Ex_max, :] = 0\n # plt.matshow(matrix_ex_compressed)\n # plt.colorbar()\n # plt.show()\n\n\n # ==== Calculate multiplicities: ====\n\n # Setup meshgrids for making boolean indexing arrays\n Egamma_mesh, Ex_mesh = np.meshgrid(Egamma_array, Ex_array)\n Egamma_max = Ex_array + dE_gamma # Maximal Egamma value for each Ex bin\n Egamma_max_grid = np.meshgrid(np.ones(Nx), Egamma_max)[1]\n if statistical_or_total == 1:\n # Statistical multiplicity calculation (i.e. trying to use statistical/continuum region only)\n slide = np.minimum( np.maximum(ThresRatio*Ex_mesh, ThresTot), ThresSta ) # The sliding lower limit for Egamma integral - sliding between ThresTot and ThresSta.\n # plt.figure(5)\n # plt.plot(slide[:,0])\n # plt.show()\n # sys.exit(0)\n # good_indices = np.where(np.logical_and(slide < Egamma_mesh, Egamma_mesh < Egamma_max_grid) , True, False)\n matrix_ex_compressed_cut = np.where(np.logical_and(slide < Egamma_mesh, Egamma_mesh < Egamma_max_grid), matrix_ex_compressed, 0)\n elif statistical_or_total == 2:\n # Total multiplicity calculation\n # good_indices = np.where(Egamma_mesh < Egamma_max_grid, True, False)\n matrix_ex_compressed_cut = np.where(Egamma_mesh < Egamma_max_grid, matrix_ex_compressed, 0)\n # for i in range(len(good_indices[:,0])):\n # print len(good_indices[i,good_indices[i,:]]) # OK, it actually works.\n \n # Cut away counts higher than Egamma = Ex + dE_gamma\n # matrix_ex_compressed_cut = np.where(good_indices, matrix_ex_compressed, 0)\n # plt.figure(1)\n # plt.pcolormesh(Egamma_array, Ex_array, matrix_ex_compressed_cut, norm=LogNorm(vmin=0.01, vmax=matrix_ex_compressed.max()))\n # plt.show()\n # sys.exit(0)\n\n # Calculate average multiplicity for each Ex channel\n area_matrix_ex_compressed_cut = np.sum(matrix_ex_compressed_cut, axis=1)\n Egamma_average = div0( np.sum(Egamma_mesh * matrix_ex_compressed_cut, axis =1) , area_matrix_ex_compressed_cut )\n if statistical_or_total == 1:\n # Statistical multiplicity - use the effective Ex0 value\n multiplicity = div0( Ex_array - np.maximum( np.minimum(Ex_array - 200, ExEntry0s), 0), Egamma_average)\n elif statistical_or_total == 2:\n # Total multiplicity - use actual Ex0 = 0\n multiplicity = div0( Ex_array, Egamma_average )\n\n\n # plt.figure(2)\n # plt.step(Ex_array, multiplicity) # This rises like a straight line from 0 to about 3-4 - seems very right!\n # plt.show()\n # sys.exit(0)\n\n # Set up dummy first-generation matrix to start iterations, made of normalized boxes:\n H = np.zeros((N_Exbins, Nx))\n for i in range(N_Exbins):\n Ni = len(Egamma_array[Egamma_array<Ex_array[i] + dE_gamma])\n # print(\"Ni =\", Ni, flush=True)\n H[i, Egamma_array < Ex_array[i] + dE_gamma] = 1/max(Ni, 1)\n # print np.sum(H0, axis=1) # Seems to work!\n\n # Set up normalization matrix N\n area = np.sum(matrix_ex_compressed_cut, axis=1) # Get total number of counts in each Ex bin\n # plt.plot(Ex_array, area)\n # plt.show()\n\n area_grid = np.tile(area, (N_Exbins, 1)) # Copy the array N_Exbins times down to make a square matrix\n # print area_grid.shape\n multiplicity_grid = np.tile(multiplicity, (N_Exbins, 1)) \n # print multiplicity_grid.shape\n normalization_matrix = div0(( np.transpose(multiplicity_grid) * area_grid ) , (multiplicity_grid * np.transpose(area_grid) )).T # The transpose gives the right result. Haven't twisted my head around exactly why.\n # normalization_matrix_check = np.zeros((N_Exbins, N_Exbins))\n # for i in range(N_Exbins):\n # for j in range(N_Exbins):\n # normalization_matrix_check[i, j] = multiplicity[i]*area[j]/(multiplicity[j]*area[i])\n normalization_matrix[np.isnan(normalization_matrix)] = 0\n # plt.matshow(normalization_matrix, origin='lower', norm=LogNorm(vmin=0.01, vmax=normalization_matrix.max()))\n # plt.show()\n # plt.matshow(normalization_matrix_check, origin='lower') # There is a difference of a transposition, check which is the right one\n # plt.show()\n\n # Set up compression parameters for Egamma axis to be used by H below:\n i_Egamma_max = np.where(Egamma_array > Ex_max+ dE_gamma)[0][0] # Get the maximal allowed gamma energy (need to make H square, thus Egamma <= Ex + dE_gamma, since that's the maximal Ex channel in the compressed matrix)\n # print i_Egamma_max, Egamma_array[i_Egamma_max], N_Exbins, int(i_Egamma_max/N_Exbins)\n # i_Egamma_max = i_Egamma_max + N_Exbins - i_Egamma_max%N_Exbins # Make sure the number of indices is a whole multiple of N_Exbins (rounded up)\n # print i_Egamma_max\n grouping_Egamma = int(np.ceil(i_Egamma_max/N_Exbins))\n # print grouping_Egamma\n # Egamma_array_compressed = Egamma_array[0:i_Egamma_max]*grouping_Egamma\n # Egamma_array_compressed = Ex_array\n\n # plt.matshow(H[:,0:i_Egamma_max])\n # plt.show()\n # H_extended = np.insert(H[:,0:i_Egamma_max], np.linspace(0,i_Egamma_max, N_Exbins - i_Egamma_max%N_Exbins), H[:,(np.linspace(0,i_Egamma_max, N_Exbins - i_Egamma_max%N_Exbins).astype(int))], axis=1)\n # H_extended[:,grouping_Egamma:-1:grouping_Egamma] /= 2\n # H_extended[:,grouping_Egamma+1:-2:grouping_Egamma] /= 2\n # H_extended = H[:,0:i_Egamma_max].repeat(N_Exbins).reshape(len(H[:,0]),N_Exbins,i_Egamma_max).sum(axis=2)/N_Exbins\n # plt.matshow(H_extended)\n # plt.show()\n # H_compressed = H[:,0:i_Egamma_max+ N_Exbins - i_Egamma_max%N_Exbins].reshape(N_Exbins, N_Exbins, grouping_Egamma).sum(axis=2)\n # plt.matshow(H_compressed)\n # plt.show()\n # H_compressed = rebin(H[:,0:i_Egamma_max], N_Exbins, 1)\n # plt.matshow(H_compressed)\n # plt.show()\n\n # H_compressed_extended = H_extended.reshape(N_Exbins, N_Exbins, grouping_Egamma).sum(axis=2)\n # plt.matshow(H_compressed_extended)\n # plt.show()\n\n # sys.exit(0)\n\n # Declare variables which will define the limits for the diff spectrum colorbar (for plotting purposes)\n vmin_spec = -200\n vmax_spec = 200\n vmin_diff = -100\n vmax_diff = 100\n\n # Prepare weight function based on Fermi gas approximation:\n a_f = 16 # 1/MeV\n n_f = 4.2 # Exponent for E_gamma\n # Make the weight array by the formula W(Ex,Eg) = Eg^n_f / (Ex-Eg)^2 * exp(2*sqrt(a*(Ex-Eg)))\n Ex_mesh, Eg_mesh = np.meshgrid(Ex_array, Ex_array, indexing=\"ij\")\n # Make mesh of final energies:\n Ef_mesh = Ex_mesh-Eg_mesh\n # Set everything above Ex=Eg diagonal to zero so the weights also become zero\n Ef_mesh[Ef_mesh < 0] = 0\n # Calculate weights. Remember that energies are in keV, while a is in 1/MeV, so we correct in the exponent:\n W_old = np.where(Eg_mesh > 0, np.power(Eg_mesh,n_f) / np.power(Ef_mesh, 2) * np.exp(2*np.sqrt(a_f*Ef_mesh/1000)), 0)\n W_old = div0(W_old, W_old.sum(axis=1).reshape(N_Exbins,1))\n\n\n dEg = 1000\n mask_W = make_mask(Ex_array, Ex_array, Ex_array[0], Ex_array[0]+dEg, Ex_array[-1], Ex_array[-1]+dEg)\n\n # Perform the iterative subtraction:\n for iteration in range(N_iterations):\n # convergence_criterion = 1\n # max_diff = 100\n # while max_diff > convergence_criterion:\n # Store H from previous iteration to compare at the end\n H_old = H\n # Compress the H matrix along gamma axis to make it square and facilitate conversion to excitation energy\n # H_compressed = H[:,0:i_Egamma_max].reshape(N_Exbins, N_Exbins, grouping_Egamma).sum(axis=2)\n H_compressed, Egamma_array_compressed = rebin(H[:,0:i_Egamma_max], Egamma_array[0:i_Egamma_max], N_Exbins, rebin_axis=1)\n\n # plt.pcolormesh(Egamma_array_compressed, Ex_array, H_compressed)\n # plt.show()\n\n # if iteration == 0:\n # Don't use H as weights for first iteration.\n # W = W_old\n # else:\n if True:\n # Convert first-generation spectra H into weights W\n W = np.zeros((N_Exbins, N_Exbins))\n for i in range(0,N_Exbins):\n # print H_compressed[i,i:0:-1].shape\n W[i,0:i] = H_compressed[i,i:0:-1]\n # TODO Consider implementing something like Massage(), but try to understand if it is necessary first.\n # plt.figure()\n # plt.pcolormesh(Ex_array, Ex_array, W, norm=LogNorm())\n # plt.show()\n # sys.exit(0)\n # Prevent oscillations, following MAMA:\n if iteration > 4:\n W = 0.7 * W + 0.3 * W_old\n # plt.matshow(W, origin='lower', vmin=W.min(), vmax=W.max())\n # plt.colorbar()\n # plt.title('Before')\n # plt.show()\n # Remove negative weights\n W[W<0] = 0\n # Apply mask \n # W = W * mask_W\n # Normalize each Ex channel to unity\n # W = np.where(np.invert(np.isnan(W/W.sum(axis=1).astype(float))), W/W.sum(axis=1).astype(float), 0)\n # Remove Inf and NaN\n W = div0(W, W.sum(axis=1).reshape(N_Exbins,1))\n # Store for next iteration:\n W_old = np.copy(W)\n # W = np.nan_to_num(W) \n # plt.matshow(W, origin='lower', vmin=W.min(), vmax=W.max())\n # plt.colorbar()\n # plt.title('After')\n # plt.show()\n\n # sys.exit(0)\n\n # print \"W = \"\n # print W\n # print \"matrix_ex_compressed = \"\n # print matrix_ex_compressed\n # print \"product =\"\n # plt.matshow(np.dot(W, matrix_ex_compressed), origin='lower', norm=LogNorm())\n # plt.show()\n\n # Calculate product of normalization matrix, weight matrix and raw count matrix\n G = np.dot( (normalization_matrix * W), matrix_ex_compressed) # Matrix of weighted sum of spectra below\n \n # Apply area correction\n if apply_area_correction:\n # Setup meshgrids for making boolean indexing arrays\n # Egamma_mesh_compressed, Ex_mesh_compressed = np.meshgrid(Egamma_array_compressed, Ex_array)\n # Egamma_max = Ex_array + dE_gamma # Maximal Egamma value for each Ex bin\n # Egamma_max_grid_compressed = np.meshgrid(np.ones(N_Exbins), Egamma_max)[1]\n # print \"Egamma_mesh_compressed, Egamma_max, Egamma_max_grid\"\n # print Egamma_mesh_compressed.shape, Egamma_max.shape, Egamma_max_grid.shape\n if statistical_or_total == 1:\n # Statistical multiplicity calculation (i.e. trying to use statistical/continuum region only)\n # slide_compressed = np.minimum( np.maximum(ThresRatio*Ex_mesh_compressed, ThresTot), ThresSta ) # The sliding lower limit for Egamma integral - sliding between ThresTot and ThresSta.\n # print \"slide_compressed\"\n # print slide_compressed.shape\n # plt.figure(5)\n # plt.plot(slide[:,0])\n # plt.show()\n # sys.exit(0)\n # good_indices_G = np.where(np.logical_and(slide_compressed < Egamma_mesh_compressed, Egamma_mesh_compressed < Egamma_max_grid) , True, False)\n G_area = np.where(np.logical_and(slide < Egamma_mesh, Egamma_mesh < Egamma_max_grid), G, 0).sum(axis=1)\n elif statistical_or_total == 2:\n # Total multiplicity calculation\n # good_indices_G = np.where(Egamma_mesh_compressed < Egamma_max_grid, True, False)\n # G_area = np.where(Egamma_mesh_compressed < Egamma_max_grid, G, 0).sum(axis=1)\n G_area = np.where(Egamma_mesh < Egamma_max_grid, G, 0).sum(axis=1)\n # G_area = np.where(good_indices_G, G, 0).sum(axis=1)\n # print \"print G_area.shape\"\n # print G_area.shape\n # print \"print G_area\"\n # print G_area\n alpha = np.where(G_area > 0, (1 - div0(1,multiplicity)) * div0( area_matrix_ex_compressed_cut, G_area ), 1)\n alpha[alpha < 0.85] = 0.85\n alpha[alpha > 1.15] = 1.15\n # print \"alpha.shape\"\n # print alpha.shape\n # print \"alpha\"\n # print alpha\n else:\n alpha = np.ones(N_Exbins)\n\n\n # The actual subtraction\n H = matrix_ex_compressed - alpha.reshape((len(alpha), 1))*G\n # print H.shape\n # Plotting:\n # vmin_diff = (H-H_old).min()\n # vmax_diff = (H-H_old).max()\n # vmin_spec = H.min()\n # vmax_spec = H.max()\n\n # plt.figure(10)\n # plt.subplot(1,2,1)\n # plt.title('First gen spectrum, current')\n # plt.pcolormesh(Egamma_array, Ex_array, H, norm=LogNorm(vmin=0.01, vmax=vmax_spec))\n # plt.colorbar()\n # plt.subplot(1,2,2)\n \n\n # plt.title('Diff with previous')\n # plt.pcolormesh(Egamma_array, Ex_array, H-H_old, vmin=vmin_diff, vmax=vmax_diff)\n # plt.colorbar()\n # plt.show()\n\n\n\n # Check convergence\n max_diff = np.max(np.abs(H-H_old))\n print(\"iteration =\", iteration, \"max_diff =\", max_diff, flush=True)\n\n # Remove negative counts\n H[H<0] = 0\n \n # Update internal variables and return True upon completion\n # return H, H-H_old, Ex_array, Egamma_array\n\n print(\"H.shape =\", H.shape)\n print(\"Ex_array.shape =\", Ex_array.shape)\n print(\"Egamma_array.shape =\", Egamma_array.shape, flush=True)\n\n self.firstgen = matrix(H, Ex_array, Egamma_array)\n return True","title":""},{"docid":"2f982723eb0a76e4667a6e7795626f76","score":"0.52906454","text":"def coin_10flips_1():","title":""},{"docid":"b4f08e760333598e2f7fc5ae9c2501cf","score":"0.5289661","text":"def algorithm():\n list_how_much_0_owes_1_with_amount_owed, list_friend = generate_data()\n\n print(\"#\" * 100)\n print(\"Algorithm\")\n print(\"#\" * 100)\n print()\n\n dict_k_pair_v_owed = defaultdict(int)\n\n # Algorithm part 1\n for i in list_how_much_0_owes_1_with_amount_owed:\n dict_k_pair_v_owed[i[0]] += i[1]\n\n # Print to show work\n print(\"Dict of Who owes Who (total amount owed)\")\n for pair, amount_owed in dict_k_pair_v_owed.items():\n print(pair, amount_owed)\n print()\n\n dict_k_pair_v_owed_total = dict()\n\n set_frozenset_used = set()\n\n # Algorithm part 2\n for pair, amount_owed in dict_k_pair_v_owed.items():\n\n frozenset_pair = frozenset(pair)\n\n if frozenset_pair not in set_frozenset_used:\n set_frozenset_used.add(frozenset_pair)\n dict_k_pair_v_owed_total[pair] = amount_owed\n continue\n\n else:\n\n pair_reversed = tuple(reversed(pair))\n\n value_of_pair_reversed = dict_k_pair_v_owed_total.get(pair_reversed)\n\n if amount_owed > value_of_pair_reversed:\n dict_k_pair_v_owed_total.pop(pair_reversed)\n dict_k_pair_v_owed_total[pair] = amount_owed - value_of_pair_reversed\n\n else:\n dict_k_pair_v_owed_total[pair_reversed] = dict_k_pair_v_owed_total[pair_reversed] - amount_owed\n\n # Print to show solution\n print(\"How much money friend index 0 owes friend index 1\")\n for pair, amount_owed in dict_k_pair_v_owed_total.items():\n print(pair, amount_owed)\n print()\n\n print(\"Proof that the Maximum amount transactions needed total is <= Combinations of size 2\")\n print(\"Combination: {} CHOOSE {} = {}\".format(len(list_friend), 2,\n math.comb(len(list_friend), 2)))\n print(\"Size of the result of my algorithm {}\".format(len(dict_k_pair_v_owed_total)))","title":""},{"docid":"56cb5f13ff6489f5912f3396b7315f99","score":"0.52889156","text":"def attractor(self, init, which_way):\n\n if which_way == 'normal':\n hebb_diff_freq_its_max = self.par['Hebb_diff_freq_its_max']\n else:\n hebb_diff_freq_its_max = self.par['Hebb_inv_diff_freq_its_max']\n\n with tf.name_scope('attractor'):\n ps = [0] * (self.par['n_recurs'] + 1)\n ps[0] = self.par['p_activation'](init)\n\n for i in range(self.par['n_recurs']):\n # get Hebbian update\n update = self.hebb_scal_prod(ps[i], i, which_way, hebb_diff_freq_its_max)\n\n ps_f = tf.split(value=ps[i], num_or_size_splits=self.par['n_place_all'], axis=1)\n for f in range(self.par['n_freq']):\n if i < hebb_diff_freq_its_max[f]:\n ps_f[f] = self.par['p_activation'](self.par['prev_p_decay'] * ps_f[f] + update[f])\n ps[i + 1] = tf.concat(ps_f, axis=1)\n\n p = ps[-1]\n\n return p","title":""},{"docid":"39ec02b0afd1aa22147f14b6d4d8c52c","score":"0.528416","text":"def enbw(self):\n pi = np.pi\n alpha = 16.7/pi\n N=self.acq_samples\n tau=self.acq_time\n fs=N/tau\n j=np.arange(N)\n def kaiser(j):\n z = 2.*j/N - 1.\n return iv(0,pi*alpha*np.sqrt(1-z**2))/iv(0,pi*alpha)\n def S1(x):\n sum = 0\n for i in x:\n sum = sum+i\n return sum\n\n def S2(x):\n sum = 0\n for i in x:\n sum = sum+i**2\n return sum\n x=kaiser(j)\n return fs*S2(x)/S1(x)**2","title":""},{"docid":"39ec02b0afd1aa22147f14b6d4d8c52c","score":"0.528416","text":"def enbw(self):\n pi = np.pi\n alpha = 16.7/pi\n N=self.acq_samples\n tau=self.acq_time\n fs=N/tau\n j=np.arange(N)\n def kaiser(j):\n z = 2.*j/N - 1.\n return iv(0,pi*alpha*np.sqrt(1-z**2))/iv(0,pi*alpha)\n def S1(x):\n sum = 0\n for i in x:\n sum = sum+i\n return sum\n\n def S2(x):\n sum = 0\n for i in x:\n sum = sum+i**2\n return sum\n x=kaiser(j)\n return fs*S2(x)/S1(x)**2","title":""},{"docid":"ff08e18a5c417e1b3cbec0a74a524d9f","score":"0.5278109","text":"def _compute_beliefs(priors: np.ndarray, messages: list) -> np.ndarray:\n beliefs = priors.copy()\n for node, belief in enumerate(beliefs):\n incoming_messages = messages[node]\n for msg in incoming_messages.values():\n belief *= msg\n belief /= np.sum(belief)\n return beliefs","title":""},{"docid":"8a75dd4a5fc363bcb3c47fa5d7d81c74","score":"0.52710605","text":"def fold_change_bohr(bohr_parameter):\n\n return 1 / (1 + np.exp((-1) * bohr_parameter))","title":""},{"docid":"0184a3c83f3afad178a9e57e7d6f61e9","score":"0.5267429","text":"def bell(n):\n result = 0\n for i in range(1, n + 1):\n result += stirling(n, i)\n return result","title":""},{"docid":"7a949c8660b6612ac91be9e670a27af3","score":"0.52673113","text":"def chooseBestGuessV3(state, numBombs):\n guessList = []\n frontier = []\n unknownsToBombs = 0\n totalCombinations = 0\n frontierValues = {}\n height = len(state[0])\n for i in range(len(state)):\n for j in range(height): \n val = state[i][j][0]\n neighbors = state[i][j][2]\n if (val > 0):\n onFrontier=False\n numNeighbors = 0\n for neighbor in neighbors:\n if (neighbor[0] == -1):\n onFrontier = True\n numNeighbors = numNeighbors + 1\n if (onFrontier):\n frontier.append((i,j,numNeighbors))\n \n for member in frontier:\n object = state[member[0]][member[1]]\n neighbors = object[2]\n unknownNeighbors = member[2]\n bombs = state[i][j][1]\n difference = object[0]-object[1]\n for neighbor in neighbors:\n neighborPos = indexToColRow(neighbor[1], height)\n neighborObject = state[neighborPos[0]][neighborPos[1]]\n if (neighborObject[0] == -1):\n val = frontierValues.get(neighbor[1], (0,0))\n frontierValues[neighbor[1]] = (val[0] + difference, val[1] + unknownNeighbors)\n max = 1.0\n for key in frontierValues:\n val = frontierValues[key]\n val = val[0] / val[1]\n #print(\"val: \",val,\", max: \",max,\"val > max: \",val > max)\n if (val < max):\n guessList = []\n guessList.append(key)\n max = val\n elif (abs(val - max) < 0.01):\n guessList.append(key)\n #print(\"Choosing 1 of \",len(guessList),\" best guesses...\") \n #print(frontierValues)\n\n \n #print(\"Frontier: \",len(frontier),\", Bombs:\",numBombs)\n #This is done because completely unknown squares are assigned a strange weight\n \n #guess = random.choice(frontier)\n #guess = colRowToIndex(guess[0], guess[1], height)\n #return guess\n if (len(guessList) == 0):\n guessList = getUnknowns(state)\n return (random.choice(guessList), max)","title":""},{"docid":"158632475cf4d5e38162707af04233e9","score":"0.52672136","text":"def gradientDescent(self, initlearningRate, numofIteration, randA=-2, randB=2, adaptLearningRate=True,\n decayingConst = 100):\n\n row, col = self.X.shape\n m = row\n\n self.W = (randB - randA) * np.random.random((1, col)) + randA\n learningRate = initlearningRate\n\n for it in range(numofIteration):\n\n # (N X M) @ (M X 1) = (N X 1)\n predicate = self.norm_X @ np.transpose(self.W)\n predicate = supp.LogisticFuction(predicate)\n # Y -> (N X 1) - predicate -> (N X 1) = (N X 1)\n diff_val = (predicate - self.Y)\n\n # (1 X N) @ (N X M)\n diff_val = np.transpose(diff_val) @ self.norm_X # (1 X M)\n diff_val = diff_val / m\n\n if adaptLearningRate:\n learningRate = self.decayLearningRate(initlearningRate, decayingConst, it)\n\n self.W = self.W - learningRate * diff_val\n\n cost = supp.CostFunction.crossEntropy(supp.LogisticFuction(self.norm_X @ np.transpose(self.W)), self.Y)\n\n self.square_error.append(cost)\n\n # regression_output = supp.CostFunction.LogisticFuction(self.W, self.norm_X)\n # To Draw the boundary decision we need to draw the line that satisfy that\n # p(Y = 1| X) = p(Y = 0| x) = 1/2\n # 1/2 = (1 / 1 + exp(-1 * W @ X) ) If we simplify this equation\n # 2 = 1 + exp(-1 * W @ X)\n # 1 = exp(-1 * W @ X) take the natural log\n # 0 = -1 * W @ X\n # W @ X = 0\n # so if we have only two feature\n # w0 + w1*x1 + w2 * x2 = 0\n # x2 = - (w1 / w2) x1 - (w0 / w2)\n # X2 = (-1 / w2) * (w1 * x1 + w0)\n\n w0 = self.W[0, 0]\n w1 = self.W[0, 1]\n w2 = self.W[0, 2]\n regression_output = (-1.0 / w2) * (w1 * self.norm_X[:, 1] + w0)\n regression_output = regression_output * self.stat_X[1,2] + self.stat_X[0,2]\n\n return self.W, regression_output","title":""},{"docid":"d5486504f88d62ba6b5f28e89817e13d","score":"0.5262246","text":"def recruit(bees, waggle_direction):\n for b in bees:\n if b.state == 0:\n if random.random() < 0.1:\n b.state = 2 # recruit bee to foraging\n b.foragingTurns = 2\n b.public_direction = waggle_direction + random.randrange(-90,90)","title":""},{"docid":"4468c17f5829302dc0e77a14be2c3b1c","score":"0.5260997","text":"def test_cooling_model_intermediate_mass_basti_co_da_10():\n wdlf2 = theoretical_lf.WDLF()\n wdlf2.set_intermediate_mass_cooling_model(model=\"basti_co_da_10\")\n wdlf2.compute_cooling_age_interpolator()\n wdlf2.compute_density(mag=mag)\n assert np.isclose(np.sum(wdlf2.number_density), 1.0)","title":""},{"docid":"c41dad878920c7f1d161a57c1dbdabd6","score":"0.5259439","text":"def sample_wf_distribution(n, reps):\n for mx, rel_branch_length in zip(sample_matrix(n, reps), sample_branch_lengths(n, reps)):\n variate = (mx.T).dot(rel_branch_length)\n err = 1 - np.sum(variate)\n variate[np.argmax(variate)] += err\n yield variate","title":""},{"docid":"4720fc01913c271e5138419c29a43e69","score":"0.52509767","text":"def far_distribution(self):\n pygame.display.set_caption(\"Far distribution\")\n self.free_coors = copy(self.out_circle_coors)\n self.distribution(self.free_coors)","title":""},{"docid":"7b4fd3a9de27665e7d48071e9ed382d8","score":"0.52505714","text":"def ksprob(alam:float)->float:\n fac = 2.0\n sum = 0.0\n termbf = 0.0\n a2 = -2.0*alam*alam\n for j in range(1,201):\n term = fac*exp(a2*j*j)\n sum = sum + term\n if fabs(term) <= (0.001*termbf) or fabs(term) < (1.0e-8*sum):\n return sum\n fac = -fac\n termbf = fabs(term)\n return 1.0 # Get here only if fails to converge; was 0.0!!","title":""},{"docid":"80a7b988d11172e92bf1d63049cb16b3","score":"0.5250242","text":"def experiment4():\n for s in data:\n l = data[s][\"seq\"][:100000]\n a = data[s][\"alpha\"]\n N = 100\n k_prev = 0\n while N < len(l):\n f = 0\n k = N\n w = N/2\n while(w > 10):\n f = max([j*ft(j*a,l[:N])/l[N] for j in range(k-10,k+10)])\n #print(k,f)\n if f > 4: k -= w\n else: k += w\n k = math.floor(k+0.5)\n w *= 1/2\n x = k+10\n for j in range(k_prev-20,x+10):\n if(j*ft(j*a,l[:N])/l[N] > 4):\n k = j\n break\n k_prev = k\n print(s,l[N],k)\n N = math.floor(N * 1.5)","title":""},{"docid":"ca6664010c65917b041577f5e0781f64","score":"0.52479565","text":"def evaluate(self, n, goal=None):","title":""},{"docid":"60ce7d2ca675f8762515730fbd145c81","score":"0.52332747","text":"def _step(self, w1, y1):\n w0 = self.w0\n p0 = self.p0\n\n dw1 = (y1 * w0) / (np.dot(y1, w0) + eps) # (eq7) weights evolve into\n\n # (eq16) cost to change portfolio\n # (excluding change in cash to avoid double counting for transaction cost)\n c1 = self.cost * (\n np.abs(dw1[1:] - w1[1:])).sum()\n\n p1 = p0 * (1 - c1) * np.dot(y1, w0) # (eq11) final portfolio value\n\n p1 = p1 * (1 - self.time_cost) # we can add a cost to holding\n\n # can't have negative holdings in this model (no shorts)\n p1 = np.clip(p1, 0, np.inf)\n\n rho1 = p1 / p0 - 1 # rate of returns\n r1 = np.log((p1 + eps) / (p0 + eps)) # (eq10) log rate of return\n # (eq22) immediate reward is log rate of return scaled by episode length\n reward = r1 / self.steps\n\n # remember for next step\n self.w0 = w1\n self.p0 = p1\n\n # if we run out of money, we're done\n done = bool(p1 == 0)\n\n # should only return single values, not list\n info = {\n \"reward\": reward,\n \"log_return\": r1,\n \"portfolio_value\": p1,\n \"market_return\": y1.mean(),\n \"rate_of_return\": rho1,\n \"weights_mean\": w1.mean(),\n \"weights_std\": w1.std(),\n \"cost\": c1,\n }\n # record weights and prices\n for i, name in enumerate(['BTCBTC'] + self.asset_names):\n info['weight_' + name] = w1[i]\n info['price_' + name] = y1[i]\n\n self.infos.append(info)\n return reward, info, done","title":""},{"docid":"91faf9ecf208ef56d24b3334879721e5","score":"0.52309537","text":"def next_day(self):\n self.weight += self.stomach - 0.25\n self.stomach = 0","title":""},{"docid":"c9d3bd4c827a6dfd8b0204ec549b7997","score":"0.52285117","text":"def calc(self):\n if abs(self.variable) >= 6999:\n self.result = self.bad_value\n return\n\n temp = 0\n for idx in range(len(self.coefs)):\n temp += float(self.coefs[idx]) * self.variable ** idx\n self.result = temp","title":""},{"docid":"83c9baec96506b6afdb975331a347f82","score":"0.5218737","text":"def bancroft(B_pass):\n\n v_light = 299792458.0\n pos = np.matrix( np.zeros(4) )\n\n for iter in range(1,3):\n B = B_pass\n [m,n] = np.shape(B)\n\n for i in range(0,m) :\n print(i)\n x = B[i][0]\n y = B[i][1]\n if iter == 1:\n traveltime = 0.072\n else:\n z = B[i][3]\n rho = (x-pos[0])**2 + (y-pos[1])**2 + (z-pos[2])**2\n traveltime = np.sqrt(rho)/v_light\n\n angle = traveltime * 7.292115147e-5\n cosa = np.cos(angle)\n sina = np.sin(angle)\n B[i][0] = cosa*x + sina*y\n B[i][1] = -sina*x + cosa*y\n\n if m > 4:\n BBB = npl.pinv(B.T*B)*B.T\n else:\n BBB = npl.pinv(B)\n\n e = np.ones(m)\n #e = np.ones(m,1)\n alpha = np.zeros((m,1))\n #for i in range(1,m+1): \n # alpha(i) = lorentz(B(i,:).T,B(i,:).T)/2.\n \n BBBe = BBB*e\n BBBalpha = BBB*alpha\n a = lorentz(BBBe,BBBe)\n b = lorentz(BBBe,BBBalpha) - 1\n c = lorentz(BBBalpha,BBBalpha)\n root = np.sqrt(b*b-a*c)\n print(\"root\",root)\n\n # Look at the two possible solutions\n r = np.zeros(2)\n print(\"r\",r,np.shape(r))\n print(\"b\",b)\n print(\"a\",a)\n r[0] = (-b-root)/a\n r[1] = (-b+root)/a\n possible_pos = np.zeros((4,2))\n\n for i in range(0,2):\n possible_pos[:,i] = r[i] * BBBe + BBBalpha\n possible_pos[3,i] = -possible_pos[3,i]\n \n for j in range(1,m+1): \n for i in range(1,3):\n c_dt = possible_pos(3,i)\n calc = norm(B[j,0:2].T - possible_pos[0:2,i])+c_dt\n omc = B[j,3]-calc\n abs_omc[i] = np.abs(omc)\n\n # discrimination between roots\n if abs_omc(1) > abs_omc(2):\n pos = possible_pos[:,2]\n else:\n pos = possible_pos[:,1]","title":""},{"docid":"5186b17ee97685583b1643cded81fb9e","score":"0.5216176","text":"def MakeBeliefs(self, guess):\r\n pmf = self.PmfPrice()\r\n self.prior = Price(pmf, self, name='prior')\r\n self.posterior = self.prior.Copy(name='posterior')\r\n self.posterior.Update(guess)","title":""},{"docid":"cfc99b3aeb5a3156eeb04df02d24d84f","score":"0.5213662","text":"def Bisection_Method(f, f_d, start_point, end_point, eps):\r\n\r\n r_l_f = Find_renge(f, start_point, end_point) # renge list f\r\n r_l_fd = Find_rengeft(f_d, start_point, end_point) # renge list f_d\r\n counter = 0\r\n result_polinom = []\r\n\r\n if r_l_f and r_l_fd:\r\n for i in range(0, len(r_l_f), 2):\r\n\r\n # Whilw loop that run until the difference between the end point and the start point is greater than epsilon\r\n while (r_l_f[i + 1] - r_l_f[i]) > eps:\r\n\r\n x_m = (r_l_f[i] + r_l_f[i + 1]) / 2\r\n\r\n counter += 1\r\n print(\"{0} - {1}\".format(counter, round(x_m, 6))) # Printing the iterations\r\n if f(r_l_f[i]) * f(x_m) > 0:\r\n r_l_f[i] = x_m\r\n\r\n else:\r\n r_l_f[i + 1] = x_m\r\n\r\n if round(x_m, 6) not in result_polinom:\r\n print(\"x: \", round(x_m, 6))\r\n result_polinom.append(round(x_m, 6)) # Save the results\r\n\r\n for i in range(0, len(r_l_fd), 2):\r\n\r\n # Whilw loop that run until the difference between the end point and the start point is greater than epsilon\r\n while (r_l_fd[i + 1] - r_l_fd[i]) > eps:\r\n\r\n x_m = (r_l_fd[i] + r_l_fd[i + 1]) / 2\r\n\r\n counter += 1\r\n print(\"{0} - {1}\".format(counter, round(x_m, 6))) # Printing the iterations\r\n\r\n if f(r_l_fd[i]) * f(x_m) > 0:\r\n r_l_fd[i] = x_m\r\n else:\r\n r_l_fd[i + 1] = x_m\r\n\r\n if round(x_m, 6) not in result_polinom:\r\n print(\"x: \", round(x_m, 6))\r\n result_polinom.append(round(x_m, 6))\r\n\r\n n = len(result_polinom)\r\n i = 0\r\n while i < n and n != 0: # list no empty\r\n if not -0.1 < f(result_polinom[i]) < 0.1:\r\n del result_polinom[i]\r\n n -= 1\r\n i = 0\r\n else:\r\n i += 1\r\n\r\n return result_polinom","title":""},{"docid":"01206a58de6bf71494556d22696d6ea2","score":"0.52111655","text":"def evaluationFunction(self, currentGameState, action):\n # Useful information you can extract from a GameState (pacman.py)\n successorGameState = currentGameState.generatePacmanSuccessor(action)\n newPos = successorGameState.getPacmanPosition()\n newFood = successorGameState.getFood()\n newGhostStates = successorGameState.getGhostStates()\n newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]\n\n \"*** YOUR CODE HERE ***\" \n fdist = 99999 # Initial closest food distance\n M = newFood.height\n N = newFood.width\n for i in range(N):\n for j in range(M):\n if newFood[i][j]:\n d = manhattanDistance(newPos, (i, j))\n if d < fdist:\n fdist = d # new closest food distance\n\n f1 = 1 / (fdist + 1) # Larger value of f1 for closer food\n\n f2 = max(newScaredTimes) # Not entirely sure what newScaredTimes is, but Im taking the largest one\n\n # List of adjacent positions to pacman\n adjacentPos = [(newPos[0] - 1, newPos[1]), (newPos[0] + 1, newPos[1]), (newPos[0], newPos[1] - 1), (newPos[0], newPos[1] + 1)]\n\n f3 = 1 # f3 is related to how close the ghosts are to pacman\n for ghost in newGhostStates:\n gpos = ghost.getPosition()\n if (newPos == gpos): # If pacman's position overlaps a ghosts position\n f3 = 0 # f3 is 0 because pacman on same position as ghost = lose\n break;\n for pos in adjacentPos:\n if gpos == pos: # If ghost is adjacent to pacman\n f3 = 0 # f3 is 0 because we do not want to be directly next to ghost\n break;\n\n if (f3 == 0): # if f3 = 0, there is a very high chance of losing ==> return low value\n return 0\n\n return successorGameState.getScore() + .1*f1 + .3*f2 + .6*f3 # return weighted sum of features","title":""},{"docid":"f07893907714241d11395aa0085373c6","score":"0.52079463","text":"def theWork(self, iteration):\n \n if(iteration == 0):\n self.numPoints = 1\n self.result = 0.5 * (self.end - self.start) * \\\n (self.integrand(self.start) + self.integrand(self.end))\n\n elif (iteration > 0):\n delta = (self.end - self.start) / self.numPoints\n \n sum = reduce(lambda x, y: x + y, \\\n [self.integrand(self.start + 0.5 * delta + i * delta) \\\n for i in range(self.numPoints)])\n \n self.result = 0.5 * (self.result + (self.end - self.start) * sum / self.numPoints)\n \n self.numPoints *= 2\n\n return self.result","title":""},{"docid":"61e0288b7a35f346c73335ef24a60192","score":"0.5207003","text":"def learn(self, f, eta=0.1): \r\n # initialize the bias and weights with random values\r\n self.bias = np.random.normal()\r\n self.weights = np.random.randn(self.num_inputs)\r\n number_of_errors = -1\r\n while number_of_errors != 0: \r\n number_of_errors = 0\r\n print \"Beginning iteration\"\r\n print \"Bias: {:.3f}\".format(self.bias)\r\n print \"Weights:\", \", \".join(\r\n \"{:.3f}\".format(wt) for wt in self.weights)\r\n for x in self.inputs:\r\n error = f(x)-self.output(x)\r\n if error:\r\n number_of_errors += 1\r\n self.bias = self.bias+eta*error\r\n self.weights = self.weights+eta*error*x\r\n print \"Number of errors:\", number_of_errors, \"\\n\"","title":""}],"string":"[\n {\n \"docid\": \"af3a903b1142e01732e6a898e55d4ef1\",\n \"score\": \"0.6557465\",\n \"text\": \"def challenge_bellman_ford():\\n from ch07.single_source_sp import bellman_ford\\n DG = nx.DiGraph()\\n DG.add_edge('d', 'e', weight=1)\\n DG.add_edge('c', 'd', weight=1)\\n DG.add_edge('b', 'c', weight=1)\\n DG.add_edge('s', 'b', weight=1)\\n\\n (defective_dist_to, _) = defective_bellman_ford(DG, 's')\\n (dist_to, _) = bellman_ford(DG, 's')\\n\\n if dist_to != defective_dist_to:\\n print('Proper Bellman-Ford computes {} but with one fewer iteration it is {}'.format(dist_to, defective_dist_to))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f0b57305711bc9d5cde5582d914665d\",\n \"score\": \"0.5987174\",\n \"text\": \"def dozen_bet(self):\\n for i in range(0, 3):\\n num = 12 * i + 1\\n self.addOutcome(Outcome(\\\"dozen #%d\\\" % (i + 1), 2), range(num, num + 12))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8deb80b70e805af8a3665a021364c92\",\n \"score\": \"0.588436\",\n \"text\": \"def incumbent_firm(wage, alpha, beta, delta, gamma, cf, psi, lambdaa, pi, grid_k, grid_z, Nz, Nk, maxit, tol): \\n\\n \\n #a. Initialize counters, initial guess. storage matriecs\\n \\n VF_old = np.zeros((Nz,Nk)) #initial guess\\n VF = np.copy(VF_old)\\n pol_k = np.copy(VF_old)\\n pol_n = np.copy(VF_old)\\n pol_inv = np.copy(VF_old)\\n pol_enter = np.copy(VF_old)\\n firm_output = np.copy(VF_old)\\n \\n \\n # c. given prices and hiring decision, iterate on incumbent firm vf\\n for it in range(maxit):\\n for iz in range(Nz):\\n for ik in prange(Nk):\\n \\n pol_n[iz,ik] = ((grid_z[iz] * gamma) / wage)**(1/(1-gamma)) * grid_k[ik]**(alpha/(1-gamma))\\n \\n firm_output[iz,ik] = grid_z[iz] * grid_k[ik] ** alpha * pol_n[iz,ik] ** gamma\\n \\n firm_profit = firm_output[iz,ik] - wage * pol_n[iz,ik] - (grid_k - (1-delta)*grid_k[ik]) \\\\\\n - psi/2 * ((grid_k- (1-delta)*grid_k[ik]) / grid_k[ik])**2 * grid_k[ik] \\n \\n val = firm_profit + beta * (1-lambdaa)*np.dot(pi[iz,:], VF_old)\\n \\n VF[iz,ik] = np.maximum( (1-delta) * grid_k[ik] - (psi * (1-delta)**2 * grid_k[ik])/2, np.max(val) - cf)\\n \\n if VF[iz,ik] == (1-delta) * grid_k[ik] - (psi * (1-delta)**2 * grid_k[ik])/2 : \\n pol_k[iz,ik] = 0\\n pol_inv[iz,ik] = 0\\n \\n else: \\n pol_k[iz,ik] = grid_k[np.argmax(val)]\\n pol_enter[iz,ik] = 1\\n pol_inv[iz,ik] = pol_k[iz,ik] - (1-delta) * grid_k[ik]\\n \\n \\n \\n dist = np.abs(VF - VF_old).max()\\n \\n if dist < tol :\\n break\\n \\n VF_old = np.copy(VF)\\n\\n\\n return VF, pol_k, pol_n, pol_enter, pol_inv, firm_output, it\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e08ed0fe6f255acb1233ddf4d9a7ace\",\n \"score\": \"0.5767544\",\n \"text\": \"def eating(self, fodder):\\n self.weight += fodder * self.default_parameters[\\\"beta\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"645b20e934ceead48b585aa6d4285d01\",\n \"score\": \"0.57584804\",\n \"text\": \"def feeding(self, fodder):\\n\\n if fodder >= self.F:\\n self.weight += self.beta * self.F\\n return self.F\\n elif (fodder > 0) and (fodder < self.F):\\n self.weight += self.beta * fodder\\n return fodder\\n if fodder < 0:\\n raise ValueError(\\\"Cannot have negative fodder value\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb0ca495aa3098fe7318c31417c6a2b8\",\n \"score\": \"0.5754847\",\n \"text\": \"def feeding(self, sorted_herb_list):\\n amount_to_eat = self.F\\n eaten_herbs = []\\n for herb in reversed(sorted_herb_list):\\n if amount_to_eat <= 0:\\n break\\n fitness_diff = self.fitness - herb.fitness\\n if fitness_diff < 0:\\n break\\n elif fitness_diff < self.DeltaPhiMax:\\n chance_to_kill = fitness_diff / self.DeltaPhiMax\\n else:\\n chance_to_kill = 1\\n number = random.uniform(0, 1)\\n if number <= chance_to_kill:\\n if amount_to_eat < herb.weight:\\n self.weight += self.beta * amount_to_eat\\n self.fitness_update()\\n eaten_herbs.append(herb)\\n return eaten_herbs\\n self.weight += self.beta * herb.weight\\n amount_to_eat -= herb.weight\\n self.fitness_update()\\n eaten_herbs.append(herb)\\n return eaten_herbs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7abbbc1e9bc8362d927a088b2a7fbcf\",\n \"score\": \"0.5750128\",\n \"text\": \"def apply_benfords_law(self):\\n self._extract_fsd()\\n print('fsd extracted')\\n self.get_counts()\\n print('counts got')\\n self.get_distribution()\\n print('distribution got')\\n stat = self.apply_chi_sq_test()\\n self.apply_visual_test(stat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dcabb753defa7206976138468350d1f\",\n \"score\": \"0.57105017\",\n \"text\": \"def part_1():\\n result = sum(calculate_fuel(int(mass)) for mass in data)\\n print(f\\\"Day 1 pt.1 Result: {result}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4fb9cd14b84b571b013bfcc974cb913\",\n \"score\": \"0.56906915\",\n \"text\": \"def BellmanFord(self, src):\\n d, p = self.initialize_src_vertex(src)\\n for i in range(self.v): # V calls\\n for node in self.graph: # Check all edges E\\n adj = self.graph[node].head\\n while adj != None: # Adj vertices\\n d, p = self.relax(node, adj.val[0], adj.val[1], d, p)\\n adj = adj.next\\n # Detect negative cost cycle\\n for node in self.graph:\\n adj = self.graph[node].head\\n while adj != None: # Adj vertices\\n if d[adj.val[0]] > d[node] + adj.val[1]:\\n return False, d, p\\n adj = adj.next\\n return True, d, p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9506945af61ed67db89fc55bbeaef18c\",\n \"score\": \"0.56712395\",\n \"text\": \"def part_2():\\n total_fuel = sum(f for mass in data for f in calculate_all_fuel(int(mass)))\\n\\n print(f\\\"Day 1 pt.2 Result: {total_fuel}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e557a2b6c40de50488c9833a8ad3d7\",\n \"score\": \"0.5633054\",\n \"text\": \"def FoBaGreedy(X, y, epsilon=0.1, maxit_f=100, maxit_b=5, backwards_freq=5):\\r\\n\\r\\n n, d = X.shape\\r\\n F = {}\\r\\n F[0] = set()\\r\\n w = {}\\r\\n w[0] = np.zeros((d, 1), dtype=np.complex64)\\r\\n k = 0\\r\\n delta = {}\\r\\n\\r\\n for forward_iter in range(maxit_f):\\r\\n\\r\\n k = k + 1\\r\\n\\r\\n # forward step\\r\\n non_zeros = np.where(w[k - 1] == 0)[0]\\r\\n err_after_addition = []\\r\\n residual = y - X.dot(w[k - 1])\\r\\n for i in range(len(non_zeros)):\\r\\n alpha = X[:, i].T.dot(residual) / np.linalg.norm(X[:, i]) ** 2\\r\\n w_added = np.copy(w[k - 1])\\r\\n w_added[i] = alpha\\r\\n err_after_addition.append(np.linalg.norm(X.dot(w_added) - y))\\r\\n i = np.argmin(err_after_addition)\\r\\n\\r\\n F[k] = F[k - 1].union({i})\\r\\n w[k] = np.zeros((d, 1), dtype=np.complex64)\\r\\n w[k][list(F[k])] = np.linalg.lstsq(X[:, list(F[k])], y)[0]\\r\\n\\r\\n # check for break condition\\r\\n delta[k] = np.linalg.norm(X.dot(w[k - 1]) - y) - np.linalg.norm(X.dot(w[k]) - y)\\r\\n if delta[k] < epsilon: return w[k - 1]\\r\\n\\r\\n # backward step, do once every few forward steps\\r\\n if forward_iter % backwards_freq == 0 and forward_iter > 0:\\r\\n\\r\\n for backward_iter in range(maxit_b):\\r\\n\\r\\n non_zeros = np.where(w[k] != 0)\\r\\n err_after_simplification = []\\r\\n for j in range(len(non_zeros)):\\r\\n w_simple = np.copy(w[k])\\r\\n w_simple[j] = 0\\r\\n err_after_simplification.append(np.linalg.norm(X.dot(w_simple) - y))\\r\\n j = np.argmin(err_after_simplification)\\r\\n w_simple = np.copy(w[k])\\r\\n w_simple[j] = 0\\r\\n\\r\\n # check for break condition on backward step\\r\\n delta_p = err_after_simplification[j] - np.linalg.norm(X.dot(w[k]) - y)\\r\\n if delta_p > 0.5 * delta[k]: break\\r\\n\\r\\n k = k - 1;\\r\\n F[k] = F[k + 1].difference({j})\\r\\n w[k] = np.zeros((d, 1), dtype=np.complex64)\\r\\n w[k][list(F[k])] = np.linalg.lstsq(X[:, list(F[k])], y)[0]\\r\\n\\r\\n return w[k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66243dd394d447c2216a4b230e5e306c\",\n \"score\": \"0.5607609\",\n \"text\": \"def solve_model(self): \\n \\n t0 = time.time() #start the clock\\n \\n \\n # a. Find the steady state wage using bisection\\n print('\\\\nSolving firm problem...')\\n \\n self.wage_ss = self.find_equilibrium_wage()\\n \\n \\n # b. Use the equilibrium wage to recover incumbent and entrant firm solutions\\n self.VF, self.pol_k, self.pol_n, self.pol_enter, self.pol_inv, self.firm_output, self.it_vf = incumbent_firm(self.wage_ss, self.alpha,\\n self.beta, self.delta, self.gamma, self.cf, self.psi, self.lambdaa, self.pi, self.grid_k, self.grid_z, self.Nz,\\n self.Nk, self.maxit, self.tol) \\n \\n self.VF_entrant, self.k_e = self.entrant_firm(self.VF)\\n \\n if self.it_vf < self.maxit:\\n print(f\\\"Value function convergence in {self.it_vf} iterations.\\\")\\n else : \\n print(\\\"Value function iteration: No convergence.\\\")\\n \\n t1 = time.time()\\n print(f'Firm problem time elapsed: {t1-t0:.2f} seconds')\\n \\n \\n # c. Invariant joint distribution with endogenous exit\\n print('\\\\nFinding stationary distribution...')\\n self.stat_dist_hat, self.it_d = solve_invariant_distribution(self.pol_k, self.grid_k, self.pol_enter, self.pi, self.nu, self.Nz,\\n self.Nk, self.maxit, self.lambdaa, self.tol)\\n \\n if self.it_d < self.maxit:\\n print(f\\\"Distribution convergence in {self.it_d} iterations.\\\")\\n else : \\n print(\\\"Distribution fixed point iteration: No convergence.\\\")\\n \\n \\n # d. Mass of entrants (m_star) in the ss equilibrium. Because labor is supplied inelastically we can use the labor market clearing condition\\n # to solve m_star\\n self.m_star = 1/np.tensordot(self.pol_n, self.stat_dist_hat)\\n \\n \\n # e. Rescale to get invariant joint distribution (mass of plants)\\n self.stat_dist = self.m_star * self.stat_dist_hat\\n \\n # invariant marginal productivity distribution by percent\\n self.stat_dist_pdf = np.sum(self.stat_dist, axis=0) / np.sum(np.sum(self.stat_dist,axis=0))\\n self.stat_dist_cdf = np.cumsum(self.stat_dist_pdf)\\n \\n t2 = time.time()\\n print(f'Stationary distribution time elapsed: {t2-t1:.2f} seconds')\\n \\n \\n # f. calculate employment distributions\\n \\n if self.plott:\\n print(\\\"\\\\nCalculating aggergate statistics and plotting...\\\")\\n else:\\n print(\\\"\\\\nCalculating aggergate statistics...\\\")\\n \\n self.dist_emp = (self.pol_n * self.stat_dist)\\n \\n # invariant marginal (capital) employment distribution by percent\\n self.dist_emp_pdf = np.sum(self.dist_emp, axis=0) / np.sum(np.sum(self.dist_emp,axis=0))\\n self.dist_emp_cdf = np.cumsum(self.dist_emp_pdf)\\n \\n \\n # g. aggregate statistics\\n \\n self.Y_ss = np.tensordot(self.firm_output, self.stat_dist)\\n self.Yfc_ss = np.tensordot(self.firm_output - self.cf, self.stat_dist)\\n self.K_ss = np.tensordot(self.pol_k, self.stat_dist)\\n self.N_ss = np.tensordot(self.pol_n, self.stat_dist) #should equal 1\\n self.Inv_ss = np.tensordot(self.pol_inv, self.stat_dist)\\n self.TFP_ss = self.Y_ss/(self.K_ss**self.alpha * self.N_ss**self.gamma)\\n\\n #use resource constraint to get aggregate consumption\\n self.C_ss = self.Yfc_ss - self.Inv_ss + \\\\\\n np.tensordot(self.psi/2 * ((self.pol_k - (1-self.delta)*self.grid_k) / self.grid_k)**2 * self.grid_k, self.stat_dist) \\\\\\n - self.m_star*(self.ce+self.k_e)\\n \\n self.average_firm_size = self.N_ss / np.sum(np.sum(self.stat_dist,axis=0))\\n self.exit_rate = 1 - np.sum((1-self.lambdaa)*np.dot(self.pi.T, self.stat_dist_hat)*self.pol_enter)/np.sum(self.stat_dist_hat) \\n \\n \\n # h. plot\\n \\n if self.plott:\\n \\n idx = [0, int(self.Nz/2), int(self.Nz-1)]\\n for ii in idx :\\n plt.plot(self.grid_k, self.VF[ii,:])\\n plt.legend(['V(k,z$_{'+str(idx[0])+'}$)','V(k,z$_{'+str(idx[1])+'}$)', 'V(k,z$_{'+str(idx[2])+'}$)'])\\n plt.title('Incumbant Firm Value Function')\\n plt.xlabel('Capital (Firm Size)')\\n #plt.savefig('vf_hopehaynv3.pdf')\\n plt.show()\\n \\n for ii in idx :\\n plt.plot(self.grid_k, self.pol_k[ii,:])\\n plt.plot(self.grid_k, (1-self.delta)*self.grid_k,':')\\n plt.legend([\\\"k'(k,z$_{\\\"+str(idx[0])+\\\"}$)\\\",\\\"k'(k,z$_{\\\"+str(idx[1])+\\\"}$)\\\", \\\"k'(k,z$_{\\\"+str(idx[2])+\\\"}$)\\\", \\\"k(1-$\\\\delta$)\\\"])\\n plt.title('Capital Policy Funciton')\\n plt.xlabel('Capital (Firm Size)')\\n #plt.savefig('pol_k_hopehaynv3.pdf')\\n plt.show()\\n \\n for ii in idx :\\n plt.plot(self.grid_k, self.pol_inv[ii,:])\\n plt.legend(['i(k,z$_{'+str(idx[0])+'}$)','i(k,z$_{'+str(idx[1])+'}$)', 'i(k,z$_{'+str(idx[2])+'}$)'])\\n plt.title(\\\"Investment: $k'(k,z)-(1-\\\\delta)k$\\\")\\n plt.xlabel('Capital (Firm Size)')\\n #plt.savefig('pol_inv_hopehaynv3.pdf')\\n plt.show()\\n \\n for ii in idx :\\n plt.plot(self.grid_k, self.pol_n[ii,:])\\n plt.legend(['n(k,z$_{'+str(idx[0])+'}$)','n(k,z$_{'+str(idx[1])+'}$)', 'n(k,z$_{'+str(idx[2])+'}$)'])\\n plt.title(\\\"Labor Demand Policy Function\\\")\\n plt.xlabel('Capital (Firm Size)')\\n #plt.savefig('pol_n_hopehaynv3.pdf')\\n plt.show()\\n \\n plt.plot(self.grid_k, self.stat_dist_pdf)\\n plt.plot(self.grid_k, self.dist_emp_pdf)\\n plt.title('Stationary Marginal PDF' )\\n plt.xlabel('Capital (Firm Size)')\\n plt.ylabel('Density')\\n plt.legend(['Productivity','Employment'])\\n #plt.savefig('pdf_hopehaynv3.pdf')\\n plt.show()\\n \\n plt.plot(self.grid_k, self.stat_dist_cdf)\\n plt.plot(self.grid_k, self.dist_emp_cdf)\\n plt.title('Stationary Marginal CDF' )\\n plt.xlabel('Capital (Firm Size)')\\n plt.ylabel('Cumulative Prob.')\\n plt.legend(['Productivity','Employment'])\\n #plt.savefig('cdf_hopehaynv3.pdf')\\n plt.show()\\n \\n t3 = time.time()\\n if self.plott : \\n print(f'Statistics and plot time elapsed: {t3-t2:.2f} seconds')\\n else :\\n print(f'Statistics time elapsed: {t3-t2:.2f} seconds')\\n \\n print(\\\"\\\\n-----------------------------------------\\\")\\n print(\\\"Stationary Equilibrium\\\")\\n print(\\\"-----------------------------------------\\\")\\n print(f\\\"ss wage = {self.wage_ss:.2f}\\\")\\n print(f\\\"exit rate = {self.exit_rate:.3f}\\\")\\n print(f\\\"avg. firm size = {self.average_firm_size:.2f}\\\")\\n print(f\\\"\\\\nss output = {self.Y_ss:.2f}\\\")\\n print(f\\\"ss investment = {self.Inv_ss:.2f}\\\")\\n print(f\\\"ss tfp = {self.TFP_ss:.2f}\\\")\\n print(f\\\"ss capital = {self.K_ss:.2f}\\\")\\n print(f\\\"ss consumption = {self.C_ss:.2f}\\\")\\n print(f\\\"ss labor = {self.N_ss:.2f}\\\")\\n \\n t4 = time.time()\\n print(f'\\\\nTotal Run Time: {t4-t0:.2f} seconds')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbc03eded1b2174d06c4e1a8fe991b16\",\n \"score\": \"0.5587307\",\n \"text\": \"def FE(dfE):\\n return w/(w-2.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2334a13634c4b30e7c81f43d6489cfea\",\n \"score\": \"0.5573583\",\n \"text\": \"def StatsBayes(dimension = 1, numIters = 100, numRuns = 1, paperCompare = False):\\n\\n # Define a dictionary to store each possible case for dimensions. Each entry is the objective function and the respective bounds.\\n problemList = {\\n 1: [Objective1D, {\\\"bField\\\": choco.uniform(0, 2000)}],\\n 2: [Objective2D, {\\\"bField\\\": choco.uniform(0, 2000), \\\"temperature\\\": choco.uniform(20, 400)}],\\n 3: [Objective3D, {\\\"bField\\\": choco.uniform(0, 2000), \\\"temperature\\\": choco.uniform(20, 400), \\\"bTheta\\\": choco.uniform(0, 180)}],\\n 4: [Objective4D, {\\\"bField\\\": choco.uniform(0, 2000), \\\"temperature\\\": choco.uniform(20, 400), \\\"bTheta\\\": choco.uniform(0, 180), \\\"eTheta\\\": choco.uniform(0, 180)}],\\n 5: [Objective5D, {\\\"bField\\\": choco.uniform(0, 2000), \\\"temperature\\\": choco.uniform(20, 400), \\\"bTheta\\\": choco.uniform(0, 180), \\\"eTheta\\\": choco.uniform(0, 180), \\\"bPhi\\\": choco.uniform(0, 180)}]\\n }\\n\\n problem = problemList.get(dimension)\\n\\n # Set up the database for the chocolate optimiser.\\n connection = choco.SQLiteConnection(\\\"sqlite:///bayes_db\\\" + str(dimension) + \\\".db\\\")\\n\\n if paperCompare:\\n\\n timeList = []\\n iterationList = []\\n\\n for run in range(numRuns):\\n\\n # Define which solver will be used.\\n solver = choco.Bayes(connection, problem[1], utility_function = \\\"ei\\\", n_bootstrap = int(np.ceil(numIters/10)), clear_db = True)\\n\\n # Clear the database. TODO: To do this?\\n connection.clear()\\n\\n # Start timing.\\n startTime = time.time()\\n\\n # Start optimisation.\\n for iteration in range(numIters):\\n\\n # Make one suggestion.\\n try:\\n token, nextParams = solver.next()\\n except:\\n print(\\\"Error suggesting a new point. Here are the last set of parameters sampled, and it's returned value:\\\")\\n print(str(nextParams))\\n print(\\\"Iteration number: \\\" + str(iteration))\\n continue\\n\\n # Check what FoM this gives. Go negative as this is a minimisation routine.\\n fEval = abs(problem[0].__call__(**nextParams))\\n\\n # Update best FoM.\\n if fEval >= globalFoM:\\n # The algorithm has managed to surpass or equal the paper value.\\n iterationList.append(iteration + 1)\\n\\n # One run complete.\\n timeElapsed = time.time() - startTime\\n timeList.append(timeElapsed)\\n\\n break\\n \\n # Tell the optimiser about the result.\\n solver.update(token, fEval)\\n\\n # Compute required values. This could probably be sped up by using created variables but I chose not to do so.\\n avgRuntime = np.average(timeList)\\n avgIters = np.average(iterationList)\\n try:\\n\\n fastestTime = np.min(timeList)\\n\\n except ValueError:\\n \\n # List is empty.\\n fastestTime = float('NaN')\\n\\n numSuccess = len(iterationList)\\n successRate = numSuccess/numRuns\\n\\n return [numSuccess, successRate, avgIters, avgRuntime, fastestTime]\\n\\n else:\\n\\n timeList = []\\n bestFoMList = []\\n\\n for run in range(numRuns):\\n\\n # Define which solver will be used.\\n solver = choco.Bayes(connection, problem[1], utility_function = \\\"ei\\\", n_bootstrap = int(np.ceil(numIters/10)), clear_db = True)\\n\\n # Clear the database. TODO: To do this?\\n connection.clear()\\n\\n # Start timing.\\n startTime = time.time()\\n bestFoM = 0\\n\\n # Start optimisation.\\n for iteration in range(numIters):\\n\\n # Make one suggestion.\\n try:\\n token, nextParams = solver.next()\\n except:\\n print(\\\"Error suggesting a new point. Here are the last set of parameters sampled, and it's returned value:\\\")\\n print(str(nextParams))\\n print(\\\"Iteration number: \\\" + str(iteration))\\n continue\\n\\n # Check what FoM this gives. Go negative as this is a minimisation routine.\\n fEval = abs(problem[0].__call__(**nextParams))\\n\\n # Update best FoM.\\n if fEval > bestFoM:\\n bestFoM = fEval\\n \\n # Tell the optimiser about the result.\\n solver.update(token, fEval)\\n\\n # One run complete.\\n timeElapsed = time.time() - startTime\\n timeList.append(timeElapsed)\\n bestFoMList.append(bestFoM)\\n \\n # Compute required values. This could probably be sped up by using created variables but I chose not to do so.\\n avgRuntime = np.average(timeList)\\n avgFoM = np.average(bestFoMList)\\n avgFoMPerTime = np.average(np.divide(bestFoMList, timeList))\\n avgFoMPerIter = np.average(np.divide(bestFoMList, numIters))\\n absBestFoM = np.max(bestFoMList)\\n\\n return [avgRuntime, avgFoM, avgFoMPerTime, avgFoMPerIter, absBestFoM]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"755be9942b813c3cf113bce7eb8b5a85\",\n \"score\": \"0.5565844\",\n \"text\": \"def selfGuidedALP_OneIteration(self,\\n BF,\\n numBasis,\\n basisItr,\\n logger,\\n prevUB,\\n initialLB,\\n initialUB,\\n timeElapsed,\\n MVC_StateList,\\n MVC_ActionList,\\n MVC_NStateList,\\n MVC_sgnStateList,\\n MVC_sgnNStateList,\\n intercept_Old,\\n linCoef_Old,\\n Perv_VFA_Coef, \\n BF_Perv_index_list,\\n BF_Perv_threshold_list):\\n rowGenCounter = 0\\n ALP = None\\n s = None\\n a = None\\n MVCval = None\\n NS = None\\n sgnS = None\\n sgnNS = None\\n cyclicPolFound = False\\n cycleLength = 4000\\n # If basis functions are ''ill''\\n prevState = np.zeros(self.mdp.dimX)\\n prevAction = np.zeros(self.mdp.dimX)\\n illCondBasisCount = 0\\n \\n #----------------------------------------------------------------------\\n # Iterate and perform constraint generation\\n while True: \\n #------------------------------------------------------------------\\n # Basis : No\\n # Generated row : No\\n if rowGenCounter ==0 and basisItr ==0:\\n #--------------------------------------------------------------\\n # Solve ALP to get VFA\\n intercept, linCoef, BF_Coef, \\\\\\n ALPval, ALP = self.ALP_GJR(BF,\\n numBasis,\\n MVC_StateList,\\n MVC_ActionList,\\n MVC_NStateList,\\n MVC_sgnStateList, \\n MVC_sgnNStateList,\\n None,\\n basisItr,\\n False,\\n None,\\n None,\\n None,\\n None)\\n \\n #------------------------------------------------------------------\\n # Basis : Yes\\n # Generated row : No\\n elif rowGenCounter ==0 and basisItr > 0:\\n #--------------------------------------------------------------\\n # Solve ALP to get VFA: use the already created Gorubi model\\n intercept, linCoef, BF_Coef, \\\\\\n ALPval, ALP = self.ALP_GJR(BF,numBasis,\\n MVC_StateList,\\n MVC_ActionList,\\n MVC_NStateList,\\n MVC_sgnStateList,\\n MVC_sgnNStateList,\\n None,\\n basisItr,\\n True,\\n Perv_VFA_Coef,\\n linCoef_Old,\\n intercept_Old,\\n BF_Perv_index_list,\\n BF_Perv_threshold_list)\\n \\n #------------------------------------------------------------------\\n # Generated row : Yes\\n else:\\n #--------------------------------------------------------------\\n # Basis : Yes\\n if basisItr >= 10: \\n #----------------------------------------------------------\\n # Solve ALP to get VFA: use the already created Gorubi model\\n intercept, linCoef, BF_Coef,\\\\\\n ALPval, ALP = self.ALP_GJR(BF,numBasis,[s],[a],\\n [NS], [sgnS],[sgnNS],\\n ALP,basisItr,True,\\n Perv_VFA_Coef,\\n linCoef_Old,\\n intercept_Old, \\n BF_Perv_index_list,\\n BF_Perv_threshold_list)\\n \\n #--------------------------------------------------------------\\n # Basis : NO\\n else:\\n intercept, linCoef, BF_Coef, ALPval,ALP = self.ALP_GJR(BF,\\n 0,\\n [s],\\n [a],\\n [NS],\\n [sgnS], \\n [sgnNS],\\n ALP,\\n basisItr,\\n False,\\n None,\\n None,\\n None,\\n None)\\n #------------------------------------------------------------------\\n # Next, perform constraint generation using the computed VFA\\n s, a, MVCval,NS,sgnS,sgnNS = self.getMostViolatedConstraint(intercept,\\n linCoef,\\n BF_Coef,\\n BF)\\n \\n #------------------------------------------------------------------ \\n \\\"\\\"\\\"\\n If the most violating state-action pair of the previous and \\n current iterations are identical, then there is an issue. In fact,\\n row generation could not cut off the previous infeasible solution.\\n If this issue happens, Gurobi parameters need to be adjusted.\\n \\\"\\\"\\\"\\n if np.linalg.norm(s-prevState,np.inf)<1e-50 and \\\\\\n np.linalg.norm(a-prevAction,np.inf)<1e-50: \\n illCondBasisCount +=1 \\n if illCondBasisCount >= 100:\\n illCondBasisCount = 0\\n msg = 'WARNING! Row generation could not cut off the previous infeasible solution. Gurobi parameters need to be adjusted for addressing this issue.'\\n print('\\\\n\\\\n'+textwrap.TextWrapper(width=50).fill(text=msg)+'\\\\n\\\\n')\\n \\n #--------------------------------------------------------------\\n # The flag shows if row generation was successful or not.\\n if basisItr == 0 :\\n flag = 0\\n else:\\n flag = 1\\n msg ='We continue solving this problem with a new set of sampled bases'\\n print('\\\\n\\\\n'+textwrap.TextWrapper(width=50).fill(text=msg)+'\\\\n\\\\n')\\n \\n #-------------------------------------------------------------- \\n # Return the results\\n return flag, initialLB, ALPval, initialUB, prevUB,\\\\\\n None, (1-initialLB/initialUB)*100, None,\\\\\\n ALPval/initialLB, None, [], MVC_StateList,\\\\\\n MVC_ActionList, MVC_NStateList, MVC_sgnStateList,\\\\\\n MVC_sgnNStateList, intercept_Old, linCoef_Old,\\\\\\n Perv_VFA_Coef, BF_Perv_index_list,\\\\\\n BF_Perv_threshold_list \\n \\n \\n #------------------------------------------------------------------ \\n # If row generation was successful, then add the newly generated \\n # state-actions.\\n MVC_StateList.append(s)\\n MVC_ActionList.append(a)\\n MVC_NStateList.append(NS)\\n MVC_sgnStateList.append(sgnS)\\n MVC_sgnNStateList.append(sgnNS)\\n \\n #------------------------------------------------------------------\\n # Store previous state and action\\n prevState = s\\n prevAction = a\\n \\n #------------------------------------------------------------------\\n # Print a header for results\\n if basisItr ==0 and rowGenCounter ==0:\\n #print('{:{fill}^{w}}'.format(' Self-guided ALP 2019 \\\\t ', fill=' ',w=148))\\n #print('\\\\n \\\\n{:{fill}^{w}}'.format(self.mdp.mdpName, fill=' ',w=148))\\n #print('\\\\n{} | {:{fill}^{w}}|'.format(''.ljust(21), 'ALP', fill=' ',w=99))\\n \\n printWithPickle('{:^3} | {:^15} | {:^8} | {:^8} | {:^8} | {:^8} | {:^8} | {:^8} | {:^5} | {:^5} | {:^5} | {:^8} |'.format(\\n 'B','VIOL','I-LB','LB','I-UB','UB','I-GAP','GAP','CYC?','C-LEN','MAX-B','T(s)'),logger)\\n printWithPickle('----------------------------------------------------------------------------------------------------------------------------',logger)\\n \\n #------------------------------------------------------------------\\n # Break the loop if constraint violation is negligible\\n isALPConverged = 0.001*prevUB\\n if basisItr ==0:\\n isALPConverged = 1e-3\\n if MVCval>=-isALPConverged:\\n \\n printWithPickle(str('{:>3d} | {:>15.5f} | {} |'.format(numBasis,(MVCval ),''.ljust(98))),logger)\\n break\\n \\n #------------------------------------------------------------------\\n # Every 50 iteration of the row generation, print results to a user.\\n elif rowGenCounter%50 ==0:\\n printWithPickle('{:>3d} | {:>15.5f} | {} |'.format(numBasis,(MVCval),''.ljust(98)),logger) \\n \\n rowGenCounter+=1 \\n\\n \\n #----------------------------------------------------------------------\\n # Only use the random stumps that have a significant weight and drop the\\n # rest of them.\\n effectiveBasis = []\\n effectiveBasisIndex = []\\n nonEffectiveBasis = []\\n for i in range(numBasis):\\n if abs(BF_Coef[i]) < 1e-3:\\n nonEffectiveBasis.append(i)\\n else:\\n effectiveBasis.append(i)\\n effectiveBasisIndex.append(i)\\n \\n #----------------------------------------------------------------------\\n # Store previous VFA\\n Perv_VFA_Coef = np.asarray([BF_Coef[i] for i in effectiveBasisIndex])\\n linCoef_Old = linCoef\\n intercept_Old = intercept\\n BF_Perv_index_list = np.asarray([BF.index_list[i] for i in effectiveBasisIndex])\\n BF_Perv_threshold_list = np.asarray([BF.threshold_list [i] for i in effectiveBasisIndex])\\n \\n #----------------------------------------------------------------------\\n # Pickle the results\\n printWithPickle('{:>3d} | {:^15} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>5} | {:>5d} | {:>5d} | {:>8.0f} |'.format(\\n numBasis,''.ljust(15),initialLB, ALPval, initialUB, prevUB, \\\\\\n (1-initialLB/initialUB)*100, (1-ALPval/prevUB)*100,\\n cyclicPolFound, cycleLength, basisItr,timeElapsed),logger) \\n \\n \\\"\\\"\\\"\\n Simulate Policy to Get Upper Bound\\n \\\"\\\"\\\" \\n #----------------------------------------------------------------------\\n # We simulate policy less frequent than updating the lower bound. Always\\n # simulate policy in the first iteration.\\n if (numBasis == 0) or (basisItr % self.ub['oftenUpdateUB'] == 0 ):\\n #----------------------------------------------------------------------\\n # Start with a random state\\n s = [np.random.uniform(low=0.0,\\n high=self.mdp.invUppBounds[_])\\n for _ in range(self.mdp.dimX)]\\n s[np.random.randint(self.mdp.dimX)] = 0.0\\n s = np.asarray(s)\\n initialState = s \\n \\n #----------------------------------------------------------------------\\n # Compute the upper bound\\n cyclicPolFound,cycleLength,cost = self.getUpperBound(state_ini=initialState, \\\\\\n BF=BF,\\n BF_Number=numBasis,\\n intercept=intercept,\\\\\\n linCoef=linCoef,\\n BF_Coef=BF_Coef)\\n \\n #------------------------------------------------------------------\\n # Save the policy cost \\n if cost > prevUB:\\n cost = prevUB\\n \\n #------------------------------------------------------------------\\n # Save the initial bounds\\n if basisItr==0:\\n initialLB =ALPval\\n initialUB =cost\\n prevUB =cost\\n \\n #------------------------------------------------------------------\\n # Print bounds obtained in the current iterate\\n printWithPickle('{:>3d} | {:^15} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>5} | {:>5d} | {:>5d} | {:8.0f} |'.format(\\n numBasis,''.ljust(15), initialLB, ALPval, initialUB, prevUB, \\\\\\n (1-initialLB/initialUB)*100, (1-ALPval/prevUB)*100,\\n cyclicPolFound, cycleLength, basisItr,timeElapsed),logger)\\n printWithPickle('----------------------------------------------------------------------------------------------------------------------------',logger)\\n \\n\\n #------------------------------------------------------------------\\n # Return the results\\n return 2, initialLB, ALPval, initialUB, prevUB, \\\\\\n cycleLength, (1-initialLB/initialUB)*100, (1-ALPval/prevUB)*100,\\\\\\n ALPval/initialLB, prevUB/ALPval, effectiveBasis, MVC_StateList, \\\\\\n MVC_ActionList, MVC_NStateList, MVC_sgnStateList, \\\\\\n MVC_sgnNStateList, intercept_Old, linCoef_Old, \\\\\\n Perv_VFA_Coef, BF_Perv_index_list, BF_Perv_threshold_list \\n \\n #----------------------------------------------------------------------\\n # Return the results in the case of not updating the policy cost\\n else:\\n printWithPickle('----------------------------------------------------------------------------------------------------------------------------',logger)\\n return 2, initialLB, ALPval, initialUB, prevUB,\\\\\\n None, (1-initialLB/initialUB)*100, (1-ALPval/prevUB)*100,\\\\\\n ALPval/initialLB, None, effectiveBasis,\\\\\\n MVC_StateList, MVC_ActionList, MVC_NStateList, \\\\\\n MVC_sgnStateList, MVC_sgnNStateList, intercept_Old,\\\\\\n linCoef_Old, Perv_VFA_Coef, BF_Perv_index_list,\\\\\\n BF_Perv_threshold_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aafd4813fd2cb4d99742b5af8d6e2a7\",\n \"score\": \"0.55521\",\n \"text\": \"def overall_fitness(self):\\n # Use a decyaing function to generate fitness\\n # Use two step decaying function\\n # First block gives importance to exploration and when as soon\\n # food has been found, the next block will focus on dropping\\n # the food on hub\\n # self.delayed_reward = round(self.beta * self.delayed_reward, 4)\\n\\n # self.individual[0].fitness = (\\n # self.ef + self.cf * 4 + self.food_collected * 8)\\n\\n self.individual[0].fitness = (1 - self.beta) * self.delayed_reward \\\\\\n + self.ef + self.cf \\\\\\n + self.food_collected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d1f668d76427c242d6d0ac7f80fb9a8\",\n \"score\": \"0.55341643\",\n \"text\": \"def _breed(self):\\n super(DifferentialEvolution, self)._breed() # call base method\\n # Differential Evolution breed operator\\n self.offspring = np.empty_like(self.population)\\n for i in range(self.population_size):\\n xr1, xr2, xr3 = np.random.choice(self.population_size, 3, replace=False) \\n trial = self.population[xr1] + \\\\\\n self.mutation_factor_*(self.population[xr2] - self.population[xr3]) \\n mask = (np.random.rand(1, self.genome_size) < self.crossover_prob_).astype(float)\\n self.offspring[i] = trial*mask + self.population[i]*(1-mask)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a466598c087b8908881c33cb75bcc3de\",\n \"score\": \"0.55249447\",\n \"text\": \"def factor(i):\\r\\n if i < k + 1:\\r\\n factor = f/population * (healthy[i] - population) + f\\r\\n elif i >= k + 1:\\r\\n factor = (f / confinement_factor) / population * (healthy[i] - population) + f / confinement_factor\\r\\n return factor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7e807c1029fc8d3d58bc690c73355d4\",\n \"score\": \"0.55227953\",\n \"text\": \"def f2(n):\\n\\tBn = 0\\n\\t#sum over k from 1 to n\\n\\tfor k in range(1, int(n)+1):\\n\\t\\tk = float(k)\\n\\t\\tBn += 1/k**2.0\\n\\tBn *= 6.0\\n\\tBn **= 0.5\\n\\treturn Bn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b57bbbe2b08ce2ba39bd5b84433d62\",\n \"score\": \"0.55149364\",\n \"text\": \"def eatCake(beta, N, Wmax=1., T=None, finite=True, plot=False):\\n states = np.linspace(0,Wmax,N) #state space vector\\n actions = np.tile(states, N).reshape((N,N)).T\\n actions = actions - actions.T\\n actions[actions<0] = 0\\n rewards = np.sqrt(actions)\\n rewards[np.triu_indices(N, k=1)] = -1e10 #pre-computed reward function\\n n_range = np.arange(N) #this is used several times, so initialize it once\\n if finite:\\n values = np.zeros((N, T+2))\\n psi = np.zeros((N, T+1))\\n for i in xrange(T,-1,-1):\\n argmaxs = np.argmax(rewards + beta*values[:,i+1].reshape(1,N), axis=1)\\n values[:,i] = (rewards + beta*values[:,i+1].reshape(1,N))[n_range,argmaxs]\\n psi[:,i] = states[argmaxs]\\n x=np.arange(0,N)\\n \\n if plot:\\n x=np.arange(0,N)\\n y=np.arange(0,T+2)\\n X,Y=np.meshgrid(x,y)\\n fig1 = plt.figure()\\n ax1= Axes3D(fig1)\\n ax1.plot_surface(states[X],Y,sp.transpose(values), cmap=cm.coolwarm)\\n plt.show ()\\n \\n fig2 = plt.figure() \\n ax2 = Axes3D(fig2)\\n y = np.arange(0,T+1)\\n X,Y=np.meshgrid(x,y)\\n ax2.plot_surface(states[X],Y,sp.transpose(psi), cmap = cm.coolwarm)\\n plt.show()\\n else:\\n values = np.zeros(N)\\n psi = np.zeros(N)\\n delta = 1.\\n while delta >= 1e-9:\\n values1 = values.copy()\\n argmaxs = np.argmax(rewards + beta*values1.reshape(1,N), axis=1)\\n values = (rewards + beta*values.reshape(1,N))[n_range, argmaxs]\\n psi = states[argmaxs]\\n delta = ((values-values1)**2).sum()\\n if plot:\\n plt.plot(states, psi)\\n plt.show()\\n \\n return values, psi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdb8a19c41709682c568e99c405f5de4\",\n \"score\": \"0.55110687\",\n \"text\": \"def schuif_hillclimber(amstel, plattegrond):\\n\\n oude_waarde = int(amstel.totale_nieuwe_huiswaarde())\\n print(f\\\"Totale wijk waarde is: {oude_waarde} euro\\\")\\n\\n\\n iteraties = 1000\\n for i in range(iteraties):\\n\\n huis, linksboven_oud = amstel.schuif_huis()\\n nieuwe_waarde = int(amstel.totale_nieuwe_huiswaarde())\\n\\n if oude_waarde > nieuwe_waarde:\\n amstel.plaats_huis(huis, linksboven_oud)\\n elif not plattegrond.grens_check(huis) or plattegrond.overlap_check(huis, amstel.wijk_lijst):\\n amstel.plaats_huis(huis, linksboven_oud)\\n else:\\n oude_waarde = nieuwe_waarde\\n if oude_waarde > amstel.hoogste_waarde:\\n amstel.hoogste_waarde = oude_waarde\\n # print(f\\\"Totale wijk waarde is: €{oude_waarde},-\\\")\\n\\n amstel.waardes_lijst.append(oude_waarde)\\n print(f\\\"____HOOGSTE waarde is: €{amstel.hoogste_waarde},- met {iteraties} iteraties___\\\")\\n # amstel.visualisatie()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c010a999984035759fd14b32b520c3ac\",\n \"score\": \"0.5508749\",\n \"text\": \"def step(self):\\n for mgene, values in zip(self.module.genes.values(), self.values):\\n values *= mgene.decay\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13b05492ca4bbf7430eaf4baca82a56d\",\n \"score\": \"0.55082846\",\n \"text\": \"def Bellman_Ford(self, w, s):\\n self.initialize_signle_source(s)\\n for i in range(1, len(self.vertices)):\\n for u, v in self.edges:\\n self.relax(u, v, w)\\n for u, v in self.edges:\\n if v.d > u.d + w(u, v):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"233dceefeb3173fff3b04aa8c03a3c05\",\n \"score\": \"0.54930604\",\n \"text\": \"def run_frank_wolfe(self):\\n self.restart()\\n\\n # Initially (t=0) we sample x_0 always\\n self.sampled_order[0] = 0\\n self.sampled_indices[0] = True\\n self.unsampled_indices = ~self.sampled_indices\\n self.W[0, 0] = 1.0\\n self.objective_curve[0] = np.nan\\n\\n for t in tqdm(range(1, self.stop_t)):\\n # The objective function of all points we can sample\\n J = self._objective_func(t)\\n # Get the index for the argmin\\n J_argmin = J.argmin()\\n # The index is not correct as we removed all indices\\n # which has been sampled, so we map back to the correct index\\n map_back_to_correct_index = self.arange_indices[self.unsampled_indices]\\n sampled_index_at_t = map_back_to_correct_index[J_argmin]\\n # Update the index arrays\\n self.sampled_order[t] = sampled_index_at_t\\n self.sampled_indices[sampled_index_at_t] = True\\n self.unsampled_indices = ~self.sampled_indices\\n self._update_W(t)\\n # Put objective value of t\\n self.objective_curve[t] = J.min()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4972a9c28df5fb030e13e00b41532b\",\n \"score\": \"0.54918635\",\n \"text\": \"def eat(self, food_available_in_cell):\\n if food_available_in_cell >= self.param_dict['F']:\\n self.weight += self.param_dict['beta'] * self.param_dict['F']\\n self.calculate_fitness()\\n return food_available_in_cell - self.param_dict['F']\\n\\n else:\\n self.weight += self.param_dict['beta'] * food_available_in_cell\\n self.calculate_fitness()\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"764a63a4c2430cc016da2db54442742b\",\n \"score\": \"0.5489449\",\n \"text\": \"def run_frank_wolfe(self):\\n self.restart()\\n\\n # Initially (t=0) we sample x_0 always\\n self.sampled_order[0] = 0\\n self.sampled_indices[0] = True\\n self.unsampled_indices = ~self.sampled_indices\\n self.W[0, 0] = 1.0\\n self.objective_curve[0] = np.nan\\n self.rho_t[0] = np.nan\\n\\n for t in tqdm(range(1, self.stop_t)):\\n # The objective function of all points we can sample\\n J = self._objective_func(t)\\n # Get the index for the argmin\\n J_argmin = J.argmin()\\n # The index is not correct as we removed all indices\\n # which has been sampled, so we map back to the correct index\\n map_back_to_correct_index = self.arange_indices[self.unsampled_indices]\\n sampled_index_at_t = map_back_to_correct_index[J_argmin]\\n # Update the index arrays\\n self.sampled_order[t] = sampled_index_at_t\\n self.sampled_indices[sampled_index_at_t] = True\\n self.unsampled_indices = ~self.sampled_indices\\n self._update_W(t)\\n # Put objective value of t\\n self.objective_curve[t] = J.min()\\n # Put rho_t\\n self.rho_t[t] = self.calculate_rho(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bde796d44444d61874620d1d119a860\",\n \"score\": \"0.5481154\",\n \"text\": \"def converge(self):\\n self.apply_tiller()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1571676e49624ab55c45c750abe675b1\",\n \"score\": \"0.5470973\",\n \"text\": \"def one_step_bellman (self):\\n pvec = self.pvec\\n values = self.values\\n dests = self.destinations\\n nx = self.nx\\n ny = self.ny\\n step_reward = self.step_reward\\n new_values = np.zeros(shape=np.shape(values))\\n \\n def val_prev(values, x, y, nx, ny, direction):\\n \\\"\\\"\\\"\\n returns the coordinates of new point after movement and retains the\\n value of the previous state\\n \\\"\\\"\\\"\\n val = values[x,y]\\n if direction == 'n':\\n x -= 1\\n elif direction == 'e':\\n y += 1\\n elif direction == 's':\\n x += 1\\n elif direction == 'w':\\n y -= 1\\n if x in range(nx) and y in range(ny):\\n val = values[x,y]\\n return val\\n \\n \\n for x in range(nx):\\n for y in range(ny):\\n if (x,y) not in dests:\\n for (p,direction) in zip(pvec,['n','e','s','w']):\\n new_values[x,y] = new_values[x,y] + p*(step_reward + \\n val_prev(values, x, y, nx, ny, direction))\\n self.values = new_values\\n return new_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca07a90b6dce5a8358c37dd1f3a7bcf7\",\n \"score\": \"0.54620963\",\n \"text\": \"def calc_fitness(self):\\n for i in range(self.n):\\n agent = self.agents[i]\\n obs = self.env.reset()\\n while True:\\n action = agent.get_action(obs)\\n obs, reward, done, _ = self.env.step(action)\\n if 0 in obs[\\\"action_mask\\\"]: # action mask activated \\n reward -= 10\\n agent.update_reward(reward)\\n if done:\\n self.ag_rewards[i] = agent.reward\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66fe1babd2aac6af7aa2a772fccab86f\",\n \"score\": \"0.5445082\",\n \"text\": \"def calcWithBernstein(self):\\n\\n tvalues = np.linspace(0,1,self.npts)\\n for i,t in enumerate(tvalues):\\n # l'i-esimo punto è uguale alla sommatoria per i da 0 a deg di cntrl[i]*bernstein(deg,i,t)\\n self.points[i] = sum([self.cntrl[j]*self.bernstein(self.deg,j,t) for j in xrange(self.deg+1)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b86c9bb43cb2afc48938ea96b3e6fadf\",\n \"score\": \"0.54448324\",\n \"text\": \"def baum_welch(Observations, Transition, Emission, Initial, iterations=1000):\\n if type(Observations) is not np.ndarray or len(Observations.shape) != 1:\\n return (None, None)\\n if type(Emission) is not np.ndarray or len(Emission.shape) != 2:\\n return None, None\\n if type(Transition) is not np.ndarray or len(Transition.shape) != 2 or \\\\\\n Transition.shape[0] != Transition.shape[1]:\\n return None, None\\n if type(Initial) is not np.ndarray or len(Initial.shape) != 2:\\n return None, None\\n if Emission.shape[0] != Transition.shape[0] != Transition.shape[0] !=\\\\\\n Initial.shape[0]:\\n return None, None\\n if Initial.shape[1] != 1:\\n return None, None\\n T = Observations.shape[0]\\n M, N = Emission.shape\\n for n in range(1, iterations):\\n alpha = forward(Observations, Emission, Transition, Initial)\\n beta = backward(Observations, Emission, Transition, Initial)\\n xi = np.zeros((M, M, T - 1))\\n for i in range(T - 1):\\n denominator = np.dot(np.dot(alpha[:, i].T, Transition) *\\n Emission[:, Observations[i + 1]].T,\\n beta[:, i + 1])\\n for j in range(M):\\n numerator = alpha[j, i] * Transition[j] *\\\\\\n Emission[:, Observations[i + 1]].T * beta[:, i + 1].T\\n xi[j, :, i] = numerator / denominator\\n gamma = np.sum(xi, axis=1)\\n Transition = np.sum(xi, 2) / np.sum(gamma, axis=1).reshape((-1, 1))\\n gamma = np.hstack((gamma,\\n np.sum(xi[:, :, T - 2], axis=0).reshape((-1, 1))))\\n denominator = np.sum(gamma, axis=1)\\n for i in range(N):\\n Emission[:, i] = np.sum(gamma[:, Observations == i], axis=1)\\n Emission = np.divide(Emission, denominator.reshape((-1, 1)))\\n return (Transition, Emission)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3479978c2a13330c504789422bd9a8f8\",\n \"score\": \"0.54438853\",\n \"text\": \"def hillFSP_mle(reactionRates):\\r\\n \\\"\\\"\\\" an input trajectory occurring in a Hill function reaction schema. \\\"\\\"\\\"\\r\\n \\\"\\\"\\\" reactionRates = ['g','g_pro','K','r'] \\\"\\\"\\\"\\r\\n global probs\\r\\n global maxN\\r\\n global numIterations\\r\\n global timeInc\\r\\n global n\\r\\n global timeVals\\r\\n if np.any(np.array(reactionRates) < 0):\\r\\n return float('Inf')\\r\\n probMatrix = np.zeros((maxN**2,maxN**2))\\r\\n for probN_A in range(maxN):\\r\\n for probN_B in range(maxN):\\r\\n probMatrix[probN_A*maxN + probN_B][probN_A*maxN + probN_B] -= \\\\\\r\\n (reactionRates[0] + reactionRates[1]/(probN_B**n + reactionRates[2]) + \\\\\\r\\n reactionRates[0] + reactionRates[1]/(probN_A**n + reactionRates[2]) + \\\\\\r\\n reactionRates[3]*probN_A + reactionRates[3]*probN_B)\\r\\n if probN_A < maxN - 1:\\r\\n probMatrix[(probN_A + 1)*maxN + probN_B][probN_A*maxN + probN_B] += \\\\\\r\\n reactionRates[0] + reactionRates[1]/(probN_B**n + reactionRates[2])\\r\\n if probN_A > 0:\\r\\n probMatrix[(probN_A - 1)*maxN + probN_B][probN_A*maxN + probN_B] += reactionRates[3]*probN_A\\r\\n if probN_B < maxN - 1:\\r\\n probMatrix[probN_A*maxN + probN_B + 1][probN_A*maxN + probN_B] += \\\\\\r\\n reactionRates[0] + reactionRates[1]/(probN_A**n + reactionRates[2])\\r\\n if probN_B > 0:\\r\\n probMatrix[probN_A*maxN + probN_B - 1][probN_A*maxN + probN_B] += reactionRates[3]*probN_B\\r\\n probMatrix = np.hstack((probMatrix,np.zeros((maxN**2,1))))\\r\\n probMatrix = np.vstack((probMatrix,-1*np.sum(probMatrix,axis=0)))\\r\\n startTime = datetime.datetime.now() \\r\\n numSteps = int(timeInc*numIterations)\\r\\n result_gpu = cudaExp(probMatrix*numSteps)\\r\\n endTime = datetime.datetime.now()\\r\\n timeVals.append((endTime - startTime).total_seconds())\\r\\n result_gpu = cp.asnumpy(result_gpu)\\r\\n loglike = -1*np.nansum(np.nansum(np.log(result_gpu)*probs.toarray()))\\r\\n tempData = open('ProgressReport_ToggleHill_' + str(simNum) + '.txt','a')\\r\\n tempData.write('g = ' + str(round(reactionRates[0],3)) + ', g_pro = ' + str(round(reactionRates[1],3)) + \\\\\\r\\n ', K = ' + str(round(reactionRates[2],4)) + ', r = ' + str(round(reactionRates[3],4)) + \\\\\\r\\n ', n = ' + str(n) + ', loglike = ' + str(round(loglike,1)) + '\\\\n')\\r\\n tempData.close()\\r\\n return loglike\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37ad7eedeef3b6bd57beb39d29b3d37e\",\n \"score\": \"0.5443434\",\n \"text\": \"def test_run_fba(self):\\n self.assertTrue(os.path.exists(os.path.join(test_file_loc, 'reaction_list.txt')))\\n reactions2run = set()\\n with open(os.path.join(test_file_loc, 'reaction_list.txt'), 'r') as f:\\n for lf in f:\\n if lf.startswith('#'):\\n continue\\n if \\\"biomass\\\" in lf.lower():\\n continue\\n r = lf.strip()\\n if r in self.__class__.modeldata.reactions:\\n reactions2run.add(r)\\n media = PyFBA.parse.pyfba_media('ArgonneLB', self.__class__.modeldata)\\n biomass = PyFBA.metabolism.biomass_equation('gram_negative')\\n status, value, growth = PyFBA.fba.run_fba(self.__class__.modeldata, reactions2run, media, biomass,\\n verbose=False)\\n self.assertTrue(growth)\\n value = float('%0.3f' % value)\\n self.assertGreaterEqual(value, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d22de6838daa779e05209eab6550428\",\n \"score\": \"0.5428214\",\n \"text\": \"def _first_order(B1, Y_res, C1, R, n1, m1, maxiter, lambdax):\\n Y_i = np.zeros((R, n1)) # Initialize 1st order contributions\\n T1 = np.zeros((m1, R, n1)) # Initialize T(emporary) matrix - 1st\\n it = 0 # Initialize iteration counter\\n\\n # First order individual estimation\\n for j in range(n1):\\n # Regularized least squares inversion ( minimize || C1 ||_2 )\\n B11 = np.matmul(np.transpose(B1[:, :, j]), B1[:, :, j])\\n\\n if (np.linalg.det(B11) == 0):\\n C1[:, j] = 0 # Ill-defined\\n else:\\n T1[:, :, j] = np.linalg.solve(np.add(B11, np.multiply(\\n lambdax, np.identity(m1))), np.transpose(B1[:, :, j]))\\n\\n C1[:, j] = np.matmul(T1[:, :, j], Y_res).reshape(m1,)\\n Y_i[:, j] = np.matmul(B1[:, :, j], C1[:, j])\\n\\n # Backfitting Method\\n var1b_old = np.sum(np.square(C1), axis=0)\\n varmax = 1\\n while (varmax > 1e-3) and (it < maxiter):\\n for j in range(n1):\\n Y_r = Y_res\\n for z in range(n1):\\n if j != z:\\n Y_r = np.subtract(Y_r, np.matmul(\\n B1[:, :, z], C1[:, z]).reshape(R, 1))\\n\\n C1[:, j] = np.matmul(T1[:, :, j], Y_r).reshape(m1,)\\n\\n var1b_new = np.sum(np.square(C1), axis=0)\\n varmax = np.max(np.absolute(np.subtract(var1b_new, var1b_old)))\\n var1b_old = var1b_new\\n it += 1\\n\\n # Now compute first-order terms\\n for j in range(n1):\\n Y_i[:, j] = np.matmul(B1[:, :, j], C1[:, j])\\n\\n # Subtract each first order term from residuals\\n Y_res = np.subtract(Y_res, Y_i[:, j].reshape(R, 1))\\n\\n return (Y_i, Y_res, C1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9392d7f1114059745e70f2c047c5e09\",\n \"score\": \"0.5421087\",\n \"text\": \"def csbal_dual_iter():\\n\\n args = sys.argv[1:]\\n if len(args) < 3:\\n print(\\\"args are: filestem, tag, freq\\\")\\n stem = args[0]\\n tag = args[1]\\n freq = float(args[2])\\n\\n # make sure the stem looks like a directory\\n if stem[-1] != os.path.sep:\\n stem = stem + os.path.sep\\n\\n # get the influence from file\\n influence = []\\n inf_file = stem+\\\"influence\\\"\\n with open(inf_file, 'rb') as filehandle:\\n influence = pickle.load(filehandle)\\n\\n tests = [('T(curr): initial unbalanced state', 't'+tag)]\\n\\n grab_data(tests, stem)\\n\\n print(\\\"Processing captured data...\\\")\\n results = batch_process(tests, stem, freq)\\n\\n print(\\\"Dual Plane Balance Results\\\")\\n correction = bal.dual_compute_weights(results, influence)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fef76a57d16a34cf0b4e1fec9e5d8e00\",\n \"score\": \"0.5416573\",\n \"text\": \"def test_bayes_factor(self):\\n res = es.bayes_factor(self.rand_s1, self.rand_s2, num_iters=2000)\\n\\n self.assertEqual (res['stop'], True)\\n self.assertAlmostEqual (res['delta'], -0.15887364780635896)\\n value025 = find_list_of_dicts_element(res['confidence_interval'], 'percentile', 2.5, 'value')\\n value975 = find_list_of_dicts_element(res['confidence_interval'], 'percentile', 97.5, 'value')\\n self.assertAlmostEqual (value025, -0.24293384641452503)\\n self.assertAlmostEqual (value975, -0.075064346336461404)\\n self.assertEqual (res['treatment_sample_size'], 1000)\\n self.assertEqual (res['control_sample_size'], 1000)\\n self.assertAlmostEqual (res['treatment_mean'], -0.045256707490195384)\\n self.assertAlmostEqual (res['control_mean'], 0.11361694031616358)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9289f419b3b4c39c8c9fff27dca68a3a\",\n \"score\": \"0.54102445\",\n \"text\": \"def step(self):\\n # py_trees.logging.level = py_trees.logging.Level.DEBUG\\n # output = py_trees.display.ascii_tree(self.bt.behaviour_tree.root)\\n\\n # Couting variables\\n self.timestamp += 1\\n self.step_count += 1\\n\\n # Increase beta\\n # self.beta = self.timestamp / self.model.iter\\n # Compute the behavior tree\\n self.bt.behaviour_tree.tick()\\n\\n # Maintain location history\\n _, gridval = self.model.grid.find_grid(self.location)\\n self.location_history.add(gridval)\\n\\n # Find the no.of food collected from the BT execution\\n self.food_collected = self.get_food_in_hub() # * self.get_food_in_hub(\\n # False)\\n\\n # Hash the phenotype with its fitness\\n # We need to move this from here to genetic step\\n self.cf = self.carrying_fitness()\\n self.ef = self.exploration_fitness()\\n\\n # Computes overall fitness using Beta function\\n self.overall_fitness()\\n\\n # Debugging\\n # decodedata = \\\"b\\\\'\\\" + self.individual[0].phenotype + \\\"\\\\'\\\"\\n # encode = self.individual[0].phenotype.encode('utf-8')\\n # print(\\n # self.name, hashlib.sha224(encode).hexdigest(\\n # ), self.food_collected, cf, ef)\\n \\\"\\\"\\\"\\n if self.individual[0].phenotype in self.phenotypes.keys():\\n e, c, f = self.phenotypes[self.individual[0].phenotype]\\n if f < self.food_collected:\\n f = self.food_collected\\n if c < self.cf:\\n c = self.cf\\n if e < self.ef:\\n e = self.ef\\n\\n self.phenotypes[self.individual[0].phenotype] = (e, c, f)\\n else:\\n # if int(cf) == 0 and int(ef) == 0\\n # and int(self.food_collected) == 0:\\n if int(self.cf) == 0 and int(self.food_collected) == 0:\\n pass\\n else:\\n self.phenotypes[self.individual[0].phenotype] = (\\n self.ef, self.cf, self.food_collected)\\n \\\"\\\"\\\"\\n self.phenotypes = dict()\\n self.phenotypes[self.individual[0].phenotype] = (\\n self.individual[0].fitness)\\n\\n # Find the nearby agents\\n cellmates = self.model.grid.get_objects_from_grid(\\n type(self).__name__, self.location)\\n\\n # If neighbours found, store the genome\\n if len(cellmates) > 1:\\n self.store_genome(cellmates)\\n\\n # Logic for gentic operations.\\n # If the genome storage has enough genomes and agents has done some\\n # exploration then compute the genetic step OR\\n # 600 time step has passed and the agent has not done anything useful\\n # then also perform genetic step\\n storage_threshold = len(\\n self.genome_storage) >= (self.model.num_agents / 10)\\n\\n # New logic to invoke genetic step\\n if self.individual[0].fitness <= 0 and self.timestamp > 100:\\n individual = initialisation(self.parameter, 10)\\n individual = evaluate_fitness(individual, self.parameter)\\n self.genome_storage = self.genome_storage + individual\\n self.genetic_step()\\n elif (\\n (\\n self.individual[0].fitness >= 0 and storage_threshold\\n ) and self.timestamp > 200 and self.food_collected <= 0):\\n self.genetic_step()\\n \\\"\\\"\\\"\\n if storage_threshold:\\n self.genetic_step()\\n elif (\\n (\\n storage_threshold is False and self.timestamp > 100\\n ) and (self.exploration_fitness() < 2)):\\n individual = initialisation(self.parameter, 10)\\n individual = evaluate_fitness(individual, self.parameter)\\n self.genome_storage = self.genome_storage + individual\\n self.genetic_step()\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57726a4a7974611fec7f15d01147751\",\n \"score\": \"0.54064596\",\n \"text\": \"def run(self):\\n # Itereer over je batch en verzamel de som bij elkaar net zoals de single threaded versie ( < 5 regels code)\\n N = round(self.n1 / self.n2)\\n self.n1 -= N\\n\\n for i in range(N):\\n self.value += f(i * self.deltaX)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c475a14b0f06dfbee285971d6de3bf7\",\n \"score\": \"0.5406371\",\n \"text\": \"def straight_bet(self):\\n for i in range(0, len(self.bins)):\\n self.addOutcome(Outcome(str(i), 35), [i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730b25cd8108317373d56630971ba534\",\n \"score\": \"0.54038376\",\n \"text\": \"def cal_BFR(c):\\r\\n # Initialize the variables\\r\\n n = len(c)\\r\\n sumc = [0, 0]\\r\\n sumsqc = [0, 0]\\r\\n varc = [0, 0]\\r\\n sdc = [0, 0]\\r\\n\\r\\n for i in range(n): # Loop for number of elements in cluster\\r\\n sumc[0] += c[i][0] # Calculate SUM\\r\\n sumc[1] += c[i][1]\\r\\n sumsqc[0] += pow(c[i][0], 2) # Calculate SUM SQUARE\\r\\n sumsqc[1] += pow(c[i][1], 2)\\r\\n\\r\\n print('==> Cluster - ', c)\\r\\n print('N for cluster is : ', n)\\r\\n print('SUM for cluster is : ', sumc)\\r\\n print('SUMSQ for cluster is : ', sumsqc, '\\\\n')\\r\\n\\r\\n varc[0] = round(sumsqc[0]/n - pow(sumc[0]/n, 2), 2) # Calculate Variance for 1st dimension\\r\\n varc[1] = round(sumsqc[1]/n - pow(sumc[1]/n, 2), 2) # Calculate Variance for 2nd dimension\\r\\n\\r\\n sdc[0] = round(math.sqrt(varc[0]), 2) # Calculate Standard Deviation for 1st dimension\\r\\n sdc[1] = round(math.sqrt(varc[1]), 2) # Calculate Standard Deviation for 2nd dimension\\r\\n\\r\\n print('Variance for cluster is : ', varc)\\r\\n print('Standard Deviation for cluster is : ', sdc, '\\\\n')\\r\\n\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d86eb3518fbef4dbb491c2d67a67744f\",\n \"score\": \"0.53985715\",\n \"text\": \"def newton_iteration(f: Callable, df: Callable, eps: float, x0: float = None, a: float = None, b: float = None,\\n weight: float = 0.9, display: bool = False, max_iterations: int = 100) -> float:\\n x = np.inf\\n if x0 is None:\\n x0 = (a + b) / 2\\n if a is not None and b is not None and a == b:\\n return a\\n x_next = x0\\n iterations = 0\\n while abs(x - x_next) > eps and iterations < max_iterations:\\n iterations += 1\\n x = x_next\\n\\n if display:\\n import matplotlib.pyplot as plt\\n xx0 = a or x-1\\n xx1 = b or x+1\\n xx = np.linspace(xx0, xx1, 100)\\n yy = np.array(list(map(f, xx)))\\n plt.plot(xx, yy)\\n plt.axvline(x=x)\\n plt.show()\\n\\n f_x = f(x)\\n try:\\n df_x = df(x)\\n except ZeroDivisionError:\\n df_x = (f_x - f(x-eps))/eps\\n if df_x != 0:\\n x_next = x - f_x / df_x\\n\\n if a is not None and x_next < a:\\n x_next = weight * a + (1 - weight) * x\\n elif b is not None and x_next > b:\\n x_next = weight * b + (1 - weight) * x\\n\\n if a is not None and x_next < a:\\n x_next = a\\n if b is not None and x_next > b:\\n x_next = b\\n\\n return x_next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64787a00a74d0a4c5e9ff29f8c86c30a\",\n \"score\": \"0.53868085\",\n \"text\": \"def problem2(config, iterations: int=1000):\\n all_returns = []\\n\\n def evaluate(p, episodes):\\n returns = []\\n for i in range(episodes):\\n r = run_gridworld_episode(p)\\n returns.append(r)\\n all_returns.append(r)\\n\\n return np.mean(returns)\\n\\n agent_policy = TabularSoftmax(25, 4)\\n agent = FCHC(agent_policy.parameters, sigma=config[0], evaluationFunction=evaluate, numEpisodes=config[1])\\n bar = range(iterations)\\n for i in bar:\\n agent_policy.parameters = agent.train()\\n # bar.set_description(\\\"Average return: {}\\\".format(evaluate(agent_policy.parameters, 5)))\\n return np.array(all_returns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2e49d62be99045459d3cabd17b61c11\",\n \"score\": \"0.5386758\",\n \"text\": \"def e2(n):\\n return sum([(1.0 / iter_fact(i)) for i in xrange(1, n+1)]) + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8948f83a08c5b3d252d780878de73bb8\",\n \"score\": \"0.53835917\",\n \"text\": \"def run_pla(trainset):\\n converged = False\\n iter_count = 0\\n \\n w = np.zeros(3)\\n\\n while not converged or iter_count<500:\\n misclass = [x for x in trainset if x[1] != do_hip(w, x[0])]\\n if len(misclass) == 0:\\n #return iter_count, w\\n break\\n else:\\n pivot = choice(misclass)\\n w = w + pivot[1] * pivot[0]\\n iter_count += 1\\n\\n return w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e97f45c79093f19d8385cc61754feb1\",\n \"score\": \"0.53780806\",\n \"text\": \"def computeForces(self, neighbors=[], eps=0.1, nu = 0.5): \\n if not self.atGoal:\\n self.F = (self.gvel-self.vel)/self.ksi #set F as F_goal\\n valid_neighbors = self.in_range(neighbors, self.dhor) #get valid agents list within the dhor range\\n #valid_neighbors = np.array(self.in_range(neighbors, self.dhor)) #use numpy.array.argsort() to get the nearest_neighbors\\n '''\\n k = 4 #k-nearest method\\n n_nearest = min(k, len(valid_neighbors))\\n dist = []\\n #dist = np.zeros(len(valid_neighbors))\\n for i in valid_neighbors:\\n dist.append(self.get_distance(i))\\n #dist[i] = self.get_distance(valid_neighbors[i])\\n #nearest_neighbors = ([x for _,x in sorted(zip(dist,valid_neighbors))])[:n_nearest] #get the nearest k agents list within the dhor range\\n #nearest_neighbors = valid_neighbors[dist.argsort()][:n_nearest]\\n '''\\n if(len(valid_neighbors)>0):\\n for target in valid_neighbors:\\n self.F+=self.get_fa(target, 0.2) #change the function called here to switch between ttc and powerlaw\\n if (np.linalg.norm(self.F) > self.maxF):\\n self.F = self.F*(self.maxF/np.linalg.norm(self.F))\\n else:\\n self.F = (self.gvel-self.vel)/self.ksi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a484b78d4cf8011768b209f769f0fbe\",\n \"score\": \"0.5371538\",\n \"text\": \"def run(self):\\n self.run_frank_wolfe()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a484b78d4cf8011768b209f769f0fbe\",\n \"score\": \"0.5371538\",\n \"text\": \"def run(self):\\n self.run_frank_wolfe()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda4c8224db04c4d408b363ac8bda9df\",\n \"score\": \"0.53707176\",\n \"text\": \"def _blowup(self, year):\\n # pylint: disable=too-many-statements\\n # pylint: disable=too-many-locals\\n AWAGE = self.BF.AWAGE[year]\\n AINTS = self.BF.AINTS[year]\\n ADIVS = self.BF.ADIVS[year]\\n ATXPY = self.BF.ATXPY[year]\\n ASCHCI = self.BF.ASCHCI[year]\\n ASCHCL = self.BF.ASCHCL[year]\\n ACGNS = self.BF.ACGNS[year]\\n ASCHEI = self.BF.ASCHEI[year]\\n ASCHEL = self.BF.ASCHEL[year]\\n ASCHF = self.BF.ASCHF[year]\\n AUCOMP = self.BF.AUCOMP[year]\\n ASOCSEC = self.BF.ASOCSEC[year]\\n ACPIM = self.BF.ACPIM[year]\\n AGDPN = self.BF.AGDPN[year]\\n ABOOK = self.BF.ABOOK[year]\\n AIPD = self.BF.AIPD[year]\\n self.e00200 *= AWAGE\\n self.e00200p *= AWAGE\\n self.e00200s *= AWAGE\\n self.e00300 *= AINTS\\n self.e00400 *= AINTS\\n self.e00600 *= ADIVS\\n self.e00650 *= ADIVS\\n self.e00700 *= ATXPY\\n self.e00800 *= ATXPY\\n self.e00900[:] = np.where(self.e00900 >= 0,\\n self.e00900 * ASCHCI,\\n self.e00900 * ASCHCL)\\n self.e00900s[:] = np.where(self.e00900s >= 0,\\n self.e00900s * ASCHCI,\\n self.e00900s * ASCHCL)\\n self.e00900p[:] = np.where(self.e00900p >= 0,\\n self.e00900p * ASCHCI,\\n self.e00900p * ASCHCL)\\n self.e01100 *= ACGNS\\n self.e01200 *= ACGNS\\n self.e01400 *= ATXPY\\n self.e01500 *= ATXPY\\n self.e01700 *= ATXPY\\n self.e02000[:] = np.where(self.e02000 >= 0,\\n self.e02000 * ASCHEI,\\n self.e02000 * ASCHEL)\\n self.e02100 *= ASCHF\\n self.e02100p *= ASCHF\\n self.e02100s *= ASCHF\\n self.e02300 *= AUCOMP\\n self.e02400 *= ASOCSEC\\n self.e03150 *= ATXPY\\n self.e03210 *= ATXPY\\n self.e03220 *= ATXPY\\n self.e03230 *= ATXPY\\n self.e03270 *= ACPIM\\n self.e03240 *= AGDPN\\n self.e03290 *= ACPIM\\n self.e03300 *= ATXPY\\n self.e03400 *= ATXPY\\n self.e03500 *= ATXPY\\n self.e07240 *= ATXPY\\n self.e07260 *= ATXPY\\n self.e07300 *= ABOOK\\n self.e07400 *= ABOOK\\n self.p08000 *= ATXPY\\n self.e09700 *= ATXPY\\n self.e09800 *= ATXPY\\n self.e09900 *= ATXPY\\n self.e11200 *= ATXPY\\n # ITEMIZED DEDUCTIONS\\n self.e17500 *= ACPIM\\n self.e18400 *= ATXPY\\n self.e18500 *= ATXPY\\n self.e19200 *= AIPD\\n self.e19800 *= ATXPY\\n self.e20100 *= ATXPY\\n self.e20400 *= ATXPY\\n self.e20500 *= ATXPY\\n # CAPITAL GAINS\\n self.p22250 *= ACGNS\\n self.p23250 *= ACGNS\\n self.e24515 *= ACGNS\\n self.e24518 *= ACGNS\\n # SCHEDULE E\\n self.p25470 *= ASCHEI\\n self.e26270 *= ASCHEI\\n self.e27200 *= ASCHEI\\n # MISCELLANOUS SCHEDULES\\n self.e07600 *= ATXPY\\n self.e32800 *= ATXPY\\n self.e58990 *= ATXPY\\n self.e62900 *= ATXPY\\n self.e87530 *= ATXPY\\n self.p87521 *= ATXPY\\n self.cmbtp *= ATXPY\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b356c35b08b6fcea275ac756ad58fa\",\n \"score\": \"0.53700113\",\n \"text\": \"def viterbi(Observation, Emission, Transition, Initial):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d429e3a081af91050eab2918d58b75\",\n \"score\": \"0.5368921\",\n \"text\": \"def __init__(self, mdp, discount = 0.9, iterations = 100):\\n self.mdp = mdp\\n self.discount = discount\\n self.iterations = iterations\\n self.values = util.Counter() # A Counter is a dict with default 0\\n # Write value iteration code here\\n\\n # CAVEAT HERE: DISTINGUISH PSEUDO-TERMINALS FROM ACTUAL TERMINALS\\n \\n \\n for i in range(0, self.iterations):\\n values_copy = self.values.copy()\\n # delta = 0\\n \\n for s in self.mdp.getStates():\\n if self.mdp.isTerminal(s):\\n continue\\n # this is to prevent the loop from evaluating the dummy terminal state\\n \\n self.values[s] = self.mdp.getReward(s, None, None) + self.discount * max([ sum([prob * values_copy[s_next] for (s_next, prob) in self.mdp.getTransitionStatesAndProbs(s, a)]) for a in self.mdp.getPossibleActions(s)])\\n #delta = max(delta, abs(values_copy[s] - values[s]))\\n\\n \\\"\\\"\\\"\\n if delta < self.epsilon * (1 - self.discount) / self.discount:\\n return self.values\\n \\n \\\"\\\"\\\"\\n #print self.values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"013b4ea1bec45e73955dcf6ecad6d060\",\n \"score\": \"0.53654516\",\n \"text\": \"def f3():\\n def f1(a,z):\\n \\\"\\\"\\\" \\n Determines the binding energy per nucleon given the atomic mass number\\n a and the atomic number z, using the semi-empirical mass formula.\\n \\\"\\\"\\\"\\n # First define our parameters (in units of MeV)\\n a1 = 15.67\\n a2 = 17.23\\n a3 = 0.75\\n a4 = 93.2\\n if a%2 == 1:\\n a5 = 0.0 # a is odd\\n elif z%2 == 0:\\n a5 = 12.0 # a and z are even\\n else:\\n a5 = -12.0 # a is even, z is odd\\n\\n # Now compute the binding energy\\n b = a1*a - a2*a**(2/3) - a3*(z**2)/(a**(1/3)) - a4*((a-2*z)**2)/a + a5/(a**0.5)\\n\\n # provide binding energy per nucleon\\n return b/a\\n def f2(z):\\n \\\"\\\"\\\"\\n Takes an atomic number z and determines\\n the maximum value of b/a for a = z to\\n a = 3z.\\n \\\"\\\"\\\"\\n out = []\\n # Make a list of tuples with entries (b/a,a)\\n for a in range(z,3*z):\\n out.append(f1(a,z))\\n # Return the max value of b/a\\n return max(out)\\n\\n out = []\\n # For each value of z, snag the output of f2()\\n for z in range(1,101):\\n out.append((f2(z),z))\\n \\n # Return the list of tuples \\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39f0fd76d77253d4537d062bfe334b33\",\n \"score\": \"0.53615665\",\n \"text\": \"def f(stakes):\\r\\n\\r\\n # INITIALIZE END_BANKROLLS AND OBJECTIVE BEFORE EACH OPTIMIZATION STEP\\r\\n end_bankrolls, objective = len(selections) * [bankroll - np.sum(stakes)] + np.asarray(existing_bets_profits), 0.00\\r\\n\\r\\n # CALCULATE END_BANKROLL FOR EACH SELECTION WITH A 'STAKES' VECTOR OF SIZE LEN(SELECTIONS) AS VARIABLE\\r\\n for index_selection, selection in enumerate(selections):\\r\\n\\r\\n # ADD WINS & LOSSES\\r\\n end_bankrolls[index_selection] += stakes[index_selection] * selection['odds_book']\\r\\n\\r\\n # RETURN THE OBJECTIVE AS A SUMPRODUCT OF PROBABILITIES AND END_BANKROLLS - THIS IS THE FUNCTION TO BE MAXIMIZED\\r\\n # SEE https://www.pinnacle.com/en/betting-articles/Betting-Strategy/the-real-kelly-criterion/HZKJTFCB3KNYN9CJ\\r\\n return -sum([p * e for p, e in zip(probs, np.log(end_bankrolls))])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88301d2850ebb382f3453172d4fffcdb\",\n \"score\": \"0.53606886\",\n \"text\": \"def single_fit_loop(self, epsilon=0):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c843fddcae3a0aecc8bc3274f229e3bd\",\n \"score\": \"0.53603166\",\n \"text\": \"def update_firm(bac, firm):\\n return 100 - bac\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ead608373e997a811506ea417fbd8b3\",\n \"score\": \"0.5357318\",\n \"text\": \"def foiling(b_one: List[str], b_two: List[str], var_type: str) -> List[str]:\\n #print(b_one, b_two, var_type)\\n b_one_string = stringify(b_one)\\n b_one_obj = Algebra(b_one_string+\\\"=0\\\")\\n\\n b_two_string = stringify(b_two)\\n b_two_obj = Algebra(b_two_string+\\\"=0\\\")\\n\\n # Create array with only coefficients\\n b_one_obj.get_coeff()\\n b_two_obj.get_coeff()\\n b_one_coeff = copy.deepcopy(b_one_obj.coeff)\\n b_two_coeff = copy.deepcopy(b_two_obj.coeff)\\n b_one_obj, b_two_obj = None, None\\n # print(\\\"b_one_coeff = \\\", b_one_coeff.eqn, \\\"b_two_coeff = \\\", b_two_coeff.eqn)\\n\\n ans_coeff = []\\n ans_powers = []\\n b_one_pow = len(b_one_coeff) - 1\\n b_two_pow = len(b_two_coeff) - 1\\n\\n # Foiling\\n for i in range(0, len(b_one_coeff)):\\n\\n for j in range(0, len(b_two_coeff)):\\n ans_coeff.append(b_one_coeff[i] * b_two_coeff[j])\\n ans_powers.append((b_one_pow - i) + (b_two_pow - j))\\n\\n # print(\\\"ans_coeff = \\\", ans_coeff)\\n # Simplifying the answer\\n simp_ans_coeff = []\\n temp_ind = []\\n temp = 0\\n for i in range(ans_powers[0], -1, -1):\\n\\n for j in range(0, len(ans_powers)):\\n\\n if ans_powers[j] == i:\\n temp += ans_coeff[j]\\n\\n simp_ans_coeff.append(temp)\\n temp = 0\\n\\n # print(\\\"simp_ans_coeff = \\\", simp_ans_coeff)\\n ans = []\\n j = 0\\n highest_deg_ans = len(simp_ans_coeff) - 1\\n\\n if highest_deg_ans > 1:\\n\\n for i in range(highest_deg_ans, 1, -1):\\n\\n if not ans:\\n\\n if simp_ans_coeff[j].imag == 0:\\n\\n if simp_ans_coeff[j] == 1:\\n\\n ans.append(var_type)\\n ans.append(\\\"^\\\")\\n ans.append(str(float(i)))\\n j += 1\\n\\n elif simp_ans_coeff[j] == 0:\\n\\n pass\\n j += 1\\n\\n elif simp_ans_coeff[j] < 0:\\n\\n if simp_ans_coeff[j] == -1:\\n\\n ans.append(\\\"-\\\" + var_type)\\n ans.append(\\\"^\\\")\\n ans.append(str(float(i)))\\n j += 1\\n\\n else:\\n\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n ans.append(\\\"^\\\")\\n ans.append(str(float(i)))\\n j += 1\\n\\n else:\\n\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n ans.append(\\\"^\\\")\\n ans.append(str(float(i)))\\n j += 1\\n\\n # simp_coeff_ans[j] is a complex number\\n else:\\n\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n ans.append(\\\"^\\\")\\n ans.append(str(float(i)))\\n j += 1\\n\\n else:\\n\\n if simp_ans_coeff[j].imag == 0:\\n\\n if simp_ans_coeff[j] == 1:\\n\\n ans.append(\\\"+\\\")\\n ans.append(var_type)\\n ans.append(\\\"^\\\")\\n ans.append(str(float(i)))\\n j += 1\\n\\n elif simp_ans_coeff[j] == 0:\\n\\n pass\\n j += 1\\n\\n elif simp_ans_coeff[j] < 0:\\n\\n if simp_ans_coeff[j] == -1:\\n\\n ans.append(\\\"-\\\")\\n ans.append(var_type)\\n ans.append(\\\"^\\\")\\n ans.append(str(float(i)))\\n j += 1\\n\\n else:\\n\\n ans.append(\\\"-\\\")\\n ans.append(str(abs(simp_ans_coeff[j])))\\n ans.append(var_type)\\n ans.append(\\\"^\\\")\\n ans.append(str(float(i)))\\n j += 1\\n\\n else:\\n\\n ans.append(\\\"+\\\")\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n ans.append(\\\"^\\\")\\n ans.append(str(float(i)))\\n j += 1\\n\\n # simp_coeff_ans[j] is a complex number\\n else:\\n\\n ans.append(\\\"+\\\")\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n ans.append(\\\"^\\\")\\n ans.append(str(float(i)))\\n j += 1\\n\\n if simp_ans_coeff[j].imag == 0:\\n\\n if simp_ans_coeff[j] == 1:\\n\\n ans.append(\\\"+\\\")\\n ans.append(var_type)\\n j += 1\\n\\n elif simp_ans_coeff[j] == 0:\\n\\n pass\\n j += 1\\n\\n elif simp_ans_coeff[j] < 0:\\n\\n if simp_ans_coeff[j] == -1:\\n\\n ans.append(\\\"-\\\")\\n ans.append(var_type)\\n j += 1\\n\\n else:\\n\\n ans.append(\\\"-\\\")\\n ans.append(str(abs(simp_ans_coeff[j])))\\n ans.append(var_type)\\n j += 1\\n\\n else:\\n\\n ans.append(\\\"+\\\")\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n j += 1\\n\\n else:\\n\\n ans.append(\\\"+\\\")\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n j += 1\\n\\n if simp_ans_coeff[j].imag == 0:\\n\\n if simp_ans_coeff[j] == 0:\\n\\n pass\\n\\n else:\\n\\n if simp_ans_coeff[j] < 0:\\n\\n ans.append(\\\"-\\\")\\n ans.append(str(abs(simp_ans_coeff[j])))\\n\\n else:\\n\\n ans.append(\\\"+\\\")\\n ans.append(str(simp_ans_coeff[j]))\\n\\n else:\\n\\n ans.append(\\\"+\\\")\\n ans.append(str(simp_ans_coeff[j]))\\n\\n else:\\n\\n if not ans:\\n\\n if simp_ans_coeff[j].imag == 0:\\n\\n if simp_ans_coeff[j] == 1:\\n\\n ans.append(var_type)\\n j += 1\\n\\n elif simp_ans_coeff[j] == 0:\\n\\n pass\\n j += 1\\n\\n elif simp_ans_coeff[j] < 0:\\n\\n if simp_ans_coeff[j] == -1:\\n\\n ans.append(\\\"-\\\")\\n ans.append(var_type)\\n j += 1\\n\\n else:\\n\\n ans.append(\\\"-\\\")\\n ans.append(str(abs(simp_ans_coeff[j])))\\n ans.append(var_type)\\n j += 1\\n\\n else:\\n\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n j += 1\\n\\n else:\\n\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n j += 1\\n\\n else:\\n\\n if simp_ans_coeff[j].imag == 0:\\n\\n if simp_ans_coeff[j] == 1:\\n\\n ans.append(\\\"+\\\")\\n ans.append(var_type)\\n j += 1\\n\\n elif simp_ans_coeff[j] == 0:\\n\\n pass\\n j += 1\\n\\n elif simp_ans_coeff[j] < 0:\\n\\n if simp_ans_coeff[j] == -1:\\n\\n ans.append(\\\"-\\\")\\n ans.append(var_type)\\n j += 1\\n\\n else:\\n\\n ans.append(\\\"-\\\")\\n ans.append(str(abs(simp_ans_coeff[j])))\\n ans.append(var_type)\\n j += 1\\n\\n else:\\n\\n ans.append(\\\"+\\\")\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n j += 1\\n\\n else:\\n\\n ans.append(\\\"+\\\")\\n ans.append(str(simp_ans_coeff[j]))\\n ans.append(var_type)\\n j += 1\\n\\n if simp_ans_coeff[j].imag == 0:\\n\\n if simp_ans_coeff[j] == 0:\\n\\n pass\\n\\n else:\\n\\n if simp_ans_coeff[j] < 0:\\n\\n ans.append(\\\"-\\\")\\n ans.append(str(abs(simp_ans_coeff[j])))\\n\\n else:\\n\\n ans.append(\\\"+\\\")\\n ans.append(str(simp_ans_coeff[j]))\\n\\n else:\\n\\n ans.append(\\\"+\\\")\\n ans.append(str(simp_ans_coeff[j]))\\n\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7051f3813e558376b199c2c3446cd0a6\",\n \"score\": \"0.5356208\",\n \"text\": \"def BO_Fockbuild(self,P,it = -1):\\n nA = self.nA\\n Pt = 2.0*TransMat(P,self.U,-1)\\n PtA = 2.0*P[:nA,:nA]\\n if self.params[\\\"Model\\\"] == \\\"TDHF\\\":\\n J,K = self.get_jk(Pt,2)\\n self.Jtilde = TransMat(J,self.U) # AO to BO\\n self.Ktilde = TransMat(K,self.U) # AO TO BO\\n\\n self.JKtilde = self.Jtilde - 0.5* self.Ktilde # combine in BO\\n\\n return self.Htilde + 0.5 * (self.JKtilde + self.JKtilde.T.conj())\\n elif self.params[\\\"Model\\\"] == \\\"TDDFT\\\":\\n #Ptb = 2.0 * P\\n #self.Jtilde = self.get_j(Ptb,2)\\n #J = self.get_j(Pt,int(2))\\n J = self.get_j_c(Pt,int(2))\\n #self.Jtilde = J\\n self.Jtilde = TransMat(J,self.U)\\n #self.Jtilde[:nA,:nA] += self.get_j(PtA,0)\\n #self.Jtilde[:nA,:nA] -= self.get_j(PtA,1)\\n Veff = self.get_vxc(Pt,int(2))\\n Veff = TransMat(Veff,self.U)\\n Veff[:nA,:nA] += self.get_vxc(PtA,int(0))\\n Veff[:nA,:nA] -= self.get_vxc(PtA,int(1))\\n #print \\\"Vxc\\\\n\\\",Veff\\n if(self.hyb[0] > 0.01):\\n self.Ktilde = np.zeros((self.n,self.n)).astype(complex)\\n\\n # this step may diverge the fockbuild(confirmed)\\n #Ktilde = self.get_k(Pt,int(2))\\n #self.Ktilde[:nA,:nA] = Ktilde[:nA,:nA].copy()\\n\\n #Ktilde = self.get_k(PtA,int(0))\\n Ktilde = self.get_k_c(PtA,int(0))\\n self.Ktilde[:nA,:nA] = Ktilde\\n #print \\\"Ktilde\\\\n\\\",-0.5 * self.hyb[0] * Ktilde\\n Veff[:nA,:nA] += -0.5 * self.hyb[0] * self.Ktilde[:nA,:nA]\\n JKtilde = self.Jtilde + Veff\\n if self.adiis and it > 0:\\n # Kevin: Do the extrapolation with all AO quantities and return the extrapolated fock matrix in BO basis.\\n #return TransMat(self.adiis.update(self.S,Pt,self.H + 0.5 * (JKtilde + JKtilde.T.conj())),self.U)\\n return self.adiis.update(self.Stilde,2*P,self.Htilde + 0.5 * (JKtilde + JKtilde.T.conj()))\\n else:\\n return self.Htilde + 0.5 * (JKtilde + JKtilde.T.conj())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c27a3d3edf442bc4dfbb19b80a2e018f\",\n \"score\": \"0.53562057\",\n \"text\": \"def reddening_cal00(lam1, ebv):\\n lam = 1e4/lam1 # Convert Angstrom to micrometres and take 1/lambda\\n rv = 4.05 # C+00 equation (5)\\n\\n # C+00 equation (3) but extrapolate for lam > 2.2\\n # C+00 equation (4) but extrapolate for lam < 0.12\\n k1 = np.where(lam >= 6300,\\n rv + 2.659*(1.040*lam - 1.857),\\n rv + 2.659*(1.509*lam - 0.198*lam**2 + 0.011*lam**3 - 2.156))\\n fact = 10**(-0.4*ebv*k1.clip(0)) # Calzetti+00 equation (2) with opposite sign\\n\\n return fact # The model spectrum has to be multiplied by this vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d688929a49e6d7833a848d43cd50582\",\n \"score\": \"0.5352535\",\n \"text\": \"def calculate_reward(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eef0d088ab2e8f030d9dbd938880b71\",\n \"score\": \"0.53505987\",\n \"text\": \"def valueIteration(self, state):\\n\\n # empirically determined optimal gamma value\\n gamma = 0.6\\n\\n # error bound for termination condition\\n epsilon = 0.001\\n\\n # inputs representing various components of the Bellman equation\\n states = self.stateDict # set of states, S\\n gridVals = self.rewardDict # set of rewards, R(s)\\n utilDict = self.utils # set of utilities initialised at 0, U\\n\\n # loop indefinitely until convergence as determined by break condition\\n while True:\\n # delta is the change in utility for any one state from one iteration to the next; reset to 0 at start\\n # of each iteration.\\n delta = 0\\n\\n # for each square in the grid, register the current utility value for later assessment and generate a\\n # temporary dictionary structure to store new utilities.\\n for square, utility in utilDict.items():\\n U = utility\\n tmp_utils = {}\\n\\n # assign a new utility value to each grid square based on the reward at that square and the utilities of\\n # the set of possible states from that square\\n for direction, state in states[square].items():\\n\\n U_s = gridVals[square] + gamma * (\\n 0.8 * utilDict[state[0]] + 0.1 * utilDict[state[1]] + 0.1 * utilDict[state[2]])\\n tmp_utils[direction] = U_s\\n\\n # take the maximum expected utility value for the set of actions A(s) at each square, assign it to the\\n # utility dictionary, U.\\n utilDict[square] = max(tmp_utils.values())\\n\\n # find the difference in utility values between any state between iterations, check whether it satisfies\\n # the termination condition and return the dictionary of final utilities if so\\n delta = max(delta, abs(utilDict[square] - U))\\n if delta < epsilon * (1 - gamma) / gamma:\\n return utilDict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9d1e75c494a74e8bd5d2595ad747d93\",\n \"score\": \"0.53504837\",\n \"text\": \"def betterEvaluationFunction(currentGameState):\\n\\n \\\"*** YOUR CODE HERE ***\\\"\\n foodGrid = currentGameState.getFood()\\n counter = 0\\n total = 0\\n returnAmount = 0\\n\\n for i in foodGrid.asList():\\n counter = counter + 1\\n total += manhattanDistance(i, currentGameState.getPacmanPosition())\\n if counter != 0:\\n average = total / counter\\n\\n returnAmount = 1 / average\\n returnAmount *= 100\\n\\n if currentGameState.getGhostState(1).scaredTimer == 38:\\n returnAmount += 100\\n\\n\\n return returnAmount + currentGameState.getScore()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6de99d3ef15af8fb471974189c53f1a0\",\n \"score\": \"0.53503096\",\n \"text\": \"def exo1():\\n niter = 200\\n u = f\\n energy = []\\n for i in 1: niter:\\n energy(i) = 1/ 2*norm(f-u, 'fro')^2 + lambda*J(u)\\n u = u - tau*(u - f + lambda*GradJ(u))\\n plot(1: niter, energy); axis('tight')\\n set_label('iteration', 'Energy')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fd20149cb166a2825ca828a2b1e9a56\",\n \"score\": \"0.5346577\",\n \"text\": \"def valueIteration(self):\\n\\n # Initialize values \\n self.values = np.zeros(len(self.s))\\n \\n # loop until it converges\\n while True:\\n \\n # values of previous iteration for checking if it has converged \\n previous_values = np.copy(self.values)\\n # loop over every state\\n for i in range(len(self.s)):\\n # get value of action a (going one state further or staying at the end state\\n # and of action b (going back to state 0) \\n if i<len(self.s)-1:\\n temp=np.array([self.values[i+1],self.values[0]])\\n else:\\n # if current state is final state 4 \\n temp=np.array([self.values[i],self.values[0]])\\n \\n # bellman equation for calculating the values \\n # multiplying the transition matrix at state i with rewards matrix at same state i\\n # ... and adding that to the previous values for action a and b, multiplied with discount, \\n # and getting the maximum value out of the whole result\\n self.values[i] = np.max(np.dot(self.t[i][:][:], self.r[i][:]) \\\\\\n + self.discount *temp)\\n # Check if values converged\\n if np.max(np.abs(self.values - previous_values)) <= self.epsilon:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd472c9d2c8dee704392aaf70d8e5516\",\n \"score\": \"0.53447545\",\n \"text\": \"def getFitness(self, items, knapsack):\\n totalFitness = 0\\n totalWeight = 0\\n for bit, item in zip(self.solution, items):\\n if bit == 1:\\n totalFitness += item.value\\n totalWeight += item.weight\\n while totalWeight > knapsack.capacity:\\n onesList = []\\n for i in range(0, len(self.solution)):\\n if self.solution[i] == 1:\\n onesList.append(i)\\n sel = random.choice(onesList)\\n self.solution[sel] -= 1\\n totalFitness -= items[sel].value\\n totalWeight -= items[sel].weight\\n self.fitness = totalFitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"add058a489d36304d9e5f4a696031955\",\n \"score\": \"0.5338278\",\n \"text\": \"def test_dozen_bets(self):\\n for dozen in range(3):\\n outcome = Outcome('Dozen {}'.format(dozen+1), RouletteOdds.dozenbet)\\n for straight in range(12):\\n self.assertIn(outcome, self.wheel.bins[dozen * 12 + straight + 1])\\n self.assertIn(outcome, self.wheel.all_outcomes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e1dc006e0415de3e74180110215d34f\",\n \"score\": \"0.53280675\",\n \"text\": \"def _F(self, X, b, c = 0.001):\\n\\n if b == 1:\\n b = 1 + c\\n\\n prefac = (b - 1) ** -2\\n\\n if isinstance(X, np.ndarray):\\n\\n X[np.where(X == 1)] = 1 - c\\n\\n output = np.empty_like(X)\\n\\n inds1 = np.where(np.absolute(X - b)<c)\\n output[inds1] = prefac*(-2 - b + (1 + b + b ** 2) * self._nfw_func(b)) * (1 + b) ** -1\\n\\n inds2 = np.where(np.absolute(X - b)>=c)\\n\\n output[inds2] = prefac * ((X[inds2] ** 2 - 1) ** -1 * (1 - b -\\n (1 - b * X[inds2] ** 2) * self._nfw_func(X[inds2])) -\\n self._nfw_func(X[inds2] * b ** -1))\\n\\n else:\\n\\n if X == 1:\\n X = 1-c\\n\\n if np.absolute(X - b)<c:\\n output = prefac * (-2 - b + (1 + b + b ** 2) * self._nfw_func(b)) * (1 + b) ** -1\\n\\n else:\\n output = prefac * ((X ** 2 - 1) ** -1 * (1 - b -\\n (1 - b * X ** 2) * self._nfw_func(X)) - self._nfw_func(X * b ** -1))\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c67fcf49732fc6a4522e26aca4e70e87\",\n \"score\": \"0.5310824\",\n \"text\": \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n R = reward\\n f = self.featExtractor.getFeatures(state, action)\\n alphadiff = self.alpha * ((R + self.discount * self.getValue(nextState)) - self.getQValue(state, action))\\n #alphadiff = self.alpha * ((R + self.discount * self.getValue(nextState)) - self.getQValue(state, action))\\n for feature in f.keys():\\n self.weights[feature] = self.weights[feature] + alphadiff * f[feature]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ad4d5576874267d785f9f90587b69b\",\n \"score\": \"0.53082305\",\n \"text\": \"def calculate_bisector(self):\\n #TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8ad80b02bbcbb412f0df608ef3d037a\",\n \"score\": \"0.5307438\",\n \"text\": \"def evaluate(factors, number, write=False):\\n wn = wntr.network.WaterNetworkModel(inpfile) # open the input file\\n # convert the variable to a diameter value\\n vals = [a*b for a,b in zip(factors, orig_diam)] \\n # now change the diameters in the network. \\n for id, diam in zip(pipes_to_change, vals):\\n wn.get_link(id).diameter=diam \\n # now create a simulation using PDD mode\\n wn.options.hydraulic.demand_model = 'PDA'\\n sim = wntr.sim.EpanetSimulator(wn)\\n # slacken the convergence criteria a bit - to save some time\\n results = sim.run_sim(version=2.2)# solver_options=dict(MAXITER=2000, TOL=1.e-2), convergence_error=False)\\n if (results.error_code==2):\\n print (\\\"\\\\nHydrfaulic analysis failed to converge!\\\\n\\\")\\n # objective 1 is : annualized cost in US$/year\\n cost = wntr.metrics.economic.annual_network_cost(wn)\\n \\n expected_demand = wntr.metrics.expected_demand(wn)[wn.junction_name_list]\\n demand = results.node['demand'][wn.junction_name_list]\\n #objective 2 is 1-adf\\n adf = demand.sum().sum()/expected_demand.sum().sum()\\n #adf=1 if adf > 1.0 else adf # Remove > 1 adf (sometimes it can happen!)\\n \\n if write:\\n # now save the files (so that we can examine them later too!)\\n inpname=f'{outputd}{os.sep}CANDIDATE_{number:03d}.inp'\\n wntr.network.io.write_inpfile(wn, inpname)\\n # write objective values to the result file\\n with open(resfile,'a') as ff:\\n ff.write(TBL.format(number,inpname, \\\"{:.9f}\\\".format(cost), '{:.9f}'.format(1-adf)))\\n LNADF= 0.0 if adf > MINADF else LN*(MINADF-adf)\\n return cost + LNADF\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0508694bd7f9493c3f3044d85b5db65a\",\n \"score\": \"0.5301106\",\n \"text\": \"def Newton_method_Approximation_step_bi(f,ax,bx,Ep,i):\\r\\n while True:\\r\\n i=i+1\\r\\n \\r\\n #print(\\\"ax=\\\",ax,\\\" and bx=\\\",bx)\\r\\n \\r\\n h_a=f(ax)/derivative(f, ax)\\r\\n ax=ax-h_a\\r\\n if(decide(abs(h_a)<=Ep)):\\r\\n #print(\\\"Root in Approximation: \\\",ax)\\r\\n return i\\r\\n \\r\\n h_b=f(bx)/derivative(f, bx)\\r\\n bx=bx-h_b\\r\\n if(decide(abs(h_b)<=Ep)):\\r\\n #print(\\\"Root in Approximation: \\\",bx)\\r\\n return i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036ac2de0de05eacd54c7198cfeb1c7b\",\n \"score\": \"0.52987486\",\n \"text\": \"def update_bac(firm, bac):\\n return 100 - firm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f79a1152bbd0d04b25eb928f529192a\",\n \"score\": \"0.52941847\",\n \"text\": \"def get_emcee_burnin(regular_indicator_chain, burnin_tolerance):\\n\\n# def eval_burnin_equation_svd(leftover_tensor,\\n# leftover_singular_vals,\\n# burnin_tolerance,\\n# nsteps):\\n# \\\"\\\"\\\"Calculate excess deviation from equilibrium PDF after nsteps.\\\"\\\"\\\"\\n#\\n# return leftover_tensor.dot(\\n# numpy.power(leftover_singular_vals, nsteps)\\n# ).max() - burnin_tolerance\\n\\n\\n def eval_burnin_equation_powers(transition_probabilities,\\n expected_distro,\\n nsteps):\\n \\\"\\\"\\\"Calculate excess deviation from equilibrium PDF after nsteps.\\\"\\\"\\\"\\n\\n return (\\n numpy.linalg.matrix_power(transition_probabilities, nsteps)\\n -\\n expected_distro\\n ).max() - burnin_tolerance\\n\\n\\n# def get_burnin_equation(fitted_markov):\\n# \\\"\\\"\\\"Return the equation to solve to find the burnin.\\\"\\\"\\\"\\n#\\n# left, singular_vals, right_trans = numpy.linalg.svd(\\n# fitted_markov.transition_probabilities.T\\n# )\\n# print('Left: ' + repr(left))\\n# print('SV: ' + repr(singular_vals))\\n# print('Right: ' + repr(right_trans))\\n# tolerance = 10.0 * float_info.epsilon * singular_vals.size\\n# assert numpy.abs(singular_vals[0] - 1) < tolerance\\n# assert (\\n# numpy.abs(\\n# left[:, 0] * right_trans[0, :]\\n# -\\n# fitted_markov.get_equilibrium_distro()\\n# )\\n# <\\n# tolerance\\n# )\\n#\\n# leftover_tensor = left[:, None, 1:] * right_trans[1:, :].T[None, :, :]\\n#\\n# return partial(eval_burnin_equation,\\n# leftover_tensor,\\n# singular_vals[1:],\\n# burnin_tolerance)\\n\\n def find_burnin_bracket(burnin_equation):\\n \\\"\\\"\\\"Return burnin range (min, max) that brackets the true value.\\\"\\\"\\\"\\n\\n burnin_min = 1\\n burnin_equation_start = burnin_equation(burnin_min)\\n if burnin_equation_start <= 0:\\n _logger.warning('Burn-in equation for burn-in of 1 step is %s'\\n %\\n repr(burnin_equation_start))\\n return 0, 1\\n burnin_max = 2\\n while burnin_equation(burnin_max) > 0:\\n burnin_min = burnin_max\\n burnin_max *= 2\\n if burnin_max > 1e6:\\n return burnin_min, None\\n\\n return burnin_min, burnin_max\\n\\n if numpy.unique(regular_indicator_chain).size == 1:\\n return 0\\n\\n fitted_markov, thin = get_approximate_markov(regular_indicator_chain)\\n\\n if fitted_markov is None:\\n return 0\\n\\n try:\\n equilibrium_distro = fitted_markov.get_equilibrium_distro()\\n except ValueError:\\n _logger.error('Failed to find equilibrium distribution')\\n return 0\\n\\n burnin_equation = partial(eval_burnin_equation_powers,\\n fitted_markov.transition_probabilities,\\n equilibrium_distro)\\n\\n burnin_min, burnin_max = find_burnin_bracket(burnin_equation)\\n\\n if burnin_max is None:\\n _logger.error('The following transition matrix fails to converge:\\\\n%s',\\n repr(fitted_markov.transition_probabilities))\\n return 0\\n\\n\\n while burnin_max - burnin_min > 1:\\n midpoint = (burnin_max + burnin_min) // 2\\n if burnin_equation(midpoint) > 0:\\n burnin_min = midpoint\\n else:\\n burnin_max = midpoint\\n\\n return burnin_max * thin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"917a7a168ce43b08f0c0afd8ca1cf1d9\",\n \"score\": \"0.5292725\",\n \"text\": \"def step(self):\\n # Increment time first so that we record accurate first-infection times.\\n self.t += 1\\n next_infected = set()\\n\\n for n in self.infected:\\n for nbr in self.G[n]:\\n # self.dprint('%d trying to infect %d at time %d' % (n, nbr, self.t))\\n # Only try to infect if this neighbor is not already infected.\\n if nbr not in self.infected:\\n if random.random() < self.G[n][nbr]['weight']:\\n # self.dprint('Infected', nbr, 'at time:', self.t)\\n next_infected.add(nbr)\\n self.infection_times[nbr] = self.t\\n\\n self.dprint('next infected set:', next_infected)\\n self.last_infected.update(self.infected)\\n self.infected.update(next_infected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166f9c417a7e66ea86be6fbc6fe01ea5\",\n \"score\": \"0.5291561\",\n \"text\": \"def baumwelchstep(stateprior,transition,emission,observations,numstates,numsym,elem_size=numpy.longdouble):\\r\\n\\txi = calcxi(stateprior,transition,emission,observations,numstates,elem_size=elem_size)\\r\\n\\tgamma = calcgamma(xi,len(observations),numstates,elem_size)\\r\\n\\tnewprior = gamma[0]\\r\\n\\tnewtrans = numpy.zeros((numstates,numstates),dtype=elem_size)\\r\\n\\tfor i in xrange(numstates):\\r\\n\\t\\tfor j in xrange(numstates):\\r\\n\\t\\t\\tnumer = 0.0\\r\\n\\t\\t\\tdenom = 0.0\\r\\n\\t\\t\\tfor t in xrange(len(observations)-1):\\r\\n\\t\\t\\t\\tnumer += xi[t][i][j]\\r\\n\\t\\t\\t\\tdenom += gamma[t][i]\\r\\n\\t\\t\\tnewtrans[i][j] = numer/denom\\r\\n\\tnewemiss = numpy.zeros( (numstates,numsym) ,dtype=elem_size)\\r\\n\\tfor j in xrange(numstates):\\r\\n\\t\\tfor k in xrange(numsym):\\r\\n\\t\\t\\tnumer = 0.0\\r\\n\\t\\t\\tdenom = 0.0\\r\\n\\t\\t\\tfor t in xrange(len(observations)):\\r\\n\\t\\t\\t\\tif observations[t] == k:\\r\\n\\t\\t\\t\\t\\tnumer += gamma[t][j]\\r\\n\\t\\t\\t\\tdenom += gamma[t][j]\\r\\n\\t\\t\\tnewemiss[j][k] = numer/denom\\r\\n\\treturn newprior,newtrans,newemiss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d5a35f352a2c8dd36ed0f26fec33e27\",\n \"score\": \"0.52910626\",\n \"text\": \"def first_generation_method(self, N_iterations=10, statistical_or_total=1):\\n\\n\\n # = Check that unfolded matrix is present \\n if self.unfolded.matrix is None:\\n raise Exception(\\\"Error: No unfolded matrix is loaded.\\\")\\n\\n # Rename variables for local use:\\n unfolded_matrix = self.unfolded.matrix\\n Ex_array_mat = self.unfolded.Ex_array\\n Egamma_array = self.unfolded.Eg_array\\n\\n # TODO check if they are None, and if so raise exception?\\n N_Exbins = self.N_Exbins_fg\\n Ex_max = self.Ex_max_fg\\n dE_gamma = self.dEg_fg\\n\\n\\n # TODO option statistical_or_total=2 (total) does not work\\n\\n\\n Ny = len(unfolded_matrix[:,0])\\n Nx = len(unfolded_matrix[0,:])\\n # Extract / calculate calibration coefficients\\n # bx = Egamma_array[0]\\n # ax = Egamma_array[1] - Egamma_array[0]\\n # by = Ex_array_mat[0]\\n # ay = Ex_array_mat[1] - Ex_array_mat[0]\\n bx = self.unfolded.calibration[\\\"a0x\\\"]\\n ax = self.unfolded.calibration[\\\"a1x\\\"]\\n by = self.unfolded.calibration[\\\"a0y\\\"]\\n ay = self.unfolded.calibration[\\\"a1y\\\"]\\n\\n \\n ThresSta = 430.0\\n # AreaCorr = 1\\n ThresTot = 200.000000\\n ThresRatio = 0.3000000\\n # ExH = 7520.00000\\n ExEntry0s = 300.000000\\n ExEntry0t = 0.00000000\\n apply_area_correction = True\\n\\n\\n # Ex_max = 7500 # keV - maximum excitation energy\\n # Ex_min = 300 # keV - minimal excitation energy, effectively moving the ground-state energy up because we cannot resolve the low-energy yrast gamma lines. This is weighed up by also using an effective multiplicity which is lower than the real one, again not considering the low-energy yrast gammas.\\n # dE_gamma = 300 # keV - allow gamma energy to exceed excitation energy by this much, to account for experimental resolution\\n # Ex_binsize = 40 # keV - bin size that we want on y axis\\n # N_Exbins = 120 # Number of excitation energy bins (NB! It will only rebin in whole multiples, so a slight change in N_Exbins might only result in getting some more empty bins on top.)\\n # N_Exbins_original = (Ex_max+dE_gamma)/ay # The number of bins between 0 and Ex_max + dE_gamma in the original matrix\\n # grouping = int(np.ceil(len(y_array[np.logical_and(0 < y_array, y_array < Ex_max + dE_gamma)])/N_Exbins)) # The integer number of bins that need to be grouped to have approximately N_Exbins bins between Ex_min and Ex_max after compression (rounded up)\\n\\n # Make arrays of Ex and Egamma axis values\\n # Ex_array = np.linspace(by, Ex_max + dE_gamma, N_Exbins)\\n # Egamma_array = np.linspace(0,Nx-1,Nx)*ax + bx # Range of Egamma values # Update: This is passed into the function.\\n\\n # matrix_ex_compressed, Ex_array = rebin(unfolded_matrix[0:int((Ex_max+dE_gamma)/Ex_array_mat.max()*Ny),:], Ex_array_mat[0:int((Ex_max+dE_gamma)/Ex_array_mat.max()*Ny)], N_Exbins, rebin_axis = 0) # This seems crazy. Does it cut away anything at all?\\n # HACK: Checking if the compression along Ex is really necessary (shouldn't it be done outside of firstgen method anyway?)\\n matrix_ex_compressed = unfolded_matrix\\n Ex_array = Ex_array_mat\\n\\n # if N_Exbins != Ny:\\n # Compress matrix along Ex\\n #matrix_ex_compressed = matrix[0:int(N_Exbins*grouping),:].reshape(N_Exbins, grouping, Nx).sum(axis=1)\\n # Update 20180828: Trying other rebin functions\\n # matrix_ex_compressed = np.zeros((N_Exbins, Nx))\\n # for i in range(Nx):\\n # # This is too slow.\\n # # TODO understand if the int((Ex_max + dE_gamma etc...)) stuff is necessary.\\n # # matrix_ex_compressed[:,i] = rebin_by_arrays_1d(matrix[0:int((Ex_max+dE_gamma)/Ex_array_mat.max()*Ny),i], Ex_array_mat[0:int((Ex_max+dE_gamma)/Ex_array_mat.max()*Ny)], Ex_array) \\n # print(\\\"i=\\\",i,flush=True)\\n # # print(\\\"Ex_array_mat.shape =\\\", Ex_array_mat.shape, flush=True)\\n # # print(\\\"matrix.shape =\\\", matrix.shape, flush=True)\\n # matrix_ex_compressed[:,i] = rebin_by_arrays_1d(matrix[:,i], Ex_array_mat, Ex_array) \\n # # matrix_ex_compressed = rebin_and_shift_2D_memoryguard(matrix[0:int((Ex_max+dE_gamma)/Ex_array_mat.max()*Ny),:], N_Exbins, rebin_axis = 0) # This seems crazy. Does it cut away anything at all?\\n # else:\\n # matrix_ex_compressed = matrix\\n\\n\\n\\n # print Ny, N_Exbins, N_Exbins_original \\n # plt.pcolormesh(Egamma_array, Ex_array, matrix_ex_compressed, norm=LogNorm(vmin=0.001, vmax=matrix_ex_compressed.max()))\\n # plt.matshow(matrix_ex_compressed)\\n # plt.colorbar()\\n # plt.show()\\n\\n # Remove counts in matrix for Ex higher than Ex_max:\\n matrix_ex_compressed[Ex_array>Ex_max, :] = 0\\n # plt.matshow(matrix_ex_compressed)\\n # plt.colorbar()\\n # plt.show()\\n\\n\\n # ==== Calculate multiplicities: ====\\n\\n # Setup meshgrids for making boolean indexing arrays\\n Egamma_mesh, Ex_mesh = np.meshgrid(Egamma_array, Ex_array)\\n Egamma_max = Ex_array + dE_gamma # Maximal Egamma value for each Ex bin\\n Egamma_max_grid = np.meshgrid(np.ones(Nx), Egamma_max)[1]\\n if statistical_or_total == 1:\\n # Statistical multiplicity calculation (i.e. trying to use statistical/continuum region only)\\n slide = np.minimum( np.maximum(ThresRatio*Ex_mesh, ThresTot), ThresSta ) # The sliding lower limit for Egamma integral - sliding between ThresTot and ThresSta.\\n # plt.figure(5)\\n # plt.plot(slide[:,0])\\n # plt.show()\\n # sys.exit(0)\\n # good_indices = np.where(np.logical_and(slide < Egamma_mesh, Egamma_mesh < Egamma_max_grid) , True, False)\\n matrix_ex_compressed_cut = np.where(np.logical_and(slide < Egamma_mesh, Egamma_mesh < Egamma_max_grid), matrix_ex_compressed, 0)\\n elif statistical_or_total == 2:\\n # Total multiplicity calculation\\n # good_indices = np.where(Egamma_mesh < Egamma_max_grid, True, False)\\n matrix_ex_compressed_cut = np.where(Egamma_mesh < Egamma_max_grid, matrix_ex_compressed, 0)\\n # for i in range(len(good_indices[:,0])):\\n # print len(good_indices[i,good_indices[i,:]]) # OK, it actually works.\\n \\n # Cut away counts higher than Egamma = Ex + dE_gamma\\n # matrix_ex_compressed_cut = np.where(good_indices, matrix_ex_compressed, 0)\\n # plt.figure(1)\\n # plt.pcolormesh(Egamma_array, Ex_array, matrix_ex_compressed_cut, norm=LogNorm(vmin=0.01, vmax=matrix_ex_compressed.max()))\\n # plt.show()\\n # sys.exit(0)\\n\\n # Calculate average multiplicity for each Ex channel\\n area_matrix_ex_compressed_cut = np.sum(matrix_ex_compressed_cut, axis=1)\\n Egamma_average = div0( np.sum(Egamma_mesh * matrix_ex_compressed_cut, axis =1) , area_matrix_ex_compressed_cut )\\n if statistical_or_total == 1:\\n # Statistical multiplicity - use the effective Ex0 value\\n multiplicity = div0( Ex_array - np.maximum( np.minimum(Ex_array - 200, ExEntry0s), 0), Egamma_average)\\n elif statistical_or_total == 2:\\n # Total multiplicity - use actual Ex0 = 0\\n multiplicity = div0( Ex_array, Egamma_average )\\n\\n\\n # plt.figure(2)\\n # plt.step(Ex_array, multiplicity) # This rises like a straight line from 0 to about 3-4 - seems very right!\\n # plt.show()\\n # sys.exit(0)\\n\\n # Set up dummy first-generation matrix to start iterations, made of normalized boxes:\\n H = np.zeros((N_Exbins, Nx))\\n for i in range(N_Exbins):\\n Ni = len(Egamma_array[Egamma_array<Ex_array[i] + dE_gamma])\\n # print(\\\"Ni =\\\", Ni, flush=True)\\n H[i, Egamma_array < Ex_array[i] + dE_gamma] = 1/max(Ni, 1)\\n # print np.sum(H0, axis=1) # Seems to work!\\n\\n # Set up normalization matrix N\\n area = np.sum(matrix_ex_compressed_cut, axis=1) # Get total number of counts in each Ex bin\\n # plt.plot(Ex_array, area)\\n # plt.show()\\n\\n area_grid = np.tile(area, (N_Exbins, 1)) # Copy the array N_Exbins times down to make a square matrix\\n # print area_grid.shape\\n multiplicity_grid = np.tile(multiplicity, (N_Exbins, 1)) \\n # print multiplicity_grid.shape\\n normalization_matrix = div0(( np.transpose(multiplicity_grid) * area_grid ) , (multiplicity_grid * np.transpose(area_grid) )).T # The transpose gives the right result. Haven't twisted my head around exactly why.\\n # normalization_matrix_check = np.zeros((N_Exbins, N_Exbins))\\n # for i in range(N_Exbins):\\n # for j in range(N_Exbins):\\n # normalization_matrix_check[i, j] = multiplicity[i]*area[j]/(multiplicity[j]*area[i])\\n normalization_matrix[np.isnan(normalization_matrix)] = 0\\n # plt.matshow(normalization_matrix, origin='lower', norm=LogNorm(vmin=0.01, vmax=normalization_matrix.max()))\\n # plt.show()\\n # plt.matshow(normalization_matrix_check, origin='lower') # There is a difference of a transposition, check which is the right one\\n # plt.show()\\n\\n # Set up compression parameters for Egamma axis to be used by H below:\\n i_Egamma_max = np.where(Egamma_array > Ex_max+ dE_gamma)[0][0] # Get the maximal allowed gamma energy (need to make H square, thus Egamma <= Ex + dE_gamma, since that's the maximal Ex channel in the compressed matrix)\\n # print i_Egamma_max, Egamma_array[i_Egamma_max], N_Exbins, int(i_Egamma_max/N_Exbins)\\n # i_Egamma_max = i_Egamma_max + N_Exbins - i_Egamma_max%N_Exbins # Make sure the number of indices is a whole multiple of N_Exbins (rounded up)\\n # print i_Egamma_max\\n grouping_Egamma = int(np.ceil(i_Egamma_max/N_Exbins))\\n # print grouping_Egamma\\n # Egamma_array_compressed = Egamma_array[0:i_Egamma_max]*grouping_Egamma\\n # Egamma_array_compressed = Ex_array\\n\\n # plt.matshow(H[:,0:i_Egamma_max])\\n # plt.show()\\n # H_extended = np.insert(H[:,0:i_Egamma_max], np.linspace(0,i_Egamma_max, N_Exbins - i_Egamma_max%N_Exbins), H[:,(np.linspace(0,i_Egamma_max, N_Exbins - i_Egamma_max%N_Exbins).astype(int))], axis=1)\\n # H_extended[:,grouping_Egamma:-1:grouping_Egamma] /= 2\\n # H_extended[:,grouping_Egamma+1:-2:grouping_Egamma] /= 2\\n # H_extended = H[:,0:i_Egamma_max].repeat(N_Exbins).reshape(len(H[:,0]),N_Exbins,i_Egamma_max).sum(axis=2)/N_Exbins\\n # plt.matshow(H_extended)\\n # plt.show()\\n # H_compressed = H[:,0:i_Egamma_max+ N_Exbins - i_Egamma_max%N_Exbins].reshape(N_Exbins, N_Exbins, grouping_Egamma).sum(axis=2)\\n # plt.matshow(H_compressed)\\n # plt.show()\\n # H_compressed = rebin(H[:,0:i_Egamma_max], N_Exbins, 1)\\n # plt.matshow(H_compressed)\\n # plt.show()\\n\\n # H_compressed_extended = H_extended.reshape(N_Exbins, N_Exbins, grouping_Egamma).sum(axis=2)\\n # plt.matshow(H_compressed_extended)\\n # plt.show()\\n\\n # sys.exit(0)\\n\\n # Declare variables which will define the limits for the diff spectrum colorbar (for plotting purposes)\\n vmin_spec = -200\\n vmax_spec = 200\\n vmin_diff = -100\\n vmax_diff = 100\\n\\n # Prepare weight function based on Fermi gas approximation:\\n a_f = 16 # 1/MeV\\n n_f = 4.2 # Exponent for E_gamma\\n # Make the weight array by the formula W(Ex,Eg) = Eg^n_f / (Ex-Eg)^2 * exp(2*sqrt(a*(Ex-Eg)))\\n Ex_mesh, Eg_mesh = np.meshgrid(Ex_array, Ex_array, indexing=\\\"ij\\\")\\n # Make mesh of final energies:\\n Ef_mesh = Ex_mesh-Eg_mesh\\n # Set everything above Ex=Eg diagonal to zero so the weights also become zero\\n Ef_mesh[Ef_mesh < 0] = 0\\n # Calculate weights. Remember that energies are in keV, while a is in 1/MeV, so we correct in the exponent:\\n W_old = np.where(Eg_mesh > 0, np.power(Eg_mesh,n_f) / np.power(Ef_mesh, 2) * np.exp(2*np.sqrt(a_f*Ef_mesh/1000)), 0)\\n W_old = div0(W_old, W_old.sum(axis=1).reshape(N_Exbins,1))\\n\\n\\n dEg = 1000\\n mask_W = make_mask(Ex_array, Ex_array, Ex_array[0], Ex_array[0]+dEg, Ex_array[-1], Ex_array[-1]+dEg)\\n\\n # Perform the iterative subtraction:\\n for iteration in range(N_iterations):\\n # convergence_criterion = 1\\n # max_diff = 100\\n # while max_diff > convergence_criterion:\\n # Store H from previous iteration to compare at the end\\n H_old = H\\n # Compress the H matrix along gamma axis to make it square and facilitate conversion to excitation energy\\n # H_compressed = H[:,0:i_Egamma_max].reshape(N_Exbins, N_Exbins, grouping_Egamma).sum(axis=2)\\n H_compressed, Egamma_array_compressed = rebin(H[:,0:i_Egamma_max], Egamma_array[0:i_Egamma_max], N_Exbins, rebin_axis=1)\\n\\n # plt.pcolormesh(Egamma_array_compressed, Ex_array, H_compressed)\\n # plt.show()\\n\\n # if iteration == 0:\\n # Don't use H as weights for first iteration.\\n # W = W_old\\n # else:\\n if True:\\n # Convert first-generation spectra H into weights W\\n W = np.zeros((N_Exbins, N_Exbins))\\n for i in range(0,N_Exbins):\\n # print H_compressed[i,i:0:-1].shape\\n W[i,0:i] = H_compressed[i,i:0:-1]\\n # TODO Consider implementing something like Massage(), but try to understand if it is necessary first.\\n # plt.figure()\\n # plt.pcolormesh(Ex_array, Ex_array, W, norm=LogNorm())\\n # plt.show()\\n # sys.exit(0)\\n # Prevent oscillations, following MAMA:\\n if iteration > 4:\\n W = 0.7 * W + 0.3 * W_old\\n # plt.matshow(W, origin='lower', vmin=W.min(), vmax=W.max())\\n # plt.colorbar()\\n # plt.title('Before')\\n # plt.show()\\n # Remove negative weights\\n W[W<0] = 0\\n # Apply mask \\n # W = W * mask_W\\n # Normalize each Ex channel to unity\\n # W = np.where(np.invert(np.isnan(W/W.sum(axis=1).astype(float))), W/W.sum(axis=1).astype(float), 0)\\n # Remove Inf and NaN\\n W = div0(W, W.sum(axis=1).reshape(N_Exbins,1))\\n # Store for next iteration:\\n W_old = np.copy(W)\\n # W = np.nan_to_num(W) \\n # plt.matshow(W, origin='lower', vmin=W.min(), vmax=W.max())\\n # plt.colorbar()\\n # plt.title('After')\\n # plt.show()\\n\\n # sys.exit(0)\\n\\n # print \\\"W = \\\"\\n # print W\\n # print \\\"matrix_ex_compressed = \\\"\\n # print matrix_ex_compressed\\n # print \\\"product =\\\"\\n # plt.matshow(np.dot(W, matrix_ex_compressed), origin='lower', norm=LogNorm())\\n # plt.show()\\n\\n # Calculate product of normalization matrix, weight matrix and raw count matrix\\n G = np.dot( (normalization_matrix * W), matrix_ex_compressed) # Matrix of weighted sum of spectra below\\n \\n # Apply area correction\\n if apply_area_correction:\\n # Setup meshgrids for making boolean indexing arrays\\n # Egamma_mesh_compressed, Ex_mesh_compressed = np.meshgrid(Egamma_array_compressed, Ex_array)\\n # Egamma_max = Ex_array + dE_gamma # Maximal Egamma value for each Ex bin\\n # Egamma_max_grid_compressed = np.meshgrid(np.ones(N_Exbins), Egamma_max)[1]\\n # print \\\"Egamma_mesh_compressed, Egamma_max, Egamma_max_grid\\\"\\n # print Egamma_mesh_compressed.shape, Egamma_max.shape, Egamma_max_grid.shape\\n if statistical_or_total == 1:\\n # Statistical multiplicity calculation (i.e. trying to use statistical/continuum region only)\\n # slide_compressed = np.minimum( np.maximum(ThresRatio*Ex_mesh_compressed, ThresTot), ThresSta ) # The sliding lower limit for Egamma integral - sliding between ThresTot and ThresSta.\\n # print \\\"slide_compressed\\\"\\n # print slide_compressed.shape\\n # plt.figure(5)\\n # plt.plot(slide[:,0])\\n # plt.show()\\n # sys.exit(0)\\n # good_indices_G = np.where(np.logical_and(slide_compressed < Egamma_mesh_compressed, Egamma_mesh_compressed < Egamma_max_grid) , True, False)\\n G_area = np.where(np.logical_and(slide < Egamma_mesh, Egamma_mesh < Egamma_max_grid), G, 0).sum(axis=1)\\n elif statistical_or_total == 2:\\n # Total multiplicity calculation\\n # good_indices_G = np.where(Egamma_mesh_compressed < Egamma_max_grid, True, False)\\n # G_area = np.where(Egamma_mesh_compressed < Egamma_max_grid, G, 0).sum(axis=1)\\n G_area = np.where(Egamma_mesh < Egamma_max_grid, G, 0).sum(axis=1)\\n # G_area = np.where(good_indices_G, G, 0).sum(axis=1)\\n # print \\\"print G_area.shape\\\"\\n # print G_area.shape\\n # print \\\"print G_area\\\"\\n # print G_area\\n alpha = np.where(G_area > 0, (1 - div0(1,multiplicity)) * div0( area_matrix_ex_compressed_cut, G_area ), 1)\\n alpha[alpha < 0.85] = 0.85\\n alpha[alpha > 1.15] = 1.15\\n # print \\\"alpha.shape\\\"\\n # print alpha.shape\\n # print \\\"alpha\\\"\\n # print alpha\\n else:\\n alpha = np.ones(N_Exbins)\\n\\n\\n # The actual subtraction\\n H = matrix_ex_compressed - alpha.reshape((len(alpha), 1))*G\\n # print H.shape\\n # Plotting:\\n # vmin_diff = (H-H_old).min()\\n # vmax_diff = (H-H_old).max()\\n # vmin_spec = H.min()\\n # vmax_spec = H.max()\\n\\n # plt.figure(10)\\n # plt.subplot(1,2,1)\\n # plt.title('First gen spectrum, current')\\n # plt.pcolormesh(Egamma_array, Ex_array, H, norm=LogNorm(vmin=0.01, vmax=vmax_spec))\\n # plt.colorbar()\\n # plt.subplot(1,2,2)\\n \\n\\n # plt.title('Diff with previous')\\n # plt.pcolormesh(Egamma_array, Ex_array, H-H_old, vmin=vmin_diff, vmax=vmax_diff)\\n # plt.colorbar()\\n # plt.show()\\n\\n\\n\\n # Check convergence\\n max_diff = np.max(np.abs(H-H_old))\\n print(\\\"iteration =\\\", iteration, \\\"max_diff =\\\", max_diff, flush=True)\\n\\n # Remove negative counts\\n H[H<0] = 0\\n \\n # Update internal variables and return True upon completion\\n # return H, H-H_old, Ex_array, Egamma_array\\n\\n print(\\\"H.shape =\\\", H.shape)\\n print(\\\"Ex_array.shape =\\\", Ex_array.shape)\\n print(\\\"Egamma_array.shape =\\\", Egamma_array.shape, flush=True)\\n\\n self.firstgen = matrix(H, Ex_array, Egamma_array)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f982723eb0a76e4667a6e7795626f76\",\n \"score\": \"0.52906454\",\n \"text\": \"def coin_10flips_1():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4f08e760333598e2f7fc5ae9c2501cf\",\n \"score\": \"0.5289661\",\n \"text\": \"def algorithm():\\n list_how_much_0_owes_1_with_amount_owed, list_friend = generate_data()\\n\\n print(\\\"#\\\" * 100)\\n print(\\\"Algorithm\\\")\\n print(\\\"#\\\" * 100)\\n print()\\n\\n dict_k_pair_v_owed = defaultdict(int)\\n\\n # Algorithm part 1\\n for i in list_how_much_0_owes_1_with_amount_owed:\\n dict_k_pair_v_owed[i[0]] += i[1]\\n\\n # Print to show work\\n print(\\\"Dict of Who owes Who (total amount owed)\\\")\\n for pair, amount_owed in dict_k_pair_v_owed.items():\\n print(pair, amount_owed)\\n print()\\n\\n dict_k_pair_v_owed_total = dict()\\n\\n set_frozenset_used = set()\\n\\n # Algorithm part 2\\n for pair, amount_owed in dict_k_pair_v_owed.items():\\n\\n frozenset_pair = frozenset(pair)\\n\\n if frozenset_pair not in set_frozenset_used:\\n set_frozenset_used.add(frozenset_pair)\\n dict_k_pair_v_owed_total[pair] = amount_owed\\n continue\\n\\n else:\\n\\n pair_reversed = tuple(reversed(pair))\\n\\n value_of_pair_reversed = dict_k_pair_v_owed_total.get(pair_reversed)\\n\\n if amount_owed > value_of_pair_reversed:\\n dict_k_pair_v_owed_total.pop(pair_reversed)\\n dict_k_pair_v_owed_total[pair] = amount_owed - value_of_pair_reversed\\n\\n else:\\n dict_k_pair_v_owed_total[pair_reversed] = dict_k_pair_v_owed_total[pair_reversed] - amount_owed\\n\\n # Print to show solution\\n print(\\\"How much money friend index 0 owes friend index 1\\\")\\n for pair, amount_owed in dict_k_pair_v_owed_total.items():\\n print(pair, amount_owed)\\n print()\\n\\n print(\\\"Proof that the Maximum amount transactions needed total is <= Combinations of size 2\\\")\\n print(\\\"Combination: {} CHOOSE {} = {}\\\".format(len(list_friend), 2,\\n math.comb(len(list_friend), 2)))\\n print(\\\"Size of the result of my algorithm {}\\\".format(len(dict_k_pair_v_owed_total)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56cb5f13ff6489f5912f3396b7315f99\",\n \"score\": \"0.52889156\",\n \"text\": \"def attractor(self, init, which_way):\\n\\n if which_way == 'normal':\\n hebb_diff_freq_its_max = self.par['Hebb_diff_freq_its_max']\\n else:\\n hebb_diff_freq_its_max = self.par['Hebb_inv_diff_freq_its_max']\\n\\n with tf.name_scope('attractor'):\\n ps = [0] * (self.par['n_recurs'] + 1)\\n ps[0] = self.par['p_activation'](init)\\n\\n for i in range(self.par['n_recurs']):\\n # get Hebbian update\\n update = self.hebb_scal_prod(ps[i], i, which_way, hebb_diff_freq_its_max)\\n\\n ps_f = tf.split(value=ps[i], num_or_size_splits=self.par['n_place_all'], axis=1)\\n for f in range(self.par['n_freq']):\\n if i < hebb_diff_freq_its_max[f]:\\n ps_f[f] = self.par['p_activation'](self.par['prev_p_decay'] * ps_f[f] + update[f])\\n ps[i + 1] = tf.concat(ps_f, axis=1)\\n\\n p = ps[-1]\\n\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39ec02b0afd1aa22147f14b6d4d8c52c\",\n \"score\": \"0.528416\",\n \"text\": \"def enbw(self):\\n pi = np.pi\\n alpha = 16.7/pi\\n N=self.acq_samples\\n tau=self.acq_time\\n fs=N/tau\\n j=np.arange(N)\\n def kaiser(j):\\n z = 2.*j/N - 1.\\n return iv(0,pi*alpha*np.sqrt(1-z**2))/iv(0,pi*alpha)\\n def S1(x):\\n sum = 0\\n for i in x:\\n sum = sum+i\\n return sum\\n\\n def S2(x):\\n sum = 0\\n for i in x:\\n sum = sum+i**2\\n return sum\\n x=kaiser(j)\\n return fs*S2(x)/S1(x)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39ec02b0afd1aa22147f14b6d4d8c52c\",\n \"score\": \"0.528416\",\n \"text\": \"def enbw(self):\\n pi = np.pi\\n alpha = 16.7/pi\\n N=self.acq_samples\\n tau=self.acq_time\\n fs=N/tau\\n j=np.arange(N)\\n def kaiser(j):\\n z = 2.*j/N - 1.\\n return iv(0,pi*alpha*np.sqrt(1-z**2))/iv(0,pi*alpha)\\n def S1(x):\\n sum = 0\\n for i in x:\\n sum = sum+i\\n return sum\\n\\n def S2(x):\\n sum = 0\\n for i in x:\\n sum = sum+i**2\\n return sum\\n x=kaiser(j)\\n return fs*S2(x)/S1(x)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff08e18a5c417e1b3cbec0a74a524d9f\",\n \"score\": \"0.5278109\",\n \"text\": \"def _compute_beliefs(priors: np.ndarray, messages: list) -> np.ndarray:\\n beliefs = priors.copy()\\n for node, belief in enumerate(beliefs):\\n incoming_messages = messages[node]\\n for msg in incoming_messages.values():\\n belief *= msg\\n belief /= np.sum(belief)\\n return beliefs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a75dd4a5fc363bcb3c47fa5d7d81c74\",\n \"score\": \"0.52710605\",\n \"text\": \"def fold_change_bohr(bohr_parameter):\\n\\n return 1 / (1 + np.exp((-1) * bohr_parameter))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0184a3c83f3afad178a9e57e7d6f61e9\",\n \"score\": \"0.5267429\",\n \"text\": \"def bell(n):\\n result = 0\\n for i in range(1, n + 1):\\n result += stirling(n, i)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a949c8660b6612ac91be9e670a27af3\",\n \"score\": \"0.52673113\",\n \"text\": \"def chooseBestGuessV3(state, numBombs):\\n guessList = []\\n frontier = []\\n unknownsToBombs = 0\\n totalCombinations = 0\\n frontierValues = {}\\n height = len(state[0])\\n for i in range(len(state)):\\n for j in range(height): \\n val = state[i][j][0]\\n neighbors = state[i][j][2]\\n if (val > 0):\\n onFrontier=False\\n numNeighbors = 0\\n for neighbor in neighbors:\\n if (neighbor[0] == -1):\\n onFrontier = True\\n numNeighbors = numNeighbors + 1\\n if (onFrontier):\\n frontier.append((i,j,numNeighbors))\\n \\n for member in frontier:\\n object = state[member[0]][member[1]]\\n neighbors = object[2]\\n unknownNeighbors = member[2]\\n bombs = state[i][j][1]\\n difference = object[0]-object[1]\\n for neighbor in neighbors:\\n neighborPos = indexToColRow(neighbor[1], height)\\n neighborObject = state[neighborPos[0]][neighborPos[1]]\\n if (neighborObject[0] == -1):\\n val = frontierValues.get(neighbor[1], (0,0))\\n frontierValues[neighbor[1]] = (val[0] + difference, val[1] + unknownNeighbors)\\n max = 1.0\\n for key in frontierValues:\\n val = frontierValues[key]\\n val = val[0] / val[1]\\n #print(\\\"val: \\\",val,\\\", max: \\\",max,\\\"val > max: \\\",val > max)\\n if (val < max):\\n guessList = []\\n guessList.append(key)\\n max = val\\n elif (abs(val - max) < 0.01):\\n guessList.append(key)\\n #print(\\\"Choosing 1 of \\\",len(guessList),\\\" best guesses...\\\") \\n #print(frontierValues)\\n\\n \\n #print(\\\"Frontier: \\\",len(frontier),\\\", Bombs:\\\",numBombs)\\n #This is done because completely unknown squares are assigned a strange weight\\n \\n #guess = random.choice(frontier)\\n #guess = colRowToIndex(guess[0], guess[1], height)\\n #return guess\\n if (len(guessList) == 0):\\n guessList = getUnknowns(state)\\n return (random.choice(guessList), max)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"158632475cf4d5e38162707af04233e9\",\n \"score\": \"0.52672136\",\n \"text\": \"def gradientDescent(self, initlearningRate, numofIteration, randA=-2, randB=2, adaptLearningRate=True,\\n decayingConst = 100):\\n\\n row, col = self.X.shape\\n m = row\\n\\n self.W = (randB - randA) * np.random.random((1, col)) + randA\\n learningRate = initlearningRate\\n\\n for it in range(numofIteration):\\n\\n # (N X M) @ (M X 1) = (N X 1)\\n predicate = self.norm_X @ np.transpose(self.W)\\n predicate = supp.LogisticFuction(predicate)\\n # Y -> (N X 1) - predicate -> (N X 1) = (N X 1)\\n diff_val = (predicate - self.Y)\\n\\n # (1 X N) @ (N X M)\\n diff_val = np.transpose(diff_val) @ self.norm_X # (1 X M)\\n diff_val = diff_val / m\\n\\n if adaptLearningRate:\\n learningRate = self.decayLearningRate(initlearningRate, decayingConst, it)\\n\\n self.W = self.W - learningRate * diff_val\\n\\n cost = supp.CostFunction.crossEntropy(supp.LogisticFuction(self.norm_X @ np.transpose(self.W)), self.Y)\\n\\n self.square_error.append(cost)\\n\\n # regression_output = supp.CostFunction.LogisticFuction(self.W, self.norm_X)\\n # To Draw the boundary decision we need to draw the line that satisfy that\\n # p(Y = 1| X) = p(Y = 0| x) = 1/2\\n # 1/2 = (1 / 1 + exp(-1 * W @ X) ) If we simplify this equation\\n # 2 = 1 + exp(-1 * W @ X)\\n # 1 = exp(-1 * W @ X) take the natural log\\n # 0 = -1 * W @ X\\n # W @ X = 0\\n # so if we have only two feature\\n # w0 + w1*x1 + w2 * x2 = 0\\n # x2 = - (w1 / w2) x1 - (w0 / w2)\\n # X2 = (-1 / w2) * (w1 * x1 + w0)\\n\\n w0 = self.W[0, 0]\\n w1 = self.W[0, 1]\\n w2 = self.W[0, 2]\\n regression_output = (-1.0 / w2) * (w1 * self.norm_X[:, 1] + w0)\\n regression_output = regression_output * self.stat_X[1,2] + self.stat_X[0,2]\\n\\n return self.W, regression_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5486504f88d62ba6b5f28e89817e13d\",\n \"score\": \"0.5262246\",\n \"text\": \"def recruit(bees, waggle_direction):\\n for b in bees:\\n if b.state == 0:\\n if random.random() < 0.1:\\n b.state = 2 # recruit bee to foraging\\n b.foragingTurns = 2\\n b.public_direction = waggle_direction + random.randrange(-90,90)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4468c17f5829302dc0e77a14be2c3b1c\",\n \"score\": \"0.5260997\",\n \"text\": \"def test_cooling_model_intermediate_mass_basti_co_da_10():\\n wdlf2 = theoretical_lf.WDLF()\\n wdlf2.set_intermediate_mass_cooling_model(model=\\\"basti_co_da_10\\\")\\n wdlf2.compute_cooling_age_interpolator()\\n wdlf2.compute_density(mag=mag)\\n assert np.isclose(np.sum(wdlf2.number_density), 1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c41dad878920c7f1d161a57c1dbdabd6\",\n \"score\": \"0.5259439\",\n \"text\": \"def sample_wf_distribution(n, reps):\\n for mx, rel_branch_length in zip(sample_matrix(n, reps), sample_branch_lengths(n, reps)):\\n variate = (mx.T).dot(rel_branch_length)\\n err = 1 - np.sum(variate)\\n variate[np.argmax(variate)] += err\\n yield variate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4720fc01913c271e5138419c29a43e69\",\n \"score\": \"0.52509767\",\n \"text\": \"def far_distribution(self):\\n pygame.display.set_caption(\\\"Far distribution\\\")\\n self.free_coors = copy(self.out_circle_coors)\\n self.distribution(self.free_coors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b4fd3a9de27665e7d48071e9ed382d8\",\n \"score\": \"0.52505714\",\n \"text\": \"def ksprob(alam:float)->float:\\n fac = 2.0\\n sum = 0.0\\n termbf = 0.0\\n a2 = -2.0*alam*alam\\n for j in range(1,201):\\n term = fac*exp(a2*j*j)\\n sum = sum + term\\n if fabs(term) <= (0.001*termbf) or fabs(term) < (1.0e-8*sum):\\n return sum\\n fac = -fac\\n termbf = fabs(term)\\n return 1.0 # Get here only if fails to converge; was 0.0!!\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80a7b988d11172e92bf1d63049cb16b3\",\n \"score\": \"0.5250242\",\n \"text\": \"def experiment4():\\n for s in data:\\n l = data[s][\\\"seq\\\"][:100000]\\n a = data[s][\\\"alpha\\\"]\\n N = 100\\n k_prev = 0\\n while N < len(l):\\n f = 0\\n k = N\\n w = N/2\\n while(w > 10):\\n f = max([j*ft(j*a,l[:N])/l[N] for j in range(k-10,k+10)])\\n #print(k,f)\\n if f > 4: k -= w\\n else: k += w\\n k = math.floor(k+0.5)\\n w *= 1/2\\n x = k+10\\n for j in range(k_prev-20,x+10):\\n if(j*ft(j*a,l[:N])/l[N] > 4):\\n k = j\\n break\\n k_prev = k\\n print(s,l[N],k)\\n N = math.floor(N * 1.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca6664010c65917b041577f5e0781f64\",\n \"score\": \"0.52479565\",\n \"text\": \"def evaluate(self, n, goal=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ce7d2ca675f8762515730fbd145c81\",\n \"score\": \"0.52332747\",\n \"text\": \"def _step(self, w1, y1):\\n w0 = self.w0\\n p0 = self.p0\\n\\n dw1 = (y1 * w0) / (np.dot(y1, w0) + eps) # (eq7) weights evolve into\\n\\n # (eq16) cost to change portfolio\\n # (excluding change in cash to avoid double counting for transaction cost)\\n c1 = self.cost * (\\n np.abs(dw1[1:] - w1[1:])).sum()\\n\\n p1 = p0 * (1 - c1) * np.dot(y1, w0) # (eq11) final portfolio value\\n\\n p1 = p1 * (1 - self.time_cost) # we can add a cost to holding\\n\\n # can't have negative holdings in this model (no shorts)\\n p1 = np.clip(p1, 0, np.inf)\\n\\n rho1 = p1 / p0 - 1 # rate of returns\\n r1 = np.log((p1 + eps) / (p0 + eps)) # (eq10) log rate of return\\n # (eq22) immediate reward is log rate of return scaled by episode length\\n reward = r1 / self.steps\\n\\n # remember for next step\\n self.w0 = w1\\n self.p0 = p1\\n\\n # if we run out of money, we're done\\n done = bool(p1 == 0)\\n\\n # should only return single values, not list\\n info = {\\n \\\"reward\\\": reward,\\n \\\"log_return\\\": r1,\\n \\\"portfolio_value\\\": p1,\\n \\\"market_return\\\": y1.mean(),\\n \\\"rate_of_return\\\": rho1,\\n \\\"weights_mean\\\": w1.mean(),\\n \\\"weights_std\\\": w1.std(),\\n \\\"cost\\\": c1,\\n }\\n # record weights and prices\\n for i, name in enumerate(['BTCBTC'] + self.asset_names):\\n info['weight_' + name] = w1[i]\\n info['price_' + name] = y1[i]\\n\\n self.infos.append(info)\\n return reward, info, done\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91faf9ecf208ef56d24b3334879721e5\",\n \"score\": \"0.52309537\",\n \"text\": \"def next_day(self):\\n self.weight += self.stomach - 0.25\\n self.stomach = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9d3bd4c827a6dfd8b0204ec549b7997\",\n \"score\": \"0.52285117\",\n \"text\": \"def calc(self):\\n if abs(self.variable) >= 6999:\\n self.result = self.bad_value\\n return\\n\\n temp = 0\\n for idx in range(len(self.coefs)):\\n temp += float(self.coefs[idx]) * self.variable ** idx\\n self.result = temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c9baec96506b6afdb975331a347f82\",\n \"score\": \"0.5218737\",\n \"text\": \"def bancroft(B_pass):\\n\\n v_light = 299792458.0\\n pos = np.matrix( np.zeros(4) )\\n\\n for iter in range(1,3):\\n B = B_pass\\n [m,n] = np.shape(B)\\n\\n for i in range(0,m) :\\n print(i)\\n x = B[i][0]\\n y = B[i][1]\\n if iter == 1:\\n traveltime = 0.072\\n else:\\n z = B[i][3]\\n rho = (x-pos[0])**2 + (y-pos[1])**2 + (z-pos[2])**2\\n traveltime = np.sqrt(rho)/v_light\\n\\n angle = traveltime * 7.292115147e-5\\n cosa = np.cos(angle)\\n sina = np.sin(angle)\\n B[i][0] = cosa*x + sina*y\\n B[i][1] = -sina*x + cosa*y\\n\\n if m > 4:\\n BBB = npl.pinv(B.T*B)*B.T\\n else:\\n BBB = npl.pinv(B)\\n\\n e = np.ones(m)\\n #e = np.ones(m,1)\\n alpha = np.zeros((m,1))\\n #for i in range(1,m+1): \\n # alpha(i) = lorentz(B(i,:).T,B(i,:).T)/2.\\n \\n BBBe = BBB*e\\n BBBalpha = BBB*alpha\\n a = lorentz(BBBe,BBBe)\\n b = lorentz(BBBe,BBBalpha) - 1\\n c = lorentz(BBBalpha,BBBalpha)\\n root = np.sqrt(b*b-a*c)\\n print(\\\"root\\\",root)\\n\\n # Look at the two possible solutions\\n r = np.zeros(2)\\n print(\\\"r\\\",r,np.shape(r))\\n print(\\\"b\\\",b)\\n print(\\\"a\\\",a)\\n r[0] = (-b-root)/a\\n r[1] = (-b+root)/a\\n possible_pos = np.zeros((4,2))\\n\\n for i in range(0,2):\\n possible_pos[:,i] = r[i] * BBBe + BBBalpha\\n possible_pos[3,i] = -possible_pos[3,i]\\n \\n for j in range(1,m+1): \\n for i in range(1,3):\\n c_dt = possible_pos(3,i)\\n calc = norm(B[j,0:2].T - possible_pos[0:2,i])+c_dt\\n omc = B[j,3]-calc\\n abs_omc[i] = np.abs(omc)\\n\\n # discrimination between roots\\n if abs_omc(1) > abs_omc(2):\\n pos = possible_pos[:,2]\\n else:\\n pos = possible_pos[:,1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5186b17ee97685583b1643cded81fb9e\",\n \"score\": \"0.5216176\",\n \"text\": \"def MakeBeliefs(self, guess):\\r\\n pmf = self.PmfPrice()\\r\\n self.prior = Price(pmf, self, name='prior')\\r\\n self.posterior = self.prior.Copy(name='posterior')\\r\\n self.posterior.Update(guess)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc99b3aeb5a3156eeb04df02d24d84f\",\n \"score\": \"0.5213662\",\n \"text\": \"def Bisection_Method(f, f_d, start_point, end_point, eps):\\r\\n\\r\\n r_l_f = Find_renge(f, start_point, end_point) # renge list f\\r\\n r_l_fd = Find_rengeft(f_d, start_point, end_point) # renge list f_d\\r\\n counter = 0\\r\\n result_polinom = []\\r\\n\\r\\n if r_l_f and r_l_fd:\\r\\n for i in range(0, len(r_l_f), 2):\\r\\n\\r\\n # Whilw loop that run until the difference between the end point and the start point is greater than epsilon\\r\\n while (r_l_f[i + 1] - r_l_f[i]) > eps:\\r\\n\\r\\n x_m = (r_l_f[i] + r_l_f[i + 1]) / 2\\r\\n\\r\\n counter += 1\\r\\n print(\\\"{0} - {1}\\\".format(counter, round(x_m, 6))) # Printing the iterations\\r\\n if f(r_l_f[i]) * f(x_m) > 0:\\r\\n r_l_f[i] = x_m\\r\\n\\r\\n else:\\r\\n r_l_f[i + 1] = x_m\\r\\n\\r\\n if round(x_m, 6) not in result_polinom:\\r\\n print(\\\"x: \\\", round(x_m, 6))\\r\\n result_polinom.append(round(x_m, 6)) # Save the results\\r\\n\\r\\n for i in range(0, len(r_l_fd), 2):\\r\\n\\r\\n # Whilw loop that run until the difference between the end point and the start point is greater than epsilon\\r\\n while (r_l_fd[i + 1] - r_l_fd[i]) > eps:\\r\\n\\r\\n x_m = (r_l_fd[i] + r_l_fd[i + 1]) / 2\\r\\n\\r\\n counter += 1\\r\\n print(\\\"{0} - {1}\\\".format(counter, round(x_m, 6))) # Printing the iterations\\r\\n\\r\\n if f(r_l_fd[i]) * f(x_m) > 0:\\r\\n r_l_fd[i] = x_m\\r\\n else:\\r\\n r_l_fd[i + 1] = x_m\\r\\n\\r\\n if round(x_m, 6) not in result_polinom:\\r\\n print(\\\"x: \\\", round(x_m, 6))\\r\\n result_polinom.append(round(x_m, 6))\\r\\n\\r\\n n = len(result_polinom)\\r\\n i = 0\\r\\n while i < n and n != 0: # list no empty\\r\\n if not -0.1 < f(result_polinom[i]) < 0.1:\\r\\n del result_polinom[i]\\r\\n n -= 1\\r\\n i = 0\\r\\n else:\\r\\n i += 1\\r\\n\\r\\n return result_polinom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01206a58de6bf71494556d22696d6ea2\",\n \"score\": \"0.52111655\",\n \"text\": \"def evaluationFunction(self, currentGameState, action):\\n # Useful information you can extract from a GameState (pacman.py)\\n successorGameState = currentGameState.generatePacmanSuccessor(action)\\n newPos = successorGameState.getPacmanPosition()\\n newFood = successorGameState.getFood()\\n newGhostStates = successorGameState.getGhostStates()\\n newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]\\n\\n \\\"*** YOUR CODE HERE ***\\\" \\n fdist = 99999 # Initial closest food distance\\n M = newFood.height\\n N = newFood.width\\n for i in range(N):\\n for j in range(M):\\n if newFood[i][j]:\\n d = manhattanDistance(newPos, (i, j))\\n if d < fdist:\\n fdist = d # new closest food distance\\n\\n f1 = 1 / (fdist + 1) # Larger value of f1 for closer food\\n\\n f2 = max(newScaredTimes) # Not entirely sure what newScaredTimes is, but Im taking the largest one\\n\\n # List of adjacent positions to pacman\\n adjacentPos = [(newPos[0] - 1, newPos[1]), (newPos[0] + 1, newPos[1]), (newPos[0], newPos[1] - 1), (newPos[0], newPos[1] + 1)]\\n\\n f3 = 1 # f3 is related to how close the ghosts are to pacman\\n for ghost in newGhostStates:\\n gpos = ghost.getPosition()\\n if (newPos == gpos): # If pacman's position overlaps a ghosts position\\n f3 = 0 # f3 is 0 because pacman on same position as ghost = lose\\n break;\\n for pos in adjacentPos:\\n if gpos == pos: # If ghost is adjacent to pacman\\n f3 = 0 # f3 is 0 because we do not want to be directly next to ghost\\n break;\\n\\n if (f3 == 0): # if f3 = 0, there is a very high chance of losing ==> return low value\\n return 0\\n\\n return successorGameState.getScore() + .1*f1 + .3*f2 + .6*f3 # return weighted sum of features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f07893907714241d11395aa0085373c6\",\n \"score\": \"0.52079463\",\n \"text\": \"def theWork(self, iteration):\\n \\n if(iteration == 0):\\n self.numPoints = 1\\n self.result = 0.5 * (self.end - self.start) * \\\\\\n (self.integrand(self.start) + self.integrand(self.end))\\n\\n elif (iteration > 0):\\n delta = (self.end - self.start) / self.numPoints\\n \\n sum = reduce(lambda x, y: x + y, \\\\\\n [self.integrand(self.start + 0.5 * delta + i * delta) \\\\\\n for i in range(self.numPoints)])\\n \\n self.result = 0.5 * (self.result + (self.end - self.start) * sum / self.numPoints)\\n \\n self.numPoints *= 2\\n\\n return self.result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61e0288b7a35f346c73335ef24a60192\",\n \"score\": \"0.5207003\",\n \"text\": \"def learn(self, f, eta=0.1): \\r\\n # initialize the bias and weights with random values\\r\\n self.bias = np.random.normal()\\r\\n self.weights = np.random.randn(self.num_inputs)\\r\\n number_of_errors = -1\\r\\n while number_of_errors != 0: \\r\\n number_of_errors = 0\\r\\n print \\\"Beginning iteration\\\"\\r\\n print \\\"Bias: {:.3f}\\\".format(self.bias)\\r\\n print \\\"Weights:\\\", \\\", \\\".join(\\r\\n \\\"{:.3f}\\\".format(wt) for wt in self.weights)\\r\\n for x in self.inputs:\\r\\n error = f(x)-self.output(x)\\r\\n if error:\\r\\n number_of_errors += 1\\r\\n self.bias = self.bias+eta*error\\r\\n self.weights = self.weights+eta*error*x\\r\\n print \\\"Number of errors:\\\", number_of_errors, \\\"\\\\n\\\"\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1783,"cells":{"query_id":{"kind":"string","value":"260535276547a50ec4d4b9c711855ac5"},"query":{"kind":"string","value":"Pads a sequences of sequences so that they're all the same length. Inplace."},"positive_passages":{"kind":"list like","value":[{"docid":"f84708de8511a0ff35a99d7162a72692","score":"0.6910745","text":"def pad_sequences(sequences, token=0, max_length=None):\n\n if max_length is not None:\n for s in sequences:\n while len(s) > max_length:\n s.pop()\n\n mx = max([len(s) for s in sequences])\n\n for seq in sequences:\n while(len(seq) < mx):\n seq.append(token)","title":""}],"string":"[\n {\n \"docid\": \"f84708de8511a0ff35a99d7162a72692\",\n \"score\": \"0.6910745\",\n \"text\": \"def pad_sequences(sequences, token=0, max_length=None):\\n\\n if max_length is not None:\\n for s in sequences:\\n while len(s) > max_length:\\n s.pop()\\n\\n mx = max([len(s) for s in sequences])\\n\\n for seq in sequences:\\n while(len(seq) < mx):\\n seq.append(token)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"eed944a1d27bddfe7f93a8af99bfc0bc","score":"0.7702567","text":"def _pad_sequences(sequences, max_steps):\n if isinstance(sequences, np.ndarray): return sequences\n assert isinstance(sequences, list)\n checker.check_positive_integer(max_steps)\n checker.check_type(sequences, np.ndarray)\n\n if all([s.shape[0] == sequences[0].shape[0] for s in sequences]):\n return np.stack(sequences, axis=0)\n\n sample_shape = sequences[0].shape[1:]\n assert len(sample_shape) > 0\n stack = np.zeros(shape=(len(sequences), max_steps, *sample_shape))\n for i, s in enumerate(sequences): stack[i, :len(s)] = s\n\n return stack","title":""},{"docid":"06298802e374d839e405d04e66417e56","score":"0.7578034","text":"def pad_sequence(cls, sequences):\n # ensure that all waveforms in the batch have the same length by applying zero-padding\n sequences = [sequence.T for sequence in sequences]\n sequences = torch.nn.utils.rnn.pad_sequence(sequences, batch_first=True, padding_value=0.)\n return sequences.permute(0, 2, 1)","title":""},{"docid":"ae9f7f05506ff852adb8f83adc9083ed","score":"0.73381835","text":"def pad_list(sequences, dim0_pad=None, dim1_pad=None,\n align_right=False, pad_value=0):\n\n sequences = [np.asarray(sublist) for sublist in sequences]\n\n if not dim0_pad:\n dim0_pad = len(sequences)\n\n if not dim1_pad:\n dim1_pad = max(len(seq) for seq in sequences)\n\n out = np.full(shape=(dim0_pad, dim1_pad), fill_value=pad_value)\n\n lengths = []\n for i in range(len(sequences)):\n data_length = len(sequences[i])\n lengths.append(data_length)\n offset = dim1_pad - data_length if align_right else 0\n np.put(out[i], range(offset, offset + data_length), sequences[i])\n\n lengths = np.array(lengths)\n\n return out, lengths","title":""},{"docid":"d0cbf808f870ad159c19a2f5146a226d","score":"0.72260326","text":"def pad_sequence(seq, size, padding=None):\n return (list(seq) + [padding for _ in xrange(size)])[:size]","title":""},{"docid":"c66d929d4971cf29d311f33a2215c417","score":"0.70857394","text":"def pad_sequences(sequences, maxlen=None, dtype='int32',\r\n padding='pre', truncating='pre', value=0.):\r\n if not hasattr(sequences, '__len__'):\r\n raise ValueError('`sequences` must be iterable.')\r\n lengths = []\r\n for x in sequences:\r\n if not hasattr(x, '__len__'):\r\n raise ValueError('`sequences` must be a list of iterables. '\r\n 'Found non-iterable: ' + str(x))\r\n lengths.append(len(x))\r\n\r\n num_samples = len(sequences)\r\n if maxlen is None:\r\n maxlen = np.max(lengths)\r\n\r\n # take the sample shape from the first non empty sequence\r\n # checking for consistency in the main loop below.\r\n sample_shape = tuple()\r\n for s in sequences:\r\n if len(s) > 0:\r\n sample_shape = np.asarray(s).shape[1:]\r\n break\r\n\r\n x = (np.ones((num_samples, maxlen) + sample_shape) * value).astype(dtype)\r\n for idx, s in enumerate(sequences):\r\n if not len(s):\r\n continue # empty list/array was found\r\n if truncating == 'pre':\r\n trunc = s[-maxlen:]\r\n elif truncating == 'post':\r\n trunc = s[:maxlen]\r\n else:\r\n raise ValueError('Truncating type \"%s\" '\r\n 'not understood' % truncating)\r\n\r\n # check `trunc` has expected shape\r\n trunc = np.asarray(trunc, dtype=dtype)\r\n if trunc.shape[1:] != sample_shape:\r\n raise ValueError('Shape of sample %s of sequence at position %s '\r\n 'is different from expected shape %s' %\r\n (trunc.shape[1:], idx, sample_shape))\r\n\r\n if padding == 'post':\r\n x[idx, :len(trunc)] = trunc\r\n elif padding == 'pre':\r\n x[idx, -len(trunc):] = trunc\r\n else:\r\n raise ValueError('Padding type \"%s\" not understood' % padding)\r\n #transpose -> (max_length, len(sequence))\r\n #x = x.transpose()\r\n return x","title":""},{"docid":"a468ce03e69cfcb43b7c496e06ed62e8","score":"0.7021152","text":"def _pad_sequence(self, seq):\n batch_size = sum(s.size(0) for s in seq)\n max_len = max(s.size(1) for s in seq)\n padded_batch = torch.full(\n (batch_size, max_len), self.tokenizer.pad_token_id, dtype=torch.long\n )\n i = 0\n for data in seq:\n block, length = data.size()\n padded_batch[i:i+block, :length] = data\n i+= block\n return padded_batch","title":""},{"docid":"35fc8d820813ea701e4a892fb22b6291","score":"0.69403857","text":"def pad_sequences(sequences, maxlen=None, dtype='int32',\n padding='pre', truncating='pre', value=0.):\n if not hasattr(sequences, '__len__'):\n raise ValueError('`sequences` must be iterable.')\n lengths = []\n for x in sequences:\n if not hasattr(x, '__len__'):\n raise ValueError('`sequences` must be a list of iterables. '\n 'Found non-iterable: ' + str(x))\n lengths.append(len(x))\n\n num_samples = len(sequences)\n if maxlen is None:\n maxlen = np.max(lengths)\n\n # take the sample shape from the first non empty sequence\n # checking for consistency in the main loop below.\n sample_shape = tuple()\n for s in sequences:\n if len(s) > 0:\n sample_shape = np.asarray(s).shape[1:]\n break\n\n is_dtype_str = np.issubdtype(dtype, np.str_) or np.issubdtype(dtype, np.unicode_)\n if isinstance(value, six.string_types) and dtype != object and not is_dtype_str:\n raise ValueError(\"`dtype` {} is not compatible with `value`'s type: {}\\n\"\n \"You should set `dtype=object` for variable length strings.\"\n .format(dtype, type(value)))\n\n x = np.full((num_samples, maxlen) + sample_shape, value, dtype=dtype)\n for idx, s in enumerate(sequences):\n if not len(s):\n continue # empty list/array was found\n if truncating == 'pre':\n trunc = s[-maxlen:]\n elif truncating == 'post':\n trunc = s[:maxlen]\n else:\n raise ValueError('Truncating type \"%s\" '\n 'not understood' % truncating)\n\n # check `trunc` has expected shape\n trunc = np.asarray(trunc, dtype=dtype)\n if trunc.shape[1:] != sample_shape:\n raise ValueError('Shape of sample %s of sequence at position %s '\n 'is different from expected shape %s' %\n (trunc.shape[1:], idx, sample_shape))\n\n if padding == 'post':\n x[idx, :len(trunc)] = trunc\n elif padding == 'pre':\n x[idx, -len(trunc):] = trunc\n else:\n raise ValueError('Padding type \"%s\" not understood' % padding)\n return x","title":""},{"docid":"0abae5f078895c4a2bf9b54d281b2e61","score":"0.6880613","text":"def pad_sequences(sequences, maxlen=None, dtype='int32',\n padding='pre', truncating='pre', value=0.):\n if not hasattr(sequences, '__len__'):\n raise ValueError('`sequences` must be iterable.')\n lengths = []\n for x in sequences:\n if not hasattr(x, '__len__'):\n raise ValueError('`sequences` must be a list of iterables. '\n 'Found non-iterable: ' + str(x))\n lengths.append(len(x))\n\n num_samples = len(sequences)\n if maxlen is None:\n maxlen = np.max(lengths)\n\n # take the sample shape from the first non empty sequence\n # checking for consistency in the main loop below.\n sample_shape = tuple()\n for s in sequences:\n if len(s) > 0:\n sample_shape = np.asarray(s).shape[1:]\n break\n\n x = (np.ones((num_samples, maxlen) + sample_shape) * value).astype(dtype)\n for idx, s in enumerate(sequences):\n if not len(s):\n continue # empty list/array was found\n if truncating == 'pre':\n trunc = s[-maxlen:]\n elif truncating == 'post':\n trunc = s[:maxlen]\n else:\n raise ValueError('Truncating type \"%s\" '\n 'not understood' % truncating)\n\n # check `trunc` has expected shape\n trunc = np.asarray(trunc, dtype=dtype)\n if trunc.shape[1:] != sample_shape:\n raise ValueError('Shape of sample %s of sequence at position %s '\n 'is different from expected shape %s' %\n (trunc.shape[1:], idx, sample_shape))\n\n if padding == 'post':\n x[idx, :len(trunc)] = trunc\n elif padding == 'pre':\n x[idx, -len(trunc):] = trunc\n else:\n raise ValueError('Padding type \"%s\" not understood' % padding)\n return x","title":""},{"docid":"f15e48291c39cb177a6174aab1b3ca87","score":"0.68363106","text":"def pad_sequences_1d(sequences, max_len=None, padding='post', truncating='post', value=0.):\n return pad_sequences(sequences, maxlen=max_len, padding=padding, truncating=truncating, value=value)","title":""},{"docid":"bcc646192857f9bd547cb6bf442fda06","score":"0.6833937","text":"def pad_txt_sequences(sequences, pad_tok):\n\n max_length = max(len(x) for x in sequences)\n\n sequence_padded, sequence_length = [], []\n\n for seq in sequences:\n seq = list(seq)\n seq_ = seq + [pad_tok] * max(max_length - len(seq), 0)\n\n sequence_padded += [seq_]\n sequence_length += [len(seq)]\n\n return sequence_padded, sequence_length","title":""},{"docid":"6ca91f3ee5f411464b2d9567ee90c4b8","score":"0.67387444","text":"def _pad_batch(batch, pad_left, pad_right, dtype=np.int32):\r\n max_len = max(len(x) for x in batch)\r\n batch_array = pad_right * np.ones((len(batch), 2 + max_len), dtype=dtype)\r\n\r\n for i, x in enumerate(batch):\r\n batch_array[i, 0] = pad_left\r\n batch_array[i, 1:len(x) + 1] = x\r\n\r\n return batch_array","title":""},{"docid":"b9195f3a03908ae66f08a65b2f67ae7b","score":"0.67275894","text":"def padding(sequences, maxlen=None, dtype='int32',\n padding='pre', truncating='pre', value=0.):\n assert padding in [\"pre\", \"post\"], \"Invalid padding={}.\".format(padding)\n assert truncating in [\"pre\", \"post\"], \"Invalid truncating={}.\".format(truncating)\n \n if maxlen is None:\n maxlen = max(len(x) for x in sequences)\n arr = np.full((len(sequences), maxlen), value, dtype=dtype)\n for idx, x in enumerate(sequences):\n if len(x) == 0:\n continue # empty list\n if truncating == 'pre':\n trunc = x[-maxlen:]\n else:\n trunc = x[:maxlen]\n trunc = np.asarray(trunc, dtype=dtype)\n\n if padding == 'pre':\n arr[idx, -len(trunc):] = trunc\n else:\n arr[idx, :len(trunc)] = trunc\n return arr","title":""},{"docid":"2a7bc84900f9cdb815ee7f8dbcc26fea","score":"0.6693861","text":"def pad_seq(sequence):\n remainder = len(sequence) % 3\n return sequence if remainder == 0 else sequence + Seq('N' * (3 - remainder))","title":""},{"docid":"b4a2a4b409b7531e5edd8696cbd1aa35","score":"0.66649985","text":"def pad_list(xs, pad_value):\n n_batch = len(xs)\n max_len = max(x.size(0) for x in xs)\n pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)\n for i in range(n_batch):\n pad[i, :xs[i].size(0)] = xs[i]\n return pad","title":""},{"docid":"f9491efb77cfd776ef9f716e5ff86933","score":"0.6624278","text":"def pad(seq, n, fillvalue=None):\n\n length = len(seq)\n if length == n:\n return seq\n else:\n needed = n - length\n return seq + type(seq)([fillvalue] * needed)\n\n # itertools equivalent:\n # return chain(iterable, repeat(fillvalue, n-len(iterable)))","title":""},{"docid":"0f936c730a4bede1a0f71f21aee114c5","score":"0.65772593","text":"def pad4(seq):\n return_value = []\n for thing in seq:\n return_value.append(\"0\" * (4 - len(thing)) + thing)\n return return_value","title":""},{"docid":"0cb1704cd2b898d507ff14dc915e63cb","score":"0.6560269","text":"def pad_video_sequences(sequences):\n lengths = torch.LongTensor([len(seq) for seq in sequences])\n v_dim = sequences[0].size(1)\n padded_seqs = torch.zeros(len(sequences), max(lengths), v_dim).float()\n length_masks = torch.zeros(len(sequences), max(lengths))\n for idx, seq in enumerate(sequences):\n end = lengths[idx]\n padded_seqs[idx, :end] = seq\n length_masks[idx, :end] = torch.ones(end.item())\n return padded_seqs, lengths, length_masks","title":""},{"docid":"004d844432ce75fdc7671efc25b37954","score":"0.64507955","text":"def _pad(ids, pad_id, length):\n assert pad_id is not None\n assert length is not None\n\n if len(ids) < length:\n a = [pad_id] * (length - len(ids))\n return ids + a\n else:\n return ids[:length]","title":""},{"docid":"40ff2d7a2d0a556422d3117c2e14aa3f","score":"0.6449326","text":"def pad_to_max_length(self, input_sequences: List[List[int]]):\n max_length = max([len(s) for s in input_sequences])\n copy_input_sequences = []\n for i in range(len(input_sequences)):\n copy_input_sequences.append([self.pad_token_id] * (max_length - len(input_sequences[i])) + input_sequences[i])\n\n return copy_input_sequences","title":""},{"docid":"fe4064b1c7ccfbd31f3318f247f497a0","score":"0.64092594","text":"def padding(A):\n # O(d)\n d = len(A)\n for i in range(d):\n A[i].append(0)\n A.append([0]*(d+1))","title":""},{"docid":"3ff43a7f9f266b01b35386009389c079","score":"0.63736403","text":"def pad_2d_sequences(seq: Sequence[Any], maxlen: Optional[int] = None,\n embedding_size: Optional[int] = None) -> np.ndarray:\n\n # find the maximum length by looking through the sequence\n if maxlen is None:\n maxlen = -1\n for item in seq:\n maxlen = max(maxlen, len(item))\n\n # find the embedding dimension by looking through the sequence until there is a non-empty item\n if embedding_size is None:\n for item in seq:\n if len(item) != 0:\n embedding_size = len(item[0])\n break\n\n result = np.zeros((len(seq), maxlen, embedding_size))\n for i, item in enumerate(seq):\n assert len(item) > 0\n result[i, -len(item):] = item\n return result","title":""},{"docid":"da4134470cf237a7ce1404d121444a58","score":"0.6347811","text":"def pad_array(arr, length, pad=0):\n arr = np.array(arr)\n tail_length = length - len(arr)\n if tail_length > 0:\n tail = np.repeat(pad, tail_length)\n return np.concatenate([arr, tail])\n else:\n return arr","title":""},{"docid":"7b6ab05ca2a5c50040de72adfa9f6199","score":"0.633389","text":"def pad(self, docs: List[List[int]]) -> List[List[int]]:\n # First apply indexing on all the rows then pad_sequnces (i found this\n # faster than trying to do these steps on each row\n return pad_sequences(docs,\n maxlen=self.padding_maxlen,\n dtype=self.padding_dtype,\n padding=self.padding,\n truncating=self.truncating,\n value=self.padding_value)","title":""},{"docid":"1748fa4919f62a6bc0dafb72f998b9f0","score":"0.6321214","text":"def sequence_to_padding(self, x, lengths):\n ret_tensor = torch.zeros(\n (lengths.shape[0], torch.max(lengths).int()) + tuple(x.shape[1:])\n ).to(x.device)\n cum_len = 0\n for i, l in enumerate(lengths):\n ret_tensor[i, :l] = x[cum_len : cum_len + l]\n cum_len += l\n return ret_tensor","title":""},{"docid":"6e027635aed374ee1c02899d0a621358","score":"0.63164455","text":"def pad_1d_list_with_zero(array: np.ndarray, length: int) -> np.ndarray:\n zero_array = np.zeros(length - len(array), dtype=array.dtype)\n return np.concatenate((array, zero_array), axis=0)","title":""},{"docid":"5997e366bf0e479c886b55a25addc759","score":"0.6314174","text":"def pad(id_sequences):\n max_length = max([len(s) for s in id_sequences])\n padded_data = np.zeros((len(id_sequences), max_length))\n mask = np.zeros_like(padded_data)\n for i, sample in enumerate(id_sequences):\n padded_data[i, : len(sample)] = sample\n mask[i, : len(sample)] = 1\n return padded_data, mask","title":""},{"docid":"bc2d8b395c282f7656e56541d8388dc2","score":"0.6292906","text":"def pad_video_tensors(sequences):\n lengths = torch.LongTensor([len(seq) for seq in sequences])\n v_l, f, t, h, w = sequences[0].size()\n padded_seqs = torch.zeros(len(sequences), max(lengths), f, t, h, w).float()\n length_masks = torch.zeros(len(sequences), max(lengths))\n for idx, seq in enumerate(sequences):\n end = lengths[idx]\n padded_seqs[idx, :end] = seq\n length_masks[idx, :end] = torch.ones(end.item())\n return padded_seqs, lengths, length_masks","title":""},{"docid":"218964b3c905ffaf6b1ed4800b4cc632","score":"0.6292905","text":"def pad(self, data, length):\n\n from keras.preprocessing.sequence import pad_sequences\n return pad_sequences(data, maxlen=length, padding='post', truncating='post', value=0)","title":""},{"docid":"4df111b72bf86b544950e1ba09a04f61","score":"0.6276021","text":"def fill_pad(haps, pos, maxlen):\n sites = [0] + [int(p) for p in pos] + [maxlen]\n nsamples = haps.shape[1]\n padwidth = sites[:-1]\n for i in range(1, len(sites)):\n padwidth[i-1] = sites[i] - sites[i-1] - 1\n x = np.zeros((padwidth[0], nsamples)).astype(\"int8\")\n for i in range(1, len(padwidth)):\n x = np.append(x, haps[i-1:i, :], axis=0)\n x = np.append(x, np.zeros((padwidth[i], nsamples)).astype(\"int8\"), axis=0)\n return x","title":""},{"docid":"d0748e5eb667200286fa9e16ba05d353","score":"0.6264318","text":"def _pad_to_seq_length(self,\n inputs,\n override_max_seq_length = None):\n if override_max_seq_length is not None:\n max_seq_length = override_max_seq_length\n else:\n max_seq_length = self._max_seq_length\n while len(inputs) > max_seq_length:\n inputs.pop()\n while len(inputs) < max_seq_length:\n inputs.append(0)","title":""},{"docid":"18ac9947e0f39072488f48653d2b5a4f","score":"0.6253948","text":"def stack_pad(tensors, pad_axes=None, pad_to_lengths=None, dtype=np.float32,\n pad_value=0):\n tensors = [np.asarray(t) for t in tensors]\n max_lengths = [max(l) for l in zip(*[t.shape for t in tensors])]\n same_axes = dict(enumerate(max_lengths))\n if pad_axes is None:\n pad_axes = []\n if isinstance(pad_axes, six.integer_types):\n if pad_to_lengths is not None:\n max_lengths[pad_axes] = pad_to_lengths\n del same_axes[pad_axes]\n else:\n if pad_to_lengths is None:\n pad_to_lengths = [None] * len(pad_axes)\n for i, l in zip(pad_axes, pad_to_lengths):\n if l is not None:\n max_lengths[i] = l\n del same_axes[i]\n same_axes_items = same_axes.items()\n dest = np.full([len(tensors)] + max_lengths, pad_value, dtype=dtype)\n for i, t in enumerate(tensors):\n for j, l in same_axes_items:\n if t.shape[j] != l:\n raise ValueError(\n 'Tensor at index %d does not have size %d along axis %d'\n % (i, l, j))\n dest[[i] + [slice(0, d) for d in t.shape]] = t\n return dest","title":""},{"docid":"1e833762b283b1be7fae226f97e258a7","score":"0.6215426","text":"def padding(data, pad, max_len=None):\n if not max_len:\n seq_len = [len(x) for x in data]\n max_len = max(seq_len)\n\n for i in range(len(data)):\n data[i] += [pad] * (max_len-len(data[i]))\n\n return data","title":""},{"docid":"a0e46976f6999292f154a67974f9d495","score":"0.6211645","text":"def uniformed_sequences(\n self,\n sequences: np.array,\n length: Optional[int] = None) -> np.array:\n if length is None:\n length = self.seq_length\n return tf.keras.preprocessing.sequence.pad_sequences(sequences, padding='post', truncating='post', maxlen=length)","title":""},{"docid":"1caaab4928a8ad6104da20f24a842878","score":"0.6201801","text":"def pad_sequences(self, source: list, max_length: int = 0) -> list:\n assert type(source) is list\n assert type(source[0]) is list\n assert type(source[0][0]) is int, 'Sequences must already be indexed!'\n assert type(max_length) is int\n\n pad_idx = self.get_pad_idx()\n assert pad_idx is not None\n\n #if max_length is 0 or less, it means we calculate it\n if max_length <= 0:\n max_length = max(list(map(len, source)))\n\n #get the true value of the lengths before padding, to potentially use later\n lengths = list(map(len, source))\n\n temp = []\n \n\n for indices in tqdm(source, desc='Padding sequences'):\n assert len(indices) <= max_length, 'Indices should already be truncated to max_length!'\n indices = indices + [pad_idx for _ in range(max_length - len(indices))]\n assert len(indices) == max_length, 'Padding error! Padded sequence != max_length!'\n temp.append(indices)\n\n assert len(lengths) == len(temp)\n assert len(source) == len(temp)\n\n return temp, lengths","title":""},{"docid":"ff688bcd40291bd42bd1d51bc3599f89","score":"0.6200217","text":"def pad_list(l, n, pad=''):\n if len(l) < n:\n for i in range(n-len(l)): l.append(pad)\n return l","title":""},{"docid":"2ca46ef550bb36f6deca881c8a689cdd","score":"0.61930496","text":"def __pad_sequence(self, sequence):\n length = len(sequence)\n if length == self.Tx:\n return np.array(sequence)\n elif length < self.Tx:\n sequence.extend([0 for i in range(self.Tx-length)])\n return np.array(sequence)\n else:\n return np.array(sequence[:self.Tx])","title":""},{"docid":"de7f5cd36217d4b739a689cbbe0d33b3","score":"0.61857396","text":"def pad_sequence(sequences: List[paddle.Tensor],\n batch_first: bool=False,\n padding_value: float=0.0) -> paddle.Tensor:\n\n # assuming trailing dimensions and type of all the Tensors\n # in sequences are same and fetching those from sequences[0]\n max_size = paddle.shape(sequences[0])\n # (TODO Hui Zhang): slice not supprot `end==start`\n # trailing_dims = max_size[1:]\n trailing_dims = tuple(\n max_size[1:].numpy().tolist()) if sequences[0].ndim >= 2 else ()\n max_len = max([s.shape[0] for s in sequences])\n if batch_first:\n out_dims = (len(sequences), max_len) + trailing_dims\n else:\n out_dims = (max_len, len(sequences)) + trailing_dims\n\n out_tensor = sequences[0].new_full(out_dims, padding_value)\n for i, tensor in enumerate(sequences):\n length = tensor.shape[0]\n # use index notation to prevent duplicate references to the tensor\n logger.debug(\n f\"length {length}, out_tensor {out_tensor.shape}, tensor {tensor.shape}\"\n )\n if batch_first:\n # TODO (Hui Zhang): set_value op not supprot `end==start`\n # TODO (Hui Zhang): set_value op not support int16\n # TODO (Hui Zhang): set_varbase 2 rank not support [0,0,...]\n # out_tensor[i, :length, ...] = tensor\n if length != 0:\n out_tensor[i, :length] = tensor\n else:\n out_tensor[i, length] = tensor\n else:\n # TODO (Hui Zhang): set_value op not supprot `end==start`\n # out_tensor[:length, i, ...] = tensor\n if length != 0:\n out_tensor[:length, i] = tensor\n else:\n out_tensor[length, i] = tensor\n\n return out_tensor","title":""},{"docid":"679c8d8f431cad350452d1d1ecc50085","score":"0.617733","text":"def pad_stim(stim,length,axis=1,pad=np.nan,start=0):\n\tif axis==0:\n\t\traise(ValueError(\"axis cannot be set to 0. axis 0 is the trials axis and it cannot be changed\"))\n\tpadded_stim = []\n\tfor s in stim:\n\t\tshape_s = list(s.shape)\n\t\tshape_temp = list(s.shape)\n\t\tshape_temp[axis-1] = int(length)\n\t\ttemp = pad*np.ones(tuple(shape_temp))\n\t\tslices = [range(sh) for sh in shape_temp]\n\t\tslices[axis-1] = range(start,min(int(length),shape_s[axis-1]+int(start)))\n\t\ttemp[np.ix_(*slices)] = s.copy()\n\t\tpadded_stim.append(temp)\n\treturn np.array(padded_stim)","title":""},{"docid":"4eb87d602eeaa8b280e2df035d1012bd","score":"0.6172475","text":"def pad_sequence_to_length(sequence: List,\n desired_length: int,\n default_value: Callable[[], Any] = lambda: 0,\n padding_on_right: bool = True) -> List:\n # Truncates the sequence to the desired length.\n if padding_on_right:\n padded_sequence = sequence[:desired_length]\n else:\n padded_sequence = sequence[-desired_length:]\n # Continues to pad with default_value() until we reach the desired length.\n for _ in range(desired_length - len(padded_sequence)):\n if padding_on_right:\n padded_sequence.append(default_value())\n else:\n padded_sequence.insert(0, default_value())\n return padded_sequence","title":""},{"docid":"469374bc24f4799ca9a0ba767909f2cd","score":"0.6170578","text":"def pad(A):\n if A.shape[1] % 2 is not 0:\n pad_flag = 1\n j, k = A.shape[0], A.shape[1]\n zers1 = np.zeros((j, k)).reshape((j, k, 1))\n A = np.concatenate((A, zers1), 2)\n zers2 = np.zeros((j, k + 1)).reshape((j, 1, k + 1))\n A = np.concatenate((A, zers2), 1)\n else:\n pad_flag = 0\n return A, pad_flag","title":""},{"docid":"b5709f4955872b8e4239395901052488","score":"0.6168596","text":"def pad_input(input_list_of_list: list, max_input_length: int) -> list:\n for input_list in input_list_of_list:\n for i in range(max_input_length - len(input_list)):\n input_list.insert(0, 0)\n\n return input_list_of_list","title":""},{"docid":"2d769d50c1aab5a2af4ce0d521ff0591","score":"0.6142439","text":"def pad_batch(batch):\n\n flatten = [u for s in batch for u in s]\n\n return pad_sequence(flatten, batch_first=True, padding_value=0)","title":""},{"docid":"9a5b6b1a442db8d4b661760080350259","score":"0.61392164","text":"def pad(self, padding_lengths: Dict[str, int]):\n desired_num_slots = padding_lengths['num_slots']\n if len(self.word_indices) > desired_num_slots:\n # We concatenate the indices from the slots at the end to make word_indices the right\n # length.\n num_extra_slots = len(self.word_indices) - desired_num_slots\n all_word_indices = self.word_indices\n self.word_indices = all_word_indices[:desired_num_slots]\n for i in range(num_extra_slots):\n self.word_indices[desired_num_slots - 1] += all_word_indices[desired_num_slots + i]\n elif len(self.word_indices) < desired_num_slots:\n additional_slots = [[] for _ in range(desired_num_slots - len(self.word_indices))]\n self.word_indices += additional_slots\n self.word_indices = [self.pad_word_sequence(indices, padding_lengths) for\n indices in self.word_indices]","title":""},{"docid":"d044de5a6f571c0ddc5e793563df7195","score":"0.61383486","text":"def pad_sentences(sentences, padding_word=\"<PAD/>\",sequence_length = 0):\n if sequence_length == 0:\n sequence_length = max(len(x) for x in sentences)\n padded_sentences = []\n for i in range(len(sentences)):\n sentence = sentences[i]\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * num_padding\n padded_sentences.append(new_sentence)\n return padded_sentences","title":""},{"docid":"d5d8fb534124785a06856df54673076a","score":"0.6138309","text":"def padded(seq):\n try:\n for elt in seq:\n yield elt\n while True:\n yield None\n except GeneratorExit:\n pass","title":""},{"docid":"d21dced90dd8db61c17ecaa666e18160","score":"0.6107195","text":"def padding(self, seq):\n # padded_seq = np.zeros([len(seq)])\n padded_seq = np.pad(seq, (0, self.sequence_max_len-len(seq)), 'constant')\n # padded_seq.append(row)\n return padded_seq.tolist()","title":""},{"docid":"b3ead67d958cf49a2907dafc35ad06a4","score":"0.6089033","text":"def pad_to_same_length(ss, pad, l_max=None):\n ls = []\n for s in ss:\n ls.append(len(s))\n if l_max == None:\n l_max = max(ls)\n ss_new = []\n for s, l in zip(ss, ls):\n if len(s) <= l_max:\n s.extend([pad] * (l_max - l))\n else:\n s = s[:l_max]\n ss_new.append(s)\n return ss_new","title":""},{"docid":"cd9d2c02f947c258fe5287d16eb805bf","score":"0.60366124","text":"def padding(self, sentences):\n sentence_pad = [sentence[:self.__sequence_length] if len(sentence) > self.__sequence_length\n else sentence + [0] * (self.__sequence_length - len(sentence))\n for sentence in sentences]\n return sentence_pad","title":""},{"docid":"a499defe198a4b2c6c1806b17c2f9e87","score":"0.60362464","text":"def pad_sentences2(sentences,sequence_length, padding_word=\"<PAD/>\"):\n padded_sentences = []\n for i in range(len(sentences)):\n sentence = sentences[i]\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * num_padding\n padded_sentences.append(new_sentence)\n return padded_sentences","title":""},{"docid":"116b00f54e7f1cfbbee79c2b17f0a8ed","score":"0.60174894","text":"def pad_graphlet(self, graphlet: List[List[int]], padding_lengths: Dict):\n desired_num_alignments = padding_lengths['num_alignments']\n graphlet = self.pad_sequence_to_length(graphlet, desired_num_alignments,\n default_value=lambda: [], truncate_from_right=False)\n # Pad the alignment to the desired number of features.\n desired_num_features = padding_lengths['num_features']\n graphlet = [self.pad_sequence_to_length(alignment, desired_num_features, default_value=lambda: 0.0,\n truncate_from_right=False) for alignment in graphlet]\n return graphlet","title":""},{"docid":"6c2a1d977d9c8543fd9a3c33ee8bf187","score":"0.6012757","text":"def pad_list(xs: torch.Tensor, pad_value=0, dim=0) -> torch.Tensor:\n if dim == 0:\n return pad_sequence(xs, batch_first=True, padding_value=pad_value)\n else:\n xs = [x.transpose(0, dim) for x in xs]\n padded = pad_sequence(xs, batch_first=True, padding_value=pad_value)\n return padded.transpose(1, dim+1).contiguous()","title":""},{"docid":"2f0243b7632e8c5f5f62b10375e1b6b6","score":"0.600301","text":"def _pad_to_grid(self, seq: torch.Tensor):\n batch, c, seq_len = seq.size()\n h,w = self.patch_size\n need = h*w - seq_len % (h*w)\n logger.debug('need {}'.format(need))\n seq = F.pad(seq,(0,need))\n return seq","title":""},{"docid":"2f0243b7632e8c5f5f62b10375e1b6b6","score":"0.600301","text":"def _pad_to_grid(self, seq: torch.Tensor):\n batch, c, seq_len = seq.size()\n h,w = self.patch_size\n need = h*w - seq_len % (h*w)\n logger.debug('need {}'.format(need))\n seq = F.pad(seq,(0,need))\n return seq","title":""},{"docid":"517616f3908baa66f5f5a959b7ad3066","score":"0.5982693","text":"def _pad_and_collate_audio(audios_list: list[Tensor], fill_value: float) -> tuple[Tensor, Tensor]:\n\ttime_dim = -1\n\taudios_lens = torch.as_tensor([audio.shape[time_dim] for audio in audios_list])\n\n\tmax_audio_len = int(audios_lens.max().item())\n\tpad = Pad(max_audio_len, fill_value=fill_value, dim=time_dim)\n\n\taudios_batch_pad = torch.stack([\n\t\tpad(audio) for audio in audios_list\n\t])\n\treturn audios_batch_pad, audios_lens","title":""},{"docid":"366e2d2d72852389d2cc97770580eabe","score":"0.5977594","text":"def pad_to_fixed_length(inputs, target_length):\n pad_n = int(target_length) - inputs.shape[0]\n if pad_n < 0:\n inputs = inputs[0: int(target_length),:]\n else:\n inputs = np.pad(inputs,[[0, pad_n], [0,0]])\n return inputs","title":""},{"docid":"e903e803182c8cc772a1fcf8717de413","score":"0.5977333","text":"def pad_seq(self, seq_idx):\n # Pad each sequence to be the same length to process in batches\n # pad_token = 0\n if len(seq_idx) >= self.max_seq_len:\n seq_idx = seq_idx[:self.max_seq_len]\n else:\n seq_idx += [0]*(self.max_seq_len - len(seq_idx))\n seq = seq_idx\n\n return seq","title":""},{"docid":"a7efd2dcbafb2243d3bd50ae2f382dc4","score":"0.5960234","text":"def pad_sentences(sentences, padding_word=\"<PAD/>\"):\n sequence_length = max(len(x) for x in sentences)\n padded_sentences = []\n for i in range(len(sentences)):\n sentence = sentences[i]\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * num_padding\n padded_sentences.append(new_sentence)\n return padded_sentences","title":""},{"docid":"bc1d10fff790f68051aea49fe362d84c","score":"0.59584653","text":"def test_list(self):\n data = [\n [1, 5, 4, 3],\n [1, 2, 4, 0, 9],\n [9, 3],\n [9, 4, 3]\n ]\n\n padded_data = gd.pad_sequence(data)\n\n assert (\n padded_data == self.get_manually_padded_2d()).all(), \"Pad sequence did not pad list of lists correctly.\"","title":""},{"docid":"560d1364b879f13ba679f4ec32be41c0","score":"0.5957906","text":"def roundrobin(seqOfSeqs, num, dopad=0, pad=None):\n ret = []\n if not seqOfSeqs: return ret\n cur = [0 for s in seqOfSeqs]\n while len(ret) < num and max(cur) >= 0:\n for i, seq in enumerate(seqOfSeqs):\n if cur[i] < 0: continue\n try:\n ret.append(seq[cur[i]])\n if len(ret) == num: break\n cur[i] += 1\n except IndexError:\n cur[i] = -1\n # if we don't have enough items and we want to pad, do it\n if len(ret) < num and dopad:\n ret.extend([pad] * range(num-len(ret)))\n return ret","title":""},{"docid":"aac7060861e750bfca9f6787377813f8","score":"0.59514123","text":"def pad_to_sequence_length(sentence, seq_length):\n \n padding_length = seq_length - (sentence.shape[0] % seq_length)\n target_length = sentence.shape[0] + padding_length\n return pad_last(sentence, target_length)","title":""},{"docid":"bc9f95e5711a54450899404ede07d457","score":"0.5950457","text":"def pad_sentences(self, sentences, sequence_length, padding_word=\"<PAD/>\"):\n padded_sentences = []\n for i in range(len(sentences)):\n sentence = sentences[i]\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * num_padding\n padded_sentences.append(new_sentence)\n return padded_sentences","title":""},{"docid":"3a91b7e334749bae82537ee16eafa2d1","score":"0.5946171","text":"def pad_sentences(sentences, padding_word=\"<PAD/>\"):\n sequence_length = max(len(x) for x in sentences)\n padded_sentences = []\n for i in range(len(sentences)):\n sentence = sentences[i]\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * num_padding\n padded_sentences.append(new_sentence)\n return padded_sentences","title":""},{"docid":"3a91b7e334749bae82537ee16eafa2d1","score":"0.5946171","text":"def pad_sentences(sentences, padding_word=\"<PAD/>\"):\n sequence_length = max(len(x) for x in sentences)\n padded_sentences = []\n for i in range(len(sentences)):\n sentence = sentences[i]\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * num_padding\n padded_sentences.append(new_sentence)\n return padded_sentences","title":""},{"docid":"3a91b7e334749bae82537ee16eafa2d1","score":"0.5946171","text":"def pad_sentences(sentences, padding_word=\"<PAD/>\"):\n sequence_length = max(len(x) for x in sentences)\n padded_sentences = []\n for i in range(len(sentences)):\n sentence = sentences[i]\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * num_padding\n padded_sentences.append(new_sentence)\n return padded_sentences","title":""},{"docid":"1a1423467aa038b2968bd00c00560910","score":"0.5898101","text":"def pad_sentences(sentences, padding_word=''):\n sequence_length = max(len(x) for x in sentences)\n padded_sentences = []\n\n for sentence in sentences:\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * num_padding\n padded_sentences.append(new_sentence)\n\n return padded_sentences","title":""},{"docid":"a9c26440cde9da053647b222ab59f617","score":"0.58606505","text":"def pad(iterable, end):\n return chain(iterable, repeat(end))","title":""},{"docid":"4c99c244c54436f0cddb0dcf235e6bd2","score":"0.58314925","text":"def pad_transitions(sentences_transitions):\n max_len = max(len(transitions) for transitions in sentences_transitions)\n for transitions in sentences_transitions:\n if len(transitions) < max_len:\n transitions.extend([PAD_CODE] * (max_len - len(transitions)))\n return np.array(sentences_transitions, dtype=np.int64)","title":""},{"docid":"2e4c4087fcb6f3615b428dbd09c97995","score":"0.5817333","text":"def pad_batch(batch):\n batch_pad = np.zeros((len(batch), len(batch[-1])))\n # fill the array\n for idx, sentence in enumerate(batch):\n batch_pad[idx, 0:len(sentence)] = sentence\n batch_pad = np.swapaxes(batch_pad, 0, 1)\n\n return batch_pad","title":""},{"docid":"b51635c560976e7bcb4003ab78670ef3","score":"0.5804639","text":"def pad(pts):\n return np.vstack([pts, [[0, 0]]])","title":""},{"docid":"f63e6172bb52d95c8802e5f6279e0247","score":"0.5804279","text":"def test_pad_sequence2(self):\n\n word_embeddings = [19, 1, 22, 1, 306, 50]\n processor = TextPreprocessing(10, 1000)\n result = processor.pad_sequence(word_embeddings)\n\n expected_result = [19, 1, 22, 1, 306, 50, 0, 0, 0, 0]\n\n self.assertEqual(result, expected_result)","title":""},{"docid":"42cbf539f6257333bd0709bb5181f7e0","score":"0.58000654","text":"def unpad(padded: Tensor, length: Tensor) -> List[Tensor]:\n output = []\n v: Tensor\n n: int\n for v, n in zip(padded, length):\n output.append(v[:n])\n return output","title":""},{"docid":"3154d6cf8b85c8145f5193b6b3fcf4c3","score":"0.5796609","text":"def pad(array: np.array, n: int, fill_value=None):\n assert array.shape[0] <= n\n if fill_value is None:\n pad = np.repeat(array[0:1], n - array.shape[0], axis=0)\n else:\n pad = np.full((n - array.shape[0],) + array.shape[1:], fill_value, dtype=array.dtype)\n return np.concatenate([array, pad], axis=0)","title":""},{"docid":"d505d0623aa73f97a3eba0cd5e681bf2","score":"0.57948244","text":"def pad_to_len(t, length, fill_value=-1):\n shape = t.shape \n if len(shape) == 2:\n # handle case with batch dimension = 1\n assert shape[0] == 1, \"can only handle case with batch dim = 1\"\n t = t.flatten() \n elif len(shape) > 2:\n raise Exception(\"Trying to pad array with shape \", shape)\n assert t.shape[0] <= length, \"tensor too long to be padded\"\n padding = torch.ones(length-len(t), dtype=torch.int64)*-1\n padded_t = torch.cat([t, padding])\n return padded_t","title":""},{"docid":"31b107996648407cfb96cdfd62ad7ad8","score":"0.5786162","text":"def pad(tensors, padding_value=-1):\n max_len = max(t.size(-1) for t in tensors)\n if tensors[0].dim() == 1:\n out = torch.LongTensor(len(tensors), max_len).fill_(padding_value)\n for i, t in enumerate(tensors):\n out[i, : t.size(0)] = t\n return out\n elif tensors[0].dim() == 2:\n max_width = max(t.size(0) for t in tensors)\n out = torch.LongTensor(len(tensors), max_width, max_len).fill_(padding_value)\n for i, t in enumerate(tensors):\n out[i, : t.size(0), : t.size(1)] = t\n return out\n else:\n raise ValueError(\"Input tensors must be either 1D or 2D!\")","title":""},{"docid":"e7aca69017d82110cbbfee3501d1c533","score":"0.5784187","text":"def pad(self, padding_lengths: Dict[str, int]):\n desired_num_options = padding_lengths['num_options']\n desired_num_graphlets = padding_lengths['num_graphlets']\n\n # Pad the number of answers. Note that while we use the superclass method when we need to add empty\n # answer_options, we don't want to remove answer options.\n num_options = len(self.answers_indexed)\n if num_options > desired_num_options:\n raise Exception(\"Case of too many answer options ({0} provided) isn't currently \"\n \"handled.\".format(num_options))\n elif num_options < desired_num_options:\n # Add empty answer option(s).\n self.answers_indexed = self.pad_sequence_to_length(self.answers_indexed,\n desired_num_options,\n lambda: [],\n truncate_from_right=False)\n # Pad the number of graphlets per option.\n for answer_index in range(len(self.answers_indexed)):\n self.answers_indexed[answer_index] = self.pad_sequence_to_length(self.answers_indexed[answer_index],\n desired_num_graphlets,\n lambda: [],\n truncate_from_right=False)\n # Pad each of the graphlets for num alignments and features per alignment\n for answer_index in range(len(self.answers_indexed)):\n self.answers_indexed[answer_index] = [self.pad_graphlet(answer_graphlet, padding_lengths)\n for answer_graphlet in self.answers_indexed[answer_index]]","title":""},{"docid":"861e6b172311805007ee9ed4a7f600cc","score":"0.5776464","text":"def pad_documents(sentences, padding_word='<PAD>'):\n sequence_length = max(len(x) for x in sentences)\n padded_sentences = []\n for i in range(len(sentences)):\n sentence = sentences[i]\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * num_padding\n padded_sentences.append(new_sentence)\n return padded_sentences","title":""},{"docid":"e2d2b30577123cf18715f947f49e206a","score":"0.5739352","text":"def pad_list(listA, val=-1, length=None):\n # pad list with zeros in order to have all lists of the same size\n assert isinstance(listA, list), 'Input must be a list. Got {}, expected {}' \\\n .format(type(listA), type(list))\n\n # get size of the biggest list\n if length:\n max_size = length\n else:\n max_size = len(max(listA, key=len))\n\n # pad all lists with the a padding value\n return [elem + [val] * int(max_size - len(elem)) for elem in listA]","title":""},{"docid":"4f93eb51ce5c92ddbf0da2df49f47c48","score":"0.5730438","text":"def packing_sequence(seq: torch.Tensor, seq_lens: np.ndarray, new_index) -> torch.Tensor:\n return rnn_utils.pack_padded_sequence(seq[new_index], seq_lens[new_index], batch_first = True)","title":""},{"docid":"a5e3ddaac4b9d61d875e75bd2cf880af","score":"0.5728993","text":"def pad(data, end=True):\n pad = np.ones((len(data), 1))\n if end:\n return np.hstack([data, pad])\n else:\n return np.hstack([pad, data])","title":""},{"docid":"f8381ec82100d4767e6f5fd92f4d689d","score":"0.5716155","text":"def concat(seq):\n return sum(seq, [])","title":""},{"docid":"a28221ed5042c308181b62eca2d84f57","score":"0.5710812","text":"def _zero_pad(sequence_length: int) -> datasets.Transform:\n\n def _zero_pad_transform(sample: reverb.ReplaySample) -> reverb.ReplaySample:\n trajectory: reverb_base.Trajectory = sample.data\n\n # Split steps and extras data (the extras won't be padded as they only\n # contain one element)\n trajectory_steps = trajectory._asdict()\n trajectory_extras = trajectory_steps.pop('extras')\n\n unpadded_length = len(tree.flatten(trajectory_steps)[0])\n\n # Do nothing if the sequence is already full.\n if unpadded_length != sequence_length:\n to_pad = sequence_length - unpadded_length\n pad = lambda x: tf.pad(x, [[0, to_pad]] + [[0, 0]] * (len(x.shape) - 1))\n\n trajectory_steps = tree.map_structure(pad, trajectory_steps)\n\n # Set the shape to be statically known, and checks it at runtime.\n def _ensure_shape(x):\n shape = tf.TensorShape([sequence_length]).concatenate(x.shape[1:])\n return tf.ensure_shape(x, shape)\n\n trajectory_steps = tree.map_structure(_ensure_shape, trajectory_steps)\n return reverb.ReplaySample(\n info=sample.info,\n data=reverb_base.Trajectory(\n **trajectory_steps, extras=trajectory_extras))\n\n return _zero_pad_transform","title":""},{"docid":"334d4b4814e895f7eaa38e6311795128","score":"0.57006264","text":"def test_nparray_oflists(self):\n data = np.array([\n [1, 5, 4, 3],\n [1, 2, 4, 0, 9],\n [9, 3],\n [9, 4, 3]\n ])\n\n assert data.dtype is np.dtype('O'), \"The test was not wired up properly\"\n\n padded_data = gd.pad_sequence(data)\n assert (\n padded_data == self.get_manually_padded_2d()).all(), \"Pad sequence did not pad np arr of lists as intended.\"","title":""},{"docid":"0edc35ff3dddf91bd097a389ddaa00fa","score":"0.5686566","text":"def pad_missing_fragments(self, seq, ions, weights):\n # TODO check that this works as intended and not superslowly\n temp = np.zeros(len(seq) - 1)\n for i, w in zip(ions, weights):\n temp[i - 1] = w\n\n ions = list(range(1, len(seq)))\n weights = temp.tolist()\n assert len(ions) == len(weights)\n\n return ions, weights","title":""},{"docid":"02134fcf590502e0f93818ce1fde8642","score":"0.56765175","text":"def adjust_sequences(data, length=50):\n for sample in data['data']:\n # adjust the sequence of input words\n if len(sample['words']) < length:\n # add <EOS> and <PAD> if sentence is shorter than maximum length\n sample['words'].append('<EOS>')\n while len(sample['words']) < length:\n sample['words'].append('<PAD>')\n else:\n # otherwise truncate and add <EOS> at last position\n sample['words'] = sample['words'][:length]\n sample['words'][-1] = '<EOS>'\n\n # adjust in the same way the sequence of output slots\n if len(sample['slots']) < length:\n sample['slots'].append('<EOS>')\n while len(sample['slots']) < length:\n sample['slots'].append('<PAD>')\n else:\n sample['slots'] = sample['slots'][:length]\n sample['slots'][-1] = '<EOS>'\n\n return data","title":""},{"docid":"9a8929525fb71a594db0783c5b1cb608","score":"0.5669793","text":"def pad_sequence(sequence, max_seq_length, truncating='pre', dtype='float32', value=0.0):\n \n if not hasattr(sequence, '__len__'):\n raise ValueError('sequence must be iterable.')\n\n sample_shape = tuple()\n sample_shape = np.asarray(sequence).shape[1:]\n \n length = len(sequence)\n\n padded_sequence = (np.ones((max_seq_length,) + sample_shape) * value).astype(dtype)\n\n # truncation\n if truncating == 'pre':\n trunc = sequence[-max_seq_length:]\n elif truncating == 'post':\n trunc = sequence[:max_seq_length]\n else:\n raise ValueError('Truncating type \"{}\" not understood'.format(truncating))\n\n # check 'trunc' has expected shape\n trunc = np.asarray(trunc, dtype=dtype)\n if trunc.shape[1:] != sample_shape:\n raise ValueError('Shape of sample {} of sequence is different from expected shape {}'.\n format(trunc.shape[1:], sample_shape))\n \n # padding\n padded_sequence[:len(trunc)] = trunc\n\n if length > max_seq_length:\n length = max_seq_length\n\n return padded_sequence, length","title":""},{"docid":"6d53bb01a16682a0310b95962794636d","score":"0.56646353","text":"def stretching_seq_nonzeros(self, x,max_len):\n x_len = len(x)\n dif_len = max_len - x_len #Number of padding zeros \n seq = \"\" #New encoding sequence to build\n if x_len > dif_len:\n #x_len > dif_len\n xIndex = 0\n for i in range(dif_len):\n xChunk = int(round(x_len / (dif_len + 1)))\n if xChunk==1:\n seq = \"\".join(string for string in [seq, x[xIndex:(xIndex+xChunk)], x[(xIndex)]]) \n else:\n seq = \"\".join(string for string in [seq, x[xIndex:(xIndex+xChunk)], x[(xIndex+xChunk-1)]])\n xIndex = xIndex + xChunk\n x_len = x_len - xChunk\n dif_len = dif_len - 1\n \n seq = seq + x[xIndex]\n if x_len <= dif_len:\n lastPad = round((dif_len)/(x_len))\n for idx, c in enumerate(x[:-1]):\n yPad = int(round(dif_len/(x_len-1)))\n miniPad = int(yPad/2)\n if yPad%2 ==0:\n seq = \"\".join(string for string in [seq, c, c*miniPad])\n seq = \"\".join(string for string in [seq, x[idx+1]*miniPad])\n else:\n if miniPad == 0:\n seq = \"\".join(string for string in [seq, c, c])\n else:\n seq = \"\".join(string for string in [seq, c, c*miniPad])\n seq = \"\".join(string for string in [seq, x[idx+1]*(miniPad+1)])\n x_len = x_len - 1\n dif_len = dif_len - yPad\n seq = seq + x[-1]\n return seq","title":""},{"docid":"a0f6e860e3ec5b9ddb4a86b7784ebe92","score":"0.56575364","text":"def pad_batch(self, batch):\n max_input_length = self.max_input_length\n max_label_length = self.max_label_length\n if max_input_length is None or max_label_length is None:\n max_input_length, max_label_length = max_length_seq(batch)\n for i in range(len(batch)):\n while len(batch[i][0]) < max_input_length:\n batch[i][0].append(self.type_to_ix['_PAD'])\n while len(batch[i][1]) < max_label_length:\n batch[i][1].append(self.type_to_ix['_PAD'])\n return batch","title":""},{"docid":"d86ed80fa59b03713e3e5db118e37f25","score":"0.5648334","text":"def periodicpad(inputs, pad):\n n = inputs.dim()\n inputs = inputs.permute(*list(range(n-1,-1,-1)))\n pad = iter(pad)\n i = 0\n indx = []\n for a in pad:\n b = next(pad)\n assert a<inputs.size()[i] and b<inputs.size()[i]\n permute = list(range(n))\n permute[i] = 0\n permute[0] = i\n inputs = inputs.permute(*permute)\n inputlist = [inputs,]\n if a > 0:\n inputlist = [inputs[slice(-a,None)],inputs]\n if b > 0:\n inputlist = inputlist+[inputs[slice(0,b)],]\n if a+b > 0:\n inputs = torch.cat(inputlist,dim=0)\n inputs = inputs.permute(*permute)\n i += 1\n inputs = inputs.permute(*list(range(n-1,-1,-1)))\n return inputs","title":""},{"docid":"b9260f43b98d7fc9da4faf69e44261c3","score":"0.56459534","text":"def test_lesser_len(self):\n data = [\n [1, 5, 4, 3],\n [1, 2, 4, 0, 9],\n [9, 3],\n [9, 4, 3]\n ]\n padded_data = gd.pad_sequence(data, max_length=4)\n manually_padded_data = np.array([\n [1, 5, 4, 3],\n [1, 2, 4, 0],\n [9, 3, 0, 0],\n [9, 4, 3, 0]\n ])\n assert (manually_padded_data == padded_data).all(), \\\n \"Cannot properly pad when max length is less than the longest arr in data.\"","title":""},{"docid":"181c271eddaca217be4354ab560e0fb4","score":"0.5642768","text":"def pad_to_fix_length(dim_2_list, pad_default, fix_length = None):\n max_len = 0\n if fix_length is None:\n len_list = [len(l) for l in dim_2_list]\n max_len = max(len_list)\n else:\n max_len = fix_length\n dim_2_list_new = []\n for l in dim_2_list:\n l_new = l + [pad_default] * max((max_len - len(l)), 0)\n dim_2_list_new.append(l_new)\n return dim_2_list_new","title":""},{"docid":"a663469f7387e0866b6878ffc5308538","score":"0.5640025","text":"def fill_one_space(self) -> None:\n self.sequences = self.build_sequences(self.sequences)\n while self.sequences and self.count_available_bits(self.sequences[0]) == 1:\n self.sequences = self.build_sequences(self.sequences)","title":""},{"docid":"2ea21bc3c2bd0c4745e99fbf984e853d","score":"0.56307036","text":"def test_pad_and_stack_tensors(self):\n # Width of all arrays must be the same to represent the \"batch_size\"\n # argument.\n my_arrays = [\n np.array([[4, 2, 1], [2, 1, 1]]), # 2 x 3.\n np.array([[4, 2, 1], [2, 1, 1], [1, 1, 7]]), # 3 x 3.\n np.array([[1, 2, 3]]) # 1 x 3.\n ] # pyformat: disable\n\n padded_and_stacked = trajectory_collector.pad_and_stack_tensors(my_arrays)\n\n # Maximum trajectory length was 3 so we expect the output to be that.\n self.assertEqual(padded_and_stacked.shape, (3, 9))\n\n # Check that padding should only happen where it is required.\n self.assertTrue(np.allclose(padded_and_stacked[1:, 6:], 0))\n self.assertFalse(np.allclose(padded_and_stacked[:, 3:7], 0))","title":""},{"docid":"89914604a28cd3f2ac1d9e1b298cbe85","score":"0.56142145","text":"def periodicpad(inputs, pad):\n n = inputs.dim()\n inputs = inputs.permute(*list(range(n - 1, -1, -1)))\n pad = iter(pad)\n i = 0\n indx = []\n for a in pad:\n b = next(pad)\n assert a < inputs.size()[i] and b < inputs.size()[i]\n permute = list(range(n))\n permute[i] = 0\n permute[0] = i\n inputs = inputs.permute(*permute)\n inputlist = [\n inputs,\n ]\n if a > 0:\n inputlist = [inputs[slice(-a, None)], inputs]\n if b > 0:\n inputlist = inputlist + [\n inputs[slice(0, b)],\n ]\n if a + b > 0:\n inputs = torch.cat(inputlist, dim=0)\n inputs = inputs.permute(*permute)\n i += 1\n inputs = inputs.permute(*list(range(n - 1, -1, -1)))\n return inputs","title":""},{"docid":"42eb9fa6e603dcb34dbc0e1dcab03786","score":"0.56067705","text":"def padarr(x, align=8):\n nitems = len(x.ravel())\n nbytes = x.nbytes\n dtypenbytes = x.dtype.itemsize\n rem = nbytes % align\n npadbytes = align - rem if rem else 0 # nbytes to pad with for 8 byte alignment\n if npadbytes == 0:\n return x\n if npadbytes % dtypenbytes != 0:\n raise RuntimeError(\"Can't pad %d byte array to %d byte alignment\" %\n (dtypenbytes, align))\n npaditems = npadbytes / dtypenbytes\n x = x.ravel().copy() # don't modify in place\n # pads with npaditems zeros, each of length dtypenbytes:\n x.resize(nitems + npaditems, refcheck=False)\n assert x.nbytes % align == 0\n return x","title":""},{"docid":"42eb9fa6e603dcb34dbc0e1dcab03786","score":"0.56067705","text":"def padarr(x, align=8):\n nitems = len(x.ravel())\n nbytes = x.nbytes\n dtypenbytes = x.dtype.itemsize\n rem = nbytes % align\n npadbytes = align - rem if rem else 0 # nbytes to pad with for 8 byte alignment\n if npadbytes == 0:\n return x\n if npadbytes % dtypenbytes != 0:\n raise RuntimeError(\"Can't pad %d byte array to %d byte alignment\" %\n (dtypenbytes, align))\n npaditems = npadbytes / dtypenbytes\n x = x.ravel().copy() # don't modify in place\n # pads with npaditems zeros, each of length dtypenbytes:\n x.resize(nitems + npaditems, refcheck=False)\n assert x.nbytes % align == 0\n return x","title":""},{"docid":"5ade6b6418ebae4e88fe9c28b79c0e80","score":"0.5601883","text":"def padnone(iterable):\n return chain(iterable, repeat(None))","title":""},{"docid":"5ade6b6418ebae4e88fe9c28b79c0e80","score":"0.5601883","text":"def padnone(iterable):\n return chain(iterable, repeat(None))","title":""},{"docid":"761182dd0786b2baa44609c82203e742","score":"0.5601394","text":"def _pad_arrs_to_max_length(arrs, pad_axis, pad_val, round_to=None, max_length=None):\n if not isinstance(arrs[0], torch.Tensor):\n arrs = [torch.as_tensor(ele) for ele in arrs]\n\n original_length = [ele.size(pad_axis) for ele in arrs]\n max_arr_len = max(original_length)\n\n if round_to is not None:\n max_arr_len = round_to * math.ceil(max_arr_len / round_to)\n elif max_length is not None:\n if max_length < max_arr_len:\n raise ValueError(\n f\"If max_length is specified, max_length={max_length} must be larger \"\n f\"than the maximum length {max_arr_len} of the given arrays at axis={pad_axis}\"\n )\n max_arr_len = max_length\n\n size = arrs[0].size()\n prev_trailing_dims = size[:pad_axis]\n after_trailing_dims = size[pad_axis + 1 :]\n\n out_dims = (len(arrs),) + prev_trailing_dims + (max_arr_len,) + after_trailing_dims\n out_tensor = arrs[0].new_full(out_dims, pad_val)\n for i, tensor in enumerate(arrs):\n length = tensor.size(pad_axis)\n out_tensor[i].narrow(pad_axis, 0, length)[:] = tensor\n\n original_length = torch.as_tensor(original_length)\n\n return out_tensor, original_length","title":""},{"docid":"cfa26cfc7476e599671422777ac99557","score":"0.55938154","text":"def padded_indices(\n sequences,\n index_dict=None,\n ndim=2,\n padding='pre',\n add_start_symbol=True,\n add_end_symbol=True,\n max_len=None):\n index_sequences = sequences_to_indices(\n sequences=sequences,\n index_dict=index_dict,\n add_start_symbol=add_start_symbol,\n add_end_symbol=add_end_symbol)\n \n if max_len == None:\n max_len = max(len(s) for s in index_sequences)\n n_samples = len(index_sequences)\n if ndim < 2:\n raise ValueError(\"Padded input must have at least 2 dims\")\n\n shape = (n_samples, max_len) + (1,) * (ndim - 2)\n result = np.zeros(shape, dtype=int)\n for i, x in enumerate(index_sequences):\n if padding == 'post':\n result[i, :len(x)] = x\n elif padding == 'pre':\n result[i, -len(x):] = x\n return result","title":""},{"docid":"10a0bf3f2a9e1c5c5ed3a8d4596de4b5","score":"0.55916065","text":"def sequences(lengths=None, elements=None):\n elements = _defaulted(elements, default_sequence_elements)\n lengths = _defaulted(lengths, default_sequence_lengths)\n \n return (islice(elements, length) for length in lengths)","title":""}],"string":"[\n {\n \"docid\": \"eed944a1d27bddfe7f93a8af99bfc0bc\",\n \"score\": \"0.7702567\",\n \"text\": \"def _pad_sequences(sequences, max_steps):\\n if isinstance(sequences, np.ndarray): return sequences\\n assert isinstance(sequences, list)\\n checker.check_positive_integer(max_steps)\\n checker.check_type(sequences, np.ndarray)\\n\\n if all([s.shape[0] == sequences[0].shape[0] for s in sequences]):\\n return np.stack(sequences, axis=0)\\n\\n sample_shape = sequences[0].shape[1:]\\n assert len(sample_shape) > 0\\n stack = np.zeros(shape=(len(sequences), max_steps, *sample_shape))\\n for i, s in enumerate(sequences): stack[i, :len(s)] = s\\n\\n return stack\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06298802e374d839e405d04e66417e56\",\n \"score\": \"0.7578034\",\n \"text\": \"def pad_sequence(cls, sequences):\\n # ensure that all waveforms in the batch have the same length by applying zero-padding\\n sequences = [sequence.T for sequence in sequences]\\n sequences = torch.nn.utils.rnn.pad_sequence(sequences, batch_first=True, padding_value=0.)\\n return sequences.permute(0, 2, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae9f7f05506ff852adb8f83adc9083ed\",\n \"score\": \"0.73381835\",\n \"text\": \"def pad_list(sequences, dim0_pad=None, dim1_pad=None,\\n align_right=False, pad_value=0):\\n\\n sequences = [np.asarray(sublist) for sublist in sequences]\\n\\n if not dim0_pad:\\n dim0_pad = len(sequences)\\n\\n if not dim1_pad:\\n dim1_pad = max(len(seq) for seq in sequences)\\n\\n out = np.full(shape=(dim0_pad, dim1_pad), fill_value=pad_value)\\n\\n lengths = []\\n for i in range(len(sequences)):\\n data_length = len(sequences[i])\\n lengths.append(data_length)\\n offset = dim1_pad - data_length if align_right else 0\\n np.put(out[i], range(offset, offset + data_length), sequences[i])\\n\\n lengths = np.array(lengths)\\n\\n return out, lengths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0cbf808f870ad159c19a2f5146a226d\",\n \"score\": \"0.72260326\",\n \"text\": \"def pad_sequence(seq, size, padding=None):\\n return (list(seq) + [padding for _ in xrange(size)])[:size]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c66d929d4971cf29d311f33a2215c417\",\n \"score\": \"0.70857394\",\n \"text\": \"def pad_sequences(sequences, maxlen=None, dtype='int32',\\r\\n padding='pre', truncating='pre', value=0.):\\r\\n if not hasattr(sequences, '__len__'):\\r\\n raise ValueError('`sequences` must be iterable.')\\r\\n lengths = []\\r\\n for x in sequences:\\r\\n if not hasattr(x, '__len__'):\\r\\n raise ValueError('`sequences` must be a list of iterables. '\\r\\n 'Found non-iterable: ' + str(x))\\r\\n lengths.append(len(x))\\r\\n\\r\\n num_samples = len(sequences)\\r\\n if maxlen is None:\\r\\n maxlen = np.max(lengths)\\r\\n\\r\\n # take the sample shape from the first non empty sequence\\r\\n # checking for consistency in the main loop below.\\r\\n sample_shape = tuple()\\r\\n for s in sequences:\\r\\n if len(s) > 0:\\r\\n sample_shape = np.asarray(s).shape[1:]\\r\\n break\\r\\n\\r\\n x = (np.ones((num_samples, maxlen) + sample_shape) * value).astype(dtype)\\r\\n for idx, s in enumerate(sequences):\\r\\n if not len(s):\\r\\n continue # empty list/array was found\\r\\n if truncating == 'pre':\\r\\n trunc = s[-maxlen:]\\r\\n elif truncating == 'post':\\r\\n trunc = s[:maxlen]\\r\\n else:\\r\\n raise ValueError('Truncating type \\\"%s\\\" '\\r\\n 'not understood' % truncating)\\r\\n\\r\\n # check `trunc` has expected shape\\r\\n trunc = np.asarray(trunc, dtype=dtype)\\r\\n if trunc.shape[1:] != sample_shape:\\r\\n raise ValueError('Shape of sample %s of sequence at position %s '\\r\\n 'is different from expected shape %s' %\\r\\n (trunc.shape[1:], idx, sample_shape))\\r\\n\\r\\n if padding == 'post':\\r\\n x[idx, :len(trunc)] = trunc\\r\\n elif padding == 'pre':\\r\\n x[idx, -len(trunc):] = trunc\\r\\n else:\\r\\n raise ValueError('Padding type \\\"%s\\\" not understood' % padding)\\r\\n #transpose -> (max_length, len(sequence))\\r\\n #x = x.transpose()\\r\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a468ce03e69cfcb43b7c496e06ed62e8\",\n \"score\": \"0.7021152\",\n \"text\": \"def _pad_sequence(self, seq):\\n batch_size = sum(s.size(0) for s in seq)\\n max_len = max(s.size(1) for s in seq)\\n padded_batch = torch.full(\\n (batch_size, max_len), self.tokenizer.pad_token_id, dtype=torch.long\\n )\\n i = 0\\n for data in seq:\\n block, length = data.size()\\n padded_batch[i:i+block, :length] = data\\n i+= block\\n return padded_batch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35fc8d820813ea701e4a892fb22b6291\",\n \"score\": \"0.69403857\",\n \"text\": \"def pad_sequences(sequences, maxlen=None, dtype='int32',\\n padding='pre', truncating='pre', value=0.):\\n if not hasattr(sequences, '__len__'):\\n raise ValueError('`sequences` must be iterable.')\\n lengths = []\\n for x in sequences:\\n if not hasattr(x, '__len__'):\\n raise ValueError('`sequences` must be a list of iterables. '\\n 'Found non-iterable: ' + str(x))\\n lengths.append(len(x))\\n\\n num_samples = len(sequences)\\n if maxlen is None:\\n maxlen = np.max(lengths)\\n\\n # take the sample shape from the first non empty sequence\\n # checking for consistency in the main loop below.\\n sample_shape = tuple()\\n for s in sequences:\\n if len(s) > 0:\\n sample_shape = np.asarray(s).shape[1:]\\n break\\n\\n is_dtype_str = np.issubdtype(dtype, np.str_) or np.issubdtype(dtype, np.unicode_)\\n if isinstance(value, six.string_types) and dtype != object and not is_dtype_str:\\n raise ValueError(\\\"`dtype` {} is not compatible with `value`'s type: {}\\\\n\\\"\\n \\\"You should set `dtype=object` for variable length strings.\\\"\\n .format(dtype, type(value)))\\n\\n x = np.full((num_samples, maxlen) + sample_shape, value, dtype=dtype)\\n for idx, s in enumerate(sequences):\\n if not len(s):\\n continue # empty list/array was found\\n if truncating == 'pre':\\n trunc = s[-maxlen:]\\n elif truncating == 'post':\\n trunc = s[:maxlen]\\n else:\\n raise ValueError('Truncating type \\\"%s\\\" '\\n 'not understood' % truncating)\\n\\n # check `trunc` has expected shape\\n trunc = np.asarray(trunc, dtype=dtype)\\n if trunc.shape[1:] != sample_shape:\\n raise ValueError('Shape of sample %s of sequence at position %s '\\n 'is different from expected shape %s' %\\n (trunc.shape[1:], idx, sample_shape))\\n\\n if padding == 'post':\\n x[idx, :len(trunc)] = trunc\\n elif padding == 'pre':\\n x[idx, -len(trunc):] = trunc\\n else:\\n raise ValueError('Padding type \\\"%s\\\" not understood' % padding)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0abae5f078895c4a2bf9b54d281b2e61\",\n \"score\": \"0.6880613\",\n \"text\": \"def pad_sequences(sequences, maxlen=None, dtype='int32',\\n padding='pre', truncating='pre', value=0.):\\n if not hasattr(sequences, '__len__'):\\n raise ValueError('`sequences` must be iterable.')\\n lengths = []\\n for x in sequences:\\n if not hasattr(x, '__len__'):\\n raise ValueError('`sequences` must be a list of iterables. '\\n 'Found non-iterable: ' + str(x))\\n lengths.append(len(x))\\n\\n num_samples = len(sequences)\\n if maxlen is None:\\n maxlen = np.max(lengths)\\n\\n # take the sample shape from the first non empty sequence\\n # checking for consistency in the main loop below.\\n sample_shape = tuple()\\n for s in sequences:\\n if len(s) > 0:\\n sample_shape = np.asarray(s).shape[1:]\\n break\\n\\n x = (np.ones((num_samples, maxlen) + sample_shape) * value).astype(dtype)\\n for idx, s in enumerate(sequences):\\n if not len(s):\\n continue # empty list/array was found\\n if truncating == 'pre':\\n trunc = s[-maxlen:]\\n elif truncating == 'post':\\n trunc = s[:maxlen]\\n else:\\n raise ValueError('Truncating type \\\"%s\\\" '\\n 'not understood' % truncating)\\n\\n # check `trunc` has expected shape\\n trunc = np.asarray(trunc, dtype=dtype)\\n if trunc.shape[1:] != sample_shape:\\n raise ValueError('Shape of sample %s of sequence at position %s '\\n 'is different from expected shape %s' %\\n (trunc.shape[1:], idx, sample_shape))\\n\\n if padding == 'post':\\n x[idx, :len(trunc)] = trunc\\n elif padding == 'pre':\\n x[idx, -len(trunc):] = trunc\\n else:\\n raise ValueError('Padding type \\\"%s\\\" not understood' % padding)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15e48291c39cb177a6174aab1b3ca87\",\n \"score\": \"0.68363106\",\n \"text\": \"def pad_sequences_1d(sequences, max_len=None, padding='post', truncating='post', value=0.):\\n return pad_sequences(sequences, maxlen=max_len, padding=padding, truncating=truncating, value=value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcc646192857f9bd547cb6bf442fda06\",\n \"score\": \"0.6833937\",\n \"text\": \"def pad_txt_sequences(sequences, pad_tok):\\n\\n max_length = max(len(x) for x in sequences)\\n\\n sequence_padded, sequence_length = [], []\\n\\n for seq in sequences:\\n seq = list(seq)\\n seq_ = seq + [pad_tok] * max(max_length - len(seq), 0)\\n\\n sequence_padded += [seq_]\\n sequence_length += [len(seq)]\\n\\n return sequence_padded, sequence_length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ca91f3ee5f411464b2d9567ee90c4b8\",\n \"score\": \"0.67387444\",\n \"text\": \"def _pad_batch(batch, pad_left, pad_right, dtype=np.int32):\\r\\n max_len = max(len(x) for x in batch)\\r\\n batch_array = pad_right * np.ones((len(batch), 2 + max_len), dtype=dtype)\\r\\n\\r\\n for i, x in enumerate(batch):\\r\\n batch_array[i, 0] = pad_left\\r\\n batch_array[i, 1:len(x) + 1] = x\\r\\n\\r\\n return batch_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9195f3a03908ae66f08a65b2f67ae7b\",\n \"score\": \"0.67275894\",\n \"text\": \"def padding(sequences, maxlen=None, dtype='int32',\\n padding='pre', truncating='pre', value=0.):\\n assert padding in [\\\"pre\\\", \\\"post\\\"], \\\"Invalid padding={}.\\\".format(padding)\\n assert truncating in [\\\"pre\\\", \\\"post\\\"], \\\"Invalid truncating={}.\\\".format(truncating)\\n \\n if maxlen is None:\\n maxlen = max(len(x) for x in sequences)\\n arr = np.full((len(sequences), maxlen), value, dtype=dtype)\\n for idx, x in enumerate(sequences):\\n if len(x) == 0:\\n continue # empty list\\n if truncating == 'pre':\\n trunc = x[-maxlen:]\\n else:\\n trunc = x[:maxlen]\\n trunc = np.asarray(trunc, dtype=dtype)\\n\\n if padding == 'pre':\\n arr[idx, -len(trunc):] = trunc\\n else:\\n arr[idx, :len(trunc)] = trunc\\n return arr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7bc84900f9cdb815ee7f8dbcc26fea\",\n \"score\": \"0.6693861\",\n \"text\": \"def pad_seq(sequence):\\n remainder = len(sequence) % 3\\n return sequence if remainder == 0 else sequence + Seq('N' * (3 - remainder))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4a2a4b409b7531e5edd8696cbd1aa35\",\n \"score\": \"0.66649985\",\n \"text\": \"def pad_list(xs, pad_value):\\n n_batch = len(xs)\\n max_len = max(x.size(0) for x in xs)\\n pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)\\n for i in range(n_batch):\\n pad[i, :xs[i].size(0)] = xs[i]\\n return pad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9491efb77cfd776ef9f716e5ff86933\",\n \"score\": \"0.6624278\",\n \"text\": \"def pad(seq, n, fillvalue=None):\\n\\n length = len(seq)\\n if length == n:\\n return seq\\n else:\\n needed = n - length\\n return seq + type(seq)([fillvalue] * needed)\\n\\n # itertools equivalent:\\n # return chain(iterable, repeat(fillvalue, n-len(iterable)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f936c730a4bede1a0f71f21aee114c5\",\n \"score\": \"0.65772593\",\n \"text\": \"def pad4(seq):\\n return_value = []\\n for thing in seq:\\n return_value.append(\\\"0\\\" * (4 - len(thing)) + thing)\\n return return_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb1704cd2b898d507ff14dc915e63cb\",\n \"score\": \"0.6560269\",\n \"text\": \"def pad_video_sequences(sequences):\\n lengths = torch.LongTensor([len(seq) for seq in sequences])\\n v_dim = sequences[0].size(1)\\n padded_seqs = torch.zeros(len(sequences), max(lengths), v_dim).float()\\n length_masks = torch.zeros(len(sequences), max(lengths))\\n for idx, seq in enumerate(sequences):\\n end = lengths[idx]\\n padded_seqs[idx, :end] = seq\\n length_masks[idx, :end] = torch.ones(end.item())\\n return padded_seqs, lengths, length_masks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"004d844432ce75fdc7671efc25b37954\",\n \"score\": \"0.64507955\",\n \"text\": \"def _pad(ids, pad_id, length):\\n assert pad_id is not None\\n assert length is not None\\n\\n if len(ids) < length:\\n a = [pad_id] * (length - len(ids))\\n return ids + a\\n else:\\n return ids[:length]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40ff2d7a2d0a556422d3117c2e14aa3f\",\n \"score\": \"0.6449326\",\n \"text\": \"def pad_to_max_length(self, input_sequences: List[List[int]]):\\n max_length = max([len(s) for s in input_sequences])\\n copy_input_sequences = []\\n for i in range(len(input_sequences)):\\n copy_input_sequences.append([self.pad_token_id] * (max_length - len(input_sequences[i])) + input_sequences[i])\\n\\n return copy_input_sequences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe4064b1c7ccfbd31f3318f247f497a0\",\n \"score\": \"0.64092594\",\n \"text\": \"def padding(A):\\n # O(d)\\n d = len(A)\\n for i in range(d):\\n A[i].append(0)\\n A.append([0]*(d+1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ff43a7f9f266b01b35386009389c079\",\n \"score\": \"0.63736403\",\n \"text\": \"def pad_2d_sequences(seq: Sequence[Any], maxlen: Optional[int] = None,\\n embedding_size: Optional[int] = None) -> np.ndarray:\\n\\n # find the maximum length by looking through the sequence\\n if maxlen is None:\\n maxlen = -1\\n for item in seq:\\n maxlen = max(maxlen, len(item))\\n\\n # find the embedding dimension by looking through the sequence until there is a non-empty item\\n if embedding_size is None:\\n for item in seq:\\n if len(item) != 0:\\n embedding_size = len(item[0])\\n break\\n\\n result = np.zeros((len(seq), maxlen, embedding_size))\\n for i, item in enumerate(seq):\\n assert len(item) > 0\\n result[i, -len(item):] = item\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da4134470cf237a7ce1404d121444a58\",\n \"score\": \"0.6347811\",\n \"text\": \"def pad_array(arr, length, pad=0):\\n arr = np.array(arr)\\n tail_length = length - len(arr)\\n if tail_length > 0:\\n tail = np.repeat(pad, tail_length)\\n return np.concatenate([arr, tail])\\n else:\\n return arr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b6ab05ca2a5c50040de72adfa9f6199\",\n \"score\": \"0.633389\",\n \"text\": \"def pad(self, docs: List[List[int]]) -> List[List[int]]:\\n # First apply indexing on all the rows then pad_sequnces (i found this\\n # faster than trying to do these steps on each row\\n return pad_sequences(docs,\\n maxlen=self.padding_maxlen,\\n dtype=self.padding_dtype,\\n padding=self.padding,\\n truncating=self.truncating,\\n value=self.padding_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1748fa4919f62a6bc0dafb72f998b9f0\",\n \"score\": \"0.6321214\",\n \"text\": \"def sequence_to_padding(self, x, lengths):\\n ret_tensor = torch.zeros(\\n (lengths.shape[0], torch.max(lengths).int()) + tuple(x.shape[1:])\\n ).to(x.device)\\n cum_len = 0\\n for i, l in enumerate(lengths):\\n ret_tensor[i, :l] = x[cum_len : cum_len + l]\\n cum_len += l\\n return ret_tensor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e027635aed374ee1c02899d0a621358\",\n \"score\": \"0.63164455\",\n \"text\": \"def pad_1d_list_with_zero(array: np.ndarray, length: int) -> np.ndarray:\\n zero_array = np.zeros(length - len(array), dtype=array.dtype)\\n return np.concatenate((array, zero_array), axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5997e366bf0e479c886b55a25addc759\",\n \"score\": \"0.6314174\",\n \"text\": \"def pad(id_sequences):\\n max_length = max([len(s) for s in id_sequences])\\n padded_data = np.zeros((len(id_sequences), max_length))\\n mask = np.zeros_like(padded_data)\\n for i, sample in enumerate(id_sequences):\\n padded_data[i, : len(sample)] = sample\\n mask[i, : len(sample)] = 1\\n return padded_data, mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc2d8b395c282f7656e56541d8388dc2\",\n \"score\": \"0.6292906\",\n \"text\": \"def pad_video_tensors(sequences):\\n lengths = torch.LongTensor([len(seq) for seq in sequences])\\n v_l, f, t, h, w = sequences[0].size()\\n padded_seqs = torch.zeros(len(sequences), max(lengths), f, t, h, w).float()\\n length_masks = torch.zeros(len(sequences), max(lengths))\\n for idx, seq in enumerate(sequences):\\n end = lengths[idx]\\n padded_seqs[idx, :end] = seq\\n length_masks[idx, :end] = torch.ones(end.item())\\n return padded_seqs, lengths, length_masks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"218964b3c905ffaf6b1ed4800b4cc632\",\n \"score\": \"0.6292905\",\n \"text\": \"def pad(self, data, length):\\n\\n from keras.preprocessing.sequence import pad_sequences\\n return pad_sequences(data, maxlen=length, padding='post', truncating='post', value=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4df111b72bf86b544950e1ba09a04f61\",\n \"score\": \"0.6276021\",\n \"text\": \"def fill_pad(haps, pos, maxlen):\\n sites = [0] + [int(p) for p in pos] + [maxlen]\\n nsamples = haps.shape[1]\\n padwidth = sites[:-1]\\n for i in range(1, len(sites)):\\n padwidth[i-1] = sites[i] - sites[i-1] - 1\\n x = np.zeros((padwidth[0], nsamples)).astype(\\\"int8\\\")\\n for i in range(1, len(padwidth)):\\n x = np.append(x, haps[i-1:i, :], axis=0)\\n x = np.append(x, np.zeros((padwidth[i], nsamples)).astype(\\\"int8\\\"), axis=0)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0748e5eb667200286fa9e16ba05d353\",\n \"score\": \"0.6264318\",\n \"text\": \"def _pad_to_seq_length(self,\\n inputs,\\n override_max_seq_length = None):\\n if override_max_seq_length is not None:\\n max_seq_length = override_max_seq_length\\n else:\\n max_seq_length = self._max_seq_length\\n while len(inputs) > max_seq_length:\\n inputs.pop()\\n while len(inputs) < max_seq_length:\\n inputs.append(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ac9947e0f39072488f48653d2b5a4f\",\n \"score\": \"0.6253948\",\n \"text\": \"def stack_pad(tensors, pad_axes=None, pad_to_lengths=None, dtype=np.float32,\\n pad_value=0):\\n tensors = [np.asarray(t) for t in tensors]\\n max_lengths = [max(l) for l in zip(*[t.shape for t in tensors])]\\n same_axes = dict(enumerate(max_lengths))\\n if pad_axes is None:\\n pad_axes = []\\n if isinstance(pad_axes, six.integer_types):\\n if pad_to_lengths is not None:\\n max_lengths[pad_axes] = pad_to_lengths\\n del same_axes[pad_axes]\\n else:\\n if pad_to_lengths is None:\\n pad_to_lengths = [None] * len(pad_axes)\\n for i, l in zip(pad_axes, pad_to_lengths):\\n if l is not None:\\n max_lengths[i] = l\\n del same_axes[i]\\n same_axes_items = same_axes.items()\\n dest = np.full([len(tensors)] + max_lengths, pad_value, dtype=dtype)\\n for i, t in enumerate(tensors):\\n for j, l in same_axes_items:\\n if t.shape[j] != l:\\n raise ValueError(\\n 'Tensor at index %d does not have size %d along axis %d'\\n % (i, l, j))\\n dest[[i] + [slice(0, d) for d in t.shape]] = t\\n return dest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e833762b283b1be7fae226f97e258a7\",\n \"score\": \"0.6215426\",\n \"text\": \"def padding(data, pad, max_len=None):\\n if not max_len:\\n seq_len = [len(x) for x in data]\\n max_len = max(seq_len)\\n\\n for i in range(len(data)):\\n data[i] += [pad] * (max_len-len(data[i]))\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0e46976f6999292f154a67974f9d495\",\n \"score\": \"0.6211645\",\n \"text\": \"def uniformed_sequences(\\n self,\\n sequences: np.array,\\n length: Optional[int] = None) -> np.array:\\n if length is None:\\n length = self.seq_length\\n return tf.keras.preprocessing.sequence.pad_sequences(sequences, padding='post', truncating='post', maxlen=length)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1caaab4928a8ad6104da20f24a842878\",\n \"score\": \"0.6201801\",\n \"text\": \"def pad_sequences(self, source: list, max_length: int = 0) -> list:\\n assert type(source) is list\\n assert type(source[0]) is list\\n assert type(source[0][0]) is int, 'Sequences must already be indexed!'\\n assert type(max_length) is int\\n\\n pad_idx = self.get_pad_idx()\\n assert pad_idx is not None\\n\\n #if max_length is 0 or less, it means we calculate it\\n if max_length <= 0:\\n max_length = max(list(map(len, source)))\\n\\n #get the true value of the lengths before padding, to potentially use later\\n lengths = list(map(len, source))\\n\\n temp = []\\n \\n\\n for indices in tqdm(source, desc='Padding sequences'):\\n assert len(indices) <= max_length, 'Indices should already be truncated to max_length!'\\n indices = indices + [pad_idx for _ in range(max_length - len(indices))]\\n assert len(indices) == max_length, 'Padding error! Padded sequence != max_length!'\\n temp.append(indices)\\n\\n assert len(lengths) == len(temp)\\n assert len(source) == len(temp)\\n\\n return temp, lengths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff688bcd40291bd42bd1d51bc3599f89\",\n \"score\": \"0.6200217\",\n \"text\": \"def pad_list(l, n, pad=''):\\n if len(l) < n:\\n for i in range(n-len(l)): l.append(pad)\\n return l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ca46ef550bb36f6deca881c8a689cdd\",\n \"score\": \"0.61930496\",\n \"text\": \"def __pad_sequence(self, sequence):\\n length = len(sequence)\\n if length == self.Tx:\\n return np.array(sequence)\\n elif length < self.Tx:\\n sequence.extend([0 for i in range(self.Tx-length)])\\n return np.array(sequence)\\n else:\\n return np.array(sequence[:self.Tx])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de7f5cd36217d4b739a689cbbe0d33b3\",\n \"score\": \"0.61857396\",\n \"text\": \"def pad_sequence(sequences: List[paddle.Tensor],\\n batch_first: bool=False,\\n padding_value: float=0.0) -> paddle.Tensor:\\n\\n # assuming trailing dimensions and type of all the Tensors\\n # in sequences are same and fetching those from sequences[0]\\n max_size = paddle.shape(sequences[0])\\n # (TODO Hui Zhang): slice not supprot `end==start`\\n # trailing_dims = max_size[1:]\\n trailing_dims = tuple(\\n max_size[1:].numpy().tolist()) if sequences[0].ndim >= 2 else ()\\n max_len = max([s.shape[0] for s in sequences])\\n if batch_first:\\n out_dims = (len(sequences), max_len) + trailing_dims\\n else:\\n out_dims = (max_len, len(sequences)) + trailing_dims\\n\\n out_tensor = sequences[0].new_full(out_dims, padding_value)\\n for i, tensor in enumerate(sequences):\\n length = tensor.shape[0]\\n # use index notation to prevent duplicate references to the tensor\\n logger.debug(\\n f\\\"length {length}, out_tensor {out_tensor.shape}, tensor {tensor.shape}\\\"\\n )\\n if batch_first:\\n # TODO (Hui Zhang): set_value op not supprot `end==start`\\n # TODO (Hui Zhang): set_value op not support int16\\n # TODO (Hui Zhang): set_varbase 2 rank not support [0,0,...]\\n # out_tensor[i, :length, ...] = tensor\\n if length != 0:\\n out_tensor[i, :length] = tensor\\n else:\\n out_tensor[i, length] = tensor\\n else:\\n # TODO (Hui Zhang): set_value op not supprot `end==start`\\n # out_tensor[:length, i, ...] = tensor\\n if length != 0:\\n out_tensor[:length, i] = tensor\\n else:\\n out_tensor[length, i] = tensor\\n\\n return out_tensor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679c8d8f431cad350452d1d1ecc50085\",\n \"score\": \"0.617733\",\n \"text\": \"def pad_stim(stim,length,axis=1,pad=np.nan,start=0):\\n\\tif axis==0:\\n\\t\\traise(ValueError(\\\"axis cannot be set to 0. axis 0 is the trials axis and it cannot be changed\\\"))\\n\\tpadded_stim = []\\n\\tfor s in stim:\\n\\t\\tshape_s = list(s.shape)\\n\\t\\tshape_temp = list(s.shape)\\n\\t\\tshape_temp[axis-1] = int(length)\\n\\t\\ttemp = pad*np.ones(tuple(shape_temp))\\n\\t\\tslices = [range(sh) for sh in shape_temp]\\n\\t\\tslices[axis-1] = range(start,min(int(length),shape_s[axis-1]+int(start)))\\n\\t\\ttemp[np.ix_(*slices)] = s.copy()\\n\\t\\tpadded_stim.append(temp)\\n\\treturn np.array(padded_stim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb87d602eeaa8b280e2df035d1012bd\",\n \"score\": \"0.6172475\",\n \"text\": \"def pad_sequence_to_length(sequence: List,\\n desired_length: int,\\n default_value: Callable[[], Any] = lambda: 0,\\n padding_on_right: bool = True) -> List:\\n # Truncates the sequence to the desired length.\\n if padding_on_right:\\n padded_sequence = sequence[:desired_length]\\n else:\\n padded_sequence = sequence[-desired_length:]\\n # Continues to pad with default_value() until we reach the desired length.\\n for _ in range(desired_length - len(padded_sequence)):\\n if padding_on_right:\\n padded_sequence.append(default_value())\\n else:\\n padded_sequence.insert(0, default_value())\\n return padded_sequence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469374bc24f4799ca9a0ba767909f2cd\",\n \"score\": \"0.6170578\",\n \"text\": \"def pad(A):\\n if A.shape[1] % 2 is not 0:\\n pad_flag = 1\\n j, k = A.shape[0], A.shape[1]\\n zers1 = np.zeros((j, k)).reshape((j, k, 1))\\n A = np.concatenate((A, zers1), 2)\\n zers2 = np.zeros((j, k + 1)).reshape((j, 1, k + 1))\\n A = np.concatenate((A, zers2), 1)\\n else:\\n pad_flag = 0\\n return A, pad_flag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5709f4955872b8e4239395901052488\",\n \"score\": \"0.6168596\",\n \"text\": \"def pad_input(input_list_of_list: list, max_input_length: int) -> list:\\n for input_list in input_list_of_list:\\n for i in range(max_input_length - len(input_list)):\\n input_list.insert(0, 0)\\n\\n return input_list_of_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d769d50c1aab5a2af4ce0d521ff0591\",\n \"score\": \"0.6142439\",\n \"text\": \"def pad_batch(batch):\\n\\n flatten = [u for s in batch for u in s]\\n\\n return pad_sequence(flatten, batch_first=True, padding_value=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a5b6b1a442db8d4b661760080350259\",\n \"score\": \"0.61392164\",\n \"text\": \"def pad(self, padding_lengths: Dict[str, int]):\\n desired_num_slots = padding_lengths['num_slots']\\n if len(self.word_indices) > desired_num_slots:\\n # We concatenate the indices from the slots at the end to make word_indices the right\\n # length.\\n num_extra_slots = len(self.word_indices) - desired_num_slots\\n all_word_indices = self.word_indices\\n self.word_indices = all_word_indices[:desired_num_slots]\\n for i in range(num_extra_slots):\\n self.word_indices[desired_num_slots - 1] += all_word_indices[desired_num_slots + i]\\n elif len(self.word_indices) < desired_num_slots:\\n additional_slots = [[] for _ in range(desired_num_slots - len(self.word_indices))]\\n self.word_indices += additional_slots\\n self.word_indices = [self.pad_word_sequence(indices, padding_lengths) for\\n indices in self.word_indices]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d044de5a6f571c0ddc5e793563df7195\",\n \"score\": \"0.61383486\",\n \"text\": \"def pad_sentences(sentences, padding_word=\\\"<PAD/>\\\",sequence_length = 0):\\n if sequence_length == 0:\\n sequence_length = max(len(x) for x in sentences)\\n padded_sentences = []\\n for i in range(len(sentences)):\\n sentence = sentences[i]\\n num_padding = sequence_length - len(sentence)\\n new_sentence = sentence + [padding_word] * num_padding\\n padded_sentences.append(new_sentence)\\n return padded_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5d8fb534124785a06856df54673076a\",\n \"score\": \"0.6138309\",\n \"text\": \"def padded(seq):\\n try:\\n for elt in seq:\\n yield elt\\n while True:\\n yield None\\n except GeneratorExit:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d21dced90dd8db61c17ecaa666e18160\",\n \"score\": \"0.6107195\",\n \"text\": \"def padding(self, seq):\\n # padded_seq = np.zeros([len(seq)])\\n padded_seq = np.pad(seq, (0, self.sequence_max_len-len(seq)), 'constant')\\n # padded_seq.append(row)\\n return padded_seq.tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ead67d958cf49a2907dafc35ad06a4\",\n \"score\": \"0.6089033\",\n \"text\": \"def pad_to_same_length(ss, pad, l_max=None):\\n ls = []\\n for s in ss:\\n ls.append(len(s))\\n if l_max == None:\\n l_max = max(ls)\\n ss_new = []\\n for s, l in zip(ss, ls):\\n if len(s) <= l_max:\\n s.extend([pad] * (l_max - l))\\n else:\\n s = s[:l_max]\\n ss_new.append(s)\\n return ss_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd9d2c02f947c258fe5287d16eb805bf\",\n \"score\": \"0.60366124\",\n \"text\": \"def padding(self, sentences):\\n sentence_pad = [sentence[:self.__sequence_length] if len(sentence) > self.__sequence_length\\n else sentence + [0] * (self.__sequence_length - len(sentence))\\n for sentence in sentences]\\n return sentence_pad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a499defe198a4b2c6c1806b17c2f9e87\",\n \"score\": \"0.60362464\",\n \"text\": \"def pad_sentences2(sentences,sequence_length, padding_word=\\\"<PAD/>\\\"):\\n padded_sentences = []\\n for i in range(len(sentences)):\\n sentence = sentences[i]\\n num_padding = sequence_length - len(sentence)\\n new_sentence = sentence + [padding_word] * num_padding\\n padded_sentences.append(new_sentence)\\n return padded_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"116b00f54e7f1cfbbee79c2b17f0a8ed\",\n \"score\": \"0.60174894\",\n \"text\": \"def pad_graphlet(self, graphlet: List[List[int]], padding_lengths: Dict):\\n desired_num_alignments = padding_lengths['num_alignments']\\n graphlet = self.pad_sequence_to_length(graphlet, desired_num_alignments,\\n default_value=lambda: [], truncate_from_right=False)\\n # Pad the alignment to the desired number of features.\\n desired_num_features = padding_lengths['num_features']\\n graphlet = [self.pad_sequence_to_length(alignment, desired_num_features, default_value=lambda: 0.0,\\n truncate_from_right=False) for alignment in graphlet]\\n return graphlet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c2a1d977d9c8543fd9a3c33ee8bf187\",\n \"score\": \"0.6012757\",\n \"text\": \"def pad_list(xs: torch.Tensor, pad_value=0, dim=0) -> torch.Tensor:\\n if dim == 0:\\n return pad_sequence(xs, batch_first=True, padding_value=pad_value)\\n else:\\n xs = [x.transpose(0, dim) for x in xs]\\n padded = pad_sequence(xs, batch_first=True, padding_value=pad_value)\\n return padded.transpose(1, dim+1).contiguous()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f0243b7632e8c5f5f62b10375e1b6b6\",\n \"score\": \"0.600301\",\n \"text\": \"def _pad_to_grid(self, seq: torch.Tensor):\\n batch, c, seq_len = seq.size()\\n h,w = self.patch_size\\n need = h*w - seq_len % (h*w)\\n logger.debug('need {}'.format(need))\\n seq = F.pad(seq,(0,need))\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f0243b7632e8c5f5f62b10375e1b6b6\",\n \"score\": \"0.600301\",\n \"text\": \"def _pad_to_grid(self, seq: torch.Tensor):\\n batch, c, seq_len = seq.size()\\n h,w = self.patch_size\\n need = h*w - seq_len % (h*w)\\n logger.debug('need {}'.format(need))\\n seq = F.pad(seq,(0,need))\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"517616f3908baa66f5f5a959b7ad3066\",\n \"score\": \"0.5982693\",\n \"text\": \"def _pad_and_collate_audio(audios_list: list[Tensor], fill_value: float) -> tuple[Tensor, Tensor]:\\n\\ttime_dim = -1\\n\\taudios_lens = torch.as_tensor([audio.shape[time_dim] for audio in audios_list])\\n\\n\\tmax_audio_len = int(audios_lens.max().item())\\n\\tpad = Pad(max_audio_len, fill_value=fill_value, dim=time_dim)\\n\\n\\taudios_batch_pad = torch.stack([\\n\\t\\tpad(audio) for audio in audios_list\\n\\t])\\n\\treturn audios_batch_pad, audios_lens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"366e2d2d72852389d2cc97770580eabe\",\n \"score\": \"0.5977594\",\n \"text\": \"def pad_to_fixed_length(inputs, target_length):\\n pad_n = int(target_length) - inputs.shape[0]\\n if pad_n < 0:\\n inputs = inputs[0: int(target_length),:]\\n else:\\n inputs = np.pad(inputs,[[0, pad_n], [0,0]])\\n return inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e903e803182c8cc772a1fcf8717de413\",\n \"score\": \"0.5977333\",\n \"text\": \"def pad_seq(self, seq_idx):\\n # Pad each sequence to be the same length to process in batches\\n # pad_token = 0\\n if len(seq_idx) >= self.max_seq_len:\\n seq_idx = seq_idx[:self.max_seq_len]\\n else:\\n seq_idx += [0]*(self.max_seq_len - len(seq_idx))\\n seq = seq_idx\\n\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7efd2dcbafb2243d3bd50ae2f382dc4\",\n \"score\": \"0.5960234\",\n \"text\": \"def pad_sentences(sentences, padding_word=\\\"<PAD/>\\\"):\\n sequence_length = max(len(x) for x in sentences)\\n padded_sentences = []\\n for i in range(len(sentences)):\\n sentence = sentences[i]\\n num_padding = sequence_length - len(sentence)\\n new_sentence = sentence + [padding_word] * num_padding\\n padded_sentences.append(new_sentence)\\n return padded_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc1d10fff790f68051aea49fe362d84c\",\n \"score\": \"0.59584653\",\n \"text\": \"def test_list(self):\\n data = [\\n [1, 5, 4, 3],\\n [1, 2, 4, 0, 9],\\n [9, 3],\\n [9, 4, 3]\\n ]\\n\\n padded_data = gd.pad_sequence(data)\\n\\n assert (\\n padded_data == self.get_manually_padded_2d()).all(), \\\"Pad sequence did not pad list of lists correctly.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"560d1364b879f13ba679f4ec32be41c0\",\n \"score\": \"0.5957906\",\n \"text\": \"def roundrobin(seqOfSeqs, num, dopad=0, pad=None):\\n ret = []\\n if not seqOfSeqs: return ret\\n cur = [0 for s in seqOfSeqs]\\n while len(ret) < num and max(cur) >= 0:\\n for i, seq in enumerate(seqOfSeqs):\\n if cur[i] < 0: continue\\n try:\\n ret.append(seq[cur[i]])\\n if len(ret) == num: break\\n cur[i] += 1\\n except IndexError:\\n cur[i] = -1\\n # if we don't have enough items and we want to pad, do it\\n if len(ret) < num and dopad:\\n ret.extend([pad] * range(num-len(ret)))\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac7060861e750bfca9f6787377813f8\",\n \"score\": \"0.59514123\",\n \"text\": \"def pad_to_sequence_length(sentence, seq_length):\\n \\n padding_length = seq_length - (sentence.shape[0] % seq_length)\\n target_length = sentence.shape[0] + padding_length\\n return pad_last(sentence, target_length)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9f95e5711a54450899404ede07d457\",\n \"score\": \"0.5950457\",\n \"text\": \"def pad_sentences(self, sentences, sequence_length, padding_word=\\\"<PAD/>\\\"):\\n padded_sentences = []\\n for i in range(len(sentences)):\\n sentence = sentences[i]\\n num_padding = sequence_length - len(sentence)\\n new_sentence = sentence + [padding_word] * num_padding\\n padded_sentences.append(new_sentence)\\n return padded_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a91b7e334749bae82537ee16eafa2d1\",\n \"score\": \"0.5946171\",\n \"text\": \"def pad_sentences(sentences, padding_word=\\\"<PAD/>\\\"):\\n sequence_length = max(len(x) for x in sentences)\\n padded_sentences = []\\n for i in range(len(sentences)):\\n sentence = sentences[i]\\n num_padding = sequence_length - len(sentence)\\n new_sentence = sentence + [padding_word] * num_padding\\n padded_sentences.append(new_sentence)\\n return padded_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a91b7e334749bae82537ee16eafa2d1\",\n \"score\": \"0.5946171\",\n \"text\": \"def pad_sentences(sentences, padding_word=\\\"<PAD/>\\\"):\\n sequence_length = max(len(x) for x in sentences)\\n padded_sentences = []\\n for i in range(len(sentences)):\\n sentence = sentences[i]\\n num_padding = sequence_length - len(sentence)\\n new_sentence = sentence + [padding_word] * num_padding\\n padded_sentences.append(new_sentence)\\n return padded_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a91b7e334749bae82537ee16eafa2d1\",\n \"score\": \"0.5946171\",\n \"text\": \"def pad_sentences(sentences, padding_word=\\\"<PAD/>\\\"):\\n sequence_length = max(len(x) for x in sentences)\\n padded_sentences = []\\n for i in range(len(sentences)):\\n sentence = sentences[i]\\n num_padding = sequence_length - len(sentence)\\n new_sentence = sentence + [padding_word] * num_padding\\n padded_sentences.append(new_sentence)\\n return padded_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a1423467aa038b2968bd00c00560910\",\n \"score\": \"0.5898101\",\n \"text\": \"def pad_sentences(sentences, padding_word=''):\\n sequence_length = max(len(x) for x in sentences)\\n padded_sentences = []\\n\\n for sentence in sentences:\\n num_padding = sequence_length - len(sentence)\\n new_sentence = sentence + [padding_word] * num_padding\\n padded_sentences.append(new_sentence)\\n\\n return padded_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9c26440cde9da053647b222ab59f617\",\n \"score\": \"0.58606505\",\n \"text\": \"def pad(iterable, end):\\n return chain(iterable, repeat(end))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c99c244c54436f0cddb0dcf235e6bd2\",\n \"score\": \"0.58314925\",\n \"text\": \"def pad_transitions(sentences_transitions):\\n max_len = max(len(transitions) for transitions in sentences_transitions)\\n for transitions in sentences_transitions:\\n if len(transitions) < max_len:\\n transitions.extend([PAD_CODE] * (max_len - len(transitions)))\\n return np.array(sentences_transitions, dtype=np.int64)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e4c4087fcb6f3615b428dbd09c97995\",\n \"score\": \"0.5817333\",\n \"text\": \"def pad_batch(batch):\\n batch_pad = np.zeros((len(batch), len(batch[-1])))\\n # fill the array\\n for idx, sentence in enumerate(batch):\\n batch_pad[idx, 0:len(sentence)] = sentence\\n batch_pad = np.swapaxes(batch_pad, 0, 1)\\n\\n return batch_pad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b51635c560976e7bcb4003ab78670ef3\",\n \"score\": \"0.5804639\",\n \"text\": \"def pad(pts):\\n return np.vstack([pts, [[0, 0]]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63e6172bb52d95c8802e5f6279e0247\",\n \"score\": \"0.5804279\",\n \"text\": \"def test_pad_sequence2(self):\\n\\n word_embeddings = [19, 1, 22, 1, 306, 50]\\n processor = TextPreprocessing(10, 1000)\\n result = processor.pad_sequence(word_embeddings)\\n\\n expected_result = [19, 1, 22, 1, 306, 50, 0, 0, 0, 0]\\n\\n self.assertEqual(result, expected_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42cbf539f6257333bd0709bb5181f7e0\",\n \"score\": \"0.58000654\",\n \"text\": \"def unpad(padded: Tensor, length: Tensor) -> List[Tensor]:\\n output = []\\n v: Tensor\\n n: int\\n for v, n in zip(padded, length):\\n output.append(v[:n])\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3154d6cf8b85c8145f5193b6b3fcf4c3\",\n \"score\": \"0.5796609\",\n \"text\": \"def pad(array: np.array, n: int, fill_value=None):\\n assert array.shape[0] <= n\\n if fill_value is None:\\n pad = np.repeat(array[0:1], n - array.shape[0], axis=0)\\n else:\\n pad = np.full((n - array.shape[0],) + array.shape[1:], fill_value, dtype=array.dtype)\\n return np.concatenate([array, pad], axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d505d0623aa73f97a3eba0cd5e681bf2\",\n \"score\": \"0.57948244\",\n \"text\": \"def pad_to_len(t, length, fill_value=-1):\\n shape = t.shape \\n if len(shape) == 2:\\n # handle case with batch dimension = 1\\n assert shape[0] == 1, \\\"can only handle case with batch dim = 1\\\"\\n t = t.flatten() \\n elif len(shape) > 2:\\n raise Exception(\\\"Trying to pad array with shape \\\", shape)\\n assert t.shape[0] <= length, \\\"tensor too long to be padded\\\"\\n padding = torch.ones(length-len(t), dtype=torch.int64)*-1\\n padded_t = torch.cat([t, padding])\\n return padded_t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b107996648407cfb96cdfd62ad7ad8\",\n \"score\": \"0.5786162\",\n \"text\": \"def pad(tensors, padding_value=-1):\\n max_len = max(t.size(-1) for t in tensors)\\n if tensors[0].dim() == 1:\\n out = torch.LongTensor(len(tensors), max_len).fill_(padding_value)\\n for i, t in enumerate(tensors):\\n out[i, : t.size(0)] = t\\n return out\\n elif tensors[0].dim() == 2:\\n max_width = max(t.size(0) for t in tensors)\\n out = torch.LongTensor(len(tensors), max_width, max_len).fill_(padding_value)\\n for i, t in enumerate(tensors):\\n out[i, : t.size(0), : t.size(1)] = t\\n return out\\n else:\\n raise ValueError(\\\"Input tensors must be either 1D or 2D!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7aca69017d82110cbbfee3501d1c533\",\n \"score\": \"0.5784187\",\n \"text\": \"def pad(self, padding_lengths: Dict[str, int]):\\n desired_num_options = padding_lengths['num_options']\\n desired_num_graphlets = padding_lengths['num_graphlets']\\n\\n # Pad the number of answers. Note that while we use the superclass method when we need to add empty\\n # answer_options, we don't want to remove answer options.\\n num_options = len(self.answers_indexed)\\n if num_options > desired_num_options:\\n raise Exception(\\\"Case of too many answer options ({0} provided) isn't currently \\\"\\n \\\"handled.\\\".format(num_options))\\n elif num_options < desired_num_options:\\n # Add empty answer option(s).\\n self.answers_indexed = self.pad_sequence_to_length(self.answers_indexed,\\n desired_num_options,\\n lambda: [],\\n truncate_from_right=False)\\n # Pad the number of graphlets per option.\\n for answer_index in range(len(self.answers_indexed)):\\n self.answers_indexed[answer_index] = self.pad_sequence_to_length(self.answers_indexed[answer_index],\\n desired_num_graphlets,\\n lambda: [],\\n truncate_from_right=False)\\n # Pad each of the graphlets for num alignments and features per alignment\\n for answer_index in range(len(self.answers_indexed)):\\n self.answers_indexed[answer_index] = [self.pad_graphlet(answer_graphlet, padding_lengths)\\n for answer_graphlet in self.answers_indexed[answer_index]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861e6b172311805007ee9ed4a7f600cc\",\n \"score\": \"0.5776464\",\n \"text\": \"def pad_documents(sentences, padding_word='<PAD>'):\\n sequence_length = max(len(x) for x in sentences)\\n padded_sentences = []\\n for i in range(len(sentences)):\\n sentence = sentences[i]\\n num_padding = sequence_length - len(sentence)\\n new_sentence = sentence + [padding_word] * num_padding\\n padded_sentences.append(new_sentence)\\n return padded_sentences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2d2b30577123cf18715f947f49e206a\",\n \"score\": \"0.5739352\",\n \"text\": \"def pad_list(listA, val=-1, length=None):\\n # pad list with zeros in order to have all lists of the same size\\n assert isinstance(listA, list), 'Input must be a list. Got {}, expected {}' \\\\\\n .format(type(listA), type(list))\\n\\n # get size of the biggest list\\n if length:\\n max_size = length\\n else:\\n max_size = len(max(listA, key=len))\\n\\n # pad all lists with the a padding value\\n return [elem + [val] * int(max_size - len(elem)) for elem in listA]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f93eb51ce5c92ddbf0da2df49f47c48\",\n \"score\": \"0.5730438\",\n \"text\": \"def packing_sequence(seq: torch.Tensor, seq_lens: np.ndarray, new_index) -> torch.Tensor:\\n return rnn_utils.pack_padded_sequence(seq[new_index], seq_lens[new_index], batch_first = True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e3ddaac4b9d61d875e75bd2cf880af\",\n \"score\": \"0.5728993\",\n \"text\": \"def pad(data, end=True):\\n pad = np.ones((len(data), 1))\\n if end:\\n return np.hstack([data, pad])\\n else:\\n return np.hstack([pad, data])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8381ec82100d4767e6f5fd92f4d689d\",\n \"score\": \"0.5716155\",\n \"text\": \"def concat(seq):\\n return sum(seq, [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a28221ed5042c308181b62eca2d84f57\",\n \"score\": \"0.5710812\",\n \"text\": \"def _zero_pad(sequence_length: int) -> datasets.Transform:\\n\\n def _zero_pad_transform(sample: reverb.ReplaySample) -> reverb.ReplaySample:\\n trajectory: reverb_base.Trajectory = sample.data\\n\\n # Split steps and extras data (the extras won't be padded as they only\\n # contain one element)\\n trajectory_steps = trajectory._asdict()\\n trajectory_extras = trajectory_steps.pop('extras')\\n\\n unpadded_length = len(tree.flatten(trajectory_steps)[0])\\n\\n # Do nothing if the sequence is already full.\\n if unpadded_length != sequence_length:\\n to_pad = sequence_length - unpadded_length\\n pad = lambda x: tf.pad(x, [[0, to_pad]] + [[0, 0]] * (len(x.shape) - 1))\\n\\n trajectory_steps = tree.map_structure(pad, trajectory_steps)\\n\\n # Set the shape to be statically known, and checks it at runtime.\\n def _ensure_shape(x):\\n shape = tf.TensorShape([sequence_length]).concatenate(x.shape[1:])\\n return tf.ensure_shape(x, shape)\\n\\n trajectory_steps = tree.map_structure(_ensure_shape, trajectory_steps)\\n return reverb.ReplaySample(\\n info=sample.info,\\n data=reverb_base.Trajectory(\\n **trajectory_steps, extras=trajectory_extras))\\n\\n return _zero_pad_transform\",\n \"title\": \"\"\n },\n {\n \"docid\": \"334d4b4814e895f7eaa38e6311795128\",\n \"score\": \"0.57006264\",\n \"text\": \"def test_nparray_oflists(self):\\n data = np.array([\\n [1, 5, 4, 3],\\n [1, 2, 4, 0, 9],\\n [9, 3],\\n [9, 4, 3]\\n ])\\n\\n assert data.dtype is np.dtype('O'), \\\"The test was not wired up properly\\\"\\n\\n padded_data = gd.pad_sequence(data)\\n assert (\\n padded_data == self.get_manually_padded_2d()).all(), \\\"Pad sequence did not pad np arr of lists as intended.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0edc35ff3dddf91bd097a389ddaa00fa\",\n \"score\": \"0.5686566\",\n \"text\": \"def pad_missing_fragments(self, seq, ions, weights):\\n # TODO check that this works as intended and not superslowly\\n temp = np.zeros(len(seq) - 1)\\n for i, w in zip(ions, weights):\\n temp[i - 1] = w\\n\\n ions = list(range(1, len(seq)))\\n weights = temp.tolist()\\n assert len(ions) == len(weights)\\n\\n return ions, weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02134fcf590502e0f93818ce1fde8642\",\n \"score\": \"0.56765175\",\n \"text\": \"def adjust_sequences(data, length=50):\\n for sample in data['data']:\\n # adjust the sequence of input words\\n if len(sample['words']) < length:\\n # add <EOS> and <PAD> if sentence is shorter than maximum length\\n sample['words'].append('<EOS>')\\n while len(sample['words']) < length:\\n sample['words'].append('<PAD>')\\n else:\\n # otherwise truncate and add <EOS> at last position\\n sample['words'] = sample['words'][:length]\\n sample['words'][-1] = '<EOS>'\\n\\n # adjust in the same way the sequence of output slots\\n if len(sample['slots']) < length:\\n sample['slots'].append('<EOS>')\\n while len(sample['slots']) < length:\\n sample['slots'].append('<PAD>')\\n else:\\n sample['slots'] = sample['slots'][:length]\\n sample['slots'][-1] = '<EOS>'\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a8929525fb71a594db0783c5b1cb608\",\n \"score\": \"0.5669793\",\n \"text\": \"def pad_sequence(sequence, max_seq_length, truncating='pre', dtype='float32', value=0.0):\\n \\n if not hasattr(sequence, '__len__'):\\n raise ValueError('sequence must be iterable.')\\n\\n sample_shape = tuple()\\n sample_shape = np.asarray(sequence).shape[1:]\\n \\n length = len(sequence)\\n\\n padded_sequence = (np.ones((max_seq_length,) + sample_shape) * value).astype(dtype)\\n\\n # truncation\\n if truncating == 'pre':\\n trunc = sequence[-max_seq_length:]\\n elif truncating == 'post':\\n trunc = sequence[:max_seq_length]\\n else:\\n raise ValueError('Truncating type \\\"{}\\\" not understood'.format(truncating))\\n\\n # check 'trunc' has expected shape\\n trunc = np.asarray(trunc, dtype=dtype)\\n if trunc.shape[1:] != sample_shape:\\n raise ValueError('Shape of sample {} of sequence is different from expected shape {}'.\\n format(trunc.shape[1:], sample_shape))\\n \\n # padding\\n padded_sequence[:len(trunc)] = trunc\\n\\n if length > max_seq_length:\\n length = max_seq_length\\n\\n return padded_sequence, length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d53bb01a16682a0310b95962794636d\",\n \"score\": \"0.56646353\",\n \"text\": \"def stretching_seq_nonzeros(self, x,max_len):\\n x_len = len(x)\\n dif_len = max_len - x_len #Number of padding zeros \\n seq = \\\"\\\" #New encoding sequence to build\\n if x_len > dif_len:\\n #x_len > dif_len\\n xIndex = 0\\n for i in range(dif_len):\\n xChunk = int(round(x_len / (dif_len + 1)))\\n if xChunk==1:\\n seq = \\\"\\\".join(string for string in [seq, x[xIndex:(xIndex+xChunk)], x[(xIndex)]]) \\n else:\\n seq = \\\"\\\".join(string for string in [seq, x[xIndex:(xIndex+xChunk)], x[(xIndex+xChunk-1)]])\\n xIndex = xIndex + xChunk\\n x_len = x_len - xChunk\\n dif_len = dif_len - 1\\n \\n seq = seq + x[xIndex]\\n if x_len <= dif_len:\\n lastPad = round((dif_len)/(x_len))\\n for idx, c in enumerate(x[:-1]):\\n yPad = int(round(dif_len/(x_len-1)))\\n miniPad = int(yPad/2)\\n if yPad%2 ==0:\\n seq = \\\"\\\".join(string for string in [seq, c, c*miniPad])\\n seq = \\\"\\\".join(string for string in [seq, x[idx+1]*miniPad])\\n else:\\n if miniPad == 0:\\n seq = \\\"\\\".join(string for string in [seq, c, c])\\n else:\\n seq = \\\"\\\".join(string for string in [seq, c, c*miniPad])\\n seq = \\\"\\\".join(string for string in [seq, x[idx+1]*(miniPad+1)])\\n x_len = x_len - 1\\n dif_len = dif_len - yPad\\n seq = seq + x[-1]\\n return seq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0f6e860e3ec5b9ddb4a86b7784ebe92\",\n \"score\": \"0.56575364\",\n \"text\": \"def pad_batch(self, batch):\\n max_input_length = self.max_input_length\\n max_label_length = self.max_label_length\\n if max_input_length is None or max_label_length is None:\\n max_input_length, max_label_length = max_length_seq(batch)\\n for i in range(len(batch)):\\n while len(batch[i][0]) < max_input_length:\\n batch[i][0].append(self.type_to_ix['_PAD'])\\n while len(batch[i][1]) < max_label_length:\\n batch[i][1].append(self.type_to_ix['_PAD'])\\n return batch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d86ed80fa59b03713e3e5db118e37f25\",\n \"score\": \"0.5648334\",\n \"text\": \"def periodicpad(inputs, pad):\\n n = inputs.dim()\\n inputs = inputs.permute(*list(range(n-1,-1,-1)))\\n pad = iter(pad)\\n i = 0\\n indx = []\\n for a in pad:\\n b = next(pad)\\n assert a<inputs.size()[i] and b<inputs.size()[i]\\n permute = list(range(n))\\n permute[i] = 0\\n permute[0] = i\\n inputs = inputs.permute(*permute)\\n inputlist = [inputs,]\\n if a > 0:\\n inputlist = [inputs[slice(-a,None)],inputs]\\n if b > 0:\\n inputlist = inputlist+[inputs[slice(0,b)],]\\n if a+b > 0:\\n inputs = torch.cat(inputlist,dim=0)\\n inputs = inputs.permute(*permute)\\n i += 1\\n inputs = inputs.permute(*list(range(n-1,-1,-1)))\\n return inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9260f43b98d7fc9da4faf69e44261c3\",\n \"score\": \"0.56459534\",\n \"text\": \"def test_lesser_len(self):\\n data = [\\n [1, 5, 4, 3],\\n [1, 2, 4, 0, 9],\\n [9, 3],\\n [9, 4, 3]\\n ]\\n padded_data = gd.pad_sequence(data, max_length=4)\\n manually_padded_data = np.array([\\n [1, 5, 4, 3],\\n [1, 2, 4, 0],\\n [9, 3, 0, 0],\\n [9, 4, 3, 0]\\n ])\\n assert (manually_padded_data == padded_data).all(), \\\\\\n \\\"Cannot properly pad when max length is less than the longest arr in data.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"181c271eddaca217be4354ab560e0fb4\",\n \"score\": \"0.5642768\",\n \"text\": \"def pad_to_fix_length(dim_2_list, pad_default, fix_length = None):\\n max_len = 0\\n if fix_length is None:\\n len_list = [len(l) for l in dim_2_list]\\n max_len = max(len_list)\\n else:\\n max_len = fix_length\\n dim_2_list_new = []\\n for l in dim_2_list:\\n l_new = l + [pad_default] * max((max_len - len(l)), 0)\\n dim_2_list_new.append(l_new)\\n return dim_2_list_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a663469f7387e0866b6878ffc5308538\",\n \"score\": \"0.5640025\",\n \"text\": \"def fill_one_space(self) -> None:\\n self.sequences = self.build_sequences(self.sequences)\\n while self.sequences and self.count_available_bits(self.sequences[0]) == 1:\\n self.sequences = self.build_sequences(self.sequences)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ea21bc3c2bd0c4745e99fbf984e853d\",\n \"score\": \"0.56307036\",\n \"text\": \"def test_pad_and_stack_tensors(self):\\n # Width of all arrays must be the same to represent the \\\"batch_size\\\"\\n # argument.\\n my_arrays = [\\n np.array([[4, 2, 1], [2, 1, 1]]), # 2 x 3.\\n np.array([[4, 2, 1], [2, 1, 1], [1, 1, 7]]), # 3 x 3.\\n np.array([[1, 2, 3]]) # 1 x 3.\\n ] # pyformat: disable\\n\\n padded_and_stacked = trajectory_collector.pad_and_stack_tensors(my_arrays)\\n\\n # Maximum trajectory length was 3 so we expect the output to be that.\\n self.assertEqual(padded_and_stacked.shape, (3, 9))\\n\\n # Check that padding should only happen where it is required.\\n self.assertTrue(np.allclose(padded_and_stacked[1:, 6:], 0))\\n self.assertFalse(np.allclose(padded_and_stacked[:, 3:7], 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89914604a28cd3f2ac1d9e1b298cbe85\",\n \"score\": \"0.56142145\",\n \"text\": \"def periodicpad(inputs, pad):\\n n = inputs.dim()\\n inputs = inputs.permute(*list(range(n - 1, -1, -1)))\\n pad = iter(pad)\\n i = 0\\n indx = []\\n for a in pad:\\n b = next(pad)\\n assert a < inputs.size()[i] and b < inputs.size()[i]\\n permute = list(range(n))\\n permute[i] = 0\\n permute[0] = i\\n inputs = inputs.permute(*permute)\\n inputlist = [\\n inputs,\\n ]\\n if a > 0:\\n inputlist = [inputs[slice(-a, None)], inputs]\\n if b > 0:\\n inputlist = inputlist + [\\n inputs[slice(0, b)],\\n ]\\n if a + b > 0:\\n inputs = torch.cat(inputlist, dim=0)\\n inputs = inputs.permute(*permute)\\n i += 1\\n inputs = inputs.permute(*list(range(n - 1, -1, -1)))\\n return inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42eb9fa6e603dcb34dbc0e1dcab03786\",\n \"score\": \"0.56067705\",\n \"text\": \"def padarr(x, align=8):\\n nitems = len(x.ravel())\\n nbytes = x.nbytes\\n dtypenbytes = x.dtype.itemsize\\n rem = nbytes % align\\n npadbytes = align - rem if rem else 0 # nbytes to pad with for 8 byte alignment\\n if npadbytes == 0:\\n return x\\n if npadbytes % dtypenbytes != 0:\\n raise RuntimeError(\\\"Can't pad %d byte array to %d byte alignment\\\" %\\n (dtypenbytes, align))\\n npaditems = npadbytes / dtypenbytes\\n x = x.ravel().copy() # don't modify in place\\n # pads with npaditems zeros, each of length dtypenbytes:\\n x.resize(nitems + npaditems, refcheck=False)\\n assert x.nbytes % align == 0\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42eb9fa6e603dcb34dbc0e1dcab03786\",\n \"score\": \"0.56067705\",\n \"text\": \"def padarr(x, align=8):\\n nitems = len(x.ravel())\\n nbytes = x.nbytes\\n dtypenbytes = x.dtype.itemsize\\n rem = nbytes % align\\n npadbytes = align - rem if rem else 0 # nbytes to pad with for 8 byte alignment\\n if npadbytes == 0:\\n return x\\n if npadbytes % dtypenbytes != 0:\\n raise RuntimeError(\\\"Can't pad %d byte array to %d byte alignment\\\" %\\n (dtypenbytes, align))\\n npaditems = npadbytes / dtypenbytes\\n x = x.ravel().copy() # don't modify in place\\n # pads with npaditems zeros, each of length dtypenbytes:\\n x.resize(nitems + npaditems, refcheck=False)\\n assert x.nbytes % align == 0\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ade6b6418ebae4e88fe9c28b79c0e80\",\n \"score\": \"0.5601883\",\n \"text\": \"def padnone(iterable):\\n return chain(iterable, repeat(None))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ade6b6418ebae4e88fe9c28b79c0e80\",\n \"score\": \"0.5601883\",\n \"text\": \"def padnone(iterable):\\n return chain(iterable, repeat(None))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"761182dd0786b2baa44609c82203e742\",\n \"score\": \"0.5601394\",\n \"text\": \"def _pad_arrs_to_max_length(arrs, pad_axis, pad_val, round_to=None, max_length=None):\\n if not isinstance(arrs[0], torch.Tensor):\\n arrs = [torch.as_tensor(ele) for ele in arrs]\\n\\n original_length = [ele.size(pad_axis) for ele in arrs]\\n max_arr_len = max(original_length)\\n\\n if round_to is not None:\\n max_arr_len = round_to * math.ceil(max_arr_len / round_to)\\n elif max_length is not None:\\n if max_length < max_arr_len:\\n raise ValueError(\\n f\\\"If max_length is specified, max_length={max_length} must be larger \\\"\\n f\\\"than the maximum length {max_arr_len} of the given arrays at axis={pad_axis}\\\"\\n )\\n max_arr_len = max_length\\n\\n size = arrs[0].size()\\n prev_trailing_dims = size[:pad_axis]\\n after_trailing_dims = size[pad_axis + 1 :]\\n\\n out_dims = (len(arrs),) + prev_trailing_dims + (max_arr_len,) + after_trailing_dims\\n out_tensor = arrs[0].new_full(out_dims, pad_val)\\n for i, tensor in enumerate(arrs):\\n length = tensor.size(pad_axis)\\n out_tensor[i].narrow(pad_axis, 0, length)[:] = tensor\\n\\n original_length = torch.as_tensor(original_length)\\n\\n return out_tensor, original_length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfa26cfc7476e599671422777ac99557\",\n \"score\": \"0.55938154\",\n \"text\": \"def padded_indices(\\n sequences,\\n index_dict=None,\\n ndim=2,\\n padding='pre',\\n add_start_symbol=True,\\n add_end_symbol=True,\\n max_len=None):\\n index_sequences = sequences_to_indices(\\n sequences=sequences,\\n index_dict=index_dict,\\n add_start_symbol=add_start_symbol,\\n add_end_symbol=add_end_symbol)\\n \\n if max_len == None:\\n max_len = max(len(s) for s in index_sequences)\\n n_samples = len(index_sequences)\\n if ndim < 2:\\n raise ValueError(\\\"Padded input must have at least 2 dims\\\")\\n\\n shape = (n_samples, max_len) + (1,) * (ndim - 2)\\n result = np.zeros(shape, dtype=int)\\n for i, x in enumerate(index_sequences):\\n if padding == 'post':\\n result[i, :len(x)] = x\\n elif padding == 'pre':\\n result[i, -len(x):] = x\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10a0bf3f2a9e1c5c5ed3a8d4596de4b5\",\n \"score\": \"0.55916065\",\n \"text\": \"def sequences(lengths=None, elements=None):\\n elements = _defaulted(elements, default_sequence_elements)\\n lengths = _defaulted(lengths, default_sequence_lengths)\\n \\n return (islice(elements, length) for length in lengths)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1784,"cells":{"query_id":{"kind":"string","value":"6aa881b9a2f30de1ffe583b1ffce1eb5"},"query":{"kind":"string","value":"Put relevant metrics data of class into dict"},"positive_passages":{"kind":"list like","value":[{"docid":"5afb20a90d6603e24821ab4cd00afd40","score":"0.64603966","text":"def metrics_dict(self):\n return {\"num_msgs\" : self.num_msg,\n \"throughput\" : self.throughput, \n \"queue_length\" : self.qlength,\n \"latency\" : self.latency,\n \"num_clients\" : self.num_clients}","title":""}],"string":"[\n {\n \"docid\": \"5afb20a90d6603e24821ab4cd00afd40\",\n \"score\": \"0.64603966\",\n \"text\": \"def metrics_dict(self):\\n return {\\\"num_msgs\\\" : self.num_msg,\\n \\\"throughput\\\" : self.throughput, \\n \\\"queue_length\\\" : self.qlength,\\n \\\"latency\\\" : self.latency,\\n \\\"num_clients\\\" : self.num_clients}\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6d0d663c48cf8e46387df0b963c73172","score":"0.69854665","text":"def stats_by_class(dataset):\n\n splitted = split_by_class(dataset)\n stats = {}\n for c, values in splitted.items():\n stats[c] = get_stats(values)\n return stats","title":""},{"docid":"fc0a30a1ff8e7d599561ab6219a85a5a","score":"0.68985754","text":"def metrics_to_use(self):","title":""},{"docid":"56e88e66c537741a433016541ce75089","score":"0.6875784","text":"def collect_metrics(self):\n pass","title":""},{"docid":"24546c717b1eeed21d65a57bda8209b3","score":"0.67222846","text":"def get_metrics(self) -> {}:\n return {'metrics': self._metrics, 'groups': self._metrics_groups}","title":""},{"docid":"398f556890ff85f8f3cc4f7fd71a3d53","score":"0.6629476","text":"def get_custom_metrics(self) -> Dict:\n return {}","title":""},{"docid":"dda17e32ac689f89f112cff6d41b9586","score":"0.6573594","text":"def as_dict(self) -> Dict:\n return self._metrics","title":""},{"docid":"31a7e16eed535b75ac14a2587b13d324","score":"0.65656495","text":"def metrics(self):\n raise NotImplementedError(\"This needs to be implemented by the inheriting class.\")","title":""},{"docid":"7907d7881fd5646a29a7d05fc15980c3","score":"0.64915085","text":"def _module_metrics(self) -> Dict[str, Any]:\n return {}","title":""},{"docid":"9c61a3fb3a39f348e250c6441b731cf9","score":"0.6294655","text":"def initialize_metrics():\n metrics = {\n 'top_comp_share': [], \n 'top_'+str(params.k)+'comp_share': [], \n 'comp_GINI': [],\n 'comp_ranking': [],\n 'comps': []\n }\n return metrics","title":""},{"docid":"ed1cca1c7112f87615e5241a5ee3195d","score":"0.6252564","text":"def compute_metrics(self):\n pass","title":""},{"docid":"ed1cca1c7112f87615e5241a5ee3195d","score":"0.6252564","text":"def compute_metrics(self):\n pass","title":""},{"docid":"ed85cc9b949e3c0e073e7021565e3baf","score":"0.62160367","text":"def metric(self):","title":""},{"docid":"07934c9ebeab5e8c727acb88a4612a3c","score":"0.6204308","text":"def report(self):\n m = super().report()\n examples = self.metrics['examples']\n if examples > 0:\n m['examples'] = examples\n m['mean_loss'] = self.metrics['loss'] / examples\n\n # get prec/recall metrics\n confmat = self.metrics['confusion_matrix']\n if self.opt.get('get_all_metrics'):\n metrics_list = self.class_list\n else:\n # only give prec/recall metrics for ref class\n metrics_list = [self.ref_class]\n\n examples_per_class = []\n for class_i in metrics_list:\n class_total = self._report_prec_recall_metrics(confmat, class_i, m)\n examples_per_class.append(class_total)\n\n if len(examples_per_class) > 1:\n # get weighted f1\n f1 = 0\n total_exs = sum(examples_per_class)\n for i in range(len(self.class_list)):\n f1 += (examples_per_class[i] / total_exs) * m[\n 'class_{}_f1'.format(self.class_list[i])\n ]\n m['weighted_f1'] = f1\n\n for k, v in m.items():\n m[k] = round_sigfigs(v, 4)\n\n return m","title":""},{"docid":"c35f38dd1cf836d747e5d93d16ed96ba","score":"0.6186239","text":"def __post_init__(self) -> None:\n\n self._metric_container_fields = [\n self.__dict__[field.name]\n for field in dataclasses.fields(self)\n if isinstance(self.__dict__[field.name], MetricsBase)\n ]","title":""},{"docid":"750bf03efd8230bc71da795362161962","score":"0.6182728","text":"def process(self) -> Dict[str, float]:\n\n epoch_preds = np.array(self.epoch_preds)\n epoch_targets = np.array(self.epoch_targets)\n epoch_breakdown = np.array(self.collected_data['breakdown_class'])\n\n # first find operation points\n if isinstance(self._class_thresholds, Callable):\n class_thresholds = self._class_thresholds(epoch_preds, epoch_targets)\n else:\n class_thresholds = self._class_thresholds\n\n # get class predictions\n epoch_class_preds = FuseMetricsToolBox.convert_probabilities_to_class(epoch_preds, thresholds=class_thresholds)\n\n # get class names\n num_classes = epoch_preds[0].shape[0]\n class_names = FuseMetricsToolBox.get_class_names(num_classes, self._class_names)\n\n # get breakdown classes (sub types)\n breakdown_classes = sorted(list(set(epoch_breakdown)), key=lambda x: str(x))\n breakdown_classes_count = self.get_list_type_count(breakdown_classes, epoch_breakdown)\n\n # fill in a 2D table of breakdown_cls x predicted cls_\n breakdown_count_per_class = {str(cls): [] for cls in breakdown_classes}\n for breakdown_cls in breakdown_classes:\n breakdown_cls_name = str(breakdown_cls)\n for class_idx, class_name in enumerate(class_names):\n detect_count = np.logical_and(np.where(epoch_breakdown == breakdown_cls, 1, 0),\n np.where(epoch_class_preds == class_idx, 1, 0)).sum()\n all_count = breakdown_classes_count[breakdown_cls]\n count_percent = '(' + str(round(detect_count / int(all_count) * 100, 1)) + '%)'\n breakdown_count_per_class[breakdown_cls_name].append(str(detect_count) + '/' + all_count + count_percent)\n\n return pd.DataFrame(breakdown_count_per_class, index=class_names).transpose().to_string()","title":""},{"docid":"8c3210f0a03237415e33268b4cec8326","score":"0.61660165","text":"def get_metrics(self): # pragma: no cover\n raise NotImplementedError()","title":""},{"docid":"2f7050bf69dfb4787278c7ec2b3e13f4","score":"0.61603546","text":"def get_metrics(self) -> DocableDict:\n return DocableDict(\n metrics_desc,\n {\n \"trip_requirements\": self._total_trips,\n \"bike_shortage\": self._total_shortages,\n \"operation_number\": self._total_operate_num,\n },\n )","title":""},{"docid":"ac67756ff1da3a3fec457bdcab6e521f","score":"0.6112048","text":"def __init__(self, metrics_dict: Dict) -> None:\n self._metrics = metrics_dict","title":""},{"docid":"ce97a7f9eb2a54916fb89dc7cc1b12c2","score":"0.6110764","text":"def get_measurement_dict(self):\n meas_dict = {}\n for key in list(self.__dict__.keys()):\n if key.find(\"meas_\") == 0:\n meas_attr = getattr(self, key)\n meas_key = meas_attr.chtype\n meas_dict[meas_key] = meas_attr\n\n return meas_dict","title":""},{"docid":"4c1f1b1b9078dfa472cbaf7ff830ea09","score":"0.6100069","text":"def _update_metrics(self):\n new_points_dict = {}\n if 'performance' in self.plot_options:\n new_points_dict['performance'] = [[self.global_classification_metrics[i].get_performance(),\n self.partial_classification_metrics[i].get_performance()]\n for i in range(self.n_classifiers)]\n\n if 'kappa' in self.plot_options:\n new_points_dict['kappa'] = [[self.global_classification_metrics[i].get_kappa(),\n self.partial_classification_metrics[i].get_kappa()]\n for i in range(self.n_classifiers)]\n\n if 'kappa_t' in self.plot_options:\n new_points_dict['kappa_t'] = [[self.global_classification_metrics[i].get_kappa_t(),\n self.partial_classification_metrics[i].get_kappa_t()]\n for i in range(self.n_classifiers)]\n\n if 'kappa_m' in self.plot_options:\n new_points_dict['kappa_m'] = [[self.global_classification_metrics[i].get_kappa_m(),\n self.partial_classification_metrics[i].get_kappa_m()]\n for i in range(self.n_classifiers)]\n\n if 'scatter' in self.plot_options:\n true, pred = [], []\n for i in range(self.n_classifiers):\n t, p = self.global_classification_metrics[i].get_last()\n true.append(t)\n pred.append(p)\n new_points_dict['scatter'] = [[true[i], pred[i]] for i in range(self.n_classifiers)]\n\n if 'hamming_score' in self.plot_options:\n new_points_dict['hamming_score'] = [[self.global_classification_metrics[i].get_hamming_score(),\n self.partial_classification_metrics[i].get_hamming_score()]\n for i in range(self.n_classifiers)]\n\n if 'hamming_loss' in self.plot_options:\n new_points_dict['hamming_loss'] = [[self.global_classification_metrics[i].get_hamming_loss(),\n self.partial_classification_metrics[i].get_hamming_loss()]\n for i in range(self.n_classifiers)]\n\n if 'exact_match' in self.plot_options:\n new_points_dict['exact_match'] = [[self.global_classification_metrics[i].get_exact_match(),\n self.partial_classification_metrics[i].get_exact_match()]\n for i in range(self.n_classifiers)]\n\n if 'j_index' in self.plot_options:\n new_points_dict['j_index'] = [[self.global_classification_metrics[i].get_j_index(),\n self.partial_classification_metrics[i].get_j_index()]\n for i in range(self.n_classifiers)]\n\n if 'mean_square_error' in self.plot_options:\n new_points_dict['mean_square_error'] = [[self.global_classification_metrics[i].get_mean_square_error(),\n self.partial_classification_metrics[i].get_mean_square_error()]\n for i in range(self.n_classifiers)]\n\n if 'mean_absolute_error' in self.plot_options:\n new_points_dict['mean_absolute_error'] = [[self.global_classification_metrics[i].get_average_error(),\n self.partial_classification_metrics[i].get_average_error()]\n for i in range(self.n_classifiers)]\n\n if 'true_vs_predicts' in self.plot_options:\n true, pred = [], []\n for i in range(self.n_classifiers):\n t, p = self.global_classification_metrics[i].get_last()\n true.append(t)\n pred.append(p)\n new_points_dict['true_vs_predicts'] = [[true[i], pred[i]] for i in range(self.n_classifiers)]\n\n self.__update_plot(self.global_sample_count, new_points_dict)","title":""},{"docid":"75b6056946d2486216dc13aaf5f9e67d","score":"0.60146326","text":"def __init__(self, data, logs_path):\n self.data = data\n self.logs_path = logs_path\n self.train_metrics = defaultdict(list)\n self.eval_metrics = defaultdict(list)\n self.test_metrics = defaultdict(list)","title":""},{"docid":"9bfbdfb9bbc33defb33955b7d8ffa19f","score":"0.5977449","text":"def _cache_metrics_metadata(self, instance):\n # ## <TEST-INSTRUMENTATION>\n t = Timer()\n # ## </TEST-INSTRUMENTATION>\n\n i_key = self._instance_key(instance)\n self.metadata_cache.init_instance(i_key)\n self.log.info(\"Warming metrics metadata cache for instance %s\", i_key)\n server_instance = self._get_server_instance(instance)\n perfManager = server_instance.content.perfManager\n\n new_metadata = {}\n metric_ids = []\n # Use old behaviour with metrics to collect defined by our constants\n if self.in_compatibility_mode(instance, log_warning=True):\n for counter in perfManager.perfCounter:\n metric_name = self.format_metric_name(counter, compatibility=True)\n new_metadata[counter.key] = {'name': metric_name, 'unit': counter.unitInfo.key}\n # Build the list of metrics we will want to collect\n if instance.get(\"all_metrics\") or metric_name in BASIC_METRICS:\n metric_ids.append(vim.PerformanceManager.MetricId(counterId=counter.key, instance=\"*\"))\n else:\n collection_level = instance.get(\"collection_level\", 1)\n for counter in perfManager.QueryPerfCounterByLevel(collection_level):\n new_metadata[counter.key] = {\"name\": self.format_metric_name(counter), \"unit\": counter.unitInfo.key}\n # Build the list of metrics we will want to collect\n metric_ids.append(vim.PerformanceManager.MetricId(counterId=counter.key, instance=\"*\"))\n\n self.log.info(\"Finished metadata collection for instance %s\", i_key)\n # Reset metadata\n self.metadata_cache.set_metadata(i_key, new_metadata)\n self.metadata_cache.set_metric_ids(i_key, metric_ids)\n\n self.cache_config.set_last(CacheConfig.Metadata, i_key, time.time())\n\n # ## <TEST-INSTRUMENTATION>\n custom_tags = instance.get('tags', []) + ['instance:{}'.format(i_key)]\n self.histogram('datadog.agent.vsphere.metric_metadata_collection.time', t.total(), tags=custom_tags)\n # ## </TEST-INSTRUMENTATION>","title":""},{"docid":"f771b4e0ac477c08d6ec9adeb17a37be","score":"0.5959972","text":"def __init__(self, bucketPrefix, testBucketNumber, dataFormat):\n \n total = 0\n classes = {}\n # counts used for attributes that are not numeric\n counts = {}\n # totals used for attributes that are numereric\n # we will use these to compute the mean and sample standard deviation for\n # each attribute - class pair.\n totals = {}\n numericValues = {}\n \n \n # reading the data in from the file\n \n self.format = dataFormat.strip().split('\\t')\n # \n self.prior = {}\n self.conditional = {}\n \n # for each of the buckets numbered 1 through 10:\n for i in range(1, 11):\n # if it is not the bucket we should ignore, read in the data\n if i != testBucketNumber:\n filename = \"%s-%02i\" % (bucketPrefix, i)\n f = open(filename)\n lines = f.readlines()\n f.close()\n for line in lines:\n fields = line.strip().split('\\t')\n ignore = []\n vector = []\n nums = []\n for i in range(len(fields)):\n if self.format[i] == 'num':\n nums.append(float(fields[i]))\n elif self.format[i] == 'attr':\n vector.append(fields[i]) \n elif self.format[i] == 'comment':\n ignore.append(fields[i])\n elif self.format[i] == 'class':\n category = fields[i]\n # now process this instance\n total += 1\n classes.setdefault(category, 0)\n counts.setdefault(category, {})\n totals.setdefault(category, {})\n numericValues.setdefault(category, {})\n classes[category] += 1\n # now process each non-numeric attribute of the instance\n col = 0\n for columnValue in vector:\n col += 1\n counts[category].setdefault(col, {})\n counts[category][col].setdefault(columnValue, 0)\n counts[category][col][columnValue] += 1\n # process numeric attributes\n col = 0\n for columnValue in nums:\n col += 1\n totals[category].setdefault(col, 0)\n #totals[category][col].setdefault(columnValue, 0)\n totals[category][col] += columnValue\n numericValues[category].setdefault(col, [])\n numericValues[category][col].append(columnValue)\n \n \n #\n # ok done counting. now compute probabilities\n #\n # first prior probabilities p(h)\n #\n for (category, count) in classes.items():\n self.prior[category] = count / total\n #\n # now compute conditional probabilities p(h|D)\n #\n for (category, columns) in counts.items():\n self.conditional.setdefault(category, {})\n for (col, valueCounts) in columns.items():\n self.conditional[category].setdefault(col, {})\n for (attrValue, count) in valueCounts.items():\n self.conditional[category][col][attrValue] = (\n count / classes[category])\n self.tmp = counts \n #\n # now compute mean and sample standard deviation\n #\n self.means = {}\n self.ssd = {}\n # ADD YOUR CODE HERE","title":""},{"docid":"9012f6be7a84cd209e5bbcfdd54b33fa","score":"0.59237885","text":"def initialize_class():\n for cl in class_name:\n class_dict[cl] = {'nbr_occ': 0,\n 'voc_occ': 0}","title":""},{"docid":"6b9af8cd2feae4860ffe2ecf0a12f9c8","score":"0.58834255","text":"def reset_metrics(self):\n self._metrics = collections.defaultdict(float)","title":""},{"docid":"4883dacf9fafcf40e191024d70a6cd16","score":"0.587903","text":"def _make_metric_trackers(self):\n train_metrics = {name:FnValueTracker(fn) for name, fn in self.train_metrics.items()}\n val_metrics = {name:FnValueTracker(fn) for name, fn in self.val_metrics.items()}\n return train_metrics, val_metrics","title":""},{"docid":"2adeb3b810e5e60ef23e1a33a3d950fb","score":"0.5876828","text":"def __init__(self, precision=1):\n self.__statistics = collections.defaultdict(dict)\n self.__precision = precision\n self.__properties = []\n self.__properties_remote = []","title":""},{"docid":"0e6f260f7ceefb40711aa78ff67e3dfe","score":"0.5858176","text":"def store_output_metrics(self):\n # total power\n self.total_power=[int(i) if i is not None else 0 for i in self.total_power] \n self.total_power=np.sum(self.total_power)\n # gpu power\n self.gpu_power=[int(i) if i is not None else 0 for i in self.gpu_power] \n self.gpu_power=np.sum(self.gpu_power)\n # cpu power\n self.cpu_power=[int(i) if i is not None else 0 for i in self.cpu_power] \n self.cpu_power=np.sum(self.cpu_power)\n \n # total temp\n self.total_temp=[int(i) if i is not None else 0 for i in self.total_temp] \n self.total_temp=np.mean(self.total_temp)\n # gpu temp\n self.gpu_temp=[int(i) if i is not None else 0 for i in self.gpu_temp] \n self.gpu_temp=np.mean(self.gpu_temp)\n # cpu temp\n self.cpu_temp=[int(i) if i is not None else 0 for i in self.cpu_temp] \n self.cpu_temp=np.mean(self.cpu_temp)\n \n # output\n output={'cur_total_power' : int(self.total_power),\n 'cur_gpu_power' : int(self.gpu_power),\n 'cur_cpu_power' : int(self.cpu_power),\n 'cur_inference' : float(self.inference_time),\n 'cur_total_temp' : float(self.total_temp),\n 'cur_gpu_temp' : float(self.gpu_temp),\n 'cur_cpu_temp' : float(self.cpu_temp)}\n with open(self.fname,'w') as f:\n json.dump(output, f)","title":""},{"docid":"3840da8a927481867d695a318fd11fbd","score":"0.5816547","text":"def __init__(self):\n super(StatisticsCollector, self).__init__()\n self.statistics = dict()\n self.formatting = dict()\n\n # Add default statistics with formatting.\n self.add_statistic('episode', '{:06d}')\n self.add_statistic('loss', '{:12.10f}')","title":""},{"docid":"effb39f0da31471dddac60305c6957ac","score":"0.58144295","text":"def insert_metrics(self):\n\t\t\n\t\tself.check_or_load_kb()\n\n\t\t# computing statistics\n\t\tfor line_num in range(1, len(self.lines) + 1):\n\t\t\tent_type_set = self.get_ent_type(line_num)\n\t\t\tent_type_set_index = repr(set(ent_type_set)) # using normal set for indexing because {1,3,2} == {1,2,3} but OrderedSet([1,3,2]) != OrderedSet([1,2,3])\n\t\t\tself.metrics.setdefault(ent_type_set_index, {})\n\t\t\tself.metrics[ent_type_set_index].setdefault('columns_number', []).append(self.nonempty_columns(line_num))\n\t\t\tself.metrics[ent_type_set_index].setdefault('description_length', []).append(self.description_length(line_num))\n\t\t\tif self.get_wiki_value(line_num, 'backlinks'):\n\t\t\t\tself.metrics[ent_type_set_index].setdefault('wiki_backlinks', []).append(int(self.get_wiki_value(line_num, 'backlinks')))\n\t\t\t\tself.metrics[ent_type_set_index].setdefault('wiki_hits', []).append(int(self.get_wiki_value(line_num, 'hits')))\n\t\t\t\tself.metrics[ent_type_set_index].setdefault('wiki_ps', []).append(int(self.get_wiki_value(line_num, 'ps')))\n\t\t\n\t\t# sorting statistics\n\t\tfor i in self.metrics:\n\t\t\tfor j in self.metrics[i]:\n\t\t\t\tself.metrics[i][j].sort()\n\n\t\t# indexing statistics\n\t\tfor i in self.metrics:\n\t\t\tfor j in self.metrics[i]:\n\t\t\t\tfor k in range(0, len(self.metrics[i][j])):\n\t\t\t\t\tif self.metrics[i][j][k] not in self.metric_index.setdefault(i, {}).setdefault(j, {}):\n\t\t\t\t\t\tmax_value = float(self.metrics[i][j][-1])\n\t\t\t\t\t\tif j in ['wiki_backlinks', 'wiki_hits']:\n\t\t\t\t\t\t\tmax_value = 0.25 * max_value\n\t\t\t\t\t\tif max_value:\n\t\t\t\t\t\t\tnormalized_value = float(self.metrics[i][j][k]) / max_value\n\t\t\t\t\t\t\tself.metric_index[i][j][self.metrics[i][j][k]] = min(normalized_value, 1.0)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tself.metric_index[i][j][self.metrics[i][j][k]] = 1.0\n\n\t\t# computing SCORE WIKI, SCORE METRICS and CONFIDENCE\n\t\tfor line_num in range(1, len(self.lines) + 1):\n\n\t\t\tcolumns = self.lines[line_num - 1]\n\t\t\t\n\t\t\t# computing SCORE WIKI\n\t\t\tscore_wiki = 0\n\t\t\tif self.get_wiki_value(columns, 'backlinks'):\n\t\t\t\twiki_backlinks = self.metric_percentile(columns, 'wiki_backlinks')\n\t\t\t\twiki_hits = self.metric_percentile(columns, 'wiki_hits')\n\t\t\t\twiki_ps = self.metric_percentile(columns, 'wiki_ps')\n\t\t\t\tscore_wiki = 100 * numpy.average([wiki_backlinks, wiki_hits, wiki_ps], weights=[5, 5, 1])\n\t\t\tcolumns[self.get_col_for(columns, \"SCORE WIKI\")] = \"%.2f\" % score_wiki\n\n\t\t\t# computing SCORE METRICS\n\t\t\tdescription_length = self.metric_percentile(columns, 'description_length')\n\t\t\tcolumns_number = self.metric_percentile(columns, 'columns_number')\n\t\t\tscore_metrics = 100 * numpy.average([description_length, columns_number])\n\t\t\tcolumns[self.get_col_for(columns, \"SCORE METRICS\")] = \"%.2f\" % score_metrics\n\n\t\t\t# computing CONFIDENCE\n\t\t\tcolumns[self.get_col_for(columns, \"CONFIDENCE\")] = \"%.2f\" % numpy.average([score_wiki, score_metrics], weights=[5, 1])","title":""},{"docid":"737ab5b06cd89c90dab4eb193efa0da5","score":"0.5800046","text":"def metric(self):\n pass","title":""},{"docid":"0d01e6e6d66dcad4070855efdfc189a1","score":"0.5794874","text":"def to_dict(self, *args):\n\n out = super().to_dict()\n\n out['stats'] = {str(j): w for j, w in self.stats.items()}\n\n return out","title":""},{"docid":"51bea03b0dc7dedb265c10f17fb09b5f","score":"0.5785863","text":"def calculateDailyMetrics(self):\n\n # we really want these by class -- ignore time periods and income levels\n vmt_byclass = self.vmt_vht_metrics.sum(level='vehicle class') # vehicles, not people\n transit_byclass = self.transit_times_by_acc_mode_egr.sum(level='Mode')\n nonmot_byclass = self.nonmot_times.sum(level='Mode') # person trips\n auto_byclass = self.auto_times.sum(level='Mode') # person trips\n\n daily_results = collections.OrderedDict()\n\n ######################################################################################\n cat1 = 'Travel Time'\n cat2 = 'Auto/Truck (Hours)'\n daily_results[(cat1,cat2,'SOV (PHT)' )] = vmt_byclass.loc[['DA','DAT'],'VHT'].sum()\n daily_results[(cat1,cat2,'HOV2 (PHT)' )] = vmt_byclass.loc[['S2','S2T'],'VHT'].sum()*2\n daily_results[(cat1,cat2,'HOV3+ (PHT)')] = vmt_byclass.loc[['S3','S3T'],'VHT'].sum()*3.5\n daily_results[(cat1,cat2,'Truck (VHT)')] = vmt_byclass.loc[['SM','SMT','HV','HVT'],'VHT'].sum()\n\n # TODO: These are vehicle hours, I think. Why not make them person hours?\n cat2 = 'Non-Recurring Freeway Delay (Hours)'\n daily_results[(cat1,cat2,'Auto' )] = \\\n vmt_byclass.loc[['DA','DAT','S2','S2T','S3','S3T'],'Non-Recurring Freeway Delay'].sum()\n daily_results[(cat1,cat2,'Truck')] = \\\n vmt_byclass.loc[['SM','SMT','HV','HVT'],'Non-Recurring Freeway Delay'].sum()\n\n cat2 = 'Transit In-Vehicle (Hours)'\n daily_results[(cat1,cat2,'Local Bus' )] = transit_byclass.loc['loc','In-vehicle hours']\n daily_results[(cat1,cat2,'Light Rail/Ferry')] = transit_byclass.loc['lrf','In-vehicle hours']\n daily_results[(cat1,cat2,'Express Bus' )] = transit_byclass.loc['exp','In-vehicle hours']\n daily_results[(cat1,cat2,'Heavy Rail' )] = transit_byclass.loc['hvy','In-vehicle hours']\n daily_results[(cat1,cat2,'Commuter Rail' )] = transit_byclass.loc['com','In-vehicle hours']\n\n cat2 = 'Transit Out-of-Vehicle (Hours)'\n daily_results[(cat1,cat2,'Walk Access+Egress' )] = transit_byclass.loc[:,'Walk acc & egr hours'].sum() + \\\n transit_byclass.loc[:,'Aux walk hours'].sum()\n daily_results[(cat1,cat2,'Drive Access+Egress')] = transit_byclass.loc[:,'Drive acc & egr hours'].sum()\n daily_results[(cat1,cat2,'Wait' )] = transit_byclass.loc[:,'Init wait hours'].sum() + \\\n transit_byclass.loc[:,'Xfer wait hours'].sum()\n # Out-of-Vehicle ajustment\n auto_person_trips = auto_byclass.loc[['da','datoll','sr2','sr2toll','sr3','sr3toll'],'Daily Trips'].sum()\n # If this is base dir, ovtt adjustment comes from project\n\n if self.is_base_dir:\n self.ovtt_adjustment = self.config.loc['ovtt_adjustment']\n elif self.config.loc['Project Mode'] in ['com','hvy','exp','lrf','loc']:\n self.ovtt_adjustment = transit_byclass.loc[self.config.loc['Project Mode'],'Out-of-vehicle hours'] / \\\n transit_byclass.loc[self.config.loc['Project Mode'],'Transit Trips']\n elif self.config.loc['Project Mode'] == 'road':\n self.ovtt_adjustment = transit_byclass.loc[:,'Out-of-vehicle hours'].sum() / \\\n transit_byclass.loc[:,'Transit Trips'].sum()\n else:\n raise Exception(\"Invalid Project Mode:'%s'; Should be one of 'road','com','hvy','exp','lrf','loc'\" % \\\n self.config.loc['Project Mode'])\n\n daily_results[(cat1,cat2,'Ajustment for %s' % self.config.loc['Project Mode'])] = \\\n self.ovtt_adjustment * auto_person_trips\n \n\n cat2 = 'Walk/Bike (Hours)'\n daily_results[(cat1,cat2,'Walk')] = nonmot_byclass.loc['Walk','Total Time (Hours)']\n daily_results[(cat1,cat2,'Bike')] = nonmot_byclass.loc['Bike','Total Time (Hours)']\n\n ######################################################################################\n cat1 = 'Travel Cost'\n cat2 = 'VMT'\n daily_results[(cat1,cat2,'Auto' )] = \\\n vmt_byclass.loc[['DA','DAT','S2','S2T','S3','S3T'],'VMT'].sum()\n daily_results[(cat1,cat2,'Truck')] = \\\n vmt_byclass.loc[['SM','SMT','HV','HVT'],'VMT'].sum()\n\n cat2 = 'Operating Costs'\n daily_results[(cat1,cat2,'Auto ($2000)' )] = \\\n 0.01*auto_byclass.loc[['da','datoll','s2','s2toll','s3','s3toll'],'Total Cost'].sum()\n daily_results[(cat1,cat2,'Truck ($2000)')] = \\\n 0.01*auto_byclass.loc['truck','Total Cost'].sum()\n\n # Parking\n cat2 = 'Auto Trips' # These are by person; change to vehicle\n daily_results[(cat1,cat2,'SOV' )] = auto_byclass.loc[['da' ,'datoll' ],'Daily Trips'].sum()\n daily_results[(cat1,cat2,'HOV2' )] = auto_byclass.loc[['sr2','sr2toll'],'Daily Trips'].sum()/2.0\n daily_results[(cat1,cat2,'HOV3+')] = auto_byclass.loc[['sr3','sr3toll'],'Daily Trips'].sum()/3.5\n total_autotrips = daily_results[(cat1,cat2,'SOV')] + daily_results[(cat1,cat2,'HOV2')] + daily_results[(cat1,cat2,'HOV3+')]\n\n cat2 = 'Parking Costs'\n try:\n for county in RunResults.PARKING_COST_PER_TRIP_WORK.keys():\n daily_results[(cat1,cat2,'Work Trips in %s' % county)] = total_autotrips * \\\n RunResults.PERCENT_PARKING_NONHOME * RunResults.PERCENT_PARKING_WORK * \\\n float(self.config.loc['percent parking cost incurred in %s' % county])\n for county in RunResults.PARKING_COST_PER_TRIP_WORK.keys():\n daily_results[(cat1,cat2,'Non-Work Trips in %s' % county)] = total_autotrips * \\\n RunResults.PERCENT_PARKING_NONHOME * (1.0-RunResults.PERCENT_PARKING_WORK) * \\\n float(self.config.loc['percent parking cost incurred in %s' % county])\n except:\n # base dirs don't have parking costs\n assert(self.is_base_dir)\n\n # Vehicles Owned\n cat2 = 'Vehicle Ownership (Modeled)'\n daily_results[(cat1,cat2,'Total')] = self.autos_owned['total autos'].sum()\n\n # Vehicles Owned - estimated from auto trips\n cat2 = 'Vehicle Ownership (Est. from Auto Trips)'\n daily_results[(cat1,cat2,'Total')] =total_autotrips*RunResults.ANNUALIZATION/RunResults.YEARLY_AUTO_TRIPS_PER_AUTO\n\n # Noise\n cat2 = 'Noise'\n daily_results[(cat1,cat2,'Auto VMT')] = \\\n vmt_byclass.loc[['DA','DAT','S2','S2T','S3','S3T'],'VMT'].sum()\n daily_results[(cat1,cat2,'Truck VMT')] = \\\n vmt_byclass.loc[['SM','SMT','HV','HVT'],'VMT'].sum()\n ######################################################################################\n cat1 = 'Air Pollutant'\n cat2 = 'PM2.5 (tons)'\n daily_results[(cat1,cat2,'PM2.5 Gasoline')] = \\\n vmt_byclass.loc[:,'Gas_PM2.5'].sum()*RunResults.EMISSIONS_SCALEUP + \\\n (daily_results[('Travel Cost','VMT','Auto')]*(RunResults.PM25_MAGIC_A+RunResults.PM25_MAGIC_B)*RunResults.PM25_MAGIC_C)\n daily_results[(cat1,cat2,'PM2.5 Diesel' )] = \\\n vmt_byclass.loc[:,'Diesel_PM2.5'].sum()*RunResults.EMISSIONS_SCALEUP + \\\n (daily_results[('Travel Cost','VMT','Truck')]*(RunResults.PM25_MAGIC_A+RunResults.PM25_MAGIC_B)*RunResults.PM25_MAGIC_C)\n\n cat2 = 'CO2 (metric tons)'\n daily_results[(cat1,cat2,'CO2')] = vmt_byclass.loc[:,'CO2' ].sum()\n\n cat2 = 'Other'\n daily_results[(cat1,cat2,'NOX (tons)')] = vmt_byclass.loc[:,'W_NOx'].sum()*RunResults.EMISSIONS_SCALEUP\n daily_results[(cat1,cat2,'SO2 (tons)')] = vmt_byclass.loc[:,'SOx' ].sum()*RunResults.EMISSIONS_SCALEUP\n\n # http://en.wikipedia.org/wiki/Volatile_organic_compound\n daily_results[(cat1,cat2,'VOC: Acetaldehyde (metric tons)' )] = vmt_byclass.loc[:,'Acetaldehyde' ].sum()*RunResults.EMISSIONS_SCALEUP\n daily_results[(cat1,cat2,'VOC: Benzene (metric tons)' )] = vmt_byclass.loc[:,'Benzene' ].sum()*RunResults.EMISSIONS_SCALEUP\n daily_results[(cat1,cat2,'VOC: 1,3-Butadiene (metric tons)')] = vmt_byclass.loc[:,'Butadiene' ].sum()*RunResults.EMISSIONS_SCALEUP\n daily_results[(cat1,cat2,'VOC: Formaldehyde (metric tons)' )] = vmt_byclass.loc[:,'Formaldehyde' ].sum()*RunResults.EMISSIONS_SCALEUP\n daily_results[(cat1,cat2,'All other VOC (metric tons)' )] = vmt_byclass.loc[:,'ROG'].sum()*RunResults.EMISSIONS_SCALEUP \\\n - daily_results[(cat1,cat2,'VOC: Acetaldehyde (metric tons)' )] \\\n - daily_results[(cat1,cat2,'VOC: Benzene (metric tons)' )] \\\n - daily_results[(cat1,cat2,'VOC: 1,3-Butadiene (metric tons)')] \\\n - daily_results[(cat1,cat2,'VOC: Formaldehyde (metric tons)' )]\n\n ######################################################################################\n cat1 = 'Collisions & Active Transport'\n cat2 = 'Fatalies due to Collisions'\n daily_results[(cat1,cat2,'Motor Vehicle')] = vmt_byclass.loc[:,'Motor Vehicle Fatality'].sum()\n daily_results[(cat1,cat2,'Walk' )] = vmt_byclass.loc[:,'Walk Fatality' ].sum()\n daily_results[(cat1,cat2,'Bike' )] = vmt_byclass.loc[:,'Bike Fatality' ].sum()\n\n cat2 = 'Injuries due to Collisions'\n daily_results[(cat1,cat2,'Motor Vehicle')] = vmt_byclass.loc[:,'Motor Vehicle Injury' ].sum()\n daily_results[(cat1,cat2,'Walk' )] = vmt_byclass.loc[:,'Walk Injury' ].sum()\n daily_results[(cat1,cat2,'Bike' )] = vmt_byclass.loc[:,'Bike Injury' ].sum()\n\n cat2 = 'Property Damage Only (PDO) Collisions'\n daily_results[(cat1,cat2,'Property Damage')] = vmt_byclass.loc[:,'Motor Vehicle Property'].sum()\n\n ######################################################################################\n cat2 = 'Trips with Active Transportation'\n # \"Off-Model Benefits\" -- see\n # J:\\PROJECT\\2013 RTP_SCS\\Performance Assessment\\Project Assessment (Apr 2012)\\B-C Methodology\\Off-Model Benefits Calculator.xlsx\n daily_results[(cat1,cat2,'Walk' )] = nonmot_byclass.loc['Walk','Daily Trips']\n daily_results[(cat1,cat2,'Bike' )] = nonmot_byclass.loc['Bike','Daily Trips']\n daily_results[(cat1,cat2,'Transit')] = transit_byclass.loc[:,'Transit Trips'].sum() # all transit trips\n\n cat2 = 'Avg Minutes Active Tranport per Person'\n daily_results[(cat1,cat2,'Walk' )] = nonmot_byclass.loc['Walk','Total Time (Hours)'] * 60.0 / RunResults.PROJECTED_2040_POPULATION\n daily_results[(cat1,cat2,'Bike' )] = nonmot_byclass.loc['Bike','Total Time (Hours)'] * 60.0 / RunResults.PROJECTED_2040_POPULATION\n daily_results[(cat1,cat2,'Transit')] = (transit_byclass.loc[:,'Walk acc & egr hours'].sum() + \\\n transit_byclass.loc[:,'Aux walk hours'].sum()) * 60.0 / RunResults.PROJECTED_2040_POPULATION\n avg_min_total = daily_results[(cat1,cat2,'Walk' )] + \\\n daily_results[(cat1,cat2,'Bike' )] + \\\n daily_results[(cat1,cat2,'Transit')]\n\n cat2 = 'Active Individuals'\n # (active daily min per bay area person) * (inactive persons) = \n # total active daily min per day _by inactive persons_\n # Divide by active minute per day requirement to get \"active people\"\n # TODO: Why multiply by inactive population?\n # Are we just removing the population that is assumed to be active anyway?\n # Wouldn't subtracting the the base give newly active population?\n daily_results[(cat1,cat2,'Total' )] = avg_min_total * \\\n (RunResults.PERCENT_POP_INACTIVE * RunResults.PROJECTED_2040_POPULATION) / RunResults.ACTIVE_MIN_REQUIREMENT\n\n idx = pd.MultiIndex.from_tuples(daily_results.keys(), \n names=['category1','category2','variable_name'])\n self.daily_results = pd.Series(daily_results, index=idx)\n\n # figure out the small cat2s\n self.lil_cats = {}\n grouped = self.daily_results.groupby(level=['category1','category2'])\n for name,group in grouped:\n if group.sum() < 1000:\n self.lil_cats[name] = 1\n\n # sum to categories\n self.daily_category_results = self.daily_results.sum(level=[0,1])\n print self.daily_category_results","title":""},{"docid":"b95ab8a91ab9213ec24c8f3fc36e7a71","score":"0.5781445","text":"def custom_stats(self):\n return {}","title":""},{"docid":"55e68c912dda72b7d0cce1d91d9f6a18","score":"0.57657784","text":"def _collect_data(self):\n\n raise NotImplementedError(\"Subclasses should implement this!\")","title":""},{"docid":"55e68c912dda72b7d0cce1d91d9f6a18","score":"0.57657784","text":"def _collect_data(self):\n\n raise NotImplementedError(\"Subclasses should implement this!\")","title":""},{"docid":"1af9bb933ebe157d6d2f010220c9f97d","score":"0.57626987","text":"def new_int_metric_dict(self):\n return {METRICS_BACKEND: {self.host: {}},\n METRICS_CONTROLLER: {self.host: {}}}","title":""},{"docid":"bcda9e8c586a4e5d1b3ac717b8d2f9ee","score":"0.57587206","text":"def _prepare_raw_data(self):\n for key, value in self.__class__.__dict__.items():\n if inspect.isclass(value):\n self.raw_data[key] = getattr(self, key)","title":""},{"docid":"b929488884f5eefd93c2ee7fffc1653e","score":"0.5746382","text":"def make_dict(msg_time, class_name): \n \n global class_time_dict\n \n # Adds class and corresponding classtime to dictionary\n class_time_dict.update({class_name: int(msg_time)})\n \n return class_time_dict","title":""},{"docid":"e146186bf4d7d6ad903a4e37f1509713","score":"0.5746343","text":"def get_metrics(self, tool, workload, logs):\n\n stderr = logs[0]['stderr']\n lines = stderr.split('\\n')\n\n # timed duration is the only the one in the \"PASSED\" line\n passed = [l for l in lines if 'PASSED' in l][0]\n # time is the only number in the line\n timed_duration = [n for n in passed.split() if n.isdigit()][0]\n\n # there is a line with \"completed warmup\" for each warmup executed\n warmup_iters = len([l for l in lines if 'completed warmup' in l])\n\n return {\n 'timed_duration': {'value': int(timed_duration), 'unit': 'ms'},\n 'warmup_iters': {'value': int(warmup_iters), 'unit': 'num'}\n }","title":""},{"docid":"8d351a283546267736c9baf7ff05b4d4","score":"0.57460517","text":"def summarize_class_data(data):\n mean = {}\n std = {}\n for label in data:\n n = len(data[label][0])\n mean[label] = []\n std[label] = []\n for i in range(n):\n mean[label].append(np.mean([v[i] for v in data[label]]))\n std[label].append(np.std([v[i] for v in data[label]]))\n mean[label].append(n + 1)\n # std[label].append(n + 1)\n return mean, std","title":""},{"docid":"fcb50cc6e170ea3c669e290b06cbda75","score":"0.5714996","text":"def precompute_statistical_class(cls,\n y: t.Optional[np.ndarray] = None,\n **kwargs) -> t.Dict[str, t.Any]:\n precomp_vals = {}\n\n if y is not None and not {\"classes\", \"class_freqs\"}.issubset(kwargs):\n classes, class_freqs = np.unique(y, return_counts=True)\n\n precomp_vals[\"classes\"] = classes\n precomp_vals[\"class_freqs\"] = class_freqs\n\n return precomp_vals","title":""},{"docid":"10a000bb34f0c35ad8c3af0b00a069fe","score":"0.57134384","text":"def _run_stats(self):\n class_scores_ = {}\n for lang_ in LANGUAGES:\n if lang_ in self.class_occ:\n class_scores_[lang_] = ClassScore(self.class_occ[lang_])\n\n result_list: List[Score] # sorted in reverse order\n for result_list in self.results:\n for count, score in enumerate(result_list):\n first = count == 0\n correct = score.is_correct\n\n if score.guessed_lang in class_scores_:\n if first and correct:\n class_scores_[score.guessed_lang].true_positive += 1\n elif first and not correct:\n class_scores_[score.guessed_lang].false_positive += 1\n elif not first and correct:\n class_scores_[score.guessed_lang].false_negative += 1\n elif not first and not correct:\n class_scores_[score.guessed_lang].true_negative += 1\n\n for lang_ in class_scores_:\n if class_scores_[lang_].true_positive != 0 or class_scores_[lang_].false_positive != 0:\n class_scores_[lang_].precision = class_scores_[lang_].true_positive / \\\n (class_scores_[lang_].true_positive + class_scores_[\n lang_].false_positive)\n\n if class_scores_[lang_].true_positive != 0 or class_scores_[lang_].false_negative != 0:\n class_scores_[lang_].recall = class_scores_[lang_].true_positive \\\n / (class_scores_[lang_].true_positive + class_scores_[lang_]\n .false_negative)\n\n if class_scores_[lang_].precision != 0 or class_scores_[lang_].recall != 0:\n class_scores_[lang_].f1 = 2 * ((class_scores_[lang_].precision * class_scores_[lang_].recall)\n / (class_scores_[lang_].precision + class_scores_[lang_].recall))\n\n self.class_scores = class_scores_\n self.final_macro_f1 = sum([class_scores_[lang_].f1 for lang_ in class_scores_]) / len(class_scores_)\n self.final_weighed_avg_f1 = sum([class_scores_[lang].f1 * class_scores_[lang_].count\n for lang_ in class_scores_]) / self.count\n\n self._output_to_eval_file()","title":""},{"docid":"bd3fd15b6e9f55c40e1ee9c2112141ca","score":"0.57","text":"def get_metrics(self, reset: bool = False) -> Dict[str, float]:\n raise NotImplementedError()","title":""},{"docid":"99d641802df50b6627d148506ddf2b02","score":"0.56737125","text":"def __init__( self ):\n self.summaries = {}","title":""},{"docid":"8112fcff68c1ba843618cdf551c5a60e","score":"0.56588453","text":"def _calculate_stats(self) -> Dict[str, int]:\n return self.statistics","title":""},{"docid":"05411033847bea6b6ea5d31877c4c9f7","score":"0.5650323","text":"def get_metrics(self, matrix):\n assert matrix.shape[0] == matrix.shape[1]\n metrics_dict = {\n \"in_domain\": self.in_domain(matrix),\n \"next_domain\": self.next_domain(matrix),\n \"accuracy\": self.accuracy(matrix),\n \"forward_transfer\": self.forward_transfer(matrix),\n \"backward_transfer\": self.backward_transfer(matrix),\n }\n return metrics_dict","title":""},{"docid":"f81d7383ca1f71d03d9ec52a014938c4","score":"0.56493306","text":"def summarize(count_data):\n class_data = defaultdict(float)\n family_data = defaultdict(lambda : defaultdict(float))\n \n # class level counts\n for class_ in count_data:\n for family in count_data[class_]:\n for elem in count_data[class_][family]:\n count = float(count_data[class_][family][elem])\n class_data[class_] += count\n \n # family level counts\n for class_ in count_data:\n for family in count_data[class_]:\n for elem in count_data[class_][family]:\n count = float(count_data[class_][family][elem])\n family_data[class_][family] += count\n \n return class_data, family_data","title":""},{"docid":"a60422a1ba1956231a948a688e9f6a0d","score":"0.5649169","text":"async def metrics(self):\n\n metrics = {}\n\n # Count of known domains (in our state db)...\n metrics[\"total_domains\"] = len(await Domain.objects.all())\n\n # Count of hosted/registered domains (on Marcaria, Namecheap, IONOS)...\n metrics[\"registrar_registered_domains\"] = len(await self.gather_registered_domains())\n\n # Count of domains hosted via DNS (on Route53, CloudFlare etc)...\n metrics[\"dns_hosted_domains\"] = len(await self.gather_hosted_domains())\n\n # TODO: Statuscheck metrics breakdown\n\n # Summary count of active agents\n metrics[\"agent_counts\"] = {\n \"registrar\": len(self.registrar_agents),\n \"dns\": len(self.dns_agents),\n \"hosting\": len(self.hosting_agents),\n \"waf\": len(self.waf_agents)\n }\n\n return metrics","title":""},{"docid":"05dab1af4998c01300b2132be7ccfdf1","score":"0.5640886","text":"def _dict_rep(self):\n props = get_class_properties(self.__class__)\n out = {k: getattr(self, k) for k in props}\n out['group'] = self.name\n return out","title":""},{"docid":"fc92ddcdb6a4017c0e6195da92957bb3","score":"0.5634014","text":"def compute_validation_metrics(self, step_id: StepID) -> Dict[str, Any]:\n pass","title":""},{"docid":"7150897739d4f3601f233ab4938cd567","score":"0.5633559","text":"def calc_global_metrics(metrics_per_class, decimal_digits=2):\n\n metrics_global = {}\n\n # macro averaging - consider only classes with elements\n N_P_nonzero_classes = np.count_nonzero(metrics_per_class['N_P']) # number of classes with non zero predicted positive elements\n N_GT = metrics_per_class['N_GT'] # number of classes with ground truth positive elements\n N_GT_nonzero_classes = np.count_nonzero(N_GT) # number of classes with non zero ground truth positive elements\n ind_N_GT_nonzero = np.nonzero(N_GT)\n metrics_global['macro'] = {}\n with np.errstate(all='ignore'):\n metrics_global['macro']['recall'] = np.nan_to_num(metrics_per_class['recall'][ind_N_GT_nonzero].sum() / N_GT_nonzero_classes) if N_GT_nonzero_classes > 0 else 1.\n metrics_global['macro']['precision'] = np.nan_to_num(metrics_per_class['precision'].sum() / N_P_nonzero_classes) # FIXME: should we divide by N_P_nonzero_classes or N_GT_nonzero_classes ?\n metrics_global['macro']['fdr'] = np.nan_to_num(metrics_per_class['fdr'].sum() / N_P_nonzero_classes) # FIXME: should we divide by N_P_nonzero_classes or N_GT_nonzero_classes ?\n metrics_global['macro']['miss_detection_rate'] = np.nan_to_num(metrics_per_class['miss_detection_rate'].sum() / N_GT_nonzero_classes)\n metrics_global['macro']['false_detection_rate_vs_gt'] = np.nan_to_num(metrics_per_class['false_detection_rate_vs_gt'].sum() / N_GT_nonzero_classes)\n metrics_global['macro']['false_detection_rate_vs_pred'] = np.nan_to_num(metrics_per_class['false_detection_rate_vs_pred'].sum() / N_P_nonzero_classes) # FIXME: should we divide by N_P_nonzero_classes or N_GT_nonzero_classes ?\n metrics_global['macro']['N_GT_nonzero_classes'] = np.nan_to_num(N_GT_nonzero_classes) # FIXME: should we divide by N_P_nonzero_classes or N_GT_nonzero_classes ?\n metrics_global['macro']['N_P_nonzero_classes'] = np.nan_to_num(N_P_nonzero_classes) # FIXME: should we divide by N_P_nonzero_classes or N_GT_nonzero_classes ?\n\n # micro averaging\n TP = metrics_per_class['TP'].sum()\n FP = metrics_per_class['FP'].sum()\n FN = metrics_per_class['FN'].sum()\n miss_detection = metrics_per_class['miss_detection'].sum()\n false_detection = metrics_per_class['false_detection'].sum()\n N_P = metrics_per_class['N_P_total']\n N_GT = metrics_per_class['N_GT_total']\n\n with np.errstate(all='ignore'):\n recall = np.nan_to_num(TP / N_GT) if N_GT > 0 else 1. # set recall of classes without GT to 1 (since recall = 0/0 we can chose either 0 or 1).\n precision = np.nan_to_num(TP / N_P)\n fdr = np.nan_to_num(FP / N_P)\n miss_detection_rate = np.nan_to_num(miss_detection / N_GT)\n false_detection_rate_vs_gt = np.nan_to_num(false_detection / N_GT)\n false_detection_rate_vs_pred = np.nan_to_num(false_detection / N_P)\n\n metrics_global['micro'] = {}\n metrics_global['micro']['TP'] = TP\n metrics_global['micro']['FP'] = FP\n metrics_global['micro']['FN'] = FN\n metrics_global['micro']['miss_detection'] = miss_detection\n metrics_global['micro']['false_detection'] = false_detection\n metrics_global['micro']['N_P'] = N_P\n metrics_global['micro']['N_GT'] = N_GT\n metrics_global['micro']['recall'] = recall\n metrics_global['micro']['precision'] = precision\n metrics_global['micro']['fdr'] = fdr\n metrics_global['micro']['miss_detection_rate'] = miss_detection_rate\n metrics_global['micro']['false_detection_rate_vs_gt'] = false_detection_rate_vs_gt\n metrics_global['micro']['false_detection_rate_vs_pred'] = false_detection_rate_vs_pred\n\n # round metrics\n if decimal_digits is not None:\n metrics_global['macro'] = {key: np.around(val, decimal_digits) for key, val in metrics_global['macro'].items()}\n metrics_global['micro'] = {key: np.around(val, decimal_digits) for key, val in metrics_global['micro'].items()}\n\n return metrics_global","title":""},{"docid":"33218901119116f40c200d7cc6eef637","score":"0.561803","text":"def get_metrics(self):\n return self.metrics","title":""},{"docid":"4dffe3214ab2594aa8b2651297587c66","score":"0.5611316","text":"def as_dict(self):\n return {\n \"@module\": self.__class__.__module__,\n \"@class\": self.__class__.__name__,\n \"params\": {\"mean\": self.mean.tolist(), \"std\": self.std.tolist()},\n }","title":""},{"docid":"ff6a252c4152c585d98d0e52de1de9c6","score":"0.5609094","text":"def meta(self) -> dict[str, Any]:\n return {\n \"name\": self.__class__.__name__,\n \"objective_weights\": self._objective_weights,\n }","title":""},{"docid":"34cf9e551a839cf2a6d3c0f4628a5444","score":"0.5600248","text":"def __init__(self):\r\n self.meter_items: Dict[str, MeterItem] = dict()","title":""},{"docid":"ca471cb4bbea5d97f7ea507470e88c1d","score":"0.5598264","text":"def _add_metrics_to_meta(self, fold_i, trainer):\n result = {\n 'name': self.name,\n 'fold': fold_i\n }\n for name, obj in trainer.train_metrics.items():\n result['train_' + name] = obj.get_last_epoch_mean()\n result['train_' + name + '_all'] = obj.get_all_epoch_means().tolist()\n for name, obj in trainer.val_metrics.items():\n result['val_' + name] = obj.get_last_epoch_mean()\n result['val_' + name + '_all'] = obj.get_all_epoch_means().tolist()\n self.meta['results'].append(result)\n\n # save to metadata\n with open(os.path.join(self.experiment_dir, 'metadata.json'), 'w') as f:\n json.dump(self.meta, f)","title":""},{"docid":"d032150ee541dac46d61ef9d31388600","score":"0.55967885","text":"def result(\n metrics: Dict[metric_types.MetricKey, Any]\n ) -> Dict[metric_types.MetricKey, float]:\n class_weights_from_labels = metrics[class_weights_from_labels_key]\n total_value = 0.0\n total_weight = 0.0\n for sub_key in sub_keys:\n child_key = metric_types.MetricKey(\n name=metric_name,\n model_name=model_name,\n output_name=output_name,\n sub_key=sub_key,\n example_weighted=example_weighted)\n if child_key not in metrics:\n # Use private name if not found under metric name\n child_key = metric_types.MetricKey(\n name='_' + metric_name,\n model_name=model_name,\n output_name=output_name,\n sub_key=sub_key,\n example_weighted=example_weighted)\n weight = 1.0 if not class_weights else 0.0\n offset = None\n if (child_key.sub_key is not None and\n child_key.sub_key.class_id is not None):\n offset = child_key.sub_key.class_id\n elif child_key.sub_key is not None and child_key.sub_key.k is not None:\n offset = child_key.sub_key.k\n if offset is not None:\n if (class_weights_from_labels and\n child_key.sub_key.class_id in class_weights_from_labels):\n weight = class_weights_from_labels[offset]\n if class_weights and child_key.sub_key.class_id in class_weights:\n weight *= class_weights[offset]\n total_value += _to_float(metrics[child_key]) * weight\n total_weight += weight\n average = total_value / total_weight if total_weight else float('nan')\n return {key: average}","title":""},{"docid":"11290a9f9b5b9dcef944213fbf30a96a","score":"0.55930245","text":"def grasp_metrics(grasps, data):\n grasp_metrics = {}\n for grasp in grasps:\n grasp_id = grasp.id\n grasp_key = GRASP_KEY + '_' + str(grasp_id)\n grasp_metrics[grasp_id] = {}\n if grasp_key in list(data.keys()):\n grasp_metric_data = data[grasp_key][GRASP_METRICS_KEY]\n for metric_name in list(grasp_metric_data.attrs.keys()):\n grasp_metrics[grasp_id][metric_name] = grasp_metric_data.attrs[metric_name]\n return grasp_metrics","title":""},{"docid":"f7a81f6af7e24fe3eadcdf43e8f583a6","score":"0.5584385","text":"def statistics(self, quickly_collected_only: bool = False) -> Dict[str, object]:","title":""},{"docid":"52d37edcfc4f29e1c06ba10ca9ea713e","score":"0.5582157","text":"def get_performance_metrics(self, data, labels):\n metrics = {}\n if self.__is_trained == True and self.__data_processor.check_dataset_validity(data, labels) == True:\n filled_labels = labels.copy().fillna(labels.mean())\n for index, model in enumerate(self.__models):\n metrics[(self.__parameters['model']['metric'] + '_' + str(index))] = np.sqrt(\n mean_squared_error(self.predict_data(model, data).predicted_mean[-(data.shape[0]):].interpolate().fillna(0), filled_labels)\n )\n return metrics","title":""},{"docid":"9a38f41a1665bd966373f73b1783444f","score":"0.557402","text":"def metrics(self, X, y):\n check_is_fitted(self, \"is_fitted_\")\n X, y = check_X_y(X, y, accept_sparse=False)\n\n self.__alternatives_ = [\n c for c in sorted(np.unique(y)) if c != self.target_class\n ]\n\n CSPS_ = {}\n for class_ in self.__alternatives_:\n mask = y == class_\n CSPS_[class_] = 1.0 - np.sum(\n self.__model_.predict(X[mask])\n ) / np.sum(mask)\n\n mask = y != self.target_class\n if np.sum(mask) == 0:\n # Testing on nothing but the target class, can't evaluate TSPS\n TSPS_ = np.nan\n else:\n TSPS_ = 1.0 - np.sum(self.__model_.predict(X[mask])) / np.sum(mask)\n\n mask = y == self.target_class\n if np.sum(mask) == 0:\n # Testing on nothing but alternative classes, can't evaluate TSNS\n TSNS_ = np.nan\n else:\n TSNS_ = np.sum(self.__model_.predict(X[mask])) / np.sum(\n mask\n ) # TSNS = CSNS for SIMCA\n\n if np.isnan(TSNS_):\n TEFF_ = TSPS_\n elif np.isnan(TSPS_):\n TEFF_ = TSNS_\n else:\n TEFF_ = np.sqrt(TSNS_ * TSPS_)\n\n metrics = {\n \"teff\": TEFF_,\n \"tsns\": TSNS_,\n \"tsps\": TSPS_,\n \"csps\": CSPS_,\n }\n return metrics","title":""},{"docid":"fdbb3cf1758032452cc07b4b2d9ab2d6","score":"0.55587864","text":"def init_app_metrics(self, app):\n if app not in self.metrics:\n with self.metrics_lock:\n self.metrics[app] = {\n 'GAUGE': collections.defaultdict(_gauge_pair),\n 'COUNTER': collections.defaultdict(int),\n 'PERCENTILE': collections.defaultdict(list)\n }","title":""},{"docid":"f796696c43679dbe302f24327b3c7534","score":"0.5551965","text":"def _get_class_dict(self):\n with open(self._opts._ecfile, \"r\") as ec_fobj:\n for line in ec_fobj:\n fields = line.strip().split()\n if fields[1].endswith('.csv'): #TODO delete this when error is fixed\n fields[1] = fields[1].rstrip('.csv')\n\n if self._opts._labels == 'EC':\n self.class_dict[fields[1]] = {'id': len(self.class_dict),\n 'size': int(fields[0]),\n }\n if self._opts._labels == 'GO':\n self.class_dict[fields[1].split('_')[1]] = {'id': len(self.class_dict),\n 'size': int(fields[0]),\n }","title":""},{"docid":"32947c4acc4fc99cd3e0fae9aa5351a3","score":"0.5548595","text":"def create_summary_metrics(self):\n # create keras summary objects for loss\n self.summary_metrics = {}\n self.summary_metrics[\"train_loss_umap\"] = tf.keras.metrics.Mean(\n \"train_loss_umap\", dtype=tf.float32\n )\n if self.decoding_method in [\"autoencoder\", \"network\"]:\n self.summary_metrics[\"train_loss_recon\"] = tf.keras.metrics.Mean(\n \"train_loss_recon\", dtype=tf.float32\n )\n if self.valid_X is not None:\n self.summary_metrics[\"valid_loss_recon\"] = tf.keras.metrics.Mean(\n \"valid_loss_recon\", dtype=tf.float32\n )\n\n if self.train_classifier:\n self.summary_metrics[\"train_loss_classif\"] = tf.keras.metrics.Mean(\n \"train_loss_classif\", dtype=tf.float32\n )\n self.summary_metrics[\"valid_loss_classif\"] = tf.keras.metrics.Mean(\n \"valid_loss_classif\", dtype=tf.float32\n )\n self.summary_metrics[\"train_acc_classif\"] = tf.keras.metrics.Mean(\n \"train_acc_classif\", dtype=tf.float32\n )\n self.summary_metrics[\"valid_acc_classif\"] = tf.keras.metrics.Mean(\n \"valid_acc_classif\", dtype=tf.float32\n )","title":""},{"docid":"3c9cda2a3f65a3600b5339b3c6a4eff6","score":"0.5544639","text":"def _collect_stat(self):\n train_stats = {}\n val_stats = {}\n deviation_stats = {}\n # collect stats\n grouping = self.data.groupby(\"type\")\n train_group = grouping.get_group(\"train\")\n val_group = grouping.get_group(\"val\")\n train_label_fractions = train_group.value_counts(\"label\") / train_group.shape[0]\n val_label_fractions = val_group.value_counts(\"label\") / val_group.shape[0]\n for key, value in val_label_fractions.items():\n val_stats[\"label_fraction_\"+key] = value\n for key, value in train_label_fractions.items():\n train_stats[\"label_fraction_\"+key] = value\n val_stats[\"area_min\"] = val_group[\"area\"].min()\n val_stats[\"area_max\"] = val_group[\"area\"].max()\n val_stats[\"area_avg\"] = val_group[\"area\"].mean()\n val_stats[\"area_std\"] = val_group[\"area\"].std()\n train_stats[\"area_min\"] = train_group[\"area\"].min()\n train_stats[\"area_max\"] = train_group[\"area\"].max()\n train_stats[\"area_avg\"] = train_group[\"area\"].mean()\n train_stats[\"area_std\"] = train_group[\"area\"].std()\n\n # calculated deviations dict\n for key, value in train_stats.items():\n deviation_stats[key] = 1-val_stats[key]/value\n # convert the dicts to dfs\n train_stats = pd.DataFrame(train_stats, index=[\"train\"])\n val_stats = pd.DataFrame(val_stats, index=[\"val\"])\n deviation_stats = pd.DataFrame(deviation_stats, index=[\"deviation (1-val/train)\"])\n data = pd.concat([train_stats, val_stats, deviation_stats]).T\n data = data.round(2)\n data = data.reset_index()\n data[\"index\"] = data[\"index\"].apply(lambda x: x.replace(\"_\", \" \").title())\n return data","title":""},{"docid":"f65f9868e47911011f832a45f1db7f00","score":"0.554401","text":"def __init__(self, data=None, k=1, metric='cosine', n=5):\n self.k = k\n self.n = n\n self.username2id = {}\n self.userid2name = {}\n self.productid2name = {}\n # for some reason I want to save the name of the metric\n self.metric = metric\n if self.metric == 'cosine':\n self.fn = self.cosine\n #\n # if data is dictionary set recommender data to it\n #\n if type(data).__name__ == 'dict':\n self.data = data","title":""},{"docid":"c05cdcc434059b7281802e96b5c8459c","score":"0.5539755","text":"def build_metrics(self) -> None:\n self.metric_functions = define_metric_functions(self.metric_names)","title":""},{"docid":"94f4f74fe26d47dacbaab7e9991e01f6","score":"0.55359995","text":"def to_dict(self):\n mydict = {}\n for attr in dir(self):\n try:\n is_callable = callable(getattr(BaseDataSet, attr))\n except AttributeError:\n is_callable = False\n if attr.startswith(\"__\") or is_callable:\n continue\n try:\n val = getattr(self, attr)\n mydict[attr] = val\n except AttributeError:\n pass\n return mydict","title":""},{"docid":"81c8290b730ba3e2df0c74f38f907034","score":"0.5530914","text":"def report_metrics(self) -> Dict[str, Any]:\n metrics = self._module_metrics()\n for child_module in self.modules():\n if isinstance(child_module, ModuleWithMetrics):\n child_metrics = child_module._module_metrics()\n if len(child_metrics) > 0:\n metrics.update(child_metrics)\n return metrics","title":""},{"docid":"e009767d9eb2932866e5be6b2b679a36","score":"0.5524761","text":"def _record_prof_data(cls, class_name: str, prof_path: str, data: any):\n\n full_path = f\"{class_name}{Cache.CACHE_PATH_SEP}{prof_path}{Cache.CACHE_PATH_SEP}values\"\n prof_dict = cls.prof.get_path_dict(\n path=full_path, create=True,\n none_when_missing=False\n )\n\n prof_dict[\"values\"] = prof_dict.get(\"values\", [ ].copy()) + [ data ]","title":""},{"docid":"119de258f14937b3727d93e484e7f00c","score":"0.5524322","text":"def _get_summary_dicts(self, data_loader, split, dataset_name,\n keys, probs, slide_ids, loss_meter, w_loss_meter):\n metrics, curves = {}, {}\n\n probs = torch.cat(probs)\n keys = torch.cat(keys)\n data_subset = f'{dataset_name}-{split}'\n\n # Load the dataset classes, and the classes the model outputs values for.\n original_tasks = list(data_loader.dataset.original_tasks)\n tasks = list(data_loader.dataset.target_tasks)\n\n #competition_tasks = list(TASK_SEQUENCES['competition'])\n for i in range(0, keys.shape[1]):\n\n # Don't evaluate on missing categories\n #if tasks[i] not in original_tasks:\n # continue\n\n task_keys = keys[:, i]\n task_probs = probs[:, i]\n task_name = tasks[i]\n\n\n task_keys, task_probs = np.array(task_keys.cpu()), np.array(task_probs.cpu())\n task_preds = (task_probs > 0.5)\n\n '''\n if (self.rad_perf is not None\n and split in ['valid', 'test']\n and dataset_name == 'stanford'):\n eval_metrics += ['rads_below_ROC', 'rads_below_PR']\n '''\n\n for curve in ['PRC', 'ROC']:\n self._update_metric(curves, curve, task_name, task_keys,\n task_probs, task_preds, data_subset, slide_ids)\n\n eval_metrics = ['AUPRC', 'AUROC', 'log_loss', 'accuracy', 'precision', 'recall']\n\n #TODO: Replace this with command line arg?\n if len(slide_ids) > 0:\n eval_metrics.append('collate_accuracy')\n\n for metric in eval_metrics:\n self._update_metric(metrics, metric, task_name, task_keys,\n task_probs, task_preds, data_subset, slide_ids=slide_ids)\n\n\n metrics.update({\n data_subset + '_' + 'loss': loss_meter.avg,\n data_subset + '_' + 'weighted_loss': w_loss_meter.avg\n })\n\n\n return metrics, curves","title":""},{"docid":"6cc11aa232d4ca8520eaf2775df1e0bf","score":"0.5523234","text":"def _instrument_dict(self, cls, obj: Any, with_class_info: bool = False):\n\n return jsonify","title":""},{"docid":"f809e0ea9e1fe1a98e56751415584dd2","score":"0.5523008","text":"def __init__(self, data):\n self._total_time = {}\n # precompute total time to have time callback in O(1)\n for from_node in xrange(data.num_locations):\n self._total_time[from_node] = {}\n for to_node in xrange(data.num_locations):\n if from_node == to_node:\n self._total_time[from_node][to_node] = 0\n else:\n self._total_time[from_node][to_node] = int(\n self.service_time(data, from_node) +\n self.travel_time(data, from_node, to_node))","title":""},{"docid":"992ee311f39d14e676ed27e76aebe8bb","score":"0.5521787","text":"def compute_metrics(self, results) -> dict:\n results = dict()\n for key in ['eqt_int', 'eqt_frac', 'eqr']:\n if key not in self.fake_results:\n continue\n sums = torch.stack(self.fake_results[key], dim=0)\n mses = (sums[0::2] / sums[1::2]).mean()\n psnrs = np.log10(2) * 20 - mses.log10() * 10\n psnrs = psnrs.cpu().numpy()\n results[key] = psnrs\n return results","title":""},{"docid":"c32a7ecc8c534e1392153391550f2738","score":"0.55208254","text":"def stats(self) -> Dict[str, int]:\n\n return {\n \"data\": self._log.stats(),\n \"metadata\": self._metalog.stats()\n }","title":""},{"docid":"e2359d07a4b830ce4064021c7e4e3c6a","score":"0.5519282","text":"def __init__(self, metrics_map, include_val_metrics=False):\n self.metrics_map = metrics_map\n self.include_val_metrics = include_val_metrics\n self.metrics_history = self.__createMetricsHistory()","title":""},{"docid":"270b4686790675143fa5c2ebecf21060","score":"0.55147254","text":"def calculate_data_stats(self, data):\n self.stats_obj = stats.describe(data)\n\n # stats_obj is a NamedTuple type\n for key, val in self.stats_obj._asdict().items():\n print(key + \":\")\n pprint.pprint(val)","title":""},{"docid":"fa6b31b7afec073347920f3e926bbca3","score":"0.551372","text":"def as_dict(self) -> dict:\n return dataclasses.asdict(self)","title":""},{"docid":"d212f8d9338162431ab965485921f07d","score":"0.5513227","text":"def metrics(self) -> List[Metric]:\n pass","title":""},{"docid":"216e844ddb17e68363d8354a3144fcd5","score":"0.5505174","text":"def get_basic_metrics(self):\n basic_stats = dict(\n total_read=self.total_read,\n total_base=self.total_base,\n mean_length=self.mean_length,\n q20=self.q20,\n gc_content=self.gc_content\n )\n return basic_stats","title":""},{"docid":"8c81e923c07b0e2e416cfe820fadc7ee","score":"0.5504363","text":"def __start_metrics(self):\n self.global_classification_metrics = []\n self.partial_classification_metrics = []\n\n if self.task_type in ['classification']:\n for i in range(self.n_classifiers):\n self.global_classification_metrics.append(ClassificationMeasurements())\n self.partial_classification_metrics.append(WindowClassificationMeasurements(window_size=self.n_wait))\n\n elif self.task_type in ['multi_output']:\n for i in range(self.n_classifiers):\n self.global_classification_metrics.append(MultiOutputMeasurements())\n self.partial_classification_metrics.append(WindowMultiOutputMeasurements(window_size=self.n_wait))\n\n elif self.task_type in ['regression']:\n for i in range(self.n_classifiers):\n self.global_classification_metrics.append(RegressionMeasurements())\n self.partial_classification_metrics.append(WindowRegressionMeasurements(window_size=self.n_wait))","title":""},{"docid":"38c74ab0f37b1f8edb130cd7004e19f8","score":"0.55036974","text":"def get_prometheus_data():\n prometheus_data = {}\n prometheus_validity = {}\n prometheus_validity['failed_accounts'] = 0\n prometheus_validity['total_accounts'] = 0\n prometheus_validity['total_checks'] = 0\n for user in prometheus_credentials:\n alerting_servers = {}\n down_servers = []\n queries = {}\n # Node cpu is a number of cpu cycles so we need to look at the rate\n # to calculate the cpu usage percentage, we previously used the last 1\n # minute but have changed it to 10 to avoid missing data. If having\n # the very latest data is crucial we could get the last minute and the\n # last 10 minutes and then use the last minute where possible, for now this\n # doesn't seem necessary\n # We are using irate over rate as it seems more suitable for the speed\n # cpu usage will be changing at:\n # https://prometheus.io/docs/prometheus/latest/querying/functions/\n queries['cpu'] = '(1 - ((avg(irate(node_cpu_seconds_total{cpu_mode}[10m])) by (instance{labels_to_filter_based_on})) or (avg(irate(node_cpu{cpu_mode}[10m])) by (instance{labels_to_filter_based_on})))) * 100'\n queries['memory'] = '(1 - ((node_memory_MemAvailable_bytes or node_memory_MemAvailable) / (node_memory_MemTotal_bytes or node_memory_MemTotal))) * 100'\n # We want all data for each instance\n # We are only interested in the disk with greatest disk io\n # We are calculating disk io for each disk in the same way as cpu\n # The /10 is to convert to seconds (/1000) and then to a percentage\n # (*1000)\n queries['disk_io'] = '(((max(avg(irate(node_disk_io_time_seconds_total[10m]) * 1000) by (instance{labels_to_filter_based_on}, device)) by (instance{labels_to_filter_based_on}))) or ((max(avg(irate(node_disk_io_time_ms[10m])) by (instance{labels_to_filter_based_on}, device)) by (instance{labels_to_filter_based_on})))) /10'\n # We need want to exclude temporary file systems, docker and rootfs as it\n # is reported as well as the device that it is mounted on\n # Including just ext4 and vfat covers all the systems we currently want\n # but could easily be wrong in the future or in a different enviroment\n # I'm going to test it like this and consider what a good exclude line\n # would be or how we want to be notified of unexpected data\n # This is configured in the config file as different set-ups may\n # have different filesystems to monitor. If no argument is supplied\n # all of the disks will be monitored for each instance.\n queries['fullest_disk'] = 'max((((node_filesystem_size_bytes{fullest_disk_sub} or node_filesystem_size{fullest_disk_sub}) - (node_filesystem_free_bytes{fullest_disk_sub} or node_filesystem_free{fullest_disk_sub})) / (node_filesystem_size_bytes{fullest_disk_sub} or node_filesystem_size{fullest_disk_sub})) * 100) by (instance{labels_to_filter_based_on})'\n\n # We will either sub in a blank string or the intermittent_tag\n # depending on if one is present\n labels_to_filter_based_on = ''\n if 'intermittent_tag' in prometheus_credentials[user]:\n labels_to_filter_based_on = ', {}'.format(prometheus_credentials[user]['intermittent_tag'])\n\n for query in queries:\n # CPU mode it necessary to avoid it being counted as a key\n # fullest_disk_sub needs to be substituted here or after\n # or it will be counted as a key\n # We are trying to substitute the intermittent_tag into\n # every group by in order to keep the tag\n queries[query] = queries[query].format(labels_to_filter_based_on = labels_to_filter_based_on, cpu_mode = '{mode=\"idle\"}', fullest_disk_sub = prometheus_credentials[user].get('fullest_disk_tags', ''))\n\n try:\n alerting_servers, down_servers = get_alerting_servers(user)\n except Exception as e:\n logger.error('The following error occured whilst getting the list of alerting servers for {}: {}'.format(user, e))\n\n #logger.error('Down servers:{}\\nAlerting servers{}'.format(down_servers, alerting_servers), 'info')\n\n prometheus_data[user] = {}\n prometheus_validity['total_accounts'] += 1\n responses = {}\n for query in queries:\n try:\n metrics_response = requests.get(\n '{}/api/v1/query'.format(prometheus_credentials[user]['url']),\n auth=HTTPBasicAuth(prometheus_credentials[user]['username'],\n prometheus_credentials[user]['password']),\n params={'query': queries[query]},\n timeout=prometheus_credentials[user]['timeout'])\n metrics_response.raise_for_status()\n except requests.exceptions.RequestException as e:\n logger.error('Could not get prometheus data for {}: Error: {}'.format(user, e))\n continue\n\n responses[query] = json.loads(metrics_response.text)\n\n #logger.error('Responses: {}'.format(responses), 'info')\n if len(responses) < len(queries):\n prometheus_validity['failed_accounts'] += 1\n continue\n\n #logger.error('Looping through responses', 'info')\n for metric in responses:\n if responses[metric]['status'] != \"success\":\n continue\n\n for instance_data in responses[metric]['data']['result']:\n hostname = instance_data['metric']['instance']\n if hostname not in prometheus_data[user]:\n prometheus_validity['total_checks'] += 1\n prometheus_data[user][hostname] = {}\n # Strip the port number if it exists from the hostname\n prometheus_data[user][hostname]['name'] = re.match('(^.*?)(:\\d{1,5})?$', hostname).group(1)\n prometheus_data[user][hostname]['summary'] = {}\n\n if 'intermittent_tag' in prometheus_credentials[user]:\n if instance_data['metric'].get(prometheus_credentials[user]['intermittent_tag']) in prometheus_credentials[user].get('intermittent_true_values', []):\n prometheus_data[user][hostname]['volatile'] = True\n\n prometheus_data[user][hostname]['summary'][metric] = float(instance_data['value'][1])\n\n to_remove = []\n #logger.error('Setting health status and orderby for servers: {}'.format(prometheus_data[user]), 'info')\n for host in prometheus_data[user]:\n # Check if the server is alerting and set the health status\n health_status = 'green'\n if host in alerting_servers:\n alert_level = max(alerting_servers[host])\n if alert_level == 1:\n health_status = 'orange'\n if alert_level == 2:\n health_status = 'red'\n\n prometheus_data[user][host]['health_status'] = health_status\n # Calculate the order by. If the server isn't reporting every\n # metric flag it as unreporting and log the issue.\n metrics = []\n values = []\n for metric in prometheus_data[user][host]['summary']:\n metrics.append(metric)\n values.append(prometheus_data[user][host]['summary'][metric])\n\n if len(values) != len(queries):\n # Mark servers to be removed so we don't encounter a\n # run time error due to removing items from a dictionary\n # we are looping through\n to_remove.append(host)\n # If a server is transient this is INFO otherwise WARN\n if prometheus_data[user][host].get('volatile') == True:\n logger.info('Volatile host {} only returned data for the following metrics {}'.format(host, metrics))\n else:\n logger.warning('{} only returned data for the following metrics {}'.format(host, metrics))\n else:\n prometheus_data[user][host]['orderby'] = max(values)\n\n #logger.error('Removing unreporting servers: {}'.format(to_remove), 'info')\n for server_to_remove in to_remove:\n del prometheus_data[user][server_to_remove]\n\n for host in down_servers:\n if host not in prometheus_data[user]:\n prometheus_validity['total_checks'] += 1\n prometheus_data[user][host] = {}\n prometheus_data[user][host]['name'] = host.rstrip(':9100')\n prometheus_data[user][host]['summary'] = {}\n\n prometheus_data[user][host]['health_status'] = 'blue'\n prometheus_data[user][host]['orderby'] = 0\n\n # Data should be considerd stale after 5 minutes\n prometheus_validity['valid_until'] = time.time() * 1000 + 300000\n return prometheus_data, prometheus_validity","title":""},{"docid":"9cded0b70ae673af040136c4298db57b","score":"0.5501371","text":"def _get_class_dict(self):\n with open(self._opts._ecfile, \"r\") as ec_fobj:\n for line in ec_fobj:\n fields = line.strip().split()\n if fields[1].endswith('.csv'): \n fields[1] = fields[1].rstrip('.csv')\n\n if self._opts._labels == 'EC':\n self.class_dict[fields[1]] = {'id': len(self.class_dict),\n 'size': int(fields[0]),\n }\n if self._opts._labels == 'GO':\n self.class_dict[fields[1].split('_')[1]] = {'id': len(self.class_dict),\n 'size': int(fields[0]),\n }\n\n # get a reverse dict:\n for key in self.class_dict.keys():\n self.id_to_class[self.class_dict[key]['id']] = key","title":""},{"docid":"a571171799d69f23a6ea26cd979ae633","score":"0.549581","text":"def to_dict(self, cls):\n return {\n 'id': self.id,\n 'cloud': self.cloud,\n 'type': _type_name(cls),\n }","title":""},{"docid":"a14a096fed028a92bc937b0d046a7376","score":"0.5495606","text":"def get_stats(self, obj):\n stats = {}\n stats['severity'] = dict(obj.get_vuls_sev_count())\n stats['cwe'] = dict(obj.get_vuls_cwe_count())\n stats['owasp'] = dict(obj.get_vuls_owasp_count())\n stats['true_positive_count'] = obj.get_true_positive_vuls_count()\n stats['false_positive_count'] = obj.get_false_positive_vuls_count()\n stats['vuls_count'] = obj.get_vuls_count()\n return stats","title":""},{"docid":"3e4efebc885a79324b15b96aafcf4b9a","score":"0.5493421","text":"def create_counts(cls, dataset):\n prior_counts = defaultdict(int)\n # Triple nested dictionary. Key of first dict contains attribute names, second dict\n # contains attribute values as keys, third dict contains class names as keys\n posterior_counts = defaultdict(lambda: defaultdict(lambda: defaultdict(int)))\n # Structure to store how many missing values exist for each attribute. A key refers\n # to the attribute name, second key refers to class name\n missing_counts = defaultdict(lambda: defaultdict(int))\n\n # Add prior counts to default dict\n for row in dataset.table:\n # Assuming last column is the class\n prior_counts[row[-1]] += 1\n # Add posterior counts to data structure\n for row in dataset.table:\n row_index = 0\n for attribute in row[:-1]:\n # If attribute == ?, then add to missing counts dict\n if attribute == '?':\n missing_counts[row_index][row[-1]] += 1\n else:\n # Initialise all the dictionaries of each possible attribute value\n # to contain all possible classes. Note missing values will not contribute\n # to the counts and are treated as a separate count that will not be used\n for key, value in prior_counts.items():\n posterior_counts[row_index][attribute][key]\n row_index += 1\n # Now add the counts\n row_index = 0\n for attribute in row[:-1]:\n # Skip adding missing values\n if attribute == '?':\n row_index += 1\n else:\n # Add to the counts\n posterior_counts[row_index][attribute][row[-1]] += 1\n row_index += 1\n\n return prior_counts, posterior_counts, missing_counts","title":""},{"docid":"0e2c8486f687f51d181609e20eaf51f1","score":"0.54889596","text":"def __init__(self):\n self.__samples_counter = 0\n self.__metric_accumulator = 0.0","title":""},{"docid":"5f1533330e583465d252dedf5ea069d8","score":"0.5488704","text":"def merge_metrics(self):\n\n self.metrics.extend(self._metrics.values())\n del self._metrics","title":""},{"docid":"962dedde064cffd5b66d6590710823d3","score":"0.54857165","text":"def to_dict(self) -> dict:\n attack_attrs = {} if not self.attack else \\\n {'max_iter': self.attack.max_iter}\n return {\n 'dataset': self.config.dataset,\n 'dataset_name': utility.name_only(self.config.dataset),\n 'n_records': self.n_records,\n 'n_attributes': self.n_attr,\n 'attrs': self.attrs,\n 'immutable': self.mask_cols,\n 'attr_mutable': self.cls.mutable,\n 'attr_immutable': self.cls.immutable,\n 'robust': self.config.robust,\n 'classes': self.cls.class_names,\n 'k_folds': self.config.folds,\n 'validator': self.config.validator,\n 'classifier': self.config.cls,\n 'sample_size': self.config.sample_size,\n 'sample_times': self.config.sample_times,\n 'attack': self.config.attack,\n 'attr_ranges': dict(\n zip(self.attrs, self.attr_ranges.values())),\n 'start': self.start_time,\n 'end': self.end_time,\n 'current_utc': int(time.time_ns()),\n **attack_attrs,\n **self.stats.__dict__,\n 'machine': machine.machine_details()\n }","title":""},{"docid":"869be8edc977bd0d90e95961841e393c","score":"0.54809195","text":"def metrics(self, metrics):\n\n self._metrics = metrics","title":""},{"docid":"91b835368569072aac2f3436aee608a3","score":"0.5479385","text":"def collect(self): # pragma: NO COVER\n # Make a shallow copy of self._view_name_to_data_map, to avoid seeing\n # concurrent modifications when iterating through the dictionary.\n metrics_map = {}\n for v_name, view_data in self._view_name_to_data_map.copy().items():\n if v_name not in self.registered_views:\n continue\n desc = self.registered_views[v_name]\n for tag_values in view_data.tag_value_aggregation_data_map:\n agg_data = view_data.tag_value_aggregation_data_map[tag_values]\n metric = self.to_metric(desc, tag_values, agg_data, metrics_map)\n\n for metric in metrics_map.values():\n yield metric","title":""},{"docid":"cd96fef38a4b84ec61ebd006ad2f93dd","score":"0.5478706","text":"def create(self, metrics):\n for metric_type, metric_list in metrics.items():\n for metric in metric_list:\n module = import_module(\n name=u\"telemetry.metrics\", package=metric_type.capitalize()\n )\n self.metric_registry[metric[u\"name\"]] = getattr(\n module, metric_type.capitalize()\n )(**metric)","title":""},{"docid":"fe4551ba87b0cd086632042fd2203714","score":"0.5476459","text":"def to_dict(self):\n obj_dict = super().to_dict()\n obj_dict['class'] = str(self.__class__),\n return obj_dict","title":""},{"docid":"5cf454024a4190215a4427740da398c7","score":"0.5475734","text":"def collect(self) -> Statistics:","title":""},{"docid":"498f81d22643a3c3303f2537a6bbd52c","score":"0.5471475","text":"def extract_data(self):\n self.avg_array = []\n self.title_line = self.data_array[0]\n self.kbmemused_index = self.data_array[0].index(\"kbmemused\")\n self.kbmemfree_index = self.data_array[0].index(\"kbmemfree\")\n self.kbbuffers_index = self.data_array[0].index(\"kbbuffers\")\n self.kbcached_index = self.data_array[0].index(\"kbcached\")\n self.ts_index = self.data_array[0].index(\"TimeStamp\")\n del self.data_array[0]\n\n # time stamps\n self.time_stamp_array = []\n for self.row in self.data_array:\n self.time_stamp_array.append((int(self.row[self.ts_index])))\n\n # extract kbmemused metric\n self.kbmemused = []\n for self.row in self.data_array:\n self.kbmemused.append(int(self.row[self.kbmemused_index]))\n\n # extract kbmemfree metric\n self.kbmemfree = []\n for self.index, self.row in enumerate(self.data_array):\n self.kbmemfree.append(int(self.row[self.kbmemfree_index]))\n\n # extract kbbuffers metric\n self.kbbuffers = []\n for self.row in self.data_array:\n self.kbbuffers.append(int(self.row[self.kbbuffers_index]))\n\n # extract kbcached metric\n self.kbcached = []\n for self.index, self.row in enumerate(self.data_array):\n self.kbcached.append(int(self.row[self.kbcached_index]))\n\n # calculate the application memory utilization metric\n # The application level memory used is the total memory used minus buffers and cached memory\n self.kbappmemused = []\n for self.row in self.data_array:\n self.kbappmemused.append(int(self.row[self.kbmemused_index]) - int(self.row[self.kbbuffers_index]) - int(\n self.row[self.kbcached_index]))\n\n self.data_array.insert(0, self.title_line)\n\n self.avg_array.append(self.time_stamp_array)\n self.avg_array.append(self.kbmemfree)\n self.avg_array.append(self.kbmemused)\n self.avg_array.append(self.kbbuffers)\n self.avg_array.append(self.kbcached)\n self.avg_array.append(self.kbappmemused)\n\n return self.avg_array","title":""},{"docid":"ffd464e4ace82c22acbca9d7c73de7ca","score":"0.5470789","text":"def _print_metrics(self):\n raise NotImplementedError()","title":""},{"docid":"a64cfbe77879edbe43e8dedeaa52d847","score":"0.5470612","text":"def __data_map_to_service_metrics(self, data_map):\n result = {}\n for service, metrics in data_map.items():\n actual_metrics = metrics.get('metrics', None)\n if actual_metrics is None:\n logging.error('Unexpected response from \"%s\"', service)\n else:\n result[service] = actual_metrics\n return result","title":""},{"docid":"b9ba6f0a5a121842fe5edd4fe3061202","score":"0.5470285","text":"def report(self) -> Dict:\n array = np.array(self._values)\n return {\n \"min\": array.min(),\n \"max\": array.max(),\n \"medium\": np.median(array),\n \"mean\": array.mean(),\n \"stdDev\": array.std(),\n \"percentile\": {\n \"75\": np.percentile(array, 75),\n \"95\": np.percentile(array, 95),\n \"99\": np.percentile(array, 99)\n }\n }","title":""},{"docid":"bafccfdb46eed6d7eaf8ba768480b1eb","score":"0.54575604","text":"def cell_measures(self) -> Dict[str, List[str]]:\n\n obj = self._obj\n all_attrs = [\n ChainMap(da.attrs, da.encoding).get(\"cell_measures\", \"\")\n for da in obj.coords.values()\n ]\n if isinstance(obj, DataArray):\n all_attrs += [ChainMap(obj.attrs, obj.encoding).get(\"cell_measures\", \"\")]\n elif isinstance(obj, Dataset):\n all_attrs += [\n ChainMap(da.attrs, da.encoding).get(\"cell_measures\", \"\")\n for da in obj.data_vars.values()\n ]\n\n keys = {}\n for attr in all_attrs:\n keys.update(parse_cell_methods_attr(attr))\n measures = {key: _get_all(self._obj, key) for key in keys}\n\n return {k: sorted(set(v)) for k, v in measures.items() if v}","title":""}],"string":"[\n {\n \"docid\": \"6d0d663c48cf8e46387df0b963c73172\",\n \"score\": \"0.69854665\",\n \"text\": \"def stats_by_class(dataset):\\n\\n splitted = split_by_class(dataset)\\n stats = {}\\n for c, values in splitted.items():\\n stats[c] = get_stats(values)\\n return stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0a30a1ff8e7d599561ab6219a85a5a\",\n \"score\": \"0.68985754\",\n \"text\": \"def metrics_to_use(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e88e66c537741a433016541ce75089\",\n \"score\": \"0.6875784\",\n \"text\": \"def collect_metrics(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24546c717b1eeed21d65a57bda8209b3\",\n \"score\": \"0.67222846\",\n \"text\": \"def get_metrics(self) -> {}:\\n return {'metrics': self._metrics, 'groups': self._metrics_groups}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"398f556890ff85f8f3cc4f7fd71a3d53\",\n \"score\": \"0.6629476\",\n \"text\": \"def get_custom_metrics(self) -> Dict:\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda17e32ac689f89f112cff6d41b9586\",\n \"score\": \"0.6573594\",\n \"text\": \"def as_dict(self) -> Dict:\\n return self._metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31a7e16eed535b75ac14a2587b13d324\",\n \"score\": \"0.65656495\",\n \"text\": \"def metrics(self):\\n raise NotImplementedError(\\\"This needs to be implemented by the inheriting class.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7907d7881fd5646a29a7d05fc15980c3\",\n \"score\": \"0.64915085\",\n \"text\": \"def _module_metrics(self) -> Dict[str, Any]:\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c61a3fb3a39f348e250c6441b731cf9\",\n \"score\": \"0.6294655\",\n \"text\": \"def initialize_metrics():\\n metrics = {\\n 'top_comp_share': [], \\n 'top_'+str(params.k)+'comp_share': [], \\n 'comp_GINI': [],\\n 'comp_ranking': [],\\n 'comps': []\\n }\\n return metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed1cca1c7112f87615e5241a5ee3195d\",\n \"score\": \"0.6252564\",\n \"text\": \"def compute_metrics(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed1cca1c7112f87615e5241a5ee3195d\",\n \"score\": \"0.6252564\",\n \"text\": \"def compute_metrics(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed85cc9b949e3c0e073e7021565e3baf\",\n \"score\": \"0.62160367\",\n \"text\": \"def metric(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07934c9ebeab5e8c727acb88a4612a3c\",\n \"score\": \"0.6204308\",\n \"text\": \"def report(self):\\n m = super().report()\\n examples = self.metrics['examples']\\n if examples > 0:\\n m['examples'] = examples\\n m['mean_loss'] = self.metrics['loss'] / examples\\n\\n # get prec/recall metrics\\n confmat = self.metrics['confusion_matrix']\\n if self.opt.get('get_all_metrics'):\\n metrics_list = self.class_list\\n else:\\n # only give prec/recall metrics for ref class\\n metrics_list = [self.ref_class]\\n\\n examples_per_class = []\\n for class_i in metrics_list:\\n class_total = self._report_prec_recall_metrics(confmat, class_i, m)\\n examples_per_class.append(class_total)\\n\\n if len(examples_per_class) > 1:\\n # get weighted f1\\n f1 = 0\\n total_exs = sum(examples_per_class)\\n for i in range(len(self.class_list)):\\n f1 += (examples_per_class[i] / total_exs) * m[\\n 'class_{}_f1'.format(self.class_list[i])\\n ]\\n m['weighted_f1'] = f1\\n\\n for k, v in m.items():\\n m[k] = round_sigfigs(v, 4)\\n\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c35f38dd1cf836d747e5d93d16ed96ba\",\n \"score\": \"0.6186239\",\n \"text\": \"def __post_init__(self) -> None:\\n\\n self._metric_container_fields = [\\n self.__dict__[field.name]\\n for field in dataclasses.fields(self)\\n if isinstance(self.__dict__[field.name], MetricsBase)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"750bf03efd8230bc71da795362161962\",\n \"score\": \"0.6182728\",\n \"text\": \"def process(self) -> Dict[str, float]:\\n\\n epoch_preds = np.array(self.epoch_preds)\\n epoch_targets = np.array(self.epoch_targets)\\n epoch_breakdown = np.array(self.collected_data['breakdown_class'])\\n\\n # first find operation points\\n if isinstance(self._class_thresholds, Callable):\\n class_thresholds = self._class_thresholds(epoch_preds, epoch_targets)\\n else:\\n class_thresholds = self._class_thresholds\\n\\n # get class predictions\\n epoch_class_preds = FuseMetricsToolBox.convert_probabilities_to_class(epoch_preds, thresholds=class_thresholds)\\n\\n # get class names\\n num_classes = epoch_preds[0].shape[0]\\n class_names = FuseMetricsToolBox.get_class_names(num_classes, self._class_names)\\n\\n # get breakdown classes (sub types)\\n breakdown_classes = sorted(list(set(epoch_breakdown)), key=lambda x: str(x))\\n breakdown_classes_count = self.get_list_type_count(breakdown_classes, epoch_breakdown)\\n\\n # fill in a 2D table of breakdown_cls x predicted cls_\\n breakdown_count_per_class = {str(cls): [] for cls in breakdown_classes}\\n for breakdown_cls in breakdown_classes:\\n breakdown_cls_name = str(breakdown_cls)\\n for class_idx, class_name in enumerate(class_names):\\n detect_count = np.logical_and(np.where(epoch_breakdown == breakdown_cls, 1, 0),\\n np.where(epoch_class_preds == class_idx, 1, 0)).sum()\\n all_count = breakdown_classes_count[breakdown_cls]\\n count_percent = '(' + str(round(detect_count / int(all_count) * 100, 1)) + '%)'\\n breakdown_count_per_class[breakdown_cls_name].append(str(detect_count) + '/' + all_count + count_percent)\\n\\n return pd.DataFrame(breakdown_count_per_class, index=class_names).transpose().to_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c3210f0a03237415e33268b4cec8326\",\n \"score\": \"0.61660165\",\n \"text\": \"def get_metrics(self): # pragma: no cover\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f7050bf69dfb4787278c7ec2b3e13f4\",\n \"score\": \"0.61603546\",\n \"text\": \"def get_metrics(self) -> DocableDict:\\n return DocableDict(\\n metrics_desc,\\n {\\n \\\"trip_requirements\\\": self._total_trips,\\n \\\"bike_shortage\\\": self._total_shortages,\\n \\\"operation_number\\\": self._total_operate_num,\\n },\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac67756ff1da3a3fec457bdcab6e521f\",\n \"score\": \"0.6112048\",\n \"text\": \"def __init__(self, metrics_dict: Dict) -> None:\\n self._metrics = metrics_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce97a7f9eb2a54916fb89dc7cc1b12c2\",\n \"score\": \"0.6110764\",\n \"text\": \"def get_measurement_dict(self):\\n meas_dict = {}\\n for key in list(self.__dict__.keys()):\\n if key.find(\\\"meas_\\\") == 0:\\n meas_attr = getattr(self, key)\\n meas_key = meas_attr.chtype\\n meas_dict[meas_key] = meas_attr\\n\\n return meas_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1f1b1b9078dfa472cbaf7ff830ea09\",\n \"score\": \"0.6100069\",\n \"text\": \"def _update_metrics(self):\\n new_points_dict = {}\\n if 'performance' in self.plot_options:\\n new_points_dict['performance'] = [[self.global_classification_metrics[i].get_performance(),\\n self.partial_classification_metrics[i].get_performance()]\\n for i in range(self.n_classifiers)]\\n\\n if 'kappa' in self.plot_options:\\n new_points_dict['kappa'] = [[self.global_classification_metrics[i].get_kappa(),\\n self.partial_classification_metrics[i].get_kappa()]\\n for i in range(self.n_classifiers)]\\n\\n if 'kappa_t' in self.plot_options:\\n new_points_dict['kappa_t'] = [[self.global_classification_metrics[i].get_kappa_t(),\\n self.partial_classification_metrics[i].get_kappa_t()]\\n for i in range(self.n_classifiers)]\\n\\n if 'kappa_m' in self.plot_options:\\n new_points_dict['kappa_m'] = [[self.global_classification_metrics[i].get_kappa_m(),\\n self.partial_classification_metrics[i].get_kappa_m()]\\n for i in range(self.n_classifiers)]\\n\\n if 'scatter' in self.plot_options:\\n true, pred = [], []\\n for i in range(self.n_classifiers):\\n t, p = self.global_classification_metrics[i].get_last()\\n true.append(t)\\n pred.append(p)\\n new_points_dict['scatter'] = [[true[i], pred[i]] for i in range(self.n_classifiers)]\\n\\n if 'hamming_score' in self.plot_options:\\n new_points_dict['hamming_score'] = [[self.global_classification_metrics[i].get_hamming_score(),\\n self.partial_classification_metrics[i].get_hamming_score()]\\n for i in range(self.n_classifiers)]\\n\\n if 'hamming_loss' in self.plot_options:\\n new_points_dict['hamming_loss'] = [[self.global_classification_metrics[i].get_hamming_loss(),\\n self.partial_classification_metrics[i].get_hamming_loss()]\\n for i in range(self.n_classifiers)]\\n\\n if 'exact_match' in self.plot_options:\\n new_points_dict['exact_match'] = [[self.global_classification_metrics[i].get_exact_match(),\\n self.partial_classification_metrics[i].get_exact_match()]\\n for i in range(self.n_classifiers)]\\n\\n if 'j_index' in self.plot_options:\\n new_points_dict['j_index'] = [[self.global_classification_metrics[i].get_j_index(),\\n self.partial_classification_metrics[i].get_j_index()]\\n for i in range(self.n_classifiers)]\\n\\n if 'mean_square_error' in self.plot_options:\\n new_points_dict['mean_square_error'] = [[self.global_classification_metrics[i].get_mean_square_error(),\\n self.partial_classification_metrics[i].get_mean_square_error()]\\n for i in range(self.n_classifiers)]\\n\\n if 'mean_absolute_error' in self.plot_options:\\n new_points_dict['mean_absolute_error'] = [[self.global_classification_metrics[i].get_average_error(),\\n self.partial_classification_metrics[i].get_average_error()]\\n for i in range(self.n_classifiers)]\\n\\n if 'true_vs_predicts' in self.plot_options:\\n true, pred = [], []\\n for i in range(self.n_classifiers):\\n t, p = self.global_classification_metrics[i].get_last()\\n true.append(t)\\n pred.append(p)\\n new_points_dict['true_vs_predicts'] = [[true[i], pred[i]] for i in range(self.n_classifiers)]\\n\\n self.__update_plot(self.global_sample_count, new_points_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b6056946d2486216dc13aaf5f9e67d\",\n \"score\": \"0.60146326\",\n \"text\": \"def __init__(self, data, logs_path):\\n self.data = data\\n self.logs_path = logs_path\\n self.train_metrics = defaultdict(list)\\n self.eval_metrics = defaultdict(list)\\n self.test_metrics = defaultdict(list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bfbdfb9bbc33defb33955b7d8ffa19f\",\n \"score\": \"0.5977449\",\n \"text\": \"def _cache_metrics_metadata(self, instance):\\n # ## <TEST-INSTRUMENTATION>\\n t = Timer()\\n # ## </TEST-INSTRUMENTATION>\\n\\n i_key = self._instance_key(instance)\\n self.metadata_cache.init_instance(i_key)\\n self.log.info(\\\"Warming metrics metadata cache for instance %s\\\", i_key)\\n server_instance = self._get_server_instance(instance)\\n perfManager = server_instance.content.perfManager\\n\\n new_metadata = {}\\n metric_ids = []\\n # Use old behaviour with metrics to collect defined by our constants\\n if self.in_compatibility_mode(instance, log_warning=True):\\n for counter in perfManager.perfCounter:\\n metric_name = self.format_metric_name(counter, compatibility=True)\\n new_metadata[counter.key] = {'name': metric_name, 'unit': counter.unitInfo.key}\\n # Build the list of metrics we will want to collect\\n if instance.get(\\\"all_metrics\\\") or metric_name in BASIC_METRICS:\\n metric_ids.append(vim.PerformanceManager.MetricId(counterId=counter.key, instance=\\\"*\\\"))\\n else:\\n collection_level = instance.get(\\\"collection_level\\\", 1)\\n for counter in perfManager.QueryPerfCounterByLevel(collection_level):\\n new_metadata[counter.key] = {\\\"name\\\": self.format_metric_name(counter), \\\"unit\\\": counter.unitInfo.key}\\n # Build the list of metrics we will want to collect\\n metric_ids.append(vim.PerformanceManager.MetricId(counterId=counter.key, instance=\\\"*\\\"))\\n\\n self.log.info(\\\"Finished metadata collection for instance %s\\\", i_key)\\n # Reset metadata\\n self.metadata_cache.set_metadata(i_key, new_metadata)\\n self.metadata_cache.set_metric_ids(i_key, metric_ids)\\n\\n self.cache_config.set_last(CacheConfig.Metadata, i_key, time.time())\\n\\n # ## <TEST-INSTRUMENTATION>\\n custom_tags = instance.get('tags', []) + ['instance:{}'.format(i_key)]\\n self.histogram('datadog.agent.vsphere.metric_metadata_collection.time', t.total(), tags=custom_tags)\\n # ## </TEST-INSTRUMENTATION>\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f771b4e0ac477c08d6ec9adeb17a37be\",\n \"score\": \"0.5959972\",\n \"text\": \"def __init__(self, bucketPrefix, testBucketNumber, dataFormat):\\n \\n total = 0\\n classes = {}\\n # counts used for attributes that are not numeric\\n counts = {}\\n # totals used for attributes that are numereric\\n # we will use these to compute the mean and sample standard deviation for\\n # each attribute - class pair.\\n totals = {}\\n numericValues = {}\\n \\n \\n # reading the data in from the file\\n \\n self.format = dataFormat.strip().split('\\\\t')\\n # \\n self.prior = {}\\n self.conditional = {}\\n \\n # for each of the buckets numbered 1 through 10:\\n for i in range(1, 11):\\n # if it is not the bucket we should ignore, read in the data\\n if i != testBucketNumber:\\n filename = \\\"%s-%02i\\\" % (bucketPrefix, i)\\n f = open(filename)\\n lines = f.readlines()\\n f.close()\\n for line in lines:\\n fields = line.strip().split('\\\\t')\\n ignore = []\\n vector = []\\n nums = []\\n for i in range(len(fields)):\\n if self.format[i] == 'num':\\n nums.append(float(fields[i]))\\n elif self.format[i] == 'attr':\\n vector.append(fields[i]) \\n elif self.format[i] == 'comment':\\n ignore.append(fields[i])\\n elif self.format[i] == 'class':\\n category = fields[i]\\n # now process this instance\\n total += 1\\n classes.setdefault(category, 0)\\n counts.setdefault(category, {})\\n totals.setdefault(category, {})\\n numericValues.setdefault(category, {})\\n classes[category] += 1\\n # now process each non-numeric attribute of the instance\\n col = 0\\n for columnValue in vector:\\n col += 1\\n counts[category].setdefault(col, {})\\n counts[category][col].setdefault(columnValue, 0)\\n counts[category][col][columnValue] += 1\\n # process numeric attributes\\n col = 0\\n for columnValue in nums:\\n col += 1\\n totals[category].setdefault(col, 0)\\n #totals[category][col].setdefault(columnValue, 0)\\n totals[category][col] += columnValue\\n numericValues[category].setdefault(col, [])\\n numericValues[category][col].append(columnValue)\\n \\n \\n #\\n # ok done counting. now compute probabilities\\n #\\n # first prior probabilities p(h)\\n #\\n for (category, count) in classes.items():\\n self.prior[category] = count / total\\n #\\n # now compute conditional probabilities p(h|D)\\n #\\n for (category, columns) in counts.items():\\n self.conditional.setdefault(category, {})\\n for (col, valueCounts) in columns.items():\\n self.conditional[category].setdefault(col, {})\\n for (attrValue, count) in valueCounts.items():\\n self.conditional[category][col][attrValue] = (\\n count / classes[category])\\n self.tmp = counts \\n #\\n # now compute mean and sample standard deviation\\n #\\n self.means = {}\\n self.ssd = {}\\n # ADD YOUR CODE HERE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9012f6be7a84cd209e5bbcfdd54b33fa\",\n \"score\": \"0.59237885\",\n \"text\": \"def initialize_class():\\n for cl in class_name:\\n class_dict[cl] = {'nbr_occ': 0,\\n 'voc_occ': 0}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b9af8cd2feae4860ffe2ecf0a12f9c8\",\n \"score\": \"0.58834255\",\n \"text\": \"def reset_metrics(self):\\n self._metrics = collections.defaultdict(float)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4883dacf9fafcf40e191024d70a6cd16\",\n \"score\": \"0.587903\",\n \"text\": \"def _make_metric_trackers(self):\\n train_metrics = {name:FnValueTracker(fn) for name, fn in self.train_metrics.items()}\\n val_metrics = {name:FnValueTracker(fn) for name, fn in self.val_metrics.items()}\\n return train_metrics, val_metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2adeb3b810e5e60ef23e1a33a3d950fb\",\n \"score\": \"0.5876828\",\n \"text\": \"def __init__(self, precision=1):\\n self.__statistics = collections.defaultdict(dict)\\n self.__precision = precision\\n self.__properties = []\\n self.__properties_remote = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e6f260f7ceefb40711aa78ff67e3dfe\",\n \"score\": \"0.5858176\",\n \"text\": \"def store_output_metrics(self):\\n # total power\\n self.total_power=[int(i) if i is not None else 0 for i in self.total_power] \\n self.total_power=np.sum(self.total_power)\\n # gpu power\\n self.gpu_power=[int(i) if i is not None else 0 for i in self.gpu_power] \\n self.gpu_power=np.sum(self.gpu_power)\\n # cpu power\\n self.cpu_power=[int(i) if i is not None else 0 for i in self.cpu_power] \\n self.cpu_power=np.sum(self.cpu_power)\\n \\n # total temp\\n self.total_temp=[int(i) if i is not None else 0 for i in self.total_temp] \\n self.total_temp=np.mean(self.total_temp)\\n # gpu temp\\n self.gpu_temp=[int(i) if i is not None else 0 for i in self.gpu_temp] \\n self.gpu_temp=np.mean(self.gpu_temp)\\n # cpu temp\\n self.cpu_temp=[int(i) if i is not None else 0 for i in self.cpu_temp] \\n self.cpu_temp=np.mean(self.cpu_temp)\\n \\n # output\\n output={'cur_total_power' : int(self.total_power),\\n 'cur_gpu_power' : int(self.gpu_power),\\n 'cur_cpu_power' : int(self.cpu_power),\\n 'cur_inference' : float(self.inference_time),\\n 'cur_total_temp' : float(self.total_temp),\\n 'cur_gpu_temp' : float(self.gpu_temp),\\n 'cur_cpu_temp' : float(self.cpu_temp)}\\n with open(self.fname,'w') as f:\\n json.dump(output, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3840da8a927481867d695a318fd11fbd\",\n \"score\": \"0.5816547\",\n \"text\": \"def __init__(self):\\n super(StatisticsCollector, self).__init__()\\n self.statistics = dict()\\n self.formatting = dict()\\n\\n # Add default statistics with formatting.\\n self.add_statistic('episode', '{:06d}')\\n self.add_statistic('loss', '{:12.10f}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"effb39f0da31471dddac60305c6957ac\",\n \"score\": \"0.58144295\",\n \"text\": \"def insert_metrics(self):\\n\\t\\t\\n\\t\\tself.check_or_load_kb()\\n\\n\\t\\t# computing statistics\\n\\t\\tfor line_num in range(1, len(self.lines) + 1):\\n\\t\\t\\tent_type_set = self.get_ent_type(line_num)\\n\\t\\t\\tent_type_set_index = repr(set(ent_type_set)) # using normal set for indexing because {1,3,2} == {1,2,3} but OrderedSet([1,3,2]) != OrderedSet([1,2,3])\\n\\t\\t\\tself.metrics.setdefault(ent_type_set_index, {})\\n\\t\\t\\tself.metrics[ent_type_set_index].setdefault('columns_number', []).append(self.nonempty_columns(line_num))\\n\\t\\t\\tself.metrics[ent_type_set_index].setdefault('description_length', []).append(self.description_length(line_num))\\n\\t\\t\\tif self.get_wiki_value(line_num, 'backlinks'):\\n\\t\\t\\t\\tself.metrics[ent_type_set_index].setdefault('wiki_backlinks', []).append(int(self.get_wiki_value(line_num, 'backlinks')))\\n\\t\\t\\t\\tself.metrics[ent_type_set_index].setdefault('wiki_hits', []).append(int(self.get_wiki_value(line_num, 'hits')))\\n\\t\\t\\t\\tself.metrics[ent_type_set_index].setdefault('wiki_ps', []).append(int(self.get_wiki_value(line_num, 'ps')))\\n\\t\\t\\n\\t\\t# sorting statistics\\n\\t\\tfor i in self.metrics:\\n\\t\\t\\tfor j in self.metrics[i]:\\n\\t\\t\\t\\tself.metrics[i][j].sort()\\n\\n\\t\\t# indexing statistics\\n\\t\\tfor i in self.metrics:\\n\\t\\t\\tfor j in self.metrics[i]:\\n\\t\\t\\t\\tfor k in range(0, len(self.metrics[i][j])):\\n\\t\\t\\t\\t\\tif self.metrics[i][j][k] not in self.metric_index.setdefault(i, {}).setdefault(j, {}):\\n\\t\\t\\t\\t\\t\\tmax_value = float(self.metrics[i][j][-1])\\n\\t\\t\\t\\t\\t\\tif j in ['wiki_backlinks', 'wiki_hits']:\\n\\t\\t\\t\\t\\t\\t\\tmax_value = 0.25 * max_value\\n\\t\\t\\t\\t\\t\\tif max_value:\\n\\t\\t\\t\\t\\t\\t\\tnormalized_value = float(self.metrics[i][j][k]) / max_value\\n\\t\\t\\t\\t\\t\\t\\tself.metric_index[i][j][self.metrics[i][j][k]] = min(normalized_value, 1.0)\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tself.metric_index[i][j][self.metrics[i][j][k]] = 1.0\\n\\n\\t\\t# computing SCORE WIKI, SCORE METRICS and CONFIDENCE\\n\\t\\tfor line_num in range(1, len(self.lines) + 1):\\n\\n\\t\\t\\tcolumns = self.lines[line_num - 1]\\n\\t\\t\\t\\n\\t\\t\\t# computing SCORE WIKI\\n\\t\\t\\tscore_wiki = 0\\n\\t\\t\\tif self.get_wiki_value(columns, 'backlinks'):\\n\\t\\t\\t\\twiki_backlinks = self.metric_percentile(columns, 'wiki_backlinks')\\n\\t\\t\\t\\twiki_hits = self.metric_percentile(columns, 'wiki_hits')\\n\\t\\t\\t\\twiki_ps = self.metric_percentile(columns, 'wiki_ps')\\n\\t\\t\\t\\tscore_wiki = 100 * numpy.average([wiki_backlinks, wiki_hits, wiki_ps], weights=[5, 5, 1])\\n\\t\\t\\tcolumns[self.get_col_for(columns, \\\"SCORE WIKI\\\")] = \\\"%.2f\\\" % score_wiki\\n\\n\\t\\t\\t# computing SCORE METRICS\\n\\t\\t\\tdescription_length = self.metric_percentile(columns, 'description_length')\\n\\t\\t\\tcolumns_number = self.metric_percentile(columns, 'columns_number')\\n\\t\\t\\tscore_metrics = 100 * numpy.average([description_length, columns_number])\\n\\t\\t\\tcolumns[self.get_col_for(columns, \\\"SCORE METRICS\\\")] = \\\"%.2f\\\" % score_metrics\\n\\n\\t\\t\\t# computing CONFIDENCE\\n\\t\\t\\tcolumns[self.get_col_for(columns, \\\"CONFIDENCE\\\")] = \\\"%.2f\\\" % numpy.average([score_wiki, score_metrics], weights=[5, 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"737ab5b06cd89c90dab4eb193efa0da5\",\n \"score\": \"0.5800046\",\n \"text\": \"def metric(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d01e6e6d66dcad4070855efdfc189a1\",\n \"score\": \"0.5794874\",\n \"text\": \"def to_dict(self, *args):\\n\\n out = super().to_dict()\\n\\n out['stats'] = {str(j): w for j, w in self.stats.items()}\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51bea03b0dc7dedb265c10f17fb09b5f\",\n \"score\": \"0.5785863\",\n \"text\": \"def calculateDailyMetrics(self):\\n\\n # we really want these by class -- ignore time periods and income levels\\n vmt_byclass = self.vmt_vht_metrics.sum(level='vehicle class') # vehicles, not people\\n transit_byclass = self.transit_times_by_acc_mode_egr.sum(level='Mode')\\n nonmot_byclass = self.nonmot_times.sum(level='Mode') # person trips\\n auto_byclass = self.auto_times.sum(level='Mode') # person trips\\n\\n daily_results = collections.OrderedDict()\\n\\n ######################################################################################\\n cat1 = 'Travel Time'\\n cat2 = 'Auto/Truck (Hours)'\\n daily_results[(cat1,cat2,'SOV (PHT)' )] = vmt_byclass.loc[['DA','DAT'],'VHT'].sum()\\n daily_results[(cat1,cat2,'HOV2 (PHT)' )] = vmt_byclass.loc[['S2','S2T'],'VHT'].sum()*2\\n daily_results[(cat1,cat2,'HOV3+ (PHT)')] = vmt_byclass.loc[['S3','S3T'],'VHT'].sum()*3.5\\n daily_results[(cat1,cat2,'Truck (VHT)')] = vmt_byclass.loc[['SM','SMT','HV','HVT'],'VHT'].sum()\\n\\n # TODO: These are vehicle hours, I think. Why not make them person hours?\\n cat2 = 'Non-Recurring Freeway Delay (Hours)'\\n daily_results[(cat1,cat2,'Auto' )] = \\\\\\n vmt_byclass.loc[['DA','DAT','S2','S2T','S3','S3T'],'Non-Recurring Freeway Delay'].sum()\\n daily_results[(cat1,cat2,'Truck')] = \\\\\\n vmt_byclass.loc[['SM','SMT','HV','HVT'],'Non-Recurring Freeway Delay'].sum()\\n\\n cat2 = 'Transit In-Vehicle (Hours)'\\n daily_results[(cat1,cat2,'Local Bus' )] = transit_byclass.loc['loc','In-vehicle hours']\\n daily_results[(cat1,cat2,'Light Rail/Ferry')] = transit_byclass.loc['lrf','In-vehicle hours']\\n daily_results[(cat1,cat2,'Express Bus' )] = transit_byclass.loc['exp','In-vehicle hours']\\n daily_results[(cat1,cat2,'Heavy Rail' )] = transit_byclass.loc['hvy','In-vehicle hours']\\n daily_results[(cat1,cat2,'Commuter Rail' )] = transit_byclass.loc['com','In-vehicle hours']\\n\\n cat2 = 'Transit Out-of-Vehicle (Hours)'\\n daily_results[(cat1,cat2,'Walk Access+Egress' )] = transit_byclass.loc[:,'Walk acc & egr hours'].sum() + \\\\\\n transit_byclass.loc[:,'Aux walk hours'].sum()\\n daily_results[(cat1,cat2,'Drive Access+Egress')] = transit_byclass.loc[:,'Drive acc & egr hours'].sum()\\n daily_results[(cat1,cat2,'Wait' )] = transit_byclass.loc[:,'Init wait hours'].sum() + \\\\\\n transit_byclass.loc[:,'Xfer wait hours'].sum()\\n # Out-of-Vehicle ajustment\\n auto_person_trips = auto_byclass.loc[['da','datoll','sr2','sr2toll','sr3','sr3toll'],'Daily Trips'].sum()\\n # If this is base dir, ovtt adjustment comes from project\\n\\n if self.is_base_dir:\\n self.ovtt_adjustment = self.config.loc['ovtt_adjustment']\\n elif self.config.loc['Project Mode'] in ['com','hvy','exp','lrf','loc']:\\n self.ovtt_adjustment = transit_byclass.loc[self.config.loc['Project Mode'],'Out-of-vehicle hours'] / \\\\\\n transit_byclass.loc[self.config.loc['Project Mode'],'Transit Trips']\\n elif self.config.loc['Project Mode'] == 'road':\\n self.ovtt_adjustment = transit_byclass.loc[:,'Out-of-vehicle hours'].sum() / \\\\\\n transit_byclass.loc[:,'Transit Trips'].sum()\\n else:\\n raise Exception(\\\"Invalid Project Mode:'%s'; Should be one of 'road','com','hvy','exp','lrf','loc'\\\" % \\\\\\n self.config.loc['Project Mode'])\\n\\n daily_results[(cat1,cat2,'Ajustment for %s' % self.config.loc['Project Mode'])] = \\\\\\n self.ovtt_adjustment * auto_person_trips\\n \\n\\n cat2 = 'Walk/Bike (Hours)'\\n daily_results[(cat1,cat2,'Walk')] = nonmot_byclass.loc['Walk','Total Time (Hours)']\\n daily_results[(cat1,cat2,'Bike')] = nonmot_byclass.loc['Bike','Total Time (Hours)']\\n\\n ######################################################################################\\n cat1 = 'Travel Cost'\\n cat2 = 'VMT'\\n daily_results[(cat1,cat2,'Auto' )] = \\\\\\n vmt_byclass.loc[['DA','DAT','S2','S2T','S3','S3T'],'VMT'].sum()\\n daily_results[(cat1,cat2,'Truck')] = \\\\\\n vmt_byclass.loc[['SM','SMT','HV','HVT'],'VMT'].sum()\\n\\n cat2 = 'Operating Costs'\\n daily_results[(cat1,cat2,'Auto ($2000)' )] = \\\\\\n 0.01*auto_byclass.loc[['da','datoll','s2','s2toll','s3','s3toll'],'Total Cost'].sum()\\n daily_results[(cat1,cat2,'Truck ($2000)')] = \\\\\\n 0.01*auto_byclass.loc['truck','Total Cost'].sum()\\n\\n # Parking\\n cat2 = 'Auto Trips' # These are by person; change to vehicle\\n daily_results[(cat1,cat2,'SOV' )] = auto_byclass.loc[['da' ,'datoll' ],'Daily Trips'].sum()\\n daily_results[(cat1,cat2,'HOV2' )] = auto_byclass.loc[['sr2','sr2toll'],'Daily Trips'].sum()/2.0\\n daily_results[(cat1,cat2,'HOV3+')] = auto_byclass.loc[['sr3','sr3toll'],'Daily Trips'].sum()/3.5\\n total_autotrips = daily_results[(cat1,cat2,'SOV')] + daily_results[(cat1,cat2,'HOV2')] + daily_results[(cat1,cat2,'HOV3+')]\\n\\n cat2 = 'Parking Costs'\\n try:\\n for county in RunResults.PARKING_COST_PER_TRIP_WORK.keys():\\n daily_results[(cat1,cat2,'Work Trips in %s' % county)] = total_autotrips * \\\\\\n RunResults.PERCENT_PARKING_NONHOME * RunResults.PERCENT_PARKING_WORK * \\\\\\n float(self.config.loc['percent parking cost incurred in %s' % county])\\n for county in RunResults.PARKING_COST_PER_TRIP_WORK.keys():\\n daily_results[(cat1,cat2,'Non-Work Trips in %s' % county)] = total_autotrips * \\\\\\n RunResults.PERCENT_PARKING_NONHOME * (1.0-RunResults.PERCENT_PARKING_WORK) * \\\\\\n float(self.config.loc['percent parking cost incurred in %s' % county])\\n except:\\n # base dirs don't have parking costs\\n assert(self.is_base_dir)\\n\\n # Vehicles Owned\\n cat2 = 'Vehicle Ownership (Modeled)'\\n daily_results[(cat1,cat2,'Total')] = self.autos_owned['total autos'].sum()\\n\\n # Vehicles Owned - estimated from auto trips\\n cat2 = 'Vehicle Ownership (Est. from Auto Trips)'\\n daily_results[(cat1,cat2,'Total')] =total_autotrips*RunResults.ANNUALIZATION/RunResults.YEARLY_AUTO_TRIPS_PER_AUTO\\n\\n # Noise\\n cat2 = 'Noise'\\n daily_results[(cat1,cat2,'Auto VMT')] = \\\\\\n vmt_byclass.loc[['DA','DAT','S2','S2T','S3','S3T'],'VMT'].sum()\\n daily_results[(cat1,cat2,'Truck VMT')] = \\\\\\n vmt_byclass.loc[['SM','SMT','HV','HVT'],'VMT'].sum()\\n ######################################################################################\\n cat1 = 'Air Pollutant'\\n cat2 = 'PM2.5 (tons)'\\n daily_results[(cat1,cat2,'PM2.5 Gasoline')] = \\\\\\n vmt_byclass.loc[:,'Gas_PM2.5'].sum()*RunResults.EMISSIONS_SCALEUP + \\\\\\n (daily_results[('Travel Cost','VMT','Auto')]*(RunResults.PM25_MAGIC_A+RunResults.PM25_MAGIC_B)*RunResults.PM25_MAGIC_C)\\n daily_results[(cat1,cat2,'PM2.5 Diesel' )] = \\\\\\n vmt_byclass.loc[:,'Diesel_PM2.5'].sum()*RunResults.EMISSIONS_SCALEUP + \\\\\\n (daily_results[('Travel Cost','VMT','Truck')]*(RunResults.PM25_MAGIC_A+RunResults.PM25_MAGIC_B)*RunResults.PM25_MAGIC_C)\\n\\n cat2 = 'CO2 (metric tons)'\\n daily_results[(cat1,cat2,'CO2')] = vmt_byclass.loc[:,'CO2' ].sum()\\n\\n cat2 = 'Other'\\n daily_results[(cat1,cat2,'NOX (tons)')] = vmt_byclass.loc[:,'W_NOx'].sum()*RunResults.EMISSIONS_SCALEUP\\n daily_results[(cat1,cat2,'SO2 (tons)')] = vmt_byclass.loc[:,'SOx' ].sum()*RunResults.EMISSIONS_SCALEUP\\n\\n # http://en.wikipedia.org/wiki/Volatile_organic_compound\\n daily_results[(cat1,cat2,'VOC: Acetaldehyde (metric tons)' )] = vmt_byclass.loc[:,'Acetaldehyde' ].sum()*RunResults.EMISSIONS_SCALEUP\\n daily_results[(cat1,cat2,'VOC: Benzene (metric tons)' )] = vmt_byclass.loc[:,'Benzene' ].sum()*RunResults.EMISSIONS_SCALEUP\\n daily_results[(cat1,cat2,'VOC: 1,3-Butadiene (metric tons)')] = vmt_byclass.loc[:,'Butadiene' ].sum()*RunResults.EMISSIONS_SCALEUP\\n daily_results[(cat1,cat2,'VOC: Formaldehyde (metric tons)' )] = vmt_byclass.loc[:,'Formaldehyde' ].sum()*RunResults.EMISSIONS_SCALEUP\\n daily_results[(cat1,cat2,'All other VOC (metric tons)' )] = vmt_byclass.loc[:,'ROG'].sum()*RunResults.EMISSIONS_SCALEUP \\\\\\n - daily_results[(cat1,cat2,'VOC: Acetaldehyde (metric tons)' )] \\\\\\n - daily_results[(cat1,cat2,'VOC: Benzene (metric tons)' )] \\\\\\n - daily_results[(cat1,cat2,'VOC: 1,3-Butadiene (metric tons)')] \\\\\\n - daily_results[(cat1,cat2,'VOC: Formaldehyde (metric tons)' )]\\n\\n ######################################################################################\\n cat1 = 'Collisions & Active Transport'\\n cat2 = 'Fatalies due to Collisions'\\n daily_results[(cat1,cat2,'Motor Vehicle')] = vmt_byclass.loc[:,'Motor Vehicle Fatality'].sum()\\n daily_results[(cat1,cat2,'Walk' )] = vmt_byclass.loc[:,'Walk Fatality' ].sum()\\n daily_results[(cat1,cat2,'Bike' )] = vmt_byclass.loc[:,'Bike Fatality' ].sum()\\n\\n cat2 = 'Injuries due to Collisions'\\n daily_results[(cat1,cat2,'Motor Vehicle')] = vmt_byclass.loc[:,'Motor Vehicle Injury' ].sum()\\n daily_results[(cat1,cat2,'Walk' )] = vmt_byclass.loc[:,'Walk Injury' ].sum()\\n daily_results[(cat1,cat2,'Bike' )] = vmt_byclass.loc[:,'Bike Injury' ].sum()\\n\\n cat2 = 'Property Damage Only (PDO) Collisions'\\n daily_results[(cat1,cat2,'Property Damage')] = vmt_byclass.loc[:,'Motor Vehicle Property'].sum()\\n\\n ######################################################################################\\n cat2 = 'Trips with Active Transportation'\\n # \\\"Off-Model Benefits\\\" -- see\\n # J:\\\\PROJECT\\\\2013 RTP_SCS\\\\Performance Assessment\\\\Project Assessment (Apr 2012)\\\\B-C Methodology\\\\Off-Model Benefits Calculator.xlsx\\n daily_results[(cat1,cat2,'Walk' )] = nonmot_byclass.loc['Walk','Daily Trips']\\n daily_results[(cat1,cat2,'Bike' )] = nonmot_byclass.loc['Bike','Daily Trips']\\n daily_results[(cat1,cat2,'Transit')] = transit_byclass.loc[:,'Transit Trips'].sum() # all transit trips\\n\\n cat2 = 'Avg Minutes Active Tranport per Person'\\n daily_results[(cat1,cat2,'Walk' )] = nonmot_byclass.loc['Walk','Total Time (Hours)'] * 60.0 / RunResults.PROJECTED_2040_POPULATION\\n daily_results[(cat1,cat2,'Bike' )] = nonmot_byclass.loc['Bike','Total Time (Hours)'] * 60.0 / RunResults.PROJECTED_2040_POPULATION\\n daily_results[(cat1,cat2,'Transit')] = (transit_byclass.loc[:,'Walk acc & egr hours'].sum() + \\\\\\n transit_byclass.loc[:,'Aux walk hours'].sum()) * 60.0 / RunResults.PROJECTED_2040_POPULATION\\n avg_min_total = daily_results[(cat1,cat2,'Walk' )] + \\\\\\n daily_results[(cat1,cat2,'Bike' )] + \\\\\\n daily_results[(cat1,cat2,'Transit')]\\n\\n cat2 = 'Active Individuals'\\n # (active daily min per bay area person) * (inactive persons) = \\n # total active daily min per day _by inactive persons_\\n # Divide by active minute per day requirement to get \\\"active people\\\"\\n # TODO: Why multiply by inactive population?\\n # Are we just removing the population that is assumed to be active anyway?\\n # Wouldn't subtracting the the base give newly active population?\\n daily_results[(cat1,cat2,'Total' )] = avg_min_total * \\\\\\n (RunResults.PERCENT_POP_INACTIVE * RunResults.PROJECTED_2040_POPULATION) / RunResults.ACTIVE_MIN_REQUIREMENT\\n\\n idx = pd.MultiIndex.from_tuples(daily_results.keys(), \\n names=['category1','category2','variable_name'])\\n self.daily_results = pd.Series(daily_results, index=idx)\\n\\n # figure out the small cat2s\\n self.lil_cats = {}\\n grouped = self.daily_results.groupby(level=['category1','category2'])\\n for name,group in grouped:\\n if group.sum() < 1000:\\n self.lil_cats[name] = 1\\n\\n # sum to categories\\n self.daily_category_results = self.daily_results.sum(level=[0,1])\\n print self.daily_category_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b95ab8a91ab9213ec24c8f3fc36e7a71\",\n \"score\": \"0.5781445\",\n \"text\": \"def custom_stats(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55e68c912dda72b7d0cce1d91d9f6a18\",\n \"score\": \"0.57657784\",\n \"text\": \"def _collect_data(self):\\n\\n raise NotImplementedError(\\\"Subclasses should implement this!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55e68c912dda72b7d0cce1d91d9f6a18\",\n \"score\": \"0.57657784\",\n \"text\": \"def _collect_data(self):\\n\\n raise NotImplementedError(\\\"Subclasses should implement this!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af9bb933ebe157d6d2f010220c9f97d\",\n \"score\": \"0.57626987\",\n \"text\": \"def new_int_metric_dict(self):\\n return {METRICS_BACKEND: {self.host: {}},\\n METRICS_CONTROLLER: {self.host: {}}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcda9e8c586a4e5d1b3ac717b8d2f9ee\",\n \"score\": \"0.57587206\",\n \"text\": \"def _prepare_raw_data(self):\\n for key, value in self.__class__.__dict__.items():\\n if inspect.isclass(value):\\n self.raw_data[key] = getattr(self, key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b929488884f5eefd93c2ee7fffc1653e\",\n \"score\": \"0.5746382\",\n \"text\": \"def make_dict(msg_time, class_name): \\n \\n global class_time_dict\\n \\n # Adds class and corresponding classtime to dictionary\\n class_time_dict.update({class_name: int(msg_time)})\\n \\n return class_time_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e146186bf4d7d6ad903a4e37f1509713\",\n \"score\": \"0.5746343\",\n \"text\": \"def get_metrics(self, tool, workload, logs):\\n\\n stderr = logs[0]['stderr']\\n lines = stderr.split('\\\\n')\\n\\n # timed duration is the only the one in the \\\"PASSED\\\" line\\n passed = [l for l in lines if 'PASSED' in l][0]\\n # time is the only number in the line\\n timed_duration = [n for n in passed.split() if n.isdigit()][0]\\n\\n # there is a line with \\\"completed warmup\\\" for each warmup executed\\n warmup_iters = len([l for l in lines if 'completed warmup' in l])\\n\\n return {\\n 'timed_duration': {'value': int(timed_duration), 'unit': 'ms'},\\n 'warmup_iters': {'value': int(warmup_iters), 'unit': 'num'}\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d351a283546267736c9baf7ff05b4d4\",\n \"score\": \"0.57460517\",\n \"text\": \"def summarize_class_data(data):\\n mean = {}\\n std = {}\\n for label in data:\\n n = len(data[label][0])\\n mean[label] = []\\n std[label] = []\\n for i in range(n):\\n mean[label].append(np.mean([v[i] for v in data[label]]))\\n std[label].append(np.std([v[i] for v in data[label]]))\\n mean[label].append(n + 1)\\n # std[label].append(n + 1)\\n return mean, std\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcb50cc6e170ea3c669e290b06cbda75\",\n \"score\": \"0.5714996\",\n \"text\": \"def precompute_statistical_class(cls,\\n y: t.Optional[np.ndarray] = None,\\n **kwargs) -> t.Dict[str, t.Any]:\\n precomp_vals = {}\\n\\n if y is not None and not {\\\"classes\\\", \\\"class_freqs\\\"}.issubset(kwargs):\\n classes, class_freqs = np.unique(y, return_counts=True)\\n\\n precomp_vals[\\\"classes\\\"] = classes\\n precomp_vals[\\\"class_freqs\\\"] = class_freqs\\n\\n return precomp_vals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10a000bb34f0c35ad8c3af0b00a069fe\",\n \"score\": \"0.57134384\",\n \"text\": \"def _run_stats(self):\\n class_scores_ = {}\\n for lang_ in LANGUAGES:\\n if lang_ in self.class_occ:\\n class_scores_[lang_] = ClassScore(self.class_occ[lang_])\\n\\n result_list: List[Score] # sorted in reverse order\\n for result_list in self.results:\\n for count, score in enumerate(result_list):\\n first = count == 0\\n correct = score.is_correct\\n\\n if score.guessed_lang in class_scores_:\\n if first and correct:\\n class_scores_[score.guessed_lang].true_positive += 1\\n elif first and not correct:\\n class_scores_[score.guessed_lang].false_positive += 1\\n elif not first and correct:\\n class_scores_[score.guessed_lang].false_negative += 1\\n elif not first and not correct:\\n class_scores_[score.guessed_lang].true_negative += 1\\n\\n for lang_ in class_scores_:\\n if class_scores_[lang_].true_positive != 0 or class_scores_[lang_].false_positive != 0:\\n class_scores_[lang_].precision = class_scores_[lang_].true_positive / \\\\\\n (class_scores_[lang_].true_positive + class_scores_[\\n lang_].false_positive)\\n\\n if class_scores_[lang_].true_positive != 0 or class_scores_[lang_].false_negative != 0:\\n class_scores_[lang_].recall = class_scores_[lang_].true_positive \\\\\\n / (class_scores_[lang_].true_positive + class_scores_[lang_]\\n .false_negative)\\n\\n if class_scores_[lang_].precision != 0 or class_scores_[lang_].recall != 0:\\n class_scores_[lang_].f1 = 2 * ((class_scores_[lang_].precision * class_scores_[lang_].recall)\\n / (class_scores_[lang_].precision + class_scores_[lang_].recall))\\n\\n self.class_scores = class_scores_\\n self.final_macro_f1 = sum([class_scores_[lang_].f1 for lang_ in class_scores_]) / len(class_scores_)\\n self.final_weighed_avg_f1 = sum([class_scores_[lang].f1 * class_scores_[lang_].count\\n for lang_ in class_scores_]) / self.count\\n\\n self._output_to_eval_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd3fd15b6e9f55c40e1ee9c2112141ca\",\n \"score\": \"0.57\",\n \"text\": \"def get_metrics(self, reset: bool = False) -> Dict[str, float]:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d641802df50b6627d148506ddf2b02\",\n \"score\": \"0.56737125\",\n \"text\": \"def __init__( self ):\\n self.summaries = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8112fcff68c1ba843618cdf551c5a60e\",\n \"score\": \"0.56588453\",\n \"text\": \"def _calculate_stats(self) -> Dict[str, int]:\\n return self.statistics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05411033847bea6b6ea5d31877c4c9f7\",\n \"score\": \"0.5650323\",\n \"text\": \"def get_metrics(self, matrix):\\n assert matrix.shape[0] == matrix.shape[1]\\n metrics_dict = {\\n \\\"in_domain\\\": self.in_domain(matrix),\\n \\\"next_domain\\\": self.next_domain(matrix),\\n \\\"accuracy\\\": self.accuracy(matrix),\\n \\\"forward_transfer\\\": self.forward_transfer(matrix),\\n \\\"backward_transfer\\\": self.backward_transfer(matrix),\\n }\\n return metrics_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f81d7383ca1f71d03d9ec52a014938c4\",\n \"score\": \"0.56493306\",\n \"text\": \"def summarize(count_data):\\n class_data = defaultdict(float)\\n family_data = defaultdict(lambda : defaultdict(float))\\n \\n # class level counts\\n for class_ in count_data:\\n for family in count_data[class_]:\\n for elem in count_data[class_][family]:\\n count = float(count_data[class_][family][elem])\\n class_data[class_] += count\\n \\n # family level counts\\n for class_ in count_data:\\n for family in count_data[class_]:\\n for elem in count_data[class_][family]:\\n count = float(count_data[class_][family][elem])\\n family_data[class_][family] += count\\n \\n return class_data, family_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a60422a1ba1956231a948a688e9f6a0d\",\n \"score\": \"0.5649169\",\n \"text\": \"async def metrics(self):\\n\\n metrics = {}\\n\\n # Count of known domains (in our state db)...\\n metrics[\\\"total_domains\\\"] = len(await Domain.objects.all())\\n\\n # Count of hosted/registered domains (on Marcaria, Namecheap, IONOS)...\\n metrics[\\\"registrar_registered_domains\\\"] = len(await self.gather_registered_domains())\\n\\n # Count of domains hosted via DNS (on Route53, CloudFlare etc)...\\n metrics[\\\"dns_hosted_domains\\\"] = len(await self.gather_hosted_domains())\\n\\n # TODO: Statuscheck metrics breakdown\\n\\n # Summary count of active agents\\n metrics[\\\"agent_counts\\\"] = {\\n \\\"registrar\\\": len(self.registrar_agents),\\n \\\"dns\\\": len(self.dns_agents),\\n \\\"hosting\\\": len(self.hosting_agents),\\n \\\"waf\\\": len(self.waf_agents)\\n }\\n\\n return metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05dab1af4998c01300b2132be7ccfdf1\",\n \"score\": \"0.5640886\",\n \"text\": \"def _dict_rep(self):\\n props = get_class_properties(self.__class__)\\n out = {k: getattr(self, k) for k in props}\\n out['group'] = self.name\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc92ddcdb6a4017c0e6195da92957bb3\",\n \"score\": \"0.5634014\",\n \"text\": \"def compute_validation_metrics(self, step_id: StepID) -> Dict[str, Any]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7150897739d4f3601f233ab4938cd567\",\n \"score\": \"0.5633559\",\n \"text\": \"def calc_global_metrics(metrics_per_class, decimal_digits=2):\\n\\n metrics_global = {}\\n\\n # macro averaging - consider only classes with elements\\n N_P_nonzero_classes = np.count_nonzero(metrics_per_class['N_P']) # number of classes with non zero predicted positive elements\\n N_GT = metrics_per_class['N_GT'] # number of classes with ground truth positive elements\\n N_GT_nonzero_classes = np.count_nonzero(N_GT) # number of classes with non zero ground truth positive elements\\n ind_N_GT_nonzero = np.nonzero(N_GT)\\n metrics_global['macro'] = {}\\n with np.errstate(all='ignore'):\\n metrics_global['macro']['recall'] = np.nan_to_num(metrics_per_class['recall'][ind_N_GT_nonzero].sum() / N_GT_nonzero_classes) if N_GT_nonzero_classes > 0 else 1.\\n metrics_global['macro']['precision'] = np.nan_to_num(metrics_per_class['precision'].sum() / N_P_nonzero_classes) # FIXME: should we divide by N_P_nonzero_classes or N_GT_nonzero_classes ?\\n metrics_global['macro']['fdr'] = np.nan_to_num(metrics_per_class['fdr'].sum() / N_P_nonzero_classes) # FIXME: should we divide by N_P_nonzero_classes or N_GT_nonzero_classes ?\\n metrics_global['macro']['miss_detection_rate'] = np.nan_to_num(metrics_per_class['miss_detection_rate'].sum() / N_GT_nonzero_classes)\\n metrics_global['macro']['false_detection_rate_vs_gt'] = np.nan_to_num(metrics_per_class['false_detection_rate_vs_gt'].sum() / N_GT_nonzero_classes)\\n metrics_global['macro']['false_detection_rate_vs_pred'] = np.nan_to_num(metrics_per_class['false_detection_rate_vs_pred'].sum() / N_P_nonzero_classes) # FIXME: should we divide by N_P_nonzero_classes or N_GT_nonzero_classes ?\\n metrics_global['macro']['N_GT_nonzero_classes'] = np.nan_to_num(N_GT_nonzero_classes) # FIXME: should we divide by N_P_nonzero_classes or N_GT_nonzero_classes ?\\n metrics_global['macro']['N_P_nonzero_classes'] = np.nan_to_num(N_P_nonzero_classes) # FIXME: should we divide by N_P_nonzero_classes or N_GT_nonzero_classes ?\\n\\n # micro averaging\\n TP = metrics_per_class['TP'].sum()\\n FP = metrics_per_class['FP'].sum()\\n FN = metrics_per_class['FN'].sum()\\n miss_detection = metrics_per_class['miss_detection'].sum()\\n false_detection = metrics_per_class['false_detection'].sum()\\n N_P = metrics_per_class['N_P_total']\\n N_GT = metrics_per_class['N_GT_total']\\n\\n with np.errstate(all='ignore'):\\n recall = np.nan_to_num(TP / N_GT) if N_GT > 0 else 1. # set recall of classes without GT to 1 (since recall = 0/0 we can chose either 0 or 1).\\n precision = np.nan_to_num(TP / N_P)\\n fdr = np.nan_to_num(FP / N_P)\\n miss_detection_rate = np.nan_to_num(miss_detection / N_GT)\\n false_detection_rate_vs_gt = np.nan_to_num(false_detection / N_GT)\\n false_detection_rate_vs_pred = np.nan_to_num(false_detection / N_P)\\n\\n metrics_global['micro'] = {}\\n metrics_global['micro']['TP'] = TP\\n metrics_global['micro']['FP'] = FP\\n metrics_global['micro']['FN'] = FN\\n metrics_global['micro']['miss_detection'] = miss_detection\\n metrics_global['micro']['false_detection'] = false_detection\\n metrics_global['micro']['N_P'] = N_P\\n metrics_global['micro']['N_GT'] = N_GT\\n metrics_global['micro']['recall'] = recall\\n metrics_global['micro']['precision'] = precision\\n metrics_global['micro']['fdr'] = fdr\\n metrics_global['micro']['miss_detection_rate'] = miss_detection_rate\\n metrics_global['micro']['false_detection_rate_vs_gt'] = false_detection_rate_vs_gt\\n metrics_global['micro']['false_detection_rate_vs_pred'] = false_detection_rate_vs_pred\\n\\n # round metrics\\n if decimal_digits is not None:\\n metrics_global['macro'] = {key: np.around(val, decimal_digits) for key, val in metrics_global['macro'].items()}\\n metrics_global['micro'] = {key: np.around(val, decimal_digits) for key, val in metrics_global['micro'].items()}\\n\\n return metrics_global\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33218901119116f40c200d7cc6eef637\",\n \"score\": \"0.561803\",\n \"text\": \"def get_metrics(self):\\n return self.metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dffe3214ab2594aa8b2651297587c66\",\n \"score\": \"0.5611316\",\n \"text\": \"def as_dict(self):\\n return {\\n \\\"@module\\\": self.__class__.__module__,\\n \\\"@class\\\": self.__class__.__name__,\\n \\\"params\\\": {\\\"mean\\\": self.mean.tolist(), \\\"std\\\": self.std.tolist()},\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6a252c4152c585d98d0e52de1de9c6\",\n \"score\": \"0.5609094\",\n \"text\": \"def meta(self) -> dict[str, Any]:\\n return {\\n \\\"name\\\": self.__class__.__name__,\\n \\\"objective_weights\\\": self._objective_weights,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34cf9e551a839cf2a6d3c0f4628a5444\",\n \"score\": \"0.5600248\",\n \"text\": \"def __init__(self):\\r\\n self.meter_items: Dict[str, MeterItem] = dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca471cb4bbea5d97f7ea507470e88c1d\",\n \"score\": \"0.5598264\",\n \"text\": \"def _add_metrics_to_meta(self, fold_i, trainer):\\n result = {\\n 'name': self.name,\\n 'fold': fold_i\\n }\\n for name, obj in trainer.train_metrics.items():\\n result['train_' + name] = obj.get_last_epoch_mean()\\n result['train_' + name + '_all'] = obj.get_all_epoch_means().tolist()\\n for name, obj in trainer.val_metrics.items():\\n result['val_' + name] = obj.get_last_epoch_mean()\\n result['val_' + name + '_all'] = obj.get_all_epoch_means().tolist()\\n self.meta['results'].append(result)\\n\\n # save to metadata\\n with open(os.path.join(self.experiment_dir, 'metadata.json'), 'w') as f:\\n json.dump(self.meta, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d032150ee541dac46d61ef9d31388600\",\n \"score\": \"0.55967885\",\n \"text\": \"def result(\\n metrics: Dict[metric_types.MetricKey, Any]\\n ) -> Dict[metric_types.MetricKey, float]:\\n class_weights_from_labels = metrics[class_weights_from_labels_key]\\n total_value = 0.0\\n total_weight = 0.0\\n for sub_key in sub_keys:\\n child_key = metric_types.MetricKey(\\n name=metric_name,\\n model_name=model_name,\\n output_name=output_name,\\n sub_key=sub_key,\\n example_weighted=example_weighted)\\n if child_key not in metrics:\\n # Use private name if not found under metric name\\n child_key = metric_types.MetricKey(\\n name='_' + metric_name,\\n model_name=model_name,\\n output_name=output_name,\\n sub_key=sub_key,\\n example_weighted=example_weighted)\\n weight = 1.0 if not class_weights else 0.0\\n offset = None\\n if (child_key.sub_key is not None and\\n child_key.sub_key.class_id is not None):\\n offset = child_key.sub_key.class_id\\n elif child_key.sub_key is not None and child_key.sub_key.k is not None:\\n offset = child_key.sub_key.k\\n if offset is not None:\\n if (class_weights_from_labels and\\n child_key.sub_key.class_id in class_weights_from_labels):\\n weight = class_weights_from_labels[offset]\\n if class_weights and child_key.sub_key.class_id in class_weights:\\n weight *= class_weights[offset]\\n total_value += _to_float(metrics[child_key]) * weight\\n total_weight += weight\\n average = total_value / total_weight if total_weight else float('nan')\\n return {key: average}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11290a9f9b5b9dcef944213fbf30a96a\",\n \"score\": \"0.55930245\",\n \"text\": \"def grasp_metrics(grasps, data):\\n grasp_metrics = {}\\n for grasp in grasps:\\n grasp_id = grasp.id\\n grasp_key = GRASP_KEY + '_' + str(grasp_id)\\n grasp_metrics[grasp_id] = {}\\n if grasp_key in list(data.keys()):\\n grasp_metric_data = data[grasp_key][GRASP_METRICS_KEY]\\n for metric_name in list(grasp_metric_data.attrs.keys()):\\n grasp_metrics[grasp_id][metric_name] = grasp_metric_data.attrs[metric_name]\\n return grasp_metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a81f6af7e24fe3eadcdf43e8f583a6\",\n \"score\": \"0.5584385\",\n \"text\": \"def statistics(self, quickly_collected_only: bool = False) -> Dict[str, object]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52d37edcfc4f29e1c06ba10ca9ea713e\",\n \"score\": \"0.5582157\",\n \"text\": \"def get_performance_metrics(self, data, labels):\\n metrics = {}\\n if self.__is_trained == True and self.__data_processor.check_dataset_validity(data, labels) == True:\\n filled_labels = labels.copy().fillna(labels.mean())\\n for index, model in enumerate(self.__models):\\n metrics[(self.__parameters['model']['metric'] + '_' + str(index))] = np.sqrt(\\n mean_squared_error(self.predict_data(model, data).predicted_mean[-(data.shape[0]):].interpolate().fillna(0), filled_labels)\\n )\\n return metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a38f41a1665bd966373f73b1783444f\",\n \"score\": \"0.557402\",\n \"text\": \"def metrics(self, X, y):\\n check_is_fitted(self, \\\"is_fitted_\\\")\\n X, y = check_X_y(X, y, accept_sparse=False)\\n\\n self.__alternatives_ = [\\n c for c in sorted(np.unique(y)) if c != self.target_class\\n ]\\n\\n CSPS_ = {}\\n for class_ in self.__alternatives_:\\n mask = y == class_\\n CSPS_[class_] = 1.0 - np.sum(\\n self.__model_.predict(X[mask])\\n ) / np.sum(mask)\\n\\n mask = y != self.target_class\\n if np.sum(mask) == 0:\\n # Testing on nothing but the target class, can't evaluate TSPS\\n TSPS_ = np.nan\\n else:\\n TSPS_ = 1.0 - np.sum(self.__model_.predict(X[mask])) / np.sum(mask)\\n\\n mask = y == self.target_class\\n if np.sum(mask) == 0:\\n # Testing on nothing but alternative classes, can't evaluate TSNS\\n TSNS_ = np.nan\\n else:\\n TSNS_ = np.sum(self.__model_.predict(X[mask])) / np.sum(\\n mask\\n ) # TSNS = CSNS for SIMCA\\n\\n if np.isnan(TSNS_):\\n TEFF_ = TSPS_\\n elif np.isnan(TSPS_):\\n TEFF_ = TSNS_\\n else:\\n TEFF_ = np.sqrt(TSNS_ * TSPS_)\\n\\n metrics = {\\n \\\"teff\\\": TEFF_,\\n \\\"tsns\\\": TSNS_,\\n \\\"tsps\\\": TSPS_,\\n \\\"csps\\\": CSPS_,\\n }\\n return metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdbb3cf1758032452cc07b4b2d9ab2d6\",\n \"score\": \"0.55587864\",\n \"text\": \"def init_app_metrics(self, app):\\n if app not in self.metrics:\\n with self.metrics_lock:\\n self.metrics[app] = {\\n 'GAUGE': collections.defaultdict(_gauge_pair),\\n 'COUNTER': collections.defaultdict(int),\\n 'PERCENTILE': collections.defaultdict(list)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f796696c43679dbe302f24327b3c7534\",\n \"score\": \"0.5551965\",\n \"text\": \"def _get_class_dict(self):\\n with open(self._opts._ecfile, \\\"r\\\") as ec_fobj:\\n for line in ec_fobj:\\n fields = line.strip().split()\\n if fields[1].endswith('.csv'): #TODO delete this when error is fixed\\n fields[1] = fields[1].rstrip('.csv')\\n\\n if self._opts._labels == 'EC':\\n self.class_dict[fields[1]] = {'id': len(self.class_dict),\\n 'size': int(fields[0]),\\n }\\n if self._opts._labels == 'GO':\\n self.class_dict[fields[1].split('_')[1]] = {'id': len(self.class_dict),\\n 'size': int(fields[0]),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32947c4acc4fc99cd3e0fae9aa5351a3\",\n \"score\": \"0.5548595\",\n \"text\": \"def create_summary_metrics(self):\\n # create keras summary objects for loss\\n self.summary_metrics = {}\\n self.summary_metrics[\\\"train_loss_umap\\\"] = tf.keras.metrics.Mean(\\n \\\"train_loss_umap\\\", dtype=tf.float32\\n )\\n if self.decoding_method in [\\\"autoencoder\\\", \\\"network\\\"]:\\n self.summary_metrics[\\\"train_loss_recon\\\"] = tf.keras.metrics.Mean(\\n \\\"train_loss_recon\\\", dtype=tf.float32\\n )\\n if self.valid_X is not None:\\n self.summary_metrics[\\\"valid_loss_recon\\\"] = tf.keras.metrics.Mean(\\n \\\"valid_loss_recon\\\", dtype=tf.float32\\n )\\n\\n if self.train_classifier:\\n self.summary_metrics[\\\"train_loss_classif\\\"] = tf.keras.metrics.Mean(\\n \\\"train_loss_classif\\\", dtype=tf.float32\\n )\\n self.summary_metrics[\\\"valid_loss_classif\\\"] = tf.keras.metrics.Mean(\\n \\\"valid_loss_classif\\\", dtype=tf.float32\\n )\\n self.summary_metrics[\\\"train_acc_classif\\\"] = tf.keras.metrics.Mean(\\n \\\"train_acc_classif\\\", dtype=tf.float32\\n )\\n self.summary_metrics[\\\"valid_acc_classif\\\"] = tf.keras.metrics.Mean(\\n \\\"valid_acc_classif\\\", dtype=tf.float32\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c9cda2a3f65a3600b5339b3c6a4eff6\",\n \"score\": \"0.5544639\",\n \"text\": \"def _collect_stat(self):\\n train_stats = {}\\n val_stats = {}\\n deviation_stats = {}\\n # collect stats\\n grouping = self.data.groupby(\\\"type\\\")\\n train_group = grouping.get_group(\\\"train\\\")\\n val_group = grouping.get_group(\\\"val\\\")\\n train_label_fractions = train_group.value_counts(\\\"label\\\") / train_group.shape[0]\\n val_label_fractions = val_group.value_counts(\\\"label\\\") / val_group.shape[0]\\n for key, value in val_label_fractions.items():\\n val_stats[\\\"label_fraction_\\\"+key] = value\\n for key, value in train_label_fractions.items():\\n train_stats[\\\"label_fraction_\\\"+key] = value\\n val_stats[\\\"area_min\\\"] = val_group[\\\"area\\\"].min()\\n val_stats[\\\"area_max\\\"] = val_group[\\\"area\\\"].max()\\n val_stats[\\\"area_avg\\\"] = val_group[\\\"area\\\"].mean()\\n val_stats[\\\"area_std\\\"] = val_group[\\\"area\\\"].std()\\n train_stats[\\\"area_min\\\"] = train_group[\\\"area\\\"].min()\\n train_stats[\\\"area_max\\\"] = train_group[\\\"area\\\"].max()\\n train_stats[\\\"area_avg\\\"] = train_group[\\\"area\\\"].mean()\\n train_stats[\\\"area_std\\\"] = train_group[\\\"area\\\"].std()\\n\\n # calculated deviations dict\\n for key, value in train_stats.items():\\n deviation_stats[key] = 1-val_stats[key]/value\\n # convert the dicts to dfs\\n train_stats = pd.DataFrame(train_stats, index=[\\\"train\\\"])\\n val_stats = pd.DataFrame(val_stats, index=[\\\"val\\\"])\\n deviation_stats = pd.DataFrame(deviation_stats, index=[\\\"deviation (1-val/train)\\\"])\\n data = pd.concat([train_stats, val_stats, deviation_stats]).T\\n data = data.round(2)\\n data = data.reset_index()\\n data[\\\"index\\\"] = data[\\\"index\\\"].apply(lambda x: x.replace(\\\"_\\\", \\\" \\\").title())\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f65f9868e47911011f832a45f1db7f00\",\n \"score\": \"0.554401\",\n \"text\": \"def __init__(self, data=None, k=1, metric='cosine', n=5):\\n self.k = k\\n self.n = n\\n self.username2id = {}\\n self.userid2name = {}\\n self.productid2name = {}\\n # for some reason I want to save the name of the metric\\n self.metric = metric\\n if self.metric == 'cosine':\\n self.fn = self.cosine\\n #\\n # if data is dictionary set recommender data to it\\n #\\n if type(data).__name__ == 'dict':\\n self.data = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c05cdcc434059b7281802e96b5c8459c\",\n \"score\": \"0.5539755\",\n \"text\": \"def build_metrics(self) -> None:\\n self.metric_functions = define_metric_functions(self.metric_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94f4f74fe26d47dacbaab7e9991e01f6\",\n \"score\": \"0.55359995\",\n \"text\": \"def to_dict(self):\\n mydict = {}\\n for attr in dir(self):\\n try:\\n is_callable = callable(getattr(BaseDataSet, attr))\\n except AttributeError:\\n is_callable = False\\n if attr.startswith(\\\"__\\\") or is_callable:\\n continue\\n try:\\n val = getattr(self, attr)\\n mydict[attr] = val\\n except AttributeError:\\n pass\\n return mydict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c8290b730ba3e2df0c74f38f907034\",\n \"score\": \"0.5530914\",\n \"text\": \"def report_metrics(self) -> Dict[str, Any]:\\n metrics = self._module_metrics()\\n for child_module in self.modules():\\n if isinstance(child_module, ModuleWithMetrics):\\n child_metrics = child_module._module_metrics()\\n if len(child_metrics) > 0:\\n metrics.update(child_metrics)\\n return metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e009767d9eb2932866e5be6b2b679a36\",\n \"score\": \"0.5524761\",\n \"text\": \"def _record_prof_data(cls, class_name: str, prof_path: str, data: any):\\n\\n full_path = f\\\"{class_name}{Cache.CACHE_PATH_SEP}{prof_path}{Cache.CACHE_PATH_SEP}values\\\"\\n prof_dict = cls.prof.get_path_dict(\\n path=full_path, create=True,\\n none_when_missing=False\\n )\\n\\n prof_dict[\\\"values\\\"] = prof_dict.get(\\\"values\\\", [ ].copy()) + [ data ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119de258f14937b3727d93e484e7f00c\",\n \"score\": \"0.5524322\",\n \"text\": \"def _get_summary_dicts(self, data_loader, split, dataset_name,\\n keys, probs, slide_ids, loss_meter, w_loss_meter):\\n metrics, curves = {}, {}\\n\\n probs = torch.cat(probs)\\n keys = torch.cat(keys)\\n data_subset = f'{dataset_name}-{split}'\\n\\n # Load the dataset classes, and the classes the model outputs values for.\\n original_tasks = list(data_loader.dataset.original_tasks)\\n tasks = list(data_loader.dataset.target_tasks)\\n\\n #competition_tasks = list(TASK_SEQUENCES['competition'])\\n for i in range(0, keys.shape[1]):\\n\\n # Don't evaluate on missing categories\\n #if tasks[i] not in original_tasks:\\n # continue\\n\\n task_keys = keys[:, i]\\n task_probs = probs[:, i]\\n task_name = tasks[i]\\n\\n\\n task_keys, task_probs = np.array(task_keys.cpu()), np.array(task_probs.cpu())\\n task_preds = (task_probs > 0.5)\\n\\n '''\\n if (self.rad_perf is not None\\n and split in ['valid', 'test']\\n and dataset_name == 'stanford'):\\n eval_metrics += ['rads_below_ROC', 'rads_below_PR']\\n '''\\n\\n for curve in ['PRC', 'ROC']:\\n self._update_metric(curves, curve, task_name, task_keys,\\n task_probs, task_preds, data_subset, slide_ids)\\n\\n eval_metrics = ['AUPRC', 'AUROC', 'log_loss', 'accuracy', 'precision', 'recall']\\n\\n #TODO: Replace this with command line arg?\\n if len(slide_ids) > 0:\\n eval_metrics.append('collate_accuracy')\\n\\n for metric in eval_metrics:\\n self._update_metric(metrics, metric, task_name, task_keys,\\n task_probs, task_preds, data_subset, slide_ids=slide_ids)\\n\\n\\n metrics.update({\\n data_subset + '_' + 'loss': loss_meter.avg,\\n data_subset + '_' + 'weighted_loss': w_loss_meter.avg\\n })\\n\\n\\n return metrics, curves\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cc11aa232d4ca8520eaf2775df1e0bf\",\n \"score\": \"0.5523234\",\n \"text\": \"def _instrument_dict(self, cls, obj: Any, with_class_info: bool = False):\\n\\n return jsonify\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f809e0ea9e1fe1a98e56751415584dd2\",\n \"score\": \"0.5523008\",\n \"text\": \"def __init__(self, data):\\n self._total_time = {}\\n # precompute total time to have time callback in O(1)\\n for from_node in xrange(data.num_locations):\\n self._total_time[from_node] = {}\\n for to_node in xrange(data.num_locations):\\n if from_node == to_node:\\n self._total_time[from_node][to_node] = 0\\n else:\\n self._total_time[from_node][to_node] = int(\\n self.service_time(data, from_node) +\\n self.travel_time(data, from_node, to_node))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"992ee311f39d14e676ed27e76aebe8bb\",\n \"score\": \"0.5521787\",\n \"text\": \"def compute_metrics(self, results) -> dict:\\n results = dict()\\n for key in ['eqt_int', 'eqt_frac', 'eqr']:\\n if key not in self.fake_results:\\n continue\\n sums = torch.stack(self.fake_results[key], dim=0)\\n mses = (sums[0::2] / sums[1::2]).mean()\\n psnrs = np.log10(2) * 20 - mses.log10() * 10\\n psnrs = psnrs.cpu().numpy()\\n results[key] = psnrs\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c32a7ecc8c534e1392153391550f2738\",\n \"score\": \"0.55208254\",\n \"text\": \"def stats(self) -> Dict[str, int]:\\n\\n return {\\n \\\"data\\\": self._log.stats(),\\n \\\"metadata\\\": self._metalog.stats()\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2359d07a4b830ce4064021c7e4e3c6a\",\n \"score\": \"0.5519282\",\n \"text\": \"def __init__(self, metrics_map, include_val_metrics=False):\\n self.metrics_map = metrics_map\\n self.include_val_metrics = include_val_metrics\\n self.metrics_history = self.__createMetricsHistory()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"270b4686790675143fa5c2ebecf21060\",\n \"score\": \"0.55147254\",\n \"text\": \"def calculate_data_stats(self, data):\\n self.stats_obj = stats.describe(data)\\n\\n # stats_obj is a NamedTuple type\\n for key, val in self.stats_obj._asdict().items():\\n print(key + \\\":\\\")\\n pprint.pprint(val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa6b31b7afec073347920f3e926bbca3\",\n \"score\": \"0.551372\",\n \"text\": \"def as_dict(self) -> dict:\\n return dataclasses.asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d212f8d9338162431ab965485921f07d\",\n \"score\": \"0.5513227\",\n \"text\": \"def metrics(self) -> List[Metric]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"216e844ddb17e68363d8354a3144fcd5\",\n \"score\": \"0.5505174\",\n \"text\": \"def get_basic_metrics(self):\\n basic_stats = dict(\\n total_read=self.total_read,\\n total_base=self.total_base,\\n mean_length=self.mean_length,\\n q20=self.q20,\\n gc_content=self.gc_content\\n )\\n return basic_stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c81e923c07b0e2e416cfe820fadc7ee\",\n \"score\": \"0.5504363\",\n \"text\": \"def __start_metrics(self):\\n self.global_classification_metrics = []\\n self.partial_classification_metrics = []\\n\\n if self.task_type in ['classification']:\\n for i in range(self.n_classifiers):\\n self.global_classification_metrics.append(ClassificationMeasurements())\\n self.partial_classification_metrics.append(WindowClassificationMeasurements(window_size=self.n_wait))\\n\\n elif self.task_type in ['multi_output']:\\n for i in range(self.n_classifiers):\\n self.global_classification_metrics.append(MultiOutputMeasurements())\\n self.partial_classification_metrics.append(WindowMultiOutputMeasurements(window_size=self.n_wait))\\n\\n elif self.task_type in ['regression']:\\n for i in range(self.n_classifiers):\\n self.global_classification_metrics.append(RegressionMeasurements())\\n self.partial_classification_metrics.append(WindowRegressionMeasurements(window_size=self.n_wait))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c74ab0f37b1f8edb130cd7004e19f8\",\n \"score\": \"0.55036974\",\n \"text\": \"def get_prometheus_data():\\n prometheus_data = {}\\n prometheus_validity = {}\\n prometheus_validity['failed_accounts'] = 0\\n prometheus_validity['total_accounts'] = 0\\n prometheus_validity['total_checks'] = 0\\n for user in prometheus_credentials:\\n alerting_servers = {}\\n down_servers = []\\n queries = {}\\n # Node cpu is a number of cpu cycles so we need to look at the rate\\n # to calculate the cpu usage percentage, we previously used the last 1\\n # minute but have changed it to 10 to avoid missing data. If having\\n # the very latest data is crucial we could get the last minute and the\\n # last 10 minutes and then use the last minute where possible, for now this\\n # doesn't seem necessary\\n # We are using irate over rate as it seems more suitable for the speed\\n # cpu usage will be changing at:\\n # https://prometheus.io/docs/prometheus/latest/querying/functions/\\n queries['cpu'] = '(1 - ((avg(irate(node_cpu_seconds_total{cpu_mode}[10m])) by (instance{labels_to_filter_based_on})) or (avg(irate(node_cpu{cpu_mode}[10m])) by (instance{labels_to_filter_based_on})))) * 100'\\n queries['memory'] = '(1 - ((node_memory_MemAvailable_bytes or node_memory_MemAvailable) / (node_memory_MemTotal_bytes or node_memory_MemTotal))) * 100'\\n # We want all data for each instance\\n # We are only interested in the disk with greatest disk io\\n # We are calculating disk io for each disk in the same way as cpu\\n # The /10 is to convert to seconds (/1000) and then to a percentage\\n # (*1000)\\n queries['disk_io'] = '(((max(avg(irate(node_disk_io_time_seconds_total[10m]) * 1000) by (instance{labels_to_filter_based_on}, device)) by (instance{labels_to_filter_based_on}))) or ((max(avg(irate(node_disk_io_time_ms[10m])) by (instance{labels_to_filter_based_on}, device)) by (instance{labels_to_filter_based_on})))) /10'\\n # We need want to exclude temporary file systems, docker and rootfs as it\\n # is reported as well as the device that it is mounted on\\n # Including just ext4 and vfat covers all the systems we currently want\\n # but could easily be wrong in the future or in a different enviroment\\n # I'm going to test it like this and consider what a good exclude line\\n # would be or how we want to be notified of unexpected data\\n # This is configured in the config file as different set-ups may\\n # have different filesystems to monitor. If no argument is supplied\\n # all of the disks will be monitored for each instance.\\n queries['fullest_disk'] = 'max((((node_filesystem_size_bytes{fullest_disk_sub} or node_filesystem_size{fullest_disk_sub}) - (node_filesystem_free_bytes{fullest_disk_sub} or node_filesystem_free{fullest_disk_sub})) / (node_filesystem_size_bytes{fullest_disk_sub} or node_filesystem_size{fullest_disk_sub})) * 100) by (instance{labels_to_filter_based_on})'\\n\\n # We will either sub in a blank string or the intermittent_tag\\n # depending on if one is present\\n labels_to_filter_based_on = ''\\n if 'intermittent_tag' in prometheus_credentials[user]:\\n labels_to_filter_based_on = ', {}'.format(prometheus_credentials[user]['intermittent_tag'])\\n\\n for query in queries:\\n # CPU mode it necessary to avoid it being counted as a key\\n # fullest_disk_sub needs to be substituted here or after\\n # or it will be counted as a key\\n # We are trying to substitute the intermittent_tag into\\n # every group by in order to keep the tag\\n queries[query] = queries[query].format(labels_to_filter_based_on = labels_to_filter_based_on, cpu_mode = '{mode=\\\"idle\\\"}', fullest_disk_sub = prometheus_credentials[user].get('fullest_disk_tags', ''))\\n\\n try:\\n alerting_servers, down_servers = get_alerting_servers(user)\\n except Exception as e:\\n logger.error('The following error occured whilst getting the list of alerting servers for {}: {}'.format(user, e))\\n\\n #logger.error('Down servers:{}\\\\nAlerting servers{}'.format(down_servers, alerting_servers), 'info')\\n\\n prometheus_data[user] = {}\\n prometheus_validity['total_accounts'] += 1\\n responses = {}\\n for query in queries:\\n try:\\n metrics_response = requests.get(\\n '{}/api/v1/query'.format(prometheus_credentials[user]['url']),\\n auth=HTTPBasicAuth(prometheus_credentials[user]['username'],\\n prometheus_credentials[user]['password']),\\n params={'query': queries[query]},\\n timeout=prometheus_credentials[user]['timeout'])\\n metrics_response.raise_for_status()\\n except requests.exceptions.RequestException as e:\\n logger.error('Could not get prometheus data for {}: Error: {}'.format(user, e))\\n continue\\n\\n responses[query] = json.loads(metrics_response.text)\\n\\n #logger.error('Responses: {}'.format(responses), 'info')\\n if len(responses) < len(queries):\\n prometheus_validity['failed_accounts'] += 1\\n continue\\n\\n #logger.error('Looping through responses', 'info')\\n for metric in responses:\\n if responses[metric]['status'] != \\\"success\\\":\\n continue\\n\\n for instance_data in responses[metric]['data']['result']:\\n hostname = instance_data['metric']['instance']\\n if hostname not in prometheus_data[user]:\\n prometheus_validity['total_checks'] += 1\\n prometheus_data[user][hostname] = {}\\n # Strip the port number if it exists from the hostname\\n prometheus_data[user][hostname]['name'] = re.match('(^.*?)(:\\\\d{1,5})?$', hostname).group(1)\\n prometheus_data[user][hostname]['summary'] = {}\\n\\n if 'intermittent_tag' in prometheus_credentials[user]:\\n if instance_data['metric'].get(prometheus_credentials[user]['intermittent_tag']) in prometheus_credentials[user].get('intermittent_true_values', []):\\n prometheus_data[user][hostname]['volatile'] = True\\n\\n prometheus_data[user][hostname]['summary'][metric] = float(instance_data['value'][1])\\n\\n to_remove = []\\n #logger.error('Setting health status and orderby for servers: {}'.format(prometheus_data[user]), 'info')\\n for host in prometheus_data[user]:\\n # Check if the server is alerting and set the health status\\n health_status = 'green'\\n if host in alerting_servers:\\n alert_level = max(alerting_servers[host])\\n if alert_level == 1:\\n health_status = 'orange'\\n if alert_level == 2:\\n health_status = 'red'\\n\\n prometheus_data[user][host]['health_status'] = health_status\\n # Calculate the order by. If the server isn't reporting every\\n # metric flag it as unreporting and log the issue.\\n metrics = []\\n values = []\\n for metric in prometheus_data[user][host]['summary']:\\n metrics.append(metric)\\n values.append(prometheus_data[user][host]['summary'][metric])\\n\\n if len(values) != len(queries):\\n # Mark servers to be removed so we don't encounter a\\n # run time error due to removing items from a dictionary\\n # we are looping through\\n to_remove.append(host)\\n # If a server is transient this is INFO otherwise WARN\\n if prometheus_data[user][host].get('volatile') == True:\\n logger.info('Volatile host {} only returned data for the following metrics {}'.format(host, metrics))\\n else:\\n logger.warning('{} only returned data for the following metrics {}'.format(host, metrics))\\n else:\\n prometheus_data[user][host]['orderby'] = max(values)\\n\\n #logger.error('Removing unreporting servers: {}'.format(to_remove), 'info')\\n for server_to_remove in to_remove:\\n del prometheus_data[user][server_to_remove]\\n\\n for host in down_servers:\\n if host not in prometheus_data[user]:\\n prometheus_validity['total_checks'] += 1\\n prometheus_data[user][host] = {}\\n prometheus_data[user][host]['name'] = host.rstrip(':9100')\\n prometheus_data[user][host]['summary'] = {}\\n\\n prometheus_data[user][host]['health_status'] = 'blue'\\n prometheus_data[user][host]['orderby'] = 0\\n\\n # Data should be considerd stale after 5 minutes\\n prometheus_validity['valid_until'] = time.time() * 1000 + 300000\\n return prometheus_data, prometheus_validity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cded0b70ae673af040136c4298db57b\",\n \"score\": \"0.5501371\",\n \"text\": \"def _get_class_dict(self):\\n with open(self._opts._ecfile, \\\"r\\\") as ec_fobj:\\n for line in ec_fobj:\\n fields = line.strip().split()\\n if fields[1].endswith('.csv'): \\n fields[1] = fields[1].rstrip('.csv')\\n\\n if self._opts._labels == 'EC':\\n self.class_dict[fields[1]] = {'id': len(self.class_dict),\\n 'size': int(fields[0]),\\n }\\n if self._opts._labels == 'GO':\\n self.class_dict[fields[1].split('_')[1]] = {'id': len(self.class_dict),\\n 'size': int(fields[0]),\\n }\\n\\n # get a reverse dict:\\n for key in self.class_dict.keys():\\n self.id_to_class[self.class_dict[key]['id']] = key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a571171799d69f23a6ea26cd979ae633\",\n \"score\": \"0.549581\",\n \"text\": \"def to_dict(self, cls):\\n return {\\n 'id': self.id,\\n 'cloud': self.cloud,\\n 'type': _type_name(cls),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14a096fed028a92bc937b0d046a7376\",\n \"score\": \"0.5495606\",\n \"text\": \"def get_stats(self, obj):\\n stats = {}\\n stats['severity'] = dict(obj.get_vuls_sev_count())\\n stats['cwe'] = dict(obj.get_vuls_cwe_count())\\n stats['owasp'] = dict(obj.get_vuls_owasp_count())\\n stats['true_positive_count'] = obj.get_true_positive_vuls_count()\\n stats['false_positive_count'] = obj.get_false_positive_vuls_count()\\n stats['vuls_count'] = obj.get_vuls_count()\\n return stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e4efebc885a79324b15b96aafcf4b9a\",\n \"score\": \"0.5493421\",\n \"text\": \"def create_counts(cls, dataset):\\n prior_counts = defaultdict(int)\\n # Triple nested dictionary. Key of first dict contains attribute names, second dict\\n # contains attribute values as keys, third dict contains class names as keys\\n posterior_counts = defaultdict(lambda: defaultdict(lambda: defaultdict(int)))\\n # Structure to store how many missing values exist for each attribute. A key refers\\n # to the attribute name, second key refers to class name\\n missing_counts = defaultdict(lambda: defaultdict(int))\\n\\n # Add prior counts to default dict\\n for row in dataset.table:\\n # Assuming last column is the class\\n prior_counts[row[-1]] += 1\\n # Add posterior counts to data structure\\n for row in dataset.table:\\n row_index = 0\\n for attribute in row[:-1]:\\n # If attribute == ?, then add to missing counts dict\\n if attribute == '?':\\n missing_counts[row_index][row[-1]] += 1\\n else:\\n # Initialise all the dictionaries of each possible attribute value\\n # to contain all possible classes. Note missing values will not contribute\\n # to the counts and are treated as a separate count that will not be used\\n for key, value in prior_counts.items():\\n posterior_counts[row_index][attribute][key]\\n row_index += 1\\n # Now add the counts\\n row_index = 0\\n for attribute in row[:-1]:\\n # Skip adding missing values\\n if attribute == '?':\\n row_index += 1\\n else:\\n # Add to the counts\\n posterior_counts[row_index][attribute][row[-1]] += 1\\n row_index += 1\\n\\n return prior_counts, posterior_counts, missing_counts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e2c8486f687f51d181609e20eaf51f1\",\n \"score\": \"0.54889596\",\n \"text\": \"def __init__(self):\\n self.__samples_counter = 0\\n self.__metric_accumulator = 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f1533330e583465d252dedf5ea069d8\",\n \"score\": \"0.5488704\",\n \"text\": \"def merge_metrics(self):\\n\\n self.metrics.extend(self._metrics.values())\\n del self._metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"962dedde064cffd5b66d6590710823d3\",\n \"score\": \"0.54857165\",\n \"text\": \"def to_dict(self) -> dict:\\n attack_attrs = {} if not self.attack else \\\\\\n {'max_iter': self.attack.max_iter}\\n return {\\n 'dataset': self.config.dataset,\\n 'dataset_name': utility.name_only(self.config.dataset),\\n 'n_records': self.n_records,\\n 'n_attributes': self.n_attr,\\n 'attrs': self.attrs,\\n 'immutable': self.mask_cols,\\n 'attr_mutable': self.cls.mutable,\\n 'attr_immutable': self.cls.immutable,\\n 'robust': self.config.robust,\\n 'classes': self.cls.class_names,\\n 'k_folds': self.config.folds,\\n 'validator': self.config.validator,\\n 'classifier': self.config.cls,\\n 'sample_size': self.config.sample_size,\\n 'sample_times': self.config.sample_times,\\n 'attack': self.config.attack,\\n 'attr_ranges': dict(\\n zip(self.attrs, self.attr_ranges.values())),\\n 'start': self.start_time,\\n 'end': self.end_time,\\n 'current_utc': int(time.time_ns()),\\n **attack_attrs,\\n **self.stats.__dict__,\\n 'machine': machine.machine_details()\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"869be8edc977bd0d90e95961841e393c\",\n \"score\": \"0.54809195\",\n \"text\": \"def metrics(self, metrics):\\n\\n self._metrics = metrics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91b835368569072aac2f3436aee608a3\",\n \"score\": \"0.5479385\",\n \"text\": \"def collect(self): # pragma: NO COVER\\n # Make a shallow copy of self._view_name_to_data_map, to avoid seeing\\n # concurrent modifications when iterating through the dictionary.\\n metrics_map = {}\\n for v_name, view_data in self._view_name_to_data_map.copy().items():\\n if v_name not in self.registered_views:\\n continue\\n desc = self.registered_views[v_name]\\n for tag_values in view_data.tag_value_aggregation_data_map:\\n agg_data = view_data.tag_value_aggregation_data_map[tag_values]\\n metric = self.to_metric(desc, tag_values, agg_data, metrics_map)\\n\\n for metric in metrics_map.values():\\n yield metric\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd96fef38a4b84ec61ebd006ad2f93dd\",\n \"score\": \"0.5478706\",\n \"text\": \"def create(self, metrics):\\n for metric_type, metric_list in metrics.items():\\n for metric in metric_list:\\n module = import_module(\\n name=u\\\"telemetry.metrics\\\", package=metric_type.capitalize()\\n )\\n self.metric_registry[metric[u\\\"name\\\"]] = getattr(\\n module, metric_type.capitalize()\\n )(**metric)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe4551ba87b0cd086632042fd2203714\",\n \"score\": \"0.5476459\",\n \"text\": \"def to_dict(self):\\n obj_dict = super().to_dict()\\n obj_dict['class'] = str(self.__class__),\\n return obj_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cf454024a4190215a4427740da398c7\",\n \"score\": \"0.5475734\",\n \"text\": \"def collect(self) -> Statistics:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"498f81d22643a3c3303f2537a6bbd52c\",\n \"score\": \"0.5471475\",\n \"text\": \"def extract_data(self):\\n self.avg_array = []\\n self.title_line = self.data_array[0]\\n self.kbmemused_index = self.data_array[0].index(\\\"kbmemused\\\")\\n self.kbmemfree_index = self.data_array[0].index(\\\"kbmemfree\\\")\\n self.kbbuffers_index = self.data_array[0].index(\\\"kbbuffers\\\")\\n self.kbcached_index = self.data_array[0].index(\\\"kbcached\\\")\\n self.ts_index = self.data_array[0].index(\\\"TimeStamp\\\")\\n del self.data_array[0]\\n\\n # time stamps\\n self.time_stamp_array = []\\n for self.row in self.data_array:\\n self.time_stamp_array.append((int(self.row[self.ts_index])))\\n\\n # extract kbmemused metric\\n self.kbmemused = []\\n for self.row in self.data_array:\\n self.kbmemused.append(int(self.row[self.kbmemused_index]))\\n\\n # extract kbmemfree metric\\n self.kbmemfree = []\\n for self.index, self.row in enumerate(self.data_array):\\n self.kbmemfree.append(int(self.row[self.kbmemfree_index]))\\n\\n # extract kbbuffers metric\\n self.kbbuffers = []\\n for self.row in self.data_array:\\n self.kbbuffers.append(int(self.row[self.kbbuffers_index]))\\n\\n # extract kbcached metric\\n self.kbcached = []\\n for self.index, self.row in enumerate(self.data_array):\\n self.kbcached.append(int(self.row[self.kbcached_index]))\\n\\n # calculate the application memory utilization metric\\n # The application level memory used is the total memory used minus buffers and cached memory\\n self.kbappmemused = []\\n for self.row in self.data_array:\\n self.kbappmemused.append(int(self.row[self.kbmemused_index]) - int(self.row[self.kbbuffers_index]) - int(\\n self.row[self.kbcached_index]))\\n\\n self.data_array.insert(0, self.title_line)\\n\\n self.avg_array.append(self.time_stamp_array)\\n self.avg_array.append(self.kbmemfree)\\n self.avg_array.append(self.kbmemused)\\n self.avg_array.append(self.kbbuffers)\\n self.avg_array.append(self.kbcached)\\n self.avg_array.append(self.kbappmemused)\\n\\n return self.avg_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd464e4ace82c22acbca9d7c73de7ca\",\n \"score\": \"0.5470789\",\n \"text\": \"def _print_metrics(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a64cfbe77879edbe43e8dedeaa52d847\",\n \"score\": \"0.5470612\",\n \"text\": \"def __data_map_to_service_metrics(self, data_map):\\n result = {}\\n for service, metrics in data_map.items():\\n actual_metrics = metrics.get('metrics', None)\\n if actual_metrics is None:\\n logging.error('Unexpected response from \\\"%s\\\"', service)\\n else:\\n result[service] = actual_metrics\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ba6f0a5a121842fe5edd4fe3061202\",\n \"score\": \"0.5470285\",\n \"text\": \"def report(self) -> Dict:\\n array = np.array(self._values)\\n return {\\n \\\"min\\\": array.min(),\\n \\\"max\\\": array.max(),\\n \\\"medium\\\": np.median(array),\\n \\\"mean\\\": array.mean(),\\n \\\"stdDev\\\": array.std(),\\n \\\"percentile\\\": {\\n \\\"75\\\": np.percentile(array, 75),\\n \\\"95\\\": np.percentile(array, 95),\\n \\\"99\\\": np.percentile(array, 99)\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bafccfdb46eed6d7eaf8ba768480b1eb\",\n \"score\": \"0.54575604\",\n \"text\": \"def cell_measures(self) -> Dict[str, List[str]]:\\n\\n obj = self._obj\\n all_attrs = [\\n ChainMap(da.attrs, da.encoding).get(\\\"cell_measures\\\", \\\"\\\")\\n for da in obj.coords.values()\\n ]\\n if isinstance(obj, DataArray):\\n all_attrs += [ChainMap(obj.attrs, obj.encoding).get(\\\"cell_measures\\\", \\\"\\\")]\\n elif isinstance(obj, Dataset):\\n all_attrs += [\\n ChainMap(da.attrs, da.encoding).get(\\\"cell_measures\\\", \\\"\\\")\\n for da in obj.data_vars.values()\\n ]\\n\\n keys = {}\\n for attr in all_attrs:\\n keys.update(parse_cell_methods_attr(attr))\\n measures = {key: _get_all(self._obj, key) for key in keys}\\n\\n return {k: sorted(set(v)) for k, v in measures.items() if v}\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1785,"cells":{"query_id":{"kind":"string","value":"112830f08bad1cb2397356518037ea65"},"query":{"kind":"string","value":"Stop EC2 instance by ID"},"positive_passages":{"kind":"list like","value":[{"docid":"a2b50f25f8f1f38e24fc4166ab3f75f9","score":"0.77420604","text":"def stop_instance(self, arg_instance_id):\n try:\n print(f'Instance \"{arg_instance_id}\" stopping begins.')\n instance = self.ec2_resource.Instance(arg_instance_id)\n instance.stop()\n instance.wait_until_stopped()\n print(f'Instance \"{instance.id}\" has stopped.')\n return [True, '']\n except ClientError as err:\n return [False, self.parse_exception('stop_instance', err)]","title":""}],"string":"[\n {\n \"docid\": \"a2b50f25f8f1f38e24fc4166ab3f75f9\",\n \"score\": \"0.77420604\",\n \"text\": \"def stop_instance(self, arg_instance_id):\\n try:\\n print(f'Instance \\\"{arg_instance_id}\\\" stopping begins.')\\n instance = self.ec2_resource.Instance(arg_instance_id)\\n instance.stop()\\n instance.wait_until_stopped()\\n print(f'Instance \\\"{instance.id}\\\" has stopped.')\\n return [True, '']\\n except ClientError as err:\\n return [False, self.parse_exception('stop_instance', err)]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"cd1761b40bdc3960e6b73585e5a48752","score":"0.8403922","text":"def aws_stop(self, msg, args):\n id = args\n instance = self._get_instance_by_id(id)\n response = \"\"\n if self._is_instance_permitted(id):\n i = self.ec2.instances.filter(InstanceIds=[id]).stop()\n reponse = \"Instance {} stopped\".format(self._get_instance_name(instance))\n else:\n response = \"Instance not in permitted list. Use `aws addpermission ID` command.\"\n return reponse","title":""},{"docid":"bddf37580f8e17135ef26a036759cde9","score":"0.8211793","text":"def stop_ec2(ec2_id, instance):\n\n ec2 = boto3.resource('ec2')\n\n # If the given ec2_id is for an EC2 template, then terminate the EC2 instance\n if ec2_id.split('-')[0] == 'lt':\n ec2.instances.filter(InstanceIds=[instance.id]).terminate()\n logging.info('Terminated EC2 instance {}'.format(instance.id))\n\n # If the given ec2_id is for an existing EC2 instance, then stop it\n else:\n instance.stop()\n logging.info('Stopped EC2 instance {}'.format(ec2_id))","title":""},{"docid":"7f8a95a8c40863c8c18956346e940905","score":"0.79622316","text":"def stop_instance(instance_id):\n try:\n response = ec2.Instance(instance_id).stop()\n logger.info(\"Stopped instance %s.\", instance_id)\n except ClientError:\n logger.exception(\"Couldn't stop instance %s.\", instance_id)\n raise\n else:\n return response","title":""},{"docid":"282e53e14241dc76b5040c3937f197e3","score":"0.766608","text":"def stop_instance(instance):\n instance.stop()","title":""},{"docid":"01c68c61309c640643e6fb975a0c5065","score":"0.7646813","text":"def stop_ec2():\n print(\"------------------------------\")\n print(\"Try to stop the EC2 instance.\")\n print(\"------------------------------\")\n\n try:\n ec2.stop_instances(InstanceIds=[Mem.instance_id], DryRun=True)\n except ClientError as e:\n if 'DryRunOperation' not in str(e):\n raise\n\n # Dry run succeeded, call stop_instances without dryrun\n try:\n response = ec2.stop_instances(InstanceIds=[Mem.instance_id], DryRun=False)\n print(\"Success\",\"Stopped\",response)\n except ClientError as e:\n print(e)","title":""},{"docid":"e053cbe85f12d37ae5b4c63f5fc0604f","score":"0.7582602","text":"def stop(self, instance_id=0):\n packet = self._create_packet('Stop', {'InstanceID': instance_id, 'Speed': 1})\n _send_tcp((self.ip, self.port), packet)","title":""},{"docid":"7a0960f94a864c4f05f5bff9cd7577fc","score":"0.74688095","text":"def terminate_current_instance():\n metadata = requests.get(\n \"http://instance-data/latest/dynamic/instance-identity/document\"\n ).json()\n return subprocess.run(\n [\n \"aws\",\n \"ec2\",\n \"terminate-instances\",\n \"--instance-ids\",\n metadata[\"instanceId\"],\n \"--region\",\n metadata[\"region\"],\n ],\n check=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n )","title":""},{"docid":"c5e17b395c7ed1b297567785fad34a74","score":"0.7275048","text":"def _stop(self, instance_id, timeout=None, expected_rcs=frozenset({0})):\n service_manager = self.instances[instance_id]['service_manager']\n service_manager.stop(timeout=timeout, expected_rcs=expected_rcs)","title":""},{"docid":"124130959f8d13bb3e37b7bd5f135fe7","score":"0.7208143","text":"def ec2_instance_stop(instance_list) :\n instances = instance_list\n if instances:\n if isinstance(instance_list, list) :\n instances = ','.join(instance_list)\n cmdargs = [\n 'aws', 'ec2', 'terminate-instances', '--instance-ids', instances\n ]\n cmd = ' '.join(cmdargs)\n os.system(cmd)\n return instances.split(\",\")","title":""},{"docid":"abbc4afe08f8044660a5387f259c38aa","score":"0.7182307","text":"def _stop(self, instance):\n container = OvzContainer.find(uuid=instance['uuid'])\n sf = ovzshutdown.OVZShutdownFile(container.ovz_id, 700)\n with sf:\n sf.read()\n sf.run_contents()\n\n ovz_utils.execute('vzctl', 'stop', container.ovz_id, run_as_root=True)\n\n # Update instance state\n self.virtapi.instance_update(\n context.get_admin_context(), instance['uuid'],\n {'power_state': power_state.SHUTDOWN})","title":""},{"docid":"c98efe5713edd41a89f0f45e45169a92","score":"0.7180752","text":"def terminate_instance(self, dryrun, instanceids):\n from wrapper.ec2 import Ec2\n ec2 = Ec2(session=self.session)\n ret = ec2.terminate_instance(dryrun=dryrun, instanceids=instanceids)\n return ret","title":""},{"docid":"2e08c30dc0954818e41cef30a2f96732","score":"0.7144824","text":"def terminate_instance():\n\n # get public IP\n public_ip = get_public_ip()\n\n # disassociate IP with this instance and terminate the instance\n cmd = '/usr/bin/euca-disassociate-address %s' % public_ip\n log.debug('Doing: %s' % cmd)\n with os.popen(cmd) as fd:\n result = fd.readline()\n log.debug('result: %s' % str(result))\n\n cmd = '/usr/bin/euca-terminate-instances %s' % Instance\n log.debug('Doing: %s' % cmd)\n with os.popen(cmd) as fd:\n result = fd.readline()\n log.debug('result: %s' % str(result))\n\n sys.exit(0)","title":""},{"docid":"46c38d8e0ead1ef8cb25a58075dcb7f8","score":"0.7132969","text":"def force_stop_server(self, req, id, body):\n context = req.environ['nova.context']\n instance = common.get_instance(self.compute_api, context, id)\n extensions.check_compute_policy(context, 'stop', instance)\n\n LOG.debug(\"context = %s\", context)\n\n try:\n self.compute_api.stop(context, instance, clean_shutdown=False)\n except exception.InstanceInvalidState as state_error:\n common.raise_http_conflict_for_instance_invalid_state(\n state_error, 'force_stop', id)\n except (exception.InstanceNotReady, exception.InstanceIsLocked) as e:\n raise webob.exc.HTTPConflict(explanation=e.format_message())\n return webob.Response(status_int=202)","title":""},{"docid":"a1ec5e1c4e7aff352e955cc9c64bbaad","score":"0.711742","text":"def stop(self, name):\n res=[]\n if name == self.Name:\n ec2 = boto3.client('ec2', self.Region)\n res=ec2.stop_instances(\n InstanceIds=[\n self.ID\n ]\n )\n return res","title":""},{"docid":"7ffc949fd13f97f7e21b851cb81de7a3","score":"0.7049946","text":"def stop(self, _id):\n\n self._run_action(_id, 'stop')","title":""},{"docid":"d07b8d5e86b414cee87c11099ad6a4e5","score":"0.7047405","text":"def nova_stop_server(self,instance_name):\n try:\n server = self.novaclient.servers.find(name = instance_name)\n id = server.id\n ret = self.novaclient.servers.stop(id)\n \n\n except (ClientException, Exception) as e:\n return (400, e.message, [])\n return (200, \"success\", ret)","title":""},{"docid":"365abc5df5baa0c44268afcdf3df8804","score":"0.69981503","text":"def terminate():\n conn = ec2.connect_to_region(env.farmboy_aws_region)\n # check for instances\n running_instances = conn.get_only_instances(\n filters={'tag-key': 'farmboy',\n 'instance-state-name': 'pending',\n 'instance-state-name': 'running'})\n\n # TODO(termie): we _probably_ want to just terminate them all and start\n # new ones, is there a use case for keeping them?\n for instance in running_instances:\n util.puts('[aws] Terminating running instance: %s (%s)'\n % (instance.id, instance.tags['farmboy']))\n instance.terminate()","title":""},{"docid":"ba6e2ae04e59896e728a769dd17f0375","score":"0.69607925","text":"def stop(self, name=None, **kwargs):\n result = None\n compute_service = self._get_compute_service()\n _operation = None\n if name is None:\n return\n try:\n\n project_id = kwargs.get('project_id',\n self.auth_config[\"project_id\"])\n zone = kwargs.get('zone', self.default_config[\"zone\"])\n\n _operation = compute_service.instances().stop(\n project=project_id,\n zone=zone,\n instance=name).execute()\n\n self._wait_for_operation(compute_service,\n _operation,\n project_id,\n zone,\n name)\n\n # Get the instance details to update DB.\n result = self._info(name, displayType=\"vm\")\n\n except Exception as se:\n if type(se) == HttpError:\n Console.error(\n f'Unable to stop instance {name}. '\n f'Reason: {se._get_reason()}')\n else:\n Console.error(f'Unable to stop instance {name}.')\n\n return result","title":""},{"docid":"9984119c195c71b5c8d7588adbfebc6b","score":"0.6957024","text":"def stop(self):\n requests = self.describe_active_spot_instance_requests()\n request_ids = self._extract_spot_requests_ids(requests)\n\n if request_ids:\n instance_ids = self._extract_spot_requests_instance_ids(requests)\n self.ec2_client.cancel_spot_instance_requests(SpotInstanceRequestIds=request_ids)\n self.ec2_client.terminate_instances(InstanceIds=instance_ids)","title":""},{"docid":"29ef460820d5ceacc88ca3d9519b7b95","score":"0.69186205","text":"def stop(instance, instance_name):\n try:\n instance.stop()\n except novaclient.exceptions.Conflict, conflict_exception:\n pattern = re.compile(ALREADY_STOPPED_PATTERN)\n if pattern.match(conflict_exception.message):\n logging.warning(\"Instance '{}' \" \\\n \"already stopped\".format(instance_name))\n else:\n raise conflict_exception","title":""},{"docid":"5937763e204ccad1bdea99f8cc344e1f","score":"0.6883898","text":"def InstanceStop(self):\n resp = self.gen_instmgr.Stop(self.instance_id, force=False)\n logging.info('Instance stop issued: %s', self.instance_id)\n # force stop again\n resp = self.gen_instmgr.Stop(self.instance_id, force=True)\n status = self._GetStatus(resp)\n logging.info('Instance stop force issued: %s, %s', self.instance_id, status)\n if status != States.STOPPED:\n endtime = time.time() + FLAGS.ibmcloud_timeout\n status = self._GetStatus(self.gen_instmgr.Show(self.instance_id))\n while status != States.STOPPED and time.time() < endtime:\n time.sleep(FLAGS.ibmcloud_polling_delay * 2)\n status = self._GetStatus(self.gen_instmgr.Show(self.instance_id))\n logging.info('Checking instance stop status: %s, %s', self.instance_id,\n status)\n return status","title":""},{"docid":"9771ffe61be299a74a122b79f52369e3","score":"0.6856986","text":"def stop_vm(name: str, zone: str):\n try:\n click.echo('Stopping instance {}'.format(name))\n call(['gcloud', 'compute', 'instances', 'stop', name, '--zone', zone])\n except CalledProcessError:\n print('Cannot stop VM')","title":""},{"docid":"7ade147c2513eb4c00702eaea9562740","score":"0.68492407","text":"def test_stop_ec2_instance(aws_region, tag_key, tag_value, result_count):\n client = boto3.client(\"ec2\", region_name=aws_region)\n launch_ec2_instances(3, aws_region, tag_key, tag_value)\n\n ec2_scheduler = InstanceScheduler(aws_region)\n ec2_scheduler.cloudwatch_alarm = CloudWatchAlarmScheduler(aws_region)\n ec2_scheduler.stop(\"tostop\", \"true\")\n instances = client.describe_instances()[\"Reservations\"][0][\"Instances\"]\n assert len(instances) == 3\n for instance in instances:\n assert instance[\"State\"] == result_count","title":""},{"docid":"7b197ecffa9979624bef7533b36c529c","score":"0.6820726","text":"def stop_sound_instance(instance_id: int) -> None:\n pass","title":""},{"docid":"343cb1085693a3f80835b3865b3483ef","score":"0.6792971","text":"def handle_stop(args):\n _latest = LATEST if args[\"latest\"] else NEXT\n #: rewrite so this is not in two places\n instance_name = \"run_{}_{}\".format(CONFD[\"instance_name\"], _latest)\n cmd = \"{} container kill {}\".format(OCI_CMD, instance_name)\n run_subprocess(cmd)","title":""},{"docid":"9ae084f54c5b597015363926a219b471","score":"0.678534","text":"def terminate_instance(self):\n try:\n logging.info('Stopping %s' % self.instance_name)\n gce_helper = self.create_gce_helper()\n gce_helper.stop_instance(self.instance_name, self.zone)\n except (gce.ApiError, gce.ApiOperationError, ValueError) as e:\n logging.error(DELETE_ERROR, {'name': self.instance_name})\n logging.error(e)\n return False\n return True","title":""},{"docid":"e5b3e107ff2f5da2d672e516547f2c6a","score":"0.6779469","text":"def terminate_ec2():\n print(\"------------------------------\")\n print(\"Try to Terminate the EC2 instance.\")\n print(\"------------------------------\")\n\n try:\n ec2.terminate_instances(InstanceIds=[Mem.instance_id], DryRun=True)\n except ClientError as e:\n if 'DryRunOperation' not in str(e):\n raise\n\n # Dry run succeeded, call terminate_instances without dryrun\n try:\n response = ec2.terminate_instances(InstanceIds=[Mem.instance_id], DryRun=False)\n print(\"Success\",\"Terminate\",response)\n except ClientError as e:\n print(e)","title":""},{"docid":"4ab70d588af63486d5a449b043781a33","score":"0.67780674","text":"def stop( self, trans, id ):\n uci = get_uci( trans, id )\n if ( uci.state != uci_states.DELETING ) and \\\n ( uci.state != uci_states.DELETING_UCI ) and \\\n ( uci.state != uci_states.ERROR ) and \\\n ( uci.state != uci_states.SHUTTING_DOWN_UCI ) and \\\n ( uci.state != uci_states.SHUTTING_DOWN ) and \\\n ( uci.state != uci_states.ADD_STORAGE_UCI ) and \\\n ( uci.state != uci_states.ADD_STORAGE ) and \\\n ( uci.state != uci_states.AVAILABLE ):\n uci.state = uci_states.SHUTTING_DOWN_UCI\n session = trans.sa_session\n session.add( uci )\n session.flush()\n trans.log_event( \"User stopped cloud instance '%s' (id: %s)\" % ( uci.name, uci.id ) )\n trans.set_message( \"Stopping of Galaxy instance '%s' initiated.\" % uci.name )\n \n return self.list( trans )\n \n trans.show_error_message( \"Cannot stop instance that is in state '%s'.\" % uci.state )\n return self.list( trans )","title":""},{"docid":"2a3a29572cd3849a858604ba41dc928b","score":"0.6777065","text":"def cmd_stop(client: RemoteDockerClient):\n print(client.stop_instance())","title":""},{"docid":"b76252b26795301b000fcfd408061739","score":"0.6742772","text":"def test_stop_ec2_instance(aws_region, aws_tags, result_count):\n client = boto3.client(\"ec2\", region_name=aws_region)\n launch_ec2_instances(3, aws_region, \"tostop\", \"true\")\n\n ec2_scheduler = InstanceScheduler(aws_region)\n ec2_scheduler.cloudwatch_alarm = CloudWatchAlarmScheduler(aws_region)\n ec2_scheduler.stop(aws_tags)\n instances = client.describe_instances()[\"Reservations\"][0][\"Instances\"]\n assert len(instances) == 3\n for instance in instances:\n assert instance[\"State\"] == result_count","title":""},{"docid":"aefa36432b7e0d0f591526baf7b53c9f","score":"0.6730674","text":"def halt():\n instance = _get_proxy_instance(TAG_NAME)\n if instance and instance['state'] in ['running']:\n print yellow('Stopping instance.')\n local('osascript %s/vpnconnection.scpt %s halted'\n '' % (PROJECT_ROOT, TAG_NAME))\n conn.stop_instances([instance['id']])\n return exit(0)\n print red('FAILURE: Instance cannot be stopped.')\n print instance\n return exit(1)","title":""},{"docid":"9a7f6f625e471ccd28b832da28a57f95","score":"0.6710691","text":"def remove_instance(self, arg_instance_id):\n try:\n print(f'Instance \"{arg_instance_id}\" removing begins.')\n instance = self.ec2_resource.Instance(arg_instance_id)\n instance.terminate()\n instance.wait_until_terminated()\n return [True, '']\n except ClientError as err:\n return [False, self.parse_exception('remove_instance', err)]","title":""},{"docid":"6707dd13a3abefb0acfc7ebb6d6a93a7","score":"0.6706253","text":"def do_stop(self, machine_id):\n try:\n machine_ref = self.session.xenapi.VM.get_by_uuid(machine_id)\n\n print \"stopping machine with id %s\" % machine_id\n self.session.xenapi.VM.clean_shutdown(machine_ref)\n except Failure:\n print \"machine with id %s is not found\" % machine_id","title":""},{"docid":"40ac8b61b04875f903f461ea53fb11e0","score":"0.6681216","text":"def setup_instance_stop(r, **attr):\n\n setup_instance_method(r.component_id, \"stop\")\n\n current.session.confirmation = current.T(\"Instance Stopped\")\n\n redirect(URL(c=\"setup\", f=\"deployment\",\n args = [r.id, \"instance\"]),\n )","title":""},{"docid":"9ef0ec2c95938c63a389ba805b0eb025","score":"0.66621834","text":"def terminate():\n conn = client.Client(env.farmboy_os_username,\n env.farmboy_os_password,\n env.farmboy_os_tenant_name,\n env.farmboy_os_auth_url,\n service_type='compute')\n\n # check for instances\n running_instances = conn.servers.list()\n\n # TODO(termie): we _probably_ want to just terminate them all and start\n # new ones, is there a use case for keeping them?\n for instance in running_instances:\n if (instance.metadata.get('farmboy')\n and instance.status == 'ACTIVE'\n and getattr(instance, 'OS-EXT-STS:task_state') == None):\n util.puts('[os] Terminating running instance: %s (%s)'\n % (instance.id, instance.metadata['farmboy']))\n instance.stop()\n instance.delete()","title":""},{"docid":"04b50d83372c8410ba11833d7953ec45","score":"0.66532624","text":"def stop(self, instance_name):\n\n vstop_input = RwVcsYang.VStopInput()\n vstop_input.instance_name = instance_name\n\n response_xml = self.mgmt_proxy.rpc(vstop_input.to_xml(yang_model.yang_model))\n response_xml = self.mgmt_proxy.get_GI_compatible_xml(response_xml, rpc_name='vstop')\n\n vstop_output = RwVcsYang.VStopOutput()\n vstop_output.from_xml(yang_model.yang_model, response_xml)\n\n return vstop_output","title":""},{"docid":"c071420f896991e2fc4cc01efae68227","score":"0.66297984","text":"def is_instance_stopped( self, instance_id, verbose=False ):\n return self.poll_instances( [instance_id], 0, 0, awsext.ec2.INSTANCE_STATE_CODE_STOPPED, verbose, is_instance_check=True )","title":""},{"docid":"27cb2fc5f28c791ff06bb1d7899cb1d0","score":"0.66289014","text":"def stop(self, force=False):\n logging.info(\"Stop VM\")\n params = {}\n params.setdefault(\"InstanceId\", self.id)\n if force:\n params.setdefault(\"ForceStop\", \"True\")\n return sdk.stop_instance(params)","title":""},{"docid":"d7b3ba6c5373d6c8272379545d2c74fd","score":"0.6619052","text":"def stopped():\n stopped = aws_cli().get_stopped_instances()\n click.echo(stopped)","title":""},{"docid":"4a3f08ce56b315f1280936484d22c1c0","score":"0.6618662","text":"def stop(self):\n if self.instance is None:\n logger.info(\"No instance to stop.\")\n return\n\n try:\n response = self.instance.stop()\n self.instance.wait_until_stopped()\n except ClientError as err:\n logger.error(\n \"Couldn't stop instance %s. Here's why: %s: %s\", self.instance.id,\n err.response['Error']['Code'], err.response['Error']['Message'])\n raise\n else:\n return response","title":""},{"docid":"1dfc5fbf6638c6a8438890921e151889","score":"0.6582461","text":"def stop_instances(self):\n self.aws.stop_instances()\n self.azure.stop_instances()","title":""},{"docid":"6cb993eadeb2aa6a8f6d170bf97b3fee","score":"0.6553835","text":"def stop_running():\n instances = [instance for instance in get_state(\"running\")]\n if len(instances) > 0:\n stop(*instances)\n else:\n out.out(\"No running instances.\")","title":""},{"docid":"59c59f401123232e8a34760973119229","score":"0.65253055","text":"def StopInstance(self, instance, force=False, retry=False, name=None,\n timeout=None):\n assert(timeout is None or force is not None)\n\n if name is None:\n name = instance.name\n if not self._IsAlive(name):\n raise errors.HypervisorError(\"Failed to stop instance %s: %s\" %\n (name, \"not running\"))\n self._MarkDown(name)","title":""},{"docid":"8c3b20e21059d160cb7c02147234a82b","score":"0.6518349","text":"def remove_instance(self, instance_id=''):\n if not instance_id:\n log.warning(\"Tried to remove an instance but did not receive instance ID\")\n return False\n log.debug(\"Specific termination of instance '%s' requested.\" % instance_id)\n for inst in self.worker_instances:\n if inst.id == instance_id:\n inst.worker_status = 'Stopping'\n log.debug(\"Set instance {0} state to {1}\".format(inst.get_desc(),\n inst.worker_status))\n for job_manager_svc in self.service_registry.active(\n service_role=ServiceRole.JOB_MANAGER):\n job_manager_svc.remove_node(inst)\n # Remove the given instance from /etc/hosts files\n misc.remove_from_etc_hosts(inst.private_ip)\n self.sync_etc_hosts()\n # Terminate the instance\n inst.terminate()\n log.info(\"Initiated requested termination of instance. \"\n \"Terminating '%s'.\" % instance_id)","title":""},{"docid":"ea3c7c6fc8265432323af3d865a683bc","score":"0.64868414","text":"def stop(name=None):\n\n Console.ok(f\"shutting down server {name}\")\n\n registry = Registry()\n\n entries = registry.list(name=name)\n pid = \"\"\n result = None\n if len(entries) > 1:\n Console.error(f\"Aborting, returned more than one entry from the Registry with the name {name}\")\n raise Exception\n elif len(entries) == 1:\n pid = str(entries[0]['pid'])\n else:\n result = Server.ps(name=name)\n\n try:\n if len(pid) > 0: # check if pid found in registry and if found kill\n print(\"Killing:\", pid)\n Shell.kill_pid(pid)\n registry.delete(name=name)\n elif result: # no server found in registry so kill based on ps output\n for process in result:\n pid = str(process[\"pid\"])\n if len(pid) > 0:\n print(\"Killing:\", pid)\n Shell.kill_pid(pid)\n registry.delete(name=name)\n else:\n print()\n Console.error(f\"PS output generated invalid PID for server name {name}\")\n else:\n print()\n Console.error(f\"No Cloudmesh OpenAPI Server found with the name {name}\")\n except:\n print(\"got to exception\")\n Console.error(\n f\"No Cloudmesh OpenAPI Server found with the name {name}\")","title":""},{"docid":"dc0b6b8d9142190c364349f2c8c5b4b6","score":"0.647184","text":"def StopInstance(self, instance, force=False, retry=False, name=None,\n timeout=None):\n assert(timeout is None or force is not None)\n\n if name is None:\n name = instance.name\n\n return self._StopInstance(name, force, instance.hvparams, timeout)","title":""},{"docid":"6add74246d539fe4709544df35d505c6","score":"0.6461264","text":"def stop(self):\r\n\t\tstopCmd = self.replaceTag(self.actionstop, self._properties)\r\n\t\tif not self.executeCmd(stopCmd, self.timeout):\r\n\t\t\traise RuntimeError(\"Error stopping action\")","title":""},{"docid":"51c241563bf9a182d531dc0fa3ade2bf","score":"0.6432939","text":"def stop(inst):\n if not os.path.exists(inst.uwsgi_vassal):\n print 'vassal %s not running' % inst.name\n return\n try:\n os.unlink(inst.uwsgi_vassal)\n print 'stop request submitted for %s' % inst.name\n except:\n print 'error unlinking vassal'\n return\n inst.uwsgi_port = None\n return","title":""},{"docid":"a90a43d5c281edce42412fdd8a5bf1e5","score":"0.6430357","text":"def stop(self):\n if self.is_spot():\n raise exception.InvalidOperation(\n \"spot instances can not be stopped\")\n elif not self.is_ebs_backed():\n raise exception.InvalidOperation(\n \"Only EBS-backed instances can be stopped\")\n if not self.is_stopped():\n log.info(\"Stopping node: %s (%s)\" % (self.alias, self.id))\n return self.instance.stop()\n else:\n log.info(\"Node '%s' is already stopped\" % self.alias)","title":""},{"docid":"9ba45ebc9396754768b420a951c63234","score":"0.6426427","text":"def stop_experiment(api, exp_id):\n return api.stop_experiment(exp_id)","title":""},{"docid":"99c331e21fc4b4ad0d28c10e6d294943","score":"0.6414884","text":"def stop(self):\n logging.info('Stopping %s on %s...', self.description, self.fqdn)\n return self.ambari.execute(self.ambari_url, self.stop_command)","title":""},{"docid":"be2f1479cdede3610fdd22c7d526a991","score":"0.6414199","text":"def ec2_launch_and_terminate_instance(ec2_conn):\n\n # Launch a specific instance with this image\n ec2_conn.run_instance('<ami-image-id>')\n # FROM: http://star.mit.edu/cluster/ Latest AMI IDs (us-east-1)\n # Sample 32bit 12.04 Ubuntu with StarCluster: ami-7c5c3915 (i386)\n # Sample 64bit 12.04 Ubuntu with StarCluster: ami-765b3e1f (x86_64)\n\n # 'Graceful' stop instance (shut down, but not terminates so still billed)\n ec2_conn.stop_instances(instance_ids=['instance-id-1', 'instance-id-2'])\n\n # Completely done with instance, terminate/destroy it, never get back\n ec2_conn.terminate_instances(instance_ids=\n ['instance-id-1', 'instance-id-2'])","title":""},{"docid":"a91cafddc5bf75a1270df3bba264a762","score":"0.6413049","text":"def stop(**_):\n ctx.logger.info(\"VM stops.\")","title":""},{"docid":"025a36932bca4a6448f6a56316a9632a","score":"0.6409263","text":"def stop_server(ServerId=None):\n pass","title":""},{"docid":"3890cbc41756f7a28922e35b09449b63","score":"0.6408638","text":"def __terminate_instance(self, instance):\n\n self.getLogger().info(\n '[aws] Terminating instance [{0}]'.format(instance.id))\n\n try:\n instance.terminate()\n except boto.exception.EC2ResponseError as exc:\n self.getLogger().warning(\n 'Error while terminating instance [{0}]: {1}'.format(\n instance.id, exc.message))","title":""},{"docid":"b40ad8f85f4fa35101dd8984b5759d50","score":"0.6404182","text":"def delete_instance(controller, instance_id=None, instance_name=None):","title":""},{"docid":"318cf92986494b8baa0fde4ff90d866a","score":"0.63966775","text":"def poll_instance_stopped( self, instance_id, interval_secs, max_minutes, verbose=False ):\n return self.poll_instances( [instance_id], interval_secs, max_minutes, awsext.ec2.INSTANCE_STATE_CODE_STOPPED, verbose )","title":""},{"docid":"c6c1eb866ddbe702e9ae577d4367ff7b","score":"0.6391912","text":"def stop_all(self, exclude=[]):\n for instance in self.list_instances(exclude=exclude):\n # print(instance['InstanceId'])\n if instance['State']['Name'] == 'running':\n self.ec2.stop_all(InstanceIds=[instance['InstanceId']])\n else:\n print('Nothing to stop')","title":""},{"docid":"79f9e6256bfd8dd2f09c64962e5b6321","score":"0.6374421","text":"def do_stop(remove=False):\n\tfor name in getInstances():\t\n\t\tinst = mdb.getInstance(name)\n\t\tif not inst:\n\t\t\tlogError('Instance does not exist on this node',name)\n\t\t\tcontinue\n\n\t\tif not isRunning(name):\n\t\t\tlogError('Instance is already stopped',name)\n\t\t\tcontinue\n\t\t\t\t\n\t\tlogBegin('Stopping',name)\t\t\t\n\t\tssd = \"/dev/%s/%s\" % (inst['cache'],name)\t\t\n\t\t\n\t\tif run(\"/sbin/dmsetup remove %s\" % name):\n\t\t\tlogEnd('dmsetup Failed',False)\n\t\t\tcontinue\n\n\t\tlogMiddle('dm stopped',\"green\")\n\t\tif inst['type'] == 'RAID':\n\t\t\tif not do_down(inst): continue\n\t\telse:\n\t\t\tdev = mdb.getDevice(HOSTNAME,name)\n\t\t\tif not dev:\n\t\t\t\tlogError('No device known for',name)\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tif run(\"qemu-nbd -d /dev/\"+dev):\n\t\t\t\tlogMiddle(dev,\"red\")\n\t\t\t\tlogEnd('Fail',False)\n\t\t\t\tcontinue\n\t\t\telse:\t\n\t\t\t\tlogMiddle(dev,\"blue\")\n\n\t\tif not remove:\n\t\t\tlogEnd('Ok',True)\n\t\telse:\n\t\t\tlogMiddle('remove','blue')\n\t\t\tif run(\"lvremove -f \"+ssd):\n\t\t\t\tlogEnd('Fail',False)\t\n\t\t\telse:\t\n\t\t\t\tmdb.delDevice(HOSTNAME,name)\n\t\t\t\tlogEnd('Ok',True)","title":""},{"docid":"aa764335756afc992abb8444934fd653","score":"0.6364227","text":"def post(self):\n instance_ids = simplejson.loads(self.GetRequiredParameter('instance_ids'))\n if not instance_ids:\n logging.error('Missing Instance ID.')\n self.response.out.write('Missing Instance ID.')\n ec2 = ec2_manager.EC2Manager()\n bots_instances = ec2.StopInstances(instance_ids)\n self.response.out.write(\n simplejson.dumps([ec2_manager.BotoEC2DataObject.Encode(x)\n for x in bots_instances]))","title":""},{"docid":"ead9dad50d2e0b75ffaf6c58368b998d","score":"0.63527024","text":"def terminate(self, instance_ids):\n self.conn.terminate_instances(instance_ids)","title":""},{"docid":"5470e6a8d47eac78d1684732f94ace0b","score":"0.6347496","text":"def delete_instance(self, instance_id):\n pass","title":""},{"docid":"c92b63ee6c6e43d5045d83162eeb95d6","score":"0.6345559","text":"def stop_vm(self, node, vmid, **kwargs):\n self.api.nodes(node).qemu(vmid).status.stop.post(**kwargs)\n return","title":""},{"docid":"4cc7b73bfdf83d2005f68f83cd1e409f","score":"0.6343248","text":"def start_instance(instance_id: str):\n\n instance = ec2.Instance(instance_id)\n if instance.state[\"Name\"] not in [\"running\", \"pending\"]:\n instance.start()","title":""},{"docid":"e01bf34bf2a194af1b710db31d0f0c44","score":"0.6332923","text":"def cleanup_instances():\n instances = describe_instances()\n stop_instance_ids = []\n for i in instances:\n if i[\"name\"].startswith(\"buildkite\") or i[\"name\"] == \"website\":\n # Don't kill buildkite server(s) or the website\n continue\n\n launched_time = i[\"LaunchTime\"]\n uptime_delta = datetime.utcnow() - launched_time.replace(tzinfo=None)\n hours = uptime_delta.total_seconds() // 3600\n if hours > settings.EC2_MAX_HOURS:\n launch_time_str = launched_time.isoformat()\n logger.info(\n f\"Stopping instance {i['name']} with id {i['InstanceId']} with {hours}h uptime since {launch_time_str}\"\n )\n stop_instance_ids.append(i[\"InstanceId\"])\n\n if stop_instance_ids:\n logger.info(\"Stopping instance ids %s\", stop_instance_ids)\n client.terminate_instances(InstanceIds=stop_instance_ids)\n else:\n logger.info(\"No instances to stop.\")","title":""},{"docid":"734c65c082199ebdfbb543044cb022a8","score":"0.6321483","text":"def do_stop(self, arg):\n self.fallback_select(\n 'Stop Task',\n lambda tid: TaskService.stop(tid),\n arg=arg,\n query='+ACTIVE'\n )","title":""},{"docid":"1341bcb4c41ffc56c80f4bac2d1b62e9","score":"0.6302743","text":"def stop(args, **_):\n credentials = _get_credentials(args)\n\n resource_id = ctx.instance.runtime_properties['resource_id']\n _stop_droplet(resource_id, credentials)\n # TODO: try power_off if shutdown is not successful","title":""},{"docid":"700985d62e1350c7e25c353bf663d970","score":"0.62850386","text":"def stop_service(self, service_name, instance=None):\n raise NotImplementedError","title":""},{"docid":"0b83211d262e4dc347c85e1f7cb14607","score":"0.6279845","text":"def stop():\n ret = _LIB.eve_click_stop()\n if ret < 0:\n raise Exception(\"eve click stop failed\")","title":""},{"docid":"d115aafbfeb876ad61929061901f7890","score":"0.6273752","text":"def terminate_self():\n\n logger.info(\"terminating running Google Compute Engine instance\")\n logger.info(\"trying to infer the identity of the instance...\")\n name = discover_instance_name()\n zone_url = discover_instance_zone()\n zone = zone_url.split(\"/\")[-1]\n project = discover_project_id()\n logger.info(\n \"identity inference done. name=%s zone=%s project=%s\", name, zone, project\n )\n\n logger.info(\"sending DELETE request to terminate instance\")\n compute_client = googleapiclient.discovery.build(\"compute\", \"v1\")\n operation = compute_client.instances().delete(\n project=project, zone=zone, instance=name,\n )\n\n logger.info(\"Blocking to wait for the delete to complete\")\n compute_client.zoneOperations().wait(\n project=project, zone=zone, operation=operation[\"name\"]\n )","title":""},{"docid":"56871b79e431ebb84b78c1b2a3645efb","score":"0.62714607","text":"def stop(self, *args):\n vm_name = ' '.join(args)\n host = get_vm_host(self._vbo, vm_name)\n if host:\n return host.stop(vm_name)\n host = get_vm_host(self._vbo, vm_name)\n if host:\n return host.co","title":""},{"docid":"5d00257614dcc2191512b1a9e63ed691","score":"0.6266016","text":"def stop(self):\n self.send_command('stop')","title":""},{"docid":"94a17f329abad61c3ecdafeacfd5b77e","score":"0.62649524","text":"def terminate(self):\n if self.instance is None:\n logger.info(\"No instance to terminate.\")\n return\n\n instance_id = self.instance.id\n try:\n self.instance.terminate()\n self.instance.wait_until_terminated()\n self.instance = None\n except ClientError as err:\n logging.error(\n \"Couldn't terminate instance %s. Here's why: %s: %s\", instance_id,\n err.response['Error']['Code'], err.response['Error']['Message'])\n raise","title":""},{"docid":"c023e89d5555945c67dd90da7ec05569","score":"0.62500906","text":"def stop_guest(userid):\n sdk_client.send_request('guest_start', userid)","title":""},{"docid":"0a3fc0d100483d43cfd1f08b93318163","score":"0.62450475","text":"def stop(self, instance_id, timeout=None, ignore_failed=False, ignore_inactive=False):\n expected_rcs = {RC_SUCCESS}\n if ignore_failed:\n expected_rcs.add(RC_SERVICE_FAILED)\n if ignore_inactive:\n expected_rcs.add(RC_SERVICE_INACTIVE)\n\n self._stop(instance_id, timeout=timeout, expected_rcs=expected_rcs)","title":""},{"docid":"1368193559e847f62502929834a91d02","score":"0.62420565","text":"def stop(self):\n pid = self.get_pid()\n if pid:\n os.kill(pid, signal.SIGTERM)","title":""},{"docid":"e2af3a285a8f459c02af3fbd394b26d6","score":"0.6234463","text":"def leave (name, instance_id = None):\r\n if instance_id is None:\r\n try:\r\n instance_id = __grains__[\"ec2_instance-id\"]\r\n except KeyError:\r\n return False\r\n lb = _get_elb (name)\r\n try:\r\n lb.deregister_instances ([instance_id,])\r\n except Exception:\r\n import traceback\r\n LOG.error (\"ELB %s: Error while deregistering instance %s\", name, instance_id)\r\n LOG.debug (traceback.format_exc ())\r\n return False\r\n LOG.debug (\"ELB %s: Removed instance %s\", name, instance_id)\r\n return True","title":""},{"docid":"3fe05238cc7ce36b9cc750d87aad5edb","score":"0.6231611","text":"def stop(self):\n id = self._get_next_id()\n json.dump(obj={\"id\": id, \"command\": \"stop\"}, fp=self._file)\n self._file.flush()\n quit()","title":""},{"docid":"7095cde3a1ab820e6b4202848cd8f589","score":"0.6230381","text":"def detach_instance(c, as_group, id):\n c.detach_instances(\n InstanceIds=[id],\n AutoScalingGroupName=as_group,\n ShouldDecrementDesiredCapacity=True,\n )","title":""},{"docid":"017243f9e31cf66bec2ba98981ef045c","score":"0.6213323","text":"def _ShutdownInstance(self, name, hvparams, timeout):\n info = self.GetInstanceInfo(name, hvparams=hvparams)\n\n if info is None or hv_base.HvInstanceState.IsShutdown(info[4]):\n logging.info(\"Failed to shutdown instance %s, not running\", name)\n return None\n\n return self._RunXen([\"shutdown\", \"-w\", name], timeout)","title":""},{"docid":"ebfd6e21a99bfbbb2636661e6538dff3","score":"0.6195339","text":"def terminate_instances(self, ids, dry_run=False):\n LOGGER.info(\"terminating: %s dry_run=%s\", ids, dry_run)\n self.ec2.instances.filter(InstanceIds=ids).terminate(DryRun=dry_run)","title":""},{"docid":"c788f27b368dfbb71d705a5987414d11","score":"0.6192878","text":"def do_stop(self, params):\n return 0","title":""},{"docid":"bc36521fede7024f7e2c531ada690edf","score":"0.61689293","text":"def leave(name, instance_id=None):\n if instance_id is None:\n try:\n instance_id = __grains__[\"ec2_instance-id\"]\n except KeyError:\n return False\n lb = _get_elb(name)\n try:\n lb.deregister_instances([instance_id, ])\n except Exception:\n import traceback\n LOG.error(\"ELB %s: Error while deregistering instance %s\", name, instance_id)\n LOG.debug(traceback.format_exc())\n return False\n LOG.debug(\"ELB %s: Removed instance %s\", name, instance_id)\n return True","title":""},{"docid":"9bb59dbff1c648cbc2ebc1ffe69c02a1","score":"0.61669004","text":"def terminate_instances(instance_ids):\n\n\n # Terminate each instance in the argument list\n ec2 = boto3.client('ec2')\n try:\n states = ec2.terminate_instances(InstanceIds=instance_ids)\n except ClientError as e:\n logging.error(e)\n return None\n return states['TerminatingInstances']","title":""},{"docid":"428a4f870998762b5e9f0c4f6f0ece36","score":"0.61622167","text":"def shutdown_vm():\n try:\n session_cache_id = session['session_cache_id']\n cached_session = session_cache[session_cache_id]\n acc_key = cached_session['acc_key']\n secret_key = cached_session['secret_key']\n credentials = {'access_key': acc_key, 'secret_access_key': secret_key}\n _cf = _create_connection('cloudformation', **credentials)\n _ec2 = _create_connection('ec2', **credentials)\n stack = _cf.describe_stacks('BitnamiWordpressStack')[0]\n for resource in stack.list_resources():\n if 'AWS::EC2::Instance' in resource.resource_type:\n _ec2.stop_instances(instance_ids=[resource.physical_resource_id])\n flash('You were able to send the stop signal to the VM.')\n return redirect(url_for('hello'))\n flash(\"Unable to find and shut down the VM.\")\n return redirect(url_for('hello'))\n except Exception as e:\n return str(e)","title":""},{"docid":"f3ad1d8d655d21d37e2cfe9e20f93b42","score":"0.61613613","text":"def stop(self, container, instances=None, map_name=None, **kwargs):\n return self.run_actions('stop', container, instances=instances, map_name=map_name, **kwargs)","title":""},{"docid":"acb2eb1db226ca57f2db25e04d595082","score":"0.6160417","text":"def StopInstances(self, request):\n try:\n params = request._serialize()\n headers = request.headers\n body = self.call(\"StopInstances\", params, headers=headers)\n response = json.loads(body)\n model = models.StopInstancesResponse()\n model._deserialize(response[\"Response\"])\n return model\n except Exception as e:\n if isinstance(e, TencentCloudSDKException):\n raise\n else:\n raise TencentCloudSDKException(type(e).__name__, str(e))","title":""},{"docid":"2f290dcbd21f41f9696b9d35d7faa83e","score":"0.615903","text":"def stop(self):\n # stop instance\n self.terminate_instance()\n # delete disk\n self.delete_disk()","title":""},{"docid":"43f03a2093b3fa7450656079524c57d2","score":"0.6154438","text":"def stop_server(context: Context):\n context.run(\"kill $(pgrep -f api_server | grep -v ^$$\\$)\", warn=True)","title":""},{"docid":"f1dd9fd6854dd0e8030e6c62c1427be0","score":"0.61472404","text":"def _destroy(self, instance_id):\n try:\n container = OvzContainer.find(nova_id=instance_id)\n container.delete()\n except exception.InstanceNotFound:\n LOG.info(\"Instance %s cannot be deleted, since it does not exist\"\n % instance_id)\n return\n except BaseException:\n raise","title":""},{"docid":"0fa05a8ce6739c6989001aa58879c8fc","score":"0.6140421","text":"def admin_ide_stop_id(id: str):\n\n # Get the course context\n course = get_course_context()\n\n # Search for the theia session\n session = TheiaSession.query.filter(\n TheiaSession.id == id,\n TheiaSession.course_id == course.id,\n ).first()\n\n # Verify it exists\n req_assert(session is not None, message='session does not exist')\n\n # Set all the things as stopped\n session.active = False\n session.ended = datetime.now()\n session.state = \"Ending\"\n\n # Commit the stop\n db.session.commit()\n\n # Enqueue the theia stop cleanup\n enqueue_ide_stop(session.id)\n\n # Hand back response\n return success_response({\"status\": \"Session Killed.\"})","title":""},{"docid":"75c897a97eddd10e43aecb314735abcd","score":"0.6130715","text":"def stop(self):\n command = [\n 'ssserver',\n '--pid-file', self.pidfile(original=True),\n '--log-file', self.logfile(original=True),\n '-d', 'stop',\n ]\n (rc, stdout, stderr) = self.call(command)\n if rc == 0:\n logger.debug(stdout)\n else:\n logger.error('stderr: %s\\nstdout: %s' % (stderr, stdout))","title":""},{"docid":"15ae967e50ca6e66e109bbe41556cab0","score":"0.6127036","text":"def stop(self):\n inst = self.instance\n\n if inst.is_online:\n\n # If the metadata isn't up to date and this instance *is not*\n # online, we don't want to error out. Just act like it worked.\n\n try:\n self.__run_cmd(self.__get_cmd('stop'))\n except Exception, e:\n if not 'not running' in str(e) and not 'not exist' in str(e):\n raise\n\n inst.is_online = False\n inst.save()","title":""},{"docid":"bfd1ba97c51d4e003fbf5f3c023aaeb5","score":"0.6108744","text":"def stop(cmd):\n\tcmd.icp.stop()","title":""},{"docid":"607adae8d3ddef7981fcafe876a9feb3","score":"0.6100318","text":"def Stop(self):\n payload = { \"Arg1\": self.href }\n return self._execute('stop', payload=payload, response_object=None)","title":""},{"docid":"5257bedf856595142da04000547c5c15","score":"0.6093426","text":"def stop(self, uuid, args, okay, fail):\n try:\n process = self.processes[uuid]\n except KeyError:\n fail('does-not-exist')\n else:\n process.stop(args, okay, fail)","title":""},{"docid":"54f11111fc0b90cc16731c72461f0cf8","score":"0.6074993","text":"def stop(self, ts_id, addr):\n if addr.find(':') >= 0:\n host, port = addr.split(':', 1)\n status = self.quit(host, port)\n return \"Stopped test system #{0}, server @{1}: stop={2}\".format(\n ts_id, addr, status)","title":""},{"docid":"57f3b69a6f6439167145df2ec1194244","score":"0.6070596","text":"def test_run_terminate(self):\n instance_id = self._create_instance()\n\n self.compute.run_instance(self.context, instance_id)\n\n instances = db.instance_get_all(context.get_admin_context())\n LOG.info(_(\"Running instances: %s\"), instances)\n self.assertEqual(len(instances), 1)\n\n self.compute.terminate_instance(self.context, instance_id)\n\n instances = db.instance_get_all(context.get_admin_context())\n LOG.info(_(\"After terminating instances: %s\"), instances)\n self.assertEqual(len(instances), 0)","title":""},{"docid":"c2b6cc35ec704ae5a3b7300da5883d31","score":"0.6070342","text":"def stop(self, name=None):\n pass","title":""},{"docid":"030bcd5c8de9535f7c1da84f9edf12ea","score":"0.60684955","text":"def _stop_swap(self, swap_uuid):\n utils.run_vtctl(['WorkflowStop', swap_uuid], auto_log=True)","title":""}],"string":"[\n {\n \"docid\": \"cd1761b40bdc3960e6b73585e5a48752\",\n \"score\": \"0.8403922\",\n \"text\": \"def aws_stop(self, msg, args):\\n id = args\\n instance = self._get_instance_by_id(id)\\n response = \\\"\\\"\\n if self._is_instance_permitted(id):\\n i = self.ec2.instances.filter(InstanceIds=[id]).stop()\\n reponse = \\\"Instance {} stopped\\\".format(self._get_instance_name(instance))\\n else:\\n response = \\\"Instance not in permitted list. Use `aws addpermission ID` command.\\\"\\n return reponse\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bddf37580f8e17135ef26a036759cde9\",\n \"score\": \"0.8211793\",\n \"text\": \"def stop_ec2(ec2_id, instance):\\n\\n ec2 = boto3.resource('ec2')\\n\\n # If the given ec2_id is for an EC2 template, then terminate the EC2 instance\\n if ec2_id.split('-')[0] == 'lt':\\n ec2.instances.filter(InstanceIds=[instance.id]).terminate()\\n logging.info('Terminated EC2 instance {}'.format(instance.id))\\n\\n # If the given ec2_id is for an existing EC2 instance, then stop it\\n else:\\n instance.stop()\\n logging.info('Stopped EC2 instance {}'.format(ec2_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f8a95a8c40863c8c18956346e940905\",\n \"score\": \"0.79622316\",\n \"text\": \"def stop_instance(instance_id):\\n try:\\n response = ec2.Instance(instance_id).stop()\\n logger.info(\\\"Stopped instance %s.\\\", instance_id)\\n except ClientError:\\n logger.exception(\\\"Couldn't stop instance %s.\\\", instance_id)\\n raise\\n else:\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"282e53e14241dc76b5040c3937f197e3\",\n \"score\": \"0.766608\",\n \"text\": \"def stop_instance(instance):\\n instance.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01c68c61309c640643e6fb975a0c5065\",\n \"score\": \"0.7646813\",\n \"text\": \"def stop_ec2():\\n print(\\\"------------------------------\\\")\\n print(\\\"Try to stop the EC2 instance.\\\")\\n print(\\\"------------------------------\\\")\\n\\n try:\\n ec2.stop_instances(InstanceIds=[Mem.instance_id], DryRun=True)\\n except ClientError as e:\\n if 'DryRunOperation' not in str(e):\\n raise\\n\\n # Dry run succeeded, call stop_instances without dryrun\\n try:\\n response = ec2.stop_instances(InstanceIds=[Mem.instance_id], DryRun=False)\\n print(\\\"Success\\\",\\\"Stopped\\\",response)\\n except ClientError as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e053cbe85f12d37ae5b4c63f5fc0604f\",\n \"score\": \"0.7582602\",\n \"text\": \"def stop(self, instance_id=0):\\n packet = self._create_packet('Stop', {'InstanceID': instance_id, 'Speed': 1})\\n _send_tcp((self.ip, self.port), packet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a0960f94a864c4f05f5bff9cd7577fc\",\n \"score\": \"0.74688095\",\n \"text\": \"def terminate_current_instance():\\n metadata = requests.get(\\n \\\"http://instance-data/latest/dynamic/instance-identity/document\\\"\\n ).json()\\n return subprocess.run(\\n [\\n \\\"aws\\\",\\n \\\"ec2\\\",\\n \\\"terminate-instances\\\",\\n \\\"--instance-ids\\\",\\n metadata[\\\"instanceId\\\"],\\n \\\"--region\\\",\\n metadata[\\\"region\\\"],\\n ],\\n check=True,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5e17b395c7ed1b297567785fad34a74\",\n \"score\": \"0.7275048\",\n \"text\": \"def _stop(self, instance_id, timeout=None, expected_rcs=frozenset({0})):\\n service_manager = self.instances[instance_id]['service_manager']\\n service_manager.stop(timeout=timeout, expected_rcs=expected_rcs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"124130959f8d13bb3e37b7bd5f135fe7\",\n \"score\": \"0.7208143\",\n \"text\": \"def ec2_instance_stop(instance_list) :\\n instances = instance_list\\n if instances:\\n if isinstance(instance_list, list) :\\n instances = ','.join(instance_list)\\n cmdargs = [\\n 'aws', 'ec2', 'terminate-instances', '--instance-ids', instances\\n ]\\n cmd = ' '.join(cmdargs)\\n os.system(cmd)\\n return instances.split(\\\",\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abbc4afe08f8044660a5387f259c38aa\",\n \"score\": \"0.7182307\",\n \"text\": \"def _stop(self, instance):\\n container = OvzContainer.find(uuid=instance['uuid'])\\n sf = ovzshutdown.OVZShutdownFile(container.ovz_id, 700)\\n with sf:\\n sf.read()\\n sf.run_contents()\\n\\n ovz_utils.execute('vzctl', 'stop', container.ovz_id, run_as_root=True)\\n\\n # Update instance state\\n self.virtapi.instance_update(\\n context.get_admin_context(), instance['uuid'],\\n {'power_state': power_state.SHUTDOWN})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98efe5713edd41a89f0f45e45169a92\",\n \"score\": \"0.7180752\",\n \"text\": \"def terminate_instance(self, dryrun, instanceids):\\n from wrapper.ec2 import Ec2\\n ec2 = Ec2(session=self.session)\\n ret = ec2.terminate_instance(dryrun=dryrun, instanceids=instanceids)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e08c30dc0954818e41cef30a2f96732\",\n \"score\": \"0.7144824\",\n \"text\": \"def terminate_instance():\\n\\n # get public IP\\n public_ip = get_public_ip()\\n\\n # disassociate IP with this instance and terminate the instance\\n cmd = '/usr/bin/euca-disassociate-address %s' % public_ip\\n log.debug('Doing: %s' % cmd)\\n with os.popen(cmd) as fd:\\n result = fd.readline()\\n log.debug('result: %s' % str(result))\\n\\n cmd = '/usr/bin/euca-terminate-instances %s' % Instance\\n log.debug('Doing: %s' % cmd)\\n with os.popen(cmd) as fd:\\n result = fd.readline()\\n log.debug('result: %s' % str(result))\\n\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c38d8e0ead1ef8cb25a58075dcb7f8\",\n \"score\": \"0.7132969\",\n \"text\": \"def force_stop_server(self, req, id, body):\\n context = req.environ['nova.context']\\n instance = common.get_instance(self.compute_api, context, id)\\n extensions.check_compute_policy(context, 'stop', instance)\\n\\n LOG.debug(\\\"context = %s\\\", context)\\n\\n try:\\n self.compute_api.stop(context, instance, clean_shutdown=False)\\n except exception.InstanceInvalidState as state_error:\\n common.raise_http_conflict_for_instance_invalid_state(\\n state_error, 'force_stop', id)\\n except (exception.InstanceNotReady, exception.InstanceIsLocked) as e:\\n raise webob.exc.HTTPConflict(explanation=e.format_message())\\n return webob.Response(status_int=202)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ec5e1c4e7aff352e955cc9c64bbaad\",\n \"score\": \"0.711742\",\n \"text\": \"def stop(self, name):\\n res=[]\\n if name == self.Name:\\n ec2 = boto3.client('ec2', self.Region)\\n res=ec2.stop_instances(\\n InstanceIds=[\\n self.ID\\n ]\\n )\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ffc949fd13f97f7e21b851cb81de7a3\",\n \"score\": \"0.7049946\",\n \"text\": \"def stop(self, _id):\\n\\n self._run_action(_id, 'stop')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07b8d5e86b414cee87c11099ad6a4e5\",\n \"score\": \"0.7047405\",\n \"text\": \"def nova_stop_server(self,instance_name):\\n try:\\n server = self.novaclient.servers.find(name = instance_name)\\n id = server.id\\n ret = self.novaclient.servers.stop(id)\\n \\n\\n except (ClientException, Exception) as e:\\n return (400, e.message, [])\\n return (200, \\\"success\\\", ret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"365abc5df5baa0c44268afcdf3df8804\",\n \"score\": \"0.69981503\",\n \"text\": \"def terminate():\\n conn = ec2.connect_to_region(env.farmboy_aws_region)\\n # check for instances\\n running_instances = conn.get_only_instances(\\n filters={'tag-key': 'farmboy',\\n 'instance-state-name': 'pending',\\n 'instance-state-name': 'running'})\\n\\n # TODO(termie): we _probably_ want to just terminate them all and start\\n # new ones, is there a use case for keeping them?\\n for instance in running_instances:\\n util.puts('[aws] Terminating running instance: %s (%s)'\\n % (instance.id, instance.tags['farmboy']))\\n instance.terminate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba6e2ae04e59896e728a769dd17f0375\",\n \"score\": \"0.69607925\",\n \"text\": \"def stop(self, name=None, **kwargs):\\n result = None\\n compute_service = self._get_compute_service()\\n _operation = None\\n if name is None:\\n return\\n try:\\n\\n project_id = kwargs.get('project_id',\\n self.auth_config[\\\"project_id\\\"])\\n zone = kwargs.get('zone', self.default_config[\\\"zone\\\"])\\n\\n _operation = compute_service.instances().stop(\\n project=project_id,\\n zone=zone,\\n instance=name).execute()\\n\\n self._wait_for_operation(compute_service,\\n _operation,\\n project_id,\\n zone,\\n name)\\n\\n # Get the instance details to update DB.\\n result = self._info(name, displayType=\\\"vm\\\")\\n\\n except Exception as se:\\n if type(se) == HttpError:\\n Console.error(\\n f'Unable to stop instance {name}. '\\n f'Reason: {se._get_reason()}')\\n else:\\n Console.error(f'Unable to stop instance {name}.')\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9984119c195c71b5c8d7588adbfebc6b\",\n \"score\": \"0.6957024\",\n \"text\": \"def stop(self):\\n requests = self.describe_active_spot_instance_requests()\\n request_ids = self._extract_spot_requests_ids(requests)\\n\\n if request_ids:\\n instance_ids = self._extract_spot_requests_instance_ids(requests)\\n self.ec2_client.cancel_spot_instance_requests(SpotInstanceRequestIds=request_ids)\\n self.ec2_client.terminate_instances(InstanceIds=instance_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ef460820d5ceacc88ca3d9519b7b95\",\n \"score\": \"0.69186205\",\n \"text\": \"def stop(instance, instance_name):\\n try:\\n instance.stop()\\n except novaclient.exceptions.Conflict, conflict_exception:\\n pattern = re.compile(ALREADY_STOPPED_PATTERN)\\n if pattern.match(conflict_exception.message):\\n logging.warning(\\\"Instance '{}' \\\" \\\\\\n \\\"already stopped\\\".format(instance_name))\\n else:\\n raise conflict_exception\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5937763e204ccad1bdea99f8cc344e1f\",\n \"score\": \"0.6883898\",\n \"text\": \"def InstanceStop(self):\\n resp = self.gen_instmgr.Stop(self.instance_id, force=False)\\n logging.info('Instance stop issued: %s', self.instance_id)\\n # force stop again\\n resp = self.gen_instmgr.Stop(self.instance_id, force=True)\\n status = self._GetStatus(resp)\\n logging.info('Instance stop force issued: %s, %s', self.instance_id, status)\\n if status != States.STOPPED:\\n endtime = time.time() + FLAGS.ibmcloud_timeout\\n status = self._GetStatus(self.gen_instmgr.Show(self.instance_id))\\n while status != States.STOPPED and time.time() < endtime:\\n time.sleep(FLAGS.ibmcloud_polling_delay * 2)\\n status = self._GetStatus(self.gen_instmgr.Show(self.instance_id))\\n logging.info('Checking instance stop status: %s, %s', self.instance_id,\\n status)\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9771ffe61be299a74a122b79f52369e3\",\n \"score\": \"0.6856986\",\n \"text\": \"def stop_vm(name: str, zone: str):\\n try:\\n click.echo('Stopping instance {}'.format(name))\\n call(['gcloud', 'compute', 'instances', 'stop', name, '--zone', zone])\\n except CalledProcessError:\\n print('Cannot stop VM')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ade147c2513eb4c00702eaea9562740\",\n \"score\": \"0.68492407\",\n \"text\": \"def test_stop_ec2_instance(aws_region, tag_key, tag_value, result_count):\\n client = boto3.client(\\\"ec2\\\", region_name=aws_region)\\n launch_ec2_instances(3, aws_region, tag_key, tag_value)\\n\\n ec2_scheduler = InstanceScheduler(aws_region)\\n ec2_scheduler.cloudwatch_alarm = CloudWatchAlarmScheduler(aws_region)\\n ec2_scheduler.stop(\\\"tostop\\\", \\\"true\\\")\\n instances = client.describe_instances()[\\\"Reservations\\\"][0][\\\"Instances\\\"]\\n assert len(instances) == 3\\n for instance in instances:\\n assert instance[\\\"State\\\"] == result_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b197ecffa9979624bef7533b36c529c\",\n \"score\": \"0.6820726\",\n \"text\": \"def stop_sound_instance(instance_id: int) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"343cb1085693a3f80835b3865b3483ef\",\n \"score\": \"0.6792971\",\n \"text\": \"def handle_stop(args):\\n _latest = LATEST if args[\\\"latest\\\"] else NEXT\\n #: rewrite so this is not in two places\\n instance_name = \\\"run_{}_{}\\\".format(CONFD[\\\"instance_name\\\"], _latest)\\n cmd = \\\"{} container kill {}\\\".format(OCI_CMD, instance_name)\\n run_subprocess(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae084f54c5b597015363926a219b471\",\n \"score\": \"0.678534\",\n \"text\": \"def terminate_instance(self):\\n try:\\n logging.info('Stopping %s' % self.instance_name)\\n gce_helper = self.create_gce_helper()\\n gce_helper.stop_instance(self.instance_name, self.zone)\\n except (gce.ApiError, gce.ApiOperationError, ValueError) as e:\\n logging.error(DELETE_ERROR, {'name': self.instance_name})\\n logging.error(e)\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5b3e107ff2f5da2d672e516547f2c6a\",\n \"score\": \"0.6779469\",\n \"text\": \"def terminate_ec2():\\n print(\\\"------------------------------\\\")\\n print(\\\"Try to Terminate the EC2 instance.\\\")\\n print(\\\"------------------------------\\\")\\n\\n try:\\n ec2.terminate_instances(InstanceIds=[Mem.instance_id], DryRun=True)\\n except ClientError as e:\\n if 'DryRunOperation' not in str(e):\\n raise\\n\\n # Dry run succeeded, call terminate_instances without dryrun\\n try:\\n response = ec2.terminate_instances(InstanceIds=[Mem.instance_id], DryRun=False)\\n print(\\\"Success\\\",\\\"Terminate\\\",response)\\n except ClientError as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab70d588af63486d5a449b043781a33\",\n \"score\": \"0.67780674\",\n \"text\": \"def stop( self, trans, id ):\\n uci = get_uci( trans, id )\\n if ( uci.state != uci_states.DELETING ) and \\\\\\n ( uci.state != uci_states.DELETING_UCI ) and \\\\\\n ( uci.state != uci_states.ERROR ) and \\\\\\n ( uci.state != uci_states.SHUTTING_DOWN_UCI ) and \\\\\\n ( uci.state != uci_states.SHUTTING_DOWN ) and \\\\\\n ( uci.state != uci_states.ADD_STORAGE_UCI ) and \\\\\\n ( uci.state != uci_states.ADD_STORAGE ) and \\\\\\n ( uci.state != uci_states.AVAILABLE ):\\n uci.state = uci_states.SHUTTING_DOWN_UCI\\n session = trans.sa_session\\n session.add( uci )\\n session.flush()\\n trans.log_event( \\\"User stopped cloud instance '%s' (id: %s)\\\" % ( uci.name, uci.id ) )\\n trans.set_message( \\\"Stopping of Galaxy instance '%s' initiated.\\\" % uci.name )\\n \\n return self.list( trans )\\n \\n trans.show_error_message( \\\"Cannot stop instance that is in state '%s'.\\\" % uci.state )\\n return self.list( trans )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a3a29572cd3849a858604ba41dc928b\",\n \"score\": \"0.6777065\",\n \"text\": \"def cmd_stop(client: RemoteDockerClient):\\n print(client.stop_instance())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b76252b26795301b000fcfd408061739\",\n \"score\": \"0.6742772\",\n \"text\": \"def test_stop_ec2_instance(aws_region, aws_tags, result_count):\\n client = boto3.client(\\\"ec2\\\", region_name=aws_region)\\n launch_ec2_instances(3, aws_region, \\\"tostop\\\", \\\"true\\\")\\n\\n ec2_scheduler = InstanceScheduler(aws_region)\\n ec2_scheduler.cloudwatch_alarm = CloudWatchAlarmScheduler(aws_region)\\n ec2_scheduler.stop(aws_tags)\\n instances = client.describe_instances()[\\\"Reservations\\\"][0][\\\"Instances\\\"]\\n assert len(instances) == 3\\n for instance in instances:\\n assert instance[\\\"State\\\"] == result_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aefa36432b7e0d0f591526baf7b53c9f\",\n \"score\": \"0.6730674\",\n \"text\": \"def halt():\\n instance = _get_proxy_instance(TAG_NAME)\\n if instance and instance['state'] in ['running']:\\n print yellow('Stopping instance.')\\n local('osascript %s/vpnconnection.scpt %s halted'\\n '' % (PROJECT_ROOT, TAG_NAME))\\n conn.stop_instances([instance['id']])\\n return exit(0)\\n print red('FAILURE: Instance cannot be stopped.')\\n print instance\\n return exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a7f6f625e471ccd28b832da28a57f95\",\n \"score\": \"0.6710691\",\n \"text\": \"def remove_instance(self, arg_instance_id):\\n try:\\n print(f'Instance \\\"{arg_instance_id}\\\" removing begins.')\\n instance = self.ec2_resource.Instance(arg_instance_id)\\n instance.terminate()\\n instance.wait_until_terminated()\\n return [True, '']\\n except ClientError as err:\\n return [False, self.parse_exception('remove_instance', err)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6707dd13a3abefb0acfc7ebb6d6a93a7\",\n \"score\": \"0.6706253\",\n \"text\": \"def do_stop(self, machine_id):\\n try:\\n machine_ref = self.session.xenapi.VM.get_by_uuid(machine_id)\\n\\n print \\\"stopping machine with id %s\\\" % machine_id\\n self.session.xenapi.VM.clean_shutdown(machine_ref)\\n except Failure:\\n print \\\"machine with id %s is not found\\\" % machine_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40ac8b61b04875f903f461ea53fb11e0\",\n \"score\": \"0.6681216\",\n \"text\": \"def setup_instance_stop(r, **attr):\\n\\n setup_instance_method(r.component_id, \\\"stop\\\")\\n\\n current.session.confirmation = current.T(\\\"Instance Stopped\\\")\\n\\n redirect(URL(c=\\\"setup\\\", f=\\\"deployment\\\",\\n args = [r.id, \\\"instance\\\"]),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef0ec2c95938c63a389ba805b0eb025\",\n \"score\": \"0.66621834\",\n \"text\": \"def terminate():\\n conn = client.Client(env.farmboy_os_username,\\n env.farmboy_os_password,\\n env.farmboy_os_tenant_name,\\n env.farmboy_os_auth_url,\\n service_type='compute')\\n\\n # check for instances\\n running_instances = conn.servers.list()\\n\\n # TODO(termie): we _probably_ want to just terminate them all and start\\n # new ones, is there a use case for keeping them?\\n for instance in running_instances:\\n if (instance.metadata.get('farmboy')\\n and instance.status == 'ACTIVE'\\n and getattr(instance, 'OS-EXT-STS:task_state') == None):\\n util.puts('[os] Terminating running instance: %s (%s)'\\n % (instance.id, instance.metadata['farmboy']))\\n instance.stop()\\n instance.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04b50d83372c8410ba11833d7953ec45\",\n \"score\": \"0.66532624\",\n \"text\": \"def stop(self, instance_name):\\n\\n vstop_input = RwVcsYang.VStopInput()\\n vstop_input.instance_name = instance_name\\n\\n response_xml = self.mgmt_proxy.rpc(vstop_input.to_xml(yang_model.yang_model))\\n response_xml = self.mgmt_proxy.get_GI_compatible_xml(response_xml, rpc_name='vstop')\\n\\n vstop_output = RwVcsYang.VStopOutput()\\n vstop_output.from_xml(yang_model.yang_model, response_xml)\\n\\n return vstop_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c071420f896991e2fc4cc01efae68227\",\n \"score\": \"0.66297984\",\n \"text\": \"def is_instance_stopped( self, instance_id, verbose=False ):\\n return self.poll_instances( [instance_id], 0, 0, awsext.ec2.INSTANCE_STATE_CODE_STOPPED, verbose, is_instance_check=True )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27cb2fc5f28c791ff06bb1d7899cb1d0\",\n \"score\": \"0.66289014\",\n \"text\": \"def stop(self, force=False):\\n logging.info(\\\"Stop VM\\\")\\n params = {}\\n params.setdefault(\\\"InstanceId\\\", self.id)\\n if force:\\n params.setdefault(\\\"ForceStop\\\", \\\"True\\\")\\n return sdk.stop_instance(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7b3ba6c5373d6c8272379545d2c74fd\",\n \"score\": \"0.6619052\",\n \"text\": \"def stopped():\\n stopped = aws_cli().get_stopped_instances()\\n click.echo(stopped)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a3f08ce56b315f1280936484d22c1c0\",\n \"score\": \"0.6618662\",\n \"text\": \"def stop(self):\\n if self.instance is None:\\n logger.info(\\\"No instance to stop.\\\")\\n return\\n\\n try:\\n response = self.instance.stop()\\n self.instance.wait_until_stopped()\\n except ClientError as err:\\n logger.error(\\n \\\"Couldn't stop instance %s. Here's why: %s: %s\\\", self.instance.id,\\n err.response['Error']['Code'], err.response['Error']['Message'])\\n raise\\n else:\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dfc5fbf6638c6a8438890921e151889\",\n \"score\": \"0.6582461\",\n \"text\": \"def stop_instances(self):\\n self.aws.stop_instances()\\n self.azure.stop_instances()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cb993eadeb2aa6a8f6d170bf97b3fee\",\n \"score\": \"0.6553835\",\n \"text\": \"def stop_running():\\n instances = [instance for instance in get_state(\\\"running\\\")]\\n if len(instances) > 0:\\n stop(*instances)\\n else:\\n out.out(\\\"No running instances.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59c59f401123232e8a34760973119229\",\n \"score\": \"0.65253055\",\n \"text\": \"def StopInstance(self, instance, force=False, retry=False, name=None,\\n timeout=None):\\n assert(timeout is None or force is not None)\\n\\n if name is None:\\n name = instance.name\\n if not self._IsAlive(name):\\n raise errors.HypervisorError(\\\"Failed to stop instance %s: %s\\\" %\\n (name, \\\"not running\\\"))\\n self._MarkDown(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c3b20e21059d160cb7c02147234a82b\",\n \"score\": \"0.6518349\",\n \"text\": \"def remove_instance(self, instance_id=''):\\n if not instance_id:\\n log.warning(\\\"Tried to remove an instance but did not receive instance ID\\\")\\n return False\\n log.debug(\\\"Specific termination of instance '%s' requested.\\\" % instance_id)\\n for inst in self.worker_instances:\\n if inst.id == instance_id:\\n inst.worker_status = 'Stopping'\\n log.debug(\\\"Set instance {0} state to {1}\\\".format(inst.get_desc(),\\n inst.worker_status))\\n for job_manager_svc in self.service_registry.active(\\n service_role=ServiceRole.JOB_MANAGER):\\n job_manager_svc.remove_node(inst)\\n # Remove the given instance from /etc/hosts files\\n misc.remove_from_etc_hosts(inst.private_ip)\\n self.sync_etc_hosts()\\n # Terminate the instance\\n inst.terminate()\\n log.info(\\\"Initiated requested termination of instance. \\\"\\n \\\"Terminating '%s'.\\\" % instance_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3c7c6fc8265432323af3d865a683bc\",\n \"score\": \"0.64868414\",\n \"text\": \"def stop(name=None):\\n\\n Console.ok(f\\\"shutting down server {name}\\\")\\n\\n registry = Registry()\\n\\n entries = registry.list(name=name)\\n pid = \\\"\\\"\\n result = None\\n if len(entries) > 1:\\n Console.error(f\\\"Aborting, returned more than one entry from the Registry with the name {name}\\\")\\n raise Exception\\n elif len(entries) == 1:\\n pid = str(entries[0]['pid'])\\n else:\\n result = Server.ps(name=name)\\n\\n try:\\n if len(pid) > 0: # check if pid found in registry and if found kill\\n print(\\\"Killing:\\\", pid)\\n Shell.kill_pid(pid)\\n registry.delete(name=name)\\n elif result: # no server found in registry so kill based on ps output\\n for process in result:\\n pid = str(process[\\\"pid\\\"])\\n if len(pid) > 0:\\n print(\\\"Killing:\\\", pid)\\n Shell.kill_pid(pid)\\n registry.delete(name=name)\\n else:\\n print()\\n Console.error(f\\\"PS output generated invalid PID for server name {name}\\\")\\n else:\\n print()\\n Console.error(f\\\"No Cloudmesh OpenAPI Server found with the name {name}\\\")\\n except:\\n print(\\\"got to exception\\\")\\n Console.error(\\n f\\\"No Cloudmesh OpenAPI Server found with the name {name}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc0b6b8d9142190c364349f2c8c5b4b6\",\n \"score\": \"0.647184\",\n \"text\": \"def StopInstance(self, instance, force=False, retry=False, name=None,\\n timeout=None):\\n assert(timeout is None or force is not None)\\n\\n if name is None:\\n name = instance.name\\n\\n return self._StopInstance(name, force, instance.hvparams, timeout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6add74246d539fe4709544df35d505c6\",\n \"score\": \"0.6461264\",\n \"text\": \"def stop(self):\\r\\n\\t\\tstopCmd = self.replaceTag(self.actionstop, self._properties)\\r\\n\\t\\tif not self.executeCmd(stopCmd, self.timeout):\\r\\n\\t\\t\\traise RuntimeError(\\\"Error stopping action\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c241563bf9a182d531dc0fa3ade2bf\",\n \"score\": \"0.6432939\",\n \"text\": \"def stop(inst):\\n if not os.path.exists(inst.uwsgi_vassal):\\n print 'vassal %s not running' % inst.name\\n return\\n try:\\n os.unlink(inst.uwsgi_vassal)\\n print 'stop request submitted for %s' % inst.name\\n except:\\n print 'error unlinking vassal'\\n return\\n inst.uwsgi_port = None\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a90a43d5c281edce42412fdd8a5bf1e5\",\n \"score\": \"0.6430357\",\n \"text\": \"def stop(self):\\n if self.is_spot():\\n raise exception.InvalidOperation(\\n \\\"spot instances can not be stopped\\\")\\n elif not self.is_ebs_backed():\\n raise exception.InvalidOperation(\\n \\\"Only EBS-backed instances can be stopped\\\")\\n if not self.is_stopped():\\n log.info(\\\"Stopping node: %s (%s)\\\" % (self.alias, self.id))\\n return self.instance.stop()\\n else:\\n log.info(\\\"Node '%s' is already stopped\\\" % self.alias)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba45ebc9396754768b420a951c63234\",\n \"score\": \"0.6426427\",\n \"text\": \"def stop_experiment(api, exp_id):\\n return api.stop_experiment(exp_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99c331e21fc4b4ad0d28c10e6d294943\",\n \"score\": \"0.6414884\",\n \"text\": \"def stop(self):\\n logging.info('Stopping %s on %s...', self.description, self.fqdn)\\n return self.ambari.execute(self.ambari_url, self.stop_command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be2f1479cdede3610fdd22c7d526a991\",\n \"score\": \"0.6414199\",\n \"text\": \"def ec2_launch_and_terminate_instance(ec2_conn):\\n\\n # Launch a specific instance with this image\\n ec2_conn.run_instance('<ami-image-id>')\\n # FROM: http://star.mit.edu/cluster/ Latest AMI IDs (us-east-1)\\n # Sample 32bit 12.04 Ubuntu with StarCluster: ami-7c5c3915 (i386)\\n # Sample 64bit 12.04 Ubuntu with StarCluster: ami-765b3e1f (x86_64)\\n\\n # 'Graceful' stop instance (shut down, but not terminates so still billed)\\n ec2_conn.stop_instances(instance_ids=['instance-id-1', 'instance-id-2'])\\n\\n # Completely done with instance, terminate/destroy it, never get back\\n ec2_conn.terminate_instances(instance_ids=\\n ['instance-id-1', 'instance-id-2'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a91cafddc5bf75a1270df3bba264a762\",\n \"score\": \"0.6413049\",\n \"text\": \"def stop(**_):\\n ctx.logger.info(\\\"VM stops.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"025a36932bca4a6448f6a56316a9632a\",\n \"score\": \"0.6409263\",\n \"text\": \"def stop_server(ServerId=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3890cbc41756f7a28922e35b09449b63\",\n \"score\": \"0.6408638\",\n \"text\": \"def __terminate_instance(self, instance):\\n\\n self.getLogger().info(\\n '[aws] Terminating instance [{0}]'.format(instance.id))\\n\\n try:\\n instance.terminate()\\n except boto.exception.EC2ResponseError as exc:\\n self.getLogger().warning(\\n 'Error while terminating instance [{0}]: {1}'.format(\\n instance.id, exc.message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b40ad8f85f4fa35101dd8984b5759d50\",\n \"score\": \"0.6404182\",\n \"text\": \"def delete_instance(controller, instance_id=None, instance_name=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"318cf92986494b8baa0fde4ff90d866a\",\n \"score\": \"0.63966775\",\n \"text\": \"def poll_instance_stopped( self, instance_id, interval_secs, max_minutes, verbose=False ):\\n return self.poll_instances( [instance_id], interval_secs, max_minutes, awsext.ec2.INSTANCE_STATE_CODE_STOPPED, verbose )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6c1eb866ddbe702e9ae577d4367ff7b\",\n \"score\": \"0.6391912\",\n \"text\": \"def stop_all(self, exclude=[]):\\n for instance in self.list_instances(exclude=exclude):\\n # print(instance['InstanceId'])\\n if instance['State']['Name'] == 'running':\\n self.ec2.stop_all(InstanceIds=[instance['InstanceId']])\\n else:\\n print('Nothing to stop')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f9e6256bfd8dd2f09c64962e5b6321\",\n \"score\": \"0.6374421\",\n \"text\": \"def do_stop(remove=False):\\n\\tfor name in getInstances():\\t\\n\\t\\tinst = mdb.getInstance(name)\\n\\t\\tif not inst:\\n\\t\\t\\tlogError('Instance does not exist on this node',name)\\n\\t\\t\\tcontinue\\n\\n\\t\\tif not isRunning(name):\\n\\t\\t\\tlogError('Instance is already stopped',name)\\n\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\n\\t\\tlogBegin('Stopping',name)\\t\\t\\t\\n\\t\\tssd = \\\"/dev/%s/%s\\\" % (inst['cache'],name)\\t\\t\\n\\t\\t\\n\\t\\tif run(\\\"/sbin/dmsetup remove %s\\\" % name):\\n\\t\\t\\tlogEnd('dmsetup Failed',False)\\n\\t\\t\\tcontinue\\n\\n\\t\\tlogMiddle('dm stopped',\\\"green\\\")\\n\\t\\tif inst['type'] == 'RAID':\\n\\t\\t\\tif not do_down(inst): continue\\n\\t\\telse:\\n\\t\\t\\tdev = mdb.getDevice(HOSTNAME,name)\\n\\t\\t\\tif not dev:\\n\\t\\t\\t\\tlogError('No device known for',name)\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\n\\t\\t\\tif run(\\\"qemu-nbd -d /dev/\\\"+dev):\\n\\t\\t\\t\\tlogMiddle(dev,\\\"red\\\")\\n\\t\\t\\t\\tlogEnd('Fail',False)\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\telse:\\t\\n\\t\\t\\t\\tlogMiddle(dev,\\\"blue\\\")\\n\\n\\t\\tif not remove:\\n\\t\\t\\tlogEnd('Ok',True)\\n\\t\\telse:\\n\\t\\t\\tlogMiddle('remove','blue')\\n\\t\\t\\tif run(\\\"lvremove -f \\\"+ssd):\\n\\t\\t\\t\\tlogEnd('Fail',False)\\t\\n\\t\\t\\telse:\\t\\n\\t\\t\\t\\tmdb.delDevice(HOSTNAME,name)\\n\\t\\t\\t\\tlogEnd('Ok',True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa764335756afc992abb8444934fd653\",\n \"score\": \"0.6364227\",\n \"text\": \"def post(self):\\n instance_ids = simplejson.loads(self.GetRequiredParameter('instance_ids'))\\n if not instance_ids:\\n logging.error('Missing Instance ID.')\\n self.response.out.write('Missing Instance ID.')\\n ec2 = ec2_manager.EC2Manager()\\n bots_instances = ec2.StopInstances(instance_ids)\\n self.response.out.write(\\n simplejson.dumps([ec2_manager.BotoEC2DataObject.Encode(x)\\n for x in bots_instances]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ead9dad50d2e0b75ffaf6c58368b998d\",\n \"score\": \"0.63527024\",\n \"text\": \"def terminate(self, instance_ids):\\n self.conn.terminate_instances(instance_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5470e6a8d47eac78d1684732f94ace0b\",\n \"score\": \"0.6347496\",\n \"text\": \"def delete_instance(self, instance_id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92b63ee6c6e43d5045d83162eeb95d6\",\n \"score\": \"0.6345559\",\n \"text\": \"def stop_vm(self, node, vmid, **kwargs):\\n self.api.nodes(node).qemu(vmid).status.stop.post(**kwargs)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cc7b73bfdf83d2005f68f83cd1e409f\",\n \"score\": \"0.6343248\",\n \"text\": \"def start_instance(instance_id: str):\\n\\n instance = ec2.Instance(instance_id)\\n if instance.state[\\\"Name\\\"] not in [\\\"running\\\", \\\"pending\\\"]:\\n instance.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e01bf34bf2a194af1b710db31d0f0c44\",\n \"score\": \"0.6332923\",\n \"text\": \"def cleanup_instances():\\n instances = describe_instances()\\n stop_instance_ids = []\\n for i in instances:\\n if i[\\\"name\\\"].startswith(\\\"buildkite\\\") or i[\\\"name\\\"] == \\\"website\\\":\\n # Don't kill buildkite server(s) or the website\\n continue\\n\\n launched_time = i[\\\"LaunchTime\\\"]\\n uptime_delta = datetime.utcnow() - launched_time.replace(tzinfo=None)\\n hours = uptime_delta.total_seconds() // 3600\\n if hours > settings.EC2_MAX_HOURS:\\n launch_time_str = launched_time.isoformat()\\n logger.info(\\n f\\\"Stopping instance {i['name']} with id {i['InstanceId']} with {hours}h uptime since {launch_time_str}\\\"\\n )\\n stop_instance_ids.append(i[\\\"InstanceId\\\"])\\n\\n if stop_instance_ids:\\n logger.info(\\\"Stopping instance ids %s\\\", stop_instance_ids)\\n client.terminate_instances(InstanceIds=stop_instance_ids)\\n else:\\n logger.info(\\\"No instances to stop.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"734c65c082199ebdfbb543044cb022a8\",\n \"score\": \"0.6321483\",\n \"text\": \"def do_stop(self, arg):\\n self.fallback_select(\\n 'Stop Task',\\n lambda tid: TaskService.stop(tid),\\n arg=arg,\\n query='+ACTIVE'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1341bcb4c41ffc56c80f4bac2d1b62e9\",\n \"score\": \"0.6302743\",\n \"text\": \"def stop(args, **_):\\n credentials = _get_credentials(args)\\n\\n resource_id = ctx.instance.runtime_properties['resource_id']\\n _stop_droplet(resource_id, credentials)\\n # TODO: try power_off if shutdown is not successful\",\n \"title\": \"\"\n },\n {\n \"docid\": \"700985d62e1350c7e25c353bf663d970\",\n \"score\": \"0.62850386\",\n \"text\": \"def stop_service(self, service_name, instance=None):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83211d262e4dc347c85e1f7cb14607\",\n \"score\": \"0.6279845\",\n \"text\": \"def stop():\\n ret = _LIB.eve_click_stop()\\n if ret < 0:\\n raise Exception(\\\"eve click stop failed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d115aafbfeb876ad61929061901f7890\",\n \"score\": \"0.6273752\",\n \"text\": \"def terminate_self():\\n\\n logger.info(\\\"terminating running Google Compute Engine instance\\\")\\n logger.info(\\\"trying to infer the identity of the instance...\\\")\\n name = discover_instance_name()\\n zone_url = discover_instance_zone()\\n zone = zone_url.split(\\\"/\\\")[-1]\\n project = discover_project_id()\\n logger.info(\\n \\\"identity inference done. name=%s zone=%s project=%s\\\", name, zone, project\\n )\\n\\n logger.info(\\\"sending DELETE request to terminate instance\\\")\\n compute_client = googleapiclient.discovery.build(\\\"compute\\\", \\\"v1\\\")\\n operation = compute_client.instances().delete(\\n project=project, zone=zone, instance=name,\\n )\\n\\n logger.info(\\\"Blocking to wait for the delete to complete\\\")\\n compute_client.zoneOperations().wait(\\n project=project, zone=zone, operation=operation[\\\"name\\\"]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56871b79e431ebb84b78c1b2a3645efb\",\n \"score\": \"0.62714607\",\n \"text\": \"def stop(self, *args):\\n vm_name = ' '.join(args)\\n host = get_vm_host(self._vbo, vm_name)\\n if host:\\n return host.stop(vm_name)\\n host = get_vm_host(self._vbo, vm_name)\\n if host:\\n return host.co\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d00257614dcc2191512b1a9e63ed691\",\n \"score\": \"0.6266016\",\n \"text\": \"def stop(self):\\n self.send_command('stop')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a17f329abad61c3ecdafeacfd5b77e\",\n \"score\": \"0.62649524\",\n \"text\": \"def terminate(self):\\n if self.instance is None:\\n logger.info(\\\"No instance to terminate.\\\")\\n return\\n\\n instance_id = self.instance.id\\n try:\\n self.instance.terminate()\\n self.instance.wait_until_terminated()\\n self.instance = None\\n except ClientError as err:\\n logging.error(\\n \\\"Couldn't terminate instance %s. Here's why: %s: %s\\\", instance_id,\\n err.response['Error']['Code'], err.response['Error']['Message'])\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c023e89d5555945c67dd90da7ec05569\",\n \"score\": \"0.62500906\",\n \"text\": \"def stop_guest(userid):\\n sdk_client.send_request('guest_start', userid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a3fc0d100483d43cfd1f08b93318163\",\n \"score\": \"0.62450475\",\n \"text\": \"def stop(self, instance_id, timeout=None, ignore_failed=False, ignore_inactive=False):\\n expected_rcs = {RC_SUCCESS}\\n if ignore_failed:\\n expected_rcs.add(RC_SERVICE_FAILED)\\n if ignore_inactive:\\n expected_rcs.add(RC_SERVICE_INACTIVE)\\n\\n self._stop(instance_id, timeout=timeout, expected_rcs=expected_rcs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1368193559e847f62502929834a91d02\",\n \"score\": \"0.62420565\",\n \"text\": \"def stop(self):\\n pid = self.get_pid()\\n if pid:\\n os.kill(pid, signal.SIGTERM)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2af3a285a8f459c02af3fbd394b26d6\",\n \"score\": \"0.6234463\",\n \"text\": \"def leave (name, instance_id = None):\\r\\n if instance_id is None:\\r\\n try:\\r\\n instance_id = __grains__[\\\"ec2_instance-id\\\"]\\r\\n except KeyError:\\r\\n return False\\r\\n lb = _get_elb (name)\\r\\n try:\\r\\n lb.deregister_instances ([instance_id,])\\r\\n except Exception:\\r\\n import traceback\\r\\n LOG.error (\\\"ELB %s: Error while deregistering instance %s\\\", name, instance_id)\\r\\n LOG.debug (traceback.format_exc ())\\r\\n return False\\r\\n LOG.debug (\\\"ELB %s: Removed instance %s\\\", name, instance_id)\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fe05238cc7ce36b9cc750d87aad5edb\",\n \"score\": \"0.6231611\",\n \"text\": \"def stop(self):\\n id = self._get_next_id()\\n json.dump(obj={\\\"id\\\": id, \\\"command\\\": \\\"stop\\\"}, fp=self._file)\\n self._file.flush()\\n quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7095cde3a1ab820e6b4202848cd8f589\",\n \"score\": \"0.6230381\",\n \"text\": \"def detach_instance(c, as_group, id):\\n c.detach_instances(\\n InstanceIds=[id],\\n AutoScalingGroupName=as_group,\\n ShouldDecrementDesiredCapacity=True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"017243f9e31cf66bec2ba98981ef045c\",\n \"score\": \"0.6213323\",\n \"text\": \"def _ShutdownInstance(self, name, hvparams, timeout):\\n info = self.GetInstanceInfo(name, hvparams=hvparams)\\n\\n if info is None or hv_base.HvInstanceState.IsShutdown(info[4]):\\n logging.info(\\\"Failed to shutdown instance %s, not running\\\", name)\\n return None\\n\\n return self._RunXen([\\\"shutdown\\\", \\\"-w\\\", name], timeout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebfd6e21a99bfbbb2636661e6538dff3\",\n \"score\": \"0.6195339\",\n \"text\": \"def terminate_instances(self, ids, dry_run=False):\\n LOGGER.info(\\\"terminating: %s dry_run=%s\\\", ids, dry_run)\\n self.ec2.instances.filter(InstanceIds=ids).terminate(DryRun=dry_run)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c788f27b368dfbb71d705a5987414d11\",\n \"score\": \"0.6192878\",\n \"text\": \"def do_stop(self, params):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc36521fede7024f7e2c531ada690edf\",\n \"score\": \"0.61689293\",\n \"text\": \"def leave(name, instance_id=None):\\n if instance_id is None:\\n try:\\n instance_id = __grains__[\\\"ec2_instance-id\\\"]\\n except KeyError:\\n return False\\n lb = _get_elb(name)\\n try:\\n lb.deregister_instances([instance_id, ])\\n except Exception:\\n import traceback\\n LOG.error(\\\"ELB %s: Error while deregistering instance %s\\\", name, instance_id)\\n LOG.debug(traceback.format_exc())\\n return False\\n LOG.debug(\\\"ELB %s: Removed instance %s\\\", name, instance_id)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb59dbff1c648cbc2ebc1ffe69c02a1\",\n \"score\": \"0.61669004\",\n \"text\": \"def terminate_instances(instance_ids):\\n\\n\\n # Terminate each instance in the argument list\\n ec2 = boto3.client('ec2')\\n try:\\n states = ec2.terminate_instances(InstanceIds=instance_ids)\\n except ClientError as e:\\n logging.error(e)\\n return None\\n return states['TerminatingInstances']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428a4f870998762b5e9f0c4f6f0ece36\",\n \"score\": \"0.61622167\",\n \"text\": \"def shutdown_vm():\\n try:\\n session_cache_id = session['session_cache_id']\\n cached_session = session_cache[session_cache_id]\\n acc_key = cached_session['acc_key']\\n secret_key = cached_session['secret_key']\\n credentials = {'access_key': acc_key, 'secret_access_key': secret_key}\\n _cf = _create_connection('cloudformation', **credentials)\\n _ec2 = _create_connection('ec2', **credentials)\\n stack = _cf.describe_stacks('BitnamiWordpressStack')[0]\\n for resource in stack.list_resources():\\n if 'AWS::EC2::Instance' in resource.resource_type:\\n _ec2.stop_instances(instance_ids=[resource.physical_resource_id])\\n flash('You were able to send the stop signal to the VM.')\\n return redirect(url_for('hello'))\\n flash(\\\"Unable to find and shut down the VM.\\\")\\n return redirect(url_for('hello'))\\n except Exception as e:\\n return str(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ad1d8d655d21d37e2cfe9e20f93b42\",\n \"score\": \"0.61613613\",\n \"text\": \"def stop(self, container, instances=None, map_name=None, **kwargs):\\n return self.run_actions('stop', container, instances=instances, map_name=map_name, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb2eb1db226ca57f2db25e04d595082\",\n \"score\": \"0.6160417\",\n \"text\": \"def StopInstances(self, request):\\n try:\\n params = request._serialize()\\n headers = request.headers\\n body = self.call(\\\"StopInstances\\\", params, headers=headers)\\n response = json.loads(body)\\n model = models.StopInstancesResponse()\\n model._deserialize(response[\\\"Response\\\"])\\n return model\\n except Exception as e:\\n if isinstance(e, TencentCloudSDKException):\\n raise\\n else:\\n raise TencentCloudSDKException(type(e).__name__, str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f290dcbd21f41f9696b9d35d7faa83e\",\n \"score\": \"0.615903\",\n \"text\": \"def stop(self):\\n # stop instance\\n self.terminate_instance()\\n # delete disk\\n self.delete_disk()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f03a2093b3fa7450656079524c57d2\",\n \"score\": \"0.6154438\",\n \"text\": \"def stop_server(context: Context):\\n context.run(\\\"kill $(pgrep -f api_server | grep -v ^$$\\\\$)\\\", warn=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1dd9fd6854dd0e8030e6c62c1427be0\",\n \"score\": \"0.61472404\",\n \"text\": \"def _destroy(self, instance_id):\\n try:\\n container = OvzContainer.find(nova_id=instance_id)\\n container.delete()\\n except exception.InstanceNotFound:\\n LOG.info(\\\"Instance %s cannot be deleted, since it does not exist\\\"\\n % instance_id)\\n return\\n except BaseException:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa05a8ce6739c6989001aa58879c8fc\",\n \"score\": \"0.6140421\",\n \"text\": \"def admin_ide_stop_id(id: str):\\n\\n # Get the course context\\n course = get_course_context()\\n\\n # Search for the theia session\\n session = TheiaSession.query.filter(\\n TheiaSession.id == id,\\n TheiaSession.course_id == course.id,\\n ).first()\\n\\n # Verify it exists\\n req_assert(session is not None, message='session does not exist')\\n\\n # Set all the things as stopped\\n session.active = False\\n session.ended = datetime.now()\\n session.state = \\\"Ending\\\"\\n\\n # Commit the stop\\n db.session.commit()\\n\\n # Enqueue the theia stop cleanup\\n enqueue_ide_stop(session.id)\\n\\n # Hand back response\\n return success_response({\\\"status\\\": \\\"Session Killed.\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c897a97eddd10e43aecb314735abcd\",\n \"score\": \"0.6130715\",\n \"text\": \"def stop(self):\\n command = [\\n 'ssserver',\\n '--pid-file', self.pidfile(original=True),\\n '--log-file', self.logfile(original=True),\\n '-d', 'stop',\\n ]\\n (rc, stdout, stderr) = self.call(command)\\n if rc == 0:\\n logger.debug(stdout)\\n else:\\n logger.error('stderr: %s\\\\nstdout: %s' % (stderr, stdout))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15ae967e50ca6e66e109bbe41556cab0\",\n \"score\": \"0.6127036\",\n \"text\": \"def stop(self):\\n inst = self.instance\\n\\n if inst.is_online:\\n\\n # If the metadata isn't up to date and this instance *is not*\\n # online, we don't want to error out. Just act like it worked.\\n\\n try:\\n self.__run_cmd(self.__get_cmd('stop'))\\n except Exception, e:\\n if not 'not running' in str(e) and not 'not exist' in str(e):\\n raise\\n\\n inst.is_online = False\\n inst.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfd1ba97c51d4e003fbf5f3c023aaeb5\",\n \"score\": \"0.6108744\",\n \"text\": \"def stop(cmd):\\n\\tcmd.icp.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607adae8d3ddef7981fcafe876a9feb3\",\n \"score\": \"0.6100318\",\n \"text\": \"def Stop(self):\\n payload = { \\\"Arg1\\\": self.href }\\n return self._execute('stop', payload=payload, response_object=None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5257bedf856595142da04000547c5c15\",\n \"score\": \"0.6093426\",\n \"text\": \"def stop(self, uuid, args, okay, fail):\\n try:\\n process = self.processes[uuid]\\n except KeyError:\\n fail('does-not-exist')\\n else:\\n process.stop(args, okay, fail)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f11111fc0b90cc16731c72461f0cf8\",\n \"score\": \"0.6074993\",\n \"text\": \"def stop(self, ts_id, addr):\\n if addr.find(':') >= 0:\\n host, port = addr.split(':', 1)\\n status = self.quit(host, port)\\n return \\\"Stopped test system #{0}, server @{1}: stop={2}\\\".format(\\n ts_id, addr, status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f3b69a6f6439167145df2ec1194244\",\n \"score\": \"0.6070596\",\n \"text\": \"def test_run_terminate(self):\\n instance_id = self._create_instance()\\n\\n self.compute.run_instance(self.context, instance_id)\\n\\n instances = db.instance_get_all(context.get_admin_context())\\n LOG.info(_(\\\"Running instances: %s\\\"), instances)\\n self.assertEqual(len(instances), 1)\\n\\n self.compute.terminate_instance(self.context, instance_id)\\n\\n instances = db.instance_get_all(context.get_admin_context())\\n LOG.info(_(\\\"After terminating instances: %s\\\"), instances)\\n self.assertEqual(len(instances), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2b6cc35ec704ae5a3b7300da5883d31\",\n \"score\": \"0.6070342\",\n \"text\": \"def stop(self, name=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"030bcd5c8de9535f7c1da84f9edf12ea\",\n \"score\": \"0.60684955\",\n \"text\": \"def _stop_swap(self, swap_uuid):\\n utils.run_vtctl(['WorkflowStop', swap_uuid], auto_log=True)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1786,"cells":{"query_id":{"kind":"string","value":"3595cadc708f0a78b7a0b73784621f8f"},"query":{"kind":"string","value":"Ensures correctness of _primary_and_secondary_sources_to_table."},"positive_passages":{"kind":"list like","value":[{"docid":"216133a72349cc172d2720c5c0bac587","score":"0.78826684","text":"def test_primary_and_secondary_sources_to_table(self):\n\n this_actual_table = raw_wind_io._primary_and_secondary_sources_to_table()\n\n this_actual_table.sort_values(\n raw_wind_io.PRIMARY_SOURCE_COLUMN, axis=0, ascending=True,\n inplace=True)\n\n this_actual_table.fillna(value='FOO', axis=1, inplace=True)\n this_actual_table.reset_index(inplace=True, drop=True)\n\n this_expected_table = (\n PRIMARY_AND_SECONDARY_SOURCE_PAIRS_AS_TABLE.sort_values(\n raw_wind_io.PRIMARY_SOURCE_COLUMN, axis=0, ascending=True,\n inplace=False)\n )\n\n this_expected_table.fillna(value='FOO', axis=1, inplace=True)\n this_expected_table.reset_index(inplace=True, drop=True)\n\n self.assertTrue(this_actual_table.equals(this_expected_table))","title":""}],"string":"[\n {\n \"docid\": \"216133a72349cc172d2720c5c0bac587\",\n \"score\": \"0.78826684\",\n \"text\": \"def test_primary_and_secondary_sources_to_table(self):\\n\\n this_actual_table = raw_wind_io._primary_and_secondary_sources_to_table()\\n\\n this_actual_table.sort_values(\\n raw_wind_io.PRIMARY_SOURCE_COLUMN, axis=0, ascending=True,\\n inplace=True)\\n\\n this_actual_table.fillna(value='FOO', axis=1, inplace=True)\\n this_actual_table.reset_index(inplace=True, drop=True)\\n\\n this_expected_table = (\\n PRIMARY_AND_SECONDARY_SOURCE_PAIRS_AS_TABLE.sort_values(\\n raw_wind_io.PRIMARY_SOURCE_COLUMN, axis=0, ascending=True,\\n inplace=False)\\n )\\n\\n this_expected_table.fillna(value='FOO', axis=1, inplace=True)\\n this_expected_table.reset_index(inplace=True, drop=True)\\n\\n self.assertTrue(this_actual_table.equals(this_expected_table))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"899c520089ce88e397c2dfe17e9130c9","score":"0.5935422","text":"def test_check_data_sources_fake_secondary(self):\n\n with self.assertRaises(ValueError):\n raw_wind_io.check_data_sources(\n primary_source=raw_wind_io.MADIS_DATA_SOURCE,\n secondary_source=FAKE_SECONDARY_DATA_SOURCE)","title":""},{"docid":"1beb98ea7219593951b5bc96b094da2f","score":"0.5878489","text":"def test_check_data_sources_fake_primary(self):\n\n with self.assertRaises(ValueError):\n raw_wind_io.check_data_sources(\n primary_source=FAKE_PRIMARY_DATA_SOURCE)","title":""},{"docid":"c349ad925bbb385ab4a06b27e28dc8df","score":"0.57140356","text":"def helper_create_tables(self):\n ## creating tables\n r.table_create('original_data', primary_key ='original_data_id').run(self.conn)\n r.table_create('road_data', primary_key = 'road_data_id').run(self.conn)\n r.table_create('flow_item', primary_key = 'flow_item_id').run(self.conn)\n r.table_create('crawled_batch', primary_key = 'crawled_batch_id').run(self.conn)\n r.table_create('flow_data', primary_key = 'flow_data_id').run(self.conn)\n r.table_create('route_cached', primary_key = 'route_cached_id').run(self.conn)\n r.table_create('analytics_monitored_area', primary_key = 'analytics_monitored_area_id').run(self.conn)\n r.table_create('analytics_traffic_pattern', primary_key = 'analytics_traffic_pattern_id').run(self.conn)\n\n ## creating index\n r.table('road_data').index_create('flow_item_id').run(self.conn)\n r.table('road_data').index_create('geometry', geo=True).run(self.conn)\n r.table('crawled_batch').index_create('crawled_timestamp').run(self.conn)\n r.table(\"flow_data\").index_create(\"flow_crawled_batch\", [r.row[\"flow_item_id\"], r.row[\"crawled_batch_id\"]]).run(self.conn)\n r.table('analytics_monitored_area').index_create('description').run(self.conn)\n r.table(\"analytics_traffic_pattern\").index_create(\"analytics_crawled_batch\", [r.row[\"analytics_monitored_area_id\"], r.row[\"crawled_batch_id\"]]).run(self.conn)\n\n ## depreciated index\n # r.table('flow_data').index_create('created_timestamp', r.row[\"CUSTOM\"][\"created_timestamp\"]).run(self.conn)\n # r.table('road_data').index_create('created_timestamp').run(self.conn)\n # r.table('road_data').index_create('crawled_batch_id').run(self.conn)\n # r.table('flow_data').index_create('crawled_batch_id', r.row[\"CUSTOM\"][\"crawled_batch_id\"]).run(self.conn)\n # r.table('flow_data').index_create('original_data_id', r.row[\"CUSTOM\"][\"original_data_id\"]).run(self.conn)\n # r.table('original_data').index_create('crawled_batch_id').run(self.conn)\n #r.table('original_data').index_create('crawled_batch_id').run(self.conn)","title":""},{"docid":"e8767fd863c5297798909f533aa5d4c2","score":"0.5648351","text":"def __transfer_tables_and_data(self):\n print \"getting table names...\"\n tables = self.__get_tidal_points_from_ACDB()\n print \"have tables names.\"\n for table in tables:\n data = self.__get_tidal_data_from_ACDB(table)\n self.__insert_tidal_data_into_db(data,table)\n self.__create_aux_info_table()","title":""},{"docid":"aaf8574708a803fe5ac0aaca9b36ca5e","score":"0.5588258","text":"def check_tables_populated(self) -> bool:\n with self.conn.cursor() as cur:\n cur.execute(self._check_sources_query)\n results = cur.fetchall()\n if len(results) < len(SourceName):\n logger.info(\"Gene sources table is missing expected sources.\")\n return False\n\n with self.conn.cursor() as cur:\n cur.execute(self._check_concepts_query)\n result = cur.fetchone()\n if not result or result[0] < 1:\n logger.info(\"Gene records table is empty.\")\n return False\n\n with self.conn.cursor() as cur:\n cur.execute(self._check_merged_query)\n result = cur.fetchone()\n if not result or result[0] < 1:\n logger.info(\"Normalized gene records table is empty.\")\n return False\n\n return True","title":""},{"docid":"30a9e8fe20979f515d0be437ee99e282","score":"0.5578816","text":"def test_fk_error_on_source_creation(rse_factory, did_factory, root_account):\n\n if get_session().bind.dialect.name == 'sqlite':\n pytest.skip('Will not run on sqlite')\n\n src_rse, src_rse_id = rse_factory.make_mock_rse()\n dst_rse, dst_rse_id = rse_factory.make_mock_rse()\n all_rses = [src_rse_id, dst_rse_id]\n add_distance(src_rse_id, dst_rse_id, distance=10)\n\n topology = Topology(rse_ids=all_rses).configure_multihop()\n did = did_factory.random_file_did()\n file = {'scope': did['scope'], 'name': did['name'], 'type': 'FILE', 'bytes': 1, 'adler32': 'beefdead'}\n add_replicas(rse_id=src_rse_id, files=[file], account=root_account)\n rule_core.add_rule(dids=[did], account=root_account, copies=1, rse_expression=dst_rse, grouping='ALL', weight=None, lifetime=None, locked=False, subscription_id=None)\n\n requests_by_id = list_and_mark_transfer_requests_and_source_replicas(rse_collection=topology, rses=[src_rse_id, dst_rse_id])\n requests, *_ = build_transfer_paths(topology=topology, protocol_factory=ProtocolFactory(), requests_with_sources=requests_by_id.values())\n request_id, [transfer_path] = next(iter(requests.items()))\n\n transfer_path[0].rws.request_id = generate_uuid()\n to_submit, *_ = assign_paths_to_transfertool_and_create_hops(requests, default_tombstone_delay=0)\n assert not to_submit","title":""},{"docid":"7b61e18302ef0e28a7f1aad31636871f","score":"0.5551415","text":"def _init_db(self):\n\n try:\n if not database_exists(self.src_engine.url) or not \\\n database_exists(self.dst_engine.url):\n raise RuntimeError(\n 'Source or destination db does not exist ¯\\_(ツ)_/¯')\n except sqlalchemy.exc.OperationalError as e:\n if \"password\" in e.args[0]:\n raise RuntimeError(\n 'Unable to log in with that user and password (╯°□°）╯︵ ┻━━━━┻')\n if \"could not connect\" in e.args[0] or \"starting up\" in e.args[0]:\n raise RuntimeError(\n 'Database unreachable ¯\\_(⊙︿⊙)_/¯')\n\n # get metadata for both dbs\n self.src_metadata = MetaData(self.src_engine, reflect=True)\n self.dst_metadata = MetaData(self.dst_engine, reflect=True)\n\n if self.table_name not in self.src_metadata.tables:\n import pdb\n pdb.set_trace()\n raise RuntimeError(\n 'Table \"{}\" does not exist in source db ¯\\_(ツ)_/¯'\n .format(self.table_name))\n\n if self.table_name not in self.dst_metadata.tables:\n # create table in destination db if it doesn't exist\n tbl = Table(self.table_name, self.src_metadata, autoload=True)\n tbl.metadata.create_all(self.dst_engine)\n\n return self","title":""},{"docid":"cf997db1f3230a8ce2b1c724dd8efc75","score":"0.5479704","text":"def ensure_tables(cursor):\n\n try:\n cursor.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS individuals (\n name VARCHAR(64) PRIMARY KEY,\n gender VARCHAR(6)\n )\n \"\"\")\n except sqlite3.OperationalError as e:\n logger.warning(\"Creating individuals table for {} failed with: '{}'. Trying to continue anyways.\".format(database, e))\n\n try:\n cursor.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS populations (\n name VARCHAR(64) PRIMARY KEY,\n size INT,\n description VARCHAR(64)\n )\n \"\"\")\n except sqlite3.OperationalError as e:\n logger.warning(\"Creating populations table for {} failed with: '{}'. Trying to continue anyways.\".format(database, e))\n\n try:\n cursor.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS individuals_populations (\n individual VARCHAR(64),\n population VARCHAR(64),\n UNIQUE(individual, population),\n FOREIGN KEY (individual) REFERENCES individuals(name),\n FOREIGN KEY (population) REFERENCES populations(name)\n )\n \"\"\")\n except sqlite3.OperationalError as e:\n logger.warning(\"Creating populations table for {} failed with: '{}'. Trying to continue anyways.\".format(database, e))","title":""},{"docid":"1c68cda6f2bd9a1217de4e6ecf28a4d8","score":"0.5416401","text":"def create_tables():","title":""},{"docid":"863a238e1f5c9f9be3ec8c3ac5ba50ae","score":"0.5389179","text":"def test_generate_required_tables(self):\n output_schema: OutputSchema = run_sync(\n get_output_schema(include=[SmallTable._meta.tablename])\n )\n self.assertEqual(len(output_schema.tables), 1)\n SmallTable_ = output_schema.get_table_with_name(\"SmallTable\")\n self._compare_table_columns(SmallTable, SmallTable_)","title":""},{"docid":"72c8bf2ab600a13a7101328b3c26c032","score":"0.5383621","text":"def checkRoutingTable(self) -> None:\n for sink in self.routingSinks:\n if sink[1] != 'X' and sink not in self.routingSources:\n QSWATUtils.error('Internal error: routing sink category {1} id {0} not found as a source'.format(sink[0], sink[1]),\n self.isBatch)\n self.checkRoutedPointsSubbasinsAndLSUsDefined()\n # check routing table for circularities\n errors, warnings = self.checkRouting(self.conn)\n for error in errors:\n QSWATUtils.error(error, self.isBatch)\n if len(warnings) > 5:\n QSWATUtils.information('See QSWAT+ log for {0} warnings about circularities in routing'.\n format(len(warnings)), self.isBatch)\n for warning in warnings:\n QSWATUtils.loginfo(warning)\n else:\n for warning in warnings:\n QSWATUtils.information(warning, self.isBatch)","title":""},{"docid":"e8b1bcf377e6a66451acdf37967ee737","score":"0.534483","text":"def resolve(self):\n if not self.first_table:\n self.first_table = self.first_table_identifier.get_unique_reference() \n \n if not self.second_table:\n self.second_table = self.second_table_identifier.get_unique_reference() \n\n columns1 = []\n if self.first_table:\n for column_name in self.first_table_columns:\n columns1.append(self.first_table.find_column(column_name))\n \n self.first_table_columns = columns1\n del columns1\n \n columns2 = []\n if self.second_table:\n for column_name in self.second_table_columns:\n columns2.append(self.second_table.find_column(column_name))\n\n self.second_table_columns = columns2\n del columns2","title":""},{"docid":"cf207df841ba33a369f0936052a1cfce","score":"0.5326868","text":"def test_postgres_to_dolt(postgres_with_table, db_with_table):\n postgres_conn, postgres_table = postgres_with_table\n dssc, dolt_table = db_with_table\n validate_dolt_as_target(postgres_conn,\n postgres_table,\n get_source_reader,\n get_postgres_target_writer,\n get_table_reader,\n dssc,\n dolt_table)","title":""},{"docid":"20f7fc8279ab12239ceb67f51ee74fb0","score":"0.52978605","text":"def set_up_connection_table(self, connections):\n # set up connection table\n self.connection_table = np.zeros((len(self.flat_sources),\n len(self.sinks)))\n for start, stop in connections:\n if start in self.flat_sources and stop in self.sinks:\n start_idx = self.flat_sources.index(start)\n stop_idx = self.sinks.index(stop)\n self.connection_table[start_idx, stop_idx] = 1","title":""},{"docid":"d1a7cc2248ac9d6d712ce58002dfa7de","score":"0.52798563","text":"def create_table_links():\n conn = psycopg2.connect(dbname=DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST, port=PORT)\n cursor = conn.cursor()\n try:\n cursor.execute(\"\"\"CREATE TABLE links2\n (\n Id INT PRIMARY KEY NOT NULL, \n link CHAR(50) UNIQUE\n );\"\"\")\n conn.commit()\n return True\n except Exception as err:\n print(f'Ошибка psycopg2: {errorcodes.lookup(err.pgcode)}')\n return False\n finally:\n conn.close()","title":""},{"docid":"27fbf697bd7a2191c7836810c73b42c9","score":"0.52739185","text":"def append_table(src_con: \"SimpleSQLite\", dst_con: \"SimpleSQLite\", table_name: str) -> bool:\n\n logger.debug(\n \"append table: src={src_db}.{src_tbl}, dst={dst_db}.{dst_tbl}\".format(\n src_db=src_con.database_path,\n src_tbl=table_name,\n dst_db=dst_con.database_path,\n dst_tbl=table_name,\n )\n )\n\n src_con.verify_table_existence(table_name)\n dst_con.validate_access_permission([\"w\", \"a\"])\n\n if dst_con.has_table(table_name):\n src_attrs = src_con.fetch_attr_names(table_name)\n dst_attrs = dst_con.fetch_attr_names(table_name)\n if src_attrs != dst_attrs:\n raise ValueError(\n dedent(\n \"\"\"\n source and destination attribute is different from each other\n src: {}\n dst: {}\n \"\"\".format(\n src_attrs, dst_attrs\n )\n )\n )\n\n primary_key, index_attrs, type_hints = extract_table_metadata(src_con, table_name)\n\n dst_con.create_table_from_tabledata(\n src_con.select_as_tabledata(table_name, type_hints=type_hints),\n primary_key=primary_key,\n index_attrs=index_attrs,\n )\n\n return True","title":""},{"docid":"4bd9486bc2b90b2e40066792029b780c","score":"0.5259045","text":"def zip_code_map_SODA( dict_db_connect, source_table, target_table, custom_map_col): \n try:\n # Start runtime\n start_time = datetime.datetime.now()\n print(\"Starting temp table (\" + target_table + \") creation at time (\" + str(start_time) + \").\")\n \n # Connect to database\n conn = psycopg2.connect( database = dict_db_connect[\"database\"], \n user = dict_db_connect[\"user\"],\n password = dict_db_connect[\"password\"],\n host = dict_db_connect[\"host\"],\n port = dict_db_connect[\"port\"])\n cur = conn.cursor()\n\n print(\"Dropping temp table if exists.\")\n cur.execute(\"DROP TABLE IF EXISTS \" + target_table + \";\")\n \n sql = \"CREATE TABLE \" + target_table + \" AS (SELECT \" + source_table\n sql += \".*, COALESCE(zip_code_map.zip_code,99999) AS zip_code FROM \" + source_table\n sql += \" LEFT JOIN zip_code_map ON zip_code_map.location = \" + source_table + \".\" + custom_map_col + \");\" \n\n print sql\n cur.execute(sql)\n conn.commit()\n conn.close()\n print \"Created temp table with status message: \" + cur.statusmessage \n \n print (\"Ended temp table creation for table (\" + source_table + \") at time (\" + \n str(datetime.datetime.now()) + \").\")\n return True\n\n except Exception as inst:\n print(inst.args)\n print(inst)\n return False","title":""},{"docid":"b5b82181cab224a1c1bc96ac1dbf0327","score":"0.5253207","text":"def checkYouHaveTheRightTables( self ):\n\n expectedTables = []\n foundTables = []\n\n # Fill a list containing all the expected tables from self.files dictionary.\n # And self.files is THE true in this class.\n for myFile in self.files:\n expectedTables.append( myFile['table'] )\n\n # Just get data to be possible to enter into postgresql.\n username = self.getConfiguration( 'database', 'user' )\n\n # Get all tables from the EXPECTED relational database you've created.\n sqlTables = \"SELECT table_name FROM information_schema.tables WHERE table_schema='public' AND table_type='BASE TABLE';\"\n\n # Actual get the data said above executing the 'psql' command.\n p = subprocess.Popen([\"psql\", '-U', 'kegg2017', '-t', '-c', sqlTables ], stdout=subprocess.PIPE)\n\n # Store the result of the command above.\n out, err = p.communicate()\n\n # The result is not cool, we have to split that in lines.\n tables = out.split('\\n')\n\n # And we have to remove empty lines (the result has it!).\n reRemoveEmptyLines = re.compile('^$')\n\n # Iterate through the result tables from the actual, real, relational database you've created.\n for t in tables:\n # Actual ignore empty lines.\n if reRemoveEmptyLines.search( t ):\n continue\n\n # Remove blank spaces and weird useless '.' characters.\n tableName = re.sub( '\\ ', '', t )\n tableName = re.sub( '\\.', '', tableName )\n\n # Now we fill the found tables from your relational database.\n foundTables.append( tableName )\n\n\n # TODO: fix lack of expected tables.\n # -------------------------------------------------------------------------------------------\n # Some table doesn't belong to the Loader but it exists...\n # It means tables that have to be populated manually or tables you know are\n # critical but you don't populate those using this Loader.\n # If you're doing smart things in your relational database, this thing have to \n # be concerned.\n expectedTables.append( 'source_databases' ) # this goes manually.\n expectedTables.append( 'protein_pdbs' ) # this is important and will be filled later.\n expectedTables.append( 'ec_reaction_classes' ) # this goes manually.\n expectedTables.append( 'clusters' ) # this goes by outside process.\n expectedTables.append( 'clustering_methods' ) # this goes by outside process.\n # END of not fancy workaround. But loaders... come on... you know... loaders... \n # If you're a developer, you're horrified. If you're a database administrator\n # you're horrified. But if you are a guy that deals with loaders through big data\n # well... you know...\n # -------------------------------------------------------------------------------------------\n\n # Remove possible duplications (we never know).\n expectedTables = set(expectedTables)\n expectedTables = list(expectedTables)\n\n # Oh my god... if all the expected tables are exacly the same as the tables we're going\n # to fill using this class... everything is so fine and cool!!\n # It means you created a database with exacly the same tables this class expect. Congratulations!!\n if set(foundTables) == set(expectedTables):\n return True\n else:\n return False","title":""},{"docid":"ffeac41fdda2fa64d2f69a0896bb514e","score":"0.52497804","text":"def data_upload(self):\n table_sql = \"CREATE TABLE IF NOT EXISTS SE_BRIDGE.dbo.\" + \\\n self.tableName + \"(\"\n for table_col in self.columns:\n table_sql = table_sql + \\\n table_col[\"name\"] + \" \" + table_col[\"dataType\"] + \", \"\n table_sql = table_sql[:-2] + \")\"\n\n # Build the MERGE statement that will copy the data from the staging table into the production table.\n # Production table should have the same name and columns as the staging table.\n merge_sql = \"MERGE INTO SE_BRIDGE.dbo.\" + self.tableName + \\\n \" t USING (SELECT * FROM SOURCE_STAGING.SE_BRIDGE.\" + \\\n self.tableName + \") x ON \"\n for key_col in self.columns:\n if key_col[\"isKey\"] == True:\n merge_sql = merge_sql + \"t.\" + \\\n key_col[\"name\"] + \" = x.\" + key_col[\"name\"] + \" AND \"\n merge_sql = merge_sql[:-4] + \"WHEN MATCHED THEN UPDATE SET \"\n for update_col in self.columns:\n if update_col[\"isKey\"] == False:\n merge_sql = merge_sql + \\\n update_col[\"name\"] + \" = x.\" + update_col[\"name\"] + \", \"\n merge_sql = merge_sql[:-2] + \" WHEN NOT MATCHED THEN INSERT (\"\n for insert_col in self.columns:\n merge_sql = merge_sql + insert_col[\"name\"] + \", \"\n merge_sql = merge_sql[:-2] + \") VALUES (\"\n for val_col in self.columns:\n merge_sql = merge_sql + \"x.\" + val_col[\"name\"] + \", \"\n merge_sql = merge_sql[:-2] + \");\"\n\n con = snowflake.connector.connect(\n user=SNOWFLAKE_USER,\n password=SNOWFLAKE_PASSWORD,\n account=SNOWFLAKE_ACCOUNT\n )\n cur = con.cursor()\n\n try:\n self.__upload(cur, \"SE_BRIDGE\")\n cur.execute(\"USE SCHEMA SE_BRIDGE.dbo\")\n\n # Create production table if it doesn't exist\n cur.execute(table_sql)\n\n # Merge the data from staging to production table\n print(\"Merging data into Production table\")\n cur.execute(merge_sql)\n\n finally:\n cur.close()\n con.close()","title":""},{"docid":"50c0782f67009f76750c69ab8a269edc","score":"0.5243017","text":"def copy_table(\n src_db, dest_db, table,\n pkey, fkeys,\n *, batch_size=50, condition=None, transform=None, validators=None\n):\n query = table.select()\n if pkey is not None:\n query = query.order_by(pkey)\n if condition is not None:\n query = query.where(condition)\n query = src_db.execute(query)\n assert pkey is None or pkey in query.keys()\n assert all(field in query.keys() for field in fkeys)\n mapping = {}\n batch = query.fetchmany(batch_size)\n orig_pkey = None # Avoids warning\n while batch:\n for row in batch:\n row = dict(row.items())\n # Get primary key, remove it\n if pkey is not None:\n orig_pkey = row[pkey]\n row = dict(row)\n row.pop(pkey)\n # Map foreign keys\n for field, fkey_map in fkeys.items():\n row[field] = fkey_map[row[field]]\n # Generic transform\n if transform is not None:\n row = transform(row)\n # Validate\n if validators:\n for key, value in row.items():\n if key in validators:\n try:\n if not validators[key](value):\n raise ValueError(\n \"Data failed validation (column %r)\" % key,\n )\n except validate.InvalidFormat:\n raise ValueError(\n \"Data failed validation (column %r)\" % key,\n )\n\n # Have to insert one-by-one for inserted_primary_key\n ins = dest_db.execute(\n table.insert(),\n row,\n )\n # Store new primary key\n if pkey is not None:\n mapping[orig_pkey], = ins.inserted_primary_key\n batch = query.fetchmany(batch_size)\n return mapping","title":""},{"docid":"18e73a1b25d653f8f5ba1d226c5b04de","score":"0.5241757","text":"def checkRoutedPointsSubbasinsAndLSUsDefined(self) -> None:\n for (sourceId, sourceCategory) in self.routingSources:\n if sourceCategory == 'PT':\n self.checkKeyInTable('gis_points', sourceId)\n elif sourceCategory == 'LSU':\n self.checkKeyInTable('gis_lsus', sourceId)\n elif sourceCategory == 'SUB':\n self.checkKeyInTable('gis_subbasins', sourceId)","title":""},{"docid":"5a5390237a3377ff8bc22094669b9d11","score":"0.5193516","text":"def test_oracle_to_mysql(oracle_with_table, create_dolt_test_data_commits):\n oracle_conn, oracle_table = oracle_with_table\n dssc, dolt_table = create_dolt_test_data_commits\n validate_dolt_as_source(oracle_conn, oracle_table, get_oracle_target_writer, dssc, dolt_table)","title":""},{"docid":"3795f7fa7886b4f0898dbe766980edbc","score":"0.5180515","text":"def migrate_onto(self):","title":""},{"docid":"9f2a8ec9e8a31dd16673b52f830a70fa","score":"0.51792276","text":"def __create_tables(self):\n self.conn.execute('create table if not exists col_resamples (col_id int, nrows int, nresamples int, lowest_raw_exps text, lowest_std_exps text)')","title":""},{"docid":"f40d29f02b5eb596d16342f0429d8807","score":"0.516509","text":"def partial_sync_table(table: tuple, args: Namespace) -> Union[bool, str]:\n snowflake = FastSyncTargetSnowflake(args.target, args.transform)\n tap_id = args.target.get('tap_id')\n\n try:\n table_name = table[0]\n\n column_name = table[1]['column']\n\n drop_target_table = table[1]['drop_target_table']\n args.drop_target_table = drop_target_table\n args.table = table_name\n\n mysql = FastSyncTapMySql(args.tap, tap_type_to_target_type)\n\n mysql.open_connections()\n\n start_value = utils.validate_boundary_value(mysql.query, table[1]['start_value'])\n end_value = utils.validate_boundary_value(mysql.query, table[1]['end_value'])\n\n # Get bookmark - Binlog position or Incremental Key value\n bookmark = common_utils.get_bookmark_for_table(table_name, args.properties, mysql)\n\n target_schema = common_utils.get_target_schema(args.target, table_name)\n table_dict = common_utils.tablename_to_dict(table_name)\n target_table = table_dict.get('table_name')\n\n target_sf = {\n 'sf_object': snowflake,\n 'schema': target_schema,\n 'table': target_table,\n 'temp': table_dict.get('temp_table_name')\n }\n\n snowflake_types = mysql.map_column_types_to_target(table_name)\n\n # making target table if not exists\n snowflake.create_schema(target_schema)\n snowflake.create_table(\n target_schema=target_schema,\n table_name=target_table,\n columns=snowflake_types['columns'],\n primary_key=snowflake_types.get('primary_key'),\n is_temporary=False,\n sort_columns=False,\n allow_replace_table=False\n )\n\n source_columns = snowflake_types.get('columns', [])\n columns_diff = utils.diff_source_target_columns(target_sf, source_columns=source_columns)\n\n start_value_for_query = start_value if start_value == 'NULL' else f'\\'{start_value}\\''\n where_clause_sql = f' WHERE {column_name} >= {start_value_for_query}'\n if end_value:\n where_clause_sql += f' AND {column_name} <= \\'{end_value}\\''\n\n # export data from source\n file_parts = mysql.export_source_table_data(args, tap_id, where_clause_sql)\n\n # mark partial data as deleted in the target\n snowflake.query(f'UPDATE {target_schema}.\"{target_table.upper()}\"'\n f' SET _SDC_DELETEd_AT = CURRENT_TIMESTAMP(){where_clause_sql} AND _SDC_DELETED_AT IS NULL')\n\n # Creating temp table in Snowflake\n primary_keys = snowflake_types.get('primary_key')\n snowflake.create_schema(target_schema)\n snowflake.create_table(\n target_schema, target_table, source_columns, primary_keys, is_temporary=True\n )\n\n mysql.close_connections()\n\n size_bytes = sum([os.path.getsize(file_part) for file_part in file_parts])\n _, s3_key_pattern = utils.upload_to_s3(snowflake, file_parts, args.temp_dir)\n\n utils.load_into_snowflake(\n target_sf, args, columns_diff, primary_keys, s3_key_pattern, size_bytes, where_clause_sql)\n\n if file_parts:\n utils.update_state_file(args, bookmark)\n\n return True\n except Exception as exc:\n LOGGER.exception(exc)\n return f'{table_name}: {exc}'","title":""},{"docid":"264b2269bbeaf006f0a96508226c8785","score":"0.51531166","text":"def test_run_incremental(self):\n\n # First run - should create tracking table, target table, and insert values\n # TODO: check constraints are picked up correctly\n run_druzhba(self.args)\n\n with self.target_conn.cursor() as cur:\n cur.execute(\n \"SELECT COUNT(*), MAX(updated_at), MAX(value1) FROM druzhba_test.test_basic\"\n )\n result = cur.fetchall()\n self.assertTupleEqual(result[0], (2, t.t1, 1))\n\n with self.source_conn.cursor() as cur:\n cur.execute(\n \"UPDATE test_basic SET value1 = 2, updated_at = %s WHERE pk = 2\",\n (t.t2,),\n )\n cur.execute(\n \"INSERT INTO test_basic VALUES (%s, %s, %s, %s);\", (3, t.t2, \"drop\", 3),\n )\n\n # Second run - should pick up the new row and the updated row\n run_druzhba(self.args)\n\n with self.target_conn.cursor() as cur:\n cur.execute(\"SELECT * FROM druzhba_test.test_basic ORDER BY pk\")\n results = cur.fetchall()\n\n self.assertListEqual(results, [(1, t.t0, 0), (2, t.t2, 2), (3, t.t2, 3)])\n\n cur.execute(\n \"SELECT * FROM druzhba_test.pipeline_table_index ORDER BY created_ts\"\n )\n results = cur.fetchall()\n\n self.assertListEqual(\n results,\n [\n (\n \"mysqltest\",\n \"druzhba_test\",\n \"test_basic\",\n t.t1.strftime(\"%Y-%m-%d %H:%M:%S.%f\"),\n ANY,\n ),\n (\n \"mysqltest\",\n \"druzhba_test\",\n \"test_basic\",\n t.t2.strftime(\"%Y-%m-%d %H:%M:%S.%f\"),\n ANY,\n ),\n ],\n )","title":""},{"docid":"f4dde5d0cf5b7a9611f06a291d9e4a63","score":"0.5147834","text":"def check_keys(pk_frame, fk_frame):\n\n assert len(pk_frame.columns) == len(fk_frame.columns), (\n 'The number of columns must be the same for primary and foreign keys')\n\n assert pk_frame.notnull().any(axis=1).all(), 'Primary keys cannot be null.'\n\n assert not pk_frame.duplicated().any(), 'Primary keys must be unique.'\n\n # Check that each foreign is a primary key\n distinct_foreign_keys = (\n fk_frame.dropna(how='all').drop_duplicates())\n merged = distinct_foreign_keys.merge(\n pk_frame, how='left', left_on=list(distinct_foreign_keys.columns),\n right_on=list(pk_frame.columns), indicator=True)\n fk_is_pk = (merged._merge == 'both').all()\n assert fk_is_pk, 'Every foreign key must match a primary key.'","title":""},{"docid":"ddfb4e16f8fa9ddd4b4f342626856abf","score":"0.5141495","text":"def test_append_only(self):\n\n with self.source_conn.cursor() as cur:\n cur.executemany(\n f\"INSERT INTO druzhba_test.{self.table_name} VALUES (%s, %s, %s);\",\n [(1, t.t0, None), (2, t.t0, None),],\n )\n\n run_druzhba(self.args)\n\n with self.source_conn.cursor() as cur:\n cur.executemany(\n f\"INSERT INTO druzhba_test.{self.table_name} VALUES (%s, %s, %s);\",\n [(2, t.t0, t.t1), (3, t.t1, None),],\n )\n\n run_druzhba(self.args)\n\n with self.target_conn.cursor() as cur:\n cur.execute(f\"SELECT * FROM druzhba_test.{self.table_name}\")\n results = cur.fetchall()\n\n self.assertSetEqual(\n set(results),\n {(1, t.t0, None), (2, t.t0, None), (2, t.t0, t.t1), (3, t.t1, None),},\n )","title":""},{"docid":"9f8f2019cdb568fbd4b0a23252de2382","score":"0.5137385","text":"def create_databases_tables(client_src: CogniteClient, client_dst: CogniteClient) -> Dict[str, List[str]]:\n db_list_src = client_src.raw.databases.list(limit=None)\n db_list_dst = client_dst.raw.databases.list(limit=None)\n\n db_names_src, db_not_created = get_not_created_names(db_list_src, db_list_dst)\n\n db_create = client_dst.raw.databases.create(name=db_not_created)\n\n assert len(db_create) == len(db_not_created)\n\n db_tb = {}\n for db_name in db_names_src:\n tb_list_src = client_src.raw.tables.list(db_name=db_name, limit=None)\n tb_list_dst = client_dst.raw.tables.list(db_name=db_name, limit=None)\n\n tb_names_src, tb_not_created = get_not_created_names(tb_list_src, tb_list_dst)\n\n tb_create = client_dst.raw.tables.create(db_name=db_name, name=tb_not_created)\n\n assert len(tb_create) == len(tb_not_created)\n\n db_tb[db_name] = tb_names_src\n\n return db_tb","title":""},{"docid":"5725ed1cc6c1b52b9251f99130b1beb1","score":"0.51236075","text":"def glue_table_copy(original_glue_db, new_glue_db, original_glue_table, dest_location, region):\n dest_bucket = bucket_dict(dest_location)['bucket_name']\n try:\n table_get_response = glue_get_table(database=new_glue_db,table=original_glue_table, region=region)\n new_s3_loc = table_get_response[\"Table\"][\"StorageDescriptor\"][\"Location\"]\n except:\n table_get_response = glue_get_table(\n database=original_glue_db,\n table=original_glue_table,\n region=region,\n )\n index_response = glue_get_partition_indexes(\n database=original_glue_db,\n table=original_glue_table,\n region=region\n )\n orig_s3_loc = table_get_response[\"Table\"][\"StorageDescriptor\"][\n \"Location\"\n ].split(\"/\")\n new_s3_loc = (\n f\"{dest_bucket}/spectrumfiles/{'/'.join(orig_s3_loc[2:])}\"\n )\n table_input = (\n {\n \"Name\": table_get_response[\"Table\"][\"Name\"],\n \"Description\": \"For use with Redshfit candidate release testing\",\n \"StorageDescriptor\": {\n \"Columns\": table_get_response[\"Table\"][\"StorageDescriptor\"][\n \"Columns\"\n ],\n \"Location\": new_s3_loc,\n },\n \"PartitionKeys\": table_get_response[\"Table\"][\"PartitionKeys\"],\n },\n )\n if index_response[\"PartitionIndexDescriptorList\"]:\n glue_create_table(\n new_database=new_glue_db,\n table_input=table_input.update(\n {\n 'PartitionIndexes\"': index_response[\n \"PartitionIndexDescriptorList\"\n ]\n }\n ),\n region=region)\n else:\n glue_create_table(\n new_database=new_glue_db,\n table_input=table_input[0],\n region=region)\n return new_s3_loc","title":""},{"docid":"c7b1f9816c0fd4e3f0e6f2d29f36974e","score":"0.51025957","text":"def _table_setup(cls):\r\n db = self.db_table.split(':') # Split the database name and table name\r\n try:\r\n with cls._get_conn() as conn:\r\n r.db(db[0]).table_create(db[1]).run(conn)\r\n #r.db(db[0]).table(db[1]).index_create('updated_at').run(conn)\r\n print(\"ReOBJ:table_setup: {} Table initialized in {} Database.\".format(db[1], db[0]))\r\n except RqlRuntimeError:\r\n print(\"ReOBJ:table_setup: {} Table exists in {} Database.\".format(db[1], db[0]))","title":""},{"docid":"e2efa1de768a8230f06e1ff86bc3028e","score":"0.50998634","text":"def constrains_source_location_on_internal_material(self):\n if self.pr_material_req == 'create_internal_transfer' and self.location_id.id == self.source_loc_id.id:\n raise UserError(_('You can not select Same Location for Source and destination.!!!'))","title":""},{"docid":"9c24623a4fab0bdb5aab5d19bee7762b","score":"0.5084189","text":"def _reorg_tables(self):\n pass","title":""},{"docid":"7ebf705c8f97bb611c97d5243dd1fef0","score":"0.5082431","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":"3d9b5e86ae8adc905c8149b5e1553f3a","score":"0.5079562","text":"def _get_comparison_pairs(db_pair, test_ingest_info):\n\n fuzzy_match_percentage = 15\n\n # Strip the random suffix from the test_ingest_info table and use it\n # for other tables.\n random_suffix = re.match(r'.+(_\\d+)', test_ingest_info).groups(1)\n\n # Match the datasets from Database 2 to those in Database 1\n sql = (\n \"CREATE TEMPORARY TABLE datasets_join_info AS SELECT DISTINCT\\n\" +\n \"a.acquisition_id AS acquisition_id1, ti.acquisition_id AS\\n\" +\n \"acquisition_id2, level_id FROM acquisition a\\n\" +\n \"INNER JOIN \" + test_ingest_info + \" ti ON \" +\n \" a.satellite_id=ti.satellite_id AND \" +\n \" a.sensor_id=ti.sensor_id AND \" +\n \" a.start_datetime BETWEEN \" +\n \" ti.start_datetime - \" + str(fuzzy_match_percentage/100.) +\n \" *ti.aq_len AND\\n\" +\n \" ti.start_datetime + \" + str(fuzzy_match_percentage/100.) +\n \" *ti.aq_len AND\\n\" +\n \" a.end_datetime BETWEEN \" +\n \" ti.end_datetime - \" + str(fuzzy_match_percentage/100.) +\n \" *ti.aq_len AND\\n\" +\n \" ti.end_datetime + \" + str(fuzzy_match_percentage/100.) +\n \" *ti.aq_len;\"\n )\n\n # Find all tiles from Database 1 which appear in the datasets\n sqltemp = (\n \"CREATE TEMPORARY TABLE tiles1 AS SELECT\\n\" +\n \"acquisition_id1, acquisition_id2, dji.level_id,\\n\" +\n \"tile_class_id AS tile_class_id1, tile_pathname AS path1,\\n\" +\n \"x_index, y_index FROM datasets_join_info dji\\n\" +\n \"INNER JOIN acquisition a ON a.acquisition_id=dji.acquisition_id1\\n\" +\n \"INNER JOIN dataset d on d.acquisition_id=a.acquisition_id AND\\n\" +\n \" d.level_id=dji.level_id\\n\" +\n \"INNER JOIN tile t ON t.dataset_id=d.dataset_id\\n\" +\n \"WHERE t.tile_class_id<>2;\"\n )\n\n sql = sql + sqltemp\n\n # Find all tiles from test ingestion\n sqltemp = (\n \"CREATE TEMPORARY TABLE tiles2 AS SELECT\\n\" +\n \"acquisition_id1, acquisition_id2, dji.level_id,\\n\" +\n \"tile_class_id AS tile_class_id2, tile_pathname AS path2,\\n\" +\n \"x_index, y_index FROM datasets_join_info dji\\n\" +\n \"INNER JOIN \" + test_ingest_info + \" ti ON \\n\" +\n \" ti.acquisition_id=dji.acquisition_id2 AND\\n\" +\n \" ti.level_id=dji.level_id;\"\n )\n\n sql = sql + sqltemp\n\n # For each Database 1 tile found in the test ingest datasets, find the\n # corresponding Database 2 tile if it exists.\n production_all_tiles = 'tiles1_all%s' %random_suffix\n test_ingest_all_tiles = 'tiles2_all%s' %random_suffix\n sqltemp = (\n \"CREATE TABLE \" + production_all_tiles + \" AS SELECT\\n\" +\n \"level_name, tile_class_id1, tile_class_id2, path1, path2\\n\" +\n \"FROM tiles1 t1 LEFT OUTER JOIN tiles2 t2 ON\\n\" +\n \"t1.acquisition_id1=t2.acquisition_id1 AND\\n\" +\n \"t1.level_id=t2.level_id AND\\n\" +\n \"t1.x_index=t2.x_index AND t1.y_index=t2.y_index\\n\" +\n \"INNER JOIN processing_level p on p.level_id=t1.level_id;\"\n )\n\n sql = sql + sqltemp\n\n # For each Database 2 tile found in the test ingest datasets, find the\n # corresponding Database 1 tile if it exists.\n sqltemp = (\n \"CREATE TABLE \" + test_ingest_all_tiles + \" AS SELECT\\n\" +\n \"level_name, tile_class_id1, tile_class_id2, path1, path2\\n\" +\n \"FROM tiles2 t2 LEFT OUTER JOIN tiles1 t1 ON\\n\" +\n \"t1.acquisition_id1=t2.acquisition_id1 AND\\n\" +\n \"t1.level_id=t2.level_id AND\\n\" +\n \"t1.x_index=t2.x_index AND t1.y_index=t2.y_index\\n\" +\n \"INNER JOIN processing_level p on p.level_id=t2.level_id; \"\n )\n\n sql = sql+sqltemp\n\n # Generate list of tiles found in Database 1 and Database 2\n sql_fetch_both = (\"SELECT\\n\" +\n \"t1.level_name, t1.tile_class_id1, t2.tile_class_id2, \\n\" +\n \"t1.path1, t2.path2 FROM\\n\" +\n production_all_tiles + \" t1 INNER JOIN \" +\n test_ingest_all_tiles + \" t2 ON\\n\" +\n \"t1.path1=t2.path1 AND t1.path2=t2.path2;\")\n\n # Generate list of tiles found in Database 1 but not Database 2\n sql_fetch_production_not_test = (\"SELECT\\n\" +\n \"level_name, tile_class_id1, tile_class_id2, \\n\" +\n \"path1, path2 FROM\\n\" +\n production_all_tiles + \" WHERE path2 is NULL;\")\n\n # Generate list of tiles found in Database 2 but not Database 1\n sql_fetch_test_not_production = (\"SELECT\\n\" +\n \"level_name, tile_class_id1, tile_class_id2,\\n\" +\n \"path1, path2 FROM\\n\" +\n test_ingest_all_tiles + \" WHERE path1 is NULL;\")\n\n\n with db_pair.db1.cursor() as cur:\n cur.execute(sql, {})\n cur.execute(sql_fetch_both, {})\n production_and_test = cur.fetchall()\n cur.execute(sql_fetch_production_not_test, {})\n production_not_test = cur.fetchall()\n cur.execute(sql_fetch_test_not_production, {})\n test_not_production = cur.fetchall()\n\n db_pair.db1.drop_table(test_ingest_info)\n db_pair.db1.drop_table(production_all_tiles)\n db_pair.db1.drop_table(test_ingest_all_tiles)\n\n return (production_and_test, production_not_test, test_not_production)","title":""},{"docid":"fb7d9f75d260cf1d1a05a3d7d8187cc8","score":"0.5079055","text":"def test_no_common_keys(self):\n t1 = self.t1\n t2 = self.t2\n del t1['a']\n del t1['b']\n del t2['a']\n del t2['b']\n with pytest.raises(np_utils.TableMergeError):\n t1.join(t2)","title":""},{"docid":"10cc1b1e980c708a6ac4bee7a8d2d5e6","score":"0.5074507","text":"def test_rebuild_tables_is_idempotent(self):\n rebuild_tables()\n rebuild_tables()\n conn = connect()\n cur = conn.cursor()\n cur.execute('SELECT * FROM users')\n self.assertEqual([], cur.fetchall(), \"no rows in users\")\n conn.close()","title":""},{"docid":"ba9a98305fed1b7f3cd11cdc3cadedb0","score":"0.50727737","text":"def test_check_same_table_name(self):\n self.assertTrue(check_same_table_list(get_path_list()))\n self.assertFalse(check_same_table_list(get_bad_path_list()))","title":""},{"docid":"e738952073d20f04ce936329675a5571","score":"0.5060211","text":"async def test_minimal_update_own_source(standard_database: Tuple[CouchDB, dict], http_client: dict) -> None:\n session, objs = standard_database\n\n response = await http_client['put'](f'/api/sources/{objs[\"sources\"][\"two\"][\"_id\"]}',\n body={'type': 'sources',\n 'id': objs['sources']['two']['_id'],\n 'attributes': {\n }},\n token=auth_token(objs['users']['provider']))\n assert response.code == 200\n source = json.load(response.buffer)['data']\n sources_db = await session['sources']\n db_source = await sources_db[source['id']]\n assert db_source['type'] == 'newspaper'\n assert db_source['title'] == 'The Daily Joke'\n assert db_source['subtitle'] == ''\n assert db_source['date'] == '1872-04-13'\n assert db_source['publisher'] == ''\n assert db_source['location'] == ''\n assert db_source['page_numbers'] == ''\n assert db_source['updated'] > db_source['created']","title":""},{"docid":"af505f8cba2b0f57a6772f8b4ed83147","score":"0.5052527","text":"def test_check_data_sources_merged_not_allowed(self):\n\n with self.assertRaises(ValueError):\n raw_wind_io.check_data_sources(\n primary_source=raw_wind_io.MERGED_DATA_SOURCE,\n allow_merged=False)","title":""},{"docid":"62a46bbd714b86cff10a465a4c4b52a2","score":"0.50484306","text":"def test_oracle_to_dolt(oracle_with_table, db_with_table):\n oracle_engine, oracle_table = oracle_with_table\n dssc, dolt_table = db_with_table\n validate_dolt_as_target(oracle_engine,\n oracle_table,\n get_source_reader,\n get_oracle_target_writer,\n get_table_reader,\n dssc,\n dolt_table,\n datetime_strict=False)","title":""},{"docid":"3e352519511098a2740df1e8eccb283a","score":"0.5045271","text":"def check_tables(table, dbu1, dbu2):\n out = []\n table = table.lower()\n assert table in ('code', 'file', 'process', 'product')\n nameattr = '{}_name'.format(table)\n # Get all records for this table.\n gettername = 'getAll{}{}s'.format(table.title(),\n 'e' if table.endswith('s') else '')\n if table == 'file': # There is no getAllFiles\n entries1 = [dbu1.getEntry('File', i) for i in dbu1.getAllFileIds()]\n entries2 = [dbu2.getEntry('File', i) for i in dbu2.getAllFileIds()]\n else:\n entries1 = getattr(dbu1, gettername)()\n entries2 = getattr(dbu2, gettername)()\n # E.g. getAllCodes returns dicts, of which only one part is the code record\n # So recover just that part\n if entries1 and isinstance(entries1[0], collections.Mapping):\n entries1 = [e[table] for e in entries1]\n entries2 = [e[table] for e in entries2]\n # Codes also have a description not a name, and files have no _\n for name_suffix in ('_name', '_description', 'name'):\n nameattr = '{}{}'.format(table, name_suffix)\n if entries1 and hasattr(entries1[0], nameattr):\n break\n # Code description is often not unique, so make something\n # that might be\n if table == 'code':\n entries1 = { '{} {} {}'.format(\n p.code_description, p.filename, p.arguments): p for p in entries1 }\n entries2 = { '{} {} {}'.format(\n p.code_description, p.filename, p.arguments): p for p in entries2 }\n else:\n entries1 = { getattr(p, nameattr): p for p in entries1 }\n entries2 = { getattr(p, nameattr): p for p in entries2 }\n # Check what's in only one database\n names1 = set(entries1.keys())\n names2 = set(entries2.keys())\n for n in sorted(names1.difference(names2)):\n out.append('{} {} is in mission1 but not mission2.'.format(\n table.title(), n))\n for n in sorted(names2.difference(names1)):\n out.append('{} {} is in mission2 but not mission1.'.format(\n table.title(), n))\n # For every column in the record, compare across databases\n for n in sorted(names1.intersection(names2)):\n e1, e2 = entries1[n], entries2[n]\n for a in list(dir(e1)):\n if a.startswith('_'):\n # Skip the non-public\n continue\n if a in (nameattr, '{}_id'.format(table)):\n # Don't compare IDs for this table\n continue\n if a in ('verbose_provenance', 'shasum', 'file_create_date'):\n # Don't compare stuff that's likely to be different\n continue\n if a.endswith('_id') or a in ('output_product'):\n # Reference to another table, compare *names*\n othertbl = 'product' if a == 'output_product' \\\n else a.split('_')[0]\n id1 = getattr(e1, a) #IDs into the other table\n id2 = getattr(e2, a)\n # Allow for not having a reference (e.g. no output product)\n if id1 in (None, ''):\n n1 = ''\n else:\n n1 = getattr(dbu1.getEntry(othertbl.title(), id1),\n '{}_name'.format(othertbl))\n if id2 in (None, ''):\n n2 = ''\n else:\n n2 = getattr(dbu2.getEntry(othertbl.title(), id2),\n '{}_name'.format(othertbl))\n if n1 != n2:\n out.append('{} {} {} is {} in mission 1 but {} in mission 2'\n .format(table.title(), n, a, n1, n2))\n elif getattr(e1, a) != getattr(e2, a):\n # Something else. Compare values\n out.append(\n '{} {} {} is {} in mission 1 but {} in mission2'.format(\n table.title(), n, a, getattr(e1, a), getattr(e2, a)))\n return out","title":""},{"docid":"f0c2b6ee84d5865a313eacd38ada2689","score":"0.50444263","text":"def load_staging_tables(cur, conn): \n for query in copy_table_queries:\n cur.execute(query)\n conn.commit()","title":""},{"docid":"4561e4f623755ed808637c2f72aa27d0","score":"0.5039051","text":"def test_table_schema(self, table_name, columns_names, columns_types, table_fk, fk_on):\n # connection instance\n con = self.connection.con\n with con:\n cursor = con.cursor()\n # collect column information in the query result\n # Every column will be represented by a tuple with the following attributes:\n # (id, name, type, not null, default_value, primary_key)\n cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name))\n ti_result = cursor.fetchall()\n names = [tup[1] for tup in ti_result]\n types = [tup[2] for tup in ti_result]\n\n # Check and assert the names and their types with default ones\n self.assertEqual(names, columns_names)\n self.assertEqual(types, columns_types)\n\n if fk_on:\n # get the foreign key data using the the query below\n # the returned tuple has the following attributes\n # (id, seq, table, from, to, on_update, on_delete, match)\n # so we take elements (2, 3, 4) -> (table, from, to)\n # reference:\n # https://stackoverflow.com/questions/44424476/output-of-the-sqlites-foreign-key-list-pragma\n cursor.execute('PRAGMA FOREIGN_KEY_LIST({})'.format(table_name))\n fk_result = cursor.fetchall()\n # Check and assert that foreign keys are correctly set\n result_filtered = [(tup[2], tup[3], tup[4]) for tup in fk_result]\n for tup in result_filtered:\n # Test that each tup is included in the list of all default foreign keys\n self.assertIn(tup, table_fk)","title":""},{"docid":"a7e0f344b2fa5188b84de57e62948abc","score":"0.50386643","text":"def _check_sources(self):\n\t\tfrom ..source import PointSource, CharacteristicFaultSource\n\n\t\tfor src in self.source_model.sources:\n\t\t\tif not isinstance(src, (PointSource, CharacteristicFaultSource)):\n\t\t\t\tmsg = \"Source type (%s) of source %s not allowed!\"\n\t\t\t\tmsg %= (src.__class__, src.source_id)\n\t\t\t\traise Exception(msg)","title":""},{"docid":"42ea73f7a9261597846fb1b3851a3a17","score":"0.5038446","text":"def copy_table(\n src_con: \"SimpleSQLite\",\n dst_con: \"SimpleSQLite\",\n src_table_name: str,\n dst_table_name: str,\n is_overwrite: bool = True,\n) -> bool:\n\n logger.debug(\n \"copy table: src={src_db}.{src_tbl}, dst={dst_db}.{dst_tbl}\".format(\n src_db=src_con.database_path,\n src_tbl=src_table_name,\n dst_db=dst_con.database_path,\n dst_tbl=dst_table_name,\n )\n )\n\n src_con.verify_table_existence(src_table_name)\n dst_con.validate_access_permission([\"w\", \"a\"])\n\n if dst_con.has_table(dst_table_name):\n if is_overwrite:\n dst_con.drop_table(dst_table_name)\n else:\n logger.error(\n \"failed to copy table: the table already exists \"\n \"(src_table={}, dst_table={})\".format(src_table_name, dst_table_name)\n )\n return False\n\n primary_key, index_attrs, _ = extract_table_metadata(src_con, src_table_name)\n\n result = src_con.select(select=\"*\", table_name=src_table_name)\n if result is None:\n return False\n\n dst_con.create_table_from_data_matrix(\n dst_table_name,\n src_con.fetch_attr_names(src_table_name),\n result.fetchall(),\n primary_key=primary_key,\n index_attrs=index_attrs,\n )\n\n return True","title":""},{"docid":"1cb864f9f6230a9b24aedc93104bc6bf","score":"0.5032729","text":"def copy_db(from_, to, list_tables):\n query_detach = \"\"\"DETACH sourcefile;\"\"\"\n query_attach = \"\"\"ATTACH '{}' AS sourcefile;\"\"\".format(from_)\n list_tables_out = get_list_tables(to)\n\n print(\"from_: {}\".format(from_), file=sys.stderr)\n print(\"to: {}\".format(to), file=sys.stderr)\n print(\"list_tables in {}: {}\".format(to, list_tables_out), file=sys.stderr)\n print(\"query_attach: {}\".format(query_attach), file=sys.stderr)\n conn = sqlite3.connect(to)\n c = conn.cursor()\n c.execute(query_attach)\n\n for table in list_tables:\n query_insert = \"\"\"INSERT INTO {} SELECT * FROM sourcefile.{};\"\"\".format(table, table)\n if table in list_tables_out:\n print(\"table '{}' in {}. it wont be created\".format(table, to),\n file=sys.stderr)\n else:\n print(\"table '{}' not in {}.\".format(table, to),\n file=sys.stderr)\n query_create_to = get_query_create(table, from_)\n print(query_create_to, file=sys.stderr)\n c.execute(query_create_to)\n print(\"TABLE '{}' created in table '{}'\".format(table, to),\n file=sys.stderr)\n print(\"query_insert: {}\".format(query_insert), file=sys.stderr)\n c.execute(query_insert)\n conn.commit()\n print(\"query_detach: {}\".format(query_detach), file=sys.stderr)\n c.execute(query_detach)\n\n conn.close()\n print(\"Copy ended!\", file=sys.stderr)","title":""},{"docid":"7b1b1763127f0d78abe0a211c1f856a9","score":"0.502104","text":"def migrate_data(schema,source_config,target_config,migration_config):\n # create database connections\n source_engine = connect_to_source(source_config)\n target_engine = connect_to_target(target_config,target_config['database'])\n \n # load the schema metadata profile\n source_metadata = sqlalchemy.MetaData(source_engine)\n source_metadata.reflect(schema=schema)\n\n # iterate the tables, loading the data\n for t in source_metadata.sorted_tables:\n migrate.copy_data(source_engine,schema,target_engine,t,migration_config['chunksize'],\n migration_config['logged'],trialrun=migration_config['trialrun'])","title":""},{"docid":"11c97a38cf342ac7f21b71a707131bb8","score":"0.50171417","text":"def run():\n\n # scan for available tables\n util_log.info('Scanning tables')\n tables = [f.replace('.csv', '') for f in os.listdir(config.TABLES_PATH) if os.path.isfile(os.path.join(config.TABLES_PATH, f)) and f.endswith('.csv')]\n db.addTables(tables)\n util_log.info(' ... done')\n\n # identify target files\n # target filenames are somewhat changing, so we just look for specific acronyms\n targets = [f for f in os.listdir(config.TARGETS_PATH) if os.path.isfile(os.path.join(config.TARGETS_PATH, f)) and f.endswith('.csv')]\n CEA_TARGET = [f for f in targets if 'CEA' in f]\n CEA_TARGET = CEA_TARGET[0] if len(CEA_TARGET) > 0 else None\n CTA_TARGET = [f for f in targets if 'CTA' in f]\n CTA_TARGET = CTA_TARGET[0] if len(CTA_TARGET) > 0 else None\n CPA_TARGET = [f for f in targets if 'CPA' in f]\n CPA_TARGET = CPA_TARGET[0] if len(CPA_TARGET) > 0 else None\n\n # get a mapping from a table to its ID\n tableToId = db.getTableToId()\n\n # copy CTA targets to db\n util_log.info('Importing CTA')\n if CTA_TARGET:\n path = os.path.join(config.TARGETS_PATH, CTA_TARGET)\n parseFile(tableToId, path, 'addCTA')\n util_log.info(' ... done')\n else:\n util_log.info(' ... skipped')\n\n # copy CEA targets to db\n util_log.info('Importing CEA')\n if CEA_TARGET:\n path = os.path.join(config.TARGETS_PATH, CEA_TARGET)\n parseFile(tableToId, path, 'addCEA')\n util_log.info(' ... done')\n else:\n util_log.info(' ... skipped')\n\n # copy CPA targets to db\n util_log.info('Importing CPA')\n if CPA_TARGET:\n path = os.path.join(config.TARGETS_PATH, CPA_TARGET)\n parseFile(tableToId, path, 'addCPA')\n util_log.info(' ... done')\n else:\n util_log.info(' ... skipped')","title":""},{"docid":"17477abab339e9c0e39b02923d89b3e4","score":"0.5012929","text":"def test_check_data_sources_madis(self):\n\n raw_wind_io.check_data_sources(\n primary_source=raw_wind_io.MADIS_DATA_SOURCE,\n secondary_source=SECONDARY_DATA_SOURCE)","title":""},{"docid":"70f18377c69c7420d845fda62c608bf6","score":"0.50094277","text":"def _checktables(self):\n self._cur.execute(\"PRAGMA TABLE_INFO(NODES)\")\n if (self._cur.fetchone() is None):\n # table doesn't exist, create it\n # SQLite does have constraints implemented at the moment\n # so datatype will just be a string\n self._cur.execute(\"CREATE TABLE NODES \"\n \"(ID INTEGER PRIMARY KEY AUTOINCREMENT,\"\n \"DATA BLOB NOT NULL)\")\n self._cur.execute(\"CREATE TABLE TAGS \"\n \"(ID INTEGER PRIMARY KEY AUTOINCREMENT,\"\n \"DATA BLOB NOT NULL UNIQUE)\")\n self._cur.execute(\"CREATE TABLE LOOKUP \"\n \"(NODE INTEGER NOT NULL, TAG INTEGER NOT NULL,\"\n \" PRIMARY KEY(NODE, TAG))\")\n\n self._cur.execute(\"CREATE TABLE KEY \"\n + \"(THEKEY TEXT NOT NULL DEFAULT '')\")\n self._cur.execute(\"INSERT INTO KEY VALUES('')\")\n try:\n self._con.commit()\n except DatabaseException as e:\n self._con.rollback()\n raise e","title":""},{"docid":"5ec7def10f184f5e5adbd2cb1f5ae39f","score":"0.50025654","text":"def test_mysql_to_dolt(mysql_with_table, db_with_table):\n mysql_engine, mysql_table = mysql_with_table\n dssc, dolt_table = db_with_table\n validate_dolt_as_target(mysql_engine,\n mysql_table,\n get_source_reader,\n get_mysql_target_writer,\n get_table_reader,\n dssc,\n dolt_table)","title":""},{"docid":"65e0f82f0eb591ae12fd7dff7693b94e","score":"0.49964797","text":"def setUp(self):\n self.db = Database(\"testsources.db\")\n self.db.create_tables()","title":""},{"docid":"fae60c1bc828c81ae00987c6c23cc44e","score":"0.49960947","text":"async def setup(self):\n try:\n engine = await self.db\n created = False\n if not await engine.has_table(self.table_name):\n # create table\n logger.info(\"Creating SQL table [{}]\".format(self.table_name))\n items = self._get_table()\n await engine.execute(CreateTable(items))\n # create indeces\n conn = await engine.connect()\n await conn.execute(\n \"CREATE INDEX `lb_last_updated` ON `{}` (`source_id` DESC,`updated` DESC);\".format(self.table_name))\n await conn.execute(\n \"CREATE INDEX `lb_post` ON `{}` (`target_id` DESC,`post_id` DESC);\".format(self.table_name))\n await conn.close()\n created = True\n # create control table if not already created.\n if self.control_table_name and not await engine.has_table(self.control_table_name):\n # create table\n logger.info(\"Creating SQL control table [{}]\".format(self.control_table_name))\n items = self._get_control_table()\n await engine.execute(CreateTable(items))\n created = True\n return created\n except Exception as exc:\n logger.error(\"[DB] Error when setting up SQL table: {}\".format(exc))\n return False","title":""},{"docid":"f71a0808e33bbbc6f447f1633f4306da","score":"0.49860498","text":"def load_staging_tables(cur, conn):\n for query in copy_table_queries:\n # print out to see which query is being executed\n print(query)\n cur.execute(query)\n conn.commit()","title":""},{"docid":"da7981c4db14305036515017fdb11022","score":"0.4976375","text":"def test_check_data_sources_non_madis(self):\n\n raw_wind_io.check_data_sources(primary_source=NON_MADIS_PRIMARY_SOURCE)","title":""},{"docid":"6bd939dc4d9f365de91904c29ceec181","score":"0.49721465","text":"async def test_admin_update_other_source(standard_database: Tuple[CouchDB, dict], http_client: dict) -> None:\n session, objs = standard_database\n\n image_data = f'data:image/png;base64,{b64encode(resources.open_binary(sources, \"one.png\").read()).decode(\"utf-8\")}' # noqa: E501\n response = await http_client['put'](f'/api/sources/{objs[\"sources\"][\"two\"][\"_id\"]}',\n body={'type': 'sources',\n 'id': objs['sources']['two']['_id'],\n 'attributes': {\n 'type': 'book',\n 'title': 'All change',\n 'subtitle': 'New subtitle',\n 'date': '2021-04-01',\n 'publisher': 'No such press',\n 'location': 'Nowhere',\n 'page_numbers': '10',\n 'data': image_data,\n }},\n token=auth_token(objs['users']['admin']))\n assert response.code == 200\n source = json.load(response.buffer)['data']\n sources_db = await session['sources']\n db_source = await sources_db[source['id']]\n assert db_source['type'] == 'book'\n assert db_source['title'] == 'All change'\n assert db_source['subtitle'] == 'New subtitle'\n assert db_source['date'] == '2021-04-01'\n assert db_source['publisher'] == 'No such press'\n assert db_source['location'] == 'Nowhere'\n assert db_source['page_numbers'] == '10'\n assert db_source['updated'] > db_source['created']","title":""},{"docid":"9dec7e7e965cf970ee777e0efd84aaf7","score":"0.49721265","text":"def _make_table_from_rule(\n schema: Namespace,\n table_type: str,\n table_name: ty.Union[str, ty.List[str]],\n src_path: ty.Optional[str] = None,\n tablefmt: str = \"github\",\n):\n if isinstance(table_name, str):\n table_name = [table_name]\n\n elements: dict[str, str | dict[str, str]] = {}\n for table in table_name:\n if table_type == \"metadata\":\n table_schema = schema.rules.sidecars[table]\n new_elements = table_schema.fields\n elif table_type == \"columns\":\n table_schema = schema.rules.tabular_data[table]\n new_elements = table_schema.columns\n # Since only one table may be provided for columns, we can just use this directly.\n additional_columns = table_schema.get(\"additional_columns\", \"not_allowed\")\n initial_columns = table_schema.get(\"initial_columns\", [])\n index_columns = table_schema.get(\"index_columns\", [])\n else:\n raise ValueError(f\"Unsupported 'table_type': '{table_type}'\")\n\n overlap = set(new_elements) & set(elements)\n if overlap:\n raise BIDSSchemaError(\n f\"Schema tables {table_name} share overlapping fields: {overlap}\"\n )\n elements.update(new_elements)\n\n subschema = schema.objects[table_type]\n retained_elements = [f for f in elements if f in subschema]\n dropped_elements = [f for f in elements if f not in subschema]\n if dropped_elements:\n print(\"Warning: Missing elements: {}\".format(\", \".join(dropped_elements)))\n\n subschema = {k: v for k, v in subschema.items() if k in retained_elements}\n element_info = {}\n for element, val in elements.items():\n if isinstance(val, str):\n level = val\n level_addendum = \"\"\n description_addendum = \"\"\n else:\n level = val[\"level\"]\n level_addendum = val.get(\"level_addendum\", \"\")\n description_addendum = val.get(\"description_addendum\", \"\")\n\n if level_addendum:\n if level_addendum.startswith((\"required\", \"recommended\", \"optional\")):\n level = f\"{level}, but {level_addendum}\"\n else:\n # Typically begins with \"if\"\n level = f\"{level} {level_addendum}\"\n\n if table_type == \"columns\" and element in index_columns:\n if len(index_columns) == 1:\n msg = f\"Values in `{schema.objects.columns[element].name}`\"\n else:\n cols = [f\"`{schema.objects.columns[col].name}`\" for col in index_columns]\n msg = f\"The combination of {', '.join(cols[:-1])} and {cols[-1]}\"\n description_addendum += f\"\\n\\n\\n\\n{msg} MUST be unique.\"\n\n if table_type == \"columns\" and initial_columns:\n if element in initial_columns:\n order = initial_columns.index(element) + 1\n order_str = utils.num2words(order, to=\"ordinal\")\n description_addendum += (\n f\"\\n\\n\\n\\nThis column must appear **{order_str}** in the file.\"\n )\n else:\n description_addendum += \"\\n\\n\\n\\nThis column may appear anywhere in the file.\"\n\n element_info[element] = {\"table_info\": (level, description_addendum)}\n\n if table_type == \"columns\":\n # Add info about additional columns to table.\n # its_complicated won't add any info about additional columns.\n if additional_columns == \"not_allowed\":\n element_info[\"Additional Columns\"] = {\n \"table_info\": (\"NOT ALLOWED\", \"Additional columns are not allowed.\")\n }\n elif additional_columns == \"allowed_if_defined\":\n element_info[\"Additional Columns\"] = {\n \"table_info\": (\n \"OPTIONAL\",\n (\n \"Additional columns are allowed if they are defined in the associated \"\n \"metadata file.\"\n ),\n )\n }\n elif additional_columns == \"allowed\":\n element_info[\"Additional Columns\"] = {\n \"table_info\": (\"OPTIONAL\", \"Additional columns are allowed.\")\n }\n\n subschema.update(\n {\n \"Additional Columns\": {\n \"name\": \"**Additional Columns**\",\n \"description\": \"\",\n \"type\": \"`n/a`\",\n }\n }\n )\n\n table_str = _make_object_table(\n subschema,\n field_info=element_info,\n table_type=table_type,\n src_path=src_path,\n tablefmt=tablefmt,\n )\n return table_str","title":""},{"docid":"a706bcfb7c972410995e81c3fc3e5584","score":"0.49702713","text":"def _check_srcs(self):\r\n allow_dup_src_type_list = ['cc_binary', 'cc_test']\r\n for s in self.srcs:\r\n if '..' in s or s.startswith('/'):\r\n raise Exception, (\r\n 'Invalid source file path: %s. '\r\n 'can only be relative path, and must in current directory or '\r\n 'subdirectories') % s\r\n\r\n src_key = os.path.normpath('%s/%s' % (self.path, s))\r\n src_value = '%s %s:%s' % (\r\n self.type, self.path, self.name)\r\n if src_key in Target.__src_target_map:\r\n value_existed = Target.__src_target_map[src_key]\r\n # May insert multiple time in test because of not unloading module\r\n if (value_existed != src_value and\r\n not (value_existed.split(': ')[0] in allow_dup_src_type_list and\r\n self.type in allow_dup_src_type_list)):\r\n # Just warn here, not raising exception\r\n console.warning('Source file %s belongs to both %s and %s' % (\r\n s, Target.__src_target_map[src_key], src_value))\r\n Target.__src_target_map[src_key] = src_value","title":""},{"docid":"63f8dc619b3fb14dcf37a0cc0cfe2c77","score":"0.4968429","text":"def assert_equal_sql_table(self, other):\n assert self.name == other.name\n for my_column, other_column in izip(self.columns, other.columns):\n assert_equal_sql_column(my_column, other_column)\n assert self.metadata == other.metadata","title":""},{"docid":"f61daee8e07099d5b59dd63eddff2211","score":"0.49625042","text":"def _check_constraints(self, df, table_name, schema='current'):\n self._check_connection()\n \n foreign_ids = {}\n \n try:\n # Query for all columns that have a foreign key in the current table\n self.cur.execute(\"SELECT source_column, target_table, target_column FROM {schema}.foreign_keys_view WHERE source_table = '{schema}.{table_name}'::regClass\".format(\n schema=schema,table_name=table_name))\n results = self.cur.fetchall()\n # Create a dict of column names as keys and a tuple of (schema.table, column name) for foreign keys\n result_dict = {key: None for (key,ftab,fcol) in results}\n for key in result_dict.keys():\n result_dict[key] = [result[1:] for result in results if result[0]==key]\n # Set of unique table, column tuples to query to check contstraints for\n foreign_id_set = set([value for value in itertools.chain.from_iterable(result_dict.values())])\n # Get the ids for all table, column tuples\n for id_set in foreign_id_set:\n self.cur.execute(\"SELECT {id} FROM {table}\".format(id=id_set[1], table=id_set[0]))\n foreign_ids[id_set] = [id for (id,) in self.cur.fetchall()]\n # Restrict the results for only ids that satisfy the foreign key restraints\n for key in result_dict.keys():\n df = df[df[key].isin(itertools.chain.from_iterable(foreign_ids[n] for n in result_dict[key]))]\n \n except psycopg2.Error as e:\n self.conn.rollback()\n \n return df","title":""},{"docid":"a5fb851ec97376518567baee905b6168","score":"0.496166","text":"def create_tables(self):\n conn = self._get_connection()\n try:\n conn.execute(schema.create_audio_files_table)\n conn.execute(schema.create_audio_files_index)\n conn.execute(schema.create_id3_tags_table)\n conn.execute(schema.create_id3_tags_index)\n except sqlite3.OperationalError, ex:\n return False\n return True","title":""},{"docid":"3d16d55dd840027c2fd7f8af727190e6","score":"0.49611425","text":"def createBasinsDataTables(self, cursor: Any) -> bool: \n # remove old table completely, for backward compatibility, in case structure changed\n tableBasins = self._BASINSDATA\n dropSQL = 'DROP TABLE IF EXISTS ' + tableBasins\n try:\n cursor.execute(dropSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not drop table {0} from project database {1}'.format(tableBasins, self.dbFile), self.isBatch)\n return False\n createSQL = 'CREATE TABLE ' + tableBasins + self._BASINSDATATABLE\n try:\n cursor.execute(createSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not create table {0} in project database {1}'.format(tableBasins, self.dbFile), self.isBatch)\n return False\n tableLus = self._LSUSDATA\n dropSQL = 'DROP TABLE IF EXISTS ' + tableLus\n try:\n cursor.execute(dropSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not drop table {0} from project database {1}'.format(tableLus, self.dbFile), self.isBatch)\n return False\n createSQL = 'CREATE TABLE ' + tableLus + ' ' + self._LSUSDATATABLE\n try:\n cursor.execute(createSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not create table {0} in project database {1}'.format(tableLus, self.dbFile), self.isBatch)\n return False\n # table searched by basin\n indexSQL = 'CREATE INDEX IF NOT EXISTS basin_index on {0} (basin)'.format(tableLus)\n try:\n cursor.execute(indexSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not create index for basin in table {0} in project database {1}'.format(tableLus, self.dbFile), self.isBatch)\n tableWater = self._WATERDATA\n dropSQL = 'DROP TABLE IF EXISTS ' + tableWater\n try:\n cursor.execute(dropSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not drop table1 {0} from project database {1}'.format(tableWater, self.dbFile), self.isBatch)\n return False\n createSQL = 'CREATE TABLE ' + tableWater + ' ' + self._WATERDATATABLE\n try:\n cursor.execute(createSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not create table {0} in project database {1}'.format(tableWater, self.dbFile), self.isBatch)\n return False\n # table searched by lsu\n indexSQL = 'CREATE INDEX IF NOT EXISTS lsu_index on {0} (lsu)'.format(tableWater)\n try:\n cursor.execute(indexSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not create index for lsu in table {0} in project database {1}'.format(tableWater, self.dbFile), self.isBatch)\n return False \n tableHrus = self._HRUSDATA\n dropSQL = 'DROP TABLE IF EXISTS ' + tableHrus\n try:\n cursor.execute(dropSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not drop table1 {0} from project database {1}'.format(tableHrus, self.dbFile), self.isBatch)\n return False\n createSQL = 'CREATE TABLE ' + tableHrus + ' ' + self._HRUSDATATABLE\n try:\n cursor.execute(createSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not create table {0} in project database {1}'.format(tableHrus, self.dbFile), self.isBatch)\n return False\n # table searched by lsu\n indexSQL = 'CREATE INDEX IF NOT EXISTS lsu_index on {0} (lsu)'.format(tableHrus)\n try:\n cursor.execute(indexSQL)\n except Exception:\n QSWATUtils.exceptionError('Could not create index for lsu in table {0} in project database {1}'.format(tableHrus, self.dbFile), self.isBatch)\n return False\n return True","title":""},{"docid":"9465f104200b93f63bf522380c1fe574","score":"0.4959584","text":"def populate_all_scrips_table(db_metadata):\n nse_stocks_dict = get_nse_stocks_dict()\n nse_isins = nse_stocks_dict.keys()\n\n bse_stocks_dict = get_bse_stocks_dict()\n bse_isins = bse_stocks_dict.keys()\n\n common_isins = set(nse_isins) & set(bse_isins)\n bse_only_isins = set(bse_isins) - common_isins\n nse_only_isins = set(nse_isins) - common_isins\n\n table = create_or_get_all_scrips_table(metadata=db_metadata)\n\n count = 0\n insert_statements = []\n for isin in common_isins:\n nstock = nse_stocks_dict[isin]\n bstock = bse_stocks_dict[isin]\n\n nstart_datetime = dt.strptime(nstock.listing_date,\n '%d-%b-%Y')\n nstart_date = dt.date(nstart_datetime)\n\n ins = table.insert().values(security_isin=nstock.isin,\n company_name=nstock.name,\n nse_traded=True,\n nse_start_date=nstart_date,\n nse_symbol=nstock.symbol,\n #nse_suspended default is False,\n bse_traded=True,\n bse_start_date=nstart_date,\n bse_id=bstock.bseid,\n bse_symbol=bstock.symbol,\n bse_group=bstock.group)\n module_logger.debug(ins.compile().params)\n\n insert_statements.append(ins)\n count += 1\n\n common_count = count\n module_logger.info(\"common securities count: %d\", common_count)\n\n for isin in bse_only_isins:\n bstock = bse_stocks_dict[isin]\n\n ins = table.insert().values(security_isin=bstock.isin,\n company_name=bstock.name,\n bse_traded=True,\n bse_id=bstock.bseid,\n bse_symbol=bstock.symbol,\n bse_group=bstock.group)\n module_logger.debug(ins.compile().params)\n\n insert_statements.append(ins)\n count += 1\n\n bse_only_count = count - common_count\n module_logger.info(\"bse_only securities count: %d\", bse_only_count)\n\n for isin in nse_only_isins:\n nstock = nse_stocks_dict[isin]\n\n nstart_datetime = dt.strptime(nstock.listing_date,\n '%d-%b-%Y')\n nstart_date = dt.date(nstart_datetime)\n\n ins = table.insert().values(security_isin=nstock.isin,\n company_name=nstock.name,\n nse_traded=True,\n nse_start_date=nstart_date,\n nse_symbol=nstock.symbol\n #nse_suspended default is False,\n )\n module_logger.debug(ins.compile().params)\n\n insert_statements.append(ins)\n count += 1\n\n nse_only_count = count - bse_only_count - common_count\n module_logger.info(\"nse_only securities count: %d\", nse_only_count)\n\n return insert_statements","title":""},{"docid":"cf87639424267de38ca492c69e69a1f1","score":"0.4956618","text":"def load_staging_tables(cur, conn):\n for query in copy_table_queries:\n cur.execute(query)\n conn.commit()","title":""},{"docid":"cf87639424267de38ca492c69e69a1f1","score":"0.4956618","text":"def load_staging_tables(cur, conn):\n for query in copy_table_queries:\n cur.execute(query)\n conn.commit()","title":""},{"docid":"cf87639424267de38ca492c69e69a1f1","score":"0.4956618","text":"def load_staging_tables(cur, conn):\n for query in copy_table_queries:\n cur.execute(query)\n conn.commit()","title":""},{"docid":"cf87639424267de38ca492c69e69a1f1","score":"0.4956618","text":"def load_staging_tables(cur, conn):\n for query in copy_table_queries:\n cur.execute(query)\n conn.commit()","title":""},{"docid":"cf87639424267de38ca492c69e69a1f1","score":"0.4956618","text":"def load_staging_tables(cur, conn):\n for query in copy_table_queries:\n cur.execute(query)\n conn.commit()","title":""},{"docid":"cf87639424267de38ca492c69e69a1f1","score":"0.4956618","text":"def load_staging_tables(cur, conn):\n for query in copy_table_queries:\n cur.execute(query)\n conn.commit()","title":""},{"docid":"41b5776f8cfc3cc41ed87b6ef2e134e4","score":"0.49493736","text":"def create_table_and_insert_dataset_resulted_from_dedupe(info_db, provider_name, file_name):\n db_connection = create_database_connection(info_db)\n db_cursor = db_connection.cursor()\n\n provider_table_name = 'bi_' + provider_name\n\n create_stmt, copy_stmt = get_statements_for_creating_table_with_fk_and_for_copying_data_into_table_from_csv(\n file_name, provider_table_name)\n\n db_cursor.execute(\"DROP TABLE IF EXISTS \" + provider_table_name)\n db_cursor.execute(create_stmt)\n\n # copy the rows from the csv file into the table in the database\n with open(file_name, 'r') as f:\n db_cursor.copy_expert(copy_stmt, f)\n\n db_cursor.close()\n db_connection.close()","title":""},{"docid":"976e72f08b629d7b90adf9c690d62f3d","score":"0.4939348","text":"def analyze(schema_name, table_name, param_src_sys_cd, constraint_type, constraint_name,\n reference_table_name,\n reference_keys,\n reference_column_name):\n\n arr_reference_keys = reference_keys.split(',')\n\n if constraint_type == 'p':\n # f^FK1_ADDR^CTRY_CTY^CTRY_CTY_ID,SRC_SYS_CD^CTRY_CTY_ID,SRC_SYS_CD\n # need to separate fields and re-arrange\n pk_list_temp = ''\n for i_field in range(len(arr_reference_keys)):\n pk_list_temp += \"nvl(cast (case when t.{0} is null then null else t.{0} end as string),'NULL')\".format(\n arr_reference_keys[i_field])\n if i_field != len(arr_reference_keys) - 1:\n pk_list_temp += ','\n pk_list_formatted = \"concat_ws('^', {0})\".format(pk_list_temp)\n\n sql_check_pk = \\\n (\"select concat_ws('^', collect_set(A.s)) from ( select {0} as s from ( select {1} \"\n \"from {2}.{3} where SRC_SYS_CD='{4}' group by {1} \"\n \"having count(1) > 1 )t limit 10 )A\").format(pk_list_formatted, reference_keys, schema_name, table_name,\n param_src_sys_cd)\n res_check_pk = create_tmp_file(sql_check_pk)\n\n if res_check_pk.strip() == '':\n print('No PK issue found!')\n else:\n print('SQL:')\n print(\"\\033[31m select {0} from {1}.{2} where SRC_SYS_CD='{3}' \"\n \"group by {0} having count(1) > 1 )t limit 10 )A\\n \\033[0m\".format(reference_keys, schema_name,\n table_name, param_src_sys_cd))\n print(\"-[ RECORD ]---+----------------\\n\"\n \"Schema Name | {0}\\n\"\n \"Table Name | {1}\\n\"\n \"Column Names | {2}\\n\"\n \"Constraint Name | {3}\\n\"\n \"Constraint Type | PRIMARY\\n\"\n \"----------------+----------------\".format(schema_name, table_name, reference_keys,\n constraint_name))\n if res_check_pk.find('^') == -1:\n print(\"Column Values | ('{0}')\\n\".format(res_check_pk))\n else:\n output_data(len(reference_keys.split(',')), res_check_pk)\n elif constraint_type == 'u':\n uk_list_temp = ''\n for i_field in range(len(arr_reference_keys)):\n uk_list_temp += \"nvl(cast (case when t.{0} is null then null else t.{0} end as string),'NULL')\".format(\n arr_reference_keys[i_field])\n if i_field != len(arr_reference_keys) - 1:\n uk_list_temp += ','\n uk_list_formatted = \"concat_ws('^', {0})\".format(uk_list_temp)\n\n sql_check_uk = \\\n (\"select concat_ws('^', collect_set(A.s)) from ( select {0} as s from ( select {1} \"\n \"from {2}.{3} where SRC_SYS_CD='{4}' group by {1} \"\n \"having count(1) > 1 )t limit 10 )A\").format(uk_list_formatted, reference_keys, schema_name, table_name,\n param_src_sys_cd)\n res_check_uk = create_tmp_file(sql_check_uk)\n\n if res_check_uk.strip() == '':\n print('No UK issue found!')\n else:\n print('SQL:')\n print(\"\\033[31m select {0} from {1}.{2} where SRC_SYS_CD='{3}' \"\n \"group by {0} having count(1) > 1 )t limit 10 )A\\n \\033[0m\".format(reference_keys, schema_name,\n table_name, param_src_sys_cd))\n print(\"-[ RECORD ]---+----------------\\n\"\n \"Schema Name | {0}\\n\"\n \"Table Name | {1}\\n\"\n \"Column Names | {2}\\n\"\n \"Constraint Name | {3}\\n\"\n \"Constraint Type | UNIQUE\\n\"\n \"----------------+----------------\".format(schema_name, table_name, reference_keys,\n constraint_name))\n if res_check_uk.find('^') == -1:\n print(\"Column Values | ('{0}')\\n\".format(res_check_uk))\n else:\n output_data(len(reference_keys.split(',')), res_check_uk)\n elif constraint_type == 'n':\n nk_list_temp = ''\n null_condition = ''\n for i_field in range(len(arr_reference_keys)):\n null_condition = '{0}.{1} is null'.format(table_name, arr_reference_keys[i_field])\n if arr_reference_keys[i_field] == 'RADAR_UPD_TS':\n nk_list_temp += \"case when RADAR_UPD_TS is null then 'NULL' else from_unixtime(unix_timestamp(),'yyyy-MM-dd HH:mm:ss') end\"\n else:\n nk_list_temp += \"nvl(cast (case when {0} is null then null else {0} end as string),'NULL')\".format(\n arr_reference_keys[i_field])\n if i_field != len(arr_reference_keys) - 1:\n nk_list_temp += ','\n null_condition += ' or '\n nk_list_formatted = \"concat_ws('^', {0})\".format(nk_list_temp)\n\n sql_check_nk = \\\n (\"select concat_ws('^', collect_set(A.s)) from ( select {0} as s \"\n \"from {1}.{2} where SRC_SYS_CD='{3}' and ( {4} ) limit 10 )A\").format(nk_list_formatted, schema_name,\n table_name,\n param_src_sys_cd, null_condition)\n res_check_nk = create_tmp_file(sql_check_nk)\n\n if res_check_nk.strip() == '':\n print('No Null issue found!')\n else:\n print('SQL:')\n print(\"\\033[31m select {0} from {1}.{2} where SRC_SYS_CD='{3}' and {4}\\n \\033[0m\".format(nk_list_formatted,\n schema_name,\n table_name,\n param_src_sys_cd,\n null_condition))\n print(\"-[ RECORD ]---+----------------\\n\"\n \"Schema Name | {0}\\n\"\n \"Table Name | {1}\\n\"\n \"Column Names | {2}\\n\"\n \"Constraint Name | {3}\\n\"\n \"Constraint Type | NULL\\n\"\n \"----------------+----------------\".format(schema_name, table_name, reference_keys,\n constraint_name))\n if res_check_nk.find('^') == -1:\n print(\"Column Values | ('{0}')\\n\".format(res_check_nk))\n else:\n output_data(len(reference_keys.split(',')), res_check_nk)\n elif constraint_type == 'f':\n if table_name.upper() == reference_table_name.upper():\n print(\"Expression returned a reference to target table itself, skipping...\")\n else:\n fk_list_temp = ''\n join_condition = ''\n null_condition = ''\n source_column_name = ''\n rfnc_column_name = ''\n for i_field in range(len(arr_reference_keys)):\n join_condition += \"{0}.{1}={2}.{3}\".format(table_name, arr_reference_keys[i_field],\n reference_table_name,\n reference_column_name.split(',')[i_field])\n null_condition += '{0}.{1} is not null'.format(table_name, arr_reference_keys[i_field])\n source_column_name += \"{0}.{1}\".format(table_name, arr_reference_keys[i_field])\n rfnc_column_name += \"{0}.{1}\".format(reference_table_name, reference_column_name.split(',')[i_field])\n fk_list_temp += \"nvl(cast (case when t.{0} is null then null else t.{0} end as string),'NULL')\".format(\n arr_reference_keys[i_field])\n if i_field != len(arr_reference_keys) - 1:\n fk_list_temp += ','\n join_condition += ' and '\n null_condition += ' and '\n source_column_name += ','\n rfnc_column_name += ','\n fk_list_formatted = \"concat_ws('^', {0})\".format(fk_list_temp)\n\n sql_check_fk = \\\n (\"select concat_ws('^', collect_set(A.s)) from ( select {0} as s from ( \"\n \"select distinct {1} from {2}.{3} where not exists ( select distinct {4} from {2}.{5} \"\n \"where {5}.SRC_SYS_CD='{6}' and {7} ) \"\n \" and {8} and {3}.SRC_SYS_CD='{6}' )t limit 10 )A\").format(fk_list_formatted, source_column_name,\n schema_name, table_name, rfnc_column_name,\n reference_table_name,\n param_src_sys_cd, join_condition,\n null_condition)\n res_check_fk = create_tmp_file(sql_check_fk)\n\n if res_check_fk.strip() == '':\n print('No FK issue found for reference table {0}! FK: {1}.'.format(reference_table_name, reference_keys))\n else:\n print('SQL:')\n print(\"\\033[31m select distinct {0} from {1}.{2} where not exists ( select distinct {3} from {1}.{4} \"\n \"where {4}.SRC_SYS_CD='{5}' and {6} ) \"\n \" and {7} and {2}.SRC_SYS_CD='{5}' limit 10\\n \\033[0m\").format(source_column_name,\n schema_name, table_name,\n rfnc_column_name,\n reference_table_name,\n param_src_sys_cd,\n join_condition,\n null_condition)\n print(\"-[ RECORD ]---+----------------\\n\"\n \"Schema Name | {0}\\n\"\n \"Table Name | {1}\\n\"\n\t\t \"Reference Table | {2}\\n\"\n \"Column Names | {3}\\n\"\n \"Constraint Name | {4}\\n\"\n \"Constraint Type | FOREIGN\\n\"\n \"----------------+----------------\".format(schema_name, table_name, reference_table_name, reference_keys,\n constraint_name))\n if res_check_fk.find('^') == -1:\n print(\"Column Values | ('{0}')\\n\".format(res_check_fk))\n else:\n output_data(len(reference_keys.split(',')), res_check_fk)\n else:\n pass","title":""},{"docid":"3eacf0b1689346e5ff49199ec93b8c8b","score":"0.49354535","text":"def _ensure_schema(self):\n with psycopg2.connect(\n host=self.config[\"host\"],\n port=self.config[\"port\"],\n user=self.config[\"user\"],\n dbname=self.config[\"default_dbname\"],\n password=self.password,\n sslmode='require') as bootstrap_connection:\n bootstrap_connection.autocommit = True\n cursor = bootstrap_connection.cursor()\n try:\n cursor.execute(\"CREATE DATABASE {config[dbname]};\".format(config=self.config))\n except psycopg2.errors.DuplicateDatabase:\n logger.info(\"Database %s exists\", self.config[\"dbname\"])\n\n with psycopg2.connect(\n host=self.config[\"host\"],\n port=self.config[\"port\"],\n user=self.config[\"user\"],\n dbname=self.config[\"dbname\"],\n password=self.password,\n sslmode='require') as bootstrap_connection:\n cursor = bootstrap_connection.cursor()\n cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS {table} (\n timestamp TIMESTAMP NOT NULL,\n url VARCHAR NOT NULL,\n status_code NUMERIC NOT NULL,\n response_time NUMERIC NOT NULL,\n match_content BOOLEAN,\n PRIMARY KEY (timestamp, url)\n );\"\"\".format(table=self.table))","title":""},{"docid":"760badfe0525110f4d2c6b84007eb334","score":"0.49313623","text":"def sync_tables(self, table):\n\t\tif table != \"*\":\n\t\t\ttable_limit = table.split(',')\n\t\t\t\n\t\t\tself.my_eng.mysql_con.tables_limit = table_limit\n\t\t\tself.my_eng.get_table_metadata()\n\t\t\tself.my_eng.lock_tables()\n\t\t\tself.pg_eng.table_limit = table_limit\n\t\t\tself.pg_eng.master_status = self.my_eng.master_status\n\t\t\tself.pg_eng.set_source_id('initialising')\n\t\t\tself.stop_replica(allow_restart=False)\n\t\t\tself.pg_eng.build_tab_ddl()\n\t\t\tself.pg_eng.build_idx_ddl(self.global_config.obfdic)\n\t\t\tself.pg_eng.drop_tables()\n\t\t\tself.pg_eng.create_tables()\n\t\t\tself.my_eng.copy_table_data(self.pg_eng, self.global_config.copy_max_memory, False, False)\n\t\t\tself.pg_eng.set_source_id('initialised')\n\t\t\tself.my_eng.unlock_tables()\n\t\t\tif self.global_config.obfdic != {}:\n\t\t\t\tself.pg_eng.sync_obfuscation(self.global_config.obfdic)\n\t\t\tself.pg_eng.create_indices()\n\t\t\tself.pg_eng.delete_table_events()\n\t\t\tself.enable_replica()\n\t\telse:\n\t\t\tprint(\"You should specify at least one table to synchronise.\")","title":""},{"docid":"73ad87d2a046394e06d38ec0caa9f696","score":"0.49273452","text":"async def test_update_own_source(standard_database: Tuple[CouchDB, dict], http_client: dict) -> None:\n session, objs = standard_database\n\n image_data = f'data:image/png;base64,{b64encode(resources.open_binary(sources, \"one.png\").read()).decode(\"utf-8\")}' # noqa: E501\n response = await http_client['put'](f'/api/sources/{objs[\"sources\"][\"two\"][\"_id\"]}',\n body={'type': 'sources',\n 'id': objs['sources']['two']['_id'],\n 'attributes': {\n 'type': 'book',\n 'title': 'All change',\n 'subtitle': 'New subtitle',\n 'date': '2021-04-01',\n 'publisher': 'No such press',\n 'location': 'Nowhere',\n 'page_numbers': '10',\n 'data': image_data,\n }},\n token=auth_token(objs['users']['provider']))\n assert response.code == 200\n source = json.load(response.buffer)['data']\n sources_db = await session['sources']\n db_source = await sources_db[source['id']]\n assert db_source['type'] == 'book'\n assert db_source['title'] == 'All change'\n assert db_source['subtitle'] == 'New subtitle'\n assert db_source['date'] == '2021-04-01'\n assert db_source['publisher'] == 'No such press'\n assert db_source['location'] == 'Nowhere'\n assert db_source['page_numbers'] == '10'\n assert db_source['updated'] > db_source['created']","title":""},{"docid":"e5f17b46212581ada8ebb2f97ae4381c","score":"0.4918272","text":"def _create_tables(self) -> None:\n logger.debug(\"Creating new gene normalizer tables.\")\n tables_query = (SCRIPTS_DIR / \"create_tables.sql\").read_bytes()\n\n with self.conn.cursor() as cur:\n cur.execute(tables_query)\n self.conn.commit()","title":""},{"docid":"ddcf8f75c5e6d9fdba10b303a6f710e2","score":"0.49150348","text":"def create_tables(source, ticker_name):\n columns = ['Date',\n f'{source}{ticker_name}_Open',\n f'{source}{ticker_name}_High',\n f'{source}{ticker_name}_Low',\n f'{source}{ticker_name}_Close',\n f'{source}{ticker_name}_Volume',\n f'{source}{ticker_name}_Ex_Dividend',\n f'{source}{ticker_name}_Split_Ratio',\n f'{source}{ticker_name}_Adj_Open',\n f'{source}{ticker_name}_Adj_High',\n f'{source}{ticker_name}_Adj_Low',\n f'{source}{ticker_name}_Adj_Close',\n f'{source}{ticker_name}_Adj_Volume']\n commands = (\n f\"\"\"\n CREATE TABLE {ticker_name} (\n {columns[0]} date PRIMARY KEY,\n {columns[1]} float(8) ,\n {columns[2]} float(8),\n {columns[3]} float(8),\n {columns[4]} float(8),\n {columns[5]} float(8),\n {columns[6]} float(8),\n {columns[7]} float(8),\n {columns[8]} float(8),\n {columns[9]} float(8),\n {columns[10]} float(8),\n {columns[11]} float(8),\n {columns[12]} float(8)\n )\n \"\"\")\n conn = None\n try:\n # read the connection parameters\n params = config()\n # connect to the PostgreSQL server\n conn = psycopg2.connect(**params)\n cur = conn.cursor()\n # create table one by one\n cur.execute(commands)\n # close communication with the PostgreSQL database server\n cur.close()\n # commit the changes\n conn.commit()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()","title":""},{"docid":"2c7994f3204d23f252728ba191957c22","score":"0.49068668","text":"def test_update_data_source(self):\n source_id = 13\n credentials = {\"project_id\": \"test_project\"}\n test_dataset_table_id = \"test_table_id\"\n data_source = {\"dataset\": \"test_dataset\"}\n\n gcp_source = {\n \"source_id\": source_id,\n \"source_uuid\": FAKE.uuid4(),\n \"name\": \"Provider GCP\",\n \"source_type\": \"GCP\",\n \"authentication\": {\"credentials\": credentials},\n \"billing_source\": {\"data_source\": data_source},\n \"auth_header\": \"fakeauthheader\",\n \"account_id\": \"acct10001\",\n \"offset\": 12,\n }\n provider = Sources(**gcp_source)\n provider.save()\n\n provider = GCPProvider()\n updated_data_source = copy.deepcopy(data_source)\n updated_data_source[\"table_id\"] = test_dataset_table_id\n provider.update_source_data_source(credentials, updated_data_source)\n\n db_obj = Sources.objects.get(source_id=source_id)\n\n self.assertEquals(db_obj.billing_source, {\"data_source\": updated_data_source})\n\n gcp2_source_id = 14\n gcp2_dataset_table_id = \"test_table_id_2\"\n data_source_2 = {\"dataset\": \"test_dataset_2\"}\n\n gcp_source2 = {\n \"source_id\": gcp2_source_id,\n \"source_uuid\": FAKE.uuid4(),\n \"name\": \"Provider GCP 2\",\n \"source_type\": \"GCP\",\n \"authentication\": {\"credentials\": credentials},\n \"billing_source\": {\"data_source\": data_source_2},\n \"auth_header\": \"fakeauthheader\",\n \"account_id\": \"acct10001\",\n \"offset\": 12,\n }\n provider_2 = Sources(**gcp_source2)\n provider_2.save()\n\n updated_data_source_2 = copy.deepcopy(data_source_2)\n updated_data_source_2[\"table_id\"] = gcp2_dataset_table_id\n\n GCPProvider().update_source_data_source(credentials, updated_data_source_2)\n\n db_obj_2 = Sources.objects.get(source_id=gcp2_source_id)\n self.assertEquals(\n Sources.objects.get(source_id=source_id).billing_source, {\"data_source\": updated_data_source}\n )\n self.assertEquals(db_obj_2.billing_source, {\"data_source\": updated_data_source_2})","title":""},{"docid":"61fb08e00db85377c442f447e8302755","score":"0.49032906","text":"def test_ensure_valid_calibration_table_passes_with_valid_table(self):\n self._assert_calibration_table_passes(self.VALID_CALIBRATION_TABLE)","title":""},{"docid":"718c5f7e1ce72d422292b4c3a791df30","score":"0.4902795","text":"def create_table_data(self, data, table_name):\n update_error = False\n #print 'create_table_data', data\n hostname = ''\n object_name = ''\n if type(data) != type(list):\n data = [data]\n for record in data:\n fields = []\n values = []\n object_name = ''\n for r in record:\n # print 'create', r, record[r]\n if r == 'name':\n object_name = record[r]\n elif r in ['hostname', 'type'] and not object_name:\n object_name = record[r]\n if r != 'pkid':\n if record[r] != 'NULL':\n fields.append(r)\n values.append(str(record[r]))\n if self.is_testbed_table(table_name):\n fields.append('fk_testbeds')\n values.append(str(self.get_active_testbed()))\n sql = \"insert into {table_name} ({fields}) values ('{values}') \".format(table_name=table_name,\n fields=','.join(fields),\n values=\"','\".join(values))\n #print sql\n self.db.db_command(sql=sql)\n #print sql_conn.sql_execute_result\n if self.db.error:\n update_error = True\n if not update_error:\n unique_col = self.get_unique_column(table_name)\n sql = 'select pkid from {table_name} where {unique_col} = \"{object_name}\"'.format(table_name=table_name,\n object_name=object_name,\n unique_col=unique_col)\n print(sql)\n \n result = self.db.db_command(sql=sql).one()\n print(result)\n if result:\n return result\n else:\n return {\n 'error': 'create_table_data is unable to retrieve inserted data collections with SQL query: {}'.format(\n sql)}\n else:\n return {'error': 'create_table_data is unable to update data collections with SQL query: {}'.format(sql)}","title":""},{"docid":"e1dd8a6a7754ba38d2915326f14da7f3","score":"0.48987654","text":"def test_issue_79(self):\r\n conn = self.connections[0]\r\n c = conn.cursor(pymysql.cursors.DictCursor)\r\n\r\n c.execute(\"drop table if exists a\")\r\n c.execute(\"drop table if exists b\")\r\n c.execute(\"\"\"CREATE TABLE a (id int, value int)\"\"\")\r\n c.execute(\"\"\"CREATE TABLE b (id int, value int)\"\"\")\r\n\r\n a=(1,11)\r\n b=(1,22)\r\n try:\r\n c.execute(\"insert into a values (%s, %s)\", a)\r\n c.execute(\"insert into b values (%s, %s)\", b)\r\n\r\n c.execute(\"SELECT * FROM a inner join b on a.id = b.id\")\r\n r = c.fetchall()[0]\r\n self.assertEqual(r['id'], 1)\r\n self.assertEqual(r['value'], 11)\r\n self.assertEqual(r['b.value'], 22)\r\n finally:\r\n c.execute(\"drop table a\")\r\n c.execute(\"drop table b\")","title":""},{"docid":"cd3fd0f72b4c0fb1c29ef8e506e7e026","score":"0.48976326","text":"def on_create_table_error(self, exc):\n # errors other than code listed below will be raised\n # - 1050: table exists\n if exc.orig.args[0] not in [1050]:\n raise exc","title":""},{"docid":"b0ea013cacb1160341a134ae2b80719c","score":"0.48952124","text":"def test_check_data_sources_merged_allowed(self):\n\n raw_wind_io.check_data_sources(\n primary_source=raw_wind_io.MERGED_DATA_SOURCE, allow_merged=True)","title":""},{"docid":"00ca6699f20bec260239ee94ea4b7ca4","score":"0.4887721","text":"def test__associate_generator_tables_1():\n gen_assoc_1_expected = pd.DataFrame({\n 'plant_id_eia': [50307, 50307, 50307, 50307, 50307, 50307, 50307],\n 'generator_id': ['GEN1', 'GEN2', 'GEN3', 'GEN4', 'GEN5', np.nan, np.nan],\n 'report_date': ['2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01', ],\n 'prime_mover_code': ['ST', 'ST', 'ST', 'ST', 'IC', 'IC', 'ST'],\n 'capacity_mw': [7.5, 2.5, 2.5, 4.3, 1.8, np.nan, np.nan],\n 'fuel_type_count': [2.0, 2.0, 2.0, 2.0, 2.0, np.nan, np.nan],\n 'energy_source_code_num': ['energy_source_code_1', 'energy_source_code_1', 'energy_source_code_1', 'energy_source_code_1', 'energy_source_code_1', np.nan, np.nan],\n 'fuel_type': ['NG', 'NG', 'NG', 'NG', 'DFO', 'RFO', 'RFO'],\n 'net_generation_mwh_g_tbl': [14.0, 1.0, 0.0, 0.0, np.nan, np.nan, np.nan],\n 'net_generation_mwh_gf_tbl': [15.0, 15.0, 15.0, 15.0, 0.0, np.nan, np.nan],\n 'fuel_consumed_mmbtu': [100000.0, 100000.0, 100000.0, 100000.0, 0.0, np.nan, np.nan],\n 'capacity_mw_fuel': [16.8, 16.8, 16.8, 16.8, 1.8, np.nan, np.nan],\n 'net_generation_mwh_g_tbl_fuel': [15.0, 15.0, 15.0, 15.0, np.nan, np.nan, np.nan],\n },\n ).pipe(helpers.convert_cols_dtypes, 'eia')\n\n gen_assoc_1_actual = (\n allocate_net_gen.associate_generator_tables(\n gf=GF_1, gen=GEN_1, gens=GENS_1)\n .pipe(helpers.convert_cols_dtypes, 'eia')\n )\n\n pd.testing.assert_frame_equal(gen_assoc_1_expected, gen_assoc_1_actual)","title":""},{"docid":"83dab9ef676042ac1bfc8699136b1833","score":"0.48870736","text":"def step_2_create_new_copy_table():\n print \"Running Step 2 - Making a copy of QueueMetrics table\"\n try:\n conn.execute(\"\"\"USE LifeNetDW SELECT * INTO QueueMetricsCopy\n FROM QueueMetrics\"\"\")\n print \"Successfully Made a Copy of QueueMetrics table\"\n except IOError as my_msg:\n print \"Error creating a new copy table:\", my_msg","title":""},{"docid":"77ecf56fea1141b8c034debef01b04a9","score":"0.4876897","text":"def setUp(self):\n self.connection = DatabaseConnection()\n with self.connection as cursor:\n cursor.Execute(\"\"\"CREATE TABLE `versionedAuthor` (\n `ID` smallint(5) unsigned NOT NULL AUTO_INCREMENT,\n `versionedAuthorID` smallint(5) unsigned NOT NULL,\n `name` varchar(32) NOT NULL,\n PRIMARY KEY (`ID`),\n KEY `recordKey` (`versionedAuthorID`)\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8\"\"\")\n cursor.Execute(\"\"\"CREATE TABLE `versionedBook` (\n `ID` smallint(5) unsigned NOT NULL AUTO_INCREMENT,\n `versionedBookID` smallint(5) unsigned NOT NULL,\n `versionedAuthor` smallint(5) unsigned NOT NULL,\n `title` varchar(32) NOT NULL,\n PRIMARY KEY (`ID`),\n KEY `recordKey` (`versionedBookID`)\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8\"\"\")","title":""},{"docid":"712737e9a447e70d6733b7ac2ff084cb","score":"0.4876497","text":"def create_tables():\n\n db = pymongo.MongoClient(DB_URI).get_default_database()\n tables = db.list_collection_names()\n\n if netcat.DBT_STATUS not in tables:\n db[netcat.DBT_INFO].create_index([(\"device_name\", pymongo.ASCENDING), (\"snapshot_timestamp\", pymongo.DESCENDING)])\n netcat.LOGGER.warning(f\"Pymongo: Created '{netcat.DBT_STATUS}' table\")\n\n if netcat.DBT_INFO not in tables:\n db[netcat.DBT_INFO].create_index([(\"snapshot_timestamp\", pymongo.DESCENDING)])\n db[netcat.DBT_INFO].create_index([(\"device_name\", pymongo.ASCENDING)])\n db[netcat.DBT_INFO].create_index([(\"device_type\", pymongo.ASCENDING)])\n db[netcat.DBT_INFO].create_index([(\"device_name\", pymongo.ASCENDING), (\"snapshot_timestamp\", pymongo.DESCENDING)])\n db[netcat.DBT_INFO].create_index([(\"device_type\", pymongo.ASCENDING), (\"snapshot_timestamp\", pymongo.DESCENDING)])\n netcat.LOGGER.warning(f\"Pymongo: Created '{netcat.DBT_INFO}' table\")\n\n if netcat.DBT_BACKUP not in tables:\n db[netcat.DBT_BACKUP].create_index([(\"snapshot_timestamp\", pymongo.DESCENDING)])\n db[netcat.DBT_BACKUP].create_index([(\"device_name\", pymongo.ASCENDING)])\n db[netcat.DBT_BACKUP].create_index([(\"device_name\", pymongo.ASCENDING), (\"snapshot_timestamp\", pymongo.DESCENDING)])\n netcat.LOGGER.warning(f\"Pymongo: Created '{netcat.DBT_BACKUP}' table\")","title":""},{"docid":"d2db8eeecfcbb674cdc7729a3e738d30","score":"0.4858888","text":"def test_rename_conflict(self):\n t1 = self.t1\n t2 = self.t2\n t1['b_1'] = 1 # Add a new column b_1 that will conflict with auto-rename\n with pytest.raises(np_utils.TableMergeError):\n t1.join(t2, keys='a')","title":""},{"docid":"43bd8e400e24ba995de63ac687b3e460","score":"0.48502496","text":"def writeLanduseTables(self) -> bool:\n isProjDb = filecmp.cmp(self.plantSoilDatabase, self.dbFile)\n isRefDb = filecmp.cmp(self.plantSoilDatabase, self.dbRefFile)\n copyPlant = True\n copyUrban = True\n if isProjDb:\n conn = self.conn\n # avoid trying to copy table to itself\n copyPlant = self.plantTable != DBUtils._PLANTS_PLT_NAME\n copyUrban = self.urbanTable != DBUtils._URBAN_URB_NAME\n if not (copyPlant or copyUrban):\n return True\n elif isRefDb:\n conn = self.connRef\n else:\n conn = sqlite3.connect(self.plantSoilDatabase)\n # suspend row_factory setting\n # normal setting to sqlite3.Row produces dictionaries for rows, which have random order \n # row_factory needs to be a function taking a cursor and row as tuple and returning a row\n # so we use an identity\n conn.row_factory = lambda _, row: row\n readCursor = conn.cursor()\n writeCursor = self.conn.cursor()\n try:\n if copyPlant:\n sql = 'DROP TABLE IF EXISTS {0}'.format(DBUtils._PLANTS_PLT_NAME)\n writeCursor.execute(sql)\n sql = 'CREATE TABLE {0} {1}'.format(DBUtils._PLANTS_PLT_NAME, DBUtils._PLANTS_PLT_TABLE)\n writeCursor.execute(sql)\n insertPlant = 'INSERT INTO {0} VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)'.format(DBUtils._PLANTS_PLT_NAME)\n if copyUrban:\n sql = 'DROP TABLE IF EXISTS {0}'.format(DBUtils._URBAN_URB_NAME)\n writeCursor.execute(sql)\n sql = 'CREATE TABLE {0} {1}'.format(DBUtils._URBAN_URB_NAME, DBUtils._URBAN_URB_TABLE)\n writeCursor.execute(sql)\n insertUrban = 'INSERT INTO {0} VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?)'.format(DBUtils._URBAN_URB_NAME)\n plantSelect = self.sqlSelect(self.plantTable, '*', '', 'name=? COLLATE NOCASE')\n urbanSelect = self.sqlSelect(self.urbanTable, '*', '', 'name=? COLLATE NOCASE')\n # changed to copy all of plant and urban tables\n # pid = 0\n # uid = 0\n for crop in self.landuseVals:\n name = self.getLanduseCode(crop)\n if name.upper() == 'BARR' or name.upper() == 'WATR':\n # BARR will become NULL later, WATR will become WETW; neither in plant table\n continue\n args = (name,)\n # look first in plantTable\n row = readCursor.execute(plantSelect, args).fetchone()\n if row is None:\n row = readCursor.execute(urbanSelect, args).fetchone()\n if row is None:\n QSWATUtils.error('Landuse name {0} (and perhaps others) not defined in {1} or {2} tables in database {3}.'.\n format(name, self.plantTable, self.urbanTable, self.plantSoilDatabase), self.isBatch)\n return False\n # changed to copy all of plant and urban tables\n # elif copyUrban:\n # uid += 1\n # # copy row, replacing id\n # writeCursor.execute(insertUrban, (uid,) + row[1:])\n # changed to copy all of plant and urban tables\n # elif copyPlant:\n # pid += 1\n # writeCursor.execute(insertPlant, (pid,) + row[1:])\n # copy all of plant and urban tables\n if copyPlant:\n plantAll = self.sqlSelect(self.plantTable, '*', '', '')\n for row in readCursor.execute(plantAll):\n writeCursor.execute(insertPlant, tuple(row))\n if copyUrban:\n urbanAll = self.sqlSelect(self.urbanTable, '*', '', '')\n for row in readCursor.execute(urbanAll):\n writeCursor.execute(insertUrban, tuple(row))\n return True\n except Exception:\n QSWATUtils.exceptionError('Could not create {3} and {4} tables from {0} and {1} tables in landuse and soil database {2}'.\n format(self.plantTable, self.urbanTable, self.plantSoilDatabase, DBUtils._PLANTS_PLT_NAME, DBUtils._URBAN_URB_NAME), \n self.isBatch)\n return False\n finally:\n self.conn.commit()\n self.hashDbTable(self.conn, DBUtils._PLANTS_PLT_NAME)\n self.hashDbTable(self.conn, DBUtils._URBAN_URB_NAME)\n if isProjDb or isRefDb:\n conn.row_factory = sqlite3.Row\n else:\n conn.close()","title":""},{"docid":"746d9a09a84e9ef4cb7bbc83ca024a23","score":"0.48501724","text":"def create_marker_table(self):\r\n connection = self.connect()\r\n cursor = connection.cursor()\r\n if cursor.tables(table=self.marker_table, tableType='TABLE').fetchone():\r\n pass\r\n else:\r\n try:\r\n connection.execute(\r\n \"\"\" CREATE TABLE {marker_table} (\r\n id BIGINT NOT NULL IDENTITY(1,1),\r\n update_id VARCHAR(128) NOT NULL,\r\n target_table VARCHAR(128),\r\n inserted DATETIME DEFAULT(GETDATE()),\r\n PRIMARY KEY (update_id)\r\n )\r\n \"\"\"\r\n .format(marker_table=self.marker_table)\r\n )\r\n except pyodbc.Error as e:\r\n raise\r\n cursor.close()\r\n connection.close()","title":""},{"docid":"7ef9c2feea3fa34ac516673eb726bb5e","score":"0.484886","text":"def _pools_compatible_for_migration(self, src_pd, src_sp, dst_pd, dst_sp):\n\n client = self._get_client()\n src_zero_padding_enabled = client.is_volume_creation_safe(src_pd,\n src_sp)\n dst_zero_padding_enabled = client.is_volume_creation_safe(dst_pd,\n dst_sp)\n src_is_fg_pool = self._is_fine_granularity_pool(src_pd, src_sp)\n dst_is_fg_pool = self._is_fine_granularity_pool(dst_pd, dst_sp)\n if src_is_fg_pool:\n return True\n elif dst_is_fg_pool:\n if not src_zero_padding_enabled:\n LOG.debug(\"Migration from Medium Granularity storage pool \"\n \"with zero padding disabled to Fine Granularity one \"\n \"is not allowed.\")\n return False\n return True\n elif not src_zero_padding_enabled == dst_zero_padding_enabled:\n LOG.debug(\"Zero padding must be either enabled or disabled on \"\n \"both storage pools.\")\n return False\n return True","title":""},{"docid":"54354d74744722377800edb1e8e3ac67","score":"0.48460242","text":"def transferTables(s1, s2):\n if not(s2.tables):\n s2.tables = s1.tables\n return\n\n for (_, t1) in s1.tables.items():\n add = True\n for (_, t2) in s2.tables.items():\n if (t1.name() == t2.name()) and (t1.type() == t2.type()):\n add = False\n break\n if add:\n s2.tables[s2._tableNextId()] = t1\n return","title":""},{"docid":"d4cd31cce0f8f66b2463e8c54cfc90d5","score":"0.4837329","text":"def test_create_table(self):\r\n db = mydbconnection.Connect()\r\n db1 = db.connection_db('dev')\r\n cursor = db1.cursor()\r\n folder_path = os.getcwd()\r\n file_path = os.path.join('\\\\'.join(folder_path.split('\\\\')[:-1]), 'files')\r\n cursor.execute(\"SELECT table_name FROM information_schema.tables\"\r\n \" WHERE table_schema='mymodel'\")\r\n table_name = cursor.fetchall()\r\n table_names = []\r\n for i in table_name:\r\n for j in i:\r\n table_names.append(j)\r\n num1 = len(table_names)\r\n for i in range(num1):\r\n for filename in os.listdir(file_path):\r\n if filename.split('.')[1] == 'sql' and filename.split('.')[0] == table_names[i]:\r\n self.assertEqual(filename.split('.')[0], table_names[i])\r\n self.assertNotEqual(filename.split('.')[1], table_names[i])","title":""},{"docid":"1fb9c971ecdbaca4132cefd5e44a82bd","score":"0.48335573","text":"def check_sqlite_tables(self, conn, source=None, table_error_callback=None, tablenames=None, progress=None):\n if progress is None:\n progress = dict()\n if not self.package_filename in (submission_schema_json, constituent_schema_json, portal_schema_json):\n raise ValueError('check_sqlite_tables() is only valid for built-in datapackages')\n if source is None:\n source = self\n tables_doc = source.model_doc['schemas']['CFDE']['tables']\n if tablenames is None:\n tablenames = set(tables_doc.keys())\n\n # collect errors and defer exception so we can give more detailed feedback\n errors_by_tname = {}\n first_error_mesg = None\n\n cur = None\n try:\n cur = conn.cursor()\n for table in tables_topo_sorted(self.doc_cfde_schema.tables.values()):\n if table.name not in tablenames:\n continue\n\n sql_checks = table.annotations.get(self.resource_tag, {}).get('check_sql_paths', [])\n skip_checks = json.loads(os.getenv('CFDE_SKIP_SQL_CHECKS', '{}'))\n resource = tables_doc[table.name][\"annotations\"].get(self.resource_tag, {})\n\n progress.setdefault(table.name, {})\n\n for path in sql_checks:\n if skip_checks is True or skip_checks.get(path[0:-4]):\n logger.info('Skipping custom SQL check %r for table %r due to CFDE_SKIP_SQL_CHECKS env. var.' % (path, table.name))\n continue\n progress[table.name].setdefault(path, None)\n if progress[table.name][path] is not None:\n logger.info('Skipping custom SQL check %r for table %r due to progress marker' % (path, table.name))\n continue\n logger.info('Running custom SQL check %r for table %r' % (path, table.name,))\n sql = self.package_filename.get_data_str(path)\n cur.execute(sql)\n row = cur.fetchone()\n if row:\n description, count, example_nid, example_data = row\n mesg = '%s in row %s %s%s' % (\n description,\n example_nid,\n example_data,\n (' (and %s others...)' % (count - 1)) if count > 1 else '',\n )\n errors_by_tname.setdefault(table.name, []).append(mesg)\n if first_error_mesg is None:\n first_error_mesg = 'Table %s %s' % (table.name, mesg)\n logger.error('custom SQL check %r for table %r ERROR: %s' % (path, table.name, mesg))\n progress[table.name][path] = False\n else:\n logger.info('custom SQL check %r for table %r OK' % (path, table.name))\n progress[table.name][path] = True\n\n if table.foreign_keys:\n logger.info('Checking foreign keys for table %r' % (table.name,))\n for fkey in table.foreign_keys:\n if fkey.pk_table.schema.name != table.schema.name:\n # only consider single schema in this sqite db\n progress[table.name][fkey.constraint_name] = 'skip'\n logger.info(\"Skipping foreign key %r which references outside schema %r\" % (fkey.constraint_name, table.schema.name))\n continue\n progress[table.name].setdefault(fkey.constraint_name, None)\n if progress[table.name][fkey.constraint_name] is not None:\n logger.info(\"Skipping foreign key %r due to progress marker\" % (fkey.constraint_name,))\n continue\n col_map = list(fkey.column_map.items())\n sql = \"\"\"\nWITH fk AS (\n SELECT fk.nid, %(fkcols)s\n FROM %(fk_table)s fk\n LEFT JOIN %(pk_table)s pk ON ( %(on)s )\n WHERE (%(nonnulls)s) AND (%(nulls)s)\n)\nSELECT\n (SELECT count(*) FROM fk) AS num_errors,\n nid,\n %(fkcols)s\nFROM fk\nORDER BY nid\nLIMIT 1;\n\"\"\" % {\n \"fk_table\": sql_identifier(table.name),\n \"fkcols\": \", \".join([\n 'fk.%s' % (sql_identifier(fkc.name),)\n for fkc, pkc in col_map\n ]),\n \"pk_table\": sql_identifier(fkey.pk_table.name),\n \"on\": \" AND \".join([\n 'fk.%s = pk.%s' % (sql_identifier(fkc.name), sql_identifier(pkc.name))\n for fkc, pkc in col_map\n ]),\n \"nonnulls\": \" AND \".join([\n \"fk.%s IS NOT NULL\" % (sql_identifier(fkc.name),)\n for fkc, pkc in col_map\n ]),\n \"nulls\": \" OR \".join([\n \"pk.%s IS NULL\" % (sql_identifier(pkc.name),)\n for fkc, pkc in col_map\n ]),\n}\n cur.execute(sql)\n row = cur.fetchone()\n if row:\n mesg = 'foreign key %r in row %s%s not present in referenced table %r' % (\n dict(zip([ fkc.name for fkc, pkc in col_map ], row[2:])), # first bad fkey\n row[1], # nid for first bad row\n (' (and %s others...)' % (row[0] - 1)) if row[0] > 1 else '',\n fkey.pk_table.name,\n )\n errors_by_tname.setdefault(table.name, []).append(mesg)\n if first_error_mesg is None:\n first_error_mesg = 'Table %s %s' % (table.name, mesg)\n logger.error('foreign key %r ERROR: %s' % (fkey.constraint_name, mesg))\n progress[table.name][fkey.constraint_name] = False\n else:\n logger.info(\"foreign key %r OK\" % (fkey.constraint_name,))\n progress[table.name][fkey.constraint_name] = True\n\n if table.name in errors_by_tname:\n if table_error_callback:\n table_error_callback(table.name, resource.get('path'), '; '.join(errors_by_tname[table.name]))\n\n if errors_by_tname:\n raise InvalidDatapackage('Errors found in %s tables %r. First error: %s' % (len(errors_by_tname), list(errors_by_tname), first_error_mesg))\n finally:\n if cur is not None:\n cur.close()","title":""},{"docid":"01160660ec6b361ea760e0e826839b39","score":"0.48321587","text":"def check_schema_initialized(self) -> bool:\n try:\n with self.conn.cursor() as cur:\n cur.execute((SCRIPTS_DIR / \"create_tables.sql\").read_bytes())\n except DuplicateTable:\n self.conn.rollback()\n else:\n logger.info(\"Gene table existence check failed.\")\n self.conn.rollback()\n return False\n try:\n with self.conn.cursor() as cur:\n cur.execute((SCRIPTS_DIR / \"add_fkeys.sql\").read_bytes())\n except DuplicateObject:\n self.conn.rollback()\n else:\n logger.info(\"Gene foreign key existence check failed.\")\n self.conn.rollback()\n return False\n try:\n with self.conn.cursor() as cur:\n cur.execute(\n (SCRIPTS_DIR / \"create_record_lookup_view.sql\").read_bytes()\n )\n except DuplicateTable:\n self.conn.rollback()\n else:\n logger.info(\"Gene normalized view lookup failed.\")\n self.conn.rollback()\n return False\n try:\n with self.conn.cursor() as cur:\n cur.execute((SCRIPTS_DIR / \"add_indexes.sql\").read_bytes())\n except DuplicateTable:\n self.conn.rollback()\n else:\n logger.info(\"Gene indexes check failed.\")\n self.conn.rollback()\n return False\n\n return True","title":""},{"docid":"c146da03873836ec32e040937dbe9d36","score":"0.48291188","text":"def load_staging_tables(cur, conn): \n for query in copy_table_queries:\n cur.execute(query)\n conn.commit()\n print(\"Query completed\")","title":""},{"docid":"60beafca3e213d2e1b52859075988ad9","score":"0.48216653","text":"def load_staging_tables(cur, conn):\n for table_name, query in copy_table_queries.items():\n cur.execute(query)\n conn.commit()\n log.info(f\"loaded data into staging table: {table_name}\")","title":""},{"docid":"f4ebf273660202046498824c971d8eae","score":"0.48206288","text":"def checkRouting(conn: Any) -> Tuple[List[str], List[str]]:\n errors: List[str] = []\n erroneous: Dict[str, Set[int]] # @UnusedVariable\n warnings: List[str] = []\n table = 'gis_routing'\n # mapping category -> id-set showing sources that are known to drain to an outlet\n # to save tracking every source all the way\n done: Dict[str, Set[int]] = dict()\n # mapping category -> id-set showing sources currently under investigation\n # (so all can be marked as done if the chain terminates)\n pending: Dict[str, Set[int]] = dict()\n # mapping category -> id -> hyd_typ -> percent used to check sources with multiple sinks have 100% accounted for\n percentages: Dict[str, Dict[int, Dict[str, float]]] = dict()\n nextSql = DBUtils.sqlSelect(table, '*', '', '')\n findSql = DBUtils.sqlSelect(table, 'hyd_typ, sinkid, sinkcat, percent', '', 'sourceid=? AND sourcecat=?')\n # Might be tempted to use pending to detect loops, but note below we don't\n # add to pending for multiple sinks, so then e.g. A -> A 90% would just loop.\n # So we prefer a count.\n maxSteps = int(conn.execute('SELECT COUNT(*) FROM ' + table).fetchone()[0])\n for row in conn.execute(nextSql):\n sid = int(row['sourceid'])\n scat = row['sourcecat']\n hydTyp = row['hyd_typ']\n tid = int(row['sinkid'])\n tcat = row['sinkcat']\n percent = float(row['percent'])\n hasZero = percent == 0\n count = maxSteps\n while count > 0:\n if scat in done and sid in done[scat]:\n # move pending to done\n for pcat, pids in pending.items():\n if pcat in done:\n done[pcat].update(pids)\n else:\n done[pcat] = pids\n pending = dict()\n break # from while loop\n if percent < 100:\n # add percent to percentages for this source\n sourcePercentages = percentages.get(scat, None)\n if sourcePercentages is None:\n percentages[scat] = {sid: {hydTyp: percent}}\n else:\n hydTypPercentages = sourcePercentages.get(sid, None)\n if hydTypPercentages is None:\n hydTypPercentages = {hydTyp: percent}\n else:\n hydTypPercentages[hydTyp] += percent\n else:\n # Only include source in pending (which should become done) \n # if there is only one sink for it, else other sinks will be ignored.\n # Pending is only used for efficiency, to avoid tracking every source to a final outlet,\n # so little harm in omitting some sources from it.\n if scat in pending:\n pending[scat].add(sid)\n else:\n pending[scat] = {sid}\n if tcat == 'X':\n # move pending to done\n for pcat, pids in pending.items():\n if pcat in done:\n done[pcat].update(pids)\n else:\n done[pcat] = pids\n pending = dict()\n break # from while loop\n count -= 1\n if count == 0:\n if hasZero:\n warnings.append('WARNING: There is a loop in the {0} table involving id {1}, category {2}, flow type {3} but with a zero percentage' \\\n .format(table, sid, scat, hydTyp))\n else:\n errors.append('There is a loop in the {0} table involving id {1}, category {2}, flow type {3}'.format(table, sid, scat, hydTyp))\n # move pending to done\n for pcat, pids in pending.items():\n if pcat in done:\n done[pcat].update(pids)\n else:\n done[pcat] = pids\n pending = dict()\n break # from while loop\n findRow = conn.execute(findSql, (tid, tcat)).fetchone()\n if findRow is None:\n # unnecessary to report as done in checkRoutingTable\n# report = False\n# if tcat not in erroneous:\n# report = True\n# erroneous[tcat] = {tid}\n# elif tid not in erroneous[tcat]:\n# erroneous[tcat].add(tid)\n# report = True\n# if report:\n# errors.append('Cannot find id {1} category {2} as a source in the {0} table'.format(table, tid, tcat))\n break # from while loop\n sid = tid\n scat = tcat\n hydTyp = findRow['hyd_typ']\n tid = int(findRow['sinkid'])\n tcat = findRow['sinkcat']\n percent = findRow['percent']\n hasZero = hasZero or percent == 0\n #end of while loop\n # end of for loop\n # check all percentages are approximately 100\n for scat, sourceDdata in percentages.items():\n for sid, data in sourceDdata.items():\n for hydTyp, percent in data.items():\n if int(percent + 0.5) != 100:\n if percent == 0 and scat in {'RES', 'PND', 'WETL'}:\n # an unused lake exit: ignore\n pass\n else:\n errors.append('The percentages for id {1}, category {2}, flow type {3} in the {0} table sum to {4} rather than 100' \\\n .format(table, sid, scat, hydTyp, int(percent + 0.5)))\n return errors, warnings","title":""},{"docid":"4c7300adbcaac05c49907e7955aefef8","score":"0.48168144","text":"def load_staging_tables(cur, conn):\n for query in copy_table_queries:\n print('Running:\\n%s' % query)\n cur.execute(query)\n conn.commit()","title":""},{"docid":"776c06663e484885839217d91a63d1a7","score":"0.48118332","text":"def test_rebuild_tables_is_idempotent(self):\n rebuild_tables()\n rebuild_tables()\n assert_sql_count(self, \"SELECT * FROM candy\", 0)","title":""}],"string":"[\n {\n \"docid\": \"899c520089ce88e397c2dfe17e9130c9\",\n \"score\": \"0.5935422\",\n \"text\": \"def test_check_data_sources_fake_secondary(self):\\n\\n with self.assertRaises(ValueError):\\n raw_wind_io.check_data_sources(\\n primary_source=raw_wind_io.MADIS_DATA_SOURCE,\\n secondary_source=FAKE_SECONDARY_DATA_SOURCE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1beb98ea7219593951b5bc96b094da2f\",\n \"score\": \"0.5878489\",\n \"text\": \"def test_check_data_sources_fake_primary(self):\\n\\n with self.assertRaises(ValueError):\\n raw_wind_io.check_data_sources(\\n primary_source=FAKE_PRIMARY_DATA_SOURCE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c349ad925bbb385ab4a06b27e28dc8df\",\n \"score\": \"0.57140356\",\n \"text\": \"def helper_create_tables(self):\\n ## creating tables\\n r.table_create('original_data', primary_key ='original_data_id').run(self.conn)\\n r.table_create('road_data', primary_key = 'road_data_id').run(self.conn)\\n r.table_create('flow_item', primary_key = 'flow_item_id').run(self.conn)\\n r.table_create('crawled_batch', primary_key = 'crawled_batch_id').run(self.conn)\\n r.table_create('flow_data', primary_key = 'flow_data_id').run(self.conn)\\n r.table_create('route_cached', primary_key = 'route_cached_id').run(self.conn)\\n r.table_create('analytics_monitored_area', primary_key = 'analytics_monitored_area_id').run(self.conn)\\n r.table_create('analytics_traffic_pattern', primary_key = 'analytics_traffic_pattern_id').run(self.conn)\\n\\n ## creating index\\n r.table('road_data').index_create('flow_item_id').run(self.conn)\\n r.table('road_data').index_create('geometry', geo=True).run(self.conn)\\n r.table('crawled_batch').index_create('crawled_timestamp').run(self.conn)\\n r.table(\\\"flow_data\\\").index_create(\\\"flow_crawled_batch\\\", [r.row[\\\"flow_item_id\\\"], r.row[\\\"crawled_batch_id\\\"]]).run(self.conn)\\n r.table('analytics_monitored_area').index_create('description').run(self.conn)\\n r.table(\\\"analytics_traffic_pattern\\\").index_create(\\\"analytics_crawled_batch\\\", [r.row[\\\"analytics_monitored_area_id\\\"], r.row[\\\"crawled_batch_id\\\"]]).run(self.conn)\\n\\n ## depreciated index\\n # r.table('flow_data').index_create('created_timestamp', r.row[\\\"CUSTOM\\\"][\\\"created_timestamp\\\"]).run(self.conn)\\n # r.table('road_data').index_create('created_timestamp').run(self.conn)\\n # r.table('road_data').index_create('crawled_batch_id').run(self.conn)\\n # r.table('flow_data').index_create('crawled_batch_id', r.row[\\\"CUSTOM\\\"][\\\"crawled_batch_id\\\"]).run(self.conn)\\n # r.table('flow_data').index_create('original_data_id', r.row[\\\"CUSTOM\\\"][\\\"original_data_id\\\"]).run(self.conn)\\n # r.table('original_data').index_create('crawled_batch_id').run(self.conn)\\n #r.table('original_data').index_create('crawled_batch_id').run(self.conn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8767fd863c5297798909f533aa5d4c2\",\n \"score\": \"0.5648351\",\n \"text\": \"def __transfer_tables_and_data(self):\\n print \\\"getting table names...\\\"\\n tables = self.__get_tidal_points_from_ACDB()\\n print \\\"have tables names.\\\"\\n for table in tables:\\n data = self.__get_tidal_data_from_ACDB(table)\\n self.__insert_tidal_data_into_db(data,table)\\n self.__create_aux_info_table()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf8574708a803fe5ac0aaca9b36ca5e\",\n \"score\": \"0.5588258\",\n \"text\": \"def check_tables_populated(self) -> bool:\\n with self.conn.cursor() as cur:\\n cur.execute(self._check_sources_query)\\n results = cur.fetchall()\\n if len(results) < len(SourceName):\\n logger.info(\\\"Gene sources table is missing expected sources.\\\")\\n return False\\n\\n with self.conn.cursor() as cur:\\n cur.execute(self._check_concepts_query)\\n result = cur.fetchone()\\n if not result or result[0] < 1:\\n logger.info(\\\"Gene records table is empty.\\\")\\n return False\\n\\n with self.conn.cursor() as cur:\\n cur.execute(self._check_merged_query)\\n result = cur.fetchone()\\n if not result or result[0] < 1:\\n logger.info(\\\"Normalized gene records table is empty.\\\")\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a9e8fe20979f515d0be437ee99e282\",\n \"score\": \"0.5578816\",\n \"text\": \"def test_fk_error_on_source_creation(rse_factory, did_factory, root_account):\\n\\n if get_session().bind.dialect.name == 'sqlite':\\n pytest.skip('Will not run on sqlite')\\n\\n src_rse, src_rse_id = rse_factory.make_mock_rse()\\n dst_rse, dst_rse_id = rse_factory.make_mock_rse()\\n all_rses = [src_rse_id, dst_rse_id]\\n add_distance(src_rse_id, dst_rse_id, distance=10)\\n\\n topology = Topology(rse_ids=all_rses).configure_multihop()\\n did = did_factory.random_file_did()\\n file = {'scope': did['scope'], 'name': did['name'], 'type': 'FILE', 'bytes': 1, 'adler32': 'beefdead'}\\n add_replicas(rse_id=src_rse_id, files=[file], account=root_account)\\n rule_core.add_rule(dids=[did], account=root_account, copies=1, rse_expression=dst_rse, grouping='ALL', weight=None, lifetime=None, locked=False, subscription_id=None)\\n\\n requests_by_id = list_and_mark_transfer_requests_and_source_replicas(rse_collection=topology, rses=[src_rse_id, dst_rse_id])\\n requests, *_ = build_transfer_paths(topology=topology, protocol_factory=ProtocolFactory(), requests_with_sources=requests_by_id.values())\\n request_id, [transfer_path] = next(iter(requests.items()))\\n\\n transfer_path[0].rws.request_id = generate_uuid()\\n to_submit, *_ = assign_paths_to_transfertool_and_create_hops(requests, default_tombstone_delay=0)\\n assert not to_submit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b61e18302ef0e28a7f1aad31636871f\",\n \"score\": \"0.5551415\",\n \"text\": \"def _init_db(self):\\n\\n try:\\n if not database_exists(self.src_engine.url) or not \\\\\\n database_exists(self.dst_engine.url):\\n raise RuntimeError(\\n 'Source or destination db does not exist ¯\\\\_(ツ)_/¯')\\n except sqlalchemy.exc.OperationalError as e:\\n if \\\"password\\\" in e.args[0]:\\n raise RuntimeError(\\n 'Unable to log in with that user and password (╯°□°）╯︵ ┻━━━━┻')\\n if \\\"could not connect\\\" in e.args[0] or \\\"starting up\\\" in e.args[0]:\\n raise RuntimeError(\\n 'Database unreachable ¯\\\\_(⊙︿⊙)_/¯')\\n\\n # get metadata for both dbs\\n self.src_metadata = MetaData(self.src_engine, reflect=True)\\n self.dst_metadata = MetaData(self.dst_engine, reflect=True)\\n\\n if self.table_name not in self.src_metadata.tables:\\n import pdb\\n pdb.set_trace()\\n raise RuntimeError(\\n 'Table \\\"{}\\\" does not exist in source db ¯\\\\_(ツ)_/¯'\\n .format(self.table_name))\\n\\n if self.table_name not in self.dst_metadata.tables:\\n # create table in destination db if it doesn't exist\\n tbl = Table(self.table_name, self.src_metadata, autoload=True)\\n tbl.metadata.create_all(self.dst_engine)\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf997db1f3230a8ce2b1c724dd8efc75\",\n \"score\": \"0.5479704\",\n \"text\": \"def ensure_tables(cursor):\\n\\n try:\\n cursor.execute(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS individuals (\\n name VARCHAR(64) PRIMARY KEY,\\n gender VARCHAR(6)\\n )\\n \\\"\\\"\\\")\\n except sqlite3.OperationalError as e:\\n logger.warning(\\\"Creating individuals table for {} failed with: '{}'. Trying to continue anyways.\\\".format(database, e))\\n\\n try:\\n cursor.execute(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS populations (\\n name VARCHAR(64) PRIMARY KEY,\\n size INT,\\n description VARCHAR(64)\\n )\\n \\\"\\\"\\\")\\n except sqlite3.OperationalError as e:\\n logger.warning(\\\"Creating populations table for {} failed with: '{}'. Trying to continue anyways.\\\".format(database, e))\\n\\n try:\\n cursor.execute(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS individuals_populations (\\n individual VARCHAR(64),\\n population VARCHAR(64),\\n UNIQUE(individual, population),\\n FOREIGN KEY (individual) REFERENCES individuals(name),\\n FOREIGN KEY (population) REFERENCES populations(name)\\n )\\n \\\"\\\"\\\")\\n except sqlite3.OperationalError as e:\\n logger.warning(\\\"Creating populations table for {} failed with: '{}'. Trying to continue anyways.\\\".format(database, e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c68cda6f2bd9a1217de4e6ecf28a4d8\",\n \"score\": \"0.5416401\",\n \"text\": \"def create_tables():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863a238e1f5c9f9be3ec8c3ac5ba50ae\",\n \"score\": \"0.5389179\",\n \"text\": \"def test_generate_required_tables(self):\\n output_schema: OutputSchema = run_sync(\\n get_output_schema(include=[SmallTable._meta.tablename])\\n )\\n self.assertEqual(len(output_schema.tables), 1)\\n SmallTable_ = output_schema.get_table_with_name(\\\"SmallTable\\\")\\n self._compare_table_columns(SmallTable, SmallTable_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c8bf2ab600a13a7101328b3c26c032\",\n \"score\": \"0.5383621\",\n \"text\": \"def checkRoutingTable(self) -> None:\\n for sink in self.routingSinks:\\n if sink[1] != 'X' and sink not in self.routingSources:\\n QSWATUtils.error('Internal error: routing sink category {1} id {0} not found as a source'.format(sink[0], sink[1]),\\n self.isBatch)\\n self.checkRoutedPointsSubbasinsAndLSUsDefined()\\n # check routing table for circularities\\n errors, warnings = self.checkRouting(self.conn)\\n for error in errors:\\n QSWATUtils.error(error, self.isBatch)\\n if len(warnings) > 5:\\n QSWATUtils.information('See QSWAT+ log for {0} warnings about circularities in routing'.\\n format(len(warnings)), self.isBatch)\\n for warning in warnings:\\n QSWATUtils.loginfo(warning)\\n else:\\n for warning in warnings:\\n QSWATUtils.information(warning, self.isBatch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8b1bcf377e6a66451acdf37967ee737\",\n \"score\": \"0.534483\",\n \"text\": \"def resolve(self):\\n if not self.first_table:\\n self.first_table = self.first_table_identifier.get_unique_reference() \\n \\n if not self.second_table:\\n self.second_table = self.second_table_identifier.get_unique_reference() \\n\\n columns1 = []\\n if self.first_table:\\n for column_name in self.first_table_columns:\\n columns1.append(self.first_table.find_column(column_name))\\n \\n self.first_table_columns = columns1\\n del columns1\\n \\n columns2 = []\\n if self.second_table:\\n for column_name in self.second_table_columns:\\n columns2.append(self.second_table.find_column(column_name))\\n\\n self.second_table_columns = columns2\\n del columns2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf207df841ba33a369f0936052a1cfce\",\n \"score\": \"0.5326868\",\n \"text\": \"def test_postgres_to_dolt(postgres_with_table, db_with_table):\\n postgres_conn, postgres_table = postgres_with_table\\n dssc, dolt_table = db_with_table\\n validate_dolt_as_target(postgres_conn,\\n postgres_table,\\n get_source_reader,\\n get_postgres_target_writer,\\n get_table_reader,\\n dssc,\\n dolt_table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f7fc8279ab12239ceb67f51ee74fb0\",\n \"score\": \"0.52978605\",\n \"text\": \"def set_up_connection_table(self, connections):\\n # set up connection table\\n self.connection_table = np.zeros((len(self.flat_sources),\\n len(self.sinks)))\\n for start, stop in connections:\\n if start in self.flat_sources and stop in self.sinks:\\n start_idx = self.flat_sources.index(start)\\n stop_idx = self.sinks.index(stop)\\n self.connection_table[start_idx, stop_idx] = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1a7cc2248ac9d6d712ce58002dfa7de\",\n \"score\": \"0.52798563\",\n \"text\": \"def create_table_links():\\n conn = psycopg2.connect(dbname=DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST, port=PORT)\\n cursor = conn.cursor()\\n try:\\n cursor.execute(\\\"\\\"\\\"CREATE TABLE links2\\n (\\n Id INT PRIMARY KEY NOT NULL, \\n link CHAR(50) UNIQUE\\n );\\\"\\\"\\\")\\n conn.commit()\\n return True\\n except Exception as err:\\n print(f'Ошибка psycopg2: {errorcodes.lookup(err.pgcode)}')\\n return False\\n finally:\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27fbf697bd7a2191c7836810c73b42c9\",\n \"score\": \"0.52739185\",\n \"text\": \"def append_table(src_con: \\\"SimpleSQLite\\\", dst_con: \\\"SimpleSQLite\\\", table_name: str) -> bool:\\n\\n logger.debug(\\n \\\"append table: src={src_db}.{src_tbl}, dst={dst_db}.{dst_tbl}\\\".format(\\n src_db=src_con.database_path,\\n src_tbl=table_name,\\n dst_db=dst_con.database_path,\\n dst_tbl=table_name,\\n )\\n )\\n\\n src_con.verify_table_existence(table_name)\\n dst_con.validate_access_permission([\\\"w\\\", \\\"a\\\"])\\n\\n if dst_con.has_table(table_name):\\n src_attrs = src_con.fetch_attr_names(table_name)\\n dst_attrs = dst_con.fetch_attr_names(table_name)\\n if src_attrs != dst_attrs:\\n raise ValueError(\\n dedent(\\n \\\"\\\"\\\"\\n source and destination attribute is different from each other\\n src: {}\\n dst: {}\\n \\\"\\\"\\\".format(\\n src_attrs, dst_attrs\\n )\\n )\\n )\\n\\n primary_key, index_attrs, type_hints = extract_table_metadata(src_con, table_name)\\n\\n dst_con.create_table_from_tabledata(\\n src_con.select_as_tabledata(table_name, type_hints=type_hints),\\n primary_key=primary_key,\\n index_attrs=index_attrs,\\n )\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bd9486bc2b90b2e40066792029b780c\",\n \"score\": \"0.5259045\",\n \"text\": \"def zip_code_map_SODA( dict_db_connect, source_table, target_table, custom_map_col): \\n try:\\n # Start runtime\\n start_time = datetime.datetime.now()\\n print(\\\"Starting temp table (\\\" + target_table + \\\") creation at time (\\\" + str(start_time) + \\\").\\\")\\n \\n # Connect to database\\n conn = psycopg2.connect( database = dict_db_connect[\\\"database\\\"], \\n user = dict_db_connect[\\\"user\\\"],\\n password = dict_db_connect[\\\"password\\\"],\\n host = dict_db_connect[\\\"host\\\"],\\n port = dict_db_connect[\\\"port\\\"])\\n cur = conn.cursor()\\n\\n print(\\\"Dropping temp table if exists.\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS \\\" + target_table + \\\";\\\")\\n \\n sql = \\\"CREATE TABLE \\\" + target_table + \\\" AS (SELECT \\\" + source_table\\n sql += \\\".*, COALESCE(zip_code_map.zip_code,99999) AS zip_code FROM \\\" + source_table\\n sql += \\\" LEFT JOIN zip_code_map ON zip_code_map.location = \\\" + source_table + \\\".\\\" + custom_map_col + \\\");\\\" \\n\\n print sql\\n cur.execute(sql)\\n conn.commit()\\n conn.close()\\n print \\\"Created temp table with status message: \\\" + cur.statusmessage \\n \\n print (\\\"Ended temp table creation for table (\\\" + source_table + \\\") at time (\\\" + \\n str(datetime.datetime.now()) + \\\").\\\")\\n return True\\n\\n except Exception as inst:\\n print(inst.args)\\n print(inst)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5b82181cab224a1c1bc96ac1dbf0327\",\n \"score\": \"0.5253207\",\n \"text\": \"def checkYouHaveTheRightTables( self ):\\n\\n expectedTables = []\\n foundTables = []\\n\\n # Fill a list containing all the expected tables from self.files dictionary.\\n # And self.files is THE true in this class.\\n for myFile in self.files:\\n expectedTables.append( myFile['table'] )\\n\\n # Just get data to be possible to enter into postgresql.\\n username = self.getConfiguration( 'database', 'user' )\\n\\n # Get all tables from the EXPECTED relational database you've created.\\n sqlTables = \\\"SELECT table_name FROM information_schema.tables WHERE table_schema='public' AND table_type='BASE TABLE';\\\"\\n\\n # Actual get the data said above executing the 'psql' command.\\n p = subprocess.Popen([\\\"psql\\\", '-U', 'kegg2017', '-t', '-c', sqlTables ], stdout=subprocess.PIPE)\\n\\n # Store the result of the command above.\\n out, err = p.communicate()\\n\\n # The result is not cool, we have to split that in lines.\\n tables = out.split('\\\\n')\\n\\n # And we have to remove empty lines (the result has it!).\\n reRemoveEmptyLines = re.compile('^$')\\n\\n # Iterate through the result tables from the actual, real, relational database you've created.\\n for t in tables:\\n # Actual ignore empty lines.\\n if reRemoveEmptyLines.search( t ):\\n continue\\n\\n # Remove blank spaces and weird useless '.' characters.\\n tableName = re.sub( '\\\\ ', '', t )\\n tableName = re.sub( '\\\\.', '', tableName )\\n\\n # Now we fill the found tables from your relational database.\\n foundTables.append( tableName )\\n\\n\\n # TODO: fix lack of expected tables.\\n # -------------------------------------------------------------------------------------------\\n # Some table doesn't belong to the Loader but it exists...\\n # It means tables that have to be populated manually or tables you know are\\n # critical but you don't populate those using this Loader.\\n # If you're doing smart things in your relational database, this thing have to \\n # be concerned.\\n expectedTables.append( 'source_databases' ) # this goes manually.\\n expectedTables.append( 'protein_pdbs' ) # this is important and will be filled later.\\n expectedTables.append( 'ec_reaction_classes' ) # this goes manually.\\n expectedTables.append( 'clusters' ) # this goes by outside process.\\n expectedTables.append( 'clustering_methods' ) # this goes by outside process.\\n # END of not fancy workaround. But loaders... come on... you know... loaders... \\n # If you're a developer, you're horrified. If you're a database administrator\\n # you're horrified. But if you are a guy that deals with loaders through big data\\n # well... you know...\\n # -------------------------------------------------------------------------------------------\\n\\n # Remove possible duplications (we never know).\\n expectedTables = set(expectedTables)\\n expectedTables = list(expectedTables)\\n\\n # Oh my god... if all the expected tables are exacly the same as the tables we're going\\n # to fill using this class... everything is so fine and cool!!\\n # It means you created a database with exacly the same tables this class expect. Congratulations!!\\n if set(foundTables) == set(expectedTables):\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffeac41fdda2fa64d2f69a0896bb514e\",\n \"score\": \"0.52497804\",\n \"text\": \"def data_upload(self):\\n table_sql = \\\"CREATE TABLE IF NOT EXISTS SE_BRIDGE.dbo.\\\" + \\\\\\n self.tableName + \\\"(\\\"\\n for table_col in self.columns:\\n table_sql = table_sql + \\\\\\n table_col[\\\"name\\\"] + \\\" \\\" + table_col[\\\"dataType\\\"] + \\\", \\\"\\n table_sql = table_sql[:-2] + \\\")\\\"\\n\\n # Build the MERGE statement that will copy the data from the staging table into the production table.\\n # Production table should have the same name and columns as the staging table.\\n merge_sql = \\\"MERGE INTO SE_BRIDGE.dbo.\\\" + self.tableName + \\\\\\n \\\" t USING (SELECT * FROM SOURCE_STAGING.SE_BRIDGE.\\\" + \\\\\\n self.tableName + \\\") x ON \\\"\\n for key_col in self.columns:\\n if key_col[\\\"isKey\\\"] == True:\\n merge_sql = merge_sql + \\\"t.\\\" + \\\\\\n key_col[\\\"name\\\"] + \\\" = x.\\\" + key_col[\\\"name\\\"] + \\\" AND \\\"\\n merge_sql = merge_sql[:-4] + \\\"WHEN MATCHED THEN UPDATE SET \\\"\\n for update_col in self.columns:\\n if update_col[\\\"isKey\\\"] == False:\\n merge_sql = merge_sql + \\\\\\n update_col[\\\"name\\\"] + \\\" = x.\\\" + update_col[\\\"name\\\"] + \\\", \\\"\\n merge_sql = merge_sql[:-2] + \\\" WHEN NOT MATCHED THEN INSERT (\\\"\\n for insert_col in self.columns:\\n merge_sql = merge_sql + insert_col[\\\"name\\\"] + \\\", \\\"\\n merge_sql = merge_sql[:-2] + \\\") VALUES (\\\"\\n for val_col in self.columns:\\n merge_sql = merge_sql + \\\"x.\\\" + val_col[\\\"name\\\"] + \\\", \\\"\\n merge_sql = merge_sql[:-2] + \\\");\\\"\\n\\n con = snowflake.connector.connect(\\n user=SNOWFLAKE_USER,\\n password=SNOWFLAKE_PASSWORD,\\n account=SNOWFLAKE_ACCOUNT\\n )\\n cur = con.cursor()\\n\\n try:\\n self.__upload(cur, \\\"SE_BRIDGE\\\")\\n cur.execute(\\\"USE SCHEMA SE_BRIDGE.dbo\\\")\\n\\n # Create production table if it doesn't exist\\n cur.execute(table_sql)\\n\\n # Merge the data from staging to production table\\n print(\\\"Merging data into Production table\\\")\\n cur.execute(merge_sql)\\n\\n finally:\\n cur.close()\\n con.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c0782f67009f76750c69ab8a269edc\",\n \"score\": \"0.5243017\",\n \"text\": \"def copy_table(\\n src_db, dest_db, table,\\n pkey, fkeys,\\n *, batch_size=50, condition=None, transform=None, validators=None\\n):\\n query = table.select()\\n if pkey is not None:\\n query = query.order_by(pkey)\\n if condition is not None:\\n query = query.where(condition)\\n query = src_db.execute(query)\\n assert pkey is None or pkey in query.keys()\\n assert all(field in query.keys() for field in fkeys)\\n mapping = {}\\n batch = query.fetchmany(batch_size)\\n orig_pkey = None # Avoids warning\\n while batch:\\n for row in batch:\\n row = dict(row.items())\\n # Get primary key, remove it\\n if pkey is not None:\\n orig_pkey = row[pkey]\\n row = dict(row)\\n row.pop(pkey)\\n # Map foreign keys\\n for field, fkey_map in fkeys.items():\\n row[field] = fkey_map[row[field]]\\n # Generic transform\\n if transform is not None:\\n row = transform(row)\\n # Validate\\n if validators:\\n for key, value in row.items():\\n if key in validators:\\n try:\\n if not validators[key](value):\\n raise ValueError(\\n \\\"Data failed validation (column %r)\\\" % key,\\n )\\n except validate.InvalidFormat:\\n raise ValueError(\\n \\\"Data failed validation (column %r)\\\" % key,\\n )\\n\\n # Have to insert one-by-one for inserted_primary_key\\n ins = dest_db.execute(\\n table.insert(),\\n row,\\n )\\n # Store new primary key\\n if pkey is not None:\\n mapping[orig_pkey], = ins.inserted_primary_key\\n batch = query.fetchmany(batch_size)\\n return mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18e73a1b25d653f8f5ba1d226c5b04de\",\n \"score\": \"0.5241757\",\n \"text\": \"def checkRoutedPointsSubbasinsAndLSUsDefined(self) -> None:\\n for (sourceId, sourceCategory) in self.routingSources:\\n if sourceCategory == 'PT':\\n self.checkKeyInTable('gis_points', sourceId)\\n elif sourceCategory == 'LSU':\\n self.checkKeyInTable('gis_lsus', sourceId)\\n elif sourceCategory == 'SUB':\\n self.checkKeyInTable('gis_subbasins', sourceId)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a5390237a3377ff8bc22094669b9d11\",\n \"score\": \"0.5193516\",\n \"text\": \"def test_oracle_to_mysql(oracle_with_table, create_dolt_test_data_commits):\\n oracle_conn, oracle_table = oracle_with_table\\n dssc, dolt_table = create_dolt_test_data_commits\\n validate_dolt_as_source(oracle_conn, oracle_table, get_oracle_target_writer, dssc, dolt_table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3795f7fa7886b4f0898dbe766980edbc\",\n \"score\": \"0.5180515\",\n \"text\": \"def migrate_onto(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f2a8ec9e8a31dd16673b52f830a70fa\",\n \"score\": \"0.51792276\",\n \"text\": \"def __create_tables(self):\\n self.conn.execute('create table if not exists col_resamples (col_id int, nrows int, nresamples int, lowest_raw_exps text, lowest_std_exps text)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40d29f02b5eb596d16342f0429d8807\",\n \"score\": \"0.516509\",\n \"text\": \"def partial_sync_table(table: tuple, args: Namespace) -> Union[bool, str]:\\n snowflake = FastSyncTargetSnowflake(args.target, args.transform)\\n tap_id = args.target.get('tap_id')\\n\\n try:\\n table_name = table[0]\\n\\n column_name = table[1]['column']\\n\\n drop_target_table = table[1]['drop_target_table']\\n args.drop_target_table = drop_target_table\\n args.table = table_name\\n\\n mysql = FastSyncTapMySql(args.tap, tap_type_to_target_type)\\n\\n mysql.open_connections()\\n\\n start_value = utils.validate_boundary_value(mysql.query, table[1]['start_value'])\\n end_value = utils.validate_boundary_value(mysql.query, table[1]['end_value'])\\n\\n # Get bookmark - Binlog position or Incremental Key value\\n bookmark = common_utils.get_bookmark_for_table(table_name, args.properties, mysql)\\n\\n target_schema = common_utils.get_target_schema(args.target, table_name)\\n table_dict = common_utils.tablename_to_dict(table_name)\\n target_table = table_dict.get('table_name')\\n\\n target_sf = {\\n 'sf_object': snowflake,\\n 'schema': target_schema,\\n 'table': target_table,\\n 'temp': table_dict.get('temp_table_name')\\n }\\n\\n snowflake_types = mysql.map_column_types_to_target(table_name)\\n\\n # making target table if not exists\\n snowflake.create_schema(target_schema)\\n snowflake.create_table(\\n target_schema=target_schema,\\n table_name=target_table,\\n columns=snowflake_types['columns'],\\n primary_key=snowflake_types.get('primary_key'),\\n is_temporary=False,\\n sort_columns=False,\\n allow_replace_table=False\\n )\\n\\n source_columns = snowflake_types.get('columns', [])\\n columns_diff = utils.diff_source_target_columns(target_sf, source_columns=source_columns)\\n\\n start_value_for_query = start_value if start_value == 'NULL' else f'\\\\'{start_value}\\\\''\\n where_clause_sql = f' WHERE {column_name} >= {start_value_for_query}'\\n if end_value:\\n where_clause_sql += f' AND {column_name} <= \\\\'{end_value}\\\\''\\n\\n # export data from source\\n file_parts = mysql.export_source_table_data(args, tap_id, where_clause_sql)\\n\\n # mark partial data as deleted in the target\\n snowflake.query(f'UPDATE {target_schema}.\\\"{target_table.upper()}\\\"'\\n f' SET _SDC_DELETEd_AT = CURRENT_TIMESTAMP(){where_clause_sql} AND _SDC_DELETED_AT IS NULL')\\n\\n # Creating temp table in Snowflake\\n primary_keys = snowflake_types.get('primary_key')\\n snowflake.create_schema(target_schema)\\n snowflake.create_table(\\n target_schema, target_table, source_columns, primary_keys, is_temporary=True\\n )\\n\\n mysql.close_connections()\\n\\n size_bytes = sum([os.path.getsize(file_part) for file_part in file_parts])\\n _, s3_key_pattern = utils.upload_to_s3(snowflake, file_parts, args.temp_dir)\\n\\n utils.load_into_snowflake(\\n target_sf, args, columns_diff, primary_keys, s3_key_pattern, size_bytes, where_clause_sql)\\n\\n if file_parts:\\n utils.update_state_file(args, bookmark)\\n\\n return True\\n except Exception as exc:\\n LOGGER.exception(exc)\\n return f'{table_name}: {exc}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"264b2269bbeaf006f0a96508226c8785\",\n \"score\": \"0.51531166\",\n \"text\": \"def test_run_incremental(self):\\n\\n # First run - should create tracking table, target table, and insert values\\n # TODO: check constraints are picked up correctly\\n run_druzhba(self.args)\\n\\n with self.target_conn.cursor() as cur:\\n cur.execute(\\n \\\"SELECT COUNT(*), MAX(updated_at), MAX(value1) FROM druzhba_test.test_basic\\\"\\n )\\n result = cur.fetchall()\\n self.assertTupleEqual(result[0], (2, t.t1, 1))\\n\\n with self.source_conn.cursor() as cur:\\n cur.execute(\\n \\\"UPDATE test_basic SET value1 = 2, updated_at = %s WHERE pk = 2\\\",\\n (t.t2,),\\n )\\n cur.execute(\\n \\\"INSERT INTO test_basic VALUES (%s, %s, %s, %s);\\\", (3, t.t2, \\\"drop\\\", 3),\\n )\\n\\n # Second run - should pick up the new row and the updated row\\n run_druzhba(self.args)\\n\\n with self.target_conn.cursor() as cur:\\n cur.execute(\\\"SELECT * FROM druzhba_test.test_basic ORDER BY pk\\\")\\n results = cur.fetchall()\\n\\n self.assertListEqual(results, [(1, t.t0, 0), (2, t.t2, 2), (3, t.t2, 3)])\\n\\n cur.execute(\\n \\\"SELECT * FROM druzhba_test.pipeline_table_index ORDER BY created_ts\\\"\\n )\\n results = cur.fetchall()\\n\\n self.assertListEqual(\\n results,\\n [\\n (\\n \\\"mysqltest\\\",\\n \\\"druzhba_test\\\",\\n \\\"test_basic\\\",\\n t.t1.strftime(\\\"%Y-%m-%d %H:%M:%S.%f\\\"),\\n ANY,\\n ),\\n (\\n \\\"mysqltest\\\",\\n \\\"druzhba_test\\\",\\n \\\"test_basic\\\",\\n t.t2.strftime(\\\"%Y-%m-%d %H:%M:%S.%f\\\"),\\n ANY,\\n ),\\n ],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4dde5d0cf5b7a9611f06a291d9e4a63\",\n \"score\": \"0.5147834\",\n \"text\": \"def check_keys(pk_frame, fk_frame):\\n\\n assert len(pk_frame.columns) == len(fk_frame.columns), (\\n 'The number of columns must be the same for primary and foreign keys')\\n\\n assert pk_frame.notnull().any(axis=1).all(), 'Primary keys cannot be null.'\\n\\n assert not pk_frame.duplicated().any(), 'Primary keys must be unique.'\\n\\n # Check that each foreign is a primary key\\n distinct_foreign_keys = (\\n fk_frame.dropna(how='all').drop_duplicates())\\n merged = distinct_foreign_keys.merge(\\n pk_frame, how='left', left_on=list(distinct_foreign_keys.columns),\\n right_on=list(pk_frame.columns), indicator=True)\\n fk_is_pk = (merged._merge == 'both').all()\\n assert fk_is_pk, 'Every foreign key must match a primary key.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddfb4e16f8fa9ddd4b4f342626856abf\",\n \"score\": \"0.5141495\",\n \"text\": \"def test_append_only(self):\\n\\n with self.source_conn.cursor() as cur:\\n cur.executemany(\\n f\\\"INSERT INTO druzhba_test.{self.table_name} VALUES (%s, %s, %s);\\\",\\n [(1, t.t0, None), (2, t.t0, None),],\\n )\\n\\n run_druzhba(self.args)\\n\\n with self.source_conn.cursor() as cur:\\n cur.executemany(\\n f\\\"INSERT INTO druzhba_test.{self.table_name} VALUES (%s, %s, %s);\\\",\\n [(2, t.t0, t.t1), (3, t.t1, None),],\\n )\\n\\n run_druzhba(self.args)\\n\\n with self.target_conn.cursor() as cur:\\n cur.execute(f\\\"SELECT * FROM druzhba_test.{self.table_name}\\\")\\n results = cur.fetchall()\\n\\n self.assertSetEqual(\\n set(results),\\n {(1, t.t0, None), (2, t.t0, None), (2, t.t0, t.t1), (3, t.t1, None),},\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f8f2019cdb568fbd4b0a23252de2382\",\n \"score\": \"0.5137385\",\n \"text\": \"def create_databases_tables(client_src: CogniteClient, client_dst: CogniteClient) -> Dict[str, List[str]]:\\n db_list_src = client_src.raw.databases.list(limit=None)\\n db_list_dst = client_dst.raw.databases.list(limit=None)\\n\\n db_names_src, db_not_created = get_not_created_names(db_list_src, db_list_dst)\\n\\n db_create = client_dst.raw.databases.create(name=db_not_created)\\n\\n assert len(db_create) == len(db_not_created)\\n\\n db_tb = {}\\n for db_name in db_names_src:\\n tb_list_src = client_src.raw.tables.list(db_name=db_name, limit=None)\\n tb_list_dst = client_dst.raw.tables.list(db_name=db_name, limit=None)\\n\\n tb_names_src, tb_not_created = get_not_created_names(tb_list_src, tb_list_dst)\\n\\n tb_create = client_dst.raw.tables.create(db_name=db_name, name=tb_not_created)\\n\\n assert len(tb_create) == len(tb_not_created)\\n\\n db_tb[db_name] = tb_names_src\\n\\n return db_tb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5725ed1cc6c1b52b9251f99130b1beb1\",\n \"score\": \"0.51236075\",\n \"text\": \"def glue_table_copy(original_glue_db, new_glue_db, original_glue_table, dest_location, region):\\n dest_bucket = bucket_dict(dest_location)['bucket_name']\\n try:\\n table_get_response = glue_get_table(database=new_glue_db,table=original_glue_table, region=region)\\n new_s3_loc = table_get_response[\\\"Table\\\"][\\\"StorageDescriptor\\\"][\\\"Location\\\"]\\n except:\\n table_get_response = glue_get_table(\\n database=original_glue_db,\\n table=original_glue_table,\\n region=region,\\n )\\n index_response = glue_get_partition_indexes(\\n database=original_glue_db,\\n table=original_glue_table,\\n region=region\\n )\\n orig_s3_loc = table_get_response[\\\"Table\\\"][\\\"StorageDescriptor\\\"][\\n \\\"Location\\\"\\n ].split(\\\"/\\\")\\n new_s3_loc = (\\n f\\\"{dest_bucket}/spectrumfiles/{'/'.join(orig_s3_loc[2:])}\\\"\\n )\\n table_input = (\\n {\\n \\\"Name\\\": table_get_response[\\\"Table\\\"][\\\"Name\\\"],\\n \\\"Description\\\": \\\"For use with Redshfit candidate release testing\\\",\\n \\\"StorageDescriptor\\\": {\\n \\\"Columns\\\": table_get_response[\\\"Table\\\"][\\\"StorageDescriptor\\\"][\\n \\\"Columns\\\"\\n ],\\n \\\"Location\\\": new_s3_loc,\\n },\\n \\\"PartitionKeys\\\": table_get_response[\\\"Table\\\"][\\\"PartitionKeys\\\"],\\n },\\n )\\n if index_response[\\\"PartitionIndexDescriptorList\\\"]:\\n glue_create_table(\\n new_database=new_glue_db,\\n table_input=table_input.update(\\n {\\n 'PartitionIndexes\\\"': index_response[\\n \\\"PartitionIndexDescriptorList\\\"\\n ]\\n }\\n ),\\n region=region)\\n else:\\n glue_create_table(\\n new_database=new_glue_db,\\n table_input=table_input[0],\\n region=region)\\n return new_s3_loc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7b1f9816c0fd4e3f0e6f2d29f36974e\",\n \"score\": \"0.51025957\",\n \"text\": \"def _table_setup(cls):\\r\\n db = self.db_table.split(':') # Split the database name and table name\\r\\n try:\\r\\n with cls._get_conn() as conn:\\r\\n r.db(db[0]).table_create(db[1]).run(conn)\\r\\n #r.db(db[0]).table(db[1]).index_create('updated_at').run(conn)\\r\\n print(\\\"ReOBJ:table_setup: {} Table initialized in {} Database.\\\".format(db[1], db[0]))\\r\\n except RqlRuntimeError:\\r\\n print(\\\"ReOBJ:table_setup: {} Table exists in {} Database.\\\".format(db[1], db[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2efa1de768a8230f06e1ff86bc3028e\",\n \"score\": \"0.50998634\",\n \"text\": \"def constrains_source_location_on_internal_material(self):\\n if self.pr_material_req == 'create_internal_transfer' and self.location_id.id == self.source_loc_id.id:\\n raise UserError(_('You can not select Same Location for Source and destination.!!!'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c24623a4fab0bdb5aab5d19bee7762b\",\n \"score\": \"0.5084189\",\n \"text\": \"def _reorg_tables(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ebf705c8f97bb611c97d5243dd1fef0\",\n \"score\": \"0.5082431\",\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\": \"3d9b5e86ae8adc905c8149b5e1553f3a\",\n \"score\": \"0.5079562\",\n \"text\": \"def _get_comparison_pairs(db_pair, test_ingest_info):\\n\\n fuzzy_match_percentage = 15\\n\\n # Strip the random suffix from the test_ingest_info table and use it\\n # for other tables.\\n random_suffix = re.match(r'.+(_\\\\d+)', test_ingest_info).groups(1)\\n\\n # Match the datasets from Database 2 to those in Database 1\\n sql = (\\n \\\"CREATE TEMPORARY TABLE datasets_join_info AS SELECT DISTINCT\\\\n\\\" +\\n \\\"a.acquisition_id AS acquisition_id1, ti.acquisition_id AS\\\\n\\\" +\\n \\\"acquisition_id2, level_id FROM acquisition a\\\\n\\\" +\\n \\\"INNER JOIN \\\" + test_ingest_info + \\\" ti ON \\\" +\\n \\\" a.satellite_id=ti.satellite_id AND \\\" +\\n \\\" a.sensor_id=ti.sensor_id AND \\\" +\\n \\\" a.start_datetime BETWEEN \\\" +\\n \\\" ti.start_datetime - \\\" + str(fuzzy_match_percentage/100.) +\\n \\\" *ti.aq_len AND\\\\n\\\" +\\n \\\" ti.start_datetime + \\\" + str(fuzzy_match_percentage/100.) +\\n \\\" *ti.aq_len AND\\\\n\\\" +\\n \\\" a.end_datetime BETWEEN \\\" +\\n \\\" ti.end_datetime - \\\" + str(fuzzy_match_percentage/100.) +\\n \\\" *ti.aq_len AND\\\\n\\\" +\\n \\\" ti.end_datetime + \\\" + str(fuzzy_match_percentage/100.) +\\n \\\" *ti.aq_len;\\\"\\n )\\n\\n # Find all tiles from Database 1 which appear in the datasets\\n sqltemp = (\\n \\\"CREATE TEMPORARY TABLE tiles1 AS SELECT\\\\n\\\" +\\n \\\"acquisition_id1, acquisition_id2, dji.level_id,\\\\n\\\" +\\n \\\"tile_class_id AS tile_class_id1, tile_pathname AS path1,\\\\n\\\" +\\n \\\"x_index, y_index FROM datasets_join_info dji\\\\n\\\" +\\n \\\"INNER JOIN acquisition a ON a.acquisition_id=dji.acquisition_id1\\\\n\\\" +\\n \\\"INNER JOIN dataset d on d.acquisition_id=a.acquisition_id AND\\\\n\\\" +\\n \\\" d.level_id=dji.level_id\\\\n\\\" +\\n \\\"INNER JOIN tile t ON t.dataset_id=d.dataset_id\\\\n\\\" +\\n \\\"WHERE t.tile_class_id<>2;\\\"\\n )\\n\\n sql = sql + sqltemp\\n\\n # Find all tiles from test ingestion\\n sqltemp = (\\n \\\"CREATE TEMPORARY TABLE tiles2 AS SELECT\\\\n\\\" +\\n \\\"acquisition_id1, acquisition_id2, dji.level_id,\\\\n\\\" +\\n \\\"tile_class_id AS tile_class_id2, tile_pathname AS path2,\\\\n\\\" +\\n \\\"x_index, y_index FROM datasets_join_info dji\\\\n\\\" +\\n \\\"INNER JOIN \\\" + test_ingest_info + \\\" ti ON \\\\n\\\" +\\n \\\" ti.acquisition_id=dji.acquisition_id2 AND\\\\n\\\" +\\n \\\" ti.level_id=dji.level_id;\\\"\\n )\\n\\n sql = sql + sqltemp\\n\\n # For each Database 1 tile found in the test ingest datasets, find the\\n # corresponding Database 2 tile if it exists.\\n production_all_tiles = 'tiles1_all%s' %random_suffix\\n test_ingest_all_tiles = 'tiles2_all%s' %random_suffix\\n sqltemp = (\\n \\\"CREATE TABLE \\\" + production_all_tiles + \\\" AS SELECT\\\\n\\\" +\\n \\\"level_name, tile_class_id1, tile_class_id2, path1, path2\\\\n\\\" +\\n \\\"FROM tiles1 t1 LEFT OUTER JOIN tiles2 t2 ON\\\\n\\\" +\\n \\\"t1.acquisition_id1=t2.acquisition_id1 AND\\\\n\\\" +\\n \\\"t1.level_id=t2.level_id AND\\\\n\\\" +\\n \\\"t1.x_index=t2.x_index AND t1.y_index=t2.y_index\\\\n\\\" +\\n \\\"INNER JOIN processing_level p on p.level_id=t1.level_id;\\\"\\n )\\n\\n sql = sql + sqltemp\\n\\n # For each Database 2 tile found in the test ingest datasets, find the\\n # corresponding Database 1 tile if it exists.\\n sqltemp = (\\n \\\"CREATE TABLE \\\" + test_ingest_all_tiles + \\\" AS SELECT\\\\n\\\" +\\n \\\"level_name, tile_class_id1, tile_class_id2, path1, path2\\\\n\\\" +\\n \\\"FROM tiles2 t2 LEFT OUTER JOIN tiles1 t1 ON\\\\n\\\" +\\n \\\"t1.acquisition_id1=t2.acquisition_id1 AND\\\\n\\\" +\\n \\\"t1.level_id=t2.level_id AND\\\\n\\\" +\\n \\\"t1.x_index=t2.x_index AND t1.y_index=t2.y_index\\\\n\\\" +\\n \\\"INNER JOIN processing_level p on p.level_id=t2.level_id; \\\"\\n )\\n\\n sql = sql+sqltemp\\n\\n # Generate list of tiles found in Database 1 and Database 2\\n sql_fetch_both = (\\\"SELECT\\\\n\\\" +\\n \\\"t1.level_name, t1.tile_class_id1, t2.tile_class_id2, \\\\n\\\" +\\n \\\"t1.path1, t2.path2 FROM\\\\n\\\" +\\n production_all_tiles + \\\" t1 INNER JOIN \\\" +\\n test_ingest_all_tiles + \\\" t2 ON\\\\n\\\" +\\n \\\"t1.path1=t2.path1 AND t1.path2=t2.path2;\\\")\\n\\n # Generate list of tiles found in Database 1 but not Database 2\\n sql_fetch_production_not_test = (\\\"SELECT\\\\n\\\" +\\n \\\"level_name, tile_class_id1, tile_class_id2, \\\\n\\\" +\\n \\\"path1, path2 FROM\\\\n\\\" +\\n production_all_tiles + \\\" WHERE path2 is NULL;\\\")\\n\\n # Generate list of tiles found in Database 2 but not Database 1\\n sql_fetch_test_not_production = (\\\"SELECT\\\\n\\\" +\\n \\\"level_name, tile_class_id1, tile_class_id2,\\\\n\\\" +\\n \\\"path1, path2 FROM\\\\n\\\" +\\n test_ingest_all_tiles + \\\" WHERE path1 is NULL;\\\")\\n\\n\\n with db_pair.db1.cursor() as cur:\\n cur.execute(sql, {})\\n cur.execute(sql_fetch_both, {})\\n production_and_test = cur.fetchall()\\n cur.execute(sql_fetch_production_not_test, {})\\n production_not_test = cur.fetchall()\\n cur.execute(sql_fetch_test_not_production, {})\\n test_not_production = cur.fetchall()\\n\\n db_pair.db1.drop_table(test_ingest_info)\\n db_pair.db1.drop_table(production_all_tiles)\\n db_pair.db1.drop_table(test_ingest_all_tiles)\\n\\n return (production_and_test, production_not_test, test_not_production)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb7d9f75d260cf1d1a05a3d7d8187cc8\",\n \"score\": \"0.5079055\",\n \"text\": \"def test_no_common_keys(self):\\n t1 = self.t1\\n t2 = self.t2\\n del t1['a']\\n del t1['b']\\n del t2['a']\\n del t2['b']\\n with pytest.raises(np_utils.TableMergeError):\\n t1.join(t2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10cc1b1e980c708a6ac4bee7a8d2d5e6\",\n \"score\": \"0.5074507\",\n \"text\": \"def test_rebuild_tables_is_idempotent(self):\\n rebuild_tables()\\n rebuild_tables()\\n conn = connect()\\n cur = conn.cursor()\\n cur.execute('SELECT * FROM users')\\n self.assertEqual([], cur.fetchall(), \\\"no rows in users\\\")\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba9a98305fed1b7f3cd11cdc3cadedb0\",\n \"score\": \"0.50727737\",\n \"text\": \"def test_check_same_table_name(self):\\n self.assertTrue(check_same_table_list(get_path_list()))\\n self.assertFalse(check_same_table_list(get_bad_path_list()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e738952073d20f04ce936329675a5571\",\n \"score\": \"0.5060211\",\n \"text\": \"async def test_minimal_update_own_source(standard_database: Tuple[CouchDB, dict], http_client: dict) -> None:\\n session, objs = standard_database\\n\\n response = await http_client['put'](f'/api/sources/{objs[\\\"sources\\\"][\\\"two\\\"][\\\"_id\\\"]}',\\n body={'type': 'sources',\\n 'id': objs['sources']['two']['_id'],\\n 'attributes': {\\n }},\\n token=auth_token(objs['users']['provider']))\\n assert response.code == 200\\n source = json.load(response.buffer)['data']\\n sources_db = await session['sources']\\n db_source = await sources_db[source['id']]\\n assert db_source['type'] == 'newspaper'\\n assert db_source['title'] == 'The Daily Joke'\\n assert db_source['subtitle'] == ''\\n assert db_source['date'] == '1872-04-13'\\n assert db_source['publisher'] == ''\\n assert db_source['location'] == ''\\n assert db_source['page_numbers'] == ''\\n assert db_source['updated'] > db_source['created']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af505f8cba2b0f57a6772f8b4ed83147\",\n \"score\": \"0.5052527\",\n \"text\": \"def test_check_data_sources_merged_not_allowed(self):\\n\\n with self.assertRaises(ValueError):\\n raw_wind_io.check_data_sources(\\n primary_source=raw_wind_io.MERGED_DATA_SOURCE,\\n allow_merged=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62a46bbd714b86cff10a465a4c4b52a2\",\n \"score\": \"0.50484306\",\n \"text\": \"def test_oracle_to_dolt(oracle_with_table, db_with_table):\\n oracle_engine, oracle_table = oracle_with_table\\n dssc, dolt_table = db_with_table\\n validate_dolt_as_target(oracle_engine,\\n oracle_table,\\n get_source_reader,\\n get_oracle_target_writer,\\n get_table_reader,\\n dssc,\\n dolt_table,\\n datetime_strict=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e352519511098a2740df1e8eccb283a\",\n \"score\": \"0.5045271\",\n \"text\": \"def check_tables(table, dbu1, dbu2):\\n out = []\\n table = table.lower()\\n assert table in ('code', 'file', 'process', 'product')\\n nameattr = '{}_name'.format(table)\\n # Get all records for this table.\\n gettername = 'getAll{}{}s'.format(table.title(),\\n 'e' if table.endswith('s') else '')\\n if table == 'file': # There is no getAllFiles\\n entries1 = [dbu1.getEntry('File', i) for i in dbu1.getAllFileIds()]\\n entries2 = [dbu2.getEntry('File', i) for i in dbu2.getAllFileIds()]\\n else:\\n entries1 = getattr(dbu1, gettername)()\\n entries2 = getattr(dbu2, gettername)()\\n # E.g. getAllCodes returns dicts, of which only one part is the code record\\n # So recover just that part\\n if entries1 and isinstance(entries1[0], collections.Mapping):\\n entries1 = [e[table] for e in entries1]\\n entries2 = [e[table] for e in entries2]\\n # Codes also have a description not a name, and files have no _\\n for name_suffix in ('_name', '_description', 'name'):\\n nameattr = '{}{}'.format(table, name_suffix)\\n if entries1 and hasattr(entries1[0], nameattr):\\n break\\n # Code description is often not unique, so make something\\n # that might be\\n if table == 'code':\\n entries1 = { '{} {} {}'.format(\\n p.code_description, p.filename, p.arguments): p for p in entries1 }\\n entries2 = { '{} {} {}'.format(\\n p.code_description, p.filename, p.arguments): p for p in entries2 }\\n else:\\n entries1 = { getattr(p, nameattr): p for p in entries1 }\\n entries2 = { getattr(p, nameattr): p for p in entries2 }\\n # Check what's in only one database\\n names1 = set(entries1.keys())\\n names2 = set(entries2.keys())\\n for n in sorted(names1.difference(names2)):\\n out.append('{} {} is in mission1 but not mission2.'.format(\\n table.title(), n))\\n for n in sorted(names2.difference(names1)):\\n out.append('{} {} is in mission2 but not mission1.'.format(\\n table.title(), n))\\n # For every column in the record, compare across databases\\n for n in sorted(names1.intersection(names2)):\\n e1, e2 = entries1[n], entries2[n]\\n for a in list(dir(e1)):\\n if a.startswith('_'):\\n # Skip the non-public\\n continue\\n if a in (nameattr, '{}_id'.format(table)):\\n # Don't compare IDs for this table\\n continue\\n if a in ('verbose_provenance', 'shasum', 'file_create_date'):\\n # Don't compare stuff that's likely to be different\\n continue\\n if a.endswith('_id') or a in ('output_product'):\\n # Reference to another table, compare *names*\\n othertbl = 'product' if a == 'output_product' \\\\\\n else a.split('_')[0]\\n id1 = getattr(e1, a) #IDs into the other table\\n id2 = getattr(e2, a)\\n # Allow for not having a reference (e.g. no output product)\\n if id1 in (None, ''):\\n n1 = ''\\n else:\\n n1 = getattr(dbu1.getEntry(othertbl.title(), id1),\\n '{}_name'.format(othertbl))\\n if id2 in (None, ''):\\n n2 = ''\\n else:\\n n2 = getattr(dbu2.getEntry(othertbl.title(), id2),\\n '{}_name'.format(othertbl))\\n if n1 != n2:\\n out.append('{} {} {} is {} in mission 1 but {} in mission 2'\\n .format(table.title(), n, a, n1, n2))\\n elif getattr(e1, a) != getattr(e2, a):\\n # Something else. Compare values\\n out.append(\\n '{} {} {} is {} in mission 1 but {} in mission2'.format(\\n table.title(), n, a, getattr(e1, a), getattr(e2, a)))\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0c2b6ee84d5865a313eacd38ada2689\",\n \"score\": \"0.50444263\",\n \"text\": \"def load_staging_tables(cur, conn): \\n for query in copy_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4561e4f623755ed808637c2f72aa27d0\",\n \"score\": \"0.5039051\",\n \"text\": \"def test_table_schema(self, table_name, columns_names, columns_types, table_fk, fk_on):\\n # connection instance\\n con = self.connection.con\\n with con:\\n cursor = con.cursor()\\n # collect column information in the query result\\n # Every column will be represented by a tuple with the following attributes:\\n # (id, name, type, not null, default_value, primary_key)\\n cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name))\\n ti_result = cursor.fetchall()\\n names = [tup[1] for tup in ti_result]\\n types = [tup[2] for tup in ti_result]\\n\\n # Check and assert the names and their types with default ones\\n self.assertEqual(names, columns_names)\\n self.assertEqual(types, columns_types)\\n\\n if fk_on:\\n # get the foreign key data using the the query below\\n # the returned tuple has the following attributes\\n # (id, seq, table, from, to, on_update, on_delete, match)\\n # so we take elements (2, 3, 4) -> (table, from, to)\\n # reference:\\n # https://stackoverflow.com/questions/44424476/output-of-the-sqlites-foreign-key-list-pragma\\n cursor.execute('PRAGMA FOREIGN_KEY_LIST({})'.format(table_name))\\n fk_result = cursor.fetchall()\\n # Check and assert that foreign keys are correctly set\\n result_filtered = [(tup[2], tup[3], tup[4]) for tup in fk_result]\\n for tup in result_filtered:\\n # Test that each tup is included in the list of all default foreign keys\\n self.assertIn(tup, table_fk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7e0f344b2fa5188b84de57e62948abc\",\n \"score\": \"0.50386643\",\n \"text\": \"def _check_sources(self):\\n\\t\\tfrom ..source import PointSource, CharacteristicFaultSource\\n\\n\\t\\tfor src in self.source_model.sources:\\n\\t\\t\\tif not isinstance(src, (PointSource, CharacteristicFaultSource)):\\n\\t\\t\\t\\tmsg = \\\"Source type (%s) of source %s not allowed!\\\"\\n\\t\\t\\t\\tmsg %= (src.__class__, src.source_id)\\n\\t\\t\\t\\traise Exception(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42ea73f7a9261597846fb1b3851a3a17\",\n \"score\": \"0.5038446\",\n \"text\": \"def copy_table(\\n src_con: \\\"SimpleSQLite\\\",\\n dst_con: \\\"SimpleSQLite\\\",\\n src_table_name: str,\\n dst_table_name: str,\\n is_overwrite: bool = True,\\n) -> bool:\\n\\n logger.debug(\\n \\\"copy table: src={src_db}.{src_tbl}, dst={dst_db}.{dst_tbl}\\\".format(\\n src_db=src_con.database_path,\\n src_tbl=src_table_name,\\n dst_db=dst_con.database_path,\\n dst_tbl=dst_table_name,\\n )\\n )\\n\\n src_con.verify_table_existence(src_table_name)\\n dst_con.validate_access_permission([\\\"w\\\", \\\"a\\\"])\\n\\n if dst_con.has_table(dst_table_name):\\n if is_overwrite:\\n dst_con.drop_table(dst_table_name)\\n else:\\n logger.error(\\n \\\"failed to copy table: the table already exists \\\"\\n \\\"(src_table={}, dst_table={})\\\".format(src_table_name, dst_table_name)\\n )\\n return False\\n\\n primary_key, index_attrs, _ = extract_table_metadata(src_con, src_table_name)\\n\\n result = src_con.select(select=\\\"*\\\", table_name=src_table_name)\\n if result is None:\\n return False\\n\\n dst_con.create_table_from_data_matrix(\\n dst_table_name,\\n src_con.fetch_attr_names(src_table_name),\\n result.fetchall(),\\n primary_key=primary_key,\\n index_attrs=index_attrs,\\n )\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cb864f9f6230a9b24aedc93104bc6bf\",\n \"score\": \"0.5032729\",\n \"text\": \"def copy_db(from_, to, list_tables):\\n query_detach = \\\"\\\"\\\"DETACH sourcefile;\\\"\\\"\\\"\\n query_attach = \\\"\\\"\\\"ATTACH '{}' AS sourcefile;\\\"\\\"\\\".format(from_)\\n list_tables_out = get_list_tables(to)\\n\\n print(\\\"from_: {}\\\".format(from_), file=sys.stderr)\\n print(\\\"to: {}\\\".format(to), file=sys.stderr)\\n print(\\\"list_tables in {}: {}\\\".format(to, list_tables_out), file=sys.stderr)\\n print(\\\"query_attach: {}\\\".format(query_attach), file=sys.stderr)\\n conn = sqlite3.connect(to)\\n c = conn.cursor()\\n c.execute(query_attach)\\n\\n for table in list_tables:\\n query_insert = \\\"\\\"\\\"INSERT INTO {} SELECT * FROM sourcefile.{};\\\"\\\"\\\".format(table, table)\\n if table in list_tables_out:\\n print(\\\"table '{}' in {}. it wont be created\\\".format(table, to),\\n file=sys.stderr)\\n else:\\n print(\\\"table '{}' not in {}.\\\".format(table, to),\\n file=sys.stderr)\\n query_create_to = get_query_create(table, from_)\\n print(query_create_to, file=sys.stderr)\\n c.execute(query_create_to)\\n print(\\\"TABLE '{}' created in table '{}'\\\".format(table, to),\\n file=sys.stderr)\\n print(\\\"query_insert: {}\\\".format(query_insert), file=sys.stderr)\\n c.execute(query_insert)\\n conn.commit()\\n print(\\\"query_detach: {}\\\".format(query_detach), file=sys.stderr)\\n c.execute(query_detach)\\n\\n conn.close()\\n print(\\\"Copy ended!\\\", file=sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b1b1763127f0d78abe0a211c1f856a9\",\n \"score\": \"0.502104\",\n \"text\": \"def migrate_data(schema,source_config,target_config,migration_config):\\n # create database connections\\n source_engine = connect_to_source(source_config)\\n target_engine = connect_to_target(target_config,target_config['database'])\\n \\n # load the schema metadata profile\\n source_metadata = sqlalchemy.MetaData(source_engine)\\n source_metadata.reflect(schema=schema)\\n\\n # iterate the tables, loading the data\\n for t in source_metadata.sorted_tables:\\n migrate.copy_data(source_engine,schema,target_engine,t,migration_config['chunksize'],\\n migration_config['logged'],trialrun=migration_config['trialrun'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c97a38cf342ac7f21b71a707131bb8\",\n \"score\": \"0.50171417\",\n \"text\": \"def run():\\n\\n # scan for available tables\\n util_log.info('Scanning tables')\\n tables = [f.replace('.csv', '') for f in os.listdir(config.TABLES_PATH) if os.path.isfile(os.path.join(config.TABLES_PATH, f)) and f.endswith('.csv')]\\n db.addTables(tables)\\n util_log.info(' ... done')\\n\\n # identify target files\\n # target filenames are somewhat changing, so we just look for specific acronyms\\n targets = [f for f in os.listdir(config.TARGETS_PATH) if os.path.isfile(os.path.join(config.TARGETS_PATH, f)) and f.endswith('.csv')]\\n CEA_TARGET = [f for f in targets if 'CEA' in f]\\n CEA_TARGET = CEA_TARGET[0] if len(CEA_TARGET) > 0 else None\\n CTA_TARGET = [f for f in targets if 'CTA' in f]\\n CTA_TARGET = CTA_TARGET[0] if len(CTA_TARGET) > 0 else None\\n CPA_TARGET = [f for f in targets if 'CPA' in f]\\n CPA_TARGET = CPA_TARGET[0] if len(CPA_TARGET) > 0 else None\\n\\n # get a mapping from a table to its ID\\n tableToId = db.getTableToId()\\n\\n # copy CTA targets to db\\n util_log.info('Importing CTA')\\n if CTA_TARGET:\\n path = os.path.join(config.TARGETS_PATH, CTA_TARGET)\\n parseFile(tableToId, path, 'addCTA')\\n util_log.info(' ... done')\\n else:\\n util_log.info(' ... skipped')\\n\\n # copy CEA targets to db\\n util_log.info('Importing CEA')\\n if CEA_TARGET:\\n path = os.path.join(config.TARGETS_PATH, CEA_TARGET)\\n parseFile(tableToId, path, 'addCEA')\\n util_log.info(' ... done')\\n else:\\n util_log.info(' ... skipped')\\n\\n # copy CPA targets to db\\n util_log.info('Importing CPA')\\n if CPA_TARGET:\\n path = os.path.join(config.TARGETS_PATH, CPA_TARGET)\\n parseFile(tableToId, path, 'addCPA')\\n util_log.info(' ... done')\\n else:\\n util_log.info(' ... skipped')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17477abab339e9c0e39b02923d89b3e4\",\n \"score\": \"0.5012929\",\n \"text\": \"def test_check_data_sources_madis(self):\\n\\n raw_wind_io.check_data_sources(\\n primary_source=raw_wind_io.MADIS_DATA_SOURCE,\\n secondary_source=SECONDARY_DATA_SOURCE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70f18377c69c7420d845fda62c608bf6\",\n \"score\": \"0.50094277\",\n \"text\": \"def _checktables(self):\\n self._cur.execute(\\\"PRAGMA TABLE_INFO(NODES)\\\")\\n if (self._cur.fetchone() is None):\\n # table doesn't exist, create it\\n # SQLite does have constraints implemented at the moment\\n # so datatype will just be a string\\n self._cur.execute(\\\"CREATE TABLE NODES \\\"\\n \\\"(ID INTEGER PRIMARY KEY AUTOINCREMENT,\\\"\\n \\\"DATA BLOB NOT NULL)\\\")\\n self._cur.execute(\\\"CREATE TABLE TAGS \\\"\\n \\\"(ID INTEGER PRIMARY KEY AUTOINCREMENT,\\\"\\n \\\"DATA BLOB NOT NULL UNIQUE)\\\")\\n self._cur.execute(\\\"CREATE TABLE LOOKUP \\\"\\n \\\"(NODE INTEGER NOT NULL, TAG INTEGER NOT NULL,\\\"\\n \\\" PRIMARY KEY(NODE, TAG))\\\")\\n\\n self._cur.execute(\\\"CREATE TABLE KEY \\\"\\n + \\\"(THEKEY TEXT NOT NULL DEFAULT '')\\\")\\n self._cur.execute(\\\"INSERT INTO KEY VALUES('')\\\")\\n try:\\n self._con.commit()\\n except DatabaseException as e:\\n self._con.rollback()\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ec7def10f184f5e5adbd2cb1f5ae39f\",\n \"score\": \"0.50025654\",\n \"text\": \"def test_mysql_to_dolt(mysql_with_table, db_with_table):\\n mysql_engine, mysql_table = mysql_with_table\\n dssc, dolt_table = db_with_table\\n validate_dolt_as_target(mysql_engine,\\n mysql_table,\\n get_source_reader,\\n get_mysql_target_writer,\\n get_table_reader,\\n dssc,\\n dolt_table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65e0f82f0eb591ae12fd7dff7693b94e\",\n \"score\": \"0.49964797\",\n \"text\": \"def setUp(self):\\n self.db = Database(\\\"testsources.db\\\")\\n self.db.create_tables()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fae60c1bc828c81ae00987c6c23cc44e\",\n \"score\": \"0.49960947\",\n \"text\": \"async def setup(self):\\n try:\\n engine = await self.db\\n created = False\\n if not await engine.has_table(self.table_name):\\n # create table\\n logger.info(\\\"Creating SQL table [{}]\\\".format(self.table_name))\\n items = self._get_table()\\n await engine.execute(CreateTable(items))\\n # create indeces\\n conn = await engine.connect()\\n await conn.execute(\\n \\\"CREATE INDEX `lb_last_updated` ON `{}` (`source_id` DESC,`updated` DESC);\\\".format(self.table_name))\\n await conn.execute(\\n \\\"CREATE INDEX `lb_post` ON `{}` (`target_id` DESC,`post_id` DESC);\\\".format(self.table_name))\\n await conn.close()\\n created = True\\n # create control table if not already created.\\n if self.control_table_name and not await engine.has_table(self.control_table_name):\\n # create table\\n logger.info(\\\"Creating SQL control table [{}]\\\".format(self.control_table_name))\\n items = self._get_control_table()\\n await engine.execute(CreateTable(items))\\n created = True\\n return created\\n except Exception as exc:\\n logger.error(\\\"[DB] Error when setting up SQL table: {}\\\".format(exc))\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f71a0808e33bbbc6f447f1633f4306da\",\n \"score\": \"0.49860498\",\n \"text\": \"def load_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n # print out to see which query is being executed\\n print(query)\\n cur.execute(query)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da7981c4db14305036515017fdb11022\",\n \"score\": \"0.4976375\",\n \"text\": \"def test_check_data_sources_non_madis(self):\\n\\n raw_wind_io.check_data_sources(primary_source=NON_MADIS_PRIMARY_SOURCE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bd939dc4d9f365de91904c29ceec181\",\n \"score\": \"0.49721465\",\n \"text\": \"async def test_admin_update_other_source(standard_database: Tuple[CouchDB, dict], http_client: dict) -> None:\\n session, objs = standard_database\\n\\n image_data = f'data:image/png;base64,{b64encode(resources.open_binary(sources, \\\"one.png\\\").read()).decode(\\\"utf-8\\\")}' # noqa: E501\\n response = await http_client['put'](f'/api/sources/{objs[\\\"sources\\\"][\\\"two\\\"][\\\"_id\\\"]}',\\n body={'type': 'sources',\\n 'id': objs['sources']['two']['_id'],\\n 'attributes': {\\n 'type': 'book',\\n 'title': 'All change',\\n 'subtitle': 'New subtitle',\\n 'date': '2021-04-01',\\n 'publisher': 'No such press',\\n 'location': 'Nowhere',\\n 'page_numbers': '10',\\n 'data': image_data,\\n }},\\n token=auth_token(objs['users']['admin']))\\n assert response.code == 200\\n source = json.load(response.buffer)['data']\\n sources_db = await session['sources']\\n db_source = await sources_db[source['id']]\\n assert db_source['type'] == 'book'\\n assert db_source['title'] == 'All change'\\n assert db_source['subtitle'] == 'New subtitle'\\n assert db_source['date'] == '2021-04-01'\\n assert db_source['publisher'] == 'No such press'\\n assert db_source['location'] == 'Nowhere'\\n assert db_source['page_numbers'] == '10'\\n assert db_source['updated'] > db_source['created']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dec7e7e965cf970ee777e0efd84aaf7\",\n \"score\": \"0.49721265\",\n \"text\": \"def _make_table_from_rule(\\n schema: Namespace,\\n table_type: str,\\n table_name: ty.Union[str, ty.List[str]],\\n src_path: ty.Optional[str] = None,\\n tablefmt: str = \\\"github\\\",\\n):\\n if isinstance(table_name, str):\\n table_name = [table_name]\\n\\n elements: dict[str, str | dict[str, str]] = {}\\n for table in table_name:\\n if table_type == \\\"metadata\\\":\\n table_schema = schema.rules.sidecars[table]\\n new_elements = table_schema.fields\\n elif table_type == \\\"columns\\\":\\n table_schema = schema.rules.tabular_data[table]\\n new_elements = table_schema.columns\\n # Since only one table may be provided for columns, we can just use this directly.\\n additional_columns = table_schema.get(\\\"additional_columns\\\", \\\"not_allowed\\\")\\n initial_columns = table_schema.get(\\\"initial_columns\\\", [])\\n index_columns = table_schema.get(\\\"index_columns\\\", [])\\n else:\\n raise ValueError(f\\\"Unsupported 'table_type': '{table_type}'\\\")\\n\\n overlap = set(new_elements) & set(elements)\\n if overlap:\\n raise BIDSSchemaError(\\n f\\\"Schema tables {table_name} share overlapping fields: {overlap}\\\"\\n )\\n elements.update(new_elements)\\n\\n subschema = schema.objects[table_type]\\n retained_elements = [f for f in elements if f in subschema]\\n dropped_elements = [f for f in elements if f not in subschema]\\n if dropped_elements:\\n print(\\\"Warning: Missing elements: {}\\\".format(\\\", \\\".join(dropped_elements)))\\n\\n subschema = {k: v for k, v in subschema.items() if k in retained_elements}\\n element_info = {}\\n for element, val in elements.items():\\n if isinstance(val, str):\\n level = val\\n level_addendum = \\\"\\\"\\n description_addendum = \\\"\\\"\\n else:\\n level = val[\\\"level\\\"]\\n level_addendum = val.get(\\\"level_addendum\\\", \\\"\\\")\\n description_addendum = val.get(\\\"description_addendum\\\", \\\"\\\")\\n\\n if level_addendum:\\n if level_addendum.startswith((\\\"required\\\", \\\"recommended\\\", \\\"optional\\\")):\\n level = f\\\"{level}, but {level_addendum}\\\"\\n else:\\n # Typically begins with \\\"if\\\"\\n level = f\\\"{level} {level_addendum}\\\"\\n\\n if table_type == \\\"columns\\\" and element in index_columns:\\n if len(index_columns) == 1:\\n msg = f\\\"Values in `{schema.objects.columns[element].name}`\\\"\\n else:\\n cols = [f\\\"`{schema.objects.columns[col].name}`\\\" for col in index_columns]\\n msg = f\\\"The combination of {', '.join(cols[:-1])} and {cols[-1]}\\\"\\n description_addendum += f\\\"\\\\n\\\\n\\\\n\\\\n{msg} MUST be unique.\\\"\\n\\n if table_type == \\\"columns\\\" and initial_columns:\\n if element in initial_columns:\\n order = initial_columns.index(element) + 1\\n order_str = utils.num2words(order, to=\\\"ordinal\\\")\\n description_addendum += (\\n f\\\"\\\\n\\\\n\\\\n\\\\nThis column must appear **{order_str}** in the file.\\\"\\n )\\n else:\\n description_addendum += \\\"\\\\n\\\\n\\\\n\\\\nThis column may appear anywhere in the file.\\\"\\n\\n element_info[element] = {\\\"table_info\\\": (level, description_addendum)}\\n\\n if table_type == \\\"columns\\\":\\n # Add info about additional columns to table.\\n # its_complicated won't add any info about additional columns.\\n if additional_columns == \\\"not_allowed\\\":\\n element_info[\\\"Additional Columns\\\"] = {\\n \\\"table_info\\\": (\\\"NOT ALLOWED\\\", \\\"Additional columns are not allowed.\\\")\\n }\\n elif additional_columns == \\\"allowed_if_defined\\\":\\n element_info[\\\"Additional Columns\\\"] = {\\n \\\"table_info\\\": (\\n \\\"OPTIONAL\\\",\\n (\\n \\\"Additional columns are allowed if they are defined in the associated \\\"\\n \\\"metadata file.\\\"\\n ),\\n )\\n }\\n elif additional_columns == \\\"allowed\\\":\\n element_info[\\\"Additional Columns\\\"] = {\\n \\\"table_info\\\": (\\\"OPTIONAL\\\", \\\"Additional columns are allowed.\\\")\\n }\\n\\n subschema.update(\\n {\\n \\\"Additional Columns\\\": {\\n \\\"name\\\": \\\"**Additional Columns**\\\",\\n \\\"description\\\": \\\"\\\",\\n \\\"type\\\": \\\"`n/a`\\\",\\n }\\n }\\n )\\n\\n table_str = _make_object_table(\\n subschema,\\n field_info=element_info,\\n table_type=table_type,\\n src_path=src_path,\\n tablefmt=tablefmt,\\n )\\n return table_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a706bcfb7c972410995e81c3fc3e5584\",\n \"score\": \"0.49702713\",\n \"text\": \"def _check_srcs(self):\\r\\n allow_dup_src_type_list = ['cc_binary', 'cc_test']\\r\\n for s in self.srcs:\\r\\n if '..' in s or s.startswith('/'):\\r\\n raise Exception, (\\r\\n 'Invalid source file path: %s. '\\r\\n 'can only be relative path, and must in current directory or '\\r\\n 'subdirectories') % s\\r\\n\\r\\n src_key = os.path.normpath('%s/%s' % (self.path, s))\\r\\n src_value = '%s %s:%s' % (\\r\\n self.type, self.path, self.name)\\r\\n if src_key in Target.__src_target_map:\\r\\n value_existed = Target.__src_target_map[src_key]\\r\\n # May insert multiple time in test because of not unloading module\\r\\n if (value_existed != src_value and\\r\\n not (value_existed.split(': ')[0] in allow_dup_src_type_list and\\r\\n self.type in allow_dup_src_type_list)):\\r\\n # Just warn here, not raising exception\\r\\n console.warning('Source file %s belongs to both %s and %s' % (\\r\\n s, Target.__src_target_map[src_key], src_value))\\r\\n Target.__src_target_map[src_key] = src_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63f8dc619b3fb14dcf37a0cc0cfe2c77\",\n \"score\": \"0.4968429\",\n \"text\": \"def assert_equal_sql_table(self, other):\\n assert self.name == other.name\\n for my_column, other_column in izip(self.columns, other.columns):\\n assert_equal_sql_column(my_column, other_column)\\n assert self.metadata == other.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61daee8e07099d5b59dd63eddff2211\",\n \"score\": \"0.49625042\",\n \"text\": \"def _check_constraints(self, df, table_name, schema='current'):\\n self._check_connection()\\n \\n foreign_ids = {}\\n \\n try:\\n # Query for all columns that have a foreign key in the current table\\n self.cur.execute(\\\"SELECT source_column, target_table, target_column FROM {schema}.foreign_keys_view WHERE source_table = '{schema}.{table_name}'::regClass\\\".format(\\n schema=schema,table_name=table_name))\\n results = self.cur.fetchall()\\n # Create a dict of column names as keys and a tuple of (schema.table, column name) for foreign keys\\n result_dict = {key: None for (key,ftab,fcol) in results}\\n for key in result_dict.keys():\\n result_dict[key] = [result[1:] for result in results if result[0]==key]\\n # Set of unique table, column tuples to query to check contstraints for\\n foreign_id_set = set([value for value in itertools.chain.from_iterable(result_dict.values())])\\n # Get the ids for all table, column tuples\\n for id_set in foreign_id_set:\\n self.cur.execute(\\\"SELECT {id} FROM {table}\\\".format(id=id_set[1], table=id_set[0]))\\n foreign_ids[id_set] = [id for (id,) in self.cur.fetchall()]\\n # Restrict the results for only ids that satisfy the foreign key restraints\\n for key in result_dict.keys():\\n df = df[df[key].isin(itertools.chain.from_iterable(foreign_ids[n] for n in result_dict[key]))]\\n \\n except psycopg2.Error as e:\\n self.conn.rollback()\\n \\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5fb851ec97376518567baee905b6168\",\n \"score\": \"0.496166\",\n \"text\": \"def create_tables(self):\\n conn = self._get_connection()\\n try:\\n conn.execute(schema.create_audio_files_table)\\n conn.execute(schema.create_audio_files_index)\\n conn.execute(schema.create_id3_tags_table)\\n conn.execute(schema.create_id3_tags_index)\\n except sqlite3.OperationalError, ex:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d16d55dd840027c2fd7f8af727190e6\",\n \"score\": \"0.49611425\",\n \"text\": \"def createBasinsDataTables(self, cursor: Any) -> bool: \\n # remove old table completely, for backward compatibility, in case structure changed\\n tableBasins = self._BASINSDATA\\n dropSQL = 'DROP TABLE IF EXISTS ' + tableBasins\\n try:\\n cursor.execute(dropSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not drop table {0} from project database {1}'.format(tableBasins, self.dbFile), self.isBatch)\\n return False\\n createSQL = 'CREATE TABLE ' + tableBasins + self._BASINSDATATABLE\\n try:\\n cursor.execute(createSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not create table {0} in project database {1}'.format(tableBasins, self.dbFile), self.isBatch)\\n return False\\n tableLus = self._LSUSDATA\\n dropSQL = 'DROP TABLE IF EXISTS ' + tableLus\\n try:\\n cursor.execute(dropSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not drop table {0} from project database {1}'.format(tableLus, self.dbFile), self.isBatch)\\n return False\\n createSQL = 'CREATE TABLE ' + tableLus + ' ' + self._LSUSDATATABLE\\n try:\\n cursor.execute(createSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not create table {0} in project database {1}'.format(tableLus, self.dbFile), self.isBatch)\\n return False\\n # table searched by basin\\n indexSQL = 'CREATE INDEX IF NOT EXISTS basin_index on {0} (basin)'.format(tableLus)\\n try:\\n cursor.execute(indexSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not create index for basin in table {0} in project database {1}'.format(tableLus, self.dbFile), self.isBatch)\\n tableWater = self._WATERDATA\\n dropSQL = 'DROP TABLE IF EXISTS ' + tableWater\\n try:\\n cursor.execute(dropSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not drop table1 {0} from project database {1}'.format(tableWater, self.dbFile), self.isBatch)\\n return False\\n createSQL = 'CREATE TABLE ' + tableWater + ' ' + self._WATERDATATABLE\\n try:\\n cursor.execute(createSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not create table {0} in project database {1}'.format(tableWater, self.dbFile), self.isBatch)\\n return False\\n # table searched by lsu\\n indexSQL = 'CREATE INDEX IF NOT EXISTS lsu_index on {0} (lsu)'.format(tableWater)\\n try:\\n cursor.execute(indexSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not create index for lsu in table {0} in project database {1}'.format(tableWater, self.dbFile), self.isBatch)\\n return False \\n tableHrus = self._HRUSDATA\\n dropSQL = 'DROP TABLE IF EXISTS ' + tableHrus\\n try:\\n cursor.execute(dropSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not drop table1 {0} from project database {1}'.format(tableHrus, self.dbFile), self.isBatch)\\n return False\\n createSQL = 'CREATE TABLE ' + tableHrus + ' ' + self._HRUSDATATABLE\\n try:\\n cursor.execute(createSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not create table {0} in project database {1}'.format(tableHrus, self.dbFile), self.isBatch)\\n return False\\n # table searched by lsu\\n indexSQL = 'CREATE INDEX IF NOT EXISTS lsu_index on {0} (lsu)'.format(tableHrus)\\n try:\\n cursor.execute(indexSQL)\\n except Exception:\\n QSWATUtils.exceptionError('Could not create index for lsu in table {0} in project database {1}'.format(tableHrus, self.dbFile), self.isBatch)\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9465f104200b93f63bf522380c1fe574\",\n \"score\": \"0.4959584\",\n \"text\": \"def populate_all_scrips_table(db_metadata):\\n nse_stocks_dict = get_nse_stocks_dict()\\n nse_isins = nse_stocks_dict.keys()\\n\\n bse_stocks_dict = get_bse_stocks_dict()\\n bse_isins = bse_stocks_dict.keys()\\n\\n common_isins = set(nse_isins) & set(bse_isins)\\n bse_only_isins = set(bse_isins) - common_isins\\n nse_only_isins = set(nse_isins) - common_isins\\n\\n table = create_or_get_all_scrips_table(metadata=db_metadata)\\n\\n count = 0\\n insert_statements = []\\n for isin in common_isins:\\n nstock = nse_stocks_dict[isin]\\n bstock = bse_stocks_dict[isin]\\n\\n nstart_datetime = dt.strptime(nstock.listing_date,\\n '%d-%b-%Y')\\n nstart_date = dt.date(nstart_datetime)\\n\\n ins = table.insert().values(security_isin=nstock.isin,\\n company_name=nstock.name,\\n nse_traded=True,\\n nse_start_date=nstart_date,\\n nse_symbol=nstock.symbol,\\n #nse_suspended default is False,\\n bse_traded=True,\\n bse_start_date=nstart_date,\\n bse_id=bstock.bseid,\\n bse_symbol=bstock.symbol,\\n bse_group=bstock.group)\\n module_logger.debug(ins.compile().params)\\n\\n insert_statements.append(ins)\\n count += 1\\n\\n common_count = count\\n module_logger.info(\\\"common securities count: %d\\\", common_count)\\n\\n for isin in bse_only_isins:\\n bstock = bse_stocks_dict[isin]\\n\\n ins = table.insert().values(security_isin=bstock.isin,\\n company_name=bstock.name,\\n bse_traded=True,\\n bse_id=bstock.bseid,\\n bse_symbol=bstock.symbol,\\n bse_group=bstock.group)\\n module_logger.debug(ins.compile().params)\\n\\n insert_statements.append(ins)\\n count += 1\\n\\n bse_only_count = count - common_count\\n module_logger.info(\\\"bse_only securities count: %d\\\", bse_only_count)\\n\\n for isin in nse_only_isins:\\n nstock = nse_stocks_dict[isin]\\n\\n nstart_datetime = dt.strptime(nstock.listing_date,\\n '%d-%b-%Y')\\n nstart_date = dt.date(nstart_datetime)\\n\\n ins = table.insert().values(security_isin=nstock.isin,\\n company_name=nstock.name,\\n nse_traded=True,\\n nse_start_date=nstart_date,\\n nse_symbol=nstock.symbol\\n #nse_suspended default is False,\\n )\\n module_logger.debug(ins.compile().params)\\n\\n insert_statements.append(ins)\\n count += 1\\n\\n nse_only_count = count - bse_only_count - common_count\\n module_logger.info(\\\"nse_only securities count: %d\\\", nse_only_count)\\n\\n return insert_statements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf87639424267de38ca492c69e69a1f1\",\n \"score\": \"0.4956618\",\n \"text\": \"def load_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf87639424267de38ca492c69e69a1f1\",\n \"score\": \"0.4956618\",\n \"text\": \"def load_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf87639424267de38ca492c69e69a1f1\",\n \"score\": \"0.4956618\",\n \"text\": \"def load_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf87639424267de38ca492c69e69a1f1\",\n \"score\": \"0.4956618\",\n \"text\": \"def load_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf87639424267de38ca492c69e69a1f1\",\n \"score\": \"0.4956618\",\n \"text\": \"def load_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf87639424267de38ca492c69e69a1f1\",\n \"score\": \"0.4956618\",\n \"text\": \"def load_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41b5776f8cfc3cc41ed87b6ef2e134e4\",\n \"score\": \"0.49493736\",\n \"text\": \"def create_table_and_insert_dataset_resulted_from_dedupe(info_db, provider_name, file_name):\\n db_connection = create_database_connection(info_db)\\n db_cursor = db_connection.cursor()\\n\\n provider_table_name = 'bi_' + provider_name\\n\\n create_stmt, copy_stmt = get_statements_for_creating_table_with_fk_and_for_copying_data_into_table_from_csv(\\n file_name, provider_table_name)\\n\\n db_cursor.execute(\\\"DROP TABLE IF EXISTS \\\" + provider_table_name)\\n db_cursor.execute(create_stmt)\\n\\n # copy the rows from the csv file into the table in the database\\n with open(file_name, 'r') as f:\\n db_cursor.copy_expert(copy_stmt, f)\\n\\n db_cursor.close()\\n db_connection.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976e72f08b629d7b90adf9c690d62f3d\",\n \"score\": \"0.4939348\",\n \"text\": \"def analyze(schema_name, table_name, param_src_sys_cd, constraint_type, constraint_name,\\n reference_table_name,\\n reference_keys,\\n reference_column_name):\\n\\n arr_reference_keys = reference_keys.split(',')\\n\\n if constraint_type == 'p':\\n # f^FK1_ADDR^CTRY_CTY^CTRY_CTY_ID,SRC_SYS_CD^CTRY_CTY_ID,SRC_SYS_CD\\n # need to separate fields and re-arrange\\n pk_list_temp = ''\\n for i_field in range(len(arr_reference_keys)):\\n pk_list_temp += \\\"nvl(cast (case when t.{0} is null then null else t.{0} end as string),'NULL')\\\".format(\\n arr_reference_keys[i_field])\\n if i_field != len(arr_reference_keys) - 1:\\n pk_list_temp += ','\\n pk_list_formatted = \\\"concat_ws('^', {0})\\\".format(pk_list_temp)\\n\\n sql_check_pk = \\\\\\n (\\\"select concat_ws('^', collect_set(A.s)) from ( select {0} as s from ( select {1} \\\"\\n \\\"from {2}.{3} where SRC_SYS_CD='{4}' group by {1} \\\"\\n \\\"having count(1) > 1 )t limit 10 )A\\\").format(pk_list_formatted, reference_keys, schema_name, table_name,\\n param_src_sys_cd)\\n res_check_pk = create_tmp_file(sql_check_pk)\\n\\n if res_check_pk.strip() == '':\\n print('No PK issue found!')\\n else:\\n print('SQL:')\\n print(\\\"\\\\033[31m select {0} from {1}.{2} where SRC_SYS_CD='{3}' \\\"\\n \\\"group by {0} having count(1) > 1 )t limit 10 )A\\\\n \\\\033[0m\\\".format(reference_keys, schema_name,\\n table_name, param_src_sys_cd))\\n print(\\\"-[ RECORD ]---+----------------\\\\n\\\"\\n \\\"Schema Name | {0}\\\\n\\\"\\n \\\"Table Name | {1}\\\\n\\\"\\n \\\"Column Names | {2}\\\\n\\\"\\n \\\"Constraint Name | {3}\\\\n\\\"\\n \\\"Constraint Type | PRIMARY\\\\n\\\"\\n \\\"----------------+----------------\\\".format(schema_name, table_name, reference_keys,\\n constraint_name))\\n if res_check_pk.find('^') == -1:\\n print(\\\"Column Values | ('{0}')\\\\n\\\".format(res_check_pk))\\n else:\\n output_data(len(reference_keys.split(',')), res_check_pk)\\n elif constraint_type == 'u':\\n uk_list_temp = ''\\n for i_field in range(len(arr_reference_keys)):\\n uk_list_temp += \\\"nvl(cast (case when t.{0} is null then null else t.{0} end as string),'NULL')\\\".format(\\n arr_reference_keys[i_field])\\n if i_field != len(arr_reference_keys) - 1:\\n uk_list_temp += ','\\n uk_list_formatted = \\\"concat_ws('^', {0})\\\".format(uk_list_temp)\\n\\n sql_check_uk = \\\\\\n (\\\"select concat_ws('^', collect_set(A.s)) from ( select {0} as s from ( select {1} \\\"\\n \\\"from {2}.{3} where SRC_SYS_CD='{4}' group by {1} \\\"\\n \\\"having count(1) > 1 )t limit 10 )A\\\").format(uk_list_formatted, reference_keys, schema_name, table_name,\\n param_src_sys_cd)\\n res_check_uk = create_tmp_file(sql_check_uk)\\n\\n if res_check_uk.strip() == '':\\n print('No UK issue found!')\\n else:\\n print('SQL:')\\n print(\\\"\\\\033[31m select {0} from {1}.{2} where SRC_SYS_CD='{3}' \\\"\\n \\\"group by {0} having count(1) > 1 )t limit 10 )A\\\\n \\\\033[0m\\\".format(reference_keys, schema_name,\\n table_name, param_src_sys_cd))\\n print(\\\"-[ RECORD ]---+----------------\\\\n\\\"\\n \\\"Schema Name | {0}\\\\n\\\"\\n \\\"Table Name | {1}\\\\n\\\"\\n \\\"Column Names | {2}\\\\n\\\"\\n \\\"Constraint Name | {3}\\\\n\\\"\\n \\\"Constraint Type | UNIQUE\\\\n\\\"\\n \\\"----------------+----------------\\\".format(schema_name, table_name, reference_keys,\\n constraint_name))\\n if res_check_uk.find('^') == -1:\\n print(\\\"Column Values | ('{0}')\\\\n\\\".format(res_check_uk))\\n else:\\n output_data(len(reference_keys.split(',')), res_check_uk)\\n elif constraint_type == 'n':\\n nk_list_temp = ''\\n null_condition = ''\\n for i_field in range(len(arr_reference_keys)):\\n null_condition = '{0}.{1} is null'.format(table_name, arr_reference_keys[i_field])\\n if arr_reference_keys[i_field] == 'RADAR_UPD_TS':\\n nk_list_temp += \\\"case when RADAR_UPD_TS is null then 'NULL' else from_unixtime(unix_timestamp(),'yyyy-MM-dd HH:mm:ss') end\\\"\\n else:\\n nk_list_temp += \\\"nvl(cast (case when {0} is null then null else {0} end as string),'NULL')\\\".format(\\n arr_reference_keys[i_field])\\n if i_field != len(arr_reference_keys) - 1:\\n nk_list_temp += ','\\n null_condition += ' or '\\n nk_list_formatted = \\\"concat_ws('^', {0})\\\".format(nk_list_temp)\\n\\n sql_check_nk = \\\\\\n (\\\"select concat_ws('^', collect_set(A.s)) from ( select {0} as s \\\"\\n \\\"from {1}.{2} where SRC_SYS_CD='{3}' and ( {4} ) limit 10 )A\\\").format(nk_list_formatted, schema_name,\\n table_name,\\n param_src_sys_cd, null_condition)\\n res_check_nk = create_tmp_file(sql_check_nk)\\n\\n if res_check_nk.strip() == '':\\n print('No Null issue found!')\\n else:\\n print('SQL:')\\n print(\\\"\\\\033[31m select {0} from {1}.{2} where SRC_SYS_CD='{3}' and {4}\\\\n \\\\033[0m\\\".format(nk_list_formatted,\\n schema_name,\\n table_name,\\n param_src_sys_cd,\\n null_condition))\\n print(\\\"-[ RECORD ]---+----------------\\\\n\\\"\\n \\\"Schema Name | {0}\\\\n\\\"\\n \\\"Table Name | {1}\\\\n\\\"\\n \\\"Column Names | {2}\\\\n\\\"\\n \\\"Constraint Name | {3}\\\\n\\\"\\n \\\"Constraint Type | NULL\\\\n\\\"\\n \\\"----------------+----------------\\\".format(schema_name, table_name, reference_keys,\\n constraint_name))\\n if res_check_nk.find('^') == -1:\\n print(\\\"Column Values | ('{0}')\\\\n\\\".format(res_check_nk))\\n else:\\n output_data(len(reference_keys.split(',')), res_check_nk)\\n elif constraint_type == 'f':\\n if table_name.upper() == reference_table_name.upper():\\n print(\\\"Expression returned a reference to target table itself, skipping...\\\")\\n else:\\n fk_list_temp = ''\\n join_condition = ''\\n null_condition = ''\\n source_column_name = ''\\n rfnc_column_name = ''\\n for i_field in range(len(arr_reference_keys)):\\n join_condition += \\\"{0}.{1}={2}.{3}\\\".format(table_name, arr_reference_keys[i_field],\\n reference_table_name,\\n reference_column_name.split(',')[i_field])\\n null_condition += '{0}.{1} is not null'.format(table_name, arr_reference_keys[i_field])\\n source_column_name += \\\"{0}.{1}\\\".format(table_name, arr_reference_keys[i_field])\\n rfnc_column_name += \\\"{0}.{1}\\\".format(reference_table_name, reference_column_name.split(',')[i_field])\\n fk_list_temp += \\\"nvl(cast (case when t.{0} is null then null else t.{0} end as string),'NULL')\\\".format(\\n arr_reference_keys[i_field])\\n if i_field != len(arr_reference_keys) - 1:\\n fk_list_temp += ','\\n join_condition += ' and '\\n null_condition += ' and '\\n source_column_name += ','\\n rfnc_column_name += ','\\n fk_list_formatted = \\\"concat_ws('^', {0})\\\".format(fk_list_temp)\\n\\n sql_check_fk = \\\\\\n (\\\"select concat_ws('^', collect_set(A.s)) from ( select {0} as s from ( \\\"\\n \\\"select distinct {1} from {2}.{3} where not exists ( select distinct {4} from {2}.{5} \\\"\\n \\\"where {5}.SRC_SYS_CD='{6}' and {7} ) \\\"\\n \\\" and {8} and {3}.SRC_SYS_CD='{6}' )t limit 10 )A\\\").format(fk_list_formatted, source_column_name,\\n schema_name, table_name, rfnc_column_name,\\n reference_table_name,\\n param_src_sys_cd, join_condition,\\n null_condition)\\n res_check_fk = create_tmp_file(sql_check_fk)\\n\\n if res_check_fk.strip() == '':\\n print('No FK issue found for reference table {0}! FK: {1}.'.format(reference_table_name, reference_keys))\\n else:\\n print('SQL:')\\n print(\\\"\\\\033[31m select distinct {0} from {1}.{2} where not exists ( select distinct {3} from {1}.{4} \\\"\\n \\\"where {4}.SRC_SYS_CD='{5}' and {6} ) \\\"\\n \\\" and {7} and {2}.SRC_SYS_CD='{5}' limit 10\\\\n \\\\033[0m\\\").format(source_column_name,\\n schema_name, table_name,\\n rfnc_column_name,\\n reference_table_name,\\n param_src_sys_cd,\\n join_condition,\\n null_condition)\\n print(\\\"-[ RECORD ]---+----------------\\\\n\\\"\\n \\\"Schema Name | {0}\\\\n\\\"\\n \\\"Table Name | {1}\\\\n\\\"\\n\\t\\t \\\"Reference Table | {2}\\\\n\\\"\\n \\\"Column Names | {3}\\\\n\\\"\\n \\\"Constraint Name | {4}\\\\n\\\"\\n \\\"Constraint Type | FOREIGN\\\\n\\\"\\n \\\"----------------+----------------\\\".format(schema_name, table_name, reference_table_name, reference_keys,\\n constraint_name))\\n if res_check_fk.find('^') == -1:\\n print(\\\"Column Values | ('{0}')\\\\n\\\".format(res_check_fk))\\n else:\\n output_data(len(reference_keys.split(',')), res_check_fk)\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eacf0b1689346e5ff49199ec93b8c8b\",\n \"score\": \"0.49354535\",\n \"text\": \"def _ensure_schema(self):\\n with psycopg2.connect(\\n host=self.config[\\\"host\\\"],\\n port=self.config[\\\"port\\\"],\\n user=self.config[\\\"user\\\"],\\n dbname=self.config[\\\"default_dbname\\\"],\\n password=self.password,\\n sslmode='require') as bootstrap_connection:\\n bootstrap_connection.autocommit = True\\n cursor = bootstrap_connection.cursor()\\n try:\\n cursor.execute(\\\"CREATE DATABASE {config[dbname]};\\\".format(config=self.config))\\n except psycopg2.errors.DuplicateDatabase:\\n logger.info(\\\"Database %s exists\\\", self.config[\\\"dbname\\\"])\\n\\n with psycopg2.connect(\\n host=self.config[\\\"host\\\"],\\n port=self.config[\\\"port\\\"],\\n user=self.config[\\\"user\\\"],\\n dbname=self.config[\\\"dbname\\\"],\\n password=self.password,\\n sslmode='require') as bootstrap_connection:\\n cursor = bootstrap_connection.cursor()\\n cursor.execute(\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {table} (\\n timestamp TIMESTAMP NOT NULL,\\n url VARCHAR NOT NULL,\\n status_code NUMERIC NOT NULL,\\n response_time NUMERIC NOT NULL,\\n match_content BOOLEAN,\\n PRIMARY KEY (timestamp, url)\\n );\\\"\\\"\\\".format(table=self.table))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"760badfe0525110f4d2c6b84007eb334\",\n \"score\": \"0.49313623\",\n \"text\": \"def sync_tables(self, table):\\n\\t\\tif table != \\\"*\\\":\\n\\t\\t\\ttable_limit = table.split(',')\\n\\t\\t\\t\\n\\t\\t\\tself.my_eng.mysql_con.tables_limit = table_limit\\n\\t\\t\\tself.my_eng.get_table_metadata()\\n\\t\\t\\tself.my_eng.lock_tables()\\n\\t\\t\\tself.pg_eng.table_limit = table_limit\\n\\t\\t\\tself.pg_eng.master_status = self.my_eng.master_status\\n\\t\\t\\tself.pg_eng.set_source_id('initialising')\\n\\t\\t\\tself.stop_replica(allow_restart=False)\\n\\t\\t\\tself.pg_eng.build_tab_ddl()\\n\\t\\t\\tself.pg_eng.build_idx_ddl(self.global_config.obfdic)\\n\\t\\t\\tself.pg_eng.drop_tables()\\n\\t\\t\\tself.pg_eng.create_tables()\\n\\t\\t\\tself.my_eng.copy_table_data(self.pg_eng, self.global_config.copy_max_memory, False, False)\\n\\t\\t\\tself.pg_eng.set_source_id('initialised')\\n\\t\\t\\tself.my_eng.unlock_tables()\\n\\t\\t\\tif self.global_config.obfdic != {}:\\n\\t\\t\\t\\tself.pg_eng.sync_obfuscation(self.global_config.obfdic)\\n\\t\\t\\tself.pg_eng.create_indices()\\n\\t\\t\\tself.pg_eng.delete_table_events()\\n\\t\\t\\tself.enable_replica()\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"You should specify at least one table to synchronise.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73ad87d2a046394e06d38ec0caa9f696\",\n \"score\": \"0.49273452\",\n \"text\": \"async def test_update_own_source(standard_database: Tuple[CouchDB, dict], http_client: dict) -> None:\\n session, objs = standard_database\\n\\n image_data = f'data:image/png;base64,{b64encode(resources.open_binary(sources, \\\"one.png\\\").read()).decode(\\\"utf-8\\\")}' # noqa: E501\\n response = await http_client['put'](f'/api/sources/{objs[\\\"sources\\\"][\\\"two\\\"][\\\"_id\\\"]}',\\n body={'type': 'sources',\\n 'id': objs['sources']['two']['_id'],\\n 'attributes': {\\n 'type': 'book',\\n 'title': 'All change',\\n 'subtitle': 'New subtitle',\\n 'date': '2021-04-01',\\n 'publisher': 'No such press',\\n 'location': 'Nowhere',\\n 'page_numbers': '10',\\n 'data': image_data,\\n }},\\n token=auth_token(objs['users']['provider']))\\n assert response.code == 200\\n source = json.load(response.buffer)['data']\\n sources_db = await session['sources']\\n db_source = await sources_db[source['id']]\\n assert db_source['type'] == 'book'\\n assert db_source['title'] == 'All change'\\n assert db_source['subtitle'] == 'New subtitle'\\n assert db_source['date'] == '2021-04-01'\\n assert db_source['publisher'] == 'No such press'\\n assert db_source['location'] == 'Nowhere'\\n assert db_source['page_numbers'] == '10'\\n assert db_source['updated'] > db_source['created']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5f17b46212581ada8ebb2f97ae4381c\",\n \"score\": \"0.4918272\",\n \"text\": \"def _create_tables(self) -> None:\\n logger.debug(\\\"Creating new gene normalizer tables.\\\")\\n tables_query = (SCRIPTS_DIR / \\\"create_tables.sql\\\").read_bytes()\\n\\n with self.conn.cursor() as cur:\\n cur.execute(tables_query)\\n self.conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddcf8f75c5e6d9fdba10b303a6f710e2\",\n \"score\": \"0.49150348\",\n \"text\": \"def create_tables(source, ticker_name):\\n columns = ['Date',\\n f'{source}{ticker_name}_Open',\\n f'{source}{ticker_name}_High',\\n f'{source}{ticker_name}_Low',\\n f'{source}{ticker_name}_Close',\\n f'{source}{ticker_name}_Volume',\\n f'{source}{ticker_name}_Ex_Dividend',\\n f'{source}{ticker_name}_Split_Ratio',\\n f'{source}{ticker_name}_Adj_Open',\\n f'{source}{ticker_name}_Adj_High',\\n f'{source}{ticker_name}_Adj_Low',\\n f'{source}{ticker_name}_Adj_Close',\\n f'{source}{ticker_name}_Adj_Volume']\\n commands = (\\n f\\\"\\\"\\\"\\n CREATE TABLE {ticker_name} (\\n {columns[0]} date PRIMARY KEY,\\n {columns[1]} float(8) ,\\n {columns[2]} float(8),\\n {columns[3]} float(8),\\n {columns[4]} float(8),\\n {columns[5]} float(8),\\n {columns[6]} float(8),\\n {columns[7]} float(8),\\n {columns[8]} float(8),\\n {columns[9]} float(8),\\n {columns[10]} float(8),\\n {columns[11]} float(8),\\n {columns[12]} float(8)\\n )\\n \\\"\\\"\\\")\\n conn = None\\n try:\\n # read the connection parameters\\n params = config()\\n # connect to the PostgreSQL server\\n conn = psycopg2.connect(**params)\\n cur = conn.cursor()\\n # create table one by one\\n cur.execute(commands)\\n # close communication with the PostgreSQL database server\\n cur.close()\\n # commit the changes\\n conn.commit()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if conn is not None:\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c7994f3204d23f252728ba191957c22\",\n \"score\": \"0.49068668\",\n \"text\": \"def test_update_data_source(self):\\n source_id = 13\\n credentials = {\\\"project_id\\\": \\\"test_project\\\"}\\n test_dataset_table_id = \\\"test_table_id\\\"\\n data_source = {\\\"dataset\\\": \\\"test_dataset\\\"}\\n\\n gcp_source = {\\n \\\"source_id\\\": source_id,\\n \\\"source_uuid\\\": FAKE.uuid4(),\\n \\\"name\\\": \\\"Provider GCP\\\",\\n \\\"source_type\\\": \\\"GCP\\\",\\n \\\"authentication\\\": {\\\"credentials\\\": credentials},\\n \\\"billing_source\\\": {\\\"data_source\\\": data_source},\\n \\\"auth_header\\\": \\\"fakeauthheader\\\",\\n \\\"account_id\\\": \\\"acct10001\\\",\\n \\\"offset\\\": 12,\\n }\\n provider = Sources(**gcp_source)\\n provider.save()\\n\\n provider = GCPProvider()\\n updated_data_source = copy.deepcopy(data_source)\\n updated_data_source[\\\"table_id\\\"] = test_dataset_table_id\\n provider.update_source_data_source(credentials, updated_data_source)\\n\\n db_obj = Sources.objects.get(source_id=source_id)\\n\\n self.assertEquals(db_obj.billing_source, {\\\"data_source\\\": updated_data_source})\\n\\n gcp2_source_id = 14\\n gcp2_dataset_table_id = \\\"test_table_id_2\\\"\\n data_source_2 = {\\\"dataset\\\": \\\"test_dataset_2\\\"}\\n\\n gcp_source2 = {\\n \\\"source_id\\\": gcp2_source_id,\\n \\\"source_uuid\\\": FAKE.uuid4(),\\n \\\"name\\\": \\\"Provider GCP 2\\\",\\n \\\"source_type\\\": \\\"GCP\\\",\\n \\\"authentication\\\": {\\\"credentials\\\": credentials},\\n \\\"billing_source\\\": {\\\"data_source\\\": data_source_2},\\n \\\"auth_header\\\": \\\"fakeauthheader\\\",\\n \\\"account_id\\\": \\\"acct10001\\\",\\n \\\"offset\\\": 12,\\n }\\n provider_2 = Sources(**gcp_source2)\\n provider_2.save()\\n\\n updated_data_source_2 = copy.deepcopy(data_source_2)\\n updated_data_source_2[\\\"table_id\\\"] = gcp2_dataset_table_id\\n\\n GCPProvider().update_source_data_source(credentials, updated_data_source_2)\\n\\n db_obj_2 = Sources.objects.get(source_id=gcp2_source_id)\\n self.assertEquals(\\n Sources.objects.get(source_id=source_id).billing_source, {\\\"data_source\\\": updated_data_source}\\n )\\n self.assertEquals(db_obj_2.billing_source, {\\\"data_source\\\": updated_data_source_2})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61fb08e00db85377c442f447e8302755\",\n \"score\": \"0.49032906\",\n \"text\": \"def test_ensure_valid_calibration_table_passes_with_valid_table(self):\\n self._assert_calibration_table_passes(self.VALID_CALIBRATION_TABLE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"718c5f7e1ce72d422292b4c3a791df30\",\n \"score\": \"0.4902795\",\n \"text\": \"def create_table_data(self, data, table_name):\\n update_error = False\\n #print 'create_table_data', data\\n hostname = ''\\n object_name = ''\\n if type(data) != type(list):\\n data = [data]\\n for record in data:\\n fields = []\\n values = []\\n object_name = ''\\n for r in record:\\n # print 'create', r, record[r]\\n if r == 'name':\\n object_name = record[r]\\n elif r in ['hostname', 'type'] and not object_name:\\n object_name = record[r]\\n if r != 'pkid':\\n if record[r] != 'NULL':\\n fields.append(r)\\n values.append(str(record[r]))\\n if self.is_testbed_table(table_name):\\n fields.append('fk_testbeds')\\n values.append(str(self.get_active_testbed()))\\n sql = \\\"insert into {table_name} ({fields}) values ('{values}') \\\".format(table_name=table_name,\\n fields=','.join(fields),\\n values=\\\"','\\\".join(values))\\n #print sql\\n self.db.db_command(sql=sql)\\n #print sql_conn.sql_execute_result\\n if self.db.error:\\n update_error = True\\n if not update_error:\\n unique_col = self.get_unique_column(table_name)\\n sql = 'select pkid from {table_name} where {unique_col} = \\\"{object_name}\\\"'.format(table_name=table_name,\\n object_name=object_name,\\n unique_col=unique_col)\\n print(sql)\\n \\n result = self.db.db_command(sql=sql).one()\\n print(result)\\n if result:\\n return result\\n else:\\n return {\\n 'error': 'create_table_data is unable to retrieve inserted data collections with SQL query: {}'.format(\\n sql)}\\n else:\\n return {'error': 'create_table_data is unable to update data collections with SQL query: {}'.format(sql)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1dd8a6a7754ba38d2915326f14da7f3\",\n \"score\": \"0.48987654\",\n \"text\": \"def test_issue_79(self):\\r\\n conn = self.connections[0]\\r\\n c = conn.cursor(pymysql.cursors.DictCursor)\\r\\n\\r\\n c.execute(\\\"drop table if exists a\\\")\\r\\n c.execute(\\\"drop table if exists b\\\")\\r\\n c.execute(\\\"\\\"\\\"CREATE TABLE a (id int, value int)\\\"\\\"\\\")\\r\\n c.execute(\\\"\\\"\\\"CREATE TABLE b (id int, value int)\\\"\\\"\\\")\\r\\n\\r\\n a=(1,11)\\r\\n b=(1,22)\\r\\n try:\\r\\n c.execute(\\\"insert into a values (%s, %s)\\\", a)\\r\\n c.execute(\\\"insert into b values (%s, %s)\\\", b)\\r\\n\\r\\n c.execute(\\\"SELECT * FROM a inner join b on a.id = b.id\\\")\\r\\n r = c.fetchall()[0]\\r\\n self.assertEqual(r['id'], 1)\\r\\n self.assertEqual(r['value'], 11)\\r\\n self.assertEqual(r['b.value'], 22)\\r\\n finally:\\r\\n c.execute(\\\"drop table a\\\")\\r\\n c.execute(\\\"drop table b\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd3fd0f72b4c0fb1c29ef8e506e7e026\",\n \"score\": \"0.48976326\",\n \"text\": \"def on_create_table_error(self, exc):\\n # errors other than code listed below will be raised\\n # - 1050: table exists\\n if exc.orig.args[0] not in [1050]:\\n raise exc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0ea013cacb1160341a134ae2b80719c\",\n \"score\": \"0.48952124\",\n \"text\": \"def test_check_data_sources_merged_allowed(self):\\n\\n raw_wind_io.check_data_sources(\\n primary_source=raw_wind_io.MERGED_DATA_SOURCE, allow_merged=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00ca6699f20bec260239ee94ea4b7ca4\",\n \"score\": \"0.4887721\",\n \"text\": \"def test__associate_generator_tables_1():\\n gen_assoc_1_expected = pd.DataFrame({\\n 'plant_id_eia': [50307, 50307, 50307, 50307, 50307, 50307, 50307],\\n 'generator_id': ['GEN1', 'GEN2', 'GEN3', 'GEN4', 'GEN5', np.nan, np.nan],\\n 'report_date': ['2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01', ],\\n 'prime_mover_code': ['ST', 'ST', 'ST', 'ST', 'IC', 'IC', 'ST'],\\n 'capacity_mw': [7.5, 2.5, 2.5, 4.3, 1.8, np.nan, np.nan],\\n 'fuel_type_count': [2.0, 2.0, 2.0, 2.0, 2.0, np.nan, np.nan],\\n 'energy_source_code_num': ['energy_source_code_1', 'energy_source_code_1', 'energy_source_code_1', 'energy_source_code_1', 'energy_source_code_1', np.nan, np.nan],\\n 'fuel_type': ['NG', 'NG', 'NG', 'NG', 'DFO', 'RFO', 'RFO'],\\n 'net_generation_mwh_g_tbl': [14.0, 1.0, 0.0, 0.0, np.nan, np.nan, np.nan],\\n 'net_generation_mwh_gf_tbl': [15.0, 15.0, 15.0, 15.0, 0.0, np.nan, np.nan],\\n 'fuel_consumed_mmbtu': [100000.0, 100000.0, 100000.0, 100000.0, 0.0, np.nan, np.nan],\\n 'capacity_mw_fuel': [16.8, 16.8, 16.8, 16.8, 1.8, np.nan, np.nan],\\n 'net_generation_mwh_g_tbl_fuel': [15.0, 15.0, 15.0, 15.0, np.nan, np.nan, np.nan],\\n },\\n ).pipe(helpers.convert_cols_dtypes, 'eia')\\n\\n gen_assoc_1_actual = (\\n allocate_net_gen.associate_generator_tables(\\n gf=GF_1, gen=GEN_1, gens=GENS_1)\\n .pipe(helpers.convert_cols_dtypes, 'eia')\\n )\\n\\n pd.testing.assert_frame_equal(gen_assoc_1_expected, gen_assoc_1_actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83dab9ef676042ac1bfc8699136b1833\",\n \"score\": \"0.48870736\",\n \"text\": \"def step_2_create_new_copy_table():\\n print \\\"Running Step 2 - Making a copy of QueueMetrics table\\\"\\n try:\\n conn.execute(\\\"\\\"\\\"USE LifeNetDW SELECT * INTO QueueMetricsCopy\\n FROM QueueMetrics\\\"\\\"\\\")\\n print \\\"Successfully Made a Copy of QueueMetrics table\\\"\\n except IOError as my_msg:\\n print \\\"Error creating a new copy table:\\\", my_msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77ecf56fea1141b8c034debef01b04a9\",\n \"score\": \"0.4876897\",\n \"text\": \"def setUp(self):\\n self.connection = DatabaseConnection()\\n with self.connection as cursor:\\n cursor.Execute(\\\"\\\"\\\"CREATE TABLE `versionedAuthor` (\\n `ID` smallint(5) unsigned NOT NULL AUTO_INCREMENT,\\n `versionedAuthorID` smallint(5) unsigned NOT NULL,\\n `name` varchar(32) NOT NULL,\\n PRIMARY KEY (`ID`),\\n KEY `recordKey` (`versionedAuthorID`)\\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8\\\"\\\"\\\")\\n cursor.Execute(\\\"\\\"\\\"CREATE TABLE `versionedBook` (\\n `ID` smallint(5) unsigned NOT NULL AUTO_INCREMENT,\\n `versionedBookID` smallint(5) unsigned NOT NULL,\\n `versionedAuthor` smallint(5) unsigned NOT NULL,\\n `title` varchar(32) NOT NULL,\\n PRIMARY KEY (`ID`),\\n KEY `recordKey` (`versionedBookID`)\\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8\\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"712737e9a447e70d6733b7ac2ff084cb\",\n \"score\": \"0.4876497\",\n \"text\": \"def create_tables():\\n\\n db = pymongo.MongoClient(DB_URI).get_default_database()\\n tables = db.list_collection_names()\\n\\n if netcat.DBT_STATUS not in tables:\\n db[netcat.DBT_INFO].create_index([(\\\"device_name\\\", pymongo.ASCENDING), (\\\"snapshot_timestamp\\\", pymongo.DESCENDING)])\\n netcat.LOGGER.warning(f\\\"Pymongo: Created '{netcat.DBT_STATUS}' table\\\")\\n\\n if netcat.DBT_INFO not in tables:\\n db[netcat.DBT_INFO].create_index([(\\\"snapshot_timestamp\\\", pymongo.DESCENDING)])\\n db[netcat.DBT_INFO].create_index([(\\\"device_name\\\", pymongo.ASCENDING)])\\n db[netcat.DBT_INFO].create_index([(\\\"device_type\\\", pymongo.ASCENDING)])\\n db[netcat.DBT_INFO].create_index([(\\\"device_name\\\", pymongo.ASCENDING), (\\\"snapshot_timestamp\\\", pymongo.DESCENDING)])\\n db[netcat.DBT_INFO].create_index([(\\\"device_type\\\", pymongo.ASCENDING), (\\\"snapshot_timestamp\\\", pymongo.DESCENDING)])\\n netcat.LOGGER.warning(f\\\"Pymongo: Created '{netcat.DBT_INFO}' table\\\")\\n\\n if netcat.DBT_BACKUP not in tables:\\n db[netcat.DBT_BACKUP].create_index([(\\\"snapshot_timestamp\\\", pymongo.DESCENDING)])\\n db[netcat.DBT_BACKUP].create_index([(\\\"device_name\\\", pymongo.ASCENDING)])\\n db[netcat.DBT_BACKUP].create_index([(\\\"device_name\\\", pymongo.ASCENDING), (\\\"snapshot_timestamp\\\", pymongo.DESCENDING)])\\n netcat.LOGGER.warning(f\\\"Pymongo: Created '{netcat.DBT_BACKUP}' table\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2db8eeecfcbb674cdc7729a3e738d30\",\n \"score\": \"0.4858888\",\n \"text\": \"def test_rename_conflict(self):\\n t1 = self.t1\\n t2 = self.t2\\n t1['b_1'] = 1 # Add a new column b_1 that will conflict with auto-rename\\n with pytest.raises(np_utils.TableMergeError):\\n t1.join(t2, keys='a')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43bd8e400e24ba995de63ac687b3e460\",\n \"score\": \"0.48502496\",\n \"text\": \"def writeLanduseTables(self) -> bool:\\n isProjDb = filecmp.cmp(self.plantSoilDatabase, self.dbFile)\\n isRefDb = filecmp.cmp(self.plantSoilDatabase, self.dbRefFile)\\n copyPlant = True\\n copyUrban = True\\n if isProjDb:\\n conn = self.conn\\n # avoid trying to copy table to itself\\n copyPlant = self.plantTable != DBUtils._PLANTS_PLT_NAME\\n copyUrban = self.urbanTable != DBUtils._URBAN_URB_NAME\\n if not (copyPlant or copyUrban):\\n return True\\n elif isRefDb:\\n conn = self.connRef\\n else:\\n conn = sqlite3.connect(self.plantSoilDatabase)\\n # suspend row_factory setting\\n # normal setting to sqlite3.Row produces dictionaries for rows, which have random order \\n # row_factory needs to be a function taking a cursor and row as tuple and returning a row\\n # so we use an identity\\n conn.row_factory = lambda _, row: row\\n readCursor = conn.cursor()\\n writeCursor = self.conn.cursor()\\n try:\\n if copyPlant:\\n sql = 'DROP TABLE IF EXISTS {0}'.format(DBUtils._PLANTS_PLT_NAME)\\n writeCursor.execute(sql)\\n sql = 'CREATE TABLE {0} {1}'.format(DBUtils._PLANTS_PLT_NAME, DBUtils._PLANTS_PLT_TABLE)\\n writeCursor.execute(sql)\\n insertPlant = 'INSERT INTO {0} VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)'.format(DBUtils._PLANTS_PLT_NAME)\\n if copyUrban:\\n sql = 'DROP TABLE IF EXISTS {0}'.format(DBUtils._URBAN_URB_NAME)\\n writeCursor.execute(sql)\\n sql = 'CREATE TABLE {0} {1}'.format(DBUtils._URBAN_URB_NAME, DBUtils._URBAN_URB_TABLE)\\n writeCursor.execute(sql)\\n insertUrban = 'INSERT INTO {0} VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?)'.format(DBUtils._URBAN_URB_NAME)\\n plantSelect = self.sqlSelect(self.plantTable, '*', '', 'name=? COLLATE NOCASE')\\n urbanSelect = self.sqlSelect(self.urbanTable, '*', '', 'name=? COLLATE NOCASE')\\n # changed to copy all of plant and urban tables\\n # pid = 0\\n # uid = 0\\n for crop in self.landuseVals:\\n name = self.getLanduseCode(crop)\\n if name.upper() == 'BARR' or name.upper() == 'WATR':\\n # BARR will become NULL later, WATR will become WETW; neither in plant table\\n continue\\n args = (name,)\\n # look first in plantTable\\n row = readCursor.execute(plantSelect, args).fetchone()\\n if row is None:\\n row = readCursor.execute(urbanSelect, args).fetchone()\\n if row is None:\\n QSWATUtils.error('Landuse name {0} (and perhaps others) not defined in {1} or {2} tables in database {3}.'.\\n format(name, self.plantTable, self.urbanTable, self.plantSoilDatabase), self.isBatch)\\n return False\\n # changed to copy all of plant and urban tables\\n # elif copyUrban:\\n # uid += 1\\n # # copy row, replacing id\\n # writeCursor.execute(insertUrban, (uid,) + row[1:])\\n # changed to copy all of plant and urban tables\\n # elif copyPlant:\\n # pid += 1\\n # writeCursor.execute(insertPlant, (pid,) + row[1:])\\n # copy all of plant and urban tables\\n if copyPlant:\\n plantAll = self.sqlSelect(self.plantTable, '*', '', '')\\n for row in readCursor.execute(plantAll):\\n writeCursor.execute(insertPlant, tuple(row))\\n if copyUrban:\\n urbanAll = self.sqlSelect(self.urbanTable, '*', '', '')\\n for row in readCursor.execute(urbanAll):\\n writeCursor.execute(insertUrban, tuple(row))\\n return True\\n except Exception:\\n QSWATUtils.exceptionError('Could not create {3} and {4} tables from {0} and {1} tables in landuse and soil database {2}'.\\n format(self.plantTable, self.urbanTable, self.plantSoilDatabase, DBUtils._PLANTS_PLT_NAME, DBUtils._URBAN_URB_NAME), \\n self.isBatch)\\n return False\\n finally:\\n self.conn.commit()\\n self.hashDbTable(self.conn, DBUtils._PLANTS_PLT_NAME)\\n self.hashDbTable(self.conn, DBUtils._URBAN_URB_NAME)\\n if isProjDb or isRefDb:\\n conn.row_factory = sqlite3.Row\\n else:\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746d9a09a84e9ef4cb7bbc83ca024a23\",\n \"score\": \"0.48501724\",\n \"text\": \"def create_marker_table(self):\\r\\n connection = self.connect()\\r\\n cursor = connection.cursor()\\r\\n if cursor.tables(table=self.marker_table, tableType='TABLE').fetchone():\\r\\n pass\\r\\n else:\\r\\n try:\\r\\n connection.execute(\\r\\n \\\"\\\"\\\" CREATE TABLE {marker_table} (\\r\\n id BIGINT NOT NULL IDENTITY(1,1),\\r\\n update_id VARCHAR(128) NOT NULL,\\r\\n target_table VARCHAR(128),\\r\\n inserted DATETIME DEFAULT(GETDATE()),\\r\\n PRIMARY KEY (update_id)\\r\\n )\\r\\n \\\"\\\"\\\"\\r\\n .format(marker_table=self.marker_table)\\r\\n )\\r\\n except pyodbc.Error as e:\\r\\n raise\\r\\n cursor.close()\\r\\n connection.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef9c2feea3fa34ac516673eb726bb5e\",\n \"score\": \"0.484886\",\n \"text\": \"def _pools_compatible_for_migration(self, src_pd, src_sp, dst_pd, dst_sp):\\n\\n client = self._get_client()\\n src_zero_padding_enabled = client.is_volume_creation_safe(src_pd,\\n src_sp)\\n dst_zero_padding_enabled = client.is_volume_creation_safe(dst_pd,\\n dst_sp)\\n src_is_fg_pool = self._is_fine_granularity_pool(src_pd, src_sp)\\n dst_is_fg_pool = self._is_fine_granularity_pool(dst_pd, dst_sp)\\n if src_is_fg_pool:\\n return True\\n elif dst_is_fg_pool:\\n if not src_zero_padding_enabled:\\n LOG.debug(\\\"Migration from Medium Granularity storage pool \\\"\\n \\\"with zero padding disabled to Fine Granularity one \\\"\\n \\\"is not allowed.\\\")\\n return False\\n return True\\n elif not src_zero_padding_enabled == dst_zero_padding_enabled:\\n LOG.debug(\\\"Zero padding must be either enabled or disabled on \\\"\\n \\\"both storage pools.\\\")\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54354d74744722377800edb1e8e3ac67\",\n \"score\": \"0.48460242\",\n \"text\": \"def transferTables(s1, s2):\\n if not(s2.tables):\\n s2.tables = s1.tables\\n return\\n\\n for (_, t1) in s1.tables.items():\\n add = True\\n for (_, t2) in s2.tables.items():\\n if (t1.name() == t2.name()) and (t1.type() == t2.type()):\\n add = False\\n break\\n if add:\\n s2.tables[s2._tableNextId()] = t1\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4cd31cce0f8f66b2463e8c54cfc90d5\",\n \"score\": \"0.4837329\",\n \"text\": \"def test_create_table(self):\\r\\n db = mydbconnection.Connect()\\r\\n db1 = db.connection_db('dev')\\r\\n cursor = db1.cursor()\\r\\n folder_path = os.getcwd()\\r\\n file_path = os.path.join('\\\\\\\\'.join(folder_path.split('\\\\\\\\')[:-1]), 'files')\\r\\n cursor.execute(\\\"SELECT table_name FROM information_schema.tables\\\"\\r\\n \\\" WHERE table_schema='mymodel'\\\")\\r\\n table_name = cursor.fetchall()\\r\\n table_names = []\\r\\n for i in table_name:\\r\\n for j in i:\\r\\n table_names.append(j)\\r\\n num1 = len(table_names)\\r\\n for i in range(num1):\\r\\n for filename in os.listdir(file_path):\\r\\n if filename.split('.')[1] == 'sql' and filename.split('.')[0] == table_names[i]:\\r\\n self.assertEqual(filename.split('.')[0], table_names[i])\\r\\n self.assertNotEqual(filename.split('.')[1], table_names[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb9c971ecdbaca4132cefd5e44a82bd\",\n \"score\": \"0.48335573\",\n \"text\": \"def check_sqlite_tables(self, conn, source=None, table_error_callback=None, tablenames=None, progress=None):\\n if progress is None:\\n progress = dict()\\n if not self.package_filename in (submission_schema_json, constituent_schema_json, portal_schema_json):\\n raise ValueError('check_sqlite_tables() is only valid for built-in datapackages')\\n if source is None:\\n source = self\\n tables_doc = source.model_doc['schemas']['CFDE']['tables']\\n if tablenames is None:\\n tablenames = set(tables_doc.keys())\\n\\n # collect errors and defer exception so we can give more detailed feedback\\n errors_by_tname = {}\\n first_error_mesg = None\\n\\n cur = None\\n try:\\n cur = conn.cursor()\\n for table in tables_topo_sorted(self.doc_cfde_schema.tables.values()):\\n if table.name not in tablenames:\\n continue\\n\\n sql_checks = table.annotations.get(self.resource_tag, {}).get('check_sql_paths', [])\\n skip_checks = json.loads(os.getenv('CFDE_SKIP_SQL_CHECKS', '{}'))\\n resource = tables_doc[table.name][\\\"annotations\\\"].get(self.resource_tag, {})\\n\\n progress.setdefault(table.name, {})\\n\\n for path in sql_checks:\\n if skip_checks is True or skip_checks.get(path[0:-4]):\\n logger.info('Skipping custom SQL check %r for table %r due to CFDE_SKIP_SQL_CHECKS env. var.' % (path, table.name))\\n continue\\n progress[table.name].setdefault(path, None)\\n if progress[table.name][path] is not None:\\n logger.info('Skipping custom SQL check %r for table %r due to progress marker' % (path, table.name))\\n continue\\n logger.info('Running custom SQL check %r for table %r' % (path, table.name,))\\n sql = self.package_filename.get_data_str(path)\\n cur.execute(sql)\\n row = cur.fetchone()\\n if row:\\n description, count, example_nid, example_data = row\\n mesg = '%s in row %s %s%s' % (\\n description,\\n example_nid,\\n example_data,\\n (' (and %s others...)' % (count - 1)) if count > 1 else '',\\n )\\n errors_by_tname.setdefault(table.name, []).append(mesg)\\n if first_error_mesg is None:\\n first_error_mesg = 'Table %s %s' % (table.name, mesg)\\n logger.error('custom SQL check %r for table %r ERROR: %s' % (path, table.name, mesg))\\n progress[table.name][path] = False\\n else:\\n logger.info('custom SQL check %r for table %r OK' % (path, table.name))\\n progress[table.name][path] = True\\n\\n if table.foreign_keys:\\n logger.info('Checking foreign keys for table %r' % (table.name,))\\n for fkey in table.foreign_keys:\\n if fkey.pk_table.schema.name != table.schema.name:\\n # only consider single schema in this sqite db\\n progress[table.name][fkey.constraint_name] = 'skip'\\n logger.info(\\\"Skipping foreign key %r which references outside schema %r\\\" % (fkey.constraint_name, table.schema.name))\\n continue\\n progress[table.name].setdefault(fkey.constraint_name, None)\\n if progress[table.name][fkey.constraint_name] is not None:\\n logger.info(\\\"Skipping foreign key %r due to progress marker\\\" % (fkey.constraint_name,))\\n continue\\n col_map = list(fkey.column_map.items())\\n sql = \\\"\\\"\\\"\\nWITH fk AS (\\n SELECT fk.nid, %(fkcols)s\\n FROM %(fk_table)s fk\\n LEFT JOIN %(pk_table)s pk ON ( %(on)s )\\n WHERE (%(nonnulls)s) AND (%(nulls)s)\\n)\\nSELECT\\n (SELECT count(*) FROM fk) AS num_errors,\\n nid,\\n %(fkcols)s\\nFROM fk\\nORDER BY nid\\nLIMIT 1;\\n\\\"\\\"\\\" % {\\n \\\"fk_table\\\": sql_identifier(table.name),\\n \\\"fkcols\\\": \\\", \\\".join([\\n 'fk.%s' % (sql_identifier(fkc.name),)\\n for fkc, pkc in col_map\\n ]),\\n \\\"pk_table\\\": sql_identifier(fkey.pk_table.name),\\n \\\"on\\\": \\\" AND \\\".join([\\n 'fk.%s = pk.%s' % (sql_identifier(fkc.name), sql_identifier(pkc.name))\\n for fkc, pkc in col_map\\n ]),\\n \\\"nonnulls\\\": \\\" AND \\\".join([\\n \\\"fk.%s IS NOT NULL\\\" % (sql_identifier(fkc.name),)\\n for fkc, pkc in col_map\\n ]),\\n \\\"nulls\\\": \\\" OR \\\".join([\\n \\\"pk.%s IS NULL\\\" % (sql_identifier(pkc.name),)\\n for fkc, pkc in col_map\\n ]),\\n}\\n cur.execute(sql)\\n row = cur.fetchone()\\n if row:\\n mesg = 'foreign key %r in row %s%s not present in referenced table %r' % (\\n dict(zip([ fkc.name for fkc, pkc in col_map ], row[2:])), # first bad fkey\\n row[1], # nid for first bad row\\n (' (and %s others...)' % (row[0] - 1)) if row[0] > 1 else '',\\n fkey.pk_table.name,\\n )\\n errors_by_tname.setdefault(table.name, []).append(mesg)\\n if first_error_mesg is None:\\n first_error_mesg = 'Table %s %s' % (table.name, mesg)\\n logger.error('foreign key %r ERROR: %s' % (fkey.constraint_name, mesg))\\n progress[table.name][fkey.constraint_name] = False\\n else:\\n logger.info(\\\"foreign key %r OK\\\" % (fkey.constraint_name,))\\n progress[table.name][fkey.constraint_name] = True\\n\\n if table.name in errors_by_tname:\\n if table_error_callback:\\n table_error_callback(table.name, resource.get('path'), '; '.join(errors_by_tname[table.name]))\\n\\n if errors_by_tname:\\n raise InvalidDatapackage('Errors found in %s tables %r. First error: %s' % (len(errors_by_tname), list(errors_by_tname), first_error_mesg))\\n finally:\\n if cur is not None:\\n cur.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01160660ec6b361ea760e0e826839b39\",\n \"score\": \"0.48321587\",\n \"text\": \"def check_schema_initialized(self) -> bool:\\n try:\\n with self.conn.cursor() as cur:\\n cur.execute((SCRIPTS_DIR / \\\"create_tables.sql\\\").read_bytes())\\n except DuplicateTable:\\n self.conn.rollback()\\n else:\\n logger.info(\\\"Gene table existence check failed.\\\")\\n self.conn.rollback()\\n return False\\n try:\\n with self.conn.cursor() as cur:\\n cur.execute((SCRIPTS_DIR / \\\"add_fkeys.sql\\\").read_bytes())\\n except DuplicateObject:\\n self.conn.rollback()\\n else:\\n logger.info(\\\"Gene foreign key existence check failed.\\\")\\n self.conn.rollback()\\n return False\\n try:\\n with self.conn.cursor() as cur:\\n cur.execute(\\n (SCRIPTS_DIR / \\\"create_record_lookup_view.sql\\\").read_bytes()\\n )\\n except DuplicateTable:\\n self.conn.rollback()\\n else:\\n logger.info(\\\"Gene normalized view lookup failed.\\\")\\n self.conn.rollback()\\n return False\\n try:\\n with self.conn.cursor() as cur:\\n cur.execute((SCRIPTS_DIR / \\\"add_indexes.sql\\\").read_bytes())\\n except DuplicateTable:\\n self.conn.rollback()\\n else:\\n logger.info(\\\"Gene indexes check failed.\\\")\\n self.conn.rollback()\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c146da03873836ec32e040937dbe9d36\",\n \"score\": \"0.48291188\",\n \"text\": \"def load_staging_tables(cur, conn): \\n for query in copy_table_queries:\\n cur.execute(query)\\n conn.commit()\\n print(\\\"Query completed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60beafca3e213d2e1b52859075988ad9\",\n \"score\": \"0.48216653\",\n \"text\": \"def load_staging_tables(cur, conn):\\n for table_name, query in copy_table_queries.items():\\n cur.execute(query)\\n conn.commit()\\n log.info(f\\\"loaded data into staging table: {table_name}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ebf273660202046498824c971d8eae\",\n \"score\": \"0.48206288\",\n \"text\": \"def checkRouting(conn: Any) -> Tuple[List[str], List[str]]:\\n errors: List[str] = []\\n erroneous: Dict[str, Set[int]] # @UnusedVariable\\n warnings: List[str] = []\\n table = 'gis_routing'\\n # mapping category -> id-set showing sources that are known to drain to an outlet\\n # to save tracking every source all the way\\n done: Dict[str, Set[int]] = dict()\\n # mapping category -> id-set showing sources currently under investigation\\n # (so all can be marked as done if the chain terminates)\\n pending: Dict[str, Set[int]] = dict()\\n # mapping category -> id -> hyd_typ -> percent used to check sources with multiple sinks have 100% accounted for\\n percentages: Dict[str, Dict[int, Dict[str, float]]] = dict()\\n nextSql = DBUtils.sqlSelect(table, '*', '', '')\\n findSql = DBUtils.sqlSelect(table, 'hyd_typ, sinkid, sinkcat, percent', '', 'sourceid=? AND sourcecat=?')\\n # Might be tempted to use pending to detect loops, but note below we don't\\n # add to pending for multiple sinks, so then e.g. A -> A 90% would just loop.\\n # So we prefer a count.\\n maxSteps = int(conn.execute('SELECT COUNT(*) FROM ' + table).fetchone()[0])\\n for row in conn.execute(nextSql):\\n sid = int(row['sourceid'])\\n scat = row['sourcecat']\\n hydTyp = row['hyd_typ']\\n tid = int(row['sinkid'])\\n tcat = row['sinkcat']\\n percent = float(row['percent'])\\n hasZero = percent == 0\\n count = maxSteps\\n while count > 0:\\n if scat in done and sid in done[scat]:\\n # move pending to done\\n for pcat, pids in pending.items():\\n if pcat in done:\\n done[pcat].update(pids)\\n else:\\n done[pcat] = pids\\n pending = dict()\\n break # from while loop\\n if percent < 100:\\n # add percent to percentages for this source\\n sourcePercentages = percentages.get(scat, None)\\n if sourcePercentages is None:\\n percentages[scat] = {sid: {hydTyp: percent}}\\n else:\\n hydTypPercentages = sourcePercentages.get(sid, None)\\n if hydTypPercentages is None:\\n hydTypPercentages = {hydTyp: percent}\\n else:\\n hydTypPercentages[hydTyp] += percent\\n else:\\n # Only include source in pending (which should become done) \\n # if there is only one sink for it, else other sinks will be ignored.\\n # Pending is only used for efficiency, to avoid tracking every source to a final outlet,\\n # so little harm in omitting some sources from it.\\n if scat in pending:\\n pending[scat].add(sid)\\n else:\\n pending[scat] = {sid}\\n if tcat == 'X':\\n # move pending to done\\n for pcat, pids in pending.items():\\n if pcat in done:\\n done[pcat].update(pids)\\n else:\\n done[pcat] = pids\\n pending = dict()\\n break # from while loop\\n count -= 1\\n if count == 0:\\n if hasZero:\\n warnings.append('WARNING: There is a loop in the {0} table involving id {1}, category {2}, flow type {3} but with a zero percentage' \\\\\\n .format(table, sid, scat, hydTyp))\\n else:\\n errors.append('There is a loop in the {0} table involving id {1}, category {2}, flow type {3}'.format(table, sid, scat, hydTyp))\\n # move pending to done\\n for pcat, pids in pending.items():\\n if pcat in done:\\n done[pcat].update(pids)\\n else:\\n done[pcat] = pids\\n pending = dict()\\n break # from while loop\\n findRow = conn.execute(findSql, (tid, tcat)).fetchone()\\n if findRow is None:\\n # unnecessary to report as done in checkRoutingTable\\n# report = False\\n# if tcat not in erroneous:\\n# report = True\\n# erroneous[tcat] = {tid}\\n# elif tid not in erroneous[tcat]:\\n# erroneous[tcat].add(tid)\\n# report = True\\n# if report:\\n# errors.append('Cannot find id {1} category {2} as a source in the {0} table'.format(table, tid, tcat))\\n break # from while loop\\n sid = tid\\n scat = tcat\\n hydTyp = findRow['hyd_typ']\\n tid = int(findRow['sinkid'])\\n tcat = findRow['sinkcat']\\n percent = findRow['percent']\\n hasZero = hasZero or percent == 0\\n #end of while loop\\n # end of for loop\\n # check all percentages are approximately 100\\n for scat, sourceDdata in percentages.items():\\n for sid, data in sourceDdata.items():\\n for hydTyp, percent in data.items():\\n if int(percent + 0.5) != 100:\\n if percent == 0 and scat in {'RES', 'PND', 'WETL'}:\\n # an unused lake exit: ignore\\n pass\\n else:\\n errors.append('The percentages for id {1}, category {2}, flow type {3} in the {0} table sum to {4} rather than 100' \\\\\\n .format(table, sid, scat, hydTyp, int(percent + 0.5)))\\n return errors, warnings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c7300adbcaac05c49907e7955aefef8\",\n \"score\": \"0.48168144\",\n \"text\": \"def load_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n print('Running:\\\\n%s' % query)\\n cur.execute(query)\\n conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776c06663e484885839217d91a63d1a7\",\n \"score\": \"0.48118332\",\n \"text\": \"def test_rebuild_tables_is_idempotent(self):\\n rebuild_tables()\\n rebuild_tables()\\n assert_sql_count(self, \\\"SELECT * FROM candy\\\", 0)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1787,"cells":{"query_id":{"kind":"string","value":"35cdb17813597bb09f951742cda5a40e"},"query":{"kind":"string","value":"Return the type a pointer points to."},"positive_passages":{"kind":"list like","value":[{"docid":"dcfc041fc41b2a95998a5c16d6c490ab","score":"0.692451","text":"def get_ptr_type(structure, member):\n if len(member) > 1:\n _, tp = get_obj_offset(types, [structure, member[0]])\n if tp == 'array':\n return types[structure][1][member[0]][1][2][1]\n else:\n return get_ptr_type(tp, member[1:])\n else:\n return types[structure][1][member[0]][1][1]","title":""}],"string":"[\n {\n \"docid\": \"dcfc041fc41b2a95998a5c16d6c490ab\",\n \"score\": \"0.692451\",\n \"text\": \"def get_ptr_type(structure, member):\\n if len(member) > 1:\\n _, tp = get_obj_offset(types, [structure, member[0]])\\n if tp == 'array':\\n return types[structure][1][member[0]][1][2][1]\\n else:\\n return get_ptr_type(tp, member[1:])\\n else:\\n return types[structure][1][member[0]][1][1]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4230d3cdb70d0eae25061363bec4071a","score":"0.8147374","text":"def get_pointer_type(self):\n return self.get_c_type() + ' *'","title":""},{"docid":"26bfe339aabfbf2261fa45d63d1e0261","score":"0.6892176","text":"def get_type_declaration(self):\n c_type = self.field_type.get_c_type()\n\n if self.is_dynamically_allocated():\n # Need to make it a pointer type if it is dynamically allocated\n type_declaration = c_type + '*'\n else:\n type_declaration = c_type\n\n return type_declaration","title":""},{"docid":"c543ad991ef842ae3d1343ace8d4f51d","score":"0.68215513","text":"def type(self):\n return self.types[self._internal.get_type()]","title":""},{"docid":"91fb4a13ec573f844158965171078609","score":"0.68138367","text":"def get_type(self):\n return self.p_type","title":""},{"docid":"e69d75cbdd4b7d486e93817466126cd5","score":"0.66998494","text":"def get_list_pointer_type(self):\n return self.get_list_c_type() + ' *'","title":""},{"docid":"9619cfca37a8309396903195ffef4fbd","score":"0.65724957","text":"def objtype(self):\n return self._ptr.contents.objtype","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"31b87e7bd9b39ffb09ff2863e85e9510","score":"0.65688413","text":"def _get_type(self):\n return self.__type","title":""},{"docid":"5e46ef2be8e2faac079edd76ba14b9cd","score":"0.6528469","text":"def type(self):\n return self._type_class.id if self._type_class else None","title":""},{"docid":"140a9f560ea3b02886f625b6b7c6fe8b","score":"0.6520387","text":"def get_constant_pointer_type(self):\n return self.get_c_type() + ' const *'","title":""},{"docid":"c67c9b46dad730f8fcc48c11ac4a10eb","score":"0.65053684","text":"def peek_type(self):\n ret_val = self.__tokens[self.__i]\n return ret_val[0]","title":""},{"docid":"86ef0935abd4266d0e496293427b8ec4","score":"0.64847386","text":"def GetPointerType(self):\n return _lldb.SBType_GetPointerType(self)","title":""},{"docid":"58c6fdf7f16fad7ae442ea2b1783d52d","score":"0.64731723","text":"def dereference_type(t):\n if t.strip() in [\"void *\", \"char *\"]:\n return t.strip()\n try:\n return t[:t.rindex(\"*\")].strip()\n except:\n return t.strip()","title":""},{"docid":"318d53be8fc4f0dd4d7e08d10ceba1d1","score":"0.646643","text":"def get_type(self):\n return self._type","title":""},{"docid":"f06a06188331637b01b1a751df440423","score":"0.6460006","text":"def getType(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"aeac52f50dc1923961727412d33e6159","score":"0.6412614","text":"def _gettype(obj):\n return type(obj)","title":""},{"docid":"a82eaed34195e8e946f0515a29b36fdf","score":"0.64093155","text":"def get_type(self):\n\n\t\treturn self.__type","title":""},{"docid":"a82eaed34195e8e946f0515a29b36fdf","score":"0.64093155","text":"def get_type(self):\n\n\t\treturn self.__type","title":""},{"docid":"5a7a4a3b7e0983205bf2166f106607f6","score":"0.6398719","text":"def is_type_ptr(*args):\n return _ida_typeinf.is_type_ptr(*args)","title":""},{"docid":"bb2e7eb7a90f84c1b2975a98c5954f6f","score":"0.63882965","text":"def ref_type(self):\n if self.is_ref:\n return self.schema.get_defined_type(self._raw_value)\n return None","title":""},{"docid":"e2457aa28ef22fc80d8e2076ee1586c3","score":"0.63820946","text":"def getType(self):\n try:\n return self.__type\n except Exception:\n traceback.print_exc()\n return None","title":""},{"docid":"f2cbafb450434b9512f0317bfc12097d","score":"0.63692075","text":"def type_name(self, t, generate=True):\n if isinstance(t, typedesc.PointerType):\n return \"ctypes.POINTER(%s)\" % (self.type_name(t.typ, generate))\n else:\n return super(MyGenerator, self).type_name(t, generate)","title":""},{"docid":"3bb88d0f668a98d35912a0ff08d68f66","score":"0.6368306","text":"def typename(o):\n return str(type(o))[7:-1]","title":""},{"docid":"d24671d7b8f954e8931ee98de58dff32","score":"0.6359439","text":"def type(self) -> _ST_T:\n return self.__type","title":""},{"docid":"565827c7130573060bd91180f47f445d","score":"0.63545394","text":"def typeof(info):\n\ttype = info[\"type\"]\n\tif type == \"string\":\n\t\treturn \"const char*\"\n\telif isenum(info):\n\t\treturn \"enum \" + enumname(info)\n\telse:\n\t\treturn \"kdb_\"+type+\"_t\"","title":""},{"docid":"d0226aa14ba6dbbdfdd35f2af1ddaef2","score":"0.6353944","text":"def typedef(object_to_type):\n return type(object_to_type).__name__","title":""},{"docid":"28598ebbc1517cc5707b8b8957ef8a5e","score":"0.6351155","text":"def GetPointeeType(self):\n return _lldb.SBType_GetPointeeType(self)","title":""},{"docid":"dd7dccd54333ab926c6d26c8bb5f233d","score":"0.6340978","text":"def getType(self):\n return self.__type","title":""},{"docid":"dd7dccd54333ab926c6d26c8bb5f233d","score":"0.6340978","text":"def getType(self):\n return self.__type","title":""},{"docid":"794ec1d6d6ec9351fb56c62269af2312","score":"0.6325502","text":"def python_builtin_type(self) -> Type[Union[int, str, float, bool]]:\n return self.PYTHON_BUILT_IN_TYPE_MAPPING[self.type]","title":""},{"docid":"5b440cac75e477b70c7aac9d3b927a29","score":"0.631853","text":"def get_type(obj):\n return type(obj)","title":""},{"docid":"d87a4a01b632bfa7fca3ec014e16b72d","score":"0.6306189","text":"def get_type():\n return sp.TRecord(owner = sp.TAddress, token_id = sp.TNat).layout((\"owner\", \"token_id\"))","title":""},{"docid":"ec734e2a50f6f83b4ca3f2ee095c5c15","score":"0.6294773","text":"def type_of(self, name):\n\t\tif name in self.subroutine_symbol_table.keys():\n\t\t\treturn self.subroutine_symbol_table[name][0]\n\t\telif name in self.class_symbol_table.keys():\n\t\t\treturn self.class_symbol_table[name][0]\n\t\treturn None","title":""},{"docid":"35eb1e8a94143ed7df744f068bd3c603","score":"0.6271728","text":"def type(self): # noqa: D401,D402\n return self.__type","title":""},{"docid":"1faa9bd5606adb3c5a7a7dae423a004b","score":"0.6265362","text":"def type(self):\n if self.bound and self.__type is None:\n self.pull()\n return self.__type","title":""},{"docid":"a3a5f085c3bfe45b57dd17174f1bcf22","score":"0.6234992","text":"def gettype(self) -> type:\n raise NotImplementedError","title":""},{"docid":"defe07719740dc0544f5dfe1b83a50b0","score":"0.6227067","text":"def get_type(self):\n return self._type","title":""},{"docid":"defe07719740dc0544f5dfe1b83a50b0","score":"0.6227067","text":"def get_type(self):\n return self._type","title":""},{"docid":"defe07719740dc0544f5dfe1b83a50b0","score":"0.6227067","text":"def get_type(self):\n return self._type","title":""},{"docid":"defe07719740dc0544f5dfe1b83a50b0","score":"0.6227067","text":"def get_type(self):\n return self._type","title":""},{"docid":"defe07719740dc0544f5dfe1b83a50b0","score":"0.6227067","text":"def get_type(self):\n return self._type","title":""},{"docid":"defe07719740dc0544f5dfe1b83a50b0","score":"0.6227067","text":"def get_type(self):\n return self._type","title":""},{"docid":"defe07719740dc0544f5dfe1b83a50b0","score":"0.6227067","text":"def get_type(self):\n return self._type","title":""},{"docid":"05fb148ae1d78901db19d6fc4559dc40","score":"0.62072676","text":"def type(self):\n return self.__data__.get(\"type\")","title":""},{"docid":"ff866f0721599c8a3005780879441973","score":"0.6203452","text":"def type(self):\n if \"type\" in self._prop_dict:\n return self._prop_dict[\"type\"]\n else:\n return None","title":""},{"docid":"85aedef22126ca5c38bd5aacee1cb42f","score":"0.62006474","text":"def type(self) :\n return self.__type","title":""},{"docid":"03e476aaa627bbfa480b0c107722fb94","score":"0.620034","text":"def type(self):\n P = self._check_valid()\n s = P._eval_line(self.name())\n i = s.rfind('Type:')\n return P(s[i+5:].strip())","title":""},{"docid":"30b88716adf2c9c4e8f34982c45bd2c4","score":"0.6189541","text":"def type(self): # noqa: D401\n return self.__type","title":""},{"docid":"30b88716adf2c9c4e8f34982c45bd2c4","score":"0.6189541","text":"def type(self): # noqa: D401\n return self.__type","title":""},{"docid":"5c2a5d5995cd7a6b5423cb04229d0189","score":"0.61822575","text":"def _type(self): # noqa: D401\n return type(self)._type","title":""},{"docid":"429445bb9f1bbd0b4813c45fd16f2014","score":"0.6181241","text":"def get_type():\n return sp.TRecord(owner = sp.TAddress, operator = sp.TAddress, token_id = sp.TNat).layout((\"owner\", (\"operator\", \"token_id\")))","title":""},{"docid":"949a1f157b0cec863f77188a7b8c4952","score":"0.6179102","text":"def get_type(self):\n return self.Type.value()","title":""},{"docid":"5e7b3bbaaeb621aa8382024e2878f864","score":"0.6177387","text":"def get_type(self):\n pass","title":""},{"docid":"5e7b3bbaaeb621aa8382024e2878f864","score":"0.6177387","text":"def get_type(self):\n pass","title":""},{"docid":"5e7b3bbaaeb621aa8382024e2878f864","score":"0.6177387","text":"def get_type(self):\n pass","title":""},{"docid":"5e7b3bbaaeb621aa8382024e2878f864","score":"0.6177387","text":"def get_type(self):\n pass","title":""},{"docid":"5e7b3bbaaeb621aa8382024e2878f864","score":"0.6177387","text":"def get_type(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"4230d3cdb70d0eae25061363bec4071a\",\n \"score\": \"0.8147374\",\n \"text\": \"def get_pointer_type(self):\\n return self.get_c_type() + ' *'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26bfe339aabfbf2261fa45d63d1e0261\",\n \"score\": \"0.6892176\",\n \"text\": \"def get_type_declaration(self):\\n c_type = self.field_type.get_c_type()\\n\\n if self.is_dynamically_allocated():\\n # Need to make it a pointer type if it is dynamically allocated\\n type_declaration = c_type + '*'\\n else:\\n type_declaration = c_type\\n\\n return type_declaration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c543ad991ef842ae3d1343ace8d4f51d\",\n \"score\": \"0.68215513\",\n \"text\": \"def type(self):\\n return self.types[self._internal.get_type()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91fb4a13ec573f844158965171078609\",\n \"score\": \"0.68138367\",\n \"text\": \"def get_type(self):\\n return self.p_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e69d75cbdd4b7d486e93817466126cd5\",\n \"score\": \"0.66998494\",\n \"text\": \"def get_list_pointer_type(self):\\n return self.get_list_c_type() + ' *'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9619cfca37a8309396903195ffef4fbd\",\n \"score\": \"0.65724957\",\n \"text\": \"def objtype(self):\\n return self._ptr.contents.objtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b87e7bd9b39ffb09ff2863e85e9510\",\n \"score\": \"0.65688413\",\n \"text\": \"def _get_type(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e46ef2be8e2faac079edd76ba14b9cd\",\n \"score\": \"0.6528469\",\n \"text\": \"def type(self):\\n return self._type_class.id if self._type_class else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140a9f560ea3b02886f625b6b7c6fe8b\",\n \"score\": \"0.6520387\",\n \"text\": \"def get_constant_pointer_type(self):\\n return self.get_c_type() + ' const *'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c67c9b46dad730f8fcc48c11ac4a10eb\",\n \"score\": \"0.65053684\",\n \"text\": \"def peek_type(self):\\n ret_val = self.__tokens[self.__i]\\n return ret_val[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86ef0935abd4266d0e496293427b8ec4\",\n \"score\": \"0.64847386\",\n \"text\": \"def GetPointerType(self):\\n return _lldb.SBType_GetPointerType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c6fdf7f16fad7ae442ea2b1783d52d\",\n \"score\": \"0.64731723\",\n \"text\": \"def dereference_type(t):\\n if t.strip() in [\\\"void *\\\", \\\"char *\\\"]:\\n return t.strip()\\n try:\\n return t[:t.rindex(\\\"*\\\")].strip()\\n except:\\n return t.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"318d53be8fc4f0dd4d7e08d10ceba1d1\",\n \"score\": \"0.646643\",\n \"text\": \"def get_type(self):\\n return self._type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06a06188331637b01b1a751df440423\",\n \"score\": \"0.6460006\",\n \"text\": \"def getType(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeac52f50dc1923961727412d33e6159\",\n \"score\": \"0.6412614\",\n \"text\": \"def _gettype(obj):\\n return type(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a82eaed34195e8e946f0515a29b36fdf\",\n \"score\": \"0.64093155\",\n \"text\": \"def get_type(self):\\n\\n\\t\\treturn self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a82eaed34195e8e946f0515a29b36fdf\",\n \"score\": \"0.64093155\",\n \"text\": \"def get_type(self):\\n\\n\\t\\treturn self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a7a4a3b7e0983205bf2166f106607f6\",\n \"score\": \"0.6398719\",\n \"text\": \"def is_type_ptr(*args):\\n return _ida_typeinf.is_type_ptr(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb2e7eb7a90f84c1b2975a98c5954f6f\",\n \"score\": \"0.63882965\",\n \"text\": \"def ref_type(self):\\n if self.is_ref:\\n return self.schema.get_defined_type(self._raw_value)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2457aa28ef22fc80d8e2076ee1586c3\",\n \"score\": \"0.63820946\",\n \"text\": \"def getType(self):\\n try:\\n return self.__type\\n except Exception:\\n traceback.print_exc()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2cbafb450434b9512f0317bfc12097d\",\n \"score\": \"0.63692075\",\n \"text\": \"def type_name(self, t, generate=True):\\n if isinstance(t, typedesc.PointerType):\\n return \\\"ctypes.POINTER(%s)\\\" % (self.type_name(t.typ, generate))\\n else:\\n return super(MyGenerator, self).type_name(t, generate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb88d0f668a98d35912a0ff08d68f66\",\n \"score\": \"0.6368306\",\n \"text\": \"def typename(o):\\n return str(type(o))[7:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d24671d7b8f954e8931ee98de58dff32\",\n \"score\": \"0.6359439\",\n \"text\": \"def type(self) -> _ST_T:\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"565827c7130573060bd91180f47f445d\",\n \"score\": \"0.63545394\",\n \"text\": \"def typeof(info):\\n\\ttype = info[\\\"type\\\"]\\n\\tif type == \\\"string\\\":\\n\\t\\treturn \\\"const char*\\\"\\n\\telif isenum(info):\\n\\t\\treturn \\\"enum \\\" + enumname(info)\\n\\telse:\\n\\t\\treturn \\\"kdb_\\\"+type+\\\"_t\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0226aa14ba6dbbdfdd35f2af1ddaef2\",\n \"score\": \"0.6353944\",\n \"text\": \"def typedef(object_to_type):\\n return type(object_to_type).__name__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28598ebbc1517cc5707b8b8957ef8a5e\",\n \"score\": \"0.6351155\",\n \"text\": \"def GetPointeeType(self):\\n return _lldb.SBType_GetPointeeType(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd7dccd54333ab926c6d26c8bb5f233d\",\n \"score\": \"0.6340978\",\n \"text\": \"def getType(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd7dccd54333ab926c6d26c8bb5f233d\",\n \"score\": \"0.6340978\",\n \"text\": \"def getType(self):\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"794ec1d6d6ec9351fb56c62269af2312\",\n \"score\": \"0.6325502\",\n \"text\": \"def python_builtin_type(self) -> Type[Union[int, str, float, bool]]:\\n return self.PYTHON_BUILT_IN_TYPE_MAPPING[self.type]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b440cac75e477b70c7aac9d3b927a29\",\n \"score\": \"0.631853\",\n \"text\": \"def get_type(obj):\\n return type(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87a4a01b632bfa7fca3ec014e16b72d\",\n \"score\": \"0.6306189\",\n \"text\": \"def get_type():\\n return sp.TRecord(owner = sp.TAddress, token_id = sp.TNat).layout((\\\"owner\\\", \\\"token_id\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec734e2a50f6f83b4ca3f2ee095c5c15\",\n \"score\": \"0.6294773\",\n \"text\": \"def type_of(self, name):\\n\\t\\tif name in self.subroutine_symbol_table.keys():\\n\\t\\t\\treturn self.subroutine_symbol_table[name][0]\\n\\t\\telif name in self.class_symbol_table.keys():\\n\\t\\t\\treturn self.class_symbol_table[name][0]\\n\\t\\treturn None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35eb1e8a94143ed7df744f068bd3c603\",\n \"score\": \"0.6271728\",\n \"text\": \"def type(self): # noqa: D401,D402\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1faa9bd5606adb3c5a7a7dae423a004b\",\n \"score\": \"0.6265362\",\n \"text\": \"def type(self):\\n if self.bound and self.__type is None:\\n self.pull()\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a5f085c3bfe45b57dd17174f1bcf22\",\n \"score\": \"0.6234992\",\n \"text\": \"def gettype(self) -> type:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"defe07719740dc0544f5dfe1b83a50b0\",\n \"score\": \"0.6227067\",\n \"text\": \"def get_type(self):\\n return self._type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"defe07719740dc0544f5dfe1b83a50b0\",\n \"score\": \"0.6227067\",\n \"text\": \"def get_type(self):\\n return self._type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"defe07719740dc0544f5dfe1b83a50b0\",\n \"score\": \"0.6227067\",\n \"text\": \"def get_type(self):\\n return self._type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"defe07719740dc0544f5dfe1b83a50b0\",\n \"score\": \"0.6227067\",\n \"text\": \"def get_type(self):\\n return self._type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"defe07719740dc0544f5dfe1b83a50b0\",\n \"score\": \"0.6227067\",\n \"text\": \"def get_type(self):\\n return self._type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"defe07719740dc0544f5dfe1b83a50b0\",\n \"score\": \"0.6227067\",\n \"text\": \"def get_type(self):\\n return self._type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"defe07719740dc0544f5dfe1b83a50b0\",\n \"score\": \"0.6227067\",\n \"text\": \"def get_type(self):\\n return self._type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05fb148ae1d78901db19d6fc4559dc40\",\n \"score\": \"0.62072676\",\n \"text\": \"def type(self):\\n return self.__data__.get(\\\"type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff866f0721599c8a3005780879441973\",\n \"score\": \"0.6203452\",\n \"text\": \"def type(self):\\n if \\\"type\\\" in self._prop_dict:\\n return self._prop_dict[\\\"type\\\"]\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85aedef22126ca5c38bd5aacee1cb42f\",\n \"score\": \"0.62006474\",\n \"text\": \"def type(self) :\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03e476aaa627bbfa480b0c107722fb94\",\n \"score\": \"0.620034\",\n \"text\": \"def type(self):\\n P = self._check_valid()\\n s = P._eval_line(self.name())\\n i = s.rfind('Type:')\\n return P(s[i+5:].strip())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b88716adf2c9c4e8f34982c45bd2c4\",\n \"score\": \"0.6189541\",\n \"text\": \"def type(self): # noqa: D401\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b88716adf2c9c4e8f34982c45bd2c4\",\n \"score\": \"0.6189541\",\n \"text\": \"def type(self): # noqa: D401\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c2a5d5995cd7a6b5423cb04229d0189\",\n \"score\": \"0.61822575\",\n \"text\": \"def _type(self): # noqa: D401\\n return type(self)._type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"429445bb9f1bbd0b4813c45fd16f2014\",\n \"score\": \"0.6181241\",\n \"text\": \"def get_type():\\n return sp.TRecord(owner = sp.TAddress, operator = sp.TAddress, token_id = sp.TNat).layout((\\\"owner\\\", (\\\"operator\\\", \\\"token_id\\\")))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"949a1f157b0cec863f77188a7b8c4952\",\n \"score\": \"0.6179102\",\n \"text\": \"def get_type(self):\\n return self.Type.value()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7b3bbaaeb621aa8382024e2878f864\",\n \"score\": \"0.6177387\",\n \"text\": \"def get_type(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7b3bbaaeb621aa8382024e2878f864\",\n \"score\": \"0.6177387\",\n \"text\": \"def get_type(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7b3bbaaeb621aa8382024e2878f864\",\n \"score\": \"0.6177387\",\n \"text\": \"def get_type(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7b3bbaaeb621aa8382024e2878f864\",\n \"score\": \"0.6177387\",\n \"text\": \"def get_type(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7b3bbaaeb621aa8382024e2878f864\",\n \"score\": \"0.6177387\",\n \"text\": \"def get_type(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1788,"cells":{"query_id":{"kind":"string","value":"321b679eff58416edcbfb834701f2f07"},"query":{"kind":"string","value":"Return a dict of keyword arguments."},"positive_passages":{"kind":"list like","value":[{"docid":"e04521da7751862a8690f24947faa438","score":"0.60982734","text":"def get_kwd_args(func):\n try:\n sig = inspect.signature(func)\n except AttributeError:\n args, _, _, defaults = inspect.getargspec(func)\n if defaults:\n kwonlyargs = args[-len(defaults):]\n else:\n kwonlyargs = []\n else:\n kwonlyargs = {p.name:p.default for p in sig.parameters.values()\n if p.default is not p.empty}\n\n return kwonlyargs","title":""}],"string":"[\n {\n \"docid\": \"e04521da7751862a8690f24947faa438\",\n \"score\": \"0.60982734\",\n \"text\": \"def get_kwd_args(func):\\n try:\\n sig = inspect.signature(func)\\n except AttributeError:\\n args, _, _, defaults = inspect.getargspec(func)\\n if defaults:\\n kwonlyargs = args[-len(defaults):]\\n else:\\n kwonlyargs = []\\n else:\\n kwonlyargs = {p.name:p.default for p in sig.parameters.values()\\n if p.default is not p.empty}\\n\\n return kwonlyargs\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a8bb33e673ce46d959e05908a59bb319","score":"0.76821774","text":"def get_kwargs(self):\n return {}","title":""},{"docid":"b212d19548ebe4a7d81bd1ead37b7578","score":"0.7656699","text":"def as_kwargs(self) -> Dict[str, Any]:\n ret = {}\n for arg in self.args.values():\n ret[arg.name] = arg.value\n return ret","title":""},{"docid":"71e7176f45aee752a386efce79f4b2d5","score":"0.7279411","text":"def get_dict(**kwargs):\n return kwargs","title":""},{"docid":"9283f2c217468e61d172e47e09b7627d","score":"0.704392","text":"def get_kwargs():\n\treturn get_kwargs_raw(sys.argv)","title":""},{"docid":"a856e5d9db18fb414d498e97ea8b2d65","score":"0.69955087","text":"def as_dict(self):\n d = {}\n for k in self._kwargs:\n d[k] = getattr(self, k)\n return d","title":""},{"docid":"add6956e6c6b46c9fc75cee486cd3efb","score":"0.69642377","text":"def get_keyword_args(function):\n argspec = inspect.getargspec(function)\n kwargs = argspec.args[len(argspec.args) - len(argspec.defaults):]\n kwargs = {arg: value for arg, value in zip(kwargs, argspec.defaults)}\n return kwargs","title":""},{"docid":"76ae4fa7eae097e66de3443da91b6925","score":"0.69118226","text":"def _get_kwargs_for_backend(self):\n return dict()","title":""},{"docid":"6751a17c60d6a26235c8d463ed38f725","score":"0.68862104","text":"def kwarg_to_str_dict(self) -> dict:\n kwarg_dict = {}\n for key, val in {**self.kwargs}.items():\n if hasattr(val, \"_ref\"):\n kwarg_dict[key] = val.ref\n else:\n kwarg_dict[key] = val\n return kwarg_dict","title":""},{"docid":"98a75aa29ab74605dba54ea6858076ea","score":"0.6885258","text":"def _kwargs(self):\n dict = {\"name\":self.name}\n return dict","title":""},{"docid":"ab28d1a7351d6324a347f15809ebc5e9","score":"0.68752354","text":"def kwargs(self):\n return self._kwargs","title":""},{"docid":"ab28d1a7351d6324a347f15809ebc5e9","score":"0.68752354","text":"def kwargs(self):\n return self._kwargs","title":""},{"docid":"92acd2a398efced513e173227b3fa958","score":"0.6813853","text":"def get_filter_kwargs(self, *_, **__) -> Dict[str, Any]:","title":""},{"docid":"6fc82cfad7acb250e5abbede826668c4","score":"0.6760701","text":"def token_kwargs(bits, parser):\r\n if not bits:\r\n return {}\r\n kwargs = SortedDict()\r\n while bits:\r\n match = kwarg_re.match(bits[0])\r\n if not match or not match.group(1):\r\n return kwargs\r\n key, value = match.groups()\r\n del bits[:1]\r\n kwargs[parser.compile_filter(key)] = parser.compile_filter(value)\r\n return kwargs","title":""},{"docid":"5a8b38ad3736aa95a5b0a4a59c8b207a","score":"0.67337054","text":"def _kwargs(self):\n dict = DAG._kwargs(self) \n dict[\"inputpaths\"] = self.inputpaths\n dict[\"outputpath\"] = self.outputpath\n dict[\"query\"] = self.query\n return dict","title":""},{"docid":"690033a7d1df8882236d1a40e59d7b23","score":"0.67321855","text":"def get_arguments(self, **include):\n d = dict(self._kwargs)\n\n for k in include:\n if not include[k] and k in d:\n d.pop(k)\n return d","title":""},{"docid":"56d1a562eefa791669ac05fd0f0e2506","score":"0.6680707","text":"def params(self, **kwargs):\n return kwargs","title":""},{"docid":"ff52db35da12453528caece763bb5ad2","score":"0.6660299","text":"def kwds(self):\n return self._kwds","title":""},{"docid":"8515e05bcb488f08400a9224ae211bce","score":"0.66316825","text":"def _get_init_kwds(self):\n kwds = dict(((key, getattr(self, key, None))\n for key in self._init_keys))\n return kwds","title":""},{"docid":"e0d53c9e942d6b23a77f04cb77090fa6","score":"0.66147864","text":"def get_kwargs(d):\n raise NotImplementedError(\"subclass must implement get_kwargs()\")","title":""},{"docid":"0d561f9b50d36056115c38847c2bf0b3","score":"0.65711325","text":"def get_argdict(cls, toolchain, args):\n return {} # Empty must be overloaded (if required)","title":""},{"docid":"1284ed6900efd034f80e3a845ebcf5be","score":"0.6425623","text":"def test_kw_args_with_dict():\n arg_dict = {'visited_color': 'blue',\n 'link_color': 'red',\n 'back_color': 'yellow',\n 'fore_color': 'orange'}\n assert arguments.fun_opt_kw_params(**arg_dict) == ('orange', 'yellow',\n 'red', 'blue')","title":""},{"docid":"cf405fb34ccc513d929683925333a0b5","score":"0.6424063","text":"def pykwarg(self):\n return self._pykwarg","title":""},{"docid":"a4a8cfe37c5c99d6fc729f7eea880406","score":"0.6389129","text":"def _parse_kwargs(self):\n re_kwargs = r'^[\\w_][\\w\\d_]*=.+$'\n kwargs = [a.split('=') for a in self.args if re.findall(re_kwargs, a)]\n self.kwargs = {k: self._load_json(v) for k, v in kwargs}\n self.args = [a for a in self.args if not re.findall(re_kwargs, a)]","title":""},{"docid":"e24a86a37567c798684a21852a144dcd","score":"0.63804466","text":"def appropriate_kwargs(kwargs, func):\n sig = inspect.signature(func)\n filter_keys = [\n param.name\n for param in sig.parameters.values()\n if param.kind == param.POSITIONAL_OR_KEYWORD and param.name in kwargs.keys()\n ]\n appropriate_dict = {filter_key: kwargs[filter_key] for filter_key in filter_keys}\n return appropriate_dict","title":""},{"docid":"b0b42807c239c4a864c3ab4aaafccdad","score":"0.63767886","text":"def kwargs(self):\n return self.environ.get('router.kwargs', {})","title":""},{"docid":"3b0ae4d38a5b840974c05d2367de22c1","score":"0.6361644","text":"def parseKwargs(acceptable,kwargs):\n \n output = {}\n\n if kwargs:\n for key in kwargs.keys():\n \n if key in acceptable:\n output[key] = kwargs[key]\n\n return output","title":""},{"docid":"5da5da071c71a3efeccb31dd681ac3b1","score":"0.63515216","text":"def enumerate_kwarg_dict(**kwargs):\n keys = []\n lens = []\n for key in kwargs:\n keys.append(key)\n lens.append(len(kwargs[key]))\n for ndind in np.ndindex(*lens):\n nowdict = dict()\n for ind, key in zip(ndind, keys):\n nowdict[key] = kwargs[key][ind]\n yield nowdict","title":""},{"docid":"26dba2014a9c296dbe7028224a90acfc","score":"0.6340735","text":"def _kwargs(self):\n dict = DAG._kwargs(self) \n if (self.job): \n dict[\"inputpaths\"] = self.job.inputpaths\n dict[\"outputpath\"] = self.job.outputpath\n dict[\"job\"] = \"%s()\" % self.job.__class__.__name__\n return dict","title":""},{"docid":"090861a994f99c394708584b71e9b5d3","score":"0.6333885","text":"def get_required_params():\n return {}","title":""},{"docid":"b529825b75ba950c110c2b1e06b16191","score":"0.6333812","text":"def _get_method_kwargs(self, kwargs, method_args):\n if isinstance(method_args, str):\n method_args = getattr(self.instance, method_args)()\n\n method_kwargs = dict()\n for arg in method_args:\n name = arg['name']\n keyword = arg.get('keyword', name)\n\n if name in kwargs:\n value = kwargs[name]\n elif 'default' in arg:\n value = arg['default']\n elif arg.get('required', True):\n raise TypeError(\"missing expected argument '{}'\".format(name))\n\n method_kwargs[keyword] = value\n\n return method_kwargs","title":""},{"docid":"d22e1a29b291b62c265daad8410ce272","score":"0.63290614","text":"def kwargs (self):\n return copy.deepcopy (self._kwargs)","title":""},{"docid":"a0acdda15a21709ae3803ade8913cdf8","score":"0.62981397","text":"def test_kw_args_with_keywords():\n assert arguments.fun_opt_kw_params(visited_color='blue',\n link_color='red',\n back_color='yellow',\n fore_color='orange') == ('orange',\n 'yellow',\n 'red', 'blue')","title":""},{"docid":"e828482765a9d5a7346e6e28ee72c0ad","score":"0.627379","text":"def get_params(self):\r\n param_names = ['aws_access_key_id', 'aws_secret_access_key',\r\n 'is_secure', 'port', 'proxy', 'proxy_port',\r\n 'proxy_user', 'proxy_pass',\r\n 'debug', 'https_connection_factory']\r\n params = {}\r\n for name in param_names:\r\n params[name] = getattr(self, name)\r\n return params","title":""},{"docid":"1c8f0f19c5bee5f1d40aed15047992aa","score":"0.6244705","text":"def parameters(self):\n return dict(self._register)","title":""},{"docid":"528cb6ff4b4d225d4b6b2a6695ce0ae5","score":"0.622424","text":"def unused_kwargs(kw):\n fn_kw = dict(base_class=None,\n base_name=None, name=None, base_arg=None, base_kw=None, parent=None,\n infer_kw=None, in_shape='BCD', base_shape=None, out_shape='BCD', tuple_out=False,\n forward_arg=None, forward_kw=None, initialization=None, activation=None, )\n return {k:v for k, v in kw.items() if k not in fn_kw}","title":""},{"docid":"8e9d87ef921bb56498989846afb2d9fb","score":"0.62157387","text":"def get_used_kwargs(self):\n return self._used_kwargs","title":""},{"docid":"b8aa036cce309c19ce5611f1d05d88f1","score":"0.6214156","text":"def get_parameters(**kwargs):\r\n parameters = vars(global_file.params)\r\n for key, value in kwargs.items():\r\n parameters[str(key)] = value\r\n return parameters","title":""},{"docid":"d12c44c5bc5ba30fcba596d1b88878bd","score":"0.6208521","text":"def token_kwargs(bits, parser, support_legacy=False):\n if not bits:\n return {}\n match = kwarg_re.match(bits[0])\n kwarg_format = match and match.group(1)\n if not kwarg_format:\n if not support_legacy:\n return {}\n if len(bits) < 3 or bits[1] != 'as':\n return {}\n\n kwargs = {}\n while bits:\n if kwarg_format:\n match = kwarg_re.match(bits[0])\n if not match or not match.group(1):\n return kwargs\n key, value = match.groups()\n del bits[:1]\n else:\n if len(bits) < 3 or bits[1] != 'as':\n return kwargs\n key, value = bits[2], bits[0]\n del bits[:3]\n kwargs[key] = parser.compile_filter(value)\n if bits and not kwarg_format:\n if bits[0] != 'and':\n return kwargs\n del bits[:1]\n return kwargs","title":""},{"docid":"13dabbb34e39c4d4513efb583dfe61c7","score":"0.62005055","text":"def test_kwargs(self):\n kwargs = forge.kwargs\n assert isinstance(kwargs, forge._signature.VarKeyword)\n assert kwargs.name == 'kwargs'\n assert kwargs.converter is None\n assert kwargs.validator is None","title":""},{"docid":"9bf8dd41b4d7128106e97115c1644f7f","score":"0.61869496","text":"def get_argument_as_keywords(self):\n status = True\n arg_kv = self.get_values_for_mandatory_args()\n if len(arg_kv) != len(self.req_args_list):\n msg = 'could not execute %s without mandatory arguments' % (object)\n self.data_repository = skip_and_report_status(self.data_repository, msg)\n status = False\n arg_kv = self.get_values_for_optional_args(arg_kv)\n return arg_kv, status","title":""},{"docid":"42195342199260fc075a40ecd7aa0894","score":"0.61833036","text":"def get_x_args_dict(self):\n return self.__x_args","title":""},{"docid":"41dff13de95b1723fca3c87a85b1d312","score":"0.61821073","text":"def argdict(self):\n return dict((arg.name, val) for arg, val in zip(self.sig, self))","title":""},{"docid":"e7dc7c57ad1ed2f6be18925297fc21ff","score":"0.61788714","text":"def get_unused_kwargs(self):\n return self._unused_kwargs","title":""},{"docid":"26ca83249f8e6ca0470e4ec952aa4ec9","score":"0.6163019","text":"def filter_extra_accepted_kwargs(fun, kwargs, skip_positional=0):\n sig = inspect.signature(fun)\n # the params from signature with up to skip_positional filtered out\n # (less only if there is not enough of positional args)\n params = [(name, param) for i, (name, param) in enumerate(sig.parameters.items())\n if i >= skip_positional or param.kind not in\n [inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.POSITIONAL_ONLY]]\n extra = [\n name for (name, param) in params\n if param.kind in [inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.KEYWORD_ONLY]\n ]\n return {name: value for name, value in kwargs.items() if name in extra}","title":""},{"docid":"cf66d7f10a32cbb036215d20b293aed6","score":"0.6145562","text":"def as_dict(self, **kwargs):\n m = {}\n self.add_to_dict(m, **kwargs)\n return m","title":""},{"docid":"d9b12dad537798be8444595e21b603ff","score":"0.61445916","text":"def _get_init_args(self):\n\n return dict(enum=self.enum, dflt=self._defname,\n base=self.base, shape=self.shape)","title":""},{"docid":"33d20581c1a260fc0ee745b043c77284","score":"0.6137059","text":"def get_run_method_kwargs(self, **kwargs) -> dict:\n return {\n key: value\n for key, value in kwargs.items()\n if self.input_definitions.get(key=key).run_method_input\n }","title":""},{"docid":"206631ee8f99ac786b38d4e163556960","score":"0.61126196","text":"def _get_reproducing_arguments(self):\n reproducing_arguments = {\n 'include': self.include,\n 'exclude': self.exclude,\n 'copy': self.copy,\n }\n args_names = {name: getattr(self, name) for name in self.args_names}\n reproducing_arguments.update(args_names)\n return reproducing_arguments","title":""},{"docid":"af951536280a24a1926260e27b2f7961","score":"0.61104506","text":"def filter_args_dict(self, args):\n return dict((k,v) for (k,v) in viewitems(args) if self.has_arg(k))","title":""},{"docid":"169451f81f81c81fdb1e1c4f671f7287","score":"0.6106871","text":"def get_params(self):\n return {}","title":""},{"docid":"87df088ff5adfc753412d0f4298b05fe","score":"0.6104978","text":"def set_keywords(self, **kwargs):\n keywords = dict()\n\n for key, value in self.allowed_keys.items():\n keywords[key] = value[1]\n\n for key, value in kwargs.items():\n if key not in self.allowed_keys:\n error = 'Keyword %s for %s object not found' % \\\n (key, self.__class__.__name__)\n MASTError(self.__class__.__name__, error)\n\n# raise RuntimeError('Keyword %s for %s object not found' % \\\n# (key, self.__class__.__name__))\n\n if isinstance(value, self.allowed_keys[key][0]):\n keywords[key] = value\n else:\n error = 'Keyword %s value %s invalid; expected type %s, got type %s' % (key, str(value), self.allowed_keys[key][0], type(value))\n MASTError(self.__class__.__name__, error)\n# raise RuntimeError('Keyword %s value invalid' % key)\n\n return keywords","title":""},{"docid":"0bbd7569fda53d09b240244ff97bf898","score":"0.6093302","text":"def get_interface_initialization_kwargs(self, **kwargs) -> dict:\n return {\n key: value\n for key, value in kwargs.items()\n if not self.input_definitions.get(key=key).run_method_input\n }","title":""},{"docid":"7cc5770ec6276a9e821b6569b0d65a68","score":"0.60870683","text":"def add_kwargs():\n pass","title":""},{"docid":"9d67bd389a01bbd3d615dbce79e1089d","score":"0.608291","text":"def get_kwargs(self):\n return {\n 'user': self.user,\n }","title":""},{"docid":"82d9188fa24a75f9bc16424874442892","score":"0.6068618","text":"def keywords(self):\n return list(self._kw)","title":""},{"docid":"ab17884ce2cab6037bef04e6a8b10343","score":"0.6042146","text":"def get_kwargs(d):\n return {\"categories\": d.get(\"categories\", None)}","title":""},{"docid":"90b9fdd6c17b34ea280da22bd9864b69","score":"0.6038479","text":"def keywords(self):\n return {\n \"unary\": {\n k: v[0] for k, v in self.unary_commands.items()\n },\n \"terminal\": {\n k: v[0] for k, v in self.terminal_commands.items()\n },\n \"binary\": {\n k: v[0] for k, v in self.binary_commands.items()\n },\n }","title":""},{"docid":"ec910a867ede5dc46e0ff70f4517c70e","score":"0.60223377","text":"def get_kwargs(d):\n return {\"values\": d.get(\"values\", None)}","title":""},{"docid":"99267e883d93e40444a2df2459d95c89","score":"0.6022138","text":"def parameters(self):\n return {}","title":""},{"docid":"6cc7bd09b640e88abaadb907af68a624","score":"0.6011194","text":"def get_xx_args_dict(self):\n return self.__xx_args","title":""},{"docid":"25755864700935fb9547dfbb400d7dda","score":"0.6009005","text":"def get_keys_info() -> Dict[str, List[str]]:\n args_dict = {}\n\n for api in API_DICT:\n arg_list = list(\n getattr(\n sys.modules[__name__], \"set_\" + str(api) + \"_key\"\n ).__code__.co_varnames\n )\n arg_list.remove(\"persist\")\n arg_list.remove(\"show_output\")\n args_dict[api] = arg_list\n\n return args_dict","title":""},{"docid":"2043dae7c91fac71a27e12d53fd157ac","score":"0.6005944","text":"def transformer_arguments(self) -> Dict[str, Any]:\n return {**self.transformer_required_arguments(), **self.transformer_optional_arguments()}","title":""},{"docid":"e77e04f982f4eb441b93d9280d7aebde","score":"0.60049504","text":"def test_kw_args_with_positional():\n assert arguments.fun_opt_kw_params('blue', 'red', 'yellow',\n 'orange') == ('blue', 'red', 'yellow',\n 'orange')","title":""},{"docid":"9498fc08a1178f221c01bb8ff69114e6","score":"0.5992732","text":"def as_kwargs(self) -> Dict[str, Parameter]:\n dict_out = {}\n\n # make all parameter paths absolute\n try:\n absolute_parameters = self.make_unc_paths(self.parameters)\n except NoAbsoluteRootPathException as e:\n raise ParameterMappingError(e) from e\n\n for parameter in absolute_parameters:\n if self.is_non_keyword(parameter):\n # This parameter should not be included in kwargs. Skip\n continue\n elif self.is_source_identifier(parameter):\n if self.is_pacs_type(parameter):\n dict_out[\"source_instance_id\"] = get_legacy_idis_value(\n parameter.value\n )\n elif self.is_path_type(parameter):\n dict_out[\"source_path\"] = str(parameter.value)\n else:\n raise ParameterMappingError(\n f\"Unknown source parameter '{parameter}'\"\n )\n else:\n try:\n dict_out[self.PARAMETER_KEYWORDS[type(parameter)]] = str(\n parameter.value\n )\n except KeyError as e:\n raise ParameterMappingError(\n f\"Unknown parameter '{parameter}'\"\n ) from e\n\n return dict_out","title":""},{"docid":"c868f874b5d18d95c823fde6e50ffcbc","score":"0.5988258","text":"def get_params(self):\n return {'k': self.k, 'q': self.q, 'sigma_s': self.sigma_s, 'm': self.m}","title":""},{"docid":"d9f1c1c13cd09c0266b56be78dab43c7","score":"0.59880024","text":"def get_args(self):\n rqst = self.request\n args = rqst.arguments()\n resp = {}\n for arg in args:\n resp[arg] = repr(rqst.get_all(arg))\n return resp","title":""},{"docid":"460734bae9fda381962d0810fe0df1c7","score":"0.59809464","text":"def _collect_kwargs(step):\n dicts = {}\n for s in _expand_inputs(step):\n name = s.name if s.name is not None else s.__class__.__name__\n if name in dicts.keys():\n raise ValueError(\"Duplicate step names: %s\" % name)\n\n d = dict(s._kwargs)\n d.pop('inputs', None)\n dicts[name] = d\n\n return dicts","title":""},{"docid":"cd149768ff6070df7c98527d1cf0dfa7","score":"0.5974165","text":"def _get_url_params_as_dict(_request):\n return _multi_dict_to_dict(_request.args)","title":""},{"docid":"a8e5b9d06c76aa0df898341bd1cd2299","score":"0.5972164","text":"def build_arg_list(fn, env):\r\n kw = {}\r\n argspec = inspect.getargspec(fn)\r\n\r\n # if there is a **kw argument in the fn definition,\r\n # just pass along the environment\r\n if argspec[2]:\r\n kw = env\r\n #else for each entry in the arglist set the value from the environment\r\n else:\r\n #skip self\r\n argnames = argspec[0][1:]\r\n for name in argnames:\r\n if name in env:\r\n kw[name] = env[name]\r\n return kw","title":""},{"docid":"cd54fbd03d445fa19aece5f4de879437","score":"0.5968557","text":"def _build_flags_hparam_dict():\n logging.info('Show FLAGS for debugging:')\n for f in HPARAM_FLAGS:\n logging.info('%s=%s', f, FLAGS[f].value)\n hparam_dict = collections.OrderedDict([\n (name, FLAGS[name].value) for name in HPARAM_FLAGS\n ])\n return hparam_dict","title":""},{"docid":"f7a53d75fb0e00ad24c8a4318847f739","score":"0.59648883","text":"def get_input_arguments(kwargs, function, warn=True):\n np.set_printoptions(threshold=20)\n print('\\narguments to {}:'.format(function.__qualname__))\n params = inspect.signature(function)\n input_kwargs = {}\n not_arguments = {}\n for k, v in kwargs.items():\n if k in params.parameters:\n input_kwargs[k] = v\n print_item(k, v)\n else:\n not_arguments[k] = v\n if warn:\n print('\\nother arguments:')\n for k, v in not_arguments.items():\n #print('{}: {}'.format(k, v))\n print_item(k, v)\n print('\\n')\n return input_kwargs","title":""},{"docid":"f7a53d75fb0e00ad24c8a4318847f739","score":"0.59648883","text":"def get_input_arguments(kwargs, function, warn=True):\n np.set_printoptions(threshold=20)\n print('\\narguments to {}:'.format(function.__qualname__))\n params = inspect.signature(function)\n input_kwargs = {}\n not_arguments = {}\n for k, v in kwargs.items():\n if k in params.parameters:\n input_kwargs[k] = v\n print_item(k, v)\n else:\n not_arguments[k] = v\n if warn:\n print('\\nother arguments:')\n for k, v in not_arguments.items():\n #print('{}: {}'.format(k, v))\n print_item(k, v)\n print('\\n')\n return input_kwargs","title":""},{"docid":"e94ef3b6d295e31ad969dc254197e2a4","score":"0.59642196","text":"def filter_args(**kwargs):\n valid_args = \"tol_num_frames\"\n\n d = dict((k, kwargs[k]) for k in valid_args if k in kwargs)\n\n return d","title":""},{"docid":"31f67f16c01a6954ba9e6bc8cd18c995","score":"0.5963277","text":"def arglist2dict(args):\n arg_dict = {}\n\n if len(args) == 0:\n return arg_dict\n\n if not args[0].startswith('--'):\n raise ValueError(f\"Positional keywords are not supported: {args[0]}\")\n\n i = 0\n while i < len(args):\n arg = args[i]\n i = i + 1\n if arg.startswith('--'):\n dest = arg[2:]\n j, arglist = Parser.get_args(args[i:])\n i = i + j\n Parser.update_arg_dict(arg_dict, dest, arglist)\n return arg_dict","title":""},{"docid":"a155c58acc6d5e6034b7afbf9d4aeed0","score":"0.5962944","text":"def format_arguments(self, **kwargs):\n return kwargs","title":""},{"docid":"6be268ad041e4c815ebf082212a0fa6f","score":"0.59613454","text":"def parameters_dict(self):\n return","title":""},{"docid":"29ade32566fbe20afbeb3bd2e060dd7f","score":"0.59456944","text":"def parameters(self) -> Dict[str, Any]:\n return self.data[\"args\"].get(\"parameters\", {})","title":""},{"docid":"860345a34389995083ecf9585ffd8009","score":"0.5921932","text":"def keyword_subset(keyword_arguments, allowed_keywords):\n keywords = set(keyword_arguments.keys())\n keyswords_to_extract = keywords.intersection(allowed_keywords)\n\n new_kw = {key: keyword_arguments[key] for key in keyswords_to_extract}\n\n return new_kw","title":""},{"docid":"fcb889e90380c4dc7e5b3f6fef63a83d","score":"0.5918953","text":"def parameters_dict(self):\n return dict(zip(self.parameters_names(), self.parameters_list))","title":""},{"docid":"cd3ed5bd746d57e16c70d34a075ac181","score":"0.59147024","text":"def get_args_dict(class_, options: Options) -> dict:\n\n argspec = getfullargspec(class_.__init__)\n init_args = argspec.args\n init_args.pop(0) # self\n result = {k: v for k, v in options.items() if k in init_args}\n\n positional_args = init_args[:-len(argspec.defaults)]\n\n missing_args = [a for a in positional_args if a not in options]\n if missing_args:\n raise BadConfigError(\n f'Some required parameters are missing in \"{options[\"name\"]}\" config: ' +\n ', '.join(missing_args)\n )\n return result","title":""},{"docid":"5c1c36486fb85f005ca29d6e9e0fd724","score":"0.5905406","text":"def __repr_args__(self):\n args = dict(super().__repr_args__())\n try:\n del args['type_hint']\n except KeyError:\n pass\n return args.items()","title":""},{"docid":"da6140dbd644f216bbbd8a2b9a53e443","score":"0.59033114","text":"def _get_param_names(self):\n temp_params = {'function': self.function, 'target': self.target}\n\n temp_params.update(self.kwargs)\n\n return temp_params","title":""},{"docid":"130cb9b8206a065137be5053b46043a7","score":"0.5897415","text":"def get_required_kwargs(fun, skip_positional=0):\n sig = inspect.signature(fun)\n # the params from signature with up to skip_positional filtered out\n # (less only if there is not enough of positional args)\n params = [(name, param) for i, (name, param) in enumerate(sig.parameters.items())\n if i >= skip_positional or param.kind not in\n [inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.POSITIONAL_ONLY]]\n return [\n name for name, param in params if param.default is inspect.Parameter.empty\n and param.kind in [inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.KEYWORD_ONLY]\n ]","title":""},{"docid":"c2165a4a5fb8606118c0911edbbd05bb","score":"0.58956957","text":"def mof_metadata(self):\n\n arg_dict = collections.defaultdict(dict)\n\n arg_dict[self.name]['type'] = self.arg_type\n arg_dict[self.name]['qualifiers'] = self.qualifiers\n arg_dict[self.name]['valuemap'] = self.valuemap\n\n return dict(arg_dict)","title":""},{"docid":"afdfe6231dcf892a074060b09b9a4797","score":"0.5893621","text":"def init(kwargs: Dict[str, str]):\n\n return {key: value for key, value in kwargs.items() if '-' not in key}","title":""},{"docid":"2098ff7013874ca8eb0bc4b564d992ac","score":"0.5883135","text":"def request_params(self):\n return {'key': self.key, 'hash': self.hash}","title":""},{"docid":"cef83b78c2cc05c47867d8b442fcb147","score":"0.5882102","text":"def get_params(self, deep: bool = True) -> Dict[str, Any]:\n return _get_params(self, deep)","title":""},{"docid":"e2a1fba9e6d90910ccf5a3fc6ce26525","score":"0.58641005","text":"def get_request_extra_params(self, **kwargs):\n params = self.request_extra_params.copy()\n params.update(kwargs)\n return params","title":""},{"docid":"59795fe24e20c11750d7a8bce8556516","score":"0.5859504","text":"def kwargs (self):\n return dict (bins=self.bins, range=self.range)","title":""},{"docid":"59795fe24e20c11750d7a8bce8556516","score":"0.5859504","text":"def kwargs (self):\n return dict (bins=self.bins, range=self.range)","title":""},{"docid":"b6d56ef93043de9e1a4b06b66c2cee1c","score":"0.58571553","text":"def keyword_only(func):\n\n @wraps(func)\n def wrapper(self, *args, **kwargs):\n if len(args) > 0:\n raise TypeError(\"Method %s forces keyword arguments.\" % func.__name__)\n self._input_kwargs = kwargs\n return func(self, **kwargs)\n\n return wrapper","title":""},{"docid":"a09960ba4e51351436445e51007f003a","score":"0.5836156","text":"def getInitParams(self):\n return {}","title":""},{"docid":"6f75209bd48311eb0f28581e576f1824","score":"0.5832235","text":"def get_kwargs_applicable_to_function(function, kwargs):\n return {\n key: value\n for key, value in kwargs.items()\n if key in inspect.getfullargspec(function).args\n }","title":""},{"docid":"fc6d7bb715747a85634d316b55d6a949","score":"0.5819433","text":"def data(self, **kw):\n return dict(params=kw)","title":""},{"docid":"fc6d7bb715747a85634d316b55d6a949","score":"0.5819433","text":"def data(self, **kw):\n return dict(params=kw)","title":""},{"docid":"2c11c02815fa940c752aeb55a8ab0fb2","score":"0.58167654","text":"def keywords(self, **kwargs):\n\n path = self._get_movie_id_path('keywords')\n resp = self._get_method(path, kwargs)\n return resp","title":""},{"docid":"e1e582e7a519e49f6e67e826c95309ac","score":"0.5814989","text":"def _get_init_args(self):\n signature = inspect.signature(self.__init__)\n parameters = signature.parameters\n args = [arg for arg, p in parameters.items()\n if p.kind is p.POSITIONAL_OR_KEYWORD]\n\n return {arg: getattr(self, arg) for arg in args if arg != 'self'}","title":""},{"docid":"ac9cb4dec37dc3f5a316774d99226707","score":"0.5805785","text":"def extra_target_arguments(self):\n return {}","title":""},{"docid":"b2ff3aeb392b2d4d82f292bc0161596c","score":"0.5802602","text":"def _get_context(argspec, kwargs):\n if argspec.has_kwargs:\n return kwargs\n return dict((arg, kwargs[arg]) for arg in argspec.args if arg in kwargs)","title":""},{"docid":"9a2fb4075af7fff84fca517cac0e82cf","score":"0.5802534","text":"def get_kwargs(d):\n return {\"range\": d.get(\"range\", None)}","title":""}],"string":"[\n {\n \"docid\": \"a8bb33e673ce46d959e05908a59bb319\",\n \"score\": \"0.76821774\",\n \"text\": \"def get_kwargs(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b212d19548ebe4a7d81bd1ead37b7578\",\n \"score\": \"0.7656699\",\n \"text\": \"def as_kwargs(self) -> Dict[str, Any]:\\n ret = {}\\n for arg in self.args.values():\\n ret[arg.name] = arg.value\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71e7176f45aee752a386efce79f4b2d5\",\n \"score\": \"0.7279411\",\n \"text\": \"def get_dict(**kwargs):\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9283f2c217468e61d172e47e09b7627d\",\n \"score\": \"0.704392\",\n \"text\": \"def get_kwargs():\\n\\treturn get_kwargs_raw(sys.argv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a856e5d9db18fb414d498e97ea8b2d65\",\n \"score\": \"0.69955087\",\n \"text\": \"def as_dict(self):\\n d = {}\\n for k in self._kwargs:\\n d[k] = getattr(self, k)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"add6956e6c6b46c9fc75cee486cd3efb\",\n \"score\": \"0.69642377\",\n \"text\": \"def get_keyword_args(function):\\n argspec = inspect.getargspec(function)\\n kwargs = argspec.args[len(argspec.args) - len(argspec.defaults):]\\n kwargs = {arg: value for arg, value in zip(kwargs, argspec.defaults)}\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ae4fa7eae097e66de3443da91b6925\",\n \"score\": \"0.69118226\",\n \"text\": \"def _get_kwargs_for_backend(self):\\n return dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6751a17c60d6a26235c8d463ed38f725\",\n \"score\": \"0.68862104\",\n \"text\": \"def kwarg_to_str_dict(self) -> dict:\\n kwarg_dict = {}\\n for key, val in {**self.kwargs}.items():\\n if hasattr(val, \\\"_ref\\\"):\\n kwarg_dict[key] = val.ref\\n else:\\n kwarg_dict[key] = val\\n return kwarg_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98a75aa29ab74605dba54ea6858076ea\",\n \"score\": \"0.6885258\",\n \"text\": \"def _kwargs(self):\\n dict = {\\\"name\\\":self.name}\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab28d1a7351d6324a347f15809ebc5e9\",\n \"score\": \"0.68752354\",\n \"text\": \"def kwargs(self):\\n return self._kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab28d1a7351d6324a347f15809ebc5e9\",\n \"score\": \"0.68752354\",\n \"text\": \"def kwargs(self):\\n return self._kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92acd2a398efced513e173227b3fa958\",\n \"score\": \"0.6813853\",\n \"text\": \"def get_filter_kwargs(self, *_, **__) -> Dict[str, Any]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc82cfad7acb250e5abbede826668c4\",\n \"score\": \"0.6760701\",\n \"text\": \"def token_kwargs(bits, parser):\\r\\n if not bits:\\r\\n return {}\\r\\n kwargs = SortedDict()\\r\\n while bits:\\r\\n match = kwarg_re.match(bits[0])\\r\\n if not match or not match.group(1):\\r\\n return kwargs\\r\\n key, value = match.groups()\\r\\n del bits[:1]\\r\\n kwargs[parser.compile_filter(key)] = parser.compile_filter(value)\\r\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a8b38ad3736aa95a5b0a4a59c8b207a\",\n \"score\": \"0.67337054\",\n \"text\": \"def _kwargs(self):\\n dict = DAG._kwargs(self) \\n dict[\\\"inputpaths\\\"] = self.inputpaths\\n dict[\\\"outputpath\\\"] = self.outputpath\\n dict[\\\"query\\\"] = self.query\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"690033a7d1df8882236d1a40e59d7b23\",\n \"score\": \"0.67321855\",\n \"text\": \"def get_arguments(self, **include):\\n d = dict(self._kwargs)\\n\\n for k in include:\\n if not include[k] and k in d:\\n d.pop(k)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56d1a562eefa791669ac05fd0f0e2506\",\n \"score\": \"0.6680707\",\n \"text\": \"def params(self, **kwargs):\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff52db35da12453528caece763bb5ad2\",\n \"score\": \"0.6660299\",\n \"text\": \"def kwds(self):\\n return self._kwds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8515e05bcb488f08400a9224ae211bce\",\n \"score\": \"0.66316825\",\n \"text\": \"def _get_init_kwds(self):\\n kwds = dict(((key, getattr(self, key, None))\\n for key in self._init_keys))\\n return kwds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d53c9e942d6b23a77f04cb77090fa6\",\n \"score\": \"0.66147864\",\n \"text\": \"def get_kwargs(d):\\n raise NotImplementedError(\\\"subclass must implement get_kwargs()\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d561f9b50d36056115c38847c2bf0b3\",\n \"score\": \"0.65711325\",\n \"text\": \"def get_argdict(cls, toolchain, args):\\n return {} # Empty must be overloaded (if required)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1284ed6900efd034f80e3a845ebcf5be\",\n \"score\": \"0.6425623\",\n \"text\": \"def test_kw_args_with_dict():\\n arg_dict = {'visited_color': 'blue',\\n 'link_color': 'red',\\n 'back_color': 'yellow',\\n 'fore_color': 'orange'}\\n assert arguments.fun_opt_kw_params(**arg_dict) == ('orange', 'yellow',\\n 'red', 'blue')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf405fb34ccc513d929683925333a0b5\",\n \"score\": \"0.6424063\",\n \"text\": \"def pykwarg(self):\\n return self._pykwarg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4a8cfe37c5c99d6fc729f7eea880406\",\n \"score\": \"0.6389129\",\n \"text\": \"def _parse_kwargs(self):\\n re_kwargs = r'^[\\\\w_][\\\\w\\\\d_]*=.+$'\\n kwargs = [a.split('=') for a in self.args if re.findall(re_kwargs, a)]\\n self.kwargs = {k: self._load_json(v) for k, v in kwargs}\\n self.args = [a for a in self.args if not re.findall(re_kwargs, a)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e24a86a37567c798684a21852a144dcd\",\n \"score\": \"0.63804466\",\n \"text\": \"def appropriate_kwargs(kwargs, func):\\n sig = inspect.signature(func)\\n filter_keys = [\\n param.name\\n for param in sig.parameters.values()\\n if param.kind == param.POSITIONAL_OR_KEYWORD and param.name in kwargs.keys()\\n ]\\n appropriate_dict = {filter_key: kwargs[filter_key] for filter_key in filter_keys}\\n return appropriate_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0b42807c239c4a864c3ab4aaafccdad\",\n \"score\": \"0.63767886\",\n \"text\": \"def kwargs(self):\\n return self.environ.get('router.kwargs', {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b0ae4d38a5b840974c05d2367de22c1\",\n \"score\": \"0.6361644\",\n \"text\": \"def parseKwargs(acceptable,kwargs):\\n \\n output = {}\\n\\n if kwargs:\\n for key in kwargs.keys():\\n \\n if key in acceptable:\\n output[key] = kwargs[key]\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da5da071c71a3efeccb31dd681ac3b1\",\n \"score\": \"0.63515216\",\n \"text\": \"def enumerate_kwarg_dict(**kwargs):\\n keys = []\\n lens = []\\n for key in kwargs:\\n keys.append(key)\\n lens.append(len(kwargs[key]))\\n for ndind in np.ndindex(*lens):\\n nowdict = dict()\\n for ind, key in zip(ndind, keys):\\n nowdict[key] = kwargs[key][ind]\\n yield nowdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26dba2014a9c296dbe7028224a90acfc\",\n \"score\": \"0.6340735\",\n \"text\": \"def _kwargs(self):\\n dict = DAG._kwargs(self) \\n if (self.job): \\n dict[\\\"inputpaths\\\"] = self.job.inputpaths\\n dict[\\\"outputpath\\\"] = self.job.outputpath\\n dict[\\\"job\\\"] = \\\"%s()\\\" % self.job.__class__.__name__\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"090861a994f99c394708584b71e9b5d3\",\n \"score\": \"0.6333885\",\n \"text\": \"def get_required_params():\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b529825b75ba950c110c2b1e06b16191\",\n \"score\": \"0.6333812\",\n \"text\": \"def _get_method_kwargs(self, kwargs, method_args):\\n if isinstance(method_args, str):\\n method_args = getattr(self.instance, method_args)()\\n\\n method_kwargs = dict()\\n for arg in method_args:\\n name = arg['name']\\n keyword = arg.get('keyword', name)\\n\\n if name in kwargs:\\n value = kwargs[name]\\n elif 'default' in arg:\\n value = arg['default']\\n elif arg.get('required', True):\\n raise TypeError(\\\"missing expected argument '{}'\\\".format(name))\\n\\n method_kwargs[keyword] = value\\n\\n return method_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22e1a29b291b62c265daad8410ce272\",\n \"score\": \"0.63290614\",\n \"text\": \"def kwargs (self):\\n return copy.deepcopy (self._kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0acdda15a21709ae3803ade8913cdf8\",\n \"score\": \"0.62981397\",\n \"text\": \"def test_kw_args_with_keywords():\\n assert arguments.fun_opt_kw_params(visited_color='blue',\\n link_color='red',\\n back_color='yellow',\\n fore_color='orange') == ('orange',\\n 'yellow',\\n 'red', 'blue')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e828482765a9d5a7346e6e28ee72c0ad\",\n \"score\": \"0.627379\",\n \"text\": \"def get_params(self):\\r\\n param_names = ['aws_access_key_id', 'aws_secret_access_key',\\r\\n 'is_secure', 'port', 'proxy', 'proxy_port',\\r\\n 'proxy_user', 'proxy_pass',\\r\\n 'debug', 'https_connection_factory']\\r\\n params = {}\\r\\n for name in param_names:\\r\\n params[name] = getattr(self, name)\\r\\n return params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c8f0f19c5bee5f1d40aed15047992aa\",\n \"score\": \"0.6244705\",\n \"text\": \"def parameters(self):\\n return dict(self._register)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"528cb6ff4b4d225d4b6b2a6695ce0ae5\",\n \"score\": \"0.622424\",\n \"text\": \"def unused_kwargs(kw):\\n fn_kw = dict(base_class=None,\\n base_name=None, name=None, base_arg=None, base_kw=None, parent=None,\\n infer_kw=None, in_shape='BCD', base_shape=None, out_shape='BCD', tuple_out=False,\\n forward_arg=None, forward_kw=None, initialization=None, activation=None, )\\n return {k:v for k, v in kw.items() if k not in fn_kw}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9d87ef921bb56498989846afb2d9fb\",\n \"score\": \"0.62157387\",\n \"text\": \"def get_used_kwargs(self):\\n return self._used_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8aa036cce309c19ce5611f1d05d88f1\",\n \"score\": \"0.6214156\",\n \"text\": \"def get_parameters(**kwargs):\\r\\n parameters = vars(global_file.params)\\r\\n for key, value in kwargs.items():\\r\\n parameters[str(key)] = value\\r\\n return parameters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d12c44c5bc5ba30fcba596d1b88878bd\",\n \"score\": \"0.6208521\",\n \"text\": \"def token_kwargs(bits, parser, support_legacy=False):\\n if not bits:\\n return {}\\n match = kwarg_re.match(bits[0])\\n kwarg_format = match and match.group(1)\\n if not kwarg_format:\\n if not support_legacy:\\n return {}\\n if len(bits) < 3 or bits[1] != 'as':\\n return {}\\n\\n kwargs = {}\\n while bits:\\n if kwarg_format:\\n match = kwarg_re.match(bits[0])\\n if not match or not match.group(1):\\n return kwargs\\n key, value = match.groups()\\n del bits[:1]\\n else:\\n if len(bits) < 3 or bits[1] != 'as':\\n return kwargs\\n key, value = bits[2], bits[0]\\n del bits[:3]\\n kwargs[key] = parser.compile_filter(value)\\n if bits and not kwarg_format:\\n if bits[0] != 'and':\\n return kwargs\\n del bits[:1]\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13dabbb34e39c4d4513efb583dfe61c7\",\n \"score\": \"0.62005055\",\n \"text\": \"def test_kwargs(self):\\n kwargs = forge.kwargs\\n assert isinstance(kwargs, forge._signature.VarKeyword)\\n assert kwargs.name == 'kwargs'\\n assert kwargs.converter is None\\n assert kwargs.validator is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf8dd41b4d7128106e97115c1644f7f\",\n \"score\": \"0.61869496\",\n \"text\": \"def get_argument_as_keywords(self):\\n status = True\\n arg_kv = self.get_values_for_mandatory_args()\\n if len(arg_kv) != len(self.req_args_list):\\n msg = 'could not execute %s without mandatory arguments' % (object)\\n self.data_repository = skip_and_report_status(self.data_repository, msg)\\n status = False\\n arg_kv = self.get_values_for_optional_args(arg_kv)\\n return arg_kv, status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42195342199260fc075a40ecd7aa0894\",\n \"score\": \"0.61833036\",\n \"text\": \"def get_x_args_dict(self):\\n return self.__x_args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41dff13de95b1723fca3c87a85b1d312\",\n \"score\": \"0.61821073\",\n \"text\": \"def argdict(self):\\n return dict((arg.name, val) for arg, val in zip(self.sig, self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7dc7c57ad1ed2f6be18925297fc21ff\",\n \"score\": \"0.61788714\",\n \"text\": \"def get_unused_kwargs(self):\\n return self._unused_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26ca83249f8e6ca0470e4ec952aa4ec9\",\n \"score\": \"0.6163019\",\n \"text\": \"def filter_extra_accepted_kwargs(fun, kwargs, skip_positional=0):\\n sig = inspect.signature(fun)\\n # the params from signature with up to skip_positional filtered out\\n # (less only if there is not enough of positional args)\\n params = [(name, param) for i, (name, param) in enumerate(sig.parameters.items())\\n if i >= skip_positional or param.kind not in\\n [inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.POSITIONAL_ONLY]]\\n extra = [\\n name for (name, param) in params\\n if param.kind in [inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.KEYWORD_ONLY]\\n ]\\n return {name: value for name, value in kwargs.items() if name in extra}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf66d7f10a32cbb036215d20b293aed6\",\n \"score\": \"0.6145562\",\n \"text\": \"def as_dict(self, **kwargs):\\n m = {}\\n self.add_to_dict(m, **kwargs)\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9b12dad537798be8444595e21b603ff\",\n \"score\": \"0.61445916\",\n \"text\": \"def _get_init_args(self):\\n\\n return dict(enum=self.enum, dflt=self._defname,\\n base=self.base, shape=self.shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33d20581c1a260fc0ee745b043c77284\",\n \"score\": \"0.6137059\",\n \"text\": \"def get_run_method_kwargs(self, **kwargs) -> dict:\\n return {\\n key: value\\n for key, value in kwargs.items()\\n if self.input_definitions.get(key=key).run_method_input\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"206631ee8f99ac786b38d4e163556960\",\n \"score\": \"0.61126196\",\n \"text\": \"def _get_reproducing_arguments(self):\\n reproducing_arguments = {\\n 'include': self.include,\\n 'exclude': self.exclude,\\n 'copy': self.copy,\\n }\\n args_names = {name: getattr(self, name) for name in self.args_names}\\n reproducing_arguments.update(args_names)\\n return reproducing_arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af951536280a24a1926260e27b2f7961\",\n \"score\": \"0.61104506\",\n \"text\": \"def filter_args_dict(self, args):\\n return dict((k,v) for (k,v) in viewitems(args) if self.has_arg(k))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"169451f81f81c81fdb1e1c4f671f7287\",\n \"score\": \"0.6106871\",\n \"text\": \"def get_params(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87df088ff5adfc753412d0f4298b05fe\",\n \"score\": \"0.6104978\",\n \"text\": \"def set_keywords(self, **kwargs):\\n keywords = dict()\\n\\n for key, value in self.allowed_keys.items():\\n keywords[key] = value[1]\\n\\n for key, value in kwargs.items():\\n if key not in self.allowed_keys:\\n error = 'Keyword %s for %s object not found' % \\\\\\n (key, self.__class__.__name__)\\n MASTError(self.__class__.__name__, error)\\n\\n# raise RuntimeError('Keyword %s for %s object not found' % \\\\\\n# (key, self.__class__.__name__))\\n\\n if isinstance(value, self.allowed_keys[key][0]):\\n keywords[key] = value\\n else:\\n error = 'Keyword %s value %s invalid; expected type %s, got type %s' % (key, str(value), self.allowed_keys[key][0], type(value))\\n MASTError(self.__class__.__name__, error)\\n# raise RuntimeError('Keyword %s value invalid' % key)\\n\\n return keywords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bbd7569fda53d09b240244ff97bf898\",\n \"score\": \"0.6093302\",\n \"text\": \"def get_interface_initialization_kwargs(self, **kwargs) -> dict:\\n return {\\n key: value\\n for key, value in kwargs.items()\\n if not self.input_definitions.get(key=key).run_method_input\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc5770ec6276a9e821b6569b0d65a68\",\n \"score\": \"0.60870683\",\n \"text\": \"def add_kwargs():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d67bd389a01bbd3d615dbce79e1089d\",\n \"score\": \"0.608291\",\n \"text\": \"def get_kwargs(self):\\n return {\\n 'user': self.user,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d9188fa24a75f9bc16424874442892\",\n \"score\": \"0.6068618\",\n \"text\": \"def keywords(self):\\n return list(self._kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab17884ce2cab6037bef04e6a8b10343\",\n \"score\": \"0.6042146\",\n \"text\": \"def get_kwargs(d):\\n return {\\\"categories\\\": d.get(\\\"categories\\\", None)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b9fdd6c17b34ea280da22bd9864b69\",\n \"score\": \"0.6038479\",\n \"text\": \"def keywords(self):\\n return {\\n \\\"unary\\\": {\\n k: v[0] for k, v in self.unary_commands.items()\\n },\\n \\\"terminal\\\": {\\n k: v[0] for k, v in self.terminal_commands.items()\\n },\\n \\\"binary\\\": {\\n k: v[0] for k, v in self.binary_commands.items()\\n },\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec910a867ede5dc46e0ff70f4517c70e\",\n \"score\": \"0.60223377\",\n \"text\": \"def get_kwargs(d):\\n return {\\\"values\\\": d.get(\\\"values\\\", None)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99267e883d93e40444a2df2459d95c89\",\n \"score\": \"0.6022138\",\n \"text\": \"def parameters(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cc7bd09b640e88abaadb907af68a624\",\n \"score\": \"0.6011194\",\n \"text\": \"def get_xx_args_dict(self):\\n return self.__xx_args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25755864700935fb9547dfbb400d7dda\",\n \"score\": \"0.6009005\",\n \"text\": \"def get_keys_info() -> Dict[str, List[str]]:\\n args_dict = {}\\n\\n for api in API_DICT:\\n arg_list = list(\\n getattr(\\n sys.modules[__name__], \\\"set_\\\" + str(api) + \\\"_key\\\"\\n ).__code__.co_varnames\\n )\\n arg_list.remove(\\\"persist\\\")\\n arg_list.remove(\\\"show_output\\\")\\n args_dict[api] = arg_list\\n\\n return args_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2043dae7c91fac71a27e12d53fd157ac\",\n \"score\": \"0.6005944\",\n \"text\": \"def transformer_arguments(self) -> Dict[str, Any]:\\n return {**self.transformer_required_arguments(), **self.transformer_optional_arguments()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77e04f982f4eb441b93d9280d7aebde\",\n \"score\": \"0.60049504\",\n \"text\": \"def test_kw_args_with_positional():\\n assert arguments.fun_opt_kw_params('blue', 'red', 'yellow',\\n 'orange') == ('blue', 'red', 'yellow',\\n 'orange')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9498fc08a1178f221c01bb8ff69114e6\",\n \"score\": \"0.5992732\",\n \"text\": \"def as_kwargs(self) -> Dict[str, Parameter]:\\n dict_out = {}\\n\\n # make all parameter paths absolute\\n try:\\n absolute_parameters = self.make_unc_paths(self.parameters)\\n except NoAbsoluteRootPathException as e:\\n raise ParameterMappingError(e) from e\\n\\n for parameter in absolute_parameters:\\n if self.is_non_keyword(parameter):\\n # This parameter should not be included in kwargs. Skip\\n continue\\n elif self.is_source_identifier(parameter):\\n if self.is_pacs_type(parameter):\\n dict_out[\\\"source_instance_id\\\"] = get_legacy_idis_value(\\n parameter.value\\n )\\n elif self.is_path_type(parameter):\\n dict_out[\\\"source_path\\\"] = str(parameter.value)\\n else:\\n raise ParameterMappingError(\\n f\\\"Unknown source parameter '{parameter}'\\\"\\n )\\n else:\\n try:\\n dict_out[self.PARAMETER_KEYWORDS[type(parameter)]] = str(\\n parameter.value\\n )\\n except KeyError as e:\\n raise ParameterMappingError(\\n f\\\"Unknown parameter '{parameter}'\\\"\\n ) from e\\n\\n return dict_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c868f874b5d18d95c823fde6e50ffcbc\",\n \"score\": \"0.5988258\",\n \"text\": \"def get_params(self):\\n return {'k': self.k, 'q': self.q, 'sigma_s': self.sigma_s, 'm': self.m}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9f1c1c13cd09c0266b56be78dab43c7\",\n \"score\": \"0.59880024\",\n \"text\": \"def get_args(self):\\n rqst = self.request\\n args = rqst.arguments()\\n resp = {}\\n for arg in args:\\n resp[arg] = repr(rqst.get_all(arg))\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"460734bae9fda381962d0810fe0df1c7\",\n \"score\": \"0.59809464\",\n \"text\": \"def _collect_kwargs(step):\\n dicts = {}\\n for s in _expand_inputs(step):\\n name = s.name if s.name is not None else s.__class__.__name__\\n if name in dicts.keys():\\n raise ValueError(\\\"Duplicate step names: %s\\\" % name)\\n\\n d = dict(s._kwargs)\\n d.pop('inputs', None)\\n dicts[name] = d\\n\\n return dicts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd149768ff6070df7c98527d1cf0dfa7\",\n \"score\": \"0.5974165\",\n \"text\": \"def _get_url_params_as_dict(_request):\\n return _multi_dict_to_dict(_request.args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8e5b9d06c76aa0df898341bd1cd2299\",\n \"score\": \"0.5972164\",\n \"text\": \"def build_arg_list(fn, env):\\r\\n kw = {}\\r\\n argspec = inspect.getargspec(fn)\\r\\n\\r\\n # if there is a **kw argument in the fn definition,\\r\\n # just pass along the environment\\r\\n if argspec[2]:\\r\\n kw = env\\r\\n #else for each entry in the arglist set the value from the environment\\r\\n else:\\r\\n #skip self\\r\\n argnames = argspec[0][1:]\\r\\n for name in argnames:\\r\\n if name in env:\\r\\n kw[name] = env[name]\\r\\n return kw\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd54fbd03d445fa19aece5f4de879437\",\n \"score\": \"0.5968557\",\n \"text\": \"def _build_flags_hparam_dict():\\n logging.info('Show FLAGS for debugging:')\\n for f in HPARAM_FLAGS:\\n logging.info('%s=%s', f, FLAGS[f].value)\\n hparam_dict = collections.OrderedDict([\\n (name, FLAGS[name].value) for name in HPARAM_FLAGS\\n ])\\n return hparam_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a53d75fb0e00ad24c8a4318847f739\",\n \"score\": \"0.59648883\",\n \"text\": \"def get_input_arguments(kwargs, function, warn=True):\\n np.set_printoptions(threshold=20)\\n print('\\\\narguments to {}:'.format(function.__qualname__))\\n params = inspect.signature(function)\\n input_kwargs = {}\\n not_arguments = {}\\n for k, v in kwargs.items():\\n if k in params.parameters:\\n input_kwargs[k] = v\\n print_item(k, v)\\n else:\\n not_arguments[k] = v\\n if warn:\\n print('\\\\nother arguments:')\\n for k, v in not_arguments.items():\\n #print('{}: {}'.format(k, v))\\n print_item(k, v)\\n print('\\\\n')\\n return input_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a53d75fb0e00ad24c8a4318847f739\",\n \"score\": \"0.59648883\",\n \"text\": \"def get_input_arguments(kwargs, function, warn=True):\\n np.set_printoptions(threshold=20)\\n print('\\\\narguments to {}:'.format(function.__qualname__))\\n params = inspect.signature(function)\\n input_kwargs = {}\\n not_arguments = {}\\n for k, v in kwargs.items():\\n if k in params.parameters:\\n input_kwargs[k] = v\\n print_item(k, v)\\n else:\\n not_arguments[k] = v\\n if warn:\\n print('\\\\nother arguments:')\\n for k, v in not_arguments.items():\\n #print('{}: {}'.format(k, v))\\n print_item(k, v)\\n print('\\\\n')\\n return input_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e94ef3b6d295e31ad969dc254197e2a4\",\n \"score\": \"0.59642196\",\n \"text\": \"def filter_args(**kwargs):\\n valid_args = \\\"tol_num_frames\\\"\\n\\n d = dict((k, kwargs[k]) for k in valid_args if k in kwargs)\\n\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31f67f16c01a6954ba9e6bc8cd18c995\",\n \"score\": \"0.5963277\",\n \"text\": \"def arglist2dict(args):\\n arg_dict = {}\\n\\n if len(args) == 0:\\n return arg_dict\\n\\n if not args[0].startswith('--'):\\n raise ValueError(f\\\"Positional keywords are not supported: {args[0]}\\\")\\n\\n i = 0\\n while i < len(args):\\n arg = args[i]\\n i = i + 1\\n if arg.startswith('--'):\\n dest = arg[2:]\\n j, arglist = Parser.get_args(args[i:])\\n i = i + j\\n Parser.update_arg_dict(arg_dict, dest, arglist)\\n return arg_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a155c58acc6d5e6034b7afbf9d4aeed0\",\n \"score\": \"0.5962944\",\n \"text\": \"def format_arguments(self, **kwargs):\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6be268ad041e4c815ebf082212a0fa6f\",\n \"score\": \"0.59613454\",\n \"text\": \"def parameters_dict(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ade32566fbe20afbeb3bd2e060dd7f\",\n \"score\": \"0.59456944\",\n \"text\": \"def parameters(self) -> Dict[str, Any]:\\n return self.data[\\\"args\\\"].get(\\\"parameters\\\", {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"860345a34389995083ecf9585ffd8009\",\n \"score\": \"0.5921932\",\n \"text\": \"def keyword_subset(keyword_arguments, allowed_keywords):\\n keywords = set(keyword_arguments.keys())\\n keyswords_to_extract = keywords.intersection(allowed_keywords)\\n\\n new_kw = {key: keyword_arguments[key] for key in keyswords_to_extract}\\n\\n return new_kw\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcb889e90380c4dc7e5b3f6fef63a83d\",\n \"score\": \"0.5918953\",\n \"text\": \"def parameters_dict(self):\\n return dict(zip(self.parameters_names(), self.parameters_list))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd3ed5bd746d57e16c70d34a075ac181\",\n \"score\": \"0.59147024\",\n \"text\": \"def get_args_dict(class_, options: Options) -> dict:\\n\\n argspec = getfullargspec(class_.__init__)\\n init_args = argspec.args\\n init_args.pop(0) # self\\n result = {k: v for k, v in options.items() if k in init_args}\\n\\n positional_args = init_args[:-len(argspec.defaults)]\\n\\n missing_args = [a for a in positional_args if a not in options]\\n if missing_args:\\n raise BadConfigError(\\n f'Some required parameters are missing in \\\"{options[\\\"name\\\"]}\\\" config: ' +\\n ', '.join(missing_args)\\n )\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c1c36486fb85f005ca29d6e9e0fd724\",\n \"score\": \"0.5905406\",\n \"text\": \"def __repr_args__(self):\\n args = dict(super().__repr_args__())\\n try:\\n del args['type_hint']\\n except KeyError:\\n pass\\n return args.items()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da6140dbd644f216bbbd8a2b9a53e443\",\n \"score\": \"0.59033114\",\n \"text\": \"def _get_param_names(self):\\n temp_params = {'function': self.function, 'target': self.target}\\n\\n temp_params.update(self.kwargs)\\n\\n return temp_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"130cb9b8206a065137be5053b46043a7\",\n \"score\": \"0.5897415\",\n \"text\": \"def get_required_kwargs(fun, skip_positional=0):\\n sig = inspect.signature(fun)\\n # the params from signature with up to skip_positional filtered out\\n # (less only if there is not enough of positional args)\\n params = [(name, param) for i, (name, param) in enumerate(sig.parameters.items())\\n if i >= skip_positional or param.kind not in\\n [inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.POSITIONAL_ONLY]]\\n return [\\n name for name, param in params if param.default is inspect.Parameter.empty\\n and param.kind in [inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.KEYWORD_ONLY]\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2165a4a5fb8606118c0911edbbd05bb\",\n \"score\": \"0.58956957\",\n \"text\": \"def mof_metadata(self):\\n\\n arg_dict = collections.defaultdict(dict)\\n\\n arg_dict[self.name]['type'] = self.arg_type\\n arg_dict[self.name]['qualifiers'] = self.qualifiers\\n arg_dict[self.name]['valuemap'] = self.valuemap\\n\\n return dict(arg_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afdfe6231dcf892a074060b09b9a4797\",\n \"score\": \"0.5893621\",\n \"text\": \"def init(kwargs: Dict[str, str]):\\n\\n return {key: value for key, value in kwargs.items() if '-' not in key}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2098ff7013874ca8eb0bc4b564d992ac\",\n \"score\": \"0.5883135\",\n \"text\": \"def request_params(self):\\n return {'key': self.key, 'hash': self.hash}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cef83b78c2cc05c47867d8b442fcb147\",\n \"score\": \"0.5882102\",\n \"text\": \"def get_params(self, deep: bool = True) -> Dict[str, Any]:\\n return _get_params(self, deep)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2a1fba9e6d90910ccf5a3fc6ce26525\",\n \"score\": \"0.58641005\",\n \"text\": \"def get_request_extra_params(self, **kwargs):\\n params = self.request_extra_params.copy()\\n params.update(kwargs)\\n return params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59795fe24e20c11750d7a8bce8556516\",\n \"score\": \"0.5859504\",\n \"text\": \"def kwargs (self):\\n return dict (bins=self.bins, range=self.range)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59795fe24e20c11750d7a8bce8556516\",\n \"score\": \"0.5859504\",\n \"text\": \"def kwargs (self):\\n return dict (bins=self.bins, range=self.range)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d56ef93043de9e1a4b06b66c2cee1c\",\n \"score\": \"0.58571553\",\n \"text\": \"def keyword_only(func):\\n\\n @wraps(func)\\n def wrapper(self, *args, **kwargs):\\n if len(args) > 0:\\n raise TypeError(\\\"Method %s forces keyword arguments.\\\" % func.__name__)\\n self._input_kwargs = kwargs\\n return func(self, **kwargs)\\n\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a09960ba4e51351436445e51007f003a\",\n \"score\": \"0.5836156\",\n \"text\": \"def getInitParams(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f75209bd48311eb0f28581e576f1824\",\n \"score\": \"0.5832235\",\n \"text\": \"def get_kwargs_applicable_to_function(function, kwargs):\\n return {\\n key: value\\n for key, value in kwargs.items()\\n if key in inspect.getfullargspec(function).args\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc6d7bb715747a85634d316b55d6a949\",\n \"score\": \"0.5819433\",\n \"text\": \"def data(self, **kw):\\n return dict(params=kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc6d7bb715747a85634d316b55d6a949\",\n \"score\": \"0.5819433\",\n \"text\": \"def data(self, **kw):\\n return dict(params=kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c11c02815fa940c752aeb55a8ab0fb2\",\n \"score\": \"0.58167654\",\n \"text\": \"def keywords(self, **kwargs):\\n\\n path = self._get_movie_id_path('keywords')\\n resp = self._get_method(path, kwargs)\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1e582e7a519e49f6e67e826c95309ac\",\n \"score\": \"0.5814989\",\n \"text\": \"def _get_init_args(self):\\n signature = inspect.signature(self.__init__)\\n parameters = signature.parameters\\n args = [arg for arg, p in parameters.items()\\n if p.kind is p.POSITIONAL_OR_KEYWORD]\\n\\n return {arg: getattr(self, arg) for arg in args if arg != 'self'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac9cb4dec37dc3f5a316774d99226707\",\n \"score\": \"0.5805785\",\n \"text\": \"def extra_target_arguments(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ff3aeb392b2d4d82f292bc0161596c\",\n \"score\": \"0.5802602\",\n \"text\": \"def _get_context(argspec, kwargs):\\n if argspec.has_kwargs:\\n return kwargs\\n return dict((arg, kwargs[arg]) for arg in argspec.args if arg in kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a2fb4075af7fff84fca517cac0e82cf\",\n \"score\": \"0.5802534\",\n \"text\": \"def get_kwargs(d):\\n return {\\\"range\\\": d.get(\\\"range\\\", None)}\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1789,"cells":{"query_id":{"kind":"string","value":"b212fc884b3873a73d7366dea6bc607c"},"query":{"kind":"string","value":"Create 'Submit' tab and add to UI frame."},"positive_passages":{"kind":"list like","value":[{"docid":"1003d833f4bf399f5e9c94f583707d2a","score":"0.5240085","text":"def __init__(self, base, frame):\n self.base = base\n self.label = \"Submit\"\n self.page = maya.form_layout(enableBackground=True) \n self.select_pool_type = 1\n self.select_instances = 1\n \n with utils.ScrollLayout(height=475, parent=self.page) as scroll:\n box_label = \"Pool Settings\"\n with utils.FrameLayout(label=box_label, collapsable=True):\n self.pool_settings = maya.col_layout(\n numberOfColumns=2,\n columnWidth=((1, 100), (2, 200)),\n rowSpacing=(1, 10),\n rowOffset=((1, \"top\", 20), (2, \"bottom\", 20)))\n maya.text(label=\"Pools: \", align=\"right\")\n maya.radio_group(\n labelArray3=(\"Auto provision a pool for this job\",\n \"Reuse an existing persistent pool\",\n \"Create a new persistent pool\"),\n numberOfRadioButtons=3,\n select=self.select_pool_type,\n vertical=True,\n onCommand1=self.set_pool_auto,\n onCommand2=self.set_pool_reuse,\n onCommand3=self.set_pool_new)\n self.pool_text = maya.text(\n label=\"Instances: \", align=\"right\")\n self.control = maya.int_slider(\n field=True, value=self.select_instances,\n minValue=1,\n maxValue=1000,\n fieldMinValue=1,\n fieldMaxValue=1000,\n changeCommand=self.set_pool_instances,\n annotation=\"Number of instances in pool\")\n maya.parent()\n\n box_label = \"Render Settings\"\n with utils.FrameLayout(label=box_label, collapsable=True) as box:\n self.render_module = box\n\n with utils.Row(3, 2, (120,200,35)) as watch:\n self.job_watcher_ui()\n\n with utils.Row(1, 1, 355, \"center\") as s_btn:\n self.submit_button = utils.ProcButton(\n \"Submit Job\", \"Submitting...\", self.submit)\n\n with utils.Row(1, 1, 355, \"center\", (1,\"bottom\",0)) as r_btn:\n self.refresh_button = utils.ProcButton(\n \"Refresh\", \"Refreshing...\", self.refresh)\n\n maya.form_layout(\n self.page, edit=True,\n attachForm=[(scroll, 'top', 5),\n (scroll, 'left', 5), (scroll, 'right', 5),\n (watch, 'left', 0), (watch, 'right', 0),\n (s_btn, 'left', 0), (s_btn, 'right', 0),\n (r_btn, 'bottom', 5),\n (r_btn, 'left', 0), (r_btn, 'right', 0)],\n attachControl=[(scroll, \"bottom\", 5, watch),\n (watch, \"bottom\" ,5, s_btn),\n (s_btn, \"bottom\", 5, r_btn)])\n frame.add_tab(self)","title":""}],"string":"[\n {\n \"docid\": \"1003d833f4bf399f5e9c94f583707d2a\",\n \"score\": \"0.5240085\",\n \"text\": \"def __init__(self, base, frame):\\n self.base = base\\n self.label = \\\"Submit\\\"\\n self.page = maya.form_layout(enableBackground=True) \\n self.select_pool_type = 1\\n self.select_instances = 1\\n \\n with utils.ScrollLayout(height=475, parent=self.page) as scroll:\\n box_label = \\\"Pool Settings\\\"\\n with utils.FrameLayout(label=box_label, collapsable=True):\\n self.pool_settings = maya.col_layout(\\n numberOfColumns=2,\\n columnWidth=((1, 100), (2, 200)),\\n rowSpacing=(1, 10),\\n rowOffset=((1, \\\"top\\\", 20), (2, \\\"bottom\\\", 20)))\\n maya.text(label=\\\"Pools: \\\", align=\\\"right\\\")\\n maya.radio_group(\\n labelArray3=(\\\"Auto provision a pool for this job\\\",\\n \\\"Reuse an existing persistent pool\\\",\\n \\\"Create a new persistent pool\\\"),\\n numberOfRadioButtons=3,\\n select=self.select_pool_type,\\n vertical=True,\\n onCommand1=self.set_pool_auto,\\n onCommand2=self.set_pool_reuse,\\n onCommand3=self.set_pool_new)\\n self.pool_text = maya.text(\\n label=\\\"Instances: \\\", align=\\\"right\\\")\\n self.control = maya.int_slider(\\n field=True, value=self.select_instances,\\n minValue=1,\\n maxValue=1000,\\n fieldMinValue=1,\\n fieldMaxValue=1000,\\n changeCommand=self.set_pool_instances,\\n annotation=\\\"Number of instances in pool\\\")\\n maya.parent()\\n\\n box_label = \\\"Render Settings\\\"\\n with utils.FrameLayout(label=box_label, collapsable=True) as box:\\n self.render_module = box\\n\\n with utils.Row(3, 2, (120,200,35)) as watch:\\n self.job_watcher_ui()\\n\\n with utils.Row(1, 1, 355, \\\"center\\\") as s_btn:\\n self.submit_button = utils.ProcButton(\\n \\\"Submit Job\\\", \\\"Submitting...\\\", self.submit)\\n\\n with utils.Row(1, 1, 355, \\\"center\\\", (1,\\\"bottom\\\",0)) as r_btn:\\n self.refresh_button = utils.ProcButton(\\n \\\"Refresh\\\", \\\"Refreshing...\\\", self.refresh)\\n\\n maya.form_layout(\\n self.page, edit=True,\\n attachForm=[(scroll, 'top', 5),\\n (scroll, 'left', 5), (scroll, 'right', 5),\\n (watch, 'left', 0), (watch, 'right', 0),\\n (s_btn, 'left', 0), (s_btn, 'right', 0),\\n (r_btn, 'bottom', 5),\\n (r_btn, 'left', 0), (r_btn, 'right', 0)],\\n attachControl=[(scroll, \\\"bottom\\\", 5, watch),\\n (watch, \\\"bottom\\\" ,5, s_btn),\\n (s_btn, \\\"bottom\\\", 5, r_btn)])\\n frame.add_tab(self)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"49a8b321adaf67148f61d57eef97bcc1","score":"0.62406707","text":"def create_new_tab(self, page=-1):\n new_tab = STATDotWindow.Tab()\n tab_label_box = gtk.EventBox()\n tab_label_box.add(new_tab.label)\n tab_label_box.connect('event', self.on_tab_clicked, new_tab.widget)\n new_tab.label.show()\n new_tab.widget.connect('clicked', self.on_node_clicked)\n if page == -1:\n page = len(self.tabs)\n self.notebook.insert_page(new_tab.hpaned, tab_label_box, page)\n self.tabs.insert(page, new_tab)\n self.set_focus(new_tab.widget)\n self.show_all()","title":""},{"docid":"eded1d52a34df50f44ebaab92c221a27","score":"0.61924666","text":"def new_tab(self):\n layout = QtWidgets.QGridLayout()\n layout.setSpacing(1)\n layout.setContentsMargins(0, 0, 0, 0)\n widget = GlueMdiArea(self)\n widget.setLayout(layout)\n tab = self.tab_widget\n self._total_tab_count += 1\n tab.addTab(widget, str(\"Tab %i\" % self._total_tab_count))\n tab.setCurrentWidget(widget)\n widget.subWindowActivated.connect(self._update_plot_dashboard)","title":""},{"docid":"b7d8d9e3eb89d001fe00712c8e5d31ae","score":"0.61313283","text":"def add_tab(self, text, icon=None):\n# button = ButtonTab() # If a pushbutton\n button = ButtonTab()\n button.setCheckable(True)\n button.setText(text)\n button.setToolButtonStyle(Qt.ToolButtonTextUnderIcon)\n button.setObjectName(text.lower())\n\n if icon:\n button.setIcon(icon)\n\n self.main_layout.addWidget(button)\n self.buttons.append(button)\n index = self.buttons.index(button)\n button.clicked.connect(lambda b=button, i=index:\n self.refresh(button, index))","title":""},{"docid":"8ac950075c326805e74324938b4e5126","score":"0.6129389","text":"def submit(self):\r\n self.btn_submit.click()","title":""},{"docid":"67d105e01d071cee06bb926128b0ca8f","score":"0.6030572","text":"def __createGreetingsTab(self):\r\n\r\n #Define the tab itself and add it to the notebook\r\n greetingsTab = ttk.Frame(self.note)\r\n self.note.add(greetingsTab, text = 'Greeting')\r\n\r\n #Define the greeting that will appear\r\n greeting = \\\r\n 'Welcome to the DCT Reduction Pipeline\\n' \\\r\n 'GUI. This program was created by the BU\\n' \\\r\n 'PyCon team and is designed for reducing\\n' \\\r\n 'and analyzing images specifically from\\n' \\\r\n 'the 4.3 m Discovery Channel Telescope.'\r\n\r\n #Place the greeting as a label and create two buttons, the configure the layout\r\n ttk.Label(greetingsTab, text = greeting, font = ('Century Gothic', 14, 'bold'), anchor = 'w', justify = 'left').grid(row = 0, column = 0, columnspan = 2, padx = 20, pady = 20)\r\n ttk.Button(greetingsTab, text = 'START', command = lambda: self.note.select(self.inputTab)).grid(row = 1, column = 0, sticky = 'nswe', padx = 5, pady = 5)\r\n ttk.Button(greetingsTab, text = 'EXIT', command = lambda: self.root.destroy()).grid(row = 1, column = 1, sticky = 'nswe', padx = 5, pady = 5)\r\n greetingsTab.rowconfigure(1, minsize = 50)\r\n greetingsTab.columnconfigure(0, weight = 10)\r\n greetingsTab.columnconfigure(1, weight = 1)\r\n\r\n return greetingsTab","title":""},{"docid":"d1b6a9485558278028681947f70db196","score":"0.60176766","text":"def submit():\n return render_template('submit.html')","title":""},{"docid":"d9aa05aa2bd66ace649a132f84a00fac","score":"0.59464854","text":"def make_tabs(self):\n tab1 = self.arima_tab()\n tab2 = self.prophet_tab()\n tab3 = self.lstm_tab()\n\n tabs = Tabs(tabs=[tab1, tab2, tab3])\n\n dt_pckr_strt = DatePicker(title='Select start of sync date',\n min_date=date(2017, 1, 1),\n max_date=date.today())\n start_picker_callback = CustomJS(args=dict(plot=self.plot),\n code=js.BUTTON_DATE)\n dt_pckr_strt.js_on_change('value', start_picker_callback)\n\n button_pred = Button(label=\"Predict\",\n button_type=\"success\")\n\n button_pred_callback = CustomJS(args=dict(plot=tabs),\n code=js.BUTTON_PRED)\n button_pred.js_on_click(button_pred_callback)\n\n tab_callback = CustomJS(args=dict(plot=tabs),\n code=js.BUTTON_TAB)\n tabs.js_on_change('active', tab_callback)\n\n end_date = date.today() + timedelta(weeks=+4)\n dt_pckr_end = DatePicker(title='Select end of sync date',\n min_date=date.today(),\n max_date=end_date)\n end_picker_callback = CustomJS(args=dict(plot=self.plot),\n code=js.BUTTON_DATE_END)\n dt_pckr_end.js_on_change('value', end_picker_callback)\n\n but_picker = column(dt_pckr_strt,\n dt_pckr_end,\n button_pred)\n\n layout = row(but_picker, tabs)\n\n script_tab, div_tab = components(layout)\n\n return script_tab, div_tab","title":""},{"docid":"7992036a5c02a11dfbb79fd525cbfe34","score":"0.58786106","text":"def MainTabs(self):\r\n self.AddingEventTab=ttk.Notebook(self.root)\r\n self.tab1= ttk.Frame(self.AddingEventTab)\r\n self.tab2= ttk.Frame(self.AddingEventTab)\r\n self.tab3= ttk.Frame(self.AddingEventTab)\r\n self.AddingEventTab.add(self.tab1, text=\"ADD EVENT\")\r\n self.AddingEventTab.add(self.tab2, text=\"RETRIEVE EVENT\")\r\n self.AddingEventTab.add(self.tab3, text=\"DELETE EVENT\")\r\n self.AddingEventTab.pack(expand=1,pady=5, fill=\"both\")","title":""},{"docid":"db7863502149502b87274f790a8695b2","score":"0.5824937","text":"def submit_assignment():\n postback_form = PostbackForm()\n return render_template(\"mturk/postback.html\",\n mturk_post_url=session[\"mturk_post_url\"],\n mturk_assignmentId=session[\"mturk_assignmentId\"],\n mturk_hitId=session[\"mturk_hitId\"],\n postback_form=postback_form)","title":""},{"docid":"2d45e7e5d07794b2a48a85364b2d0610","score":"0.58155704","text":"def createTab(self, tab_name='New tab', file_name=None, highlight=True):\n new_tab = Tab(self.notebook, file_name=file_name, highlight=highlight)\n self.notebook.add(new_tab, text=tab_name)\n last_added = self.notebook.index('end')\n self.notebook.select([last_added - 1])\n return new_tab","title":""},{"docid":"10ebf42f3b7b3aca3f5bb03df2012d7d","score":"0.5793726","text":"def add_tab(self, interface):\n self.notebook.AddPage(interface, interface.name)","title":""},{"docid":"7da771df3eb19adfcf45b87461c1addd","score":"0.57392085","text":"def do(self):\n self.ui.model.add_frame(self.frame)\n self.ui.append_frame_tree(-1)\n self.ui.select_frame_at_index(-1)","title":""},{"docid":"0718f78b46d95c12584016809e633466","score":"0.57334876","text":"def _place_submit_button(self) -> None:\r\n self._submit_button = tk.Button(self._frame, text='Submit', command=self._search,\r\n font=('arialnarrow', 20), bg='forest green', fg='white')\r\n self._submit_button.pack()\r\n self._submit_button.place(x=(WINDOW_WIDTH)//2, y=WINDOW_HEIGHT//1.25, anchor='center',\r\n width=WINDOW_WIDTH//4, height=100)","title":""},{"docid":"7cc53b6dc51afebe480b9b9a83e7ce82","score":"0.5688593","text":"def create_page(self):\n self.lastname_label.grid(row=0, column=0, padx=20, pady=(0, 5), stick=tk.W)\n self.lastname_entry.grid(row=0, column=1, pady=(0, 5))\n self.firstname_label.grid(row=1, column=0, padx=20, pady=(0, 5), sticky=tk.W)\n self.firstname_entry.grid(row=1, column=1, pady=(0, 5))\n self.submit_button.grid(row=0, column=3, rowspan=2, sticky=tk.N+tk.S, padx=20, pady=(0, 5))\n self.message_label.grid(row=2, column=0, columnspan=4, padx=20, pady=(15, 15), ipady=5, sticky=tk.W+tk.E)\n\n self.title_label.pack(fill=\"both\", pady=20)\n self.frame.pack(expand=True)\n\n return self.container","title":""},{"docid":"f532f6179f8118686fd8d90c1cb0fd12","score":"0.5680229","text":"def create_display_tabs(self):","title":""},{"docid":"3bbb6ac1297486af9ca0cb0ede6598e8","score":"0.5680064","text":"def _place_submit_button(self) -> None:\r\n self._submit_button = tk.Button(self._frame, text='Submit', command=self._search,\r\n font=('arialnarrow', 15), bg='forest green', fg='white')\r\n self._submit_button.pack()\r\n self._submit_button.place(x=WINDOW_WIDTH - 10, y=(WINDOW_HEIGHT //15),\r\n width=WINDOW_WIDTH//10, height=50, anchor='e',)","title":""},{"docid":"e54d49b52bbbbc1b3a6e1a5e44cffc3e","score":"0.56779075","text":"def new_tab(self, url=\"\", tab_name=\"\"):\r\n if self.open:\r\n pyprint.write(\"Creating new tab...\")\r\n self.browser.find_element_by_tag_name('body').send_keys(Keys.CONTROL + 't')\r\n self.browser.switch_to.window(self.browser.window_handles[len(self.urls)])\r\n if url and tab_name:\r\n self.urls.append(url)\r\n self.tab_names.append(tab_name)\r\n self.current_tab = len(self.urls) - 1\r\n self.load(url, tab_name, self.current_tab)\r\n else:\r\n self.urls.append(\"\")\r\n self.tab_names.append(\"\")\r\n self.current_tab = len(self.urls) - 1\r\n return self.current_tab","title":""},{"docid":"ae29b91b4e2da990334028341a2b9834","score":"0.56756747","text":"def addTab(self, QWidget, *__args): # real signature unknown; restored from __doc__ with multiple overloads\n return 0","title":""},{"docid":"9b6cc0c9c75baeb52462b19db73b152a","score":"0.56719065","text":"def do(self):\n self.ui.clear_form()\n\n frame = SoziFrame(self.ui.model)\n frame.refid = self.ui.model.get_next_selected_id()\n\n self.ui.model.add_frame(frame)\n self.ui.append_frame_tree(-1)\n self.ui.select_frame_at_index(-1)","title":""},{"docid":"fe3ca88e6fb98a68994ef1257e9c7ece","score":"0.56595266","text":"def add_tab(self, tid, tab: TabButton, label: str = None, callbacks: list = None):\n if not callbacks:\n callbacks = []\n callbacks.append((self.__tab_controller.show_tab, tid, None))\n self.tabs.append((tab, label))\n self.__tab_controller.create_controller(tab_id=tid)\n tab._tab_group = self\n for callback in callbacks or []:\n callback, args, kwargs = callback\n tab.add_callback(callback, args, kwargs)","title":""},{"docid":"4c874c7ab4a7d7d0950db96355272c98","score":"0.56564707","text":"def _submit(self):\n el = self.d.find_element(*self.LOCS[\"submit\"])\n el.click()","title":""},{"docid":"685d566c2b16f9ade7eb9ad70c3d3e39","score":"0.564697","text":"def submit(self):\n pass","title":""},{"docid":"9a0671c8474ee55a23be8ee7682b4c7b","score":"0.56117016","text":"def add_experiment_tab(self, experiment, is_new): \n # If the welcome tab is open, close it.\n if self.is_welcome_tab_open:\n self.DeletePage(0) \n \n # Create new notebook with Experiment controls and display\n self.count += 1\n etab = tab_experiment.TabExperiment(self, self.top, self.db,\n experiment, is_new)\n \n self.AddPage(etab, \"Experiment%d\" % self.count, True)\n if is_new:\n etab.SetSelection(0)\n etab.simulate.SetFocus()","title":""},{"docid":"e1190f3badd7da1193b243a860edcc87","score":"0.5610358","text":"def init_new_experimental_step(self):\n \n # Create new experiment ui \n upload_btn = widgets.Button(description='Upload', tooltip='Upload')\n upload_btn.on_click(self.on_upload_clicked)\n \n exp_step_name = widgets.Text(description='New participant name:', placeholder='PROJECT_SUBJECT', style={'description_width':'initial'})\n exp_name = widgets.Text(description='Experiment:', placeholder='EXPERIMENT', style={'description_width':'initial'})\n project = widgets.Text(description='Project:', placeholder='PROJECT')\n space = widgets.Text(description='Space:', placeholder='SPACE')\n self.new_exp_ui = widgets.VBox([exp_step_name, exp_name, project, space, upload_btn])","title":""},{"docid":"a66715308b9e72977723fa00dddc04a0","score":"0.5577774","text":"def onNewFile(self):\n self.createTab()","title":""},{"docid":"a77556d1732130fa576db39462f3d3dd","score":"0.5552963","text":"def on_add_watref_tab(self, event):\n # If there's already a tab, just activate it.\n if self._tabs[\"quant\"]:\n self.NotebookDataset.activate_tab(self._tabs[\"quant\"])\n else:\n if self._tabs[\"fit\"]:\n # Create a new quant block and add a tab to go with it.\n block = self.dataset.add_watref()\n \n # The quant tab follows fitting\n tab = self._tabs[\"fit\"]\n index = self.NotebookDataset.get_tab_index(tab) + 1\n \n # We know the insertion point. Create and insert new tab.\n tab = tab_watref.TabWatref(self, self.top, block)\n self._tabs[\"quant\"] = tab\n \n self.NotebookDataset.InsertPage(index, tab, 'Quant', True)","title":""},{"docid":"32133d4fa8251046148d1aca93f36895","score":"0.5505739","text":"def on_tab_clicked(self, widget, event, child):\n if event.type == gtk.gdk.BUTTON_PRESS:\n n = -1\n for tab in self.tabs:\n n += 1\n if child == tab.widget:\n break\n self.notebook.set_current_page(n)\n if event.button == 3:\n options = ['New Tab', 'Close Tab']\n menu = gtk.Menu()\n for option in options:\n menu_item = gtk.MenuItem(option)\n menu.append(menu_item)\n menu_item.connect('activate', self.menu_item_response, option)\n menu_item.show()\n menu.popup(None, None, None, event.button, event.time)","title":""},{"docid":"0d3b0c65aa4105f07182827ff035b3c6","score":"0.5441618","text":"def submit(self):\n return","title":""},{"docid":"0d3b0c65aa4105f07182827ff035b3c6","score":"0.5441618","text":"def submit(self):\n return","title":""},{"docid":"b566b4ea651fd3ba7028eae597737dcc","score":"0.5430729","text":"def addTab(self, title):\n tab = Q.QWidget()\n tab.setContentsMargins(9, 9, 9, 9)\n self._tab.addTab(tab, title)\n self._tab.show()\n return tab","title":""},{"docid":"c21d82d00b75f5bf5cb54daca0e15dd6","score":"0.54004943","text":"def addTab(self, label, content):\n new_tab = Tab(label, content)\n self.tabs[label] = new_tab\n self.tab_list.append(new_tab)\n self.updateContent()\n return new_tab","title":""},{"docid":"02a51073bc442f83559de0eefc266238","score":"0.5390192","text":"def on_new_clicked(self):\n\n editor = PythonEditor()\n self.tab_widget.addTab(editor, self.py_icon, editor.filename)\n\n self.tab_widget.setCurrentWidget(editor)\n\n return editor","title":""},{"docid":"e45712feab20d9795097d4d5262b49b1","score":"0.5388206","text":"def addInitialTab(self, id, label, content):\n assert getattr(self, '_athenaID', None) is None, (\n \"Cannot call this after the widget has rendered.\")\n if self.widgetArgs is EMPTY:\n self.widgetArgs = []\n self.widgetArgs.append((id, label, content))","title":""},{"docid":"e6a5379244133ec4e9cf04822aed68f7","score":"0.5360094","text":"def build_tabs():\n temp_tab = \"<div style='display: flex;margin: 0 auto;width: 100%;' name='tabHolder'>\"\n for i in range(0, TAB_COUNT+1):\n # set tab 0 to selected by default\n className = 'tab-button-selected' if i ==0 else 'tab-button'\n # each button is triggers a function in the js file\n temp_tab += \"<button onclick='tabChanged(\"+str(i)+\")' name=tab-button-\"+str(i)+\" class='\"+className+\"' style='flex-grow:\" +str(100/TAB_COUNT)+ \"'> \"+TAB_NAMES[i]+\"</button>\"\n temp_tab += \"</div>\"\n return temp_tab","title":""},{"docid":"c5513e36f116c4b2e09f462c1e603668","score":"0.5346864","text":"def new_tab(self, widget, title, scrolled=True):\n\t\t\n\t\tif scrolled:\n\t\t\twindow = gtk.ScrolledWindow()\n\t\t\twindow.add(widget)\n\t\t\twindow.show_all()\n\t\telse:\n\t\t\twindow = widget\n\t\t\twindow.show()\n\t\t\t\n\t\t# Check if we have more than one tab, if so, show the tabs\n\t\tif (self.get_n_pages() + 1) > 1:\n\t\t\tself.set_property('show-tabs', True)\n\t\t\t\n\t\t# Create the tab label\n\t\tlabel = self.create_tab_label(title, window)\n\n\t\t# Append the page\n\t\tself.append_page(window)\n\t\tself.set_tab_label(window, label)\n\t\t\n\t\tself.set_current_page(self.get_n_pages())","title":""},{"docid":"d32e79c845dbe3909f8e2461fb1b1391","score":"0.5313238","text":"def insertTab(self, p_int, QWidget, *__args): # real signature unknown; restored from __doc__ with multiple overloads\n return 0","title":""},{"docid":"88d5b25b76462d9473aec304f69a9583","score":"0.53058505","text":"def newTab(self, text, bibtext, showBibtex, query):\n if self.neverOpened:\n self.resultsTabs.removeTab(0)\n self.neverOpened = False\n self.savedReformat.setEnabled(True)\n self.savedShowBibtex.setEnabled(True)\n self.openInBatch.setEnabled(True)\n tab = QTextBrowser()\n # enable editing\n # TODO save bibtex before reformat\n tab.setTextInteractionFlags(tab.textInteractionFlags() |\n Qt.TextEditable | Qt.TextEditorInteraction)\n tab.setOpenExternalLinks(True)\n tab.formattedtext = text\n tab.bibtext = bibtext\n tab.lastBibtex = showBibtex\n\n if showBibtex:\n tab.setText(self.HTMLsafe(bibtext, True))\n else:\n tab.setText(text)\n\n self.tabWidget.tabBar().setCurrentIndex(1)\n self.resultsTabs.addTab(tab, query)\n self.resultsTabs.tabBar().setCurrentIndex(self.resultsTabs.count()-1)\n tab.setFocus(True)","title":""},{"docid":"c01bae2410e5b794fec621941129c965","score":"0.5275982","text":"def new_tab(self, url, active = True):\n response = self.request({\n 'url': url,\n 'active': active\n }, 'newTab', True)\n\n return Tab.from_json(self, response['tab'])","title":""},{"docid":"9a29821c2f60c9c72c4ae753c30db3f8","score":"0.5274316","text":"def create_tab_label(self, title, tab):\n\t\t\n\t\thbox = gtk.HBox()\n\t\tcbtn = gtk.Button()\n\t\tlabel = gtk.Label(title)\n\t\timage = gtk.Image()\n\n\t\timage.set_from_stock(gtk.STOCK_CLOSE, gtk.ICON_SIZE_MENU)\n\t\tcbtn.set_image(image)\n\t\tcbtn.set_relief(gtk.RELIEF_NONE)\n\t\tcbtn.connect(\"clicked\", self.close_tab, tab)\n\n\t\thbox.pack_start(label, True, True)\n\t\thbox.pack_end(cbtn, False, False)\n\t\thbox.show_all()\n\t\t\n\t\treturn hbox","title":""},{"docid":"fd514c7f2f88d3b7b31168e376c00dac","score":"0.5264573","text":"def add_tab(self, file_path, tab_title):\n\n try:\n mapobject = sunpy.make_map(file_path)\n tab_page = TabPage(mapobject, self.tabWidget)\n self.tabWidget.addTab(tab_page, tab_title)\n \n # Focus new tab\n self.tabWidget.setCurrentIndex(self.tabWidget.count() - 1)\n \n # Set color options dialog appropriately\n self.initialize_color_options()\n if self.tabWidget.count() == 1:\n self.colorOptionsDockWidget.show()\n except TypeError, e:\n file_err = QMessageBox()\n file_err.setText(str(e) + '\\n' + file_path)\n file_err.exec_()","title":""},{"docid":"a41ed26b7567b76153fdf99dfaf74799","score":"0.5256108","text":"def menu_item_response(self, widget, string):\n if string == \"New Tab\":\n page = self.notebook.get_current_page()\n self.create_new_tab(page + 1)\n self.notebook.set_current_page(page + 1)\n elif string == \"Close Tab\":\n if len(self.tabs) == 1:\n self.create_new_tab()\n page = 0\n else:\n page = self.notebook.get_current_page()\n self.notebook.remove_page(page)\n self.tabs.remove(self.tabs[page])","title":""},{"docid":"c05a3c5a1114ac55a1ebabb9be27b4d8","score":"0.5223002","text":"def submitContent(self):\n newContent = self.newText.get(1.0,END) # Putting user entry into text box in a variable.\n webPage_functions.writeBody(\"pyWebPage.html\", newContent) # Write new content to file.","title":""},{"docid":"e4c8eabddbc374413deaceebc5619bbe","score":"0.5194204","text":"def submit_button_clicked(self):\n\t\t# check all entry variables for valid paths/exts\n\t\t# we do global check on variables so all input errors can be shown at once\n\t\tself.check_verbatims()\n\t\tself.check_output()\n\t\t\n\t\t# ask if we're ready to be submitted\n\t\t# here we stack checks based on priority for report\n\t\t\n\t\t# for appendix, all input fields need to be filled out for report to run\n\t\tif self.check_verbatims():\n\t\t\tif self.check_output():\n\t\t\t\t verbatims_path = os.path.join(self.verbatims_entry.get())\n\t\t\t\t output_path = os.path.join(self.output_entry.get())\n\t\t\t\t # ask controller to run files\n\t\t\t\t self.controller.run_appendix(verbatims_path, output_path)","title":""},{"docid":"292de5ba0281cee894c18985d3c19f0b","score":"0.51780456","text":"def load_view_attack_tab(self):\n layout = QtWidgets.QVBoxLayout()\n layout.addWidget(self.view_attack)\n\n tab = QtWidgets.QWidget()\n tab.setLayout(layout)\n\n self.view_tabs.addTab(tab, \"MITRE\")","title":""},{"docid":"ebc90118b510d57a4f46956ef39bc42d","score":"0.516823","text":"def _create_assessment_inputs_page(self, notebook): # pylint: disable=R0914, R0915\r\n\r\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\r\n # Build-up the containers for the tab. #\r\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\r\n _bbox = gtk.VButtonBox()\r\n _bbox.set_layout(gtk.BUTTONBOX_START)\r\n\r\n _hbox = gtk.HBox()\r\n _hbox.pack_start(_bbox, False, True)\r\n\r\n _hpaned = gtk.HPaned()\r\n\r\n _hbox.pack_end(_hpaned, True, True)\r\n\r\n _hpaned2 = gtk.HPaned()\r\n _hpaned.pack1(_hpaned2, True, False)\r\n\r\n _bbox.pack_start(self.btnCalculate, False, False)\r\n _bbox.pack_end(self.btnCalculateAll, False, False)\r\n\r\n # --------------------------------------------------------------#\r\n # Build the quadrant 1 (left) container. #\r\n # --------------------------------------------------------------#\r\n # Add the layout for stress inputs.\r\n _fixed1 = gtk.Fixed()\r\n\r\n _scrollwindow = gtk.ScrolledWindow()\r\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\r\n _scrollwindow.add_with_viewport(_fixed1)\r\n\r\n _frame = Widgets.make_frame(label=_(u\"Stress Inputs\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(_scrollwindow)\r\n\r\n _hpaned2.pack1(_frame, True, False)\r\n\r\n # --------------------------------------------------------------#\r\n # Build the quadrant 2 (middle) container. #\r\n # --------------------------------------------------------------#\r\n # Add the layout for reliability inputs.\r\n _fixed2 = gtk.Fixed()\r\n\r\n _scrollwindow = gtk.ScrolledWindow()\r\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\r\n _scrollwindow.add_with_viewport(_fixed2)\r\n\r\n _frame = Widgets.make_frame(label=_(u\"Reliability Inputs\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(_scrollwindow)\r\n\r\n self.vpnReliabilityInputs.pack1(_frame, True, True)\r\n\r\n _hpaned2.pack2(self.vpnReliabilityInputs, True, False)\r\n\r\n # --------------------------------------------------------------#\r\n # Build the quadrant 2 (upper right) container. #\r\n # --------------------------------------------------------------#\r\n _vpaned = gtk.VPaned()\r\n _hpaned.pack2(_vpaned, True, False)\r\n\r\n # Add the layout for common maintainability inputs. These input\r\n # widgets will be used for assembly and component inputs.\r\n _fixed3 = gtk.Fixed()\r\n\r\n _scrollwindow = gtk.ScrolledWindow()\r\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\r\n _scrollwindow.add_with_viewport(_fixed3)\r\n\r\n _frame = Widgets.make_frame(label=_(u\"Maintainability Inputs\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(_scrollwindow)\r\n\r\n _vpaned.pack1(_frame, True, False)\r\n\r\n # --------------------------------------------------------------#\r\n # Build the quadrant 4 (lower right) container. #\r\n # --------------------------------------------------------------#\r\n # Add the layout for common miscellaneous inputs. These input\r\n # widgets will be used for assembly and component inputs.\r\n _fixed4 = gtk.Fixed()\r\n\r\n _scrollwindow = gtk.ScrolledWindow()\r\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\r\n _scrollwindow.add_with_viewport(_fixed4)\r\n\r\n _frame = Widgets.make_frame(label=_(u\"Miscellaneous Inputs\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(_scrollwindow)\r\n\r\n _vpaned.pack2(_frame, True, False)\r\n\r\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\r\n # Place the widgets used to display the assessment inputs. #\r\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\r\n # Load all the gtk.ComboBox() widgets.\r\n _model = self.cmbHRMethod.get_model()\r\n _model.clear()\r\n self.cmbHRMethod.append_text(\"\")\r\n for __, _type in enumerate(Configuration.RTK_HR_TYPE):\r\n self.cmbHRMethod.append_text(_type)\r\n\r\n _model = self.cmbHRModel.get_model()\r\n _model.clear()\r\n self.cmbHRModel.append_text(\"\")\r\n for __, _model in enumerate(Configuration.RTK_HR_MODEL):\r\n self.cmbHRModel.append_text(_model)\r\n\r\n _model = self.cmbFailDist.get_model()\r\n _model.clear()\r\n self.cmbFailDist.append_text(\"\")\r\n for __, _dist in enumerate(Configuration.RTK_S_DIST):\r\n self.cmbFailDist.append_text(_dist)\r\n\r\n _model = self.cmbActEnviron.get_model()\r\n _model.clear()\r\n self.cmbActEnviron.append_text(\"\")\r\n for __, _environ in enumerate(Configuration.RTK_ACTIVE_ENVIRON):\r\n self.cmbActEnviron.append_text(_environ[0])\r\n\r\n _model = self.cmbDormantEnviron.get_model()\r\n _model.clear()\r\n self.cmbDormantEnviron.append_text(\"\")\r\n for __, _environ in enumerate(Configuration.RTK_DORMANT_ENVIRON):\r\n self.cmbDormantEnviron.append_text(_environ)\r\n\r\n # Create the labels for quadrant 1.\r\n _labels = [_(u\"Active Environment:\"), _(u\"Active Temp:\"),\r\n _(u\"Dormant Environment:\"), _(u\"Dormant Temp:\"),\r\n _(u\"Duty Cycle:\"), _(u\"Humidity:\"), _(u\"Vibration:\"),\r\n _(u\"RPM:\"), _(u\"Min Rated Temp:\"), _(u\"Knee Temp:\"),\r\n _(u\"Max Rated Temp:\"), _(u\"Rated Voltage:\"),\r\n _(u\"Operating Voltage:\"), _(u\"Rated Current:\"),\r\n _(u\"Operating Current:\"), _(u\"Rated Power:\"),\r\n _(u\"Operating Power:\"), _(u\"theta JC:\"),\r\n _(u\"Temperature Rise:\"), _(u\"Case Temperature:\")]\r\n (_x_pos, _y_pos) = Widgets.make_labels(_labels, _fixed1, 5, 5)\r\n _x_pos += 50\r\n\r\n # Place the quadrant 1 widgets.\r\n self.cmbActEnviron.set_tooltip_text(_(u\"Selects the active \"\r\n u\"operating environment for \"\r\n u\"the selected hardware item.\"))\r\n self.txtActTemp.set_tooltip_text(_(u\"Displays the active \"\r\n u\"environment operating \"\r\n u\"temperature for the selected \"\r\n u\"hardware item.\"))\r\n self.cmbDormantEnviron.set_tooltip_text(_(u\"Selects the dormant \"\r\n u\"environment for the \"\r\n u\"selected hardware item.\"))\r\n self.txtDormantTemp.set_tooltip_text(_(u\"Displays the dormant \"\r\n u\"environment temperature \"\r\n u\"for the selected \"\r\n u\"hardware item.\"))\r\n self.txtDutyCycle.set_tooltip_text(_(u\"Displays the operating \"\r\n u\"duty cycle for the \"\r\n u\"selected hardware item.\"))\r\n self.txtHumidity.set_tooltip_text(_(u\"Displays the active \"\r\n u\"environment operating \"\r\n u\"humidity for the selected \"\r\n u\"hardware item.\"))\r\n self.txtVibration.set_tooltip_text(_(u\"Displays the active \"\r\n u\"environment operating \"\r\n u\"vibration level for the \"\r\n u\"selected hardware item.\"))\r\n self.txtRPM.set_tooltip_text(_(u\"Displays the active environment \"\r\n u\"operating RPM for the selected \"\r\n u\"hardware item.\"))\r\n self.txtMinTemp.set_tooltip_text(_(u\"The minimum design operating \"\r\n u\"temperature for the selected\"\r\n u\"hardware item.\"))\r\n self.txtKneeTemp.set_tooltip_text(_(u\"The knee temperature for \"\r\n u\"the selected hardware item.\"))\r\n self.txtMaxTemp.set_tooltip_text(_(u\"The maximum design operating \"\r\n u\"temperature for the selected \"\r\n u\"hardware item.\"))\r\n self.txtRatedVoltage.set_tooltip_text(_(u\"The maximum rated \"\r\n u\"voltage for the \"\r\n u\"selected hardware item.\"))\r\n self.txtOpVoltage.set_tooltip_text(_(u\"The operating voltage for \"\r\n u\"the selected hardware item.\"))\r\n self.txtRatedCurrent.set_tooltip_text(_(u\"The maximum rated \"\r\n u\"current for the \"\r\n u\"selected hardware item.\"))\r\n self.txtOpCurrent.set_tooltip_text(_(u\"The operating current for \"\r\n u\"the selected hardware item.\"))\r\n self.txtRatedPower.set_tooltip_text(_(u\"The maximum rated power \"\r\n u\"for the selected \"\r\n u\"hardware item.\"))\r\n self.txtOpPower.set_tooltip_text(_(u\"The operating power for the \"\r\n u\"selected hardware item.\"))\r\n self.txtThetaJC.set_tooltip_text(_(u\"The junction-to-case thermal \"\r\n u\"resistance for the selected \"\r\n u\"hardware item.\"))\r\n self.txtTempRise.set_tooltip_text(_(u\"The ambient to case \"\r\n u\"temperature rise for the \"\r\n u\"selected hardware item.\"))\r\n self.txtCaseTemp.set_tooltip_text(_(u\"The case temperature for \"\r\n u\"the selected hardware item.\"))\r\n\r\n _fixed1.put(self.cmbActEnviron, _x_pos, _y_pos[0])\r\n _fixed1.put(self.txtActTemp, _x_pos, _y_pos[1])\r\n _fixed1.put(self.cmbDormantEnviron, _x_pos, _y_pos[2])\r\n _fixed1.put(self.txtDormantTemp, _x_pos, _y_pos[3])\r\n _fixed1.put(self.txtDutyCycle, _x_pos, _y_pos[4])\r\n _fixed1.put(self.txtHumidity, _x_pos, _y_pos[5])\r\n _fixed1.put(self.txtVibration, _x_pos, _y_pos[6])\r\n _fixed1.put(self.txtRPM, _x_pos, _y_pos[7])\r\n _fixed1.put(self.txtMinTemp, _x_pos, _y_pos[8])\r\n _fixed1.put(self.txtKneeTemp, _x_pos, _y_pos[9])\r\n _fixed1.put(self.txtMaxTemp, _x_pos, _y_pos[10])\r\n _fixed1.put(self.txtRatedVoltage, _x_pos, _y_pos[11])\r\n _fixed1.put(self.txtOpVoltage, _x_pos, _y_pos[12])\r\n _fixed1.put(self.txtRatedCurrent, _x_pos, _y_pos[13])\r\n _fixed1.put(self.txtOpCurrent, _x_pos, _y_pos[14])\r\n _fixed1.put(self.txtRatedPower, _x_pos, _y_pos[15])\r\n _fixed1.put(self.txtOpPower, _x_pos, _y_pos[16])\r\n _fixed1.put(self.txtThetaJC, _x_pos, _y_pos[17])\r\n _fixed1.put(self.txtTempRise, _x_pos, _y_pos[18])\r\n _fixed1.put(self.txtCaseTemp, _x_pos, _y_pos[19])\r\n\r\n self._lst_handler_id.append(\r\n self.cmbActEnviron.connect('changed', self._on_combo_changed, 22))\r\n self._lst_handler_id.append(\r\n self.txtActTemp.connect('focus-out-event', self._on_focus_out, 23))\r\n self._lst_handler_id.append(\r\n self.cmbDormantEnviron.connect('changed',\r\n self._on_combo_changed, 24))\r\n self._lst_handler_id.append(\r\n self.txtDormantTemp.connect('focus-out-event',\r\n self._on_focus_out, 25))\r\n self._lst_handler_id.append(\r\n self.txtDutyCycle.connect('focus-out-event',\r\n self._on_focus_out, 26))\r\n self._lst_handler_id.append(\r\n self.txtHumidity.connect('focus-out-event',\r\n self._on_focus_out, 27))\r\n self._lst_handler_id.append(\r\n self.txtVibration.connect('focus-out-event',\r\n self._on_focus_out, 28))\r\n self._lst_handler_id.append(\r\n self.txtRPM.connect('focus-out-event', self._on_focus_out, 29))\r\n\r\n # Create the labels for quadrant 2.\r\n _labels = [_(u\"Calculation Method:\"), _(u\"Calculation Model:\"),\r\n _(u\"Specified h(t):\"), _(u\"Specified MTBF:\"),\r\n _(u\"Software h(t):\"), _(u\"Additive Adj:\"),\r\n _(u\"Multiplicative Adj:\"), _(u\"Failure Distribution:\"),\r\n _(u\"Scale:\"), _(u\"Shape:\"), _(u\"Location:\")]\r\n (_x_pos,\r\n _y_pos) = Widgets.make_labels(_labels, _fixed2, 5, 5)\r\n _x_pos += 50\r\n\r\n # Place the quadrant 2 widgets.\r\n self.cmbHRMethod.set_tooltip_text(_(u\"Selects the method of \"\r\n u\"assessing the hazard rate for \"\r\n u\"the selected hardware item.\"))\r\n self.cmbHRModel.set_tooltip_text(_(u\"Selects the hazard rate model to \"\r\n u\"use when calculating the hazard \"\r\n u\"rate for the selected hardware \"\r\n u\"item.\"))\r\n self.txtSpecifiedHt.set_tooltip_text(_(u\"Displays the specified \"\r\n u\"failure intensity for \"\r\n u\"the selected hardware item.\"))\r\n self.txtSpecifiedMTBF.set_tooltip_text(_(u\"Displays the specified \"\r\n u\"mean time between \"\r\n u\"failure (MTBF) for the \"\r\n u\"selected hardware item.\"))\r\n self.txtSoftwareHt.set_tooltip_text(_(u\"Displays the software \"\r\n u\"failure rate for the \"\r\n u\"selected hardware item.\"))\r\n self.txtAddAdj.set_tooltip_text(_(u\"Displays any hazard rate \"\r\n u\"assessment additive \"\r\n u\"adjustment factor for the \"\r\n u\"selected hardware item.\"))\r\n self.txtMultAdj.set_tooltip_text(_(u\"Displays any hazard rate \"\r\n u\"assessment multiplicative \"\r\n u\"adjustment factor for the \"\r\n u\"selected hardware item.\"))\r\n self.cmbFailDist.set_tooltip_text(_(u\"Selects the distribution of \"\r\n u\"times to failure for the \"\r\n u\"selected hardware item.\"))\r\n self.txtFailScale.set_tooltip_text(_(u\"Displays the time to \"\r\n u\"failure distribution scale \"\r\n u\"factor for the selected \"\r\n u\"hardware item.\"))\r\n self.txtFailShape.set_tooltip_text(_(u\"Displays the time to \"\r\n u\"failure distribution shape \"\r\n u\"factor for the selected \"\r\n u\"hardware item.\"))\r\n self.txtFailLoc.set_tooltip_text(_(u\"Displays the time to failure \"\r\n u\"distribution location \"\r\n u\"factor for the selected \"\r\n u\"hardware item.\"))\r\n\r\n _fixed2.put(self.cmbHRMethod, _x_pos, _y_pos[0])\r\n _fixed2.put(self.cmbHRModel, _x_pos, _y_pos[1])\r\n _fixed2.put(self.txtSpecifiedHt, _x_pos, _y_pos[2])\r\n _fixed2.put(self.txtSpecifiedMTBF, _x_pos, _y_pos[3])\r\n _fixed2.put(self.txtSoftwareHt, _x_pos, _y_pos[4])\r\n _fixed2.put(self.txtAddAdj, _x_pos, _y_pos[5])\r\n _fixed2.put(self.txtMultAdj, _x_pos, _y_pos[6])\r\n _fixed2.put(self.cmbFailDist, _x_pos, _y_pos[7])\r\n _fixed2.put(self.txtFailScale, _x_pos, _y_pos[8])\r\n _fixed2.put(self.txtFailShape, _x_pos, _y_pos[9])\r\n _fixed2.put(self.txtFailLoc, _x_pos, _y_pos[10])\r\n\r\n self._lst_handler_id.append(\r\n self.cmbHRMethod.connect('changed', self._on_combo_changed, 30))\r\n self._lst_handler_id.append(\r\n self.cmbHRModel.connect('changed', self._on_combo_changed, 31))\r\n self._lst_handler_id.append(\r\n self.txtSpecifiedHt.connect('focus-out-event',\r\n self._on_focus_out, 32))\r\n self._lst_handler_id.append(\r\n self.txtSpecifiedMTBF.connect('focus-out-event',\r\n self._on_focus_out, 33))\r\n self._lst_handler_id.append(\r\n self.txtSoftwareHt.connect('focus-out-event',\r\n self._on_focus_out, 34))\r\n self._lst_handler_id.append(self.txtAddAdj.connect('focus-out-event',\r\n self._on_focus_out,\r\n 35))\r\n self._lst_handler_id.append(\r\n self.txtMultAdj.connect('focus-out-event',\r\n self._on_focus_out, 36))\r\n self._lst_handler_id.append(\r\n self.cmbFailDist.connect('changed', self._on_combo_changed, 37))\r\n self._lst_handler_id.append(\r\n self.txtFailScale.connect('focus-out-event',\r\n self._on_focus_out, 38))\r\n self._lst_handler_id.append(\r\n self.txtFailShape.connect('focus-out-event',\r\n self._on_focus_out, 39))\r\n self._lst_handler_id.append(self.txtFailLoc.connect('focus-out-event',\r\n self._on_focus_out,\r\n 40))\r\n\r\n _fixed1.show_all()\r\n _fixed2.show_all()\r\n\r\n # Create the labels for quadrant #2.\r\n _labels = [_(u\"MTTR Method:\"), _(u\"Specified MTTR:\"),\r\n _(u\"Additive Adj:\"), _(u\"Multiplicative Adj:\"),\r\n _(u\"Repair Distribution:\"), _(u\"Scale:\"), _(u\"Shape:\")]\r\n (_x_pos,\r\n _y_pos) = Widgets.make_labels(_labels, _fixed3, 5, 5)\r\n\r\n # Create the labels for quadrant #4.\r\n _labels = [_(u\"Cost Method:\"), _(u\"Unit Cost:\")]\r\n (_x_pos2,\r\n _y_pos2) = Widgets.make_labels(_labels, _fixed4, 5, 5)\r\n _x_pos = max(_x_pos, _x_pos2) + 50\r\n\r\n # Place the quadrant #3 widgets.\r\n _model = self.cmbMTTRMethod.get_model()\r\n _model.clear()\r\n self.cmbMTTRMethod.append_text(\"\")\r\n for __, _type in enumerate(Configuration.RTK_MTTR_TYPE):\r\n self.cmbMTTRMethod.append_text(_type)\r\n\r\n _model = self.cmbRepairDist.get_model()\r\n _model.clear()\r\n self.cmbRepairDist.append_text(\"\")\r\n for __, _dist in enumerate(Configuration.RTK_S_DIST):\r\n self.cmbRepairDist.append_text(_dist)\r\n\r\n self.cmbMTTRMethod.set_tooltip_text(_(u\"Selects the method of \"\r\n u\"assessing the mean time to \"\r\n u\"repair (MTTR) for the \"\r\n u\"selected hardware item.\"))\r\n self.txtSpecifiedMTTR.set_tooltip_text(_(u\"Displays the specified \"\r\n u\"mean time to repair \"\r\n u\"(MTTR) for the \"\r\n u\"selected hardware item.\"))\r\n self.txtMTTRAddAdj.set_tooltip_text(_(u\"Displays the mean time to \"\r\n u\"repair (MTTR) assessment \"\r\n u\"additive adjustment \"\r\n u\"factor for the selected \"\r\n u\"hardware item.\"))\r\n self.txtMTTRMultAdj.set_tooltip_text(_(u\"Displays the mean time \"\r\n u\"to repair (MTTR) \"\r\n u\"assessment multiplicative \"\r\n u\"adjustment factor for \"\r\n u\"the selected hardware item.\"))\r\n self.cmbRepairDist.set_tooltip_text(_(u\"Selects the time to \"\r\n u\"repair distribution for \"\r\n u\"the selected hardware item.\"))\r\n self.txtRepairScale.set_tooltip_text(_(u\"Displays the time to \"\r\n u\"repair distribution \"\r\n u\"scale parameter for the \"\r\n u\"selected hardware item.\"))\r\n self.txtRepairShape.set_tooltip_text(_(u\"Displays the time to \"\r\n u\"repair distribution \"\r\n u\"shape parameter for the \"\r\n u\"selected hardware item.\"))\r\n\r\n _fixed3.put(self.cmbMTTRMethod, _x_pos, _y_pos[0])\r\n _fixed3.put(self.txtSpecifiedMTTR, _x_pos, _y_pos[1])\r\n _fixed3.put(self.txtMTTRAddAdj, _x_pos, _y_pos[2])\r\n _fixed3.put(self.txtMTTRMultAdj, _x_pos, _y_pos[3])\r\n _fixed3.put(self.cmbRepairDist, _x_pos, _y_pos[4])\r\n _fixed3.put(self.txtRepairScale, _x_pos, _y_pos[5])\r\n _fixed3.put(self.txtRepairShape, _x_pos, _y_pos[6])\r\n\r\n self._lst_handler_id.append(\r\n self.cmbMTTRMethod.connect('changed', self._on_combo_changed, 41))\r\n self._lst_handler_id.append(\r\n self.txtSpecifiedMTTR.connect('focus-out-event',\r\n self._on_focus_out, 42))\r\n self._lst_handler_id.append(\r\n self.txtMTTRAddAdj.connect('focus-out-event',\r\n self._on_focus_out, 43))\r\n self._lst_handler_id.append(\r\n self.txtMTTRMultAdj.connect('focus-out-event',\r\n self._on_focus_out, 44))\r\n self._lst_handler_id.append(\r\n self.cmbRepairDist.connect('changed', self._on_combo_changed, 45))\r\n self._lst_handler_id.append(\r\n self.txtRepairScale.connect('focus-out-event',\r\n self._on_focus_out, 46))\r\n self._lst_handler_id.append(\r\n self.txtRepairShape.connect('focus-out-event',\r\n self._on_focus_out, 47))\r\n\r\n # Place the quadrant #4 widgets.\r\n _model = self.cmbCostMethod.get_model()\r\n _model.clear()\r\n self.cmbCostMethod.append_text(\"\")\r\n for __, _type in enumerate(Configuration.RTK_COST_TYPE):\r\n self.cmbCostMethod.append_text(_type)\r\n\r\n self.cmbCostMethod.set_tooltip_text(_(u\"Select the method for \"\r\n u\"assessing the cost of the \"\r\n u\"selected hardware item.\"))\r\n self.txtCost.set_tooltip_text(_(u\"The unit cost of the selected \"\r\n u\"hardware item.\"))\r\n\r\n _fixed4.put(self.cmbCostMethod, _x_pos, _y_pos2[0])\r\n _fixed4.put(self.txtCost, _x_pos, _y_pos2[1])\r\n\r\n self._lst_handler_id.append(\r\n self.cmbCostMethod.connect('changed', self._on_combo_changed, 48))\r\n self._lst_handler_id.append(\r\n self.txtCost.connect('focus-out-event',\r\n self._on_focus_out, 49))\r\n self._lst_handler_id.append(\r\n self.txtMinTemp.connect('focus-out-event', self._on_focus_out, 50))\r\n self._lst_handler_id.append(\r\n self.txtKneeTemp.connect('focus-out-event',\r\n self._on_focus_out, 51))\r\n self._lst_handler_id.append(\r\n self.txtMaxTemp.connect('focus-out-event', self._on_focus_out, 52))\r\n self._lst_handler_id.append(\r\n self.txtRatedVoltage.connect('focus-out-event',\r\n self._on_focus_out, 53))\r\n self._lst_handler_id.append(\r\n self.txtOpVoltage.connect('focus-out-event',\r\n self._on_focus_out, 54))\r\n self._lst_handler_id.append(\r\n self.txtRatedCurrent.connect('focus-out-event',\r\n self._on_focus_out, 55))\r\n self._lst_handler_id.append(\r\n self.txtOpCurrent.connect('focus-out-event',\r\n self._on_focus_out, 56))\r\n self._lst_handler_id.append(\r\n self.txtRatedPower.connect('focus-out-event',\r\n self._on_focus_out, 57))\r\n self._lst_handler_id.append(self.txtOpPower.connect('focus-out-event',\r\n self._on_focus_out,\r\n 58))\r\n self._lst_handler_id.append(self.txtThetaJC.connect('focus-out-event',\r\n self._on_focus_out,\r\n 59))\r\n self._lst_handler_id.append(\r\n self.txtTempRise.connect('focus-out-event',\r\n self._on_focus_out, 60))\r\n self._lst_handler_id.append(\r\n self.txtCaseTemp.connect('focus-out-event',\r\n self._on_focus_out, 61))\r\n\r\n _fixed3.show_all()\r\n _fixed4.show_all()\r\n\r\n _label = gtk.Label()\r\n _label.set_markup(\"<span weight='bold'>\" + _(u\"Assessment\\nInputs\") +\r\n \"</span>\")\r\n _label.set_alignment(xalign=0.5, yalign=0.5)\r\n _label.set_justify(gtk.JUSTIFY_CENTER)\r\n _label.show_all()\r\n _label.set_tooltip_text(_(u\"Allows entering reliability, \"\r\n u\"maintainability, and other assessment \"\r\n u\"inputs for the selected hardware item.\"))\r\n\r\n notebook.insert_page(_hbox, tab_label=_label, position=-1)\r\n\r\n return False","title":""},{"docid":"29985308aeea799ad462f0f94e0c8e09","score":"0.5151647","text":"def make_btn(self, text, clicked):\n btn = qtw.QPushButton(\n text,\n pressed=clicked\n )\n btn.setSizePolicy(\n qtw.QSizePolicy.Fixed,\n qtw.QSizePolicy.Fixed\n )\n self.btn_frame.layout().addWidget(btn)\n self.buttons.append(btn)","title":""},{"docid":"ffb38f3653029ae1914c86369b42c366","score":"0.51320344","text":"def createClicked(self):\n self.browseGrp.setHidden(True)\n self.createGroup.setHidden(False)\n self.status = 2","title":""},{"docid":"3fd81d34517bec9e193b85f161ff10ce","score":"0.5126743","text":"def post_manual_faculty_submit_html():\n return ''","title":""},{"docid":"17b06d50130206cac108eff3cc608532","score":"0.511716","text":"def add_job_widgets(self):\n\n # add the fixture processing option widgets form.\n self.fp_form = FixtureProcessingForm(\n self.engineering_flag, self, self.ntbk_tabs)\n self.ntbk_tabs.add(self.fp_form, text=\"Fixture Processor Options.\")\n \n # get user options, and fill the checkboxes\n self.fp_form.get_user_options()","title":""},{"docid":"545d068b2da16f4c6373b2cd987ec001","score":"0.5115603","text":"def create_button(self, text, _class):\n tkinter.Button(\n self.frame, text=text,\n command=lambda: self.new_window(_class)).pack(fill=tkinter.X, pady=100, ipadx=10, ipady=10)","title":""},{"docid":"bfcd8743db5d56575b781c71a6cc0248","score":"0.51048666","text":"def add_widget(self, widget):\n\n # Check if widget has the required attributes\n self.validate_widget(widget)\n\n has_item = self.tab_widget.widget(widget.order)\n if has_item:\n raise RuntimeError(\"Current index `%i` is occupied\" % widget.order)\n\n self.tab_widget.insertTab(widget.order, widget, widget.label)","title":""},{"docid":"e481a6e2b97212ca4d606ab6e1dc33a6","score":"0.5100388","text":"def on_create_btn_pressed(self):\n pass","title":""},{"docid":"8f93a64248a58de4335ea9997dd4c75d","score":"0.5098779","text":"def _create_descrip_tab(self, nb: ttk.Notebook) -> None:\n frame = ttk.Frame(nb, name='descrip')\n msg = [\"Ttk is the new Tk themed widget set. One of the widgets \",\n \"it includes is the notebook widget, which provides a set \",\n \"of tabs that allow the selection of a group of panels, \",\n \"each with distinct content. They are a feature of many \",\n \"modern user interfaces. Not only can the tabs be selected \",\n \"with the mouse, but they can also be switched between \",\n \"using Ctrl+Tab when the notebook page heading itself is \",\n \"selected. Note that the second tab is disabled, and cannot \"\n \"be selected.\"] # widgets to be displayed on 'Description' tab\n lbl = ttk.Label(frame, wraplength='4i', justify=LEFT, anchor=N, text=''.join(msg))\n neatVar = tk.StringVar()\n btn = ttk.Button(frame, text='Neat!', underline=0, command=lambda v=neatVar: self._say_neat(v))\n neat = ttk.Label(frame, textvariable=neatVar, name='neat')\n\n # position and set resize behaviour\n lbl.grid(row=0, column=0, columnspan=2, sticky='new', pady=5)\n btn.grid(row=1, column=0, pady=(2, 4))\n neat.grid(row=1, column=1, pady=(2, 4))\n frame.rowconfigure(1, weight=1)\n frame.columnconfigure((0, 1), weight=1, uniform=1)\n\n # bind for button short-cut key\n # (must be bound to toplevel window)\n self.winfo_toplevel().bind('<Alt-n>', lambda e, v=neatVar: self._say_neat(v))\n\n # add to notebook (underline = index for short-cut character)\n nb.add(frame, text='Description', underline=0, padding=2)","title":""},{"docid":"3f2bdb055d842d84f214e61f1b854d2f","score":"0.50981313","text":"def __init__(self, initParams):\n super().__init__()\n\n self.initParams = initParams\n self.addTab(tabEx1(self.initParams), \"Tab Ex1\")\n self.addTab(tabEx2(self.initParams), \"Tab Ex2\")","title":""},{"docid":"ae5b9764755650c5ba71bd099b2f533c","score":"0.50971574","text":"def input_button(self):\r\n self.submit_button = Tk.Button(self.root, text = \"Submit\", command = self.save_and_close)\r\n self.submit_button.pack()\r\n self.root.bind('<Return>', self.save_and_close)","title":""},{"docid":"f5401d60d76edc303e22465631a8ae22","score":"0.5078489","text":"def action_newbrowser(self, url):","title":""},{"docid":"51d27e1089e522b58352443afc12827e","score":"0.5078393","text":"def new_tab(self, value):\r\n self.logger.warn(\"Setting values on new_tab will NOT update the remote Canvas instance.\")\r\n self._new_tab = value","title":""},{"docid":"bac5709765cf99e8574d89d398c972b8","score":"0.5075329","text":"def button_6_click(self, **event_args):\r\n open_form('Passenger')","title":""},{"docid":"e6dda5b01ae3ff2b93f35358751ca1b9","score":"0.5072264","text":"def on_button_submit_clicked(self, button_submit):\n\t\tself.entry_job_starting_number.set_text(repr(self.job_starting_number))\n\t\tself.dialog_submit.show()","title":""},{"docid":"d567751f28470ff505ff31a81876ef47","score":"0.50675994","text":"def on_pb_submit_clicked(self):\n if self.lineEdit_username.text() == \"\":\n QMessageBox.warning(self, \"警告\", \"用户名没有输入\")\n self.lineEdit_username.setFocus()\n elif self.lineEdit_pwd.text() == \"\":\n QMessageBox.warning(self, \"警告\", \"密码没有输入\")\n self.lineEdit_pwd.setFocus()\n else:\n name = self.lineEdit_username.text()\n pwd = self.lineEdit_pwd.text()\n jscode = \"PyQt52WebValue('{}','{}');\".format(name, pwd)\n self.view.page().runJavaScript(jscode)","title":""},{"docid":"a6cedd70c421c64356a3a87431b7ed64","score":"0.50664186","text":"def changeName(self):\n new_window = Toplevel(self.master, height=100, width=300, takefocus=True)\n new_window.resizable(False, False)\n\n frame = Frame(new_window, padx=5, pady=5)\n frame.pack()\n\n label = Label(frame, text='Enter the new tab name', padx=10, pady=10)\n label.grid(row=2, column=1, rowspan=2, columnspan=5)\n\n name_entry = Entry(frame, width=20)\n name_entry.grid(row=4, column=1, rowspan=2, columnspan=5)\n name_entry.focus_set()\n\n CancelButton = Button(frame, text='Cancel', command=lambda: self.closeWidget(new_window), width=10, padx=5)\n OkButton = Button(frame, text='Ok', command=lambda: self.changeTabName(name_entry.get(), new_window), width=10, padx=5)\n CancelButton.grid(row=7, column=4, columnspan=2)\n OkButton.grid(row=7, column=2, columnspan=2)","title":""},{"docid":"863eb27f32b0d920dfd8170ee250003c","score":"0.50634223","text":"def __init__(self, ui):\n # The new frame will be added at the end of the presentation\n new_frame_number = str(len(ui.model.frames) + 1)\n\n SoziAction.__init__(self,\n _(\"Delete the new frame {0}\").format(new_frame_number),\n _(\"Create a new frame at {0}\").format(new_frame_number)\n )\n \n self.ui = ui","title":""},{"docid":"07b425370525af02f0e9552b6590d0df","score":"0.5061895","text":"def __createInputTab(self):\r\n\r\n #Define an empty dict which will contain refernces to the StringVar\r\n #objects associated with each text field\r\n self.inputEntryTxt = {}\r\n \r\n #Define the tab itself and add it to the notebook\r\n inputTab = ttk.Frame(self.note)\r\n self.note.add(inputTab, text = 'Input')\r\n\r\n #The label and help button at the top\r\n ttk.Label(inputTab, text = 'Enter the relevant information below', font = ('Century Gothic', 10, 'bold')).grid(row = 0, column = 0, columnspan = 2, sticky = 'W', padx = (2,0), pady = 2)\r\n ttk.Button(inputTab, text = '?', command = lambda: self.showInputHelp(), width = 2).grid(row = 0, column = 2, sticky = 'E', pady = 2)\r\n\r\n #The input path label and entry box\r\n ttk.Label(inputTab, text = 'Input Path', font = ('Cenutry Gothic', 9)).grid(row = 1, column = 0, stick = 'w', padx = (2,8), pady = 4)\r\n self.inputEntryTxt['Input Path'] = tk.StringVar()\r\n ttk.Entry(inputTab, textvariable = self.inputEntryTxt['Input Path'], width = 50).grid(row = 1, column = 1, columnspan = 2)\r\n\r\n #The input bias filenames label and entry box\r\n ttk.Label(inputTab, text = 'Bias Filenames', font = ('Cenutry Gothic', 9)).grid(row = 2, column = 0, stick = 'w', padx = (2,8), pady = 4)\r\n self.inputEntryTxt['Bias Filenames'] = tk.StringVar()\r\n ttk.Entry(inputTab, textvariable = self.inputEntryTxt['Bias Filenames'], width = 50).grid(row = 2, column = 1, columnspan = 2)\r\n\r\n #The input flat filenames label and entry box\r\n ttk.Label(inputTab, text = 'Flat Filenames', font = ('Cenutry Gothic', 9)).grid(row = 3, column = 0, stick = 'w', padx = (2,8), pady = 4)\r\n self.inputEntryTxt['Flat Filenames'] = tk.StringVar()\r\n ttk.Entry(inputTab, textvariable = self.inputEntryTxt['Flat Filenames'], width = 50).grid(row = 3, column = 1, columnspan = 2)\r\n\r\n #The input image filenames label and entry box\r\n ttk.Label(inputTab, text = 'Image Filenames', font = ('Cenutry Gothic', 9)).grid(row = 4, column = 0, stick = 'w', padx = (2,8), pady = 4)\r\n self.inputEntryTxt['Image Filenames'] = tk.StringVar()\r\n ttk.Entry(inputTab, textvariable = self.inputEntryTxt['Image Filenames'], width = 50).grid(row = 4, column = 1, columnspan = 2)\r\n\r\n #The load images button\r\n ttk.Button(inputTab, text = 'LOAD IMAGES', command = lambda: self.loadImages()).grid(row = 5, column = 0, columnspan = 3, stick = 'nswe', padx = (2,0), pady = 10)\r\n inputTab.rowconfigure(5, minsize = 55)\r\n \r\n return inputTab","title":""},{"docid":"12e937ebec0f1161fb3ed04d491aadc1","score":"0.5057023","text":"def create_plot_page(self):\r\n\r\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\r\n # Build-up the containers for the tab. #\r\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\r\n _vbox = gtk.VBox()\r\n self.pack_start(_vbox, True, True)\r\n\r\n _frame = Widgets.make_frame(_(u\"Survival Function\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(self.pltPlot1)\r\n _vbox.pack_start(_frame, True, True)\r\n\r\n _frame = Widgets.make_frame(_(u\"Hazard Function\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(self.pltPlot2)\r\n _vbox.pack_end(_frame, True, True)\r\n\r\n _vbox = gtk.VBox()\r\n self.pack_end(_vbox, True, True)\r\n\r\n _frame = Widgets.make_frame(_(u\"Cumulative Hazard Function\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(self.pltPlot3)\r\n _vbox.pack_start(_frame, True, True)\r\n\r\n _frame = Widgets.make_frame(_(u\"Log Cumulative Hazard Function\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(self.pltPlot4)\r\n _vbox.pack_end(_frame, True, True)\r\n\r\n # Insert the page.\r\n self.lblPage.set_markup(\"<span weight='bold'>Analysis\\nPlots</span>\")\r\n self.lblPage.set_alignment(xalign=0.5, yalign=0.5)\r\n self.lblPage.set_justify(gtk.JUSTIFY_CENTER)\r\n self.lblPage.show_all()\r\n self.lblPage.set_tooltip_text(_(u\"Displays survival analyses plots.\"))\r\n\r\n return False","title":""},{"docid":"a70758b0a9c74585e94c101f12261ef6","score":"0.50532055","text":"def addButtons(self, frame, buttonsText):\n button = ttk.Button(frame, text=buttonsText[0], style=BUTTON_STYLE, command=lambda: self.done(0))\n button.pack(side=tk.LEFT, fill=BOTH, expand=1, padx=BUTTON_PAD_X, pady=BUTTON_PAD_Y)","title":""},{"docid":"2de1d4e5f57ccf4e813cc902846647e5","score":"0.50530213","text":"def open_new_tab(url):\n\tpass","title":""},{"docid":"fc0a6f2c33ef3c8c368ae38410abfc66","score":"0.5047902","text":"def on_addprojectBtn_clicked(self): \r\n print \"En este nivel se abre el window\"\r\n\r\n\r\n self.addProject()","title":""},{"docid":"a38794e8e9e8c337b8f18d00011b804f","score":"0.5046592","text":"def NewFile(self):\n self.NewFileInterface = tk.Toplevel()\n Label = ttk.Label(master = self.NewFileInterface, text = \"Enter name of file\")\n Label.pack()\n self.Entry = ttk.Entry(master = self.NewFileInterface)\n self.Entry.pack()\n Button = ttk.Button(master = self.NewFileInterface, text = \"Press to save\", command = self.SaveNewFile)\n Button.pack()","title":""},{"docid":"6a4782fe380a7d85433b011ef247bf0b","score":"0.50457335","text":"def add_tab():\n try:\n data = json.loads(request.data)\n name = data['name']\n priority = int(data.get('priority', 0))\n assert isinstance(name, basestring)\n except:\n return {'error': 'illegal format'}\n username = current_user.username\n doc = get_user(username)\n for tab in doc['tab']:\n if tab['name'] == name:\n return {'error': 'tab with this name already exists!'}\n doc['tab'].append({\n 'name': name,\n 'priority': priority,\n 'tags': []\n })\n get_mongo('user').save(doc)\n log_info('user {0} add tab {1}'.format(username, name))\n return {'success': 1}","title":""},{"docid":"8826f5449f29f2a656a10939bc707fde","score":"0.5034962","text":"def _submit_employee_form(self):\n\n # Make the questions non editable for the current part.\n for question in self.questions:\n question.is_editable = False\n question.save()\n\n # Move the form in next state\n type_ = self.type_\n if type_ == 'self_appraisal':\n self.employee_form_appraisal.submit_to_reporting([self.employee_form_appraisal])\n elif type_ == 'appraisal':\n self.employee_form_reporting.submit_to_reviewing([self.employee_form_reporting])\n elif type_ == 'reviewing':\n if self.employee_form_reviewing.acceptance_authority:\n self.employee_form_reviewing.submit_to_accepting([self.employee_form_reviewing])\n else:\n self.employee_form_reviewing.disclose([self.employee_form_reviewing])\n elif type_ == 'accepting':\n self.employee_form_accepting.disclose([self.employee_form_accepting])","title":""},{"docid":"214cfbd666ac70becc1401740e0188c3","score":"0.5032395","text":"def submission_create(request) -> HttpResponse:\r\n if request.method == 'POST':\r\n form = SubmissionForm(request.POST, initial={\r\n 'assignment': request.assignment,\r\n })\r\n if form.is_valid():\r\n submission = form.save(commit=False)\r\n submission.user = request.user\r\n submission.assignment = request.assignment\r\n submission = form.save()\r\n tasks.automatic_mark_and_feedback.delay(submission.pk)\r\n AuditLog.objects.create(date=datetime.now(pytz.UTC), unit=request.unit, message=\"%s[%s] submitted %s[%s]\" % (request.user, request.user.pk, submission, submission.pk))\r\n return redirect(submission)\r\n else:\r\n for error in form.non_field_errors():\r\n messages.error(request, error)\r\n else:\r\n form = SubmissionForm(initial={\r\n 'solution': request.assignment.template\r\n })\r\n\r\n context = {\r\n 'form': form,\r\n 'unit': request.unit,\r\n 'assignment': request.assignment,\r\n }\r\n\r\n return render(request, 'decentmark/submission_create.html', context)","title":""},{"docid":"90f0d7f51da0a521eb5e8d9abe936dcb","score":"0.50318515","text":"def _create_new_form(self, extra_data, post_data, count, initial=None):\n prefix = '%s_new_%s' % (self.assertion_type, str(count))\n form = self.form_class(self.authority_records, extra_data,\n prefix=prefix, data=post_data, initial=initial)\n form.is_new_form = True\n return form","title":""},{"docid":"b3160e79d99c6bf5469e3b1f4a9dcb99","score":"0.50307053","text":"def submit(self):\n raise NotImplementedError","title":""},{"docid":"30acbcc4497c8ba5d9031592dd9a5432","score":"0.5027584","text":"def add(self, image, title, view, name=None, order=0):\n item = TabControllerItem(\n image=image,\n title=title,\n view=view,\n name=name,\n action=self.action)\n\n self.tabs.append({\n 'button': item,\n 'order': order,\n })\n\n return self","title":""},{"docid":"9c6a04555b67a6664f7d32de4bfde16d","score":"0.5026459","text":"def ide_go_final_tab():","title":""},{"docid":"3503dca7358876396e18fea8503f4796","score":"0.5024592","text":"def create_button(oDoc, oPage, sName, sLabel, index):\n # Change to be a file. required by \"AssignAction(). ScriptEventDescriptor.ScriptCode\" \n sScriptURL = \"vnd.sun.star.script:draw_uno_plan.py$button_push_event?language=Python&location=user\"\n # Displays in Design Mode, Button Right click, Control Properties... Events... \n # draw_uno_plan.py$button_push_event (user, Python)\n \n oButtonModel = add_new_button(sName, sLabel, oDoc, oPage, index) \n \n oForm = oPage.getForms().getByIndex(0)\n # Find index inside the form container\n nIndex = get_index(oButtonModel, oForm)\n #print(str(nIndex)) # -1\n \n assign_action(nIndex, sScriptURL, oForm, oDoc)","title":""},{"docid":"5021e9bca74b63505127e8327fadc6ac","score":"0.50217193","text":"def addEmpButtonPressed(self):\n newEmp = addEmpWindow()","title":""},{"docid":"6788098df779a90f41b426f8096e94d3","score":"0.5021367","text":"def test_submit(self):\n self.jurisdictions_field = self.driver.find_element_by_name(\"name\")\n self.sick_people_field = self.driver.find_element_by_name(\"victims\")\n\n self.jurisdictions_field.send_keys(\"Antioquia\")\n self.sick_people_field.send_keys(\"1 million\")\n self.sick_people_field.submit()\n\n continue_link = self.driver.find_element_by_link_text('Submit')\n self.assertTrue(\"New jurisdiction added succesfully\" in continue_link)","title":""},{"docid":"42733339908d56d47fa102f6d90970c7","score":"0.5005546","text":"def on_ok(self, keypress=None):\n self.parentApp.setNextForm('MAIN')\n self.parentApp.switchFormNow()","title":""},{"docid":"42733339908d56d47fa102f6d90970c7","score":"0.5005546","text":"def on_ok(self, keypress=None):\n self.parentApp.setNextForm('MAIN')\n self.parentApp.switchFormNow()","title":""},{"docid":"3fe6463ac0e1d9f8cf8b749d59e835d2","score":"0.5003776","text":"def new_tab(self):\r\n return self._new_tab","title":""},{"docid":"fe1a2db599f31e41b81624e73432b64f","score":"0.5000268","text":"def new_report_click(self, **event_args):\n open_form('reports.main')","title":""},{"docid":"63e8208f158dd43cb198ea95896b3d80","score":"0.49832997","text":"def change_tab(self, index):\n if self.ui.tabMain.tabText(index) == \"Action\":\n utils.populate_action_fields(self)\n self.ui.listActions.setCurrentRow(self.ui.listActions.count() - 1)\n elif self.ui.tabMain.tabText(index) == \"MD Integration\":\n utils.populate_md_integration_levels(self)\n elif self.ui.tabMain.tabText(index) == \"Input File Preview\":\n self.ui.txtInputFile.setPlainText(utils.generate_input_file(self))","title":""},{"docid":"7a5054e57ad906e78b8e7ab2209a3fb5","score":"0.4965369","text":"def clicked_button_set_window(self):\r\n self.clear_layout()\r\n self.current_input = WindowForm()\r\n\r\n self.layout.addWidget(self.current_input)\r\n self.current_input.push_button_start.setEnabled(False)\r\n # Configure layout\r\n self.current_input.push_button_input.clicked.connect(self.clicked_push_button_input)\r\n self.current_input.push_button_output.clicked.connect(self.clicked_push_button_output)\r\n\r\n self.current_input.line_edit_output.setText(self._current_path)\r\n # self.current_input.line_edit_template.setEnabled(False)\r\n self.enable_line_edits(False)\r\n self.enable_line_edits(True)\r\n\r\n self.current_input.push_button_start.clicked.connect(self.clicked_push_button_start)\r\n\r\n self.current_input.set_progress_bar_maximum(10)","title":""},{"docid":"dd44a0796bac567ecebc9ade02f4c3ea","score":"0.4957268","text":"def on_btn_add(self, sender):\n index = self.add_buttons.index(sender)\n\n self.shift_region_frames(index)\n self.add_region_bar(index, \"\", \"\")\n\n self.update_region_indexes()\n self.update_all_swap_buttons()\n\n # add region to the parameter_data\n self.parameter_data.add_region_at(index)\n\n self.update_atoms(change_region=True)","title":""},{"docid":"95caae7b5678f580776fb8503d3132f7","score":"0.4955552","text":"def click_create_scenario_button(self):\n create_scenario_button_element = self.wait().until(EC.presence_of_element_located(self.create_scenario_locator), 'create scenario locator not found before specified time')\n self.hover(create_scenario_button_element)\n self.script_executor_click(create_scenario_button_element)\n self.wait_for_ajax_spinner_load()","title":""},{"docid":"f82b45a2c3080652b78f4366accbccf7","score":"0.49554682","text":"def create_ui(self):\n \n pass","title":""},{"docid":"f82b45a2c3080652b78f4366accbccf7","score":"0.49554682","text":"def create_ui(self):\n \n pass","title":""},{"docid":"17e91208e18453098f2746b0f5c68a54","score":"0.49518228","text":"def add_help_tab(self):\n from App import App\n from RibbonPanes import QHelpPane\n\n try:\n tab = self.addTab('Help') # \"Help\"\n\n # Action Section\n cSection = tab.addSection('Help')\n pane = QHelpPane()\n pane.ui.toolButtonHelp.clicked.connect(App.mainWnd().on_help)\n cSection.addCustomWidget(pane)\n except Exception as ex:\n message = f\"error occurred({repr(ex)}) in {sys.exc_info()[-1].tb_frame.f_code.co_filename}:\" \\\n f\"{sys.exc_info()[-1].tb_lineno}\"\n pass\n\n return # Everything after this is old","title":""},{"docid":"8a7ed4786bfd6e0b1cbee040468ce366","score":"0.49490008","text":"def POST(self, name):\n\n\t\tweb.header(\"Content-Type\",\"text/html; charset=utf-8\")\n\t\tweb.header('Access-Control-Allow-Origin', '*')\n\t\tweb.header('Access-Control-Allow-Methods', 'GET, POST, OPTIONS')\n\n\t\tis_git_auth = github_sync.is_git_auth()\n\t\tf = forms.get_form(conf.ask_form)\n\t\tuser = session['username']\n\t\tsession['ip_address'] = str(web.ctx['ip'])\n\t\tu.write_ip(str(datetime.datetime.now()), str(web.ctx['ip']), 'POST')\n\t\tblock_user, limit = u.check_ip(str(web.ctx['ip']), str(datetime.datetime.now()) )\n\t\twhereto = prefixLocal+'/' if user == 'anonymous' else prefixLocal+'welcome-1'\n\n\t\t# form validation (ask_class)\n\t\tif not f.validates():\n\t\t\tu.log_output('SUBMIT INVALID FORM', session['logged_in'], session['username'],name)\n\t\t\treturn render.record(record_form=f, pageID=name, user=user, alert=block_user,\n\t\t\t\t\t\t\t\tlimit=limit, is_git_auth=is_git_auth,invalid=True,\n\t\t\t\t\t\t\t\tproject=conf.myProject,template=None)\n\t\telse:\n\t\t\trecordData = web.input()\n\n\t\t\t# load the template selected by the user\n\t\t\tif 'res_name' in recordData:\n\t\t\t\tif recordData.res_name != 'None':\n\t\t\t\t\tf = forms.get_form(recordData.res_name)\n\t\t\t\t\treturn render.record(record_form=f, pageID=name, user=user, alert=block_user,\n\t\t\t\t\t\t\t\t\tlimit=limit, is_git_auth=is_git_auth,invalid=False,\n\t\t\t\t\t\t\t\t\tproject=conf.myProject,template=recordData.res_name)\n\t\t\t\telse:\n\t\t\t\t\traise web.seeother(prefixLocal+'record-'+name)\n\n\t\t\tif 'action' in recordData:\n\t\t\t\tcreate_record(recordData)\n\n\t\t\trecordID = recordData.recordID if 'recordID' in recordData else None\n\t\t\ttemplateID = recordData.templateID if 'templateID' in recordData else None\n\t\t\tu.log_output('CREATED RECORD', session['logged_in'], session['username'],recordID)\n\n\t\t\tif recordID:\n\t\t\t\tuserID = user.replace('@','-at-').replace('.','-dot-')\n\t\t\t\tfile_path = mapping.inputToRDF(recordData, userID, 'not modified',tpl_form=templateID)\n\t\t\t\tif conf.github_backup == \"True\":\n\t\t\t\t\ttry:\n\t\t\t\t\t\tgithub_sync.push(file_path,\"main\", session['gituser'], session['username'], session['bearer_token'])\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\tprint(e)\n\n\t\t\t\traise web.seeother(whereto)\n\t\t\telse:\n\t\t\t\tcreate_record(recordData)","title":""},{"docid":"52a58a81d1d618f5b1bb4ce59962657a","score":"0.4946528","text":"def setUp(self):\n self.tab_group = QtTabGroup(None, uuid4().hex, QtLocalPipe(uuid4))\n self.tab_group.create()","title":""},{"docid":"ccb393dfce1c4bd60c1a3ac2466dc8c1","score":"0.49464187","text":"def on_submit_clicked(self):\n LOG.info('Submit button is clicked')\n global_constants.FROM_DB = False\n global_constants.TEXT_BOARD = self.gui_utils.text_board\n global_constants.TEXT_INTERFACE = self.gui_utils.text_interface\n global_constants.TEXT_DEVICE = self.gui_utils.text_device\n LOG.info('The User selected \"BOARD:\"{0}, \"INTERFACE:\"{1}, \"DEVICE:\"{2}'.\n format(self.gui_utils.text_board,\n self.gui_utils.text_interface,\n self.gui_utils.text_device))\n self.str_file_name = '{0}_{1}_{2}'.format(self.gui_utils.text_board,\n self.gui_utils.text_interface,\n self.gui_utils.text_device)\n LOG.debug('The File name Loaded {0}'.format(self.str_file_name))\n # Getting back to current script directory\n LOG.debug('Getting back to current script directory')\n OutputWrite.change_to_script_directory(__file__)\n # Opening the test case files\n LOG.debug('Opening the test case files')\n try:\n LOG.info('Getting the test cases from a file')\n with open('../resources/' + self.str_file_name, 'r') as file_obj:\n list_of_test_cases = file_obj.readlines()\n\n self.gui_utils.count = len(list_of_test_cases)\n\n item = QtWidgets.QTableWidgetItem()\n item.setText('Test Id')\n self.gui_utils.tableWidget.setHorizontalHeaderItem(0, item)\n self.gui_utils.tableWidget.horizontalHeaderItem(0). \\\n setTextAlignment(QtCore.Qt.AlignLeft)\n item = QtWidgets.QTableWidgetItem()\n item.setText('Description')\n self.gui_utils.tableWidget.setHorizontalHeaderItem(1, item)\n self.gui_utils.tableWidget.horizontalHeaderItem(1). \\\n setTextAlignment(QtCore.Qt.AlignLeft)\n # adding the checkboxes to the table widget\n self.gui_utils.tableWidget.setRowCount(self.gui_utils.count)\n LOG.debug('Populating the test cases to the QTableWidget')\n for i in range(self.gui_utils.count):\n test_case = list_of_test_cases[i].split(',')\n for j in range(2):\n if j == 1:\n item.setFlags(QtCore.Qt.ItemIsUserCheckable |\n QtCore.Qt.ItemIsEnabled)\n item.setCheckState(QtCore.Qt.Unchecked)\n item = QtWidgets.QTableWidgetItem()\n item.setText(test_case[j])\n item.setFlags(QtCore.Qt.ItemIsEnabled)\n self.gui_utils.tableWidget.setItem(i, j, item)\n except FileNotFoundError as file_error:\n LOG.exception(file_error)\n QMessageBox.warning(None, 'Warning', \"Select Proper Test Case File\")","title":""},{"docid":"883ea41fae826ef80f592e168ff2fcdb","score":"0.49347147","text":"def show_new_exp_ui(self):\n \n display(self.new_exp_ui)","title":""},{"docid":"6d76765578d085b243a179bad8cc9483","score":"0.49254894","text":"def form():\n return render_template(\"explorer_upload.html\")","title":""},{"docid":"b6f95593b9402459a9631ec0dfbc6662","score":"0.4923823","text":"def new_job():\n if current_user.account_type != AccountTypes.c:\n flash(f'You cannot access this page.')\n return redirect(url_for('main.index'))\n\n form = JobPostForm()\n if form.validate_on_submit():\n deets = extract_details(form)\n post_id = new_jobpost(current_user._company.id, deets.pop('title'), **deets)\n\n # now update the cache for this new post\n update_cache(jobpost_id=post_id)\n\n flash(f\"Created job post with ID {post_id}\")\n return redirect(url_for('main.job_page', job_id=post_id))\n return render_template('company/jobpost_editor.html',\n form=form, skill_list=SKILL_NAMES, attitude_list=ATTITUDE_NAMES\n )","title":""},{"docid":"a5c06ad11a9cbf2de185c0fd75324bb0","score":"0.49214414","text":"def new_thread():\n\n form = NewThreadForm()\n if form.validate_on_submit():\n\n data = form.data.copy()\n poster = Poster.from_details(data.pop('name'))\n t = poster.create_thread(**data)\n return redirect(url_for('thread', thread_id=t.id))\n\n else:\n flash_form_errors(form)\n return redirect(url_for('index'))","title":""},{"docid":"3db64c5c9f749abd32e14f6f22479919","score":"0.49211615","text":"def __init__(self, ui):\n\n # The new frame is a copy of the currently selected frame\n self.index = ui.get_selected_frame_index()\n\n # The new frame will be added at the end of the presentation\n new_frame_number = str(len(ui.model.frames) + 1)\n\n SoziAction.__init__(self,\n _(\"Delete duplicated frame {0}\").format(new_frame_number),\n _(\"Duplicate frame {0}\").format(self.index + 1)\n )\n \n self.ui = ui\n \n self.frame = ui.model.frames[self.index].copy()","title":""},{"docid":"382deb9ffec80d2ce7f4eda2f068d223","score":"0.4916559","text":"async def handle_submit(request):\n name = request.match_info.get(\"name\", \"\")\n token = request.match_info.get(\"token\", \"\")\n blob = await request.read()\n\n try:\n submit_app(name, token, blob)\n except Exception as err:\n return web.Response(status=403, text=str(err))\n else:\n return web.Response(status=200, text=\"Thanks for submitting app %r!\" % name)","title":""},{"docid":"4d3a1802fa40e258086104f8dcaa8906","score":"0.49132928","text":"def _openProtocolForm(self, prot):\n \n w = pwgui.form.FormWindow(Message.TITLE_NAME_RUN + prot.getClassName(), prot, \n self._executeSaveProtocol, self.windows,\n hostList=self.project.getHostNames(), updateProtocolCallback=self._updateProtocol(prot))\n w.adjustSize()\n w.show(center=True)","title":""},{"docid":"97d1d03dd4886d6c750cfa20cca4c462","score":"0.49073058","text":"def action(self, data_object, response_data):\n\n db_name, table_name = self.tree_view.get_selected_item_and_parent_text()\n self.tab_table.add_tab( ui_tabTable.TAB_TYPE_TABLE, db_name, table_name )","title":""}],"string":"[\n {\n \"docid\": \"49a8b321adaf67148f61d57eef97bcc1\",\n \"score\": \"0.62406707\",\n \"text\": \"def create_new_tab(self, page=-1):\\n new_tab = STATDotWindow.Tab()\\n tab_label_box = gtk.EventBox()\\n tab_label_box.add(new_tab.label)\\n tab_label_box.connect('event', self.on_tab_clicked, new_tab.widget)\\n new_tab.label.show()\\n new_tab.widget.connect('clicked', self.on_node_clicked)\\n if page == -1:\\n page = len(self.tabs)\\n self.notebook.insert_page(new_tab.hpaned, tab_label_box, page)\\n self.tabs.insert(page, new_tab)\\n self.set_focus(new_tab.widget)\\n self.show_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eded1d52a34df50f44ebaab92c221a27\",\n \"score\": \"0.61924666\",\n \"text\": \"def new_tab(self):\\n layout = QtWidgets.QGridLayout()\\n layout.setSpacing(1)\\n layout.setContentsMargins(0, 0, 0, 0)\\n widget = GlueMdiArea(self)\\n widget.setLayout(layout)\\n tab = self.tab_widget\\n self._total_tab_count += 1\\n tab.addTab(widget, str(\\\"Tab %i\\\" % self._total_tab_count))\\n tab.setCurrentWidget(widget)\\n widget.subWindowActivated.connect(self._update_plot_dashboard)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d8d9e3eb89d001fe00712c8e5d31ae\",\n \"score\": \"0.61313283\",\n \"text\": \"def add_tab(self, text, icon=None):\\n# button = ButtonTab() # If a pushbutton\\n button = ButtonTab()\\n button.setCheckable(True)\\n button.setText(text)\\n button.setToolButtonStyle(Qt.ToolButtonTextUnderIcon)\\n button.setObjectName(text.lower())\\n\\n if icon:\\n button.setIcon(icon)\\n\\n self.main_layout.addWidget(button)\\n self.buttons.append(button)\\n index = self.buttons.index(button)\\n button.clicked.connect(lambda b=button, i=index:\\n self.refresh(button, index))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac950075c326805e74324938b4e5126\",\n \"score\": \"0.6129389\",\n \"text\": \"def submit(self):\\r\\n self.btn_submit.click()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d105e01d071cee06bb926128b0ca8f\",\n \"score\": \"0.6030572\",\n \"text\": \"def __createGreetingsTab(self):\\r\\n\\r\\n #Define the tab itself and add it to the notebook\\r\\n greetingsTab = ttk.Frame(self.note)\\r\\n self.note.add(greetingsTab, text = 'Greeting')\\r\\n\\r\\n #Define the greeting that will appear\\r\\n greeting = \\\\\\r\\n 'Welcome to the DCT Reduction Pipeline\\\\n' \\\\\\r\\n 'GUI. This program was created by the BU\\\\n' \\\\\\r\\n 'PyCon team and is designed for reducing\\\\n' \\\\\\r\\n 'and analyzing images specifically from\\\\n' \\\\\\r\\n 'the 4.3 m Discovery Channel Telescope.'\\r\\n\\r\\n #Place the greeting as a label and create two buttons, the configure the layout\\r\\n ttk.Label(greetingsTab, text = greeting, font = ('Century Gothic', 14, 'bold'), anchor = 'w', justify = 'left').grid(row = 0, column = 0, columnspan = 2, padx = 20, pady = 20)\\r\\n ttk.Button(greetingsTab, text = 'START', command = lambda: self.note.select(self.inputTab)).grid(row = 1, column = 0, sticky = 'nswe', padx = 5, pady = 5)\\r\\n ttk.Button(greetingsTab, text = 'EXIT', command = lambda: self.root.destroy()).grid(row = 1, column = 1, sticky = 'nswe', padx = 5, pady = 5)\\r\\n greetingsTab.rowconfigure(1, minsize = 50)\\r\\n greetingsTab.columnconfigure(0, weight = 10)\\r\\n greetingsTab.columnconfigure(1, weight = 1)\\r\\n\\r\\n return greetingsTab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1b6a9485558278028681947f70db196\",\n \"score\": \"0.60176766\",\n \"text\": \"def submit():\\n return render_template('submit.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9aa05aa2bd66ace649a132f84a00fac\",\n \"score\": \"0.59464854\",\n \"text\": \"def make_tabs(self):\\n tab1 = self.arima_tab()\\n tab2 = self.prophet_tab()\\n tab3 = self.lstm_tab()\\n\\n tabs = Tabs(tabs=[tab1, tab2, tab3])\\n\\n dt_pckr_strt = DatePicker(title='Select start of sync date',\\n min_date=date(2017, 1, 1),\\n max_date=date.today())\\n start_picker_callback = CustomJS(args=dict(plot=self.plot),\\n code=js.BUTTON_DATE)\\n dt_pckr_strt.js_on_change('value', start_picker_callback)\\n\\n button_pred = Button(label=\\\"Predict\\\",\\n button_type=\\\"success\\\")\\n\\n button_pred_callback = CustomJS(args=dict(plot=tabs),\\n code=js.BUTTON_PRED)\\n button_pred.js_on_click(button_pred_callback)\\n\\n tab_callback = CustomJS(args=dict(plot=tabs),\\n code=js.BUTTON_TAB)\\n tabs.js_on_change('active', tab_callback)\\n\\n end_date = date.today() + timedelta(weeks=+4)\\n dt_pckr_end = DatePicker(title='Select end of sync date',\\n min_date=date.today(),\\n max_date=end_date)\\n end_picker_callback = CustomJS(args=dict(plot=self.plot),\\n code=js.BUTTON_DATE_END)\\n dt_pckr_end.js_on_change('value', end_picker_callback)\\n\\n but_picker = column(dt_pckr_strt,\\n dt_pckr_end,\\n button_pred)\\n\\n layout = row(but_picker, tabs)\\n\\n script_tab, div_tab = components(layout)\\n\\n return script_tab, div_tab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7992036a5c02a11dfbb79fd525cbfe34\",\n \"score\": \"0.58786106\",\n \"text\": \"def MainTabs(self):\\r\\n self.AddingEventTab=ttk.Notebook(self.root)\\r\\n self.tab1= ttk.Frame(self.AddingEventTab)\\r\\n self.tab2= ttk.Frame(self.AddingEventTab)\\r\\n self.tab3= ttk.Frame(self.AddingEventTab)\\r\\n self.AddingEventTab.add(self.tab1, text=\\\"ADD EVENT\\\")\\r\\n self.AddingEventTab.add(self.tab2, text=\\\"RETRIEVE EVENT\\\")\\r\\n self.AddingEventTab.add(self.tab3, text=\\\"DELETE EVENT\\\")\\r\\n self.AddingEventTab.pack(expand=1,pady=5, fill=\\\"both\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db7863502149502b87274f790a8695b2\",\n \"score\": \"0.5824937\",\n \"text\": \"def submit_assignment():\\n postback_form = PostbackForm()\\n return render_template(\\\"mturk/postback.html\\\",\\n mturk_post_url=session[\\\"mturk_post_url\\\"],\\n mturk_assignmentId=session[\\\"mturk_assignmentId\\\"],\\n mturk_hitId=session[\\\"mturk_hitId\\\"],\\n postback_form=postback_form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d45e7e5d07794b2a48a85364b2d0610\",\n \"score\": \"0.58155704\",\n \"text\": \"def createTab(self, tab_name='New tab', file_name=None, highlight=True):\\n new_tab = Tab(self.notebook, file_name=file_name, highlight=highlight)\\n self.notebook.add(new_tab, text=tab_name)\\n last_added = self.notebook.index('end')\\n self.notebook.select([last_added - 1])\\n return new_tab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ebf42f3b7b3aca3f5bb03df2012d7d\",\n \"score\": \"0.5793726\",\n \"text\": \"def add_tab(self, interface):\\n self.notebook.AddPage(interface, interface.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7da771df3eb19adfcf45b87461c1addd\",\n \"score\": \"0.57392085\",\n \"text\": \"def do(self):\\n self.ui.model.add_frame(self.frame)\\n self.ui.append_frame_tree(-1)\\n self.ui.select_frame_at_index(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0718f78b46d95c12584016809e633466\",\n \"score\": \"0.57334876\",\n \"text\": \"def _place_submit_button(self) -> None:\\r\\n self._submit_button = tk.Button(self._frame, text='Submit', command=self._search,\\r\\n font=('arialnarrow', 20), bg='forest green', fg='white')\\r\\n self._submit_button.pack()\\r\\n self._submit_button.place(x=(WINDOW_WIDTH)//2, y=WINDOW_HEIGHT//1.25, anchor='center',\\r\\n width=WINDOW_WIDTH//4, height=100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc53b6dc51afebe480b9b9a83e7ce82\",\n \"score\": \"0.5688593\",\n \"text\": \"def create_page(self):\\n self.lastname_label.grid(row=0, column=0, padx=20, pady=(0, 5), stick=tk.W)\\n self.lastname_entry.grid(row=0, column=1, pady=(0, 5))\\n self.firstname_label.grid(row=1, column=0, padx=20, pady=(0, 5), sticky=tk.W)\\n self.firstname_entry.grid(row=1, column=1, pady=(0, 5))\\n self.submit_button.grid(row=0, column=3, rowspan=2, sticky=tk.N+tk.S, padx=20, pady=(0, 5))\\n self.message_label.grid(row=2, column=0, columnspan=4, padx=20, pady=(15, 15), ipady=5, sticky=tk.W+tk.E)\\n\\n self.title_label.pack(fill=\\\"both\\\", pady=20)\\n self.frame.pack(expand=True)\\n\\n return self.container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f532f6179f8118686fd8d90c1cb0fd12\",\n \"score\": \"0.5680229\",\n \"text\": \"def create_display_tabs(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bbb6ac1297486af9ca0cb0ede6598e8\",\n \"score\": \"0.5680064\",\n \"text\": \"def _place_submit_button(self) -> None:\\r\\n self._submit_button = tk.Button(self._frame, text='Submit', command=self._search,\\r\\n font=('arialnarrow', 15), bg='forest green', fg='white')\\r\\n self._submit_button.pack()\\r\\n self._submit_button.place(x=WINDOW_WIDTH - 10, y=(WINDOW_HEIGHT //15),\\r\\n width=WINDOW_WIDTH//10, height=50, anchor='e',)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54d49b52bbbbc1b3a6e1a5e44cffc3e\",\n \"score\": \"0.56779075\",\n \"text\": \"def new_tab(self, url=\\\"\\\", tab_name=\\\"\\\"):\\r\\n if self.open:\\r\\n pyprint.write(\\\"Creating new tab...\\\")\\r\\n self.browser.find_element_by_tag_name('body').send_keys(Keys.CONTROL + 't')\\r\\n self.browser.switch_to.window(self.browser.window_handles[len(self.urls)])\\r\\n if url and tab_name:\\r\\n self.urls.append(url)\\r\\n self.tab_names.append(tab_name)\\r\\n self.current_tab = len(self.urls) - 1\\r\\n self.load(url, tab_name, self.current_tab)\\r\\n else:\\r\\n self.urls.append(\\\"\\\")\\r\\n self.tab_names.append(\\\"\\\")\\r\\n self.current_tab = len(self.urls) - 1\\r\\n return self.current_tab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae29b91b4e2da990334028341a2b9834\",\n \"score\": \"0.56756747\",\n \"text\": \"def addTab(self, QWidget, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b6cc0c9c75baeb52462b19db73b152a\",\n \"score\": \"0.56719065\",\n \"text\": \"def do(self):\\n self.ui.clear_form()\\n\\n frame = SoziFrame(self.ui.model)\\n frame.refid = self.ui.model.get_next_selected_id()\\n\\n self.ui.model.add_frame(frame)\\n self.ui.append_frame_tree(-1)\\n self.ui.select_frame_at_index(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe3ca88e6fb98a68994ef1257e9c7ece\",\n \"score\": \"0.56595266\",\n \"text\": \"def add_tab(self, tid, tab: TabButton, label: str = None, callbacks: list = None):\\n if not callbacks:\\n callbacks = []\\n callbacks.append((self.__tab_controller.show_tab, tid, None))\\n self.tabs.append((tab, label))\\n self.__tab_controller.create_controller(tab_id=tid)\\n tab._tab_group = self\\n for callback in callbacks or []:\\n callback, args, kwargs = callback\\n tab.add_callback(callback, args, kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c874c7ab4a7d7d0950db96355272c98\",\n \"score\": \"0.56564707\",\n \"text\": \"def _submit(self):\\n el = self.d.find_element(*self.LOCS[\\\"submit\\\"])\\n el.click()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"685d566c2b16f9ade7eb9ad70c3d3e39\",\n \"score\": \"0.564697\",\n \"text\": \"def submit(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a0671c8474ee55a23be8ee7682b4c7b\",\n \"score\": \"0.56117016\",\n \"text\": \"def add_experiment_tab(self, experiment, is_new): \\n # If the welcome tab is open, close it.\\n if self.is_welcome_tab_open:\\n self.DeletePage(0) \\n \\n # Create new notebook with Experiment controls and display\\n self.count += 1\\n etab = tab_experiment.TabExperiment(self, self.top, self.db,\\n experiment, is_new)\\n \\n self.AddPage(etab, \\\"Experiment%d\\\" % self.count, True)\\n if is_new:\\n etab.SetSelection(0)\\n etab.simulate.SetFocus()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1190f3badd7da1193b243a860edcc87\",\n \"score\": \"0.5610358\",\n \"text\": \"def init_new_experimental_step(self):\\n \\n # Create new experiment ui \\n upload_btn = widgets.Button(description='Upload', tooltip='Upload')\\n upload_btn.on_click(self.on_upload_clicked)\\n \\n exp_step_name = widgets.Text(description='New participant name:', placeholder='PROJECT_SUBJECT', style={'description_width':'initial'})\\n exp_name = widgets.Text(description='Experiment:', placeholder='EXPERIMENT', style={'description_width':'initial'})\\n project = widgets.Text(description='Project:', placeholder='PROJECT')\\n space = widgets.Text(description='Space:', placeholder='SPACE')\\n self.new_exp_ui = widgets.VBox([exp_step_name, exp_name, project, space, upload_btn])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a66715308b9e72977723fa00dddc04a0\",\n \"score\": \"0.5577774\",\n \"text\": \"def onNewFile(self):\\n self.createTab()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a77556d1732130fa576db39462f3d3dd\",\n \"score\": \"0.5552963\",\n \"text\": \"def on_add_watref_tab(self, event):\\n # If there's already a tab, just activate it.\\n if self._tabs[\\\"quant\\\"]:\\n self.NotebookDataset.activate_tab(self._tabs[\\\"quant\\\"])\\n else:\\n if self._tabs[\\\"fit\\\"]:\\n # Create a new quant block and add a tab to go with it.\\n block = self.dataset.add_watref()\\n \\n # The quant tab follows fitting\\n tab = self._tabs[\\\"fit\\\"]\\n index = self.NotebookDataset.get_tab_index(tab) + 1\\n \\n # We know the insertion point. Create and insert new tab.\\n tab = tab_watref.TabWatref(self, self.top, block)\\n self._tabs[\\\"quant\\\"] = tab\\n \\n self.NotebookDataset.InsertPage(index, tab, 'Quant', True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32133d4fa8251046148d1aca93f36895\",\n \"score\": \"0.5505739\",\n \"text\": \"def on_tab_clicked(self, widget, event, child):\\n if event.type == gtk.gdk.BUTTON_PRESS:\\n n = -1\\n for tab in self.tabs:\\n n += 1\\n if child == tab.widget:\\n break\\n self.notebook.set_current_page(n)\\n if event.button == 3:\\n options = ['New Tab', 'Close Tab']\\n menu = gtk.Menu()\\n for option in options:\\n menu_item = gtk.MenuItem(option)\\n menu.append(menu_item)\\n menu_item.connect('activate', self.menu_item_response, option)\\n menu_item.show()\\n menu.popup(None, None, None, event.button, event.time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3b0c65aa4105f07182827ff035b3c6\",\n \"score\": \"0.5441618\",\n \"text\": \"def submit(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3b0c65aa4105f07182827ff035b3c6\",\n \"score\": \"0.5441618\",\n \"text\": \"def submit(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b566b4ea651fd3ba7028eae597737dcc\",\n \"score\": \"0.5430729\",\n \"text\": \"def addTab(self, title):\\n tab = Q.QWidget()\\n tab.setContentsMargins(9, 9, 9, 9)\\n self._tab.addTab(tab, title)\\n self._tab.show()\\n return tab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c21d82d00b75f5bf5cb54daca0e15dd6\",\n \"score\": \"0.54004943\",\n \"text\": \"def addTab(self, label, content):\\n new_tab = Tab(label, content)\\n self.tabs[label] = new_tab\\n self.tab_list.append(new_tab)\\n self.updateContent()\\n return new_tab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02a51073bc442f83559de0eefc266238\",\n \"score\": \"0.5390192\",\n \"text\": \"def on_new_clicked(self):\\n\\n editor = PythonEditor()\\n self.tab_widget.addTab(editor, self.py_icon, editor.filename)\\n\\n self.tab_widget.setCurrentWidget(editor)\\n\\n return editor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e45712feab20d9795097d4d5262b49b1\",\n \"score\": \"0.5388206\",\n \"text\": \"def addInitialTab(self, id, label, content):\\n assert getattr(self, '_athenaID', None) is None, (\\n \\\"Cannot call this after the widget has rendered.\\\")\\n if self.widgetArgs is EMPTY:\\n self.widgetArgs = []\\n self.widgetArgs.append((id, label, content))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6a5379244133ec4e9cf04822aed68f7\",\n \"score\": \"0.5360094\",\n \"text\": \"def build_tabs():\\n temp_tab = \\\"<div style='display: flex;margin: 0 auto;width: 100%;' name='tabHolder'>\\\"\\n for i in range(0, TAB_COUNT+1):\\n # set tab 0 to selected by default\\n className = 'tab-button-selected' if i ==0 else 'tab-button'\\n # each button is triggers a function in the js file\\n temp_tab += \\\"<button onclick='tabChanged(\\\"+str(i)+\\\")' name=tab-button-\\\"+str(i)+\\\" class='\\\"+className+\\\"' style='flex-grow:\\\" +str(100/TAB_COUNT)+ \\\"'> \\\"+TAB_NAMES[i]+\\\"</button>\\\"\\n temp_tab += \\\"</div>\\\"\\n return temp_tab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5513e36f116c4b2e09f462c1e603668\",\n \"score\": \"0.5346864\",\n \"text\": \"def new_tab(self, widget, title, scrolled=True):\\n\\t\\t\\n\\t\\tif scrolled:\\n\\t\\t\\twindow = gtk.ScrolledWindow()\\n\\t\\t\\twindow.add(widget)\\n\\t\\t\\twindow.show_all()\\n\\t\\telse:\\n\\t\\t\\twindow = widget\\n\\t\\t\\twindow.show()\\n\\t\\t\\t\\n\\t\\t# Check if we have more than one tab, if so, show the tabs\\n\\t\\tif (self.get_n_pages() + 1) > 1:\\n\\t\\t\\tself.set_property('show-tabs', True)\\n\\t\\t\\t\\n\\t\\t# Create the tab label\\n\\t\\tlabel = self.create_tab_label(title, window)\\n\\n\\t\\t# Append the page\\n\\t\\tself.append_page(window)\\n\\t\\tself.set_tab_label(window, label)\\n\\t\\t\\n\\t\\tself.set_current_page(self.get_n_pages())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32e79c845dbe3909f8e2461fb1b1391\",\n \"score\": \"0.5313238\",\n \"text\": \"def insertTab(self, p_int, QWidget, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88d5b25b76462d9473aec304f69a9583\",\n \"score\": \"0.53058505\",\n \"text\": \"def newTab(self, text, bibtext, showBibtex, query):\\n if self.neverOpened:\\n self.resultsTabs.removeTab(0)\\n self.neverOpened = False\\n self.savedReformat.setEnabled(True)\\n self.savedShowBibtex.setEnabled(True)\\n self.openInBatch.setEnabled(True)\\n tab = QTextBrowser()\\n # enable editing\\n # TODO save bibtex before reformat\\n tab.setTextInteractionFlags(tab.textInteractionFlags() |\\n Qt.TextEditable | Qt.TextEditorInteraction)\\n tab.setOpenExternalLinks(True)\\n tab.formattedtext = text\\n tab.bibtext = bibtext\\n tab.lastBibtex = showBibtex\\n\\n if showBibtex:\\n tab.setText(self.HTMLsafe(bibtext, True))\\n else:\\n tab.setText(text)\\n\\n self.tabWidget.tabBar().setCurrentIndex(1)\\n self.resultsTabs.addTab(tab, query)\\n self.resultsTabs.tabBar().setCurrentIndex(self.resultsTabs.count()-1)\\n tab.setFocus(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c01bae2410e5b794fec621941129c965\",\n \"score\": \"0.5275982\",\n \"text\": \"def new_tab(self, url, active = True):\\n response = self.request({\\n 'url': url,\\n 'active': active\\n }, 'newTab', True)\\n\\n return Tab.from_json(self, response['tab'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a29821c2f60c9c72c4ae753c30db3f8\",\n \"score\": \"0.5274316\",\n \"text\": \"def create_tab_label(self, title, tab):\\n\\t\\t\\n\\t\\thbox = gtk.HBox()\\n\\t\\tcbtn = gtk.Button()\\n\\t\\tlabel = gtk.Label(title)\\n\\t\\timage = gtk.Image()\\n\\n\\t\\timage.set_from_stock(gtk.STOCK_CLOSE, gtk.ICON_SIZE_MENU)\\n\\t\\tcbtn.set_image(image)\\n\\t\\tcbtn.set_relief(gtk.RELIEF_NONE)\\n\\t\\tcbtn.connect(\\\"clicked\\\", self.close_tab, tab)\\n\\n\\t\\thbox.pack_start(label, True, True)\\n\\t\\thbox.pack_end(cbtn, False, False)\\n\\t\\thbox.show_all()\\n\\t\\t\\n\\t\\treturn hbox\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd514c7f2f88d3b7b31168e376c00dac\",\n \"score\": \"0.5264573\",\n \"text\": \"def add_tab(self, file_path, tab_title):\\n\\n try:\\n mapobject = sunpy.make_map(file_path)\\n tab_page = TabPage(mapobject, self.tabWidget)\\n self.tabWidget.addTab(tab_page, tab_title)\\n \\n # Focus new tab\\n self.tabWidget.setCurrentIndex(self.tabWidget.count() - 1)\\n \\n # Set color options dialog appropriately\\n self.initialize_color_options()\\n if self.tabWidget.count() == 1:\\n self.colorOptionsDockWidget.show()\\n except TypeError, e:\\n file_err = QMessageBox()\\n file_err.setText(str(e) + '\\\\n' + file_path)\\n file_err.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a41ed26b7567b76153fdf99dfaf74799\",\n \"score\": \"0.5256108\",\n \"text\": \"def menu_item_response(self, widget, string):\\n if string == \\\"New Tab\\\":\\n page = self.notebook.get_current_page()\\n self.create_new_tab(page + 1)\\n self.notebook.set_current_page(page + 1)\\n elif string == \\\"Close Tab\\\":\\n if len(self.tabs) == 1:\\n self.create_new_tab()\\n page = 0\\n else:\\n page = self.notebook.get_current_page()\\n self.notebook.remove_page(page)\\n self.tabs.remove(self.tabs[page])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c05a3c5a1114ac55a1ebabb9be27b4d8\",\n \"score\": \"0.5223002\",\n \"text\": \"def submitContent(self):\\n newContent = self.newText.get(1.0,END) # Putting user entry into text box in a variable.\\n webPage_functions.writeBody(\\\"pyWebPage.html\\\", newContent) # Write new content to file.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4c8eabddbc374413deaceebc5619bbe\",\n \"score\": \"0.5194204\",\n \"text\": \"def submit_button_clicked(self):\\n\\t\\t# check all entry variables for valid paths/exts\\n\\t\\t# we do global check on variables so all input errors can be shown at once\\n\\t\\tself.check_verbatims()\\n\\t\\tself.check_output()\\n\\t\\t\\n\\t\\t# ask if we're ready to be submitted\\n\\t\\t# here we stack checks based on priority for report\\n\\t\\t\\n\\t\\t# for appendix, all input fields need to be filled out for report to run\\n\\t\\tif self.check_verbatims():\\n\\t\\t\\tif self.check_output():\\n\\t\\t\\t\\t verbatims_path = os.path.join(self.verbatims_entry.get())\\n\\t\\t\\t\\t output_path = os.path.join(self.output_entry.get())\\n\\t\\t\\t\\t # ask controller to run files\\n\\t\\t\\t\\t self.controller.run_appendix(verbatims_path, output_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"292de5ba0281cee894c18985d3c19f0b\",\n \"score\": \"0.51780456\",\n \"text\": \"def load_view_attack_tab(self):\\n layout = QtWidgets.QVBoxLayout()\\n layout.addWidget(self.view_attack)\\n\\n tab = QtWidgets.QWidget()\\n tab.setLayout(layout)\\n\\n self.view_tabs.addTab(tab, \\\"MITRE\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc90118b510d57a4f46956ef39bc42d\",\n \"score\": \"0.516823\",\n \"text\": \"def _create_assessment_inputs_page(self, notebook): # pylint: disable=R0914, R0915\\r\\n\\r\\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\\r\\n # Build-up the containers for the tab. #\\r\\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\\r\\n _bbox = gtk.VButtonBox()\\r\\n _bbox.set_layout(gtk.BUTTONBOX_START)\\r\\n\\r\\n _hbox = gtk.HBox()\\r\\n _hbox.pack_start(_bbox, False, True)\\r\\n\\r\\n _hpaned = gtk.HPaned()\\r\\n\\r\\n _hbox.pack_end(_hpaned, True, True)\\r\\n\\r\\n _hpaned2 = gtk.HPaned()\\r\\n _hpaned.pack1(_hpaned2, True, False)\\r\\n\\r\\n _bbox.pack_start(self.btnCalculate, False, False)\\r\\n _bbox.pack_end(self.btnCalculateAll, False, False)\\r\\n\\r\\n # --------------------------------------------------------------#\\r\\n # Build the quadrant 1 (left) container. #\\r\\n # --------------------------------------------------------------#\\r\\n # Add the layout for stress inputs.\\r\\n _fixed1 = gtk.Fixed()\\r\\n\\r\\n _scrollwindow = gtk.ScrolledWindow()\\r\\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\\r\\n _scrollwindow.add_with_viewport(_fixed1)\\r\\n\\r\\n _frame = Widgets.make_frame(label=_(u\\\"Stress Inputs\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(_scrollwindow)\\r\\n\\r\\n _hpaned2.pack1(_frame, True, False)\\r\\n\\r\\n # --------------------------------------------------------------#\\r\\n # Build the quadrant 2 (middle) container. #\\r\\n # --------------------------------------------------------------#\\r\\n # Add the layout for reliability inputs.\\r\\n _fixed2 = gtk.Fixed()\\r\\n\\r\\n _scrollwindow = gtk.ScrolledWindow()\\r\\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\\r\\n _scrollwindow.add_with_viewport(_fixed2)\\r\\n\\r\\n _frame = Widgets.make_frame(label=_(u\\\"Reliability Inputs\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(_scrollwindow)\\r\\n\\r\\n self.vpnReliabilityInputs.pack1(_frame, True, True)\\r\\n\\r\\n _hpaned2.pack2(self.vpnReliabilityInputs, True, False)\\r\\n\\r\\n # --------------------------------------------------------------#\\r\\n # Build the quadrant 2 (upper right) container. #\\r\\n # --------------------------------------------------------------#\\r\\n _vpaned = gtk.VPaned()\\r\\n _hpaned.pack2(_vpaned, True, False)\\r\\n\\r\\n # Add the layout for common maintainability inputs. These input\\r\\n # widgets will be used for assembly and component inputs.\\r\\n _fixed3 = gtk.Fixed()\\r\\n\\r\\n _scrollwindow = gtk.ScrolledWindow()\\r\\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\\r\\n _scrollwindow.add_with_viewport(_fixed3)\\r\\n\\r\\n _frame = Widgets.make_frame(label=_(u\\\"Maintainability Inputs\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(_scrollwindow)\\r\\n\\r\\n _vpaned.pack1(_frame, True, False)\\r\\n\\r\\n # --------------------------------------------------------------#\\r\\n # Build the quadrant 4 (lower right) container. #\\r\\n # --------------------------------------------------------------#\\r\\n # Add the layout for common miscellaneous inputs. These input\\r\\n # widgets will be used for assembly and component inputs.\\r\\n _fixed4 = gtk.Fixed()\\r\\n\\r\\n _scrollwindow = gtk.ScrolledWindow()\\r\\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\\r\\n _scrollwindow.add_with_viewport(_fixed4)\\r\\n\\r\\n _frame = Widgets.make_frame(label=_(u\\\"Miscellaneous Inputs\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(_scrollwindow)\\r\\n\\r\\n _vpaned.pack2(_frame, True, False)\\r\\n\\r\\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\\r\\n # Place the widgets used to display the assessment inputs. #\\r\\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\\r\\n # Load all the gtk.ComboBox() widgets.\\r\\n _model = self.cmbHRMethod.get_model()\\r\\n _model.clear()\\r\\n self.cmbHRMethod.append_text(\\\"\\\")\\r\\n for __, _type in enumerate(Configuration.RTK_HR_TYPE):\\r\\n self.cmbHRMethod.append_text(_type)\\r\\n\\r\\n _model = self.cmbHRModel.get_model()\\r\\n _model.clear()\\r\\n self.cmbHRModel.append_text(\\\"\\\")\\r\\n for __, _model in enumerate(Configuration.RTK_HR_MODEL):\\r\\n self.cmbHRModel.append_text(_model)\\r\\n\\r\\n _model = self.cmbFailDist.get_model()\\r\\n _model.clear()\\r\\n self.cmbFailDist.append_text(\\\"\\\")\\r\\n for __, _dist in enumerate(Configuration.RTK_S_DIST):\\r\\n self.cmbFailDist.append_text(_dist)\\r\\n\\r\\n _model = self.cmbActEnviron.get_model()\\r\\n _model.clear()\\r\\n self.cmbActEnviron.append_text(\\\"\\\")\\r\\n for __, _environ in enumerate(Configuration.RTK_ACTIVE_ENVIRON):\\r\\n self.cmbActEnviron.append_text(_environ[0])\\r\\n\\r\\n _model = self.cmbDormantEnviron.get_model()\\r\\n _model.clear()\\r\\n self.cmbDormantEnviron.append_text(\\\"\\\")\\r\\n for __, _environ in enumerate(Configuration.RTK_DORMANT_ENVIRON):\\r\\n self.cmbDormantEnviron.append_text(_environ)\\r\\n\\r\\n # Create the labels for quadrant 1.\\r\\n _labels = [_(u\\\"Active Environment:\\\"), _(u\\\"Active Temp:\\\"),\\r\\n _(u\\\"Dormant Environment:\\\"), _(u\\\"Dormant Temp:\\\"),\\r\\n _(u\\\"Duty Cycle:\\\"), _(u\\\"Humidity:\\\"), _(u\\\"Vibration:\\\"),\\r\\n _(u\\\"RPM:\\\"), _(u\\\"Min Rated Temp:\\\"), _(u\\\"Knee Temp:\\\"),\\r\\n _(u\\\"Max Rated Temp:\\\"), _(u\\\"Rated Voltage:\\\"),\\r\\n _(u\\\"Operating Voltage:\\\"), _(u\\\"Rated Current:\\\"),\\r\\n _(u\\\"Operating Current:\\\"), _(u\\\"Rated Power:\\\"),\\r\\n _(u\\\"Operating Power:\\\"), _(u\\\"theta JC:\\\"),\\r\\n _(u\\\"Temperature Rise:\\\"), _(u\\\"Case Temperature:\\\")]\\r\\n (_x_pos, _y_pos) = Widgets.make_labels(_labels, _fixed1, 5, 5)\\r\\n _x_pos += 50\\r\\n\\r\\n # Place the quadrant 1 widgets.\\r\\n self.cmbActEnviron.set_tooltip_text(_(u\\\"Selects the active \\\"\\r\\n u\\\"operating environment for \\\"\\r\\n u\\\"the selected hardware item.\\\"))\\r\\n self.txtActTemp.set_tooltip_text(_(u\\\"Displays the active \\\"\\r\\n u\\\"environment operating \\\"\\r\\n u\\\"temperature for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.cmbDormantEnviron.set_tooltip_text(_(u\\\"Selects the dormant \\\"\\r\\n u\\\"environment for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtDormantTemp.set_tooltip_text(_(u\\\"Displays the dormant \\\"\\r\\n u\\\"environment temperature \\\"\\r\\n u\\\"for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.txtDutyCycle.set_tooltip_text(_(u\\\"Displays the operating \\\"\\r\\n u\\\"duty cycle for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtHumidity.set_tooltip_text(_(u\\\"Displays the active \\\"\\r\\n u\\\"environment operating \\\"\\r\\n u\\\"humidity for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.txtVibration.set_tooltip_text(_(u\\\"Displays the active \\\"\\r\\n u\\\"environment operating \\\"\\r\\n u\\\"vibration level for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtRPM.set_tooltip_text(_(u\\\"Displays the active environment \\\"\\r\\n u\\\"operating RPM for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.txtMinTemp.set_tooltip_text(_(u\\\"The minimum design operating \\\"\\r\\n u\\\"temperature for the selected\\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.txtKneeTemp.set_tooltip_text(_(u\\\"The knee temperature for \\\"\\r\\n u\\\"the selected hardware item.\\\"))\\r\\n self.txtMaxTemp.set_tooltip_text(_(u\\\"The maximum design operating \\\"\\r\\n u\\\"temperature for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.txtRatedVoltage.set_tooltip_text(_(u\\\"The maximum rated \\\"\\r\\n u\\\"voltage for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtOpVoltage.set_tooltip_text(_(u\\\"The operating voltage for \\\"\\r\\n u\\\"the selected hardware item.\\\"))\\r\\n self.txtRatedCurrent.set_tooltip_text(_(u\\\"The maximum rated \\\"\\r\\n u\\\"current for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtOpCurrent.set_tooltip_text(_(u\\\"The operating current for \\\"\\r\\n u\\\"the selected hardware item.\\\"))\\r\\n self.txtRatedPower.set_tooltip_text(_(u\\\"The maximum rated power \\\"\\r\\n u\\\"for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.txtOpPower.set_tooltip_text(_(u\\\"The operating power for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtThetaJC.set_tooltip_text(_(u\\\"The junction-to-case thermal \\\"\\r\\n u\\\"resistance for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.txtTempRise.set_tooltip_text(_(u\\\"The ambient to case \\\"\\r\\n u\\\"temperature rise for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtCaseTemp.set_tooltip_text(_(u\\\"The case temperature for \\\"\\r\\n u\\\"the selected hardware item.\\\"))\\r\\n\\r\\n _fixed1.put(self.cmbActEnviron, _x_pos, _y_pos[0])\\r\\n _fixed1.put(self.txtActTemp, _x_pos, _y_pos[1])\\r\\n _fixed1.put(self.cmbDormantEnviron, _x_pos, _y_pos[2])\\r\\n _fixed1.put(self.txtDormantTemp, _x_pos, _y_pos[3])\\r\\n _fixed1.put(self.txtDutyCycle, _x_pos, _y_pos[4])\\r\\n _fixed1.put(self.txtHumidity, _x_pos, _y_pos[5])\\r\\n _fixed1.put(self.txtVibration, _x_pos, _y_pos[6])\\r\\n _fixed1.put(self.txtRPM, _x_pos, _y_pos[7])\\r\\n _fixed1.put(self.txtMinTemp, _x_pos, _y_pos[8])\\r\\n _fixed1.put(self.txtKneeTemp, _x_pos, _y_pos[9])\\r\\n _fixed1.put(self.txtMaxTemp, _x_pos, _y_pos[10])\\r\\n _fixed1.put(self.txtRatedVoltage, _x_pos, _y_pos[11])\\r\\n _fixed1.put(self.txtOpVoltage, _x_pos, _y_pos[12])\\r\\n _fixed1.put(self.txtRatedCurrent, _x_pos, _y_pos[13])\\r\\n _fixed1.put(self.txtOpCurrent, _x_pos, _y_pos[14])\\r\\n _fixed1.put(self.txtRatedPower, _x_pos, _y_pos[15])\\r\\n _fixed1.put(self.txtOpPower, _x_pos, _y_pos[16])\\r\\n _fixed1.put(self.txtThetaJC, _x_pos, _y_pos[17])\\r\\n _fixed1.put(self.txtTempRise, _x_pos, _y_pos[18])\\r\\n _fixed1.put(self.txtCaseTemp, _x_pos, _y_pos[19])\\r\\n\\r\\n self._lst_handler_id.append(\\r\\n self.cmbActEnviron.connect('changed', self._on_combo_changed, 22))\\r\\n self._lst_handler_id.append(\\r\\n self.txtActTemp.connect('focus-out-event', self._on_focus_out, 23))\\r\\n self._lst_handler_id.append(\\r\\n self.cmbDormantEnviron.connect('changed',\\r\\n self._on_combo_changed, 24))\\r\\n self._lst_handler_id.append(\\r\\n self.txtDormantTemp.connect('focus-out-event',\\r\\n self._on_focus_out, 25))\\r\\n self._lst_handler_id.append(\\r\\n self.txtDutyCycle.connect('focus-out-event',\\r\\n self._on_focus_out, 26))\\r\\n self._lst_handler_id.append(\\r\\n self.txtHumidity.connect('focus-out-event',\\r\\n self._on_focus_out, 27))\\r\\n self._lst_handler_id.append(\\r\\n self.txtVibration.connect('focus-out-event',\\r\\n self._on_focus_out, 28))\\r\\n self._lst_handler_id.append(\\r\\n self.txtRPM.connect('focus-out-event', self._on_focus_out, 29))\\r\\n\\r\\n # Create the labels for quadrant 2.\\r\\n _labels = [_(u\\\"Calculation Method:\\\"), _(u\\\"Calculation Model:\\\"),\\r\\n _(u\\\"Specified h(t):\\\"), _(u\\\"Specified MTBF:\\\"),\\r\\n _(u\\\"Software h(t):\\\"), _(u\\\"Additive Adj:\\\"),\\r\\n _(u\\\"Multiplicative Adj:\\\"), _(u\\\"Failure Distribution:\\\"),\\r\\n _(u\\\"Scale:\\\"), _(u\\\"Shape:\\\"), _(u\\\"Location:\\\")]\\r\\n (_x_pos,\\r\\n _y_pos) = Widgets.make_labels(_labels, _fixed2, 5, 5)\\r\\n _x_pos += 50\\r\\n\\r\\n # Place the quadrant 2 widgets.\\r\\n self.cmbHRMethod.set_tooltip_text(_(u\\\"Selects the method of \\\"\\r\\n u\\\"assessing the hazard rate for \\\"\\r\\n u\\\"the selected hardware item.\\\"))\\r\\n self.cmbHRModel.set_tooltip_text(_(u\\\"Selects the hazard rate model to \\\"\\r\\n u\\\"use when calculating the hazard \\\"\\r\\n u\\\"rate for the selected hardware \\\"\\r\\n u\\\"item.\\\"))\\r\\n self.txtSpecifiedHt.set_tooltip_text(_(u\\\"Displays the specified \\\"\\r\\n u\\\"failure intensity for \\\"\\r\\n u\\\"the selected hardware item.\\\"))\\r\\n self.txtSpecifiedMTBF.set_tooltip_text(_(u\\\"Displays the specified \\\"\\r\\n u\\\"mean time between \\\"\\r\\n u\\\"failure (MTBF) for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtSoftwareHt.set_tooltip_text(_(u\\\"Displays the software \\\"\\r\\n u\\\"failure rate for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtAddAdj.set_tooltip_text(_(u\\\"Displays any hazard rate \\\"\\r\\n u\\\"assessment additive \\\"\\r\\n u\\\"adjustment factor for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtMultAdj.set_tooltip_text(_(u\\\"Displays any hazard rate \\\"\\r\\n u\\\"assessment multiplicative \\\"\\r\\n u\\\"adjustment factor for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.cmbFailDist.set_tooltip_text(_(u\\\"Selects the distribution of \\\"\\r\\n u\\\"times to failure for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtFailScale.set_tooltip_text(_(u\\\"Displays the time to \\\"\\r\\n u\\\"failure distribution scale \\\"\\r\\n u\\\"factor for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.txtFailShape.set_tooltip_text(_(u\\\"Displays the time to \\\"\\r\\n u\\\"failure distribution shape \\\"\\r\\n u\\\"factor for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.txtFailLoc.set_tooltip_text(_(u\\\"Displays the time to failure \\\"\\r\\n u\\\"distribution location \\\"\\r\\n u\\\"factor for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n\\r\\n _fixed2.put(self.cmbHRMethod, _x_pos, _y_pos[0])\\r\\n _fixed2.put(self.cmbHRModel, _x_pos, _y_pos[1])\\r\\n _fixed2.put(self.txtSpecifiedHt, _x_pos, _y_pos[2])\\r\\n _fixed2.put(self.txtSpecifiedMTBF, _x_pos, _y_pos[3])\\r\\n _fixed2.put(self.txtSoftwareHt, _x_pos, _y_pos[4])\\r\\n _fixed2.put(self.txtAddAdj, _x_pos, _y_pos[5])\\r\\n _fixed2.put(self.txtMultAdj, _x_pos, _y_pos[6])\\r\\n _fixed2.put(self.cmbFailDist, _x_pos, _y_pos[7])\\r\\n _fixed2.put(self.txtFailScale, _x_pos, _y_pos[8])\\r\\n _fixed2.put(self.txtFailShape, _x_pos, _y_pos[9])\\r\\n _fixed2.put(self.txtFailLoc, _x_pos, _y_pos[10])\\r\\n\\r\\n self._lst_handler_id.append(\\r\\n self.cmbHRMethod.connect('changed', self._on_combo_changed, 30))\\r\\n self._lst_handler_id.append(\\r\\n self.cmbHRModel.connect('changed', self._on_combo_changed, 31))\\r\\n self._lst_handler_id.append(\\r\\n self.txtSpecifiedHt.connect('focus-out-event',\\r\\n self._on_focus_out, 32))\\r\\n self._lst_handler_id.append(\\r\\n self.txtSpecifiedMTBF.connect('focus-out-event',\\r\\n self._on_focus_out, 33))\\r\\n self._lst_handler_id.append(\\r\\n self.txtSoftwareHt.connect('focus-out-event',\\r\\n self._on_focus_out, 34))\\r\\n self._lst_handler_id.append(self.txtAddAdj.connect('focus-out-event',\\r\\n self._on_focus_out,\\r\\n 35))\\r\\n self._lst_handler_id.append(\\r\\n self.txtMultAdj.connect('focus-out-event',\\r\\n self._on_focus_out, 36))\\r\\n self._lst_handler_id.append(\\r\\n self.cmbFailDist.connect('changed', self._on_combo_changed, 37))\\r\\n self._lst_handler_id.append(\\r\\n self.txtFailScale.connect('focus-out-event',\\r\\n self._on_focus_out, 38))\\r\\n self._lst_handler_id.append(\\r\\n self.txtFailShape.connect('focus-out-event',\\r\\n self._on_focus_out, 39))\\r\\n self._lst_handler_id.append(self.txtFailLoc.connect('focus-out-event',\\r\\n self._on_focus_out,\\r\\n 40))\\r\\n\\r\\n _fixed1.show_all()\\r\\n _fixed2.show_all()\\r\\n\\r\\n # Create the labels for quadrant #2.\\r\\n _labels = [_(u\\\"MTTR Method:\\\"), _(u\\\"Specified MTTR:\\\"),\\r\\n _(u\\\"Additive Adj:\\\"), _(u\\\"Multiplicative Adj:\\\"),\\r\\n _(u\\\"Repair Distribution:\\\"), _(u\\\"Scale:\\\"), _(u\\\"Shape:\\\")]\\r\\n (_x_pos,\\r\\n _y_pos) = Widgets.make_labels(_labels, _fixed3, 5, 5)\\r\\n\\r\\n # Create the labels for quadrant #4.\\r\\n _labels = [_(u\\\"Cost Method:\\\"), _(u\\\"Unit Cost:\\\")]\\r\\n (_x_pos2,\\r\\n _y_pos2) = Widgets.make_labels(_labels, _fixed4, 5, 5)\\r\\n _x_pos = max(_x_pos, _x_pos2) + 50\\r\\n\\r\\n # Place the quadrant #3 widgets.\\r\\n _model = self.cmbMTTRMethod.get_model()\\r\\n _model.clear()\\r\\n self.cmbMTTRMethod.append_text(\\\"\\\")\\r\\n for __, _type in enumerate(Configuration.RTK_MTTR_TYPE):\\r\\n self.cmbMTTRMethod.append_text(_type)\\r\\n\\r\\n _model = self.cmbRepairDist.get_model()\\r\\n _model.clear()\\r\\n self.cmbRepairDist.append_text(\\\"\\\")\\r\\n for __, _dist in enumerate(Configuration.RTK_S_DIST):\\r\\n self.cmbRepairDist.append_text(_dist)\\r\\n\\r\\n self.cmbMTTRMethod.set_tooltip_text(_(u\\\"Selects the method of \\\"\\r\\n u\\\"assessing the mean time to \\\"\\r\\n u\\\"repair (MTTR) for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtSpecifiedMTTR.set_tooltip_text(_(u\\\"Displays the specified \\\"\\r\\n u\\\"mean time to repair \\\"\\r\\n u\\\"(MTTR) for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtMTTRAddAdj.set_tooltip_text(_(u\\\"Displays the mean time to \\\"\\r\\n u\\\"repair (MTTR) assessment \\\"\\r\\n u\\\"additive adjustment \\\"\\r\\n u\\\"factor for the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n self.txtMTTRMultAdj.set_tooltip_text(_(u\\\"Displays the mean time \\\"\\r\\n u\\\"to repair (MTTR) \\\"\\r\\n u\\\"assessment multiplicative \\\"\\r\\n u\\\"adjustment factor for \\\"\\r\\n u\\\"the selected hardware item.\\\"))\\r\\n self.cmbRepairDist.set_tooltip_text(_(u\\\"Selects the time to \\\"\\r\\n u\\\"repair distribution for \\\"\\r\\n u\\\"the selected hardware item.\\\"))\\r\\n self.txtRepairScale.set_tooltip_text(_(u\\\"Displays the time to \\\"\\r\\n u\\\"repair distribution \\\"\\r\\n u\\\"scale parameter for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtRepairShape.set_tooltip_text(_(u\\\"Displays the time to \\\"\\r\\n u\\\"repair distribution \\\"\\r\\n u\\\"shape parameter for the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n\\r\\n _fixed3.put(self.cmbMTTRMethod, _x_pos, _y_pos[0])\\r\\n _fixed3.put(self.txtSpecifiedMTTR, _x_pos, _y_pos[1])\\r\\n _fixed3.put(self.txtMTTRAddAdj, _x_pos, _y_pos[2])\\r\\n _fixed3.put(self.txtMTTRMultAdj, _x_pos, _y_pos[3])\\r\\n _fixed3.put(self.cmbRepairDist, _x_pos, _y_pos[4])\\r\\n _fixed3.put(self.txtRepairScale, _x_pos, _y_pos[5])\\r\\n _fixed3.put(self.txtRepairShape, _x_pos, _y_pos[6])\\r\\n\\r\\n self._lst_handler_id.append(\\r\\n self.cmbMTTRMethod.connect('changed', self._on_combo_changed, 41))\\r\\n self._lst_handler_id.append(\\r\\n self.txtSpecifiedMTTR.connect('focus-out-event',\\r\\n self._on_focus_out, 42))\\r\\n self._lst_handler_id.append(\\r\\n self.txtMTTRAddAdj.connect('focus-out-event',\\r\\n self._on_focus_out, 43))\\r\\n self._lst_handler_id.append(\\r\\n self.txtMTTRMultAdj.connect('focus-out-event',\\r\\n self._on_focus_out, 44))\\r\\n self._lst_handler_id.append(\\r\\n self.cmbRepairDist.connect('changed', self._on_combo_changed, 45))\\r\\n self._lst_handler_id.append(\\r\\n self.txtRepairScale.connect('focus-out-event',\\r\\n self._on_focus_out, 46))\\r\\n self._lst_handler_id.append(\\r\\n self.txtRepairShape.connect('focus-out-event',\\r\\n self._on_focus_out, 47))\\r\\n\\r\\n # Place the quadrant #4 widgets.\\r\\n _model = self.cmbCostMethod.get_model()\\r\\n _model.clear()\\r\\n self.cmbCostMethod.append_text(\\\"\\\")\\r\\n for __, _type in enumerate(Configuration.RTK_COST_TYPE):\\r\\n self.cmbCostMethod.append_text(_type)\\r\\n\\r\\n self.cmbCostMethod.set_tooltip_text(_(u\\\"Select the method for \\\"\\r\\n u\\\"assessing the cost of the \\\"\\r\\n u\\\"selected hardware item.\\\"))\\r\\n self.txtCost.set_tooltip_text(_(u\\\"The unit cost of the selected \\\"\\r\\n u\\\"hardware item.\\\"))\\r\\n\\r\\n _fixed4.put(self.cmbCostMethod, _x_pos, _y_pos2[0])\\r\\n _fixed4.put(self.txtCost, _x_pos, _y_pos2[1])\\r\\n\\r\\n self._lst_handler_id.append(\\r\\n self.cmbCostMethod.connect('changed', self._on_combo_changed, 48))\\r\\n self._lst_handler_id.append(\\r\\n self.txtCost.connect('focus-out-event',\\r\\n self._on_focus_out, 49))\\r\\n self._lst_handler_id.append(\\r\\n self.txtMinTemp.connect('focus-out-event', self._on_focus_out, 50))\\r\\n self._lst_handler_id.append(\\r\\n self.txtKneeTemp.connect('focus-out-event',\\r\\n self._on_focus_out, 51))\\r\\n self._lst_handler_id.append(\\r\\n self.txtMaxTemp.connect('focus-out-event', self._on_focus_out, 52))\\r\\n self._lst_handler_id.append(\\r\\n self.txtRatedVoltage.connect('focus-out-event',\\r\\n self._on_focus_out, 53))\\r\\n self._lst_handler_id.append(\\r\\n self.txtOpVoltage.connect('focus-out-event',\\r\\n self._on_focus_out, 54))\\r\\n self._lst_handler_id.append(\\r\\n self.txtRatedCurrent.connect('focus-out-event',\\r\\n self._on_focus_out, 55))\\r\\n self._lst_handler_id.append(\\r\\n self.txtOpCurrent.connect('focus-out-event',\\r\\n self._on_focus_out, 56))\\r\\n self._lst_handler_id.append(\\r\\n self.txtRatedPower.connect('focus-out-event',\\r\\n self._on_focus_out, 57))\\r\\n self._lst_handler_id.append(self.txtOpPower.connect('focus-out-event',\\r\\n self._on_focus_out,\\r\\n 58))\\r\\n self._lst_handler_id.append(self.txtThetaJC.connect('focus-out-event',\\r\\n self._on_focus_out,\\r\\n 59))\\r\\n self._lst_handler_id.append(\\r\\n self.txtTempRise.connect('focus-out-event',\\r\\n self._on_focus_out, 60))\\r\\n self._lst_handler_id.append(\\r\\n self.txtCaseTemp.connect('focus-out-event',\\r\\n self._on_focus_out, 61))\\r\\n\\r\\n _fixed3.show_all()\\r\\n _fixed4.show_all()\\r\\n\\r\\n _label = gtk.Label()\\r\\n _label.set_markup(\\\"<span weight='bold'>\\\" + _(u\\\"Assessment\\\\nInputs\\\") +\\r\\n \\\"</span>\\\")\\r\\n _label.set_alignment(xalign=0.5, yalign=0.5)\\r\\n _label.set_justify(gtk.JUSTIFY_CENTER)\\r\\n _label.show_all()\\r\\n _label.set_tooltip_text(_(u\\\"Allows entering reliability, \\\"\\r\\n u\\\"maintainability, and other assessment \\\"\\r\\n u\\\"inputs for the selected hardware item.\\\"))\\r\\n\\r\\n notebook.insert_page(_hbox, tab_label=_label, position=-1)\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29985308aeea799ad462f0f94e0c8e09\",\n \"score\": \"0.5151647\",\n \"text\": \"def make_btn(self, text, clicked):\\n btn = qtw.QPushButton(\\n text,\\n pressed=clicked\\n )\\n btn.setSizePolicy(\\n qtw.QSizePolicy.Fixed,\\n qtw.QSizePolicy.Fixed\\n )\\n self.btn_frame.layout().addWidget(btn)\\n self.buttons.append(btn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb38f3653029ae1914c86369b42c366\",\n \"score\": \"0.51320344\",\n \"text\": \"def createClicked(self):\\n self.browseGrp.setHidden(True)\\n self.createGroup.setHidden(False)\\n self.status = 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd81d34517bec9e193b85f161ff10ce\",\n \"score\": \"0.5126743\",\n \"text\": \"def post_manual_faculty_submit_html():\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17b06d50130206cac108eff3cc608532\",\n \"score\": \"0.511716\",\n \"text\": \"def add_job_widgets(self):\\n\\n # add the fixture processing option widgets form.\\n self.fp_form = FixtureProcessingForm(\\n self.engineering_flag, self, self.ntbk_tabs)\\n self.ntbk_tabs.add(self.fp_form, text=\\\"Fixture Processor Options.\\\")\\n \\n # get user options, and fill the checkboxes\\n self.fp_form.get_user_options()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"545d068b2da16f4c6373b2cd987ec001\",\n \"score\": \"0.5115603\",\n \"text\": \"def create_button(self, text, _class):\\n tkinter.Button(\\n self.frame, text=text,\\n command=lambda: self.new_window(_class)).pack(fill=tkinter.X, pady=100, ipadx=10, ipady=10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfcd8743db5d56575b781c71a6cc0248\",\n \"score\": \"0.51048666\",\n \"text\": \"def add_widget(self, widget):\\n\\n # Check if widget has the required attributes\\n self.validate_widget(widget)\\n\\n has_item = self.tab_widget.widget(widget.order)\\n if has_item:\\n raise RuntimeError(\\\"Current index `%i` is occupied\\\" % widget.order)\\n\\n self.tab_widget.insertTab(widget.order, widget, widget.label)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e481a6e2b97212ca4d606ab6e1dc33a6\",\n \"score\": \"0.5100388\",\n \"text\": \"def on_create_btn_pressed(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f93a64248a58de4335ea9997dd4c75d\",\n \"score\": \"0.5098779\",\n \"text\": \"def _create_descrip_tab(self, nb: ttk.Notebook) -> None:\\n frame = ttk.Frame(nb, name='descrip')\\n msg = [\\\"Ttk is the new Tk themed widget set. One of the widgets \\\",\\n \\\"it includes is the notebook widget, which provides a set \\\",\\n \\\"of tabs that allow the selection of a group of panels, \\\",\\n \\\"each with distinct content. They are a feature of many \\\",\\n \\\"modern user interfaces. Not only can the tabs be selected \\\",\\n \\\"with the mouse, but they can also be switched between \\\",\\n \\\"using Ctrl+Tab when the notebook page heading itself is \\\",\\n \\\"selected. Note that the second tab is disabled, and cannot \\\"\\n \\\"be selected.\\\"] # widgets to be displayed on 'Description' tab\\n lbl = ttk.Label(frame, wraplength='4i', justify=LEFT, anchor=N, text=''.join(msg))\\n neatVar = tk.StringVar()\\n btn = ttk.Button(frame, text='Neat!', underline=0, command=lambda v=neatVar: self._say_neat(v))\\n neat = ttk.Label(frame, textvariable=neatVar, name='neat')\\n\\n # position and set resize behaviour\\n lbl.grid(row=0, column=0, columnspan=2, sticky='new', pady=5)\\n btn.grid(row=1, column=0, pady=(2, 4))\\n neat.grid(row=1, column=1, pady=(2, 4))\\n frame.rowconfigure(1, weight=1)\\n frame.columnconfigure((0, 1), weight=1, uniform=1)\\n\\n # bind for button short-cut key\\n # (must be bound to toplevel window)\\n self.winfo_toplevel().bind('<Alt-n>', lambda e, v=neatVar: self._say_neat(v))\\n\\n # add to notebook (underline = index for short-cut character)\\n nb.add(frame, text='Description', underline=0, padding=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2bdb055d842d84f214e61f1b854d2f\",\n \"score\": \"0.50981313\",\n \"text\": \"def __init__(self, initParams):\\n super().__init__()\\n\\n self.initParams = initParams\\n self.addTab(tabEx1(self.initParams), \\\"Tab Ex1\\\")\\n self.addTab(tabEx2(self.initParams), \\\"Tab Ex2\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae5b9764755650c5ba71bd099b2f533c\",\n \"score\": \"0.50971574\",\n \"text\": \"def input_button(self):\\r\\n self.submit_button = Tk.Button(self.root, text = \\\"Submit\\\", command = self.save_and_close)\\r\\n self.submit_button.pack()\\r\\n self.root.bind('<Return>', self.save_and_close)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5401d60d76edc303e22465631a8ae22\",\n \"score\": \"0.5078489\",\n \"text\": \"def action_newbrowser(self, url):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51d27e1089e522b58352443afc12827e\",\n \"score\": \"0.5078393\",\n \"text\": \"def new_tab(self, value):\\r\\n self.logger.warn(\\\"Setting values on new_tab will NOT update the remote Canvas instance.\\\")\\r\\n self._new_tab = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bac5709765cf99e8574d89d398c972b8\",\n \"score\": \"0.5075329\",\n \"text\": \"def button_6_click(self, **event_args):\\r\\n open_form('Passenger')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6dda5b01ae3ff2b93f35358751ca1b9\",\n \"score\": \"0.5072264\",\n \"text\": \"def on_button_submit_clicked(self, button_submit):\\n\\t\\tself.entry_job_starting_number.set_text(repr(self.job_starting_number))\\n\\t\\tself.dialog_submit.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d567751f28470ff505ff31a81876ef47\",\n \"score\": \"0.50675994\",\n \"text\": \"def on_pb_submit_clicked(self):\\n if self.lineEdit_username.text() == \\\"\\\":\\n QMessageBox.warning(self, \\\"警告\\\", \\\"用户名没有输入\\\")\\n self.lineEdit_username.setFocus()\\n elif self.lineEdit_pwd.text() == \\\"\\\":\\n QMessageBox.warning(self, \\\"警告\\\", \\\"密码没有输入\\\")\\n self.lineEdit_pwd.setFocus()\\n else:\\n name = self.lineEdit_username.text()\\n pwd = self.lineEdit_pwd.text()\\n jscode = \\\"PyQt52WebValue('{}','{}');\\\".format(name, pwd)\\n self.view.page().runJavaScript(jscode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6cedd70c421c64356a3a87431b7ed64\",\n \"score\": \"0.50664186\",\n \"text\": \"def changeName(self):\\n new_window = Toplevel(self.master, height=100, width=300, takefocus=True)\\n new_window.resizable(False, False)\\n\\n frame = Frame(new_window, padx=5, pady=5)\\n frame.pack()\\n\\n label = Label(frame, text='Enter the new tab name', padx=10, pady=10)\\n label.grid(row=2, column=1, rowspan=2, columnspan=5)\\n\\n name_entry = Entry(frame, width=20)\\n name_entry.grid(row=4, column=1, rowspan=2, columnspan=5)\\n name_entry.focus_set()\\n\\n CancelButton = Button(frame, text='Cancel', command=lambda: self.closeWidget(new_window), width=10, padx=5)\\n OkButton = Button(frame, text='Ok', command=lambda: self.changeTabName(name_entry.get(), new_window), width=10, padx=5)\\n CancelButton.grid(row=7, column=4, columnspan=2)\\n OkButton.grid(row=7, column=2, columnspan=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863eb27f32b0d920dfd8170ee250003c\",\n \"score\": \"0.50634223\",\n \"text\": \"def __init__(self, ui):\\n # The new frame will be added at the end of the presentation\\n new_frame_number = str(len(ui.model.frames) + 1)\\n\\n SoziAction.__init__(self,\\n _(\\\"Delete the new frame {0}\\\").format(new_frame_number),\\n _(\\\"Create a new frame at {0}\\\").format(new_frame_number)\\n )\\n \\n self.ui = ui\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07b425370525af02f0e9552b6590d0df\",\n \"score\": \"0.5061895\",\n \"text\": \"def __createInputTab(self):\\r\\n\\r\\n #Define an empty dict which will contain refernces to the StringVar\\r\\n #objects associated with each text field\\r\\n self.inputEntryTxt = {}\\r\\n \\r\\n #Define the tab itself and add it to the notebook\\r\\n inputTab = ttk.Frame(self.note)\\r\\n self.note.add(inputTab, text = 'Input')\\r\\n\\r\\n #The label and help button at the top\\r\\n ttk.Label(inputTab, text = 'Enter the relevant information below', font = ('Century Gothic', 10, 'bold')).grid(row = 0, column = 0, columnspan = 2, sticky = 'W', padx = (2,0), pady = 2)\\r\\n ttk.Button(inputTab, text = '?', command = lambda: self.showInputHelp(), width = 2).grid(row = 0, column = 2, sticky = 'E', pady = 2)\\r\\n\\r\\n #The input path label and entry box\\r\\n ttk.Label(inputTab, text = 'Input Path', font = ('Cenutry Gothic', 9)).grid(row = 1, column = 0, stick = 'w', padx = (2,8), pady = 4)\\r\\n self.inputEntryTxt['Input Path'] = tk.StringVar()\\r\\n ttk.Entry(inputTab, textvariable = self.inputEntryTxt['Input Path'], width = 50).grid(row = 1, column = 1, columnspan = 2)\\r\\n\\r\\n #The input bias filenames label and entry box\\r\\n ttk.Label(inputTab, text = 'Bias Filenames', font = ('Cenutry Gothic', 9)).grid(row = 2, column = 0, stick = 'w', padx = (2,8), pady = 4)\\r\\n self.inputEntryTxt['Bias Filenames'] = tk.StringVar()\\r\\n ttk.Entry(inputTab, textvariable = self.inputEntryTxt['Bias Filenames'], width = 50).grid(row = 2, column = 1, columnspan = 2)\\r\\n\\r\\n #The input flat filenames label and entry box\\r\\n ttk.Label(inputTab, text = 'Flat Filenames', font = ('Cenutry Gothic', 9)).grid(row = 3, column = 0, stick = 'w', padx = (2,8), pady = 4)\\r\\n self.inputEntryTxt['Flat Filenames'] = tk.StringVar()\\r\\n ttk.Entry(inputTab, textvariable = self.inputEntryTxt['Flat Filenames'], width = 50).grid(row = 3, column = 1, columnspan = 2)\\r\\n\\r\\n #The input image filenames label and entry box\\r\\n ttk.Label(inputTab, text = 'Image Filenames', font = ('Cenutry Gothic', 9)).grid(row = 4, column = 0, stick = 'w', padx = (2,8), pady = 4)\\r\\n self.inputEntryTxt['Image Filenames'] = tk.StringVar()\\r\\n ttk.Entry(inputTab, textvariable = self.inputEntryTxt['Image Filenames'], width = 50).grid(row = 4, column = 1, columnspan = 2)\\r\\n\\r\\n #The load images button\\r\\n ttk.Button(inputTab, text = 'LOAD IMAGES', command = lambda: self.loadImages()).grid(row = 5, column = 0, columnspan = 3, stick = 'nswe', padx = (2,0), pady = 10)\\r\\n inputTab.rowconfigure(5, minsize = 55)\\r\\n \\r\\n return inputTab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e937ebec0f1161fb3ed04d491aadc1\",\n \"score\": \"0.5057023\",\n \"text\": \"def create_plot_page(self):\\r\\n\\r\\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\\r\\n # Build-up the containers for the tab. #\\r\\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\\r\\n _vbox = gtk.VBox()\\r\\n self.pack_start(_vbox, True, True)\\r\\n\\r\\n _frame = Widgets.make_frame(_(u\\\"Survival Function\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(self.pltPlot1)\\r\\n _vbox.pack_start(_frame, True, True)\\r\\n\\r\\n _frame = Widgets.make_frame(_(u\\\"Hazard Function\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(self.pltPlot2)\\r\\n _vbox.pack_end(_frame, True, True)\\r\\n\\r\\n _vbox = gtk.VBox()\\r\\n self.pack_end(_vbox, True, True)\\r\\n\\r\\n _frame = Widgets.make_frame(_(u\\\"Cumulative Hazard Function\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(self.pltPlot3)\\r\\n _vbox.pack_start(_frame, True, True)\\r\\n\\r\\n _frame = Widgets.make_frame(_(u\\\"Log Cumulative Hazard Function\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(self.pltPlot4)\\r\\n _vbox.pack_end(_frame, True, True)\\r\\n\\r\\n # Insert the page.\\r\\n self.lblPage.set_markup(\\\"<span weight='bold'>Analysis\\\\nPlots</span>\\\")\\r\\n self.lblPage.set_alignment(xalign=0.5, yalign=0.5)\\r\\n self.lblPage.set_justify(gtk.JUSTIFY_CENTER)\\r\\n self.lblPage.show_all()\\r\\n self.lblPage.set_tooltip_text(_(u\\\"Displays survival analyses plots.\\\"))\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a70758b0a9c74585e94c101f12261ef6\",\n \"score\": \"0.50532055\",\n \"text\": \"def addButtons(self, frame, buttonsText):\\n button = ttk.Button(frame, text=buttonsText[0], style=BUTTON_STYLE, command=lambda: self.done(0))\\n button.pack(side=tk.LEFT, fill=BOTH, expand=1, padx=BUTTON_PAD_X, pady=BUTTON_PAD_Y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2de1d4e5f57ccf4e813cc902846647e5\",\n \"score\": \"0.50530213\",\n \"text\": \"def open_new_tab(url):\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0a6f2c33ef3c8c368ae38410abfc66\",\n \"score\": \"0.5047902\",\n \"text\": \"def on_addprojectBtn_clicked(self): \\r\\n print \\\"En este nivel se abre el window\\\"\\r\\n\\r\\n\\r\\n self.addProject()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a38794e8e9e8c337b8f18d00011b804f\",\n \"score\": \"0.5046592\",\n \"text\": \"def NewFile(self):\\n self.NewFileInterface = tk.Toplevel()\\n Label = ttk.Label(master = self.NewFileInterface, text = \\\"Enter name of file\\\")\\n Label.pack()\\n self.Entry = ttk.Entry(master = self.NewFileInterface)\\n self.Entry.pack()\\n Button = ttk.Button(master = self.NewFileInterface, text = \\\"Press to save\\\", command = self.SaveNewFile)\\n Button.pack()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a4782fe380a7d85433b011ef247bf0b\",\n \"score\": \"0.50457335\",\n \"text\": \"def add_tab():\\n try:\\n data = json.loads(request.data)\\n name = data['name']\\n priority = int(data.get('priority', 0))\\n assert isinstance(name, basestring)\\n except:\\n return {'error': 'illegal format'}\\n username = current_user.username\\n doc = get_user(username)\\n for tab in doc['tab']:\\n if tab['name'] == name:\\n return {'error': 'tab with this name already exists!'}\\n doc['tab'].append({\\n 'name': name,\\n 'priority': priority,\\n 'tags': []\\n })\\n get_mongo('user').save(doc)\\n log_info('user {0} add tab {1}'.format(username, name))\\n return {'success': 1}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8826f5449f29f2a656a10939bc707fde\",\n \"score\": \"0.5034962\",\n \"text\": \"def _submit_employee_form(self):\\n\\n # Make the questions non editable for the current part.\\n for question in self.questions:\\n question.is_editable = False\\n question.save()\\n\\n # Move the form in next state\\n type_ = self.type_\\n if type_ == 'self_appraisal':\\n self.employee_form_appraisal.submit_to_reporting([self.employee_form_appraisal])\\n elif type_ == 'appraisal':\\n self.employee_form_reporting.submit_to_reviewing([self.employee_form_reporting])\\n elif type_ == 'reviewing':\\n if self.employee_form_reviewing.acceptance_authority:\\n self.employee_form_reviewing.submit_to_accepting([self.employee_form_reviewing])\\n else:\\n self.employee_form_reviewing.disclose([self.employee_form_reviewing])\\n elif type_ == 'accepting':\\n self.employee_form_accepting.disclose([self.employee_form_accepting])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214cfbd666ac70becc1401740e0188c3\",\n \"score\": \"0.5032395\",\n \"text\": \"def submission_create(request) -> HttpResponse:\\r\\n if request.method == 'POST':\\r\\n form = SubmissionForm(request.POST, initial={\\r\\n 'assignment': request.assignment,\\r\\n })\\r\\n if form.is_valid():\\r\\n submission = form.save(commit=False)\\r\\n submission.user = request.user\\r\\n submission.assignment = request.assignment\\r\\n submission = form.save()\\r\\n tasks.automatic_mark_and_feedback.delay(submission.pk)\\r\\n AuditLog.objects.create(date=datetime.now(pytz.UTC), unit=request.unit, message=\\\"%s[%s] submitted %s[%s]\\\" % (request.user, request.user.pk, submission, submission.pk))\\r\\n return redirect(submission)\\r\\n else:\\r\\n for error in form.non_field_errors():\\r\\n messages.error(request, error)\\r\\n else:\\r\\n form = SubmissionForm(initial={\\r\\n 'solution': request.assignment.template\\r\\n })\\r\\n\\r\\n context = {\\r\\n 'form': form,\\r\\n 'unit': request.unit,\\r\\n 'assignment': request.assignment,\\r\\n }\\r\\n\\r\\n return render(request, 'decentmark/submission_create.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f0d7f51da0a521eb5e8d9abe936dcb\",\n \"score\": \"0.50318515\",\n \"text\": \"def _create_new_form(self, extra_data, post_data, count, initial=None):\\n prefix = '%s_new_%s' % (self.assertion_type, str(count))\\n form = self.form_class(self.authority_records, extra_data,\\n prefix=prefix, data=post_data, initial=initial)\\n form.is_new_form = True\\n return form\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3160e79d99c6bf5469e3b1f4a9dcb99\",\n \"score\": \"0.50307053\",\n \"text\": \"def submit(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30acbcc4497c8ba5d9031592dd9a5432\",\n \"score\": \"0.5027584\",\n \"text\": \"def add(self, image, title, view, name=None, order=0):\\n item = TabControllerItem(\\n image=image,\\n title=title,\\n view=view,\\n name=name,\\n action=self.action)\\n\\n self.tabs.append({\\n 'button': item,\\n 'order': order,\\n })\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c6a04555b67a6664f7d32de4bfde16d\",\n \"score\": \"0.5026459\",\n \"text\": \"def ide_go_final_tab():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3503dca7358876396e18fea8503f4796\",\n \"score\": \"0.5024592\",\n \"text\": \"def create_button(oDoc, oPage, sName, sLabel, index):\\n # Change to be a file. required by \\\"AssignAction(). ScriptEventDescriptor.ScriptCode\\\" \\n sScriptURL = \\\"vnd.sun.star.script:draw_uno_plan.py$button_push_event?language=Python&location=user\\\"\\n # Displays in Design Mode, Button Right click, Control Properties... Events... \\n # draw_uno_plan.py$button_push_event (user, Python)\\n \\n oButtonModel = add_new_button(sName, sLabel, oDoc, oPage, index) \\n \\n oForm = oPage.getForms().getByIndex(0)\\n # Find index inside the form container\\n nIndex = get_index(oButtonModel, oForm)\\n #print(str(nIndex)) # -1\\n \\n assign_action(nIndex, sScriptURL, oForm, oDoc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5021e9bca74b63505127e8327fadc6ac\",\n \"score\": \"0.50217193\",\n \"text\": \"def addEmpButtonPressed(self):\\n newEmp = addEmpWindow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6788098df779a90f41b426f8096e94d3\",\n \"score\": \"0.5021367\",\n \"text\": \"def test_submit(self):\\n self.jurisdictions_field = self.driver.find_element_by_name(\\\"name\\\")\\n self.sick_people_field = self.driver.find_element_by_name(\\\"victims\\\")\\n\\n self.jurisdictions_field.send_keys(\\\"Antioquia\\\")\\n self.sick_people_field.send_keys(\\\"1 million\\\")\\n self.sick_people_field.submit()\\n\\n continue_link = self.driver.find_element_by_link_text('Submit')\\n self.assertTrue(\\\"New jurisdiction added succesfully\\\" in continue_link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42733339908d56d47fa102f6d90970c7\",\n \"score\": \"0.5005546\",\n \"text\": \"def on_ok(self, keypress=None):\\n self.parentApp.setNextForm('MAIN')\\n self.parentApp.switchFormNow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42733339908d56d47fa102f6d90970c7\",\n \"score\": \"0.5005546\",\n \"text\": \"def on_ok(self, keypress=None):\\n self.parentApp.setNextForm('MAIN')\\n self.parentApp.switchFormNow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fe6463ac0e1d9f8cf8b749d59e835d2\",\n \"score\": \"0.5003776\",\n \"text\": \"def new_tab(self):\\r\\n return self._new_tab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe1a2db599f31e41b81624e73432b64f\",\n \"score\": \"0.5000268\",\n \"text\": \"def new_report_click(self, **event_args):\\n open_form('reports.main')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63e8208f158dd43cb198ea95896b3d80\",\n \"score\": \"0.49832997\",\n \"text\": \"def change_tab(self, index):\\n if self.ui.tabMain.tabText(index) == \\\"Action\\\":\\n utils.populate_action_fields(self)\\n self.ui.listActions.setCurrentRow(self.ui.listActions.count() - 1)\\n elif self.ui.tabMain.tabText(index) == \\\"MD Integration\\\":\\n utils.populate_md_integration_levels(self)\\n elif self.ui.tabMain.tabText(index) == \\\"Input File Preview\\\":\\n self.ui.txtInputFile.setPlainText(utils.generate_input_file(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a5054e57ad906e78b8e7ab2209a3fb5\",\n \"score\": \"0.4965369\",\n \"text\": \"def clicked_button_set_window(self):\\r\\n self.clear_layout()\\r\\n self.current_input = WindowForm()\\r\\n\\r\\n self.layout.addWidget(self.current_input)\\r\\n self.current_input.push_button_start.setEnabled(False)\\r\\n # Configure layout\\r\\n self.current_input.push_button_input.clicked.connect(self.clicked_push_button_input)\\r\\n self.current_input.push_button_output.clicked.connect(self.clicked_push_button_output)\\r\\n\\r\\n self.current_input.line_edit_output.setText(self._current_path)\\r\\n # self.current_input.line_edit_template.setEnabled(False)\\r\\n self.enable_line_edits(False)\\r\\n self.enable_line_edits(True)\\r\\n\\r\\n self.current_input.push_button_start.clicked.connect(self.clicked_push_button_start)\\r\\n\\r\\n self.current_input.set_progress_bar_maximum(10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd44a0796bac567ecebc9ade02f4c3ea\",\n \"score\": \"0.4957268\",\n \"text\": \"def on_btn_add(self, sender):\\n index = self.add_buttons.index(sender)\\n\\n self.shift_region_frames(index)\\n self.add_region_bar(index, \\\"\\\", \\\"\\\")\\n\\n self.update_region_indexes()\\n self.update_all_swap_buttons()\\n\\n # add region to the parameter_data\\n self.parameter_data.add_region_at(index)\\n\\n self.update_atoms(change_region=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95caae7b5678f580776fb8503d3132f7\",\n \"score\": \"0.4955552\",\n \"text\": \"def click_create_scenario_button(self):\\n create_scenario_button_element = self.wait().until(EC.presence_of_element_located(self.create_scenario_locator), 'create scenario locator not found before specified time')\\n self.hover(create_scenario_button_element)\\n self.script_executor_click(create_scenario_button_element)\\n self.wait_for_ajax_spinner_load()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f82b45a2c3080652b78f4366accbccf7\",\n \"score\": \"0.49554682\",\n \"text\": \"def create_ui(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f82b45a2c3080652b78f4366accbccf7\",\n \"score\": \"0.49554682\",\n \"text\": \"def create_ui(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17e91208e18453098f2746b0f5c68a54\",\n \"score\": \"0.49518228\",\n \"text\": \"def add_help_tab(self):\\n from App import App\\n from RibbonPanes import QHelpPane\\n\\n try:\\n tab = self.addTab('Help') # \\\"Help\\\"\\n\\n # Action Section\\n cSection = tab.addSection('Help')\\n pane = QHelpPane()\\n pane.ui.toolButtonHelp.clicked.connect(App.mainWnd().on_help)\\n cSection.addCustomWidget(pane)\\n except Exception as ex:\\n message = f\\\"error occurred({repr(ex)}) in {sys.exc_info()[-1].tb_frame.f_code.co_filename}:\\\" \\\\\\n f\\\"{sys.exc_info()[-1].tb_lineno}\\\"\\n pass\\n\\n return # Everything after this is old\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a7ed4786bfd6e0b1cbee040468ce366\",\n \"score\": \"0.49490008\",\n \"text\": \"def POST(self, name):\\n\\n\\t\\tweb.header(\\\"Content-Type\\\",\\\"text/html; charset=utf-8\\\")\\n\\t\\tweb.header('Access-Control-Allow-Origin', '*')\\n\\t\\tweb.header('Access-Control-Allow-Methods', 'GET, POST, OPTIONS')\\n\\n\\t\\tis_git_auth = github_sync.is_git_auth()\\n\\t\\tf = forms.get_form(conf.ask_form)\\n\\t\\tuser = session['username']\\n\\t\\tsession['ip_address'] = str(web.ctx['ip'])\\n\\t\\tu.write_ip(str(datetime.datetime.now()), str(web.ctx['ip']), 'POST')\\n\\t\\tblock_user, limit = u.check_ip(str(web.ctx['ip']), str(datetime.datetime.now()) )\\n\\t\\twhereto = prefixLocal+'/' if user == 'anonymous' else prefixLocal+'welcome-1'\\n\\n\\t\\t# form validation (ask_class)\\n\\t\\tif not f.validates():\\n\\t\\t\\tu.log_output('SUBMIT INVALID FORM', session['logged_in'], session['username'],name)\\n\\t\\t\\treturn render.record(record_form=f, pageID=name, user=user, alert=block_user,\\n\\t\\t\\t\\t\\t\\t\\t\\tlimit=limit, is_git_auth=is_git_auth,invalid=True,\\n\\t\\t\\t\\t\\t\\t\\t\\tproject=conf.myProject,template=None)\\n\\t\\telse:\\n\\t\\t\\trecordData = web.input()\\n\\n\\t\\t\\t# load the template selected by the user\\n\\t\\t\\tif 'res_name' in recordData:\\n\\t\\t\\t\\tif recordData.res_name != 'None':\\n\\t\\t\\t\\t\\tf = forms.get_form(recordData.res_name)\\n\\t\\t\\t\\t\\treturn render.record(record_form=f, pageID=name, user=user, alert=block_user,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tlimit=limit, is_git_auth=is_git_auth,invalid=False,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tproject=conf.myProject,template=recordData.res_name)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\traise web.seeother(prefixLocal+'record-'+name)\\n\\n\\t\\t\\tif 'action' in recordData:\\n\\t\\t\\t\\tcreate_record(recordData)\\n\\n\\t\\t\\trecordID = recordData.recordID if 'recordID' in recordData else None\\n\\t\\t\\ttemplateID = recordData.templateID if 'templateID' in recordData else None\\n\\t\\t\\tu.log_output('CREATED RECORD', session['logged_in'], session['username'],recordID)\\n\\n\\t\\t\\tif recordID:\\n\\t\\t\\t\\tuserID = user.replace('@','-at-').replace('.','-dot-')\\n\\t\\t\\t\\tfile_path = mapping.inputToRDF(recordData, userID, 'not modified',tpl_form=templateID)\\n\\t\\t\\t\\tif conf.github_backup == \\\"True\\\":\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tgithub_sync.push(file_path,\\\"main\\\", session['gituser'], session['username'], session['bearer_token'])\\n\\t\\t\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\t\\t\\tprint(e)\\n\\n\\t\\t\\t\\traise web.seeother(whereto)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcreate_record(recordData)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52a58a81d1d618f5b1bb4ce59962657a\",\n \"score\": \"0.4946528\",\n \"text\": \"def setUp(self):\\n self.tab_group = QtTabGroup(None, uuid4().hex, QtLocalPipe(uuid4))\\n self.tab_group.create()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccb393dfce1c4bd60c1a3ac2466dc8c1\",\n \"score\": \"0.49464187\",\n \"text\": \"def on_submit_clicked(self):\\n LOG.info('Submit button is clicked')\\n global_constants.FROM_DB = False\\n global_constants.TEXT_BOARD = self.gui_utils.text_board\\n global_constants.TEXT_INTERFACE = self.gui_utils.text_interface\\n global_constants.TEXT_DEVICE = self.gui_utils.text_device\\n LOG.info('The User selected \\\"BOARD:\\\"{0}, \\\"INTERFACE:\\\"{1}, \\\"DEVICE:\\\"{2}'.\\n format(self.gui_utils.text_board,\\n self.gui_utils.text_interface,\\n self.gui_utils.text_device))\\n self.str_file_name = '{0}_{1}_{2}'.format(self.gui_utils.text_board,\\n self.gui_utils.text_interface,\\n self.gui_utils.text_device)\\n LOG.debug('The File name Loaded {0}'.format(self.str_file_name))\\n # Getting back to current script directory\\n LOG.debug('Getting back to current script directory')\\n OutputWrite.change_to_script_directory(__file__)\\n # Opening the test case files\\n LOG.debug('Opening the test case files')\\n try:\\n LOG.info('Getting the test cases from a file')\\n with open('../resources/' + self.str_file_name, 'r') as file_obj:\\n list_of_test_cases = file_obj.readlines()\\n\\n self.gui_utils.count = len(list_of_test_cases)\\n\\n item = QtWidgets.QTableWidgetItem()\\n item.setText('Test Id')\\n self.gui_utils.tableWidget.setHorizontalHeaderItem(0, item)\\n self.gui_utils.tableWidget.horizontalHeaderItem(0). \\\\\\n setTextAlignment(QtCore.Qt.AlignLeft)\\n item = QtWidgets.QTableWidgetItem()\\n item.setText('Description')\\n self.gui_utils.tableWidget.setHorizontalHeaderItem(1, item)\\n self.gui_utils.tableWidget.horizontalHeaderItem(1). \\\\\\n setTextAlignment(QtCore.Qt.AlignLeft)\\n # adding the checkboxes to the table widget\\n self.gui_utils.tableWidget.setRowCount(self.gui_utils.count)\\n LOG.debug('Populating the test cases to the QTableWidget')\\n for i in range(self.gui_utils.count):\\n test_case = list_of_test_cases[i].split(',')\\n for j in range(2):\\n if j == 1:\\n item.setFlags(QtCore.Qt.ItemIsUserCheckable |\\n QtCore.Qt.ItemIsEnabled)\\n item.setCheckState(QtCore.Qt.Unchecked)\\n item = QtWidgets.QTableWidgetItem()\\n item.setText(test_case[j])\\n item.setFlags(QtCore.Qt.ItemIsEnabled)\\n self.gui_utils.tableWidget.setItem(i, j, item)\\n except FileNotFoundError as file_error:\\n LOG.exception(file_error)\\n QMessageBox.warning(None, 'Warning', \\\"Select Proper Test Case File\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"883ea41fae826ef80f592e168ff2fcdb\",\n \"score\": \"0.49347147\",\n \"text\": \"def show_new_exp_ui(self):\\n \\n display(self.new_exp_ui)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d76765578d085b243a179bad8cc9483\",\n \"score\": \"0.49254894\",\n \"text\": \"def form():\\n return render_template(\\\"explorer_upload.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6f95593b9402459a9631ec0dfbc6662\",\n \"score\": \"0.4923823\",\n \"text\": \"def new_job():\\n if current_user.account_type != AccountTypes.c:\\n flash(f'You cannot access this page.')\\n return redirect(url_for('main.index'))\\n\\n form = JobPostForm()\\n if form.validate_on_submit():\\n deets = extract_details(form)\\n post_id = new_jobpost(current_user._company.id, deets.pop('title'), **deets)\\n\\n # now update the cache for this new post\\n update_cache(jobpost_id=post_id)\\n\\n flash(f\\\"Created job post with ID {post_id}\\\")\\n return redirect(url_for('main.job_page', job_id=post_id))\\n return render_template('company/jobpost_editor.html',\\n form=form, skill_list=SKILL_NAMES, attitude_list=ATTITUDE_NAMES\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5c06ad11a9cbf2de185c0fd75324bb0\",\n \"score\": \"0.49214414\",\n \"text\": \"def new_thread():\\n\\n form = NewThreadForm()\\n if form.validate_on_submit():\\n\\n data = form.data.copy()\\n poster = Poster.from_details(data.pop('name'))\\n t = poster.create_thread(**data)\\n return redirect(url_for('thread', thread_id=t.id))\\n\\n else:\\n flash_form_errors(form)\\n return redirect(url_for('index'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3db64c5c9f749abd32e14f6f22479919\",\n \"score\": \"0.49211615\",\n \"text\": \"def __init__(self, ui):\\n\\n # The new frame is a copy of the currently selected frame\\n self.index = ui.get_selected_frame_index()\\n\\n # The new frame will be added at the end of the presentation\\n new_frame_number = str(len(ui.model.frames) + 1)\\n\\n SoziAction.__init__(self,\\n _(\\\"Delete duplicated frame {0}\\\").format(new_frame_number),\\n _(\\\"Duplicate frame {0}\\\").format(self.index + 1)\\n )\\n \\n self.ui = ui\\n \\n self.frame = ui.model.frames[self.index].copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"382deb9ffec80d2ce7f4eda2f068d223\",\n \"score\": \"0.4916559\",\n \"text\": \"async def handle_submit(request):\\n name = request.match_info.get(\\\"name\\\", \\\"\\\")\\n token = request.match_info.get(\\\"token\\\", \\\"\\\")\\n blob = await request.read()\\n\\n try:\\n submit_app(name, token, blob)\\n except Exception as err:\\n return web.Response(status=403, text=str(err))\\n else:\\n return web.Response(status=200, text=\\\"Thanks for submitting app %r!\\\" % name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d3a1802fa40e258086104f8dcaa8906\",\n \"score\": \"0.49132928\",\n \"text\": \"def _openProtocolForm(self, prot):\\n \\n w = pwgui.form.FormWindow(Message.TITLE_NAME_RUN + prot.getClassName(), prot, \\n self._executeSaveProtocol, self.windows,\\n hostList=self.project.getHostNames(), updateProtocolCallback=self._updateProtocol(prot))\\n w.adjustSize()\\n w.show(center=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97d1d03dd4886d6c750cfa20cca4c462\",\n \"score\": \"0.49073058\",\n \"text\": \"def action(self, data_object, response_data):\\n\\n db_name, table_name = self.tree_view.get_selected_item_and_parent_text()\\n self.tab_table.add_tab( ui_tabTable.TAB_TYPE_TABLE, db_name, table_name )\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1790,"cells":{"query_id":{"kind":"string","value":"a17cbb0188b345190034c00e7d3bf5c9"},"query":{"kind":"string","value":"Boolean indicating if the share is pending."},"positive_passages":{"kind":"list like","value":[{"docid":"a2afe99d58c91477255e17e25d0b592d","score":"0.77301145","text":"def is_pending(self):\n return self.status == Status.PENDING","title":""}],"string":"[\n {\n \"docid\": \"a2afe99d58c91477255e17e25d0b592d\",\n \"score\": \"0.77301145\",\n \"text\": \"def is_pending(self):\\n return self.status == Status.PENDING\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"98c322aa75b28d401d968997b511a834","score":"0.7388369","text":"def has_pending(self):\n return self._pending_count > 0","title":""},{"docid":"523f2808a5cb4de9818b788829d25604","score":"0.71935004","text":"def is_pending(self):\n\n return self._state == \"PEND\"","title":""},{"docid":"49ac405f684d0e5bd87cdebc9c7acc77","score":"0.7162429","text":"def pending(self) -> bool:\n return self._mp_notify.empty()","title":""},{"docid":"b404913d9a2f40beb23a5bbe82eedfaa","score":"0.7025647","text":"def has_pending_request(self):\n return self._pending_request is not None","title":""},{"docid":"43ea2dcb4b03b819aa9bd224f939ee84","score":"0.70212436","text":"def is_pending(self):\n return self._state == \"Q\"","title":""},{"docid":"e58dae8ad77c2d29351122ae20e40f0e","score":"0.6905378","text":"def pending(self):\n return bool(self.events)","title":""},{"docid":"2974c2c533cc6692867acb4ee6b9ff64","score":"0.65005517","text":"def isWaiting(self):\r\n return self.iswaiting","title":""},{"docid":"589fa93e12bbfe40c53550afd649e776","score":"0.64999545","text":"def is_shareable(self):\n return self._is_shareable","title":""},{"docid":"b81471ee5ef42007a334ab96c24c7de5","score":"0.6467672","text":"def can_sync(self):\n status = self.get_status()\n\n return status.get(\"state\") in self.DONE_STATUSES or (\n status.get(\"state\") not in self.DONE_STATUSES\n and self.task_date is not None\n and self.task_date < now() - timedelta(hours=MAX_TASK_RUNTIME)\n )","title":""},{"docid":"26fe933254f3da783e744f7fa554a516","score":"0.6460968","text":"def _is_pending(self, taskid):\n raise NotImplementedError","title":""},{"docid":"16c85828129dd65785fb671a4d0a94d3","score":"0.6441495","text":"def pending(self):\n return self.timer.pending or self.timer.active","title":""},{"docid":"927b3d51612b2561f135b910dbf6dd03","score":"0.64230835","text":"def is_processing(self):\r\n return self.status == 202","title":""},{"docid":"09901c610b1cce13dddfca3a33958477","score":"0.63798034","text":"def is_reporting_allowed(self):\n if self.status:\n if self.status.is_done:\n allowed = False\n else:\n allowed = True\n else:\n allowed = False\n return allowed","title":""},{"docid":"ba171afd07284637d065357b4d52c391","score":"0.6370119","text":"def is_pending(self):\n return np.any([self._is_rung_pending(i) > 0 for i, _ in enumerate(self.budgets)])","title":""},{"docid":"f4e6f70ecb1b3f68eda4a8a74876bece","score":"0.6365188","text":"def __bool__(self):\n record = self.db.crawl_queue.find_one(\n {'status': {'$ne': self.COMPLETE}}\n )\n return True if record else False","title":""},{"docid":"13a04b714e698230ed2828f43829be72","score":"0.6282428","text":"def is_in_progress(self):\n\t\treturn self._in_progress","title":""},{"docid":"ed6f733c987afd09957e9d2b87760445","score":"0.6269867","text":"def is_complete(self):\n return (self.participants > 0) and (len(self._shares) == self.participants)","title":""},{"docid":"0273761b8c29778e4d22fcdec03e4576","score":"0.62660056","text":"def is_fixed_progress(self):\n if self.is_meeting():\n return True\n if self.is_event_type('Social Credits'):\n return True\n return False","title":""},{"docid":"9c692ed952b50f467b085d1efcd9ae45","score":"0.62562937","text":"def cash_sharing(self) -> bool:\n return self._cash_sharing","title":""},{"docid":"b40cacd1154709c48d172f9b0c28a82c","score":"0.62133515","text":"def is_locked(self):\n if self.status:\n return True\n else:\n return False","title":""},{"docid":"0c6c35d2a5897998faac3384f515ee0c","score":"0.62098455","text":"def is_working(self) -> bool:\n return self in (BigPlanStatus.IN_PROGRESS, BigPlanStatus.BLOCKED)","title":""},{"docid":"107179a82199122f5275c7d3123299bd","score":"0.61892843","text":"def _is_ok(self):\n if (self._data['status']['video_status'] == 'ready' and\n self._data['published'] and\n self._data['privacy']['value'] == 'EVERYONE'):\n return True\n return False","title":""},{"docid":"a840f5ac8bc9237576fbdfe187189a0d","score":"0.61158127","text":"def is_pending_node(node):\n\n state = STATE['nodes'][node]['state']\n \n if state == 'casg-pending'\t\t\\\n or state == 'sibling-pending'\t\\\n or state == 'casg-pending'\t\\\n or state == 'hardware-pending'\t\\\n or state == 'suspect-pending':\n return True\n else:\n return False","title":""},{"docid":"2ac0f2953c8d44bab54b9a9a89d49cc5","score":"0.61049116","text":"def __bool__(self) -> bool:\n outcome = self.status\n return outcome","title":""},{"docid":"e5514b6c7aca29bbc4b1896b63e7c0e0","score":"0.6104356","text":"def _has_pending_submit(self) -> bool:\n sub_fut = [sub_job.future for sub_job in self._sub_jobs if sub_job.future is not None]\n return not all(fut.done() for fut in sub_fut)","title":""},{"docid":"93fb99932070586e175d4af39e504481","score":"0.6100507","text":"def syncing(self):\n return bool(self._sp_offline_sync_status.syncing)","title":""},{"docid":"eb9e7f1a2437580484364ae7912e4eae","score":"0.6097002","text":"def is_visible(self):\n return self.is_actual and self.status == PUBLISHED","title":""},{"docid":"4862b5db811bf1edf072eb8757a12038","score":"0.6096543","text":"def _is_busy(self):\n if self._track.get_sound_players_in_use_count() > 1:\n return True\n else:\n return False","title":""},{"docid":"e46ac5a719fe74fa8051c23f79c3f12c","score":"0.60878026","text":"def is_accepted(self):\n return self.status == Status.ACCEPTED","title":""},{"docid":"df4312893f583a0ea567d3edb707a8d2","score":"0.6086489","text":"def has_pending_commit(self):\n pending_commit = self._get_pending_commits()\n if pending_commit:\n return True\n else:\n return False","title":""},{"docid":"5193d91e587c2d14e7775cb6776044ca","score":"0.60703427","text":"def available(self):\n return self._state is not None","title":""},{"docid":"5193d91e587c2d14e7775cb6776044ca","score":"0.60703427","text":"def available(self):\n return self._state is not None","title":""},{"docid":"49028b9beb440e71fbb09b26cf470aa3","score":"0.6068486","text":"def IsPayable(self):\n from neo.Core.State.ContractState import ContractPropertyState\n return self.ContractProperties & ContractPropertyState.Payable > 0","title":""},{"docid":"d724d64065ec8c5adeb726386ae13e03","score":"0.6061687","text":"def is_pending_cancel(self, refresh_order_info: bool = True) -> bool:\n\n if refresh_order_info:\n self.trade_obj._update_order_status()\n\n if self.order_status == 'PENDING_CANCEL':\n return True\n else:\n return False","title":""},{"docid":"bb4ce1a23307778c0ba7660b71ce2f16","score":"0.6050245","text":"def __bool__(self):\n\n return self.status == \"OK\"","title":""},{"docid":"ea437f8709cfea699c5dcf0197975262","score":"0.6050007","text":"def is_with_progress(self):\n return self._is_with_progress","title":""},{"docid":"01da0d6955ecf4c6a0a2e0b255201f3c","score":"0.6040844","text":"def is_paid(self):\n return self.transaction.is_paid()","title":""},{"docid":"25063e165ae79163470aaf12913d540a","score":"0.60381234","text":"def is_paid(self) -> bool:\n return self.active and self.type not in (\"free\", \"dev\")","title":""},{"docid":"d2da9bb817fcef8ce89c36b74047e35a","score":"0.60369724","text":"def available(self):\n if self._state is not None:\n return True\n return False","title":""},{"docid":"69b4f8fba66fca045c80144c09cf0550","score":"0.6035328","text":"def is_sending(self):\n\t\treturn self.sending","title":""},{"docid":"c07aea89c3f841ccd396a02cab0e980a","score":"0.60328305","text":"def is_starved(self):\n return any(conn.is_starved for conn in self.conns)","title":""},{"docid":"099fe85e382e0bd3b1ea5f706b5b1892","score":"0.6024106","text":"def message_pending(self):\n return not self.msg_q.empty()","title":""},{"docid":"c2e927285d603793456506163e0449f6","score":"0.6018771","text":"def is_posted(self) -> bool:\n return self.status and self.id","title":""},{"docid":"384915ca79400caff1ebb28bf7e001e1","score":"0.6005207","text":"def requires_approval(self):\n return self.__requires_approval","title":""},{"docid":"fc2f5a4ab9bdaf5850fe85dc082eb003","score":"0.59875435","text":"def is_working_or_more(self) -> bool:\n return self.is_working or self.is_completed","title":""},{"docid":"51f137932877a06fe3fe25ad53663ec1","score":"0.59807897","text":"def is_empty(self):\n return not (self._pending or self._waiting or self._finished)","title":""},{"docid":"8cce8b4a94bb803d78810ae040887c0e","score":"0.59757936","text":"def is_paid(self):\n\n return self.paid","title":""},{"docid":"a329c924ef87879eb1a77c92d8c0f4f3","score":"0.5974324","text":"def is_notifying(self):\r\n return self.properties.Get(BT_CHARACTERISTIC_IFACE, 'Notifying') == 1","title":""},{"docid":"84974683341184e20d92cca57f004371","score":"0.5972231","text":"def submitted(self):\n return self.status >= SUBMITTED","title":""},{"docid":"c28e0d2d724efad0c1dd5bc7af329cf3","score":"0.59702456","text":"def _is_rung_pending(self, rung):\n if self.sh_bracket[self.budgets[rung]] > 0:\n return True\n return False","title":""},{"docid":"c117d825d79ebaa2c30b35d99089a406","score":"0.59697187","text":"def available(self):\n return self._stream.is_active","title":""},{"docid":"3c5932ff4029fa16a71c4d6eb347d372","score":"0.59406877","text":"def has_pending_packets_to_be_sent(self):\n return len(self.packet_buffer) != 0","title":""},{"docid":"69e2c7cc7ae39108f5ef0f6a1e0143b4","score":"0.5923557","text":"def has_shareable_url(self):\r\n return self.url is not None and not self.url.startswith(u\"file:\")","title":""},{"docid":"411d325881b975ed474b29522021f1b6","score":"0.59107924","text":"def is_filled(self, update=True) -> bool:\n if self.is_cancelled() or not self.is_posted():\n return False\n elif self.status and self.status == 'done':\n return True\n\n if update:\n self.update()\n\n return self.status and self.status == 'done'","title":""},{"docid":"5ddd5d0a528125b2561c507463cdd31e","score":"0.5904186","text":"def work_pending(self):\n\n # print '====> RED FLAG:',self.red_flag\n # print '====> WORKERS IDLE:',self.workers_idle()\n # print '====> EMPTY:',self.is_empty()\n \n return (not self.red_flag) and not (self.workers_idle() and self.is_empty())","title":""},{"docid":"890a81c2112876fdca707ed3ab2100aa","score":"0.59020543","text":"def is_accepted(self) -> bool:\n return self == BigPlanStatus.ACCEPTED","title":""},{"docid":"d29c5a9743118442721cb9217a03510c","score":"0.5898807","text":"def require_approval(self) -> bool:\n return pulumi.get(self, \"require_approval\")","title":""},{"docid":"6d06b3572d8753cf49cdf786c9346a36","score":"0.5898477","text":"def ready(self):\n return self._result is not NOT_USED","title":""},{"docid":"81d78e0e1cb71bf756bf5bddf9dae095","score":"0.5892744","text":"def is_accepted_or_more(self) -> bool:\n return self.is_accepted or self.is_working or self.is_completed","title":""},{"docid":"a7329a86af6f44b6edb818cc449f6fe3","score":"0.58883077","text":"def get_presence(self):\n return True","title":""},{"docid":"75b34537868d61b966ae3769f3745105","score":"0.5883458","text":"def has_shareable_url(self):\r\n url = self.get_url()\r\n return url is not None and not url.startswith(u\"file:\")","title":""},{"docid":"34a33434ca0bc102c2ae820ea18a19b2","score":"0.5879415","text":"def getStatus(self):\n return self.isBusy","title":""},{"docid":"525a116f1fdc3ab434758afbe338c81f","score":"0.58693564","text":"def __nonzero__(self):\n return bool(self.require)","title":""},{"docid":"31c3e3215d3a201e5ff6c1069c42fae0","score":"0.58668774","text":"def is_made_up_pool(self):\n return self.total_share_amount == self.amplifier == 0 and self.get_pool_pair_id() == \"unknown\"","title":""},{"docid":"6579191ab959624286ee06d0f3bfe6d6","score":"0.5865415","text":"def get_status(self):\n return True","title":""},{"docid":"b72d90b6927a1b9e87bd48b71539e9a2","score":"0.5864871","text":"def __bool__(self):\n \n return False","title":""},{"docid":"0976955a8ee224bb8ee1dd9520f9e9a2","score":"0.58623785","text":"def isNotifying(self) -> \"SbBool\":\n return _coin.SoEngine_isNotifying(self)","title":""},{"docid":"1fd46535e8b35a1e8fbc836cb47772a1","score":"0.5847602","text":"def flag(self) -> bool:\n\n return self._flag","title":""},{"docid":"f72163187bef17a761869647f264751a","score":"0.58387977","text":"def is_pending_activation(self, refresh_order_info: bool = True) -> bool:\n\n if refresh_order_info:\n self.trade_obj._update_order_status()\n\n if self.order_status == 'PENDING_ACTIVATION':\n return True\n else:\n return False","title":""},{"docid":"6a494791cb3605cca38232fe539fc928","score":"0.5831955","text":"def is_flag(self):\n return self._isflag","title":""},{"docid":"a491f9ae6ec819de887e031be5f31131","score":"0.58248043","text":"def in_use(self):\n return self.in_blocking_call or self.in_pubsub or self.in_transaction","title":""},{"docid":"797e9f141bd5b1eafdb95a1a0e97ede3","score":"0.5822704","text":"def pending(self):\n pending = Contract().pending_contracts(WidgetAbstract.username)\n return pending","title":""},{"docid":"dc1cc0016b9ab8960d4b37c479e73173","score":"0.5815581","text":"def isScheduled(self) -> \"SbBool\":\n return _coin.SoDelayQueueSensor_isScheduled(self)","title":""},{"docid":"c17d657254de99274c45def7b41a19e1","score":"0.58087957","text":"def has_completed(self):\n return self.is_completed","title":""},{"docid":"6cac10b6b06997d22ab9999c531da113","score":"0.57837754","text":"def isplaying(self):\n\t\treturn self.play is not None","title":""},{"docid":"de5732f2a2f83434b79183ad7f4d1f43","score":"0.57823294","text":"def hasProgress(self) -> bool:\n ...","title":""},{"docid":"de5732f2a2f83434b79183ad7f4d1f43","score":"0.57823294","text":"def hasProgress(self) -> bool:\n ...","title":""},{"docid":"8d7eda8ba1fcc7cc0cda145ed114e00e","score":"0.5779451","text":"def is_ask_sn(self):\n return not self.is_normal_news_item()","title":""},{"docid":"05b1aab93fe70488ceb3dadc30dbc2bb","score":"0.57786876","text":"def done(self) -> bool:\n return bool(self.time_remaining <= 0 or self._status == 'done')","title":""},{"docid":"a9338dfdd29450a639ba1deae6c03dcf","score":"0.57735914","text":"def available(self) -> bool:\n return self._serial in self._fetcher._state # type: ignore","title":""},{"docid":"146c021ce8f1f94c074c9e8d0a2c08b1","score":"0.5767116","text":"def chargeable(self):\n return not self.internal","title":""},{"docid":"56965d9318ce561f4f4717171ed96c8e","score":"0.5757424","text":"def is_done(self):\n return not (self.patrn_bfs_queue and self.sub_bfs_queue)","title":""},{"docid":"c8f775d1174e5a2d3ea60a039494b4d9","score":"0.57574016","text":"def is_active(self):\r\n return self.sentcount >= 0","title":""},{"docid":"662bb17ed7621ed0e5faf43129731d05","score":"0.57530624","text":"def is_working(self):\n return self.req_queue or (\n self.active_jobs and not all(job.is_power_off() for job in self.active_jobs)\n )","title":""},{"docid":"5d9f327357ad3f27f7d17fcc35e812b7","score":"0.5751738","text":"def has_it(self):\n if self.is_active():\n return True\n if self.is_imported():\n return True\n return False","title":""},{"docid":"c71e5f7ee9f2d14602739d36d99a752f","score":"0.574348","text":"def is_uploading(self):\r\n return self.downloader and self.downloader.get_state() == u'uploading'","title":""},{"docid":"2db8f88999fbfca3f4992b4f0d9cfe26","score":"0.57398903","text":"def status(self) -> bool:\n return pulumi.get(self, \"status\")","title":""},{"docid":"2db8f88999fbfca3f4992b4f0d9cfe26","score":"0.57398903","text":"def status(self) -> bool:\n return pulumi.get(self, \"status\")","title":""},{"docid":"2db8f88999fbfca3f4992b4f0d9cfe26","score":"0.57398903","text":"def status(self) -> bool:\n return pulumi.get(self, \"status\")","title":""},{"docid":"2db8f88999fbfca3f4992b4f0d9cfe26","score":"0.57398903","text":"def status(self) -> bool:\n return pulumi.get(self, \"status\")","title":""},{"docid":"2db8f88999fbfca3f4992b4f0d9cfe26","score":"0.57398903","text":"def status(self) -> bool:\n return pulumi.get(self, \"status\")","title":""},{"docid":"826f875a6fc7f5cc2145fa79f016c39f","score":"0.57370335","text":"def is_active(self):\n return self.scheduled_time and not self.finish_time","title":""},{"docid":"9abfff5d69f785a04ac994d72286fe03","score":"0.5728797","text":"def is_complete(self) -> bool:\n return bool(self.submission.is_finalized)","title":""},{"docid":"886c9b41421a58aa3281e2122bcf0bd7","score":"0.5725123","text":"def is_on(self):\n return self.is_streaming","title":""},{"docid":"c61bd75d1b850a677ac4333697072116","score":"0.5723523","text":"def is_available(self) -> bool:\n return self.raw[\"data\"][\"@topic\"] == \"true\"","title":""},{"docid":"14a93ca0a229d54e655f79de597978f4","score":"0.5722406","text":"def is_on(self):\n return self.unsub_tracker is not None","title":""},{"docid":"b97d67037c039a40268438d852223c88","score":"0.5719964","text":"def is_on(self) -> bool:\n return self._is_on","title":""},{"docid":"b97d67037c039a40268438d852223c88","score":"0.5719964","text":"def is_on(self) -> bool:\n return self._is_on","title":""},{"docid":"1c7124198ce1b40b9ce24af7f5fa9566","score":"0.57128775","text":"def is_on(self) -> bool:\n return self._state","title":""},{"docid":"1c7124198ce1b40b9ce24af7f5fa9566","score":"0.57128775","text":"def is_on(self) -> bool:\n return self._state","title":""}],"string":"[\n {\n \"docid\": \"98c322aa75b28d401d968997b511a834\",\n \"score\": \"0.7388369\",\n \"text\": \"def has_pending(self):\\n return self._pending_count > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"523f2808a5cb4de9818b788829d25604\",\n \"score\": \"0.71935004\",\n \"text\": \"def is_pending(self):\\n\\n return self._state == \\\"PEND\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49ac405f684d0e5bd87cdebc9c7acc77\",\n \"score\": \"0.7162429\",\n \"text\": \"def pending(self) -> bool:\\n return self._mp_notify.empty()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b404913d9a2f40beb23a5bbe82eedfaa\",\n \"score\": \"0.7025647\",\n \"text\": \"def has_pending_request(self):\\n return self._pending_request is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43ea2dcb4b03b819aa9bd224f939ee84\",\n \"score\": \"0.70212436\",\n \"text\": \"def is_pending(self):\\n return self._state == \\\"Q\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e58dae8ad77c2d29351122ae20e40f0e\",\n \"score\": \"0.6905378\",\n \"text\": \"def pending(self):\\n return bool(self.events)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2974c2c533cc6692867acb4ee6b9ff64\",\n \"score\": \"0.65005517\",\n \"text\": \"def isWaiting(self):\\r\\n return self.iswaiting\",\n \"title\": \"\"\n },\n {\n \"docid\": \"589fa93e12bbfe40c53550afd649e776\",\n \"score\": \"0.64999545\",\n \"text\": \"def is_shareable(self):\\n return self._is_shareable\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b81471ee5ef42007a334ab96c24c7de5\",\n \"score\": \"0.6467672\",\n \"text\": \"def can_sync(self):\\n status = self.get_status()\\n\\n return status.get(\\\"state\\\") in self.DONE_STATUSES or (\\n status.get(\\\"state\\\") not in self.DONE_STATUSES\\n and self.task_date is not None\\n and self.task_date < now() - timedelta(hours=MAX_TASK_RUNTIME)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26fe933254f3da783e744f7fa554a516\",\n \"score\": \"0.6460968\",\n \"text\": \"def _is_pending(self, taskid):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16c85828129dd65785fb671a4d0a94d3\",\n \"score\": \"0.6441495\",\n \"text\": \"def pending(self):\\n return self.timer.pending or self.timer.active\",\n \"title\": \"\"\n },\n {\n \"docid\": \"927b3d51612b2561f135b910dbf6dd03\",\n \"score\": \"0.64230835\",\n \"text\": \"def is_processing(self):\\r\\n return self.status == 202\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09901c610b1cce13dddfca3a33958477\",\n \"score\": \"0.63798034\",\n \"text\": \"def is_reporting_allowed(self):\\n if self.status:\\n if self.status.is_done:\\n allowed = False\\n else:\\n allowed = True\\n else:\\n allowed = False\\n return allowed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba171afd07284637d065357b4d52c391\",\n \"score\": \"0.6370119\",\n \"text\": \"def is_pending(self):\\n return np.any([self._is_rung_pending(i) > 0 for i, _ in enumerate(self.budgets)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e6f70ecb1b3f68eda4a8a74876bece\",\n \"score\": \"0.6365188\",\n \"text\": \"def __bool__(self):\\n record = self.db.crawl_queue.find_one(\\n {'status': {'$ne': self.COMPLETE}}\\n )\\n return True if record else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a04b714e698230ed2828f43829be72\",\n \"score\": \"0.6282428\",\n \"text\": \"def is_in_progress(self):\\n\\t\\treturn self._in_progress\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed6f733c987afd09957e9d2b87760445\",\n \"score\": \"0.6269867\",\n \"text\": \"def is_complete(self):\\n return (self.participants > 0) and (len(self._shares) == self.participants)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0273761b8c29778e4d22fcdec03e4576\",\n \"score\": \"0.62660056\",\n \"text\": \"def is_fixed_progress(self):\\n if self.is_meeting():\\n return True\\n if self.is_event_type('Social Credits'):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c692ed952b50f467b085d1efcd9ae45\",\n \"score\": \"0.62562937\",\n \"text\": \"def cash_sharing(self) -> bool:\\n return self._cash_sharing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b40cacd1154709c48d172f9b0c28a82c\",\n \"score\": \"0.62133515\",\n \"text\": \"def is_locked(self):\\n if self.status:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c6c35d2a5897998faac3384f515ee0c\",\n \"score\": \"0.62098455\",\n \"text\": \"def is_working(self) -> bool:\\n return self in (BigPlanStatus.IN_PROGRESS, BigPlanStatus.BLOCKED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"107179a82199122f5275c7d3123299bd\",\n \"score\": \"0.61892843\",\n \"text\": \"def _is_ok(self):\\n if (self._data['status']['video_status'] == 'ready' and\\n self._data['published'] and\\n self._data['privacy']['value'] == 'EVERYONE'):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a840f5ac8bc9237576fbdfe187189a0d\",\n \"score\": \"0.61158127\",\n \"text\": \"def is_pending_node(node):\\n\\n state = STATE['nodes'][node]['state']\\n \\n if state == 'casg-pending'\\t\\t\\\\\\n or state == 'sibling-pending'\\t\\\\\\n or state == 'casg-pending'\\t\\\\\\n or state == 'hardware-pending'\\t\\\\\\n or state == 'suspect-pending':\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ac0f2953c8d44bab54b9a9a89d49cc5\",\n \"score\": \"0.61049116\",\n \"text\": \"def __bool__(self) -> bool:\\n outcome = self.status\\n return outcome\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5514b6c7aca29bbc4b1896b63e7c0e0\",\n \"score\": \"0.6104356\",\n \"text\": \"def _has_pending_submit(self) -> bool:\\n sub_fut = [sub_job.future for sub_job in self._sub_jobs if sub_job.future is not None]\\n return not all(fut.done() for fut in sub_fut)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93fb99932070586e175d4af39e504481\",\n \"score\": \"0.6100507\",\n \"text\": \"def syncing(self):\\n return bool(self._sp_offline_sync_status.syncing)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb9e7f1a2437580484364ae7912e4eae\",\n \"score\": \"0.6097002\",\n \"text\": \"def is_visible(self):\\n return self.is_actual and self.status == PUBLISHED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4862b5db811bf1edf072eb8757a12038\",\n \"score\": \"0.6096543\",\n \"text\": \"def _is_busy(self):\\n if self._track.get_sound_players_in_use_count() > 1:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e46ac5a719fe74fa8051c23f79c3f12c\",\n \"score\": \"0.60878026\",\n \"text\": \"def is_accepted(self):\\n return self.status == Status.ACCEPTED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df4312893f583a0ea567d3edb707a8d2\",\n \"score\": \"0.6086489\",\n \"text\": \"def has_pending_commit(self):\\n pending_commit = self._get_pending_commits()\\n if pending_commit:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5193d91e587c2d14e7775cb6776044ca\",\n \"score\": \"0.60703427\",\n \"text\": \"def available(self):\\n return self._state is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5193d91e587c2d14e7775cb6776044ca\",\n \"score\": \"0.60703427\",\n \"text\": \"def available(self):\\n return self._state is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49028b9beb440e71fbb09b26cf470aa3\",\n \"score\": \"0.6068486\",\n \"text\": \"def IsPayable(self):\\n from neo.Core.State.ContractState import ContractPropertyState\\n return self.ContractProperties & ContractPropertyState.Payable > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d724d64065ec8c5adeb726386ae13e03\",\n \"score\": \"0.6061687\",\n \"text\": \"def is_pending_cancel(self, refresh_order_info: bool = True) -> bool:\\n\\n if refresh_order_info:\\n self.trade_obj._update_order_status()\\n\\n if self.order_status == 'PENDING_CANCEL':\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4ce1a23307778c0ba7660b71ce2f16\",\n \"score\": \"0.6050245\",\n \"text\": \"def __bool__(self):\\n\\n return self.status == \\\"OK\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea437f8709cfea699c5dcf0197975262\",\n \"score\": \"0.6050007\",\n \"text\": \"def is_with_progress(self):\\n return self._is_with_progress\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01da0d6955ecf4c6a0a2e0b255201f3c\",\n \"score\": \"0.6040844\",\n \"text\": \"def is_paid(self):\\n return self.transaction.is_paid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25063e165ae79163470aaf12913d540a\",\n \"score\": \"0.60381234\",\n \"text\": \"def is_paid(self) -> bool:\\n return self.active and self.type not in (\\\"free\\\", \\\"dev\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2da9bb817fcef8ce89c36b74047e35a\",\n \"score\": \"0.60369724\",\n \"text\": \"def available(self):\\n if self._state is not None:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b4f8fba66fca045c80144c09cf0550\",\n \"score\": \"0.6035328\",\n \"text\": \"def is_sending(self):\\n\\t\\treturn self.sending\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c07aea89c3f841ccd396a02cab0e980a\",\n \"score\": \"0.60328305\",\n \"text\": \"def is_starved(self):\\n return any(conn.is_starved for conn in self.conns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"099fe85e382e0bd3b1ea5f706b5b1892\",\n \"score\": \"0.6024106\",\n \"text\": \"def message_pending(self):\\n return not self.msg_q.empty()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2e927285d603793456506163e0449f6\",\n \"score\": \"0.6018771\",\n \"text\": \"def is_posted(self) -> bool:\\n return self.status and self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"384915ca79400caff1ebb28bf7e001e1\",\n \"score\": \"0.6005207\",\n \"text\": \"def requires_approval(self):\\n return self.__requires_approval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc2f5a4ab9bdaf5850fe85dc082eb003\",\n \"score\": \"0.59875435\",\n \"text\": \"def is_working_or_more(self) -> bool:\\n return self.is_working or self.is_completed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f137932877a06fe3fe25ad53663ec1\",\n \"score\": \"0.59807897\",\n \"text\": \"def is_empty(self):\\n return not (self._pending or self._waiting or self._finished)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cce8b4a94bb803d78810ae040887c0e\",\n \"score\": \"0.59757936\",\n \"text\": \"def is_paid(self):\\n\\n return self.paid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a329c924ef87879eb1a77c92d8c0f4f3\",\n \"score\": \"0.5974324\",\n \"text\": \"def is_notifying(self):\\r\\n return self.properties.Get(BT_CHARACTERISTIC_IFACE, 'Notifying') == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84974683341184e20d92cca57f004371\",\n \"score\": \"0.5972231\",\n \"text\": \"def submitted(self):\\n return self.status >= SUBMITTED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c28e0d2d724efad0c1dd5bc7af329cf3\",\n \"score\": \"0.59702456\",\n \"text\": \"def _is_rung_pending(self, rung):\\n if self.sh_bracket[self.budgets[rung]] > 0:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c117d825d79ebaa2c30b35d99089a406\",\n \"score\": \"0.59697187\",\n \"text\": \"def available(self):\\n return self._stream.is_active\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c5932ff4029fa16a71c4d6eb347d372\",\n \"score\": \"0.59406877\",\n \"text\": \"def has_pending_packets_to_be_sent(self):\\n return len(self.packet_buffer) != 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e2c7cc7ae39108f5ef0f6a1e0143b4\",\n \"score\": \"0.5923557\",\n \"text\": \"def has_shareable_url(self):\\r\\n return self.url is not None and not self.url.startswith(u\\\"file:\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"411d325881b975ed474b29522021f1b6\",\n \"score\": \"0.59107924\",\n \"text\": \"def is_filled(self, update=True) -> bool:\\n if self.is_cancelled() or not self.is_posted():\\n return False\\n elif self.status and self.status == 'done':\\n return True\\n\\n if update:\\n self.update()\\n\\n return self.status and self.status == 'done'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ddd5d0a528125b2561c507463cdd31e\",\n \"score\": \"0.5904186\",\n \"text\": \"def work_pending(self):\\n\\n # print '====> RED FLAG:',self.red_flag\\n # print '====> WORKERS IDLE:',self.workers_idle()\\n # print '====> EMPTY:',self.is_empty()\\n \\n return (not self.red_flag) and not (self.workers_idle() and self.is_empty())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"890a81c2112876fdca707ed3ab2100aa\",\n \"score\": \"0.59020543\",\n \"text\": \"def is_accepted(self) -> bool:\\n return self == BigPlanStatus.ACCEPTED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d29c5a9743118442721cb9217a03510c\",\n \"score\": \"0.5898807\",\n \"text\": \"def require_approval(self) -> bool:\\n return pulumi.get(self, \\\"require_approval\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d06b3572d8753cf49cdf786c9346a36\",\n \"score\": \"0.5898477\",\n \"text\": \"def ready(self):\\n return self._result is not NOT_USED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d78e0e1cb71bf756bf5bddf9dae095\",\n \"score\": \"0.5892744\",\n \"text\": \"def is_accepted_or_more(self) -> bool:\\n return self.is_accepted or self.is_working or self.is_completed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7329a86af6f44b6edb818cc449f6fe3\",\n \"score\": \"0.58883077\",\n \"text\": \"def get_presence(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b34537868d61b966ae3769f3745105\",\n \"score\": \"0.5883458\",\n \"text\": \"def has_shareable_url(self):\\r\\n url = self.get_url()\\r\\n return url is not None and not url.startswith(u\\\"file:\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34a33434ca0bc102c2ae820ea18a19b2\",\n \"score\": \"0.5879415\",\n \"text\": \"def getStatus(self):\\n return self.isBusy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"525a116f1fdc3ab434758afbe338c81f\",\n \"score\": \"0.58693564\",\n \"text\": \"def __nonzero__(self):\\n return bool(self.require)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31c3e3215d3a201e5ff6c1069c42fae0\",\n \"score\": \"0.58668774\",\n \"text\": \"def is_made_up_pool(self):\\n return self.total_share_amount == self.amplifier == 0 and self.get_pool_pair_id() == \\\"unknown\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6579191ab959624286ee06d0f3bfe6d6\",\n \"score\": \"0.5865415\",\n \"text\": \"def get_status(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b72d90b6927a1b9e87bd48b71539e9a2\",\n \"score\": \"0.5864871\",\n \"text\": \"def __bool__(self):\\n \\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0976955a8ee224bb8ee1dd9520f9e9a2\",\n \"score\": \"0.58623785\",\n \"text\": \"def isNotifying(self) -> \\\"SbBool\\\":\\n return _coin.SoEngine_isNotifying(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fd46535e8b35a1e8fbc836cb47772a1\",\n \"score\": \"0.5847602\",\n \"text\": \"def flag(self) -> bool:\\n\\n return self._flag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f72163187bef17a761869647f264751a\",\n \"score\": \"0.58387977\",\n \"text\": \"def is_pending_activation(self, refresh_order_info: bool = True) -> bool:\\n\\n if refresh_order_info:\\n self.trade_obj._update_order_status()\\n\\n if self.order_status == 'PENDING_ACTIVATION':\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a494791cb3605cca38232fe539fc928\",\n \"score\": \"0.5831955\",\n \"text\": \"def is_flag(self):\\n return self._isflag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a491f9ae6ec819de887e031be5f31131\",\n \"score\": \"0.58248043\",\n \"text\": \"def in_use(self):\\n return self.in_blocking_call or self.in_pubsub or self.in_transaction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797e9f141bd5b1eafdb95a1a0e97ede3\",\n \"score\": \"0.5822704\",\n \"text\": \"def pending(self):\\n pending = Contract().pending_contracts(WidgetAbstract.username)\\n return pending\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc1cc0016b9ab8960d4b37c479e73173\",\n \"score\": \"0.5815581\",\n \"text\": \"def isScheduled(self) -> \\\"SbBool\\\":\\n return _coin.SoDelayQueueSensor_isScheduled(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17d657254de99274c45def7b41a19e1\",\n \"score\": \"0.58087957\",\n \"text\": \"def has_completed(self):\\n return self.is_completed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cac10b6b06997d22ab9999c531da113\",\n \"score\": \"0.57837754\",\n \"text\": \"def isplaying(self):\\n\\t\\treturn self.play is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de5732f2a2f83434b79183ad7f4d1f43\",\n \"score\": \"0.57823294\",\n \"text\": \"def hasProgress(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de5732f2a2f83434b79183ad7f4d1f43\",\n \"score\": \"0.57823294\",\n \"text\": \"def hasProgress(self) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7eda8ba1fcc7cc0cda145ed114e00e\",\n \"score\": \"0.5779451\",\n \"text\": \"def is_ask_sn(self):\\n return not self.is_normal_news_item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05b1aab93fe70488ceb3dadc30dbc2bb\",\n \"score\": \"0.57786876\",\n \"text\": \"def done(self) -> bool:\\n return bool(self.time_remaining <= 0 or self._status == 'done')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9338dfdd29450a639ba1deae6c03dcf\",\n \"score\": \"0.57735914\",\n \"text\": \"def available(self) -> bool:\\n return self._serial in self._fetcher._state # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"146c021ce8f1f94c074c9e8d0a2c08b1\",\n \"score\": \"0.5767116\",\n \"text\": \"def chargeable(self):\\n return not self.internal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56965d9318ce561f4f4717171ed96c8e\",\n \"score\": \"0.5757424\",\n \"text\": \"def is_done(self):\\n return not (self.patrn_bfs_queue and self.sub_bfs_queue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8f775d1174e5a2d3ea60a039494b4d9\",\n \"score\": \"0.57574016\",\n \"text\": \"def is_active(self):\\r\\n return self.sentcount >= 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"662bb17ed7621ed0e5faf43129731d05\",\n \"score\": \"0.57530624\",\n \"text\": \"def is_working(self):\\n return self.req_queue or (\\n self.active_jobs and not all(job.is_power_off() for job in self.active_jobs)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d9f327357ad3f27f7d17fcc35e812b7\",\n \"score\": \"0.5751738\",\n \"text\": \"def has_it(self):\\n if self.is_active():\\n return True\\n if self.is_imported():\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c71e5f7ee9f2d14602739d36d99a752f\",\n \"score\": \"0.574348\",\n \"text\": \"def is_uploading(self):\\r\\n return self.downloader and self.downloader.get_state() == u'uploading'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db8f88999fbfca3f4992b4f0d9cfe26\",\n \"score\": \"0.57398903\",\n \"text\": \"def status(self) -> bool:\\n return pulumi.get(self, \\\"status\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db8f88999fbfca3f4992b4f0d9cfe26\",\n \"score\": \"0.57398903\",\n \"text\": \"def status(self) -> bool:\\n return pulumi.get(self, \\\"status\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db8f88999fbfca3f4992b4f0d9cfe26\",\n \"score\": \"0.57398903\",\n \"text\": \"def status(self) -> bool:\\n return pulumi.get(self, \\\"status\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db8f88999fbfca3f4992b4f0d9cfe26\",\n \"score\": \"0.57398903\",\n \"text\": \"def status(self) -> bool:\\n return pulumi.get(self, \\\"status\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db8f88999fbfca3f4992b4f0d9cfe26\",\n \"score\": \"0.57398903\",\n \"text\": \"def status(self) -> bool:\\n return pulumi.get(self, \\\"status\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"826f875a6fc7f5cc2145fa79f016c39f\",\n \"score\": \"0.57370335\",\n \"text\": \"def is_active(self):\\n return self.scheduled_time and not self.finish_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9abfff5d69f785a04ac994d72286fe03\",\n \"score\": \"0.5728797\",\n \"text\": \"def is_complete(self) -> bool:\\n return bool(self.submission.is_finalized)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"886c9b41421a58aa3281e2122bcf0bd7\",\n \"score\": \"0.5725123\",\n \"text\": \"def is_on(self):\\n return self.is_streaming\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61bd75d1b850a677ac4333697072116\",\n \"score\": \"0.5723523\",\n \"text\": \"def is_available(self) -> bool:\\n return self.raw[\\\"data\\\"][\\\"@topic\\\"] == \\\"true\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14a93ca0a229d54e655f79de597978f4\",\n \"score\": \"0.5722406\",\n \"text\": \"def is_on(self):\\n return self.unsub_tracker is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b97d67037c039a40268438d852223c88\",\n \"score\": \"0.5719964\",\n \"text\": \"def is_on(self) -> bool:\\n return self._is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b97d67037c039a40268438d852223c88\",\n \"score\": \"0.5719964\",\n \"text\": \"def is_on(self) -> bool:\\n return self._is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7124198ce1b40b9ce24af7f5fa9566\",\n \"score\": \"0.57128775\",\n \"text\": \"def is_on(self) -> bool:\\n return self._state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7124198ce1b40b9ce24af7f5fa9566\",\n \"score\": \"0.57128775\",\n \"text\": \"def is_on(self) -> bool:\\n return self._state\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1791,"cells":{"query_id":{"kind":"string","value":"de54d78ec6bc27371d496d58a3050204"},"query":{"kind":"string","value":"Get the dictionary of a dataclass"},"positive_passages":{"kind":"list like","value":[{"docid":"8b8293686f3040d330353c83f7839383","score":"0.0","text":"def default(self, o: Any) -> Any:\n if dataclassy.functions.is_dataclass_instance(o):\n return dataclassy.asdict(o)\n if isinstance(o, set):\n return list(o)\n return dataclassy.asdict(o)","title":""}],"string":"[\n {\n \"docid\": \"8b8293686f3040d330353c83f7839383\",\n \"score\": \"0.0\",\n \"text\": \"def default(self, o: Any) -> Any:\\n if dataclassy.functions.is_dataclass_instance(o):\\n return dataclassy.asdict(o)\\n if isinstance(o, set):\\n return list(o)\\n return dataclassy.asdict(o)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"14f4e99b24c54dbbdea1cfb5357aa462","score":"0.8139149","text":"def dataclass_to_dict(dataklass: C) -> Dict[str, Any]:\n data = {}\n if is_dataclass(dataklass):\n for field in fields(dataklass):\n value = getattr(dataklass, field.name)\n if is_dataclass(value):\n value = dataclass_to_dict(value)\n data[field.name] = value\n return data","title":""},{"docid":"b7b76931bec1e7b106e213b00f1a5f98","score":"0.719192","text":"def to_dict(_class):\n return_dict = {\n 'name': _class.identifier(),\n 'description': _class.description(),\n 'version': _class.version()\n }\n\n return return_dict","title":""},{"docid":"d8401f6d2058489a7506afd5fdeac890","score":"0.71298236","text":"def asdict(self: Report, ctx: Any) -> dict:\n return dataclasses.asdict(self)","title":""},{"docid":"ccfa5f472e64dec3f0e2715de2c995e7","score":"0.70431006","text":"def get_dict(self):\n raise NotImplementedError","title":""},{"docid":"c92277dc70920e789c945153e95fc381","score":"0.7032144","text":"def data_dict(self):\n return {k: getattr(self, k) for k in self.meta_dict}","title":""},{"docid":"b0eae43482f0a3f6cea14a8ae00099bc","score":"0.6996601","text":"def dict(self):\n return self.__dict__","title":""},{"docid":"f9fd6c303369a39c02c6c147e2675f62","score":"0.694164","text":"def to_dict(self):\n return { 'class': str(self.__class__.__name__) }","title":""},{"docid":"9d0f6b1274a01ace55340d4042a20a76","score":"0.69199246","text":"def as_dict(cls):\n return dict(cls.as_tuple())","title":""},{"docid":"5d6ac6d82ccb0df2dc6aeb35de56a683","score":"0.6780257","text":"def __dict__(self):\n return self.todict()","title":""},{"docid":"e7a4c1e14b21eefba1c86cbc26ac8105","score":"0.6778667","text":"def dict(self):\n return self.__dict__","title":""},{"docid":"e7a4c1e14b21eefba1c86cbc26ac8105","score":"0.6778667","text":"def dict(self):\n return self.__dict__","title":""},{"docid":"e7a4c1e14b21eefba1c86cbc26ac8105","score":"0.6778667","text":"def dict(self):\n return self.__dict__","title":""},{"docid":"e7a4c1e14b21eefba1c86cbc26ac8105","score":"0.6778667","text":"def dict(self):\n return self.__dict__","title":""},{"docid":"e7a4c1e14b21eefba1c86cbc26ac8105","score":"0.6778667","text":"def dict(self):\n return self.__dict__","title":""},{"docid":"e7a4c1e14b21eefba1c86cbc26ac8105","score":"0.6778667","text":"def dict(self):\n return self.__dict__","title":""},{"docid":"cdef7b335735ad44488cae0913b19ed9","score":"0.6689074","text":"def as_dict(self):\n return self.data","title":""},{"docid":"9b7d86d43906126e1573b116508f39f2","score":"0.6665481","text":"def asDict(self, data):\n raise NotImplementedError","title":""},{"docid":"9b7d86d43906126e1573b116508f39f2","score":"0.6665481","text":"def asDict(self, data):\n raise NotImplementedError","title":""},{"docid":"9324a82dc45488d1e63c6b896ba75e28","score":"0.66645825","text":"def dict(self):\n return self.__dict","title":""},{"docid":"ca252d7418986498ae25d57a8f12a860","score":"0.66560733","text":"def dataclass_from_dict(dataklasstype: Type[C], data: Dict[str, Any]) -> C:\n if is_dataclass(dataklasstype):\n for field in fields(dataklasstype):\n if is_dataclass(field.type):\n value = dataclass_from_dict(field.type, data[field.name])\n else:\n value = data[field.name]\n data[field.name] = value\n return dataklasstype(**data)","title":""},{"docid":"5a29f4af88510338b3a4b77539648610","score":"0.66498274","text":"def dict(self):\r\n return self.dict","title":""},{"docid":"52d637d07381991ff3b2d6ab0092c37a","score":"0.6627073","text":"def asDict(self):\n raise NotImplementedError()","title":""},{"docid":"781ab2d72bf14b5f6bcea33dda1caca6","score":"0.6609977","text":"def get_dict(self):\n\n return self._data","title":""},{"docid":"75ec739c606b66e09a565b073ef8b212","score":"0.65946776","text":"def as_dict(self):\n raise NotImplementedError","title":""},{"docid":"3bf041b0ae01ad45ea6f683f8414e7ee","score":"0.6581369","text":"def to_dict(self):","title":""},{"docid":"b32ed591f1cfb38fafdb68a8e99a7e45","score":"0.65795505","text":"def to_dict(self) -> Dict:\n return self.__dict__","title":""},{"docid":"77e7cb8610313207fcbedc6e27cd4744","score":"0.65702134","text":"def as_dict(self):\n\n # Get the properties inherited from Solution\n SolutionData_dict = super(SolutionData, self).as_dict()\n if self.field is None:\n SolutionData_dict[\"field\"] = None\n else: # Store serialized data (using cloudpickle) and str to read it in json save files\n SolutionData_dict[\"field\"] = {\n \"__class__\": str(type(self._field)),\n \"__repr__\": str(self._field.__repr__()),\n \"serialized\": dumps(self._field).decode(\"ISO-8859-2\"),\n }\n # The class name is added to the dict fordeserialisation purpose\n # Overwrite the mother class name\n SolutionData_dict[\"__class__\"] = \"SolutionData\"\n return SolutionData_dict","title":""},{"docid":"ed407f3d10126f5e3d34bb4ea6b804f6","score":"0.6567003","text":"def as_dict(self):\n return self.__dict__","title":""},{"docid":"1763f86305992d94f7b06b5ffb171dc2","score":"0.65582484","text":"def to_dict(self) -> dict:","title":""},{"docid":"bc9d1c97d4fc04677548bec4028024bc","score":"0.6546496","text":"def as_dict(self):\n pass","title":""},{"docid":"bc9d1c97d4fc04677548bec4028024bc","score":"0.6546496","text":"def as_dict(self):\n pass","title":""},{"docid":"d83d6dc556371a5d237dd5b19886ce23","score":"0.65442824","text":"def as_dict(self) -> Dict[str, Any]:\n return asdict(self)","title":""},{"docid":"fa1e3e1cb37a885c2e306d35013ef0f5","score":"0.65435463","text":"def to_dict(self):\n # name returns only the name of the object\n new_dict = self.__dict__.copy() # has all class atributes\n # to update again:\n new_dict[\"created_at\"] = self.created_at.isoformat()\n new_dict[\"updated_at\"] = self.updated_at.isoformat()\n # this class will be used in reload to deserialize:\n new_dict[\"__class__\"] = self.__class__.__name__\n return new_dict","title":""},{"docid":"1927d38f25fab898c16e163ad0b4d7e0","score":"0.6537185","text":"def _asdict(self):\n return self","title":""},{"docid":"87e74ac54559a0585eb4d4fbf7bbf8aa","score":"0.65366375","text":"def to_dict(self, **kwargs):\n data = {\n \"class\": self._name,\n \"name\": self.name,\n \"initial\": self.initial,\n }\n return data","title":""},{"docid":"7f178a00fe467da5e451cdbaaab37a0d","score":"0.6533918","text":"def toDictionary(self):\n \n pass","title":""},{"docid":"7f178a00fe467da5e451cdbaaab37a0d","score":"0.6533918","text":"def toDictionary(self):\n \n pass","title":""},{"docid":"ca4fe362779f41846ce55fbe11b4fc85","score":"0.65247595","text":"def data(self):\n items = (slot[1:] for slot in self.__slots__ \\\n if slot not in ('_id', '_meta',))\n data = {}\n for item in items:\n data[item] = getattr(self, \"_\"+item)\n return data","title":""},{"docid":"c60387739b5d05e6b771b6e9e07fa4f2","score":"0.6515189","text":"def as_dict(self):\n return {\"@module\": self.__class__.__module__,\n \"@class\": self.__class__.__name__}","title":""},{"docid":"ef678d367e044bc838a63d57b59f4596","score":"0.65135956","text":"def dict(self) -> dict:\n raise NotImplementedError","title":""},{"docid":"1b42c0ed661b46dd366614f9ca7b0dad","score":"0.6512086","text":"def _to_dict(self) -> Dict:","title":""},{"docid":"2004f21103c137b7280abf91b3a41add","score":"0.65117264","text":"def get(self) -> dict:\n return {\n \"type\": self.__class__.__name__,\n \"database\": self.database,\n \"version\": self.version,\n \"data\": self.data,\n \"uids\": self.uids,\n }","title":""},{"docid":"75dcb916e550f359dc1ee2b807645d0d","score":"0.65047544","text":"def to_dict(self) -> Dict[str, Any]:","title":""},{"docid":"9e33c295f15ad4cfc2edea6868fc2797","score":"0.6492508","text":"def GetData(self) -> Dict[str, Any]:\n ...","title":""},{"docid":"79ecda6c7036d997148cccd6faea7bb8","score":"0.6482701","text":"def dict(self):\n return self._d","title":""},{"docid":"b0cc45b073f1457ae4bbbcd9583b9067","score":"0.64721256","text":"def __call__(self):\n return self.dict","title":""},{"docid":"4f3287f71f0c1c363eb408621c76b5f1","score":"0.6471443","text":"def to_dict(self):\n d = {}\n keys = vars(self).keys()\n for k in keys:\n d[k] = self.__getattribute__(k)\n return d","title":""},{"docid":"1f18e9f605cee01c5830b5190900d5ae","score":"0.64653087","text":"def asdict(self):\n return objToDict(self)","title":""},{"docid":"4aeadd8a247f67c85590e3f425040028","score":"0.6446227","text":"def d(self):\n return self.__dict__","title":""},{"docid":"c6db79373ef784535db9de49ce737d37","score":"0.6436002","text":"def returdic(self):\n return self.__dictio","title":""},{"docid":"3a9f49ee8f35f6db748eacd5fcd4620a","score":"0.6430358","text":"def to_dict(self) -> Dict:\n raise NotImplementedError","title":""},{"docid":"35c60d8170dea04a36359063abd4486a","score":"0.6429092","text":"def to_dict(self) -> Dict:\n return utilities.asdict(self)","title":""},{"docid":"99a44781584a77ad00255e4f9e41bfda","score":"0.64258265","text":"def to_dict(self) -> dict:\n d = self.__dict__.copy()\n for key, value in d.items():\n if isinstance(value, self.__class__):\n d[key] = value.to_dict()\n return d","title":""},{"docid":"1ec0b2df3d18164837ec2f6e52547061","score":"0.6413656","text":"def asdict(self) -> dict:\n return {\n k: v\n for k, v in dataclasses.asdict(self).items()\n if k not in {\"app\", \"number\"}\n }","title":""},{"docid":"58cc4f8e9d948041f114538fbe7fff51","score":"0.640013","text":"def data_class(self) -> type:\n raise NotImplementedError()","title":""},{"docid":"8400cf6ae3ed28fa560baa80b8ab3030","score":"0.63879365","text":"def to_dictionary(self):\n\n d_object = {}\n for a in [\"id\", \"size\", 'x', 'y']:\n d_object[a] = getattr(self, a)\n\n return d_object","title":""},{"docid":"4c30c66271b4415050a0cb05f23ee3a3","score":"0.63829714","text":"def class_to_json(obj):\n return (obj.__dict__)","title":""},{"docid":"a442018a841021690b46da48f0958370","score":"0.63789856","text":"def to_dict(self):\n raise NotImplementedError","title":""},{"docid":"3fc4c8a29ca39fc1f381d16310b9f7d4","score":"0.63784707","text":"def get_data(self):\n property_data = dict()\n private_keys = ['_id', '_type', '_outV', '_inV', '_label']\n for key in self.data: # Python 3\n value = self.data[key]\n if key not in private_keys:\n property_data.update({key: value})\n return property_data","title":""},{"docid":"88f5387ed2bc3e202c6a097fbcf4f884","score":"0.63700664","text":"def _to_dict(self):\n _dict = {}\n if hasattr(self, 'name') and self.name is not None:\n _dict['name'] = self.name\n if hasattr(self, 'type') and self.type is not None:\n _dict['type'] = self.type\n if hasattr(self, 'parameters') and self.parameters is not None:\n _dict['parameters'] = self.parameters\n if hasattr(self,\n 'result_variable') and self.result_variable is not None:\n _dict['result_variable'] = self.result_variable\n if hasattr(self, 'credentials') and self.credentials is not None:\n _dict['credentials'] = self.credentials\n return _dict","title":""},{"docid":"16ac5e3a6055baf51c990a1a0624cf39","score":"0.63663256","text":"def to_dict(self, **kwargs) -> dict:\n data = {\n \"class\": self._name,\n \"start\": self.start,\n \"end\": self.end,\n \"name\": self.name,\n }\n return data","title":""},{"docid":"2d9ea1bd7d1baa441f18a3184224bd9d","score":"0.6363133","text":"def inherited_class_as_dict(class_obj):\n def try_group_name(attr):\n try:\n return class_vars[attr]._group_name\n except:\n return attr\n\n class_vars = {}\n for order in class_obj.__mro__[::-1]:\n if \"__dict__\" in dir(order):\n var_dict = {k:v for k, v in order.__dict__.items() if not k.startswith(\"__\") and k not in FIGTREE_NAMES}\n class_vars = {**class_vars, **var_dict}\n\n return {try_group_name(k): v for k, v in class_vars.items()}","title":""},{"docid":"5ae9d03a5d42ceee0e42b7ca92a9ed25","score":"0.6343375","text":"def as_dict(self):\n tmp = {}\n tmp.update(self.__dict__)\n return tmp","title":""},{"docid":"5c1654d4761378ac9fa28f0a544fdad3","score":"0.6338181","text":"def get_data(self):\n db = self.db()\n data = dict()\n for model in self.get_mapped_classes():\n query = db.session.query(model)\n data[model.__name__] = dumps(query.all())\n return data","title":""},{"docid":"7e0377f0c35cc2e72bb6002f09f244dd","score":"0.6335914","text":"def as_dict(self):\n return {\"@module\": self.__class__.__module__,\n \"@class\": self.__class__.__name__,\n \"additional_condition\": self._additional_condition}","title":""},{"docid":"929d8e26111a59101ad2c3bb37e08405","score":"0.63352484","text":"def to_dict(self):\n return self.__dict__","title":""},{"docid":"fe01f83dbc7f193bb8757a9ef1c151d1","score":"0.6333447","text":"def _make_class_dict():\n from citrine.resources.condition_template import ConditionTemplate\n from citrine.resources.parameter_template import ParameterTemplate\n from citrine.resources.property_template import PropertyTemplate\n from citrine.resources.material_template import MaterialTemplate\n from citrine.resources.measurement_template import MeasurementTemplate\n from citrine.resources.process_template import ProcessTemplate\n from citrine.resources.ingredient_spec import IngredientSpec\n from citrine.resources.material_spec import MaterialSpec\n from citrine.resources.measurement_spec import MeasurementSpec\n from citrine.resources.process_spec import ProcessSpec\n from citrine.resources.ingredient_run import IngredientRun\n from citrine.resources.material_run import MaterialRun\n from citrine.resources.measurement_run import MeasurementRun\n from citrine.resources.process_run import ProcessRun\n _clazz_list = [ConditionTemplate, ParameterTemplate, PropertyTemplate,\n MaterialTemplate, MeasurementTemplate, ProcessTemplate,\n IngredientSpec, MaterialSpec, MeasurementSpec, ProcessSpec,\n IngredientRun, MaterialRun, MeasurementRun, ProcessRun]\n for clazz in _clazz_list:\n DataConcepts.class_dict[clazz._response_key] = clazz\n DataConcepts.class_dict['link_by_uid'] = LinkByUID","title":""},{"docid":"ebbc0d2e5fd5f7a267a888427582b523","score":"0.6331466","text":"def to_dict(self):\n raise NotImplementedError()","title":""},{"docid":"40b575ac73a1d03831a47196e12da6e7","score":"0.63302416","text":"def data_util_class_getter():\n\n data_util_class_path = os.path.split(os.path.realpath(__file__))[0] + \"/datasets/util\"\n\n data_util_script_name_list = [data_util_class for data_util_class in os.listdir(data_util_class_path) if\n data_util_class not in \"__pycache__\" and data_util_class not in \"__init__.py\"]\n data_util_dict = {}\n\n for data_util_script_name in data_util_script_name_list:\n\n class_name = \"DATA\"\n data_util_script = importlib.import_module(\"autognas.datasets.util\" +\n \".\" + data_util_script_name[:-3])\n data_util_obj = getattr(data_util_script, class_name)\n data_util_obj = data_util_obj()\n data_util_dict[data_util_obj] = data_util_obj.name\n\n return data_util_dict","title":""},{"docid":"0ae8bb751e36fb1c09456ee3391680fd","score":"0.6328491","text":"def dict(self):\n dict = {}\n\n if hasattr(self, 'name'):\n dict['name'] = self.name\n\n if hasattr(self, 'tags'):\n dict['tags'] = self.tags\n\n if hasattr(self, 'type'):\n dict['type'] = self.type\n\n dict['pos'] = self.pos\n dict['size'] = self.size\n\n return dict","title":""},{"docid":"ff4689c7c0fd425b56c43332b4e0191e","score":"0.6327577","text":"def get_class_attributes(self):\n return {\n \"crypto_provider\": self.crypto_provider,\n \"dtype\": self.dtype,\n \"field\": self.field,\n \"protocol\": self.protocol,\n }","title":""},{"docid":"9155fd43c155ea6dbedf22b81f700be4","score":"0.63185734","text":"def to_dict(cls):\n return {attr: getattr(cls, attr) for attr in dir(cls) if attr.isupper()}","title":""},{"docid":"4163607e13775d192376300353856b87","score":"0.63138443","text":"def to_dict(self):\n dict_init = self.__dict__\n return dict_init","title":""},{"docid":"d26ccf069e1e1b63ce140af6411a7211","score":"0.6306848","text":"def to_dict(self):\n return asdict(self)","title":""},{"docid":"489eceb9c0b8db44bc25586c027a83b7","score":"0.6306225","text":"def as_dict(self):\n return {item: getattr(self, item) for item in self.__slots__}","title":""},{"docid":"132414e7bcf8b3b445852a60909abbe3","score":"0.63003933","text":"def as_dict(self):\n return {\"@module\": self.__class__.__module__,\n \"@class\": self.__class__.__name__,\n \"only_anion_cation_bonds\": self.only_anion_cation_bonds,\n \"csm_cutoff\": self.csm_cutoffs,\n \"voronoi_parameters_fraction\": self.voronoi_parameters_fractions}","title":""},{"docid":"1a18c544150566fe933e13b2558b2335","score":"0.62974685","text":"def save_to_dict(self):\n return self.__dict__","title":""},{"docid":"7f7242100237230fa16991ac636c3e9f","score":"0.6281997","text":"def __dict__(self):\n return {s: getattr(self, s) for s in self.__slots__ if hasattr(self, s)}","title":""},{"docid":"7399a4bd6feeeca749187ecc300f8c7c","score":"0.62783206","text":"def dict(self):\n return self._dict","title":""},{"docid":"f1c6c0aa44718c182595c60f5f8dbdcb","score":"0.6271045","text":"def get_data_handler_data_type(cls):\n return dict","title":""},{"docid":"19bfc56ca28fcc44dbc1bf29e085036a","score":"0.62638754","text":"def dict(self):\n pass","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"772c4ddfaf7a2dec87230514f27a693a","score":"0.62637264","text":"def _to_dict(self):\n return self.to_dict()","title":""},{"docid":"039cc0cc1be2236a3fda4aaa46713650","score":"0.6241955","text":"def get_dict(self) -> dict:\n return {\"name\": self.get_name(), \"probability\": float(self.get_probability())}","title":""},{"docid":"e6561055edc9d8757a0448e4aaa1fe5b","score":"0.6239511","text":"def to_dict(self):\n pass","title":""},{"docid":"e6561055edc9d8757a0448e4aaa1fe5b","score":"0.6239511","text":"def to_dict(self):\n pass","title":""},{"docid":"e6561055edc9d8757a0448e4aaa1fe5b","score":"0.6239511","text":"def to_dict(self):\n pass","title":""},{"docid":"e4f7a916faa8af867c1a60a9f830a260","score":"0.6232544","text":"def __dictattr_get__(self) -> Dict[str, Any]:\n\n return self.values","title":""},{"docid":"82e2ec6dfcb019825bad27c23035eed1","score":"0.62323916","text":"def get_full_data(self) -> Dict[str, Any]:\n try:\n serializer_class = model_serializer_classes[type(self)]\n except KeyError:\n # Because of the order of imports, it can happen, that the serializer\n # for a model is not imported yet. Try to guess the name of the\n # module and import it.\n module_name = type(self).__module__.rsplit(\".\", 1)[0] + \".serializers\"\n __import__(module_name)\n serializer_class = model_serializer_classes[type(self)]\n return serializer_class(self).data","title":""},{"docid":"54a768a55fd4159035671cad82261421","score":"0.62268794","text":"def getDict(self) -> dict:\n\n raise NotImplementedError(\"I never learned to read\")","title":""}],"string":"[\n {\n \"docid\": \"14f4e99b24c54dbbdea1cfb5357aa462\",\n \"score\": \"0.8139149\",\n \"text\": \"def dataclass_to_dict(dataklass: C) -> Dict[str, Any]:\\n data = {}\\n if is_dataclass(dataklass):\\n for field in fields(dataklass):\\n value = getattr(dataklass, field.name)\\n if is_dataclass(value):\\n value = dataclass_to_dict(value)\\n data[field.name] = value\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7b76931bec1e7b106e213b00f1a5f98\",\n \"score\": \"0.719192\",\n \"text\": \"def to_dict(_class):\\n return_dict = {\\n 'name': _class.identifier(),\\n 'description': _class.description(),\\n 'version': _class.version()\\n }\\n\\n return return_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8401f6d2058489a7506afd5fdeac890\",\n \"score\": \"0.71298236\",\n \"text\": \"def asdict(self: Report, ctx: Any) -> dict:\\n return dataclasses.asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccfa5f472e64dec3f0e2715de2c995e7\",\n \"score\": \"0.70431006\",\n \"text\": \"def get_dict(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92277dc70920e789c945153e95fc381\",\n \"score\": \"0.7032144\",\n \"text\": \"def data_dict(self):\\n return {k: getattr(self, k) for k in self.meta_dict}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0eae43482f0a3f6cea14a8ae00099bc\",\n \"score\": \"0.6996601\",\n \"text\": \"def dict(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9fd6c303369a39c02c6c147e2675f62\",\n \"score\": \"0.694164\",\n \"text\": \"def to_dict(self):\\n return { 'class': str(self.__class__.__name__) }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0f6b1274a01ace55340d4042a20a76\",\n \"score\": \"0.69199246\",\n \"text\": \"def as_dict(cls):\\n return dict(cls.as_tuple())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d6ac6d82ccb0df2dc6aeb35de56a683\",\n \"score\": \"0.6780257\",\n \"text\": \"def __dict__(self):\\n return self.todict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a4c1e14b21eefba1c86cbc26ac8105\",\n \"score\": \"0.6778667\",\n \"text\": \"def dict(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a4c1e14b21eefba1c86cbc26ac8105\",\n \"score\": \"0.6778667\",\n \"text\": \"def dict(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a4c1e14b21eefba1c86cbc26ac8105\",\n \"score\": \"0.6778667\",\n \"text\": \"def dict(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a4c1e14b21eefba1c86cbc26ac8105\",\n \"score\": \"0.6778667\",\n \"text\": \"def dict(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a4c1e14b21eefba1c86cbc26ac8105\",\n \"score\": \"0.6778667\",\n \"text\": \"def dict(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a4c1e14b21eefba1c86cbc26ac8105\",\n \"score\": \"0.6778667\",\n \"text\": \"def dict(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdef7b335735ad44488cae0913b19ed9\",\n \"score\": \"0.6689074\",\n \"text\": \"def as_dict(self):\\n return self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7d86d43906126e1573b116508f39f2\",\n \"score\": \"0.6665481\",\n \"text\": \"def asDict(self, data):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7d86d43906126e1573b116508f39f2\",\n \"score\": \"0.6665481\",\n \"text\": \"def asDict(self, data):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9324a82dc45488d1e63c6b896ba75e28\",\n \"score\": \"0.66645825\",\n \"text\": \"def dict(self):\\n return self.__dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca252d7418986498ae25d57a8f12a860\",\n \"score\": \"0.66560733\",\n \"text\": \"def dataclass_from_dict(dataklasstype: Type[C], data: Dict[str, Any]) -> C:\\n if is_dataclass(dataklasstype):\\n for field in fields(dataklasstype):\\n if is_dataclass(field.type):\\n value = dataclass_from_dict(field.type, data[field.name])\\n else:\\n value = data[field.name]\\n data[field.name] = value\\n return dataklasstype(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a29f4af88510338b3a4b77539648610\",\n \"score\": \"0.66498274\",\n \"text\": \"def dict(self):\\r\\n return self.dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52d637d07381991ff3b2d6ab0092c37a\",\n \"score\": \"0.6627073\",\n \"text\": \"def asDict(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"781ab2d72bf14b5f6bcea33dda1caca6\",\n \"score\": \"0.6609977\",\n \"text\": \"def get_dict(self):\\n\\n return self._data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75ec739c606b66e09a565b073ef8b212\",\n \"score\": \"0.65946776\",\n \"text\": \"def as_dict(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf041b0ae01ad45ea6f683f8414e7ee\",\n \"score\": \"0.6581369\",\n \"text\": \"def to_dict(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b32ed591f1cfb38fafdb68a8e99a7e45\",\n \"score\": \"0.65795505\",\n \"text\": \"def to_dict(self) -> Dict:\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77e7cb8610313207fcbedc6e27cd4744\",\n \"score\": \"0.65702134\",\n \"text\": \"def as_dict(self):\\n\\n # Get the properties inherited from Solution\\n SolutionData_dict = super(SolutionData, self).as_dict()\\n if self.field is None:\\n SolutionData_dict[\\\"field\\\"] = None\\n else: # Store serialized data (using cloudpickle) and str to read it in json save files\\n SolutionData_dict[\\\"field\\\"] = {\\n \\\"__class__\\\": str(type(self._field)),\\n \\\"__repr__\\\": str(self._field.__repr__()),\\n \\\"serialized\\\": dumps(self._field).decode(\\\"ISO-8859-2\\\"),\\n }\\n # The class name is added to the dict fordeserialisation purpose\\n # Overwrite the mother class name\\n SolutionData_dict[\\\"__class__\\\"] = \\\"SolutionData\\\"\\n return SolutionData_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed407f3d10126f5e3d34bb4ea6b804f6\",\n \"score\": \"0.6567003\",\n \"text\": \"def as_dict(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1763f86305992d94f7b06b5ffb171dc2\",\n \"score\": \"0.65582484\",\n \"text\": \"def to_dict(self) -> dict:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9d1c97d4fc04677548bec4028024bc\",\n \"score\": \"0.6546496\",\n \"text\": \"def as_dict(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9d1c97d4fc04677548bec4028024bc\",\n \"score\": \"0.6546496\",\n \"text\": \"def as_dict(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d83d6dc556371a5d237dd5b19886ce23\",\n \"score\": \"0.65442824\",\n \"text\": \"def as_dict(self) -> Dict[str, Any]:\\n return asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa1e3e1cb37a885c2e306d35013ef0f5\",\n \"score\": \"0.65435463\",\n \"text\": \"def to_dict(self):\\n # name returns only the name of the object\\n new_dict = self.__dict__.copy() # has all class atributes\\n # to update again:\\n new_dict[\\\"created_at\\\"] = self.created_at.isoformat()\\n new_dict[\\\"updated_at\\\"] = self.updated_at.isoformat()\\n # this class will be used in reload to deserialize:\\n new_dict[\\\"__class__\\\"] = self.__class__.__name__\\n return new_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1927d38f25fab898c16e163ad0b4d7e0\",\n \"score\": \"0.6537185\",\n \"text\": \"def _asdict(self):\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e74ac54559a0585eb4d4fbf7bbf8aa\",\n \"score\": \"0.65366375\",\n \"text\": \"def to_dict(self, **kwargs):\\n data = {\\n \\\"class\\\": self._name,\\n \\\"name\\\": self.name,\\n \\\"initial\\\": self.initial,\\n }\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f178a00fe467da5e451cdbaaab37a0d\",\n \"score\": \"0.6533918\",\n \"text\": \"def toDictionary(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f178a00fe467da5e451cdbaaab37a0d\",\n \"score\": \"0.6533918\",\n \"text\": \"def toDictionary(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca4fe362779f41846ce55fbe11b4fc85\",\n \"score\": \"0.65247595\",\n \"text\": \"def data(self):\\n items = (slot[1:] for slot in self.__slots__ \\\\\\n if slot not in ('_id', '_meta',))\\n data = {}\\n for item in items:\\n data[item] = getattr(self, \\\"_\\\"+item)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c60387739b5d05e6b771b6e9e07fa4f2\",\n \"score\": \"0.6515189\",\n \"text\": \"def as_dict(self):\\n return {\\\"@module\\\": self.__class__.__module__,\\n \\\"@class\\\": self.__class__.__name__}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef678d367e044bc838a63d57b59f4596\",\n \"score\": \"0.65135956\",\n \"text\": \"def dict(self) -> dict:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b42c0ed661b46dd366614f9ca7b0dad\",\n \"score\": \"0.6512086\",\n \"text\": \"def _to_dict(self) -> Dict:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2004f21103c137b7280abf91b3a41add\",\n \"score\": \"0.65117264\",\n \"text\": \"def get(self) -> dict:\\n return {\\n \\\"type\\\": self.__class__.__name__,\\n \\\"database\\\": self.database,\\n \\\"version\\\": self.version,\\n \\\"data\\\": self.data,\\n \\\"uids\\\": self.uids,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75dcb916e550f359dc1ee2b807645d0d\",\n \"score\": \"0.65047544\",\n \"text\": \"def to_dict(self) -> Dict[str, Any]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e33c295f15ad4cfc2edea6868fc2797\",\n \"score\": \"0.6492508\",\n \"text\": \"def GetData(self) -> Dict[str, Any]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79ecda6c7036d997148cccd6faea7bb8\",\n \"score\": \"0.6482701\",\n \"text\": \"def dict(self):\\n return self._d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0cc45b073f1457ae4bbbcd9583b9067\",\n \"score\": \"0.64721256\",\n \"text\": \"def __call__(self):\\n return self.dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3287f71f0c1c363eb408621c76b5f1\",\n \"score\": \"0.6471443\",\n \"text\": \"def to_dict(self):\\n d = {}\\n keys = vars(self).keys()\\n for k in keys:\\n d[k] = self.__getattribute__(k)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f18e9f605cee01c5830b5190900d5ae\",\n \"score\": \"0.64653087\",\n \"text\": \"def asdict(self):\\n return objToDict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aeadd8a247f67c85590e3f425040028\",\n \"score\": \"0.6446227\",\n \"text\": \"def d(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6db79373ef784535db9de49ce737d37\",\n \"score\": \"0.6436002\",\n \"text\": \"def returdic(self):\\n return self.__dictio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a9f49ee8f35f6db748eacd5fcd4620a\",\n \"score\": \"0.6430358\",\n \"text\": \"def to_dict(self) -> Dict:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c60d8170dea04a36359063abd4486a\",\n \"score\": \"0.6429092\",\n \"text\": \"def to_dict(self) -> Dict:\\n return utilities.asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a44781584a77ad00255e4f9e41bfda\",\n \"score\": \"0.64258265\",\n \"text\": \"def to_dict(self) -> dict:\\n d = self.__dict__.copy()\\n for key, value in d.items():\\n if isinstance(value, self.__class__):\\n d[key] = value.to_dict()\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ec0b2df3d18164837ec2f6e52547061\",\n \"score\": \"0.6413656\",\n \"text\": \"def asdict(self) -> dict:\\n return {\\n k: v\\n for k, v in dataclasses.asdict(self).items()\\n if k not in {\\\"app\\\", \\\"number\\\"}\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58cc4f8e9d948041f114538fbe7fff51\",\n \"score\": \"0.640013\",\n \"text\": \"def data_class(self) -> type:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8400cf6ae3ed28fa560baa80b8ab3030\",\n \"score\": \"0.63879365\",\n \"text\": \"def to_dictionary(self):\\n\\n d_object = {}\\n for a in [\\\"id\\\", \\\"size\\\", 'x', 'y']:\\n d_object[a] = getattr(self, a)\\n\\n return d_object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c30c66271b4415050a0cb05f23ee3a3\",\n \"score\": \"0.63829714\",\n \"text\": \"def class_to_json(obj):\\n return (obj.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a442018a841021690b46da48f0958370\",\n \"score\": \"0.63789856\",\n \"text\": \"def to_dict(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc4c8a29ca39fc1f381d16310b9f7d4\",\n \"score\": \"0.63784707\",\n \"text\": \"def get_data(self):\\n property_data = dict()\\n private_keys = ['_id', '_type', '_outV', '_inV', '_label']\\n for key in self.data: # Python 3\\n value = self.data[key]\\n if key not in private_keys:\\n property_data.update({key: value})\\n return property_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88f5387ed2bc3e202c6a097fbcf4f884\",\n \"score\": \"0.63700664\",\n \"text\": \"def _to_dict(self):\\n _dict = {}\\n if hasattr(self, 'name') and self.name is not None:\\n _dict['name'] = self.name\\n if hasattr(self, 'type') and self.type is not None:\\n _dict['type'] = self.type\\n if hasattr(self, 'parameters') and self.parameters is not None:\\n _dict['parameters'] = self.parameters\\n if hasattr(self,\\n 'result_variable') and self.result_variable is not None:\\n _dict['result_variable'] = self.result_variable\\n if hasattr(self, 'credentials') and self.credentials is not None:\\n _dict['credentials'] = self.credentials\\n return _dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ac5e3a6055baf51c990a1a0624cf39\",\n \"score\": \"0.63663256\",\n \"text\": \"def to_dict(self, **kwargs) -> dict:\\n data = {\\n \\\"class\\\": self._name,\\n \\\"start\\\": self.start,\\n \\\"end\\\": self.end,\\n \\\"name\\\": self.name,\\n }\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9ea1bd7d1baa441f18a3184224bd9d\",\n \"score\": \"0.6363133\",\n \"text\": \"def inherited_class_as_dict(class_obj):\\n def try_group_name(attr):\\n try:\\n return class_vars[attr]._group_name\\n except:\\n return attr\\n\\n class_vars = {}\\n for order in class_obj.__mro__[::-1]:\\n if \\\"__dict__\\\" in dir(order):\\n var_dict = {k:v for k, v in order.__dict__.items() if not k.startswith(\\\"__\\\") and k not in FIGTREE_NAMES}\\n class_vars = {**class_vars, **var_dict}\\n\\n return {try_group_name(k): v for k, v in class_vars.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ae9d03a5d42ceee0e42b7ca92a9ed25\",\n \"score\": \"0.6343375\",\n \"text\": \"def as_dict(self):\\n tmp = {}\\n tmp.update(self.__dict__)\\n return tmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c1654d4761378ac9fa28f0a544fdad3\",\n \"score\": \"0.6338181\",\n \"text\": \"def get_data(self):\\n db = self.db()\\n data = dict()\\n for model in self.get_mapped_classes():\\n query = db.session.query(model)\\n data[model.__name__] = dumps(query.all())\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e0377f0c35cc2e72bb6002f09f244dd\",\n \"score\": \"0.6335914\",\n \"text\": \"def as_dict(self):\\n return {\\\"@module\\\": self.__class__.__module__,\\n \\\"@class\\\": self.__class__.__name__,\\n \\\"additional_condition\\\": self._additional_condition}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"929d8e26111a59101ad2c3bb37e08405\",\n \"score\": \"0.63352484\",\n \"text\": \"def to_dict(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe01f83dbc7f193bb8757a9ef1c151d1\",\n \"score\": \"0.6333447\",\n \"text\": \"def _make_class_dict():\\n from citrine.resources.condition_template import ConditionTemplate\\n from citrine.resources.parameter_template import ParameterTemplate\\n from citrine.resources.property_template import PropertyTemplate\\n from citrine.resources.material_template import MaterialTemplate\\n from citrine.resources.measurement_template import MeasurementTemplate\\n from citrine.resources.process_template import ProcessTemplate\\n from citrine.resources.ingredient_spec import IngredientSpec\\n from citrine.resources.material_spec import MaterialSpec\\n from citrine.resources.measurement_spec import MeasurementSpec\\n from citrine.resources.process_spec import ProcessSpec\\n from citrine.resources.ingredient_run import IngredientRun\\n from citrine.resources.material_run import MaterialRun\\n from citrine.resources.measurement_run import MeasurementRun\\n from citrine.resources.process_run import ProcessRun\\n _clazz_list = [ConditionTemplate, ParameterTemplate, PropertyTemplate,\\n MaterialTemplate, MeasurementTemplate, ProcessTemplate,\\n IngredientSpec, MaterialSpec, MeasurementSpec, ProcessSpec,\\n IngredientRun, MaterialRun, MeasurementRun, ProcessRun]\\n for clazz in _clazz_list:\\n DataConcepts.class_dict[clazz._response_key] = clazz\\n DataConcepts.class_dict['link_by_uid'] = LinkByUID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebbc0d2e5fd5f7a267a888427582b523\",\n \"score\": \"0.6331466\",\n \"text\": \"def to_dict(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40b575ac73a1d03831a47196e12da6e7\",\n \"score\": \"0.63302416\",\n \"text\": \"def data_util_class_getter():\\n\\n data_util_class_path = os.path.split(os.path.realpath(__file__))[0] + \\\"/datasets/util\\\"\\n\\n data_util_script_name_list = [data_util_class for data_util_class in os.listdir(data_util_class_path) if\\n data_util_class not in \\\"__pycache__\\\" and data_util_class not in \\\"__init__.py\\\"]\\n data_util_dict = {}\\n\\n for data_util_script_name in data_util_script_name_list:\\n\\n class_name = \\\"DATA\\\"\\n data_util_script = importlib.import_module(\\\"autognas.datasets.util\\\" +\\n \\\".\\\" + data_util_script_name[:-3])\\n data_util_obj = getattr(data_util_script, class_name)\\n data_util_obj = data_util_obj()\\n data_util_dict[data_util_obj] = data_util_obj.name\\n\\n return data_util_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ae8bb751e36fb1c09456ee3391680fd\",\n \"score\": \"0.6328491\",\n \"text\": \"def dict(self):\\n dict = {}\\n\\n if hasattr(self, 'name'):\\n dict['name'] = self.name\\n\\n if hasattr(self, 'tags'):\\n dict['tags'] = self.tags\\n\\n if hasattr(self, 'type'):\\n dict['type'] = self.type\\n\\n dict['pos'] = self.pos\\n dict['size'] = self.size\\n\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff4689c7c0fd425b56c43332b4e0191e\",\n \"score\": \"0.6327577\",\n \"text\": \"def get_class_attributes(self):\\n return {\\n \\\"crypto_provider\\\": self.crypto_provider,\\n \\\"dtype\\\": self.dtype,\\n \\\"field\\\": self.field,\\n \\\"protocol\\\": self.protocol,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9155fd43c155ea6dbedf22b81f700be4\",\n \"score\": \"0.63185734\",\n \"text\": \"def to_dict(cls):\\n return {attr: getattr(cls, attr) for attr in dir(cls) if attr.isupper()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4163607e13775d192376300353856b87\",\n \"score\": \"0.63138443\",\n \"text\": \"def to_dict(self):\\n dict_init = self.__dict__\\n return dict_init\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d26ccf069e1e1b63ce140af6411a7211\",\n \"score\": \"0.6306848\",\n \"text\": \"def to_dict(self):\\n return asdict(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"489eceb9c0b8db44bc25586c027a83b7\",\n \"score\": \"0.6306225\",\n \"text\": \"def as_dict(self):\\n return {item: getattr(self, item) for item in self.__slots__}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132414e7bcf8b3b445852a60909abbe3\",\n \"score\": \"0.63003933\",\n \"text\": \"def as_dict(self):\\n return {\\\"@module\\\": self.__class__.__module__,\\n \\\"@class\\\": self.__class__.__name__,\\n \\\"only_anion_cation_bonds\\\": self.only_anion_cation_bonds,\\n \\\"csm_cutoff\\\": self.csm_cutoffs,\\n \\\"voronoi_parameters_fraction\\\": self.voronoi_parameters_fractions}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a18c544150566fe933e13b2558b2335\",\n \"score\": \"0.62974685\",\n \"text\": \"def save_to_dict(self):\\n return self.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f7242100237230fa16991ac636c3e9f\",\n \"score\": \"0.6281997\",\n \"text\": \"def __dict__(self):\\n return {s: getattr(self, s) for s in self.__slots__ if hasattr(self, s)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7399a4bd6feeeca749187ecc300f8c7c\",\n \"score\": \"0.62783206\",\n \"text\": \"def dict(self):\\n return self._dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c6c0aa44718c182595c60f5f8dbdcb\",\n \"score\": \"0.6271045\",\n \"text\": \"def get_data_handler_data_type(cls):\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19bfc56ca28fcc44dbc1bf29e085036a\",\n \"score\": \"0.62638754\",\n \"text\": \"def dict(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"772c4ddfaf7a2dec87230514f27a693a\",\n \"score\": \"0.62637264\",\n \"text\": \"def _to_dict(self):\\n return self.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039cc0cc1be2236a3fda4aaa46713650\",\n \"score\": \"0.6241955\",\n \"text\": \"def get_dict(self) -> dict:\\n return {\\\"name\\\": self.get_name(), \\\"probability\\\": float(self.get_probability())}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6561055edc9d8757a0448e4aaa1fe5b\",\n \"score\": \"0.6239511\",\n \"text\": \"def to_dict(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6561055edc9d8757a0448e4aaa1fe5b\",\n \"score\": \"0.6239511\",\n \"text\": \"def to_dict(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6561055edc9d8757a0448e4aaa1fe5b\",\n \"score\": \"0.6239511\",\n \"text\": \"def to_dict(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4f7a916faa8af867c1a60a9f830a260\",\n \"score\": \"0.6232544\",\n \"text\": \"def __dictattr_get__(self) -> Dict[str, Any]:\\n\\n return self.values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e2ec6dfcb019825bad27c23035eed1\",\n \"score\": \"0.62323916\",\n \"text\": \"def get_full_data(self) -> Dict[str, Any]:\\n try:\\n serializer_class = model_serializer_classes[type(self)]\\n except KeyError:\\n # Because of the order of imports, it can happen, that the serializer\\n # for a model is not imported yet. Try to guess the name of the\\n # module and import it.\\n module_name = type(self).__module__.rsplit(\\\".\\\", 1)[0] + \\\".serializers\\\"\\n __import__(module_name)\\n serializer_class = model_serializer_classes[type(self)]\\n return serializer_class(self).data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54a768a55fd4159035671cad82261421\",\n \"score\": \"0.62268794\",\n \"text\": \"def getDict(self) -> dict:\\n\\n raise NotImplementedError(\\\"I never learned to read\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1792,"cells":{"query_id":{"kind":"string","value":"4960b8336d863a9cccb69e67c9ac431c"},"query":{"kind":"string","value":"Calculate the covariance matrix between orbital elements, and initial state vector using all Monte Carlo trajectories. The covariance matrix is weighted by the timing residuals. The orbital covariance matrix is calculated for radians and the inital state vector matrix in meters and meters per second."},"positive_passages":{"kind":"list like","value":[{"docid":"6a93c0efd13f0123b7c5d3e5e7e2d430","score":"0.70054585","text":"def calcCovMatrices(mc_traj_list):\n\n # Filter out those trajectories for which the last perihelion time could not be estimated\n mc_traj_list = [traj for traj in mc_traj_list if traj.orbit.last_perihelion is not None]\n\n # If there are no good orbits, do not estimate the covariance matrix\n if not mc_traj_list:\n return np.zeros((6, 6)) - 1, np.zeros((6, 6)) - 1\n\n # Extract timing residuals\n timing_res_list = np.array([traj.timing_res for traj in mc_traj_list])\n\n # Make sure the timing residual is not 0\n timing_res_list[timing_res_list == 0] = 1e-10\n\n # Calculate the weights using timing residuals\n weights = np.min(timing_res_list)/timing_res_list\n weights = weights\n\n # Extract orbit elements\n e_list = np.array([traj.orbit.e for traj in mc_traj_list])\n q_list = np.array([traj.orbit.q for traj in mc_traj_list])\n tp_list = np.array([datetime2JD(traj.orbit.last_perihelion) for traj in mc_traj_list])\n node_list = np.degrees(normalizeAngleWrap(np.array([traj.orbit.node for traj in mc_traj_list])))\n peri_list = np.degrees(normalizeAngleWrap(np.array([traj.orbit.peri for traj in mc_traj_list])))\n i_list = np.degrees(normalizeAngleWrap(np.array([traj.orbit.i for traj in mc_traj_list])))\n \n\n # Calculate the orbital covariance (angles in degrees)\n orbit_input = np.c_[e_list, q_list, tp_list, node_list, peri_list, i_list].T\n orbit_cov = np.cov(orbit_input, aweights=weights)\n\n\n # Extract inital state vectors\n state_vect_list = np.array([traj.state_vect_mini for traj in mc_traj_list])\n initial_vel_vect_list = np.array([traj.v_init*traj.radiant_eci_mini for traj in mc_traj_list])\n\n # Calculate inital state vector covariance\n state_vect_input = np.hstack([state_vect_list, initial_vel_vect_list]).T\n state_vect_cov = np.cov(state_vect_input, aweights=weights)\n\n\n return orbit_cov, state_vect_cov","title":""}],"string":"[\n {\n \"docid\": \"6a93c0efd13f0123b7c5d3e5e7e2d430\",\n \"score\": \"0.70054585\",\n \"text\": \"def calcCovMatrices(mc_traj_list):\\n\\n # Filter out those trajectories for which the last perihelion time could not be estimated\\n mc_traj_list = [traj for traj in mc_traj_list if traj.orbit.last_perihelion is not None]\\n\\n # If there are no good orbits, do not estimate the covariance matrix\\n if not mc_traj_list:\\n return np.zeros((6, 6)) - 1, np.zeros((6, 6)) - 1\\n\\n # Extract timing residuals\\n timing_res_list = np.array([traj.timing_res for traj in mc_traj_list])\\n\\n # Make sure the timing residual is not 0\\n timing_res_list[timing_res_list == 0] = 1e-10\\n\\n # Calculate the weights using timing residuals\\n weights = np.min(timing_res_list)/timing_res_list\\n weights = weights\\n\\n # Extract orbit elements\\n e_list = np.array([traj.orbit.e for traj in mc_traj_list])\\n q_list = np.array([traj.orbit.q for traj in mc_traj_list])\\n tp_list = np.array([datetime2JD(traj.orbit.last_perihelion) for traj in mc_traj_list])\\n node_list = np.degrees(normalizeAngleWrap(np.array([traj.orbit.node for traj in mc_traj_list])))\\n peri_list = np.degrees(normalizeAngleWrap(np.array([traj.orbit.peri for traj in mc_traj_list])))\\n i_list = np.degrees(normalizeAngleWrap(np.array([traj.orbit.i for traj in mc_traj_list])))\\n \\n\\n # Calculate the orbital covariance (angles in degrees)\\n orbit_input = np.c_[e_list, q_list, tp_list, node_list, peri_list, i_list].T\\n orbit_cov = np.cov(orbit_input, aweights=weights)\\n\\n\\n # Extract inital state vectors\\n state_vect_list = np.array([traj.state_vect_mini for traj in mc_traj_list])\\n initial_vel_vect_list = np.array([traj.v_init*traj.radiant_eci_mini for traj in mc_traj_list])\\n\\n # Calculate inital state vector covariance\\n state_vect_input = np.hstack([state_vect_list, initial_vel_vect_list]).T\\n state_vect_cov = np.cov(state_vect_input, aweights=weights)\\n\\n\\n return orbit_cov, state_vect_cov\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"bc9b754996a3685fcb12d5c38d26cd34","score":"0.6350269","text":"def covariance(self):\n c,r = self.size(); float(c); float(r)\n mean_vector = self.mean()\n covariance_mat = mmatrix((c,r))\n for index_c in range(c):\n tally = float(0)\n for index2_c in range(c):\n for i in range(r):\n #print self[index_c, i], self[index2_c, i]\n tally += (self[index_c, i] - mean_vector[index_c])*(self[index2_c, i] - mean_vector[index_c])\n print (\"(%f - %f) * (%f - %f)\") % (self[index_c, i], mean_vector[index_c], self[index2_c, i], mean_vector[index_c])\n #print (\"(%d, %d) %d\") % (index_c, index2_c, tally)\n try:\n temp = (1 / (r-1)) #'r' here is really \"N\" or 'number of observations\n except (ZeroDivisionError):\n print(\"division by zero on this pairing.\")\n temp = 1\n #covariance_mat[index_c, index2_c] = float((tally/temp))","title":""},{"docid":"d091fb7c45e898408132a82c3c76f3e9","score":"0.6111639","text":"def _mn_cov_ ( self , size = -1 , root = False ) :\n #\n if size <= 0 : size = len ( self )\n size = min ( size , len ( self ) ) \n #\n from array import array\n matrix = array ( 'd' , [ 0 for i in range ( 0 , size * size ) ] )\n self.mnemat ( matrix , size )\n #\n import ostap.math.linalg\n from ostap.core.core import Ostap \n mtrx = Ostap.Math.SymMatrix ( size )() \n for i in range ( 0 , size ) :\n for j in range ( i , size ) : \n mtrx [ i , j ] = matrix [ i * size + j ]\n \n return mtrx","title":""},{"docid":"a29e4d0a5aeb19566111253242bc6eaf","score":"0.59871054","text":"def _mn_cov_(self, size=-1, root=False):\n #\n if size <= 0:\n size = len(self)\n size = min(size, len(self))\n #\n import array\n matrix = array.array('d', [0 for i in range(0, size * size)])\n self.mnemat(matrix, size)\n #\n if 1 == size and not root:\n mtrx = cpp.Gaudi.Math.SymMatrix1x1()\n elif 2 == size and not root:\n mtrx = cpp.Gaudi.Math.SymMatrix2x2()\n elif 3 == size and not root:\n mtrx = cpp.Gaudi.Math.SymMatrix3x3()\n elif 4 == size and not root:\n mtrx = cpp.Gaudi.Math.SymMatrix4x4()\n elif 5 == size and not root:\n mtrx = cpp.Gaudi.Math.SymMatrix5x5()\n elif 6 == size and not root:\n mtrx = cpp.Gaudi.Math.SymMatrix6x6()\n elif 7 == size and not root:\n mtrx = cpp.Gaudi.Math.SymMatrix7x7()\n # no 8x8!\n # elif 9 == size and not root : mtrx = cpp.Gaudi.Math.SymMatrix9x9 ()\n else:\n # use ROOT matrices\n mtrx = ROOT.TMatrix(size, size)\n for i in range(0, size):\n for j in range(0, size):\n mtrx[i][j] = matrix[i * size + j]\n return mtrx # RETURN\n\n for i in range(0, size):\n for j in range(i, size):\n mtrx[i, j] = matrix[i * size + j]\n\n return mtrx","title":""},{"docid":"3da2d978eb1a6874b90e68d66382e7af","score":"0.59445214","text":"def calcECIEqAltAz(self, state_vect, radiant_eci, observations):\n\n # Determine the first time of observation\n t0 = min([obs.time_data[0] for obs in observations])\n\n # Go through observations from all stations\n for obs in observations:\n\n # Init array for modelled ECI positons\n obs.model_eci = []\n\n # Init array for modelled RA, Dec positions\n obs.model_ra = np.zeros_like(obs.time_data)\n obs.model_dec = np.zeros_like(obs.time_data)\n\n # Init arrays for modelled alt, az\n obs.model_azim = np.zeros_like(obs.time_data)\n obs.model_elev = np.zeros_like(obs.time_data)\n\n\n # Go through all individual position measurement from each site\n for i, (t, jd, stat, meas) in enumerate(zip(obs.time_data, obs.JD_data, obs.stat_eci_los, \\\n obs.meas_eci_los)):\n\n # Calculate closest points of approach (observed line of sight to radiant line)\n obs_cpa, rad_cpa, d = findClosestPoints(stat, meas, state_vect, radiant_eci)\n\n\n # # Calculate the time in seconds from the beginning of the meteor\n # t_rel = t - t0\n\n # # Calculate the gravitational acceleration at the given height\n # g = G*EARTH.MASS/(vectMag(rad_cpa)**2)\n\n # # Determine the sign of the initial time\n # time_sign = np.sign(t_rel)\n\n # # Calculate the amount of gravity drop from a straight trajectory (handle the case when the\n # # time is negative)\n # drop = time_sign*(1/2.0)*g*t_rel**2\n\n # # Apply gravity drop to ECI coordinates\n # rad_cpa -= drop*vectNorm(rad_cpa)\n\n\n # Set the ECI position of the CPA on the radiant line, as seen by this observer\n obs.model_eci.append(rad_cpa)\n\n # Calculate the right ascension and declination of the modelled point from the observer's \n # point of view\n stat_rad_eci = rad_cpa - stat\n model_ra, model_dec = eci2RaDec(stat_rad_eci)\n\n obs.model_ra[i] = model_ra\n obs.model_dec[i] = model_dec\n\n # Calculate the azimuth and elevation of the modelled point from the observer's point of view\n model_azim, model_elev = raDec2AltAz(model_ra, model_dec, obs.JD_data[i], obs.lat, obs.lon)\n\n obs.model_azim[i] = model_azim\n obs.model_elev[i] = model_elev\n\n\n obs.model_eci = np.array(obs.model_eci)\n\n\n ### Assign model_fit1, model_fit2, so they are in the same format as the input meas1, meas2 data\n ######################################################################################################\n\n # If inputs were RA and Dec\n if self.meastype == 1:\n\n obs.model_fit1 = obs.model_ra\n obs.model_fit2 = obs.model_dec\n\n # If inputs were azimuth +east of due north, and elevation angle\n elif self.meastype == 2:\n\n obs.model_fit1 = obs.model_azim\n obs.model_fit2 = obs.model_elev\n\n # If inputs were azimuth +west of due south, and zenith angle\n elif self.meastype == 3:\n\n obs.model_fit1 = (obs.model_azim + np.pi)%(2*np.pi)\n obs.model_fit2 = np.pi/2.0 - obs.model_elev\n\n # If input were azimuth +north of due east, and zenith angle\n elif self.meastype == 4:\n\n obs.model_fit1 = (np.pi/2.0 - obs.model_azim)%(2*np.pi)\n obs.model_fit2 = np.pi/2.0 - obs.model_elev\n\n ######################################################################################################","title":""},{"docid":"5cbba21331a7ee03a5427f6d90be8685","score":"0.5922897","text":"def cov(self):\r\n if 'correlation' not in self._paras.keys():\r\n print('correlation model has not been fitted yet!')\r\n return\r\n T = self._corrs.shape[2]\r\n d = self._corrs.shape[0]\r\n cov = np.empty([d, d, T])\r\n for i in range(T):\r\n corr = self._corrs[:, :, i]\r\n std = np.diag(self._stds[i, :]) # diagonal matrix of sigma\r\n cov[:, :, i] = std.dot(corr).dot(std)\r\n return cov","title":""},{"docid":"d91dea75ef10db2aea4b1f0c6e962730","score":"0.5781341","text":"def cokurtosis(self):\n r = self._X.values \n mu = np.mean(r, axis=0).reshape(1, -1)\n x = r - mu\n n = x.shape[1] # number of assets\n m = x.shape[0]\n M4 = []\n for i in range(n):\n for j in range(n):\n K = np.empty([n, n]) # k block inside Kronecker product\n for k in range(n):\n for l in range(n):\n # E[(r_i - mu_i)*(r_j - mu_j)*(r_k - mu_k)*(r_l - mu_l)]\n K[k, l] = np.sum(x[:, i]*x[:, j]*x[:, k]*x[:, l])/(m-1)\n M4.append(K)\n M4 = np.concatenate(M4, axis=1)\n return M4","title":""},{"docid":"550787b7b14ff37216ff3eae40c7dfb7","score":"0.57466966","text":"def calculate_cov(self):\n self.Q = np.matmul(self.G, self.q)\n self.Q = np.matmul(self.Q, self.G.transpose())\n \n self.P = np.matmul(self.F, self.P)\n self.P = np.matmul(self.P, self.F.transpose()) + self.Q","title":""},{"docid":"e77279bde8c4bd4b133efe70e54da35c","score":"0.57117057","text":"def covariance_matrix(sub,full,N_sub_vol):\n Nr = full.shape[0] # Nr is the number of radial bins\n cov = np.zeros((Nr,Nr)) # 2D array that keeps the covariance matrix \n after_subtraction = sub - np.mean(sub,axis=0)\n tmp = 0\n for i in range(Nr):\n for j in range(Nr):\n tmp = 0.0\n for k in range(N_sub_vol):\n tmp = tmp + after_subtraction[k,i]*after_subtraction[k,j]\n cov[i,j] = (((N_sub_vol-1)/N_sub_vol)*tmp)\n \n return cov","title":""},{"docid":"cfe485259c2269281d1492f312ce71c1","score":"0.5705652","text":"def __init__(self, init_x, init_y, Q=0.1 * np.eye(4), R=0.1 * np.eye(2)):\n self.state = np.array([init_x, init_y, 0., 0.]) # state\n self.cov = 0.1 * np.eye(4)\n self.dt = np.eye(4)\n self.dt[0,2] = 1\n self.dt[1,3] = 1\n self.sigma_dt = Q\n self.mt = np.zeros((2,4))\n self.mt[0,0] = 1\n self.mt[1,1] = 1 \n self.sigma_mt = R","title":""},{"docid":"a05aa115ab7fbe987ee451cea1584a1c","score":"0.5686504","text":"def __simulate__(self,initialstate,reortho=False,verbose=False):\n dtype=np.result_type(self._matvec(initialstate))\n Dim=1\n for d in initialstate.shape:\n Dim*=d\n #initialization:\n xn=copy.deepcopy(initialstate)\n xn/=np.sqrt(self._dot(xn.conjugate(),xn))\n\n xn_minus_1=self._zeros(initialstate.shape,dtype=dtype)\n converged=False\n it=0\n kn=[]\n epsn=[]\n self._vecs=[]\n first=True\n while converged==False:\n #normalize the current vector:\n knval=np.sqrt(self._dot(xn.conjugate(),xn))\n\n if knval<self._delta:\n converged=True\n break\n kn.append(knval)\n xn=xn/kn[-1]\n #store the Lanczos vector for later\n\n if reortho==True:\n for v in self._vecs:\n xn-=self._dot(v.conjugate(),xn)*v\n self._vecs.append(xn) \n Hxn=self._matvec(xn) \n epsn.append(self._dot(xn.conjugate(),Hxn))\n if ((it%self._Ndiag)==0)&(len(epsn)>=self._numeig):\n #diagonalize the effective Hamiltonian\n Heff=np.diag(epsn)+np.diag(kn[1:],1)+np.diag(np.conj(kn[1:]),-1)\n eta,u=np.linalg.eigh(Heff)\n if first==False:\n if np.linalg.norm(eta[0:self._numeig]-etaold[0:self._numeig])<self._deltaEta:\n converged=True\n first=False\n etaold=eta[0:self._numeig]\n if it>0:\n Hxn-=(self._vecs[-1]*epsn[-1])\n Hxn-=(self._vecs[-2]*kn[-1])\n else:\n Hxn-=(self._vecs[-1]*epsn[-1])\n xn=Hxn\n it=it+1\n if it>self._ncv:\n break\n\n self._Heff=np.diag(epsn)+np.diag(kn[1:],1)+np.diag(np.conj(kn[1:]),-1)\n eta,u=np.linalg.eigh(self._Heff)\n states=[]\n for n2 in range(min(self._numeig,len(eta))):\n state=np.zeros(initialstate.shape,dtype=initialstate.dtype)\n for n1 in range(len(self._vecs)):\n state+=self._vecs[n1]*u[n1,n2]\n states.append(state/np.sqrt(self._dot(state.conjugate(),state)))\n return eta[0:min(self._numeig,len(eta))],states,converged","title":""},{"docid":"3c1483e760ed81e2d2aeb2bc7235fc75","score":"0.5680264","text":"def vector_field(self, state):\r\n A = state[: (p := len(self.config.network.populations))]\r\n R = state[p : 2*p]\r\n S = 1 - A - R\r\n CAA = _internals._unflat_scalar_triangle(\r\n state[2*p : 2*p + round(p*(p+1)/2)])\r\n CRR = _internals._unflat_scalar_triangle(\r\n state[2*p + round(p*(p+1)/2) : 2*p + p*(p+1)])\r\n CAR = (state[2*p + p*(p+1) :]).reshape((p,p))\r\n CAS = - CAA - CAR\r\n CRS = - CRR - CAR.transpose()\r\n B = self.config.Q.copy()\r\n VarB = np.zeros(p)\r\n CAB = np.zeros((p,p))\r\n CRB = np.zeros((p,p))\r\n CSB = np.zeros((p,p))\r\n for J, K in np.ndindex((p,p)):\r\n B[J] += self.config.network.c[J,K] * A[K]\r\n for L in range(p):\r\n VarB[J] += (self.config.network.c[J,K] \r\n * self.config.network.c[J,L] * CAA[K,L])\r\n CAB[J,K] += self.config.network.c[K,L] * CAA[J,L]\r\n CRB[J,K] += self.config.network.c[K,L] * CAR[L,J]\r\n CSB[J,K] += self.config.network.c[K,L] * CAS[L,J]\r\n f = np.zeros(self.dim)\r\n dCAA = np.zeros((p,p))\r\n dCRR = np.zeros((p,p))\r\n dCAR = np.zeros((p,p))\r\n for J, popJ in enumerate(self.config.network.populations):\r\n SG = np.where(S[J] == 0, 0,\r\n S[J] * popJ.G(B[J] + CSB[J,J]/S[J], VarB[J]))\r\n f[J] = (- popJ.beta * A[J] + popJ.alpha * SG)\r\n f[J+p] = - popJ.gamma * R[J] + popJ.beta * A[J]\r\n for K, popK in enumerate(self.config.network.populations):\r\n if J <= K:\r\n dCAA[J,K] = (- (popJ.beta + popK.beta) * CAA[J,K]\r\n + popJ.alpha * popJ.H(A[K], S[J], B[J],\r\n CAS[K,J], CAB[K,J], CSB[J,J], VarB[J])\r\n + popK.alpha * popK.H(A[J], S[K], B[K],\r\n CAS[J,K], CAB[J,K], CSB[K,K], VarB[K]))\r\n dCRR[J,K] = (- (popJ.gamma + popK.gamma) * CRR[J,K]\r\n + popJ.beta * CAR[J,K] + popK.beta * CAR[K,J])\r\n dCAR[J,K] = (- (popJ.beta + popK.gamma) * CAR[J,K]\r\n + popK.beta * CAA[J,K]\r\n + popJ.alpha * popJ.H(R[K], S[J], B[J],\r\n CRS[K,J], CRB[K,J], CSB[J,J], VarB[J]))\r\n f[2*p : 2*p + round(p*(p+1)/2)] = dCAA[np.triu_indices(p)]\r\n f[2*p + round(p*(p+1)/2) : 2*p + p*(p+1)] = dCRR[np.triu_indices(p)]\r\n f[2*p + p*(p+1) :] = dCAR.flatten()\r\n return np.array(f, float)","title":""},{"docid":"e604194857aaf26f204c3f135fd4488a","score":"0.56795895","text":"def covariance_matrix(self, start_date=None, end_date=None):\n\n indiv_ret = self.daily_individual_rates_of_return(start_date, end_date)\n cov_matrix = indiv_ret.cov()\n\n return cov_matrix","title":""},{"docid":"177706cfddb4d4881a51b0e32f1afb00","score":"0.5662406","text":"def calcCovarianceMatrix(self,phase_volume):\n input_array = []\n for particle in phase_volume:\n row = []\n cylindrical_coordinates = particle.x.convertToCylindrical()\n cylindrical_momentum = particle.p.convertToCylindrical(particle.x)\n row.append(cylindrical_coordinates.rho)\n row.append(cylindrical_coordinates.rho*cylindrical_coordinates.phi)\n row.append(cylindrical_coordinates.z)\n row.append(cylindrical_momentum.rho)\n row.append(cylindrical_momentum.phi)\n row.append(cylindrical_momentum.z)\n row.append(particle.getEnergy())\n input_array.append(row)\n input_array = np.array(input_array).T #Transpose\n return np.cov(input_array)","title":""},{"docid":"55dec521fc23d9d1215e9dce26c64bca","score":"0.56399816","text":"def a_cov(self):\n a_cov = np.linalg.inv(splinal.sqrtm(np.eye(self.size_ens) + np.dot(self.hmx_mat,\n np.dot(np.linalg.inv(self.rmatrix), self.hmx_mat.T))))\n return a_cov","title":""},{"docid":"4e19c0d0bc6a98a469409f23386bc3d6","score":"0.560998","text":"def test_sumcovariancenormsmetric_compute_sum_covariancenorms():\n generator = SumofCovarianceNormsMetric()\n\n time = datetime.datetime.now()\n track = Track(states=[GaussianState(state_vector=[[1], [2], [1], [2]],\n timestamp=time,\n covar=np.diag([i, i, i, i]))\n for i in range(5)])\n\n metric = generator.compute_sum_covariancenorms(track.states)\n\n assert metric.title == \"Covariance Matrix Norm Sum\"\n assert metric.value == 20\n assert metric.timestamp == time\n assert metric.generator == generator\n with pytest.raises(ValueError,\n match=\"All states must be from the same time to compute total uncertainty\"):\n generator.compute_sum_covariancenorms([\n GaussianState(state_vector=[[1], [2], [1], [2]],\n timestamp=time,\n covar=np.diag([0, 0, 0, 0])),\n GaussianState(state_vector=[[1], [2], [1], [2]],\n timestamp=time+datetime.timedelta(seconds=1),\n covar=np.diag([0, 0, 0, 0]))])","title":""},{"docid":"e3f9edf7a8042630d60904c6660604c7","score":"0.5602733","text":"def calcVelocity(self, state_vect, radiant_eci, observations, weights, calc_res=False):\n\n\n # Go through observations from all stations\n for obs in observations:\n\n # List of distances from the first trajectory point on the radiant line\n first_pt_distances = []\n\n # List of distances from the state vector\n state_vect_dist = []\n\n # Go through all individual position measurement from each site\n for i, (stat, meas) in enumerate(zip(obs.stat_eci_los, obs.meas_eci_los)):\n\n # Calculate closest points of approach (observed line of sight to radiant line)\n obs_cpa, rad_cpa, d = findClosestPoints(stat, meas, state_vect, radiant_eci)\n\n # Take the position of the first point as the reference point\n if i == 0:\n ref_point = np.copy(rad_cpa)\n\n # Calculate the distance from the first observed point to the projected point on the radiant line\n dist = vectMag(ref_point - rad_cpa)\n \n first_pt_distances.append(dist)\n\n # Distance from the state vector to the projected point on the radiant line\n state_vect_dist.append(vectMag(state_vect - rad_cpa))\n\n\n # Convert the distances (length along the trail) into a numpy array\n obs.length = np.array(first_pt_distances)\n obs.state_vect_dist = np.array(state_vect_dist)\n\n\n ### Calculate average velocity including all points up to the given point ###\n velocities_prev_point = []\n for i, (t, l) in enumerate(zip(obs.time_data, obs.length)):\n\n # For the first 4 points compute the velocity using the first 4 points\n if i < 4:\n time_part = obs.time_data[:4]\n len_part = obs.length[:4]\n\n # Otherwise include all points up to the current point\n else:\n time_part = obs.time_data[: i+1]\n len_part = obs.length[: i+1]\n\n # If there are NaNs or infs, drop them\n filter_mask = np.logical_not(np.isnan(time_part) | np.isinf(time_part) | np.isnan(len_part) \\\n | np.isinf(len_part))\n\n time_part = time_part[filter_mask]\n len_part = len_part[filter_mask]\n\n if len(time_part) > 1:\n\n # Fit a line through time vs. length data\n try:\n popt, _ = scipy.optimize.curve_fit(lineFunc, time_part, len_part)\n\n # Check for this fit error, which happens extrememly rarely:\n # RuntimeError: Optimal parameters not found: gtol=0.000000 is too small, func(x) is \n # orthogonal to the columns of the Jacobian to machine precision.\n except RuntimeError:\n print(\"A velocity fit failed with a RuntimeError, skipping this iteration.\")\n popt = [np.nan]\n\n velocities_prev_point.append(popt[0])\n\n else:\n\n # If there are no good points to estimate the velocity on, use NaN\n velocities_prev_point.append(np.nan)\n\n\n\n obs.velocities_prev_point = np.array(velocities_prev_point)\n\n\n ### ###\n\n ### Length vs. time\n\n # plt.plot(obs.state_vect_dist, obs.time_data, marker='x', label=str(obs.station_id), zorder=3)\n\n ##########\n\n ### Calculate point to point velocities ###\n\n # Shift the radiant distances one element down (for difference calculation)\n dists_shifted = np.r_[0, obs.length][:-1]\n\n # Calculate distance differences from point to point (first is always 0)\n dists_diffs = obs.length - dists_shifted\n\n # Shift the time one element down (for difference calculation)\n time_shifted = np.r_[0, obs.time_data][:-1]\n\n # Calculate the time differences from point to point\n time_diffs = obs.time_data - time_shifted\n\n # Replace zeros in time by machine precision value to avoid division by zero errors\n time_diffs[time_diffs == 0] = np.finfo(np.float64).eps\n\n # Calculate velocity for every point\n obs.velocities = dists_diffs/time_diffs\n\n ### ###\n\n\n # plt.ylabel('Time (s)')\n # plt.xlabel('Distance from state vector (m)')\n\n # plt.gca().invert_yaxis()\n\n # plt.legend()\n # plt.grid()\n # plt.savefig('mc_time_offsets.' + self.plot_file_type, dpi=300)\n # plt.show()\n\n\n\n if calc_res:\n\n # Because the timing offsets have already been applied, the timing offsets are 0\n zero_timing_res = np.zeros(len(self.observations))\n\n # Calculate the timing offset between the meteor time vs. length\n \n if self.timing_res is None:\n self.timing_res = timingResiduals(zero_timing_res, self.observations, self.stations_time_dict, \n weights=weights)\n\n self.timing_stddev = timingResiduals(zero_timing_res, self.observations, self.stations_time_dict, \n weights=weights, ret_stddev=True)","title":""},{"docid":"f35a88516013a2246ef02adbdbd6bf1a","score":"0.5575701","text":"def covariance(psd, omega_vec):\n d_omega_covariance = np.array([psd[:, :, i] + psd[:, :, i].T for i in range(psd.shape[-1])])\n print(d_omega_covariance.shape)\n covariance = np.zeros((psd.shape[0], psd.shape[0]))\n for i in range(psd.shape[0]):\n for j in range(psd.shape[0]):\n covariance[i, j] = 0.5 * np.trapz(d_omega_covariance[:, i, j], omega_vec)\n rms = np.sqrt(np.diag(covariance))\n print(rms)\n\n return covariance, rms","title":""},{"docid":"b7466566ca9fca84c28e27d98ca30f7c","score":"0.55721","text":"def CalcCovarMatrix(vects, cov_mat, avg, flags): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"91e48aa9f98238873776fb64f707a6fe","score":"0.55661136","text":"def create_matrix_drift_diffusion_varying_cs(self):\n\t\t# initializing the matrix of coefficients\n\t\tself.M = np.zeros((self.nL, self.nL))\n\t\tM1 = np.zeros((self.nL, self.nL))\n\t\tM2 = np.zeros((self.nL, self.nL))\n\t\tM3 = np.zeros((self.nL, self.nL))\n\t\tM4 = np.zeros((self.nL, self.nL))\n\t\tM5 = np.zeros((self.nL, self.nL))\n\n\t\t# matrix 1 (dD/dx * dY/dx)\n\t\tM1 = np.diag([(self.De_vector[i]-self.De_vector[i-2])/(4*self.dL**2) if i != 1 else (self.De_vector[1]-self.De_vector[0])/(4*self.dL**2) for i in range(1,self.nL)], k=1)\n\t\tM1 += np.diag([-(self.De_vector[i+1]-self.De_vector[i-1])/(4*self.dL**2) if i != self.nL-1 else -(self.De_vector[self.nL-1]-self.De_vector[self.nL-2])/(4*self.dL**2) for i in range(1,self.nL)], k=-1)\n\t\tM1[0,0] = -(self.De_vector[1]-self.De_vector[0])/(4*self.dL**2)\n\t\tM1[-1,-1] = (self.De_vector[self.nL-1]-self.De_vector[self.nL-2])/(4*self.dL**2)\n\t\t# matrix 2 (D * d2Y/dx2)\n\t\tM2 = np.diag([-2*self.De_vector[i-1]/(self.dL**2) for i in range(1,self.nL+1)])\n\t\tM2 += np.diag([self.De_vector[i-1]/(self.dL**2) for i in range(1,self.nL)], k=1)\n\t\tM2 += np.diag([self.De_vector[i]/(self.dL**2) for i in range(1,self.nL)], k=-1)\n\t\tM2[0,0] = -self.De_vector[0]/(self.dL**2)\n\t\tM2[-1,-1] = -self.De_vector[self.nL-1]/(self.dL**2)\n\t\t# matrix 3 (du/dx * E * Y)\n\t\tM3 = np.diag([(self.mu_vector[i] - self.mu_vector[i-2])/(2*self.dL)*self.E_vector[i-1] if i not in [1,self.nL] else 0 for i in range(1,self.nL+1)])\n\t\tM3[0,0] = (self.mu_vector[1] - self.mu_vector[0])/(2*self.dL)*self.E_vector[0]\n\t\tM3[-1, -1] = (self.mu_vector[self.nL-1] - self.mu_vector[self.nL-2])/(2*self.dL)*self.E_vector[self.nL-1]\n\t\t# matrix 4 (u * dE/dx * Y)\n\t\tM4 = np.diag([(self.E_vector[i] - self.E_vector[i-2])/(2*self.dL)*self.mu_vector[i-1] if i not in [1,self.nL] else 0 for i in range(1,self.nL+1)])\n\t\tM4[0,0] = (self.E_vector[1] - self.E_vector[0])/(2*self.dL)*self.mu_vector[0]\n\t\tM4[-1, -1] = (self.E_vector[self.nL-1] - self.E_vector[self.nL-2])/(2*self.dL)*self.mu_vector[self.nL-1]\n\t\t# matrix 5 (u * E* dY/dx)\n\t\tM5 = np.diag([self.mu_vector[i-1]*self.E_vector[i-1]/(2*self.dL) for i in range(1,self.nL)], k=1)\n\t\tM5 += np.diag([-self.mu_vector[i-1]*self.E_vector[i-1]/(2*self.dL) for i in range(1,self.nL)], k=-1)\n\t\tM5[0,0] = self.mu_vector[0]*self.E_vector[0]/(2*self.dL)\n\t\tM5[-1,-1] = -self.mu_vector[self.nL-1]*self.E_vector[self.nL-1]/(2*self.dL)\n\t\t# total matrix\n\t\tself.M = M1 + M2 + M3 + M4 + M5\n\n\t\t# # debugging lines\n\t\t# print(M4)\n\t\t# quit()\n\t\t# # end debugging lines","title":""},{"docid":"de3db26dd7b14232762f504c92cbf89f","score":"0.55653065","text":"def CorrectionCenterOfVelocity(self):\n self.vx= self.vx-sum(self.m*self.vx)/self.Mt()\n self.vy= self.vy-sum(self.m*self.vy)/self.Mt()\n self.vz= self.vz-sum(self.m*self.vz)/self.Mt()\n self.UptoDate=False","title":""},{"docid":"7cf8979a81bbd742a8d0adc5071549fe","score":"0.5562396","text":"def cov(\n self,\n t,\n i=defaults[\"i\"],\n p=defaults[\"p\"],\n u=defaults[\"u\"][: defaults[\"udeg\"]],\n ):\n cov = self._flux.cov(t, i, p, u)\n if self._time_variable:\n cov *= self._temporal_kernel(t, t, self._tau)\n if self._normalized:\n mean = self._flux.mean(t, i, p, u)[0]\n return self._normalize(1.0 + mean, cov)\n else:\n return cov","title":""},{"docid":"5d0a537a64649a0f98908d198dc90ff5","score":"0.5559678","text":"def _mn_cor_ ( self , size = -1 , root = False ) :\n #\n cov = self.cov ( size , root )\n #\n from math import sqrt\n #\n if isinstance ( cov , ROOT.TMatrix ) :\n\n size = cov.GetNrows()\n root = True\n \n else : size = cov.kRows\n\n ## use ROOT matrices \n if root : cor = ROOT.TMatrix ( size , size )\n else : cor = cov.__class__ () \n\n for i in range ( 0 , size ) :\n \n d_i = cov ( i , i )\n cor [ i , i ] = 1 if 0 < d_i else 0\n \n for j in range ( i + 1 , size ) :\n \n d_j = cov ( j , j )\n \n if 0 != cov ( i , j ) and 0 < d_i and 0 < d_j :\n \n if root and root_major < 6 : cor [ i ] [ j ] = cov ( i , j ) / sqrt ( d_i * d_j )\n else : cor [ i , j ] = cov ( i , j ) / sqrt ( d_i * d_j )\n \n else :\n \n if root and root_major < 6 : cor [ i ] [ j ] = 0 \n else : cor [ i , j ] = 0\n\n return cor","title":""},{"docid":"6d41d7d45fe5255f8e844f49fe5cb3ee","score":"0.5556089","text":"def get_C(self,Xtest):\n\n test_cov = self.model.kern.K(Xtest,Xtest)\n sigmasqr = self.model.Gaussian_noise.variance[0]\n K_NN_diags = self.model.kern.Kdiag(self.model.X)\n K_NN = self.model.kern.K(self.model.X)\n \n K_star = self.model.kern.K(Xtest,self.model.Z.values)\n print self.model.Z.values\n K_NM = self.model.kern.K(self.model.X,self.model.Z.values)\n K_MM = self.model.kern.K(self.model.Z.values)\n invK_MM = np.linalg.inv(K_MM)\n \n #lambda values are the diagonal of the training input covariances minus \n #(cov of training+pseudo).(inv cov of pseudo).(transpose of cov of training+pseudo)\n lamb = np.zeros(len(self.model.X))\n for i,t_in in enumerate(self.model.X):\n lamb[i] = K_NN_diags[i] - np.dot(np.dot(K_NM[i,:].T,invK_MM),K_NM[i,:])\n\n #this finds (\\Lambda + \\sigma^2 I)^{-1}\n diag = 1.0/(lamb + sigmasqr) #diagonal values\n\n Q = K_MM + np.dot(K_NM.T * diag,K_NM)\n C = np.dot(np.dot(K_star, np.linalg.inv(Q)),K_NM.T) * diag\n return C","title":""},{"docid":"702e1d2719a7c9600efd100df7a9da71","score":"0.55528426","text":"def motion_jacobian_state_vector(self):\n # States\n theta = self.state_vector[2]\n \n # Velocities\n v = self.velocity_vector[0]\n w = self.velocity_vector[1]\n \n # Expected value of error\n dv = 0\n dw = 0\n \n # Identity matrix\n self.F = np.identity((self.state_vector_size))\n \n # Calculate Jacobian\n # Non linear motion model when there's no angular velocity\n if abs(w + dw) > 1e-4: \n d = (v + dv) / (w + dw)\n self.F[0][2] = d * (-np.cos(theta) + np.cos(theta + (w + dw) * self.dt))\n self.F[1][2] = d * (-np.sin(theta) + np.sin(theta + (w + dw) * self.dt))\n\n # Linear motion model\n else: \n self.F[0][2] = -np.sin(theta) * (v + dv) * self.dt\n self.F[1][2] = np.cos(theta) * (v + dv) * self.dt","title":""},{"docid":"b185985bfdeb6036d413f44c85920b87","score":"0.5540962","text":"def cov(self):\n\t\tif self.v is not None and self.r is not None:\n\t\t\tassert self.v.dim() == 1\n\t\t\tdim = self.v.dim()\n\t\t\tv = self.v.unsqueeze(1) # D * 1 vector\n\t\t\trt = self.r.unsqueeze(0) # 1 * D vector\n\t\t\tA = torch.eye(dim) + v.mm(rt)\n\t\t\treturn A.mm(torch.diag(self.sigma.pow(2)).mm(A.t()))\n\t\telse:\n\t\t\treturn torch.diag(self.sigma.pow(2))","title":""},{"docid":"3b60c18dee3ba101f1f9287b53effe07","score":"0.5536518","text":"def covariances(self):\n # n_gauss x 2 x 2\n # this has no derivatives, since the radii are fixed.\n return (self.radii**2)[:, None, None] * np.eye(2)","title":""},{"docid":"3b60c18dee3ba101f1f9287b53effe07","score":"0.5536518","text":"def covariances(self):\n # n_gauss x 2 x 2\n # this has no derivatives, since the radii are fixed.\n return (self.radii**2)[:, None, None] * np.eye(2)","title":""},{"docid":"12309147b8d5bc944697b6a6667e9279","score":"0.55254924","text":"def covariance(time,\n body_id,\n fpa_size = 4096,\n fov = 30.0 * np.pi/180.0,\n theta_max = 70 * np.pi/180.0,\n sigma_pix = 0.063,\n n_max = 1000,\n spacecraft_id = -5440,\n statistics = False):\n r_p = spice.bodvcd(body_id, 'RADII', 3)[1][2] * 1000.0\n d_x = 1 / (2.0 * np.tan(fov / 2))\n r_sc_wrt_body = spice.spkezp(spacecraft_id, time, 'J2000', 'NONE', body_id)[0] * 1000.0\n rho = norm(r_sc_wrt_body)\n r_pix = r_p * fpa_size / (2 * rho * np.tan(fov / 2))\n\n # FIXME: find theta_max instead of specifying it\n \n # Don't allow n to exceed our processing capabilities, if applicable\n n = 2 * theta_max * r_pix\n if n > n_max:\n n = n_max\n\n D = (theta_max / 4) * (2 * theta_max + np.sin(2*theta_max)) - np.sin(theta_max)**2\n\n T_p_to_c = compute_T_pa_to_cam(time, body_id, spacecraft_id)\n\n corner = np.sqrt(rho**2 - r_p**2) * np.sin(theta_max) / (D * r_p)\n M = np.array([[theta_max / D, 0.0, corner],\n [0.0, 4 / (2 * theta_max - np.sin(2*theta_max)), 0.0],\n [corner, 0.0, (rho**2 - r_p**2) * (2 * theta_max + np.sin(2*theta_max)) / (4 * D * r_p**2)]])\n scalar = sigma_pix**2 * rho**4 * theta_max / (n * d_x**2 * (rho**2 - r_p**2))\n P = T_p_to_c.dot(M).dot(T_p_to_c.T) * scalar\n\n if statistics:\n r_sun_wrt_body = spice.spkezp(body_id, time, 'J2000', 'LT', 10)[0]\n e_sun = r_sun_wrt_body / norm(r_sun_wrt_body)\n e_cam = r_sc_wrt_body / norm(r_sc_wrt_body)\n \n data = {\n 'rho': rho,\n 'sun_angle': np.arccos(np.dot(e_sun, e_cam)),\n 'r_pix': r_pix,\n 'n': n\n }\n\n return P, data\n\n return P","title":""},{"docid":"39e12ca0c4f96276d92050d198e7cbfb","score":"0.5524313","text":"def find_covariance(self):\n\n n = len(self.combinations)\n self.cov = np.zeros((n,n),dtype=np.float64)\n\n for i in range(n):\n vec1 = self.find_vec(self.combinations[i])\n for j in range(n):\n vec2 = self.find_vec(self.combinations[j])\n for k in range(len(self.modes)):\n self.cov[i][j] += vec1[k]*vec2[k]*self.modes[k].dfreq*self.modes[k].dfreq\n\n self.invcov = np.linalg.inv(self.cov)","title":""},{"docid":"6417229006075d715c3cc6ca449097a3","score":"0.5516294","text":"def generate_covariance_matrix(self) -> np.ndarray:\r\n n_vars = self.price_vector.shape[0]\r\n samples = np.random.uniform(low=0, high=config_dict['Exchange']['max_variance_factor'], size=(n_vars,10))\r\n return np.cov(samples, bias=True)","title":""},{"docid":"f03219a0e0f80a65475c1121c1cd36b5","score":"0.5502516","text":"def _cov_setup(self):\n if self._P is not None: # then already computed so return\n return\n # get the weights\n self._w = self.kern.w\n\n # get the p x p matrix A if ness\n if self._A is None: # then compute, note this is expensive\n self._Phi = self.kern.cov(self.X)[0]\n self._A = self._Phi.T.dot(self._Phi) # O(np^2) operation!\n\n # compute the P matrix and factorize\n self._P = self._A + np.diag(self.noise_var/self._w)\n self._Pchol = cho_factor(self._P)","title":""},{"docid":"53216a39e966381088958369fada741d","score":"0.549624","text":"def _mn_cor_(self, size=-1, root=False):\n #\n cov = self.cov(size, root)\n #\n from math import sqrt\n #\n if isinstance(cov, ROOT.TMatrix):\n\n size = cov.GetNrows()\n root = True\n\n else:\n size = cov.kRows\n\n # use ROOT matrices\n if root:\n cor = ROOT.TMatrix(size, size)\n else:\n cor = cov.__class__()\n\n for i in range(0, size):\n\n d_i = cov(i, i)\n cor[i, i] = 1 if 0 < d_i else 0\n\n for j in range(i + 1, size):\n\n d_j = cov(j, j)\n\n if 0 != cov(i, j) and 0 < d_i and 0 < d_j:\n\n if root:\n cor[i][j] = cov(i, j) / sqrt(d_i * d_j)\n else:\n cor[i, j] = cov(i, j) / sqrt(d_i * d_j)\n\n else:\n\n if _root:\n cor[i][j] = 0\n else:\n cor[i, j] = 0\n\n return cor","title":""},{"docid":"f6e7249a8336fa90b17e8860733e18fc","score":"0.54872376","text":"def mb_covariance(self, mb_params):\r\n dim = len(mb_params)\r\n grad_outer= np.zeros((dim, dim))\r\n hess = np.zeros((dim, dim))\r\n\r\n for t in range(self.T):\r\n data_block, raw_data_block = self.X[t], self.X_raw[t]\r\n a, y = raw_data_block[:, 1], raw_data_block[:, 2]\r\n y_next = self.y[t]\r\n for l in range(self.L):\r\n x_raw, x = raw_data_block[l, :], data_block[l, :]\r\n\r\n # gradient\r\n if raw_data_block[l, 2]:\r\n # No recovery\r\n pass\r\n else:\r\n def mb_log_lik_at_x(mb_params_):\r\n lik = mb_log_lik_single(a, y, y_next, l, self.L, mb_params_, self.ADJACENCY_MATRIX, self.DISTANCE_MATRIX,\r\n self.PRODUCT_MATRIX)\r\n return lik\r\n\r\n mb_grad = gradient.central_diff_grad(mb_log_lik_at_x, mb_params)\r\n mb_hess = gradient.central_diff_hess(mb_log_lik_at_x, mb_params)\r\n\r\n grad_outer_lt = np.outer(mb_grad, mb_grad)\r\n grad_outer += grad_outer_lt\r\n hess += mb_hess\r\n\r\n hess_inv = np.linalg.inv(hess + 0.1 * np.eye(dim))\r\n cov = np.dot(hess_inv, np.dot(grad_outer, hess_inv)) / float(self.L * self.T)\r\n return cov","title":""},{"docid":"7ecec6225c7448e3cf58d7ffff5624e6","score":"0.54739594","text":"def vector_field(self, state):\r\n A = state[: (p := len(self.config.network.populations))]\r\n R = state[p : 2*p]\r\n S = 1 - A - R\r\n CAA = _internals._unflat_scalar_triangle(\r\n state[2*p : 2*p + round(p*(p+1)/2)])\r\n CRR = _internals._unflat_scalar_triangle(\r\n state[2*p + round(p*(p+1)/2) : 2*p+p*(p+1)])\r\n CAR = (state[2*p + p*(p+1) :]).reshape((p,p))\r\n CAS = - CAA - CAR\r\n CSR = - CRR - CAR\r\n B = self.config.Q.copy()\r\n VarB = np.zeros(p)\r\n CAB = np.zeros((p,p))\r\n CRB = np.zeros((p,p))\r\n for J, K in np.ndindex((p,p)):\r\n B[J] += self.config.network.c[J,K] * A[K]\r\n for L in range(p):\r\n VarB[J] += (self.config.network.c[J,K] \r\n * self.config.network.c[J,L] * CAA[K,L])\r\n CAB[J,K] += self.config.network.c[K,L] * CAA[J,L]\r\n CRB[J,K] += self.config.network.c[K,L] * CAR[L,J]\r\n f = np.zeros(self.dim)\r\n dCAA = np.zeros((p,p))\r\n dCRR = np.zeros((p,p))\r\n dCAR = np.zeros((p,p))\r\n for J, popJ in enumerate(self.config.network.populations):\r\n f[J] = (- popJ.beta * A[J] + popJ.alpha*popJ.F(B[J]) * S[J]\r\n - popJ.alpha*popJ.dF(B[J]) * (CAB[J,J] + CRB[J,J])\r\n + popJ.alpha/2*popJ.ddF(B[J]) * S[J] * VarB[J])\r\n f[J+p] = - popJ.gamma * R[J] + popJ.beta * A[J]\r\n for K, popK in enumerate(self.config.network.populations):\r\n dCAA[J,K] = (- (popJ.beta + popK.beta) * CAA[J,K]\r\n + popJ.alpha*popJ.F(B[J]) * CAS[K,J]\r\n + popK.alpha*popK.F(B[K]) * CAS[J,K]\r\n + popJ.alpha*popJ.dF(B[J]) * S[J] * CAB[K,J]\r\n + popK.alpha*popK.dF(B[K]) * S[K] * CAB[J,K])\r\n dCRR[J,K] = (- (popJ.gamma + popK.gamma) * CRR[J,K]\r\n + popJ.beta * CAR[J,K] + popK.beta * CAR[K,J])\r\n dCAR[J,K] = (- (popJ.beta + popK.gamma) * CAR[J,K]\r\n + popK.beta * CAA[J,K]\r\n + popJ.alpha*popJ.F(B[J]) * CSR[J,K]\r\n + popJ.alpha*popJ.dF(B[J]) * S[J] * CRB[K,J])\r\n f[2*p : 2*p + round(p*(p+1)/2)] = dCAA[np.triu_indices(p)]\r\n f[2*p + round(p*(p+1)/2) : 2*p + p*(p+1)] = dCRR[np.triu_indices(p)]\r\n f[2*p + p*(p+1) :] = dCAR.flatten()\r\n return np.array(f, float)","title":""},{"docid":"4c00d4f81fb1acf44c9fcc399350f216","score":"0.5471497","text":"def _monte_carlo_sweep(self, random_update = False):\n\n nlen = self.nlen\n j = self.j\n h = self.h\n de = 0.0\n naccept = 0\n\n for i0 in range(nlen):\n ic = i0\n if random_update: ic = numpy.random.randint(nlen)\n im = numpy.mod(ic - 1 + nlen, nlen)\n ip = numpy.mod(ic + 1, nlen)\n\n for j0 in range(nlen):\n jc = j0\n if random_update: jc = numpy.random.randint(nlen)\n jm = numpy.mod(jc - 1 + nlen, nlen)\n jp = numpy.mod(jc + 1, nlen)\n\n # Existing state and new trial state (just flip\n # with probability one)\n\n s0 = self.s[ic, jc]\n s1 = -s0\n\n # Compute difference in energy\n\n ds = s1 - s0\n delta = -j*ds*(self.s[im, jc] + self.s[ip, jc] + self.s[ic, jm] + self.s[ic, jp]) - h*ds\n\n # Metropolis\n\n if delta < 0.0:\n self.s[ic, jc] = s1\n naccept += 1\n de += delta\n elif numpy.random.uniform() < numpy.exp(-delta/self.kT):\n self.s[ic, jc] = s1\n naccept += 1\n de += delta\n\n return naccept, de","title":""},{"docid":"68e9f596bcc055ef1648ee7638968c15","score":"0.54613376","text":"def _compute_covariance(self):\n self.factor = self.covariance_factor()\n # Cache covariance and inverse covariance of the data\n if not hasattr(self, '_data_inv_cov'):\n self._data_covariance = atleast_2d(cov(self.dataset, rowvar=1,\n bias=False,\n aweights=self.weights))\n self._data_inv_cov = linalg.inv(self._data_covariance)\n\n self.covariance = self._data_covariance * self.factor**2\n self.inv_cov = self._data_inv_cov / self.factor**2\n self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n","title":""},{"docid":"c5fcd7625fe8811ab7a2f3cbba09b36d","score":"0.54596156","text":"def _initialize(self):\n if self._mean is None or self._cov is None:\n return\n\n Q = self._cov[\"QS\"][0][0]\n S = self._cov[\"QS\"][1]\n\n if S.size > 0:\n self.tau[:] = 1 / npsum((Q * sqrt(S)) ** 2, axis=1)\n else:\n self.tau[:] = 0.0\n self.eta[:] = self._mean\n self.eta[:] *= self.tau","title":""},{"docid":"12858a9541bc4cffa02bbf9c48ac1444","score":"0.5456833","text":"def cov_0(self):\n # TODO: check this!\n# return self.sigma_n**2 + self.sigma_f**2\n return self.sigma_f**2","title":""},{"docid":"d6e0af82ee40f999b127dbc26a78f572","score":"0.54477686","text":"def calcCovarianceMatrix(self,phase_volume):\n input_array = []\n for particle in phase_volume:\n row = []\n row.append(particle.x.x)\n row.append(particle.x.y)\n row.append(particle.x.z)\n row.append(particle.p.x)\n row.append(particle.p.y)\n row.append(particle.p.z)\n row.append(particle.getEnergy())\n input_array.append(row)\n input_array = np.array(input_array).T #Transpose\n return np.cov(input_array)","title":""},{"docid":"7ad884b3b8629f0762343d9f0d12fdb2","score":"0.5444077","text":"def covariances(self):\n # n_gauss x 2 x 2\n # this has no derivatives, since the radii are fixed.\n return np.zeros([1, 2, 2])","title":""},{"docid":"9820e868e2de21d16afdb6c8b31c7180","score":"0.5438816","text":"def getmomentofinertia(self):\n \n I = np.zeros((3,3), np.float64)\n centerOfMass = self.center_of_mass()\n for atom_index in range(self.atomnumber):\n mass = self.masses[atom_index]\n coord = self.coords[atom_index] - centerOfMass\n I[0,0] += mass * (coord[1] * coord[1] + coord[2] * coord[2])\n I[1,1] += mass * (coord[0] * coord[0] + coord[2] * coord[2])\n I[2,2] += mass * (coord[0] * coord[0] + coord[1] * coord[1])\n I[0,1] -= mass * coord[0] * coord[1]\n I[0,2] -= mass * coord[0] * coord[2]\n I[1,2] -= mass * coord[1] * coord[2]\n I[1,0] = I[0,1]\n I[2,0] = I[0,2]\n I[2,1] = I[1,2]\n values1, vectors1 = np.linalg.eig(I)\n return I, values1, vectors1","title":""},{"docid":"e46340b10f64a45e76cd546ad7b84151","score":"0.54383546","text":"def cov_0(self):\n #return self.sigma_n**2 + self.sigma_f**2\n return self.sigma_f**2","title":""},{"docid":"82819b5bb3b219f4e4446689ee548cab","score":"0.54332715","text":"def calcVectorFromOrbit(self):\n # calculate distance from CoM using semimajor axis, eccentricity and true anomaly\n\n magpos = self.semilat / (1.0+ self.e * np.cos(self.trueanom)) \n\n self.position = Vector3D(0.0,0.0,0.0)\n self.velocity = Vector3D(0.0,0.0,0.0)\n \n # Calculate self.position vector in orbital plane\n self.position.x = magpos * np.cos(self.trueanom) \n self.position.y = magpos * np.sin(self.trueanom) \n self.position.z = 0.0 \n\n # Calculate self.velocity vector in orbital plane */\n gravparam = self.G * self.totalMass \n\n try:\n magvel = np.sqrt(gravparam/self.semilat)\n except ZeroDivisionError:\n magvel = np.sqrt(2.0*gravparam/magpos)\n \n self.velocity.x = -magvel * np.sin(self.trueanom) \n self.velocity.y = magvel * (np.cos(self.trueanom) + self.e) \n self.velocity.z = 0.0 \n\n # Begin rotations:\n # Firstly, Rotation around z axis by -self.argper */\n\n if(self.argper>tiny):\n self.position.rotateZ(self.argper) \n self.velocity.rotateZ(self.argper) \n \n # Secondly, Rotate around x by -inclination */\n\n if(self.i >tiny):\n self.position.rotateX(self.i) \n self.velocity.rotateX(self.i) \n \n # Lastly, Rotate around z by self.longascend */\n\n if(self.longascend >tiny):\n self.position.rotateZ(self.longascend) \n self.velocity.rotateZ(self.longascend)","title":""},{"docid":"b0503b02b6db5a3601b4dcd43100578b","score":"0.54150593","text":"def covariance_matrix(self) -> np.ndarray:\n matrices = [samples.covariance_matrix() for samples in self.samples_list]\n prior_count = self.samples_list[0].model.prior_count\n size = prior_count * len(self.samples_list)\n array = np.zeros((size, size))\n for i, matrix in enumerate(matrices):\n array[\n i * prior_count : (i + 1) * prior_count,\n i * prior_count : (i + 1) * prior_count,\n ] = matrix\n return array","title":""},{"docid":"d8e050934ba2effe85eb64ecf8794611","score":"0.54073244","text":"def _covariance_matrix(self, X):\n sig_aa = self._rbf_kernel(self.X_train, self.X_train, self.alpha, self.l) + self.sigma ** 2 * np.identity(\n self.n)\n sig_ab = self._rbf_kernel(self.X_train, X, self.alpha, self.l)\n sig_ba = self._rbf_kernel(X, self.X_train, self.alpha, self.l)\n sig_bb = self._rbf_kernel(X, X, self.alpha, self.l)\n return sig_aa, sig_ab, sig_ba, sig_bb","title":""},{"docid":"0026a2f1c6099e5248b2b40d606837a0","score":"0.54066795","text":"def test_construct_covariance_matrix_nsources(self):\n\n cvec = np.zeros((2, 15))\n cvec[0] = np.arange(15) + 1\n cvec[1] = -cvec[0]\n parallax = np.array([16, -16])\n vrad = np.array([17, -17]) * au_km_year_per_sec\n vrad_error = np.array([18, -18]) * au_km_year_per_sec\n\n cmat = construct_covariance_matrix(cvec, parallax, vrad, vrad_error)\n\n self.assertEquals(2, cmat.shape[0])\n self.assertEquals(6, cmat.shape[1])\n self.assertEquals(6, cmat.shape[2])\n\n iu = np.triu_indices(6, k=1)\n\n np.testing.assert_equal(cmat[0, iu[0], iu[1]], cmat[0, iu[1], iu[0]])\n np.testing.assert_equal((np.arange(5) + 1) ** 2, np.diag(cmat[0])[0:-1])\n\n expected = np.array([12, 21, 32, 45, 60, 88, 120, 156, 210, 300])\n iuu = np.triu_indices(5, k=1)\n np.testing.assert_equal(expected, cmat[0, :-1, :-1][iuu])\n\n for i in range(5):\n np.testing.assert_almost_equal(cmat[0, i, 5], cmat[0, i, 2] * 17, 1)\n\n np.testing.assert_almost_equal(\n cmat[0, 5, 5], 9 * (17**2 + 18**2) + (16 * 18) ** 2, 1\n )\n\n np.testing.assert_equal(cmat[1, iu[0], iu[1]], cmat[1, iu[1], iu[0]])\n np.testing.assert_equal((np.arange(5) + 1) ** 2, np.diag(cmat[1])[0:-1])\n\n expected = -np.array([12, 21, 32, 45, 60, 88, 120, 156, 210, 300])\n iuu = np.triu_indices(5, k=1)\n np.testing.assert_equal(expected, cmat[1, :-1, :-1][iuu])\n\n for i in range(5):\n np.testing.assert_almost_equal(cmat[1, i, 5], cmat[1, i, 2] * (-17), 1)\n\n np.testing.assert_almost_equal(\n cmat[1, 5, 5], 9 * (17**2 + 18**2) + (16 * 18) ** 2, 1\n )","title":""},{"docid":"5cc0fc41cad711864935da749ca3c217","score":"0.5399837","text":"def test_construct_covariance_matrix_onesource(self):\n\n cvec = np.arange(15) + 1\n parallax = 16\n vrad = 17 * au_km_year_per_sec\n vrad_error = 18 * au_km_year_per_sec\n\n cmat = construct_covariance_matrix(cvec, parallax, vrad, vrad_error)\n\n self.assertEquals(6, cmat.shape[0])\n self.assertEquals(6, cmat.shape[1])\n\n iu = np.triu_indices(6, k=1)\n\n np.testing.assert_equal(cmat[iu], cmat[iu[1], iu[0]])\n np.testing.assert_equal((np.arange(5) + 1) ** 2, np.diag(cmat)[0:-1])\n\n expected = np.array([12, 21, 32, 45, 60, 88, 120, 156, 210, 300])\n iuu = np.triu_indices(5, k=1)\n np.testing.assert_equal(expected, cmat[:-1, :-1][iuu])\n\n for i in range(5):\n np.testing.assert_almost_equal(cmat[i, 5], cmat[i, 2] * 17, 1)\n\n np.testing.assert_almost_equal(\n cmat[5, 5], 9 * (17**2 + 18**2) + (16 * 18) ** 2, 1\n )","title":""},{"docid":"d07d1e662bc1c1c66f66085c61796064","score":"0.5391459","text":"def test_measurement_jacobian():\n N = 10\n measurement_model = LinearKalmanFilterMeasurementModel()\n C_autograd = measurement_model.jacobian(states=torch.zeros((N, state_dim)))\n\n for i in range(N):\n torch.testing.assert_allclose(C_autograd[i], C)","title":""},{"docid":"b4c207310874fe155436032d6ebf7997","score":"0.53850037","text":"def test_caustic_for_orbital_motion():\n q = 0.1\n s = 1.3\n params = {'t_0': 100., 'u_0': 0.1, 't_E': 10., 'q': q,\n 's': s, 'ds_dt': 0.5, 'alpha': 0., 'dalpha_dt': 0.}\n model = mm.Model(parameters=params)\n\n model.update_caustics()\n almost(model.caustics.get_caustics(),\n mm.Caustics(q=q, s=s).get_caustics())\n\n model.update_caustics(100.+365.25/2)\n almost(model.caustics.get_caustics(),\n mm.Caustics(q=q, s=1.55).get_caustics())","title":""},{"docid":"f2e8899b83663fe9f9a06c6befeec7b2","score":"0.5378065","text":"def covariance(Z):\n N = Z.shape[0]\n S = np.zeros((Z.shape[1], Z.shape[1]))\n for i in range(N):\n S += 1.0 / N * np.outer(Z[i, :], Z[i, :])\n return S","title":""},{"docid":"09d52ed44a3f34b8dcba757eed31554b","score":"0.5371901","text":"def covariance_matrices(self, p):\n\n p = self.to_params(p)\n\n covc = self._cov_matrix(p['covc'])\n covb = self._cov_matrix(p['covb'])\n\n return (covc, covb)","title":""},{"docid":"bd13a78ac96cafbfa20f2aed881b1094","score":"0.5363501","text":"def fit(self, X):\n\n assert X.dtype in ['float32', 'float64']\n assert not np.any(np.isnan(X))\n assert len(X.shape) == 2\n n_samples = X.shape[0]\n if self.copy:\n X = X.copy()\n # Center data\n self.mean_ = np.mean(X, axis=0)\n X -= self.mean_\n\n log.info('computing zca of a {0} matrix'.format(X.shape))\n t1 = time.time()\n\n bias = self.filter_bias * scipy.sparse.identity(X.shape[1],\n theano.config.floatX)\n\n covariance = ZCA._gpu_matrix_dot(X.T, X) / X.shape[0] + bias\n t2 = time.time()\n log.info(\"cov estimate took {0} seconds\".format(t2 - t1))\n\n t1 = time.time()\n eigs, eigv = linalg.eigh(covariance)\n t2 = time.time()\n\n log.info(\"eigh() took {0} seconds\".format(t2 - t1))\n assert not np.any(np.isnan(eigs))\n assert not np.any(np.isnan(eigv))\n assert eigs.min() > 0\n\n if self.n_components and self.n_drop_components:\n raise ValueError('Either n_components or n_drop_components'\n 'should be specified')\n\n if self.n_components:\n eigs = eigs[-self.n_components:]\n eigv = eigv[:, -self.n_components:]\n\n if self.n_drop_components:\n eigs = eigs[self.n_drop_components:]\n eigv = eigv[:, self.n_drop_components:]\n\n t1 = time.time()\n\n sqrt_eigs = np.sqrt(eigs)\n try:\n self.P_ = ZCA._gpu_mdmt(eigv, 1.0 / sqrt_eigs)\n except MemoryError:\n warnings.warn()\n self.P_ = np.dot(eigv * (1.0 / sqrt_eigs), eigv.T)\n\n t2 = time.time()\n assert not np.any(np.isnan(self.P_))\n self.has_fit_ = True\n\n if self.store_inverse:\n self.inv_P_ = ZCA._gpu_mdmt(eigv, sqrt_eigs)\n else:\n self.inv_P_ = None","title":""},{"docid":"c565072334314e5253f447a303fa3bc7","score":"0.53611517","text":"def correlation_matrix(covariance_matrix, rms):\n corr_coeff = np.zeros_like(covariance_matrix)\n for i in range(covariance_matrix.shape[0]):\n for j in range(covariance_matrix.shape[0]):\n corr_coeff[i, j] = covariance_matrix[i, j] / rms[i] / rms[j]\n\n return corr_coeff","title":""},{"docid":"29bac20d28afe3a4b3303fe89feb7996","score":"0.5356866","text":"def evaluate(self):\n cls_attr_name = self.__class__.__name__+\".time_series\"\n self.time_series.trait[\"data\"].log_debug(owner = cls_attr_name)\n \n #(tpts, nodes, nodes, state-variables, modes)\n result_shape = self.result_shape(self.time_series.data.shape)\n LOG.info(\"result shape will be: %s\" % str(result_shape))\n \n result = numpy.zeros(result_shape)\n \n #TODO: For region level, 4s, 2000Hz, this takes ~3hours... (which makes node_coherence seem positively speedy...)\n #TODO: Probably best to add a keyword for offsets, so we just compute +- some \"small\" range...\n #One inter-node correllation, across offsets, for each state-var & mode.\n for mode in range(result_shape[4]):\n for var in range(result_shape[3]):\n data = self.time_series.data[:, var, :, mode]\n data = data - data.mean(axis=0)[numpy.newaxis, :]\n #TODO: Work out a way around the 4 level loop,\n for n1 in range(result_shape[1]):\n for n2 in range(result_shape[2]):\n result[:, n1, n2, var, mode] = correlate(data[:, n1],\n data[:, n2],\n mode=\"same\")\n \n util.log_debug_array(LOG, result, \"result\")\n \n offset = (self.time_series.sample_period *\n numpy.arange(-(numpy.floor(result_shape[0] / 2.0)),\n numpy.ceil(result_shape[0] / 2.0)))\n \n cross_corr = temporal_correlations.CrossCorrelation(\n source = self.time_series,\n array_data = result,\n time = offset,\n use_storage = False)\n \n return cross_corr","title":""},{"docid":"e9f30530998c3ccc31ee9221f39e880c","score":"0.5352657","text":"def statePCovarianceCalc(self):\n\t\tprint(\"prev process covariance matrix P: \", self.P)\n\t\tA = np.array([[1, self.delta_T],\n\t\t\t [0, 1]])\n\t\t# State Covariance Matrix\n\t\tself.PP = np.matmul(np.matmul(A, self.P), A.transpose()) # + self.Q\n\t\tself.PP = np.array([[self.PP[0][0], 0],\n\t\t\t\t [ 0, self.PP[1][1]]])\n\t\t\n\t\tprint(\"predicted covariance matrix: \", self.PP)\t\n\t\treturn self.PP # 2x2 matrix","title":""},{"docid":"903399203d56cd54ddb12d76f5cff991","score":"0.53515756","text":"def do_CI(N_orb, a):\n N_config = int(comb(N_orb, 2)) # number of configurations possible, ignoring spin\n Etot, _, h, G, c = do_scf(a)\n config_list = np.zeros((N_config, 2)).astype(int) # initialize possible configs as [i,j]th occupied orbitals\n count = 0\n for i in range(N_orb):\n for j in range(i + 1, N_orb):\n config_list[count, :] = [i, j]\n count += 1\n\n # initialize CI matrix\n H_CI = np.zeros((N_config, N_config))\n\n # populate CI matrix elements \\bra \\psi_ij | H | \\psi_ij \\ket, where \\psi is a configuration\n for config1 in range(N_config):\n i, j = config_list[config1, :]\n for config2 in range(N_config):\n k, l = config_list[config2, :]\n if i == l:\n H_CI[config1, config2] -= c[:, j] @ h @ c[:, k]\n if j == k:\n H_CI[config1, config2] -= c[:, i] @ h @ c[:, l]\n if j == l:\n H_CI[config1, config2] += c[:, i] @ h @ c[:, k]\n if i == k:\n H_CI[config1, config2] += c[:, j] @ h @ c[:, l]\n\n H_CI[config1, config2] += a * ((c[:, i] @ G @ c[:, k]) * (c[:, j] @ G @ c[:, l])\n - (c[:, j] @ G @ c[:, k]) * (c[:, i] @ G @ c[:, l]))\n\n E, b = eig(H_CI)\n order = np.argsort(E)\n E = np.real(np.sort(E))\n return E, b, order, config_list","title":""},{"docid":"65eff0aaee4644f07367375516305992","score":"0.5351557","text":"def get_covariance_matrix(self):\n if self.Ss is None:\n _ = self.get_Ss_from_Us()\n\n assert self.nbatch == 1, 'covariance matrix is returned only for single batch sample, but ' \\\n 'self.nbatch={}'.format(self.nbatch)\n\n # assume single batch sample\n batch_idx = 0\n S = np.zeros((self.ndims, self.ndims))\n\n a = self.alphas[batch_idx, :].data.numpy().squeeze()\n mus = self.mus[batch_idx, :, :].data.numpy().squeeze()\n ss = self.Ss[batch_idx, :, :, :].squeeze()\n\n m = np.dot(a, mus.T)\n\n # get covariance shifted by the means, weighted with alpha\n for k in range(self.n_components):\n S += a[k] * (ss[k, :, :] + np.outer(mus[:, k], mus[:, k]))\n\n # subtract weighted means\n S -= np.outer(m, m)\n\n return S","title":""},{"docid":"576a220087b35bf37330830124f447a4","score":"0.5350341","text":"def compute_C(self):\n self.C = self.K.dot(self.P.dot(self.R.dot(self.T)))","title":""},{"docid":"ae0d45c02568b4850d5d3ddd87f8e35f","score":"0.5345444","text":"def _cov_matrix(self, x):\n\n y = np.zeros((3,3))\n y[np.tril_indices(3)] = x\n y.flat[::4] = np.exp(y.flat[::4])\n\n return np.dot(y, y.T)","title":""},{"docid":"50ff774408fd93bc444b48c28eb41949","score":"0.53283805","text":"def res_causal(self):\n sigma1 = self.param.covshocks[0]\n sigma2 = self.param.covshocks[1]\n rho = self.param.covshocks[2]\n cov12 = (sigma1**(1/2))*(sigma2**(1/2))*rho\n covshocks = [[sigma1,cov12],[cov12,sigma2]]\n \n s = np.random.multivariate_normal(self.param.meanshocks, covshocks, self.N)\n omega = np.reshape(s[:,0],(self.N,1))\n theta = np.reshape(s[:,1],(self.N,1))\n \n return omega, theta","title":""},{"docid":"28ed7a1c08919d8024faab84702cfdb0","score":"0.5328304","text":"def calculate_cca(dat_x, dat_y, timeaxis=-2):\n assert (len(dat_x.data.shape) == len(dat_y.data.shape) == 2 and\n dat_x.data.shape[timeaxis] == dat_y.data.shape[timeaxis])\n\n if timeaxis == 0 or timeaxis == -2:\n x = dat_x.data.copy()\n y = dat_y.data.copy()\n else:\n x = dat_x.data.T.copy()\n y = dat_y.data.T.copy()\n\n # calculate covariances and it's inverses\n x -= x.mean(axis=0)\n y -= y.mean(axis=0)\n N = x.shape[0]\n c_xx = np.dot(x.T, x) / N\n c_yy = np.dot(y.T, y) / N\n c_xy = np.dot(x.T, y) / N\n c_yx = np.dot(y.T, x) / N\n ic_xx = np.linalg.pinv(c_xx)\n ic_yy = np.linalg.pinv(c_yy)\n # calculate w_x\n w, v = np.linalg.eig(functools.reduce(np.dot, [ic_xx, c_xy, ic_yy, c_yx]))\n w_x = v[:, np.argmax(w)].real\n w_x = w_x / np.sqrt(functools.reduce(np.dot, [w_x.T, c_xx, w_x]))\n # calculate w_y\n w, v = np.linalg.eig(functools.reduce(np.dot, [ic_yy, c_yx, ic_xx, c_xy]))\n w_y = v[:, np.argmax(w)].real\n w_y = w_y / np.sqrt(functools.reduce(np.dot, [w_y.T, c_yy, w_y]))\n # calculate rho\n rho = abs(functools.reduce(np.dot, [w_x.T, c_xy, w_y]))\n return rho, w_x, w_y","title":""},{"docid":"4d823b2847dd30c3e3066aca2820cc51","score":"0.53281224","text":"def __init__(self, markers, sample_time):\n self.markers = markers\n self.last_time = None # Used to keep track of time between measurements \n self.Q_t = np.diag([0.1, 0.1])\n self.R_t = np.diag([0.01, 0.01, np.pi/180/2])\n # YOUR CODE HERE\n \n self.sample_time = sample_time\n self.x_t = np.zeros(3) # initialize state variables\n self.P_t = np.diag([10, 10, 10]) # initialize covariance matrix","title":""},{"docid":"43adbe87e0aa57a7f4e840dddfe0f435","score":"0.532233","text":"def get_covariance_matrix(self, X):\n # It is recommended that you try to compute the covariance matrix\n # by performing the needed operations by hand, instead of using np.cov()\n # function, this way you will have better idea of the covariance matrix\n\n # Also take into account that the random variables for which you want to\n # compute the covariance matrix are the columns of X, not the rows!\n\n # don't forget the 1/(N-K) term in this case\n # YOUR CODE HERE\n\n return (1 / (self.nr_data - self.nr_labels)) * (X - X.mean(axis=0)).T @ (X - X.mean(axis=0))","title":""},{"docid":"e106732ff3a750f8037961318bab7896","score":"0.53162223","text":"def mixture_of_variational_distributions_moments(\n relaxation, rng, n_init=100, n_step=1000, step_size=0.2,\n init_scale=0.5, tol=1e-4):\n var_obj_list = []\n var_first_mom_list = []\n var_biases_list = []\n for j in range(n_init):\n var_biases = rng.normal(size=(relaxation.n_unit,)) * init_scale\n for i in range(n_step):\n var_obj, grads_wrt_var_biases, var_first_mom = (\n var.var_obj_and_grads_mean_field(\n var_biases, relaxation.W, relaxation.b)\n )\n grads_wrt_var_biases = np.array(grads_wrt_var_biases)\n var_biases -= step_size * grads_wrt_var_biases\n in_list = False\n for vm in var_first_mom_list:\n diff = vm - var_first_mom\n dist = diff.dot(diff)**0.5\n if dist < tol:\n in_list = True\n if not in_list:\n var_obj_list.append(var_obj)\n var_first_mom_list.append(np.array(var_first_mom))\n var_biases_list.append(var_biases)\n var_weights = np.exp(-np.array(var_obj_list))\n var_weights /= var_weights.sum()\n var_mean = np.zeros((relaxation.n_dim_r))\n var_covar = np.zeros((relaxation.n_dim_r, relaxation.n_dim_r))\n for var_first_mom, w in zip(var_first_mom_list, var_weights):\n m = relaxation.Q.T.dot(var_first_mom)\n var_mean += w * m\n var_covar += w * np.outer(m, m)\n var_covar += np.eye(relaxation.n_dim_r) - np.outer(var_mean, var_mean)\n var_covar_chol = la.cholesky(var_covar, True)\n var_log_norm = (\n 0.5 * relaxation.n_dim_r * np.log(2 * np.pi) -\n relaxation.n_unit * np.log(2) +\n 0.5 * relaxation.D.diagonal().sum() +\n np.log(np.exp(-np.array(var_obj_list)).sum())\n )\n return var_mean, var_covar_chol, var_log_norm","title":""},{"docid":"e872ac3732c0d3429702a20ebd1231e5","score":"0.5311863","text":"def modcovar_marple (X,IP):\n Pv = []\n N = len(X)\n A = np.zeros(N, dtype=complex)\n D = np.zeros(N, dtype=complex)\n C = np.zeros(N, dtype=complex)\n R = np.zeros(N, dtype=complex)\n # Initialization\n R1=0.\n for K in range(1, N-1):\n R1=R1 + 2.*(X[K].real**2 + X[K].imag**2)\n R2 = X[0].real**2 + X[0].imag**2\n R3 = X[N-1].real**2 + X[N-1].imag**2\n R4 = 1. / (R1 + 2. * (R2 + R3))\n P = R1 + R2 + R3\n DELTA = 1. - R2 * R4\n GAMMA = 1. - R3 * R4\n LAMBDA = (X[0] * X[N-1]).conjugate()*R4\n C[0] = X[N-1] * R4\n D[0] = X[0].conjugate() * R4\n\n M = 0\n if (IP ==0):\n P = (.5*R1+R2+R3)/float(N)\n return [], P, []\n\n #C Main loop\n\n for M in range(0, IP):\n #print '---------------------', M\n SAVE1 = 0+0j\n for K in range(M+1, N):\n SAVE1 = SAVE1 + X[K]*X[K-M-1].conjugate()\n SAVE1 *= 2.\n R[M] = SAVE1.conjugate()\n THETA = X[N-1]*D[0]\n PSI=X[N-1]*C[0]\n XI = X[0].conjugate() * D[0]\n if M==0:\n pass\n else:\n for K in range(0, M):\n THETA=THETA+X[N-K-2]*D[K+1] # Eq. (8.D.45)\n PSI = PSI + X[N-K-2]*C[K+1] # Eq. (8.D.45)\n XI = XI + X[K+1].conjugate() * D[K+1] # Eq. (8.D.45)\n R[K] = R[K]-X[N-M-1] * X[N+1-M+K-1].conjugate() - X[M].conjugate() * X[M-K-1] # Eq. (8.D.37)\n SAVE1=SAVE1+R[K].conjugate()*A[M-K-1] # Eq. (8.D.24)\n #print 'withini loop', K, THETA, PSI, XI, R[K], SAVE1\n #print 'x -------', C[K], C[K+1]\n #print 'x -------', D[K], D[K+1]\n #print 'x -------', N-K-2, K+1,N-M-1, M, M-K-1\n #print 'x -------', X[N-K-2], X[K+1],X[N-M-1], X[M], X[M-K-1]\n\n\n # Order update of A vector\n\n C1 = -SAVE1/P\n A[M]=C1 # Eq. (8.D.23)\n P=P*(1.-C1.real**2-C1.imag**2) # Eq. (8.D.25)\n #print 'C1=' , C1, 'A[M]', A[M], 'P=',P\n if M==0:\n pass\n else:\n #for k in range(0, (M-1)/2+1):\n #print 'BEFORE', A[0:4]\n for K in range(0, (M+1)//2):\n MK = M-K-1\n SAVE1=A[K]\n A[K]=SAVE1+C1*A[MK].conjugate() # Eq. (8.D.22)\n #print 'A uopdate---->',M, K, MK, A[K], A[MK], SAVE1, C1\n if (K != MK):\n #print 'ENTERI IF', K, MK, A[MK], C1, SAVE1\n A[MK]=A[MK]+C1*(SAVE1.conjugate()) # Eq. (8.D.22)\n #print 'AFTER', A[0:4]\n #print M+1, IP\n if M+1 == IP:\n #Pv.append(P)\n P=.5*P/float(N-M-1)\n Pv.append(P)\n return A, P, Pv\n else:\n Pv.append(.5*P/float(N-M-1))\n #Pv.append(P)\n\n # Time update of C,D vectors and GAMMA,DELTA,LAMBDA scalars\n\n R1=1./(DELTA*GAMMA-(LAMBDA.real)**2-(LAMBDA.imag)**2)\n C1=(THETA*(LAMBDA.conjugate())+PSI*DELTA)*R1\n C2=(PSI*LAMBDA+THETA*GAMMA)*R1\n C3=(XI*(LAMBDA.conjugate())+THETA*DELTA)*R1\n C4=(THETA*LAMBDA+XI*GAMMA)*R1\n\n #print 'C1C2C3C4', C1, C2, C3, C4\n #print M, M/2+1, (M-1)/2+1\n for K in range(0, (M)//2+1):\n MK=M-K\n SAVE1=C[K].conjugate()\n SAVE2=D[K].conjugate()\n SAVE3=C[MK].conjugate()\n SAVE4=D[MK].conjugate()\n C[K]=C[K]+C1*SAVE3+C2*SAVE4 # Eq. (8.D.43)\n D[K]=D[K]+C3*SAVE3+C4*SAVE4 # Eq. (8.D.44)\n #print '-------->', K,MK, SAVE1, SAVE2, SAVE3, SAVE4\n if K != MK:\n C[MK]=C[MK]+C1*SAVE1+C2*SAVE2 # Eq. (8.D.43)\n D[MK]=D[MK]+C3*SAVE1+C4*SAVE2 # Eq. (8.D.44)\n #print 'witninloop loop ',K,MK,C[MK], D[MK]\n #print 'after loop'\n #print 'C=',C[0:2]\n #print 'D=',D[0:2]\n R2=PSI.real**2+PSI.imag**2\n R3=THETA.real**2+THETA.imag**2\n R4=XI.real**2+XI.imag**2\n R5=GAMMA-(R2*DELTA+R3*GAMMA+2.*np.real(PSI*LAMBDA*THETA.conjugate()))*R1\n R2=DELTA-(R3*DELTA+R4*GAMMA+2.*np.real(THETA*LAMBDA*XI.conjugate()))*R1\n GAMMA=R5 # Eq. (8.D.46)\n DELTA=R2 # Eq. (8.D.47)\n LAMBDA=LAMBDA+C3*PSI.conjugate()+C4*THETA.conjugate() # Eq. (8.D.48)\n #print 'before time updaqt',R2, R3, R4, R5, LAMBDA\n if P <= 0.:\n raise ValueError('Found an invalid negative value for P.')\n if (DELTA > 0. and DELTA <= 1. and GAMMA > 0. and GAMMA <=1.):\n pass\n else:\n raise ValueError('Found an invalid DELTA or GAMMA value.')\n # Time update of A vector; order updates of C,D vectors aand GAMMA,\n # DELTA,LAMBDA scalars\n #print 'time update--------------'\n R1=1./P\n R2=1./(DELTA*GAMMA-LAMBDA.real**2-LAMBDA.imag**2) # Eq. (8.D.41)\n EF=X[M+1]\n EB=X[N-M-2]\n #print 'efeb', EF, EB\n #print 'A=',A[0:2]\n for K in range(0, M+1):\n #print 'efeb lloop', K,A[K], X[M-K], X[N-M+K-1]\n EF=EF+A[K]*X[M-K] # Eq. (8.D.1)\n EB=EB+A[K].conjugate()*X[N-M+K-1] # Eq. (8.D.2)\n C1=EB*R1 # Eq. (8.D.28)\n C2=EF.conjugate()*R1 # Eq. (8.D.29)\n C3=(EB.conjugate()*DELTA+EF*LAMBDA)*R2\n C4=(EF*GAMMA+(EB*LAMBDA).conjugate())*R2\n\n #print 'c1c2c3c4', C1 ,C2, C3, C4\n #print 'efeb',EF, EB\n\n for K in range(M, -1, -1):\n SAVE1=A[K]\n A[K]=SAVE1+C3*C[K]+C4*D[K] # Eq. (8.D.38)\n C[K+1]=C[K]+C1*SAVE1 # Eq. (8.D.26)\n D[K+1]=D[K]+C2*SAVE1 # Eq. (8.D.27)\n #print 'ACD',K, A[K], C[K+1], D[K+1]\n C[0]=C1\n D[0]=C2\n R3=EB.real**2+EB.imag**2\n R4=EF.real**2+EF.imag**2\n P=P-(R3*DELTA+R4*GAMMA+2.*np.real(EF*EB*LAMBDA))*R2 # Eq. (8.D.42)\n DELTA=DELTA-R4*R1 # Eq. (8.D.32)\n GAMMA=GAMMA-R3*R1 # Eq. (8.D.33)\n LAMBDA=LAMBDA+(EF*EB).conjugate()*R1 # Eq. (8.D.35)\n\n #print 'final'\n #print R3, R4, P, DELTA, GAMMA, LAMBDA\n #print 'Afinal=',A[0:2]\n #print 'P=',P\n if (P > 0.):\n pass\n else:\n raise ValueError(\"Found a invalid negative P value \")\n if (DELTA > 0. and DELTA <= 1. and GAMMA > 0. and GAMMA <= 1.):\n pass\n else:\n raise ValueError(\"Found an invalid negative GAMMA or DELTA value \")","title":""},{"docid":"581b0683258d223ef2c7229e9d5a7ee7","score":"0.5294292","text":"def covariance(self, S, S0 = None):\n I = self.fishermatrix(S, S0 = S0)\n return np.linalg.linalg.inv(I)","title":""},{"docid":"89201c72cbed1eda36b00d1990e87ab3","score":"0.5293026","text":"def covariance_matrix(epsrel=0.0, verbosity=2):\n\n # Test if the current data pipe exists.\n check_pipe()\n\n # The specific analysis API object.\n api = return_api()\n\n # Loop over the models.\n for model_info in api.model_loop():\n # Get the Jacobian and weighting matrix.\n jacobian, weights = api.covariance_matrix(model_info=model_info, verbosity=verbosity)\n\n # Calculate the covariance matrix.\n pcov = statistics.multifit_covar(J=jacobian, weights=weights)\n\n # To compute one standard deviation errors on the parameters, take the square root of the diagonal covariance.\n sd = sqrt(diag(pcov))\n\n # Loop over the parameters.\n index = 0\n for name in api.get_param_names(model_info):\n # Set the parameter error.\n api.set_error(index, sd[index], model_info=model_info)\n\n # Increment the parameter index.\n index = index + 1","title":""},{"docid":"874c480feab4d3a3da841c54575ef1ef","score":"0.5282582","text":"def run_sim(R_star, transit_duration, bodies):\n\n # Run 3-body sim for one full orbit of the outermost moon\n loop(bodies, orbit_duration)\n \n\n # Move resulting data from lists to numpy arrays\n ttv_array = numpy.array([])\n ttv_array = ttv_list\n tdv_array = numpy.array([])\n tdv_array = tdv_list\n\n # Zeropoint correction\n middle_point = numpy.amin(ttv_array) + numpy.amax(ttv_array)\n ttv_array = numpy.subtract(ttv_array, 0.5 * middle_point)\n ttv_array = numpy.divide(ttv_array, 1000) # km/s\n\n # Compensate for barycenter offset of planet at start of simulation:\n planet.px = 0.5 * (gravity_firstmoon + gravity_secondmoon)\n stretch_factor = 1 / ((planet.px / 1000) / numpy.amax(ttv_array))\n ttv_array = numpy.divide(ttv_array, stretch_factor)\n\n # Convert to time units, TTV\n ttv_array = numpy.divide(ttv_array, R_star)\n ttv_array = numpy.multiply(ttv_array, transit_duration * 60 * 24) # minutes\n\n # Convert to time units, TDV\n oldspeed = (2 * R_star / transit_duration) * 1000 / 24 / 60 / 60 # m/sec\n newspeed = oldspeed - numpy.amax(tdv_array)\n difference = (transit_duration - (transit_duration * newspeed / oldspeed)) * 24 * 60\n conversion_factor = difference / numpy.amax(tdv_array)\n tdv_array = numpy.multiply(tdv_array, conversion_factor)\n\n return ttv_array, tdv_array","title":""},{"docid":"0091e773f187b0f6aaaaff379da769da","score":"0.5281733","text":"def acic_covariates():\n mat = covariate_matrix()\n mat, _, _ = normalize(mat)\n return mat","title":""},{"docid":"42eb49fd5e7eafac20923b70995bfaa9","score":"0.5281578","text":"def cov_naive(X):\n D, N = X.shape\n ### Edit the code below to compute the covariance matrix by iterating over the dataset.\n covariance = np.zeros((D, D))\n covMatrix = np.cov(X,bias=True)\n print(covMatrix)\n ### Update covariance\n\n ###\n return covMatrix","title":""},{"docid":"f1884cf20c01a2d290e8f9ba0877a791","score":"0.52797663","text":"def covolume(M):\n from regular_models.RR_spaces.RR_lattices import RRSpace\n M_K = M.RR_space();\n return (Matrix([M_K.function_space().vector(f) for f in M.basis()])).determinant()","title":""},{"docid":"23c3d9dd4f5148774d882ab2c9a124ee","score":"0.5279012","text":"def c2tMatrix(sec, init, consts):\r\n # Polar motion(arcsec->radians)\r\n xp = 0.0349282 * consts.asec2rad\r\n yp = 0.4833163 * consts.asec2rad\r\n\r\n # CIP offsets wrt IAU 2000A(mas->radians)\r\n dx = 0.1725 * consts.asec2rad / 1000\r\n dy = -0.2650 * consts.asec2rad / 1000\r\n\r\n mjdjd0 = 2400000.5\r\n\r\n tm = sec / 86400\r\n utc = init.mjd + tm\r\n delta = deltaAT(init.year, init.month, init.mjd, tm)\r\n tai = utc + delta / 86400\r\n tt = tai + 32.184 / 86400\r\n tut = tm + consts.dUT1 / 86400\r\n\r\n # IAU 2000A, CIO based, using classical angels\r\n # CIP and CIO, IAU 2000A\r\n x, y, s = calcXYS(mjdjd0, tt, consts)\r\n\r\n # Add CIP corrections\r\n x = x + dx\r\n y = y + dy\r\n\r\n # Obtain the spherical angles E and d\r\n r2 = x**2 + y**2\r\n if r2 > 0:\r\n e = atan2(y, x)\r\n else:\r\n e = 0\r\n d = atan(sqrt(r2 / (1 - r2)))\r\n\r\n # GCRS to CIRS matrix\r\n matr = np.eye(3)\r\n matr = rotateRZ(e, matr)\r\n matr = rotateRY(d, matr)\r\n matr = rotateRZ(-(e + s), matr)\r\n\r\n # Earth rotation angle\r\n dj1 = mjdjd0 + init.mjd\r\n dj2 = tut\r\n # Days since fundamental epoch\r\n if dj1 < dj2:\r\n date1 = dj1\r\n date2 = dj2\r\n else:\r\n date1 = dj2\r\n date2 = dj1\r\n\r\n t = date1 + (date2 - consts.dj00)\r\n\r\n # Fractional part of T(days)\r\n f = date1 % 1 + date2 % 1\r\n\r\n # Earth rotation angle at this UT1\r\n era = (2*pi*(f + 0.7790572732640 + 0.00273781191135448 * t)) % (2*pi)\r\n if era < 0:\r\n era += 2*pi\r\n\r\n # Form celestial - terrestial matrix(no polar motion yet)\r\n celTerMatrix = np.copy(matr)\r\n celTerMatrix = rotateRZ(era, celTerMatrix)\r\n\r\n # Polar motion matrix(TRIRS->ITRS, IERS 2003)\r\n # Interval between fundamental epoch J2000.0 and current date(JC).\r\n t = ((mjdjd0 - consts.dj00) + tt) / consts.djc\r\n\r\n # Approximate s'\r\n sp = -47e-6 * t * consts.asec2rad\r\n\r\n polarMatrix = np.eye(3)\r\n polarMatrix = rotateRZ(sp, polarMatrix)\r\n polarMatrix = rotateRY(-xp, polarMatrix)\r\n polarMatrix = rotateRX(-yp, polarMatrix)\r\n\r\n # Form celestial - terrestial matrix(including polar motion)\r\n polarCelTerMatrix = polarMatrix.dot(celTerMatrix)\r\n return polarCelTerMatrix","title":""},{"docid":"b327a70bf42ecd46a584efae575aad61","score":"0.52783996","text":"def compute_cov_mat(self, beta, lamz, lams=None, verbose=False):\n if verbose:\n print('in covMat, beta shape ',beta.shape,\n ', lamz shape ',lamz.shape,\n ', sqdist shape ',self.sqdist.shape)\n # lamz is the process precision, nominally 1 for scaled data\n # lams is a 'nugget' or noise variance\n if self.sqdist.shape[0] == 0:\n sqdist_exp_sum = np.array([])\n else:\n sqdist_sum = np.squeeze(self.sqdist @ beta)\n sqdist_exp_sum = np.exp(-sqdist_sum) / lamz\n if self.type == 1:\n cov = np.zeros((self.n, self.n))\n cov[self.ind] = sqdist_exp_sum\n cov = cov + cov.T\n lam = 1/lamz \n if lams is not None: lam += 1/lams\n np.fill_diagonal(cov, lam)\n elif self.type == 2:\n cov = np.zeros((self.n, self.m))\n cov[self.ind] = sqdist_exp_sum\n return cov","title":""},{"docid":"e2b93f58b2eab2a7b4bd89d0e1d60320","score":"0.52736324","text":"def covariance(datapoints, num_features, mean_feature_vector_for_c):\n cov_matrix = [[0]*num_features]*num_features\n for feature_number_i in range(num_features):\n for feature_number_j in range(num_features):\n running_sum = 0\n for datapoint in datapoints:\n ith_diff = datapoint[feature_number_i] - mean_feature_vector_for_c[feature_number_i]\n jth_diff = datapoint[feature_number_j] - mean_feature_vector_for_c[feature_number_j]\n running_sum += ith_diff * jth_diff\n\n if len(datapoints) <= 0:\n running_sum = 0\n else:\n running_sum = running_sum / float(len(datapoints))\n\n cov_matrix[feature_number_i][feature_number_j] = running_sum\n return cov_matrix","title":""},{"docid":"10b498204be43769cf9a699d505b81c5","score":"0.5266135","text":"def cov(self):\n return self.F @ self.F.t() + torch.diag(self.variance)","title":""},{"docid":"31d9c4fde0e85a19c293849a5246ff6f","score":"0.5255149","text":"def run(self):\n\n S = self.model.stoichiometry_matrix\n amat = self.model.propensities\n ymat = self.model.species\n n_species = len(ymat)\n\n # dPdt is matrix of each species differentiated w.r.t. time\n # The code below literally multiplies the stoichiometry matrix to a column vector of propensities\n # from the right (::math::`\\frac{dP}{dt} = \\mathbf{Sa}`)\n dPdt = S * amat\n\n\n # A Is a matrix of each species (rows) and the derivatives of their stoichiometry matrix rows\n # against each other species\n # Code below computes the matrix A, that is of size `len(ymat) x len(ymat)`, for which each entry\n # ::math::`A_{ik} = \\sum_j S_{ij} \\frac{\\partial a_j}{\\partial y_k} = \\mathfb{S_i} \\frac{\\partial \\mathbf{a}}{\\partial y_k}`\n A = sp.Matrix(len(ymat), len(ymat), lambda i, j: 0)\n for i in range(A.rows):\n for k in range(A.cols):\n A[i, k] = reduce(operator.add, [S[i, j] * sp.diff(amat[j], ymat[k]) for j in range(len(amat))])\n\n\n # `diagA` is a matrix that has values sqrt(a[i]) on the diagonal (0 elsewhere)\n diagA = sp.Matrix(len(amat), len(amat), lambda i, j: amat[i] ** sp.Rational(1,2) if i==j else 0)\n # E is stoichiometry matrix times diagA\n E = S * diagA\n\n variance_terms = []\n cov_matrix = []\n for i in range(len(ymat)):\n row = []\n for j in range(len(ymat)):\n if i <= j:\n symbol = 'V_{0}_{1}'.format(i, j)\n variance_terms.append(VarianceTerm(position=(i,j), symbol=symbol))\n else:\n # Since Vi,j = Vj,i, i.e. covariance are equal, we only record Vi,j but not Vj,i\n symbol = 'V_{0}_{1}'.format(j, i)\n variance_terms.append(VarianceTerm(position=(j,i), symbol=symbol))\n row.append(symbol)\n cov_matrix.append(row)\n\n V = sp.Matrix(cov_matrix)\n\n\n # Matrix of variances (diagonal) and covariances of species i and j differentiated wrt time.\n # I.e. if i=j, V_ij is the variance, and if i!=j, V_ij is the covariance between species i and species j\n dVdt = A * V + V * (A.T) + E * (E.T)\n\n\n # build ODEProblem object\n rhs_redundant = sp.Matrix([i for i in dPdt] + [i for i in dVdt])\n\n\n #generate ODE terms\n n_vectors = [tuple([1 if i==j else 0 for i in range(n_species)]) for j in range(n_species)]\n moment_terms = [Moment(nvec,lhs) for (lhs, nvec) in zip(ymat, n_vectors)]\n\n ode_description = moment_terms + variance_terms\n\n\n non_redundant_idx = []\n ode_terms = []\n # remove repetitive covariances, as Vij = Vji\n for i, cov in enumerate(ode_description):\n if cov in ode_terms:\n continue\n else:\n ode_terms.append(cov)\n non_redundant_idx.append(i)\n rhs = []\n for i in non_redundant_idx:\n rhs.append(rhs_redundant[i])\n\n\n out_problem = ODEProblem(\"LNA\", ode_terms, rhs, sp.Matrix(self.model.parameters))\n\n\n return out_problem","title":""},{"docid":"5d30d5cfb20028f3869498d3f1691b88","score":"0.5253129","text":"def solve_for_c(T, T_prev, phi, D_eff, rho_v_dT, nz, dt, dz, Eterms):\r\n\r\n a = np.zeros(nz)\r\n beta = np.zeros(nz)\r\n Bc = np.zeros([nz, nz])\r\n c = np.zeros(nz)\r\n main_Ac = np.ones(nz)\r\n upper_Ac = np.zeros(nz - 1)\r\n lower_Ac = np.zeros(nz - 1)\r\n Ac = np.zeros([nz, nz])\r\n main_Bc = np.zeros(nz)\r\n Ec = np.zeros([nz, nz])\r\n upper_Ec = np.zeros(nz - 1)\r\n lower_Ec = np.zeros(nz - 1)\r\n part1 = np.zeros(nz)\r\n part2 = np.zeros(nz)\r\n # Variables\r\n beta = D_eff * rho_v_dT\r\n a = (1 - phi) * rho_v_dT / dt\r\n # Set up matrix Ac\r\n dz_nz = np.zeros(nz)\r\n dz_nz[:-1] = dz[:]\r\n dz_nz[-1] = (dz[-1] - dz[-2]) / 2 + dz[-1]\r\n ## Elements of matrix Ac (LHS)\r\n main_Ac[1:-1] = a[1:-1] + 2 * 2 * beta[1:-1] / (dz_nz[1:-1] ** 2 + dz_nz[:-2] ** 2)\r\n for i in range(1, nz - 1): # k+1\r\n upper_Ac[i] = -((beta[i + 1] - beta[i - 1])) / (\r\n dz_nz[i] + dz_nz[i - 1]\r\n ) ** 2 - (2 * beta[i]) / (dz_nz[i] ** 2 + dz_nz[i - 1] ** 2)\r\n for i in range(0, nz - 2): # k-1\r\n lower_Ac[i] = (beta[i + 2] - beta[i]) / (dz_nz[i + 1] + dz_nz[i]) ** 2 - (\r\n 2 * beta[i + 1]\r\n ) / (dz_nz[i + 1] ** 2 + dz_nz[i] ** 2)\r\n main_Ac[0] = a[0]\r\n upper_Ac[0] = 0\r\n main_Ac[-1] = a[-1]\r\n lower_Ac[-1] = 0\r\n ### Set up tridiagonal Matrix Ac\r\n Ac = (\r\n np.diag(np.ones(nz) * main_Ac, k=0)\r\n + np.diag(np.ones(nz - 1) * lower_Ac, k=-1)\r\n + np.diag(np.ones(nz - 1) * upper_Ac, k=1)\r\n )\r\n # Elements of matrix Ec\r\n if Eterms:\r\n for i in range(1, nz - 1): # k+1\r\n upper_Ec[i] = (\r\n (2 * beta[i])\r\n / (dz_nz[i] ** 2 + dz_nz[i - 1] ** 2)\r\n * (dz_nz[i] - dz_nz[i - 1])\r\n / (dz_nz[i] + dz_nz[i - 1])\r\n )\r\n for i in range(0, nz - 2): # k-1\r\n lower_Ec[i] = (\r\n -(2 * beta[i + 1])\r\n / (dz_nz[i + 1] ** 2 + dz_nz[i] ** 2)\r\n * (dz_nz[i + 1] - dz_nz[i])\r\n / (dz_nz[i + 1] + dz_nz[i])\r\n )\r\n else:\r\n pass\r\n upper_Ec[0] = 0\r\n lower_Ec[-1] = 0\r\n ### Set up Matrix Ec\r\n Ec = np.diag(np.ones(nz - 1) * lower_Ec, k=-1) + np.diag(\r\n np.ones(nz - 1) * upper_Ec, k=1\r\n )\r\n # Set up matrix Bc\r\n main_Bc = -a\r\n Bc = np.diag(np.ones(nz) * (main_Bc), k=0)\r\n # Compute c\r\n if Eterms:\r\n part1 = np.dot((Ac + Ec), T)\r\n else:\r\n part1 = np.dot(Ac, T)\r\n part2 = np.dot(Bc, T_prev)\r\n c = -(part1 + part2)\r\n return c","title":""},{"docid":"274369ab4476ad6fe371f39bbec752f3","score":"0.5252565","text":"def _solve_marginal_cov(self):\n status = self.mcov_solver.initialize(\n self.graph, self.optimized_results)\n\n # Record noise model (covariance) of last pose\n self.last_optimized_cov = self.get_marignal_cov_matrix(\n self._idc_in_graph[-1])\n\n if status != ms.MarginalCovarianceSolverStatus.SUCCESS:\n print(\"maginal covariance error\")\n print(status)","title":""},{"docid":"fd05fc24b0d47a71d17ebb1bbb4798c5","score":"0.5251973","text":"def _determine_prior_covariance_matrices(self):\n scaled_x = np.divide(self.X, self.l)\n scaled_m = np.divide(self.M, self.l)\n distance_xm = euclidean_distances(scaled_x, scaled_m, squared=True)\n distance_mm = euclidean_distances(scaled_m, scaled_m, squared=True)\n self.SIG_XM = self.a * np.exp(-distance_xm / 2)\n self.SIG_MM = self.a * np.exp(-distance_mm / 2) + np.identity(self.M.shape[0]) * self.lam * self.a\n self.B = self.a + self.b - np.sum(np.multiply(np.linalg.solve(self.SIG_MM, self.SIG_XM.T), self.SIG_XM.T), 0)","title":""},{"docid":"ad431bbf94579b152c070c174280cc11","score":"0.5250714","text":"def makeCovMat(\n self, gsAltitudes, gsPositions, layerHeights, cn2, L0):\n nLayers = len(layerHeights)\n\n # Convert gsPositions to radians from arcsecs\n gsPositions = gsPositions * (1./3600) * (numpy.pi/180)\n\n # Actually do covariance matrix calculation\n self.covmat = covarianceMatrix.matcov(\n self.nGS, self.nSubaps, self.nxSubaps, self.subapSizes,\n self.subapPositions, gsAltitudes, gsPositions, nLayers,\n layerHeights, cn2, L0, self.covmat)\n\n return self.covmat","title":""},{"docid":"a07fa2b0013004ee9d4a7d52410cd42d","score":"0.5250694","text":"def _compute_all(self) -> np.ndarray:\n _assert_numerical_iterable(self.acc, 'Gravitational acceleration vector')\n self.acc = np.copy(self.acc)\n if self.acc.ndim < 2:\n return self.estimate(self.acc, self.mag, self.representation)\n # Normalization of 2D arrays\n a = self.acc/np.linalg.norm(self.acc, axis=1)[:, None]\n angles = np.zeros((len(a), 3)) # Allocation of angles array\n # Estimate tilt angles\n angles[:, 0] = np.arctan2(a[:, 1], a[:, 2])\n angles[:, 1] = np.arctan2(-a[:, 0], np.sqrt(a[:, 1]**2 + a[:, 2]**2))\n if self.mag is not None:\n _assert_numerical_iterable(self.mag, 'Geomagnetic field vector')\n # Estimate heading angle\n m = self.mag/np.linalg.norm(self.mag, axis=1)[:, None]\n my2 = m[:, 2]*np.sin(angles[:, 0]) - m[:, 1]*np.cos(angles[:, 0])\n mz2 = m[:, 1]*np.sin(angles[:, 0]) + m[:, 2]*np.cos(angles[:, 0])\n mx3 = m[:, 0]*np.cos(angles[:, 1]) + mz2*np.sin(angles[:, 1])\n angles[:, 2] = np.arctan2(my2, mx3)\n # Return angles in degrees\n if self.representation == 'angles':\n return angles*RAD2DEG\n # RPY to Quaternion\n cp = np.cos(0.5*angles[:, 1])\n sp = np.sin(0.5*angles[:, 1])\n cr = np.cos(0.5*angles[:, 0])\n sr = np.sin(0.5*angles[:, 0])\n cy = np.cos(0.5*angles[:, 2])\n sy = np.sin(0.5*angles[:, 2])\n Q = np.zeros((len(self.acc), 4))\n Q[:, 0] = cy*cp*cr + sy*sp*sr\n Q[:, 1] = cy*cp*sr - sy*sp*cr\n Q[:, 2] = sy*cp*sr + cy*sp*cr\n Q[:, 3] = sy*cp*cr - cy*sp*sr\n if self.representation == 'quaternion':\n return Q/np.linalg.norm(Q, axis=1)[:, None]\n return q2R(Q)","title":""},{"docid":"bfbaf91e3905e2e4c03fd2bdcea6ef3c","score":"0.52389455","text":"def chi_xyz(self):\n z = np.abs(self.grid[2] + 0.5 * self.csize)\n a = z - 0.5 * self.csize\n b = z + 0.5 * self.csize\n\n a = np.where(b <= self.params['geometry']['r_0'], np.NaN, a)\n b = np.where(b <= self.params['geometry']['r_0'], np.NaN, b)\n\n a = np.where(a <= self.params['geometry']['r_0'],\n self.params['geometry']['r_0'], a)\n\n z *= con.au\n a *= con.au\n b *= con.au\n\n def t_z(z):\n \"\"\"\n Time as a function of z. Defined purely for informative purposes\n \"\"\"\n r_0 = self.params['geometry']['r_0'] * con.au\n v_0 = self.params['properties']['v_0'] * 1000\n q_v = self.params['power_laws']['q_v']\n return (r_0 ** q_v * z ** (1. - q_v) - r_0) / (v_0 * (1. - q_v))\n\n def int_t_z(z):\n \"\"\"\n Integral of t_z defined above for use in average value finding\n \"\"\"\n r_0 = self.params['geometry']['r_0'] * con.au\n v_0 = self.params['properties']['v_0'] * 1000.\n q_v = self.params['power_laws']['q_v']\n num = r_0 ** q_v * z ** (2. - q_v) + (q_v - 2.) * r_0 * z\n den = v_0 * (q_v - 2.) * (q_v - 1.)\n return num / den\n\n av_ts = 1. / (b - a)\n av_ts *= int_t_z(b) - int_t_z(a)\n\n # So that times start at 0 at r_0 and to progress to current model time\n av_ts = self.time - av_ts\n\n av_ts = np.where(self.fill_factor > 0, av_ts, np.NaN)\n\n av_chis = self._jml_t(av_ts) / self._ss_jml\n\n return av_chis","title":""},{"docid":"03e84202120235991ed925cbd564595d","score":"0.5234817","text":"def TMS_cov(r, phi):\r\n cp = np.cos(phi)\r\n sp = np.sin(phi)\r\n ch = np.cosh(r)\r\n sh = np.sinh(r)\r\n\r\n S = np.array(\r\n [\r\n [ch, cp * sh, 0, sp * sh],\r\n [cp * sh, ch, sp * sh, 0],\r\n [0, sp * sh, ch, -cp * sh],\r\n [sp * sh, 0, -cp * sh, ch],\r\n ]\r\n )\r\n\r\n return S @ S.T","title":""},{"docid":"c33e11cf17027bad58f949e09ac40730","score":"0.5226706","text":"def covariance(self, data):\n vectors = []\n for doc in data:\n vectors.append(doc.vector)\n datmatrix = numpy.matrix(vectors)\n sigma = numpy.matrix(numpy.cov(numpy.concatenate((datmatrix, datmatrix), 0).H))\n for i in range(numpy.shape(sigma)[0]):\n sigma[i, i] += 0.001\n\n return sigma","title":""},{"docid":"ee23912da49ce6bd631c1567a213602a","score":"0.52259743","text":"def construct_covariance_matrix(self, vol_matrix, corr_matrix):\n cov_matrix = np.dot(vol_matrix, np.dot(corr_matrix, vol_matrix))\n return cov_matrix","title":""},{"docid":"e9bf7c75c8e3257f89b4537d0d76749b","score":"0.52246374","text":"def cov(self):\n x_T = np.transpose(self.x)\n x_cov = np.cov(x_T)\n return x_cov","title":""},{"docid":"6b459b03141731d4844a6791f4e5ffee","score":"0.52108973","text":"def dynamics_model(state, dt, moonEph, sunEph, main_thrust_info):\n moonEph = moonEph*1000\n sunEph = sunEph*1000\n rec = state[0:3].T*1000\n rec_dot = state[3:6].T*1000\n rem = moonEph[0, 0:3]\n res = sunEph[0, 0:3]\n rcm = rec-rem\n rcs = rec-res\n position = rec + rec_dot * dt\n # Convert thrust vector into ECI frame\n net_acceleration_thrust = np.zeros((1,3))\n if main_thrust_info is not None:\n kick_orientation = main_thrust_info['kick_orientation']\n acc_mag = main_thrust_info['acceleration_magnitude']\n Aq = attitudeMatrix(kick_orientation).reshape(3,3)\n local_vector = np.array([1, 0, 0, 0]) # last 0 is padding\n net_acceleration_thrust = (np.dot(Aq,local_vector[:3].T.reshape(3,1)) * acc_mag).reshape(1,3)\n \n # Inlined RK4\n n1 = G(position, rem, rcm, rcs, res, thrust=net_acceleration_thrust)\n net_acceleration_thrust = np.zeros((1,3))\n n2 = G(position+dt*n1/2, rem, rcm, rcs, res, thrust=net_acceleration_thrust)\n n3 = G(position+dt*n2/2, rem, rcm, rcs, res, thrust=net_acceleration_thrust)\n n4 = G(position+n3*dt, rem, rcm, rcs, res, thrust=net_acceleration_thrust)\n velocity = rec_dot + (1.0/6.0)*(n1 + 2*n2 + 2*n3 + n4)*dt # TODO: main thrust fire delta time\n\n return np.concatenate((position, velocity), axis=None) / 1000","title":""},{"docid":"f1ed3bc35b2ee491dacd201220953e96","score":"0.51990944","text":"def _calculate_initial_coefficients(\n self, iteration: int, n_iterations: int\n ) -> Tuple[float, float]:\n\n r1 = r.generate_uniform_random_number()\n\n E_0 = 2 * r1 - 1\n J = 2 * (1 - r1)\n\n # Calculates the energy (eq. 3)\n E = 2 * E_0 * (1 - (iteration / n_iterations))\n\n return np.fabs(E), J","title":""},{"docid":"9855b7f1d5aa2f3e249a6126e473da48","score":"0.5186826","text":"def get_covariance_matrix(self, X):\n # what should be instead of K in this term 1/(N-K) in this case ?!\n covariance_matrix = (1 / (self.nr_data - 1)) * (X - X.mean(axis=0)).T @ (X - X.mean(axis=0))\n return covariance_matrix","title":""},{"docid":"e64969d1650cbe45bf4f30a6fc2cf490","score":"0.518431","text":"def process_noise_covariance():\n # noise only in the vel part of the state which depends on a*t (not measurable)\n Q = np.zeros((6,6))\n Q[3,3] = Q[4,4] = Q[5,5] = 0.05 \n return Q","title":""},{"docid":"b84389aa3230cc8401ab26b98b8d531c","score":"0.51784927","text":"def _CMatrix(epoch):\n B = ICRSCoordinates.frameBiasJ2000\n if epoch is None:\n return B\n else:\n from math import sin,cos,atan,atan2,sqrt\n from ..utils import rotation_matrix\n \n P = _precession_matrix_J2000_Capitaine(epoch)\n N = _nutation_matrix(epoch)\n \n \n x,y,z = (N*P*B).A[2]\n xsq,ysq = x**2,y**2\n bz = 1/(1+z)\n s = CIRSCoordinates._CIOLocator(epoch)\n \n #matrix components - see Circular 179 or IERS Conventions 2003\n a,b,c = 1-bz*xsq , -bz*x*y , -x\n d,e,f = -bz*x*y , 1 - bz*ysq , -y\n g,h,i = x , y , 1 - bz*(xsq+ysq)\n \n #return rotation_matrix(-s,'z',defrees=False)*np.mat([[a,b,c],\n # [d,e,f],\n # [g,h,i]]) \n \n si = sin(s)\n co = cos(s)\n \n M = [[a*co - d*si,b*co - e*si,c*co - f*si],\n [a*si + d*co,b*si + e*co,c*si + f*co],\n [ g, h, i ]] \n return np.mat(M)","title":""},{"docid":"0c5acdbed42c2bf00df94bd4cb7bd96b","score":"0.5175023","text":"def modcovar(x, order):\n from spectrum import corrmtx\n import scipy.linalg\n X = corrmtx(x, order, 'modified')\n Xc = np.array(X[:,1:])\n X1 = np.array(X[:,0])\n\n # Coefficients estimated via the covariance method\n # Here we use lstsq rathre than solve function because Xc is not square matrix\n a, residues, rank, singular_values = scipy.linalg.lstsq(-Xc, X1)\n\n # Estimate the input white noise variance\n\n\n Cz = np.dot(X1.conj().transpose(), Xc)\n e = np.dot(X1.conj().transpose(), X1) + np.dot(Cz, a)\n assert e.imag < 1e-4, 'wierd behaviour'\n e = float(e.real) # ignore imag part that should be small\n\n return a, e","title":""},{"docid":"aa4391f36787822df6b776c1efe3014d","score":"0.5170733","text":"def cov_estimator(TS):\n return np.cov(TS.T)","title":""},{"docid":"9754513ee7ed8b57e972e40cade7617d","score":"0.51630664","text":"def cov(M):\r\n N = M.shape[0]\r\n u = M.mean(axis=0)\r\n M = M - np.kron(np.ones((N, 1)), u)\r\n C = np.dot(M.T, M) / (N-1)\r\n return C","title":""},{"docid":"fb9149d41185c6b9a05e0f11d5d29b56","score":"0.5162007","text":"def initialization_SIS_withMCMC(self, temperature_ts, daytype_ts, elec_ts, len_initialization_MCMC):\n ###1. Create variables to store the MCMC results between date = 0 and date = len_initialization_MCMC - 1\n #(Convergence can therefore be checked by the user)\n self.MCMC_s = np.zeros((len_initialization_MCMC, self.nb_particles)) \n self.MCMC_g_heat = np.zeros((len_initialization_MCMC, self.nb_particles))\n self.MCMC_sigma2_s_star = np.zeros((1, self.nb_particles)) #See the article: we assume here that sigma_s and sigma_g do not follow any dynamic (they remain fixed to sigma_s_star and sigma_g_star)\n self.MCMC_sigma2_g_star = np.zeros((1, self.nb_particles))\n ###2. Gibbs-sampler \n #Gibbs Initialization\n self.MCMC_s[0,0] = truncnorm.rvs(a = 0,b = math.inf, loc= 0, scale = 10**4, size=1)[0]\n self.MCMC_g_heat[0,0] = truncnorm.rvs(a = - math.inf,b = 0, loc= 0, scale = 10**4, size=1)[0]\n self.MCMC_sigma2_s_star[0, 0] = 1 / (np.random.gamma(size = 1, shape = 0.01, scale = 100)[0])\n self.MCMC_sigma2_g_star[0, 0] = 1 / (np.random.gamma(size = 1, shape = 0.01, scale = 100)[0])\n for i in range(1, len_initialization_MCMC,1):\n self.MCMC_s[i,0] = self.sample_new_s(s_prev = self.MCMC_s[i-1,0], sigma_s_current = (self.MCMC_sigma2_s_star[0, 0])**0.5)\n self.MCMC_g_heat[i,0] = self.sample_new_g_heat(g_heat_prev = self.MCMC_g_heat[i-1,0], sigma_g_current = (self.MCMC_sigma2_g_star[0, 0])**0.5)\n #Gibbs Main\n for j in range(1, self.nb_particles, 1):\n self.MCMC_s[:,j] = self.MCMC_s[:,j-1]\n self.MCMC_g_heat[:,j] = self.MCMC_g_heat[:,j-1]\n self.MCMC_sigma2_s_star[:,j] = self.MCMC_sigma2_s_star[:,j-1]\n self.MCMC_sigma2_g_star[:,j] = self.MCMC_sigma2_g_star[:,j-1]\n ##a. Simulate s0\n #Compute the denominator of the variance and the mean\n denom_s_0 = (10**8)*self.MCMC_sigma2_s_star[0, j]*(self.kappa[daytype_ts[0]]**2) + self.sigma2*self.MCMC_sigma2_s_star[0, j] + (10**8)*self.sigma2 \n #Compute the numerator of the mean\n if (self.u_heat > temperature_ts[0]):\n numerator_mean_s_0 = (10**8)*self.sigma2* self.MCMC_s[1,j] + (10**8)*self.MCMC_sigma2_s_star[0, j]*self.kappa[daytype_ts[0]]*(elec_ts[0] - self.MCMC_g_heat[0,j]*(temperature_ts[0] - self.u_heat))\n else :\n numerator_mean_s_0 = (10**8)*self.sigma2* self.MCMC_s[1,j] + (10**8)*self.MCMC_sigma2_s_star[0, j]*self.kappa[daytype_ts[0]]*(elec_ts[0])\n #Compute the final parameters of the truncated normal that simulates from the full conditional of s_0\n mean_s_0 = numerator_mean_s_0 / denom_s_0\n var_s_0 = ((10**8) *self.sigma2*self.MCMC_sigma2_s_star[0, j]) / denom_s_0\n a_s_0, b_s_0 = (0 - mean_s_0) / (var_s_0**0.5), math.inf\n self.MCMC_s[0,j] = truncnorm.rvs(a = a_s_0, b = b_s_0, loc= mean_s_0, scale = (var_s_0**0.5), size=1)[0]#loc = mean / scale = standard deviation\n if(self.MCMC_s[0,j]==math.inf):\n self.MCMC_s[0,j]=0\n ##b. Simulate all other s(i)\n for i in range(1, len_initialization_MCMC,1):\n denom_s_i = 2*self.sigma2 + self.MCMC_sigma2_s_star[0, j]*(self.kappa[daytype_ts[i]]**2)\n if (i+1 < len_initialization_MCMC-1):\n dependence_next_s = self.MCMC_s[i+1,j]\n else : \n dependence_next_s = 0\n #Compute the numerator of the mean\n if (self.u_heat > temperature_ts[i]):\n numerator_mean_s_i = self.sigma2*(self.MCMC_s[i-1,j] + dependence_next_s) + self.MCMC_sigma2_s_star[0, j]*self.kappa[daytype_ts[i]]*(elec_ts[i] - self.MCMC_g_heat[i,j]*(temperature_ts[i] - self.u_heat))\n else :\n numerator_mean_s_i = self.sigma2*(self.MCMC_s[i-1,j] + dependence_next_s) + self.MCMC_sigma2_s_star[0, j]*self.kappa[daytype_ts[i]]*(elec_ts[i])\n mean_s_i = numerator_mean_s_i / denom_s_i\n var_s_i = (self.sigma2*self.MCMC_sigma2_s_star[0, j]) / denom_s_i\n a_s_i, b_s_i = (0 - mean_s_i) / (var_s_i**0.5), math.inf\n self.MCMC_s[i,j] = truncnorm.rvs(a = a_s_i, b = b_s_i, loc= mean_s_i, scale = (var_s_i**0.5), size=1)[0]\n if(self.MCMC_s[i,j]==math.inf):\n self.MCMC_s[i,j]=0\n ##c. Simulate g_heat0\n #Compute the numerator and the denominator of the mean\n if (self.u_heat > temperature_ts[0]):\n denom_g_0 = (10**8)*self.MCMC_sigma2_g_star[0, j]*((temperature_ts[0] - self.u_heat )**2) + self.sigma2*self.MCMC_sigma2_g_star[0, j] + (10**8)*self.sigma2 \n numerator_mean_g_0 = (10**8)*self.sigma2* self.MCMC_g_heat[1,j] + (10**8)*self.MCMC_sigma2_g_star[0, j]*(temperature_ts[0] - self.u_heat)*(elec_ts[0] - self.MCMC_s[0,j]*self.kappa[daytype_ts[0]])\n else :\n denom_g_0 = self.sigma2*self.MCMC_sigma2_g_star[0, j] + (10**8)*self.sigma2 \n numerator_mean_g_0 = (10**8)*self.sigma2* self.MCMC_g_heat[1,j]\n #Compute the final parameters of the truncated normal that simulates from the full conditional of g_0\n mean_g_0 = numerator_mean_g_0 / denom_g_0\n var_g_0 = ((10**8) *self.sigma2*self.MCMC_sigma2_g_star[0, j]) / denom_g_0\n a_g_0, b_g_0 = -math.inf, (0 - mean_g_0) / (var_g_0**0.5)\n self.MCMC_g_heat[0,j] = truncnorm.rvs(a = a_g_0, b = b_g_0, loc= mean_g_0, scale = (var_g_0**0.5), size=1)[0]\n if(self.MCMC_g_heat[0,j]==-math.inf):\n self.MCMC_g_heat[0,j]=0\n ##d. Simulate all other g_heat(i)\n for i in range(1, len_initialization_MCMC,1):\n if (i+1 < len_initialization_MCMC-1):\n dependence_next_g = self.MCMC_g_heat[i+1,j]\n else : \n dependence_next_g = 0\n if (self.u_heat > temperature_ts[i]):\n denom_g_i = 2*self.sigma2 + self.MCMC_sigma2_g_star[0, j]*((temperature_ts[i] - self.u_heat )**2)\n numerator_mean_g_i = self.sigma2*(self.MCMC_g_heat[i-1,j] + dependence_next_g) + self.MCMC_sigma2_g_star[0, j]*(temperature_ts[i] - self.u_heat )*(elec_ts[i] - self.MCMC_s[i,j]*self.kappa[daytype_ts[i]])\n else :\n denom_g_i = 2*self.sigma2\n numerator_mean_g_i = self.sigma2*(self.MCMC_g_heat[i-1,j] + dependence_next_g)\n mean_g_i = numerator_mean_g_i / denom_g_i\n var_g_i = (self.sigma2*self.MCMC_sigma2_g_star[0, j]) / denom_g_i\n a_g_i, b_g_i = -math.inf, (0 - mean_g_i) / (var_g_i**0.5)\n self.MCMC_g_heat[i,j] = truncnorm.rvs(a = a_g_i, b = b_g_i, loc= mean_g_i, scale = (var_g_i**0.5), size=1)[0]\n if(self.MCMC_g_heat[i,j]==-math.inf):\n self.MCMC_g_heat[i,j]=0\n ##e. Simulate the variances\n shape_variances = 0.01 + ((len_initialization_MCMC - 1)/2)\n s_lag = np.roll(self.MCMC_s[:,j], 1)\n s_lag[0] = self.MCMC_s[0,j]\n rate_s = 0.01 + sum((self.MCMC_s[:,j] - s_lag)**2)\n self.MCMC_sigma2_s_star[0, j] = 1 / (np.random.gamma(size = 1, shape = shape_variances, scale = 1/rate_s )[0])\n g_lag = np.roll(self.MCMC_g_heat[:,j], 1)\n g_lag[0] = self.MCMC_g_heat[0,j]\n rate_g = 0.01 + sum((self.MCMC_g_heat[:,j] - g_lag)**2)\n self.MCMC_sigma2_g_star[0, j] = 1 / (np.random.gamma(size = 1, shape = shape_variances, scale = 1/rate_g )[0])\n ###3. Add a prior on sigma_s and sigma_g based on the estimated errors\n s_lag = np.roll(self.MCMC_s, shift = 1, axis =0)\n s_lag[0,] = self.MCMC_s[0,:]\n error_s = self.MCMC_s - s_lag \n error_s = error_s[1:len_initialization_MCMC, :] #delete first row\n #compute the standard error at each time i\n error_s_std = np.std(error_s, axis =1)\n #compute the average of the standard errors among all times\n error_s_mean_of_std = np.mean(error_s_std)\n error_s_std_of_std = np.std(error_s_std)\n #same process for g_heat\n g_lag = np.roll(self.MCMC_g_heat, shift = 1, axis =0)\n g_lag[0,] = self.MCMC_g_heat[0,:]\n error_g = self.MCMC_g_heat - g_lag \n error_g = error_g[1:len_initialization_MCMC, :] #delete first row\n error_g_std = np.std(error_g, axis =1)\n error_g_mean_of_std = np.mean(error_g_std)\n error_g_std_of_std = np.std(error_g_std)\n ###4. Return the initialization of the Particle Filter at date (len_initialization_MCMC - 1)\n self.s[0,] = self.MCMC_s[len_initialization_MCMC-1,self.nb_particles-1] + np.zeros(self.nb_particles)\n self.g_heat[0,] = self.MCMC_g_heat[len_initialization_MCMC-1,self.nb_particles-1]+ np.zeros(self.nb_particles)\n self.sigma_s[0,] = self.MCMC_sigma2_s_star[0,self.nb_particles-1]**0.5+ np.zeros(self.nb_particles)\n self.sigma_g[0,] = self.MCMC_sigma2_g_star[0,self.nb_particles-1]**0.5+ np.zeros(self.nb_particles)\n #self.sigma_s[0,] = truncnorm.rvs(a = (- error_s_mean_of_std / error_s_std_of_std) ,b = math.inf, loc= error_s_mean_of_std, scale = error_s_std_of_std, size=self.nb_particles)\n #self.sigma_g[0,] = truncnorm.rvs(a = (- error_g_mean_of_std / error_g_std_of_std) ,b = math.inf, loc= error_g_mean_of_std, scale = error_g_std_of_std, size=self.nb_particles)\n self.x[0,] = self.compute_x(s = self.s[0,], g_heat = self.g_heat[0,], temperature = temperature_ts[len_initialization_MCMC-1], daytype = daytype_ts[len_initialization_MCMC-1])","title":""},{"docid":"593c5541d8ff73f1b543f7e36f9534d6","score":"0.51595795","text":"def simulation(self, T=100):\n # Vars\n x0 = np.array([0.5, 0.0, 5.0, 0.0, 0.0, 0.0]) \n A = self.system_matrix()\n # -observations\n observation_size = self.observation_dim\n obs_mean = np.zeros((observation_size,))\n obs_cov = self.observation_noise_covariance() #(2x2)\n observations = np.zeros((T,observation_size)) \n # -states\n predState_mean = np.zeros((6,))\n predState_cov = self.process_noise_covariance() #(6x6) \n states = np.zeros((T,6))\n \n # States and observations\n for i in range(T):\n if i == 0:\n states[i,:] = x0\n obs_noise = np.random.multivariate_normal(obs_mean, obs_cov)\n observations[i,:] = self.observation(x0) + obs_noise\n else:\n predState_noise = np.random.multivariate_normal(predState_mean, predState_cov)\n states[i,:] = np.dot(A,states[i-1,:]) + predState_noise\n obs_noise = np.random.multivariate_normal(obs_mean, obs_cov)\n observations[i,:] = self.observation(states[i,:]) + obs_noise\n \n return states, observations","title":""},{"docid":"b462161ee0554d9b93ab326189ecb811","score":"0.5156437","text":"def covariance(self):\n for cat in self.probabilities:\n if cat != 'Attrition':\n self.covariances[cat] = 0\n for x in [0,1]:\n for sub_cat in self.probabilities[cat]:\n self.covariances[cat] += ((float(x)-self.expectations['Attrition'])*(float(sub_cat)-self.expectations[cat])*(self.probabilities['Attrition'][x])*(self.probabilities[cat][sub_cat]))\n #print(cat,(float(x)-self.expectations['Attrition'])*(float(sub_cat)-self.expectations[cat])*(self.probabilities['Attrition'][x])*(self.probabilities[cat][sub_cat]),self.covariances[cat])\n\n else:\n self.covariances[cat] = self.variances[cat]\n\n self.rho[cat] = self.covariances[cat]/((self.variances['Attrition']*self.variances[cat])**0.5)","title":""},{"docid":"c6e95edbfc5eb889c99c172d03ada8f3","score":"0.5153598","text":"def motion_jacobian_noise_components(self):\n # States\n theta = self.state_vector[2]\n \n # Velocities\n v = self.velocity_vector[0]\n w = self.velocity_vector[1]\n \n # Expected value of error\n dv = 0\n dw = 0\n \n # Initialize\n self.G = np.zeros((self.state_vector_size, self.control_size))\n \n # Calculate Jacobian\n # Non linear motion model when there's no angular velocity\n if abs(w + dw) > 1e-4:\n angle = theta + (w + dw) * self.dt\n dc = np.cos(angle) - np.cos(theta)\n ds = np.sin(angle) - np.sin(theta)\n\n self.G[0][0] = ds / (w + dw)\n self.G[0][1] = (np.cos(angle) * (v + dv) * self.dt / (w + dw)) - (v + dv) * ds / np.square(w + dw)\n self.G[1][0] = -dc / (w + dw)\n self.G[1][1] = (v + dv) * dc / np.square(w + dw) + np.sin(angle) * (v + dv) * self.dt / (w + dw)\n self.G[2][0] = 0\n self.G[2][1] = self.dt\n\n # Linear motion model\n else:\n self.G[0][0] = np.sin(theta) * self.dt\n self.G[0][1] = np.sin(theta) * self.dt","title":""},{"docid":"0cabd4f5183b222e0a83ccfa14d6df52","score":"0.51508665","text":"def getCovarianceMatrix(self):\n return self.cov_matrix","title":""},{"docid":"1b113e765bdf246dc0415482a39592da","score":"0.5148368","text":"def simulate(self, time, seed=None):\n if self._x0 is None:\n raise InitialConditionNotDefined\n\n trans_indices_A = np.nonzero(self.A)\n nr_trans_A = trans_indices_A[0].shape[0]\n\n trans_indices_B = np.nonzero(self.B)\n nr_trans_B = trans_indices_B[0].shape[0]\n\n x = copy.copy(self._x0)\n t = 0\n T = [0]\n X = [copy.copy(x)]\n\n def transition_rates(_x):\n\n _trans_rates_A = np.zeros(shape=trans_indices_A[0].shape)\n\n for j in range(nr_trans_A):\n _k, _s, _s_prime = trans_indices_A[0][j], trans_indices_A[1][j], trans_indices_A[2][j]\n _trans_rates_A[j] = self.A[_k, _s, _s_prime] * _x[_k, _s]\n\n _trans_rates_B = np.zeros(shape=trans_indices_B[0].shape)\n\n for j in range(nr_trans_B):\n _k, _k1 = trans_indices_B[0][j], trans_indices_B[1][j]\n _s, _s1 = trans_indices_B[2][j], trans_indices_B[3][j]\n _s_prime, _s1_prime = trans_indices_B[4][j], trans_indices_B[5][j]\n _trans_rates_B[j] = self.B[_k, _k1, _s, _s1, _s_prime, _s1_prime] * _x[_k, _s] * _x[_k1, _s1]\n\n return _trans_rates_A, _trans_rates_B\n\n # set random generator to seed\n rnd.seed(seed)\n\n while t < time:\n # transition rates evaluated at x\n trans_rates_A, trans_rates_B = transition_rates(x)\n S = np.sum(trans_rates_A) + np.sum(trans_rates_B)\n\n if S <= 1e-12:\n print('System stalled (total rate = 0)')\n t = time\n else:\n a = rnd.random() * S\n # selected transition\n transition_selected = False\n for i in range(trans_rates_A.shape[0]):\n if a > trans_rates_A[i]:\n a -= trans_rates_A[i]\n else:\n transition_selected = True\n k, s, s_prime = trans_indices_A[0][i], trans_indices_A[1][i], trans_indices_A[2][i]\n # add unilateral transition to state vector\n x += (- self.e(k, s) + self.e(k, s_prime))\n break\n\n if transition_selected is False:\n for i in range(trans_rates_B.shape[0]):\n if a > trans_rates_B[i]:\n a -= trans_rates_B[i]\n else:\n k, k1 = trans_indices_B[0][i], trans_indices_B[1][i]\n s, s1 = trans_indices_B[2][i], trans_indices_B[3][i]\n s_prime, s1_prime = trans_indices_B[4][i], trans_indices_B[5][i]\n # add unilateral transition to state vector\n x += (- self.e(k, s) + self.e(k, s_prime) - self.e(k1, s1) + self.e(k1, s1_prime))\n break\n\n t += rnd.expovariate(S)\n\n T.append(t)\n X.append(copy.copy(x))\n\n X = np.array(X)\n return T, X","title":""}],"string":"[\n {\n \"docid\": \"bc9b754996a3685fcb12d5c38d26cd34\",\n \"score\": \"0.6350269\",\n \"text\": \"def covariance(self):\\n c,r = self.size(); float(c); float(r)\\n mean_vector = self.mean()\\n covariance_mat = mmatrix((c,r))\\n for index_c in range(c):\\n tally = float(0)\\n for index2_c in range(c):\\n for i in range(r):\\n #print self[index_c, i], self[index2_c, i]\\n tally += (self[index_c, i] - mean_vector[index_c])*(self[index2_c, i] - mean_vector[index_c])\\n print (\\\"(%f - %f) * (%f - %f)\\\") % (self[index_c, i], mean_vector[index_c], self[index2_c, i], mean_vector[index_c])\\n #print (\\\"(%d, %d) %d\\\") % (index_c, index2_c, tally)\\n try:\\n temp = (1 / (r-1)) #'r' here is really \\\"N\\\" or 'number of observations\\n except (ZeroDivisionError):\\n print(\\\"division by zero on this pairing.\\\")\\n temp = 1\\n #covariance_mat[index_c, index2_c] = float((tally/temp))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d091fb7c45e898408132a82c3c76f3e9\",\n \"score\": \"0.6111639\",\n \"text\": \"def _mn_cov_ ( self , size = -1 , root = False ) :\\n #\\n if size <= 0 : size = len ( self )\\n size = min ( size , len ( self ) ) \\n #\\n from array import array\\n matrix = array ( 'd' , [ 0 for i in range ( 0 , size * size ) ] )\\n self.mnemat ( matrix , size )\\n #\\n import ostap.math.linalg\\n from ostap.core.core import Ostap \\n mtrx = Ostap.Math.SymMatrix ( size )() \\n for i in range ( 0 , size ) :\\n for j in range ( i , size ) : \\n mtrx [ i , j ] = matrix [ i * size + j ]\\n \\n return mtrx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a29e4d0a5aeb19566111253242bc6eaf\",\n \"score\": \"0.59871054\",\n \"text\": \"def _mn_cov_(self, size=-1, root=False):\\n #\\n if size <= 0:\\n size = len(self)\\n size = min(size, len(self))\\n #\\n import array\\n matrix = array.array('d', [0 for i in range(0, size * size)])\\n self.mnemat(matrix, size)\\n #\\n if 1 == size and not root:\\n mtrx = cpp.Gaudi.Math.SymMatrix1x1()\\n elif 2 == size and not root:\\n mtrx = cpp.Gaudi.Math.SymMatrix2x2()\\n elif 3 == size and not root:\\n mtrx = cpp.Gaudi.Math.SymMatrix3x3()\\n elif 4 == size and not root:\\n mtrx = cpp.Gaudi.Math.SymMatrix4x4()\\n elif 5 == size and not root:\\n mtrx = cpp.Gaudi.Math.SymMatrix5x5()\\n elif 6 == size and not root:\\n mtrx = cpp.Gaudi.Math.SymMatrix6x6()\\n elif 7 == size and not root:\\n mtrx = cpp.Gaudi.Math.SymMatrix7x7()\\n # no 8x8!\\n # elif 9 == size and not root : mtrx = cpp.Gaudi.Math.SymMatrix9x9 ()\\n else:\\n # use ROOT matrices\\n mtrx = ROOT.TMatrix(size, size)\\n for i in range(0, size):\\n for j in range(0, size):\\n mtrx[i][j] = matrix[i * size + j]\\n return mtrx # RETURN\\n\\n for i in range(0, size):\\n for j in range(i, size):\\n mtrx[i, j] = matrix[i * size + j]\\n\\n return mtrx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3da2d978eb1a6874b90e68d66382e7af\",\n \"score\": \"0.59445214\",\n \"text\": \"def calcECIEqAltAz(self, state_vect, radiant_eci, observations):\\n\\n # Determine the first time of observation\\n t0 = min([obs.time_data[0] for obs in observations])\\n\\n # Go through observations from all stations\\n for obs in observations:\\n\\n # Init array for modelled ECI positons\\n obs.model_eci = []\\n\\n # Init array for modelled RA, Dec positions\\n obs.model_ra = np.zeros_like(obs.time_data)\\n obs.model_dec = np.zeros_like(obs.time_data)\\n\\n # Init arrays for modelled alt, az\\n obs.model_azim = np.zeros_like(obs.time_data)\\n obs.model_elev = np.zeros_like(obs.time_data)\\n\\n\\n # Go through all individual position measurement from each site\\n for i, (t, jd, stat, meas) in enumerate(zip(obs.time_data, obs.JD_data, obs.stat_eci_los, \\\\\\n obs.meas_eci_los)):\\n\\n # Calculate closest points of approach (observed line of sight to radiant line)\\n obs_cpa, rad_cpa, d = findClosestPoints(stat, meas, state_vect, radiant_eci)\\n\\n\\n # # Calculate the time in seconds from the beginning of the meteor\\n # t_rel = t - t0\\n\\n # # Calculate the gravitational acceleration at the given height\\n # g = G*EARTH.MASS/(vectMag(rad_cpa)**2)\\n\\n # # Determine the sign of the initial time\\n # time_sign = np.sign(t_rel)\\n\\n # # Calculate the amount of gravity drop from a straight trajectory (handle the case when the\\n # # time is negative)\\n # drop = time_sign*(1/2.0)*g*t_rel**2\\n\\n # # Apply gravity drop to ECI coordinates\\n # rad_cpa -= drop*vectNorm(rad_cpa)\\n\\n\\n # Set the ECI position of the CPA on the radiant line, as seen by this observer\\n obs.model_eci.append(rad_cpa)\\n\\n # Calculate the right ascension and declination of the modelled point from the observer's \\n # point of view\\n stat_rad_eci = rad_cpa - stat\\n model_ra, model_dec = eci2RaDec(stat_rad_eci)\\n\\n obs.model_ra[i] = model_ra\\n obs.model_dec[i] = model_dec\\n\\n # Calculate the azimuth and elevation of the modelled point from the observer's point of view\\n model_azim, model_elev = raDec2AltAz(model_ra, model_dec, obs.JD_data[i], obs.lat, obs.lon)\\n\\n obs.model_azim[i] = model_azim\\n obs.model_elev[i] = model_elev\\n\\n\\n obs.model_eci = np.array(obs.model_eci)\\n\\n\\n ### Assign model_fit1, model_fit2, so they are in the same format as the input meas1, meas2 data\\n ######################################################################################################\\n\\n # If inputs were RA and Dec\\n if self.meastype == 1:\\n\\n obs.model_fit1 = obs.model_ra\\n obs.model_fit2 = obs.model_dec\\n\\n # If inputs were azimuth +east of due north, and elevation angle\\n elif self.meastype == 2:\\n\\n obs.model_fit1 = obs.model_azim\\n obs.model_fit2 = obs.model_elev\\n\\n # If inputs were azimuth +west of due south, and zenith angle\\n elif self.meastype == 3:\\n\\n obs.model_fit1 = (obs.model_azim + np.pi)%(2*np.pi)\\n obs.model_fit2 = np.pi/2.0 - obs.model_elev\\n\\n # If input were azimuth +north of due east, and zenith angle\\n elif self.meastype == 4:\\n\\n obs.model_fit1 = (np.pi/2.0 - obs.model_azim)%(2*np.pi)\\n obs.model_fit2 = np.pi/2.0 - obs.model_elev\\n\\n ######################################################################################################\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cbba21331a7ee03a5427f6d90be8685\",\n \"score\": \"0.5922897\",\n \"text\": \"def cov(self):\\r\\n if 'correlation' not in self._paras.keys():\\r\\n print('correlation model has not been fitted yet!')\\r\\n return\\r\\n T = self._corrs.shape[2]\\r\\n d = self._corrs.shape[0]\\r\\n cov = np.empty([d, d, T])\\r\\n for i in range(T):\\r\\n corr = self._corrs[:, :, i]\\r\\n std = np.diag(self._stds[i, :]) # diagonal matrix of sigma\\r\\n cov[:, :, i] = std.dot(corr).dot(std)\\r\\n return cov\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d91dea75ef10db2aea4b1f0c6e962730\",\n \"score\": \"0.5781341\",\n \"text\": \"def cokurtosis(self):\\n r = self._X.values \\n mu = np.mean(r, axis=0).reshape(1, -1)\\n x = r - mu\\n n = x.shape[1] # number of assets\\n m = x.shape[0]\\n M4 = []\\n for i in range(n):\\n for j in range(n):\\n K = np.empty([n, n]) # k block inside Kronecker product\\n for k in range(n):\\n for l in range(n):\\n # E[(r_i - mu_i)*(r_j - mu_j)*(r_k - mu_k)*(r_l - mu_l)]\\n K[k, l] = np.sum(x[:, i]*x[:, j]*x[:, k]*x[:, l])/(m-1)\\n M4.append(K)\\n M4 = np.concatenate(M4, axis=1)\\n return M4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"550787b7b14ff37216ff3eae40c7dfb7\",\n \"score\": \"0.57466966\",\n \"text\": \"def calculate_cov(self):\\n self.Q = np.matmul(self.G, self.q)\\n self.Q = np.matmul(self.Q, self.G.transpose())\\n \\n self.P = np.matmul(self.F, self.P)\\n self.P = np.matmul(self.P, self.F.transpose()) + self.Q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77279bde8c4bd4b133efe70e54da35c\",\n \"score\": \"0.57117057\",\n \"text\": \"def covariance_matrix(sub,full,N_sub_vol):\\n Nr = full.shape[0] # Nr is the number of radial bins\\n cov = np.zeros((Nr,Nr)) # 2D array that keeps the covariance matrix \\n after_subtraction = sub - np.mean(sub,axis=0)\\n tmp = 0\\n for i in range(Nr):\\n for j in range(Nr):\\n tmp = 0.0\\n for k in range(N_sub_vol):\\n tmp = tmp + after_subtraction[k,i]*after_subtraction[k,j]\\n cov[i,j] = (((N_sub_vol-1)/N_sub_vol)*tmp)\\n \\n return cov\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfe485259c2269281d1492f312ce71c1\",\n \"score\": \"0.5705652\",\n \"text\": \"def __init__(self, init_x, init_y, Q=0.1 * np.eye(4), R=0.1 * np.eye(2)):\\n self.state = np.array([init_x, init_y, 0., 0.]) # state\\n self.cov = 0.1 * np.eye(4)\\n self.dt = np.eye(4)\\n self.dt[0,2] = 1\\n self.dt[1,3] = 1\\n self.sigma_dt = Q\\n self.mt = np.zeros((2,4))\\n self.mt[0,0] = 1\\n self.mt[1,1] = 1 \\n self.sigma_mt = R\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a05aa115ab7fbe987ee451cea1584a1c\",\n \"score\": \"0.5686504\",\n \"text\": \"def __simulate__(self,initialstate,reortho=False,verbose=False):\\n dtype=np.result_type(self._matvec(initialstate))\\n Dim=1\\n for d in initialstate.shape:\\n Dim*=d\\n #initialization:\\n xn=copy.deepcopy(initialstate)\\n xn/=np.sqrt(self._dot(xn.conjugate(),xn))\\n\\n xn_minus_1=self._zeros(initialstate.shape,dtype=dtype)\\n converged=False\\n it=0\\n kn=[]\\n epsn=[]\\n self._vecs=[]\\n first=True\\n while converged==False:\\n #normalize the current vector:\\n knval=np.sqrt(self._dot(xn.conjugate(),xn))\\n\\n if knval<self._delta:\\n converged=True\\n break\\n kn.append(knval)\\n xn=xn/kn[-1]\\n #store the Lanczos vector for later\\n\\n if reortho==True:\\n for v in self._vecs:\\n xn-=self._dot(v.conjugate(),xn)*v\\n self._vecs.append(xn) \\n Hxn=self._matvec(xn) \\n epsn.append(self._dot(xn.conjugate(),Hxn))\\n if ((it%self._Ndiag)==0)&(len(epsn)>=self._numeig):\\n #diagonalize the effective Hamiltonian\\n Heff=np.diag(epsn)+np.diag(kn[1:],1)+np.diag(np.conj(kn[1:]),-1)\\n eta,u=np.linalg.eigh(Heff)\\n if first==False:\\n if np.linalg.norm(eta[0:self._numeig]-etaold[0:self._numeig])<self._deltaEta:\\n converged=True\\n first=False\\n etaold=eta[0:self._numeig]\\n if it>0:\\n Hxn-=(self._vecs[-1]*epsn[-1])\\n Hxn-=(self._vecs[-2]*kn[-1])\\n else:\\n Hxn-=(self._vecs[-1]*epsn[-1])\\n xn=Hxn\\n it=it+1\\n if it>self._ncv:\\n break\\n\\n self._Heff=np.diag(epsn)+np.diag(kn[1:],1)+np.diag(np.conj(kn[1:]),-1)\\n eta,u=np.linalg.eigh(self._Heff)\\n states=[]\\n for n2 in range(min(self._numeig,len(eta))):\\n state=np.zeros(initialstate.shape,dtype=initialstate.dtype)\\n for n1 in range(len(self._vecs)):\\n state+=self._vecs[n1]*u[n1,n2]\\n states.append(state/np.sqrt(self._dot(state.conjugate(),state)))\\n return eta[0:min(self._numeig,len(eta))],states,converged\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c1483e760ed81e2d2aeb2bc7235fc75\",\n \"score\": \"0.5680264\",\n \"text\": \"def vector_field(self, state):\\r\\n A = state[: (p := len(self.config.network.populations))]\\r\\n R = state[p : 2*p]\\r\\n S = 1 - A - R\\r\\n CAA = _internals._unflat_scalar_triangle(\\r\\n state[2*p : 2*p + round(p*(p+1)/2)])\\r\\n CRR = _internals._unflat_scalar_triangle(\\r\\n state[2*p + round(p*(p+1)/2) : 2*p + p*(p+1)])\\r\\n CAR = (state[2*p + p*(p+1) :]).reshape((p,p))\\r\\n CAS = - CAA - CAR\\r\\n CRS = - CRR - CAR.transpose()\\r\\n B = self.config.Q.copy()\\r\\n VarB = np.zeros(p)\\r\\n CAB = np.zeros((p,p))\\r\\n CRB = np.zeros((p,p))\\r\\n CSB = np.zeros((p,p))\\r\\n for J, K in np.ndindex((p,p)):\\r\\n B[J] += self.config.network.c[J,K] * A[K]\\r\\n for L in range(p):\\r\\n VarB[J] += (self.config.network.c[J,K] \\r\\n * self.config.network.c[J,L] * CAA[K,L])\\r\\n CAB[J,K] += self.config.network.c[K,L] * CAA[J,L]\\r\\n CRB[J,K] += self.config.network.c[K,L] * CAR[L,J]\\r\\n CSB[J,K] += self.config.network.c[K,L] * CAS[L,J]\\r\\n f = np.zeros(self.dim)\\r\\n dCAA = np.zeros((p,p))\\r\\n dCRR = np.zeros((p,p))\\r\\n dCAR = np.zeros((p,p))\\r\\n for J, popJ in enumerate(self.config.network.populations):\\r\\n SG = np.where(S[J] == 0, 0,\\r\\n S[J] * popJ.G(B[J] + CSB[J,J]/S[J], VarB[J]))\\r\\n f[J] = (- popJ.beta * A[J] + popJ.alpha * SG)\\r\\n f[J+p] = - popJ.gamma * R[J] + popJ.beta * A[J]\\r\\n for K, popK in enumerate(self.config.network.populations):\\r\\n if J <= K:\\r\\n dCAA[J,K] = (- (popJ.beta + popK.beta) * CAA[J,K]\\r\\n + popJ.alpha * popJ.H(A[K], S[J], B[J],\\r\\n CAS[K,J], CAB[K,J], CSB[J,J], VarB[J])\\r\\n + popK.alpha * popK.H(A[J], S[K], B[K],\\r\\n CAS[J,K], CAB[J,K], CSB[K,K], VarB[K]))\\r\\n dCRR[J,K] = (- (popJ.gamma + popK.gamma) * CRR[J,K]\\r\\n + popJ.beta * CAR[J,K] + popK.beta * CAR[K,J])\\r\\n dCAR[J,K] = (- (popJ.beta + popK.gamma) * CAR[J,K]\\r\\n + popK.beta * CAA[J,K]\\r\\n + popJ.alpha * popJ.H(R[K], S[J], B[J],\\r\\n CRS[K,J], CRB[K,J], CSB[J,J], VarB[J]))\\r\\n f[2*p : 2*p + round(p*(p+1)/2)] = dCAA[np.triu_indices(p)]\\r\\n f[2*p + round(p*(p+1)/2) : 2*p + p*(p+1)] = dCRR[np.triu_indices(p)]\\r\\n f[2*p + p*(p+1) :] = dCAR.flatten()\\r\\n return np.array(f, float)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e604194857aaf26f204c3f135fd4488a\",\n \"score\": \"0.56795895\",\n \"text\": \"def covariance_matrix(self, start_date=None, end_date=None):\\n\\n indiv_ret = self.daily_individual_rates_of_return(start_date, end_date)\\n cov_matrix = indiv_ret.cov()\\n\\n return cov_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"177706cfddb4d4881a51b0e32f1afb00\",\n \"score\": \"0.5662406\",\n \"text\": \"def calcCovarianceMatrix(self,phase_volume):\\n input_array = []\\n for particle in phase_volume:\\n row = []\\n cylindrical_coordinates = particle.x.convertToCylindrical()\\n cylindrical_momentum = particle.p.convertToCylindrical(particle.x)\\n row.append(cylindrical_coordinates.rho)\\n row.append(cylindrical_coordinates.rho*cylindrical_coordinates.phi)\\n row.append(cylindrical_coordinates.z)\\n row.append(cylindrical_momentum.rho)\\n row.append(cylindrical_momentum.phi)\\n row.append(cylindrical_momentum.z)\\n row.append(particle.getEnergy())\\n input_array.append(row)\\n input_array = np.array(input_array).T #Transpose\\n return np.cov(input_array)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55dec521fc23d9d1215e9dce26c64bca\",\n \"score\": \"0.56399816\",\n \"text\": \"def a_cov(self):\\n a_cov = np.linalg.inv(splinal.sqrtm(np.eye(self.size_ens) + np.dot(self.hmx_mat,\\n np.dot(np.linalg.inv(self.rmatrix), self.hmx_mat.T))))\\n return a_cov\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e19c0d0bc6a98a469409f23386bc3d6\",\n \"score\": \"0.560998\",\n \"text\": \"def test_sumcovariancenormsmetric_compute_sum_covariancenorms():\\n generator = SumofCovarianceNormsMetric()\\n\\n time = datetime.datetime.now()\\n track = Track(states=[GaussianState(state_vector=[[1], [2], [1], [2]],\\n timestamp=time,\\n covar=np.diag([i, i, i, i]))\\n for i in range(5)])\\n\\n metric = generator.compute_sum_covariancenorms(track.states)\\n\\n assert metric.title == \\\"Covariance Matrix Norm Sum\\\"\\n assert metric.value == 20\\n assert metric.timestamp == time\\n assert metric.generator == generator\\n with pytest.raises(ValueError,\\n match=\\\"All states must be from the same time to compute total uncertainty\\\"):\\n generator.compute_sum_covariancenorms([\\n GaussianState(state_vector=[[1], [2], [1], [2]],\\n timestamp=time,\\n covar=np.diag([0, 0, 0, 0])),\\n GaussianState(state_vector=[[1], [2], [1], [2]],\\n timestamp=time+datetime.timedelta(seconds=1),\\n covar=np.diag([0, 0, 0, 0]))])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f9edf7a8042630d60904c6660604c7\",\n \"score\": \"0.5602733\",\n \"text\": \"def calcVelocity(self, state_vect, radiant_eci, observations, weights, calc_res=False):\\n\\n\\n # Go through observations from all stations\\n for obs in observations:\\n\\n # List of distances from the first trajectory point on the radiant line\\n first_pt_distances = []\\n\\n # List of distances from the state vector\\n state_vect_dist = []\\n\\n # Go through all individual position measurement from each site\\n for i, (stat, meas) in enumerate(zip(obs.stat_eci_los, obs.meas_eci_los)):\\n\\n # Calculate closest points of approach (observed line of sight to radiant line)\\n obs_cpa, rad_cpa, d = findClosestPoints(stat, meas, state_vect, radiant_eci)\\n\\n # Take the position of the first point as the reference point\\n if i == 0:\\n ref_point = np.copy(rad_cpa)\\n\\n # Calculate the distance from the first observed point to the projected point on the radiant line\\n dist = vectMag(ref_point - rad_cpa)\\n \\n first_pt_distances.append(dist)\\n\\n # Distance from the state vector to the projected point on the radiant line\\n state_vect_dist.append(vectMag(state_vect - rad_cpa))\\n\\n\\n # Convert the distances (length along the trail) into a numpy array\\n obs.length = np.array(first_pt_distances)\\n obs.state_vect_dist = np.array(state_vect_dist)\\n\\n\\n ### Calculate average velocity including all points up to the given point ###\\n velocities_prev_point = []\\n for i, (t, l) in enumerate(zip(obs.time_data, obs.length)):\\n\\n # For the first 4 points compute the velocity using the first 4 points\\n if i < 4:\\n time_part = obs.time_data[:4]\\n len_part = obs.length[:4]\\n\\n # Otherwise include all points up to the current point\\n else:\\n time_part = obs.time_data[: i+1]\\n len_part = obs.length[: i+1]\\n\\n # If there are NaNs or infs, drop them\\n filter_mask = np.logical_not(np.isnan(time_part) | np.isinf(time_part) | np.isnan(len_part) \\\\\\n | np.isinf(len_part))\\n\\n time_part = time_part[filter_mask]\\n len_part = len_part[filter_mask]\\n\\n if len(time_part) > 1:\\n\\n # Fit a line through time vs. length data\\n try:\\n popt, _ = scipy.optimize.curve_fit(lineFunc, time_part, len_part)\\n\\n # Check for this fit error, which happens extrememly rarely:\\n # RuntimeError: Optimal parameters not found: gtol=0.000000 is too small, func(x) is \\n # orthogonal to the columns of the Jacobian to machine precision.\\n except RuntimeError:\\n print(\\\"A velocity fit failed with a RuntimeError, skipping this iteration.\\\")\\n popt = [np.nan]\\n\\n velocities_prev_point.append(popt[0])\\n\\n else:\\n\\n # If there are no good points to estimate the velocity on, use NaN\\n velocities_prev_point.append(np.nan)\\n\\n\\n\\n obs.velocities_prev_point = np.array(velocities_prev_point)\\n\\n\\n ### ###\\n\\n ### Length vs. time\\n\\n # plt.plot(obs.state_vect_dist, obs.time_data, marker='x', label=str(obs.station_id), zorder=3)\\n\\n ##########\\n\\n ### Calculate point to point velocities ###\\n\\n # Shift the radiant distances one element down (for difference calculation)\\n dists_shifted = np.r_[0, obs.length][:-1]\\n\\n # Calculate distance differences from point to point (first is always 0)\\n dists_diffs = obs.length - dists_shifted\\n\\n # Shift the time one element down (for difference calculation)\\n time_shifted = np.r_[0, obs.time_data][:-1]\\n\\n # Calculate the time differences from point to point\\n time_diffs = obs.time_data - time_shifted\\n\\n # Replace zeros in time by machine precision value to avoid division by zero errors\\n time_diffs[time_diffs == 0] = np.finfo(np.float64).eps\\n\\n # Calculate velocity for every point\\n obs.velocities = dists_diffs/time_diffs\\n\\n ### ###\\n\\n\\n # plt.ylabel('Time (s)')\\n # plt.xlabel('Distance from state vector (m)')\\n\\n # plt.gca().invert_yaxis()\\n\\n # plt.legend()\\n # plt.grid()\\n # plt.savefig('mc_time_offsets.' + self.plot_file_type, dpi=300)\\n # plt.show()\\n\\n\\n\\n if calc_res:\\n\\n # Because the timing offsets have already been applied, the timing offsets are 0\\n zero_timing_res = np.zeros(len(self.observations))\\n\\n # Calculate the timing offset between the meteor time vs. length\\n \\n if self.timing_res is None:\\n self.timing_res = timingResiduals(zero_timing_res, self.observations, self.stations_time_dict, \\n weights=weights)\\n\\n self.timing_stddev = timingResiduals(zero_timing_res, self.observations, self.stations_time_dict, \\n weights=weights, ret_stddev=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f35a88516013a2246ef02adbdbd6bf1a\",\n \"score\": \"0.5575701\",\n \"text\": \"def covariance(psd, omega_vec):\\n d_omega_covariance = np.array([psd[:, :, i] + psd[:, :, i].T for i in range(psd.shape[-1])])\\n print(d_omega_covariance.shape)\\n covariance = np.zeros((psd.shape[0], psd.shape[0]))\\n for i in range(psd.shape[0]):\\n for j in range(psd.shape[0]):\\n covariance[i, j] = 0.5 * np.trapz(d_omega_covariance[:, i, j], omega_vec)\\n rms = np.sqrt(np.diag(covariance))\\n print(rms)\\n\\n return covariance, rms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7466566ca9fca84c28e27d98ca30f7c\",\n \"score\": \"0.55721\",\n \"text\": \"def CalcCovarMatrix(vects, cov_mat, avg, flags): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e48aa9f98238873776fb64f707a6fe\",\n \"score\": \"0.55661136\",\n \"text\": \"def create_matrix_drift_diffusion_varying_cs(self):\\n\\t\\t# initializing the matrix of coefficients\\n\\t\\tself.M = np.zeros((self.nL, self.nL))\\n\\t\\tM1 = np.zeros((self.nL, self.nL))\\n\\t\\tM2 = np.zeros((self.nL, self.nL))\\n\\t\\tM3 = np.zeros((self.nL, self.nL))\\n\\t\\tM4 = np.zeros((self.nL, self.nL))\\n\\t\\tM5 = np.zeros((self.nL, self.nL))\\n\\n\\t\\t# matrix 1 (dD/dx * dY/dx)\\n\\t\\tM1 = np.diag([(self.De_vector[i]-self.De_vector[i-2])/(4*self.dL**2) if i != 1 else (self.De_vector[1]-self.De_vector[0])/(4*self.dL**2) for i in range(1,self.nL)], k=1)\\n\\t\\tM1 += np.diag([-(self.De_vector[i+1]-self.De_vector[i-1])/(4*self.dL**2) if i != self.nL-1 else -(self.De_vector[self.nL-1]-self.De_vector[self.nL-2])/(4*self.dL**2) for i in range(1,self.nL)], k=-1)\\n\\t\\tM1[0,0] = -(self.De_vector[1]-self.De_vector[0])/(4*self.dL**2)\\n\\t\\tM1[-1,-1] = (self.De_vector[self.nL-1]-self.De_vector[self.nL-2])/(4*self.dL**2)\\n\\t\\t# matrix 2 (D * d2Y/dx2)\\n\\t\\tM2 = np.diag([-2*self.De_vector[i-1]/(self.dL**2) for i in range(1,self.nL+1)])\\n\\t\\tM2 += np.diag([self.De_vector[i-1]/(self.dL**2) for i in range(1,self.nL)], k=1)\\n\\t\\tM2 += np.diag([self.De_vector[i]/(self.dL**2) for i in range(1,self.nL)], k=-1)\\n\\t\\tM2[0,0] = -self.De_vector[0]/(self.dL**2)\\n\\t\\tM2[-1,-1] = -self.De_vector[self.nL-1]/(self.dL**2)\\n\\t\\t# matrix 3 (du/dx * E * Y)\\n\\t\\tM3 = np.diag([(self.mu_vector[i] - self.mu_vector[i-2])/(2*self.dL)*self.E_vector[i-1] if i not in [1,self.nL] else 0 for i in range(1,self.nL+1)])\\n\\t\\tM3[0,0] = (self.mu_vector[1] - self.mu_vector[0])/(2*self.dL)*self.E_vector[0]\\n\\t\\tM3[-1, -1] = (self.mu_vector[self.nL-1] - self.mu_vector[self.nL-2])/(2*self.dL)*self.E_vector[self.nL-1]\\n\\t\\t# matrix 4 (u * dE/dx * Y)\\n\\t\\tM4 = np.diag([(self.E_vector[i] - self.E_vector[i-2])/(2*self.dL)*self.mu_vector[i-1] if i not in [1,self.nL] else 0 for i in range(1,self.nL+1)])\\n\\t\\tM4[0,0] = (self.E_vector[1] - self.E_vector[0])/(2*self.dL)*self.mu_vector[0]\\n\\t\\tM4[-1, -1] = (self.E_vector[self.nL-1] - self.E_vector[self.nL-2])/(2*self.dL)*self.mu_vector[self.nL-1]\\n\\t\\t# matrix 5 (u * E* dY/dx)\\n\\t\\tM5 = np.diag([self.mu_vector[i-1]*self.E_vector[i-1]/(2*self.dL) for i in range(1,self.nL)], k=1)\\n\\t\\tM5 += np.diag([-self.mu_vector[i-1]*self.E_vector[i-1]/(2*self.dL) for i in range(1,self.nL)], k=-1)\\n\\t\\tM5[0,0] = self.mu_vector[0]*self.E_vector[0]/(2*self.dL)\\n\\t\\tM5[-1,-1] = -self.mu_vector[self.nL-1]*self.E_vector[self.nL-1]/(2*self.dL)\\n\\t\\t# total matrix\\n\\t\\tself.M = M1 + M2 + M3 + M4 + M5\\n\\n\\t\\t# # debugging lines\\n\\t\\t# print(M4)\\n\\t\\t# quit()\\n\\t\\t# # end debugging lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de3db26dd7b14232762f504c92cbf89f\",\n \"score\": \"0.55653065\",\n \"text\": \"def CorrectionCenterOfVelocity(self):\\n self.vx= self.vx-sum(self.m*self.vx)/self.Mt()\\n self.vy= self.vy-sum(self.m*self.vy)/self.Mt()\\n self.vz= self.vz-sum(self.m*self.vz)/self.Mt()\\n self.UptoDate=False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf8979a81bbd742a8d0adc5071549fe\",\n \"score\": \"0.5562396\",\n \"text\": \"def cov(\\n self,\\n t,\\n i=defaults[\\\"i\\\"],\\n p=defaults[\\\"p\\\"],\\n u=defaults[\\\"u\\\"][: defaults[\\\"udeg\\\"]],\\n ):\\n cov = self._flux.cov(t, i, p, u)\\n if self._time_variable:\\n cov *= self._temporal_kernel(t, t, self._tau)\\n if self._normalized:\\n mean = self._flux.mean(t, i, p, u)[0]\\n return self._normalize(1.0 + mean, cov)\\n else:\\n return cov\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0a537a64649a0f98908d198dc90ff5\",\n \"score\": \"0.5559678\",\n \"text\": \"def _mn_cor_ ( self , size = -1 , root = False ) :\\n #\\n cov = self.cov ( size , root )\\n #\\n from math import sqrt\\n #\\n if isinstance ( cov , ROOT.TMatrix ) :\\n\\n size = cov.GetNrows()\\n root = True\\n \\n else : size = cov.kRows\\n\\n ## use ROOT matrices \\n if root : cor = ROOT.TMatrix ( size , size )\\n else : cor = cov.__class__ () \\n\\n for i in range ( 0 , size ) :\\n \\n d_i = cov ( i , i )\\n cor [ i , i ] = 1 if 0 < d_i else 0\\n \\n for j in range ( i + 1 , size ) :\\n \\n d_j = cov ( j , j )\\n \\n if 0 != cov ( i , j ) and 0 < d_i and 0 < d_j :\\n \\n if root and root_major < 6 : cor [ i ] [ j ] = cov ( i , j ) / sqrt ( d_i * d_j )\\n else : cor [ i , j ] = cov ( i , j ) / sqrt ( d_i * d_j )\\n \\n else :\\n \\n if root and root_major < 6 : cor [ i ] [ j ] = 0 \\n else : cor [ i , j ] = 0\\n\\n return cor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d41d7d45fe5255f8e844f49fe5cb3ee\",\n \"score\": \"0.5556089\",\n \"text\": \"def get_C(self,Xtest):\\n\\n test_cov = self.model.kern.K(Xtest,Xtest)\\n sigmasqr = self.model.Gaussian_noise.variance[0]\\n K_NN_diags = self.model.kern.Kdiag(self.model.X)\\n K_NN = self.model.kern.K(self.model.X)\\n \\n K_star = self.model.kern.K(Xtest,self.model.Z.values)\\n print self.model.Z.values\\n K_NM = self.model.kern.K(self.model.X,self.model.Z.values)\\n K_MM = self.model.kern.K(self.model.Z.values)\\n invK_MM = np.linalg.inv(K_MM)\\n \\n #lambda values are the diagonal of the training input covariances minus \\n #(cov of training+pseudo).(inv cov of pseudo).(transpose of cov of training+pseudo)\\n lamb = np.zeros(len(self.model.X))\\n for i,t_in in enumerate(self.model.X):\\n lamb[i] = K_NN_diags[i] - np.dot(np.dot(K_NM[i,:].T,invK_MM),K_NM[i,:])\\n\\n #this finds (\\\\Lambda + \\\\sigma^2 I)^{-1}\\n diag = 1.0/(lamb + sigmasqr) #diagonal values\\n\\n Q = K_MM + np.dot(K_NM.T * diag,K_NM)\\n C = np.dot(np.dot(K_star, np.linalg.inv(Q)),K_NM.T) * diag\\n return C\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702e1d2719a7c9600efd100df7a9da71\",\n \"score\": \"0.55528426\",\n \"text\": \"def motion_jacobian_state_vector(self):\\n # States\\n theta = self.state_vector[2]\\n \\n # Velocities\\n v = self.velocity_vector[0]\\n w = self.velocity_vector[1]\\n \\n # Expected value of error\\n dv = 0\\n dw = 0\\n \\n # Identity matrix\\n self.F = np.identity((self.state_vector_size))\\n \\n # Calculate Jacobian\\n # Non linear motion model when there's no angular velocity\\n if abs(w + dw) > 1e-4: \\n d = (v + dv) / (w + dw)\\n self.F[0][2] = d * (-np.cos(theta) + np.cos(theta + (w + dw) * self.dt))\\n self.F[1][2] = d * (-np.sin(theta) + np.sin(theta + (w + dw) * self.dt))\\n\\n # Linear motion model\\n else: \\n self.F[0][2] = -np.sin(theta) * (v + dv) * self.dt\\n self.F[1][2] = np.cos(theta) * (v + dv) * self.dt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b185985bfdeb6036d413f44c85920b87\",\n \"score\": \"0.5540962\",\n \"text\": \"def cov(self):\\n\\t\\tif self.v is not None and self.r is not None:\\n\\t\\t\\tassert self.v.dim() == 1\\n\\t\\t\\tdim = self.v.dim()\\n\\t\\t\\tv = self.v.unsqueeze(1) # D * 1 vector\\n\\t\\t\\trt = self.r.unsqueeze(0) # 1 * D vector\\n\\t\\t\\tA = torch.eye(dim) + v.mm(rt)\\n\\t\\t\\treturn A.mm(torch.diag(self.sigma.pow(2)).mm(A.t()))\\n\\t\\telse:\\n\\t\\t\\treturn torch.diag(self.sigma.pow(2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b60c18dee3ba101f1f9287b53effe07\",\n \"score\": \"0.5536518\",\n \"text\": \"def covariances(self):\\n # n_gauss x 2 x 2\\n # this has no derivatives, since the radii are fixed.\\n return (self.radii**2)[:, None, None] * np.eye(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b60c18dee3ba101f1f9287b53effe07\",\n \"score\": \"0.5536518\",\n \"text\": \"def covariances(self):\\n # n_gauss x 2 x 2\\n # this has no derivatives, since the radii are fixed.\\n return (self.radii**2)[:, None, None] * np.eye(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12309147b8d5bc944697b6a6667e9279\",\n \"score\": \"0.55254924\",\n \"text\": \"def covariance(time,\\n body_id,\\n fpa_size = 4096,\\n fov = 30.0 * np.pi/180.0,\\n theta_max = 70 * np.pi/180.0,\\n sigma_pix = 0.063,\\n n_max = 1000,\\n spacecraft_id = -5440,\\n statistics = False):\\n r_p = spice.bodvcd(body_id, 'RADII', 3)[1][2] * 1000.0\\n d_x = 1 / (2.0 * np.tan(fov / 2))\\n r_sc_wrt_body = spice.spkezp(spacecraft_id, time, 'J2000', 'NONE', body_id)[0] * 1000.0\\n rho = norm(r_sc_wrt_body)\\n r_pix = r_p * fpa_size / (2 * rho * np.tan(fov / 2))\\n\\n # FIXME: find theta_max instead of specifying it\\n \\n # Don't allow n to exceed our processing capabilities, if applicable\\n n = 2 * theta_max * r_pix\\n if n > n_max:\\n n = n_max\\n\\n D = (theta_max / 4) * (2 * theta_max + np.sin(2*theta_max)) - np.sin(theta_max)**2\\n\\n T_p_to_c = compute_T_pa_to_cam(time, body_id, spacecraft_id)\\n\\n corner = np.sqrt(rho**2 - r_p**2) * np.sin(theta_max) / (D * r_p)\\n M = np.array([[theta_max / D, 0.0, corner],\\n [0.0, 4 / (2 * theta_max - np.sin(2*theta_max)), 0.0],\\n [corner, 0.0, (rho**2 - r_p**2) * (2 * theta_max + np.sin(2*theta_max)) / (4 * D * r_p**2)]])\\n scalar = sigma_pix**2 * rho**4 * theta_max / (n * d_x**2 * (rho**2 - r_p**2))\\n P = T_p_to_c.dot(M).dot(T_p_to_c.T) * scalar\\n\\n if statistics:\\n r_sun_wrt_body = spice.spkezp(body_id, time, 'J2000', 'LT', 10)[0]\\n e_sun = r_sun_wrt_body / norm(r_sun_wrt_body)\\n e_cam = r_sc_wrt_body / norm(r_sc_wrt_body)\\n \\n data = {\\n 'rho': rho,\\n 'sun_angle': np.arccos(np.dot(e_sun, e_cam)),\\n 'r_pix': r_pix,\\n 'n': n\\n }\\n\\n return P, data\\n\\n return P\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39e12ca0c4f96276d92050d198e7cbfb\",\n \"score\": \"0.5524313\",\n \"text\": \"def find_covariance(self):\\n\\n n = len(self.combinations)\\n self.cov = np.zeros((n,n),dtype=np.float64)\\n\\n for i in range(n):\\n vec1 = self.find_vec(self.combinations[i])\\n for j in range(n):\\n vec2 = self.find_vec(self.combinations[j])\\n for k in range(len(self.modes)):\\n self.cov[i][j] += vec1[k]*vec2[k]*self.modes[k].dfreq*self.modes[k].dfreq\\n\\n self.invcov = np.linalg.inv(self.cov)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6417229006075d715c3cc6ca449097a3\",\n \"score\": \"0.5516294\",\n \"text\": \"def generate_covariance_matrix(self) -> np.ndarray:\\r\\n n_vars = self.price_vector.shape[0]\\r\\n samples = np.random.uniform(low=0, high=config_dict['Exchange']['max_variance_factor'], size=(n_vars,10))\\r\\n return np.cov(samples, bias=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f03219a0e0f80a65475c1121c1cd36b5\",\n \"score\": \"0.5502516\",\n \"text\": \"def _cov_setup(self):\\n if self._P is not None: # then already computed so return\\n return\\n # get the weights\\n self._w = self.kern.w\\n\\n # get the p x p matrix A if ness\\n if self._A is None: # then compute, note this is expensive\\n self._Phi = self.kern.cov(self.X)[0]\\n self._A = self._Phi.T.dot(self._Phi) # O(np^2) operation!\\n\\n # compute the P matrix and factorize\\n self._P = self._A + np.diag(self.noise_var/self._w)\\n self._Pchol = cho_factor(self._P)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53216a39e966381088958369fada741d\",\n \"score\": \"0.549624\",\n \"text\": \"def _mn_cor_(self, size=-1, root=False):\\n #\\n cov = self.cov(size, root)\\n #\\n from math import sqrt\\n #\\n if isinstance(cov, ROOT.TMatrix):\\n\\n size = cov.GetNrows()\\n root = True\\n\\n else:\\n size = cov.kRows\\n\\n # use ROOT matrices\\n if root:\\n cor = ROOT.TMatrix(size, size)\\n else:\\n cor = cov.__class__()\\n\\n for i in range(0, size):\\n\\n d_i = cov(i, i)\\n cor[i, i] = 1 if 0 < d_i else 0\\n\\n for j in range(i + 1, size):\\n\\n d_j = cov(j, j)\\n\\n if 0 != cov(i, j) and 0 < d_i and 0 < d_j:\\n\\n if root:\\n cor[i][j] = cov(i, j) / sqrt(d_i * d_j)\\n else:\\n cor[i, j] = cov(i, j) / sqrt(d_i * d_j)\\n\\n else:\\n\\n if _root:\\n cor[i][j] = 0\\n else:\\n cor[i, j] = 0\\n\\n return cor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6e7249a8336fa90b17e8860733e18fc\",\n \"score\": \"0.54872376\",\n \"text\": \"def mb_covariance(self, mb_params):\\r\\n dim = len(mb_params)\\r\\n grad_outer= np.zeros((dim, dim))\\r\\n hess = np.zeros((dim, dim))\\r\\n\\r\\n for t in range(self.T):\\r\\n data_block, raw_data_block = self.X[t], self.X_raw[t]\\r\\n a, y = raw_data_block[:, 1], raw_data_block[:, 2]\\r\\n y_next = self.y[t]\\r\\n for l in range(self.L):\\r\\n x_raw, x = raw_data_block[l, :], data_block[l, :]\\r\\n\\r\\n # gradient\\r\\n if raw_data_block[l, 2]:\\r\\n # No recovery\\r\\n pass\\r\\n else:\\r\\n def mb_log_lik_at_x(mb_params_):\\r\\n lik = mb_log_lik_single(a, y, y_next, l, self.L, mb_params_, self.ADJACENCY_MATRIX, self.DISTANCE_MATRIX,\\r\\n self.PRODUCT_MATRIX)\\r\\n return lik\\r\\n\\r\\n mb_grad = gradient.central_diff_grad(mb_log_lik_at_x, mb_params)\\r\\n mb_hess = gradient.central_diff_hess(mb_log_lik_at_x, mb_params)\\r\\n\\r\\n grad_outer_lt = np.outer(mb_grad, mb_grad)\\r\\n grad_outer += grad_outer_lt\\r\\n hess += mb_hess\\r\\n\\r\\n hess_inv = np.linalg.inv(hess + 0.1 * np.eye(dim))\\r\\n cov = np.dot(hess_inv, np.dot(grad_outer, hess_inv)) / float(self.L * self.T)\\r\\n return cov\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ecec6225c7448e3cf58d7ffff5624e6\",\n \"score\": \"0.54739594\",\n \"text\": \"def vector_field(self, state):\\r\\n A = state[: (p := len(self.config.network.populations))]\\r\\n R = state[p : 2*p]\\r\\n S = 1 - A - R\\r\\n CAA = _internals._unflat_scalar_triangle(\\r\\n state[2*p : 2*p + round(p*(p+1)/2)])\\r\\n CRR = _internals._unflat_scalar_triangle(\\r\\n state[2*p + round(p*(p+1)/2) : 2*p+p*(p+1)])\\r\\n CAR = (state[2*p + p*(p+1) :]).reshape((p,p))\\r\\n CAS = - CAA - CAR\\r\\n CSR = - CRR - CAR\\r\\n B = self.config.Q.copy()\\r\\n VarB = np.zeros(p)\\r\\n CAB = np.zeros((p,p))\\r\\n CRB = np.zeros((p,p))\\r\\n for J, K in np.ndindex((p,p)):\\r\\n B[J] += self.config.network.c[J,K] * A[K]\\r\\n for L in range(p):\\r\\n VarB[J] += (self.config.network.c[J,K] \\r\\n * self.config.network.c[J,L] * CAA[K,L])\\r\\n CAB[J,K] += self.config.network.c[K,L] * CAA[J,L]\\r\\n CRB[J,K] += self.config.network.c[K,L] * CAR[L,J]\\r\\n f = np.zeros(self.dim)\\r\\n dCAA = np.zeros((p,p))\\r\\n dCRR = np.zeros((p,p))\\r\\n dCAR = np.zeros((p,p))\\r\\n for J, popJ in enumerate(self.config.network.populations):\\r\\n f[J] = (- popJ.beta * A[J] + popJ.alpha*popJ.F(B[J]) * S[J]\\r\\n - popJ.alpha*popJ.dF(B[J]) * (CAB[J,J] + CRB[J,J])\\r\\n + popJ.alpha/2*popJ.ddF(B[J]) * S[J] * VarB[J])\\r\\n f[J+p] = - popJ.gamma * R[J] + popJ.beta * A[J]\\r\\n for K, popK in enumerate(self.config.network.populations):\\r\\n dCAA[J,K] = (- (popJ.beta + popK.beta) * CAA[J,K]\\r\\n + popJ.alpha*popJ.F(B[J]) * CAS[K,J]\\r\\n + popK.alpha*popK.F(B[K]) * CAS[J,K]\\r\\n + popJ.alpha*popJ.dF(B[J]) * S[J] * CAB[K,J]\\r\\n + popK.alpha*popK.dF(B[K]) * S[K] * CAB[J,K])\\r\\n dCRR[J,K] = (- (popJ.gamma + popK.gamma) * CRR[J,K]\\r\\n + popJ.beta * CAR[J,K] + popK.beta * CAR[K,J])\\r\\n dCAR[J,K] = (- (popJ.beta + popK.gamma) * CAR[J,K]\\r\\n + popK.beta * CAA[J,K]\\r\\n + popJ.alpha*popJ.F(B[J]) * CSR[J,K]\\r\\n + popJ.alpha*popJ.dF(B[J]) * S[J] * CRB[K,J])\\r\\n f[2*p : 2*p + round(p*(p+1)/2)] = dCAA[np.triu_indices(p)]\\r\\n f[2*p + round(p*(p+1)/2) : 2*p + p*(p+1)] = dCRR[np.triu_indices(p)]\\r\\n f[2*p + p*(p+1) :] = dCAR.flatten()\\r\\n return np.array(f, float)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c00d4f81fb1acf44c9fcc399350f216\",\n \"score\": \"0.5471497\",\n \"text\": \"def _monte_carlo_sweep(self, random_update = False):\\n\\n nlen = self.nlen\\n j = self.j\\n h = self.h\\n de = 0.0\\n naccept = 0\\n\\n for i0 in range(nlen):\\n ic = i0\\n if random_update: ic = numpy.random.randint(nlen)\\n im = numpy.mod(ic - 1 + nlen, nlen)\\n ip = numpy.mod(ic + 1, nlen)\\n\\n for j0 in range(nlen):\\n jc = j0\\n if random_update: jc = numpy.random.randint(nlen)\\n jm = numpy.mod(jc - 1 + nlen, nlen)\\n jp = numpy.mod(jc + 1, nlen)\\n\\n # Existing state and new trial state (just flip\\n # with probability one)\\n\\n s0 = self.s[ic, jc]\\n s1 = -s0\\n\\n # Compute difference in energy\\n\\n ds = s1 - s0\\n delta = -j*ds*(self.s[im, jc] + self.s[ip, jc] + self.s[ic, jm] + self.s[ic, jp]) - h*ds\\n\\n # Metropolis\\n\\n if delta < 0.0:\\n self.s[ic, jc] = s1\\n naccept += 1\\n de += delta\\n elif numpy.random.uniform() < numpy.exp(-delta/self.kT):\\n self.s[ic, jc] = s1\\n naccept += 1\\n de += delta\\n\\n return naccept, de\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68e9f596bcc055ef1648ee7638968c15\",\n \"score\": \"0.54613376\",\n \"text\": \"def _compute_covariance(self):\\n self.factor = self.covariance_factor()\\n # Cache covariance and inverse covariance of the data\\n if not hasattr(self, '_data_inv_cov'):\\n self._data_covariance = atleast_2d(cov(self.dataset, rowvar=1,\\n bias=False,\\n aweights=self.weights))\\n self._data_inv_cov = linalg.inv(self._data_covariance)\\n\\n self.covariance = self._data_covariance * self.factor**2\\n self.inv_cov = self._data_inv_cov / self.factor**2\\n self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5fcd7625fe8811ab7a2f3cbba09b36d\",\n \"score\": \"0.54596156\",\n \"text\": \"def _initialize(self):\\n if self._mean is None or self._cov is None:\\n return\\n\\n Q = self._cov[\\\"QS\\\"][0][0]\\n S = self._cov[\\\"QS\\\"][1]\\n\\n if S.size > 0:\\n self.tau[:] = 1 / npsum((Q * sqrt(S)) ** 2, axis=1)\\n else:\\n self.tau[:] = 0.0\\n self.eta[:] = self._mean\\n self.eta[:] *= self.tau\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12858a9541bc4cffa02bbf9c48ac1444\",\n \"score\": \"0.5456833\",\n \"text\": \"def cov_0(self):\\n # TODO: check this!\\n# return self.sigma_n**2 + self.sigma_f**2\\n return self.sigma_f**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e0af82ee40f999b127dbc26a78f572\",\n \"score\": \"0.54477686\",\n \"text\": \"def calcCovarianceMatrix(self,phase_volume):\\n input_array = []\\n for particle in phase_volume:\\n row = []\\n row.append(particle.x.x)\\n row.append(particle.x.y)\\n row.append(particle.x.z)\\n row.append(particle.p.x)\\n row.append(particle.p.y)\\n row.append(particle.p.z)\\n row.append(particle.getEnergy())\\n input_array.append(row)\\n input_array = np.array(input_array).T #Transpose\\n return np.cov(input_array)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad884b3b8629f0762343d9f0d12fdb2\",\n \"score\": \"0.5444077\",\n \"text\": \"def covariances(self):\\n # n_gauss x 2 x 2\\n # this has no derivatives, since the radii are fixed.\\n return np.zeros([1, 2, 2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9820e868e2de21d16afdb6c8b31c7180\",\n \"score\": \"0.5438816\",\n \"text\": \"def getmomentofinertia(self):\\n \\n I = np.zeros((3,3), np.float64)\\n centerOfMass = self.center_of_mass()\\n for atom_index in range(self.atomnumber):\\n mass = self.masses[atom_index]\\n coord = self.coords[atom_index] - centerOfMass\\n I[0,0] += mass * (coord[1] * coord[1] + coord[2] * coord[2])\\n I[1,1] += mass * (coord[0] * coord[0] + coord[2] * coord[2])\\n I[2,2] += mass * (coord[0] * coord[0] + coord[1] * coord[1])\\n I[0,1] -= mass * coord[0] * coord[1]\\n I[0,2] -= mass * coord[0] * coord[2]\\n I[1,2] -= mass * coord[1] * coord[2]\\n I[1,0] = I[0,1]\\n I[2,0] = I[0,2]\\n I[2,1] = I[1,2]\\n values1, vectors1 = np.linalg.eig(I)\\n return I, values1, vectors1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e46340b10f64a45e76cd546ad7b84151\",\n \"score\": \"0.54383546\",\n \"text\": \"def cov_0(self):\\n #return self.sigma_n**2 + self.sigma_f**2\\n return self.sigma_f**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82819b5bb3b219f4e4446689ee548cab\",\n \"score\": \"0.54332715\",\n \"text\": \"def calcVectorFromOrbit(self):\\n # calculate distance from CoM using semimajor axis, eccentricity and true anomaly\\n\\n magpos = self.semilat / (1.0+ self.e * np.cos(self.trueanom)) \\n\\n self.position = Vector3D(0.0,0.0,0.0)\\n self.velocity = Vector3D(0.0,0.0,0.0)\\n \\n # Calculate self.position vector in orbital plane\\n self.position.x = magpos * np.cos(self.trueanom) \\n self.position.y = magpos * np.sin(self.trueanom) \\n self.position.z = 0.0 \\n\\n # Calculate self.velocity vector in orbital plane */\\n gravparam = self.G * self.totalMass \\n\\n try:\\n magvel = np.sqrt(gravparam/self.semilat)\\n except ZeroDivisionError:\\n magvel = np.sqrt(2.0*gravparam/magpos)\\n \\n self.velocity.x = -magvel * np.sin(self.trueanom) \\n self.velocity.y = magvel * (np.cos(self.trueanom) + self.e) \\n self.velocity.z = 0.0 \\n\\n # Begin rotations:\\n # Firstly, Rotation around z axis by -self.argper */\\n\\n if(self.argper>tiny):\\n self.position.rotateZ(self.argper) \\n self.velocity.rotateZ(self.argper) \\n \\n # Secondly, Rotate around x by -inclination */\\n\\n if(self.i >tiny):\\n self.position.rotateX(self.i) \\n self.velocity.rotateX(self.i) \\n \\n # Lastly, Rotate around z by self.longascend */\\n\\n if(self.longascend >tiny):\\n self.position.rotateZ(self.longascend) \\n self.velocity.rotateZ(self.longascend)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0503b02b6db5a3601b4dcd43100578b\",\n \"score\": \"0.54150593\",\n \"text\": \"def covariance_matrix(self) -> np.ndarray:\\n matrices = [samples.covariance_matrix() for samples in self.samples_list]\\n prior_count = self.samples_list[0].model.prior_count\\n size = prior_count * len(self.samples_list)\\n array = np.zeros((size, size))\\n for i, matrix in enumerate(matrices):\\n array[\\n i * prior_count : (i + 1) * prior_count,\\n i * prior_count : (i + 1) * prior_count,\\n ] = matrix\\n return array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8e050934ba2effe85eb64ecf8794611\",\n \"score\": \"0.54073244\",\n \"text\": \"def _covariance_matrix(self, X):\\n sig_aa = self._rbf_kernel(self.X_train, self.X_train, self.alpha, self.l) + self.sigma ** 2 * np.identity(\\n self.n)\\n sig_ab = self._rbf_kernel(self.X_train, X, self.alpha, self.l)\\n sig_ba = self._rbf_kernel(X, self.X_train, self.alpha, self.l)\\n sig_bb = self._rbf_kernel(X, X, self.alpha, self.l)\\n return sig_aa, sig_ab, sig_ba, sig_bb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0026a2f1c6099e5248b2b40d606837a0\",\n \"score\": \"0.54066795\",\n \"text\": \"def test_construct_covariance_matrix_nsources(self):\\n\\n cvec = np.zeros((2, 15))\\n cvec[0] = np.arange(15) + 1\\n cvec[1] = -cvec[0]\\n parallax = np.array([16, -16])\\n vrad = np.array([17, -17]) * au_km_year_per_sec\\n vrad_error = np.array([18, -18]) * au_km_year_per_sec\\n\\n cmat = construct_covariance_matrix(cvec, parallax, vrad, vrad_error)\\n\\n self.assertEquals(2, cmat.shape[0])\\n self.assertEquals(6, cmat.shape[1])\\n self.assertEquals(6, cmat.shape[2])\\n\\n iu = np.triu_indices(6, k=1)\\n\\n np.testing.assert_equal(cmat[0, iu[0], iu[1]], cmat[0, iu[1], iu[0]])\\n np.testing.assert_equal((np.arange(5) + 1) ** 2, np.diag(cmat[0])[0:-1])\\n\\n expected = np.array([12, 21, 32, 45, 60, 88, 120, 156, 210, 300])\\n iuu = np.triu_indices(5, k=1)\\n np.testing.assert_equal(expected, cmat[0, :-1, :-1][iuu])\\n\\n for i in range(5):\\n np.testing.assert_almost_equal(cmat[0, i, 5], cmat[0, i, 2] * 17, 1)\\n\\n np.testing.assert_almost_equal(\\n cmat[0, 5, 5], 9 * (17**2 + 18**2) + (16 * 18) ** 2, 1\\n )\\n\\n np.testing.assert_equal(cmat[1, iu[0], iu[1]], cmat[1, iu[1], iu[0]])\\n np.testing.assert_equal((np.arange(5) + 1) ** 2, np.diag(cmat[1])[0:-1])\\n\\n expected = -np.array([12, 21, 32, 45, 60, 88, 120, 156, 210, 300])\\n iuu = np.triu_indices(5, k=1)\\n np.testing.assert_equal(expected, cmat[1, :-1, :-1][iuu])\\n\\n for i in range(5):\\n np.testing.assert_almost_equal(cmat[1, i, 5], cmat[1, i, 2] * (-17), 1)\\n\\n np.testing.assert_almost_equal(\\n cmat[1, 5, 5], 9 * (17**2 + 18**2) + (16 * 18) ** 2, 1\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc0fc41cad711864935da749ca3c217\",\n \"score\": \"0.5399837\",\n \"text\": \"def test_construct_covariance_matrix_onesource(self):\\n\\n cvec = np.arange(15) + 1\\n parallax = 16\\n vrad = 17 * au_km_year_per_sec\\n vrad_error = 18 * au_km_year_per_sec\\n\\n cmat = construct_covariance_matrix(cvec, parallax, vrad, vrad_error)\\n\\n self.assertEquals(6, cmat.shape[0])\\n self.assertEquals(6, cmat.shape[1])\\n\\n iu = np.triu_indices(6, k=1)\\n\\n np.testing.assert_equal(cmat[iu], cmat[iu[1], iu[0]])\\n np.testing.assert_equal((np.arange(5) + 1) ** 2, np.diag(cmat)[0:-1])\\n\\n expected = np.array([12, 21, 32, 45, 60, 88, 120, 156, 210, 300])\\n iuu = np.triu_indices(5, k=1)\\n np.testing.assert_equal(expected, cmat[:-1, :-1][iuu])\\n\\n for i in range(5):\\n np.testing.assert_almost_equal(cmat[i, 5], cmat[i, 2] * 17, 1)\\n\\n np.testing.assert_almost_equal(\\n cmat[5, 5], 9 * (17**2 + 18**2) + (16 * 18) ** 2, 1\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07d1e662bc1c1c66f66085c61796064\",\n \"score\": \"0.5391459\",\n \"text\": \"def test_measurement_jacobian():\\n N = 10\\n measurement_model = LinearKalmanFilterMeasurementModel()\\n C_autograd = measurement_model.jacobian(states=torch.zeros((N, state_dim)))\\n\\n for i in range(N):\\n torch.testing.assert_allclose(C_autograd[i], C)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4c207310874fe155436032d6ebf7997\",\n \"score\": \"0.53850037\",\n \"text\": \"def test_caustic_for_orbital_motion():\\n q = 0.1\\n s = 1.3\\n params = {'t_0': 100., 'u_0': 0.1, 't_E': 10., 'q': q,\\n 's': s, 'ds_dt': 0.5, 'alpha': 0., 'dalpha_dt': 0.}\\n model = mm.Model(parameters=params)\\n\\n model.update_caustics()\\n almost(model.caustics.get_caustics(),\\n mm.Caustics(q=q, s=s).get_caustics())\\n\\n model.update_caustics(100.+365.25/2)\\n almost(model.caustics.get_caustics(),\\n mm.Caustics(q=q, s=1.55).get_caustics())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2e8899b83663fe9f9a06c6befeec7b2\",\n \"score\": \"0.5378065\",\n \"text\": \"def covariance(Z):\\n N = Z.shape[0]\\n S = np.zeros((Z.shape[1], Z.shape[1]))\\n for i in range(N):\\n S += 1.0 / N * np.outer(Z[i, :], Z[i, :])\\n return S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09d52ed44a3f34b8dcba757eed31554b\",\n \"score\": \"0.5371901\",\n \"text\": \"def covariance_matrices(self, p):\\n\\n p = self.to_params(p)\\n\\n covc = self._cov_matrix(p['covc'])\\n covb = self._cov_matrix(p['covb'])\\n\\n return (covc, covb)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd13a78ac96cafbfa20f2aed881b1094\",\n \"score\": \"0.5363501\",\n \"text\": \"def fit(self, X):\\n\\n assert X.dtype in ['float32', 'float64']\\n assert not np.any(np.isnan(X))\\n assert len(X.shape) == 2\\n n_samples = X.shape[0]\\n if self.copy:\\n X = X.copy()\\n # Center data\\n self.mean_ = np.mean(X, axis=0)\\n X -= self.mean_\\n\\n log.info('computing zca of a {0} matrix'.format(X.shape))\\n t1 = time.time()\\n\\n bias = self.filter_bias * scipy.sparse.identity(X.shape[1],\\n theano.config.floatX)\\n\\n covariance = ZCA._gpu_matrix_dot(X.T, X) / X.shape[0] + bias\\n t2 = time.time()\\n log.info(\\\"cov estimate took {0} seconds\\\".format(t2 - t1))\\n\\n t1 = time.time()\\n eigs, eigv = linalg.eigh(covariance)\\n t2 = time.time()\\n\\n log.info(\\\"eigh() took {0} seconds\\\".format(t2 - t1))\\n assert not np.any(np.isnan(eigs))\\n assert not np.any(np.isnan(eigv))\\n assert eigs.min() > 0\\n\\n if self.n_components and self.n_drop_components:\\n raise ValueError('Either n_components or n_drop_components'\\n 'should be specified')\\n\\n if self.n_components:\\n eigs = eigs[-self.n_components:]\\n eigv = eigv[:, -self.n_components:]\\n\\n if self.n_drop_components:\\n eigs = eigs[self.n_drop_components:]\\n eigv = eigv[:, self.n_drop_components:]\\n\\n t1 = time.time()\\n\\n sqrt_eigs = np.sqrt(eigs)\\n try:\\n self.P_ = ZCA._gpu_mdmt(eigv, 1.0 / sqrt_eigs)\\n except MemoryError:\\n warnings.warn()\\n self.P_ = np.dot(eigv * (1.0 / sqrt_eigs), eigv.T)\\n\\n t2 = time.time()\\n assert not np.any(np.isnan(self.P_))\\n self.has_fit_ = True\\n\\n if self.store_inverse:\\n self.inv_P_ = ZCA._gpu_mdmt(eigv, sqrt_eigs)\\n else:\\n self.inv_P_ = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c565072334314e5253f447a303fa3bc7\",\n \"score\": \"0.53611517\",\n \"text\": \"def correlation_matrix(covariance_matrix, rms):\\n corr_coeff = np.zeros_like(covariance_matrix)\\n for i in range(covariance_matrix.shape[0]):\\n for j in range(covariance_matrix.shape[0]):\\n corr_coeff[i, j] = covariance_matrix[i, j] / rms[i] / rms[j]\\n\\n return corr_coeff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29bac20d28afe3a4b3303fe89feb7996\",\n \"score\": \"0.5356866\",\n \"text\": \"def evaluate(self):\\n cls_attr_name = self.__class__.__name__+\\\".time_series\\\"\\n self.time_series.trait[\\\"data\\\"].log_debug(owner = cls_attr_name)\\n \\n #(tpts, nodes, nodes, state-variables, modes)\\n result_shape = self.result_shape(self.time_series.data.shape)\\n LOG.info(\\\"result shape will be: %s\\\" % str(result_shape))\\n \\n result = numpy.zeros(result_shape)\\n \\n #TODO: For region level, 4s, 2000Hz, this takes ~3hours... (which makes node_coherence seem positively speedy...)\\n #TODO: Probably best to add a keyword for offsets, so we just compute +- some \\\"small\\\" range...\\n #One inter-node correllation, across offsets, for each state-var & mode.\\n for mode in range(result_shape[4]):\\n for var in range(result_shape[3]):\\n data = self.time_series.data[:, var, :, mode]\\n data = data - data.mean(axis=0)[numpy.newaxis, :]\\n #TODO: Work out a way around the 4 level loop,\\n for n1 in range(result_shape[1]):\\n for n2 in range(result_shape[2]):\\n result[:, n1, n2, var, mode] = correlate(data[:, n1],\\n data[:, n2],\\n mode=\\\"same\\\")\\n \\n util.log_debug_array(LOG, result, \\\"result\\\")\\n \\n offset = (self.time_series.sample_period *\\n numpy.arange(-(numpy.floor(result_shape[0] / 2.0)),\\n numpy.ceil(result_shape[0] / 2.0)))\\n \\n cross_corr = temporal_correlations.CrossCorrelation(\\n source = self.time_series,\\n array_data = result,\\n time = offset,\\n use_storage = False)\\n \\n return cross_corr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9f30530998c3ccc31ee9221f39e880c\",\n \"score\": \"0.5352657\",\n \"text\": \"def statePCovarianceCalc(self):\\n\\t\\tprint(\\\"prev process covariance matrix P: \\\", self.P)\\n\\t\\tA = np.array([[1, self.delta_T],\\n\\t\\t\\t [0, 1]])\\n\\t\\t# State Covariance Matrix\\n\\t\\tself.PP = np.matmul(np.matmul(A, self.P), A.transpose()) # + self.Q\\n\\t\\tself.PP = np.array([[self.PP[0][0], 0],\\n\\t\\t\\t\\t [ 0, self.PP[1][1]]])\\n\\t\\t\\n\\t\\tprint(\\\"predicted covariance matrix: \\\", self.PP)\\t\\n\\t\\treturn self.PP # 2x2 matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903399203d56cd54ddb12d76f5cff991\",\n \"score\": \"0.53515756\",\n \"text\": \"def do_CI(N_orb, a):\\n N_config = int(comb(N_orb, 2)) # number of configurations possible, ignoring spin\\n Etot, _, h, G, c = do_scf(a)\\n config_list = np.zeros((N_config, 2)).astype(int) # initialize possible configs as [i,j]th occupied orbitals\\n count = 0\\n for i in range(N_orb):\\n for j in range(i + 1, N_orb):\\n config_list[count, :] = [i, j]\\n count += 1\\n\\n # initialize CI matrix\\n H_CI = np.zeros((N_config, N_config))\\n\\n # populate CI matrix elements \\\\bra \\\\psi_ij | H | \\\\psi_ij \\\\ket, where \\\\psi is a configuration\\n for config1 in range(N_config):\\n i, j = config_list[config1, :]\\n for config2 in range(N_config):\\n k, l = config_list[config2, :]\\n if i == l:\\n H_CI[config1, config2] -= c[:, j] @ h @ c[:, k]\\n if j == k:\\n H_CI[config1, config2] -= c[:, i] @ h @ c[:, l]\\n if j == l:\\n H_CI[config1, config2] += c[:, i] @ h @ c[:, k]\\n if i == k:\\n H_CI[config1, config2] += c[:, j] @ h @ c[:, l]\\n\\n H_CI[config1, config2] += a * ((c[:, i] @ G @ c[:, k]) * (c[:, j] @ G @ c[:, l])\\n - (c[:, j] @ G @ c[:, k]) * (c[:, i] @ G @ c[:, l]))\\n\\n E, b = eig(H_CI)\\n order = np.argsort(E)\\n E = np.real(np.sort(E))\\n return E, b, order, config_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65eff0aaee4644f07367375516305992\",\n \"score\": \"0.5351557\",\n \"text\": \"def get_covariance_matrix(self):\\n if self.Ss is None:\\n _ = self.get_Ss_from_Us()\\n\\n assert self.nbatch == 1, 'covariance matrix is returned only for single batch sample, but ' \\\\\\n 'self.nbatch={}'.format(self.nbatch)\\n\\n # assume single batch sample\\n batch_idx = 0\\n S = np.zeros((self.ndims, self.ndims))\\n\\n a = self.alphas[batch_idx, :].data.numpy().squeeze()\\n mus = self.mus[batch_idx, :, :].data.numpy().squeeze()\\n ss = self.Ss[batch_idx, :, :, :].squeeze()\\n\\n m = np.dot(a, mus.T)\\n\\n # get covariance shifted by the means, weighted with alpha\\n for k in range(self.n_components):\\n S += a[k] * (ss[k, :, :] + np.outer(mus[:, k], mus[:, k]))\\n\\n # subtract weighted means\\n S -= np.outer(m, m)\\n\\n return S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"576a220087b35bf37330830124f447a4\",\n \"score\": \"0.5350341\",\n \"text\": \"def compute_C(self):\\n self.C = self.K.dot(self.P.dot(self.R.dot(self.T)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae0d45c02568b4850d5d3ddd87f8e35f\",\n \"score\": \"0.5345444\",\n \"text\": \"def _cov_matrix(self, x):\\n\\n y = np.zeros((3,3))\\n y[np.tril_indices(3)] = x\\n y.flat[::4] = np.exp(y.flat[::4])\\n\\n return np.dot(y, y.T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50ff774408fd93bc444b48c28eb41949\",\n \"score\": \"0.53283805\",\n \"text\": \"def res_causal(self):\\n sigma1 = self.param.covshocks[0]\\n sigma2 = self.param.covshocks[1]\\n rho = self.param.covshocks[2]\\n cov12 = (sigma1**(1/2))*(sigma2**(1/2))*rho\\n covshocks = [[sigma1,cov12],[cov12,sigma2]]\\n \\n s = np.random.multivariate_normal(self.param.meanshocks, covshocks, self.N)\\n omega = np.reshape(s[:,0],(self.N,1))\\n theta = np.reshape(s[:,1],(self.N,1))\\n \\n return omega, theta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28ed7a1c08919d8024faab84702cfdb0\",\n \"score\": \"0.5328304\",\n \"text\": \"def calculate_cca(dat_x, dat_y, timeaxis=-2):\\n assert (len(dat_x.data.shape) == len(dat_y.data.shape) == 2 and\\n dat_x.data.shape[timeaxis] == dat_y.data.shape[timeaxis])\\n\\n if timeaxis == 0 or timeaxis == -2:\\n x = dat_x.data.copy()\\n y = dat_y.data.copy()\\n else:\\n x = dat_x.data.T.copy()\\n y = dat_y.data.T.copy()\\n\\n # calculate covariances and it's inverses\\n x -= x.mean(axis=0)\\n y -= y.mean(axis=0)\\n N = x.shape[0]\\n c_xx = np.dot(x.T, x) / N\\n c_yy = np.dot(y.T, y) / N\\n c_xy = np.dot(x.T, y) / N\\n c_yx = np.dot(y.T, x) / N\\n ic_xx = np.linalg.pinv(c_xx)\\n ic_yy = np.linalg.pinv(c_yy)\\n # calculate w_x\\n w, v = np.linalg.eig(functools.reduce(np.dot, [ic_xx, c_xy, ic_yy, c_yx]))\\n w_x = v[:, np.argmax(w)].real\\n w_x = w_x / np.sqrt(functools.reduce(np.dot, [w_x.T, c_xx, w_x]))\\n # calculate w_y\\n w, v = np.linalg.eig(functools.reduce(np.dot, [ic_yy, c_yx, ic_xx, c_xy]))\\n w_y = v[:, np.argmax(w)].real\\n w_y = w_y / np.sqrt(functools.reduce(np.dot, [w_y.T, c_yy, w_y]))\\n # calculate rho\\n rho = abs(functools.reduce(np.dot, [w_x.T, c_xy, w_y]))\\n return rho, w_x, w_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d823b2847dd30c3e3066aca2820cc51\",\n \"score\": \"0.53281224\",\n \"text\": \"def __init__(self, markers, sample_time):\\n self.markers = markers\\n self.last_time = None # Used to keep track of time between measurements \\n self.Q_t = np.diag([0.1, 0.1])\\n self.R_t = np.diag([0.01, 0.01, np.pi/180/2])\\n # YOUR CODE HERE\\n \\n self.sample_time = sample_time\\n self.x_t = np.zeros(3) # initialize state variables\\n self.P_t = np.diag([10, 10, 10]) # initialize covariance matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43adbe87e0aa57a7f4e840dddfe0f435\",\n \"score\": \"0.532233\",\n \"text\": \"def get_covariance_matrix(self, X):\\n # It is recommended that you try to compute the covariance matrix\\n # by performing the needed operations by hand, instead of using np.cov()\\n # function, this way you will have better idea of the covariance matrix\\n\\n # Also take into account that the random variables for which you want to\\n # compute the covariance matrix are the columns of X, not the rows!\\n\\n # don't forget the 1/(N-K) term in this case\\n # YOUR CODE HERE\\n\\n return (1 / (self.nr_data - self.nr_labels)) * (X - X.mean(axis=0)).T @ (X - X.mean(axis=0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e106732ff3a750f8037961318bab7896\",\n \"score\": \"0.53162223\",\n \"text\": \"def mixture_of_variational_distributions_moments(\\n relaxation, rng, n_init=100, n_step=1000, step_size=0.2,\\n init_scale=0.5, tol=1e-4):\\n var_obj_list = []\\n var_first_mom_list = []\\n var_biases_list = []\\n for j in range(n_init):\\n var_biases = rng.normal(size=(relaxation.n_unit,)) * init_scale\\n for i in range(n_step):\\n var_obj, grads_wrt_var_biases, var_first_mom = (\\n var.var_obj_and_grads_mean_field(\\n var_biases, relaxation.W, relaxation.b)\\n )\\n grads_wrt_var_biases = np.array(grads_wrt_var_biases)\\n var_biases -= step_size * grads_wrt_var_biases\\n in_list = False\\n for vm in var_first_mom_list:\\n diff = vm - var_first_mom\\n dist = diff.dot(diff)**0.5\\n if dist < tol:\\n in_list = True\\n if not in_list:\\n var_obj_list.append(var_obj)\\n var_first_mom_list.append(np.array(var_first_mom))\\n var_biases_list.append(var_biases)\\n var_weights = np.exp(-np.array(var_obj_list))\\n var_weights /= var_weights.sum()\\n var_mean = np.zeros((relaxation.n_dim_r))\\n var_covar = np.zeros((relaxation.n_dim_r, relaxation.n_dim_r))\\n for var_first_mom, w in zip(var_first_mom_list, var_weights):\\n m = relaxation.Q.T.dot(var_first_mom)\\n var_mean += w * m\\n var_covar += w * np.outer(m, m)\\n var_covar += np.eye(relaxation.n_dim_r) - np.outer(var_mean, var_mean)\\n var_covar_chol = la.cholesky(var_covar, True)\\n var_log_norm = (\\n 0.5 * relaxation.n_dim_r * np.log(2 * np.pi) -\\n relaxation.n_unit * np.log(2) +\\n 0.5 * relaxation.D.diagonal().sum() +\\n np.log(np.exp(-np.array(var_obj_list)).sum())\\n )\\n return var_mean, var_covar_chol, var_log_norm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e872ac3732c0d3429702a20ebd1231e5\",\n \"score\": \"0.5311863\",\n \"text\": \"def modcovar_marple (X,IP):\\n Pv = []\\n N = len(X)\\n A = np.zeros(N, dtype=complex)\\n D = np.zeros(N, dtype=complex)\\n C = np.zeros(N, dtype=complex)\\n R = np.zeros(N, dtype=complex)\\n # Initialization\\n R1=0.\\n for K in range(1, N-1):\\n R1=R1 + 2.*(X[K].real**2 + X[K].imag**2)\\n R2 = X[0].real**2 + X[0].imag**2\\n R3 = X[N-1].real**2 + X[N-1].imag**2\\n R4 = 1. / (R1 + 2. * (R2 + R3))\\n P = R1 + R2 + R3\\n DELTA = 1. - R2 * R4\\n GAMMA = 1. - R3 * R4\\n LAMBDA = (X[0] * X[N-1]).conjugate()*R4\\n C[0] = X[N-1] * R4\\n D[0] = X[0].conjugate() * R4\\n\\n M = 0\\n if (IP ==0):\\n P = (.5*R1+R2+R3)/float(N)\\n return [], P, []\\n\\n #C Main loop\\n\\n for M in range(0, IP):\\n #print '---------------------', M\\n SAVE1 = 0+0j\\n for K in range(M+1, N):\\n SAVE1 = SAVE1 + X[K]*X[K-M-1].conjugate()\\n SAVE1 *= 2.\\n R[M] = SAVE1.conjugate()\\n THETA = X[N-1]*D[0]\\n PSI=X[N-1]*C[0]\\n XI = X[0].conjugate() * D[0]\\n if M==0:\\n pass\\n else:\\n for K in range(0, M):\\n THETA=THETA+X[N-K-2]*D[K+1] # Eq. (8.D.45)\\n PSI = PSI + X[N-K-2]*C[K+1] # Eq. (8.D.45)\\n XI = XI + X[K+1].conjugate() * D[K+1] # Eq. (8.D.45)\\n R[K] = R[K]-X[N-M-1] * X[N+1-M+K-1].conjugate() - X[M].conjugate() * X[M-K-1] # Eq. (8.D.37)\\n SAVE1=SAVE1+R[K].conjugate()*A[M-K-1] # Eq. (8.D.24)\\n #print 'withini loop', K, THETA, PSI, XI, R[K], SAVE1\\n #print 'x -------', C[K], C[K+1]\\n #print 'x -------', D[K], D[K+1]\\n #print 'x -------', N-K-2, K+1,N-M-1, M, M-K-1\\n #print 'x -------', X[N-K-2], X[K+1],X[N-M-1], X[M], X[M-K-1]\\n\\n\\n # Order update of A vector\\n\\n C1 = -SAVE1/P\\n A[M]=C1 # Eq. (8.D.23)\\n P=P*(1.-C1.real**2-C1.imag**2) # Eq. (8.D.25)\\n #print 'C1=' , C1, 'A[M]', A[M], 'P=',P\\n if M==0:\\n pass\\n else:\\n #for k in range(0, (M-1)/2+1):\\n #print 'BEFORE', A[0:4]\\n for K in range(0, (M+1)//2):\\n MK = M-K-1\\n SAVE1=A[K]\\n A[K]=SAVE1+C1*A[MK].conjugate() # Eq. (8.D.22)\\n #print 'A uopdate---->',M, K, MK, A[K], A[MK], SAVE1, C1\\n if (K != MK):\\n #print 'ENTERI IF', K, MK, A[MK], C1, SAVE1\\n A[MK]=A[MK]+C1*(SAVE1.conjugate()) # Eq. (8.D.22)\\n #print 'AFTER', A[0:4]\\n #print M+1, IP\\n if M+1 == IP:\\n #Pv.append(P)\\n P=.5*P/float(N-M-1)\\n Pv.append(P)\\n return A, P, Pv\\n else:\\n Pv.append(.5*P/float(N-M-1))\\n #Pv.append(P)\\n\\n # Time update of C,D vectors and GAMMA,DELTA,LAMBDA scalars\\n\\n R1=1./(DELTA*GAMMA-(LAMBDA.real)**2-(LAMBDA.imag)**2)\\n C1=(THETA*(LAMBDA.conjugate())+PSI*DELTA)*R1\\n C2=(PSI*LAMBDA+THETA*GAMMA)*R1\\n C3=(XI*(LAMBDA.conjugate())+THETA*DELTA)*R1\\n C4=(THETA*LAMBDA+XI*GAMMA)*R1\\n\\n #print 'C1C2C3C4', C1, C2, C3, C4\\n #print M, M/2+1, (M-1)/2+1\\n for K in range(0, (M)//2+1):\\n MK=M-K\\n SAVE1=C[K].conjugate()\\n SAVE2=D[K].conjugate()\\n SAVE3=C[MK].conjugate()\\n SAVE4=D[MK].conjugate()\\n C[K]=C[K]+C1*SAVE3+C2*SAVE4 # Eq. (8.D.43)\\n D[K]=D[K]+C3*SAVE3+C4*SAVE4 # Eq. (8.D.44)\\n #print '-------->', K,MK, SAVE1, SAVE2, SAVE3, SAVE4\\n if K != MK:\\n C[MK]=C[MK]+C1*SAVE1+C2*SAVE2 # Eq. (8.D.43)\\n D[MK]=D[MK]+C3*SAVE1+C4*SAVE2 # Eq. (8.D.44)\\n #print 'witninloop loop ',K,MK,C[MK], D[MK]\\n #print 'after loop'\\n #print 'C=',C[0:2]\\n #print 'D=',D[0:2]\\n R2=PSI.real**2+PSI.imag**2\\n R3=THETA.real**2+THETA.imag**2\\n R4=XI.real**2+XI.imag**2\\n R5=GAMMA-(R2*DELTA+R3*GAMMA+2.*np.real(PSI*LAMBDA*THETA.conjugate()))*R1\\n R2=DELTA-(R3*DELTA+R4*GAMMA+2.*np.real(THETA*LAMBDA*XI.conjugate()))*R1\\n GAMMA=R5 # Eq. (8.D.46)\\n DELTA=R2 # Eq. (8.D.47)\\n LAMBDA=LAMBDA+C3*PSI.conjugate()+C4*THETA.conjugate() # Eq. (8.D.48)\\n #print 'before time updaqt',R2, R3, R4, R5, LAMBDA\\n if P <= 0.:\\n raise ValueError('Found an invalid negative value for P.')\\n if (DELTA > 0. and DELTA <= 1. and GAMMA > 0. and GAMMA <=1.):\\n pass\\n else:\\n raise ValueError('Found an invalid DELTA or GAMMA value.')\\n # Time update of A vector; order updates of C,D vectors aand GAMMA,\\n # DELTA,LAMBDA scalars\\n #print 'time update--------------'\\n R1=1./P\\n R2=1./(DELTA*GAMMA-LAMBDA.real**2-LAMBDA.imag**2) # Eq. (8.D.41)\\n EF=X[M+1]\\n EB=X[N-M-2]\\n #print 'efeb', EF, EB\\n #print 'A=',A[0:2]\\n for K in range(0, M+1):\\n #print 'efeb lloop', K,A[K], X[M-K], X[N-M+K-1]\\n EF=EF+A[K]*X[M-K] # Eq. (8.D.1)\\n EB=EB+A[K].conjugate()*X[N-M+K-1] # Eq. (8.D.2)\\n C1=EB*R1 # Eq. (8.D.28)\\n C2=EF.conjugate()*R1 # Eq. (8.D.29)\\n C3=(EB.conjugate()*DELTA+EF*LAMBDA)*R2\\n C4=(EF*GAMMA+(EB*LAMBDA).conjugate())*R2\\n\\n #print 'c1c2c3c4', C1 ,C2, C3, C4\\n #print 'efeb',EF, EB\\n\\n for K in range(M, -1, -1):\\n SAVE1=A[K]\\n A[K]=SAVE1+C3*C[K]+C4*D[K] # Eq. (8.D.38)\\n C[K+1]=C[K]+C1*SAVE1 # Eq. (8.D.26)\\n D[K+1]=D[K]+C2*SAVE1 # Eq. (8.D.27)\\n #print 'ACD',K, A[K], C[K+1], D[K+1]\\n C[0]=C1\\n D[0]=C2\\n R3=EB.real**2+EB.imag**2\\n R4=EF.real**2+EF.imag**2\\n P=P-(R3*DELTA+R4*GAMMA+2.*np.real(EF*EB*LAMBDA))*R2 # Eq. (8.D.42)\\n DELTA=DELTA-R4*R1 # Eq. (8.D.32)\\n GAMMA=GAMMA-R3*R1 # Eq. (8.D.33)\\n LAMBDA=LAMBDA+(EF*EB).conjugate()*R1 # Eq. (8.D.35)\\n\\n #print 'final'\\n #print R3, R4, P, DELTA, GAMMA, LAMBDA\\n #print 'Afinal=',A[0:2]\\n #print 'P=',P\\n if (P > 0.):\\n pass\\n else:\\n raise ValueError(\\\"Found a invalid negative P value \\\")\\n if (DELTA > 0. and DELTA <= 1. and GAMMA > 0. and GAMMA <= 1.):\\n pass\\n else:\\n raise ValueError(\\\"Found an invalid negative GAMMA or DELTA value \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"581b0683258d223ef2c7229e9d5a7ee7\",\n \"score\": \"0.5294292\",\n \"text\": \"def covariance(self, S, S0 = None):\\n I = self.fishermatrix(S, S0 = S0)\\n return np.linalg.linalg.inv(I)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89201c72cbed1eda36b00d1990e87ab3\",\n \"score\": \"0.5293026\",\n \"text\": \"def covariance_matrix(epsrel=0.0, verbosity=2):\\n\\n # Test if the current data pipe exists.\\n check_pipe()\\n\\n # The specific analysis API object.\\n api = return_api()\\n\\n # Loop over the models.\\n for model_info in api.model_loop():\\n # Get the Jacobian and weighting matrix.\\n jacobian, weights = api.covariance_matrix(model_info=model_info, verbosity=verbosity)\\n\\n # Calculate the covariance matrix.\\n pcov = statistics.multifit_covar(J=jacobian, weights=weights)\\n\\n # To compute one standard deviation errors on the parameters, take the square root of the diagonal covariance.\\n sd = sqrt(diag(pcov))\\n\\n # Loop over the parameters.\\n index = 0\\n for name in api.get_param_names(model_info):\\n # Set the parameter error.\\n api.set_error(index, sd[index], model_info=model_info)\\n\\n # Increment the parameter index.\\n index = index + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874c480feab4d3a3da841c54575ef1ef\",\n \"score\": \"0.5282582\",\n \"text\": \"def run_sim(R_star, transit_duration, bodies):\\n\\n # Run 3-body sim for one full orbit of the outermost moon\\n loop(bodies, orbit_duration)\\n \\n\\n # Move resulting data from lists to numpy arrays\\n ttv_array = numpy.array([])\\n ttv_array = ttv_list\\n tdv_array = numpy.array([])\\n tdv_array = tdv_list\\n\\n # Zeropoint correction\\n middle_point = numpy.amin(ttv_array) + numpy.amax(ttv_array)\\n ttv_array = numpy.subtract(ttv_array, 0.5 * middle_point)\\n ttv_array = numpy.divide(ttv_array, 1000) # km/s\\n\\n # Compensate for barycenter offset of planet at start of simulation:\\n planet.px = 0.5 * (gravity_firstmoon + gravity_secondmoon)\\n stretch_factor = 1 / ((planet.px / 1000) / numpy.amax(ttv_array))\\n ttv_array = numpy.divide(ttv_array, stretch_factor)\\n\\n # Convert to time units, TTV\\n ttv_array = numpy.divide(ttv_array, R_star)\\n ttv_array = numpy.multiply(ttv_array, transit_duration * 60 * 24) # minutes\\n\\n # Convert to time units, TDV\\n oldspeed = (2 * R_star / transit_duration) * 1000 / 24 / 60 / 60 # m/sec\\n newspeed = oldspeed - numpy.amax(tdv_array)\\n difference = (transit_duration - (transit_duration * newspeed / oldspeed)) * 24 * 60\\n conversion_factor = difference / numpy.amax(tdv_array)\\n tdv_array = numpy.multiply(tdv_array, conversion_factor)\\n\\n return ttv_array, tdv_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0091e773f187b0f6aaaaff379da769da\",\n \"score\": \"0.5281733\",\n \"text\": \"def acic_covariates():\\n mat = covariate_matrix()\\n mat, _, _ = normalize(mat)\\n return mat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42eb49fd5e7eafac20923b70995bfaa9\",\n \"score\": \"0.5281578\",\n \"text\": \"def cov_naive(X):\\n D, N = X.shape\\n ### Edit the code below to compute the covariance matrix by iterating over the dataset.\\n covariance = np.zeros((D, D))\\n covMatrix = np.cov(X,bias=True)\\n print(covMatrix)\\n ### Update covariance\\n\\n ###\\n return covMatrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1884cf20c01a2d290e8f9ba0877a791\",\n \"score\": \"0.52797663\",\n \"text\": \"def covolume(M):\\n from regular_models.RR_spaces.RR_lattices import RRSpace\\n M_K = M.RR_space();\\n return (Matrix([M_K.function_space().vector(f) for f in M.basis()])).determinant()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23c3d9dd4f5148774d882ab2c9a124ee\",\n \"score\": \"0.5279012\",\n \"text\": \"def c2tMatrix(sec, init, consts):\\r\\n # Polar motion(arcsec->radians)\\r\\n xp = 0.0349282 * consts.asec2rad\\r\\n yp = 0.4833163 * consts.asec2rad\\r\\n\\r\\n # CIP offsets wrt IAU 2000A(mas->radians)\\r\\n dx = 0.1725 * consts.asec2rad / 1000\\r\\n dy = -0.2650 * consts.asec2rad / 1000\\r\\n\\r\\n mjdjd0 = 2400000.5\\r\\n\\r\\n tm = sec / 86400\\r\\n utc = init.mjd + tm\\r\\n delta = deltaAT(init.year, init.month, init.mjd, tm)\\r\\n tai = utc + delta / 86400\\r\\n tt = tai + 32.184 / 86400\\r\\n tut = tm + consts.dUT1 / 86400\\r\\n\\r\\n # IAU 2000A, CIO based, using classical angels\\r\\n # CIP and CIO, IAU 2000A\\r\\n x, y, s = calcXYS(mjdjd0, tt, consts)\\r\\n\\r\\n # Add CIP corrections\\r\\n x = x + dx\\r\\n y = y + dy\\r\\n\\r\\n # Obtain the spherical angles E and d\\r\\n r2 = x**2 + y**2\\r\\n if r2 > 0:\\r\\n e = atan2(y, x)\\r\\n else:\\r\\n e = 0\\r\\n d = atan(sqrt(r2 / (1 - r2)))\\r\\n\\r\\n # GCRS to CIRS matrix\\r\\n matr = np.eye(3)\\r\\n matr = rotateRZ(e, matr)\\r\\n matr = rotateRY(d, matr)\\r\\n matr = rotateRZ(-(e + s), matr)\\r\\n\\r\\n # Earth rotation angle\\r\\n dj1 = mjdjd0 + init.mjd\\r\\n dj2 = tut\\r\\n # Days since fundamental epoch\\r\\n if dj1 < dj2:\\r\\n date1 = dj1\\r\\n date2 = dj2\\r\\n else:\\r\\n date1 = dj2\\r\\n date2 = dj1\\r\\n\\r\\n t = date1 + (date2 - consts.dj00)\\r\\n\\r\\n # Fractional part of T(days)\\r\\n f = date1 % 1 + date2 % 1\\r\\n\\r\\n # Earth rotation angle at this UT1\\r\\n era = (2*pi*(f + 0.7790572732640 + 0.00273781191135448 * t)) % (2*pi)\\r\\n if era < 0:\\r\\n era += 2*pi\\r\\n\\r\\n # Form celestial - terrestial matrix(no polar motion yet)\\r\\n celTerMatrix = np.copy(matr)\\r\\n celTerMatrix = rotateRZ(era, celTerMatrix)\\r\\n\\r\\n # Polar motion matrix(TRIRS->ITRS, IERS 2003)\\r\\n # Interval between fundamental epoch J2000.0 and current date(JC).\\r\\n t = ((mjdjd0 - consts.dj00) + tt) / consts.djc\\r\\n\\r\\n # Approximate s'\\r\\n sp = -47e-6 * t * consts.asec2rad\\r\\n\\r\\n polarMatrix = np.eye(3)\\r\\n polarMatrix = rotateRZ(sp, polarMatrix)\\r\\n polarMatrix = rotateRY(-xp, polarMatrix)\\r\\n polarMatrix = rotateRX(-yp, polarMatrix)\\r\\n\\r\\n # Form celestial - terrestial matrix(including polar motion)\\r\\n polarCelTerMatrix = polarMatrix.dot(celTerMatrix)\\r\\n return polarCelTerMatrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b327a70bf42ecd46a584efae575aad61\",\n \"score\": \"0.52783996\",\n \"text\": \"def compute_cov_mat(self, beta, lamz, lams=None, verbose=False):\\n if verbose:\\n print('in covMat, beta shape ',beta.shape,\\n ', lamz shape ',lamz.shape,\\n ', sqdist shape ',self.sqdist.shape)\\n # lamz is the process precision, nominally 1 for scaled data\\n # lams is a 'nugget' or noise variance\\n if self.sqdist.shape[0] == 0:\\n sqdist_exp_sum = np.array([])\\n else:\\n sqdist_sum = np.squeeze(self.sqdist @ beta)\\n sqdist_exp_sum = np.exp(-sqdist_sum) / lamz\\n if self.type == 1:\\n cov = np.zeros((self.n, self.n))\\n cov[self.ind] = sqdist_exp_sum\\n cov = cov + cov.T\\n lam = 1/lamz \\n if lams is not None: lam += 1/lams\\n np.fill_diagonal(cov, lam)\\n elif self.type == 2:\\n cov = np.zeros((self.n, self.m))\\n cov[self.ind] = sqdist_exp_sum\\n return cov\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2b93f58b2eab2a7b4bd89d0e1d60320\",\n \"score\": \"0.52736324\",\n \"text\": \"def covariance(datapoints, num_features, mean_feature_vector_for_c):\\n cov_matrix = [[0]*num_features]*num_features\\n for feature_number_i in range(num_features):\\n for feature_number_j in range(num_features):\\n running_sum = 0\\n for datapoint in datapoints:\\n ith_diff = datapoint[feature_number_i] - mean_feature_vector_for_c[feature_number_i]\\n jth_diff = datapoint[feature_number_j] - mean_feature_vector_for_c[feature_number_j]\\n running_sum += ith_diff * jth_diff\\n\\n if len(datapoints) <= 0:\\n running_sum = 0\\n else:\\n running_sum = running_sum / float(len(datapoints))\\n\\n cov_matrix[feature_number_i][feature_number_j] = running_sum\\n return cov_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b498204be43769cf9a699d505b81c5\",\n \"score\": \"0.5266135\",\n \"text\": \"def cov(self):\\n return self.F @ self.F.t() + torch.diag(self.variance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d9c4fde0e85a19c293849a5246ff6f\",\n \"score\": \"0.5255149\",\n \"text\": \"def run(self):\\n\\n S = self.model.stoichiometry_matrix\\n amat = self.model.propensities\\n ymat = self.model.species\\n n_species = len(ymat)\\n\\n # dPdt is matrix of each species differentiated w.r.t. time\\n # The code below literally multiplies the stoichiometry matrix to a column vector of propensities\\n # from the right (::math::`\\\\frac{dP}{dt} = \\\\mathbf{Sa}`)\\n dPdt = S * amat\\n\\n\\n # A Is a matrix of each species (rows) and the derivatives of their stoichiometry matrix rows\\n # against each other species\\n # Code below computes the matrix A, that is of size `len(ymat) x len(ymat)`, for which each entry\\n # ::math::`A_{ik} = \\\\sum_j S_{ij} \\\\frac{\\\\partial a_j}{\\\\partial y_k} = \\\\mathfb{S_i} \\\\frac{\\\\partial \\\\mathbf{a}}{\\\\partial y_k}`\\n A = sp.Matrix(len(ymat), len(ymat), lambda i, j: 0)\\n for i in range(A.rows):\\n for k in range(A.cols):\\n A[i, k] = reduce(operator.add, [S[i, j] * sp.diff(amat[j], ymat[k]) for j in range(len(amat))])\\n\\n\\n # `diagA` is a matrix that has values sqrt(a[i]) on the diagonal (0 elsewhere)\\n diagA = sp.Matrix(len(amat), len(amat), lambda i, j: amat[i] ** sp.Rational(1,2) if i==j else 0)\\n # E is stoichiometry matrix times diagA\\n E = S * diagA\\n\\n variance_terms = []\\n cov_matrix = []\\n for i in range(len(ymat)):\\n row = []\\n for j in range(len(ymat)):\\n if i <= j:\\n symbol = 'V_{0}_{1}'.format(i, j)\\n variance_terms.append(VarianceTerm(position=(i,j), symbol=symbol))\\n else:\\n # Since Vi,j = Vj,i, i.e. covariance are equal, we only record Vi,j but not Vj,i\\n symbol = 'V_{0}_{1}'.format(j, i)\\n variance_terms.append(VarianceTerm(position=(j,i), symbol=symbol))\\n row.append(symbol)\\n cov_matrix.append(row)\\n\\n V = sp.Matrix(cov_matrix)\\n\\n\\n # Matrix of variances (diagonal) and covariances of species i and j differentiated wrt time.\\n # I.e. if i=j, V_ij is the variance, and if i!=j, V_ij is the covariance between species i and species j\\n dVdt = A * V + V * (A.T) + E * (E.T)\\n\\n\\n # build ODEProblem object\\n rhs_redundant = sp.Matrix([i for i in dPdt] + [i for i in dVdt])\\n\\n\\n #generate ODE terms\\n n_vectors = [tuple([1 if i==j else 0 for i in range(n_species)]) for j in range(n_species)]\\n moment_terms = [Moment(nvec,lhs) for (lhs, nvec) in zip(ymat, n_vectors)]\\n\\n ode_description = moment_terms + variance_terms\\n\\n\\n non_redundant_idx = []\\n ode_terms = []\\n # remove repetitive covariances, as Vij = Vji\\n for i, cov in enumerate(ode_description):\\n if cov in ode_terms:\\n continue\\n else:\\n ode_terms.append(cov)\\n non_redundant_idx.append(i)\\n rhs = []\\n for i in non_redundant_idx:\\n rhs.append(rhs_redundant[i])\\n\\n\\n out_problem = ODEProblem(\\\"LNA\\\", ode_terms, rhs, sp.Matrix(self.model.parameters))\\n\\n\\n return out_problem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d30d5cfb20028f3869498d3f1691b88\",\n \"score\": \"0.5253129\",\n \"text\": \"def solve_for_c(T, T_prev, phi, D_eff, rho_v_dT, nz, dt, dz, Eterms):\\r\\n\\r\\n a = np.zeros(nz)\\r\\n beta = np.zeros(nz)\\r\\n Bc = np.zeros([nz, nz])\\r\\n c = np.zeros(nz)\\r\\n main_Ac = np.ones(nz)\\r\\n upper_Ac = np.zeros(nz - 1)\\r\\n lower_Ac = np.zeros(nz - 1)\\r\\n Ac = np.zeros([nz, nz])\\r\\n main_Bc = np.zeros(nz)\\r\\n Ec = np.zeros([nz, nz])\\r\\n upper_Ec = np.zeros(nz - 1)\\r\\n lower_Ec = np.zeros(nz - 1)\\r\\n part1 = np.zeros(nz)\\r\\n part2 = np.zeros(nz)\\r\\n # Variables\\r\\n beta = D_eff * rho_v_dT\\r\\n a = (1 - phi) * rho_v_dT / dt\\r\\n # Set up matrix Ac\\r\\n dz_nz = np.zeros(nz)\\r\\n dz_nz[:-1] = dz[:]\\r\\n dz_nz[-1] = (dz[-1] - dz[-2]) / 2 + dz[-1]\\r\\n ## Elements of matrix Ac (LHS)\\r\\n main_Ac[1:-1] = a[1:-1] + 2 * 2 * beta[1:-1] / (dz_nz[1:-1] ** 2 + dz_nz[:-2] ** 2)\\r\\n for i in range(1, nz - 1): # k+1\\r\\n upper_Ac[i] = -((beta[i + 1] - beta[i - 1])) / (\\r\\n dz_nz[i] + dz_nz[i - 1]\\r\\n ) ** 2 - (2 * beta[i]) / (dz_nz[i] ** 2 + dz_nz[i - 1] ** 2)\\r\\n for i in range(0, nz - 2): # k-1\\r\\n lower_Ac[i] = (beta[i + 2] - beta[i]) / (dz_nz[i + 1] + dz_nz[i]) ** 2 - (\\r\\n 2 * beta[i + 1]\\r\\n ) / (dz_nz[i + 1] ** 2 + dz_nz[i] ** 2)\\r\\n main_Ac[0] = a[0]\\r\\n upper_Ac[0] = 0\\r\\n main_Ac[-1] = a[-1]\\r\\n lower_Ac[-1] = 0\\r\\n ### Set up tridiagonal Matrix Ac\\r\\n Ac = (\\r\\n np.diag(np.ones(nz) * main_Ac, k=0)\\r\\n + np.diag(np.ones(nz - 1) * lower_Ac, k=-1)\\r\\n + np.diag(np.ones(nz - 1) * upper_Ac, k=1)\\r\\n )\\r\\n # Elements of matrix Ec\\r\\n if Eterms:\\r\\n for i in range(1, nz - 1): # k+1\\r\\n upper_Ec[i] = (\\r\\n (2 * beta[i])\\r\\n / (dz_nz[i] ** 2 + dz_nz[i - 1] ** 2)\\r\\n * (dz_nz[i] - dz_nz[i - 1])\\r\\n / (dz_nz[i] + dz_nz[i - 1])\\r\\n )\\r\\n for i in range(0, nz - 2): # k-1\\r\\n lower_Ec[i] = (\\r\\n -(2 * beta[i + 1])\\r\\n / (dz_nz[i + 1] ** 2 + dz_nz[i] ** 2)\\r\\n * (dz_nz[i + 1] - dz_nz[i])\\r\\n / (dz_nz[i + 1] + dz_nz[i])\\r\\n )\\r\\n else:\\r\\n pass\\r\\n upper_Ec[0] = 0\\r\\n lower_Ec[-1] = 0\\r\\n ### Set up Matrix Ec\\r\\n Ec = np.diag(np.ones(nz - 1) * lower_Ec, k=-1) + np.diag(\\r\\n np.ones(nz - 1) * upper_Ec, k=1\\r\\n )\\r\\n # Set up matrix Bc\\r\\n main_Bc = -a\\r\\n Bc = np.diag(np.ones(nz) * (main_Bc), k=0)\\r\\n # Compute c\\r\\n if Eterms:\\r\\n part1 = np.dot((Ac + Ec), T)\\r\\n else:\\r\\n part1 = np.dot(Ac, T)\\r\\n part2 = np.dot(Bc, T_prev)\\r\\n c = -(part1 + part2)\\r\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"274369ab4476ad6fe371f39bbec752f3\",\n \"score\": \"0.5252565\",\n \"text\": \"def _solve_marginal_cov(self):\\n status = self.mcov_solver.initialize(\\n self.graph, self.optimized_results)\\n\\n # Record noise model (covariance) of last pose\\n self.last_optimized_cov = self.get_marignal_cov_matrix(\\n self._idc_in_graph[-1])\\n\\n if status != ms.MarginalCovarianceSolverStatus.SUCCESS:\\n print(\\\"maginal covariance error\\\")\\n print(status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd05fc24b0d47a71d17ebb1bbb4798c5\",\n \"score\": \"0.5251973\",\n \"text\": \"def _determine_prior_covariance_matrices(self):\\n scaled_x = np.divide(self.X, self.l)\\n scaled_m = np.divide(self.M, self.l)\\n distance_xm = euclidean_distances(scaled_x, scaled_m, squared=True)\\n distance_mm = euclidean_distances(scaled_m, scaled_m, squared=True)\\n self.SIG_XM = self.a * np.exp(-distance_xm / 2)\\n self.SIG_MM = self.a * np.exp(-distance_mm / 2) + np.identity(self.M.shape[0]) * self.lam * self.a\\n self.B = self.a + self.b - np.sum(np.multiply(np.linalg.solve(self.SIG_MM, self.SIG_XM.T), self.SIG_XM.T), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad431bbf94579b152c070c174280cc11\",\n \"score\": \"0.5250714\",\n \"text\": \"def makeCovMat(\\n self, gsAltitudes, gsPositions, layerHeights, cn2, L0):\\n nLayers = len(layerHeights)\\n\\n # Convert gsPositions to radians from arcsecs\\n gsPositions = gsPositions * (1./3600) * (numpy.pi/180)\\n\\n # Actually do covariance matrix calculation\\n self.covmat = covarianceMatrix.matcov(\\n self.nGS, self.nSubaps, self.nxSubaps, self.subapSizes,\\n self.subapPositions, gsAltitudes, gsPositions, nLayers,\\n layerHeights, cn2, L0, self.covmat)\\n\\n return self.covmat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a07fa2b0013004ee9d4a7d52410cd42d\",\n \"score\": \"0.5250694\",\n \"text\": \"def _compute_all(self) -> np.ndarray:\\n _assert_numerical_iterable(self.acc, 'Gravitational acceleration vector')\\n self.acc = np.copy(self.acc)\\n if self.acc.ndim < 2:\\n return self.estimate(self.acc, self.mag, self.representation)\\n # Normalization of 2D arrays\\n a = self.acc/np.linalg.norm(self.acc, axis=1)[:, None]\\n angles = np.zeros((len(a), 3)) # Allocation of angles array\\n # Estimate tilt angles\\n angles[:, 0] = np.arctan2(a[:, 1], a[:, 2])\\n angles[:, 1] = np.arctan2(-a[:, 0], np.sqrt(a[:, 1]**2 + a[:, 2]**2))\\n if self.mag is not None:\\n _assert_numerical_iterable(self.mag, 'Geomagnetic field vector')\\n # Estimate heading angle\\n m = self.mag/np.linalg.norm(self.mag, axis=1)[:, None]\\n my2 = m[:, 2]*np.sin(angles[:, 0]) - m[:, 1]*np.cos(angles[:, 0])\\n mz2 = m[:, 1]*np.sin(angles[:, 0]) + m[:, 2]*np.cos(angles[:, 0])\\n mx3 = m[:, 0]*np.cos(angles[:, 1]) + mz2*np.sin(angles[:, 1])\\n angles[:, 2] = np.arctan2(my2, mx3)\\n # Return angles in degrees\\n if self.representation == 'angles':\\n return angles*RAD2DEG\\n # RPY to Quaternion\\n cp = np.cos(0.5*angles[:, 1])\\n sp = np.sin(0.5*angles[:, 1])\\n cr = np.cos(0.5*angles[:, 0])\\n sr = np.sin(0.5*angles[:, 0])\\n cy = np.cos(0.5*angles[:, 2])\\n sy = np.sin(0.5*angles[:, 2])\\n Q = np.zeros((len(self.acc), 4))\\n Q[:, 0] = cy*cp*cr + sy*sp*sr\\n Q[:, 1] = cy*cp*sr - sy*sp*cr\\n Q[:, 2] = sy*cp*sr + cy*sp*cr\\n Q[:, 3] = sy*cp*cr - cy*sp*sr\\n if self.representation == 'quaternion':\\n return Q/np.linalg.norm(Q, axis=1)[:, None]\\n return q2R(Q)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfbaf91e3905e2e4c03fd2bdcea6ef3c\",\n \"score\": \"0.52389455\",\n \"text\": \"def chi_xyz(self):\\n z = np.abs(self.grid[2] + 0.5 * self.csize)\\n a = z - 0.5 * self.csize\\n b = z + 0.5 * self.csize\\n\\n a = np.where(b <= self.params['geometry']['r_0'], np.NaN, a)\\n b = np.where(b <= self.params['geometry']['r_0'], np.NaN, b)\\n\\n a = np.where(a <= self.params['geometry']['r_0'],\\n self.params['geometry']['r_0'], a)\\n\\n z *= con.au\\n a *= con.au\\n b *= con.au\\n\\n def t_z(z):\\n \\\"\\\"\\\"\\n Time as a function of z. Defined purely for informative purposes\\n \\\"\\\"\\\"\\n r_0 = self.params['geometry']['r_0'] * con.au\\n v_0 = self.params['properties']['v_0'] * 1000\\n q_v = self.params['power_laws']['q_v']\\n return (r_0 ** q_v * z ** (1. - q_v) - r_0) / (v_0 * (1. - q_v))\\n\\n def int_t_z(z):\\n \\\"\\\"\\\"\\n Integral of t_z defined above for use in average value finding\\n \\\"\\\"\\\"\\n r_0 = self.params['geometry']['r_0'] * con.au\\n v_0 = self.params['properties']['v_0'] * 1000.\\n q_v = self.params['power_laws']['q_v']\\n num = r_0 ** q_v * z ** (2. - q_v) + (q_v - 2.) * r_0 * z\\n den = v_0 * (q_v - 2.) * (q_v - 1.)\\n return num / den\\n\\n av_ts = 1. / (b - a)\\n av_ts *= int_t_z(b) - int_t_z(a)\\n\\n # So that times start at 0 at r_0 and to progress to current model time\\n av_ts = self.time - av_ts\\n\\n av_ts = np.where(self.fill_factor > 0, av_ts, np.NaN)\\n\\n av_chis = self._jml_t(av_ts) / self._ss_jml\\n\\n return av_chis\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03e84202120235991ed925cbd564595d\",\n \"score\": \"0.5234817\",\n \"text\": \"def TMS_cov(r, phi):\\r\\n cp = np.cos(phi)\\r\\n sp = np.sin(phi)\\r\\n ch = np.cosh(r)\\r\\n sh = np.sinh(r)\\r\\n\\r\\n S = np.array(\\r\\n [\\r\\n [ch, cp * sh, 0, sp * sh],\\r\\n [cp * sh, ch, sp * sh, 0],\\r\\n [0, sp * sh, ch, -cp * sh],\\r\\n [sp * sh, 0, -cp * sh, ch],\\r\\n ]\\r\\n )\\r\\n\\r\\n return S @ S.T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c33e11cf17027bad58f949e09ac40730\",\n \"score\": \"0.5226706\",\n \"text\": \"def covariance(self, data):\\n vectors = []\\n for doc in data:\\n vectors.append(doc.vector)\\n datmatrix = numpy.matrix(vectors)\\n sigma = numpy.matrix(numpy.cov(numpy.concatenate((datmatrix, datmatrix), 0).H))\\n for i in range(numpy.shape(sigma)[0]):\\n sigma[i, i] += 0.001\\n\\n return sigma\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee23912da49ce6bd631c1567a213602a\",\n \"score\": \"0.52259743\",\n \"text\": \"def construct_covariance_matrix(self, vol_matrix, corr_matrix):\\n cov_matrix = np.dot(vol_matrix, np.dot(corr_matrix, vol_matrix))\\n return cov_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9bf7c75c8e3257f89b4537d0d76749b\",\n \"score\": \"0.52246374\",\n \"text\": \"def cov(self):\\n x_T = np.transpose(self.x)\\n x_cov = np.cov(x_T)\\n return x_cov\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b459b03141731d4844a6791f4e5ffee\",\n \"score\": \"0.52108973\",\n \"text\": \"def dynamics_model(state, dt, moonEph, sunEph, main_thrust_info):\\n moonEph = moonEph*1000\\n sunEph = sunEph*1000\\n rec = state[0:3].T*1000\\n rec_dot = state[3:6].T*1000\\n rem = moonEph[0, 0:3]\\n res = sunEph[0, 0:3]\\n rcm = rec-rem\\n rcs = rec-res\\n position = rec + rec_dot * dt\\n # Convert thrust vector into ECI frame\\n net_acceleration_thrust = np.zeros((1,3))\\n if main_thrust_info is not None:\\n kick_orientation = main_thrust_info['kick_orientation']\\n acc_mag = main_thrust_info['acceleration_magnitude']\\n Aq = attitudeMatrix(kick_orientation).reshape(3,3)\\n local_vector = np.array([1, 0, 0, 0]) # last 0 is padding\\n net_acceleration_thrust = (np.dot(Aq,local_vector[:3].T.reshape(3,1)) * acc_mag).reshape(1,3)\\n \\n # Inlined RK4\\n n1 = G(position, rem, rcm, rcs, res, thrust=net_acceleration_thrust)\\n net_acceleration_thrust = np.zeros((1,3))\\n n2 = G(position+dt*n1/2, rem, rcm, rcs, res, thrust=net_acceleration_thrust)\\n n3 = G(position+dt*n2/2, rem, rcm, rcs, res, thrust=net_acceleration_thrust)\\n n4 = G(position+n3*dt, rem, rcm, rcs, res, thrust=net_acceleration_thrust)\\n velocity = rec_dot + (1.0/6.0)*(n1 + 2*n2 + 2*n3 + n4)*dt # TODO: main thrust fire delta time\\n\\n return np.concatenate((position, velocity), axis=None) / 1000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1ed3bc35b2ee491dacd201220953e96\",\n \"score\": \"0.51990944\",\n \"text\": \"def _calculate_initial_coefficients(\\n self, iteration: int, n_iterations: int\\n ) -> Tuple[float, float]:\\n\\n r1 = r.generate_uniform_random_number()\\n\\n E_0 = 2 * r1 - 1\\n J = 2 * (1 - r1)\\n\\n # Calculates the energy (eq. 3)\\n E = 2 * E_0 * (1 - (iteration / n_iterations))\\n\\n return np.fabs(E), J\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9855b7f1d5aa2f3e249a6126e473da48\",\n \"score\": \"0.5186826\",\n \"text\": \"def get_covariance_matrix(self, X):\\n # what should be instead of K in this term 1/(N-K) in this case ?!\\n covariance_matrix = (1 / (self.nr_data - 1)) * (X - X.mean(axis=0)).T @ (X - X.mean(axis=0))\\n return covariance_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e64969d1650cbe45bf4f30a6fc2cf490\",\n \"score\": \"0.518431\",\n \"text\": \"def process_noise_covariance():\\n # noise only in the vel part of the state which depends on a*t (not measurable)\\n Q = np.zeros((6,6))\\n Q[3,3] = Q[4,4] = Q[5,5] = 0.05 \\n return Q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b84389aa3230cc8401ab26b98b8d531c\",\n \"score\": \"0.51784927\",\n \"text\": \"def _CMatrix(epoch):\\n B = ICRSCoordinates.frameBiasJ2000\\n if epoch is None:\\n return B\\n else:\\n from math import sin,cos,atan,atan2,sqrt\\n from ..utils import rotation_matrix\\n \\n P = _precession_matrix_J2000_Capitaine(epoch)\\n N = _nutation_matrix(epoch)\\n \\n \\n x,y,z = (N*P*B).A[2]\\n xsq,ysq = x**2,y**2\\n bz = 1/(1+z)\\n s = CIRSCoordinates._CIOLocator(epoch)\\n \\n #matrix components - see Circular 179 or IERS Conventions 2003\\n a,b,c = 1-bz*xsq , -bz*x*y , -x\\n d,e,f = -bz*x*y , 1 - bz*ysq , -y\\n g,h,i = x , y , 1 - bz*(xsq+ysq)\\n \\n #return rotation_matrix(-s,'z',defrees=False)*np.mat([[a,b,c],\\n # [d,e,f],\\n # [g,h,i]]) \\n \\n si = sin(s)\\n co = cos(s)\\n \\n M = [[a*co - d*si,b*co - e*si,c*co - f*si],\\n [a*si + d*co,b*si + e*co,c*si + f*co],\\n [ g, h, i ]] \\n return np.mat(M)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c5acdbed42c2bf00df94bd4cb7bd96b\",\n \"score\": \"0.5175023\",\n \"text\": \"def modcovar(x, order):\\n from spectrum import corrmtx\\n import scipy.linalg\\n X = corrmtx(x, order, 'modified')\\n Xc = np.array(X[:,1:])\\n X1 = np.array(X[:,0])\\n\\n # Coefficients estimated via the covariance method\\n # Here we use lstsq rathre than solve function because Xc is not square matrix\\n a, residues, rank, singular_values = scipy.linalg.lstsq(-Xc, X1)\\n\\n # Estimate the input white noise variance\\n\\n\\n Cz = np.dot(X1.conj().transpose(), Xc)\\n e = np.dot(X1.conj().transpose(), X1) + np.dot(Cz, a)\\n assert e.imag < 1e-4, 'wierd behaviour'\\n e = float(e.real) # ignore imag part that should be small\\n\\n return a, e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa4391f36787822df6b776c1efe3014d\",\n \"score\": \"0.5170733\",\n \"text\": \"def cov_estimator(TS):\\n return np.cov(TS.T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9754513ee7ed8b57e972e40cade7617d\",\n \"score\": \"0.51630664\",\n \"text\": \"def cov(M):\\r\\n N = M.shape[0]\\r\\n u = M.mean(axis=0)\\r\\n M = M - np.kron(np.ones((N, 1)), u)\\r\\n C = np.dot(M.T, M) / (N-1)\\r\\n return C\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9149d41185c6b9a05e0f11d5d29b56\",\n \"score\": \"0.5162007\",\n \"text\": \"def initialization_SIS_withMCMC(self, temperature_ts, daytype_ts, elec_ts, len_initialization_MCMC):\\n ###1. Create variables to store the MCMC results between date = 0 and date = len_initialization_MCMC - 1\\n #(Convergence can therefore be checked by the user)\\n self.MCMC_s = np.zeros((len_initialization_MCMC, self.nb_particles)) \\n self.MCMC_g_heat = np.zeros((len_initialization_MCMC, self.nb_particles))\\n self.MCMC_sigma2_s_star = np.zeros((1, self.nb_particles)) #See the article: we assume here that sigma_s and sigma_g do not follow any dynamic (they remain fixed to sigma_s_star and sigma_g_star)\\n self.MCMC_sigma2_g_star = np.zeros((1, self.nb_particles))\\n ###2. Gibbs-sampler \\n #Gibbs Initialization\\n self.MCMC_s[0,0] = truncnorm.rvs(a = 0,b = math.inf, loc= 0, scale = 10**4, size=1)[0]\\n self.MCMC_g_heat[0,0] = truncnorm.rvs(a = - math.inf,b = 0, loc= 0, scale = 10**4, size=1)[0]\\n self.MCMC_sigma2_s_star[0, 0] = 1 / (np.random.gamma(size = 1, shape = 0.01, scale = 100)[0])\\n self.MCMC_sigma2_g_star[0, 0] = 1 / (np.random.gamma(size = 1, shape = 0.01, scale = 100)[0])\\n for i in range(1, len_initialization_MCMC,1):\\n self.MCMC_s[i,0] = self.sample_new_s(s_prev = self.MCMC_s[i-1,0], sigma_s_current = (self.MCMC_sigma2_s_star[0, 0])**0.5)\\n self.MCMC_g_heat[i,0] = self.sample_new_g_heat(g_heat_prev = self.MCMC_g_heat[i-1,0], sigma_g_current = (self.MCMC_sigma2_g_star[0, 0])**0.5)\\n #Gibbs Main\\n for j in range(1, self.nb_particles, 1):\\n self.MCMC_s[:,j] = self.MCMC_s[:,j-1]\\n self.MCMC_g_heat[:,j] = self.MCMC_g_heat[:,j-1]\\n self.MCMC_sigma2_s_star[:,j] = self.MCMC_sigma2_s_star[:,j-1]\\n self.MCMC_sigma2_g_star[:,j] = self.MCMC_sigma2_g_star[:,j-1]\\n ##a. Simulate s0\\n #Compute the denominator of the variance and the mean\\n denom_s_0 = (10**8)*self.MCMC_sigma2_s_star[0, j]*(self.kappa[daytype_ts[0]]**2) + self.sigma2*self.MCMC_sigma2_s_star[0, j] + (10**8)*self.sigma2 \\n #Compute the numerator of the mean\\n if (self.u_heat > temperature_ts[0]):\\n numerator_mean_s_0 = (10**8)*self.sigma2* self.MCMC_s[1,j] + (10**8)*self.MCMC_sigma2_s_star[0, j]*self.kappa[daytype_ts[0]]*(elec_ts[0] - self.MCMC_g_heat[0,j]*(temperature_ts[0] - self.u_heat))\\n else :\\n numerator_mean_s_0 = (10**8)*self.sigma2* self.MCMC_s[1,j] + (10**8)*self.MCMC_sigma2_s_star[0, j]*self.kappa[daytype_ts[0]]*(elec_ts[0])\\n #Compute the final parameters of the truncated normal that simulates from the full conditional of s_0\\n mean_s_0 = numerator_mean_s_0 / denom_s_0\\n var_s_0 = ((10**8) *self.sigma2*self.MCMC_sigma2_s_star[0, j]) / denom_s_0\\n a_s_0, b_s_0 = (0 - mean_s_0) / (var_s_0**0.5), math.inf\\n self.MCMC_s[0,j] = truncnorm.rvs(a = a_s_0, b = b_s_0, loc= mean_s_0, scale = (var_s_0**0.5), size=1)[0]#loc = mean / scale = standard deviation\\n if(self.MCMC_s[0,j]==math.inf):\\n self.MCMC_s[0,j]=0\\n ##b. Simulate all other s(i)\\n for i in range(1, len_initialization_MCMC,1):\\n denom_s_i = 2*self.sigma2 + self.MCMC_sigma2_s_star[0, j]*(self.kappa[daytype_ts[i]]**2)\\n if (i+1 < len_initialization_MCMC-1):\\n dependence_next_s = self.MCMC_s[i+1,j]\\n else : \\n dependence_next_s = 0\\n #Compute the numerator of the mean\\n if (self.u_heat > temperature_ts[i]):\\n numerator_mean_s_i = self.sigma2*(self.MCMC_s[i-1,j] + dependence_next_s) + self.MCMC_sigma2_s_star[0, j]*self.kappa[daytype_ts[i]]*(elec_ts[i] - self.MCMC_g_heat[i,j]*(temperature_ts[i] - self.u_heat))\\n else :\\n numerator_mean_s_i = self.sigma2*(self.MCMC_s[i-1,j] + dependence_next_s) + self.MCMC_sigma2_s_star[0, j]*self.kappa[daytype_ts[i]]*(elec_ts[i])\\n mean_s_i = numerator_mean_s_i / denom_s_i\\n var_s_i = (self.sigma2*self.MCMC_sigma2_s_star[0, j]) / denom_s_i\\n a_s_i, b_s_i = (0 - mean_s_i) / (var_s_i**0.5), math.inf\\n self.MCMC_s[i,j] = truncnorm.rvs(a = a_s_i, b = b_s_i, loc= mean_s_i, scale = (var_s_i**0.5), size=1)[0]\\n if(self.MCMC_s[i,j]==math.inf):\\n self.MCMC_s[i,j]=0\\n ##c. Simulate g_heat0\\n #Compute the numerator and the denominator of the mean\\n if (self.u_heat > temperature_ts[0]):\\n denom_g_0 = (10**8)*self.MCMC_sigma2_g_star[0, j]*((temperature_ts[0] - self.u_heat )**2) + self.sigma2*self.MCMC_sigma2_g_star[0, j] + (10**8)*self.sigma2 \\n numerator_mean_g_0 = (10**8)*self.sigma2* self.MCMC_g_heat[1,j] + (10**8)*self.MCMC_sigma2_g_star[0, j]*(temperature_ts[0] - self.u_heat)*(elec_ts[0] - self.MCMC_s[0,j]*self.kappa[daytype_ts[0]])\\n else :\\n denom_g_0 = self.sigma2*self.MCMC_sigma2_g_star[0, j] + (10**8)*self.sigma2 \\n numerator_mean_g_0 = (10**8)*self.sigma2* self.MCMC_g_heat[1,j]\\n #Compute the final parameters of the truncated normal that simulates from the full conditional of g_0\\n mean_g_0 = numerator_mean_g_0 / denom_g_0\\n var_g_0 = ((10**8) *self.sigma2*self.MCMC_sigma2_g_star[0, j]) / denom_g_0\\n a_g_0, b_g_0 = -math.inf, (0 - mean_g_0) / (var_g_0**0.5)\\n self.MCMC_g_heat[0,j] = truncnorm.rvs(a = a_g_0, b = b_g_0, loc= mean_g_0, scale = (var_g_0**0.5), size=1)[0]\\n if(self.MCMC_g_heat[0,j]==-math.inf):\\n self.MCMC_g_heat[0,j]=0\\n ##d. Simulate all other g_heat(i)\\n for i in range(1, len_initialization_MCMC,1):\\n if (i+1 < len_initialization_MCMC-1):\\n dependence_next_g = self.MCMC_g_heat[i+1,j]\\n else : \\n dependence_next_g = 0\\n if (self.u_heat > temperature_ts[i]):\\n denom_g_i = 2*self.sigma2 + self.MCMC_sigma2_g_star[0, j]*((temperature_ts[i] - self.u_heat )**2)\\n numerator_mean_g_i = self.sigma2*(self.MCMC_g_heat[i-1,j] + dependence_next_g) + self.MCMC_sigma2_g_star[0, j]*(temperature_ts[i] - self.u_heat )*(elec_ts[i] - self.MCMC_s[i,j]*self.kappa[daytype_ts[i]])\\n else :\\n denom_g_i = 2*self.sigma2\\n numerator_mean_g_i = self.sigma2*(self.MCMC_g_heat[i-1,j] + dependence_next_g)\\n mean_g_i = numerator_mean_g_i / denom_g_i\\n var_g_i = (self.sigma2*self.MCMC_sigma2_g_star[0, j]) / denom_g_i\\n a_g_i, b_g_i = -math.inf, (0 - mean_g_i) / (var_g_i**0.5)\\n self.MCMC_g_heat[i,j] = truncnorm.rvs(a = a_g_i, b = b_g_i, loc= mean_g_i, scale = (var_g_i**0.5), size=1)[0]\\n if(self.MCMC_g_heat[i,j]==-math.inf):\\n self.MCMC_g_heat[i,j]=0\\n ##e. Simulate the variances\\n shape_variances = 0.01 + ((len_initialization_MCMC - 1)/2)\\n s_lag = np.roll(self.MCMC_s[:,j], 1)\\n s_lag[0] = self.MCMC_s[0,j]\\n rate_s = 0.01 + sum((self.MCMC_s[:,j] - s_lag)**2)\\n self.MCMC_sigma2_s_star[0, j] = 1 / (np.random.gamma(size = 1, shape = shape_variances, scale = 1/rate_s )[0])\\n g_lag = np.roll(self.MCMC_g_heat[:,j], 1)\\n g_lag[0] = self.MCMC_g_heat[0,j]\\n rate_g = 0.01 + sum((self.MCMC_g_heat[:,j] - g_lag)**2)\\n self.MCMC_sigma2_g_star[0, j] = 1 / (np.random.gamma(size = 1, shape = shape_variances, scale = 1/rate_g )[0])\\n ###3. Add a prior on sigma_s and sigma_g based on the estimated errors\\n s_lag = np.roll(self.MCMC_s, shift = 1, axis =0)\\n s_lag[0,] = self.MCMC_s[0,:]\\n error_s = self.MCMC_s - s_lag \\n error_s = error_s[1:len_initialization_MCMC, :] #delete first row\\n #compute the standard error at each time i\\n error_s_std = np.std(error_s, axis =1)\\n #compute the average of the standard errors among all times\\n error_s_mean_of_std = np.mean(error_s_std)\\n error_s_std_of_std = np.std(error_s_std)\\n #same process for g_heat\\n g_lag = np.roll(self.MCMC_g_heat, shift = 1, axis =0)\\n g_lag[0,] = self.MCMC_g_heat[0,:]\\n error_g = self.MCMC_g_heat - g_lag \\n error_g = error_g[1:len_initialization_MCMC, :] #delete first row\\n error_g_std = np.std(error_g, axis =1)\\n error_g_mean_of_std = np.mean(error_g_std)\\n error_g_std_of_std = np.std(error_g_std)\\n ###4. Return the initialization of the Particle Filter at date (len_initialization_MCMC - 1)\\n self.s[0,] = self.MCMC_s[len_initialization_MCMC-1,self.nb_particles-1] + np.zeros(self.nb_particles)\\n self.g_heat[0,] = self.MCMC_g_heat[len_initialization_MCMC-1,self.nb_particles-1]+ np.zeros(self.nb_particles)\\n self.sigma_s[0,] = self.MCMC_sigma2_s_star[0,self.nb_particles-1]**0.5+ np.zeros(self.nb_particles)\\n self.sigma_g[0,] = self.MCMC_sigma2_g_star[0,self.nb_particles-1]**0.5+ np.zeros(self.nb_particles)\\n #self.sigma_s[0,] = truncnorm.rvs(a = (- error_s_mean_of_std / error_s_std_of_std) ,b = math.inf, loc= error_s_mean_of_std, scale = error_s_std_of_std, size=self.nb_particles)\\n #self.sigma_g[0,] = truncnorm.rvs(a = (- error_g_mean_of_std / error_g_std_of_std) ,b = math.inf, loc= error_g_mean_of_std, scale = error_g_std_of_std, size=self.nb_particles)\\n self.x[0,] = self.compute_x(s = self.s[0,], g_heat = self.g_heat[0,], temperature = temperature_ts[len_initialization_MCMC-1], daytype = daytype_ts[len_initialization_MCMC-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"593c5541d8ff73f1b543f7e36f9534d6\",\n \"score\": \"0.51595795\",\n \"text\": \"def simulation(self, T=100):\\n # Vars\\n x0 = np.array([0.5, 0.0, 5.0, 0.0, 0.0, 0.0]) \\n A = self.system_matrix()\\n # -observations\\n observation_size = self.observation_dim\\n obs_mean = np.zeros((observation_size,))\\n obs_cov = self.observation_noise_covariance() #(2x2)\\n observations = np.zeros((T,observation_size)) \\n # -states\\n predState_mean = np.zeros((6,))\\n predState_cov = self.process_noise_covariance() #(6x6) \\n states = np.zeros((T,6))\\n \\n # States and observations\\n for i in range(T):\\n if i == 0:\\n states[i,:] = x0\\n obs_noise = np.random.multivariate_normal(obs_mean, obs_cov)\\n observations[i,:] = self.observation(x0) + obs_noise\\n else:\\n predState_noise = np.random.multivariate_normal(predState_mean, predState_cov)\\n states[i,:] = np.dot(A,states[i-1,:]) + predState_noise\\n obs_noise = np.random.multivariate_normal(obs_mean, obs_cov)\\n observations[i,:] = self.observation(states[i,:]) + obs_noise\\n \\n return states, observations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b462161ee0554d9b93ab326189ecb811\",\n \"score\": \"0.5156437\",\n \"text\": \"def covariance(self):\\n for cat in self.probabilities:\\n if cat != 'Attrition':\\n self.covariances[cat] = 0\\n for x in [0,1]:\\n for sub_cat in self.probabilities[cat]:\\n self.covariances[cat] += ((float(x)-self.expectations['Attrition'])*(float(sub_cat)-self.expectations[cat])*(self.probabilities['Attrition'][x])*(self.probabilities[cat][sub_cat]))\\n #print(cat,(float(x)-self.expectations['Attrition'])*(float(sub_cat)-self.expectations[cat])*(self.probabilities['Attrition'][x])*(self.probabilities[cat][sub_cat]),self.covariances[cat])\\n\\n else:\\n self.covariances[cat] = self.variances[cat]\\n\\n self.rho[cat] = self.covariances[cat]/((self.variances['Attrition']*self.variances[cat])**0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6e95edbfc5eb889c99c172d03ada8f3\",\n \"score\": \"0.5153598\",\n \"text\": \"def motion_jacobian_noise_components(self):\\n # States\\n theta = self.state_vector[2]\\n \\n # Velocities\\n v = self.velocity_vector[0]\\n w = self.velocity_vector[1]\\n \\n # Expected value of error\\n dv = 0\\n dw = 0\\n \\n # Initialize\\n self.G = np.zeros((self.state_vector_size, self.control_size))\\n \\n # Calculate Jacobian\\n # Non linear motion model when there's no angular velocity\\n if abs(w + dw) > 1e-4:\\n angle = theta + (w + dw) * self.dt\\n dc = np.cos(angle) - np.cos(theta)\\n ds = np.sin(angle) - np.sin(theta)\\n\\n self.G[0][0] = ds / (w + dw)\\n self.G[0][1] = (np.cos(angle) * (v + dv) * self.dt / (w + dw)) - (v + dv) * ds / np.square(w + dw)\\n self.G[1][0] = -dc / (w + dw)\\n self.G[1][1] = (v + dv) * dc / np.square(w + dw) + np.sin(angle) * (v + dv) * self.dt / (w + dw)\\n self.G[2][0] = 0\\n self.G[2][1] = self.dt\\n\\n # Linear motion model\\n else:\\n self.G[0][0] = np.sin(theta) * self.dt\\n self.G[0][1] = np.sin(theta) * self.dt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cabd4f5183b222e0a83ccfa14d6df52\",\n \"score\": \"0.51508665\",\n \"text\": \"def getCovarianceMatrix(self):\\n return self.cov_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b113e765bdf246dc0415482a39592da\",\n \"score\": \"0.5148368\",\n \"text\": \"def simulate(self, time, seed=None):\\n if self._x0 is None:\\n raise InitialConditionNotDefined\\n\\n trans_indices_A = np.nonzero(self.A)\\n nr_trans_A = trans_indices_A[0].shape[0]\\n\\n trans_indices_B = np.nonzero(self.B)\\n nr_trans_B = trans_indices_B[0].shape[0]\\n\\n x = copy.copy(self._x0)\\n t = 0\\n T = [0]\\n X = [copy.copy(x)]\\n\\n def transition_rates(_x):\\n\\n _trans_rates_A = np.zeros(shape=trans_indices_A[0].shape)\\n\\n for j in range(nr_trans_A):\\n _k, _s, _s_prime = trans_indices_A[0][j], trans_indices_A[1][j], trans_indices_A[2][j]\\n _trans_rates_A[j] = self.A[_k, _s, _s_prime] * _x[_k, _s]\\n\\n _trans_rates_B = np.zeros(shape=trans_indices_B[0].shape)\\n\\n for j in range(nr_trans_B):\\n _k, _k1 = trans_indices_B[0][j], trans_indices_B[1][j]\\n _s, _s1 = trans_indices_B[2][j], trans_indices_B[3][j]\\n _s_prime, _s1_prime = trans_indices_B[4][j], trans_indices_B[5][j]\\n _trans_rates_B[j] = self.B[_k, _k1, _s, _s1, _s_prime, _s1_prime] * _x[_k, _s] * _x[_k1, _s1]\\n\\n return _trans_rates_A, _trans_rates_B\\n\\n # set random generator to seed\\n rnd.seed(seed)\\n\\n while t < time:\\n # transition rates evaluated at x\\n trans_rates_A, trans_rates_B = transition_rates(x)\\n S = np.sum(trans_rates_A) + np.sum(trans_rates_B)\\n\\n if S <= 1e-12:\\n print('System stalled (total rate = 0)')\\n t = time\\n else:\\n a = rnd.random() * S\\n # selected transition\\n transition_selected = False\\n for i in range(trans_rates_A.shape[0]):\\n if a > trans_rates_A[i]:\\n a -= trans_rates_A[i]\\n else:\\n transition_selected = True\\n k, s, s_prime = trans_indices_A[0][i], trans_indices_A[1][i], trans_indices_A[2][i]\\n # add unilateral transition to state vector\\n x += (- self.e(k, s) + self.e(k, s_prime))\\n break\\n\\n if transition_selected is False:\\n for i in range(trans_rates_B.shape[0]):\\n if a > trans_rates_B[i]:\\n a -= trans_rates_B[i]\\n else:\\n k, k1 = trans_indices_B[0][i], trans_indices_B[1][i]\\n s, s1 = trans_indices_B[2][i], trans_indices_B[3][i]\\n s_prime, s1_prime = trans_indices_B[4][i], trans_indices_B[5][i]\\n # add unilateral transition to state vector\\n x += (- self.e(k, s) + self.e(k, s_prime) - self.e(k1, s1) + self.e(k1, s1_prime))\\n break\\n\\n t += rnd.expovariate(S)\\n\\n T.append(t)\\n X.append(copy.copy(x))\\n\\n X = np.array(X)\\n return T, X\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1793,"cells":{"query_id":{"kind":"string","value":"e61d5d6da4a061cda9ed8338be837c73"},"query":{"kind":"string","value":"This function will post a message to the room based on provided room ID"},"positive_passages":{"kind":"list like","value":[{"docid":"b8b411438b860ac6461f8d82518cf229","score":"0.7153023","text":"def post_message(room_id):\n text = input(\"test from Sam Tian \")\n URL = \"https://api.ciscospark.com/v1/messages\"\n PAYLOAD = {\"roomId\": room_id, \"text\": text}\n\n response = requests.post(url=URL, json=PAYLOAD, headers=HEADERS) \n #MISSION: requests method is missing\n\n if response.status_code == 200:\n print(\"Your message was successfully posted to the room\")\n else:\n print(\"Something went wrong.\\n\"\n \"Please check the script and run it again!\")\n exit()","title":""}],"string":"[\n {\n \"docid\": \"b8b411438b860ac6461f8d82518cf229\",\n \"score\": \"0.7153023\",\n \"text\": \"def post_message(room_id):\\n text = input(\\\"test from Sam Tian \\\")\\n URL = \\\"https://api.ciscospark.com/v1/messages\\\"\\n PAYLOAD = {\\\"roomId\\\": room_id, \\\"text\\\": text}\\n\\n response = requests.post(url=URL, json=PAYLOAD, headers=HEADERS) \\n #MISSION: requests method is missing\\n\\n if response.status_code == 200:\\n print(\\\"Your message was successfully posted to the room\\\")\\n else:\\n print(\\\"Something went wrong.\\\\n\\\"\\n \\\"Please check the script and run it again!\\\")\\n exit()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c926d331d105eb3c95f82d1c5ff3d7e5","score":"0.7540419","text":"def on_post(self, req, resp, room_id, message_id=None):\n\n room = Room.objects.get(id=room_id)\n\n message = Message(message=req.json[\"message\"])\n message.room = room\n message.author = req.session.user\n message.save()\n\n resp.status = HTTP_CREATED\n msg = message.to_mongo()\n msg[\"author\"] = message.author.email\n resp.json = msg\n\n requests.post(\"http://localhost/api/v1/pub/room/\"+room_id+\"/message\", data=resp.body)","title":""},{"docid":"4f53c8f3558e0bf42e21c4b9d0a1677a","score":"0.7364877","text":"def post_message(room_id):\n text = input(\"What message would you like to post? \")\n URL = \"https://api.ciscospark.com/v1/messages\"\n PAYLOAD = {\"roomId\": room_id, \"text\": text}\n\n response = requests.post(url=URL, json=PAYLOAD, headers=HEADERS) #MISSION: requests method is missing\n\n if response.status_code == 200:\n print(\"Your message was successfully posted to the room\")\n else:\n print(\"Something went wrong.\\n\"\n \"Please check the script and run it again!\")\n exit()","title":""},{"docid":"eb848e0100e041f1967d2e679cfaefd1","score":"0.7149647","text":"def send_message_room(room_id, message, source=None):\n\n msg = event.Event(\n kind=event.EventKind.chat_message,\n data={\n \"source\": source,\n \"message\": message,\n \"target\": {\"type\": \"room\", \"value\": room_id},\n \"type\": \"chat\"\n },\n room_id=room_id,\n token=source,\n )\n messaging.send(msg)","title":""},{"docid":"55cd30520ca8080da3dbe7d9be586a48","score":"0.7033622","text":"async def send_message(self, roomId: str, message: str) -> m.Message:\n response = await self.client.call(\"sendMessage\", {\"rid\": roomId, \"msg\": message})\n return m.create(m.Message, response)","title":""},{"docid":"1dddf473a0d11bbc6098dffe9e31fffc","score":"0.69286066","text":"def chat_post_message(self, text, room_id=None, channel=None, **kwargs):\n if room_id:\n if text:\n return self.call_api_post(\n \"chat.postMessage\", roomId=room_id, text=text, kwargs=kwargs\n )\n return self.call_api_post(\"chat.postMessage\", roomId=room_id, kwargs=kwargs)\n if channel:\n if text:\n return self.call_api_post(\n \"chat.postMessage\", channel=channel, text=text, kwargs=kwargs\n )\n return self.call_api_post(\n \"chat.postMessage\", channel=channel, kwargs=kwargs\n )\n raise RocketMissingParamException(\"roomId or channel required\")","title":""},{"docid":"723bab1d24cedc9de431784765f50c29","score":"0.6758986","text":"def post_message(self, game_id, text, spectator=False):\n path = f'api/board/game/{game_id}/chat'\n room = 'spectator' if spectator else 'player'\n payload = {'room': room, 'text': text}\n return self._r.post(path, json=payload)['ok']","title":""},{"docid":"f382e3f60fc834f1bdfa703adb96393e","score":"0.67211306","text":"def post_message(self, game_id, text, spectator=False):\n path = f'api/bot/game/{game_id}/chat'\n room = 'spectator' if spectator else 'player'\n payload = {'room': room, 'text': text}\n return self._r.post(path, json=payload)['ok']","title":""},{"docid":"e6896a21bc1964648819167d391cb585","score":"0.6552604","text":"def add_message(id: str, username: str, body: str) -> None:\n Room.verify_room(id)\n rc.lpush(Room.get_messages_list_key(id), pickle.dumps({\"username\": username, \"body\": body}))","title":""},{"docid":"bc0ee49b58c6e9ea1295d76eb6543cc7","score":"0.6546431","text":"def _send_telegram_message(self, ID, msg, **kwargs):\n self.log.info(\"Sending message '{}' to ID: {}\".format(msg, ID))\n if ID:\n self.bot.send_message(chat_id=ID, text=msg, **kwargs)\n else:\n self.log.warning(\"No ID passed, no message sent!\")","title":""},{"docid":"db193ec6b6dd7833ff25b48cf984d6e9","score":"0.64895856","text":"def post_to_channel(db, team_id, message):\n team = db.find_one({'team_id': team_id})\n requests.post('https://slack.com/api/chat.postMessage', {\n 'token': team['bot_access_token'],\n 'channel': team['post_to'],\n 'text': message,\n 'username': \"Slack-confessions\",\n }).json()","title":""},{"docid":"3ab2dd041db4c3c03e9cf98174abcca3","score":"0.64836764","text":"def send_message(self, message):\n # Send out the message to everyone in the room\n self.group.send({\n \"text\": json.dumps({\n \"room\": str(self.room.id),\n \"message\": message,\n \"username\": self.message.user.username,\n }),\n })","title":""},{"docid":"705bcfcbb84d4484df23cc764d8871c5","score":"0.64541364","text":"def message(self, msg):\n # The reply method will use the messages 'to' JID as the\n # outgoing reply's 'from' JID.\n to_jid = JID(msg['to'])\n from_jid = JID(msg['from'])\n # strip off the server\n msg_body = msg['body']\n msg_id = msg['id']\n\n to_user_key = protocol.create_user_key(str(to_jid.user))\n from_user_key = protocol.create_user_key(str(from_jid.user)) \n logging.debug(\"SENDING MESSAGE TO KEY: %s=%s\" % (str(to_user_key), str(msg_body)))\n fullmsg = dhtxmpp_protocol_msg.create_msg_str(from_user_key, to_user_key, msg_body, msg_id)\n msg_key = protocol.create_msg_key(str(to_jid.user), dhtxmpp_protocol_msg.msg_type_msg)\n self.send_msg_to_dht(msg_key, fullmsg)","title":""},{"docid":"106126f9a0ba784e7baa3cb5b1ed3f2e","score":"0.63024265","text":"def post(self, request, m_id):\n recipient_username = request.POST.get('recipient') or 'admin'\n recipient = User.objects.get(username=recipient_username)\n parent_msg_id = request.POST.get('parent_msg')\n\n message = Message.objects.get(id=int(parent_msg_id))\n message.replied_at = timezone.now()\n message.save() # update replied at\n parent_msg_id = Message.objects.get(id=m_id)\n\n payload = {\n 'sender': User.objects.get(username=request.user.username),\n 'parent_msg': message,\n 'body': request.POST.get('body'),\n 'recipient': recipient,\n } # create reply payload\n reply = Message(**payload)\n reply.sent_at = timezone.now()\n reply.save()\n\n alert.add_message(\n request, alert.SUCCESS, 'Message sent successfully.'\n )\n\n return redirect(\n reverse(\n 'message', kwargs={'m_id': message.id}\n )\n )","title":""},{"docid":"cb0ea49fc7713c836c28e3c6161fdd6a","score":"0.6291127","text":"def send_message(chatroomID: str, json_data: dict):\n\n replyID = json_data.get('replyID')\n mtype = 'text'\n\n channelID = json_data.get('channelID')\n username = json_data.get('username')\n\n keyID = None\n mtext = json_data.get('data')\n\n\n # Make sure all arguments are given\n required = [\"mtype\", \"channelID\", \"username\", \"data\"]\n for i, a in enumerate([mtype, channelID, username, mtext]):\n if a == None:\n return f\"No value supplied for required field: {required[i]}.\", 400\n\n\n # Validate user input\n values = [\"replyID\", \"channelID\"] # , \"keyID\" ]\n for i, a in enumerate([replyID, channelID]):\n if a != None and not security.is_uuid(a):\n return f\"Value for {values[i]} is not a valid UUID!\", 400\n\n\n # users should not be able to send empty messages\n if len(mtext) < 1:\n return \"Cannot send empty message\", 400\n\n\n # Check if channel exists and that the user has access to it\n permissions, status = database.get_channel_permissions(chatroomID, channelID, username)\n if status != 200: return permissions, status\n\n\n if not permissions['w']:\n return \"You do not have permissions to write in this channel\", 403\n\n # continue on from here\n return database.write_message_text(chatroomID, channelID, username, mtext, replyID)","title":""},{"docid":"e9b2372e67f40082a74441e1d5d7461e","score":"0.6256253","text":"def post(self):\n channel_id = self.get_argument('id', None)\n channel_data = {}\n channel_data['message'] = self.request.body\n channel_data['content_type'] = self.request.headers.get('Content-Type')\n channel_data['last_modified'] = self.request.headers.get('Last-Modified')\n client = brukva.Client(port=6380)\n client.connect()\n tornado.ioloop.IOLoop().instance().add_callback(\n lambda: self.publish_message(client, channel_id, channel_data))","title":""},{"docid":"95a4fd036ab7f1728db2b35575a9c50a","score":"0.6230725","text":"def send_message_with_id(PSID, text):\n r = requests.post(\"https://graph.facebook.com/v2.6/me/messages\",\n params={\"access_token\": PAT},\n data=json.dumps({\n \"recipient\": {\"id\": PSID},\n \"message\": {\"text\": text}\n }),\n headers={'Content-type': 'application/json'})\n if r.status_code != requests.codes.ok:\n print r.text","title":""},{"docid":"45d75c6ee53b349d1083d612890bb594","score":"0.6197968","text":"async def message(rooms, groupid, sensorid, msg):\n # only send the message if the room exists\n if not _does_room_exist(rooms, groupid, sensorid):\n return\n # add additional fields to the message\n # create the response object for the websocket\n resp = dict()\n resp[\"cmd\"] = \"RESP_READING\"\n resp[\"readings\"] = [{\n \"rtypeid\": msg[\"rtypeid\"],\n \"ts\": msg[\"ts\"],\n \"val\": msg[\"val\"],\n \"rstring\": msg[\"rstring\"]\n }]\n try:\n # get the rtype, so we only send to clients that ask for it specifically\n rtypeid = msg[\"rtypeid\"]\n except KeyError:\n print(\"ERROR: KeyError has occurred sending message, 'rtypeid' not found!\")\n return\n # steps through all clients in the room\n for ws, rtype in rooms[(groupid, sensorid)].items():\n if rtype == rtypeid:\n await ws.send_str(simplejson.dumps(resp))","title":""},{"docid":"19e924a893006517233d7e4c3b9ec7bb","score":"0.61862916","text":"def reply(self, message, messageID, socketID):\n encodedMessage = json.dumps((message, messageID))\n self.connections[socketID][0].send(encodedMessage + '\\n')","title":""},{"docid":"f370dcd9a878f17fbe29d817173c1bbd","score":"0.61757594","text":"def add_message(self, msg_id, location, msg):","title":""},{"docid":"eb0e36ea05646b0f7b4ae45c07cf6fbd","score":"0.61682874","text":"async def send_message_in_room(self, message: str, room_id: str, client_socket: socket.socket) -> None:\n recipients = self.rooms.get(str(room_id))\n for recipient in recipients:\n if recipient is not client_socket:\n await self.event_loop.sock_sendall(recipient, message.encode('utf-8'))","title":""},{"docid":"56a75e2cf6ace298696590933e4631d9","score":"0.616219","text":"async def chat_message(self, event):\n\n print('message: ' + event['message'])\n await self.send_json({\n 'room': event['room'],\n 'user': event['user'],\n 'message': event['message'],\n 'created': str(now())\n })","title":""},{"docid":"cc80ad74ef391aaebd09ad13f647521e","score":"0.61027426","text":"def create_room(self, room_name: str, room_id: str, channel_id: str, user_id: str, user_name) -> None:","title":""},{"docid":"77e5278ff58357ac641325102f6a8b67","score":"0.6062393","text":"def send_bot_message(self, body, message_id=None, to=None,\n subject='Just bot things...'):\n sender = self.reddit.user.me().name\n if sender != 'transcribersofreddit':\n raise InvalidUser(f'Attempting to send message as {sender}'\n f'instead of the official ToR bot')\n\n if message_id:\n self.reddit.message(message_id).reply(body)\n elif to:\n self.reddit.redditor(to).message(subject, body)\n else:\n raise NotImplementedError(\n \"Must give either a value for ``message_id`` or ``to``\"\n )","title":""},{"docid":"97f3e9d4b0386342c650b12f8e5fd1f7","score":"0.604548","text":"def send_message(self, chat_id, text):\n params = {'chat_id': chat_id, 'text': text}\n method = 'sendMessage'\n resp = requests.post(self.api_url + method, params)\n return resp","title":""},{"docid":"cc044d018731366c2827ff6a9a4d8817","score":"0.6014179","text":"def joined(message, room):\n join_room(room)\n\n print(room)\n emit('room', {'msg': room})\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)","title":""},{"docid":"324e1d59f1329e9e6412fcc8d7d4dd76","score":"0.60139734","text":"def _send_message_to_server(self, message, ID=None):\n if isinstance(message, dict):\n msg = message[\"text\"]\n from_ID = message[\"from\"][\"id\"]\n else:\n msg = str(message)\n from_ID = ID\n\n try:\n response = self.Client.send_request(\"TelegramBot\", {str(from_ID):\"{}\".format(msg)})\n except Exception as err:\n self.log.info(\"Server Error {}\".format(err))\n return err\n self.log.info(\"Server responded with {}\".format(response))\n return response","title":""},{"docid":"ff7cf2b84e26220c7ee0117627f6af12","score":"0.60011244","text":"def send_message_to_robot(self, message: BaseMessage, id_robot):\n self.put_item(id_robot, message)","title":""},{"docid":"787d6fb0f6fe2cc194b8c967e980c672","score":"0.5997031","text":"async def chat_message(self, event):\n # Send a message down to the client\n await self.send_json(\n {\n \"msg_type\": settings.MSG_TYPE_MESSAGE,\n \"room\": \"hello\",\n \"message\": event[\"message\"],\n },\n )","title":""},{"docid":"787d6fb0f6fe2cc194b8c967e980c672","score":"0.5997031","text":"async def chat_message(self, event):\n # Send a message down to the client\n await self.send_json(\n {\n \"msg_type\": settings.MSG_TYPE_MESSAGE,\n \"room\": \"hello\",\n \"message\": event[\"message\"],\n },\n )","title":""},{"docid":"eba996d0b850d3c9a95612afb8c442ac","score":"0.5988014","text":"def send_message(self, text):\n import requests\n\n endpoint = '{}rooms/{}/chatMessages'.format(self.gitter_host, self.room_id)\n response = requests.post(\n endpoint,\n headers=self.headers,\n json={'text': text}\n )\n self.logger.info('{} sending message to {}'.format(\n response.status_code, endpoint\n ))\n self._validate_status_code(response)\n return response.json()","title":""},{"docid":"b94068bc00b43d5b011a506c2b48418f","score":"0.5975957","text":"def send_message(to, text, keyboard=None):\n if keyboard is None:\n keyboard = create_keyboard(to)\n data = {\"chat_id\": int(to), \"text\": text, \"reply_markup\": json.dumps(keyboard)}\n logger.debug(\"sending message: \" + json.dumps(data))\n response = requests.post(\n url=\"https://api.telegram.org/\" + bot_id + \"/sendMessage\", data=data)\n logger.debug(\"got response from API: \" + str(response.json()))","title":""},{"docid":"448bee807c7f5e236fa161ffd8ea922f","score":"0.5967386","text":"def muc_message(self, msg):\r\n\r\n session = self.session\r\n sender=msg['mucnick']\r\n body=msg['body']\r\n\r\n session.WriteMessageToReservationOutput(self.res_id, \"\\nChatroom message: (%s) \\\"%s\\\"\" % (sender, body))","title":""},{"docid":"e9358af4b259b3de9e3d3fbcbe2130ce","score":"0.5965737","text":"def send_msg(self, message):\n time = datetime.datetime.now().strftime(\"%H:%M:%S\")\n message = str(message)\n user = str(self.root.ids.username.text)\n add_message = {user: f\"{message} {time}\"}\n database.child(\"Chat\").child(\"Messages\").push(add_message)\n self.root.ids.message_box.text = \"\"\n toast(\"Message Sent\")\n\n self.update_chats()","title":""},{"docid":"31c9d520868f6ba12f66336cba97dcc6","score":"0.5950587","text":"def to_gitter(self, msg):\n d = self.bridge.gitter.gitter_request(\n 'POST',\n 'v1/rooms/%s/chatMessages',\n {'text': matrix_to_gitter(msg)},\n self.gitter_room_id,\n user=self.user)\n d.addErrback(Errback(log,\n \"Error posting message to Gitter room {room}\",\n room=self.gitter_room_name))","title":""},{"docid":"90c79f768b82368e0590c5d31196d9fe","score":"0.5943808","text":"async def message(self, room: MatrixRoom, event: RoomMessageText):\n # Ignore messages from ourselves\n if event.sender == self.client.user:\n return\n\n # Ignore broken events\n if not event.body:\n return\n\n # We do some stripping just to remove any surrounding formatting\n formatting_chars = [\"<p>\", \"\\\\n\", \"</p>\"]\n body = self.str_strip(event.body, formatting_chars)\n formatted_body = (\n self.str_strip(event.formatted_body, formatting_chars)\n if event.formatted_body\n else None\n )\n\n # Use the formatted message text, or the basic text if no formatting is available\n msg = formatted_body or body\n if not msg:\n logger.info(\"No msg!\")\n return\n\n # Check whether this is a command\n #\n # We use event.body here as formatted bodies can start with <p> instead of the\n # command prefix\n if not body.startswith(CONFIG.command_prefix):\n return\n\n logger.debug(\"Command received: %s\", msg)\n\n # Assume this is a command and attempt to process\n command = Command(self.client, self.store, msg, room, event)\n\n try:\n await command.process()\n except CommandError as e:\n # An expected error occurred. Inform the user\n msg = f\"Error: {e.msg}\"\n await send_text_to_room(self.client, room.room_id, msg)\n\n # Print traceback\n logger.exception(\"CommandError while processing command:\")\n except Exception as e:\n # An unknown error occurred. Inform the user\n msg = f\"An unknown error occurred: {e}\"\n await send_text_to_room(self.client, room.room_id, msg)\n\n # Print traceback\n logger.exception(\"Unknown error while processing command:\")","title":""},{"docid":"08282a5c376f0325ea46f5a3b1b9b97a","score":"0.593638","text":"def server_post_message():\n token = request.form['token']\n message = request.form['message']\n email_wall = request.form['email_wall']\n response = post_message(token, message, email_wall)\n global socket_storage\n live_message(socket_storage)\n return response","title":""},{"docid":"0dc0844a96fbb3a903aef20047c254fe","score":"0.5923847","text":"def send(self, msg_id):\n r.rpush(\"%s:queue\" % self.key, msg_id)","title":""},{"docid":"eb7c1c795ae1e458a5474ec8fbdaee0e","score":"0.5913215","text":"def send(event=None): # sends message to server; event is passed by binders.\n typed_msg = my_msg.get()\n if typed_msg != '{quit}':\n msg = '{\"type\":\"chatroom-send\", \"from\":\"%s\", \"to\": [], \"message\": \"%s\", \"message-length\":%s}' % (NAME, typed_msg, str(len(typed_msg)))\n else:\n msg = '{\"type\":\"chatroom-end\", \"id\":%s}' % str(ID)\n \n my_msg.set(\"\") # Clears input field back to blankness.\n client_socket.send(bytes(msg, \"utf8\"))\n \n if typed_msg == '{quit}':\n client_socket.close()\n top.quit()","title":""},{"docid":"92a296c4e6ed6661125ba76b007df9a3","score":"0.58762765","text":"def send_message_to_chat_room(msg, room_name, author=BOT_NAME):\n channel_layer = get_channel_layer()\n async_to_sync(channel_layer.group_send)(\n group=room_name,\n message={\n 'type': 'chat_message',\n 'message': msg,\n 'author': author,\n }\n )","title":""},{"docid":"89480240eb0d0d453fe1251fb2ce9056","score":"0.58743656","text":"def post_message(self, channel_id, text, timestamp=\"\", parse=\"full\"):\n self.push_message(channel_id, str(text))","title":""},{"docid":"9a5bf6b5f3cda2dc8d17a1cc22c2de75","score":"0.58414394","text":"def text(message, room):\n # room = session.get('room')\n\n print('---------------------------')\n print(room)\n emit('message', {'msg': message['msg'], 'room': room['room'], 'name': session.get('name')}, room=room['room'])\n #emit('message', {'msg': session.get('name') + ':' + message['msg'], 'room': room['room']}, room=room['room'])","title":""},{"docid":"af37dcfe2968714be245ab7395e556dd","score":"0.58267546","text":"async def chat_message(self, event):\n if self.user and not self.user.is_authenticated:\n return\n\n await self.send(text_data=json.dumps({\n 'user_id': None,\n 'created_at': event['created_at'],\n 'message': event['message'],\n 'publisher_full_name': event['publisher_full_name'],\n }))","title":""},{"docid":"7cb94889eedbc8358df8eebf27d2d9d0","score":"0.5822937","text":"def send(msg, chat_id, token=my_token):\n\tbot = telegram.Bot(token=token)\n\treturn bot.sendMessage(chat_id=chat_id, text=msg)","title":""},{"docid":"a04fa14fe214ad199c9d90388747173c","score":"0.58200175","text":"def join(message):\n try:\n add_user_to_room(message['room'], request.sid)\n join_room(message['room'])\n send_message(\"NOTIFICATION\", \"Room status\", \"Room joined.\",\n {\"room\":message['room']})\n except InvalidRoomNameError:\n send_message(\"ERROR\", \"Room status\", \"Invalid room name.\",\n {\"room\":message['room']})","title":""},{"docid":"57a1898332faca7691032e6254284bc3","score":"0.58085006","text":"def joined(message):\n friend_id = int(message['friend_id'])\n my_id = int(message['my_id'])\n # my_id = session.get('my_id')\n print('!!!!!!!!!!!!!!!!!!!!!my_id: ', my_id)\n room_id = get_room_name(my_id, friend_id)\n session['room'] = room_id\n join_room(room_id)\n res = userService.addUserRoom(my_id, room_id)\n emit('status', {'msg': 'join', 'friend_id': friend_id, 'room_id': room_id}, room=room_id)\n return make_response(jsonify(res))","title":""},{"docid":"dbe9fb5b0c8e298b64455bc4dc393325","score":"0.58012867","text":"def join(room_id):\n token = request.args[\"token\"]\n token_valid = verify_token(token)\n if not token_valid:\n return\n current_user_id = str(g.current_user)\n join_room(room_id)\n conversation = ChatRoom.objects().get(id=room_id)\n if not conversation is None:\n if not current_user_id in conversation.participants:\n conversation.participants.append(current_user_id)\n conversation.save()\n emit(\"joined\", conversation.to_json())","title":""},{"docid":"d4d788aafb54a076012a0294f66363c5","score":"0.5800687","text":"def send_to_messenger(self, ctx):\n ctx[\"recipient\"] = {\"id\": self.__userid}\n response = requests.post(self.__url, json=ctx)\n if response.status_code != 200:\n print(\"Response Error:\", response.text)\n return response","title":""},{"docid":"1cf209221c463fe64aff81c2fcc9150a","score":"0.5795401","text":"def post(self, request, *args, **kwargs):\n recipient_username = request.POST.get('recipient') or 'admin'\n recipient = User.objects.get(username=recipient_username)\n payload = {\n 'subject': request.POST.get('subject'),\n 'sender': User.objects.get(username=request.user.username),\n 'body': request.POST.get('body'),\n 'recipient': recipient,\n } # create new message payload\n\n mesg = Message(**payload)\n mesg.sent_at = timezone.now()\n mesg.save()\n\n alert.add_message(\n request, alert.SUCCESS, 'New conversation started with {}.'\n .format(recipient.username)\n )\n\n return redirect(\n reverse(\n 'message', kwargs={'m_id': mesg.id}\n )\n )","title":""},{"docid":"68fb3ea61451506b1b9cce591d69859d","score":"0.5786168","text":"def on_post(self, req, resp, room_id=None):\n\n room = Room(name=req.json[\"name\"])\n room.admins.append(req.session.user)\n room.save()\n\n resp.status = HTTP_CREATED\n resp.json = room.to_mongo()","title":""},{"docid":"77aa95495442a8e6666509cdd240487f","score":"0.5784784","text":"def send_message(recipient_id, message_text):\n\n log(\"Sending message to {recipient}: {text}\".format(\n recipient=recipient_id, text=message_text))\n\n params = {\n \"access_token\": PAGE_ACCESS_TOKEN\n }\n headers = {\n \"Content-Type\": \"application/json\"\n }\n data = json.dumps({\n \"recipient\": {\n \"id\": recipient_id\n },\n \"message\": {\n \"text\": message_text\n }\n })\n r = requests.post(\"https://graph.facebook.com/v2.6/me/messages\",\n params=params, headers=headers, data=data)\n if r.status_code != 200:\n log(r.status_code)\n log(r.text)","title":""},{"docid":"19e651a844f5a5c5db2663da376dda82","score":"0.57818305","text":"def test_9_post_message(self):\n post_data = {\n \"text\": \"testscasetest\",\n \"sender\": 2,\n \"thread\": 4,\n }\n\n response = self.client.post('/api/create_message/', data=json.dumps(post_data), content_type='application/json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(9, response.data.get('id'))","title":""},{"docid":"dc527ca676046b5670fdd5b4a90e44a8","score":"0.5773678","text":"def send_message_location(request, location_id):\n if request.method == 'POST':\n if 'message' in request.data:\n employee_list = Employee.objects.all()\n location = get_object_or_404(Location, pk=location_id)\n message = Message(\n text=request.data['message'],\n from_user=request.user,\n to_user=location.name)\n message.save()\n for employee in employee_list:\n if employee.location == location:\n send_push_notification(employee, message.text)\n serializer = MessageSerializer(message)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n else:\n content = {'detail': config.NO_MESSAGE}\n return Response(content, status=status.HTTP_400_BAD_REQUEST)","title":""},{"docid":"818966c6af59f8df68c19f380a6f7b86","score":"0.5773557","text":"def postMessage(token, channel, message):\n print (\"Posting the following message in the channel {} : {}\".format(channel, message))\n sc = SlackClient(token)\n sc.api_call(\"chat.postMessage\",\n channel= channel,\n text=message)\n print(\"************((((((Message Posted))))))\")","title":""},{"docid":"9b4a758a33c913127bb959e97283aac2","score":"0.576747","text":"def postToChat(card):\n url = 'https://chat.googleapis.com/v1/spaces/AAAAttO5lGo/messages?key=AIzaSyDdI0hCZtE6vySjMm-WEfRq3CPzqKqqsHI&token=O8jRrOKFDlrVSMwE5IFcqiq5gSThdFhh4nG4OYbUjqk%3D'\n bot_message = {\n \"cards\": card\n }\n\n message_headers = {'Content-Type': 'application/json; charset=UTF-8'}\n\n http_obj = Http()\n# print bot_message\n response = http_obj.request(\n uri=url,\n method='POST',\n headers=message_headers,\n body=dumps(bot_message),\n )","title":""},{"docid":"2761e4624b04d160fb0e0c20199dcdab","score":"0.5766705","text":"def send (self, jid, message):\n self.client.send(xmpp.protocol.Message(jid, message))","title":""},{"docid":"8d353696860b0079555bc3caa03fd5b7","score":"0.57648885","text":"def send_message(self, message, user, msg_type=MSG_TYPE_MESSAGE):\n final_msg = {'room': str(self.id), 'message': message, 'username': user.username, 'msg_type': msg_type}\n\n # Send out the message to everyone in the room\n self.websocket_group.send(\n {\"text\": json.dumps(final_msg)}\n )","title":""},{"docid":"e38176005c5ccd37fbe5176c88c3b6d5","score":"0.57624835","text":"def send_message(self, chat_id: Union[str, int], text: str) -> dict:\n raise NotImplementedError","title":""},{"docid":"8b724f9a58919a449e5c4d373acebfa5","score":"0.575347","text":"def send(self, txt):\n self.api.messages.create(roomId=self.room.id, markdown=txt)","title":""},{"docid":"9bbc4bbd283c42c75f7e6fd85f351e63","score":"0.5744374","text":"def send_message(self, message):","title":""},{"docid":"7e27d429f9b1e144ed34016c6c6315ec","score":"0.573872","text":"def send_message(self, event):\n message = self.entry_message.get()\n if message != '':\n n = chat.MyMessage()\n n.name = self.username\n n.message = message\n self.conn.SendNote(n)\n self.entry_message.delete(0, 'end')","title":""},{"docid":"ad4ccef12b971b09f414b5f27e287df7","score":"0.5734658","text":"def send_msg(msg):\n client = Client(TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN)\n client.messages.create(from_=TWILIO_FROM_NUM, to=TWILIO_TO_NUM, body=msg)","title":""},{"docid":"71975715ce87adf02612510a2558d20b","score":"0.57293004","text":"async def room_send(\n self,\n room_id: str,\n event_type: str,\n body: Dict[Any, Any],\n tx_id: Union[str, UUID],\n ) -> MatrixResponse:\n return await self.auth_client(\n \"PUT\",\n f\"rooms/{room_id}/send/{event_type}/{tx_id}\",\n json=body,\n )","title":""},{"docid":"c3a116237b79314807f8666f45aad8da","score":"0.5718929","text":"def send_message(msg, chatid, token):\n assert type(chatid) == int\n assert type(msg) == str\n assert type(token) == str\n\n BASE_URL = f\"https://api.telegram.org/bot{token}\"\n fullmsg = f\"sendMessage?text={msg}&chat_id={chatid}\"\n # query params\n rsp = requests.get(f\"{BASE_URL}/{fullmsg}\")\n logging.debug(\"Message sent %s\", rsp.text)","title":""},{"docid":"2acb0b25a9ac22666a4ce39d2c12e480","score":"0.5708671","text":"def addMessage(chat_id): \n user_id= request.args.get(\"user_id\")\n text= request.args.get(\"text\")\n \n #check if the user has the permission to post in the chats or raise an exception\n get=db_chats.find_one({\"_id\":ObjectId(chat_id) })\n if not ObjectId(user_id) in get['users_list']:\n raise PermissionError(\"Permission denied\")\n\n #add the message to the messages collection and get the id\n \n dic={\n 'user_id': ObjectId(user_id),\n 'text':text,\n 'chat_id':ObjectId(chat_id)\n }\n message_id=db_messages.insert_one(dic)\n \n #add the message text to the messages_list of the chats\n\n post=db_chats.find_one({\"_id\":ObjectId(chat_id)})\n post['messages_list'].append(message_id.inserted_id)\n db_chats.update_one({'_id':ObjectId(chat_id)}, {\"$set\": post}, upsert=False)\n \n return json.dumps({'message_id':str(message_id.inserted_id)})","title":""},{"docid":"b56e759d6d1ec4d1a017e2ae68d7ace2","score":"0.5696409","text":"def send_message(self, text):\n if self.chat_id is 0:\n data = {'chat_id': self.master_id, 'text': text}\n else:\n data = {'chat_id': self.chat_id, 'text': text}\n response = self.post_request(data, '', self.api['sendMessage'])\n return self.construct(response)","title":""},{"docid":"7e9059ab593f6ef4d55d39be3bceafca","score":"0.568416","text":"def send_message(request):\n serializer = MessageSerializer(data=request.data)\n \n if serializer.is_valid():\n data = serializer.data\n room_id = data['room_id'] #room id gotten from client request\n \n rooms = DB.read(\"dm_rooms\")\n if type(rooms) == list:\n is_room_avalaible = len([room for room in rooms if room.get('_id', None) == room_id]) != 0\n \n if is_room_avalaible:\n response = DB.write(\"dm_messages\", data=serializer.data)\n if response.get(\"status\") == 200:\n print(\"data sent to zc core\")\n centrifugo_data = send_centrifugo_data(room=room_id,data=data) #publish data to centrifugo\n if centrifugo_data.get(\"status_code\") < 400:\n print(centrifugo_data)\n return Response(data=response, status=status.HTTP_201_CREATED)\n \n return Response(data=\"data not sent\",status=status.HTTP_400_BAD_REQUEST)\n return Response(\"No such room\",status=status.HTTP_400_BAD_REQUEST) \n return Response(\"core server not avaliable\",status=status.HTTP_424_FAILED_DEPENDENCY)\n \n return Response(status=status.HTTP_400_BAD_REQUEST)","title":""},{"docid":"ba2e1c1f8182c4e07a6e325c6b9e8bd5","score":"0.5667958","text":"def post(self, msg, wparam, lparam):\n self._PostThreadMessage(self._threadid, msg, wparam, lparam)","title":""},{"docid":"aa43b27241989c5913bd52b2c975fd04","score":"0.56650287","text":"def reply(request, pk):\n\tp = request.POST\n\tif p[\"body\"]:\n\t\tthread = Thread.objects.get(pk=pk)\n\t\tpost = Post.objects.create(thread=thread, title=p[\"subject\"], body=p[\"body\"],\n\t\t\tcreator=request.user)\n\t\tincrement_post_counter(request)\n\treturn HttpResponseRedirect(reverse(\"forum.views.thread\", args=[pk]) + \"?page=last\")","title":""},{"docid":"84de9f66c3e44325f30b749790a553b2","score":"0.5651113","text":"def send_message(text, chat_id):\n url = \"{}/sendMessage\".format(BASE_URL)\n params = {\n \"chat_id\": chat_id,\n \"text\": text,\n \"parse_mode\": \"Markdown\"\n }\n response = sess.get(url=url, params=params)\n if response.status_code != requests.codes.ok: # pylint: disable=no-member\n print(\"*\"*20, \"ERROR\", \"*\"*20)\n pprint.pprint(response.json())\n print(\"*\"*45)\n else:\n print(f\"Sent message to chat_id {chat_id}, replying with '{text}'\")","title":""},{"docid":"b1a28968a07bdae191846487b15c3460","score":"0.5645387","text":"def send_message(payload):\n requests.post('https://graph.facebook.com/v2.6/me/messages/?access_token=' + token, json=payload)","title":""},{"docid":"7724f1fc808b26b3670cac06c807bde7","score":"0.5637254","text":"def send_message(chat_id, text='Wait a second, please'):\n send_message_url = MAIN_URL + f'sendmessage?chat_id={chat_id}&text={text}'\n requests.get(send_message_url)","title":""},{"docid":"0dbb02de3dcce2e01f4fdde26fb1b9aa","score":"0.56330985","text":"def send_message(self, robot_id, message):\n self.messages_to_send.append([robot_id, message])","title":""},{"docid":"27ee184db1ef37b378078749a247ce5a","score":"0.56271166","text":"def send_message_to_channel(channel_id):\n user_id = decode_identity()\n\n video_url = request.get_json().get('videoUrl')\n img_url = request.get_json().get('imgUrl')\n\n if video_url:\n video_data = get_video_metadata(video_url)\n\n new_message = Message.create(\n channel_id=channel_id,\n user_id=user_id,\n text_content=request.get_json().get('textContent'),\n video_url=video_url,\n video_html=video_data.get('html'),\n video_source=video_data.get('source'),\n video_length=video_data.get('length'),\n )\n\n elif img_url and not video_url:\n img_data = get_img_metadata(img_url)\n\n new_message = Message.create(\n channel_id=channel_id,\n user_id=user_id,\n text_content=request.get_json().get('textContent'),\n img_url=img_url,\n img_html=img_data.get('html'),\n img_height=img_data.get('height'),\n img_width=img_data.get('width'),\n )\n else:\n new_message = Message.create(\n channel_id=channel_id,\n user_id=user_id,\n text_content=request.get_json().get('textContent'),\n )\n\n socketio.emit('message', new_message._data, room=channel_id, include_self=True)\n\n return jsonify(new_message._data)","title":""},{"docid":"7b461af097f4a067fafdd6b75e0aa337","score":"0.5620505","text":"def post(self, request, *args, **kwargs):\n message = self.respond_form(request.POST)\n if message.is_valid:\n message_instance = message.save(commit=False)\n message_instance.chat = self.get_object()\n message_instance.sender = config.CHAT_RESPONDENT\n message_instance.save()\n return redirect('chat-respond', hash=self.get_object().hash)","title":""},{"docid":"81386df7425be6e7cd813d464a9fa15e","score":"0.5612972","text":"async def send_message(sid, message):\n # Added transport mode checker\n transport_mode = sio.transport(sid)\n logger.debug('MESSAGE TRANSPORT MODE (%s): %s' % (sid, transport_mode))\n logger.debug('EVENT(\"sendMessage\"): %s' % message['data'])\n try:\n if isinstance(message, dict):\n if message.get('data') is not None:\n api_ai_message = await run_small_talk(message['data']) # TODO change to the json server_message\n # api_ai_message = await get_server_message(sio.pg, message)\n await sio.emit('sendMessageResponse',\n {'data': api_ai_message},\n room=sid, namespace='/chat')\n logger.debug('EVENT(\"sendMessageResponse\"): %s' % api_ai_message)\n else:\n raise ValueError('Message should have key(\"data\")')\n else:\n raise TypeError('Message should be dict: {\"data\": \"some text\"}')\n except ValueError as e:\n logger.error('Handle ERROR: %s' % e)\n except TypeError as e1:\n logger.error('Handle ERROR: %s' % e1)","title":""},{"docid":"5a510fb8f94a76ff11dbb89b3851df05","score":"0.56115544","text":"def print_to_channel(c_id: str, s_msg: str) -> None:\n try:\n client.chat_postMessage(channel=c_id, text=s_msg)\n except SlackApiError as e:\n logger.error(\"Error posting message: {}\".format(e))","title":""},{"docid":"358ba743ef4c2728d18e58b2bc32eeec","score":"0.56109077","text":"def send_message(self, user_id, message_body):\n response = self.client.post('https://www.kwikku.com/core/core_actions/post_message.php', {\n 'action': '1',\n 'to': user_id,\n 'body': message_body,\n 'tipe': '0'\n })\n\n # Check if send message success\n if response.text == '[{\"result\":\"1\"}]':\n return True\n else:\n return False","title":""},{"docid":"96abf5e42aa183ae8637dd381a040e19","score":"0.56046814","text":"async def send(self, room: str, *messages: str):\n string = f'{room}|{\"|\".join(messages)}'\n print(f'[{datetime.now().replace(microsecond=0).isoformat()}] >> {string}')\n await self.websocket.send(string)","title":""},{"docid":"edb6f60d1bfbef0721766d2ae5a36d82","score":"0.5597723","text":"def send_message_keyboard(user_id, text, keyboard):\n data = {\n 'message': text,\n 'user_id': user_id,\n 'access_token': config.token,\n 'keyboard': json.dumps(keyboard, ensure_ascii=False),\n 'v': api_ver\n }\n res = requests.post(config.vk_api_url + 'messages.send', data=data)\n print(res.text)","title":""},{"docid":"2c3e132cedab9b6fb81484289ffac706","score":"0.5584322","text":"def send_message(request):\n\n usr = request.POST['usr']\n to_who = get_object_or_404(User, username=usr)\n frm_who = request.user\n msg = request.POST['message']\n\n entry = Message.objects.create(to_user=to_who, from_user=frm_who, msg=msg)\n\n return HttpResponseRedirect(reverse('users.user_page',\n args=[usr]))","title":""},{"docid":"994936f0e27ff6c648d2cd5fe147993d","score":"0.55818963","text":"def joined(message):\n room = session.get('room')\n join_room(room)\n print(session.get('name'))\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)","title":""},{"docid":"8710663f90be29bdcb8e50b6a4dcc095","score":"0.5580264","text":"def create_room(room_name, team_id):\n print(\"Crating the room...\")\n URL = \"https://api.ciscospark.com/v1/rooms\"\n PAYLOAD = {\"title\": room_name, \"teamId\": team_id} \n # MISSION: Key responsible for team ID is missing\n\n response = requests.post(url=URL, json=PAYLOAD, headers=HEADERS)\n\n if response.status_code == 200:\n response = response.json()\n print(\"Room was successfully created.\")\n print(\"Name: \" + response[\"title\"])\n print(\"ID: \" + response[\"id\"])\n return response[\"id\"]\n else:\n print(\"Something went wrong.\\n\"\n \"Please check the script and run it again!\")\n exit()","title":""},{"docid":"75979349eb3b7b7c7324adbed2cb814d","score":"0.556531","text":"def create_room(room_name, team_id):\n print(\"Crating the room...\")\n URL = \"https://api.ciscospark.com/v1/rooms\"\n PAYLOAD = {\"title\": room_name, \"id\": team_id} # MISSION: Key responsible for team ID is missing\n\n response = requests.post(url=URL, json=PAYLOAD, headers=HEADERS)\n\n if response.status_code == 200:\n response = response.json()\n print(\"Room was successfully created.\")\n print(\"Name: \" + response[\"title\"])\n print(\"ID: \" + response[\"id\"])\n return response[\"id\"]\n else:\n print(\"Something went wrong.\\n\"\n \"Please check the script and run it again!\")\n exit()","title":""},{"docid":"4e4e2e9aa157597e7e96375e5e985508","score":"0.5564458","text":"def channel_for_room(self, room_id: str) -> str:","title":""},{"docid":"0921df56a9b49a7029cf1a0630e0a51d","score":"0.5560418","text":"def add_to_room(self, room_id=None, room_uuid='', box_folder_id=''):\n if not room_id:\n return None\n bucket = attribute.Attributes(actor_id=self.actor_id, bucket=\"rooms\", config=self.config)\n room = bucket.get_attr(name=room_id)\n if not room:\n room = {}\n if room_uuid:\n room[\"uuid\"] = room_uuid\n if box_folder_id:\n room[\"boxFolderId\"] = box_folder_id\n bucket.set_attr(name=room_id, data=room)\n return room","title":""},{"docid":"60afc615d880ed32eb1df4b3f0973feb","score":"0.55603796","text":"def joined(message):\n room = session.get('room')\n join_room(room)\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)","title":""},{"docid":"60afc615d880ed32eb1df4b3f0973feb","score":"0.55603796","text":"def joined(message):\n room = session.get('room')\n join_room(room)\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)","title":""},{"docid":"702d57a32e0335750ed56452f8ef9cee","score":"0.55587655","text":"async def sayIn(self, room, message):\n await self.send(f\"{room}|{message}\")","title":""},{"docid":"07879133728fe2cd6b3d47f2abc5dc69","score":"0.5557736","text":"async def PM(self, message): # pylint: disable=invalid-name\n await self.connection.whisper(self.id, message)","title":""},{"docid":"ecaaa5a2afb9427180df1a9fe77395f7","score":"0.55562365","text":"def submit(self, message):\n self.messages.put(message)","title":""},{"docid":"b7e2d751a09859acef67ea0eda1c4ba6","score":"0.5548825","text":"def sendMessage(self, remote_id, messageParts):\n request_id = b''\n self.sendMultipart(remote_id, [request_id, b''] + list(messageParts))","title":""},{"docid":"4395802e21a2e9c5c960fe319eb05490","score":"0.55426496","text":"def _send_message(self, observation, chatID):\n if self._is_end_of_conversation(observation['text']):\n data = {\n 'text': '/end',\n 'evaluation': {\n 'quality': 0,\n 'breadth': 0,\n 'engagement': 0\n }\n }\n else:\n data = {\n 'text': observation['text'],\n 'evaluation': 0\n }\n message = {\n 'chat_id': chatID,\n 'text': json.dumps(data)\n }\n\n headers = {\n 'Content-Type': 'application/json'\n }\n\n res = requests.post(self.bot_url + '/sendMessage',\n json=message, headers=headers)\n if res.status_code != 200:\n print(res.text)\n res.raise_for_status()","title":""},{"docid":"57814f4c535358fb28f5c623961cddf3","score":"0.55302465","text":"async def add_to_room(self, room_id: str, client_socket: socket.socket) -> None:\n self.rooms[str(room_id)].add(client_socket)\n client_id = self.all_clients.index(client_socket)\n insert('clients_rooms', {'client_id': client_id, 'room_id': room_id})","title":""},{"docid":"ba69cc412227fafcd7c59e18c9264284","score":"0.55248505","text":"async def create_room(self, room_id: str, room_name: str, client_socket: socket.socket) -> None:\n self.rooms[room_id].add(client_socket)\n author_id = self.all_clients.index(client_socket)\n insert('rooms', {'name': room_name, 'author_id': str(author_id)})","title":""},{"docid":"8b3e402156c734b480fbba61e74fd70d","score":"0.55201733","text":"def publish_msg(self,message,thread_identifier):\n self.topic_occupied[thread_identifier]=True\n self.session.publish_data(message)\n self.topic_occupied[thread_identifier]=False","title":""},{"docid":"f2bd3f706450fbf7bbf09a709c2f7ec1","score":"0.5513918","text":"def create_message(user_id):\n text = st.text_input('Quelle est votre message aujourd\\'hui ?', max_chars=280)\n if text:\n requests.post(f'http://127.0.0.1:8000/create_message/{user_id}?dm_text={text}')\n st.success('Votre message a bien été envoyé.')","title":""},{"docid":"05bfd9aac063d97df03c4a91b3023f5c","score":"0.5513761","text":"def _message(self, msg):\n if msg['type'] in ('chat', 'normal'):\n self.message(msg['from'].bare, msg['body'])","title":""},{"docid":"48049b10533621d45064bfce046ba784","score":"0.55128014","text":"def sendMessage(user, user_to, subject, text):\n message = Message.objects.create(user_from = user, user_to = User.objects.get(id=user_to), \n subject = subject, text = text)\n message.save()\n return True","title":""},{"docid":"2bdc1f698b265078f7a5ce30d3affca7","score":"0.55083436","text":"def chat_room(request, user_id):\n # Group name for chat. It's uniq.\n user_to_chat = get_object_or_404(User, pk=user_id)\n id_list = [request.user.pk, int(user_id)]\n id_list.sort()\n room_name = \"room_\" + str(id_list[0]) + \"_with_\" + str(id_list[1])\n\n # show messages stored id DB to user\n messages = Message.objects.filter(chat_room=room_name)\n\n return render(request, 'chat/room.html', {\n 'room_name': room_name,\n 'user_to_chat': user_to_chat,\n 'user': request.user,\n 'messages': messages\n })","title":""},{"docid":"509c287f1aefa7f5028d82047ceeba7a","score":"0.54997635","text":"def send_message(chat_id):\n #\n if request.json: # If there is data in the POST request\n # message = Message.from_dict(request.json, chat_id, request.args['user_id'])\n\n result_proxy = db.session.execute(\n '''\n INSERT INTO \n messages (chat_id, sender_id, content) \n VALUES \n (:chat_id, :sender_id, :content)\n RETURNING id, chat_id, sender_id, content, created_at\n '''\n , dict(chat_id=int(chat_id), sender_id=int(request.args['user_id']), content=request.json['content'])\n )\n\n result = result_proxy.fetchone()\n\n\n db.session.commit()\n\n return jsonify({'id': result[0],\n 'chat_id': result[1],\n 'sender_id': result[2],\n 'content': result[3],\n 'created_at': result[4]\n })\n else:\n return \"Error\"","title":""}],"string":"[\n {\n \"docid\": \"c926d331d105eb3c95f82d1c5ff3d7e5\",\n \"score\": \"0.7540419\",\n \"text\": \"def on_post(self, req, resp, room_id, message_id=None):\\n\\n room = Room.objects.get(id=room_id)\\n\\n message = Message(message=req.json[\\\"message\\\"])\\n message.room = room\\n message.author = req.session.user\\n message.save()\\n\\n resp.status = HTTP_CREATED\\n msg = message.to_mongo()\\n msg[\\\"author\\\"] = message.author.email\\n resp.json = msg\\n\\n requests.post(\\\"http://localhost/api/v1/pub/room/\\\"+room_id+\\\"/message\\\", data=resp.body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f53c8f3558e0bf42e21c4b9d0a1677a\",\n \"score\": \"0.7364877\",\n \"text\": \"def post_message(room_id):\\n text = input(\\\"What message would you like to post? \\\")\\n URL = \\\"https://api.ciscospark.com/v1/messages\\\"\\n PAYLOAD = {\\\"roomId\\\": room_id, \\\"text\\\": text}\\n\\n response = requests.post(url=URL, json=PAYLOAD, headers=HEADERS) #MISSION: requests method is missing\\n\\n if response.status_code == 200:\\n print(\\\"Your message was successfully posted to the room\\\")\\n else:\\n print(\\\"Something went wrong.\\\\n\\\"\\n \\\"Please check the script and run it again!\\\")\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb848e0100e041f1967d2e679cfaefd1\",\n \"score\": \"0.7149647\",\n \"text\": \"def send_message_room(room_id, message, source=None):\\n\\n msg = event.Event(\\n kind=event.EventKind.chat_message,\\n data={\\n \\\"source\\\": source,\\n \\\"message\\\": message,\\n \\\"target\\\": {\\\"type\\\": \\\"room\\\", \\\"value\\\": room_id},\\n \\\"type\\\": \\\"chat\\\"\\n },\\n room_id=room_id,\\n token=source,\\n )\\n messaging.send(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55cd30520ca8080da3dbe7d9be586a48\",\n \"score\": \"0.7033622\",\n \"text\": \"async def send_message(self, roomId: str, message: str) -> m.Message:\\n response = await self.client.call(\\\"sendMessage\\\", {\\\"rid\\\": roomId, \\\"msg\\\": message})\\n return m.create(m.Message, response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dddf473a0d11bbc6098dffe9e31fffc\",\n \"score\": \"0.69286066\",\n \"text\": \"def chat_post_message(self, text, room_id=None, channel=None, **kwargs):\\n if room_id:\\n if text:\\n return self.call_api_post(\\n \\\"chat.postMessage\\\", roomId=room_id, text=text, kwargs=kwargs\\n )\\n return self.call_api_post(\\\"chat.postMessage\\\", roomId=room_id, kwargs=kwargs)\\n if channel:\\n if text:\\n return self.call_api_post(\\n \\\"chat.postMessage\\\", channel=channel, text=text, kwargs=kwargs\\n )\\n return self.call_api_post(\\n \\\"chat.postMessage\\\", channel=channel, kwargs=kwargs\\n )\\n raise RocketMissingParamException(\\\"roomId or channel required\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"723bab1d24cedc9de431784765f50c29\",\n \"score\": \"0.6758986\",\n \"text\": \"def post_message(self, game_id, text, spectator=False):\\n path = f'api/board/game/{game_id}/chat'\\n room = 'spectator' if spectator else 'player'\\n payload = {'room': room, 'text': text}\\n return self._r.post(path, json=payload)['ok']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f382e3f60fc834f1bdfa703adb96393e\",\n \"score\": \"0.67211306\",\n \"text\": \"def post_message(self, game_id, text, spectator=False):\\n path = f'api/bot/game/{game_id}/chat'\\n room = 'spectator' if spectator else 'player'\\n payload = {'room': room, 'text': text}\\n return self._r.post(path, json=payload)['ok']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6896a21bc1964648819167d391cb585\",\n \"score\": \"0.6552604\",\n \"text\": \"def add_message(id: str, username: str, body: str) -> None:\\n Room.verify_room(id)\\n rc.lpush(Room.get_messages_list_key(id), pickle.dumps({\\\"username\\\": username, \\\"body\\\": body}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0ee49b58c6e9ea1295d76eb6543cc7\",\n \"score\": \"0.6546431\",\n \"text\": \"def _send_telegram_message(self, ID, msg, **kwargs):\\n self.log.info(\\\"Sending message '{}' to ID: {}\\\".format(msg, ID))\\n if ID:\\n self.bot.send_message(chat_id=ID, text=msg, **kwargs)\\n else:\\n self.log.warning(\\\"No ID passed, no message sent!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db193ec6b6dd7833ff25b48cf984d6e9\",\n \"score\": \"0.64895856\",\n \"text\": \"def post_to_channel(db, team_id, message):\\n team = db.find_one({'team_id': team_id})\\n requests.post('https://slack.com/api/chat.postMessage', {\\n 'token': team['bot_access_token'],\\n 'channel': team['post_to'],\\n 'text': message,\\n 'username': \\\"Slack-confessions\\\",\\n }).json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ab2dd041db4c3c03e9cf98174abcca3\",\n \"score\": \"0.64836764\",\n \"text\": \"def send_message(self, message):\\n # Send out the message to everyone in the room\\n self.group.send({\\n \\\"text\\\": json.dumps({\\n \\\"room\\\": str(self.room.id),\\n \\\"message\\\": message,\\n \\\"username\\\": self.message.user.username,\\n }),\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"705bcfcbb84d4484df23cc764d8871c5\",\n \"score\": \"0.64541364\",\n \"text\": \"def message(self, msg):\\n # The reply method will use the messages 'to' JID as the\\n # outgoing reply's 'from' JID.\\n to_jid = JID(msg['to'])\\n from_jid = JID(msg['from'])\\n # strip off the server\\n msg_body = msg['body']\\n msg_id = msg['id']\\n\\n to_user_key = protocol.create_user_key(str(to_jid.user))\\n from_user_key = protocol.create_user_key(str(from_jid.user)) \\n logging.debug(\\\"SENDING MESSAGE TO KEY: %s=%s\\\" % (str(to_user_key), str(msg_body)))\\n fullmsg = dhtxmpp_protocol_msg.create_msg_str(from_user_key, to_user_key, msg_body, msg_id)\\n msg_key = protocol.create_msg_key(str(to_jid.user), dhtxmpp_protocol_msg.msg_type_msg)\\n self.send_msg_to_dht(msg_key, fullmsg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"106126f9a0ba784e7baa3cb5b1ed3f2e\",\n \"score\": \"0.63024265\",\n \"text\": \"def post(self, request, m_id):\\n recipient_username = request.POST.get('recipient') or 'admin'\\n recipient = User.objects.get(username=recipient_username)\\n parent_msg_id = request.POST.get('parent_msg')\\n\\n message = Message.objects.get(id=int(parent_msg_id))\\n message.replied_at = timezone.now()\\n message.save() # update replied at\\n parent_msg_id = Message.objects.get(id=m_id)\\n\\n payload = {\\n 'sender': User.objects.get(username=request.user.username),\\n 'parent_msg': message,\\n 'body': request.POST.get('body'),\\n 'recipient': recipient,\\n } # create reply payload\\n reply = Message(**payload)\\n reply.sent_at = timezone.now()\\n reply.save()\\n\\n alert.add_message(\\n request, alert.SUCCESS, 'Message sent successfully.'\\n )\\n\\n return redirect(\\n reverse(\\n 'message', kwargs={'m_id': message.id}\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb0ea49fc7713c836c28e3c6161fdd6a\",\n \"score\": \"0.6291127\",\n \"text\": \"def send_message(chatroomID: str, json_data: dict):\\n\\n replyID = json_data.get('replyID')\\n mtype = 'text'\\n\\n channelID = json_data.get('channelID')\\n username = json_data.get('username')\\n\\n keyID = None\\n mtext = json_data.get('data')\\n\\n\\n # Make sure all arguments are given\\n required = [\\\"mtype\\\", \\\"channelID\\\", \\\"username\\\", \\\"data\\\"]\\n for i, a in enumerate([mtype, channelID, username, mtext]):\\n if a == None:\\n return f\\\"No value supplied for required field: {required[i]}.\\\", 400\\n\\n\\n # Validate user input\\n values = [\\\"replyID\\\", \\\"channelID\\\"] # , \\\"keyID\\\" ]\\n for i, a in enumerate([replyID, channelID]):\\n if a != None and not security.is_uuid(a):\\n return f\\\"Value for {values[i]} is not a valid UUID!\\\", 400\\n\\n\\n # users should not be able to send empty messages\\n if len(mtext) < 1:\\n return \\\"Cannot send empty message\\\", 400\\n\\n\\n # Check if channel exists and that the user has access to it\\n permissions, status = database.get_channel_permissions(chatroomID, channelID, username)\\n if status != 200: return permissions, status\\n\\n\\n if not permissions['w']:\\n return \\\"You do not have permissions to write in this channel\\\", 403\\n\\n # continue on from here\\n return database.write_message_text(chatroomID, channelID, username, mtext, replyID)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b2372e67f40082a74441e1d5d7461e\",\n \"score\": \"0.6256253\",\n \"text\": \"def post(self):\\n channel_id = self.get_argument('id', None)\\n channel_data = {}\\n channel_data['message'] = self.request.body\\n channel_data['content_type'] = self.request.headers.get('Content-Type')\\n channel_data['last_modified'] = self.request.headers.get('Last-Modified')\\n client = brukva.Client(port=6380)\\n client.connect()\\n tornado.ioloop.IOLoop().instance().add_callback(\\n lambda: self.publish_message(client, channel_id, channel_data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95a4fd036ab7f1728db2b35575a9c50a\",\n \"score\": \"0.6230725\",\n \"text\": \"def send_message_with_id(PSID, text):\\n r = requests.post(\\\"https://graph.facebook.com/v2.6/me/messages\\\",\\n params={\\\"access_token\\\": PAT},\\n data=json.dumps({\\n \\\"recipient\\\": {\\\"id\\\": PSID},\\n \\\"message\\\": {\\\"text\\\": text}\\n }),\\n headers={'Content-type': 'application/json'})\\n if r.status_code != requests.codes.ok:\\n print r.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d75c6ee53b349d1083d612890bb594\",\n \"score\": \"0.6197968\",\n \"text\": \"async def message(rooms, groupid, sensorid, msg):\\n # only send the message if the room exists\\n if not _does_room_exist(rooms, groupid, sensorid):\\n return\\n # add additional fields to the message\\n # create the response object for the websocket\\n resp = dict()\\n resp[\\\"cmd\\\"] = \\\"RESP_READING\\\"\\n resp[\\\"readings\\\"] = [{\\n \\\"rtypeid\\\": msg[\\\"rtypeid\\\"],\\n \\\"ts\\\": msg[\\\"ts\\\"],\\n \\\"val\\\": msg[\\\"val\\\"],\\n \\\"rstring\\\": msg[\\\"rstring\\\"]\\n }]\\n try:\\n # get the rtype, so we only send to clients that ask for it specifically\\n rtypeid = msg[\\\"rtypeid\\\"]\\n except KeyError:\\n print(\\\"ERROR: KeyError has occurred sending message, 'rtypeid' not found!\\\")\\n return\\n # steps through all clients in the room\\n for ws, rtype in rooms[(groupid, sensorid)].items():\\n if rtype == rtypeid:\\n await ws.send_str(simplejson.dumps(resp))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e924a893006517233d7e4c3b9ec7bb\",\n \"score\": \"0.61862916\",\n \"text\": \"def reply(self, message, messageID, socketID):\\n encodedMessage = json.dumps((message, messageID))\\n self.connections[socketID][0].send(encodedMessage + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f370dcd9a878f17fbe29d817173c1bbd\",\n \"score\": \"0.61757594\",\n \"text\": \"def add_message(self, msg_id, location, msg):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb0e36ea05646b0f7b4ae45c07cf6fbd\",\n \"score\": \"0.61682874\",\n \"text\": \"async def send_message_in_room(self, message: str, room_id: str, client_socket: socket.socket) -> None:\\n recipients = self.rooms.get(str(room_id))\\n for recipient in recipients:\\n if recipient is not client_socket:\\n await self.event_loop.sock_sendall(recipient, message.encode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56a75e2cf6ace298696590933e4631d9\",\n \"score\": \"0.616219\",\n \"text\": \"async def chat_message(self, event):\\n\\n print('message: ' + event['message'])\\n await self.send_json({\\n 'room': event['room'],\\n 'user': event['user'],\\n 'message': event['message'],\\n 'created': str(now())\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc80ad74ef391aaebd09ad13f647521e\",\n \"score\": \"0.61027426\",\n \"text\": \"def create_room(self, room_name: str, room_id: str, channel_id: str, user_id: str, user_name) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77e5278ff58357ac641325102f6a8b67\",\n \"score\": \"0.6062393\",\n \"text\": \"def send_bot_message(self, body, message_id=None, to=None,\\n subject='Just bot things...'):\\n sender = self.reddit.user.me().name\\n if sender != 'transcribersofreddit':\\n raise InvalidUser(f'Attempting to send message as {sender}'\\n f'instead of the official ToR bot')\\n\\n if message_id:\\n self.reddit.message(message_id).reply(body)\\n elif to:\\n self.reddit.redditor(to).message(subject, body)\\n else:\\n raise NotImplementedError(\\n \\\"Must give either a value for ``message_id`` or ``to``\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97f3e9d4b0386342c650b12f8e5fd1f7\",\n \"score\": \"0.604548\",\n \"text\": \"def send_message(self, chat_id, text):\\n params = {'chat_id': chat_id, 'text': text}\\n method = 'sendMessage'\\n resp = requests.post(self.api_url + method, params)\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc044d018731366c2827ff6a9a4d8817\",\n \"score\": \"0.6014179\",\n \"text\": \"def joined(message, room):\\n join_room(room)\\n\\n print(room)\\n emit('room', {'msg': room})\\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"324e1d59f1329e9e6412fcc8d7d4dd76\",\n \"score\": \"0.60139734\",\n \"text\": \"def _send_message_to_server(self, message, ID=None):\\n if isinstance(message, dict):\\n msg = message[\\\"text\\\"]\\n from_ID = message[\\\"from\\\"][\\\"id\\\"]\\n else:\\n msg = str(message)\\n from_ID = ID\\n\\n try:\\n response = self.Client.send_request(\\\"TelegramBot\\\", {str(from_ID):\\\"{}\\\".format(msg)})\\n except Exception as err:\\n self.log.info(\\\"Server Error {}\\\".format(err))\\n return err\\n self.log.info(\\\"Server responded with {}\\\".format(response))\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff7cf2b84e26220c7ee0117627f6af12\",\n \"score\": \"0.60011244\",\n \"text\": \"def send_message_to_robot(self, message: BaseMessage, id_robot):\\n self.put_item(id_robot, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787d6fb0f6fe2cc194b8c967e980c672\",\n \"score\": \"0.5997031\",\n \"text\": \"async def chat_message(self, event):\\n # Send a message down to the client\\n await self.send_json(\\n {\\n \\\"msg_type\\\": settings.MSG_TYPE_MESSAGE,\\n \\\"room\\\": \\\"hello\\\",\\n \\\"message\\\": event[\\\"message\\\"],\\n },\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787d6fb0f6fe2cc194b8c967e980c672\",\n \"score\": \"0.5997031\",\n \"text\": \"async def chat_message(self, event):\\n # Send a message down to the client\\n await self.send_json(\\n {\\n \\\"msg_type\\\": settings.MSG_TYPE_MESSAGE,\\n \\\"room\\\": \\\"hello\\\",\\n \\\"message\\\": event[\\\"message\\\"],\\n },\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba996d0b850d3c9a95612afb8c442ac\",\n \"score\": \"0.5988014\",\n \"text\": \"def send_message(self, text):\\n import requests\\n\\n endpoint = '{}rooms/{}/chatMessages'.format(self.gitter_host, self.room_id)\\n response = requests.post(\\n endpoint,\\n headers=self.headers,\\n json={'text': text}\\n )\\n self.logger.info('{} sending message to {}'.format(\\n response.status_code, endpoint\\n ))\\n self._validate_status_code(response)\\n return response.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b94068bc00b43d5b011a506c2b48418f\",\n \"score\": \"0.5975957\",\n \"text\": \"def send_message(to, text, keyboard=None):\\n if keyboard is None:\\n keyboard = create_keyboard(to)\\n data = {\\\"chat_id\\\": int(to), \\\"text\\\": text, \\\"reply_markup\\\": json.dumps(keyboard)}\\n logger.debug(\\\"sending message: \\\" + json.dumps(data))\\n response = requests.post(\\n url=\\\"https://api.telegram.org/\\\" + bot_id + \\\"/sendMessage\\\", data=data)\\n logger.debug(\\\"got response from API: \\\" + str(response.json()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"448bee807c7f5e236fa161ffd8ea922f\",\n \"score\": \"0.5967386\",\n \"text\": \"def muc_message(self, msg):\\r\\n\\r\\n session = self.session\\r\\n sender=msg['mucnick']\\r\\n body=msg['body']\\r\\n\\r\\n session.WriteMessageToReservationOutput(self.res_id, \\\"\\\\nChatroom message: (%s) \\\\\\\"%s\\\\\\\"\\\" % (sender, body))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9358af4b259b3de9e3d3fbcbe2130ce\",\n \"score\": \"0.5965737\",\n \"text\": \"def send_msg(self, message):\\n time = datetime.datetime.now().strftime(\\\"%H:%M:%S\\\")\\n message = str(message)\\n user = str(self.root.ids.username.text)\\n add_message = {user: f\\\"{message} {time}\\\"}\\n database.child(\\\"Chat\\\").child(\\\"Messages\\\").push(add_message)\\n self.root.ids.message_box.text = \\\"\\\"\\n toast(\\\"Message Sent\\\")\\n\\n self.update_chats()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31c9d520868f6ba12f66336cba97dcc6\",\n \"score\": \"0.5950587\",\n \"text\": \"def to_gitter(self, msg):\\n d = self.bridge.gitter.gitter_request(\\n 'POST',\\n 'v1/rooms/%s/chatMessages',\\n {'text': matrix_to_gitter(msg)},\\n self.gitter_room_id,\\n user=self.user)\\n d.addErrback(Errback(log,\\n \\\"Error posting message to Gitter room {room}\\\",\\n room=self.gitter_room_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90c79f768b82368e0590c5d31196d9fe\",\n \"score\": \"0.5943808\",\n \"text\": \"async def message(self, room: MatrixRoom, event: RoomMessageText):\\n # Ignore messages from ourselves\\n if event.sender == self.client.user:\\n return\\n\\n # Ignore broken events\\n if not event.body:\\n return\\n\\n # We do some stripping just to remove any surrounding formatting\\n formatting_chars = [\\\"<p>\\\", \\\"\\\\\\\\n\\\", \\\"</p>\\\"]\\n body = self.str_strip(event.body, formatting_chars)\\n formatted_body = (\\n self.str_strip(event.formatted_body, formatting_chars)\\n if event.formatted_body\\n else None\\n )\\n\\n # Use the formatted message text, or the basic text if no formatting is available\\n msg = formatted_body or body\\n if not msg:\\n logger.info(\\\"No msg!\\\")\\n return\\n\\n # Check whether this is a command\\n #\\n # We use event.body here as formatted bodies can start with <p> instead of the\\n # command prefix\\n if not body.startswith(CONFIG.command_prefix):\\n return\\n\\n logger.debug(\\\"Command received: %s\\\", msg)\\n\\n # Assume this is a command and attempt to process\\n command = Command(self.client, self.store, msg, room, event)\\n\\n try:\\n await command.process()\\n except CommandError as e:\\n # An expected error occurred. Inform the user\\n msg = f\\\"Error: {e.msg}\\\"\\n await send_text_to_room(self.client, room.room_id, msg)\\n\\n # Print traceback\\n logger.exception(\\\"CommandError while processing command:\\\")\\n except Exception as e:\\n # An unknown error occurred. Inform the user\\n msg = f\\\"An unknown error occurred: {e}\\\"\\n await send_text_to_room(self.client, room.room_id, msg)\\n\\n # Print traceback\\n logger.exception(\\\"Unknown error while processing command:\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08282a5c376f0325ea46f5a3b1b9b97a\",\n \"score\": \"0.593638\",\n \"text\": \"def server_post_message():\\n token = request.form['token']\\n message = request.form['message']\\n email_wall = request.form['email_wall']\\n response = post_message(token, message, email_wall)\\n global socket_storage\\n live_message(socket_storage)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc0844a96fbb3a903aef20047c254fe\",\n \"score\": \"0.5923847\",\n \"text\": \"def send(self, msg_id):\\n r.rpush(\\\"%s:queue\\\" % self.key, msg_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb7c1c795ae1e458a5474ec8fbdaee0e\",\n \"score\": \"0.5913215\",\n \"text\": \"def send(event=None): # sends message to server; event is passed by binders.\\n typed_msg = my_msg.get()\\n if typed_msg != '{quit}':\\n msg = '{\\\"type\\\":\\\"chatroom-send\\\", \\\"from\\\":\\\"%s\\\", \\\"to\\\": [], \\\"message\\\": \\\"%s\\\", \\\"message-length\\\":%s}' % (NAME, typed_msg, str(len(typed_msg)))\\n else:\\n msg = '{\\\"type\\\":\\\"chatroom-end\\\", \\\"id\\\":%s}' % str(ID)\\n \\n my_msg.set(\\\"\\\") # Clears input field back to blankness.\\n client_socket.send(bytes(msg, \\\"utf8\\\"))\\n \\n if typed_msg == '{quit}':\\n client_socket.close()\\n top.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a296c4e6ed6661125ba76b007df9a3\",\n \"score\": \"0.58762765\",\n \"text\": \"def send_message_to_chat_room(msg, room_name, author=BOT_NAME):\\n channel_layer = get_channel_layer()\\n async_to_sync(channel_layer.group_send)(\\n group=room_name,\\n message={\\n 'type': 'chat_message',\\n 'message': msg,\\n 'author': author,\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89480240eb0d0d453fe1251fb2ce9056\",\n \"score\": \"0.58743656\",\n \"text\": \"def post_message(self, channel_id, text, timestamp=\\\"\\\", parse=\\\"full\\\"):\\n self.push_message(channel_id, str(text))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a5bf6b5f3cda2dc8d17a1cc22c2de75\",\n \"score\": \"0.58414394\",\n \"text\": \"def text(message, room):\\n # room = session.get('room')\\n\\n print('---------------------------')\\n print(room)\\n emit('message', {'msg': message['msg'], 'room': room['room'], 'name': session.get('name')}, room=room['room'])\\n #emit('message', {'msg': session.get('name') + ':' + message['msg'], 'room': room['room']}, room=room['room'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af37dcfe2968714be245ab7395e556dd\",\n \"score\": \"0.58267546\",\n \"text\": \"async def chat_message(self, event):\\n if self.user and not self.user.is_authenticated:\\n return\\n\\n await self.send(text_data=json.dumps({\\n 'user_id': None,\\n 'created_at': event['created_at'],\\n 'message': event['message'],\\n 'publisher_full_name': event['publisher_full_name'],\\n }))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cb94889eedbc8358df8eebf27d2d9d0\",\n \"score\": \"0.5822937\",\n \"text\": \"def send(msg, chat_id, token=my_token):\\n\\tbot = telegram.Bot(token=token)\\n\\treturn bot.sendMessage(chat_id=chat_id, text=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a04fa14fe214ad199c9d90388747173c\",\n \"score\": \"0.58200175\",\n \"text\": \"def join(message):\\n try:\\n add_user_to_room(message['room'], request.sid)\\n join_room(message['room'])\\n send_message(\\\"NOTIFICATION\\\", \\\"Room status\\\", \\\"Room joined.\\\",\\n {\\\"room\\\":message['room']})\\n except InvalidRoomNameError:\\n send_message(\\\"ERROR\\\", \\\"Room status\\\", \\\"Invalid room name.\\\",\\n {\\\"room\\\":message['room']})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a1898332faca7691032e6254284bc3\",\n \"score\": \"0.58085006\",\n \"text\": \"def joined(message):\\n friend_id = int(message['friend_id'])\\n my_id = int(message['my_id'])\\n # my_id = session.get('my_id')\\n print('!!!!!!!!!!!!!!!!!!!!!my_id: ', my_id)\\n room_id = get_room_name(my_id, friend_id)\\n session['room'] = room_id\\n join_room(room_id)\\n res = userService.addUserRoom(my_id, room_id)\\n emit('status', {'msg': 'join', 'friend_id': friend_id, 'room_id': room_id}, room=room_id)\\n return make_response(jsonify(res))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbe9fb5b0c8e298b64455bc4dc393325\",\n \"score\": \"0.58012867\",\n \"text\": \"def join(room_id):\\n token = request.args[\\\"token\\\"]\\n token_valid = verify_token(token)\\n if not token_valid:\\n return\\n current_user_id = str(g.current_user)\\n join_room(room_id)\\n conversation = ChatRoom.objects().get(id=room_id)\\n if not conversation is None:\\n if not current_user_id in conversation.participants:\\n conversation.participants.append(current_user_id)\\n conversation.save()\\n emit(\\\"joined\\\", conversation.to_json())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4d788aafb54a076012a0294f66363c5\",\n \"score\": \"0.5800687\",\n \"text\": \"def send_to_messenger(self, ctx):\\n ctx[\\\"recipient\\\"] = {\\\"id\\\": self.__userid}\\n response = requests.post(self.__url, json=ctx)\\n if response.status_code != 200:\\n print(\\\"Response Error:\\\", response.text)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf209221c463fe64aff81c2fcc9150a\",\n \"score\": \"0.5795401\",\n \"text\": \"def post(self, request, *args, **kwargs):\\n recipient_username = request.POST.get('recipient') or 'admin'\\n recipient = User.objects.get(username=recipient_username)\\n payload = {\\n 'subject': request.POST.get('subject'),\\n 'sender': User.objects.get(username=request.user.username),\\n 'body': request.POST.get('body'),\\n 'recipient': recipient,\\n } # create new message payload\\n\\n mesg = Message(**payload)\\n mesg.sent_at = timezone.now()\\n mesg.save()\\n\\n alert.add_message(\\n request, alert.SUCCESS, 'New conversation started with {}.'\\n .format(recipient.username)\\n )\\n\\n return redirect(\\n reverse(\\n 'message', kwargs={'m_id': mesg.id}\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68fb3ea61451506b1b9cce591d69859d\",\n \"score\": \"0.5786168\",\n \"text\": \"def on_post(self, req, resp, room_id=None):\\n\\n room = Room(name=req.json[\\\"name\\\"])\\n room.admins.append(req.session.user)\\n room.save()\\n\\n resp.status = HTTP_CREATED\\n resp.json = room.to_mongo()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77aa95495442a8e6666509cdd240487f\",\n \"score\": \"0.5784784\",\n \"text\": \"def send_message(recipient_id, message_text):\\n\\n log(\\\"Sending message to {recipient}: {text}\\\".format(\\n recipient=recipient_id, text=message_text))\\n\\n params = {\\n \\\"access_token\\\": PAGE_ACCESS_TOKEN\\n }\\n headers = {\\n \\\"Content-Type\\\": \\\"application/json\\\"\\n }\\n data = json.dumps({\\n \\\"recipient\\\": {\\n \\\"id\\\": recipient_id\\n },\\n \\\"message\\\": {\\n \\\"text\\\": message_text\\n }\\n })\\n r = requests.post(\\\"https://graph.facebook.com/v2.6/me/messages\\\",\\n params=params, headers=headers, data=data)\\n if r.status_code != 200:\\n log(r.status_code)\\n log(r.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e651a844f5a5c5db2663da376dda82\",\n \"score\": \"0.57818305\",\n \"text\": \"def test_9_post_message(self):\\n post_data = {\\n \\\"text\\\": \\\"testscasetest\\\",\\n \\\"sender\\\": 2,\\n \\\"thread\\\": 4,\\n }\\n\\n response = self.client.post('/api/create_message/', data=json.dumps(post_data), content_type='application/json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n self.assertEqual(9, response.data.get('id'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc527ca676046b5670fdd5b4a90e44a8\",\n \"score\": \"0.5773678\",\n \"text\": \"def send_message_location(request, location_id):\\n if request.method == 'POST':\\n if 'message' in request.data:\\n employee_list = Employee.objects.all()\\n location = get_object_or_404(Location, pk=location_id)\\n message = Message(\\n text=request.data['message'],\\n from_user=request.user,\\n to_user=location.name)\\n message.save()\\n for employee in employee_list:\\n if employee.location == location:\\n send_push_notification(employee, message.text)\\n serializer = MessageSerializer(message)\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n else:\\n content = {'detail': config.NO_MESSAGE}\\n return Response(content, status=status.HTTP_400_BAD_REQUEST)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"818966c6af59f8df68c19f380a6f7b86\",\n \"score\": \"0.5773557\",\n \"text\": \"def postMessage(token, channel, message):\\n print (\\\"Posting the following message in the channel {} : {}\\\".format(channel, message))\\n sc = SlackClient(token)\\n sc.api_call(\\\"chat.postMessage\\\",\\n channel= channel,\\n text=message)\\n print(\\\"************((((((Message Posted))))))\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b4a758a33c913127bb959e97283aac2\",\n \"score\": \"0.576747\",\n \"text\": \"def postToChat(card):\\n url = 'https://chat.googleapis.com/v1/spaces/AAAAttO5lGo/messages?key=AIzaSyDdI0hCZtE6vySjMm-WEfRq3CPzqKqqsHI&token=O8jRrOKFDlrVSMwE5IFcqiq5gSThdFhh4nG4OYbUjqk%3D'\\n bot_message = {\\n \\\"cards\\\": card\\n }\\n\\n message_headers = {'Content-Type': 'application/json; charset=UTF-8'}\\n\\n http_obj = Http()\\n# print bot_message\\n response = http_obj.request(\\n uri=url,\\n method='POST',\\n headers=message_headers,\\n body=dumps(bot_message),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2761e4624b04d160fb0e0c20199dcdab\",\n \"score\": \"0.5766705\",\n \"text\": \"def send (self, jid, message):\\n self.client.send(xmpp.protocol.Message(jid, message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d353696860b0079555bc3caa03fd5b7\",\n \"score\": \"0.57648885\",\n \"text\": \"def send_message(self, message, user, msg_type=MSG_TYPE_MESSAGE):\\n final_msg = {'room': str(self.id), 'message': message, 'username': user.username, 'msg_type': msg_type}\\n\\n # Send out the message to everyone in the room\\n self.websocket_group.send(\\n {\\\"text\\\": json.dumps(final_msg)}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e38176005c5ccd37fbe5176c88c3b6d5\",\n \"score\": \"0.57624835\",\n \"text\": \"def send_message(self, chat_id: Union[str, int], text: str) -> dict:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b724f9a58919a449e5c4d373acebfa5\",\n \"score\": \"0.575347\",\n \"text\": \"def send(self, txt):\\n self.api.messages.create(roomId=self.room.id, markdown=txt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bbc4bbd283c42c75f7e6fd85f351e63\",\n \"score\": \"0.5744374\",\n \"text\": \"def send_message(self, message):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e27d429f9b1e144ed34016c6c6315ec\",\n \"score\": \"0.573872\",\n \"text\": \"def send_message(self, event):\\n message = self.entry_message.get()\\n if message != '':\\n n = chat.MyMessage()\\n n.name = self.username\\n n.message = message\\n self.conn.SendNote(n)\\n self.entry_message.delete(0, 'end')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad4ccef12b971b09f414b5f27e287df7\",\n \"score\": \"0.5734658\",\n \"text\": \"def send_msg(msg):\\n client = Client(TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN)\\n client.messages.create(from_=TWILIO_FROM_NUM, to=TWILIO_TO_NUM, body=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71975715ce87adf02612510a2558d20b\",\n \"score\": \"0.57293004\",\n \"text\": \"async def room_send(\\n self,\\n room_id: str,\\n event_type: str,\\n body: Dict[Any, Any],\\n tx_id: Union[str, UUID],\\n ) -> MatrixResponse:\\n return await self.auth_client(\\n \\\"PUT\\\",\\n f\\\"rooms/{room_id}/send/{event_type}/{tx_id}\\\",\\n json=body,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3a116237b79314807f8666f45aad8da\",\n \"score\": \"0.5718929\",\n \"text\": \"def send_message(msg, chatid, token):\\n assert type(chatid) == int\\n assert type(msg) == str\\n assert type(token) == str\\n\\n BASE_URL = f\\\"https://api.telegram.org/bot{token}\\\"\\n fullmsg = f\\\"sendMessage?text={msg}&chat_id={chatid}\\\"\\n # query params\\n rsp = requests.get(f\\\"{BASE_URL}/{fullmsg}\\\")\\n logging.debug(\\\"Message sent %s\\\", rsp.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2acb0b25a9ac22666a4ce39d2c12e480\",\n \"score\": \"0.5708671\",\n \"text\": \"def addMessage(chat_id): \\n user_id= request.args.get(\\\"user_id\\\")\\n text= request.args.get(\\\"text\\\")\\n \\n #check if the user has the permission to post in the chats or raise an exception\\n get=db_chats.find_one({\\\"_id\\\":ObjectId(chat_id) })\\n if not ObjectId(user_id) in get['users_list']:\\n raise PermissionError(\\\"Permission denied\\\")\\n\\n #add the message to the messages collection and get the id\\n \\n dic={\\n 'user_id': ObjectId(user_id),\\n 'text':text,\\n 'chat_id':ObjectId(chat_id)\\n }\\n message_id=db_messages.insert_one(dic)\\n \\n #add the message text to the messages_list of the chats\\n\\n post=db_chats.find_one({\\\"_id\\\":ObjectId(chat_id)})\\n post['messages_list'].append(message_id.inserted_id)\\n db_chats.update_one({'_id':ObjectId(chat_id)}, {\\\"$set\\\": post}, upsert=False)\\n \\n return json.dumps({'message_id':str(message_id.inserted_id)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56e759d6d1ec4d1a017e2ae68d7ace2\",\n \"score\": \"0.5696409\",\n \"text\": \"def send_message(self, text):\\n if self.chat_id is 0:\\n data = {'chat_id': self.master_id, 'text': text}\\n else:\\n data = {'chat_id': self.chat_id, 'text': text}\\n response = self.post_request(data, '', self.api['sendMessage'])\\n return self.construct(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e9059ab593f6ef4d55d39be3bceafca\",\n \"score\": \"0.568416\",\n \"text\": \"def send_message(request):\\n serializer = MessageSerializer(data=request.data)\\n \\n if serializer.is_valid():\\n data = serializer.data\\n room_id = data['room_id'] #room id gotten from client request\\n \\n rooms = DB.read(\\\"dm_rooms\\\")\\n if type(rooms) == list:\\n is_room_avalaible = len([room for room in rooms if room.get('_id', None) == room_id]) != 0\\n \\n if is_room_avalaible:\\n response = DB.write(\\\"dm_messages\\\", data=serializer.data)\\n if response.get(\\\"status\\\") == 200:\\n print(\\\"data sent to zc core\\\")\\n centrifugo_data = send_centrifugo_data(room=room_id,data=data) #publish data to centrifugo\\n if centrifugo_data.get(\\\"status_code\\\") < 400:\\n print(centrifugo_data)\\n return Response(data=response, status=status.HTTP_201_CREATED)\\n \\n return Response(data=\\\"data not sent\\\",status=status.HTTP_400_BAD_REQUEST)\\n return Response(\\\"No such room\\\",status=status.HTTP_400_BAD_REQUEST) \\n return Response(\\\"core server not avaliable\\\",status=status.HTTP_424_FAILED_DEPENDENCY)\\n \\n return Response(status=status.HTTP_400_BAD_REQUEST)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba2e1c1f8182c4e07a6e325c6b9e8bd5\",\n \"score\": \"0.5667958\",\n \"text\": \"def post(self, msg, wparam, lparam):\\n self._PostThreadMessage(self._threadid, msg, wparam, lparam)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa43b27241989c5913bd52b2c975fd04\",\n \"score\": \"0.56650287\",\n \"text\": \"def reply(request, pk):\\n\\tp = request.POST\\n\\tif p[\\\"body\\\"]:\\n\\t\\tthread = Thread.objects.get(pk=pk)\\n\\t\\tpost = Post.objects.create(thread=thread, title=p[\\\"subject\\\"], body=p[\\\"body\\\"],\\n\\t\\t\\tcreator=request.user)\\n\\t\\tincrement_post_counter(request)\\n\\treturn HttpResponseRedirect(reverse(\\\"forum.views.thread\\\", args=[pk]) + \\\"?page=last\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84de9f66c3e44325f30b749790a553b2\",\n \"score\": \"0.5651113\",\n \"text\": \"def send_message(text, chat_id):\\n url = \\\"{}/sendMessage\\\".format(BASE_URL)\\n params = {\\n \\\"chat_id\\\": chat_id,\\n \\\"text\\\": text,\\n \\\"parse_mode\\\": \\\"Markdown\\\"\\n }\\n response = sess.get(url=url, params=params)\\n if response.status_code != requests.codes.ok: # pylint: disable=no-member\\n print(\\\"*\\\"*20, \\\"ERROR\\\", \\\"*\\\"*20)\\n pprint.pprint(response.json())\\n print(\\\"*\\\"*45)\\n else:\\n print(f\\\"Sent message to chat_id {chat_id}, replying with '{text}'\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1a28968a07bdae191846487b15c3460\",\n \"score\": \"0.5645387\",\n \"text\": \"def send_message(payload):\\n requests.post('https://graph.facebook.com/v2.6/me/messages/?access_token=' + token, json=payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7724f1fc808b26b3670cac06c807bde7\",\n \"score\": \"0.5637254\",\n \"text\": \"def send_message(chat_id, text='Wait a second, please'):\\n send_message_url = MAIN_URL + f'sendmessage?chat_id={chat_id}&text={text}'\\n requests.get(send_message_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dbb02de3dcce2e01f4fdde26fb1b9aa\",\n \"score\": \"0.56330985\",\n \"text\": \"def send_message(self, robot_id, message):\\n self.messages_to_send.append([robot_id, message])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27ee184db1ef37b378078749a247ce5a\",\n \"score\": \"0.56271166\",\n \"text\": \"def send_message_to_channel(channel_id):\\n user_id = decode_identity()\\n\\n video_url = request.get_json().get('videoUrl')\\n img_url = request.get_json().get('imgUrl')\\n\\n if video_url:\\n video_data = get_video_metadata(video_url)\\n\\n new_message = Message.create(\\n channel_id=channel_id,\\n user_id=user_id,\\n text_content=request.get_json().get('textContent'),\\n video_url=video_url,\\n video_html=video_data.get('html'),\\n video_source=video_data.get('source'),\\n video_length=video_data.get('length'),\\n )\\n\\n elif img_url and not video_url:\\n img_data = get_img_metadata(img_url)\\n\\n new_message = Message.create(\\n channel_id=channel_id,\\n user_id=user_id,\\n text_content=request.get_json().get('textContent'),\\n img_url=img_url,\\n img_html=img_data.get('html'),\\n img_height=img_data.get('height'),\\n img_width=img_data.get('width'),\\n )\\n else:\\n new_message = Message.create(\\n channel_id=channel_id,\\n user_id=user_id,\\n text_content=request.get_json().get('textContent'),\\n )\\n\\n socketio.emit('message', new_message._data, room=channel_id, include_self=True)\\n\\n return jsonify(new_message._data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b461af097f4a067fafdd6b75e0aa337\",\n \"score\": \"0.5620505\",\n \"text\": \"def post(self, request, *args, **kwargs):\\n message = self.respond_form(request.POST)\\n if message.is_valid:\\n message_instance = message.save(commit=False)\\n message_instance.chat = self.get_object()\\n message_instance.sender = config.CHAT_RESPONDENT\\n message_instance.save()\\n return redirect('chat-respond', hash=self.get_object().hash)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81386df7425be6e7cd813d464a9fa15e\",\n \"score\": \"0.5612972\",\n \"text\": \"async def send_message(sid, message):\\n # Added transport mode checker\\n transport_mode = sio.transport(sid)\\n logger.debug('MESSAGE TRANSPORT MODE (%s): %s' % (sid, transport_mode))\\n logger.debug('EVENT(\\\"sendMessage\\\"): %s' % message['data'])\\n try:\\n if isinstance(message, dict):\\n if message.get('data') is not None:\\n api_ai_message = await run_small_talk(message['data']) # TODO change to the json server_message\\n # api_ai_message = await get_server_message(sio.pg, message)\\n await sio.emit('sendMessageResponse',\\n {'data': api_ai_message},\\n room=sid, namespace='/chat')\\n logger.debug('EVENT(\\\"sendMessageResponse\\\"): %s' % api_ai_message)\\n else:\\n raise ValueError('Message should have key(\\\"data\\\")')\\n else:\\n raise TypeError('Message should be dict: {\\\"data\\\": \\\"some text\\\"}')\\n except ValueError as e:\\n logger.error('Handle ERROR: %s' % e)\\n except TypeError as e1:\\n logger.error('Handle ERROR: %s' % e1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a510fb8f94a76ff11dbb89b3851df05\",\n \"score\": \"0.56115544\",\n \"text\": \"def print_to_channel(c_id: str, s_msg: str) -> None:\\n try:\\n client.chat_postMessage(channel=c_id, text=s_msg)\\n except SlackApiError as e:\\n logger.error(\\\"Error posting message: {}\\\".format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"358ba743ef4c2728d18e58b2bc32eeec\",\n \"score\": \"0.56109077\",\n \"text\": \"def send_message(self, user_id, message_body):\\n response = self.client.post('https://www.kwikku.com/core/core_actions/post_message.php', {\\n 'action': '1',\\n 'to': user_id,\\n 'body': message_body,\\n 'tipe': '0'\\n })\\n\\n # Check if send message success\\n if response.text == '[{\\\"result\\\":\\\"1\\\"}]':\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96abf5e42aa183ae8637dd381a040e19\",\n \"score\": \"0.56046814\",\n \"text\": \"async def send(self, room: str, *messages: str):\\n string = f'{room}|{\\\"|\\\".join(messages)}'\\n print(f'[{datetime.now().replace(microsecond=0).isoformat()}] >> {string}')\\n await self.websocket.send(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edb6f60d1bfbef0721766d2ae5a36d82\",\n \"score\": \"0.5597723\",\n \"text\": \"def send_message_keyboard(user_id, text, keyboard):\\n data = {\\n 'message': text,\\n 'user_id': user_id,\\n 'access_token': config.token,\\n 'keyboard': json.dumps(keyboard, ensure_ascii=False),\\n 'v': api_ver\\n }\\n res = requests.post(config.vk_api_url + 'messages.send', data=data)\\n print(res.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c3e132cedab9b6fb81484289ffac706\",\n \"score\": \"0.5584322\",\n \"text\": \"def send_message(request):\\n\\n usr = request.POST['usr']\\n to_who = get_object_or_404(User, username=usr)\\n frm_who = request.user\\n msg = request.POST['message']\\n\\n entry = Message.objects.create(to_user=to_who, from_user=frm_who, msg=msg)\\n\\n return HttpResponseRedirect(reverse('users.user_page',\\n args=[usr]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"994936f0e27ff6c648d2cd5fe147993d\",\n \"score\": \"0.55818963\",\n \"text\": \"def joined(message):\\n room = session.get('room')\\n join_room(room)\\n print(session.get('name'))\\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8710663f90be29bdcb8e50b6a4dcc095\",\n \"score\": \"0.5580264\",\n \"text\": \"def create_room(room_name, team_id):\\n print(\\\"Crating the room...\\\")\\n URL = \\\"https://api.ciscospark.com/v1/rooms\\\"\\n PAYLOAD = {\\\"title\\\": room_name, \\\"teamId\\\": team_id} \\n # MISSION: Key responsible for team ID is missing\\n\\n response = requests.post(url=URL, json=PAYLOAD, headers=HEADERS)\\n\\n if response.status_code == 200:\\n response = response.json()\\n print(\\\"Room was successfully created.\\\")\\n print(\\\"Name: \\\" + response[\\\"title\\\"])\\n print(\\\"ID: \\\" + response[\\\"id\\\"])\\n return response[\\\"id\\\"]\\n else:\\n print(\\\"Something went wrong.\\\\n\\\"\\n \\\"Please check the script and run it again!\\\")\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75979349eb3b7b7c7324adbed2cb814d\",\n \"score\": \"0.556531\",\n \"text\": \"def create_room(room_name, team_id):\\n print(\\\"Crating the room...\\\")\\n URL = \\\"https://api.ciscospark.com/v1/rooms\\\"\\n PAYLOAD = {\\\"title\\\": room_name, \\\"id\\\": team_id} # MISSION: Key responsible for team ID is missing\\n\\n response = requests.post(url=URL, json=PAYLOAD, headers=HEADERS)\\n\\n if response.status_code == 200:\\n response = response.json()\\n print(\\\"Room was successfully created.\\\")\\n print(\\\"Name: \\\" + response[\\\"title\\\"])\\n print(\\\"ID: \\\" + response[\\\"id\\\"])\\n return response[\\\"id\\\"]\\n else:\\n print(\\\"Something went wrong.\\\\n\\\"\\n \\\"Please check the script and run it again!\\\")\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e4e2e9aa157597e7e96375e5e985508\",\n \"score\": \"0.5564458\",\n \"text\": \"def channel_for_room(self, room_id: str) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0921df56a9b49a7029cf1a0630e0a51d\",\n \"score\": \"0.5560418\",\n \"text\": \"def add_to_room(self, room_id=None, room_uuid='', box_folder_id=''):\\n if not room_id:\\n return None\\n bucket = attribute.Attributes(actor_id=self.actor_id, bucket=\\\"rooms\\\", config=self.config)\\n room = bucket.get_attr(name=room_id)\\n if not room:\\n room = {}\\n if room_uuid:\\n room[\\\"uuid\\\"] = room_uuid\\n if box_folder_id:\\n room[\\\"boxFolderId\\\"] = box_folder_id\\n bucket.set_attr(name=room_id, data=room)\\n return room\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60afc615d880ed32eb1df4b3f0973feb\",\n \"score\": \"0.55603796\",\n \"text\": \"def joined(message):\\n room = session.get('room')\\n join_room(room)\\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60afc615d880ed32eb1df4b3f0973feb\",\n \"score\": \"0.55603796\",\n \"text\": \"def joined(message):\\n room = session.get('room')\\n join_room(room)\\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702d57a32e0335750ed56452f8ef9cee\",\n \"score\": \"0.55587655\",\n \"text\": \"async def sayIn(self, room, message):\\n await self.send(f\\\"{room}|{message}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07879133728fe2cd6b3d47f2abc5dc69\",\n \"score\": \"0.5557736\",\n \"text\": \"async def PM(self, message): # pylint: disable=invalid-name\\n await self.connection.whisper(self.id, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecaaa5a2afb9427180df1a9fe77395f7\",\n \"score\": \"0.55562365\",\n \"text\": \"def submit(self, message):\\n self.messages.put(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7e2d751a09859acef67ea0eda1c4ba6\",\n \"score\": \"0.5548825\",\n \"text\": \"def sendMessage(self, remote_id, messageParts):\\n request_id = b''\\n self.sendMultipart(remote_id, [request_id, b''] + list(messageParts))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4395802e21a2e9c5c960fe319eb05490\",\n \"score\": \"0.55426496\",\n \"text\": \"def _send_message(self, observation, chatID):\\n if self._is_end_of_conversation(observation['text']):\\n data = {\\n 'text': '/end',\\n 'evaluation': {\\n 'quality': 0,\\n 'breadth': 0,\\n 'engagement': 0\\n }\\n }\\n else:\\n data = {\\n 'text': observation['text'],\\n 'evaluation': 0\\n }\\n message = {\\n 'chat_id': chatID,\\n 'text': json.dumps(data)\\n }\\n\\n headers = {\\n 'Content-Type': 'application/json'\\n }\\n\\n res = requests.post(self.bot_url + '/sendMessage',\\n json=message, headers=headers)\\n if res.status_code != 200:\\n print(res.text)\\n res.raise_for_status()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57814f4c535358fb28f5c623961cddf3\",\n \"score\": \"0.55302465\",\n \"text\": \"async def add_to_room(self, room_id: str, client_socket: socket.socket) -> None:\\n self.rooms[str(room_id)].add(client_socket)\\n client_id = self.all_clients.index(client_socket)\\n insert('clients_rooms', {'client_id': client_id, 'room_id': room_id})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba69cc412227fafcd7c59e18c9264284\",\n \"score\": \"0.55248505\",\n \"text\": \"async def create_room(self, room_id: str, room_name: str, client_socket: socket.socket) -> None:\\n self.rooms[room_id].add(client_socket)\\n author_id = self.all_clients.index(client_socket)\\n insert('rooms', {'name': room_name, 'author_id': str(author_id)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b3e402156c734b480fbba61e74fd70d\",\n \"score\": \"0.55201733\",\n \"text\": \"def publish_msg(self,message,thread_identifier):\\n self.topic_occupied[thread_identifier]=True\\n self.session.publish_data(message)\\n self.topic_occupied[thread_identifier]=False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2bd3f706450fbf7bbf09a709c2f7ec1\",\n \"score\": \"0.5513918\",\n \"text\": \"def create_message(user_id):\\n text = st.text_input('Quelle est votre message aujourd\\\\'hui ?', max_chars=280)\\n if text:\\n requests.post(f'http://127.0.0.1:8000/create_message/{user_id}?dm_text={text}')\\n st.success('Votre message a bien été envoyé.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05bfd9aac063d97df03c4a91b3023f5c\",\n \"score\": \"0.5513761\",\n \"text\": \"def _message(self, msg):\\n if msg['type'] in ('chat', 'normal'):\\n self.message(msg['from'].bare, msg['body'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48049b10533621d45064bfce046ba784\",\n \"score\": \"0.55128014\",\n \"text\": \"def sendMessage(user, user_to, subject, text):\\n message = Message.objects.create(user_from = user, user_to = User.objects.get(id=user_to), \\n subject = subject, text = text)\\n message.save()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bdc1f698b265078f7a5ce30d3affca7\",\n \"score\": \"0.55083436\",\n \"text\": \"def chat_room(request, user_id):\\n # Group name for chat. It's uniq.\\n user_to_chat = get_object_or_404(User, pk=user_id)\\n id_list = [request.user.pk, int(user_id)]\\n id_list.sort()\\n room_name = \\\"room_\\\" + str(id_list[0]) + \\\"_with_\\\" + str(id_list[1])\\n\\n # show messages stored id DB to user\\n messages = Message.objects.filter(chat_room=room_name)\\n\\n return render(request, 'chat/room.html', {\\n 'room_name': room_name,\\n 'user_to_chat': user_to_chat,\\n 'user': request.user,\\n 'messages': messages\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509c287f1aefa7f5028d82047ceeba7a\",\n \"score\": \"0.54997635\",\n \"text\": \"def send_message(chat_id):\\n #\\n if request.json: # If there is data in the POST request\\n # message = Message.from_dict(request.json, chat_id, request.args['user_id'])\\n\\n result_proxy = db.session.execute(\\n '''\\n INSERT INTO \\n messages (chat_id, sender_id, content) \\n VALUES \\n (:chat_id, :sender_id, :content)\\n RETURNING id, chat_id, sender_id, content, created_at\\n '''\\n , dict(chat_id=int(chat_id), sender_id=int(request.args['user_id']), content=request.json['content'])\\n )\\n\\n result = result_proxy.fetchone()\\n\\n\\n db.session.commit()\\n\\n return jsonify({'id': result[0],\\n 'chat_id': result[1],\\n 'sender_id': result[2],\\n 'content': result[3],\\n 'created_at': result[4]\\n })\\n else:\\n return \\\"Error\\\"\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1794,"cells":{"query_id":{"kind":"string","value":"5fb0fc04c86be50925b545dac8c3f637"},"query":{"kind":"string","value":"Creates an acceptor from a string. This function creates a FST which accepts its input with a fixed weight (defaulting to semiring One)."},"positive_passages":{"kind":"list like","value":[{"docid":"64c1810bd7aac41df103f0b285e4ece3","score":"0.76613843","text":"def acceptor(\n astring: str, symbol_table: SymbolTable, weight: Optional[float] = None\n) -> VectorFst:\n if weight is None:\n weight = weight_one()\n\n acceptor_fst_ptr = ctypes.pointer(ctypes.c_void_p())\n ret_code = lib.utils_string_to_acceptor(\n astring.encode(\"utf-8\"),\n symbol_table.ptr,\n ctypes.c_float(weight),\n ctypes.byref(acceptor_fst_ptr),\n )\n err_msg = \"Error creating acceptor FST\"\n check_ffi_error(ret_code, err_msg)\n return VectorFst(ptr=acceptor_fst_ptr)","title":""}],"string":"[\n {\n \"docid\": \"64c1810bd7aac41df103f0b285e4ece3\",\n \"score\": \"0.76613843\",\n \"text\": \"def acceptor(\\n astring: str, symbol_table: SymbolTable, weight: Optional[float] = None\\n) -> VectorFst:\\n if weight is None:\\n weight = weight_one()\\n\\n acceptor_fst_ptr = ctypes.pointer(ctypes.c_void_p())\\n ret_code = lib.utils_string_to_acceptor(\\n astring.encode(\\\"utf-8\\\"),\\n symbol_table.ptr,\\n ctypes.c_float(weight),\\n ctypes.byref(acceptor_fst_ptr),\\n )\\n err_msg = \\\"Error creating acceptor FST\\\"\\n check_ffi_error(ret_code, err_msg)\\n return VectorFst(ptr=acceptor_fst_ptr)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3a11123790342656c29964c9455581a1","score":"0.5958145","text":"def from_string(factory, str_graph):\n\n builder = Builder(factory)\n node_sequences = [s.split('->') for s in str_graph.split(\",\")]\n\n for seq in node_sequences:\n last_id = None\n for s in seq:\n node_id = s.strip()\n builder.node(node_id)\n if last_id is not None:\n builder.edge(last_id, node_id)\n last_id = node_id\n\n return builder.build()","title":""},{"docid":"5ddae97937508d253dd15568058696f5","score":"0.5359703","text":"def transducer(\n istring: str,\n ostring: str,\n isymt: SymbolTable,\n osymt: SymbolTable,\n weight: Optional[float] = None,\n) -> VectorFst:\n\n if weight is None:\n weight = weight_one()\n\n transducer_fst_ptr = ctypes.c_void_p()\n ret_code = lib.utils_string_to_transducer(\n istring.encode(\"utf-8\"),\n ostring.encode(\"utf-8\"),\n isymt.ptr,\n osymt.ptr,\n ctypes.c_float(weight),\n ctypes.byref(transducer_fst_ptr),\n )\n err_msg = \"Error creating tranducer FST\"\n check_ffi_error(ret_code, err_msg)\n\n return VectorFst(ptr=transducer_fst_ptr)","title":""},{"docid":"0d630a8bcd89bf22a2e8cd811c2a8a38","score":"0.5194737","text":"def parse(input_string: str ) -> SgfTree:\n sgfTree, input_string = _parse_node(input_string)\n if sgfTree is None or len(input_string) > 0:\n raise ValueError(\"Error parsing SGF string.\")\n return sgfTree","title":""},{"docid":"be177ed2fab4a20fb92c9ae4765ea2da","score":"0.50749373","text":"def make_input_fst(word):\n s, accept_state = 0, 10000\n\n for i, c in enumerate(word):\n # TODO: You need to implement format_arc function in scripts/util\n print(format_arc(s, s + 1, c, c, weight=0))\n s += 1\n\n if i == len(word) - 1:\n print(format_arc(s, accept_state, EPS, EPS, weight=0))\n\n print(accept_state)","title":""},{"docid":"942305e0f39bb827c1a0090395994745","score":"0.50313365","text":"def from_string(\n cls, string: str, volume: Optional[float] = None, frozen: bool = False\n ) -> \"DefectChargeState\":\n stripped_string = string.split()\n if frozen is False:\n return cls(\n charge=int(stripped_string[0]),\n energy=float(stripped_string[1]),\n degeneracy=int(stripped_string[2]),\n )\n else:\n if volume is None:\n raise ValueError(\n \"You must specify a real, positive cell volume if passing a frozen concentration!\"\n )\n else:\n return cls(\n charge=int(stripped_string[1]),\n fixed_concentration=float(stripped_string[2]) / 1e24 * volume,\n )","title":""},{"docid":"5d21155b0b4d5d82afe15cf5330aea03","score":"0.49972406","text":"def __init__(self, string, case_insensitive=False):\r\n self.string = string\r\n self.case_insensitive = case_insensitive\r\n self.N = len(string) - 1\r\n self.nodes = [Node()]\r\n self.edges = {}\r\n self.active = Suffix(0, 0, -1)\r\n if self.case_insensitive:\r\n self.string = self.string.lower()\r\n for i in range(len(string)):\r\n self._add_prefix(i)","title":""},{"docid":"29ffc6154e3afe889e41e7e92504f508","score":"0.49869916","text":"def stringToTree_weakRef(str):\n # Reset current class members\n rootNode = NLPTree()\n rootNode.data = None\n rootNode.parent = None\n rootNode.children = []\n rootNode.terminals = []\n currentRoot = rootNode\n eIndex = -1\n\n # Blank/Empty Tree\n str = str.rstrip()\n if str == '0' or str == '(())' or str == '':\n return currentRoot;\n try:\n for token in str.split():\n if token[-1] == ')' and len(token) > 1:\n # Close subtree and add terminal node\n # Increment e-index counter\n eIndex += 1\n # How many levels are we closing?\n closingMarkerLocation = token.find(')')\n levelsToClose = len(token) - closingMarkerLocation\n # Get token name\n tokenName = token[0:closingMarkerLocation]\n\n # Create new child and add to tree\n newChild = TerminalNode(tokenName.lower())\n newChild.eIndex = eIndex\n newChild.parent = weakref.ref(currentRoot)\n newChild.parent().eIndex = eIndex\n\n # Add new child to the tree\n currentRoot.addChild(newChild)\n # Close subtree and back up to parent\n while levelsToClose > 0 and currentRoot.parent is not None:\n currentRoot = currentRoot.parent()\n levelsToClose -= 1\n elif token == ')':\n # We are finished with this tree.\n pass\n else:\n # token must begin with '('\n # Begin new subtree\n tokenName = None\n if token == '(': # We are at the root\n tokenName = \"TOP\"\n else:\n tokenName = token[1:]\n newChild = NLPTree(tokenName)\n newChild.parent = weakref.ref(currentRoot)\n if rootNode.data is None:\n rootNode.data = tokenName\n else:\n currentRoot.addChild(newChild)\n currentRoot = newChild\n except Exception as e:\n # Upon any error processing the string,\n # assume for now it is due to a malformed tree string\n # and return an empty tree.\n sys.stderr.write(\"Potentially malformed tree string: %s\\n\" % (str))\n print e\n rootNode = NLPTree()\n rootNode.data = None\n rootNode.parent = None\n rootNode.children = []\n rootNode.terminals = []\n return rootNode\n tree = addSpans(currentRoot)\n return tree","title":""},{"docid":"35d1232b008f8902a4c77d87a87ebf23","score":"0.4967768","text":"def from_string(cls, payload: str) -> GatewayDispatch:\n payload: Dict[str, Union[int, str, Dict[str, Any]]] = loads(payload)\n return cls(\n payload.get(\"op\"),\n payload.get(\"d\"),\n payload.get(\"s\"),\n payload.get(\"t\")\n )","title":""},{"docid":"04fc79ca54983e5553b3422df71675fb","score":"0.49329767","text":"def from_string(cls, string: str) -> 'Torrent':\n return cls(Bencode.read_string(string, byte_keys={'pieces'}))","title":""},{"docid":"83c8d0fb2cee180367fed375abe674d3","score":"0.49263716","text":"def from_str(string):","title":""},{"docid":"3fedd9b6a6927ec2f6de689ef0825c7d","score":"0.49197897","text":"def ast_to_nfa(ast):\n\t(initial, accepting) = ast._thompson()\n\taccepting.accepting = 1\n\n\treturn NFA(initial)","title":""},{"docid":"5448cd420cd9c2c87667f81c90fcd536","score":"0.49150044","text":"def __init__(self, input_string: string, low_bound: float, high_bound: float, number: int):\n self.raw_string = input_string\n self.left = low_bound\n self.right = high_bound\n self.number_points = number\n self.grammar_tree = GrammarTree(self.raw_string)\n self.grammar_tree.build_tree()","title":""},{"docid":"e70de25a33dcfb65222724bdeae85b54","score":"0.47806048","text":"def __init__(self, input_str=default_string):\n self.input_str = input_str\n self.node = [[0 for x in range(7)] for x in range(7)]\n self.dic = {}\n self.count_expanded_nodes = 1\n self.count_filled_peg = 0\n self.stack = []\n self.expanded = []\n self.heap = []\n self.relation_tuple = []\n self.config()","title":""},{"docid":"8a06d16b683a3d2ccad850733964bb6a","score":"0.47087893","text":"def new(string):\n\tpass","title":""},{"docid":"0f69336245a6a3606e3150bba2f591f0","score":"0.47087494","text":"def parse(requested_trees, logger):\n # First, strip off any hanging quotes at beginning/end of the strategy\n if requested_trees.startswith(\"\\\"\"):\n requested_trees = requested_trees[1:]\n if requested_trees.endswith(\"\\\"\"):\n requested_trees = requested_trees[:-1]\n\n # Define a blank strategy to initialize with the user specified string\n strat = actions.strategy.Strategy([], [])\n\n # Actions for the in and out forest are separated by a \"\\/\".\n # Split the given string by this token\n out_in_actions = requested_trees.split(\"\\\\/\")\n\n # Specify that we're starting with the out forest before we parse the in forest\n out = True\n direction = \"out\"\n # For each string representation of the action directions, in or out\n for str_actions in out_in_actions:\n # Individual action trees always end in \"|\" to signify the end - split the\n # entire action sequence into individual trees\n str_actions = str_actions.split(\"|\")\n\n # For each string representation of each tree in the forest\n for str_action in str_actions:\n # If it's an empty action, skip it\n if not str_action.strip():\n continue\n\n assert \" \" not in str_action.strip(), \"Strategy includes a space - malformed!\"\n\n # Get rid of hanging whitespace from the splitting\n str_action = str_action.strip()\n\n # ActionTree uses the last \"|\" as a sanity check for well-formed\n # strategies, so restore the \"|\" that was lost from the split\n str_action = str_action + \"|\"\n new_tree = actions.tree.ActionTree(direction)\n success = new_tree.parse(str_action, logger)\n if success is False:\n raise actions.tree.ActionTreeParseError(\"Failed to parse tree\")\n\n # Once all the actions are parsed, add this tree to the\n # current direction of actions\n if out:\n strat.out_actions.append(new_tree)\n else:\n strat.in_actions.append(new_tree)\n # Change the flag to tell it to parse the IN direction during the next loop iteration\n out = False\n direction = \"in\"\n return strat","title":""},{"docid":"714a9c80706a9daed79cee628326c507","score":"0.47065774","text":"def get_ast(self, string: str) -> Tree:\n return self.parser.parse(string)","title":""},{"docid":"de28c64a8c4853b034a51a1abd73542e","score":"0.46972725","text":"def __init__(self, policy_str=None):\n\n p = None\n if isinstance(policy_str, str):\n p = policy2DNF(policy_parser.parseString(policy_str))\n elif isinstance(policy_str, list):\n p = policy_str\n elif isinstance(policy_str, DNF):\n self.policy = policy_str\n return\n elif policy_str is None:\n self.policy = Policy([[Satisfied()]])\n return\n else:\n raise RuntimeError(\"Failed\")\n\n self.policy = DNF([])\n for clause in p:\n self.policy.add(ConjunctClause(clause))","title":""},{"docid":"45ef1d071cbec7cd50a9b559e76841ec","score":"0.46346596","text":"def from_string(cls, graph_data: str):\n\n graph = cls()\n\n for line in graph_data.splitlines():\n\n line = line.strip()\n\n if not line:\n continue\n\n splitted_line = re.split(\"\\\\s+\", line)\n\n if len(splitted_line) != 3:\n raise ValueError(f\"Invalid line found in line: {line}\")\n\n src = Vertex(splitted_line[0])\n dest = Vertex(splitted_line[1])\n weight = int(splitted_line[2])\n\n graph.add_edge(src, dest, weight)\n\n return graph","title":""},{"docid":"1432d8df9b9afb4285627e37a105e930","score":"0.46241093","text":"def parse(steno_string: str) -> ste.Steno:\n return TYPE.parseString(steno_string, parseAll=True)[0] # type: ignore","title":""},{"docid":"261d1a5517f965c0257a3d5dc55b1a61","score":"0.45987538","text":"def epsilon_machine(weight: Optional[float] = None) -> VectorFst:\n if weight is None:\n weight = weight_one()\n fst = VectorFst()\n state = fst.add_state()\n fst.set_start(state)\n fst.set_final(state, weight)\n\n return fst","title":""},{"docid":"47ae0539409f1b92f725ddcfd4cf72d2","score":"0.45962554","text":"def create_from_arg_string(cls, arg_string):\n return cls()","title":""},{"docid":"f21efdafed4f491baf88c6cc69d3049e","score":"0.45924416","text":"def regex_to_nfa(regex: str) -> RegexFaConstruction:\n re_lst = regex_to_tokens(regex)\n nfa_stack = []\n op_stack = []\n node_index = 0\n for token in re_lst:\n # current token is operand\n if token.get('type') == 1:\n f = RegexFaConstruction()\n if token.get('kind') == Kind.NORMAL:\n f.add_normal_node(f.input_port, f.output_port, pattern_char=token.get('value'))\n else:\n if token.get('kind') == Kind.SET:\n f.add_charset_node(f.input_port, f.output_port, charset=token.get(Kind.SET), negative=False)\n elif token.get('kind') == Kind.NEG_SET:\n f.add_charset_node(f.input_port, f.output_port, charset=token.get(Kind.SET), negative=True)\n elif token.get('kind') == Kind.TRANS:\n if token.get('value') == 'w':\n f.add_da_node(f.input_port, f.output_port)\n elif token.get('value') == 's':\n f.add_empty_char_node(f.input_port, f.output_port)\n else:\n f.add_digit_node(f.input_port, f.output_port)\n elif token.get('kind') == Kind.DOT:\n f.add_any_node(f.input_port, f.output_port)\n nfa_stack.append(f)\n node_index += 1\n # current token is operator\n else:\n if is_left_bracket(token):\n op_stack.append(token)\n elif is_repeat(token):\n f = nfa_stack.pop()\n if token.get('kind') == Kind.NORMAL:\n if token.get('value') == '*':\n f, inc = nodes_repeat_ge_zero(f, node_index)\n node_index += inc\n elif token.get('value') == '+':\n f, inc = nodes_repeat_ge_one(f, node_index)\n node_index += inc\n nfa_stack.append(f)\n else:\n r = token.get(Kind.RANGE)\n if r[0] == r[1]:\n new_f = nodes_repeat_eq(f, r[0])\n else:\n new_f, inc = nodes_repeat_range(f, node_index, r[0], r[1])\n nfa_stack.append(new_f)\n elif is_prefix(token) or is_postfix(token):\n while len(op_stack) > 0 and not is_left_bracket(op_stack[-1]):\n op = op_stack.pop()\n if is_concat(op):\n f1 = nfa_stack.pop()\n f2 = nfa_stack.pop()\n new_f = concat_nfa(f2, f1, 'con' + str(node_index))\n nfa_stack.append(new_f)\n node_index += 1\n elif is_postfix(op):\n f = nfa_stack.pop()\n f, inc = nodes_postfix(f, node_index)\n node_index += inc\n nfa_stack.append(f)\n elif is_prefix(op):\n f = nfa_stack.pop()\n f, inc = nodes_prefix(f, node_index)\n node_index += inc\n nfa_stack.append(f)\n op_stack.append(token)\n elif is_concat(token):\n while len(op_stack) > 0 and is_concat(op_stack[-1]):\n op_stack.pop()\n f1 = nfa_stack.pop()\n f2 = nfa_stack.pop()\n new_f = concat_nfa(f2, f1, 'con' + str(node_index))\n nfa_stack.append(new_f)\n node_index += 1\n op_stack.append(token)\n elif is_right_bracket(token):\n while len(op_stack) > 0 and not is_left_bracket(op_stack[-1]):\n op = op_stack.pop()\n if is_concat(op):\n f1 = nfa_stack.pop()\n f2 = nfa_stack.pop()\n new_f = concat_nfa(f2, f1, 'con' + str(node_index))\n nfa_stack.append(new_f)\n node_index += 1\n elif is_postfix(op):\n f = nfa_stack.pop()\n f, inc = nodes_postfix(f, node_index)\n node_index += inc\n nfa_stack.append(f)\n elif is_prefix(op):\n f = nfa_stack.pop()\n f, inc = nodes_prefix(f, node_index)\n node_index += inc\n nfa_stack.append(f)\n op_stack.pop()\n while len(op_stack):\n op = op_stack.pop()\n if is_concat(op):\n f1 = nfa_stack.pop()\n f2 = nfa_stack.pop()\n new_f = concat_nfa(f2, f1, 'con' + str(node_index))\n nfa_stack.append(new_f)\n node_index += 1\n elif is_postfix(op):\n f = nfa_stack.pop()\n f, inc = nodes_postfix(f, node_index)\n node_index += inc\n nfa_stack.append(f)\n elif is_prefix(op):\n f = nfa_stack.pop()\n f, inc = nodes_prefix(f, node_index)\n node_index += inc\n nfa_stack.append(f)\n nfa = nfa_stack.pop()\n return nfa","title":""},{"docid":"30298d502faeda7a1f948804632200f9","score":"0.45574868","text":"def __init__(self, string=\"0\"):\n self.tree = 0\n self.set_string(string)\n return","title":""},{"docid":"5baa5ad7a69b75502b4c4072c3f716e5","score":"0.45546323","text":"def to_syntax_tree(self, instring, schema):\n return self.builder.build(instring, schema)","title":""},{"docid":"114fde9702a74cbacc04e126524563b2","score":"0.45458665","text":"def read_dag():\n dag = DAG()\n\n num_edges = int(input())\n for _ in range(num_edges):\n source, target = input().split(' ')\n dag.add_edge(source, target)\n\n return dag","title":""},{"docid":"2a4f19be2f050fe5533145a181bfbdd4","score":"0.45423505","text":"def get_automaton_1():\n stateA = State(\"State A\", True)\n stateB = State(\"State B\")\n stateA.add_transition(one, stateA)\n stateA.add_transition(zero, stateB)\n stateB.add_transition(one, stateB)\n stateB.add_transition(zero, stateB)\n\n comparator = DFAComparator()\n return DeterministicFiniteAutomaton(binaryAlphabet, stateA,\n set([stateA, stateB]), comparator, \"Tomita's grammar 1 automaton\")","title":""},{"docid":"ca29a390c2243d2ebe998bc9f9d76caa","score":"0.45374167","text":"def fromstring(cls, str_addr):\n tup = str_addr.partition(':')\n return cls((tup[0], tup[2]))","title":""},{"docid":"8b29c62907cec9ed746f105c69a67219","score":"0.45075926","text":"def make_constraints_from_string(owner, target, subtarget, fstring):\n\n separator = '#'\n cns_blocks = fstring.split(separator)\n\n transform_type = ['CL', 'CR', 'CS', 'CT']\n limit_type = ['LL', 'LR', 'LS']\n track_type = ['DT', 'TT', 'ST']\n relationship_type = ['PA']\n\n for cns in cns_blocks:\n\n if cns[0:2] in transform_type:\n make_transform_constraint_from_string(owner, target, subtarget, cns)\n\n if cns[0:2] in limit_type:\n make_limit_constraint_from_string(owner, cns)\n\n if cns[0:2] in track_type:\n make_track_constraint_from_string(owner, target, subtarget, cns)\n\n if cns[0:2] in relationship_type:\n make_relation_constraint_from_string(owner, target, subtarget, cns)","title":""},{"docid":"b818a9db7c7751de4bde505bbd597b7a","score":"0.4486907","text":"def from_string(s):\n s = s.lower()\n\n if s == \"input\":\n return PortType.INPUT\n elif s == \"output\":\n return PortType.OUTPUT\n elif s == \"clock\":\n return PortType.CLOCK\n else:\n assert False, s","title":""},{"docid":"9c27ba7acfd21050b1d25eb577b65c95","score":"0.44642383","text":"def build_dfa(self, string, given_starting_node=None):\n\t\tif given_starting_node is None:\n\t\t\tstarting_node = Node(given_garbage_node=self.garbage_node)\n\t\telse:\n\t\t\tstarting_node = given_starting_node\n\t\tstarting_nodes = [starting_node]\n\t\tfinishing_nodes = []\n\t\tcurrent_node = starting_node\n\t\tprevious_nodes = []\n\t\ti = 0\n\t\twhile i < len(string):\n\t\t\tcharacter = string[i]\n\t\t\tif character == '|':\n\t\t\t\tnew_starting_nodes, new_finishing_nodes = self.build_dfa(string[i+1:], \n\t\t\t\t\tgiven_starting_node=given_starting_node)\n\t\t\t\tstarting_nodes += new_starting_nodes\n\t\t\t\tfinishing_nodes += new_finishing_nodes\n\t\t\t\tbreak\n\t\t\telif character == '*':\n\t\t\t\tcurrent_node.add_to_jumpdict(string[i-1], current_node)\n\t\t\t\ti += 1 \n\t\t\t\tif i == len(string):\n\t\t\t\t\tfinishing_nodes.append(current_node)\n\t\t\t\t\tfinishing_nodes += previous_nodes\n\t\t\t\t\treturn starting_nodes, finishing_nodes\n\t\t\t\telse:\n\t\t\t\t\tnew_node = Node(given_garbage_node=self.garbage_node)\n\t\t\t\t\tcurrent_node.add_to_jumpdict(string[i], new_node)\n\t\t\t\t\tfor previous_node in previous_nodes:\n\t\t\t\t\t\tprevious_node.add_to_jumpdict(string[i], new_node)\n\t\t\t\t\tprevious_nodes = [current_node]\n\t\t\t\t\tcurrent_node = new_node\n\t\t\t\t\ti += 1\n\t\t\telif character == ')':\n\t\t\t\traise ValueError(\"Non matching parentheses\")\n\t\t\telif character == '(':\n\t\t\t\tsubstring, position = self.find_matching_paren(string[i:])\n\t\t\t\ti += position\n\t\t\t\tsubstart, subend = self.build_dfa(substring, given_starting_node=current_node)\n\t\t\t\tif i >= len(string):\n\t\t\t\t\tfinishing_nodes += subend\n\t\t\t\t\treturn starting_nodes, finishing_nodes\n\t\t\t\telse:\n\t\t\t\t\tnew_char = string[i]\n\t\t\t\t\tauto_node = Node(given_garbage_node=self.garbage_node)\n\t\t\t\t\tnew_node = Node(given_garbage_node=self.garbage_node)\n\t\t\t\t\tfor subendnode in subend:\n\t\t\t\t\t\tsubendnode.set_auto_move(auto_node)\n\t\t\t\t\tauto_node.add_to_jumpdict(new_char, new_node)\n\t\t\t\t\tif new_char == '*':\n\t\t\t\t\t\tfor key in current_node.jumpdict:\n\t\t\t\t\t\t\tfor jump_node in current_node.jumpdict[key]:\n\t\t\t\t\t\t\t\tauto_node.add_to_jumpdict(key, jump_node)\n\t\t\t\t\t\ti += 1\n\t\t\t\t\t\tif i == len(string):\n\t\t\t\t\t\t\tfinishing_nodes.append(auto_node)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tnew_node = Node(given_garbage_node=self.garbage_node)\n\t\t\t\t\t\t\tcurrent_node.add_to_jumpdict(string[i], new_node)\n\t\t\t\t\t\t\tauto_node.add_to_jumpdict(string[i], new_node)\n\t\t\t\t\t\t\tprevious_nodes = [auto_node, current_node]\n\t\t\t\t\t\t\tcurrent_node = new_node\n\t\t\t\t\t\t\ti += 1\n\t\t\t\t\telse:\n\t\t\t\t\t\tcurrent_node = new_node\n\t\t\t\t\t\tprevious_nodes = [auto_node]\n\t\t\t\t\t\ti += 1\n\t\t\telif character == '\\\\':\n\t\t\t\ti += 1\n\t\t\t\tif i == len(string):\n\t\t\t\t\traise ValueError(\"Non terminating backlash\")\n\t\t\t\telse:\n\t\t\t\t\tnewchar = string[i]\n\t\t\t\t\tnew_node = Node(given_garbage_node=self.garbage_node)\n\t\t\t\t\tcurrent_node.add_to_jumpdict(newchar, new_node)\n\t\t\t\t\tprevious_nodes = [current_node]\n\t\t\t\t\tcurrent_node = new_node\n\t\t\t\t\ti += 1\n\t\t\telse:\n\t\t\t\tnew_node = Node(given_garbage_node=self.garbage_node)\n\t\t\t\tcurrent_node.add_to_jumpdict(character, new_node)\n\t\t\t\tprevious_nodes = [current_node]\n\t\t\t\tcurrent_node = new_node\n\t\t\t\ti += 1\n\t\tfinishing_nodes.append(current_node)\n\t\treturn starting_nodes, finishing_nodes","title":""},{"docid":"09bbaa5952b9b3f3d1a53e993b9f1f1c","score":"0.44318843","text":"def parse_node(s):\n #print(s)\n s = s.strip()\n parts = s.split(')')\n if len(parts) == 1:\n descendants, label = [], s\n else:\n if not parts[0].startswith('('):\n raise ValueError('unmatched braces %s' % parts[0][:100])\n descendants = list(_parse_siblings(')'.join(parts[:-1])[1:]))\n label = parts[-1]\n length, name = _parse_name_and_length(label)\n new_tree = TreeNode(name=name, label=length)\n new_tree.children = descendants\n print(\"new_tree\", new_tree.label)\n return new_tree","title":""},{"docid":"dd92e4fb6df07c4670563f55812658e4","score":"0.44281778","text":"def _create_node(env, s, g, prev_node, a, cost_fn):\n h = env.estim_cost(s)\n f = cost_fn(g, h)\n return Node(f, h, g, s, prev_node, a)","title":""},{"docid":"d2ae9a9d74e6af29db75a5bd55d0420f","score":"0.4421834","text":"def to_cnf(s):\n s = expr(s)\n if isinstance(s, str):\n s = expr(s)\n s = eliminate_implications(s) # Steps 1, 2 from p. 253\n s = move_not_inwards(s) # Step 3\n return distribute_and_over_or(s) # Step 4","title":""},{"docid":"ea817ba27d2c345670efa65bbd300989","score":"0.44179893","text":"def _make_bnode(string):\n bnode_pattern = ur'_:([A-Za-z][A-Za-z0-9]*)'\n match = re.compile(bnode_pattern).match(string)\n if not match:\n return False\n # else if match occurs,\n string = string[2:] # remove _: from the string\n return rb.term.BNode(string.decode(\"unicode-escape\"))","title":""},{"docid":"8229332fc72ae6ca1942bce3450cf30b","score":"0.44150883","text":"def from_string(st, var = 'x', r_dict = False):\n terms = st.replace(\" \",\"\").split(\"+\")\n i = 0\n while i < len(terms):\n if '-' in terms[i]:\n m = terms[i].split('-')\n if m[0] == '':\n m = m[1:]\n m[0] = '-' + m[0]\n j = 0 \n while j < (len(m)):\n if m[j][len(m[j]) - 1] == '^':\n m = m[:j] + [m[j] + '-' + m[j + 1]] + m[j + 2:]\n j = j + 1 \n \n terms = terms[:i] + [m[0]] + ['-' + j for j in m[1:]] + terms[i + 1:]\n i = i + 1 \n \n cofs = []\n exps = []\n for i in terms:\n ind = i.find(var)\n\n cof = 0\n if ind < 0:\n cof = float(i)\n elif ind == 0:\n cof = 1.0\n elif i[:ind] == '-':\n cof = -1.0\n else:\n cof = float(i[:ind])\n\n exp = 0\n if ind < 0:\n exp = 0\n elif len(i[ind + len(var) + 1:]) == 0:\n exp = 1.0\n else:\n exp = float(i[ind + len(var) + 1:])\n\n if exp in exps:\n cofs[exps.index(exp)] += cof\n else:\n exps += [exp]\n cofs += [cof]\n r = {exps[i]: cofs[i] for i in range(len(exps))}\n if(r_dict):\n return r\n else:\n return Polynomial(r)","title":""},{"docid":"9ff4861042564e3028890adb7822457b","score":"0.43837804","text":"def sof(value, maxSize, minSize=0, name=None, weight=1.0):\n content = StringContent(value)\n return OpenField(content, maxSize, minSize, name, weight)","title":""},{"docid":"3810ad56d4a2370129818deef3da616a","score":"0.43740287","text":"def to_nfa(self, accepting_id=1):\n\n\t\t(initial, accepting) = self._thompson()\n\t\taccepting.accepting = accepting_id\n\t\treturn NFA(initial)","title":""},{"docid":"6889d7467c9ec52bf0e44c40466776c3","score":"0.43695772","text":"def valueOf(string: str) -> 'SegmentType':\n ...","title":""},{"docid":"0bfef0b508a97a2f2fe87fc3f8848925","score":"0.4363003","text":"def parse(s, fg=False):\n global full_graph\n full_graph = fg\n return parser.parse(s)","title":""},{"docid":"094a42a07e70be7266179605983436aa","score":"0.43585694","text":"def match(infix, string):\n # call the shunt funciton on the infix string that is passed in\n postfix = shunt(infix)\n # call the compile function on the postfix string that was created above to create and NFA for each symbol in the postfix string\n nfa = compile(postfix)\n # a new set that only allows one copy of a state into it (as sets do)\n currentSet = set()\n # a new set to hold the next states\n nextSet = set()\n # call the followEdges method and set the currentSet of states equal to the states that the new NFAs intial state can reach\n currentSet |= followEdges(nfa.initial)\n # loop through each character of the string\n for s in string:\n # loop through the current set of states\n for c in currentSet:\n # check if the state has the same label as the character in the string \n if c.label == s:\n # add the edge1 state to the next set including all the states that are reachable by the edge arrows\n # nextSet = nextSet + followEdges(c.edge1)\n nextSet |= followEdges(c.edge1)\n # The currentSet of states is replaced by nextSet\n currentSet = nextSet\n # the nextSet is emptied\n nextSet = set()\n # check if the accept state is in the currentSet\n return (nfa.final in currentSet)","title":""},{"docid":"5a493b3d0a1a566ebc6e23aa8a3f52dc","score":"0.43564594","text":"def str_to_sym_or_float(string):\n\n try:\n return float(string)\n # The string is not a float value\n except ValueError:\n return Symbol(string, real=True)","title":""},{"docid":"d06cde11fd9f85fbf3156c6c27e8803a","score":"0.4356002","text":"def protocol_factory(connector=connector, node=instance._node):\n return lambda: NetstringProtocol(connector, node)","title":""},{"docid":"1dba5ec7765388faa8f3b96142dc6e22","score":"0.4348451","text":"def get_automaton_5():\n stateQ1 = State(\"State 0\", True)\n stateQ2 = State(\"State 1\")\n stateQ3 = State(\"State 2\")\n stateQ4 = State(\"State 3\")\n stateQ1.add_transition(one, stateQ2)\n stateQ1.add_transition(zero, stateQ3)\n stateQ2.add_transition(one, stateQ1)\n stateQ2.add_transition(zero, stateQ4)\n stateQ3.add_transition(one, stateQ4)\n stateQ3.add_transition(zero, stateQ1)\n stateQ4.add_transition(one, stateQ3)\n stateQ4.add_transition(zero, stateQ2)\n\n comparator = DFAComparator()\n return DeterministicFiniteAutomaton(binaryAlphabet, stateQ1,\n set([stateQ1, stateQ2, stateQ3, stateQ4]), comparator, \"Tomita's grammar 5 automaton\")","title":""},{"docid":"7fa8219aa05424dafc277f6e40374028","score":"0.43415213","text":"def from_stdin():\n edges, adj_matrix = Graph._parse_edges()\n properties = Graph._parse_properties(adj_matrix)\n\n # update weights using node properties\n for node_1, node_2 in edges:\n max_similarity = len(properties[node_1])\n similarity = np.sum(properties[node_1] == properties[node_2])\n edges[node_1, node_2] = max_similarity - similarity + 1\n\n return Graph(edges, adj_matrix)","title":""},{"docid":"86d3725b1eb3b072353961221218a7ea","score":"0.4340687","text":"def from_str(string:str) -> \"Version\":\n if string is None:\n return Version(0,0,0)\n else:\n numbers = string.split(\".\")\n return Version(\n int(numbers[0]),\n int(numbers[1]),\n int(numbers[2])\n )","title":""},{"docid":"e008b2cde8cb1e20358fe17ba0a5ea0d","score":"0.43392023","text":"def treeFromString(treeString,nodep): #Transform string into a bifurcating Tree structure without state at internal nodes\n\ttreeString=treeString.replace(\";\",\":0.0\")\n\tif treeString[0]!=\"(\":\n\t\t#Leaf case\n\t\tnode=getLeaf(treeString)\n\t\tbranch=getBranch(treeString)\n\t\tnode.branchp=branch\n\t\tnode.parent=nodep\n\t\tfor sam in node.samples:\n\t\t\tbreak\n\t\tnode.subtree=sam+\":\"+str(branch)\n\t\treturn node\t\n\telse:\n\t\t#internal node case\n\t\tindexEnd=len(treeString)-1\n\t\twhile treeString[indexEnd]!=\")\":\n\t\t\tindexEnd-=1\n\t\t\tif indexEnd<0:\n\t\t\t\tprint \"parenthesis error while parsing tree\"\n\t\t\t\treturn None\n\t\tbranch=getBranch(treeString[indexEnd+1:])\n\t\tnode=Node()\n\t\tnode.parent=nodep\n\t\tnode.branchp=branch\n\t\tif treeString[indexEnd+1]==\"[\":\n\t\t\t#node.disState=[getState(treeString[indexEnd+1:])]\n\t\t\tss=getState(treeString[indexEnd+1:])\n\t\t\tif IsInt(ss):\n\t\t\t\tnode.disState=[int(ss)]\n\t\t\telse:\n\t\t\t\tnode.disState=[ss]\n\t\tindex=0\n\t\tcountPar=0\n\t\twhile treeString[index]!=\",\" or countPar!=0:\n\t\t\tindex+=1\n\t\t\tif treeString[index]==\"(\" or treeString[index]==\"[\":\n\t\t\t\tcountPar+=1\n\t\t\telif treeString[index]==\")\" or treeString[index]==\"]\":\n\t\t\t\tcountPar-=1\n\t\t#Call iteratively function on subtrees\t\n\t\t#print \"subtree1\", treeString[1:index]\n\t\tsubtree1=treeFromString(treeString[1:index],node)\n\t\tnode.child1=subtree1\n\t\tnode.branch1=subtree1.branchp\n\t\t#print \"subtree2\", treeString[index+1:indexEnd]\n\t\tsubtree2=treeFromString(treeString[index+1:indexEnd],node)\n\t\tnode.child2=subtree2\n\t\tnode.branch2=subtree2.branchp\n\t\tnode.samples =(node.child1.samples | node.child2.samples)\n\t\t#print \"New node\",node.samples\n\t\tnode.subtree= \"(\" + subtree1.subtree + \",\" + subtree2.subtree + \"):\" + str(branch)\n\t\tnode.coalTime=node.child1.coalTime+node.branch1\n\t\treturn node","title":""},{"docid":"a73200bf857a8aba1947d14893b1d51f","score":"0.43390766","text":"def test_add_fused_gate(backend):\n c = Circuit(2)\n c.add(gates.H(0))\n c.add(gates.H(1))\n c.add(gates.CNOT(0, 1))\n fused_c = c.fuse()\n fgate = fused_c.queue[0]\n assert isinstance(fgate, gates.FusedGate)\n\n new_c = Circuit(2)\n new_c.add(fgate)\n assert c.depth == 2\n assert c.ngates == 3\n assert new_c.depth == 1\n assert new_c.ngates == 1\n backend.assert_circuitclose(fused_c, c)\n backend.assert_circuitclose(new_c, c)","title":""},{"docid":"bf0e1458790f1c04fb24388a2d799904","score":"0.43364912","text":"def parse(s:str,nonterms:List[Nonterminal], nt_of_name=None, names=None) -> Expr:\n if names is None:\n names = [nonterm.name_noargc for nonterm in nonterms]\n if nt_of_name is None:\n nt_of_name = {(nt.name_noargc,nt.argc):nt for nt in nonterms}\n\n # fn calls become tuples: (fn_name,[args])\n # example: foo(bar(x,y),z) -> ('foo',[('bar',['x','y']),'z'])\n\n # strip whitespace, remove spaces, do some basic checks\n s = s.strip()\n if '\\n' in s:\n raise ParseError(\"why is there a newline\")\n if s.count('(') != s.count(')'):\n raise ParseError(\"unequal number of opening and closing parens\")\n if len(s) == 0:\n raise ParseError(\"empty string\")\n\n # figure out what funciton / const / var is being used next\n match = re.match(r'(\\w|-)+',s)\n if match is None:\n raise ParseError(\"Expected string to start with an identifier:\",s)\n name = s[:match.end()]\n s = s[match.end():]\n if name not in names:\n raise ParseError(\"unrecognized nonterminal:\",name)\n \n # handle var and const\n if len(s) == 0:\n # since this was the last token of the string\n # its a const or var so we wanna call the Nonterminal\n # with zero arguments\n if (name,0) not in nt_of_name:\n argcs = [argc for (fname,argc) in nt_of_name if fname == name]\n raise ParseError(f\"found {name}() but can't call it with 0 args. Allowed argcs: {argcs}\")\n expr = Expr(nt_of_name[(name,0)],[])\n if expr.exception:\n raise ParseError(f\"exception during execution of subtree {expr}: {expr.val}\")\n return expr\n \n # handle function call\n if s[0] != '(':\n raise ParseError(\"expected open paren, got:\",s)\n if s[-1] != ')':\n raise ParseError(\"expected this to end with close paren:\",s)\n\n s = s[1:-1]\n # split by commas, except when already inside parens\n args = []\n while True:\n s = s.strip()\n if s == '':\n break # only happens if zero args\n i = next_comma(s) # find net comma, but aware of paren depth\n if i == -1: # no more commas, so we should parse the rest into an Expr\n args.append(parse(s,nonterms,nt_of_name,names))\n break\n else: # parse next arg into an Expr\n args.append(parse(s[:i],nonterms,nt_of_name,names))\n s = s[i+1:] # skip the ',' and continue\n\n if (name,len(args)) not in nt_of_name:\n argcs = [argc for (fname,argc) in nt_of_name if fname == name]\n raise ParseError(f'{name} applied to wrong number of args, got {len(args)} but expected one of these: {argcs}')\n\n expr = Expr(nt_of_name[(name,len(args))],args)\n if expr.exception:\n raise ParseError(f\"exception during execution of subtree {expr}: {expr.val}\")\n return expr","title":""},{"docid":"e0b7a907bc815ba86f8a4d8b3bbbbb2e","score":"0.43338928","text":"def parse(string):\n words = string.split()\n count = len(words)\n if count == 0:\n raise EmptyOptionString()\n elif count == 1:\n return Signature(words[0], [])\n else:\n head = words[0]\n tail = words[1:]\n body = []\n for index, castkeydefault in enumerate(tail):\n cast, key, default = separate(castkeydefault)\n body.append(Argument(index, key, default, cast))\n return Signature(head, body)","title":""},{"docid":"192e267bbbe058946a157877ad1812d6","score":"0.43327823","text":"def valueOf(string: str) -> 'SegmentAttribute':\n ...","title":""},{"docid":"75b0ae4d40d5c026dda37cdbd2895439","score":"0.43294743","text":"def match(infix, string):\n\n # Shunt and compile the regular expression\n postfix = shunt(infix)\n nfa = compile(postfix)\n\n # The current set of states and the next set of states\n current = set()\n next = set()\n\n # add the initial state to the current state\n current |= follows(nfa.initial)\n\n # Loop through each character in the string\n for s in string:\n # loop through the current set of states\n for c in current:\n # check if that state is labelled s\n if c.label == s:\n # add the edge1 state to the next set\n next |= follows(c.edge1)\n # set current to next, and clear out next\n current = next\n next = set()\n\n # check if the accept state is in the set of current states\n return nfa.accept in current","title":""},{"docid":"f908e484d6802ac480b0c7a5b40c9e99","score":"0.43282476","text":"def makeHuffmanCodes(string):\n frequencies = countUnique(string)\n Nodes = [Node(char, freq) for (char, freq) in frequencies]\n assert(len(Nodes) >= 2)\n while len(Nodes) > 2:\n New = Node(None, 0)\n #Merge the two rarest nodes in New\n New.addChildren(Nodes.pop(0), Nodes.pop(0))\n #Insert the new node into the array, keep it sorted\n pos = bisect([X.frequency for X in Nodes], New.frequency)\n Nodes.insert(pos, New) #there might be a more concise way\n\n #last two nodes in array are the direct descendants of the tree\n Huffman_Tree = Node(None, 0)\n Huffman_Tree.addChildren(Nodes.pop(0), Nodes.pop(0))\n assert (len(Nodes) == 0) #Nodes must be empty now\n #put a code (0/1) in every node except the root\n Huffman_Tree.assignCodes()\n D = dict()\n Huffman_Tree.makeDictionary(D)\n return D","title":""},{"docid":"65b205aa156cd627e17a932ec655e30d","score":"0.431863","text":"def from_string(s: str) -> \"Token\":\n # DO NOT MODIFY THIS METHOD\n return Token(*s.rsplit(\"/\", 1))","title":""},{"docid":"65ea5cd200ac8e69bcea97f1bb01444d","score":"0.43019292","text":"def from_str(cls, input_str):\n\n str_c = cls()\n str_c.offset = 0\n str_c.value_list = [x for x in input_str]\n str_c.length = len(str_c.value_list)\n\n return str_c","title":""},{"docid":"89041b2aab85c511ca7567c276641a6a","score":"0.4299525","text":"def string_through_NFA(string, automaton):\n current_state = [0]\n transitions = []\n next_state = []\n\n for letter in string:\n for state in current_state:\n transitions.append(automaton[0][state]) # contains the possible transitions for the current state\n\n for delta in transitions:\n for i, v in enumerate(delta):\n if v[0] == letter:\n next_state.append(v[1])\n # if no state was appended to next_state, the transition for this letter was not found. Nicolas Cage died.\n\n current_state = next_state\n next_state = []\n transitions = []\n\n for accepted_state in automaton[1]:\n for final_state in current_state:\n if accepted_state == final_state:\n return True\n return False","title":""},{"docid":"843013dfb51b1fede164ff7d3446a337","score":"0.42986515","text":"def test_two_state_accept_odds():\n transitions = [\n \"s1 a ^s2\",\n \"s2 a ^s1\"\n ]\n dfa = DFAstate(transitions, 's1', accepting_states=['s2'])\n\n input_string = \"aaaaaa\"\n current_state = 's1'\n accepting = False\n for letter in input_string:\n current_state = 's1' if current_state == 's2' else 's2'\n accepting = False if accepting else True\n assert current_state == dfa.event(letter).get_active_states()[0]\n assert accepting == dfa.in_accepting()","title":""},{"docid":"29e1e857742f347ab00a8aa83a94fca4","score":"0.42942676","text":"def from_str(cls, elementstr):\n ival, params = cls.parse(elementstr)\n return cls(ival, params)","title":""},{"docid":"d8935ad189068f057f7390c4412189c2","score":"0.42929357","text":"async def create_rule_for_string(cls, signal):\n rule = input(constants.string_prompt)\n value = input(constants.value_prompt)\n\n # Insert the rule\n payload = {\n 'signal': signal,\n 'rule': rule,\n 'value': str(value),\n 'value_type': 'string'\n }\n await DbHelper.insert(constants.rules_table, payload)","title":""},{"docid":"4e5393b030c0420f4c3ff1fe126a5909","score":"0.4282777","text":"def fromString(self, *args):\n return _yarp.Bottle_fromString(self, *args)","title":""},{"docid":"6009bb2e0f4b931203c7f9b890bf2daf","score":"0.42790574","text":"def _from_string(cls, string):\r\n m = cls.name_email_regex.search(string)\r\n if m:\r\n name, email = m.groups()\r\n return Actor(name, email)\r\n else:\r\n m = cls.name_only_regex.search(string)\r\n if m:\r\n return Actor(m.group(1), None)\r\n else:\r\n # assume best and use the whole string as name\r\n return Actor(string, None)\r\n # END special case name\r\n # END handle name/email matching\r","title":""},{"docid":"eb732683d5cc8c76030fc2a546befd0e","score":"0.4276147","text":"def create_stem(name, modality='vgg16', weights='imagenet'):\n\n # create the based network based on the modality\n if modality == 'vgg16':\n stem = tf.keras.applications.VGG16(include_top=False, weights=weights, classes=2, classifier_activation='sigmoid')\n elif modality == 'resnet50':\n stem = tf.keras.applications.ResNet50(include_top=False, weights=weights, classes=2)\n else:\n inception_builder = Inceptionv1()\n stem = inception_builder.build_inception()\n\n # modified the name to avoid name confliction\n stem._name = stem.name + '_' + name\n\n return stem","title":""},{"docid":"66af0b2f855957b771d0cb21a1218c35","score":"0.42751518","text":"def _from_string(cls, string: str) -> \"Actor\":\n m = cls.name_email_regex.search(string)\n if m:\n name, email = m.groups()\n return Actor(name, email)\n else:\n m = cls.name_only_regex.search(string)\n if m:\n return Actor(m.group(1), None)\n # assume best and use the whole string as name\n return Actor(string, None)\n # END special case name\n # END handle name/email matching","title":""},{"docid":"7b00295e6a99f060ed292f5047d44dd4","score":"0.42721355","text":"def parse(string):\n\tif string == 'quit':\n\t\tquit()\n\t\n\tstring = number_touch(string)\n\tstring = parens(string)\n\ttree = split_ops(string, ['+', '-'])\n\tif tree[0].count('*') or tree[0].count('/'):\n\t\ttree[0] = split_ops(tree[0], ['*', '/'])\n\t\ttree[0] = make_tree(tree[0], ['*', '/'])\n\n\ttree = make_tree(tree, ['+', '-'])\n\treturn calc(tree)","title":""},{"docid":"f6b2225caa20a979b56ec7890edbe928","score":"0.4269554","text":"def from_string(cls, ns_string):\n\n split_param_list = ns_string.split('=')\n parameters = {}\n nodes = []\n if len(split_param_list) != 1: # if there are parameters\n # split_statement = [name, [node0, node1,...], first_param]\n split_statement = split_param_list.pop(0).split()\n if len(split_statement) >= 3: # if there are nodes\n nodes = split_statement[1:-1]\n # start parsing parameters\n param_name = split_statement[-1]\n for n, split_param in enumerate(split_param_list):\n param_list_part = split_param.split()\n if n == len(split_param_list)-1: # if last split_parameter\n param_value = split_param\n else:\n param_value = ' '.join(param_list_part[:-1]) # join except param_name\n parameters[param_name.strip()] = param_value.strip()\n param_name = param_list_part[-1]\n else: # there are no parameters\n # split_statement = [name, [node0, node1,...]]\n split_statement = ns_string.split()\n if len(split_statement) > 1: # if there are nodes\n nodes = split_statement[1:]\n name = split_statement[0]\n return cls(name, nodes, parameters)","title":""},{"docid":"4d51e9a6371f89794ce8fa54b47609a7","score":"0.42659387","text":"def build_from_string(self, xml_str: str) -> None:\n xml = ElementTree(fromstring(xml_str))\n self._build_model(xml)","title":""},{"docid":"574aa7fc8cb931ac66a052db9a6ac822","score":"0.4265689","text":"def fromstring(text, parser=None, base_url=None): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"d949c05df3479e250877373e8ac34ab2","score":"0.42577648","text":"def add_initial_state(circuit, state_str):\n\n words = re.compile(\"([!\\w]+)\").findall(state_str)\n\n for item in words:\n\n inverted = item[0] == \"!\"\n state = 0 if inverted else 1\n net = item[1:] if inverted else item\n\n circuit[\"initial_state\"][net] = state","title":""},{"docid":"b2203da584dfa33a68e0d4d6f180c6e9","score":"0.42541292","text":"def _from_string(self, s):\n s = s.upper().replace(\"T\", \"U\") # convert to uppercase DNA\n d = self.SequenceClass(s, alphabet=self.alphabet.sub_enumerations[0])\n self._data = d.to_codons()._data","title":""},{"docid":"70ff3a582287505f953e23b54286af28","score":"0.42439976","text":"def from_string(cls, string):\n tf = cls()\n sa = tf.structarr\n lines = [\n line\n for line in string.splitlines()\n if line.strip() and not (line.startswith(\"#\") or \"3dvolreg matrices\" in line)\n ]\n\n if not lines:\n raise TransformFileError\n\n parameters = np.vstack(\n (\n np.genfromtxt([lines[0].encode()], dtype=\"f8\").reshape((3, 4)),\n (0.0, 0.0, 0.0, 1.0),\n )\n )\n sa[\"parameters\"] = parameters\n return tf","title":""},{"docid":"0e287c2f253e659b1198f50e3f10cd8d","score":"0.42419368","text":"def Dijkstra(self, node):\n INF = float('inf')\n self.validateNodes(INF)\n #we will use the nodes' tokens to store the distance info!\n node.set_token(0.0) #0-distance from itself!\n #initializing the tree\n T_root = Leaf(node)\n T_root.setDistance(node.get_token())\n T = Tree(T_root)\n #initializing a dictionary to keep trace of the leaves\n leaves = dict()\n leaves[node] = T_root\n #initializing the priority queue to mantain the fringe\n PQ = PriorityQueue()\n PQ.insert(T_root, node.get_token())\n\n while not PQ.isEmpty():\n u = PQ.deleteMin() #retrieving the min node from the leaf\n n = u.getElem()\n for v in self._adjacency[n.get_index()].getLastAddedList():\n if v.get_token() == INF:\n l = Leaf(v)\n leaves[v] = l #updating the leaves' dictionary\n PQ.insert(l, n.get_token() + 1.0) #each edge will be unitary-cost\n v.set_token(n.get_token() + 1.0)\n T.insertLeaf(l, u)\n elif n.get_token() + 1.0 < v.get_token():\n relaxed = n.get_token() + 1.0\n leaves[v].setDistance(relaxed)\n #updating the tree... (we are now saving in the priority queue the leaves)\n leaves[v].setFather(u)\n leaves[n].addSon(leaves[v])\n #updating the priority queue\n PQ.decreaseKey(leaves[v], relaxed)\n v.set_token(relaxed)\n return T","title":""},{"docid":"0665ef8b2d27eedf9924148a4aeef213","score":"0.4237808","text":"def getLeaf(treeString): #Define node associated to tree leaf.\n\tindex=0\n\twhile treeString[index]!=\":\":\n\t\tindex+=1\n\tnode=Node()\n\tif treeString[index-1]!=\"]\":\n\t\tnode.samples=set([treeString[:index]])\n\telse:\n\t\tindex2=0\n\t\twhile treeString[index2]!=\"[\":\n\t\t\tindex2+=1\n\t\tnode.samples=set([treeString[:index2]])\n\t\tnode.disState=[treeString[index2+1:index-1]]\n\t\tss=treeString[index2+1:index-1]\n\t\tif IsInt(ss):\n\t\t\tnode.disState=[int(ss)]\n\t\telse:\n\t\t\tnode.disState=[ss]\n\tnode.coalTime=0.0\n\t#print \"New leaf\", node.samples\n\treturn node","title":""},{"docid":"100b8682dd9e8dc7e8309357791ce74d","score":"0.42355517","text":"def str2action(s: str) -> Netio.ACTION:\n try:\n return Netio.ACTION[s.upper()]\n except KeyError or AttributeError:\n try:\n return Netio.ACTION(int(s))\n except ValueError:\n raise argparse.ArgumentTypeError(f\"{s!r} is not a valid {Netio.ACTION.__name__}\")","title":""},{"docid":"c297421c5e4c64f2cea1a7c68f2e0245","score":"0.4231503","text":"def __init__(self, ab_num: float, input_string: string):\n super().__init__(input_string)\n self.raw_str = input_string\n self.value = None\n self.children = []\n self.abbr = ab_num # store the value of the abbreviation of multiplication","title":""},{"docid":"dc58e042e14911d21ceb0bb2b7a90d42","score":"0.4225568","text":"def match(infix, string):\n # Shunt and compile the regular expression\n postfix = shuntingYard(infix)\n # Builds the NFA\n nfa = compile(postfix)\n\n # The current set of states and the next set of states\n current = set()\n nexts = set()\n\n # Add the initial state to the current list\n current |= followes(nfa.initial)\n\n # Loop through each character in the string\n for s in string:\n # Loop through the current set of states\n for c in current:\n # Check if that state is labelled s\n if c.label == s:\n # Add the edge1 state to the next set.\n nexts |= followes(c.edge1)\n # Set current to next, and clear out next \n current = nexts\n nexts = set()\n # Check if the accept state is in the set of current states\n return (nfa.accept in current)","title":""},{"docid":"f78ef35aed0dd9521329c0e39cfa8e04","score":"0.42242277","text":"def parse_food_phrase(tree, eat_lexicon, filters, debug=False):\n\n eat_lexicon_lower = set(tok.lower() for tok in eat_lexicon)\n state = _STATE_SCAN_EAT\n\n # Append a state to transition to complete at end of sentence.\n for stree in its.chain(tree, (('', ''),)):\n\n if state == _STATE_SCAN_EAT:\n\n if isinstance(stree, tuple) \\\n and stree[0].lower() in eat_lexicon_lower:\n state = _STATE_EAT_LAST\n\n assert state in [_STATE_SCAN_EAT, _STATE_EAT_LAST]\n\n elif state == _STATE_EAT_LAST:\n\n # We must have a noun phrase after our eat word.\n if not isinstance(stree, Tree) or stree.label() != 'NP':\n state = _STATE_SCAN_EAT\n\n else:\n # Extract food from NP after eat word.\n new_words = [(w.lower(), pos) for w, pos in stree]\n words = new_words\n\n filtered_words = [(w, pos) for w, pos\n in its.ifilter(_FILTER_POS, new_words)\n if all(f(w) for f in filters)]\n\n if len(filtered_words) > 0:\n state = _STATE_NP_FOUND\n else:\n state = _STATE_SCAN_EAT\n\n assert state in [_STATE_SCAN_EAT, _STATE_NP_FOUND]\n\n elif state == _STATE_NP_FOUND:\n\n newstate = _STATE_NP_COMPLETE\n if isinstance(stree, tuple):\n word = stree[0].lower()\n if word in ['of', 'in', 'on', 'with', 'and']:\n words.append(stree)\n filtered_words.append((word, stree[1]))\n newstate = _STATE_IN_FOUND\n\n state = newstate\n\n assert state in [_STATE_IN_FOUND, _STATE_NP_COMPLETE]\n\n elif state == _STATE_IN_FOUND:\n\n if not isinstance(stree, Tree) or stree.label() != 'NP':\n words.pop()\n filtered_words.pop()\n\n else:\n new_words = [(w.lower(), pos) for w, pos in stree]\n words.extend(new_words)\n\n filtered_words.extend((w, pos) for w, pos\n in its.ifilter(_FILTER_POS, new_words)\n if all(f(w) for f in filters))\n\n state = _STATE_NP_COMPLETE\n\n elif state == _STATE_NP_COMPLETE:\n break\n\n # At the end of the sentence, check complete.\n\n if state == _STATE_NP_COMPLETE:\n\n food = ' '.join(w for w, _ in filtered_words)\n\n if debug is True and len(filtered_words) != len(words):\n food_unfiltered = ' '.join(w for w, _ in words)\n print \"Filtered: {} => {}\".format(food_unfiltered, food)\n\n if len(food) > 0 and food not in _STOPWORDS_EN:\n return food\n\n return None","title":""},{"docid":"c75b8477ff57702de0d338d7e78e5169","score":"0.42224583","text":"def _message_factory(cls, factory_string):\n components = factory_string.split(\" \")\n x, y = int(components[1]), int(components[2])\n\n if cls.REPR_PREFIX != components[0]:\n raise ValueError(\"Expected %s, found %s\" % (cls.REPR_PREFIX, components[0]))\n\n return UTSDigMessage(x, y)","title":""},{"docid":"4da9e5aac06afc2a60d35a4459e94802","score":"0.42202806","text":"def main():\n fsa_filename = sys.argv[1]\n test_filename = sys.argv[2]\n\n with open(fsa_filename, \"r\") as fsa_file:\n fsa_rules = fsa_file.readlines()\n\n acceptor = FSAAcceptor(fsa_rules)\n\n with open(test_filename, \"r\") as test_file:\n test_strings = test_file.readlines()\n\n for string in test_strings:\n print(string.strip() + \" => \" + (\"yes\" if acceptor.can_accept_string(string) else \"no\"))","title":""},{"docid":"69b7eb6312f830ebc3ad1fa082fa2378","score":"0.42202336","text":"def get_node_from_string(string, world_interface, _verbose=False):\n has_children = False\n behavior = BEHAVIORS_DICT.get(string)\n if behavior is not None:\n node = behavior(string, world_interface)\n elif string == 'f(':\n node = pt.composites.Selector(\"Fallback\")\n has_children = True\n elif string == 's(':\n node = RSequence()\n has_children = True\n elif string == 'p(':\n node = pt.composites.Parallel(\n name=\"Parallel\",\n policy=pt.common.ParallelPolicy.SuccessOnAll(synchronise=False))\n has_children = True\n elif string == 'nonpytreesbehavior':\n return False\n else:\n raise Exception(\"Unexpected character\", string)\n return node, has_children","title":""},{"docid":"e7131cf2bb2d78b50d90d6474fd054ac","score":"0.42197636","text":"def fromTFExample(bytestr):\n import tensorflow as tf\n example = tf.train.Example()\n example.ParseFromString(bytestr)\n return example","title":""},{"docid":"5ad92979720579917146aaa9e60434d7","score":"0.4219349","text":"def cstr_train(diam, height, num_CSTRs=None, asPFR=None):\n\n # Initialize defaults of keyword arguments\n if num_CSTRs is None:\n nCSTRs = 1\n else:\n nCSTRs = num_CSTRs\n if asPFR is None:\n treat_as_PFR = True\n else:\n treat_as_PFR = asPFR\n\n if treat_as_PFR:\n cstr_height = height / nCSTRs\n train_height = height\n else:\n cstr_height = height\n train_height = height * nCSTRS\n\n # Source, sink nodes\n nodes = OrderedDict([('gas_source',\n {'type': 'source',\n 'pos': [0.0, 0.0, 0.0]}),\n ('gas_sink', \n {'type': 'sink',\n 'pos': [0.0, 0.0, train_height]})\n ])\n # Reactor nodes\n pos = 0.0\n for nCSTR in range(nCSTRs):\n cstr_name = 'R' + str(nCSTR)\n nodes[cstr_name] = {'type': 'reactor',\n 'pos': [0.0, 0.0, pos],\n 'diam': bed_diam,\n 'height': cstr_height}\n pos += cstr_height\n\n # Inlet, outlet streams\n streams = OrderedDict([('gas_inlet',\n {'nodes': ['gas_source', 'R0']}),\n ('gas_outlet',\n {'nodes': ['R' + str(nCSTRs), 'gas_sink']})\n ])\n # Internal streams\n for nCSTR in range(nCSTRs - 1): # One less internal stream than the number of CSTRs\n source_name = 'R' + str(nCSTR)\n sink_name = 'R' + str(nCSTR+1)\n stream_name = source_name + '_to_' + sink_name\n stream[stream_name] = {'nodes': [source_name, sink_name]}\n return nodes, streams","title":""},{"docid":"8e678ac84d4271998c3bb43facee4d8d","score":"0.42146686","text":"def edgefactory(edge_type):\n return edge_mapping[edge_type]","title":""},{"docid":"355acf46998b91cf79cbb393b7116df5","score":"0.4205546","text":"def _from_freqstr(cls, freqstr):\n numeric_part = \"\"\n freq_part = \"\"\n\n for x in freqstr:\n if x.isdigit():\n numeric_part += x\n else:\n freq_part += x\n\n if (\n freq_part not in cls._CODES_TO_UNITS\n or not numeric_part + freq_part == freqstr\n ):\n raise ValueError(f\"Cannot interpret frequency str: {freqstr}\")\n\n return cls(**{cls._CODES_TO_UNITS[freq_part]: int(numeric_part)})","title":""},{"docid":"87f2916d3d1eb4b7f80518381c8c014b","score":"0.4203038","text":"def from_string(cls, afield_as_string=\"\", af_size=None, tf_size=None):\n\n # split Afield header\n strlist = afield_as_string.split(\";\", 1)\n\n # make AF pos list\n pos_list = cls.pos_list_from_string(cls, string=strlist[0] + \";\")\n\n tf_str_list = [\"T\" + s.replace(\"UG\", \"UT\") for s in strlist[1].replace(\"UT\", \"UG\").split(\"T\")[1:]]\n\n # make TF list\n tf_list = [ZbaTfield.from_string(tfield_as_string=s, tf_size=tf_size) for s in tf_str_list]\n\n return cls(pos=pos_list, size=cls.default_size, t_list=tf_list)","title":""},{"docid":"f66663f4c4c432e5ff11f6ce3162a0d5","score":"0.42002225","text":"def new_string_graph(n, predicates):\n graph = DataGraph()\n for i in range(1,n):\n s = str(i)\n for p in predicates:\n o = str(i+1)\n # print s, p, o\n graph.addNode(s, p, o)\n return graph","title":""},{"docid":"4d846ef815d8e32501b38817376fa18f","score":"0.41943747","text":"def match(infix, string):\n \n # Shunt and compile the regular expression\n postfix = shunt(infix)\n nfa = compile(postfix)\n\n # The current set of states\n current = set()\n next = set()\n\n # Add the initial state to the current set\n current |= followes(nfa.initial)\n\n # Loop through each character\n for s in string:\n # Loop Through current set of states\n for c in current:\n # Check if that state is labelled s.\n if c.label == s:\n # Add the edge1 state to the next set.\n next |= followes(c.edge1)\n #set current to next and clear out next\n current = next\n next = set()\n\n # Check if the accept state is in the set of current states\n return (nfa.accept in current)","title":""},{"docid":"75a3b8e9e41d19d952efe9091bfd62d6","score":"0.41935343","text":"def get_act_fn(act_fn_string: str):\n if act_fn_string == \"relu\":\n return th.nn.ReLU\n elif act_fn_string == \"sigmoid\":\n return th.nn.Sigmoid\n elif act_fn_string == \"tanh\":\n return th.nn.Tanh","title":""},{"docid":"9a4b14b338df730241cd1944b9ed38f4","score":"0.41903028","text":"def parseString( string, parser = None ):\n\tp = SimpleXMLHandler()\n\tif parser:\n\t\tdstParser = parser().parseString\n\telse:\n\t\tdstParser = sax.parseString\n\tdstParser( string, p )\n\troot = p.rootNode\n\troot.filename = None\n\treturn root","title":""},{"docid":"35763462612e98fcf5ec409304bb21c3","score":"0.41898003","text":"def simple_parser() -> argparse.ArgumentParser:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"INPUT\", \"Generic one-param input\", type=str)\n return parser","title":""},{"docid":"0110afc8068fffb119f83060dd3ba365","score":"0.4186264","text":"def parse(string):\n return SpecParser().parse(string)","title":""},{"docid":"ff1254d3622662c9a4dd182f12214119","score":"0.41801172","text":"def tree_from_string(text, source_name, start_line=1):\n seq = element_seq_from_string(text, source_name, start_line)\n compressed_seq = compress_element_seq(seq)\n root = tree_from_element_seq(compressed_seq)\n return root","title":""},{"docid":"2589097f6332c19ebd3c47a391272fd7","score":"0.41791803","text":"def from_string(value):\n\t\traise NotImplementedError()","title":""},{"docid":"e05e6aea882ba97414d834a5b597f1ca","score":"0.41788822","text":"def string2boolean_parser(string):\n\n string = string.lower()\n if string == 'true' or string == 't':\n return True\n elif string == 'false' or string == 'f':\n return False\n else:\n raise ValueError","title":""},{"docid":"c8bb09fb833efac2968e8839c1742013","score":"0.41767716","text":"def from_string(cls: Type[DS], s: str) -> DS:\n if len(s) < 1 or s[0:1] != cls.SIGIL:\n raise SynapseError(\n 400,\n \"Expected %s string to start with '%s'\" % (cls.__name__, cls.SIGIL),\n Codes.INVALID_PARAM,\n )\n\n parts = s[1:].split(\":\", 1)\n if len(parts) != 2:\n raise SynapseError(\n 400,\n \"Expected %s of the form '%slocalname:domain'\"\n % (cls.__name__, cls.SIGIL),\n Codes.INVALID_PARAM,\n )\n\n domain = parts[1]\n # This code will need changing if we want to support multiple domain\n # names on one HS\n return cls(localpart=parts[0], domain=domain)","title":""},{"docid":"864d3c1cc0511df32cf415cd7ec53598","score":"0.41758972","text":"def from_string(cls, xml_string):\n element = ElementTree.fromstring(xml_string)\n return cls.from_element(element)","title":""},{"docid":"7ffd184b2e86502b8e7c0d81555ade50","score":"0.41740236","text":"def create_forest(request, config):\n\n if 'use_forest' in request:\n\n # {\n # \"type\": \"request\",\n # \"use_forest\": \"1\\tMit\\tmit\\t_\\tADP\\tAPPR\\t_\\t_\\t3\\t_\\t...\n # \"forest_format\": \"conll09\"\n # }\n\n format_ = request['forest_format']\n try:\n info = get_format_from_config(config, format_) #check if config file contains format, return format dict\n except KeyError as e:\n msg = 'Format not supported: %s'\n logging.warning(msg, format_)\n raise ValueError(msg % format_) from e\n return Forest.from_string(request['use_forest'], format_info=info)\n #request['use_forest']: conll string e.g.\"1\\tMit\\tmit\\t_\\tADP\\tAPPR\\t_\\t_\\t3\\t_\\t...\"\n #info: format dict\n\n\n\n#-----------------ignore for now, come back later------------------------------------------\n elif 'process' in request:\n\n # {\n # \"type\": \"request\",\n # \"process\": \"Mit Bedacht badet heute ein Lurch in einem See.\",\n # \"source_format\": \"raw\",\n # \"target_format\": \"conll09\"\n # }\n\n try:\n forest_string = process(request, config)\n except ValueError as e:\n msg = 'Cannot convert from source_format %s to target_format %s.'\n logging.warning(msg,\n request['source_format'], request['target_format'])\n raise ValueError(msg % (request['source_format'],\n request['target_format'])) from e\n try:\n info = get_format_from_config(config, request['target_format'])\n except KeyError as e:\n msg = 'target_format %s not supported.'\n logging.warning(msg, request['target_format'])\n raise ValueError(msg % (request['target_format'])) from e\n return Forest.from_string(forest_string, format_info=info)","title":""},{"docid":"cdbc626e6400ac0d4ac9c7eeae6ae262","score":"0.41736302","text":"def _new_snode(self, node1, node2):\n label = self._new_label('S')\n self.add_node(label)\n self.root = label\n self.add_edge(label, node1)\n self.add_edge(label, node2)\n\n return label","title":""},{"docid":"91d1bd09f84b6399ebb6f5b500d9f8bc","score":"0.41708004","text":"def start_new(self) -> WalAcceptor:\n\n wa_num = len(self.instances)\n wa = WalAcceptor(self.wa_binpath,\n os.path.join(self.data_dir, \"wal_acceptor_{}\".format(wa_num)),\n self.initial_port + wa_num, wa_num)\n wa.start()\n self.instances.append(wa)\n return wa","title":""},{"docid":"4154d16f8a730df56739884d2f743937","score":"0.41675404","text":"def create_function_from_string(string):\n if string is None:\n return None\n\n if 'lambda' in string:\n string = f'func = {string}'\n loc = {}\n exec(string, {}, loc)\n funcs = [loc.get(func, None) for func in loc.keys()]\n return funcs[0] if len(funcs) > 0 else None","title":""},{"docid":"28fdffa8d3dec7d7985ddfe75f5e8472","score":"0.4162198","text":"def from_binary(cls, binary_string):\n integer_part, decimal_part = binary_string.split('.')\n return cls(len(integer_part), len(decimal_part), int(integer_part + decimal_part, 2))","title":""},{"docid":"ec7b6a8ea8cc7766795bc8b7f4db932f","score":"0.41581127","text":"def from_string(cls, string):\n lines = string.split(\"\\n\")\n # throw away the first and last lines, and remove indentation\n lines = [line.strip() for line in lines[1:-1]]\n opts = {}\n for line in lines:\n split = line.split(\"=\")\n opt = split[0]\n if len(split) == 2:\n value = split[1]\n else:\n value = \"=\".join(split[1:])\n opts[opt] = value\n\n # remove the SSID from the other options and strip the quotes from it.\n ssid = opts[\"ssid\"][1:-1]\n del opts[\"ssid\"]\n\n return cls(ssid, **opts)","title":""}],"string":"[\n {\n \"docid\": \"3a11123790342656c29964c9455581a1\",\n \"score\": \"0.5958145\",\n \"text\": \"def from_string(factory, str_graph):\\n\\n builder = Builder(factory)\\n node_sequences = [s.split('->') for s in str_graph.split(\\\",\\\")]\\n\\n for seq in node_sequences:\\n last_id = None\\n for s in seq:\\n node_id = s.strip()\\n builder.node(node_id)\\n if last_id is not None:\\n builder.edge(last_id, node_id)\\n last_id = node_id\\n\\n return builder.build()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ddae97937508d253dd15568058696f5\",\n \"score\": \"0.5359703\",\n \"text\": \"def transducer(\\n istring: str,\\n ostring: str,\\n isymt: SymbolTable,\\n osymt: SymbolTable,\\n weight: Optional[float] = None,\\n) -> VectorFst:\\n\\n if weight is None:\\n weight = weight_one()\\n\\n transducer_fst_ptr = ctypes.c_void_p()\\n ret_code = lib.utils_string_to_transducer(\\n istring.encode(\\\"utf-8\\\"),\\n ostring.encode(\\\"utf-8\\\"),\\n isymt.ptr,\\n osymt.ptr,\\n ctypes.c_float(weight),\\n ctypes.byref(transducer_fst_ptr),\\n )\\n err_msg = \\\"Error creating tranducer FST\\\"\\n check_ffi_error(ret_code, err_msg)\\n\\n return VectorFst(ptr=transducer_fst_ptr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d630a8bcd89bf22a2e8cd811c2a8a38\",\n \"score\": \"0.5194737\",\n \"text\": \"def parse(input_string: str ) -> SgfTree:\\n sgfTree, input_string = _parse_node(input_string)\\n if sgfTree is None or len(input_string) > 0:\\n raise ValueError(\\\"Error parsing SGF string.\\\")\\n return sgfTree\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be177ed2fab4a20fb92c9ae4765ea2da\",\n \"score\": \"0.50749373\",\n \"text\": \"def make_input_fst(word):\\n s, accept_state = 0, 10000\\n\\n for i, c in enumerate(word):\\n # TODO: You need to implement format_arc function in scripts/util\\n print(format_arc(s, s + 1, c, c, weight=0))\\n s += 1\\n\\n if i == len(word) - 1:\\n print(format_arc(s, accept_state, EPS, EPS, weight=0))\\n\\n print(accept_state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"942305e0f39bb827c1a0090395994745\",\n \"score\": \"0.50313365\",\n \"text\": \"def from_string(\\n cls, string: str, volume: Optional[float] = None, frozen: bool = False\\n ) -> \\\"DefectChargeState\\\":\\n stripped_string = string.split()\\n if frozen is False:\\n return cls(\\n charge=int(stripped_string[0]),\\n energy=float(stripped_string[1]),\\n degeneracy=int(stripped_string[2]),\\n )\\n else:\\n if volume is None:\\n raise ValueError(\\n \\\"You must specify a real, positive cell volume if passing a frozen concentration!\\\"\\n )\\n else:\\n return cls(\\n charge=int(stripped_string[1]),\\n fixed_concentration=float(stripped_string[2]) / 1e24 * volume,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d21155b0b4d5d82afe15cf5330aea03\",\n \"score\": \"0.49972406\",\n \"text\": \"def __init__(self, string, case_insensitive=False):\\r\\n self.string = string\\r\\n self.case_insensitive = case_insensitive\\r\\n self.N = len(string) - 1\\r\\n self.nodes = [Node()]\\r\\n self.edges = {}\\r\\n self.active = Suffix(0, 0, -1)\\r\\n if self.case_insensitive:\\r\\n self.string = self.string.lower()\\r\\n for i in range(len(string)):\\r\\n self._add_prefix(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ffc6154e3afe889e41e7e92504f508\",\n \"score\": \"0.49869916\",\n \"text\": \"def stringToTree_weakRef(str):\\n # Reset current class members\\n rootNode = NLPTree()\\n rootNode.data = None\\n rootNode.parent = None\\n rootNode.children = []\\n rootNode.terminals = []\\n currentRoot = rootNode\\n eIndex = -1\\n\\n # Blank/Empty Tree\\n str = str.rstrip()\\n if str == '0' or str == '(())' or str == '':\\n return currentRoot;\\n try:\\n for token in str.split():\\n if token[-1] == ')' and len(token) > 1:\\n # Close subtree and add terminal node\\n # Increment e-index counter\\n eIndex += 1\\n # How many levels are we closing?\\n closingMarkerLocation = token.find(')')\\n levelsToClose = len(token) - closingMarkerLocation\\n # Get token name\\n tokenName = token[0:closingMarkerLocation]\\n\\n # Create new child and add to tree\\n newChild = TerminalNode(tokenName.lower())\\n newChild.eIndex = eIndex\\n newChild.parent = weakref.ref(currentRoot)\\n newChild.parent().eIndex = eIndex\\n\\n # Add new child to the tree\\n currentRoot.addChild(newChild)\\n # Close subtree and back up to parent\\n while levelsToClose > 0 and currentRoot.parent is not None:\\n currentRoot = currentRoot.parent()\\n levelsToClose -= 1\\n elif token == ')':\\n # We are finished with this tree.\\n pass\\n else:\\n # token must begin with '('\\n # Begin new subtree\\n tokenName = None\\n if token == '(': # We are at the root\\n tokenName = \\\"TOP\\\"\\n else:\\n tokenName = token[1:]\\n newChild = NLPTree(tokenName)\\n newChild.parent = weakref.ref(currentRoot)\\n if rootNode.data is None:\\n rootNode.data = tokenName\\n else:\\n currentRoot.addChild(newChild)\\n currentRoot = newChild\\n except Exception as e:\\n # Upon any error processing the string,\\n # assume for now it is due to a malformed tree string\\n # and return an empty tree.\\n sys.stderr.write(\\\"Potentially malformed tree string: %s\\\\n\\\" % (str))\\n print e\\n rootNode = NLPTree()\\n rootNode.data = None\\n rootNode.parent = None\\n rootNode.children = []\\n rootNode.terminals = []\\n return rootNode\\n tree = addSpans(currentRoot)\\n return tree\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d1232b008f8902a4c77d87a87ebf23\",\n \"score\": \"0.4967768\",\n \"text\": \"def from_string(cls, payload: str) -> GatewayDispatch:\\n payload: Dict[str, Union[int, str, Dict[str, Any]]] = loads(payload)\\n return cls(\\n payload.get(\\\"op\\\"),\\n payload.get(\\\"d\\\"),\\n payload.get(\\\"s\\\"),\\n payload.get(\\\"t\\\")\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04fc79ca54983e5553b3422df71675fb\",\n \"score\": \"0.49329767\",\n \"text\": \"def from_string(cls, string: str) -> 'Torrent':\\n return cls(Bencode.read_string(string, byte_keys={'pieces'}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c8d0fb2cee180367fed375abe674d3\",\n \"score\": \"0.49263716\",\n \"text\": \"def from_str(string):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fedd9b6a6927ec2f6de689ef0825c7d\",\n \"score\": \"0.49197897\",\n \"text\": \"def ast_to_nfa(ast):\\n\\t(initial, accepting) = ast._thompson()\\n\\taccepting.accepting = 1\\n\\n\\treturn NFA(initial)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5448cd420cd9c2c87667f81c90fcd536\",\n \"score\": \"0.49150044\",\n \"text\": \"def __init__(self, input_string: string, low_bound: float, high_bound: float, number: int):\\n self.raw_string = input_string\\n self.left = low_bound\\n self.right = high_bound\\n self.number_points = number\\n self.grammar_tree = GrammarTree(self.raw_string)\\n self.grammar_tree.build_tree()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e70de25a33dcfb65222724bdeae85b54\",\n \"score\": \"0.47806048\",\n \"text\": \"def __init__(self, input_str=default_string):\\n self.input_str = input_str\\n self.node = [[0 for x in range(7)] for x in range(7)]\\n self.dic = {}\\n self.count_expanded_nodes = 1\\n self.count_filled_peg = 0\\n self.stack = []\\n self.expanded = []\\n self.heap = []\\n self.relation_tuple = []\\n self.config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a06d16b683a3d2ccad850733964bb6a\",\n \"score\": \"0.47087893\",\n \"text\": \"def new(string):\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f69336245a6a3606e3150bba2f591f0\",\n \"score\": \"0.47087494\",\n \"text\": \"def parse(requested_trees, logger):\\n # First, strip off any hanging quotes at beginning/end of the strategy\\n if requested_trees.startswith(\\\"\\\\\\\"\\\"):\\n requested_trees = requested_trees[1:]\\n if requested_trees.endswith(\\\"\\\\\\\"\\\"):\\n requested_trees = requested_trees[:-1]\\n\\n # Define a blank strategy to initialize with the user specified string\\n strat = actions.strategy.Strategy([], [])\\n\\n # Actions for the in and out forest are separated by a \\\"\\\\/\\\".\\n # Split the given string by this token\\n out_in_actions = requested_trees.split(\\\"\\\\\\\\/\\\")\\n\\n # Specify that we're starting with the out forest before we parse the in forest\\n out = True\\n direction = \\\"out\\\"\\n # For each string representation of the action directions, in or out\\n for str_actions in out_in_actions:\\n # Individual action trees always end in \\\"|\\\" to signify the end - split the\\n # entire action sequence into individual trees\\n str_actions = str_actions.split(\\\"|\\\")\\n\\n # For each string representation of each tree in the forest\\n for str_action in str_actions:\\n # If it's an empty action, skip it\\n if not str_action.strip():\\n continue\\n\\n assert \\\" \\\" not in str_action.strip(), \\\"Strategy includes a space - malformed!\\\"\\n\\n # Get rid of hanging whitespace from the splitting\\n str_action = str_action.strip()\\n\\n # ActionTree uses the last \\\"|\\\" as a sanity check for well-formed\\n # strategies, so restore the \\\"|\\\" that was lost from the split\\n str_action = str_action + \\\"|\\\"\\n new_tree = actions.tree.ActionTree(direction)\\n success = new_tree.parse(str_action, logger)\\n if success is False:\\n raise actions.tree.ActionTreeParseError(\\\"Failed to parse tree\\\")\\n\\n # Once all the actions are parsed, add this tree to the\\n # current direction of actions\\n if out:\\n strat.out_actions.append(new_tree)\\n else:\\n strat.in_actions.append(new_tree)\\n # Change the flag to tell it to parse the IN direction during the next loop iteration\\n out = False\\n direction = \\\"in\\\"\\n return strat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"714a9c80706a9daed79cee628326c507\",\n \"score\": \"0.47065774\",\n \"text\": \"def get_ast(self, string: str) -> Tree:\\n return self.parser.parse(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de28c64a8c4853b034a51a1abd73542e\",\n \"score\": \"0.46972725\",\n \"text\": \"def __init__(self, policy_str=None):\\n\\n p = None\\n if isinstance(policy_str, str):\\n p = policy2DNF(policy_parser.parseString(policy_str))\\n elif isinstance(policy_str, list):\\n p = policy_str\\n elif isinstance(policy_str, DNF):\\n self.policy = policy_str\\n return\\n elif policy_str is None:\\n self.policy = Policy([[Satisfied()]])\\n return\\n else:\\n raise RuntimeError(\\\"Failed\\\")\\n\\n self.policy = DNF([])\\n for clause in p:\\n self.policy.add(ConjunctClause(clause))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ef1d071cbec7cd50a9b559e76841ec\",\n \"score\": \"0.46346596\",\n \"text\": \"def from_string(cls, graph_data: str):\\n\\n graph = cls()\\n\\n for line in graph_data.splitlines():\\n\\n line = line.strip()\\n\\n if not line:\\n continue\\n\\n splitted_line = re.split(\\\"\\\\\\\\s+\\\", line)\\n\\n if len(splitted_line) != 3:\\n raise ValueError(f\\\"Invalid line found in line: {line}\\\")\\n\\n src = Vertex(splitted_line[0])\\n dest = Vertex(splitted_line[1])\\n weight = int(splitted_line[2])\\n\\n graph.add_edge(src, dest, weight)\\n\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1432d8df9b9afb4285627e37a105e930\",\n \"score\": \"0.46241093\",\n \"text\": \"def parse(steno_string: str) -> ste.Steno:\\n return TYPE.parseString(steno_string, parseAll=True)[0] # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261d1a5517f965c0257a3d5dc55b1a61\",\n \"score\": \"0.45987538\",\n \"text\": \"def epsilon_machine(weight: Optional[float] = None) -> VectorFst:\\n if weight is None:\\n weight = weight_one()\\n fst = VectorFst()\\n state = fst.add_state()\\n fst.set_start(state)\\n fst.set_final(state, weight)\\n\\n return fst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47ae0539409f1b92f725ddcfd4cf72d2\",\n \"score\": \"0.45962554\",\n \"text\": \"def create_from_arg_string(cls, arg_string):\\n return cls()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f21efdafed4f491baf88c6cc69d3049e\",\n \"score\": \"0.45924416\",\n \"text\": \"def regex_to_nfa(regex: str) -> RegexFaConstruction:\\n re_lst = regex_to_tokens(regex)\\n nfa_stack = []\\n op_stack = []\\n node_index = 0\\n for token in re_lst:\\n # current token is operand\\n if token.get('type') == 1:\\n f = RegexFaConstruction()\\n if token.get('kind') == Kind.NORMAL:\\n f.add_normal_node(f.input_port, f.output_port, pattern_char=token.get('value'))\\n else:\\n if token.get('kind') == Kind.SET:\\n f.add_charset_node(f.input_port, f.output_port, charset=token.get(Kind.SET), negative=False)\\n elif token.get('kind') == Kind.NEG_SET:\\n f.add_charset_node(f.input_port, f.output_port, charset=token.get(Kind.SET), negative=True)\\n elif token.get('kind') == Kind.TRANS:\\n if token.get('value') == 'w':\\n f.add_da_node(f.input_port, f.output_port)\\n elif token.get('value') == 's':\\n f.add_empty_char_node(f.input_port, f.output_port)\\n else:\\n f.add_digit_node(f.input_port, f.output_port)\\n elif token.get('kind') == Kind.DOT:\\n f.add_any_node(f.input_port, f.output_port)\\n nfa_stack.append(f)\\n node_index += 1\\n # current token is operator\\n else:\\n if is_left_bracket(token):\\n op_stack.append(token)\\n elif is_repeat(token):\\n f = nfa_stack.pop()\\n if token.get('kind') == Kind.NORMAL:\\n if token.get('value') == '*':\\n f, inc = nodes_repeat_ge_zero(f, node_index)\\n node_index += inc\\n elif token.get('value') == '+':\\n f, inc = nodes_repeat_ge_one(f, node_index)\\n node_index += inc\\n nfa_stack.append(f)\\n else:\\n r = token.get(Kind.RANGE)\\n if r[0] == r[1]:\\n new_f = nodes_repeat_eq(f, r[0])\\n else:\\n new_f, inc = nodes_repeat_range(f, node_index, r[0], r[1])\\n nfa_stack.append(new_f)\\n elif is_prefix(token) or is_postfix(token):\\n while len(op_stack) > 0 and not is_left_bracket(op_stack[-1]):\\n op = op_stack.pop()\\n if is_concat(op):\\n f1 = nfa_stack.pop()\\n f2 = nfa_stack.pop()\\n new_f = concat_nfa(f2, f1, 'con' + str(node_index))\\n nfa_stack.append(new_f)\\n node_index += 1\\n elif is_postfix(op):\\n f = nfa_stack.pop()\\n f, inc = nodes_postfix(f, node_index)\\n node_index += inc\\n nfa_stack.append(f)\\n elif is_prefix(op):\\n f = nfa_stack.pop()\\n f, inc = nodes_prefix(f, node_index)\\n node_index += inc\\n nfa_stack.append(f)\\n op_stack.append(token)\\n elif is_concat(token):\\n while len(op_stack) > 0 and is_concat(op_stack[-1]):\\n op_stack.pop()\\n f1 = nfa_stack.pop()\\n f2 = nfa_stack.pop()\\n new_f = concat_nfa(f2, f1, 'con' + str(node_index))\\n nfa_stack.append(new_f)\\n node_index += 1\\n op_stack.append(token)\\n elif is_right_bracket(token):\\n while len(op_stack) > 0 and not is_left_bracket(op_stack[-1]):\\n op = op_stack.pop()\\n if is_concat(op):\\n f1 = nfa_stack.pop()\\n f2 = nfa_stack.pop()\\n new_f = concat_nfa(f2, f1, 'con' + str(node_index))\\n nfa_stack.append(new_f)\\n node_index += 1\\n elif is_postfix(op):\\n f = nfa_stack.pop()\\n f, inc = nodes_postfix(f, node_index)\\n node_index += inc\\n nfa_stack.append(f)\\n elif is_prefix(op):\\n f = nfa_stack.pop()\\n f, inc = nodes_prefix(f, node_index)\\n node_index += inc\\n nfa_stack.append(f)\\n op_stack.pop()\\n while len(op_stack):\\n op = op_stack.pop()\\n if is_concat(op):\\n f1 = nfa_stack.pop()\\n f2 = nfa_stack.pop()\\n new_f = concat_nfa(f2, f1, 'con' + str(node_index))\\n nfa_stack.append(new_f)\\n node_index += 1\\n elif is_postfix(op):\\n f = nfa_stack.pop()\\n f, inc = nodes_postfix(f, node_index)\\n node_index += inc\\n nfa_stack.append(f)\\n elif is_prefix(op):\\n f = nfa_stack.pop()\\n f, inc = nodes_prefix(f, node_index)\\n node_index += inc\\n nfa_stack.append(f)\\n nfa = nfa_stack.pop()\\n return nfa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30298d502faeda7a1f948804632200f9\",\n \"score\": \"0.45574868\",\n \"text\": \"def __init__(self, string=\\\"0\\\"):\\n self.tree = 0\\n self.set_string(string)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5baa5ad7a69b75502b4c4072c3f716e5\",\n \"score\": \"0.45546323\",\n \"text\": \"def to_syntax_tree(self, instring, schema):\\n return self.builder.build(instring, schema)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"114fde9702a74cbacc04e126524563b2\",\n \"score\": \"0.45458665\",\n \"text\": \"def read_dag():\\n dag = DAG()\\n\\n num_edges = int(input())\\n for _ in range(num_edges):\\n source, target = input().split(' ')\\n dag.add_edge(source, target)\\n\\n return dag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a4f19be2f050fe5533145a181bfbdd4\",\n \"score\": \"0.45423505\",\n \"text\": \"def get_automaton_1():\\n stateA = State(\\\"State A\\\", True)\\n stateB = State(\\\"State B\\\")\\n stateA.add_transition(one, stateA)\\n stateA.add_transition(zero, stateB)\\n stateB.add_transition(one, stateB)\\n stateB.add_transition(zero, stateB)\\n\\n comparator = DFAComparator()\\n return DeterministicFiniteAutomaton(binaryAlphabet, stateA,\\n set([stateA, stateB]), comparator, \\\"Tomita's grammar 1 automaton\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca29a390c2243d2ebe998bc9f9d76caa\",\n \"score\": \"0.45374167\",\n \"text\": \"def fromstring(cls, str_addr):\\n tup = str_addr.partition(':')\\n return cls((tup[0], tup[2]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b29c62907cec9ed746f105c69a67219\",\n \"score\": \"0.45075926\",\n \"text\": \"def make_constraints_from_string(owner, target, subtarget, fstring):\\n\\n separator = '#'\\n cns_blocks = fstring.split(separator)\\n\\n transform_type = ['CL', 'CR', 'CS', 'CT']\\n limit_type = ['LL', 'LR', 'LS']\\n track_type = ['DT', 'TT', 'ST']\\n relationship_type = ['PA']\\n\\n for cns in cns_blocks:\\n\\n if cns[0:2] in transform_type:\\n make_transform_constraint_from_string(owner, target, subtarget, cns)\\n\\n if cns[0:2] in limit_type:\\n make_limit_constraint_from_string(owner, cns)\\n\\n if cns[0:2] in track_type:\\n make_track_constraint_from_string(owner, target, subtarget, cns)\\n\\n if cns[0:2] in relationship_type:\\n make_relation_constraint_from_string(owner, target, subtarget, cns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b818a9db7c7751de4bde505bbd597b7a\",\n \"score\": \"0.4486907\",\n \"text\": \"def from_string(s):\\n s = s.lower()\\n\\n if s == \\\"input\\\":\\n return PortType.INPUT\\n elif s == \\\"output\\\":\\n return PortType.OUTPUT\\n elif s == \\\"clock\\\":\\n return PortType.CLOCK\\n else:\\n assert False, s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c27ba7acfd21050b1d25eb577b65c95\",\n \"score\": \"0.44642383\",\n \"text\": \"def build_dfa(self, string, given_starting_node=None):\\n\\t\\tif given_starting_node is None:\\n\\t\\t\\tstarting_node = Node(given_garbage_node=self.garbage_node)\\n\\t\\telse:\\n\\t\\t\\tstarting_node = given_starting_node\\n\\t\\tstarting_nodes = [starting_node]\\n\\t\\tfinishing_nodes = []\\n\\t\\tcurrent_node = starting_node\\n\\t\\tprevious_nodes = []\\n\\t\\ti = 0\\n\\t\\twhile i < len(string):\\n\\t\\t\\tcharacter = string[i]\\n\\t\\t\\tif character == '|':\\n\\t\\t\\t\\tnew_starting_nodes, new_finishing_nodes = self.build_dfa(string[i+1:], \\n\\t\\t\\t\\t\\tgiven_starting_node=given_starting_node)\\n\\t\\t\\t\\tstarting_nodes += new_starting_nodes\\n\\t\\t\\t\\tfinishing_nodes += new_finishing_nodes\\n\\t\\t\\t\\tbreak\\n\\t\\t\\telif character == '*':\\n\\t\\t\\t\\tcurrent_node.add_to_jumpdict(string[i-1], current_node)\\n\\t\\t\\t\\ti += 1 \\n\\t\\t\\t\\tif i == len(string):\\n\\t\\t\\t\\t\\tfinishing_nodes.append(current_node)\\n\\t\\t\\t\\t\\tfinishing_nodes += previous_nodes\\n\\t\\t\\t\\t\\treturn starting_nodes, finishing_nodes\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tnew_node = Node(given_garbage_node=self.garbage_node)\\n\\t\\t\\t\\t\\tcurrent_node.add_to_jumpdict(string[i], new_node)\\n\\t\\t\\t\\t\\tfor previous_node in previous_nodes:\\n\\t\\t\\t\\t\\t\\tprevious_node.add_to_jumpdict(string[i], new_node)\\n\\t\\t\\t\\t\\tprevious_nodes = [current_node]\\n\\t\\t\\t\\t\\tcurrent_node = new_node\\n\\t\\t\\t\\t\\ti += 1\\n\\t\\t\\telif character == ')':\\n\\t\\t\\t\\traise ValueError(\\\"Non matching parentheses\\\")\\n\\t\\t\\telif character == '(':\\n\\t\\t\\t\\tsubstring, position = self.find_matching_paren(string[i:])\\n\\t\\t\\t\\ti += position\\n\\t\\t\\t\\tsubstart, subend = self.build_dfa(substring, given_starting_node=current_node)\\n\\t\\t\\t\\tif i >= len(string):\\n\\t\\t\\t\\t\\tfinishing_nodes += subend\\n\\t\\t\\t\\t\\treturn starting_nodes, finishing_nodes\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tnew_char = string[i]\\n\\t\\t\\t\\t\\tauto_node = Node(given_garbage_node=self.garbage_node)\\n\\t\\t\\t\\t\\tnew_node = Node(given_garbage_node=self.garbage_node)\\n\\t\\t\\t\\t\\tfor subendnode in subend:\\n\\t\\t\\t\\t\\t\\tsubendnode.set_auto_move(auto_node)\\n\\t\\t\\t\\t\\tauto_node.add_to_jumpdict(new_char, new_node)\\n\\t\\t\\t\\t\\tif new_char == '*':\\n\\t\\t\\t\\t\\t\\tfor key in current_node.jumpdict:\\n\\t\\t\\t\\t\\t\\t\\tfor jump_node in current_node.jumpdict[key]:\\n\\t\\t\\t\\t\\t\\t\\t\\tauto_node.add_to_jumpdict(key, jump_node)\\n\\t\\t\\t\\t\\t\\ti += 1\\n\\t\\t\\t\\t\\t\\tif i == len(string):\\n\\t\\t\\t\\t\\t\\t\\tfinishing_nodes.append(auto_node)\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tnew_node = Node(given_garbage_node=self.garbage_node)\\n\\t\\t\\t\\t\\t\\t\\tcurrent_node.add_to_jumpdict(string[i], new_node)\\n\\t\\t\\t\\t\\t\\t\\tauto_node.add_to_jumpdict(string[i], new_node)\\n\\t\\t\\t\\t\\t\\t\\tprevious_nodes = [auto_node, current_node]\\n\\t\\t\\t\\t\\t\\t\\tcurrent_node = new_node\\n\\t\\t\\t\\t\\t\\t\\ti += 1\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tcurrent_node = new_node\\n\\t\\t\\t\\t\\t\\tprevious_nodes = [auto_node]\\n\\t\\t\\t\\t\\t\\ti += 1\\n\\t\\t\\telif character == '\\\\\\\\':\\n\\t\\t\\t\\ti += 1\\n\\t\\t\\t\\tif i == len(string):\\n\\t\\t\\t\\t\\traise ValueError(\\\"Non terminating backlash\\\")\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tnewchar = string[i]\\n\\t\\t\\t\\t\\tnew_node = Node(given_garbage_node=self.garbage_node)\\n\\t\\t\\t\\t\\tcurrent_node.add_to_jumpdict(newchar, new_node)\\n\\t\\t\\t\\t\\tprevious_nodes = [current_node]\\n\\t\\t\\t\\t\\tcurrent_node = new_node\\n\\t\\t\\t\\t\\ti += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tnew_node = Node(given_garbage_node=self.garbage_node)\\n\\t\\t\\t\\tcurrent_node.add_to_jumpdict(character, new_node)\\n\\t\\t\\t\\tprevious_nodes = [current_node]\\n\\t\\t\\t\\tcurrent_node = new_node\\n\\t\\t\\t\\ti += 1\\n\\t\\tfinishing_nodes.append(current_node)\\n\\t\\treturn starting_nodes, finishing_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09bbaa5952b9b3f3d1a53e993b9f1f1c\",\n \"score\": \"0.44318843\",\n \"text\": \"def parse_node(s):\\n #print(s)\\n s = s.strip()\\n parts = s.split(')')\\n if len(parts) == 1:\\n descendants, label = [], s\\n else:\\n if not parts[0].startswith('('):\\n raise ValueError('unmatched braces %s' % parts[0][:100])\\n descendants = list(_parse_siblings(')'.join(parts[:-1])[1:]))\\n label = parts[-1]\\n length, name = _parse_name_and_length(label)\\n new_tree = TreeNode(name=name, label=length)\\n new_tree.children = descendants\\n print(\\\"new_tree\\\", new_tree.label)\\n return new_tree\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd92e4fb6df07c4670563f55812658e4\",\n \"score\": \"0.44281778\",\n \"text\": \"def _create_node(env, s, g, prev_node, a, cost_fn):\\n h = env.estim_cost(s)\\n f = cost_fn(g, h)\\n return Node(f, h, g, s, prev_node, a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2ae9a9d74e6af29db75a5bd55d0420f\",\n \"score\": \"0.4421834\",\n \"text\": \"def to_cnf(s):\\n s = expr(s)\\n if isinstance(s, str):\\n s = expr(s)\\n s = eliminate_implications(s) # Steps 1, 2 from p. 253\\n s = move_not_inwards(s) # Step 3\\n return distribute_and_over_or(s) # Step 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea817ba27d2c345670efa65bbd300989\",\n \"score\": \"0.44179893\",\n \"text\": \"def _make_bnode(string):\\n bnode_pattern = ur'_:([A-Za-z][A-Za-z0-9]*)'\\n match = re.compile(bnode_pattern).match(string)\\n if not match:\\n return False\\n # else if match occurs,\\n string = string[2:] # remove _: from the string\\n return rb.term.BNode(string.decode(\\\"unicode-escape\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8229332fc72ae6ca1942bce3450cf30b\",\n \"score\": \"0.44150883\",\n \"text\": \"def from_string(st, var = 'x', r_dict = False):\\n terms = st.replace(\\\" \\\",\\\"\\\").split(\\\"+\\\")\\n i = 0\\n while i < len(terms):\\n if '-' in terms[i]:\\n m = terms[i].split('-')\\n if m[0] == '':\\n m = m[1:]\\n m[0] = '-' + m[0]\\n j = 0 \\n while j < (len(m)):\\n if m[j][len(m[j]) - 1] == '^':\\n m = m[:j] + [m[j] + '-' + m[j + 1]] + m[j + 2:]\\n j = j + 1 \\n \\n terms = terms[:i] + [m[0]] + ['-' + j for j in m[1:]] + terms[i + 1:]\\n i = i + 1 \\n \\n cofs = []\\n exps = []\\n for i in terms:\\n ind = i.find(var)\\n\\n cof = 0\\n if ind < 0:\\n cof = float(i)\\n elif ind == 0:\\n cof = 1.0\\n elif i[:ind] == '-':\\n cof = -1.0\\n else:\\n cof = float(i[:ind])\\n\\n exp = 0\\n if ind < 0:\\n exp = 0\\n elif len(i[ind + len(var) + 1:]) == 0:\\n exp = 1.0\\n else:\\n exp = float(i[ind + len(var) + 1:])\\n\\n if exp in exps:\\n cofs[exps.index(exp)] += cof\\n else:\\n exps += [exp]\\n cofs += [cof]\\n r = {exps[i]: cofs[i] for i in range(len(exps))}\\n if(r_dict):\\n return r\\n else:\\n return Polynomial(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ff4861042564e3028890adb7822457b\",\n \"score\": \"0.43837804\",\n \"text\": \"def sof(value, maxSize, minSize=0, name=None, weight=1.0):\\n content = StringContent(value)\\n return OpenField(content, maxSize, minSize, name, weight)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3810ad56d4a2370129818deef3da616a\",\n \"score\": \"0.43740287\",\n \"text\": \"def to_nfa(self, accepting_id=1):\\n\\n\\t\\t(initial, accepting) = self._thompson()\\n\\t\\taccepting.accepting = accepting_id\\n\\t\\treturn NFA(initial)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6889d7467c9ec52bf0e44c40466776c3\",\n \"score\": \"0.43695772\",\n \"text\": \"def valueOf(string: str) -> 'SegmentType':\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bfef0b508a97a2f2fe87fc3f8848925\",\n \"score\": \"0.4363003\",\n \"text\": \"def parse(s, fg=False):\\n global full_graph\\n full_graph = fg\\n return parser.parse(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"094a42a07e70be7266179605983436aa\",\n \"score\": \"0.43585694\",\n \"text\": \"def match(infix, string):\\n # call the shunt funciton on the infix string that is passed in\\n postfix = shunt(infix)\\n # call the compile function on the postfix string that was created above to create and NFA for each symbol in the postfix string\\n nfa = compile(postfix)\\n # a new set that only allows one copy of a state into it (as sets do)\\n currentSet = set()\\n # a new set to hold the next states\\n nextSet = set()\\n # call the followEdges method and set the currentSet of states equal to the states that the new NFAs intial state can reach\\n currentSet |= followEdges(nfa.initial)\\n # loop through each character of the string\\n for s in string:\\n # loop through the current set of states\\n for c in currentSet:\\n # check if the state has the same label as the character in the string \\n if c.label == s:\\n # add the edge1 state to the next set including all the states that are reachable by the edge arrows\\n # nextSet = nextSet + followEdges(c.edge1)\\n nextSet |= followEdges(c.edge1)\\n # The currentSet of states is replaced by nextSet\\n currentSet = nextSet\\n # the nextSet is emptied\\n nextSet = set()\\n # check if the accept state is in the currentSet\\n return (nfa.final in currentSet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a493b3d0a1a566ebc6e23aa8a3f52dc\",\n \"score\": \"0.43564594\",\n \"text\": \"def str_to_sym_or_float(string):\\n\\n try:\\n return float(string)\\n # The string is not a float value\\n except ValueError:\\n return Symbol(string, real=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d06cde11fd9f85fbf3156c6c27e8803a\",\n \"score\": \"0.4356002\",\n \"text\": \"def protocol_factory(connector=connector, node=instance._node):\\n return lambda: NetstringProtocol(connector, node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dba5ec7765388faa8f3b96142dc6e22\",\n \"score\": \"0.4348451\",\n \"text\": \"def get_automaton_5():\\n stateQ1 = State(\\\"State 0\\\", True)\\n stateQ2 = State(\\\"State 1\\\")\\n stateQ3 = State(\\\"State 2\\\")\\n stateQ4 = State(\\\"State 3\\\")\\n stateQ1.add_transition(one, stateQ2)\\n stateQ1.add_transition(zero, stateQ3)\\n stateQ2.add_transition(one, stateQ1)\\n stateQ2.add_transition(zero, stateQ4)\\n stateQ3.add_transition(one, stateQ4)\\n stateQ3.add_transition(zero, stateQ1)\\n stateQ4.add_transition(one, stateQ3)\\n stateQ4.add_transition(zero, stateQ2)\\n\\n comparator = DFAComparator()\\n return DeterministicFiniteAutomaton(binaryAlphabet, stateQ1,\\n set([stateQ1, stateQ2, stateQ3, stateQ4]), comparator, \\\"Tomita's grammar 5 automaton\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fa8219aa05424dafc277f6e40374028\",\n \"score\": \"0.43415213\",\n \"text\": \"def from_stdin():\\n edges, adj_matrix = Graph._parse_edges()\\n properties = Graph._parse_properties(adj_matrix)\\n\\n # update weights using node properties\\n for node_1, node_2 in edges:\\n max_similarity = len(properties[node_1])\\n similarity = np.sum(properties[node_1] == properties[node_2])\\n edges[node_1, node_2] = max_similarity - similarity + 1\\n\\n return Graph(edges, adj_matrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86d3725b1eb3b072353961221218a7ea\",\n \"score\": \"0.4340687\",\n \"text\": \"def from_str(string:str) -> \\\"Version\\\":\\n if string is None:\\n return Version(0,0,0)\\n else:\\n numbers = string.split(\\\".\\\")\\n return Version(\\n int(numbers[0]),\\n int(numbers[1]),\\n int(numbers[2])\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e008b2cde8cb1e20358fe17ba0a5ea0d\",\n \"score\": \"0.43392023\",\n \"text\": \"def treeFromString(treeString,nodep): #Transform string into a bifurcating Tree structure without state at internal nodes\\n\\ttreeString=treeString.replace(\\\";\\\",\\\":0.0\\\")\\n\\tif treeString[0]!=\\\"(\\\":\\n\\t\\t#Leaf case\\n\\t\\tnode=getLeaf(treeString)\\n\\t\\tbranch=getBranch(treeString)\\n\\t\\tnode.branchp=branch\\n\\t\\tnode.parent=nodep\\n\\t\\tfor sam in node.samples:\\n\\t\\t\\tbreak\\n\\t\\tnode.subtree=sam+\\\":\\\"+str(branch)\\n\\t\\treturn node\\t\\n\\telse:\\n\\t\\t#internal node case\\n\\t\\tindexEnd=len(treeString)-1\\n\\t\\twhile treeString[indexEnd]!=\\\")\\\":\\n\\t\\t\\tindexEnd-=1\\n\\t\\t\\tif indexEnd<0:\\n\\t\\t\\t\\tprint \\\"parenthesis error while parsing tree\\\"\\n\\t\\t\\t\\treturn None\\n\\t\\tbranch=getBranch(treeString[indexEnd+1:])\\n\\t\\tnode=Node()\\n\\t\\tnode.parent=nodep\\n\\t\\tnode.branchp=branch\\n\\t\\tif treeString[indexEnd+1]==\\\"[\\\":\\n\\t\\t\\t#node.disState=[getState(treeString[indexEnd+1:])]\\n\\t\\t\\tss=getState(treeString[indexEnd+1:])\\n\\t\\t\\tif IsInt(ss):\\n\\t\\t\\t\\tnode.disState=[int(ss)]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tnode.disState=[ss]\\n\\t\\tindex=0\\n\\t\\tcountPar=0\\n\\t\\twhile treeString[index]!=\\\",\\\" or countPar!=0:\\n\\t\\t\\tindex+=1\\n\\t\\t\\tif treeString[index]==\\\"(\\\" or treeString[index]==\\\"[\\\":\\n\\t\\t\\t\\tcountPar+=1\\n\\t\\t\\telif treeString[index]==\\\")\\\" or treeString[index]==\\\"]\\\":\\n\\t\\t\\t\\tcountPar-=1\\n\\t\\t#Call iteratively function on subtrees\\t\\n\\t\\t#print \\\"subtree1\\\", treeString[1:index]\\n\\t\\tsubtree1=treeFromString(treeString[1:index],node)\\n\\t\\tnode.child1=subtree1\\n\\t\\tnode.branch1=subtree1.branchp\\n\\t\\t#print \\\"subtree2\\\", treeString[index+1:indexEnd]\\n\\t\\tsubtree2=treeFromString(treeString[index+1:indexEnd],node)\\n\\t\\tnode.child2=subtree2\\n\\t\\tnode.branch2=subtree2.branchp\\n\\t\\tnode.samples =(node.child1.samples | node.child2.samples)\\n\\t\\t#print \\\"New node\\\",node.samples\\n\\t\\tnode.subtree= \\\"(\\\" + subtree1.subtree + \\\",\\\" + subtree2.subtree + \\\"):\\\" + str(branch)\\n\\t\\tnode.coalTime=node.child1.coalTime+node.branch1\\n\\t\\treturn node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a73200bf857a8aba1947d14893b1d51f\",\n \"score\": \"0.43390766\",\n \"text\": \"def test_add_fused_gate(backend):\\n c = Circuit(2)\\n c.add(gates.H(0))\\n c.add(gates.H(1))\\n c.add(gates.CNOT(0, 1))\\n fused_c = c.fuse()\\n fgate = fused_c.queue[0]\\n assert isinstance(fgate, gates.FusedGate)\\n\\n new_c = Circuit(2)\\n new_c.add(fgate)\\n assert c.depth == 2\\n assert c.ngates == 3\\n assert new_c.depth == 1\\n assert new_c.ngates == 1\\n backend.assert_circuitclose(fused_c, c)\\n backend.assert_circuitclose(new_c, c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0e1458790f1c04fb24388a2d799904\",\n \"score\": \"0.43364912\",\n \"text\": \"def parse(s:str,nonterms:List[Nonterminal], nt_of_name=None, names=None) -> Expr:\\n if names is None:\\n names = [nonterm.name_noargc for nonterm in nonterms]\\n if nt_of_name is None:\\n nt_of_name = {(nt.name_noargc,nt.argc):nt for nt in nonterms}\\n\\n # fn calls become tuples: (fn_name,[args])\\n # example: foo(bar(x,y),z) -> ('foo',[('bar',['x','y']),'z'])\\n\\n # strip whitespace, remove spaces, do some basic checks\\n s = s.strip()\\n if '\\\\n' in s:\\n raise ParseError(\\\"why is there a newline\\\")\\n if s.count('(') != s.count(')'):\\n raise ParseError(\\\"unequal number of opening and closing parens\\\")\\n if len(s) == 0:\\n raise ParseError(\\\"empty string\\\")\\n\\n # figure out what funciton / const / var is being used next\\n match = re.match(r'(\\\\w|-)+',s)\\n if match is None:\\n raise ParseError(\\\"Expected string to start with an identifier:\\\",s)\\n name = s[:match.end()]\\n s = s[match.end():]\\n if name not in names:\\n raise ParseError(\\\"unrecognized nonterminal:\\\",name)\\n \\n # handle var and const\\n if len(s) == 0:\\n # since this was the last token of the string\\n # its a const or var so we wanna call the Nonterminal\\n # with zero arguments\\n if (name,0) not in nt_of_name:\\n argcs = [argc for (fname,argc) in nt_of_name if fname == name]\\n raise ParseError(f\\\"found {name}() but can't call it with 0 args. Allowed argcs: {argcs}\\\")\\n expr = Expr(nt_of_name[(name,0)],[])\\n if expr.exception:\\n raise ParseError(f\\\"exception during execution of subtree {expr}: {expr.val}\\\")\\n return expr\\n \\n # handle function call\\n if s[0] != '(':\\n raise ParseError(\\\"expected open paren, got:\\\",s)\\n if s[-1] != ')':\\n raise ParseError(\\\"expected this to end with close paren:\\\",s)\\n\\n s = s[1:-1]\\n # split by commas, except when already inside parens\\n args = []\\n while True:\\n s = s.strip()\\n if s == '':\\n break # only happens if zero args\\n i = next_comma(s) # find net comma, but aware of paren depth\\n if i == -1: # no more commas, so we should parse the rest into an Expr\\n args.append(parse(s,nonterms,nt_of_name,names))\\n break\\n else: # parse next arg into an Expr\\n args.append(parse(s[:i],nonterms,nt_of_name,names))\\n s = s[i+1:] # skip the ',' and continue\\n\\n if (name,len(args)) not in nt_of_name:\\n argcs = [argc for (fname,argc) in nt_of_name if fname == name]\\n raise ParseError(f'{name} applied to wrong number of args, got {len(args)} but expected one of these: {argcs}')\\n\\n expr = Expr(nt_of_name[(name,len(args))],args)\\n if expr.exception:\\n raise ParseError(f\\\"exception during execution of subtree {expr}: {expr.val}\\\")\\n return expr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0b7a907bc815ba86f8a4d8b3bbbbb2e\",\n \"score\": \"0.43338928\",\n \"text\": \"def parse(string):\\n words = string.split()\\n count = len(words)\\n if count == 0:\\n raise EmptyOptionString()\\n elif count == 1:\\n return Signature(words[0], [])\\n else:\\n head = words[0]\\n tail = words[1:]\\n body = []\\n for index, castkeydefault in enumerate(tail):\\n cast, key, default = separate(castkeydefault)\\n body.append(Argument(index, key, default, cast))\\n return Signature(head, body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"192e267bbbe058946a157877ad1812d6\",\n \"score\": \"0.43327823\",\n \"text\": \"def valueOf(string: str) -> 'SegmentAttribute':\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b0ae4d40d5c026dda37cdbd2895439\",\n \"score\": \"0.43294743\",\n \"text\": \"def match(infix, string):\\n\\n # Shunt and compile the regular expression\\n postfix = shunt(infix)\\n nfa = compile(postfix)\\n\\n # The current set of states and the next set of states\\n current = set()\\n next = set()\\n\\n # add the initial state to the current state\\n current |= follows(nfa.initial)\\n\\n # Loop through each character in the string\\n for s in string:\\n # loop through the current set of states\\n for c in current:\\n # check if that state is labelled s\\n if c.label == s:\\n # add the edge1 state to the next set\\n next |= follows(c.edge1)\\n # set current to next, and clear out next\\n current = next\\n next = set()\\n\\n # check if the accept state is in the set of current states\\n return nfa.accept in current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f908e484d6802ac480b0c7a5b40c9e99\",\n \"score\": \"0.43282476\",\n \"text\": \"def makeHuffmanCodes(string):\\n frequencies = countUnique(string)\\n Nodes = [Node(char, freq) for (char, freq) in frequencies]\\n assert(len(Nodes) >= 2)\\n while len(Nodes) > 2:\\n New = Node(None, 0)\\n #Merge the two rarest nodes in New\\n New.addChildren(Nodes.pop(0), Nodes.pop(0))\\n #Insert the new node into the array, keep it sorted\\n pos = bisect([X.frequency for X in Nodes], New.frequency)\\n Nodes.insert(pos, New) #there might be a more concise way\\n\\n #last two nodes in array are the direct descendants of the tree\\n Huffman_Tree = Node(None, 0)\\n Huffman_Tree.addChildren(Nodes.pop(0), Nodes.pop(0))\\n assert (len(Nodes) == 0) #Nodes must be empty now\\n #put a code (0/1) in every node except the root\\n Huffman_Tree.assignCodes()\\n D = dict()\\n Huffman_Tree.makeDictionary(D)\\n return D\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65b205aa156cd627e17a932ec655e30d\",\n \"score\": \"0.431863\",\n \"text\": \"def from_string(s: str) -> \\\"Token\\\":\\n # DO NOT MODIFY THIS METHOD\\n return Token(*s.rsplit(\\\"/\\\", 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65ea5cd200ac8e69bcea97f1bb01444d\",\n \"score\": \"0.43019292\",\n \"text\": \"def from_str(cls, input_str):\\n\\n str_c = cls()\\n str_c.offset = 0\\n str_c.value_list = [x for x in input_str]\\n str_c.length = len(str_c.value_list)\\n\\n return str_c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89041b2aab85c511ca7567c276641a6a\",\n \"score\": \"0.4299525\",\n \"text\": \"def string_through_NFA(string, automaton):\\n current_state = [0]\\n transitions = []\\n next_state = []\\n\\n for letter in string:\\n for state in current_state:\\n transitions.append(automaton[0][state]) # contains the possible transitions for the current state\\n\\n for delta in transitions:\\n for i, v in enumerate(delta):\\n if v[0] == letter:\\n next_state.append(v[1])\\n # if no state was appended to next_state, the transition for this letter was not found. Nicolas Cage died.\\n\\n current_state = next_state\\n next_state = []\\n transitions = []\\n\\n for accepted_state in automaton[1]:\\n for final_state in current_state:\\n if accepted_state == final_state:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"843013dfb51b1fede164ff7d3446a337\",\n \"score\": \"0.42986515\",\n \"text\": \"def test_two_state_accept_odds():\\n transitions = [\\n \\\"s1 a ^s2\\\",\\n \\\"s2 a ^s1\\\"\\n ]\\n dfa = DFAstate(transitions, 's1', accepting_states=['s2'])\\n\\n input_string = \\\"aaaaaa\\\"\\n current_state = 's1'\\n accepting = False\\n for letter in input_string:\\n current_state = 's1' if current_state == 's2' else 's2'\\n accepting = False if accepting else True\\n assert current_state == dfa.event(letter).get_active_states()[0]\\n assert accepting == dfa.in_accepting()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29e1e857742f347ab00a8aa83a94fca4\",\n \"score\": \"0.42942676\",\n \"text\": \"def from_str(cls, elementstr):\\n ival, params = cls.parse(elementstr)\\n return cls(ival, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8935ad189068f057f7390c4412189c2\",\n \"score\": \"0.42929357\",\n \"text\": \"async def create_rule_for_string(cls, signal):\\n rule = input(constants.string_prompt)\\n value = input(constants.value_prompt)\\n\\n # Insert the rule\\n payload = {\\n 'signal': signal,\\n 'rule': rule,\\n 'value': str(value),\\n 'value_type': 'string'\\n }\\n await DbHelper.insert(constants.rules_table, payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e5393b030c0420f4c3ff1fe126a5909\",\n \"score\": \"0.4282777\",\n \"text\": \"def fromString(self, *args):\\n return _yarp.Bottle_fromString(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6009bb2e0f4b931203c7f9b890bf2daf\",\n \"score\": \"0.42790574\",\n \"text\": \"def _from_string(cls, string):\\r\\n m = cls.name_email_regex.search(string)\\r\\n if m:\\r\\n name, email = m.groups()\\r\\n return Actor(name, email)\\r\\n else:\\r\\n m = cls.name_only_regex.search(string)\\r\\n if m:\\r\\n return Actor(m.group(1), None)\\r\\n else:\\r\\n # assume best and use the whole string as name\\r\\n return Actor(string, None)\\r\\n # END special case name\\r\\n # END handle name/email matching\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb732683d5cc8c76030fc2a546befd0e\",\n \"score\": \"0.4276147\",\n \"text\": \"def create_stem(name, modality='vgg16', weights='imagenet'):\\n\\n # create the based network based on the modality\\n if modality == 'vgg16':\\n stem = tf.keras.applications.VGG16(include_top=False, weights=weights, classes=2, classifier_activation='sigmoid')\\n elif modality == 'resnet50':\\n stem = tf.keras.applications.ResNet50(include_top=False, weights=weights, classes=2)\\n else:\\n inception_builder = Inceptionv1()\\n stem = inception_builder.build_inception()\\n\\n # modified the name to avoid name confliction\\n stem._name = stem.name + '_' + name\\n\\n return stem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66af0b2f855957b771d0cb21a1218c35\",\n \"score\": \"0.42751518\",\n \"text\": \"def _from_string(cls, string: str) -> \\\"Actor\\\":\\n m = cls.name_email_regex.search(string)\\n if m:\\n name, email = m.groups()\\n return Actor(name, email)\\n else:\\n m = cls.name_only_regex.search(string)\\n if m:\\n return Actor(m.group(1), None)\\n # assume best and use the whole string as name\\n return Actor(string, None)\\n # END special case name\\n # END handle name/email matching\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b00295e6a99f060ed292f5047d44dd4\",\n \"score\": \"0.42721355\",\n \"text\": \"def parse(string):\\n\\tif string == 'quit':\\n\\t\\tquit()\\n\\t\\n\\tstring = number_touch(string)\\n\\tstring = parens(string)\\n\\ttree = split_ops(string, ['+', '-'])\\n\\tif tree[0].count('*') or tree[0].count('/'):\\n\\t\\ttree[0] = split_ops(tree[0], ['*', '/'])\\n\\t\\ttree[0] = make_tree(tree[0], ['*', '/'])\\n\\n\\ttree = make_tree(tree, ['+', '-'])\\n\\treturn calc(tree)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b2225caa20a979b56ec7890edbe928\",\n \"score\": \"0.4269554\",\n \"text\": \"def from_string(cls, ns_string):\\n\\n split_param_list = ns_string.split('=')\\n parameters = {}\\n nodes = []\\n if len(split_param_list) != 1: # if there are parameters\\n # split_statement = [name, [node0, node1,...], first_param]\\n split_statement = split_param_list.pop(0).split()\\n if len(split_statement) >= 3: # if there are nodes\\n nodes = split_statement[1:-1]\\n # start parsing parameters\\n param_name = split_statement[-1]\\n for n, split_param in enumerate(split_param_list):\\n param_list_part = split_param.split()\\n if n == len(split_param_list)-1: # if last split_parameter\\n param_value = split_param\\n else:\\n param_value = ' '.join(param_list_part[:-1]) # join except param_name\\n parameters[param_name.strip()] = param_value.strip()\\n param_name = param_list_part[-1]\\n else: # there are no parameters\\n # split_statement = [name, [node0, node1,...]]\\n split_statement = ns_string.split()\\n if len(split_statement) > 1: # if there are nodes\\n nodes = split_statement[1:]\\n name = split_statement[0]\\n return cls(name, nodes, parameters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d51e9a6371f89794ce8fa54b47609a7\",\n \"score\": \"0.42659387\",\n \"text\": \"def build_from_string(self, xml_str: str) -> None:\\n xml = ElementTree(fromstring(xml_str))\\n self._build_model(xml)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"574aa7fc8cb931ac66a052db9a6ac822\",\n \"score\": \"0.4265689\",\n \"text\": \"def fromstring(text, parser=None, base_url=None): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d949c05df3479e250877373e8ac34ab2\",\n \"score\": \"0.42577648\",\n \"text\": \"def add_initial_state(circuit, state_str):\\n\\n words = re.compile(\\\"([!\\\\w]+)\\\").findall(state_str)\\n\\n for item in words:\\n\\n inverted = item[0] == \\\"!\\\"\\n state = 0 if inverted else 1\\n net = item[1:] if inverted else item\\n\\n circuit[\\\"initial_state\\\"][net] = state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2203da584dfa33a68e0d4d6f180c6e9\",\n \"score\": \"0.42541292\",\n \"text\": \"def _from_string(self, s):\\n s = s.upper().replace(\\\"T\\\", \\\"U\\\") # convert to uppercase DNA\\n d = self.SequenceClass(s, alphabet=self.alphabet.sub_enumerations[0])\\n self._data = d.to_codons()._data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70ff3a582287505f953e23b54286af28\",\n \"score\": \"0.42439976\",\n \"text\": \"def from_string(cls, string):\\n tf = cls()\\n sa = tf.structarr\\n lines = [\\n line\\n for line in string.splitlines()\\n if line.strip() and not (line.startswith(\\\"#\\\") or \\\"3dvolreg matrices\\\" in line)\\n ]\\n\\n if not lines:\\n raise TransformFileError\\n\\n parameters = np.vstack(\\n (\\n np.genfromtxt([lines[0].encode()], dtype=\\\"f8\\\").reshape((3, 4)),\\n (0.0, 0.0, 0.0, 1.0),\\n )\\n )\\n sa[\\\"parameters\\\"] = parameters\\n return tf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e287c2f253e659b1198f50e3f10cd8d\",\n \"score\": \"0.42419368\",\n \"text\": \"def Dijkstra(self, node):\\n INF = float('inf')\\n self.validateNodes(INF)\\n #we will use the nodes' tokens to store the distance info!\\n node.set_token(0.0) #0-distance from itself!\\n #initializing the tree\\n T_root = Leaf(node)\\n T_root.setDistance(node.get_token())\\n T = Tree(T_root)\\n #initializing a dictionary to keep trace of the leaves\\n leaves = dict()\\n leaves[node] = T_root\\n #initializing the priority queue to mantain the fringe\\n PQ = PriorityQueue()\\n PQ.insert(T_root, node.get_token())\\n\\n while not PQ.isEmpty():\\n u = PQ.deleteMin() #retrieving the min node from the leaf\\n n = u.getElem()\\n for v in self._adjacency[n.get_index()].getLastAddedList():\\n if v.get_token() == INF:\\n l = Leaf(v)\\n leaves[v] = l #updating the leaves' dictionary\\n PQ.insert(l, n.get_token() + 1.0) #each edge will be unitary-cost\\n v.set_token(n.get_token() + 1.0)\\n T.insertLeaf(l, u)\\n elif n.get_token() + 1.0 < v.get_token():\\n relaxed = n.get_token() + 1.0\\n leaves[v].setDistance(relaxed)\\n #updating the tree... (we are now saving in the priority queue the leaves)\\n leaves[v].setFather(u)\\n leaves[n].addSon(leaves[v])\\n #updating the priority queue\\n PQ.decreaseKey(leaves[v], relaxed)\\n v.set_token(relaxed)\\n return T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0665ef8b2d27eedf9924148a4aeef213\",\n \"score\": \"0.4237808\",\n \"text\": \"def getLeaf(treeString): #Define node associated to tree leaf.\\n\\tindex=0\\n\\twhile treeString[index]!=\\\":\\\":\\n\\t\\tindex+=1\\n\\tnode=Node()\\n\\tif treeString[index-1]!=\\\"]\\\":\\n\\t\\tnode.samples=set([treeString[:index]])\\n\\telse:\\n\\t\\tindex2=0\\n\\t\\twhile treeString[index2]!=\\\"[\\\":\\n\\t\\t\\tindex2+=1\\n\\t\\tnode.samples=set([treeString[:index2]])\\n\\t\\tnode.disState=[treeString[index2+1:index-1]]\\n\\t\\tss=treeString[index2+1:index-1]\\n\\t\\tif IsInt(ss):\\n\\t\\t\\tnode.disState=[int(ss)]\\n\\t\\telse:\\n\\t\\t\\tnode.disState=[ss]\\n\\tnode.coalTime=0.0\\n\\t#print \\\"New leaf\\\", node.samples\\n\\treturn node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"100b8682dd9e8dc7e8309357791ce74d\",\n \"score\": \"0.42355517\",\n \"text\": \"def str2action(s: str) -> Netio.ACTION:\\n try:\\n return Netio.ACTION[s.upper()]\\n except KeyError or AttributeError:\\n try:\\n return Netio.ACTION(int(s))\\n except ValueError:\\n raise argparse.ArgumentTypeError(f\\\"{s!r} is not a valid {Netio.ACTION.__name__}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c297421c5e4c64f2cea1a7c68f2e0245\",\n \"score\": \"0.4231503\",\n \"text\": \"def __init__(self, ab_num: float, input_string: string):\\n super().__init__(input_string)\\n self.raw_str = input_string\\n self.value = None\\n self.children = []\\n self.abbr = ab_num # store the value of the abbreviation of multiplication\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc58e042e14911d21ceb0bb2b7a90d42\",\n \"score\": \"0.4225568\",\n \"text\": \"def match(infix, string):\\n # Shunt and compile the regular expression\\n postfix = shuntingYard(infix)\\n # Builds the NFA\\n nfa = compile(postfix)\\n\\n # The current set of states and the next set of states\\n current = set()\\n nexts = set()\\n\\n # Add the initial state to the current list\\n current |= followes(nfa.initial)\\n\\n # Loop through each character in the string\\n for s in string:\\n # Loop through the current set of states\\n for c in current:\\n # Check if that state is labelled s\\n if c.label == s:\\n # Add the edge1 state to the next set.\\n nexts |= followes(c.edge1)\\n # Set current to next, and clear out next \\n current = nexts\\n nexts = set()\\n # Check if the accept state is in the set of current states\\n return (nfa.accept in current)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f78ef35aed0dd9521329c0e39cfa8e04\",\n \"score\": \"0.42242277\",\n \"text\": \"def parse_food_phrase(tree, eat_lexicon, filters, debug=False):\\n\\n eat_lexicon_lower = set(tok.lower() for tok in eat_lexicon)\\n state = _STATE_SCAN_EAT\\n\\n # Append a state to transition to complete at end of sentence.\\n for stree in its.chain(tree, (('', ''),)):\\n\\n if state == _STATE_SCAN_EAT:\\n\\n if isinstance(stree, tuple) \\\\\\n and stree[0].lower() in eat_lexicon_lower:\\n state = _STATE_EAT_LAST\\n\\n assert state in [_STATE_SCAN_EAT, _STATE_EAT_LAST]\\n\\n elif state == _STATE_EAT_LAST:\\n\\n # We must have a noun phrase after our eat word.\\n if not isinstance(stree, Tree) or stree.label() != 'NP':\\n state = _STATE_SCAN_EAT\\n\\n else:\\n # Extract food from NP after eat word.\\n new_words = [(w.lower(), pos) for w, pos in stree]\\n words = new_words\\n\\n filtered_words = [(w, pos) for w, pos\\n in its.ifilter(_FILTER_POS, new_words)\\n if all(f(w) for f in filters)]\\n\\n if len(filtered_words) > 0:\\n state = _STATE_NP_FOUND\\n else:\\n state = _STATE_SCAN_EAT\\n\\n assert state in [_STATE_SCAN_EAT, _STATE_NP_FOUND]\\n\\n elif state == _STATE_NP_FOUND:\\n\\n newstate = _STATE_NP_COMPLETE\\n if isinstance(stree, tuple):\\n word = stree[0].lower()\\n if word in ['of', 'in', 'on', 'with', 'and']:\\n words.append(stree)\\n filtered_words.append((word, stree[1]))\\n newstate = _STATE_IN_FOUND\\n\\n state = newstate\\n\\n assert state in [_STATE_IN_FOUND, _STATE_NP_COMPLETE]\\n\\n elif state == _STATE_IN_FOUND:\\n\\n if not isinstance(stree, Tree) or stree.label() != 'NP':\\n words.pop()\\n filtered_words.pop()\\n\\n else:\\n new_words = [(w.lower(), pos) for w, pos in stree]\\n words.extend(new_words)\\n\\n filtered_words.extend((w, pos) for w, pos\\n in its.ifilter(_FILTER_POS, new_words)\\n if all(f(w) for f in filters))\\n\\n state = _STATE_NP_COMPLETE\\n\\n elif state == _STATE_NP_COMPLETE:\\n break\\n\\n # At the end of the sentence, check complete.\\n\\n if state == _STATE_NP_COMPLETE:\\n\\n food = ' '.join(w for w, _ in filtered_words)\\n\\n if debug is True and len(filtered_words) != len(words):\\n food_unfiltered = ' '.join(w for w, _ in words)\\n print \\\"Filtered: {} => {}\\\".format(food_unfiltered, food)\\n\\n if len(food) > 0 and food not in _STOPWORDS_EN:\\n return food\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c75b8477ff57702de0d338d7e78e5169\",\n \"score\": \"0.42224583\",\n \"text\": \"def _message_factory(cls, factory_string):\\n components = factory_string.split(\\\" \\\")\\n x, y = int(components[1]), int(components[2])\\n\\n if cls.REPR_PREFIX != components[0]:\\n raise ValueError(\\\"Expected %s, found %s\\\" % (cls.REPR_PREFIX, components[0]))\\n\\n return UTSDigMessage(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4da9e5aac06afc2a60d35a4459e94802\",\n \"score\": \"0.42202806\",\n \"text\": \"def main():\\n fsa_filename = sys.argv[1]\\n test_filename = sys.argv[2]\\n\\n with open(fsa_filename, \\\"r\\\") as fsa_file:\\n fsa_rules = fsa_file.readlines()\\n\\n acceptor = FSAAcceptor(fsa_rules)\\n\\n with open(test_filename, \\\"r\\\") as test_file:\\n test_strings = test_file.readlines()\\n\\n for string in test_strings:\\n print(string.strip() + \\\" => \\\" + (\\\"yes\\\" if acceptor.can_accept_string(string) else \\\"no\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b7eb6312f830ebc3ad1fa082fa2378\",\n \"score\": \"0.42202336\",\n \"text\": \"def get_node_from_string(string, world_interface, _verbose=False):\\n has_children = False\\n behavior = BEHAVIORS_DICT.get(string)\\n if behavior is not None:\\n node = behavior(string, world_interface)\\n elif string == 'f(':\\n node = pt.composites.Selector(\\\"Fallback\\\")\\n has_children = True\\n elif string == 's(':\\n node = RSequence()\\n has_children = True\\n elif string == 'p(':\\n node = pt.composites.Parallel(\\n name=\\\"Parallel\\\",\\n policy=pt.common.ParallelPolicy.SuccessOnAll(synchronise=False))\\n has_children = True\\n elif string == 'nonpytreesbehavior':\\n return False\\n else:\\n raise Exception(\\\"Unexpected character\\\", string)\\n return node, has_children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7131cf2bb2d78b50d90d6474fd054ac\",\n \"score\": \"0.42197636\",\n \"text\": \"def fromTFExample(bytestr):\\n import tensorflow as tf\\n example = tf.train.Example()\\n example.ParseFromString(bytestr)\\n return example\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad92979720579917146aaa9e60434d7\",\n \"score\": \"0.4219349\",\n \"text\": \"def cstr_train(diam, height, num_CSTRs=None, asPFR=None):\\n\\n # Initialize defaults of keyword arguments\\n if num_CSTRs is None:\\n nCSTRs = 1\\n else:\\n nCSTRs = num_CSTRs\\n if asPFR is None:\\n treat_as_PFR = True\\n else:\\n treat_as_PFR = asPFR\\n\\n if treat_as_PFR:\\n cstr_height = height / nCSTRs\\n train_height = height\\n else:\\n cstr_height = height\\n train_height = height * nCSTRS\\n\\n # Source, sink nodes\\n nodes = OrderedDict([('gas_source',\\n {'type': 'source',\\n 'pos': [0.0, 0.0, 0.0]}),\\n ('gas_sink', \\n {'type': 'sink',\\n 'pos': [0.0, 0.0, train_height]})\\n ])\\n # Reactor nodes\\n pos = 0.0\\n for nCSTR in range(nCSTRs):\\n cstr_name = 'R' + str(nCSTR)\\n nodes[cstr_name] = {'type': 'reactor',\\n 'pos': [0.0, 0.0, pos],\\n 'diam': bed_diam,\\n 'height': cstr_height}\\n pos += cstr_height\\n\\n # Inlet, outlet streams\\n streams = OrderedDict([('gas_inlet',\\n {'nodes': ['gas_source', 'R0']}),\\n ('gas_outlet',\\n {'nodes': ['R' + str(nCSTRs), 'gas_sink']})\\n ])\\n # Internal streams\\n for nCSTR in range(nCSTRs - 1): # One less internal stream than the number of CSTRs\\n source_name = 'R' + str(nCSTR)\\n sink_name = 'R' + str(nCSTR+1)\\n stream_name = source_name + '_to_' + sink_name\\n stream[stream_name] = {'nodes': [source_name, sink_name]}\\n return nodes, streams\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e678ac84d4271998c3bb43facee4d8d\",\n \"score\": \"0.42146686\",\n \"text\": \"def edgefactory(edge_type):\\n return edge_mapping[edge_type]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"355acf46998b91cf79cbb393b7116df5\",\n \"score\": \"0.4205546\",\n \"text\": \"def _from_freqstr(cls, freqstr):\\n numeric_part = \\\"\\\"\\n freq_part = \\\"\\\"\\n\\n for x in freqstr:\\n if x.isdigit():\\n numeric_part += x\\n else:\\n freq_part += x\\n\\n if (\\n freq_part not in cls._CODES_TO_UNITS\\n or not numeric_part + freq_part == freqstr\\n ):\\n raise ValueError(f\\\"Cannot interpret frequency str: {freqstr}\\\")\\n\\n return cls(**{cls._CODES_TO_UNITS[freq_part]: int(numeric_part)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f2916d3d1eb4b7f80518381c8c014b\",\n \"score\": \"0.4203038\",\n \"text\": \"def from_string(cls, afield_as_string=\\\"\\\", af_size=None, tf_size=None):\\n\\n # split Afield header\\n strlist = afield_as_string.split(\\\";\\\", 1)\\n\\n # make AF pos list\\n pos_list = cls.pos_list_from_string(cls, string=strlist[0] + \\\";\\\")\\n\\n tf_str_list = [\\\"T\\\" + s.replace(\\\"UG\\\", \\\"UT\\\") for s in strlist[1].replace(\\\"UT\\\", \\\"UG\\\").split(\\\"T\\\")[1:]]\\n\\n # make TF list\\n tf_list = [ZbaTfield.from_string(tfield_as_string=s, tf_size=tf_size) for s in tf_str_list]\\n\\n return cls(pos=pos_list, size=cls.default_size, t_list=tf_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f66663f4c4c432e5ff11f6ce3162a0d5\",\n \"score\": \"0.42002225\",\n \"text\": \"def new_string_graph(n, predicates):\\n graph = DataGraph()\\n for i in range(1,n):\\n s = str(i)\\n for p in predicates:\\n o = str(i+1)\\n # print s, p, o\\n graph.addNode(s, p, o)\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d846ef815d8e32501b38817376fa18f\",\n \"score\": \"0.41943747\",\n \"text\": \"def match(infix, string):\\n \\n # Shunt and compile the regular expression\\n postfix = shunt(infix)\\n nfa = compile(postfix)\\n\\n # The current set of states\\n current = set()\\n next = set()\\n\\n # Add the initial state to the current set\\n current |= followes(nfa.initial)\\n\\n # Loop through each character\\n for s in string:\\n # Loop Through current set of states\\n for c in current:\\n # Check if that state is labelled s.\\n if c.label == s:\\n # Add the edge1 state to the next set.\\n next |= followes(c.edge1)\\n #set current to next and clear out next\\n current = next\\n next = set()\\n\\n # Check if the accept state is in the set of current states\\n return (nfa.accept in current)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75a3b8e9e41d19d952efe9091bfd62d6\",\n \"score\": \"0.41935343\",\n \"text\": \"def get_act_fn(act_fn_string: str):\\n if act_fn_string == \\\"relu\\\":\\n return th.nn.ReLU\\n elif act_fn_string == \\\"sigmoid\\\":\\n return th.nn.Sigmoid\\n elif act_fn_string == \\\"tanh\\\":\\n return th.nn.Tanh\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a4b14b338df730241cd1944b9ed38f4\",\n \"score\": \"0.41903028\",\n \"text\": \"def parseString( string, parser = None ):\\n\\tp = SimpleXMLHandler()\\n\\tif parser:\\n\\t\\tdstParser = parser().parseString\\n\\telse:\\n\\t\\tdstParser = sax.parseString\\n\\tdstParser( string, p )\\n\\troot = p.rootNode\\n\\troot.filename = None\\n\\treturn root\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35763462612e98fcf5ec409304bb21c3\",\n \"score\": \"0.41898003\",\n \"text\": \"def simple_parser() -> argparse.ArgumentParser:\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"INPUT\\\", \\\"Generic one-param input\\\", type=str)\\n return parser\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0110afc8068fffb119f83060dd3ba365\",\n \"score\": \"0.4186264\",\n \"text\": \"def parse(string):\\n return SpecParser().parse(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff1254d3622662c9a4dd182f12214119\",\n \"score\": \"0.41801172\",\n \"text\": \"def tree_from_string(text, source_name, start_line=1):\\n seq = element_seq_from_string(text, source_name, start_line)\\n compressed_seq = compress_element_seq(seq)\\n root = tree_from_element_seq(compressed_seq)\\n return root\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2589097f6332c19ebd3c47a391272fd7\",\n \"score\": \"0.41791803\",\n \"text\": \"def from_string(value):\\n\\t\\traise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e05e6aea882ba97414d834a5b597f1ca\",\n \"score\": \"0.41788822\",\n \"text\": \"def string2boolean_parser(string):\\n\\n string = string.lower()\\n if string == 'true' or string == 't':\\n return True\\n elif string == 'false' or string == 'f':\\n return False\\n else:\\n raise ValueError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8bb09fb833efac2968e8839c1742013\",\n \"score\": \"0.41767716\",\n \"text\": \"def from_string(cls: Type[DS], s: str) -> DS:\\n if len(s) < 1 or s[0:1] != cls.SIGIL:\\n raise SynapseError(\\n 400,\\n \\\"Expected %s string to start with '%s'\\\" % (cls.__name__, cls.SIGIL),\\n Codes.INVALID_PARAM,\\n )\\n\\n parts = s[1:].split(\\\":\\\", 1)\\n if len(parts) != 2:\\n raise SynapseError(\\n 400,\\n \\\"Expected %s of the form '%slocalname:domain'\\\"\\n % (cls.__name__, cls.SIGIL),\\n Codes.INVALID_PARAM,\\n )\\n\\n domain = parts[1]\\n # This code will need changing if we want to support multiple domain\\n # names on one HS\\n return cls(localpart=parts[0], domain=domain)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"864d3c1cc0511df32cf415cd7ec53598\",\n \"score\": \"0.41758972\",\n \"text\": \"def from_string(cls, xml_string):\\n element = ElementTree.fromstring(xml_string)\\n return cls.from_element(element)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ffd184b2e86502b8e7c0d81555ade50\",\n \"score\": \"0.41740236\",\n \"text\": \"def create_forest(request, config):\\n\\n if 'use_forest' in request:\\n\\n # {\\n # \\\"type\\\": \\\"request\\\",\\n # \\\"use_forest\\\": \\\"1\\\\tMit\\\\tmit\\\\t_\\\\tADP\\\\tAPPR\\\\t_\\\\t_\\\\t3\\\\t_\\\\t...\\n # \\\"forest_format\\\": \\\"conll09\\\"\\n # }\\n\\n format_ = request['forest_format']\\n try:\\n info = get_format_from_config(config, format_) #check if config file contains format, return format dict\\n except KeyError as e:\\n msg = 'Format not supported: %s'\\n logging.warning(msg, format_)\\n raise ValueError(msg % format_) from e\\n return Forest.from_string(request['use_forest'], format_info=info)\\n #request['use_forest']: conll string e.g.\\\"1\\\\tMit\\\\tmit\\\\t_\\\\tADP\\\\tAPPR\\\\t_\\\\t_\\\\t3\\\\t_\\\\t...\\\"\\n #info: format dict\\n\\n\\n\\n#-----------------ignore for now, come back later------------------------------------------\\n elif 'process' in request:\\n\\n # {\\n # \\\"type\\\": \\\"request\\\",\\n # \\\"process\\\": \\\"Mit Bedacht badet heute ein Lurch in einem See.\\\",\\n # \\\"source_format\\\": \\\"raw\\\",\\n # \\\"target_format\\\": \\\"conll09\\\"\\n # }\\n\\n try:\\n forest_string = process(request, config)\\n except ValueError as e:\\n msg = 'Cannot convert from source_format %s to target_format %s.'\\n logging.warning(msg,\\n request['source_format'], request['target_format'])\\n raise ValueError(msg % (request['source_format'],\\n request['target_format'])) from e\\n try:\\n info = get_format_from_config(config, request['target_format'])\\n except KeyError as e:\\n msg = 'target_format %s not supported.'\\n logging.warning(msg, request['target_format'])\\n raise ValueError(msg % (request['target_format'])) from e\\n return Forest.from_string(forest_string, format_info=info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdbc626e6400ac0d4ac9c7eeae6ae262\",\n \"score\": \"0.41736302\",\n \"text\": \"def _new_snode(self, node1, node2):\\n label = self._new_label('S')\\n self.add_node(label)\\n self.root = label\\n self.add_edge(label, node1)\\n self.add_edge(label, node2)\\n\\n return label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91d1bd09f84b6399ebb6f5b500d9f8bc\",\n \"score\": \"0.41708004\",\n \"text\": \"def start_new(self) -> WalAcceptor:\\n\\n wa_num = len(self.instances)\\n wa = WalAcceptor(self.wa_binpath,\\n os.path.join(self.data_dir, \\\"wal_acceptor_{}\\\".format(wa_num)),\\n self.initial_port + wa_num, wa_num)\\n wa.start()\\n self.instances.append(wa)\\n return wa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4154d16f8a730df56739884d2f743937\",\n \"score\": \"0.41675404\",\n \"text\": \"def create_function_from_string(string):\\n if string is None:\\n return None\\n\\n if 'lambda' in string:\\n string = f'func = {string}'\\n loc = {}\\n exec(string, {}, loc)\\n funcs = [loc.get(func, None) for func in loc.keys()]\\n return funcs[0] if len(funcs) > 0 else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28fdffa8d3dec7d7985ddfe75f5e8472\",\n \"score\": \"0.4162198\",\n \"text\": \"def from_binary(cls, binary_string):\\n integer_part, decimal_part = binary_string.split('.')\\n return cls(len(integer_part), len(decimal_part), int(integer_part + decimal_part, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec7b6a8ea8cc7766795bc8b7f4db932f\",\n \"score\": \"0.41581127\",\n \"text\": \"def from_string(cls, string):\\n lines = string.split(\\\"\\\\n\\\")\\n # throw away the first and last lines, and remove indentation\\n lines = [line.strip() for line in lines[1:-1]]\\n opts = {}\\n for line in lines:\\n split = line.split(\\\"=\\\")\\n opt = split[0]\\n if len(split) == 2:\\n value = split[1]\\n else:\\n value = \\\"=\\\".join(split[1:])\\n opts[opt] = value\\n\\n # remove the SSID from the other options and strip the quotes from it.\\n ssid = opts[\\\"ssid\\\"][1:-1]\\n del opts[\\\"ssid\\\"]\\n\\n return cls(ssid, **opts)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1795,"cells":{"query_id":{"kind":"string","value":"cc834995374349d6489a52ff4e17e95b"},"query":{"kind":"string","value":"Private slot to show the contents of a table. table name of the table for which to show the contents (string)"},"positive_passages":{"kind":"list like","value":[{"docid":"7071ae00f36e28ee0871ca3d2f085a5b","score":"0.62225384","text":"def on_connections_tableActivated(self, table):\n self.showTable(table)","title":""}],"string":"[\n {\n \"docid\": \"7071ae00f36e28ee0871ca3d2f085a5b\",\n \"score\": \"0.62225384\",\n \"text\": \"def on_connections_tableActivated(self, table):\\n self.showTable(table)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3f313076ed2959f209cda71176c10ca1","score":"0.7463752","text":"def show_table(table):\n ui.print_table(table, table_structure)","title":""},{"docid":"fa6e6a9c9248556684c25af17ed59ffc","score":"0.74433726","text":"def show_table(table):\n ui.clear_scr()\n ui.print_table(table, [\"ID\", \"Name\", \"E-mail\", \"Subscribed\"], NAME)","title":""},{"docid":"9c2b31c5f9b25f6e3647fc55ad3914ab","score":"0.7123695","text":"def view_table():","title":""},{"docid":"e6bc7db5843bdc0798e45daaf88d0b95","score":"0.6997254","text":"def displayTable(self, tableName, out=sys.stdout):\r\n out.write(\"collector: \" + self.getName() + \"\\n\")\r\n out.write(\"table: \" + tableName + \"\\n\")\r\n self.getTable(tableName).show(self.getSize(tableName), truncate=False)","title":""},{"docid":"dbf384b614849ac917d5d649d48cabe2","score":"0.6794333","text":"def showTable(tableName):\n DB = connect()\n c = DB.cursor()\n c.execute(\"SELECT * from %s;\", (AsIs(tableName),))\n results = c.fetchall()\n for result in results:\n print result\n DB.close()","title":""},{"docid":"2a2d4ff39a5516622062b1843e9fc5ef","score":"0.66829437","text":"def show_table():\n\n title_list = ('ID', 'Name', 'Studio', 'Elements', 'Sales')\n\n return table, title_list","title":""},{"docid":"9e29186952e6977e62f5acef4f718308","score":"0.6672744","text":"def show_table(conn):\n cur = conn.cursor()\n cur.execute(\"SELECT * FROM %s\" % TABLE_NAME)\n rows = cur.fetchall()\n for row in rows:\n print row\n return","title":""},{"docid":"8d42872e71eb3b2945e67fc1d84e06a3","score":"0.6651799","text":"def showTables(self):\n for x in self.tables:\n print(x)","title":""},{"docid":"2186cad5043e22659a20e1c76071b183","score":"0.6628946","text":"def show_table(table):\n titles = [\"ID\", \"Title\", \"Amount\", \"Price per Item\", \"Retailer\", \"Date\"]\n output_table = [[row[ID], row[TITLE], row[AMOUNT], row[PRICE], row[RETAILER],\n '/'.join((row[YEAR], row[MONTH], row[DAY]))] for row in table]\n ui.clear_scr()\n ui.print_table(output_table, titles, TITLE)","title":""},{"docid":"1a41c9627cf51c9e8e4958e650a8bcad","score":"0.6605803","text":"def show_small_table(self, table):\n with self.connection.cursor() as cursor:\n sql = \"SELECT * FROM {0}\".format(table)\n cursor.execute(sql)\n rows = cursor.fetchall()\n for row in rows:\n print(\"{0} {1}\".format(row[0], row[1]))\n return rows","title":""},{"docid":"eff24fc9e67cb7b683244b196572e20e","score":"0.6587574","text":"def print_table(self):\n self.render()","title":""},{"docid":"d9c05afb5c886043280ee693d236267b","score":"0.6512721","text":"def table(self, name):\n\n string = \"========================================================\\n\"\n string += \"========= Table %-24s : ==========\\n\" % name\n string += \"========================================================\\n\"\n formatter = TabularOutputFormatter(format_name=\"simple\")\n request = self._sql.columns()\n records = self._db.request(request, (name,), ask=True)\n headers = [\"Name\", \"Default\", \"Nullable\", \"Type\"]\n for x in formatter.format_output(records, headers):\n string += x + \"\\n\"\n return string","title":""},{"docid":"2dd46a3f9cd0fa7f639704443ce1edd8","score":"0.6506884","text":"def render_table_view(q: Q):\n\n items = [ui.separator(label='Step 2: View the Dataset')]\n\n if q.client.working_file_path is None:\n items.append(ui.message_bar(type='warning', text='Please upload a dataset!'))\n else:\n items.append(ui.text_xl(os.path.basename(q.client.working_file_path)))\n items.append(make_ui_table(file_path=q.client.working_file_path, n_rows=10, name='head_of_table'))\n\n q.page['table'] = ui.form_card(box='4 2 8 4', items=items)","title":""},{"docid":"86cd72ac8a1e00ab45ec189fb48adf5f","score":"0.64748573","text":"def showTable(self, table):\n model = QSqlTableModel(self.table, self.connections.currentDatabase())\n model.setEditStrategy(QSqlTableModel.OnRowChange)\n model.setTable(table)\n model.select()\n if model.lastError().type() != QSqlError.NoError:\n self.statusMessage.emit(model.lastError().text())\n self.table.setModel(model)\n self.table.setEditTriggers(\n QAbstractItemView.DoubleClicked | QAbstractItemView.EditKeyPressed)\n \n self.table.resizeColumnsToContents()\n \n self.table.selectionModel().currentRowChanged.connect(\n self.updateActions)\n \n self.updateActions()","title":""},{"docid":"e2afe404c3aad81e88f590a15c926ca6","score":"0.6317729","text":"def show_all(conn,table=None):\n msg = ''\n c = conn.cursor()\n if table == None:\n c.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n tables = [t[0] for t in c.fetchall()]\n for table in tables:\n msg += f'Table: {table}\\n'\n msg += show_all(conn,table=table)\n msg += '\\n'\n msg += '\\n'\n else:\n c.execute(f'SELECT * FROM {table} ORDER BY usage DESC')\n names = list(map(lambda x: x[0], c.description))\n msg += ' '.join(names) + '\\n'\n rows = c.fetchall()\n for row in rows:\n msg += ' '.join([str(x) for x in row]) + '\\n'\n msg += '\\n'\n c.close()\n return msg.strip()","title":""},{"docid":"3b18b4cdc301f659a31bd0d5d0785fa1","score":"0.62529594","text":"def showContents():","title":""},{"docid":"8716193e500684c3a19f24328a96affa","score":"0.62337637","text":"def showTable(self):\n gd = GenericDialog(\"Roi-group table\")\n gd.addPanel(self) # Add current table instance (a subclass of Panel to gd panel)\n gd.showDialog()","title":""},{"docid":"1f1b494bc4b03c66deacabe6057fcd11","score":"0.6202329","text":"def print_table(self,table):\n built=[]\n built.append(\"<table %s>\\n\" % (self.table_attr) )\n built.append(self.print_header(table))\n built.append(self.print_rows(table))\n built.append(\"</table>\\n\")\n return ''.join(built)","title":""},{"docid":"ea50c82fe01977c906219737a9c3c33d","score":"0.61623955","text":"def show(self):\n\n if self.length() > 0:\n strings = []\n strings.append('<table class=\"tabelle\">')\n strings.append('<tr>')\n for i in range(0,len(self.__columns)):\n if self.__colspan[i] > 0:\n strings.append('<th colspan=\"%d\">%s</th>'\n % (self.__colspan[i], self.__columns[i]))\n else:\n strings.append('<th>%s</th>' % self.__columns[i])\n strings.append('</tr>\\n')\n for line in self.__lines:\n strings.append('<tr>')\n for i in range(0,len(line)):\n if line[i] is None:\n line[i] = \"\"\n if type(line[i]) in [IntType, LongType]:\n cell = '<td style=\"text-align:right;\">'\n elif isinstance(line[i], Decimal):\n cell = '<td style=\"text-align:right;\">'\n elif not self.__padding[i]:\n cell = '<td style=\"padding:0px;\">'\n else:\n cell = '<td>'\n strings.append(\"%s%s</td>\" % (cell, line[i]))\n strings.append('</tr>\\n')\n strings.append('</table>')\n print \"\".join(strings)\n else:\n print '(keine)'","title":""},{"docid":"fc855198440a4246de1772b44a8a8015","score":"0.61425245","text":"def table_names(self):","title":""},{"docid":"f67374bbbbc31542517d4dc65b3d7aa9","score":"0.6106816","text":"def __str__(self):\n return self.renderer.print_table(self)","title":""},{"docid":"da7ff0505f37ed9cac76c96843bb1525","score":"0.6086017","text":"def show_table( self, table_name = None, options = {} ):\n assert isinstance( table_name, (basestring)), \"show_table(): Argument 'table_name' must be (one) of type(s) '(basestring)'; given %s\" % type( table_name ).__name__\n assert isinstance( options, (dict)), \"show_table(): Argument 'options' must be (one) of type(s) '(dict)'; given %s\" % type( options ).__name__\n\n (REQ_SCHEMA, RSP_SCHEMA) = self.__get_schemas( \"/show/table\" )\n\n obj = {}\n obj['table_name'] = table_name\n obj['options'] = self.__sanitize_dicts( options )\n\n response = self.__post_then_get_cext( REQ_SCHEMA, RSP_SCHEMA, obj, '/show/table' )\n if not _Util.is_ok( response ):\n return AttrDict( response )\n\n # Create record types for the returned types and save them\n for __type_info in zip( response[\"type_ids\"], response[\"type_labels\"], response[\"type_schemas\"], response[\"properties\"] ):\n # Create a type only if it is not colleciton\n if (__type_info[ 1 ] != \"<collection>\"):\n record_type = RecordType.from_type_schema( __type_info[ 1 ], __type_info[ 2 ], __type_info[ 3 ] )\n self.save_known_type( __type_info[ 0 ], record_type )\n # end loop\n\n return AttrDict( response )","title":""},{"docid":"34fa2c93396b9b599f7a5209cb144f84","score":"0.6060656","text":"def get_tables(self):","title":""},{"docid":"7dff059f0beaf1261b6e5d55241a8236","score":"0.60490155","text":"def DisplayContents(self):","title":""},{"docid":"7dff059f0beaf1261b6e5d55241a8236","score":"0.60490155","text":"def DisplayContents(self):","title":""},{"docid":"ad0acfa932897780c0cdfdee5a4fe143","score":"0.6046792","text":"def print_table(self,table):\n built=[]\n built.append(self.print_header(table))\n built.append(self.print_rows(table))\n return ''.join(built)","title":""},{"docid":"061c6261f3d9c3c4e923a221482fd412","score":"0.6045626","text":"def add_table(self, tablecontent):\n self.file_handle.write('to be implemented' + tablecontent + '\\n')","title":""},{"docid":"44d82079c38cd32c21c7bacadb9169e0","score":"0.60382533","text":"def show(self, subcmd, **kwarg):\n\n if subcmd == \"tables\":\n tables = self.db._get_tables()\n\n for table in tables:\n print(table[0])\n\n elif subcmd == \"fields\":\n if \"table\" not in kwarg.keys():\n return 1\n\n cols = self.db._get_table_columns(kwarg[\"table\"])\n\n for col in cols:\n print(col[1])\n\n elif subcmd == \"data\":\n if \"table\" not in kwarg.keys():\n return 1\n\n data = self.db._get_table_data(kwarg[\"table\"])\n\n for row in data:\n print(\"|\".join(map(lambda x: str(x), row)))","title":""},{"docid":"7bd80a9e99719554ed2ea4a616ad8e4b","score":"0.603433","text":"def show_all(self):\n self.cursor.execute(\"select * from \" + self.tablename)\n table = self.cursor.fetchall()\n view.record_names()\n view.print_table_from_sql(table)","title":""},{"docid":"e5998f37ca3589d6665f31bc60e0e6a3","score":"0.5974566","text":"def tables_alone():","title":""},{"docid":"a41370db3191fc8bcb34501fa871c8ef","score":"0.5949151","text":"def df_show_table(df):\n pass","title":""},{"docid":"30e9114270c3bd1da03090008a3677fe","score":"0.59096444","text":"def table(name):\n return TABLES[name]","title":""},{"docid":"e5af22c37ca8c6f14f1dc4655980c270","score":"0.5901838","text":"def TableList():\r\n\treturn [ \"contact\", \"instruments\", \"alumni\", \"pages\", \"60thevents\", \"memories\", \"parade\", \"concert\", \"paid\", \"wallace\", \"attendance\", \"volunteers\" ]","title":""},{"docid":"68fc71010f04fc73a39057d005c36aa0","score":"0.59002477","text":"def print_table(self):\r\n print \" \",\r\n for i in range(len(self.s1)):\r\n print \"%4.2s\" % self.s1[i],\r\n print\r\n for i in range(len(self.d)):\r\n self.__print_row(i, 'd', True)\r\n self.__print_row(i, 'dis')\r\n self.__print_row(i, 'dit')\r\n print","title":""},{"docid":"7c24abd433a4b3e7867e62a72b002616","score":"0.58912396","text":"def WriteTableOfContents(self):\r\n self.WriteRow()\r\n self.WriteLine('CONTENTS', right=self.Tag(self.Slug('contents')))\r\n for i, block in enumerate(self.module.sections.values()):\r\n assert 'id' in block.locals\r\n assert 'name' in block.locals\r\n line = '%d. %s' % (i + 1, block.locals['name'])\r\n slug = self.Slug(block.locals['id'])\r\n self.WriteLine(line, indent=1, right=self.Link(slug), fill='.')\r\n self.WriteLine()","title":""},{"docid":"2b3dd939b72365920ab3061d3e10c51b","score":"0.5865623","text":"def __str__(self):\n return str(self._table)","title":""},{"docid":"2b3dd939b72365920ab3061d3e10c51b","score":"0.5865623","text":"def __str__(self):\n return str(self._table)","title":""},{"docid":"982f69c7c6dbddbd44bb923efc2bae42","score":"0.58638453","text":"def load_table(self):\n self.temp_text_table.deleteLater()\n table_widget = TabWidget(np.random.randn(60, 128, 14))\n self.table_layout.addWidget(table_widget)","title":""},{"docid":"cebe4e7fc084f584a9855dc73309b3fa","score":"0.5843006","text":"def display_tables(self):\n for item in self.display_table.get_children():\n self.display_table.delete(item)\n\n columns = self.active_table.get_column_names()\n columns.insert(0, 'Lp.')\n\n for i, j in enumerate(self.result_table):\n j.insert(0, i)\n self.display_table['columns'] = columns\n\n for i in columns:\n self.display_table.heading(i, text=i, anchor='w')\n\n self.display_table.column('#0', width=0, stretch=NO)\n self.display_table.heading('#0', text='', anchor=CENTER)\n for i, j in enumerate(self.result_table):\n self.display_table.insert(parent='', index=i, values=j)\n\n for j in self.result_table:\n del j[0]","title":""},{"docid":"c805e1def1253b8bd8fabe23491816c7","score":"0.58426553","text":"def configshowtable(self, sender, flag):\n \n if (self.showtable == flag):\n return\n\n self.showtable = flag\n \n self.unreadyall(False)\n self.queueaction(self.sendall, 'volity.show_table',\n sender, self.showtable)","title":""},{"docid":"a882cfa36cbd11304a082bc784c709ea","score":"0.5835174","text":"def get_table_description(self, *_):\n return \"\"","title":""},{"docid":"5b1ee7e99502c3c3d18d18e433ad9a8d","score":"0.5823388","text":"def showSchema(self, table):\n rec = self.connections.currentDatabase().record(table)\n model = QStandardItemModel(self.table)\n \n model.insertRows(0, rec.count())\n model.insertColumns(0, 7)\n \n model.setHeaderData(0, Qt.Horizontal, \"Fieldname\")\n model.setHeaderData(1, Qt.Horizontal, \"Type\")\n model.setHeaderData(2, Qt.Horizontal, \"Length\")\n model.setHeaderData(3, Qt.Horizontal, \"Precision\")\n model.setHeaderData(4, Qt.Horizontal, \"Required\")\n model.setHeaderData(5, Qt.Horizontal, \"Auto Value\")\n model.setHeaderData(6, Qt.Horizontal, \"Default Value\")\n \n for i in range(rec.count()):\n fld = rec.field(i)\n model.setData(model.index(i, 0), fld.name())\n if fld.typeID() == -1:\n model.setData(model.index(i, 1),\n QVariant.typeToName(fld.type()))\n else:\n model.setData(\n model.index(i, 1), \"{0} ({1})\".format(\n QVariant.typeToName(fld.type()), fld.typeID()))\n if fld.length() < 0:\n model.setData(model.index(i, 2), \"?\")\n else:\n model.setData(model.index(i, 2), fld.length())\n if fld.precision() < 0:\n model.setData(model.index(i, 3), \"?\")\n else:\n model.setData(model.index(i, 3), fld.precision())\n if fld.requiredStatus() == -1:\n model.setData(model.index(i, 4), \"?\")\n else:\n model.setData(model.index(i, 4), bool(fld.requiredStatus()))\n model.setData(model.index(i, 5), fld.isAutoValue())\n model.setData(model.index(i, 6), fld.defaultValue())\n \n self.table.setModel(model)\n self.table.setEditTriggers(QAbstractItemView.NoEditTriggers)\n \n self.table.resizeColumnsToContents()\n \n self.updateActions()","title":""},{"docid":"eecd1e50048bf8ad65aa6ae8b38753fd","score":"0.57981473","text":"def TableEditor ( *args, **traits ):\n return toolkit().table_editor( *args, **traits )","title":""},{"docid":"86902f7ab303b3f432191884d886cdad","score":"0.57886297","text":"def renderTable(table):\n res = \"\"\"\n<table>\n <thead>\n <tr>\n <th></th>\"\"\"\n for visvar in visvars:\n res += \"<th>{}</th>\".format(visvar)\n res += \"\"\"\n </tr>\n </thead>\n <tbody>\"\"\"\n for sym in table:\n res += \"\"\"\n <tr>\"\"\"\n res += \"<th>{}</th>\".format(sym.name)\n for visvar in visvars:\n var = sym.get(visvar)\n bind = sym.get_bind(visvar)\n if bind:\n res += \"<td><em>{}</em></td>\".format(sym.get(visvar))\n else:\n res += \"<td>{}</td>\".format(sym.get(visvar))\n res += \"<td><div id='{}' style='width:50px;height:50px' /></td>\".format(sym.name)\n res += \"\"\"\n </tr>\"\"\"\n\n res += \"\"\"\n</tbody>\n</table>\n\"\"\"\n return res","title":""},{"docid":"3c3b6d3ec93df5cb8c3c40edbd2a394f","score":"0.57675385","text":"def describe_table(table_name, e):\r\n return pd.read_sql(SHOW_SQL.format(table_name), e)","title":""},{"docid":"f99e44a40b0bea59ebfd4e8364977d06","score":"0.5742724","text":"def __getitem__(self, table_name):\n return Table(self.api_key, self.base_url, table_name)","title":""},{"docid":"ba95ac11b164d057c2de5b15da547552","score":"0.5721031","text":"def table(self, table_name):\n return self[table_name]","title":""},{"docid":"8cb98dce2e960ed04c23cd55c0b6d0e6","score":"0.5718282","text":"def show_table(sequence):\n if len(sequence) == 0: #***add this elsewhere outside function\n print('At the moment, there are no cards in the table pile')\n for card in sequence:\n card.view_card()","title":""},{"docid":"6b83ad8c807ec63cacb50e449f88265e","score":"0.5711241","text":"def display(self, out=sys.stdout):\r\n for tableName in self.sparkData:\r\n self.displayTable(tableName, out=out)","title":""},{"docid":"c6ac4144862dd9793a6be79fe50b5869","score":"0.5703106","text":"def get_table(self):\n pass","title":""},{"docid":"04604773860435436c759d803f9daf50","score":"0.5697406","text":"def on_listar_clicked(self, verTabla):\r\n tbCli = TablaClientes()\r\n tbCli.show_all()","title":""},{"docid":"771e7c51e16f2701ac1f2f6e318a9b93","score":"0.56892115","text":"def show_inventory(table):\r\n print('======= The Current Inventory: =======')\r\n print('ID\\tCD Title (by: Artist)\\n')\r\n if not table:\r\n print('The Inventory is currently empty \\n')\r\n else:\r\n for row in table:\r\n print('{}\\t{} (by:{})'.format(*row.values()))\r\n print('======================================\\n')","title":""},{"docid":"bc2cad3743f3e955423aa80e0c6614cb","score":"0.5684577","text":"def show_inventory(table):\r\n print('\\n======= The Current Inventory: =======')\r\n print('ID\\tCD Title (by: Artist)\\n')\r\n if table[0]['ID'] == None:\r\n print()\r\n else: \r\n for row in table:\r\n print('{}\\t{} (by: {})'.format(*row.values()))\r\n print('======================================\\n')","title":""},{"docid":"ff7672b0a4a18b02ce9f8873ae01f47f","score":"0.5671016","text":"def handle_tables(table_data: list):\n table = GFMT(table_data)\n table.inner_row_border = True\n table = table = \"{}\".format(table.table)\n return table","title":""},{"docid":"49589215dc56684dc31dec689bce1789","score":"0.56673527","text":"def on_listar_clicked(self, verTabla):\r\n tbCli = TablaServicios()\r\n tbCli.show_all()","title":""},{"docid":"daa5722a85f8f19310917c8bc4a80ef8","score":"0.5651488","text":"def display_schema(self):\n if not self.conn:\n return\n sql = \"SELECT SQL FROM sqlite_master WHERE TYPE = 'table'\"\n self.query(sql)\n tables = self.cursor.fetchall()\n for table in tables:\n print(*table, '\\n')","title":""},{"docid":"65cd09e83d963f61762fe450588c745c","score":"0.56419194","text":"def __repr__(self): #TODO atm we can't read this in!\n return repr(self.table)","title":""},{"docid":"fd2cc0c25202247391926e4671def947","score":"0.5641438","text":"def reloadTable(self):\n print \"reload table\"\n self.availableServers = self.retrieveAllStoredServerInfo()\n print self.availableServers\n table = self.serverListDialog.tableWidget\n #table.setEditTriggers(QAbstractItemView.NoEditTriggers)\n table.setRowCount(len(self.availableServers))\n table.setColumnCount(2)\n table.setHorizontalHeaderLabels(\"Server Name;Server full URL\".split(';'))\n table.horizontalHeader().setStretchLastSection(True)\n table.setSelectionBehavior(QAbstractItemView.SelectRows)\n\n for (i, item) in enumerate(self.availableServers):\n table.setItem(i, 0, QTableWidgetItem(item.getName()))\n table.setItem(i, 1, QTableWidgetItem(item.getURL()))\n print i,item","title":""},{"docid":"1395ee0cd3e3ec57c3340bb4e86b838a","score":"0.5635469","text":"def read_table_products(self):","title":""},{"docid":"529b8c62cd9589363549e567b1e7a0df","score":"0.56342435","text":"def table_list(self):\r\n sql_cmd = \"select * from sqlite_master where type = 'table';\"\r\n self.cursor.execute(sql_cmd)\r\n db_info = self.cursor.fetchall()\r\n for entry in db_info:\r\n print(entry)","title":""},{"docid":"ca4db9b8873a157ca0db1861b8db1de3","score":"0.5634146","text":"def render(self):\n self._output(self.renderer.print_table(table=self))","title":""},{"docid":"0929018cad6d8cc70e9c42b8845aaf1d","score":"0.5622588","text":"def display_inventory(table):\r\n print('======= Current Inventory =======')\r\n print('ID\\tCD Title\\tArtist\\n')\r\n for obj in lstOfCDObjects:\r\n print('{}, {}, {}'.format(obj.cd_id, obj.cd_title, obj.cd_artist))\r\n print('=================================')","title":""},{"docid":"dcb7c1e9fbfdfda8149a062a5255a135","score":"0.56062126","text":"def table(elements):\n click.echo(tabulate(elements))","title":""},{"docid":"99bc17f5e562e40d46afcaae7bc053d9","score":"0.5604074","text":"def t():\n\treturn table()","title":""},{"docid":"6a9f72e034950a9edad6e0a5875b7863","score":"0.55940145","text":"def u():\n\treturn table2()","title":""},{"docid":"0ea9cb30e9b08a8f1f95b684ba1eac20","score":"0.55819374","text":"def cur_tables():\n begin()\n cur.execute(\"SELECT name FROM sqlite_master WHERE type='table';\") \n x = [tup[0] for tup in cur.fetchall()]\n for table in x:\n result = cur.execute(\"SELECT * FROM %s;\" % table)\n \n # Get all rows.\n rows = result.fetchall()\n cur.execute(\"SELECT * FROM %s\" % table)\n col_name = [tuple[0] for tuple in cur.description]\n \n # \\n represents an end-of-line\n print (\"\\n--- TABLE \", table)\n print (col_name)\n print (rows)\n end()","title":""},{"docid":"7724fa7a6a5a8032807683b1f763ef84","score":"0.5558646","text":"def makeTable(self):\n self.table = QtWidgets.QTableWidget(self.nparts+2, 8, self)\n self.table.setHorizontalHeaderLabels(['File', 'Exists?', 'Create time', 'Size(Byte)', 'Sub.time', 'Job Id', 'Stat.', 'Log?'])\n #self.table.setVerticalHeaderLabels(['' for i in range(self.nparts+2)])\n #self.table.setRowCount(self.nparts+1)\n\n self.table.verticalHeader().hide()\n\n self.table.horizontalHeader().setDefaultSectionSize(60)\n self.table.horizontalHeader().resizeSection(0,200)\n self.table.horizontalHeader().resizeSection(1,50)\n self.table.horizontalHeader().resizeSection(2,150)\n self.table.horizontalHeader().resizeSection(3,80)\n self.table.horizontalHeader().resizeSection(4,70)\n self.table.horizontalHeader().resizeSection(5,60)\n self.table.horizontalHeader().resizeSection(6,50)\n self.table.horizontalHeader().resizeSection(7,40)\n\n self.list_of_files_work = fnm.get_list_of_files_cora_proc_work()\n self.list_of_files_work_log = fnm.get_list_of_files_cora_proc_work_log()\n self.list_of_files_main = fnm.get_list_of_files_cora_proc_main()\n self.list_of_files = fnm.get_list_of_files_cora_proc_all()\n\n self.row = -1\n self.makeTableAdd()\n\n self.list_of_items = []\n self.list_of_work_files = list(zip(fnm.get_list_of_files_cora_proc_work(), fnm.get_list_of_files_cora_proc_work_log()))\n\n for i, (fname,lname) in enumerate(self.list_of_work_files) :\n\n file_exists = os.path.exists(fname)\n logf_exists = os.path.exists(lname)\n item_fname = QtWidgets.QTableWidgetItem( str(os.path.basename(fname)) )\n item_exists = QtWidgets.QTableWidgetItem( self.dict_status[file_exists] )\n item_ctime = QtWidgets.QTableWidgetItem( 'N/A' )\n item_size = QtWidgets.QTableWidgetItem( 'N/A' )\n item_stime = QtWidgets.QTableWidgetItem( 'N/A' )\n item_jobid = QtWidgets.QTableWidgetItem( 'N/A' )\n item_jobst = QtWidgets.QTableWidgetItem( 'N/A' )\n item_lname = QtWidgets.QTableWidgetItem( 'N/A' ) # os.path.basename(lname) )\n\n item_fname.setCheckState(QtCore.Qt.Checked) # Unchecked, PartiallyChecked, Checked\n\n item_fname .setTextAlignment(QtCore.Qt.AlignLeft | QtCore.Qt.AlignVCenter ) #| QtCore.Qt.AlignAbsolute)\n item_exists.setTextAlignment(QtCore.Qt.AlignCenter)\n item_ctime .setTextAlignment(QtCore.Qt.AlignCenter)\n item_size .setTextAlignment(QtCore.Qt.AlignRight | QtCore.Qt.AlignVCenter )\n item_stime .setTextAlignment(QtCore.Qt.AlignRight | QtCore.Qt.AlignVCenter | QtCore.Qt.AlignAbsolute)\n item_jobid .setTextAlignment(QtCore.Qt.AlignCenter)\n item_jobst .setTextAlignment(QtCore.Qt.AlignCenter)\n item_lname .setTextAlignment(QtCore.Qt.AlignCenter)\n\n self.row += 1\n self.table.setItem(self.row, 0, item_fname)\n self.table.setItem(self.row, 1, item_exists)\n self.table.setItem(self.row, 2, item_ctime)\n self.table.setItem(self.row, 3, item_size)\n self.table.setItem(self.row, 4, item_stime)\n self.table.setItem(self.row, 5, item_jobid)\n self.table.setItem(self.row, 6, item_jobst)\n self.table.setItem(self.row, 7, item_lname)\n \n row_of_items = [i, fname, item_fname, item_exists, item_ctime, item_size, item_stime, item_jobid, item_jobst, lname, item_lname]\n self.list_of_items.append(row_of_items)\n\n #self.table.setSpan(self.row, 0, 1, 5) \n #self.table.setItem(self.row, 0, self.title_split)\n\n self.table.setFixedWidth(self.table.horizontalHeader().length() + 20)\n\n #self.item_fname_header = self.table.horizontalHeaderItem(0)\n #print 'self.item_fname_header.text(): ', self.item_fname_header.text() \n #flags = QtCore.Qt.ItemFlags(QtCore.Qt.NoItemFlags|QtCore.Qt.ItemIsUserCheckable )\n #self.item_fname_header.setFlags(flags)\n #self.item_fname_header.setCheckState(QtCore.Qt.PartiallyChecked) # Unchecked, PartiallyChecked, Checked\n\n self.cbx = QtWidgets.QCheckBox(self.table.horizontalHeader())\n self.cbx.setCheckState(QtCore.Qt.Checked)\n self.cbx.setGeometry(QtCore.QRect(3, 4, 16, 17)) # (self.table.columnWidth(0)/2)\n\n self.cbx.stateChanged[int].connect(self.onCBox)","title":""},{"docid":"86a51cd17fb23b011bf0c2862720fbd7","score":"0.55505896","text":"def print_info(self):\n print(f'\\t\\t[{self.table_type}] {self.table_id} ({len(self.view_query)} bytes)')","title":""},{"docid":"baac311fe9065ca85d6c22c79bd6f626","score":"0.5547397","text":"def __str__(self):\r\n _table_str = ''\r\n x_y = int(len(self._table) ** 0.5)\r\n for x in range(1, x_y+1):\r\n _table_list = []\r\n for y in range(1, x_y+1):\r\n _table_list.append(str(self._table[(x,y)]))\r\n _table_str += ' | '.join(_table_list) + '\\n'\r\n return _table_str.strip()","title":""},{"docid":"1ac388e2bbf761e7770de9412aa51124","score":"0.554105","text":"def set_table(self, table):\n self.table = table","title":""},{"docid":"11eeab3cdacc44b19e778a92474acecd","score":"0.5529487","text":"def table(self, table_name):\n self._table = table_name","title":""},{"docid":"b4516dab08b8d319aac4fdfe24d1d5a9","score":"0.551774","text":"def viewAll(self, parent=None):\n from PEATDB.Tables import TableCanvas\n from PEATDB.TableModels import TableModel\n\n tp = Toplevel(master=parent)\n tframe = Frame(tp)\n tframe.pack()\n m = TableModel()\n m.importDict(self.data)\n table = TableCanvas(tframe, model=m)\n table.createTableFrame()\n\n return","title":""},{"docid":"eae0fafc59b4aac97498615b1413d3a1","score":"0.5516669","text":"def GET(self):\n return RENDER.truthtable(TABLE)","title":""},{"docid":"e8ce701e135bc9cbcdc211a4680cf421","score":"0.55131763","text":"def __str__(self):\r\n\t\treturn str(self.tablero)","title":""},{"docid":"afc616194a81842e1a255d8f0e6071a0","score":"0.5507635","text":"def __init__(self, *args, **kwargs):\n \n super(Table, self).__init__(*args, **kwargs)\n self.type = 'table'","title":""},{"docid":"c38c7ed3a480cb1f4d35b311d55d1609","score":"0.54863995","text":"def print_table(table_data,title=None):\n table_instance = SingleTable(table_data.tolist(), title)\n table_instance.inner_heading_row_border = False\n table_instance.inner_row_border = True\n# table_instance.justify_columns = {0: 'center', 1: 'center', 2: 'center'}\n print (table_instance.table)","title":""},{"docid":"1733fee93ee6b323a16afb65b04c6baa","score":"0.54859173","text":"def display(self, cells):","title":""},{"docid":"a2b152b1554afec67cdedc5579af8132","score":"0.54843515","text":"def setup(self, table, entries_number):\n headers = ['Date', 'Time', 'Contract', 'Entry', 'Exit', 'Stop-loss', 'Positions', 'Result']\n Qt_headers = QtCore.QStringList(headers)\n table.setColumnCount(8)\n table.setRowCount(entries_number+5)\n table.setHorizontalHeaderLabels(Qt_headers)","title":""},{"docid":"00a05aced81d254535a5bdf9e69e0a4a","score":"0.5483887","text":"def __repr__(self):\r\n output = \"\"\r\n for i in range(self.tableSize):\r\n output += \"[{}]: {} \\n\".format(i, self.table[i])\r\n return output","title":""},{"docid":"a6da6a93984262026dc13a93abb6f1ec","score":"0.5460559","text":"def view_table():\n conn = sqlite3.connect(\"outofindia.db\")\n cur = conn.cursor()\n cur.execute(\"SELECT * FROM outofindia\")\n rows = cur.fetchall()\n conn.close()\n return rows","title":""},{"docid":"695410deed48ad40cea876e4b5e12843","score":"0.5459687","text":"def setup(self, table):\n \n headers = ['Date', 'Time', 'Contract', 'Entry', 'Exit', 'Stop-loss', 'Positions']\n Qt_headers = QtCore.QStringList(headers)\n table.setColumnCount(7)\n table.setRowCount(1)\n table.setHorizontalHeaderLabels(Qt_headers)","title":""},{"docid":"a679549fbb21614bdbe472f629e63f70","score":"0.5458017","text":"def tables(rows):\n table_names = [\n x[0]\n for x in r8.db.execute(\n \"SELECT name FROM sqlite_master WHERE type='table'\"\n ).fetchall()\n ]\n for table in table_names:\n click.secho(f\"[{table}]\", fg=\"green\")\n util.run_sql(f\"SELECT * FROM {table} LIMIT {rows}\")","title":""},{"docid":"e98950280a9cd2a25d6011dc493d0355","score":"0.5454108","text":"def show( ):","title":""},{"docid":"edd8a7a0acc79792df4ff7f40ad7a497","score":"0.5450475","text":"def show(self, argv):\n if len(argv) > 1:\n colname = argv[1]\n self._print(\"%20.20s: %s\" % (colname, getattr(self._obj, colname)))\n else:\n for metadata in self._metadata:\n self._print(\"%20.20s: %s\" % (metadata.colname, getattr(self._obj, metadata.colname)))","title":""},{"docid":"503b3972793b1fe2d0e8815b611ea322","score":"0.5446094","text":"def getTable(self, name: unicode) -> db.Table:\n ...","title":""},{"docid":"45d800926783fd8f1d75f48c57a17229","score":"0.5434978","text":"def load_qtable(self, file):","title":""},{"docid":"51c05d3c26cca2132fcb83177dc98712","score":"0.5428032","text":"def display_menu(): \r\n print()\r\n print('(0) Create New Table')\r\n print('(1) Search an athlete')\r\n print('(2) Print/Export/View? current table')\r\n print('(3) Quit')\r\n print()","title":""},{"docid":"9bb4a3978673692370aae8dbaee24b47","score":"0.54208183","text":"def preview_widget(self, parent=None):\n by_column = False if self.table_layout == self.layout_names[0] else True\n return TableWidget(\"Table preview\",\n columns=self.columns,\n by_column=by_column,\n refresh_time=None,\n check_status=False,\n float_digits=self.float_digits,\n parent=None,\n )","title":""},{"docid":"abfc2c72fcfcc2d006ca49713af057b4","score":"0.5417643","text":"def defineTable(self):\r\n\r\n #define items in list\r\n for i in range(len(qc.questionList)):\r\n newTitle = QTableWidgetItem(qc.questionList[i].title)\r\n questionType = QTableWidgetItem(qc.questionList[i].type)\r\n self.table.setItem(i, 0, newTitle)\r\n self.table.setItem(i, 1, questionType)\r\n\r\n # define horizontal headers\r\n horHeaders = [\"Question\", \"Type\"]\r\n self.table.setHorizontalHeaderLabels(horHeaders)\r\n\r\n # hide vertical headers (no numbers)\r\n self.table.verticalHeader().hide()\r\n\r\n # resize headers\r\n header = self.table.horizontalHeader()\r\n header.setSectionResizeMode(0, QHeaderView.Stretch)\r\n header.setSectionResizeMode(1, QHeaderView.ResizeToContents)\r\n\r\n # hide grid\r\n self.table.setShowGrid(False)\r\n\r\n # select rows instead of individual cells\r\n self.table.setSelectionBehavior(QAbstractItemView.SelectRows)\r\n self.table.setEditTriggers(QAbstractItemView.NoEditTriggers)\r\n self.table.itemSelectionChanged.connect(self.questionSelected)","title":""},{"docid":"fb08b4b67afae5f7aadbab6a19aa9bdd","score":"0.5408994","text":"def browse_web(self, table):\n \n selected_rows = []\n row_items = []\n n_cols = table.columnCount()\n for i in range(table.rowCount()):\n for j in range(n_cols):\n item = table.item(i, j)\n if item.isSelected() and i not in selected_rows:\n selected_rows.append(i)\n items = [table.item(i, k).text() for k in range(n_cols)]\n row_items.append(items)\n\n if not row_items:\n self.show_popup(\"Please try again\", QtWidgets.QMessageBox.Warning,\\\n \"No URLS selected.\", \"Select listings by clicking.\")\n\n tbls = (self.bbsResultsTable, self.sbResultsTable, self.twResultsTable)\n if table in tbls:\n urls = [item[-1] for item in row_items]\n elif table == self.csResultsTable:\n urls = [item[-2] for item in row_items]\n elif table == self.starredResultsTable:\n urls = [items[-3] for item in row_items]\n urls = list(filter(lambda url: url[:7] != \"mailto:\", urls))\n self.webEngineView_1.setUrl(QtCore.QUrl(urls[0]))\n self.webEngineView_1.urlChanged.connect(self.update_urlbar)\n self.urlBar.setText(urls[0])\n\n for i, url in enumerate(urls[1:]):\n idx = str(i + 2)\n setattr(self, \"tab\" + idx, QtWidgets.QWidget())\n getattr(self, \"tab\" + idx).setObjectName(\"tab\" + idx)\n setattr(self, \"webEngineView_\" + idx, \\\n QtWebEngineWidgets.QWebEngineView(getattr(self, \"tab\" + idx)))\n\n setattr(self, \"page_view\" + idx, \\\n WebEnginePage(getattr(self, \"webEngineView_\" + idx)))\n getattr(self, \"webEngineView_\" + idx).setPage(getattr(self, \\\n \"page_view\" + idx))\n dimensions = QtCore.QRect(0, 0, 761, 371)\n getattr(self, \"webEngineView_\" + idx).setGeometry(dimensions)\n getattr(self, \"webEngineView_\" + idx).setMouseTracking(True)\n getattr(self, \"webEngineView_\" + idx).setUrl(QtCore.QUrl(url))\n getattr(self, \"webEngineView_\" + idx).setZoomFactor(0.8)\n getattr(self, \"webEngineView_\" + idx).urlChanged.connect(\\\n self.update_urlbar)\n getattr(self, \"webEngineView_\" + idx).setObjectName(\\\n \"webEngineView\" + idx)\n self.browserTabs.addTab(getattr(self, \"tab\" + idx), \\\n \"Listing \" + idx)\n \n self.newTab = QtWidgets.QWidget()\n self.newTab.setObjectName(\"newTab\")\n self.webEngineView = QtWebEngineWidgets.QWebEngineView(self.newTab)\n self.webEngineView.setGeometry(QtCore.QRect(0, 0, 761, 371))\n self.page_view = WebEnginePage(self.webEngineView)\n self.webEngineView.setPage(self.page_view)\n self.webEngineView.setMouseTracking(True)\n self.webEngineView.setUrl(QtCore.QUrl(\"https://www.google.com/\"))\n self.webEngineView.setZoomFactor(0.8)\n self.webEngineView.setObjectName(\"webEngineView\")\n self.browserTabs.addTab(self.newTab, \"New Tab\")\n self.browserTabs.setTabText(self.browserTabs.indexOf(self.newTab), \\\n QtCore.QCoreApplication.translate(\"BrokeredDealEvaluator\", \"New Tab\"))\n\n self.actionExport.setDisabled(True)\n self.stackedWidget.setCurrentIndex(16)","title":""},{"docid":"27208347dbf1188125e4f9f0ce749421","score":"0.5405281","text":"def _htmlTable(self, vName, vType, v, vSize):\n cellLayout = \"\"\"\n <div style='font-weight:bold; text-align:center'>{0}</div></td>\"\"\" + \\\n \"\"\"<td><div style='text-align:center'>{1}</div></td>\"\"\" + \\\n \"\"\"<td><div style='text-align:center'>{2}</div></td><td>\"\"\" + \\\n \"\"\"<div style='max-height:80px; text-overflow:ellipsis; table-layout:fixed; overflow:auto'>{3}</div>\"\"\" + \\\n \"\"\"</td>\n \"\"\"\n\n self._tablab.value = \"\"\"\n <div id=\"wkspTable\">\n <table class=\"table table-bordered table-striped'\">\n <tr>\n <th><div style='text-align:center'>Name</div></th>\n <th><div style='text-align:center'>Type</div></th>\n <th><div style='min-width:40px; text-align:center'>Size</div></th>\n <th>Value</th>\n </tr>\n <tr>\n <td>\"\"\" + \\\n '</td></tr><tr><td>'.join([cellLayout.format(vName[k], vType[k], vSize[k], str(val)) for k, val in enumerate(v)]) + \\\n \"\"\"</td>\n </tr>\n </table>\n </div>\"\"\"","title":""},{"docid":"61c33ca1d1be6537546fff932f34e303","score":"0.5391382","text":"def _tables(self, args):\n\t\tline = \"SELECT name FROM sqlite_master WHERE type = 'table' ORDER BY name;\"\n\t\tinterpretor.onecmd(line)","title":""},{"docid":"4cfadd03a831052dff17c7fe3ec82f3e","score":"0.53863317","text":"def __call__(self):\n string = 'TABLE: {}\\n'.format(self.__table__)\n for i, tup in enumerate(self):\n string += '├─ ' if i < len(dict(self))-1 else '└─ '\n string += '{}: {} {}\\n'.format(tup[0], tup[1], type(tup[1]))\n string += 'RELATIONSHIPS: {}\\n'.format(self.__table__)\n mapper = self.inspect().relationships\n for i, rel in enumerate(mapper):\n string += '├─ ' if i < len(dict(mapper))-1 else '└─ '\n string += '{}, {}\\n'.format(rel, rel.direction.name)\n print string","title":""},{"docid":"70663cc043a3493d777ef0cd758b57b5","score":"0.538245","text":"def on_rows_updated(ibswgt, tblname, nRows):\n #printDBG('Rows updated in tblname=%r, nRows=%r' % (str(tblname), nRows))\n if tblname == ENCOUNTER_TABLE: # Hack\n return\n tblname = str(tblname)\n tblnice = gh.TABLE_NICE[tblname]\n view = ibswgt.views[tblname]\n index = ibswgt._tab_table_wgt.indexOf(view)\n text = tblnice + ' ' + str(nRows)\n #printDBG('Rows updated in index=%r, text=%r' % (index, text))\n ibswgt._tab_table_wgt.setTabText(index, text)","title":""},{"docid":"e2206a7695506ee7528283fedd0db659","score":"0.5380112","text":"def printTablesIndex(self, outBuffer=False):\n sz = [14, 0, 8]\n for tab in self.__tables:\n if len(tab.name) > sz[0]:\n sz[0] = len(tab.name)\n l = len(str(tab.getlinewidth()))\n if l > sz[1]:\n sz[1] = l\n\n fmtt = \"{0:\" + str(sz[0]) + \"s} {1:\" + str(sz[1]) + \"d} {2:>\" + str(sz[2]) + \"s} {3:s}\"\n buff = fmtt.format(\"ReadMe\", MAX_SIZE_README_LINE, \".\", \"this file\") + \"\\n\"\n for tab in self.__tables:\n buff += fmtt.format(self.__strFmt(tab.name),\n tab.getlinewidth(),\n str(tab.nlines),\n self.__strFmt(tab.description))\n buff += \"\\n\"\n\n if outBuffer: return buff\n sys.stdout.write(buff)","title":""},{"docid":"0467f07d70d5eeef0e2ac90df065f460","score":"0.53758734","text":"def table(tablename, readonly=True):\r\n t = tbtool()\r\n t.open(tablename, nomodify=readonly)\r\n return t","title":""},{"docid":"12b33747857aa6fa5337fafc1d6b0df5","score":"0.53654003","text":"def show_individual_field(table):\n if table == ad_feature:\n print('The schemas for table ad_feature is: ')\n ad_feature.printSchema()\n elif table == behavior_log:\n print('The schemas for table behavior_log is: ')\n behavior_log.printSchema()\n elif table == raw_sample:\n print('The schemas for table raw_sample is: ')\n raw_sample.printSchema()\n elif table == user_profile:\n print('The schemas for table user_profile is: ')\n user_profile.printSchema()","title":""},{"docid":"051afb79c30cb526e206bb5a57e06565","score":"0.53632385","text":"def create_main_table_view(self, profile_dict_obj, table_clicked_action):\n\n # Create the main table widget where data will be populated\n self.main_table_view = QTableWidget()\n\n # Do not allow the user to edit the contents of the table\n self.main_table_view.setEditTriggers(\n QtWidgets.QTableWidget.NoEditTriggers)\n # The number of rows is defined by how many objects there are in the dictionary\n self.main_table_view.setRowCount(len(profile_dict_obj))\n # 2 columns: one for the profile name and one for the delete button\n self.main_table_view.setColumnCount(2)\n\n # Iterate through the data in the dictionary and format it appropriately in the table\n for row_count, profile_name in enumerate(profile_dict_obj):\n # Modify the profile name's string and populate into a table cell\n mod_profile_name = profile_name.replace('_', ' ').title()\n mod_profile_name_widget = QTableWidgetItem(mod_profile_name)\n # Create a delete 'button' by converting a cell to act like a button\n delete_row_widget = QTableWidgetItem('Delete')\n delete_row_widget.setBackground(\n QColor(sc.COLOR_ERROR)\n )\n delete_row_widget.setForeground(\n QColor(sc.COLOR_WHITE)\n )\n mod_profile_name_widget.setFont(self.table_font)\n delete_row_widget.setFont(self.table_font)\n # Align items in their respective cells appropriately\n mod_profile_name_widget.setTextAlignment(Qt.AlignLeft)\n delete_row_widget.setTextAlignment(Qt.AlignRight)\n # Add items to the table view\n self.main_table_view.setItem(\n row_count, 0, mod_profile_name_widget)\n self.main_table_view.setItem(\n row_count, 1, delete_row_widget)\n\n # Function to execute when a cell from this table is clicked\n self.main_table_view.clicked.connect(table_clicked_action)\n\n # Capture the horizontal header of the table\n main_table_hor_head = self.main_table_view.horizontalHeader()\n\n # Hide all headers of the table\n self.main_table_view.verticalHeader().setVisible(False)\n main_table_hor_head.setVisible(False)\n\n # Resize the left column to stretch as far out as possible\n main_table_hor_head.setSectionResizeMode(0, QHeaderView.Stretch)\n # Resize the right column to wrap around its contents\n main_table_hor_head.setSectionResizeMode(\n 1, QHeaderView.ResizeToContents)\n\n self.main_table_view.resizeRowsToContents()","title":""},{"docid":"a35608e12e926eeac8b81bcb54cd2a57","score":"0.53614664","text":"def add_to_table(self, table, index):\n table.setCellWidget(index,0, self._tab_id)\n table.setCellWidget(index,1, self._tab_name)\n table.setCellWidget(index,2, self._tab_owner)\n table.setCellWidget(index,3, self._tab_status) \n table.setCellWidget(index,4, self._tab_tasks_total)\n table.setCellWidget(index,5, self._tab_tasks_left)\n table.setCellWidget(index,6, self._tab_tasks_done)\n # currently not supported\n #table.setCellWidget(index,6, self._tab_priority)\n table.setCellWidget(index,7, self._tab_pool)","title":""},{"docid":"155cebd81e6e603f22f2ae5de818afac","score":"0.53536856","text":"def print_table(table, db_file):\n \n try:\n conn, c = connect_to_db(db_file)\n rows = c.execute('SELECT * FROM {t}'.format(t=safe(table))).fetchall()\n cols = c.execute(\"PRAGMA table_info({t})\".format(t=safe(table))).fetchall()\n conn.close()\n print '\\nTABLE ', table, '\\n'\n r = 1\n for row in rows:\n print \"ROW\", r\n for i in range(len(cols)):\n print ' ', cols[i][1].ljust(16), (str(row[i]) if isinstance(row[i],(int,long)) else row[i])\n print ''\n r += 1\n except Exception as e:\n print \"Error when trying to print table\", table\n print e","title":""}],"string":"[\n {\n \"docid\": \"3f313076ed2959f209cda71176c10ca1\",\n \"score\": \"0.7463752\",\n \"text\": \"def show_table(table):\\n ui.print_table(table, table_structure)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa6e6a9c9248556684c25af17ed59ffc\",\n \"score\": \"0.74433726\",\n \"text\": \"def show_table(table):\\n ui.clear_scr()\\n ui.print_table(table, [\\\"ID\\\", \\\"Name\\\", \\\"E-mail\\\", \\\"Subscribed\\\"], NAME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2b31c5f9b25f6e3647fc55ad3914ab\",\n \"score\": \"0.7123695\",\n \"text\": \"def view_table():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6bc7db5843bdc0798e45daaf88d0b95\",\n \"score\": \"0.6997254\",\n \"text\": \"def displayTable(self, tableName, out=sys.stdout):\\r\\n out.write(\\\"collector: \\\" + self.getName() + \\\"\\\\n\\\")\\r\\n out.write(\\\"table: \\\" + tableName + \\\"\\\\n\\\")\\r\\n self.getTable(tableName).show(self.getSize(tableName), truncate=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbf384b614849ac917d5d649d48cabe2\",\n \"score\": \"0.6794333\",\n \"text\": \"def showTable(tableName):\\n DB = connect()\\n c = DB.cursor()\\n c.execute(\\\"SELECT * from %s;\\\", (AsIs(tableName),))\\n results = c.fetchall()\\n for result in results:\\n print result\\n DB.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a2d4ff39a5516622062b1843e9fc5ef\",\n \"score\": \"0.66829437\",\n \"text\": \"def show_table():\\n\\n title_list = ('ID', 'Name', 'Studio', 'Elements', 'Sales')\\n\\n return table, title_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e29186952e6977e62f5acef4f718308\",\n \"score\": \"0.6672744\",\n \"text\": \"def show_table(conn):\\n cur = conn.cursor()\\n cur.execute(\\\"SELECT * FROM %s\\\" % TABLE_NAME)\\n rows = cur.fetchall()\\n for row in rows:\\n print row\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d42872e71eb3b2945e67fc1d84e06a3\",\n \"score\": \"0.6651799\",\n \"text\": \"def showTables(self):\\n for x in self.tables:\\n print(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2186cad5043e22659a20e1c76071b183\",\n \"score\": \"0.6628946\",\n \"text\": \"def show_table(table):\\n titles = [\\\"ID\\\", \\\"Title\\\", \\\"Amount\\\", \\\"Price per Item\\\", \\\"Retailer\\\", \\\"Date\\\"]\\n output_table = [[row[ID], row[TITLE], row[AMOUNT], row[PRICE], row[RETAILER],\\n '/'.join((row[YEAR], row[MONTH], row[DAY]))] for row in table]\\n ui.clear_scr()\\n ui.print_table(output_table, titles, TITLE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a41c9627cf51c9e8e4958e650a8bcad\",\n \"score\": \"0.6605803\",\n \"text\": \"def show_small_table(self, table):\\n with self.connection.cursor() as cursor:\\n sql = \\\"SELECT * FROM {0}\\\".format(table)\\n cursor.execute(sql)\\n rows = cursor.fetchall()\\n for row in rows:\\n print(\\\"{0} {1}\\\".format(row[0], row[1]))\\n return rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff24fc9e67cb7b683244b196572e20e\",\n \"score\": \"0.6587574\",\n \"text\": \"def print_table(self):\\n self.render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9c05afb5c886043280ee693d236267b\",\n \"score\": \"0.6512721\",\n \"text\": \"def table(self, name):\\n\\n string = \\\"========================================================\\\\n\\\"\\n string += \\\"========= Table %-24s : ==========\\\\n\\\" % name\\n string += \\\"========================================================\\\\n\\\"\\n formatter = TabularOutputFormatter(format_name=\\\"simple\\\")\\n request = self._sql.columns()\\n records = self._db.request(request, (name,), ask=True)\\n headers = [\\\"Name\\\", \\\"Default\\\", \\\"Nullable\\\", \\\"Type\\\"]\\n for x in formatter.format_output(records, headers):\\n string += x + \\\"\\\\n\\\"\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd46a3f9cd0fa7f639704443ce1edd8\",\n \"score\": \"0.6506884\",\n \"text\": \"def render_table_view(q: Q):\\n\\n items = [ui.separator(label='Step 2: View the Dataset')]\\n\\n if q.client.working_file_path is None:\\n items.append(ui.message_bar(type='warning', text='Please upload a dataset!'))\\n else:\\n items.append(ui.text_xl(os.path.basename(q.client.working_file_path)))\\n items.append(make_ui_table(file_path=q.client.working_file_path, n_rows=10, name='head_of_table'))\\n\\n q.page['table'] = ui.form_card(box='4 2 8 4', items=items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86cd72ac8a1e00ab45ec189fb48adf5f\",\n \"score\": \"0.64748573\",\n \"text\": \"def showTable(self, table):\\n model = QSqlTableModel(self.table, self.connections.currentDatabase())\\n model.setEditStrategy(QSqlTableModel.OnRowChange)\\n model.setTable(table)\\n model.select()\\n if model.lastError().type() != QSqlError.NoError:\\n self.statusMessage.emit(model.lastError().text())\\n self.table.setModel(model)\\n self.table.setEditTriggers(\\n QAbstractItemView.DoubleClicked | QAbstractItemView.EditKeyPressed)\\n \\n self.table.resizeColumnsToContents()\\n \\n self.table.selectionModel().currentRowChanged.connect(\\n self.updateActions)\\n \\n self.updateActions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2afe404c3aad81e88f590a15c926ca6\",\n \"score\": \"0.6317729\",\n \"text\": \"def show_all(conn,table=None):\\n msg = ''\\n c = conn.cursor()\\n if table == None:\\n c.execute(\\\"SELECT name FROM sqlite_master WHERE type='table';\\\")\\n tables = [t[0] for t in c.fetchall()]\\n for table in tables:\\n msg += f'Table: {table}\\\\n'\\n msg += show_all(conn,table=table)\\n msg += '\\\\n'\\n msg += '\\\\n'\\n else:\\n c.execute(f'SELECT * FROM {table} ORDER BY usage DESC')\\n names = list(map(lambda x: x[0], c.description))\\n msg += ' '.join(names) + '\\\\n'\\n rows = c.fetchall()\\n for row in rows:\\n msg += ' '.join([str(x) for x in row]) + '\\\\n'\\n msg += '\\\\n'\\n c.close()\\n return msg.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b18b4cdc301f659a31bd0d5d0785fa1\",\n \"score\": \"0.62529594\",\n \"text\": \"def showContents():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8716193e500684c3a19f24328a96affa\",\n \"score\": \"0.62337637\",\n \"text\": \"def showTable(self):\\n gd = GenericDialog(\\\"Roi-group table\\\")\\n gd.addPanel(self) # Add current table instance (a subclass of Panel to gd panel)\\n gd.showDialog()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1b494bc4b03c66deacabe6057fcd11\",\n \"score\": \"0.6202329\",\n \"text\": \"def print_table(self,table):\\n built=[]\\n built.append(\\\"<table %s>\\\\n\\\" % (self.table_attr) )\\n built.append(self.print_header(table))\\n built.append(self.print_rows(table))\\n built.append(\\\"</table>\\\\n\\\")\\n return ''.join(built)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea50c82fe01977c906219737a9c3c33d\",\n \"score\": \"0.61623955\",\n \"text\": \"def show(self):\\n\\n if self.length() > 0:\\n strings = []\\n strings.append('<table class=\\\"tabelle\\\">')\\n strings.append('<tr>')\\n for i in range(0,len(self.__columns)):\\n if self.__colspan[i] > 0:\\n strings.append('<th colspan=\\\"%d\\\">%s</th>'\\n % (self.__colspan[i], self.__columns[i]))\\n else:\\n strings.append('<th>%s</th>' % self.__columns[i])\\n strings.append('</tr>\\\\n')\\n for line in self.__lines:\\n strings.append('<tr>')\\n for i in range(0,len(line)):\\n if line[i] is None:\\n line[i] = \\\"\\\"\\n if type(line[i]) in [IntType, LongType]:\\n cell = '<td style=\\\"text-align:right;\\\">'\\n elif isinstance(line[i], Decimal):\\n cell = '<td style=\\\"text-align:right;\\\">'\\n elif not self.__padding[i]:\\n cell = '<td style=\\\"padding:0px;\\\">'\\n else:\\n cell = '<td>'\\n strings.append(\\\"%s%s</td>\\\" % (cell, line[i]))\\n strings.append('</tr>\\\\n')\\n strings.append('</table>')\\n print \\\"\\\".join(strings)\\n else:\\n print '(keine)'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc855198440a4246de1772b44a8a8015\",\n \"score\": \"0.61425245\",\n \"text\": \"def table_names(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f67374bbbbc31542517d4dc65b3d7aa9\",\n \"score\": \"0.6106816\",\n \"text\": \"def __str__(self):\\n return self.renderer.print_table(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da7ff0505f37ed9cac76c96843bb1525\",\n \"score\": \"0.6086017\",\n \"text\": \"def show_table( self, table_name = None, options = {} ):\\n assert isinstance( table_name, (basestring)), \\\"show_table(): Argument 'table_name' must be (one) of type(s) '(basestring)'; given %s\\\" % type( table_name ).__name__\\n assert isinstance( options, (dict)), \\\"show_table(): Argument 'options' must be (one) of type(s) '(dict)'; given %s\\\" % type( options ).__name__\\n\\n (REQ_SCHEMA, RSP_SCHEMA) = self.__get_schemas( \\\"/show/table\\\" )\\n\\n obj = {}\\n obj['table_name'] = table_name\\n obj['options'] = self.__sanitize_dicts( options )\\n\\n response = self.__post_then_get_cext( REQ_SCHEMA, RSP_SCHEMA, obj, '/show/table' )\\n if not _Util.is_ok( response ):\\n return AttrDict( response )\\n\\n # Create record types for the returned types and save them\\n for __type_info in zip( response[\\\"type_ids\\\"], response[\\\"type_labels\\\"], response[\\\"type_schemas\\\"], response[\\\"properties\\\"] ):\\n # Create a type only if it is not colleciton\\n if (__type_info[ 1 ] != \\\"<collection>\\\"):\\n record_type = RecordType.from_type_schema( __type_info[ 1 ], __type_info[ 2 ], __type_info[ 3 ] )\\n self.save_known_type( __type_info[ 0 ], record_type )\\n # end loop\\n\\n return AttrDict( response )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34fa2c93396b9b599f7a5209cb144f84\",\n \"score\": \"0.6060656\",\n \"text\": \"def get_tables(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dff059f0beaf1261b6e5d55241a8236\",\n \"score\": \"0.60490155\",\n \"text\": \"def DisplayContents(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dff059f0beaf1261b6e5d55241a8236\",\n \"score\": \"0.60490155\",\n \"text\": \"def DisplayContents(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad0acfa932897780c0cdfdee5a4fe143\",\n \"score\": \"0.6046792\",\n \"text\": \"def print_table(self,table):\\n built=[]\\n built.append(self.print_header(table))\\n built.append(self.print_rows(table))\\n return ''.join(built)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061c6261f3d9c3c4e923a221482fd412\",\n \"score\": \"0.6045626\",\n \"text\": \"def add_table(self, tablecontent):\\n self.file_handle.write('to be implemented' + tablecontent + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44d82079c38cd32c21c7bacadb9169e0\",\n \"score\": \"0.60382533\",\n \"text\": \"def show(self, subcmd, **kwarg):\\n\\n if subcmd == \\\"tables\\\":\\n tables = self.db._get_tables()\\n\\n for table in tables:\\n print(table[0])\\n\\n elif subcmd == \\\"fields\\\":\\n if \\\"table\\\" not in kwarg.keys():\\n return 1\\n\\n cols = self.db._get_table_columns(kwarg[\\\"table\\\"])\\n\\n for col in cols:\\n print(col[1])\\n\\n elif subcmd == \\\"data\\\":\\n if \\\"table\\\" not in kwarg.keys():\\n return 1\\n\\n data = self.db._get_table_data(kwarg[\\\"table\\\"])\\n\\n for row in data:\\n print(\\\"|\\\".join(map(lambda x: str(x), row)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bd80a9e99719554ed2ea4a616ad8e4b\",\n \"score\": \"0.603433\",\n \"text\": \"def show_all(self):\\n self.cursor.execute(\\\"select * from \\\" + self.tablename)\\n table = self.cursor.fetchall()\\n view.record_names()\\n view.print_table_from_sql(table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5998f37ca3589d6665f31bc60e0e6a3\",\n \"score\": \"0.5974566\",\n \"text\": \"def tables_alone():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a41370db3191fc8bcb34501fa871c8ef\",\n \"score\": \"0.5949151\",\n \"text\": \"def df_show_table(df):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30e9114270c3bd1da03090008a3677fe\",\n \"score\": \"0.59096444\",\n \"text\": \"def table(name):\\n return TABLES[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5af22c37ca8c6f14f1dc4655980c270\",\n \"score\": \"0.5901838\",\n \"text\": \"def TableList():\\r\\n\\treturn [ \\\"contact\\\", \\\"instruments\\\", \\\"alumni\\\", \\\"pages\\\", \\\"60thevents\\\", \\\"memories\\\", \\\"parade\\\", \\\"concert\\\", \\\"paid\\\", \\\"wallace\\\", \\\"attendance\\\", \\\"volunteers\\\" ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68fc71010f04fc73a39057d005c36aa0\",\n \"score\": \"0.59002477\",\n \"text\": \"def print_table(self):\\r\\n print \\\" \\\",\\r\\n for i in range(len(self.s1)):\\r\\n print \\\"%4.2s\\\" % self.s1[i],\\r\\n print\\r\\n for i in range(len(self.d)):\\r\\n self.__print_row(i, 'd', True)\\r\\n self.__print_row(i, 'dis')\\r\\n self.__print_row(i, 'dit')\\r\\n print\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c24abd433a4b3e7867e62a72b002616\",\n \"score\": \"0.58912396\",\n \"text\": \"def WriteTableOfContents(self):\\r\\n self.WriteRow()\\r\\n self.WriteLine('CONTENTS', right=self.Tag(self.Slug('contents')))\\r\\n for i, block in enumerate(self.module.sections.values()):\\r\\n assert 'id' in block.locals\\r\\n assert 'name' in block.locals\\r\\n line = '%d. %s' % (i + 1, block.locals['name'])\\r\\n slug = self.Slug(block.locals['id'])\\r\\n self.WriteLine(line, indent=1, right=self.Link(slug), fill='.')\\r\\n self.WriteLine()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b3dd939b72365920ab3061d3e10c51b\",\n \"score\": \"0.5865623\",\n \"text\": \"def __str__(self):\\n return str(self._table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b3dd939b72365920ab3061d3e10c51b\",\n \"score\": \"0.5865623\",\n \"text\": \"def __str__(self):\\n return str(self._table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"982f69c7c6dbddbd44bb923efc2bae42\",\n \"score\": \"0.58638453\",\n \"text\": \"def load_table(self):\\n self.temp_text_table.deleteLater()\\n table_widget = TabWidget(np.random.randn(60, 128, 14))\\n self.table_layout.addWidget(table_widget)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cebe4e7fc084f584a9855dc73309b3fa\",\n \"score\": \"0.5843006\",\n \"text\": \"def display_tables(self):\\n for item in self.display_table.get_children():\\n self.display_table.delete(item)\\n\\n columns = self.active_table.get_column_names()\\n columns.insert(0, 'Lp.')\\n\\n for i, j in enumerate(self.result_table):\\n j.insert(0, i)\\n self.display_table['columns'] = columns\\n\\n for i in columns:\\n self.display_table.heading(i, text=i, anchor='w')\\n\\n self.display_table.column('#0', width=0, stretch=NO)\\n self.display_table.heading('#0', text='', anchor=CENTER)\\n for i, j in enumerate(self.result_table):\\n self.display_table.insert(parent='', index=i, values=j)\\n\\n for j in self.result_table:\\n del j[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c805e1def1253b8bd8fabe23491816c7\",\n \"score\": \"0.58426553\",\n \"text\": \"def configshowtable(self, sender, flag):\\n \\n if (self.showtable == flag):\\n return\\n\\n self.showtable = flag\\n \\n self.unreadyall(False)\\n self.queueaction(self.sendall, 'volity.show_table',\\n sender, self.showtable)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a882cfa36cbd11304a082bc784c709ea\",\n \"score\": \"0.5835174\",\n \"text\": \"def get_table_description(self, *_):\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b1ee7e99502c3c3d18d18e433ad9a8d\",\n \"score\": \"0.5823388\",\n \"text\": \"def showSchema(self, table):\\n rec = self.connections.currentDatabase().record(table)\\n model = QStandardItemModel(self.table)\\n \\n model.insertRows(0, rec.count())\\n model.insertColumns(0, 7)\\n \\n model.setHeaderData(0, Qt.Horizontal, \\\"Fieldname\\\")\\n model.setHeaderData(1, Qt.Horizontal, \\\"Type\\\")\\n model.setHeaderData(2, Qt.Horizontal, \\\"Length\\\")\\n model.setHeaderData(3, Qt.Horizontal, \\\"Precision\\\")\\n model.setHeaderData(4, Qt.Horizontal, \\\"Required\\\")\\n model.setHeaderData(5, Qt.Horizontal, \\\"Auto Value\\\")\\n model.setHeaderData(6, Qt.Horizontal, \\\"Default Value\\\")\\n \\n for i in range(rec.count()):\\n fld = rec.field(i)\\n model.setData(model.index(i, 0), fld.name())\\n if fld.typeID() == -1:\\n model.setData(model.index(i, 1),\\n QVariant.typeToName(fld.type()))\\n else:\\n model.setData(\\n model.index(i, 1), \\\"{0} ({1})\\\".format(\\n QVariant.typeToName(fld.type()), fld.typeID()))\\n if fld.length() < 0:\\n model.setData(model.index(i, 2), \\\"?\\\")\\n else:\\n model.setData(model.index(i, 2), fld.length())\\n if fld.precision() < 0:\\n model.setData(model.index(i, 3), \\\"?\\\")\\n else:\\n model.setData(model.index(i, 3), fld.precision())\\n if fld.requiredStatus() == -1:\\n model.setData(model.index(i, 4), \\\"?\\\")\\n else:\\n model.setData(model.index(i, 4), bool(fld.requiredStatus()))\\n model.setData(model.index(i, 5), fld.isAutoValue())\\n model.setData(model.index(i, 6), fld.defaultValue())\\n \\n self.table.setModel(model)\\n self.table.setEditTriggers(QAbstractItemView.NoEditTriggers)\\n \\n self.table.resizeColumnsToContents()\\n \\n self.updateActions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eecd1e50048bf8ad65aa6ae8b38753fd\",\n \"score\": \"0.57981473\",\n \"text\": \"def TableEditor ( *args, **traits ):\\n return toolkit().table_editor( *args, **traits )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86902f7ab303b3f432191884d886cdad\",\n \"score\": \"0.57886297\",\n \"text\": \"def renderTable(table):\\n res = \\\"\\\"\\\"\\n<table>\\n <thead>\\n <tr>\\n <th></th>\\\"\\\"\\\"\\n for visvar in visvars:\\n res += \\\"<th>{}</th>\\\".format(visvar)\\n res += \\\"\\\"\\\"\\n </tr>\\n </thead>\\n <tbody>\\\"\\\"\\\"\\n for sym in table:\\n res += \\\"\\\"\\\"\\n <tr>\\\"\\\"\\\"\\n res += \\\"<th>{}</th>\\\".format(sym.name)\\n for visvar in visvars:\\n var = sym.get(visvar)\\n bind = sym.get_bind(visvar)\\n if bind:\\n res += \\\"<td><em>{}</em></td>\\\".format(sym.get(visvar))\\n else:\\n res += \\\"<td>{}</td>\\\".format(sym.get(visvar))\\n res += \\\"<td><div id='{}' style='width:50px;height:50px' /></td>\\\".format(sym.name)\\n res += \\\"\\\"\\\"\\n </tr>\\\"\\\"\\\"\\n\\n res += \\\"\\\"\\\"\\n</tbody>\\n</table>\\n\\\"\\\"\\\"\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3b6d3ec93df5cb8c3c40edbd2a394f\",\n \"score\": \"0.57675385\",\n \"text\": \"def describe_table(table_name, e):\\r\\n return pd.read_sql(SHOW_SQL.format(table_name), e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f99e44a40b0bea59ebfd4e8364977d06\",\n \"score\": \"0.5742724\",\n \"text\": \"def __getitem__(self, table_name):\\n return Table(self.api_key, self.base_url, table_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba95ac11b164d057c2de5b15da547552\",\n \"score\": \"0.5721031\",\n \"text\": \"def table(self, table_name):\\n return self[table_name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb98dce2e960ed04c23cd55c0b6d0e6\",\n \"score\": \"0.5718282\",\n \"text\": \"def show_table(sequence):\\n if len(sequence) == 0: #***add this elsewhere outside function\\n print('At the moment, there are no cards in the table pile')\\n for card in sequence:\\n card.view_card()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b83ad8c807ec63cacb50e449f88265e\",\n \"score\": \"0.5711241\",\n \"text\": \"def display(self, out=sys.stdout):\\r\\n for tableName in self.sparkData:\\r\\n self.displayTable(tableName, out=out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6ac4144862dd9793a6be79fe50b5869\",\n \"score\": \"0.5703106\",\n \"text\": \"def get_table(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04604773860435436c759d803f9daf50\",\n \"score\": \"0.5697406\",\n \"text\": \"def on_listar_clicked(self, verTabla):\\r\\n tbCli = TablaClientes()\\r\\n tbCli.show_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"771e7c51e16f2701ac1f2f6e318a9b93\",\n \"score\": \"0.56892115\",\n \"text\": \"def show_inventory(table):\\r\\n print('======= The Current Inventory: =======')\\r\\n print('ID\\\\tCD Title (by: Artist)\\\\n')\\r\\n if not table:\\r\\n print('The Inventory is currently empty \\\\n')\\r\\n else:\\r\\n for row in table:\\r\\n print('{}\\\\t{} (by:{})'.format(*row.values()))\\r\\n print('======================================\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc2cad3743f3e955423aa80e0c6614cb\",\n \"score\": \"0.5684577\",\n \"text\": \"def show_inventory(table):\\r\\n print('\\\\n======= The Current Inventory: =======')\\r\\n print('ID\\\\tCD Title (by: Artist)\\\\n')\\r\\n if table[0]['ID'] == None:\\r\\n print()\\r\\n else: \\r\\n for row in table:\\r\\n print('{}\\\\t{} (by: {})'.format(*row.values()))\\r\\n print('======================================\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff7672b0a4a18b02ce9f8873ae01f47f\",\n \"score\": \"0.5671016\",\n \"text\": \"def handle_tables(table_data: list):\\n table = GFMT(table_data)\\n table.inner_row_border = True\\n table = table = \\\"{}\\\".format(table.table)\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49589215dc56684dc31dec689bce1789\",\n \"score\": \"0.56673527\",\n \"text\": \"def on_listar_clicked(self, verTabla):\\r\\n tbCli = TablaServicios()\\r\\n tbCli.show_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa5722a85f8f19310917c8bc4a80ef8\",\n \"score\": \"0.5651488\",\n \"text\": \"def display_schema(self):\\n if not self.conn:\\n return\\n sql = \\\"SELECT SQL FROM sqlite_master WHERE TYPE = 'table'\\\"\\n self.query(sql)\\n tables = self.cursor.fetchall()\\n for table in tables:\\n print(*table, '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65cd09e83d963f61762fe450588c745c\",\n \"score\": \"0.56419194\",\n \"text\": \"def __repr__(self): #TODO atm we can't read this in!\\n return repr(self.table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd2cc0c25202247391926e4671def947\",\n \"score\": \"0.5641438\",\n \"text\": \"def reloadTable(self):\\n print \\\"reload table\\\"\\n self.availableServers = self.retrieveAllStoredServerInfo()\\n print self.availableServers\\n table = self.serverListDialog.tableWidget\\n #table.setEditTriggers(QAbstractItemView.NoEditTriggers)\\n table.setRowCount(len(self.availableServers))\\n table.setColumnCount(2)\\n table.setHorizontalHeaderLabels(\\\"Server Name;Server full URL\\\".split(';'))\\n table.horizontalHeader().setStretchLastSection(True)\\n table.setSelectionBehavior(QAbstractItemView.SelectRows)\\n\\n for (i, item) in enumerate(self.availableServers):\\n table.setItem(i, 0, QTableWidgetItem(item.getName()))\\n table.setItem(i, 1, QTableWidgetItem(item.getURL()))\\n print i,item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1395ee0cd3e3ec57c3340bb4e86b838a\",\n \"score\": \"0.5635469\",\n \"text\": \"def read_table_products(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"529b8c62cd9589363549e567b1e7a0df\",\n \"score\": \"0.56342435\",\n \"text\": \"def table_list(self):\\r\\n sql_cmd = \\\"select * from sqlite_master where type = 'table';\\\"\\r\\n self.cursor.execute(sql_cmd)\\r\\n db_info = self.cursor.fetchall()\\r\\n for entry in db_info:\\r\\n print(entry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca4db9b8873a157ca0db1861b8db1de3\",\n \"score\": \"0.5634146\",\n \"text\": \"def render(self):\\n self._output(self.renderer.print_table(table=self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0929018cad6d8cc70e9c42b8845aaf1d\",\n \"score\": \"0.5622588\",\n \"text\": \"def display_inventory(table):\\r\\n print('======= Current Inventory =======')\\r\\n print('ID\\\\tCD Title\\\\tArtist\\\\n')\\r\\n for obj in lstOfCDObjects:\\r\\n print('{}, {}, {}'.format(obj.cd_id, obj.cd_title, obj.cd_artist))\\r\\n print('=================================')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb7c1e9fbfdfda8149a062a5255a135\",\n \"score\": \"0.56062126\",\n \"text\": \"def table(elements):\\n click.echo(tabulate(elements))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99bc17f5e562e40d46afcaae7bc053d9\",\n \"score\": \"0.5604074\",\n \"text\": \"def t():\\n\\treturn table()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a9f72e034950a9edad6e0a5875b7863\",\n \"score\": \"0.55940145\",\n \"text\": \"def u():\\n\\treturn table2()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea9cb30e9b08a8f1f95b684ba1eac20\",\n \"score\": \"0.55819374\",\n \"text\": \"def cur_tables():\\n begin()\\n cur.execute(\\\"SELECT name FROM sqlite_master WHERE type='table';\\\") \\n x = [tup[0] for tup in cur.fetchall()]\\n for table in x:\\n result = cur.execute(\\\"SELECT * FROM %s;\\\" % table)\\n \\n # Get all rows.\\n rows = result.fetchall()\\n cur.execute(\\\"SELECT * FROM %s\\\" % table)\\n col_name = [tuple[0] for tuple in cur.description]\\n \\n # \\\\n represents an end-of-line\\n print (\\\"\\\\n--- TABLE \\\", table)\\n print (col_name)\\n print (rows)\\n end()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7724fa7a6a5a8032807683b1f763ef84\",\n \"score\": \"0.5558646\",\n \"text\": \"def makeTable(self):\\n self.table = QtWidgets.QTableWidget(self.nparts+2, 8, self)\\n self.table.setHorizontalHeaderLabels(['File', 'Exists?', 'Create time', 'Size(Byte)', 'Sub.time', 'Job Id', 'Stat.', 'Log?'])\\n #self.table.setVerticalHeaderLabels(['' for i in range(self.nparts+2)])\\n #self.table.setRowCount(self.nparts+1)\\n\\n self.table.verticalHeader().hide()\\n\\n self.table.horizontalHeader().setDefaultSectionSize(60)\\n self.table.horizontalHeader().resizeSection(0,200)\\n self.table.horizontalHeader().resizeSection(1,50)\\n self.table.horizontalHeader().resizeSection(2,150)\\n self.table.horizontalHeader().resizeSection(3,80)\\n self.table.horizontalHeader().resizeSection(4,70)\\n self.table.horizontalHeader().resizeSection(5,60)\\n self.table.horizontalHeader().resizeSection(6,50)\\n self.table.horizontalHeader().resizeSection(7,40)\\n\\n self.list_of_files_work = fnm.get_list_of_files_cora_proc_work()\\n self.list_of_files_work_log = fnm.get_list_of_files_cora_proc_work_log()\\n self.list_of_files_main = fnm.get_list_of_files_cora_proc_main()\\n self.list_of_files = fnm.get_list_of_files_cora_proc_all()\\n\\n self.row = -1\\n self.makeTableAdd()\\n\\n self.list_of_items = []\\n self.list_of_work_files = list(zip(fnm.get_list_of_files_cora_proc_work(), fnm.get_list_of_files_cora_proc_work_log()))\\n\\n for i, (fname,lname) in enumerate(self.list_of_work_files) :\\n\\n file_exists = os.path.exists(fname)\\n logf_exists = os.path.exists(lname)\\n item_fname = QtWidgets.QTableWidgetItem( str(os.path.basename(fname)) )\\n item_exists = QtWidgets.QTableWidgetItem( self.dict_status[file_exists] )\\n item_ctime = QtWidgets.QTableWidgetItem( 'N/A' )\\n item_size = QtWidgets.QTableWidgetItem( 'N/A' )\\n item_stime = QtWidgets.QTableWidgetItem( 'N/A' )\\n item_jobid = QtWidgets.QTableWidgetItem( 'N/A' )\\n item_jobst = QtWidgets.QTableWidgetItem( 'N/A' )\\n item_lname = QtWidgets.QTableWidgetItem( 'N/A' ) # os.path.basename(lname) )\\n\\n item_fname.setCheckState(QtCore.Qt.Checked) # Unchecked, PartiallyChecked, Checked\\n\\n item_fname .setTextAlignment(QtCore.Qt.AlignLeft | QtCore.Qt.AlignVCenter ) #| QtCore.Qt.AlignAbsolute)\\n item_exists.setTextAlignment(QtCore.Qt.AlignCenter)\\n item_ctime .setTextAlignment(QtCore.Qt.AlignCenter)\\n item_size .setTextAlignment(QtCore.Qt.AlignRight | QtCore.Qt.AlignVCenter )\\n item_stime .setTextAlignment(QtCore.Qt.AlignRight | QtCore.Qt.AlignVCenter | QtCore.Qt.AlignAbsolute)\\n item_jobid .setTextAlignment(QtCore.Qt.AlignCenter)\\n item_jobst .setTextAlignment(QtCore.Qt.AlignCenter)\\n item_lname .setTextAlignment(QtCore.Qt.AlignCenter)\\n\\n self.row += 1\\n self.table.setItem(self.row, 0, item_fname)\\n self.table.setItem(self.row, 1, item_exists)\\n self.table.setItem(self.row, 2, item_ctime)\\n self.table.setItem(self.row, 3, item_size)\\n self.table.setItem(self.row, 4, item_stime)\\n self.table.setItem(self.row, 5, item_jobid)\\n self.table.setItem(self.row, 6, item_jobst)\\n self.table.setItem(self.row, 7, item_lname)\\n \\n row_of_items = [i, fname, item_fname, item_exists, item_ctime, item_size, item_stime, item_jobid, item_jobst, lname, item_lname]\\n self.list_of_items.append(row_of_items)\\n\\n #self.table.setSpan(self.row, 0, 1, 5) \\n #self.table.setItem(self.row, 0, self.title_split)\\n\\n self.table.setFixedWidth(self.table.horizontalHeader().length() + 20)\\n\\n #self.item_fname_header = self.table.horizontalHeaderItem(0)\\n #print 'self.item_fname_header.text(): ', self.item_fname_header.text() \\n #flags = QtCore.Qt.ItemFlags(QtCore.Qt.NoItemFlags|QtCore.Qt.ItemIsUserCheckable )\\n #self.item_fname_header.setFlags(flags)\\n #self.item_fname_header.setCheckState(QtCore.Qt.PartiallyChecked) # Unchecked, PartiallyChecked, Checked\\n\\n self.cbx = QtWidgets.QCheckBox(self.table.horizontalHeader())\\n self.cbx.setCheckState(QtCore.Qt.Checked)\\n self.cbx.setGeometry(QtCore.QRect(3, 4, 16, 17)) # (self.table.columnWidth(0)/2)\\n\\n self.cbx.stateChanged[int].connect(self.onCBox)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86a51cd17fb23b011bf0c2862720fbd7\",\n \"score\": \"0.55505896\",\n \"text\": \"def print_info(self):\\n print(f'\\\\t\\\\t[{self.table_type}] {self.table_id} ({len(self.view_query)} bytes)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baac311fe9065ca85d6c22c79bd6f626\",\n \"score\": \"0.5547397\",\n \"text\": \"def __str__(self):\\r\\n _table_str = ''\\r\\n x_y = int(len(self._table) ** 0.5)\\r\\n for x in range(1, x_y+1):\\r\\n _table_list = []\\r\\n for y in range(1, x_y+1):\\r\\n _table_list.append(str(self._table[(x,y)]))\\r\\n _table_str += ' | '.join(_table_list) + '\\\\n'\\r\\n return _table_str.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ac388e2bbf761e7770de9412aa51124\",\n \"score\": \"0.554105\",\n \"text\": \"def set_table(self, table):\\n self.table = table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11eeab3cdacc44b19e778a92474acecd\",\n \"score\": \"0.5529487\",\n \"text\": \"def table(self, table_name):\\n self._table = table_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4516dab08b8d319aac4fdfe24d1d5a9\",\n \"score\": \"0.551774\",\n \"text\": \"def viewAll(self, parent=None):\\n from PEATDB.Tables import TableCanvas\\n from PEATDB.TableModels import TableModel\\n\\n tp = Toplevel(master=parent)\\n tframe = Frame(tp)\\n tframe.pack()\\n m = TableModel()\\n m.importDict(self.data)\\n table = TableCanvas(tframe, model=m)\\n table.createTableFrame()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eae0fafc59b4aac97498615b1413d3a1\",\n \"score\": \"0.5516669\",\n \"text\": \"def GET(self):\\n return RENDER.truthtable(TABLE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8ce701e135bc9cbcdc211a4680cf421\",\n \"score\": \"0.55131763\",\n \"text\": \"def __str__(self):\\r\\n\\t\\treturn str(self.tablero)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afc616194a81842e1a255d8f0e6071a0\",\n \"score\": \"0.5507635\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n \\n super(Table, self).__init__(*args, **kwargs)\\n self.type = 'table'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c38c7ed3a480cb1f4d35b311d55d1609\",\n \"score\": \"0.54863995\",\n \"text\": \"def print_table(table_data,title=None):\\n table_instance = SingleTable(table_data.tolist(), title)\\n table_instance.inner_heading_row_border = False\\n table_instance.inner_row_border = True\\n# table_instance.justify_columns = {0: 'center', 1: 'center', 2: 'center'}\\n print (table_instance.table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1733fee93ee6b323a16afb65b04c6baa\",\n \"score\": \"0.54859173\",\n \"text\": \"def display(self, cells):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2b152b1554afec67cdedc5579af8132\",\n \"score\": \"0.54843515\",\n \"text\": \"def setup(self, table, entries_number):\\n headers = ['Date', 'Time', 'Contract', 'Entry', 'Exit', 'Stop-loss', 'Positions', 'Result']\\n Qt_headers = QtCore.QStringList(headers)\\n table.setColumnCount(8)\\n table.setRowCount(entries_number+5)\\n table.setHorizontalHeaderLabels(Qt_headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a05aced81d254535a5bdf9e69e0a4a\",\n \"score\": \"0.5483887\",\n \"text\": \"def __repr__(self):\\r\\n output = \\\"\\\"\\r\\n for i in range(self.tableSize):\\r\\n output += \\\"[{}]: {} \\\\n\\\".format(i, self.table[i])\\r\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6da6a93984262026dc13a93abb6f1ec\",\n \"score\": \"0.5460559\",\n \"text\": \"def view_table():\\n conn = sqlite3.connect(\\\"outofindia.db\\\")\\n cur = conn.cursor()\\n cur.execute(\\\"SELECT * FROM outofindia\\\")\\n rows = cur.fetchall()\\n conn.close()\\n return rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695410deed48ad40cea876e4b5e12843\",\n \"score\": \"0.5459687\",\n \"text\": \"def setup(self, table):\\n \\n headers = ['Date', 'Time', 'Contract', 'Entry', 'Exit', 'Stop-loss', 'Positions']\\n Qt_headers = QtCore.QStringList(headers)\\n table.setColumnCount(7)\\n table.setRowCount(1)\\n table.setHorizontalHeaderLabels(Qt_headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a679549fbb21614bdbe472f629e63f70\",\n \"score\": \"0.5458017\",\n \"text\": \"def tables(rows):\\n table_names = [\\n x[0]\\n for x in r8.db.execute(\\n \\\"SELECT name FROM sqlite_master WHERE type='table'\\\"\\n ).fetchall()\\n ]\\n for table in table_names:\\n click.secho(f\\\"[{table}]\\\", fg=\\\"green\\\")\\n util.run_sql(f\\\"SELECT * FROM {table} LIMIT {rows}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e98950280a9cd2a25d6011dc493d0355\",\n \"score\": \"0.5454108\",\n \"text\": \"def show( ):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edd8a7a0acc79792df4ff7f40ad7a497\",\n \"score\": \"0.5450475\",\n \"text\": \"def show(self, argv):\\n if len(argv) > 1:\\n colname = argv[1]\\n self._print(\\\"%20.20s: %s\\\" % (colname, getattr(self._obj, colname)))\\n else:\\n for metadata in self._metadata:\\n self._print(\\\"%20.20s: %s\\\" % (metadata.colname, getattr(self._obj, metadata.colname)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"503b3972793b1fe2d0e8815b611ea322\",\n \"score\": \"0.5446094\",\n \"text\": \"def getTable(self, name: unicode) -> db.Table:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d800926783fd8f1d75f48c57a17229\",\n \"score\": \"0.5434978\",\n \"text\": \"def load_qtable(self, file):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c05d3c26cca2132fcb83177dc98712\",\n \"score\": \"0.5428032\",\n \"text\": \"def display_menu(): \\r\\n print()\\r\\n print('(0) Create New Table')\\r\\n print('(1) Search an athlete')\\r\\n print('(2) Print/Export/View? current table')\\r\\n print('(3) Quit')\\r\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb4a3978673692370aae8dbaee24b47\",\n \"score\": \"0.54208183\",\n \"text\": \"def preview_widget(self, parent=None):\\n by_column = False if self.table_layout == self.layout_names[0] else True\\n return TableWidget(\\\"Table preview\\\",\\n columns=self.columns,\\n by_column=by_column,\\n refresh_time=None,\\n check_status=False,\\n float_digits=self.float_digits,\\n parent=None,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abfc2c72fcfcc2d006ca49713af057b4\",\n \"score\": \"0.5417643\",\n \"text\": \"def defineTable(self):\\r\\n\\r\\n #define items in list\\r\\n for i in range(len(qc.questionList)):\\r\\n newTitle = QTableWidgetItem(qc.questionList[i].title)\\r\\n questionType = QTableWidgetItem(qc.questionList[i].type)\\r\\n self.table.setItem(i, 0, newTitle)\\r\\n self.table.setItem(i, 1, questionType)\\r\\n\\r\\n # define horizontal headers\\r\\n horHeaders = [\\\"Question\\\", \\\"Type\\\"]\\r\\n self.table.setHorizontalHeaderLabels(horHeaders)\\r\\n\\r\\n # hide vertical headers (no numbers)\\r\\n self.table.verticalHeader().hide()\\r\\n\\r\\n # resize headers\\r\\n header = self.table.horizontalHeader()\\r\\n header.setSectionResizeMode(0, QHeaderView.Stretch)\\r\\n header.setSectionResizeMode(1, QHeaderView.ResizeToContents)\\r\\n\\r\\n # hide grid\\r\\n self.table.setShowGrid(False)\\r\\n\\r\\n # select rows instead of individual cells\\r\\n self.table.setSelectionBehavior(QAbstractItemView.SelectRows)\\r\\n self.table.setEditTriggers(QAbstractItemView.NoEditTriggers)\\r\\n self.table.itemSelectionChanged.connect(self.questionSelected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb08b4b67afae5f7aadbab6a19aa9bdd\",\n \"score\": \"0.5408994\",\n \"text\": \"def browse_web(self, table):\\n \\n selected_rows = []\\n row_items = []\\n n_cols = table.columnCount()\\n for i in range(table.rowCount()):\\n for j in range(n_cols):\\n item = table.item(i, j)\\n if item.isSelected() and i not in selected_rows:\\n selected_rows.append(i)\\n items = [table.item(i, k).text() for k in range(n_cols)]\\n row_items.append(items)\\n\\n if not row_items:\\n self.show_popup(\\\"Please try again\\\", QtWidgets.QMessageBox.Warning,\\\\\\n \\\"No URLS selected.\\\", \\\"Select listings by clicking.\\\")\\n\\n tbls = (self.bbsResultsTable, self.sbResultsTable, self.twResultsTable)\\n if table in tbls:\\n urls = [item[-1] for item in row_items]\\n elif table == self.csResultsTable:\\n urls = [item[-2] for item in row_items]\\n elif table == self.starredResultsTable:\\n urls = [items[-3] for item in row_items]\\n urls = list(filter(lambda url: url[:7] != \\\"mailto:\\\", urls))\\n self.webEngineView_1.setUrl(QtCore.QUrl(urls[0]))\\n self.webEngineView_1.urlChanged.connect(self.update_urlbar)\\n self.urlBar.setText(urls[0])\\n\\n for i, url in enumerate(urls[1:]):\\n idx = str(i + 2)\\n setattr(self, \\\"tab\\\" + idx, QtWidgets.QWidget())\\n getattr(self, \\\"tab\\\" + idx).setObjectName(\\\"tab\\\" + idx)\\n setattr(self, \\\"webEngineView_\\\" + idx, \\\\\\n QtWebEngineWidgets.QWebEngineView(getattr(self, \\\"tab\\\" + idx)))\\n\\n setattr(self, \\\"page_view\\\" + idx, \\\\\\n WebEnginePage(getattr(self, \\\"webEngineView_\\\" + idx)))\\n getattr(self, \\\"webEngineView_\\\" + idx).setPage(getattr(self, \\\\\\n \\\"page_view\\\" + idx))\\n dimensions = QtCore.QRect(0, 0, 761, 371)\\n getattr(self, \\\"webEngineView_\\\" + idx).setGeometry(dimensions)\\n getattr(self, \\\"webEngineView_\\\" + idx).setMouseTracking(True)\\n getattr(self, \\\"webEngineView_\\\" + idx).setUrl(QtCore.QUrl(url))\\n getattr(self, \\\"webEngineView_\\\" + idx).setZoomFactor(0.8)\\n getattr(self, \\\"webEngineView_\\\" + idx).urlChanged.connect(\\\\\\n self.update_urlbar)\\n getattr(self, \\\"webEngineView_\\\" + idx).setObjectName(\\\\\\n \\\"webEngineView\\\" + idx)\\n self.browserTabs.addTab(getattr(self, \\\"tab\\\" + idx), \\\\\\n \\\"Listing \\\" + idx)\\n \\n self.newTab = QtWidgets.QWidget()\\n self.newTab.setObjectName(\\\"newTab\\\")\\n self.webEngineView = QtWebEngineWidgets.QWebEngineView(self.newTab)\\n self.webEngineView.setGeometry(QtCore.QRect(0, 0, 761, 371))\\n self.page_view = WebEnginePage(self.webEngineView)\\n self.webEngineView.setPage(self.page_view)\\n self.webEngineView.setMouseTracking(True)\\n self.webEngineView.setUrl(QtCore.QUrl(\\\"https://www.google.com/\\\"))\\n self.webEngineView.setZoomFactor(0.8)\\n self.webEngineView.setObjectName(\\\"webEngineView\\\")\\n self.browserTabs.addTab(self.newTab, \\\"New Tab\\\")\\n self.browserTabs.setTabText(self.browserTabs.indexOf(self.newTab), \\\\\\n QtCore.QCoreApplication.translate(\\\"BrokeredDealEvaluator\\\", \\\"New Tab\\\"))\\n\\n self.actionExport.setDisabled(True)\\n self.stackedWidget.setCurrentIndex(16)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27208347dbf1188125e4f9f0ce749421\",\n \"score\": \"0.5405281\",\n \"text\": \"def _htmlTable(self, vName, vType, v, vSize):\\n cellLayout = \\\"\\\"\\\"\\n <div style='font-weight:bold; text-align:center'>{0}</div></td>\\\"\\\"\\\" + \\\\\\n \\\"\\\"\\\"<td><div style='text-align:center'>{1}</div></td>\\\"\\\"\\\" + \\\\\\n \\\"\\\"\\\"<td><div style='text-align:center'>{2}</div></td><td>\\\"\\\"\\\" + \\\\\\n \\\"\\\"\\\"<div style='max-height:80px; text-overflow:ellipsis; table-layout:fixed; overflow:auto'>{3}</div>\\\"\\\"\\\" + \\\\\\n \\\"\\\"\\\"</td>\\n \\\"\\\"\\\"\\n\\n self._tablab.value = \\\"\\\"\\\"\\n <div id=\\\"wkspTable\\\">\\n <table class=\\\"table table-bordered table-striped'\\\">\\n <tr>\\n <th><div style='text-align:center'>Name</div></th>\\n <th><div style='text-align:center'>Type</div></th>\\n <th><div style='min-width:40px; text-align:center'>Size</div></th>\\n <th>Value</th>\\n </tr>\\n <tr>\\n <td>\\\"\\\"\\\" + \\\\\\n '</td></tr><tr><td>'.join([cellLayout.format(vName[k], vType[k], vSize[k], str(val)) for k, val in enumerate(v)]) + \\\\\\n \\\"\\\"\\\"</td>\\n </tr>\\n </table>\\n </div>\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61c33ca1d1be6537546fff932f34e303\",\n \"score\": \"0.5391382\",\n \"text\": \"def _tables(self, args):\\n\\t\\tline = \\\"SELECT name FROM sqlite_master WHERE type = 'table' ORDER BY name;\\\"\\n\\t\\tinterpretor.onecmd(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cfadd03a831052dff17c7fe3ec82f3e\",\n \"score\": \"0.53863317\",\n \"text\": \"def __call__(self):\\n string = 'TABLE: {}\\\\n'.format(self.__table__)\\n for i, tup in enumerate(self):\\n string += '├─ ' if i < len(dict(self))-1 else '└─ '\\n string += '{}: {} {}\\\\n'.format(tup[0], tup[1], type(tup[1]))\\n string += 'RELATIONSHIPS: {}\\\\n'.format(self.__table__)\\n mapper = self.inspect().relationships\\n for i, rel in enumerate(mapper):\\n string += '├─ ' if i < len(dict(mapper))-1 else '└─ '\\n string += '{}, {}\\\\n'.format(rel, rel.direction.name)\\n print string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70663cc043a3493d777ef0cd758b57b5\",\n \"score\": \"0.538245\",\n \"text\": \"def on_rows_updated(ibswgt, tblname, nRows):\\n #printDBG('Rows updated in tblname=%r, nRows=%r' % (str(tblname), nRows))\\n if tblname == ENCOUNTER_TABLE: # Hack\\n return\\n tblname = str(tblname)\\n tblnice = gh.TABLE_NICE[tblname]\\n view = ibswgt.views[tblname]\\n index = ibswgt._tab_table_wgt.indexOf(view)\\n text = tblnice + ' ' + str(nRows)\\n #printDBG('Rows updated in index=%r, text=%r' % (index, text))\\n ibswgt._tab_table_wgt.setTabText(index, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2206a7695506ee7528283fedd0db659\",\n \"score\": \"0.5380112\",\n \"text\": \"def printTablesIndex(self, outBuffer=False):\\n sz = [14, 0, 8]\\n for tab in self.__tables:\\n if len(tab.name) > sz[0]:\\n sz[0] = len(tab.name)\\n l = len(str(tab.getlinewidth()))\\n if l > sz[1]:\\n sz[1] = l\\n\\n fmtt = \\\"{0:\\\" + str(sz[0]) + \\\"s} {1:\\\" + str(sz[1]) + \\\"d} {2:>\\\" + str(sz[2]) + \\\"s} {3:s}\\\"\\n buff = fmtt.format(\\\"ReadMe\\\", MAX_SIZE_README_LINE, \\\".\\\", \\\"this file\\\") + \\\"\\\\n\\\"\\n for tab in self.__tables:\\n buff += fmtt.format(self.__strFmt(tab.name),\\n tab.getlinewidth(),\\n str(tab.nlines),\\n self.__strFmt(tab.description))\\n buff += \\\"\\\\n\\\"\\n\\n if outBuffer: return buff\\n sys.stdout.write(buff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0467f07d70d5eeef0e2ac90df065f460\",\n \"score\": \"0.53758734\",\n \"text\": \"def table(tablename, readonly=True):\\r\\n t = tbtool()\\r\\n t.open(tablename, nomodify=readonly)\\r\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b33747857aa6fa5337fafc1d6b0df5\",\n \"score\": \"0.53654003\",\n \"text\": \"def show_individual_field(table):\\n if table == ad_feature:\\n print('The schemas for table ad_feature is: ')\\n ad_feature.printSchema()\\n elif table == behavior_log:\\n print('The schemas for table behavior_log is: ')\\n behavior_log.printSchema()\\n elif table == raw_sample:\\n print('The schemas for table raw_sample is: ')\\n raw_sample.printSchema()\\n elif table == user_profile:\\n print('The schemas for table user_profile is: ')\\n user_profile.printSchema()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"051afb79c30cb526e206bb5a57e06565\",\n \"score\": \"0.53632385\",\n \"text\": \"def create_main_table_view(self, profile_dict_obj, table_clicked_action):\\n\\n # Create the main table widget where data will be populated\\n self.main_table_view = QTableWidget()\\n\\n # Do not allow the user to edit the contents of the table\\n self.main_table_view.setEditTriggers(\\n QtWidgets.QTableWidget.NoEditTriggers)\\n # The number of rows is defined by how many objects there are in the dictionary\\n self.main_table_view.setRowCount(len(profile_dict_obj))\\n # 2 columns: one for the profile name and one for the delete button\\n self.main_table_view.setColumnCount(2)\\n\\n # Iterate through the data in the dictionary and format it appropriately in the table\\n for row_count, profile_name in enumerate(profile_dict_obj):\\n # Modify the profile name's string and populate into a table cell\\n mod_profile_name = profile_name.replace('_', ' ').title()\\n mod_profile_name_widget = QTableWidgetItem(mod_profile_name)\\n # Create a delete 'button' by converting a cell to act like a button\\n delete_row_widget = QTableWidgetItem('Delete')\\n delete_row_widget.setBackground(\\n QColor(sc.COLOR_ERROR)\\n )\\n delete_row_widget.setForeground(\\n QColor(sc.COLOR_WHITE)\\n )\\n mod_profile_name_widget.setFont(self.table_font)\\n delete_row_widget.setFont(self.table_font)\\n # Align items in their respective cells appropriately\\n mod_profile_name_widget.setTextAlignment(Qt.AlignLeft)\\n delete_row_widget.setTextAlignment(Qt.AlignRight)\\n # Add items to the table view\\n self.main_table_view.setItem(\\n row_count, 0, mod_profile_name_widget)\\n self.main_table_view.setItem(\\n row_count, 1, delete_row_widget)\\n\\n # Function to execute when a cell from this table is clicked\\n self.main_table_view.clicked.connect(table_clicked_action)\\n\\n # Capture the horizontal header of the table\\n main_table_hor_head = self.main_table_view.horizontalHeader()\\n\\n # Hide all headers of the table\\n self.main_table_view.verticalHeader().setVisible(False)\\n main_table_hor_head.setVisible(False)\\n\\n # Resize the left column to stretch as far out as possible\\n main_table_hor_head.setSectionResizeMode(0, QHeaderView.Stretch)\\n # Resize the right column to wrap around its contents\\n main_table_hor_head.setSectionResizeMode(\\n 1, QHeaderView.ResizeToContents)\\n\\n self.main_table_view.resizeRowsToContents()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35608e12e926eeac8b81bcb54cd2a57\",\n \"score\": \"0.53614664\",\n \"text\": \"def add_to_table(self, table, index):\\n table.setCellWidget(index,0, self._tab_id)\\n table.setCellWidget(index,1, self._tab_name)\\n table.setCellWidget(index,2, self._tab_owner)\\n table.setCellWidget(index,3, self._tab_status) \\n table.setCellWidget(index,4, self._tab_tasks_total)\\n table.setCellWidget(index,5, self._tab_tasks_left)\\n table.setCellWidget(index,6, self._tab_tasks_done)\\n # currently not supported\\n #table.setCellWidget(index,6, self._tab_priority)\\n table.setCellWidget(index,7, self._tab_pool)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"155cebd81e6e603f22f2ae5de818afac\",\n \"score\": \"0.53536856\",\n \"text\": \"def print_table(table, db_file):\\n \\n try:\\n conn, c = connect_to_db(db_file)\\n rows = c.execute('SELECT * FROM {t}'.format(t=safe(table))).fetchall()\\n cols = c.execute(\\\"PRAGMA table_info({t})\\\".format(t=safe(table))).fetchall()\\n conn.close()\\n print '\\\\nTABLE ', table, '\\\\n'\\n r = 1\\n for row in rows:\\n print \\\"ROW\\\", r\\n for i in range(len(cols)):\\n print ' ', cols[i][1].ljust(16), (str(row[i]) if isinstance(row[i],(int,long)) else row[i])\\n print ''\\n r += 1\\n except Exception as e:\\n print \\\"Error when trying to print table\\\", table\\n print e\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1796,"cells":{"query_id":{"kind":"string","value":"cd3a0cfa9f2722cf570fbbfd381ffaff"},"query":{"kind":"string","value":"Uniform log prior for the 2D Rosenbrock likelihood following Wang & Li (2017) where the prior pi(x) is a uniform distribution over [5, 5] x [5, 5] x ... for however many dimensions (dim = x.shape[1])"},"positive_passages":{"kind":"list like","value":[{"docid":"c95cb3578239edfe8a7cd908978e8fd8","score":"0.0","text":"def rosenbrockLnprior(theta):\n\n if np.any(np.fabs(theta) > 5):\n return -np.inf\n else:\n return 0.0","title":""}],"string":"[\n {\n \"docid\": \"c95cb3578239edfe8a7cd908978e8fd8\",\n \"score\": \"0.0\",\n \"text\": \"def rosenbrockLnprior(theta):\\n\\n if np.any(np.fabs(theta) > 5):\\n return -np.inf\\n else:\\n return 0.0\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3c5b98dc9d700b0e2e1aa1542cf74ec7","score":"0.7165135","text":"def log_prior(mu, log_sigma):\n return 0.5 * T.sum(1 + 2 * log_sigma - mu ** 2 - T.exp(2 * log_sigma))","title":""},{"docid":"0f64d33d9ca8e1c719d82e088ca4cd88","score":"0.7152979","text":"def log_prior(mu, log_sigma):\n return 0.5* T.sum(1 + 2*log_sigma - mu**2 - T.exp(2*log_sigma))","title":""},{"docid":"d34e3e39c1d08848cb37f4511ed42ab3","score":"0.6978056","text":"def log_prob(self, x: th.Tensor) -> th.Tensor:","title":""},{"docid":"d3b840b37afa3ddb04fb0c2f3f789ee6","score":"0.6974313","text":"def log_prior(self, x):\n if self.chi and self.has_prior:\n return self.chi.logpdf(x[self.parameters[2]])\n else:\n raise RuntimeError(\n \"log_prior is not defined when a radial parameter has been \"\n \"specified!\"\n )","title":""},{"docid":"70d0f3e095cf69577a57439fd574a752","score":"0.6945109","text":"def log_prior(self, x):\n return self.chi.logpdf(x[self.parameters[1]])","title":""},{"docid":"7484faf9c294a823ec3bf3171c0e6a47","score":"0.69407576","text":"def _logprior(rate, params, data, m=DEFAULT_M_NAME):\n alpha, mmin, mmax = params\n return np.log(rate) + priors.pareto(data, name=m, exp=alpha, minval=mmin, maxval=mmax)","title":""},{"docid":"7484faf9c294a823ec3bf3171c0e6a47","score":"0.69407576","text":"def _logprior(rate, params, data, m=DEFAULT_M_NAME):\n alpha, mmin, mmax = params\n return np.log(rate) + priors.pareto(data, name=m, exp=alpha, minval=mmin, maxval=mmax)","title":""},{"docid":"afdae4522dbb6840e5323b9701da8a06","score":"0.6869229","text":"def _calculate_log_prior(self):\n \"\"\"\n # We initially wanted to use a uniform prior over number parts, here is how we can define it.\n # To assume a uniform prior over number of parts, we can let the derivation prob. for all trees to be equal.\n # Note that we still have to keep the spatial model probability as there are many hypotheses\n # with the same number of parts but with different spatial models.\n # NOTE we are not using this prior, because it will lead to chains that are potentially quite slow since\n # the chain will spend an equal amount of time sampling quite large trees as it does sampling small trees.\n return np.log(self.shape.spatial_model.probability())\n \"\"\"\n # use the same prior with BDAoOSSShape\n return np.log(self.shape.probability()) + np.log(self.shape.spatial_model.probability())","title":""},{"docid":"4555d1641627b22879e9bac10a582a4f","score":"0.6867216","text":"def log_jeffrey_prior(x):\n\n p1 = polygamma(n=1, x=x / 2)\n p2 = polygamma(n=1, x=(x + 1) / 2) + 2 * (x + 3) / (x * (x + 1) ** 2)\n if p1 <= p2:\n return -1 * float('inf')\n return .5 * (np.log(x) - np.log(x + 3) + np.log(p1 - p2))","title":""},{"docid":"7a9a0beb4dd1a764307f6fe177eb29cb","score":"0.6852913","text":"def log_prior(self, x: np.ndarray) -> np.ndarray:\n with np.errstate(divide=\"ignore\"):\n log_p = np.log(self.in_bounds(x), dtype=float)\n log_p -= np.sum(np.log(self.upper_bounds - self.lower_bounds))\n return log_p","title":""},{"docid":"d6b61655ac30172970cdbb1dcb6329eb","score":"0.68394876","text":"def _evaluate_negative_log_prior(self, x):\n Sinv = np.diag([1/self.theta_std**2]*self.dim) \n mu = self.theta_mean*np.ones(self.dim)\n \n return 0.5*(x-mu).T.dot(Sinv.dot(x-mu))","title":""},{"docid":"1b2ab295c6765c44ac54125c9762e7f6","score":"0.6776281","text":"def log_prob(self, likelihood: Distribution, x):\n raise NotImplementedError(\"Implement me!\")","title":""},{"docid":"46c972608e819062475c238c35760ea6","score":"0.6707999","text":"def logPosterior(self, arr):\n\n nParams = self.nParams\n\n nObs = self.nObs\n\n Theta, X = arr[:nParams], arr[nParams:] # find another way\n Y = self.Y\n\n nStates = self.nStates\n\n logPrior_Theta = None # TODO find right prior - log p(theta)\n logPrior_Theta = 0\n\n logPrior_X = 0 # GP Prior - sum log p(X_k | 0, Cphi_k)\n logObs = 0 # sum log p(Y|X, sigma)\n logGradientMatch = 0 # sum of log p(f_k(x, theta) | m_k, Gamma_k)\n\n Gamma_matrices = self.Gamma_matrices\n CPhi_matrices = self.Cphi_matrices\n m_noX_matrices = self.m_noX_matrices\n\n sigma = self.sigma\n\n F = self.F\n F_val = F(X, Theta)\n\n for k in range(nStates):\n sigma_k = sigma[k]\n\n Gamma_k = Gamma_matrices[k] # cov of x_k GP derivative\n CPhi_k = CPhi_matrices[k] # cov of x_k GP Prior\n m_noX_k = m_noX_matrices[k]\n\n x_k = X[k]\n\n m_k = m_noX_k.dot(x_k) # mean of derivative of GP\n\n y_k = Y.T[k]\n\n # 1. GP prior - add log p(x_k | 0, Cphi_k)\n logPrior_X += self.logN(x=x_k,\n mean=np.zeros(nObs),\n cov=CPhi_k + np.eye(nObs)*1e-6)\n\n # 2. Observation - add log p(y_k | x_k, sigma*I)\n logObs += self.logN(x=y_k,\n mean=x_k,\n cov=np.eye(nObs) * sigma_k**2)\n\n # logObs += self.logN(x=y_k,\n # mean=x_k,\n # cov=np.eye(nObs) * sigma_k**2)\n\n # 3. gradient matching - add log p(f_k(X, theta) | m_k, Gamma_k)\n logGradientMatch += self.logN(F_val[k], # check the shape\n mean=m_k,\n cov=Gamma_k)\n\n return logPrior_Theta + logPrior_X + logObs + logGradientMatch","title":""},{"docid":"cc1af5ae5ee825a370ebc69278383fc3","score":"0.6683679","text":"def compute_log_prob(x, axis=None):\n return np.log(x) - np.log(x.sum(axis=axis).reshape(-1, 1))","title":""},{"docid":"2980143acd721ecc5457248aeda192ab","score":"0.6617951","text":"def log_prob(self, x):\n y, logp = self.f(x)\n return self.prior.log_prob(y) + logp, y","title":""},{"docid":"2f3e7a9a46099429a55b364a9f4f079c","score":"0.659146","text":"def log_probability(self, x):\n lp = -0.5*np.log(2*np.pi*self.sigmasq) -1.0/(2*self.sigmasq) * (x-self.mu)**2\n lp = np.nan_to_num(lp)\n return lp","title":""},{"docid":"4ace82be88e699faac086d00ecf53266","score":"0.6587828","text":"def logPrior(self):\n self.map()\n if self.inbounds():\n self.logP = 0.0\n else:\n self.logP = -np.inf\n return self.logP","title":""},{"docid":"8e5255246efe97b041387167513a518f","score":"0.6555751","text":"def log_prior(nat_params,alpha_pi=10,alpha_T=.1,O_mean_var=100,O_logsd_var=1): \n pi,T,O,R = to_params(nat_params)\n O_means,O_sds = O\n \n log_pi = np.log(pi+1e-16)\n log_T = np.log(T+1e-16)\n \n obj = 0\n \n obj += (alpha_pi-1)*np.sum(log_pi)\n obj += (alpha_T-1)*np.sum(log_T)\n obj += -1/(2*O_mean_var)*np.sum(np.power(O_means,2)) \n # obj += -1/(2*O_logsd_var)*np.sum(np.power(O_log_sds,2)) \n\n return -obj","title":""},{"docid":"0f6c3dc06bbcc296f51b18f6194a081d","score":"0.6549311","text":"def log_gamma_prior(x, tau=0.5):\n return np.sum(ss.expon.logpdf(x, scale=tau)) # tau - inv rate param, scale inv of rate.","title":""},{"docid":"fb876467202814acf106e79c503c3170","score":"0.6546037","text":"def logdprior(parameters, hyperparameters):\n sigma_part = parameters[0] + invgamma_logpdf(parameters[0], hyperparameters[\"sigma_shape\"], hyperparameters[\"sigma_scale\"])\n tau_part = parameters[1] + invgamma_logpdf(parameters[1], hyperparameters[\"tau_shape\"], hyperparameters[\"tau_scale\"])\n return sigma_part + tau_part","title":""},{"docid":"3a4b5b14533a352a10d9a345129fa8c0","score":"0.65450346","text":"def _logprior(rate, params, data, m1=DEFAULT_M1_NAME, m2=DEFAULT_M2_NAME, DL=DEFAULT_DL_NAME):\n ### extract samples from data\n m1 = data[m1]\n m2 = data[m2]\n dl = data[DL]\n\n ### extract hyperparameters\n alpha, mmin, mmax, dlmin, dlmax = params\n\n ### compute prior at these samples and return\n return np.log(rate) + priors.ordered_joint_pareto(data, name1=m1, name2=m2, exp=alpha, minval=mmin, maxval=mmax) + priors.pareto(data, name=DL, exp=2, minval=dlmin, maxval=dlmax)","title":""},{"docid":"ef39d47682bb8b2817cba41868b92336","score":"0.6525077","text":"def log_prob(self, x):\n dim = x.shape[-1]\n dev = x - self.mean\n if self.mean.ndim == self.cov.ndim:\n maha = ((dev * self.prec_U) ** 2).sum(axis=-1)\n else:\n maha = (dev.dot(self.prec_U) ** 2).sum(axis=-1)\n return -0.5 * (dim * _LOG_2PI + self.log_det_cov + maha)","title":""},{"docid":"cf166b9e2632d5bd76228d85cc6b12c3","score":"0.6521594","text":"def _log_llr(\n self, x: np.ndarray, mu0: float, sigma0: float, mu1: float, sigma1: float\n ) -> float:\n\n return np.sum(\n np.log(sigma1 / sigma0)\n + 0.5 * (((x - mu1) / sigma1) ** 2 - ((x - mu0) / sigma0) ** 2)\n )","title":""},{"docid":"943733280bc15070eac7c44bd30b4adc","score":"0.6498431","text":"def log_prob(self, x, u):\n z, T, nat_params, logp = self.f(x, u) \n return self.free_energy_bound(T, nat_params) - logp.mean(), z","title":""},{"docid":"838cc88615151e169b8d7805a3183d23","score":"0.64857256","text":"def log_posterior_probs(x):\n assert x.ndim == 1\n\n # TODO: enter your code here\n\n # log prior logP(Hi)\n log_prior = np.log(PRIOR_PROBS)\n\n # log likelihood logP(X|H) = [logP(X|H1), logP(X|H2), logP(X|H3)]\n log_likelihood = np.array([])\n for dist in HYPOTHESIS_SPACE:\n log_likelihood = np.append(log_likelihood, np.sum(dist.logpdf(x)))\n\n # log model evidence logP(X) = log( exp(logP(H1)+logP(X|H1)) + exp(logP(H2)+logP(X|H2)) + exp(logP(H3)+logP(X|H3)) )\n log_model_evidence = logsumexp( log_prior + log_likelihood ) # use logsumexp for numerical stability\n\n # log Bayes rule logP(Hi|X) = log{ P(X|Hi)*P(Hi)/P(X) } = logP(X|Hi) + logP(Hi) - logP(X)\n log_p = log_likelihood + log_prior - log_model_evidence\n\n assert log_p.shape == (3,)\n return log_p","title":""},{"docid":"b315092e44409c57ffcae0c89fb1a0dc","score":"0.64828515","text":"def log_prob(self, likelihood: Distribution, y):\n raise NotImplementedError(\"Implement me!\")","title":""},{"docid":"f078403aea91ee84d55d392d67b85a35","score":"0.6463773","text":"def likelihood(z, x, P, H, R):\n return np.exp(log_likelihood(z, x, P, H, R))","title":""},{"docid":"2e206df6957d5cc16b60ef9df84f4d9a","score":"0.64529216","text":"def fragilityln(x,mu,sig):\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\n return Prob","title":""},{"docid":"2e206df6957d5cc16b60ef9df84f4d9a","score":"0.64529216","text":"def fragilityln(x,mu,sig):\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\n return Prob","title":""},{"docid":"36c1967edd350b3c27def7e4a11be4db","score":"0.6438222","text":"def lnprior(X):\n T,N_y,N_gb,alpha,turn=X\n if 10< T< 50 and 0< N_y <0.06 and 0<N_gb<0.06 and 0<alpha<10 and 10<turn<50:\n # if 0 < m < 4 and -10<c<10.0:\n return 0.0\n # return -np.log(N_y)\n return -np.inf","title":""},{"docid":"2c06fb69344da1d841ecdd4c8136a68b","score":"0.6437301","text":"def log_prior_u(self, u):\n return uniform_log_prob(theta=u, lower=prior_bounds['u'][0], upper=prior_bounds['u'][1])","title":""},{"docid":"0e879b7a5da4a7a1df00c149fa6bd3ce","score":"0.6435463","text":"def log_probability(self):\n lp = self.log_likelihood()\n lp += self.log_prior()\n\n return lp","title":""},{"docid":"0e879b7a5da4a7a1df00c149fa6bd3ce","score":"0.6435463","text":"def log_probability(self):\n lp = self.log_likelihood()\n lp += self.log_prior()\n\n return lp","title":""},{"docid":"fb5e53bcdeeef08f03506ed32db5431d","score":"0.64333975","text":"def genetic_lnprior(x):\n\n x = np.array(x)\n if x.ndim > 1:\n alpha1 = x[:,0]\n alpha2 = x[:,1]\n gamma = x[:,2]\n beta = x[:,3]\n eta = x[:,4]\n K = x[:,5]\n else:\n alpha1, alpha2, gamma, beta, eta, K = x\n\n # All flat priors from Wang & Li (2017)\n if np.any(alpha1 < 120.0) or np.any(alpha1 > 200.0):\n return -np.inf\n elif np.any(alpha2 < 15.0) or np.any(alpha2 > 16.0):\n return -np.inf\n elif np.any(gamma < 2.1) or np.any(gamma > 2.9):\n return -np.inf\n elif np.any(beta < 0.85) or np.any(beta > 1.15):\n return -np.inf\n elif np.any(eta < 1.3) or np.any(eta > 2.7):\n return -np.inf\n elif np.any(K < np.log10(2.3e-5)) or np.any(K > np.log10(3.7e-5)):\n return -np.inf\n else:\n return 0.0","title":""},{"docid":"8b80deeec01502c85dcd6128a2eea8ad","score":"0.6426834","text":"def get_particle_log_prior(ripl):\n log_joint = get_particle_log_joint(ripl)\n log_likelihood = get_particle_log_likelihood(ripl)\n return compute_particle_log_prior(log_joint, log_likelihood)","title":""},{"docid":"062579914eea7df4cf09f9addf400000","score":"0.6418788","text":"def fragilityln(x,mu,sig):\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\n return Prob","title":""},{"docid":"062579914eea7df4cf09f9addf400000","score":"0.6418788","text":"def fragilityln(x,mu,sig):\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\n return Prob","title":""},{"docid":"062579914eea7df4cf09f9addf400000","score":"0.6418788","text":"def fragilityln(x,mu,sig):\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\n return Prob","title":""},{"docid":"ffeb19a0b08d7eb667b91078afaeb522","score":"0.6398503","text":"def lnprior_pdf(self, x):\n value = self.value(x)\n if self._log:\n value = np.log(value)\n return -(value - self._mu) ** 2 / (2. * self._sigma ** 2)","title":""},{"docid":"36b62f5986ed63003603e8aa2a8da3bd","score":"0.6395055","text":"def log_prob_components(self):\n params = self.params\n x, mu, R = [getattr(self.params, attr) for attr in ('data','mu','R')]\n\n log_p = np.array([params.distance(x[i], mu, R[i][:2]) for i in range(params.n_projections)])\n \n log_norm = 0.5 * params.n_dimensions * np.log(params.tau / (2*np.pi))\n \n return params.beta * (log_norm - 0.5 * params.tau * log_p)","title":""},{"docid":"47689625ee9b378ac0689aefd6788bc9","score":"0.63927907","text":"def log_prob(self, x):\n # both xi and beta have shape (*batch_shape)\n # and so do all the elements bellow\n\n x = x.unsqueeze(dim=-1)\n\n logp = -self.beta.log().double()\n logp += torch.where(\n self.xi == torch.zeros_like(self.xi),\n -x / self.beta,\n -(1 + 1.0 / (self.xi + 1e-6))\n * torch.log(1 + self.xi * x / self.beta),\n )\n logp = torch.where(\n x < torch.zeros_like(x),\n (-np.inf * torch.ones_like(x)).double(),\n logp,\n )\n return logp.squeeze(dim=-1)","title":""},{"docid":"843cf7bfd680862d2baab13b06931a03","score":"0.63919437","text":"def ln_gaussian_prior(x, mean, sigma):\n return -0.5*( ((x - mean) / sigma)**2 + np.log(2*np.pi*sigma**2) )","title":""},{"docid":"80094db15e7e8618d93b7aa633bd322e","score":"0.6391109","text":"def logit_log_prob(raw_logits, shaped_logits_logsumexp):\n return shape_logits(raw_logits) - shaped_logits_logsumexp[..., None]","title":""},{"docid":"e9e1a945656e5341806d875bb8ad284c","score":"0.63885444","text":"def log_probability(self, x):\n assert np.all(x >= self.lb) and np.all(x <= self.ub)\n\n # Center x\n z = (x-self.mu) / np.sqrt(self.sigmasq)\n # Log prob of the normalization constant\n lp = -0.5 * np.log(self.sigmasq) - np.log(self.Z)\n # Log prob of the standardized density\n lp += -0.5*np.log(2*np.pi) -0.5*z**2\n return lp","title":""},{"docid":"386bea022ce371953fed456179f25b89","score":"0.63843906","text":"def log_prior(self):\n\n log_prior = 0.0\n for param in self.parameters():\n if hasattr(param, \"prior\") and param.prior is not None:\n if hasattr(param, \"transform\") and param.transform is not None:\n val = param.transform()\n else:\n val = param.data\n log_prior += param.prior.log_prob(val).sum()\n\n return log_prior","title":""},{"docid":"a6792e07a9e2befabc1c12ea257e00f5","score":"0.6373793","text":"def logdprior(parameters, hyperparameters):\n mu_part = - 0.5 / (hyperparameters[\"mu_sd\"]**2) * (parameters[0] - hyperparameters[\"mu_mean\"])**2\n beta_part = - 0.5 / (hyperparameters[\"beta_sd\"]**2) * (parameters[1] - hyperparameters[\"beta_mean\"])**2\n xi_part = parameters[2] - hyperparameters[\"xi_rate\"] * exp(parameters[2])\n omega2_part = parameters[3] - hyperparameters[\"omega2_rate\"] * exp(parameters[3])\n lamb1_part = parameters[4] - hyperparameters[\"lambda1_rate\"] * exp(parameters[4])\n lambadd_part = parameters[5] - hyperparameters[\"lambdaadd_rate\"] * exp(parameters[5])\n w_part = parameters[6] - 2 * log(1 + exp(parameters[6]))\n return mu_part + beta_part + xi_part + omega2_part + lamb1_part + lambadd_part + w_part","title":""},{"docid":"681a41444472f9ced231f3e49fab861a","score":"0.6372184","text":"def log_probability(self, x):\n lp = x * np.log(self.p) + (1-x) * np.log(1.0-self.p)\n lp = np.nan_to_num(lp)\n return lp","title":""},{"docid":"04661a45f35b7ac1cf081023b26b2a8c","score":"0.63547295","text":"def loglike(params):\n a_cum, logC = params\n\n # always enforce this general prior on a\n if a_cum <= 0:\n return -np.inf\n\n # first the models of flare energies and rate\n result = loglike_powerlaw(a_cum + 1) + loglike_poisson(a_cum, logC)\n\n # next any priors\n result += self.a_logprior(a_cum) + self.logC_logprior(logC)\n\n return result","title":""},{"docid":"1ce15f17a800923182b801a47397fb6e","score":"0.6354573","text":"def logprior(theta):\n\n # read input parameters\n if anisotropic:\n logM, rh, g, phi0, logra = theta\n else:\n logM, rh, g, phi0 = theta\n logra = 6 - 1e-5\n\n # check boundaries\n if not ((3. < logM < 7.) & (0. < rh < 30.) &\n (1e-3 < g < 3.5) & (1.5 < phi0 < 14.) &\n (-1. < logra < 6.)):\n return -np.inf\n else:\n # g: uniform\n gprior = np.log(1. / (5.3 - 1e-3))\n\n # logM: uniform\n logMprior = np.log(1. / (7. - 3.))\n\n # phi0: uniform\n phi0prior = np.log(1. / (14 - 1.5))\n\n # rh: uniform\n rhprior = np.log(1. / (30. - 0.))\n\n # logra: uniform\n if anisotropic:\n lograprior = np.log(1. / (6. - (-1.)))\n else:\n lograprior = 0.\n\n return gprior + logMprior + phi0prior + rhprior + lograprior","title":""},{"docid":"d95c4d1a810d3baa417163f3f8d7c136","score":"0.6345897","text":"def ln_prior(self, parameters):\n lnprior = 0.0\n for (key, value) in parameters.iteritems():\n parmtype = key.split(\"_\")[1]\n if parmtype == 'radius':\n lnprior += stats.distributions.uniform.logpdf(value, 0.5, 2.5)\n elif parmtype == 'scalingFactor':\n lnprior += stats.distributions.uniform.logpdf(value, -0.8, 2.3)\n elif parmtype == 'sigma':\n lnprior += stats.distributions.invgamma.logpdf(value,1.0,1.0)\n return lnprior","title":""},{"docid":"02553acc58828166aef7727f254d6d55","score":"0.6344789","text":"def log_probability(self, x):\n z = x-self.mu\n lp = -0.5*self.D*np.log(2*np.pi) -0.5*self.D*self.logdet_Sigma \\\n -0.5 * z.T.dot(np.linalg.solve(self.Sigma, z))\n lp = np.nan_to_num(lp)\n return lp","title":""},{"docid":"31a7e8323bb5cbacc1c12982cc86043f","score":"0.6342709","text":"def log_prob(y, xb):\n if y == 1:\n xb = -xb\n return -log_sum_exp(np.array([0, xb]))","title":""},{"docid":"9856337c7f78bc2e80e70e6ae959cd03","score":"0.6337176","text":"def __lnprior_gp(self, prior_gp=1):\n return np.log(prior_gp)","title":""},{"docid":"0d63459918216014f8b9db0ba4afcc59","score":"0.6331367","text":"def pl(x, xmin=None):\n if xmin is None:\n xmin = x.min()\n return (len(x)-1)/(float(len(x)))*(1./len(x) * np.sum(np.log(x/xmin)))**-1","title":""},{"docid":"63c94aa408a24f1295c564571453be8f","score":"0.6330112","text":"def x_prime_log_prior(self, x_prime):\n if self.has_prime_prior:\n return (\n -1.5 * np.log(2 * np.pi)\n - np.sum(\n [x_prime[pp] ** 2 for pp in self.prime_parameters], axis=0\n )\n / 2\n )\n else:\n raise RuntimeError(\"x prime prior is not defined!\")","title":""},{"docid":"3ebc1a2bcd4d31a32cd0e7710a1b9fb7","score":"0.63271934","text":"def log_gamma_prior(x, tau=0.5):\n n = len(x)\n rate = 1/tau\n res = n * math.log(rate/2) - rate * np.sum(np.abs(x))\n return res","title":""},{"docid":"45e5c5ed5b558ee1350954c112da6683","score":"0.63266855","text":"def likelihood(mean, logs, x): # logs: log(sigma)\n if mean is None and logs is None:\n return -0.5 * (x ** 2 + GaussianDiag.Log2PI)\n else:\n return -0.5 * (logs * 2. + ((x - mean) ** 2) / torch.exp(logs * 2.) + GaussianDiag.Log2PI)","title":""},{"docid":"0886d70dada0b0bc3bf19a365be3929d","score":"0.63261914","text":"def log_prob(self, x: torch.Tensor) -> torch.Tensor:\n return self.__call__(x)","title":""},{"docid":"4a16fbba705d0f8271faa2d7a451018d","score":"0.63193995","text":"def ln_prob(self,param):\n\n # Calcualte prior. If not finite, this solution has an -infinity log\n # likelihood\n ln_prior = self.ln_prior(param)\n if not np.isfinite(ln_prior):\n return -np.inf\n\n # Calcualte likelihood. If not finite, this solution has an -infinity\n # log likelihood\n ln_like = self.ln_like(param)\n if not np.isfinite(ln_like):\n return -np.inf\n\n # log posterior is log prior plus log likelihood\n return ln_prior + ln_like","title":""},{"docid":"1b2470816ef718836cbdd7c4f3fcbe72","score":"0.63115937","text":"def proposal(S):\n if rng.rand() <= 0.5:\n index = rng.randint(S.u.size)\n S.u[index] += 10.**(1.5 - 6.*rng.rand())*rng.randn()\n S.u[index] = np.mod(S.u[index], 1.)\n else:\n S.lam += 10.**(1.5 - 6.*rng.rand())*rng.randn()\n S.lam = np.mod(S.lam, 1.)\n\n S.logx = np.empty(Model.logl.size)\n for i in xrange(0, Model.run_id.max() + 1):\n which = (Model.run_id==i)\n if i==0:\n factor = 1.\n else:\n factor = S.lam\n S.logx[which] = factor*np.cumsum(np.log(S.u[which]))\n S.order = np.argsort(S.logx)\n S.logx = np.sort(S.logx)[::-1]","title":""},{"docid":"ff86dcda2e071fac791469ad9ee0fd36","score":"0.631035","text":"def lnprob(theta, samples, xmin):\n\n prior = lnprior(theta)\n posterior = lnlike(theta, samples, xmin)\n if not np.isfinite(prior):\n return -np.inf\n\n return prior + posterior","title":""},{"docid":"18325074188daba43b5523b473f56716","score":"0.62930757","text":"def loglikeobs(self, params):\n low, upp = self._bounds(params)\n prob = self.prob(low, upp)\n return np.log(prob + 1e-20)","title":""},{"docid":"702e4763e4b037e52e26ae0e54ec89f0","score":"0.62866753","text":"def loglikelihood(pi, mu, data, K):\n\treturn np.sum(np.log([np.sum([pi[k] * bernoulli(x, mu[k]) for k in range(0, K)]) for x in data]))","title":""},{"docid":"902a9c6a551c92009062b7a01b081016","score":"0.62759113","text":"def loghyperprior(self):\n return self._loghyperprior(rate, self.params)","title":""},{"docid":"7cac36c74de400813f63cdcd00b103eb","score":"0.6271557","text":"def fragilityln(self, x,mu,sig):\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\n return Prob","title":""},{"docid":"24d8b02f41d0f631b8cf122060ee2d48","score":"0.62703246","text":"def log_prior_z(self, z):\n return uniform_log_prob(theta=z, lower=prior_bounds['z'][0], upper=prior_bounds['z'][1])","title":""},{"docid":"21f4afa5ea1de76d6d96c69c1f24c243","score":"0.62641776","text":"def log_normpdf(x, mu, sigma):\n diff = x-mu\n return np.log(1/(math.sqrt(2*math.pi*sigma)))-(math.pow(diff,2)/(2*sigma))","title":""},{"docid":"e2575afcfe1b12bd0c5cd953b0bae4c6","score":"0.62430704","text":"def log_likelihood(z, x, P, H, R):\n S = np.dot(H, np.dot(P, H.T)) + R\n return logpdf(z, np.dot(H, x), S)","title":""},{"docid":"90e56012db49e9d635b8954cc7e0c209","score":"0.6235775","text":"def poisson_llr(n, mu):\n return n - mu + xlogy(n, mu / numpy.maximum(1, n))","title":""},{"docid":"e32e8609af184ca9a0739bd25971f611","score":"0.6235716","text":"def prior (prob):\n\treturn uniform.pdf(prob) #uniform, uniformative prior ","title":""},{"docid":"156cc5ac377fae182361332b999a20a6","score":"0.6233888","text":"def log_prior_compute(self, zi):\n lp = 0.\n sigma = 1\n for i in range(len(zi.nn_params)):\n lp += -0.5 * torch.sum((zi.nn_params[i].weight ** 2)) / self.p_sigma**2\n if self.bias:\n lp += -0.5 * torch.sum((zi.nn_params[i].bias ** 2)) / self.p_sigma**2\n return lp","title":""},{"docid":"41bab49c8b124fcfd26201ce12b08617","score":"0.6226735","text":"def _compute_log_likelihood(self, X):\n assert type(X).__module__ == \"numpy\"\n xs = X[:, 0]\n ns = X[:, 1]\n ps = X[:, 2:4]\n ref = binom.logpmf(xs, ns, ps[:, 0])\n nonref = binom.logpmf(xs, ns, ps[:, 1])\n return np.matrix([ref, nonref]).T","title":""},{"docid":"8752189d9dc8df61e8e212ec07c074f0","score":"0.62233776","text":"def fn(x, data):\n beta, q, delta, rho, gamma_mild, gamma_wild, k = x\n S, E, I_mild, I_wild, P, N = data\n\n pC = 1 - np.exp(-rho)\n pR_mild = 1 - np.exp(-gamma_mild)\n pR_wild = 1 - np.exp(-gamma_wild)\n\n # log likelihood\n # add epsilon to avoid log 0.\n logB = np.sum(np.log(sp.stats.binom(S, P).pmf(B) + epsilon))\n logC = np.sum(np.log(sp.stats.binom(E, pC).pmf(C) + epsilon))\n logD_mild = np.sum(np.log(sp.stats.binom(I_mild, pR_mild).pmf(D_mild) + epsilon))\n logD_wild = np.sum(np.log(sp.stats.binom(I_wild, pR_wild).pmf(D_wild) + epsilon))\n\n assert not np.isnan(logB)\n assert not np.isnan(logC)\n assert not np.isnan(logD_mild)\n assert not np.isnan(logD_wild)\n\n # log prior\n log_prior = 0\n for i in range(len(priors)):\n a, b = priors[i]\n log_prior += np.log(sp.stats.gamma(a, b).pdf(x[i])+epsilon)\n assert not np.isnan(log_prior) \n return logB + logC + logD_mild + logD_wild + log_prior","title":""},{"docid":"84dcc57e5ecd360693d3c0c21f52f152","score":"0.6214991","text":"def probability(self):\r\n #self.testfunc = lambda x: x**2\r\n #self.testintegral = integrate.quad(self.testfunc,1,2)\r\n #print(\"testfunc\", self.testintegral)\r\n self.mu1likefunc = lambda x: self.likefunc1D(x,self.mpoints,self.xsqr,self.xixj,self.xmean)\r\n self.mu1probability = integrate.quad(self.mu1likefunc,-np.inf,np.inf)\r\n self.mu2likefunc = lambda y: self.likefunc1D(y,self.mpoints,self.ysqr,self.yiyj,self.ymean)\r\n self.mu2probability = integrate.quad(self.mu2likefunc,-np.inf,np.inf)\r\n self.posterior = self.mu1probability[0] * self.mu2probability[0] # it is really posterior/prior and ignored the 1/2pi to m factor\r\n print(\"p({x}) / prior =\", self.posterior)","title":""},{"docid":"967c3de3d6f4aff986df469078b3958a","score":"0.61947596","text":"def ln_prior(self, pars):\n \n a, b = pars # unpack parameters\n ln_prior_val = 0. # we'll add to this\n\n if b < -0.5 or b > 0.5:\n return -np.inf\n else:\n ln_prior_val += np.log(1E-2) # normalization, log(1/100)\n\n if a < -5 or a > 5.:\n return -np.inf\n else:\n ln_prior_val += np.log(1E-2) # normalization, log(1/100)\n\n return ln_prior_val","title":""},{"docid":"87199f56fe60df882114c91e54321a2d","score":"0.61928195","text":"def lnlikeFromProjProb(modelParams, observable):\n #xMax=10\n #xMin=-10\n sigma, m, b = modelParams\n #print('sigma {}, m {}, b {}'.format(sigma,m,b))\n y = observable # for convenience inside the function\n projectedProbabilities = getProjectedProb( y, m, b, sigma, xMin, xMax)\n logProjProbs = np.log(projectedProbabilities)\n loglikelihood = np.sum(logProjProbs)\n \n return loglikelihood","title":""},{"docid":"75cae0ca33a8bb98e387a67010923416","score":"0.618985","text":"def bernoulli_log_pdf(x, mu):\n mu = torch.clamp(mu, 1e-7, 1.-1e-7)\n return torch.sum(x * torch.log(mu) + (1. - x) * torch.log(1. - mu), dim=1)","title":""},{"docid":"75cae0ca33a8bb98e387a67010923416","score":"0.618985","text":"def bernoulli_log_pdf(x, mu):\n mu = torch.clamp(mu, 1e-7, 1.-1e-7)\n return torch.sum(x * torch.log(mu) + (1. - x) * torch.log(1. - mu), dim=1)","title":""},{"docid":"32da6ecd860fbb060d85c8730c5fc50b","score":"0.61860675","text":"def bernoulli_log_pdf(x: torch.Tensor, mu: torch.Tensor):\n mu = torch.clamp(mu, 1e-7, 1.-1e-7)\n return torch.sum(x * torch.log(mu) + (1. - x) * torch.log(1. - mu), dim=1)","title":""},{"docid":"aba435bda0dc9091c80723818eca4a71","score":"0.61832607","text":"def ln_prior(self,param):\n\n # If a paramter falls outside of the bounds, make the prior -infinity\n if np.sum(param < self.bounds[0,:]) > 0 or np.sum(param > self.bounds[1,:]) > 0:\n return -np.inf\n\n # otherwise, uniform\n return 0.0","title":""},{"docid":"564a736c29363f21fcfb887a5ceb6022","score":"0.6176178","text":"def log_likelihood(self, x):\n # *** START CODE HERE ***\n\n # Generating identity matrix of size d\n I_d = np.identity(self.d)\n\n # Obtain mu and sigma. Using results from the assignment\n # Reshaping mu so it's a (dim,) vector instead of (dim,1) to make compatible with compute_normal\n mu = self.b.flatten()\n sigma = self.W.dot(self.W.T) + self.gamma**2 * I_d\n\n # Compute log likelihood\n log_likelihood = np.log(compute_normal(x.T, mu, sigma))\n\n return log_likelihood[0]\n # *** END CODE HERE ***","title":""},{"docid":"00739b3194d8362b7028c50c423454ce","score":"0.61728406","text":"def test_log_prob():\n dims = 2\n n = 10\n x = torch.randn(n, 2)\n dist = ResampledGaussian(\n dims, lambda x: torch.ones(len(x), 1), trainable=False\n )\n out = dist.log_prob(x)\n assert len(out) == n","title":""},{"docid":"de12ee1a40b69b516831f29cb6d09faa","score":"0.6170956","text":"def compute_log_likelihood(mu_0, mu_1, sigma_0, sigma_1, pi):\n ll = 0.0\n # -------------------------------------------------------------------------\n # TODO: Code to compute ll\n\n # Iterate through all data points\n # Calculate sum of log probabilities\n for xm in X.values():\n x = xm.tolist()[0]\n #p = (1-pi)*mvg_pdf(x, mu_0, sigma_0) + pi*mvg_pdf(x, mu_1, sigma_1)\n p = (1-pi)*multivariate_normal.pdf(x, mean=mu_0, cov=sigma_0) \\\n + pi*multivariate_normal.pdf(x, mean=mu_1, cov=sigma_1)\n # Accumulate p in ll\n if p == 0.0:\n print \"ERROR: p = 0.0\"\n sys.exit()\n ll += np.log(p)\n\n # END_YOUR_CODE\n return ll","title":""},{"docid":"2fbda8157d31f43b93ab3001f5110e28","score":"0.6167279","text":"def log_prior(theta):\n N0, v0, ns0, sigma0, w0 = theta\n\n log_prior = -np.inf\n\n # Here we convert from N0, v0, ns0 to M200, c200\n\n if 0 < N0 < 5 and -1. < v0 < 3.5 and -2. < sigma0 < 4. and -0.5 < ns0 < 0.5 and -2. < w0 < 3.:\n\n logM200, logc200 = find_nfw_params(10**N0, 10**v0, ns0, 10**sigma0, 10**w0, 10., np.log10(c_M_relation(10.)))\n\n # Flat priors on all except c200:\n if -2. < sigma0 < 4. and -2. < w0 < 3. and 6 < logM200 < 14 and 0 < logc200 < 2:\n # Prior distribution on c200\n c200 = 10 ** logc200\n log_prior = log_prior_c200(c200, logM200)\n\n\n return log_prior","title":""},{"docid":"569ee3dae24a919a572e80ae6be873a2","score":"0.6162471","text":"def _log_prob(self, inputs: np.ndarray) -> np.ndarray:\n # calculate log_prob\n u, log_det = self._inverse(self._params, inputs)\n log_prob = self.prior.log_prob(u) + log_det\n # set NaN's to negative infinity (i.e. zero probability)\n log_prob = np.nan_to_num(log_prob, nan=np.NINF)\n return log_prob","title":""},{"docid":"575c27f80190f47e158602861582d60f","score":"0.616182","text":"def log_probability(theta, model, data):\n\n prior = log_prior(theta, model)\n if np.isinf(prior):\n return prior\n return prior + log_likelihood(theta, model, data)","title":""},{"docid":"ac9742972de22d45d81dac7ee6fce68a","score":"0.61460537","text":"def lnp(self, x):\n return self.distr.logpdf(x, loc=self.loc, scale=self.scale)","title":""},{"docid":"19c6d139e3557134187d81a124a4f446","score":"0.61433464","text":"def lnprobProj(modelParams, observables):\n logprior = lnPriors(modelParams)\n if not np.isfinite(logprior):\n return -np.inf\n return logprior + lnlikeFromProjProb(modelParams, observables)","title":""},{"docid":"392e923ce1fd2615443f2c7baa928642","score":"0.6143345","text":"def ln_uniform_prior(x, min_x, max_x):\n if min_x < x < max_x:\n return -np.log(max_x - min_x)\n else:\n return -np.inf","title":""},{"docid":"9b612a319e875e3d35a4e529dfb570aa","score":"0.614138","text":"def get_particle_log_likelihood(ripl):\n return ripl.evaluate('global_log_likelihood')","title":""},{"docid":"4b95c26c87a766cf8b8b002a1f8037cf","score":"0.61358666","text":"def ln_probab(pars, data, models, P):\n\n y_model, bands = ymodel(data.nus, data.z, data.dlum, models, P, *pars)\n lnp = ln_prior(data, models, P, *pars)\n\n if np.isfinite(lnp): \n posterior = lnp + ln_likelihood(data.nus, data.fluxes, data.fluxerrs, data.z, y_model) \n return posterior\n return -np.inf","title":""},{"docid":"268358e46506b1b50813f7eecdc92840","score":"0.61335814","text":"def logprob(self, Y):\n return self.build_logprob(Y)","title":""},{"docid":"fb321b63352ebe493651f276e6b2fd16","score":"0.6129187","text":"def log_probability(self, params, band):\n self.set_parameter_vector(params)\n _, _, y, ye, _, _ = np.loadtxt(self.lightcurves[band]).T\n\n # check model params are valid - checks against bounds\n lp = self.log_prior()\n if not np.isfinite(lp):\n return -np.inf\n try:\n ym = self.get_value(band)\n except ValueError as err:\n # invalid lcurve params\n print('warning: model failed ', err)\n return -np.inf\n else:\n chisq = np.sum((y - ym)**2 / ye**2)\n log_likelihood = -0.5 * (np.sum(np.log(2 * np.pi * ye**2)) + chisq)\n # log_likelihood + lp\n return log_likelihood","title":""},{"docid":"ce5f2bc40cae08a76778c68b932af4e4","score":"0.6127457","text":"def lnp(self, x):\n return self.distr.logpdf(x, loc=self.loc, scale=self.scale, df=self.df)","title":""},{"docid":"3290b02bfddf9529f621cc235fd78dc7","score":"0.61258495","text":"def log_prior(self, weights):\n shape = weights.shape\n sigma_1 = tf.constant(0.005, shape=shape, dtype=tf.float32)\n sigma_2 = tf.constant(0.00005, shape=shape, dtype=tf.float32)\n def pdf(w, sigma):\n res1 = tf.math.divide(tf.math.square(w), tf.math.square(sigma)*2)\n return tf.math.divide(tf.math.exp(tf.clip_by_value(-res1, -87.315, 88.722)), sigma*(2*math.pi)**0.5)\n part_1 = tf.clip_by_value(0.25*pdf(weights, sigma_1), tf.float32.min//2, tf.float32.max//2)\n part_2 = tf.clip_by_value(0.75*pdf(weights, sigma_2), tf.float32.min//2, tf.float32.max//2)\n return tf.math.reduce_sum(tf.math.log(part_1 + part_2))","title":""},{"docid":"86821d39fe91edc9e86142dae7394afe","score":"0.6122441","text":"def log_likelihood(self, data, hypo):\n x = data\n mu, sigma = hypo\n loglike = eval_normal_log_pdf(x, mu, sigma)\n return loglike","title":""},{"docid":"095c27f1525e25c299cc62f12a900099","score":"0.6115879","text":"def lnprior(self, pars):\n theta = pars[:self.Nbins]\n if np.any(theta < 0):\n return -np.inf\n a, tau, err = np.exp(pars[self.Nbins:-1])\n mean = pars[-1]\n kernel = a * kernels.ExpSquaredKernel(tau)\n gp = GP(kernel, mean=mean)\n gp.compute(self.bin_centers, yerr=err)\n return gp.lnlikelihood(theta) / self.smoothing","title":""},{"docid":"7b87f3b9813196bf6a30beaf03c344ae","score":"0.61149335","text":"def prior(cube, ndim, nparams):\n cube[0] = cube[0]*-10 #beta1 =uniform prior between 0:-10\n cube[1] = cube[2]*-10 #beta2 = uniform prior between 0:-10\n cube[2] = cube[3]*-10 #P_F =uniform prior between 0:-10\n cube[3] = cube[4]*-10 #P_b = uniform prior between 0:-10\n cube[4] = 10**(cube[5]*10 - 5) # F_0 = log-uniform prior between 10^-5 and 10^5\n cube[5] = 10**(cube[2]*10 + 20) # nu_b0=log-uniform prior between 10^10 and 10^30\n if ndim < 7:\n return\n \tcube[7] =cube[3]*4# s=uniform prior between 0 and 4","title":""},{"docid":"e51ce4ffd6716dde1f65e8230ca35b0e","score":"0.61113846","text":"def log_prob_from_logits(x):\n axis = len(x.get_shape())-1\n m = tf.reduce_max(x, axis, keep_dims=True)\n return x - m - tf.log(tf.reduce_sum(tf.exp(x-m), axis, keep_dims=True))","title":""},{"docid":"e26de8d54921ea9373ee20bff22ecb8f","score":"0.61059034","text":"def lnprior(theta):\n dtq, tau, Z = theta\n if -0.1 <= dtq <= 13.807108309208775 and np.log10(0.00001) <= tau <= np.log10(5.9) and 0.001 <= Z <= 1.6:\n return 0.0\n elif -0.1 <= dtq <= 13.807108309208775 and np.log10(5.9) < tau <= np.log10(6.0) and 0.001 <= Z <= 1.6:\n return 2*(np.exp(np.log10(5.9)) - np.exp(np.log10(tau)))\n else:\n return -np.inf","title":""},{"docid":"e6c89df99a6928318e8ffb32b4863ed1","score":"0.6104513","text":"def log1p(x):\n return log(1 + x)","title":""}],"string":"[\n {\n \"docid\": \"3c5b98dc9d700b0e2e1aa1542cf74ec7\",\n \"score\": \"0.7165135\",\n \"text\": \"def log_prior(mu, log_sigma):\\n return 0.5 * T.sum(1 + 2 * log_sigma - mu ** 2 - T.exp(2 * log_sigma))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f64d33d9ca8e1c719d82e088ca4cd88\",\n \"score\": \"0.7152979\",\n \"text\": \"def log_prior(mu, log_sigma):\\n return 0.5* T.sum(1 + 2*log_sigma - mu**2 - T.exp(2*log_sigma))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d34e3e39c1d08848cb37f4511ed42ab3\",\n \"score\": \"0.6978056\",\n \"text\": \"def log_prob(self, x: th.Tensor) -> th.Tensor:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b840b37afa3ddb04fb0c2f3f789ee6\",\n \"score\": \"0.6974313\",\n \"text\": \"def log_prior(self, x):\\n if self.chi and self.has_prior:\\n return self.chi.logpdf(x[self.parameters[2]])\\n else:\\n raise RuntimeError(\\n \\\"log_prior is not defined when a radial parameter has been \\\"\\n \\\"specified!\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d0f3e095cf69577a57439fd574a752\",\n \"score\": \"0.6945109\",\n \"text\": \"def log_prior(self, x):\\n return self.chi.logpdf(x[self.parameters[1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7484faf9c294a823ec3bf3171c0e6a47\",\n \"score\": \"0.69407576\",\n \"text\": \"def _logprior(rate, params, data, m=DEFAULT_M_NAME):\\n alpha, mmin, mmax = params\\n return np.log(rate) + priors.pareto(data, name=m, exp=alpha, minval=mmin, maxval=mmax)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7484faf9c294a823ec3bf3171c0e6a47\",\n \"score\": \"0.69407576\",\n \"text\": \"def _logprior(rate, params, data, m=DEFAULT_M_NAME):\\n alpha, mmin, mmax = params\\n return np.log(rate) + priors.pareto(data, name=m, exp=alpha, minval=mmin, maxval=mmax)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afdae4522dbb6840e5323b9701da8a06\",\n \"score\": \"0.6869229\",\n \"text\": \"def _calculate_log_prior(self):\\n \\\"\\\"\\\"\\n # We initially wanted to use a uniform prior over number parts, here is how we can define it.\\n # To assume a uniform prior over number of parts, we can let the derivation prob. for all trees to be equal.\\n # Note that we still have to keep the spatial model probability as there are many hypotheses\\n # with the same number of parts but with different spatial models.\\n # NOTE we are not using this prior, because it will lead to chains that are potentially quite slow since\\n # the chain will spend an equal amount of time sampling quite large trees as it does sampling small trees.\\n return np.log(self.shape.spatial_model.probability())\\n \\\"\\\"\\\"\\n # use the same prior with BDAoOSSShape\\n return np.log(self.shape.probability()) + np.log(self.shape.spatial_model.probability())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4555d1641627b22879e9bac10a582a4f\",\n \"score\": \"0.6867216\",\n \"text\": \"def log_jeffrey_prior(x):\\n\\n p1 = polygamma(n=1, x=x / 2)\\n p2 = polygamma(n=1, x=(x + 1) / 2) + 2 * (x + 3) / (x * (x + 1) ** 2)\\n if p1 <= p2:\\n return -1 * float('inf')\\n return .5 * (np.log(x) - np.log(x + 3) + np.log(p1 - p2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a9a0beb4dd1a764307f6fe177eb29cb\",\n \"score\": \"0.6852913\",\n \"text\": \"def log_prior(self, x: np.ndarray) -> np.ndarray:\\n with np.errstate(divide=\\\"ignore\\\"):\\n log_p = np.log(self.in_bounds(x), dtype=float)\\n log_p -= np.sum(np.log(self.upper_bounds - self.lower_bounds))\\n return log_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6b61655ac30172970cdbb1dcb6329eb\",\n \"score\": \"0.68394876\",\n \"text\": \"def _evaluate_negative_log_prior(self, x):\\n Sinv = np.diag([1/self.theta_std**2]*self.dim) \\n mu = self.theta_mean*np.ones(self.dim)\\n \\n return 0.5*(x-mu).T.dot(Sinv.dot(x-mu))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b2ab295c6765c44ac54125c9762e7f6\",\n \"score\": \"0.6776281\",\n \"text\": \"def log_prob(self, likelihood: Distribution, x):\\n raise NotImplementedError(\\\"Implement me!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c972608e819062475c238c35760ea6\",\n \"score\": \"0.6707999\",\n \"text\": \"def logPosterior(self, arr):\\n\\n nParams = self.nParams\\n\\n nObs = self.nObs\\n\\n Theta, X = arr[:nParams], arr[nParams:] # find another way\\n Y = self.Y\\n\\n nStates = self.nStates\\n\\n logPrior_Theta = None # TODO find right prior - log p(theta)\\n logPrior_Theta = 0\\n\\n logPrior_X = 0 # GP Prior - sum log p(X_k | 0, Cphi_k)\\n logObs = 0 # sum log p(Y|X, sigma)\\n logGradientMatch = 0 # sum of log p(f_k(x, theta) | m_k, Gamma_k)\\n\\n Gamma_matrices = self.Gamma_matrices\\n CPhi_matrices = self.Cphi_matrices\\n m_noX_matrices = self.m_noX_matrices\\n\\n sigma = self.sigma\\n\\n F = self.F\\n F_val = F(X, Theta)\\n\\n for k in range(nStates):\\n sigma_k = sigma[k]\\n\\n Gamma_k = Gamma_matrices[k] # cov of x_k GP derivative\\n CPhi_k = CPhi_matrices[k] # cov of x_k GP Prior\\n m_noX_k = m_noX_matrices[k]\\n\\n x_k = X[k]\\n\\n m_k = m_noX_k.dot(x_k) # mean of derivative of GP\\n\\n y_k = Y.T[k]\\n\\n # 1. GP prior - add log p(x_k | 0, Cphi_k)\\n logPrior_X += self.logN(x=x_k,\\n mean=np.zeros(nObs),\\n cov=CPhi_k + np.eye(nObs)*1e-6)\\n\\n # 2. Observation - add log p(y_k | x_k, sigma*I)\\n logObs += self.logN(x=y_k,\\n mean=x_k,\\n cov=np.eye(nObs) * sigma_k**2)\\n\\n # logObs += self.logN(x=y_k,\\n # mean=x_k,\\n # cov=np.eye(nObs) * sigma_k**2)\\n\\n # 3. gradient matching - add log p(f_k(X, theta) | m_k, Gamma_k)\\n logGradientMatch += self.logN(F_val[k], # check the shape\\n mean=m_k,\\n cov=Gamma_k)\\n\\n return logPrior_Theta + logPrior_X + logObs + logGradientMatch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc1af5ae5ee825a370ebc69278383fc3\",\n \"score\": \"0.6683679\",\n \"text\": \"def compute_log_prob(x, axis=None):\\n return np.log(x) - np.log(x.sum(axis=axis).reshape(-1, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2980143acd721ecc5457248aeda192ab\",\n \"score\": \"0.6617951\",\n \"text\": \"def log_prob(self, x):\\n y, logp = self.f(x)\\n return self.prior.log_prob(y) + logp, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f3e7a9a46099429a55b364a9f4f079c\",\n \"score\": \"0.659146\",\n \"text\": \"def log_probability(self, x):\\n lp = -0.5*np.log(2*np.pi*self.sigmasq) -1.0/(2*self.sigmasq) * (x-self.mu)**2\\n lp = np.nan_to_num(lp)\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ace82be88e699faac086d00ecf53266\",\n \"score\": \"0.6587828\",\n \"text\": \"def logPrior(self):\\n self.map()\\n if self.inbounds():\\n self.logP = 0.0\\n else:\\n self.logP = -np.inf\\n return self.logP\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5255246efe97b041387167513a518f\",\n \"score\": \"0.6555751\",\n \"text\": \"def log_prior(nat_params,alpha_pi=10,alpha_T=.1,O_mean_var=100,O_logsd_var=1): \\n pi,T,O,R = to_params(nat_params)\\n O_means,O_sds = O\\n \\n log_pi = np.log(pi+1e-16)\\n log_T = np.log(T+1e-16)\\n \\n obj = 0\\n \\n obj += (alpha_pi-1)*np.sum(log_pi)\\n obj += (alpha_T-1)*np.sum(log_T)\\n obj += -1/(2*O_mean_var)*np.sum(np.power(O_means,2)) \\n # obj += -1/(2*O_logsd_var)*np.sum(np.power(O_log_sds,2)) \\n\\n return -obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f6c3dc06bbcc296f51b18f6194a081d\",\n \"score\": \"0.6549311\",\n \"text\": \"def log_gamma_prior(x, tau=0.5):\\n return np.sum(ss.expon.logpdf(x, scale=tau)) # tau - inv rate param, scale inv of rate.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb876467202814acf106e79c503c3170\",\n \"score\": \"0.6546037\",\n \"text\": \"def logdprior(parameters, hyperparameters):\\n sigma_part = parameters[0] + invgamma_logpdf(parameters[0], hyperparameters[\\\"sigma_shape\\\"], hyperparameters[\\\"sigma_scale\\\"])\\n tau_part = parameters[1] + invgamma_logpdf(parameters[1], hyperparameters[\\\"tau_shape\\\"], hyperparameters[\\\"tau_scale\\\"])\\n return sigma_part + tau_part\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a4b5b14533a352a10d9a345129fa8c0\",\n \"score\": \"0.65450346\",\n \"text\": \"def _logprior(rate, params, data, m1=DEFAULT_M1_NAME, m2=DEFAULT_M2_NAME, DL=DEFAULT_DL_NAME):\\n ### extract samples from data\\n m1 = data[m1]\\n m2 = data[m2]\\n dl = data[DL]\\n\\n ### extract hyperparameters\\n alpha, mmin, mmax, dlmin, dlmax = params\\n\\n ### compute prior at these samples and return\\n return np.log(rate) + priors.ordered_joint_pareto(data, name1=m1, name2=m2, exp=alpha, minval=mmin, maxval=mmax) + priors.pareto(data, name=DL, exp=2, minval=dlmin, maxval=dlmax)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef39d47682bb8b2817cba41868b92336\",\n \"score\": \"0.6525077\",\n \"text\": \"def log_prob(self, x):\\n dim = x.shape[-1]\\n dev = x - self.mean\\n if self.mean.ndim == self.cov.ndim:\\n maha = ((dev * self.prec_U) ** 2).sum(axis=-1)\\n else:\\n maha = (dev.dot(self.prec_U) ** 2).sum(axis=-1)\\n return -0.5 * (dim * _LOG_2PI + self.log_det_cov + maha)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf166b9e2632d5bd76228d85cc6b12c3\",\n \"score\": \"0.6521594\",\n \"text\": \"def _log_llr(\\n self, x: np.ndarray, mu0: float, sigma0: float, mu1: float, sigma1: float\\n ) -> float:\\n\\n return np.sum(\\n np.log(sigma1 / sigma0)\\n + 0.5 * (((x - mu1) / sigma1) ** 2 - ((x - mu0) / sigma0) ** 2)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"943733280bc15070eac7c44bd30b4adc\",\n \"score\": \"0.6498431\",\n \"text\": \"def log_prob(self, x, u):\\n z, T, nat_params, logp = self.f(x, u) \\n return self.free_energy_bound(T, nat_params) - logp.mean(), z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"838cc88615151e169b8d7805a3183d23\",\n \"score\": \"0.64857256\",\n \"text\": \"def log_posterior_probs(x):\\n assert x.ndim == 1\\n\\n # TODO: enter your code here\\n\\n # log prior logP(Hi)\\n log_prior = np.log(PRIOR_PROBS)\\n\\n # log likelihood logP(X|H) = [logP(X|H1), logP(X|H2), logP(X|H3)]\\n log_likelihood = np.array([])\\n for dist in HYPOTHESIS_SPACE:\\n log_likelihood = np.append(log_likelihood, np.sum(dist.logpdf(x)))\\n\\n # log model evidence logP(X) = log( exp(logP(H1)+logP(X|H1)) + exp(logP(H2)+logP(X|H2)) + exp(logP(H3)+logP(X|H3)) )\\n log_model_evidence = logsumexp( log_prior + log_likelihood ) # use logsumexp for numerical stability\\n\\n # log Bayes rule logP(Hi|X) = log{ P(X|Hi)*P(Hi)/P(X) } = logP(X|Hi) + logP(Hi) - logP(X)\\n log_p = log_likelihood + log_prior - log_model_evidence\\n\\n assert log_p.shape == (3,)\\n return log_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b315092e44409c57ffcae0c89fb1a0dc\",\n \"score\": \"0.64828515\",\n \"text\": \"def log_prob(self, likelihood: Distribution, y):\\n raise NotImplementedError(\\\"Implement me!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f078403aea91ee84d55d392d67b85a35\",\n \"score\": \"0.6463773\",\n \"text\": \"def likelihood(z, x, P, H, R):\\n return np.exp(log_likelihood(z, x, P, H, R))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e206df6957d5cc16b60ef9df84f4d9a\",\n \"score\": \"0.64529216\",\n \"text\": \"def fragilityln(x,mu,sig):\\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\\n return Prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e206df6957d5cc16b60ef9df84f4d9a\",\n \"score\": \"0.64529216\",\n \"text\": \"def fragilityln(x,mu,sig):\\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\\n return Prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36c1967edd350b3c27def7e4a11be4db\",\n \"score\": \"0.6438222\",\n \"text\": \"def lnprior(X):\\n T,N_y,N_gb,alpha,turn=X\\n if 10< T< 50 and 0< N_y <0.06 and 0<N_gb<0.06 and 0<alpha<10 and 10<turn<50:\\n # if 0 < m < 4 and -10<c<10.0:\\n return 0.0\\n # return -np.log(N_y)\\n return -np.inf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c06fb69344da1d841ecdd4c8136a68b\",\n \"score\": \"0.6437301\",\n \"text\": \"def log_prior_u(self, u):\\n return uniform_log_prob(theta=u, lower=prior_bounds['u'][0], upper=prior_bounds['u'][1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e879b7a5da4a7a1df00c149fa6bd3ce\",\n \"score\": \"0.6435463\",\n \"text\": \"def log_probability(self):\\n lp = self.log_likelihood()\\n lp += self.log_prior()\\n\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e879b7a5da4a7a1df00c149fa6bd3ce\",\n \"score\": \"0.6435463\",\n \"text\": \"def log_probability(self):\\n lp = self.log_likelihood()\\n lp += self.log_prior()\\n\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb5e53bcdeeef08f03506ed32db5431d\",\n \"score\": \"0.64333975\",\n \"text\": \"def genetic_lnprior(x):\\n\\n x = np.array(x)\\n if x.ndim > 1:\\n alpha1 = x[:,0]\\n alpha2 = x[:,1]\\n gamma = x[:,2]\\n beta = x[:,3]\\n eta = x[:,4]\\n K = x[:,5]\\n else:\\n alpha1, alpha2, gamma, beta, eta, K = x\\n\\n # All flat priors from Wang & Li (2017)\\n if np.any(alpha1 < 120.0) or np.any(alpha1 > 200.0):\\n return -np.inf\\n elif np.any(alpha2 < 15.0) or np.any(alpha2 > 16.0):\\n return -np.inf\\n elif np.any(gamma < 2.1) or np.any(gamma > 2.9):\\n return -np.inf\\n elif np.any(beta < 0.85) or np.any(beta > 1.15):\\n return -np.inf\\n elif np.any(eta < 1.3) or np.any(eta > 2.7):\\n return -np.inf\\n elif np.any(K < np.log10(2.3e-5)) or np.any(K > np.log10(3.7e-5)):\\n return -np.inf\\n else:\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b80deeec01502c85dcd6128a2eea8ad\",\n \"score\": \"0.6426834\",\n \"text\": \"def get_particle_log_prior(ripl):\\n log_joint = get_particle_log_joint(ripl)\\n log_likelihood = get_particle_log_likelihood(ripl)\\n return compute_particle_log_prior(log_joint, log_likelihood)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"062579914eea7df4cf09f9addf400000\",\n \"score\": \"0.6418788\",\n \"text\": \"def fragilityln(x,mu,sig):\\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\\n return Prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"062579914eea7df4cf09f9addf400000\",\n \"score\": \"0.6418788\",\n \"text\": \"def fragilityln(x,mu,sig):\\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\\n return Prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"062579914eea7df4cf09f9addf400000\",\n \"score\": \"0.6418788\",\n \"text\": \"def fragilityln(x,mu,sig):\\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\\n return Prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffeb19a0b08d7eb667b91078afaeb522\",\n \"score\": \"0.6398503\",\n \"text\": \"def lnprior_pdf(self, x):\\n value = self.value(x)\\n if self._log:\\n value = np.log(value)\\n return -(value - self._mu) ** 2 / (2. * self._sigma ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b62f5986ed63003603e8aa2a8da3bd\",\n \"score\": \"0.6395055\",\n \"text\": \"def log_prob_components(self):\\n params = self.params\\n x, mu, R = [getattr(self.params, attr) for attr in ('data','mu','R')]\\n\\n log_p = np.array([params.distance(x[i], mu, R[i][:2]) for i in range(params.n_projections)])\\n \\n log_norm = 0.5 * params.n_dimensions * np.log(params.tau / (2*np.pi))\\n \\n return params.beta * (log_norm - 0.5 * params.tau * log_p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47689625ee9b378ac0689aefd6788bc9\",\n \"score\": \"0.63927907\",\n \"text\": \"def log_prob(self, x):\\n # both xi and beta have shape (*batch_shape)\\n # and so do all the elements bellow\\n\\n x = x.unsqueeze(dim=-1)\\n\\n logp = -self.beta.log().double()\\n logp += torch.where(\\n self.xi == torch.zeros_like(self.xi),\\n -x / self.beta,\\n -(1 + 1.0 / (self.xi + 1e-6))\\n * torch.log(1 + self.xi * x / self.beta),\\n )\\n logp = torch.where(\\n x < torch.zeros_like(x),\\n (-np.inf * torch.ones_like(x)).double(),\\n logp,\\n )\\n return logp.squeeze(dim=-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"843cf7bfd680862d2baab13b06931a03\",\n \"score\": \"0.63919437\",\n \"text\": \"def ln_gaussian_prior(x, mean, sigma):\\n return -0.5*( ((x - mean) / sigma)**2 + np.log(2*np.pi*sigma**2) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80094db15e7e8618d93b7aa633bd322e\",\n \"score\": \"0.6391109\",\n \"text\": \"def logit_log_prob(raw_logits, shaped_logits_logsumexp):\\n return shape_logits(raw_logits) - shaped_logits_logsumexp[..., None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9e1a945656e5341806d875bb8ad284c\",\n \"score\": \"0.63885444\",\n \"text\": \"def log_probability(self, x):\\n assert np.all(x >= self.lb) and np.all(x <= self.ub)\\n\\n # Center x\\n z = (x-self.mu) / np.sqrt(self.sigmasq)\\n # Log prob of the normalization constant\\n lp = -0.5 * np.log(self.sigmasq) - np.log(self.Z)\\n # Log prob of the standardized density\\n lp += -0.5*np.log(2*np.pi) -0.5*z**2\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"386bea022ce371953fed456179f25b89\",\n \"score\": \"0.63843906\",\n \"text\": \"def log_prior(self):\\n\\n log_prior = 0.0\\n for param in self.parameters():\\n if hasattr(param, \\\"prior\\\") and param.prior is not None:\\n if hasattr(param, \\\"transform\\\") and param.transform is not None:\\n val = param.transform()\\n else:\\n val = param.data\\n log_prior += param.prior.log_prob(val).sum()\\n\\n return log_prior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6792e07a9e2befabc1c12ea257e00f5\",\n \"score\": \"0.6373793\",\n \"text\": \"def logdprior(parameters, hyperparameters):\\n mu_part = - 0.5 / (hyperparameters[\\\"mu_sd\\\"]**2) * (parameters[0] - hyperparameters[\\\"mu_mean\\\"])**2\\n beta_part = - 0.5 / (hyperparameters[\\\"beta_sd\\\"]**2) * (parameters[1] - hyperparameters[\\\"beta_mean\\\"])**2\\n xi_part = parameters[2] - hyperparameters[\\\"xi_rate\\\"] * exp(parameters[2])\\n omega2_part = parameters[3] - hyperparameters[\\\"omega2_rate\\\"] * exp(parameters[3])\\n lamb1_part = parameters[4] - hyperparameters[\\\"lambda1_rate\\\"] * exp(parameters[4])\\n lambadd_part = parameters[5] - hyperparameters[\\\"lambdaadd_rate\\\"] * exp(parameters[5])\\n w_part = parameters[6] - 2 * log(1 + exp(parameters[6]))\\n return mu_part + beta_part + xi_part + omega2_part + lamb1_part + lambadd_part + w_part\",\n \"title\": \"\"\n },\n {\n \"docid\": \"681a41444472f9ced231f3e49fab861a\",\n \"score\": \"0.6372184\",\n \"text\": \"def log_probability(self, x):\\n lp = x * np.log(self.p) + (1-x) * np.log(1.0-self.p)\\n lp = np.nan_to_num(lp)\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04661a45f35b7ac1cf081023b26b2a8c\",\n \"score\": \"0.63547295\",\n \"text\": \"def loglike(params):\\n a_cum, logC = params\\n\\n # always enforce this general prior on a\\n if a_cum <= 0:\\n return -np.inf\\n\\n # first the models of flare energies and rate\\n result = loglike_powerlaw(a_cum + 1) + loglike_poisson(a_cum, logC)\\n\\n # next any priors\\n result += self.a_logprior(a_cum) + self.logC_logprior(logC)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ce15f17a800923182b801a47397fb6e\",\n \"score\": \"0.6354573\",\n \"text\": \"def logprior(theta):\\n\\n # read input parameters\\n if anisotropic:\\n logM, rh, g, phi0, logra = theta\\n else:\\n logM, rh, g, phi0 = theta\\n logra = 6 - 1e-5\\n\\n # check boundaries\\n if not ((3. < logM < 7.) & (0. < rh < 30.) &\\n (1e-3 < g < 3.5) & (1.5 < phi0 < 14.) &\\n (-1. < logra < 6.)):\\n return -np.inf\\n else:\\n # g: uniform\\n gprior = np.log(1. / (5.3 - 1e-3))\\n\\n # logM: uniform\\n logMprior = np.log(1. / (7. - 3.))\\n\\n # phi0: uniform\\n phi0prior = np.log(1. / (14 - 1.5))\\n\\n # rh: uniform\\n rhprior = np.log(1. / (30. - 0.))\\n\\n # logra: uniform\\n if anisotropic:\\n lograprior = np.log(1. / (6. - (-1.)))\\n else:\\n lograprior = 0.\\n\\n return gprior + logMprior + phi0prior + rhprior + lograprior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d95c4d1a810d3baa417163f3f8d7c136\",\n \"score\": \"0.6345897\",\n \"text\": \"def ln_prior(self, parameters):\\n lnprior = 0.0\\n for (key, value) in parameters.iteritems():\\n parmtype = key.split(\\\"_\\\")[1]\\n if parmtype == 'radius':\\n lnprior += stats.distributions.uniform.logpdf(value, 0.5, 2.5)\\n elif parmtype == 'scalingFactor':\\n lnprior += stats.distributions.uniform.logpdf(value, -0.8, 2.3)\\n elif parmtype == 'sigma':\\n lnprior += stats.distributions.invgamma.logpdf(value,1.0,1.0)\\n return lnprior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02553acc58828166aef7727f254d6d55\",\n \"score\": \"0.6344789\",\n \"text\": \"def log_probability(self, x):\\n z = x-self.mu\\n lp = -0.5*self.D*np.log(2*np.pi) -0.5*self.D*self.logdet_Sigma \\\\\\n -0.5 * z.T.dot(np.linalg.solve(self.Sigma, z))\\n lp = np.nan_to_num(lp)\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31a7e8323bb5cbacc1c12982cc86043f\",\n \"score\": \"0.6342709\",\n \"text\": \"def log_prob(y, xb):\\n if y == 1:\\n xb = -xb\\n return -log_sum_exp(np.array([0, xb]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9856337c7f78bc2e80e70e6ae959cd03\",\n \"score\": \"0.6337176\",\n \"text\": \"def __lnprior_gp(self, prior_gp=1):\\n return np.log(prior_gp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d63459918216014f8b9db0ba4afcc59\",\n \"score\": \"0.6331367\",\n \"text\": \"def pl(x, xmin=None):\\n if xmin is None:\\n xmin = x.min()\\n return (len(x)-1)/(float(len(x)))*(1./len(x) * np.sum(np.log(x/xmin)))**-1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63c94aa408a24f1295c564571453be8f\",\n \"score\": \"0.6330112\",\n \"text\": \"def x_prime_log_prior(self, x_prime):\\n if self.has_prime_prior:\\n return (\\n -1.5 * np.log(2 * np.pi)\\n - np.sum(\\n [x_prime[pp] ** 2 for pp in self.prime_parameters], axis=0\\n )\\n / 2\\n )\\n else:\\n raise RuntimeError(\\\"x prime prior is not defined!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ebc1a2bcd4d31a32cd0e7710a1b9fb7\",\n \"score\": \"0.63271934\",\n \"text\": \"def log_gamma_prior(x, tau=0.5):\\n n = len(x)\\n rate = 1/tau\\n res = n * math.log(rate/2) - rate * np.sum(np.abs(x))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e5c5ed5b558ee1350954c112da6683\",\n \"score\": \"0.63266855\",\n \"text\": \"def likelihood(mean, logs, x): # logs: log(sigma)\\n if mean is None and logs is None:\\n return -0.5 * (x ** 2 + GaussianDiag.Log2PI)\\n else:\\n return -0.5 * (logs * 2. + ((x - mean) ** 2) / torch.exp(logs * 2.) + GaussianDiag.Log2PI)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0886d70dada0b0bc3bf19a365be3929d\",\n \"score\": \"0.63261914\",\n \"text\": \"def log_prob(self, x: torch.Tensor) -> torch.Tensor:\\n return self.__call__(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a16fbba705d0f8271faa2d7a451018d\",\n \"score\": \"0.63193995\",\n \"text\": \"def ln_prob(self,param):\\n\\n # Calcualte prior. If not finite, this solution has an -infinity log\\n # likelihood\\n ln_prior = self.ln_prior(param)\\n if not np.isfinite(ln_prior):\\n return -np.inf\\n\\n # Calcualte likelihood. If not finite, this solution has an -infinity\\n # log likelihood\\n ln_like = self.ln_like(param)\\n if not np.isfinite(ln_like):\\n return -np.inf\\n\\n # log posterior is log prior plus log likelihood\\n return ln_prior + ln_like\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b2470816ef718836cbdd7c4f3fcbe72\",\n \"score\": \"0.63115937\",\n \"text\": \"def proposal(S):\\n if rng.rand() <= 0.5:\\n index = rng.randint(S.u.size)\\n S.u[index] += 10.**(1.5 - 6.*rng.rand())*rng.randn()\\n S.u[index] = np.mod(S.u[index], 1.)\\n else:\\n S.lam += 10.**(1.5 - 6.*rng.rand())*rng.randn()\\n S.lam = np.mod(S.lam, 1.)\\n\\n S.logx = np.empty(Model.logl.size)\\n for i in xrange(0, Model.run_id.max() + 1):\\n which = (Model.run_id==i)\\n if i==0:\\n factor = 1.\\n else:\\n factor = S.lam\\n S.logx[which] = factor*np.cumsum(np.log(S.u[which]))\\n S.order = np.argsort(S.logx)\\n S.logx = np.sort(S.logx)[::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff86dcda2e071fac791469ad9ee0fd36\",\n \"score\": \"0.631035\",\n \"text\": \"def lnprob(theta, samples, xmin):\\n\\n prior = lnprior(theta)\\n posterior = lnlike(theta, samples, xmin)\\n if not np.isfinite(prior):\\n return -np.inf\\n\\n return prior + posterior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18325074188daba43b5523b473f56716\",\n \"score\": \"0.62930757\",\n \"text\": \"def loglikeobs(self, params):\\n low, upp = self._bounds(params)\\n prob = self.prob(low, upp)\\n return np.log(prob + 1e-20)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702e4763e4b037e52e26ae0e54ec89f0\",\n \"score\": \"0.62866753\",\n \"text\": \"def loglikelihood(pi, mu, data, K):\\n\\treturn np.sum(np.log([np.sum([pi[k] * bernoulli(x, mu[k]) for k in range(0, K)]) for x in data]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"902a9c6a551c92009062b7a01b081016\",\n \"score\": \"0.62759113\",\n \"text\": \"def loghyperprior(self):\\n return self._loghyperprior(rate, self.params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cac36c74de400813f63cdcd00b103eb\",\n \"score\": \"0.6271557\",\n \"text\": \"def fragilityln(self, x,mu,sig):\\n Prob = 0.5*(1 + erf((log(x) - mu)/(sqrt(2)*sig)))\\n return Prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24d8b02f41d0f631b8cf122060ee2d48\",\n \"score\": \"0.62703246\",\n \"text\": \"def log_prior_z(self, z):\\n return uniform_log_prob(theta=z, lower=prior_bounds['z'][0], upper=prior_bounds['z'][1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f4afa5ea1de76d6d96c69c1f24c243\",\n \"score\": \"0.62641776\",\n \"text\": \"def log_normpdf(x, mu, sigma):\\n diff = x-mu\\n return np.log(1/(math.sqrt(2*math.pi*sigma)))-(math.pow(diff,2)/(2*sigma))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2575afcfe1b12bd0c5cd953b0bae4c6\",\n \"score\": \"0.62430704\",\n \"text\": \"def log_likelihood(z, x, P, H, R):\\n S = np.dot(H, np.dot(P, H.T)) + R\\n return logpdf(z, np.dot(H, x), S)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90e56012db49e9d635b8954cc7e0c209\",\n \"score\": \"0.6235775\",\n \"text\": \"def poisson_llr(n, mu):\\n return n - mu + xlogy(n, mu / numpy.maximum(1, n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e32e8609af184ca9a0739bd25971f611\",\n \"score\": \"0.6235716\",\n \"text\": \"def prior (prob):\\n\\treturn uniform.pdf(prob) #uniform, uniformative prior \",\n \"title\": \"\"\n },\n {\n \"docid\": \"156cc5ac377fae182361332b999a20a6\",\n \"score\": \"0.6233888\",\n \"text\": \"def log_prior_compute(self, zi):\\n lp = 0.\\n sigma = 1\\n for i in range(len(zi.nn_params)):\\n lp += -0.5 * torch.sum((zi.nn_params[i].weight ** 2)) / self.p_sigma**2\\n if self.bias:\\n lp += -0.5 * torch.sum((zi.nn_params[i].bias ** 2)) / self.p_sigma**2\\n return lp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41bab49c8b124fcfd26201ce12b08617\",\n \"score\": \"0.6226735\",\n \"text\": \"def _compute_log_likelihood(self, X):\\n assert type(X).__module__ == \\\"numpy\\\"\\n xs = X[:, 0]\\n ns = X[:, 1]\\n ps = X[:, 2:4]\\n ref = binom.logpmf(xs, ns, ps[:, 0])\\n nonref = binom.logpmf(xs, ns, ps[:, 1])\\n return np.matrix([ref, nonref]).T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8752189d9dc8df61e8e212ec07c074f0\",\n \"score\": \"0.62233776\",\n \"text\": \"def fn(x, data):\\n beta, q, delta, rho, gamma_mild, gamma_wild, k = x\\n S, E, I_mild, I_wild, P, N = data\\n\\n pC = 1 - np.exp(-rho)\\n pR_mild = 1 - np.exp(-gamma_mild)\\n pR_wild = 1 - np.exp(-gamma_wild)\\n\\n # log likelihood\\n # add epsilon to avoid log 0.\\n logB = np.sum(np.log(sp.stats.binom(S, P).pmf(B) + epsilon))\\n logC = np.sum(np.log(sp.stats.binom(E, pC).pmf(C) + epsilon))\\n logD_mild = np.sum(np.log(sp.stats.binom(I_mild, pR_mild).pmf(D_mild) + epsilon))\\n logD_wild = np.sum(np.log(sp.stats.binom(I_wild, pR_wild).pmf(D_wild) + epsilon))\\n\\n assert not np.isnan(logB)\\n assert not np.isnan(logC)\\n assert not np.isnan(logD_mild)\\n assert not np.isnan(logD_wild)\\n\\n # log prior\\n log_prior = 0\\n for i in range(len(priors)):\\n a, b = priors[i]\\n log_prior += np.log(sp.stats.gamma(a, b).pdf(x[i])+epsilon)\\n assert not np.isnan(log_prior) \\n return logB + logC + logD_mild + logD_wild + log_prior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84dcc57e5ecd360693d3c0c21f52f152\",\n \"score\": \"0.6214991\",\n \"text\": \"def probability(self):\\r\\n #self.testfunc = lambda x: x**2\\r\\n #self.testintegral = integrate.quad(self.testfunc,1,2)\\r\\n #print(\\\"testfunc\\\", self.testintegral)\\r\\n self.mu1likefunc = lambda x: self.likefunc1D(x,self.mpoints,self.xsqr,self.xixj,self.xmean)\\r\\n self.mu1probability = integrate.quad(self.mu1likefunc,-np.inf,np.inf)\\r\\n self.mu2likefunc = lambda y: self.likefunc1D(y,self.mpoints,self.ysqr,self.yiyj,self.ymean)\\r\\n self.mu2probability = integrate.quad(self.mu2likefunc,-np.inf,np.inf)\\r\\n self.posterior = self.mu1probability[0] * self.mu2probability[0] # it is really posterior/prior and ignored the 1/2pi to m factor\\r\\n print(\\\"p({x}) / prior =\\\", self.posterior)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"967c3de3d6f4aff986df469078b3958a\",\n \"score\": \"0.61947596\",\n \"text\": \"def ln_prior(self, pars):\\n \\n a, b = pars # unpack parameters\\n ln_prior_val = 0. # we'll add to this\\n\\n if b < -0.5 or b > 0.5:\\n return -np.inf\\n else:\\n ln_prior_val += np.log(1E-2) # normalization, log(1/100)\\n\\n if a < -5 or a > 5.:\\n return -np.inf\\n else:\\n ln_prior_val += np.log(1E-2) # normalization, log(1/100)\\n\\n return ln_prior_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87199f56fe60df882114c91e54321a2d\",\n \"score\": \"0.61928195\",\n \"text\": \"def lnlikeFromProjProb(modelParams, observable):\\n #xMax=10\\n #xMin=-10\\n sigma, m, b = modelParams\\n #print('sigma {}, m {}, b {}'.format(sigma,m,b))\\n y = observable # for convenience inside the function\\n projectedProbabilities = getProjectedProb( y, m, b, sigma, xMin, xMax)\\n logProjProbs = np.log(projectedProbabilities)\\n loglikelihood = np.sum(logProjProbs)\\n \\n return loglikelihood\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75cae0ca33a8bb98e387a67010923416\",\n \"score\": \"0.618985\",\n \"text\": \"def bernoulli_log_pdf(x, mu):\\n mu = torch.clamp(mu, 1e-7, 1.-1e-7)\\n return torch.sum(x * torch.log(mu) + (1. - x) * torch.log(1. - mu), dim=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75cae0ca33a8bb98e387a67010923416\",\n \"score\": \"0.618985\",\n \"text\": \"def bernoulli_log_pdf(x, mu):\\n mu = torch.clamp(mu, 1e-7, 1.-1e-7)\\n return torch.sum(x * torch.log(mu) + (1. - x) * torch.log(1. - mu), dim=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32da6ecd860fbb060d85c8730c5fc50b\",\n \"score\": \"0.61860675\",\n \"text\": \"def bernoulli_log_pdf(x: torch.Tensor, mu: torch.Tensor):\\n mu = torch.clamp(mu, 1e-7, 1.-1e-7)\\n return torch.sum(x * torch.log(mu) + (1. - x) * torch.log(1. - mu), dim=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba435bda0dc9091c80723818eca4a71\",\n \"score\": \"0.61832607\",\n \"text\": \"def ln_prior(self,param):\\n\\n # If a paramter falls outside of the bounds, make the prior -infinity\\n if np.sum(param < self.bounds[0,:]) > 0 or np.sum(param > self.bounds[1,:]) > 0:\\n return -np.inf\\n\\n # otherwise, uniform\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564a736c29363f21fcfb887a5ceb6022\",\n \"score\": \"0.6176178\",\n \"text\": \"def log_likelihood(self, x):\\n # *** START CODE HERE ***\\n\\n # Generating identity matrix of size d\\n I_d = np.identity(self.d)\\n\\n # Obtain mu and sigma. Using results from the assignment\\n # Reshaping mu so it's a (dim,) vector instead of (dim,1) to make compatible with compute_normal\\n mu = self.b.flatten()\\n sigma = self.W.dot(self.W.T) + self.gamma**2 * I_d\\n\\n # Compute log likelihood\\n log_likelihood = np.log(compute_normal(x.T, mu, sigma))\\n\\n return log_likelihood[0]\\n # *** END CODE HERE ***\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00739b3194d8362b7028c50c423454ce\",\n \"score\": \"0.61728406\",\n \"text\": \"def test_log_prob():\\n dims = 2\\n n = 10\\n x = torch.randn(n, 2)\\n dist = ResampledGaussian(\\n dims, lambda x: torch.ones(len(x), 1), trainable=False\\n )\\n out = dist.log_prob(x)\\n assert len(out) == n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de12ee1a40b69b516831f29cb6d09faa\",\n \"score\": \"0.6170956\",\n \"text\": \"def compute_log_likelihood(mu_0, mu_1, sigma_0, sigma_1, pi):\\n ll = 0.0\\n # -------------------------------------------------------------------------\\n # TODO: Code to compute ll\\n\\n # Iterate through all data points\\n # Calculate sum of log probabilities\\n for xm in X.values():\\n x = xm.tolist()[0]\\n #p = (1-pi)*mvg_pdf(x, mu_0, sigma_0) + pi*mvg_pdf(x, mu_1, sigma_1)\\n p = (1-pi)*multivariate_normal.pdf(x, mean=mu_0, cov=sigma_0) \\\\\\n + pi*multivariate_normal.pdf(x, mean=mu_1, cov=sigma_1)\\n # Accumulate p in ll\\n if p == 0.0:\\n print \\\"ERROR: p = 0.0\\\"\\n sys.exit()\\n ll += np.log(p)\\n\\n # END_YOUR_CODE\\n return ll\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fbda8157d31f43b93ab3001f5110e28\",\n \"score\": \"0.6167279\",\n \"text\": \"def log_prior(theta):\\n N0, v0, ns0, sigma0, w0 = theta\\n\\n log_prior = -np.inf\\n\\n # Here we convert from N0, v0, ns0 to M200, c200\\n\\n if 0 < N0 < 5 and -1. < v0 < 3.5 and -2. < sigma0 < 4. and -0.5 < ns0 < 0.5 and -2. < w0 < 3.:\\n\\n logM200, logc200 = find_nfw_params(10**N0, 10**v0, ns0, 10**sigma0, 10**w0, 10., np.log10(c_M_relation(10.)))\\n\\n # Flat priors on all except c200:\\n if -2. < sigma0 < 4. and -2. < w0 < 3. and 6 < logM200 < 14 and 0 < logc200 < 2:\\n # Prior distribution on c200\\n c200 = 10 ** logc200\\n log_prior = log_prior_c200(c200, logM200)\\n\\n\\n return log_prior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"569ee3dae24a919a572e80ae6be873a2\",\n \"score\": \"0.6162471\",\n \"text\": \"def _log_prob(self, inputs: np.ndarray) -> np.ndarray:\\n # calculate log_prob\\n u, log_det = self._inverse(self._params, inputs)\\n log_prob = self.prior.log_prob(u) + log_det\\n # set NaN's to negative infinity (i.e. zero probability)\\n log_prob = np.nan_to_num(log_prob, nan=np.NINF)\\n return log_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"575c27f80190f47e158602861582d60f\",\n \"score\": \"0.616182\",\n \"text\": \"def log_probability(theta, model, data):\\n\\n prior = log_prior(theta, model)\\n if np.isinf(prior):\\n return prior\\n return prior + log_likelihood(theta, model, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac9742972de22d45d81dac7ee6fce68a\",\n \"score\": \"0.61460537\",\n \"text\": \"def lnp(self, x):\\n return self.distr.logpdf(x, loc=self.loc, scale=self.scale)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c6d139e3557134187d81a124a4f446\",\n \"score\": \"0.61433464\",\n \"text\": \"def lnprobProj(modelParams, observables):\\n logprior = lnPriors(modelParams)\\n if not np.isfinite(logprior):\\n return -np.inf\\n return logprior + lnlikeFromProjProb(modelParams, observables)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"392e923ce1fd2615443f2c7baa928642\",\n \"score\": \"0.6143345\",\n \"text\": \"def ln_uniform_prior(x, min_x, max_x):\\n if min_x < x < max_x:\\n return -np.log(max_x - min_x)\\n else:\\n return -np.inf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b612a319e875e3d35a4e529dfb570aa\",\n \"score\": \"0.614138\",\n \"text\": \"def get_particle_log_likelihood(ripl):\\n return ripl.evaluate('global_log_likelihood')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b95c26c87a766cf8b8b002a1f8037cf\",\n \"score\": \"0.61358666\",\n \"text\": \"def ln_probab(pars, data, models, P):\\n\\n y_model, bands = ymodel(data.nus, data.z, data.dlum, models, P, *pars)\\n lnp = ln_prior(data, models, P, *pars)\\n\\n if np.isfinite(lnp): \\n posterior = lnp + ln_likelihood(data.nus, data.fluxes, data.fluxerrs, data.z, y_model) \\n return posterior\\n return -np.inf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"268358e46506b1b50813f7eecdc92840\",\n \"score\": \"0.61335814\",\n \"text\": \"def logprob(self, Y):\\n return self.build_logprob(Y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb321b63352ebe493651f276e6b2fd16\",\n \"score\": \"0.6129187\",\n \"text\": \"def log_probability(self, params, band):\\n self.set_parameter_vector(params)\\n _, _, y, ye, _, _ = np.loadtxt(self.lightcurves[band]).T\\n\\n # check model params are valid - checks against bounds\\n lp = self.log_prior()\\n if not np.isfinite(lp):\\n return -np.inf\\n try:\\n ym = self.get_value(band)\\n except ValueError as err:\\n # invalid lcurve params\\n print('warning: model failed ', err)\\n return -np.inf\\n else:\\n chisq = np.sum((y - ym)**2 / ye**2)\\n log_likelihood = -0.5 * (np.sum(np.log(2 * np.pi * ye**2)) + chisq)\\n # log_likelihood + lp\\n return log_likelihood\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce5f2bc40cae08a76778c68b932af4e4\",\n \"score\": \"0.6127457\",\n \"text\": \"def lnp(self, x):\\n return self.distr.logpdf(x, loc=self.loc, scale=self.scale, df=self.df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3290b02bfddf9529f621cc235fd78dc7\",\n \"score\": \"0.61258495\",\n \"text\": \"def log_prior(self, weights):\\n shape = weights.shape\\n sigma_1 = tf.constant(0.005, shape=shape, dtype=tf.float32)\\n sigma_2 = tf.constant(0.00005, shape=shape, dtype=tf.float32)\\n def pdf(w, sigma):\\n res1 = tf.math.divide(tf.math.square(w), tf.math.square(sigma)*2)\\n return tf.math.divide(tf.math.exp(tf.clip_by_value(-res1, -87.315, 88.722)), sigma*(2*math.pi)**0.5)\\n part_1 = tf.clip_by_value(0.25*pdf(weights, sigma_1), tf.float32.min//2, tf.float32.max//2)\\n part_2 = tf.clip_by_value(0.75*pdf(weights, sigma_2), tf.float32.min//2, tf.float32.max//2)\\n return tf.math.reduce_sum(tf.math.log(part_1 + part_2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86821d39fe91edc9e86142dae7394afe\",\n \"score\": \"0.6122441\",\n \"text\": \"def log_likelihood(self, data, hypo):\\n x = data\\n mu, sigma = hypo\\n loglike = eval_normal_log_pdf(x, mu, sigma)\\n return loglike\",\n \"title\": \"\"\n },\n {\n \"docid\": \"095c27f1525e25c299cc62f12a900099\",\n \"score\": \"0.6115879\",\n \"text\": \"def lnprior(self, pars):\\n theta = pars[:self.Nbins]\\n if np.any(theta < 0):\\n return -np.inf\\n a, tau, err = np.exp(pars[self.Nbins:-1])\\n mean = pars[-1]\\n kernel = a * kernels.ExpSquaredKernel(tau)\\n gp = GP(kernel, mean=mean)\\n gp.compute(self.bin_centers, yerr=err)\\n return gp.lnlikelihood(theta) / self.smoothing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b87f3b9813196bf6a30beaf03c344ae\",\n \"score\": \"0.61149335\",\n \"text\": \"def prior(cube, ndim, nparams):\\n cube[0] = cube[0]*-10 #beta1 =uniform prior between 0:-10\\n cube[1] = cube[2]*-10 #beta2 = uniform prior between 0:-10\\n cube[2] = cube[3]*-10 #P_F =uniform prior between 0:-10\\n cube[3] = cube[4]*-10 #P_b = uniform prior between 0:-10\\n cube[4] = 10**(cube[5]*10 - 5) # F_0 = log-uniform prior between 10^-5 and 10^5\\n cube[5] = 10**(cube[2]*10 + 20) # nu_b0=log-uniform prior between 10^10 and 10^30\\n if ndim < 7:\\n return\\n \\tcube[7] =cube[3]*4# s=uniform prior between 0 and 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e51ce4ffd6716dde1f65e8230ca35b0e\",\n \"score\": \"0.61113846\",\n \"text\": \"def log_prob_from_logits(x):\\n axis = len(x.get_shape())-1\\n m = tf.reduce_max(x, axis, keep_dims=True)\\n return x - m - tf.log(tf.reduce_sum(tf.exp(x-m), axis, keep_dims=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e26de8d54921ea9373ee20bff22ecb8f\",\n \"score\": \"0.61059034\",\n \"text\": \"def lnprior(theta):\\n dtq, tau, Z = theta\\n if -0.1 <= dtq <= 13.807108309208775 and np.log10(0.00001) <= tau <= np.log10(5.9) and 0.001 <= Z <= 1.6:\\n return 0.0\\n elif -0.1 <= dtq <= 13.807108309208775 and np.log10(5.9) < tau <= np.log10(6.0) and 0.001 <= Z <= 1.6:\\n return 2*(np.exp(np.log10(5.9)) - np.exp(np.log10(tau)))\\n else:\\n return -np.inf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6c89df99a6928318e8ffb32b4863ed1\",\n \"score\": \"0.6104513\",\n \"text\": \"def log1p(x):\\n return log(1 + x)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1797,"cells":{"query_id":{"kind":"string","value":"8b57b40b506a65569c2bf2211d30b5ce"},"query":{"kind":"string","value":"Deletes a specified link."},"positive_passages":{"kind":"list like","value":[{"docid":"817159d0d23e3ebac410c02f312e746c","score":"0.6469105","text":"def delete_link(\n self, drawer, id, custom_headers=None, raw=False, **operation_config):\n # Construct URL\n url = '/v1/content/links/{drawer}/{id}'\n path_format_arguments = {\n 'drawer': self._serialize.url(\"drawer\", drawer, 'int'),\n 'id': self._serialize.url(\"id\", id, 'int')\n }\n url = self._client.format_url(url, **path_format_arguments)\n\n # Construct parameters\n query_parameters = {}\n\n # Construct headers\n header_parameters = {}\n header_parameters['Content-Type'] = 'application/json; charset=utf-8'\n if custom_headers:\n header_parameters.update(custom_headers)\n\n # Construct and send request\n request = self._client.delete(url, query_parameters)\n response = self._client.send(request, header_parameters, stream=False, **operation_config)\n\n if response.status_code not in [204, 403, 404]:\n raise HttpOperationError(self._deserialize, response)\n\n deserialized = None\n\n if response.status_code == 204:\n deserialized = self._deserialize('object', response)\n\n if raw:\n client_raw_response = ClientRawResponse(deserialized, response)\n return client_raw_response\n\n return deserialized","title":""}],"string":"[\n {\n \"docid\": \"817159d0d23e3ebac410c02f312e746c\",\n \"score\": \"0.6469105\",\n \"text\": \"def delete_link(\\n self, drawer, id, custom_headers=None, raw=False, **operation_config):\\n # Construct URL\\n url = '/v1/content/links/{drawer}/{id}'\\n path_format_arguments = {\\n 'drawer': self._serialize.url(\\\"drawer\\\", drawer, 'int'),\\n 'id': self._serialize.url(\\\"id\\\", id, 'int')\\n }\\n url = self._client.format_url(url, **path_format_arguments)\\n\\n # Construct parameters\\n query_parameters = {}\\n\\n # Construct headers\\n header_parameters = {}\\n header_parameters['Content-Type'] = 'application/json; charset=utf-8'\\n if custom_headers:\\n header_parameters.update(custom_headers)\\n\\n # Construct and send request\\n request = self._client.delete(url, query_parameters)\\n response = self._client.send(request, header_parameters, stream=False, **operation_config)\\n\\n if response.status_code not in [204, 403, 404]:\\n raise HttpOperationError(self._deserialize, response)\\n\\n deserialized = None\\n\\n if response.status_code == 204:\\n deserialized = self._deserialize('object', response)\\n\\n if raw:\\n client_raw_response = ClientRawResponse(deserialized, response)\\n return client_raw_response\\n\\n return deserialized\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"75d04a8e3c7382b57f67bc652bebb46f","score":"0.8691151","text":"def delete_link(self, link):","title":""},{"docid":"330e382b80fa4d675a239b8fc57303d7","score":"0.7785142","text":"async def delete_link(self, player_tag: str):\n return await self._request(\"DELETE\", \"/links/{}\".format(correct_tag(player_tag, prefix=\"\")))","title":""},{"docid":"f75358a35d6184deb645d1621215fbde","score":"0.7599679","text":"def delete_link_by_id(link_id):\n try:\n link = Link.query.filter_by(id=link_id).delete()\n db.session.commit()\n if link:\n return APIResponseBuilder.success({\n \"deleted\": True\n })\n return APIResponseBuilder.failure({\n \"invalid_id\": f\"Unable to delete link with ID {link_id}\"\n })\n except SQLAlchemyError as e:\n return APIResponseBuilder.error(f\"Issue running query: {e}\")\n except Exception as e:\n return APIResponseBuilder.error(f\"Error encountered: {e}\")","title":""},{"docid":"44841a95ef356840f25ad2ad10c37043","score":"0.7394994","text":"def removeLink(self, _link: str):\n self._has_permission(\"removeLink\")\n target = BTPAddress.from_string_with_validation(_link)\n self._remove_link(target)","title":""},{"docid":"54729ea12132af201fafee80616900c2","score":"0.7261224","text":"def delete(self, request, *args, **kwargs):\n\n serializer = DeleteLinkShareSerializer(data=request.data, context=self.get_serializer_context())\n\n if not serializer.is_valid():\n\n return Response(\n serializer.errors, status=status.HTTP_400_BAD_REQUEST\n )\n\n link_share = serializer.validated_data.get('link_share')\n\n # delete it\n link_share.delete()\n\n return Response(status=status.HTTP_200_OK)","title":""},{"docid":"82d375780a8bd42fdbc461fcb375afcb","score":"0.7193894","text":"def delete_link(self, pos):\n # DEBUG\n #debug(\"delete_link\", (\"cond\", pos in self._my_links.viewkeys()))\n if pos in self._my_links.viewkeys():\n temp = self._my_links.pop(pos)\n del temp\n if pos in self._links.viewkeys():\n temp = self._links.pop(pos)\n del temp","title":""},{"docid":"40fd394c3713591a4464eea2e4aeaf22","score":"0.71831894","text":"def deleteLink(self):\n # get selected index\n index = self.getSelectedIndex(\"delete\")\n # check selection\n if index is not None:\n # prevent last link to use reference beacon from being deleted\n if self.isLastAnchorLink(index):\n QMessageBox.warning(\n self, \n \"Last Link To Reference Beacon\", \n \"Cannot remove last link to reference beacon\"\n )\n return\n # recursively delete dependant links\n self.deleteLinkDependants(self.beardistChain[index][0][3])\n # delete link\n del self.beardistChain[index] \n self.updateBearDistChainDependants()\n if len(self.beardistChain) == 0: \n self.pshbtn_chain_finish.setEnabled(False)\n self.pshbtn_chain_edt.setEnabled(False)\n self.pshbtn_chain_del.setEnabled(False)","title":""},{"docid":"78dd2e8d57a9fe57d74f6f0ed9fb4f75","score":"0.7053143","text":"def delete_link(self) -> ActionLink:\n return next(filter(lambda x: x.rel == 'delete', self.links), None)","title":""},{"docid":"78dd2e8d57a9fe57d74f6f0ed9fb4f75","score":"0.7053143","text":"def delete_link(self) -> ActionLink:\n return next(filter(lambda x: x.rel == 'delete', self.links), None)","title":""},{"docid":"be43a32e0ab8938da30a184b8db8ab9f","score":"0.6955712","text":"def delete_link(self, light_name):\n self.get_links().pop(light_name)","title":""},{"docid":"825aab34bc96768733af5d460e2e6ab3","score":"0.692503","text":"def _removeLink( self, link_id, source=None, container=None ):\n link = getattr( self, link_id, None)\n if link is None:\n return\n ob = source or link.getSourceObject()\n if ob is None:\n return\n\n if hasattr( ob, 'failIfLocked' ):\n ob.failIfLocked()\n\n can_remove_link = _checkPermission( CMFCorePermissions.View, ob )\n\n if not ( can_remove_link or getattr( self, '_v_force_links_remove', 0 ) ):\n raise Unauthorized, 'You are not allowed to remove link between these documents'\n\n if container is not None:\n id = link.getDestinationObject().getId()\n try:\n if id in container.objectIds():\n container._delObject( id )\n except:\n logger.error('_removeLink Cannot delete linked object: %s\\n>container: %s' % ( id, `container` ))\n\n self.unindexObject( link )\n self._delObject( link_id )","title":""},{"docid":"d559be837cb48dd1768babc5f3565093","score":"0.69047475","text":"def deleteLink( self, link, canvas ):\n pair = canvas.links.get( link, None )\n if pair is not None:\n source=pair['src']\n dest=pair['dest']\n del source.links[ dest ]\n del dest.links[ source ]\n stags = canvas.gettags( canvas.widgetToItem[ source ] )\n dtags = canvas.gettags( canvas.widgetToItem[ dest ] )\n ltags = canvas.gettags( link )\n\n self.removeGraphEdge(source[ 'text' ], dest[ 'text' ], canvas)\n\n if 'control' in ltags:\n controllerName = ''\n switchName = ''\n if 'Controller' in stags:\n controllerName = source[ 'text' ]\n switchName = dest[ 'text' ]\n else:\n controllerName = dest[ 'text' ]\n switchName = source[ 'text' ]\n\n if controllerName in canvas.switchOpts[switchName]['controllers']:\n canvas.switchOpts[switchName]['controllers'].remove(controllerName)\n\n\n if link is not None:\n del canvas.links[ link ]","title":""},{"docid":"0b9a8e4dda6a476b7adb65df4ccb6ac4","score":"0.68553734","text":"def delete_secret_link(self, identity, id_, link_id, links_config=None, uow=None):\n record, parent = self.get_parent_and_record_or_draft(id_)\n\n # Permissions\n self.require_permission(identity, \"manage\", record=record)\n\n # Fetching\n link_ids = [link.link_id for link in parent.access.links]\n if str(link_id) not in link_ids:\n raise LookupError(str(link_id))\n\n link_idx = link_ids.index(link_id)\n link = parent.access.links[link_idx].resolve()\n\n # Deletion\n parent.access.links.pop(link_idx)\n link.revoke()\n\n # Commit\n uow.register(RecordCommitOp(parent))\n if record:\n uow.register(RecordCommitOp(record))\n\n # Index all child records of the parent\n self._index_related_records(record, parent, uow=uow)\n\n return True","title":""},{"docid":"8008aacdd0a6cd63d0d7d1475de86ff5","score":"0.68308836","text":"def __delete_violation_link(self, agreement_id, violation, violation_link, extras):\n myrulesengine = rulesengine.RulesEngine()\n myrulesengine._registry.delete_resource(violation_link.identifier, extras)\n\n agreement = myrulesengine._registry.get_resource(agreement_id, None)\n agreement.identifier = agreement_id\n if violation_link in agreement.links:\n agreement.links.remove(violation_link)","title":""},{"docid":"5dd0bc165eee8f132b7ab12f10d27ea2","score":"0.67351085","text":"def deleteEvent(self, href):\n\n self._removeEvent(href)\n\n response = yield self._request(\n (NO_CONTENT, NOT_FOUND),\n 'DELETE',\n self.server[\"uri\"] + href.encode('utf-8'),\n method_label=\"DELETE{event}\",\n )\n returnValue(response)","title":""},{"docid":"0941bb0b0d2a51bcb915b1cc96cb72cc","score":"0.6733908","text":"def delete_link(\n self,\n session_id: int,\n node1_id: int,\n node2_id: int,\n iface1_id: int = None,\n iface2_id: int = None,\n source: str = None,\n ) -> core_pb2.DeleteLinkResponse:\n request = core_pb2.DeleteLinkRequest(\n session_id=session_id,\n node1_id=node1_id,\n node2_id=node2_id,\n iface1_id=iface1_id,\n iface2_id=iface2_id,\n source=source,\n )\n return self.stub.DeleteLink(request)","title":""},{"docid":"9c1a3d4c6e20ecc1c1a7795888de7aa4","score":"0.67205495","text":"def delete_links(self):\n link_names = get_selected_list(self.link_box)\n if link_names:\n self.link_obj.delete_links(link_names)\n self.populate_links()","title":""},{"docid":"3362e931bd3359f36d391a8361ac41ef","score":"0.6657411","text":"def delete(url):\n _delete(urls, url)","title":""},{"docid":"cf65ffe0a1322d544add91b1eac64fe1","score":"0.6655403","text":"def remove_link(self, links):\n self._link_manager.remove_link(links, update_external=not self._disable_sync_link_manager)","title":""},{"docid":"f7f027a69bc4afd515e70dfa376b9926","score":"0.66016877","text":"def delete(self):\n\t\ttag\t= 'link_'+self.getIdentifier()\n\t\tself.canvas.delete(tag)\n\t\tself.canvas.tag_unbind(tag,\"<Any>\")","title":""},{"docid":"36339162523fda5db2457f3e15a6bbfe","score":"0.654126","text":"def delete_by_href(self, href, etag=None, timeout=-1, auditmemento=None,\n helpers=None):\n o = urlparse.urlparse(href)\n hostname_mismatch = (o.hostname.lower() != self.session.host.lower())\n if hostname_mismatch or o.port != self.session.port:\n LOG.debug('href=%s will be modified to use %s:%s', href,\n self.session.host, self.session.port)\n return self.delete_by_path(o.path, etag=etag, timeout=timeout,\n auditmemento=auditmemento, helpers=helpers)","title":""},{"docid":"aab22dd66c79b46d64606944fe54673f","score":"0.64760023","text":"def destroy_link(self):\n self.detach_link(0)\n self.clean_up()","title":""},{"docid":"581202be2ddeba42b17d4135b37db5cd","score":"0.6454511","text":"def delete(url, header):\n requests.request('DELETE', url, headers=header)","title":""},{"docid":"accc057cb3f953c2320cc71b6aadaa5e","score":"0.6449743","text":"def deleteAtIndex(self, index):\n if index < 0 or index >= len(self._link_list):\n return\n\n self._link_list.pop(index)","title":""},{"docid":"128adb54c5e075cf5a97984cc9fb4c56","score":"0.6400765","text":"def delete(self, *args):\n self._execute('delete', *args)","title":""},{"docid":"e2ba1a9df9991f0d949451f9174a70b8","score":"0.639403","text":"def delete():","title":""},{"docid":"e2ba1a9df9991f0d949451f9174a70b8","score":"0.639403","text":"def delete():","title":""},{"docid":"e94e87c13a2b86ecf1f75724a7678498","score":"0.6387958","text":"def delProductProcessLink(self, ll):\n link = self.getEntry('Productprocesslink', ll)\n self.session.delete(link)\n self.commitDB()","title":""},{"docid":"4ae65534c895b6323c5e62c1e43c35d4","score":"0.635933","text":"def unlink(self):\n link = self.link\n if link is None:\n raise ValueError(\"Interface is not linked\")\n if link.is_ptp or link.is_lag:\n link.delete()\n else:\n raise ValueError(\"Cannot unlink non p-t-p link\")","title":""},{"docid":"c53254baa1abc493d3e29e83c2fef6f4","score":"0.63314855","text":"def delete(self, url, headers=None, data=None):\n pass","title":""},{"docid":"a3ff6f95f2599ad00e946174298a174a","score":"0.63186055","text":"def delete(self, evt=None):\n for link in self.next_links.copy():\n link.delete()","title":""},{"docid":"2bd63ab4263d568cf932dd3b533d50ab","score":"0.6315366","text":"def remove_current_link(self):\n try:\n os.unlink(self.link_path)\n except FileNotFoundError:\n pass","title":""},{"docid":"e2c9b708bc59dbb107d8c52892fb4e0f","score":"0.629047","text":"def delete(self, access_group_entry):\n self._delete(RESOURCE_PATH % common_base.getid(access_group_entry))","title":""},{"docid":"014541e29b09c169bb5d3c6ad2d50f89","score":"0.62828404","text":"def delete(self):\n api_args = {}\n DynectSession.get_session().execute(self.uri, 'DELETE', api_args)","title":""},{"docid":"751419834960390c86aabad2b1cb8645","score":"0.6247951","text":"def _delete(self, url):\n r = requests.delete(self._api_prefix + url, headers=self.headers, auth=self.auth, allow_redirects=False,)\n return self._action(r)","title":""},{"docid":"8ea132ab3a5f4430cb2129dfa5b2cac2","score":"0.6235586","text":"def delete(self, path, **kwargs):\n #TODO\n pass","title":""},{"docid":"82ef79827957e2b732db4c7e9754d0d2","score":"0.6204013","text":"def delete_bookmark(self, url):\n params = {'url': url}\n \n req = requests.get(self.base_url+self.command['delete'], \n {**params, **self.common_filters})\n \n return req.status_code","title":""},{"docid":"5efa84bea25ebc6d5faa58e47dbc6186","score":"0.6194476","text":"def sys_upload_link_remove(request):\n content_type = 'application/json; charset=utf-8'\n result = {}\n\n token = request.POST.get('t')\n if not token:\n result = {'error': _(u\"Argument missing\")}\n return HttpResponse(json.dumps(result), status=400, content_type=content_type)\n\n UploadLinkShare.objects.filter(token=token).delete()\n result = {'success': True}\n return HttpResponse(json.dumps(result), content_type=content_type)","title":""},{"docid":"cffbfc3f26485a0806fa85a6e178804c","score":"0.6193662","text":"def deleteLink(self, request, tag):\n if ihyperbola.IEditable.providedBy(self.original):\n return tag\n return ''","title":""},{"docid":"b3eac0ed4284ffabb10fd83fe5318ef5","score":"0.61924946","text":"def delete(self, url, headers=None):\r\n return self._request('DELETE', url, headers=headers)","title":""},{"docid":"d8ff542299ea3408cf68b627589c1dcd","score":"0.61709625","text":"def delete(self, site, **kwargs):\n site_handle = site.handle\n site.delete()\n\n return {\"message\": \"Site '{}' deleted\".format(site_handle)}, 200","title":""},{"docid":"9334073a6e334fe3f52d7306cdd7f28b","score":"0.61698496","text":"def delete(self, path):\n path = self.get_abs_path(path)\n return delete(path)","title":""},{"docid":"44180fe385cca531fe7ad7caeb93ca72","score":"0.616643","text":"def delete(self):\n self.request().delete()","title":""},{"docid":"44bafd259392764fffe94773d2a82c72","score":"0.6159616","text":"def do_DELETE(self, arg):\n args = arg.split()\n cl.DELETE(args[0], int(args[1]), int(args[2]))","title":""},{"docid":"19906dc2d39c88aab007acc31b54d595","score":"0.61514026","text":"def delete(self, *args, **kwargs):\n pass","title":""},{"docid":"f19b1abafbdb55cdc87a1d001a958be0","score":"0.6144609","text":"def deleteLink(self):\r\n\r\n if not self.securityManager.checkPermission('Delete objects', self.context):\r\n return None\r\n\r\n if self.contextState.is_locked():\r\n return self.context.absolute_url() + \"/@@cmsui-lock-info\"\r\n\r\n objectButtons = self.contextState.actions('object_buttons')\r\n for action in objectButtons:\r\n if action['id'] == 'delete':\r\n return \"%s\" % (action['url'])\r\n\r\n return None","title":""},{"docid":"335e1e7a45d72bd77d4e63d7f9c85ebf","score":"0.61284363","text":"def deleteEntry(entry_id):","title":""},{"docid":"59ef273fac358efc2394fd4aff17cd0d","score":"0.61128944","text":"def do_delete(self, rel_url):\n # If rel_url begins with a slash, remove it\n rel_url = rel_url.lstrip('/')\n full_url = self.url + rel_url\n c = pycurl.Curl()\n c.setopt(pycurl.CUSTOMREQUEST, 'DELETE')\n self.curl_call(full_url, c)\n c.close()\n return None","title":""},{"docid":"d276fd71be14fe2f46474ab79ce71b5d","score":"0.6104616","text":"def delete(self, path, **kwargs):\n return self._call('DELETE', path, **kwargs)","title":""},{"docid":"3c7dadcf2908da3062b4b1614e38c832","score":"0.6090667","text":"def delete(self, short_url):\n del self.url_map[short_url]","title":""},{"docid":"f63cca964800066976e41c5ed61146df","score":"0.60836476","text":"def delete(self):\n self._conn.delete(self._link('self'))","title":""},{"docid":"c7d95e6d1f26eb7bf94d91b7f88e62e7","score":"0.60824996","text":"def delete_anchor(id, action, name):\n check_admin()\n\n if action == \"request\":\n return render_template('admin/confirmation.html', id=id, name=name)\n if action == \"confirm\":\n anchor = Anchor.query.get_or_404(id)\n db.session.delete(anchor)\n db.session.commit()\n flash('You have successfully deleted the anchor.')\n\n # redirect to the roles page\n return redirect(url_for('admin.list_anchors'))","title":""},{"docid":"c001b8e19a58463d7a41a8dae648e771","score":"0.60649896","text":"def delete(self):\n self._conn.delete(self._link(\"self\"))","title":""},{"docid":"1affc1202bf19a0231a0b8a87ad6a2a8","score":"0.6064537","text":"def delete_link_post(request):\n link_post_id = request.POST.get('id')\n action = request.POST.get('action')\n if link_post_id and action:\n try:\n link_post = LinkPost.objects.get(id=link_post_id)\n if action == 'delete':\n # Only author should be able to delete its post\n if link_post.author == request.user:\n link_post.delete()\n\n return JsonResponse({\n 'status': 'ok',\n 'link_post_id': link_post_id\n })\n\n except LinkPost.DoesNotExist:\n return JsonResponse({'status': 'ko'})\n\n return JsonResponse({'status': 'ko'})","title":""},{"docid":"4554fac283d11574c52881022d0575ed","score":"0.6054866","text":"async def delete_revoked_chat_invite_link(\n self,\n chat_id: int,\n invite_link: str,\n *,\n request_id: str = None,\n request_timeout: int = None,\n skip_validation: bool = False\n ) -> Ok:\n _constructor = DeleteRevokedChatInviteLink.construct if skip_validation else DeleteRevokedChatInviteLink\n\n return await self.client.request(\n _constructor(\n chat_id=chat_id,\n invite_link=invite_link,\n ),\n request_id=request_id,\n request_timeout=request_timeout,\n )","title":""},{"docid":"c91acdecd6cb9ca1cd998175384c9508","score":"0.6051296","text":"def delete_link(self, node_one_id, node_two_id, interface_one_id, interface_two_id, link_type=LinkTypes.WIRED):\n # get node objects identified by link data\n node_one, node_two, net_one, net_two, _tunnel = self._link_nodes(node_one_id, node_two_id)\n\n if node_one:\n node_one.lock.acquire()\n if node_two:\n node_two.lock.acquire()\n\n try:\n # wireless link\n if link_type == LinkTypes.WIRELESS:\n objects = [node_one, node_two, net_one, net_two]\n self._link_wireless(objects, connect=False)\n # wired link\n else:\n if all([node_one, node_two]):\n # TODO: fix this for the case where ifindex[1,2] are not specified\n # a wired unlink event, delete the connecting bridge\n interface_one = node_one.netif(interface_one_id)\n interface_two = node_two.netif(interface_two_id)\n\n # get interfaces from common network, if no network node\n # otherwise get interfaces between a node and network\n if not interface_one and not interface_two:\n common_networks = node_one.commonnets(node_two)\n for network, common_interface_one, common_interface_two in common_networks:\n if (net_one and network == net_one) or not net_one:\n interface_one = common_interface_one\n interface_two = common_interface_two\n break\n\n if all([interface_one, interface_two]) and any([interface_one.net, interface_two.net]):\n if interface_one.net != interface_two.net and all([interface_one.up, interface_two.up]):\n raise ValueError(\"no common network found\")\n\n logging.info(\"deleting link node(%s):interface(%s) node(%s):interface(%s)\",\n node_one.name, interface_one.name, node_two.name, interface_two.name)\n net_one = interface_one.net\n interface_one.detachnet()\n interface_two.detachnet()\n if net_one.numnetif() == 0:\n self.delete_node(net_one.id)\n node_one.delnetif(interface_one.netindex)\n node_two.delnetif(interface_two.netindex)\n finally:\n if node_one:\n node_one.lock.release()\n if node_two:\n node_two.lock.release()","title":""},{"docid":"14661bb31afb8a03997e50030c2f4358","score":"0.60363173","text":"def delete(self, node):\n pass","title":""},{"docid":"d09523fffe8c9f1b0a589409329c458d","score":"0.6028819","text":"def unlink(linkpath):\n\tresult, dwRet = delete_reparse_point(linkpath, IO_REPARSE_TAG_MOUNT_POINT, check)\n\tif result: RemoveDirectory(linkpath)\n\treturn result, dwRet","title":""},{"docid":"ec5f5c216f6d51dd50b8d5e0cc2fd208","score":"0.60236895","text":"def delete(self, path):\n raise NotImplementedError","title":""},{"docid":"ec5f5c216f6d51dd50b8d5e0cc2fd208","score":"0.60236895","text":"def delete(self, path):\n raise NotImplementedError","title":""},{"docid":"c800a128065069fb9532c28ae234cf57","score":"0.6022477","text":"def delete(self, rule):\n pass","title":""},{"docid":"9e8101acec17e91ae864a5998404bccf","score":"0.60109395","text":"def delete(self):\n self._root._browser.delete(self._wadl_resource.url)","title":""},{"docid":"89e82eddd1486e12873a164de2af4942","score":"0.5990324","text":"def delete(self,path):\n raise NotImplementedError(\"Method 'delete' is not implemented.\")","title":""},{"docid":"9cd8a9239bd63843046cfb623cf5d7ff","score":"0.59826404","text":"def delete(self, path, attrs=None):\n if attrs is not None:\n path += ('&' if path.find(\"?\") != -1 else '?') + \\\n urllib.parse.urlencode(attrs)\n return self.call(path, method=\"DELETE\", token=self.token)","title":""},{"docid":"ad35c8330404bcc37aa90fca90a27a19","score":"0.5979427","text":"def delete(self) -> None:\n query = \"\"\"\n DELETE {\n ?s ?p ?o .\n } WHERE {\n ?s ?p ?o .\n }\n \"\"\"\n self.query.post(query)","title":""},{"docid":"892d246fe64c38ffc83554d0693bc4ef","score":"0.5974179","text":"def delete(self, url, params=None, **kwargs):\n return self.call_api(\"DELETE\", url, params=params, **kwargs)","title":""},{"docid":"322ac7bbc2a9a6725dac48085b16b87d","score":"0.5970807","text":"def delete(self):\n self._api.delete(self.id)\n self.id = None","title":""},{"docid":"eb1088e579b58f4b4eccc6e474338dde","score":"0.59604126","text":"def delete(self, tagpath):\n self.api[tagpath].delete()","title":""},{"docid":"ab179db8319bf6445857c1fdfd099b41","score":"0.59472543","text":"def delete(identifier):","title":""},{"docid":"ab96ebd3b6b516d54a51385d0dd56d85","score":"0.59467006","text":"def delete(path):\n response = client.delete(url=path)\n click.echo(format_response(response))","title":""},{"docid":"74af0c1bea64065d9f89545f1a0d4345","score":"0.5940008","text":"def test_delete(self):\n deleted = self.links.delete(1)\n self.assertEqual(deleted, None)","title":""},{"docid":"a06f335d8db78afe8d4f9be136ffe73c","score":"0.5939779","text":"def delFilefilelink(self, f, commit = True):\n f = self.getFileID(f) # change a name to a number\n n1 = self.session.query(self.Filefilelink).filter_by(source_file=f).delete()\n n2 = self.session.query(self.Filefilelink).filter_by(resulting_file=f).delete()\n if n1 + n2 == 0:\n raise DBNoData(\"No entry for ID={0} found\".format(f))\n elif commit:\n self.commitDB()","title":""},{"docid":"9e21dc0bbdab6fb90a1944564d74156e","score":"0.5935269","text":"def delete():\n pass","title":""},{"docid":"4b0da2fe40200342f67c4e5fb15b9442","score":"0.5915121","text":"def delete(self, element):\n # type: (ClarityElement) -> None\n\n self.lims.request('delete', element.uri)\n del self._cache[element.uri]","title":""},{"docid":"122b9313fd73a1838afacdbfd21df323","score":"0.59146017","text":"def delete_object(self, id):\r\n self.request(id, post_args={\"method\": \"delete\"})","title":""},{"docid":"0c22254ad98b020f42a09bd7c4fd2d54","score":"0.5912734","text":"def delete(self, link, extras):\n instance_id = link.source.attributes['occi.core.id']\n volume_id = link.target.attributes['occi.core.id']\n\n volume = storage.get_storage(volume_id, extras['nova_ctx'])\n vm.detach_volume(instance_id, volume, extras['nova_ctx'])","title":""},{"docid":"85936e395ecf4cc0a964a4eafe51bc95","score":"0.5898899","text":"def deleteItem(self, id):","title":""},{"docid":"60ad7c81f71d280950942b8d8bf72e56","score":"0.5884213","text":"def delLink( self, node1, node2 ):\n command = \"dellink \" + str( node1 ) + \" \" + str( node2 )\n try:\n response = self.execute(\n cmd=command,\n prompt=\"mininet>\",\n timeout=10 )\n if re.search( \"no node named\", response ):\n main.log.warn( response )\n return main.FALSE\n elif re.search( \"Error\", response ):\n main.log.warn( response )\n return main.FALSE\n elif re.search( \"usage:\", response ):\n main.log.warn( response )\n return main.FALSE\n else:\n return main.TRUE\n except pexpect.EOF:\n main.log.error( self.name + \": EOF exception found\" )\n main.log.error( self.name + \": \" + self.handle.before )\n main.cleanup()\n main.exit()","title":""},{"docid":"17fa7c8599e3b367d69f1bac64be550f","score":"0.58819383","text":"def DELETE(self, path, **kwargs):\n return self.call('DELETE', path, **kwargs)","title":""},{"docid":"d5578268d6c07879c42c57e0050dfa89","score":"0.5869283","text":"def delete(self):\n pass","title":""},{"docid":"d5578268d6c07879c42c57e0050dfa89","score":"0.5869283","text":"def delete(self):\n pass","title":""},{"docid":"d5578268d6c07879c42c57e0050dfa89","score":"0.5869283","text":"def delete(self):\n pass","title":""},{"docid":"d5578268d6c07879c42c57e0050dfa89","score":"0.5869283","text":"def delete(self):\n pass","title":""},{"docid":"d5578268d6c07879c42c57e0050dfa89","score":"0.5869283","text":"def delete(self):\n pass","title":""},{"docid":"dad4804f416acc06cd70de1d4eae882b","score":"0.5868816","text":"def delete(self, url: str, **kwargs: Any) -> Response:\n return self.request(\"DELETE\", url, **kwargs)","title":""},{"docid":"371caa3dfc567c9eb363df2208e02266","score":"0.5868621","text":"def delete_contact(self):\n contact_link = ContactLinkModel.find_by_entity_id(self._model.id)\n\n if contact_link is None:\n raise BusinessException(Error.DATA_NOT_FOUND, None)\n\n del contact_link.entity\n contact_link.commit()\n\n if not contact_link.has_links():\n contact = contact_link.contact\n contact_link.delete()\n contact.delete()\n\n return self","title":""},{"docid":"1d40acadc344687bf74b43119d1c0ebd","score":"0.58641183","text":"def delete(self, **kwargs):\n self._protocols.delete(**kwargs)","title":""},{"docid":"db43fde016db6be31a5f02a8d9362cbd","score":"0.5861543","text":"def delete(self):\n return self._api._req(\n self._path(), method='delete', assert_status=204, json_res=False)","title":""},{"docid":"4a62538eddbc9d08440270d102e8dd82","score":"0.5849253","text":"def delete(self, request, *args, **kwargs):\n self.object = self.get_object()\n success_url = self.get_success_url()\n try:\n rmtree(self.object.file)\n except FileNotFoundError:\n pass\n self.object.delete()\n return HttpResponseRedirect(success_url)","title":""},{"docid":"7646831428714e765e0d478910208815","score":"0.58464134","text":"def delete(self, *args, **kwargs):\n self.object.delete()\n return redirect(self.get_success_url())","title":""},{"docid":"eda0ae2b85991151fc0ec7890cca49e0","score":"0.5839519","text":"def delete(self):\n self._reference.Delete()","title":""},{"docid":"88e3dfa712edd295cb4c1b1cfc348f90","score":"0.58285695","text":"def delete_video(self):\n self.multipler_request('video/%s/delete' % self.file_id)","title":""},{"docid":"667dcd5f26c134cc0b4bffaa0f16eb85","score":"0.581242","text":"def delete(self, *arg, headers=None, **kwargs):\n if headers is None:\n headers = self._headers\n if self.base_url:\n return self._session.delete(self.base_url + arg[0], headers=headers, **kwargs)\n return self._session.delete(*arg, headers=headers, **kwargs)","title":""},{"docid":"d997c81be533e5b8678437be5d2b6051","score":"0.5797309","text":"def delete(self, path, **kwargs):\n return self.request(path, \"DELETE\", **kwargs)","title":""},{"docid":"629f302166bb510418aafe0ac78dfd16","score":"0.5792755","text":"def delete(self, *args, **kw):\n kw.update({'method': 'DELETE'})\n return self.fetch(*args, **kw)","title":""},{"docid":"340059dd9eba9623f0e352eab061b6af","score":"0.5790963","text":"def delete(self, path, data=None):\n data = self._to_payload(data)\n return requests.delete(\n self._get_full_url(path),\n data=data,\n headers=self.HEADERS,\n timeout=TIMEOUT,\n )","title":""},{"docid":"137081178233efd551533518a111a0ef","score":"0.5790186","text":"def deleteitem():","title":""},{"docid":"c902cb91d8e483295fad07b2b1a3f88e","score":"0.5788931","text":"def delete(self):\n self._send_data(self.path, None, \"DELETE\", \"delete\")\n utils.delete_attrs(self, self.config_attrs)","title":""},{"docid":"cca02615dabe53736e47d57f784e5d5c","score":"0.5780032","text":"def deleting_page(self, page):\n pass","title":""},{"docid":"6322ab1d90e70ea253e073559e7ac273","score":"0.57783353","text":"def delete(self, name):\n if name:\n try:\n del self._uris[name]\n except KeyError:\n pass","title":""}],"string":"[\n {\n \"docid\": \"75d04a8e3c7382b57f67bc652bebb46f\",\n \"score\": \"0.8691151\",\n \"text\": \"def delete_link(self, link):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"330e382b80fa4d675a239b8fc57303d7\",\n \"score\": \"0.7785142\",\n \"text\": \"async def delete_link(self, player_tag: str):\\n return await self._request(\\\"DELETE\\\", \\\"/links/{}\\\".format(correct_tag(player_tag, prefix=\\\"\\\")))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f75358a35d6184deb645d1621215fbde\",\n \"score\": \"0.7599679\",\n \"text\": \"def delete_link_by_id(link_id):\\n try:\\n link = Link.query.filter_by(id=link_id).delete()\\n db.session.commit()\\n if link:\\n return APIResponseBuilder.success({\\n \\\"deleted\\\": True\\n })\\n return APIResponseBuilder.failure({\\n \\\"invalid_id\\\": f\\\"Unable to delete link with ID {link_id}\\\"\\n })\\n except SQLAlchemyError as e:\\n return APIResponseBuilder.error(f\\\"Issue running query: {e}\\\")\\n except Exception as e:\\n return APIResponseBuilder.error(f\\\"Error encountered: {e}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44841a95ef356840f25ad2ad10c37043\",\n \"score\": \"0.7394994\",\n \"text\": \"def removeLink(self, _link: str):\\n self._has_permission(\\\"removeLink\\\")\\n target = BTPAddress.from_string_with_validation(_link)\\n self._remove_link(target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54729ea12132af201fafee80616900c2\",\n \"score\": \"0.7261224\",\n \"text\": \"def delete(self, request, *args, **kwargs):\\n\\n serializer = DeleteLinkShareSerializer(data=request.data, context=self.get_serializer_context())\\n\\n if not serializer.is_valid():\\n\\n return Response(\\n serializer.errors, status=status.HTTP_400_BAD_REQUEST\\n )\\n\\n link_share = serializer.validated_data.get('link_share')\\n\\n # delete it\\n link_share.delete()\\n\\n return Response(status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d375780a8bd42fdbc461fcb375afcb\",\n \"score\": \"0.7193894\",\n \"text\": \"def delete_link(self, pos):\\n # DEBUG\\n #debug(\\\"delete_link\\\", (\\\"cond\\\", pos in self._my_links.viewkeys()))\\n if pos in self._my_links.viewkeys():\\n temp = self._my_links.pop(pos)\\n del temp\\n if pos in self._links.viewkeys():\\n temp = self._links.pop(pos)\\n del temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40fd394c3713591a4464eea2e4aeaf22\",\n \"score\": \"0.71831894\",\n \"text\": \"def deleteLink(self):\\n # get selected index\\n index = self.getSelectedIndex(\\\"delete\\\")\\n # check selection\\n if index is not None:\\n # prevent last link to use reference beacon from being deleted\\n if self.isLastAnchorLink(index):\\n QMessageBox.warning(\\n self, \\n \\\"Last Link To Reference Beacon\\\", \\n \\\"Cannot remove last link to reference beacon\\\"\\n )\\n return\\n # recursively delete dependant links\\n self.deleteLinkDependants(self.beardistChain[index][0][3])\\n # delete link\\n del self.beardistChain[index] \\n self.updateBearDistChainDependants()\\n if len(self.beardistChain) == 0: \\n self.pshbtn_chain_finish.setEnabled(False)\\n self.pshbtn_chain_edt.setEnabled(False)\\n self.pshbtn_chain_del.setEnabled(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78dd2e8d57a9fe57d74f6f0ed9fb4f75\",\n \"score\": \"0.7053143\",\n \"text\": \"def delete_link(self) -> ActionLink:\\n return next(filter(lambda x: x.rel == 'delete', self.links), None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78dd2e8d57a9fe57d74f6f0ed9fb4f75\",\n \"score\": \"0.7053143\",\n \"text\": \"def delete_link(self) -> ActionLink:\\n return next(filter(lambda x: x.rel == 'delete', self.links), None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be43a32e0ab8938da30a184b8db8ab9f\",\n \"score\": \"0.6955712\",\n \"text\": \"def delete_link(self, light_name):\\n self.get_links().pop(light_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825aab34bc96768733af5d460e2e6ab3\",\n \"score\": \"0.692503\",\n \"text\": \"def _removeLink( self, link_id, source=None, container=None ):\\n link = getattr( self, link_id, None)\\n if link is None:\\n return\\n ob = source or link.getSourceObject()\\n if ob is None:\\n return\\n\\n if hasattr( ob, 'failIfLocked' ):\\n ob.failIfLocked()\\n\\n can_remove_link = _checkPermission( CMFCorePermissions.View, ob )\\n\\n if not ( can_remove_link or getattr( self, '_v_force_links_remove', 0 ) ):\\n raise Unauthorized, 'You are not allowed to remove link between these documents'\\n\\n if container is not None:\\n id = link.getDestinationObject().getId()\\n try:\\n if id in container.objectIds():\\n container._delObject( id )\\n except:\\n logger.error('_removeLink Cannot delete linked object: %s\\\\n>container: %s' % ( id, `container` ))\\n\\n self.unindexObject( link )\\n self._delObject( link_id )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d559be837cb48dd1768babc5f3565093\",\n \"score\": \"0.69047475\",\n \"text\": \"def deleteLink( self, link, canvas ):\\n pair = canvas.links.get( link, None )\\n if pair is not None:\\n source=pair['src']\\n dest=pair['dest']\\n del source.links[ dest ]\\n del dest.links[ source ]\\n stags = canvas.gettags( canvas.widgetToItem[ source ] )\\n dtags = canvas.gettags( canvas.widgetToItem[ dest ] )\\n ltags = canvas.gettags( link )\\n\\n self.removeGraphEdge(source[ 'text' ], dest[ 'text' ], canvas)\\n\\n if 'control' in ltags:\\n controllerName = ''\\n switchName = ''\\n if 'Controller' in stags:\\n controllerName = source[ 'text' ]\\n switchName = dest[ 'text' ]\\n else:\\n controllerName = dest[ 'text' ]\\n switchName = source[ 'text' ]\\n\\n if controllerName in canvas.switchOpts[switchName]['controllers']:\\n canvas.switchOpts[switchName]['controllers'].remove(controllerName)\\n\\n\\n if link is not None:\\n del canvas.links[ link ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b9a8e4dda6a476b7adb65df4ccb6ac4\",\n \"score\": \"0.68553734\",\n \"text\": \"def delete_secret_link(self, identity, id_, link_id, links_config=None, uow=None):\\n record, parent = self.get_parent_and_record_or_draft(id_)\\n\\n # Permissions\\n self.require_permission(identity, \\\"manage\\\", record=record)\\n\\n # Fetching\\n link_ids = [link.link_id for link in parent.access.links]\\n if str(link_id) not in link_ids:\\n raise LookupError(str(link_id))\\n\\n link_idx = link_ids.index(link_id)\\n link = parent.access.links[link_idx].resolve()\\n\\n # Deletion\\n parent.access.links.pop(link_idx)\\n link.revoke()\\n\\n # Commit\\n uow.register(RecordCommitOp(parent))\\n if record:\\n uow.register(RecordCommitOp(record))\\n\\n # Index all child records of the parent\\n self._index_related_records(record, parent, uow=uow)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8008aacdd0a6cd63d0d7d1475de86ff5\",\n \"score\": \"0.68308836\",\n \"text\": \"def __delete_violation_link(self, agreement_id, violation, violation_link, extras):\\n myrulesengine = rulesengine.RulesEngine()\\n myrulesengine._registry.delete_resource(violation_link.identifier, extras)\\n\\n agreement = myrulesengine._registry.get_resource(agreement_id, None)\\n agreement.identifier = agreement_id\\n if violation_link in agreement.links:\\n agreement.links.remove(violation_link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd0bc165eee8f132b7ab12f10d27ea2\",\n \"score\": \"0.67351085\",\n \"text\": \"def deleteEvent(self, href):\\n\\n self._removeEvent(href)\\n\\n response = yield self._request(\\n (NO_CONTENT, NOT_FOUND),\\n 'DELETE',\\n self.server[\\\"uri\\\"] + href.encode('utf-8'),\\n method_label=\\\"DELETE{event}\\\",\\n )\\n returnValue(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0941bb0b0d2a51bcb915b1cc96cb72cc\",\n \"score\": \"0.6733908\",\n \"text\": \"def delete_link(\\n self,\\n session_id: int,\\n node1_id: int,\\n node2_id: int,\\n iface1_id: int = None,\\n iface2_id: int = None,\\n source: str = None,\\n ) -> core_pb2.DeleteLinkResponse:\\n request = core_pb2.DeleteLinkRequest(\\n session_id=session_id,\\n node1_id=node1_id,\\n node2_id=node2_id,\\n iface1_id=iface1_id,\\n iface2_id=iface2_id,\\n source=source,\\n )\\n return self.stub.DeleteLink(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c1a3d4c6e20ecc1c1a7795888de7aa4\",\n \"score\": \"0.67205495\",\n \"text\": \"def delete_links(self):\\n link_names = get_selected_list(self.link_box)\\n if link_names:\\n self.link_obj.delete_links(link_names)\\n self.populate_links()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3362e931bd3359f36d391a8361ac41ef\",\n \"score\": \"0.6657411\",\n \"text\": \"def delete(url):\\n _delete(urls, url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf65ffe0a1322d544add91b1eac64fe1\",\n \"score\": \"0.6655403\",\n \"text\": \"def remove_link(self, links):\\n self._link_manager.remove_link(links, update_external=not self._disable_sync_link_manager)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7f027a69bc4afd515e70dfa376b9926\",\n \"score\": \"0.66016877\",\n \"text\": \"def delete(self):\\n\\t\\ttag\\t= 'link_'+self.getIdentifier()\\n\\t\\tself.canvas.delete(tag)\\n\\t\\tself.canvas.tag_unbind(tag,\\\"<Any>\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36339162523fda5db2457f3e15a6bbfe\",\n \"score\": \"0.654126\",\n \"text\": \"def delete_by_href(self, href, etag=None, timeout=-1, auditmemento=None,\\n helpers=None):\\n o = urlparse.urlparse(href)\\n hostname_mismatch = (o.hostname.lower() != self.session.host.lower())\\n if hostname_mismatch or o.port != self.session.port:\\n LOG.debug('href=%s will be modified to use %s:%s', href,\\n self.session.host, self.session.port)\\n return self.delete_by_path(o.path, etag=etag, timeout=timeout,\\n auditmemento=auditmemento, helpers=helpers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab22dd66c79b46d64606944fe54673f\",\n \"score\": \"0.64760023\",\n \"text\": \"def destroy_link(self):\\n self.detach_link(0)\\n self.clean_up()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"581202be2ddeba42b17d4135b37db5cd\",\n \"score\": \"0.6454511\",\n \"text\": \"def delete(url, header):\\n requests.request('DELETE', url, headers=header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"accc057cb3f953c2320cc71b6aadaa5e\",\n \"score\": \"0.6449743\",\n \"text\": \"def deleteAtIndex(self, index):\\n if index < 0 or index >= len(self._link_list):\\n return\\n\\n self._link_list.pop(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128adb54c5e075cf5a97984cc9fb4c56\",\n \"score\": \"0.6400765\",\n \"text\": \"def delete(self, *args):\\n self._execute('delete', *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ba1a9df9991f0d949451f9174a70b8\",\n \"score\": \"0.639403\",\n \"text\": \"def delete():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ba1a9df9991f0d949451f9174a70b8\",\n \"score\": \"0.639403\",\n \"text\": \"def delete():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e94e87c13a2b86ecf1f75724a7678498\",\n \"score\": \"0.6387958\",\n \"text\": \"def delProductProcessLink(self, ll):\\n link = self.getEntry('Productprocesslink', ll)\\n self.session.delete(link)\\n self.commitDB()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae65534c895b6323c5e62c1e43c35d4\",\n \"score\": \"0.635933\",\n \"text\": \"def unlink(self):\\n link = self.link\\n if link is None:\\n raise ValueError(\\\"Interface is not linked\\\")\\n if link.is_ptp or link.is_lag:\\n link.delete()\\n else:\\n raise ValueError(\\\"Cannot unlink non p-t-p link\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c53254baa1abc493d3e29e83c2fef6f4\",\n \"score\": \"0.63314855\",\n \"text\": \"def delete(self, url, headers=None, data=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3ff6f95f2599ad00e946174298a174a\",\n \"score\": \"0.63186055\",\n \"text\": \"def delete(self, evt=None):\\n for link in self.next_links.copy():\\n link.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bd63ab4263d568cf932dd3b533d50ab\",\n \"score\": \"0.6315366\",\n \"text\": \"def remove_current_link(self):\\n try:\\n os.unlink(self.link_path)\\n except FileNotFoundError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c9b708bc59dbb107d8c52892fb4e0f\",\n \"score\": \"0.629047\",\n \"text\": \"def delete(self, access_group_entry):\\n self._delete(RESOURCE_PATH % common_base.getid(access_group_entry))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"014541e29b09c169bb5d3c6ad2d50f89\",\n \"score\": \"0.62828404\",\n \"text\": \"def delete(self):\\n api_args = {}\\n DynectSession.get_session().execute(self.uri, 'DELETE', api_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751419834960390c86aabad2b1cb8645\",\n \"score\": \"0.6247951\",\n \"text\": \"def _delete(self, url):\\n r = requests.delete(self._api_prefix + url, headers=self.headers, auth=self.auth, allow_redirects=False,)\\n return self._action(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ea132ab3a5f4430cb2129dfa5b2cac2\",\n \"score\": \"0.6235586\",\n \"text\": \"def delete(self, path, **kwargs):\\n #TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82ef79827957e2b732db4c7e9754d0d2\",\n \"score\": \"0.6204013\",\n \"text\": \"def delete_bookmark(self, url):\\n params = {'url': url}\\n \\n req = requests.get(self.base_url+self.command['delete'], \\n {**params, **self.common_filters})\\n \\n return req.status_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5efa84bea25ebc6d5faa58e47dbc6186\",\n \"score\": \"0.6194476\",\n \"text\": \"def sys_upload_link_remove(request):\\n content_type = 'application/json; charset=utf-8'\\n result = {}\\n\\n token = request.POST.get('t')\\n if not token:\\n result = {'error': _(u\\\"Argument missing\\\")}\\n return HttpResponse(json.dumps(result), status=400, content_type=content_type)\\n\\n UploadLinkShare.objects.filter(token=token).delete()\\n result = {'success': True}\\n return HttpResponse(json.dumps(result), content_type=content_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cffbfc3f26485a0806fa85a6e178804c\",\n \"score\": \"0.6193662\",\n \"text\": \"def deleteLink(self, request, tag):\\n if ihyperbola.IEditable.providedBy(self.original):\\n return tag\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3eac0ed4284ffabb10fd83fe5318ef5\",\n \"score\": \"0.61924946\",\n \"text\": \"def delete(self, url, headers=None):\\r\\n return self._request('DELETE', url, headers=headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8ff542299ea3408cf68b627589c1dcd\",\n \"score\": \"0.61709625\",\n \"text\": \"def delete(self, site, **kwargs):\\n site_handle = site.handle\\n site.delete()\\n\\n return {\\\"message\\\": \\\"Site '{}' deleted\\\".format(site_handle)}, 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9334073a6e334fe3f52d7306cdd7f28b\",\n \"score\": \"0.61698496\",\n \"text\": \"def delete(self, path):\\n path = self.get_abs_path(path)\\n return delete(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44180fe385cca531fe7ad7caeb93ca72\",\n \"score\": \"0.616643\",\n \"text\": \"def delete(self):\\n self.request().delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44bafd259392764fffe94773d2a82c72\",\n \"score\": \"0.6159616\",\n \"text\": \"def do_DELETE(self, arg):\\n args = arg.split()\\n cl.DELETE(args[0], int(args[1]), int(args[2]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19906dc2d39c88aab007acc31b54d595\",\n \"score\": \"0.61514026\",\n \"text\": \"def delete(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f19b1abafbdb55cdc87a1d001a958be0\",\n \"score\": \"0.6144609\",\n \"text\": \"def deleteLink(self):\\r\\n\\r\\n if not self.securityManager.checkPermission('Delete objects', self.context):\\r\\n return None\\r\\n\\r\\n if self.contextState.is_locked():\\r\\n return self.context.absolute_url() + \\\"/@@cmsui-lock-info\\\"\\r\\n\\r\\n objectButtons = self.contextState.actions('object_buttons')\\r\\n for action in objectButtons:\\r\\n if action['id'] == 'delete':\\r\\n return \\\"%s\\\" % (action['url'])\\r\\n\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335e1e7a45d72bd77d4e63d7f9c85ebf\",\n \"score\": \"0.61284363\",\n \"text\": \"def deleteEntry(entry_id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ef273fac358efc2394fd4aff17cd0d\",\n \"score\": \"0.61128944\",\n \"text\": \"def do_delete(self, rel_url):\\n # If rel_url begins with a slash, remove it\\n rel_url = rel_url.lstrip('/')\\n full_url = self.url + rel_url\\n c = pycurl.Curl()\\n c.setopt(pycurl.CUSTOMREQUEST, 'DELETE')\\n self.curl_call(full_url, c)\\n c.close()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d276fd71be14fe2f46474ab79ce71b5d\",\n \"score\": \"0.6104616\",\n \"text\": \"def delete(self, path, **kwargs):\\n return self._call('DELETE', path, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c7dadcf2908da3062b4b1614e38c832\",\n \"score\": \"0.6090667\",\n \"text\": \"def delete(self, short_url):\\n del self.url_map[short_url]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63cca964800066976e41c5ed61146df\",\n \"score\": \"0.60836476\",\n \"text\": \"def delete(self):\\n self._conn.delete(self._link('self'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7d95e6d1f26eb7bf94d91b7f88e62e7\",\n \"score\": \"0.60824996\",\n \"text\": \"def delete_anchor(id, action, name):\\n check_admin()\\n\\n if action == \\\"request\\\":\\n return render_template('admin/confirmation.html', id=id, name=name)\\n if action == \\\"confirm\\\":\\n anchor = Anchor.query.get_or_404(id)\\n db.session.delete(anchor)\\n db.session.commit()\\n flash('You have successfully deleted the anchor.')\\n\\n # redirect to the roles page\\n return redirect(url_for('admin.list_anchors'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c001b8e19a58463d7a41a8dae648e771\",\n \"score\": \"0.60649896\",\n \"text\": \"def delete(self):\\n self._conn.delete(self._link(\\\"self\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1affc1202bf19a0231a0b8a87ad6a2a8\",\n \"score\": \"0.6064537\",\n \"text\": \"def delete_link_post(request):\\n link_post_id = request.POST.get('id')\\n action = request.POST.get('action')\\n if link_post_id and action:\\n try:\\n link_post = LinkPost.objects.get(id=link_post_id)\\n if action == 'delete':\\n # Only author should be able to delete its post\\n if link_post.author == request.user:\\n link_post.delete()\\n\\n return JsonResponse({\\n 'status': 'ok',\\n 'link_post_id': link_post_id\\n })\\n\\n except LinkPost.DoesNotExist:\\n return JsonResponse({'status': 'ko'})\\n\\n return JsonResponse({'status': 'ko'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4554fac283d11574c52881022d0575ed\",\n \"score\": \"0.6054866\",\n \"text\": \"async def delete_revoked_chat_invite_link(\\n self,\\n chat_id: int,\\n invite_link: str,\\n *,\\n request_id: str = None,\\n request_timeout: int = None,\\n skip_validation: bool = False\\n ) -> Ok:\\n _constructor = DeleteRevokedChatInviteLink.construct if skip_validation else DeleteRevokedChatInviteLink\\n\\n return await self.client.request(\\n _constructor(\\n chat_id=chat_id,\\n invite_link=invite_link,\\n ),\\n request_id=request_id,\\n request_timeout=request_timeout,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c91acdecd6cb9ca1cd998175384c9508\",\n \"score\": \"0.6051296\",\n \"text\": \"def delete_link(self, node_one_id, node_two_id, interface_one_id, interface_two_id, link_type=LinkTypes.WIRED):\\n # get node objects identified by link data\\n node_one, node_two, net_one, net_two, _tunnel = self._link_nodes(node_one_id, node_two_id)\\n\\n if node_one:\\n node_one.lock.acquire()\\n if node_two:\\n node_two.lock.acquire()\\n\\n try:\\n # wireless link\\n if link_type == LinkTypes.WIRELESS:\\n objects = [node_one, node_two, net_one, net_two]\\n self._link_wireless(objects, connect=False)\\n # wired link\\n else:\\n if all([node_one, node_two]):\\n # TODO: fix this for the case where ifindex[1,2] are not specified\\n # a wired unlink event, delete the connecting bridge\\n interface_one = node_one.netif(interface_one_id)\\n interface_two = node_two.netif(interface_two_id)\\n\\n # get interfaces from common network, if no network node\\n # otherwise get interfaces between a node and network\\n if not interface_one and not interface_two:\\n common_networks = node_one.commonnets(node_two)\\n for network, common_interface_one, common_interface_two in common_networks:\\n if (net_one and network == net_one) or not net_one:\\n interface_one = common_interface_one\\n interface_two = common_interface_two\\n break\\n\\n if all([interface_one, interface_two]) and any([interface_one.net, interface_two.net]):\\n if interface_one.net != interface_two.net and all([interface_one.up, interface_two.up]):\\n raise ValueError(\\\"no common network found\\\")\\n\\n logging.info(\\\"deleting link node(%s):interface(%s) node(%s):interface(%s)\\\",\\n node_one.name, interface_one.name, node_two.name, interface_two.name)\\n net_one = interface_one.net\\n interface_one.detachnet()\\n interface_two.detachnet()\\n if net_one.numnetif() == 0:\\n self.delete_node(net_one.id)\\n node_one.delnetif(interface_one.netindex)\\n node_two.delnetif(interface_two.netindex)\\n finally:\\n if node_one:\\n node_one.lock.release()\\n if node_two:\\n node_two.lock.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14661bb31afb8a03997e50030c2f4358\",\n \"score\": \"0.60363173\",\n \"text\": \"def delete(self, node):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d09523fffe8c9f1b0a589409329c458d\",\n \"score\": \"0.6028819\",\n \"text\": \"def unlink(linkpath):\\n\\tresult, dwRet = delete_reparse_point(linkpath, IO_REPARSE_TAG_MOUNT_POINT, check)\\n\\tif result: RemoveDirectory(linkpath)\\n\\treturn result, dwRet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec5f5c216f6d51dd50b8d5e0cc2fd208\",\n \"score\": \"0.60236895\",\n \"text\": \"def delete(self, path):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec5f5c216f6d51dd50b8d5e0cc2fd208\",\n \"score\": \"0.60236895\",\n \"text\": \"def delete(self, path):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c800a128065069fb9532c28ae234cf57\",\n \"score\": \"0.6022477\",\n \"text\": \"def delete(self, rule):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e8101acec17e91ae864a5998404bccf\",\n \"score\": \"0.60109395\",\n \"text\": \"def delete(self):\\n self._root._browser.delete(self._wadl_resource.url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e82eddd1486e12873a164de2af4942\",\n \"score\": \"0.5990324\",\n \"text\": \"def delete(self,path):\\n raise NotImplementedError(\\\"Method 'delete' is not implemented.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd8a9239bd63843046cfb623cf5d7ff\",\n \"score\": \"0.59826404\",\n \"text\": \"def delete(self, path, attrs=None):\\n if attrs is not None:\\n path += ('&' if path.find(\\\"?\\\") != -1 else '?') + \\\\\\n urllib.parse.urlencode(attrs)\\n return self.call(path, method=\\\"DELETE\\\", token=self.token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad35c8330404bcc37aa90fca90a27a19\",\n \"score\": \"0.5979427\",\n \"text\": \"def delete(self) -> None:\\n query = \\\"\\\"\\\"\\n DELETE {\\n ?s ?p ?o .\\n } WHERE {\\n ?s ?p ?o .\\n }\\n \\\"\\\"\\\"\\n self.query.post(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"892d246fe64c38ffc83554d0693bc4ef\",\n \"score\": \"0.5974179\",\n \"text\": \"def delete(self, url, params=None, **kwargs):\\n return self.call_api(\\\"DELETE\\\", url, params=params, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"322ac7bbc2a9a6725dac48085b16b87d\",\n \"score\": \"0.5970807\",\n \"text\": \"def delete(self):\\n self._api.delete(self.id)\\n self.id = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1088e579b58f4b4eccc6e474338dde\",\n \"score\": \"0.59604126\",\n \"text\": \"def delete(self, tagpath):\\n self.api[tagpath].delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab179db8319bf6445857c1fdfd099b41\",\n \"score\": \"0.59472543\",\n \"text\": \"def delete(identifier):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab96ebd3b6b516d54a51385d0dd56d85\",\n \"score\": \"0.59467006\",\n \"text\": \"def delete(path):\\n response = client.delete(url=path)\\n click.echo(format_response(response))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74af0c1bea64065d9f89545f1a0d4345\",\n \"score\": \"0.5940008\",\n \"text\": \"def test_delete(self):\\n deleted = self.links.delete(1)\\n self.assertEqual(deleted, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a06f335d8db78afe8d4f9be136ffe73c\",\n \"score\": \"0.5939779\",\n \"text\": \"def delFilefilelink(self, f, commit = True):\\n f = self.getFileID(f) # change a name to a number\\n n1 = self.session.query(self.Filefilelink).filter_by(source_file=f).delete()\\n n2 = self.session.query(self.Filefilelink).filter_by(resulting_file=f).delete()\\n if n1 + n2 == 0:\\n raise DBNoData(\\\"No entry for ID={0} found\\\".format(f))\\n elif commit:\\n self.commitDB()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e21dc0bbdab6fb90a1944564d74156e\",\n \"score\": \"0.5935269\",\n \"text\": \"def delete():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b0da2fe40200342f67c4e5fb15b9442\",\n \"score\": \"0.5915121\",\n \"text\": \"def delete(self, element):\\n # type: (ClarityElement) -> None\\n\\n self.lims.request('delete', element.uri)\\n del self._cache[element.uri]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"122b9313fd73a1838afacdbfd21df323\",\n \"score\": \"0.59146017\",\n \"text\": \"def delete_object(self, id):\\r\\n self.request(id, post_args={\\\"method\\\": \\\"delete\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c22254ad98b020f42a09bd7c4fd2d54\",\n \"score\": \"0.5912734\",\n \"text\": \"def delete(self, link, extras):\\n instance_id = link.source.attributes['occi.core.id']\\n volume_id = link.target.attributes['occi.core.id']\\n\\n volume = storage.get_storage(volume_id, extras['nova_ctx'])\\n vm.detach_volume(instance_id, volume, extras['nova_ctx'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85936e395ecf4cc0a964a4eafe51bc95\",\n \"score\": \"0.5898899\",\n \"text\": \"def deleteItem(self, id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ad7c81f71d280950942b8d8bf72e56\",\n \"score\": \"0.5884213\",\n \"text\": \"def delLink( self, node1, node2 ):\\n command = \\\"dellink \\\" + str( node1 ) + \\\" \\\" + str( node2 )\\n try:\\n response = self.execute(\\n cmd=command,\\n prompt=\\\"mininet>\\\",\\n timeout=10 )\\n if re.search( \\\"no node named\\\", response ):\\n main.log.warn( response )\\n return main.FALSE\\n elif re.search( \\\"Error\\\", response ):\\n main.log.warn( response )\\n return main.FALSE\\n elif re.search( \\\"usage:\\\", response ):\\n main.log.warn( response )\\n return main.FALSE\\n else:\\n return main.TRUE\\n except pexpect.EOF:\\n main.log.error( self.name + \\\": EOF exception found\\\" )\\n main.log.error( self.name + \\\": \\\" + self.handle.before )\\n main.cleanup()\\n main.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17fa7c8599e3b367d69f1bac64be550f\",\n \"score\": \"0.58819383\",\n \"text\": \"def DELETE(self, path, **kwargs):\\n return self.call('DELETE', path, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5578268d6c07879c42c57e0050dfa89\",\n \"score\": \"0.5869283\",\n \"text\": \"def delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5578268d6c07879c42c57e0050dfa89\",\n \"score\": \"0.5869283\",\n \"text\": \"def delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5578268d6c07879c42c57e0050dfa89\",\n \"score\": \"0.5869283\",\n \"text\": \"def delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5578268d6c07879c42c57e0050dfa89\",\n \"score\": \"0.5869283\",\n \"text\": \"def delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5578268d6c07879c42c57e0050dfa89\",\n \"score\": \"0.5869283\",\n \"text\": \"def delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dad4804f416acc06cd70de1d4eae882b\",\n \"score\": \"0.5868816\",\n \"text\": \"def delete(self, url: str, **kwargs: Any) -> Response:\\n return self.request(\\\"DELETE\\\", url, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"371caa3dfc567c9eb363df2208e02266\",\n \"score\": \"0.5868621\",\n \"text\": \"def delete_contact(self):\\n contact_link = ContactLinkModel.find_by_entity_id(self._model.id)\\n\\n if contact_link is None:\\n raise BusinessException(Error.DATA_NOT_FOUND, None)\\n\\n del contact_link.entity\\n contact_link.commit()\\n\\n if not contact_link.has_links():\\n contact = contact_link.contact\\n contact_link.delete()\\n contact.delete()\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d40acadc344687bf74b43119d1c0ebd\",\n \"score\": \"0.58641183\",\n \"text\": \"def delete(self, **kwargs):\\n self._protocols.delete(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db43fde016db6be31a5f02a8d9362cbd\",\n \"score\": \"0.5861543\",\n \"text\": \"def delete(self):\\n return self._api._req(\\n self._path(), method='delete', assert_status=204, json_res=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a62538eddbc9d08440270d102e8dd82\",\n \"score\": \"0.5849253\",\n \"text\": \"def delete(self, request, *args, **kwargs):\\n self.object = self.get_object()\\n success_url = self.get_success_url()\\n try:\\n rmtree(self.object.file)\\n except FileNotFoundError:\\n pass\\n self.object.delete()\\n return HttpResponseRedirect(success_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7646831428714e765e0d478910208815\",\n \"score\": \"0.58464134\",\n \"text\": \"def delete(self, *args, **kwargs):\\n self.object.delete()\\n return redirect(self.get_success_url())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda0ae2b85991151fc0ec7890cca49e0\",\n \"score\": \"0.5839519\",\n \"text\": \"def delete(self):\\n self._reference.Delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e3dfa712edd295cb4c1b1cfc348f90\",\n \"score\": \"0.58285695\",\n \"text\": \"def delete_video(self):\\n self.multipler_request('video/%s/delete' % self.file_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"667dcd5f26c134cc0b4bffaa0f16eb85\",\n \"score\": \"0.581242\",\n \"text\": \"def delete(self, *arg, headers=None, **kwargs):\\n if headers is None:\\n headers = self._headers\\n if self.base_url:\\n return self._session.delete(self.base_url + arg[0], headers=headers, **kwargs)\\n return self._session.delete(*arg, headers=headers, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d997c81be533e5b8678437be5d2b6051\",\n \"score\": \"0.5797309\",\n \"text\": \"def delete(self, path, **kwargs):\\n return self.request(path, \\\"DELETE\\\", **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629f302166bb510418aafe0ac78dfd16\",\n \"score\": \"0.5792755\",\n \"text\": \"def delete(self, *args, **kw):\\n kw.update({'method': 'DELETE'})\\n return self.fetch(*args, **kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340059dd9eba9623f0e352eab061b6af\",\n \"score\": \"0.5790963\",\n \"text\": \"def delete(self, path, data=None):\\n data = self._to_payload(data)\\n return requests.delete(\\n self._get_full_url(path),\\n data=data,\\n headers=self.HEADERS,\\n timeout=TIMEOUT,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"137081178233efd551533518a111a0ef\",\n \"score\": \"0.5790186\",\n \"text\": \"def deleteitem():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c902cb91d8e483295fad07b2b1a3f88e\",\n \"score\": \"0.5788931\",\n \"text\": \"def delete(self):\\n self._send_data(self.path, None, \\\"DELETE\\\", \\\"delete\\\")\\n utils.delete_attrs(self, self.config_attrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cca02615dabe53736e47d57f784e5d5c\",\n \"score\": \"0.5780032\",\n \"text\": \"def deleting_page(self, page):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6322ab1d90e70ea253e073559e7ac273\",\n \"score\": \"0.57783353\",\n \"text\": \"def delete(self, name):\\n if name:\\n try:\\n del self._uris[name]\\n except KeyError:\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1798,"cells":{"query_id":{"kind":"string","value":"a7b1a69abe3a81bef0ab8c3bf77ea64d"},"query":{"kind":"string","value":"Test that the check ran as expected."},"positive_passages":{"kind":"list like","value":[{"docid":"24c0a765979399b2f26022a1bca2dcce","score":"0.0","text":"async def test_did_run(coresys: CoreSys):\n core_security = CheckCoreSecurity(coresys)\n should_run = core_security.states\n should_not_run = [state for state in CoreState if state not in should_run]\n assert len(should_run) != 0\n assert len(should_not_run) != 0\n\n with patch(\n \"supervisor.resolution.checks.core_security.CheckCoreSecurity.run_check\",\n return_value=None,\n ) as check:\n for state in should_run:\n coresys.core.state = state\n await core_security()\n check.assert_called_once()\n check.reset_mock()\n\n for state in should_not_run:\n coresys.core.state = state\n await core_security()\n check.assert_not_called()\n check.reset_mock()","title":""}],"string":"[\n {\n \"docid\": \"24c0a765979399b2f26022a1bca2dcce\",\n \"score\": \"0.0\",\n \"text\": \"async def test_did_run(coresys: CoreSys):\\n core_security = CheckCoreSecurity(coresys)\\n should_run = core_security.states\\n should_not_run = [state for state in CoreState if state not in should_run]\\n assert len(should_run) != 0\\n assert len(should_not_run) != 0\\n\\n with patch(\\n \\\"supervisor.resolution.checks.core_security.CheckCoreSecurity.run_check\\\",\\n return_value=None,\\n ) as check:\\n for state in should_run:\\n coresys.core.state = state\\n await core_security()\\n check.assert_called_once()\\n check.reset_mock()\\n\\n for state in should_not_run:\\n coresys.core.state = state\\n await core_security()\\n check.assert_not_called()\\n check.reset_mock()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b0de60d9fa8572424d4f0dea91ee446f","score":"0.76596487","text":"def test_check():\n res, _ = ToolTester().check(None)\n assert res","title":""},{"docid":"0be4e31b3b916dfdca641c459ba608fd","score":"0.7338686","text":"def test_succeed():\n assert True","title":""},{"docid":"d66d05c927950139124fbd3014c84583","score":"0.7323444","text":"def test_check1(self):\n self.task.condition = 'True'\n\n test, traceback = self.task.check()\n assert test\n assert not traceback\n assert self.check.check_called","title":""},{"docid":"f2e00adcd3dde0ef4dd376332008353e","score":"0.73156786","text":"def test_example(self):\n self.assertTrue(True)","title":""},{"docid":"240b607e29d545e63c887fc8fd69b67a","score":"0.731112","text":"def test_assert_truth(self):\n\n self.assertTrue(True)","title":""},{"docid":"bdc50fb96c26fc0d0f9f9635566191e8","score":"0.7301021","text":"def test_get_check(self):\n pass","title":""},{"docid":"362111d39535a293173504b924137678","score":"0.7298853","text":"def test_that_passes():\n assert True","title":""},{"docid":"2bfa51c04773f4c74dd1eda22ef4de33","score":"0.7285278","text":"def test_always_pass():\n assert True","title":""},{"docid":"3a2ded7265a6e02a4a64cc6f783e4f4f","score":"0.72754675","text":"def test_basic(self):\n self.assertTrue(True)","title":""},{"docid":"b57eaf216dd1448b5d511c85087c44a3","score":"0.72709703","text":"def test_full_cmd_success():\r\n pass","title":""},{"docid":"b8fa5c85dcd1f4f41adedeee09d9bd63","score":"0.7245594","text":"def test_check2(self):\n self.task.condition = '*True'\n\n test, traceback = self.task.check(test_instr=True)\n assert not test\n assert len(traceback) == 1\n assert 'root/Test-condition' in traceback","title":""},{"docid":"81600e225b6e693179fff5f6040bb240","score":"0.719482","text":"def verify():","title":""},{"docid":"b0340d9a466846a2c3a0b5fdacff97f1","score":"0.7166722","text":"def test_post_check(self):\n pass","title":""},{"docid":"37792814f2c1071aaa51e508fcb6223b","score":"0.7148263","text":"def Check(self):","title":""},{"docid":"390b1f6fc26039d74990ee4ec24f73f8","score":"0.71183205","text":"def test_sample():\n\tassert True","title":""},{"docid":"f410d993da1efb5e1d47384281857ac4","score":"0.71126956","text":"def verification(self):","title":""},{"docid":"6c7051d20a11cdb7f0123959f2438a79","score":"0.70982695","text":"def test_broken(self):\n assert(2 == 3)","title":""},{"docid":"9533fe2971f60d336a11a7a6282ad429","score":"0.7085562","text":"def test1(self) -> None:\n assert 1 == 0, \"Test 1 has failed\"","title":""},{"docid":"cc6bb6b914b4a888aac3bedf8a8e51bf","score":"0.70767725","text":"def test_check_consistency_succeeds(self):\n self.initialization._check_consistency()","title":""},{"docid":"55c386c30e001f4648621dbc2f68e8c0","score":"0.70575374","text":"def test_pre_3():","title":""},{"docid":"c5f929e5757d785c245990e120fb5043","score":"0.70525193","text":"def check(self):","title":""},{"docid":"0597a0a800af25d9525d41fbe295b77c","score":"0.7041821","text":"def test_true():\n assert True","title":""},{"docid":"e3c87fca217dd875a683e0cc49e2e4bf","score":"0.7036741","text":"def sanity_check(self):","title":""},{"docid":"796170513b66550f48ee7bbfb1e22041","score":"0.7028594","text":"def a_test(self):\n self.assertTrue(True)","title":""},{"docid":"e407186a9c37da1f8268d10ae6fb1f48","score":"0.70238656","text":"def test_that_fails(self):\n self.assertEqual(True, True)","title":""},{"docid":"0b25f3d382c53c1ada2a1500dfe7d967","score":"0.70083094","text":"def test_statment():","title":""},{"docid":"f442fc20be7f6fff2d1f5b387fe488ad","score":"0.6966077","text":"def test_checkbox_4():\n assert test_main.checkbox_validation_on_success(\"Cruise\") == True","title":""},{"docid":"d3862ecd8c6ff442021ff0181ee1ad09","score":"0.69649035","text":"def test_command_changes_positive() -> None:\n collection = RulesCollection()\n collection.register(CommandHasChangesCheckRule())\n success = \"examples/playbooks/command-check-success.yml\"\n good_runner = Runner(success, rules=collection)\n assert [] == good_runner.run()","title":""},{"docid":"7968a70fb9eb73e769f7d46ecab726d7","score":"0.6961945","text":"def test_trivial(self):\n self.assertTrue(True)","title":""},{"docid":"34164233450cb4c43bc1e35ac3e92aae","score":"0.6960001","text":"def test_canary(self):\n self.assertEqual(2, 2)","title":""},{"docid":"34164233450cb4c43bc1e35ac3e92aae","score":"0.6960001","text":"def test_canary(self):\n self.assertEqual(2, 2)","title":""},{"docid":"250edae669d7c3b83e108a49c4de08de","score":"0.69591653","text":"def test_pre_6():","title":""},{"docid":"f9c36db7ffd82cffae66bfaaf4cc6d3a","score":"0.6956748","text":"def _do_unit_test(self):\n return True","title":""},{"docid":"a1728cb8d614613afc3a834a39d2cfcd","score":"0.69514483","text":"def test_checkbox_1():\n assert test_main.checkbox_validation_on_failure() == True","title":""},{"docid":"095cf8e243ec04106d36f1afa919730f","score":"0.69489825","text":"def test_testing(wait_for_crossbar):\n assert True","title":""},{"docid":"636ce0a646b3b10092dc0c6cce55f38e","score":"0.69478256","text":"def check_assumptions():","title":""},{"docid":"59c4d891ed9e263f27cc84b4c88a781a","score":"0.69239616","text":"def test_checkbox_3():\n assert test_main.checkbox_validation_on_success(\"Airplane\") == True","title":""},{"docid":"1e20aa29648f8284e67783834c5443fd","score":"0.6916858","text":"def check(self):\n pass","title":""},{"docid":"1e20aa29648f8284e67783834c5443fd","score":"0.6916858","text":"def check(self):\n pass","title":""},{"docid":"1e20aa29648f8284e67783834c5443fd","score":"0.6916858","text":"def check(self):\n pass","title":""},{"docid":"9dfc847f35a4166ae8cbf36b07fca637","score":"0.69100654","text":"def test_pre_4():","title":""},{"docid":"101b802076f24356c65cf5983693cc3d","score":"0.690627","text":"def test_check_if_snapshot_passed():\n # Confirm result is True iff all values are PASSED\n assert check_if_all_passed({\"a\": \"PASSED\", \"b\": \"PASSED\"})\n assert not check_if_all_passed({\"a\": \"PASSED\", \"b\": \"FAILED\"})\n assert not check_if_all_passed({\"a\": \"PASSED\", \"b\": \"WARNINGS\"})\n assert not check_if_all_passed({\"a\": \"FAILED\", \"b\": \"FAILED\"})","title":""},{"docid":"4a3921f97f6e011ac00ffea1c15205f1","score":"0.690392","text":"def test_pre5():","title":""},{"docid":"a9798d8fe6305390af5611e551d2b11c","score":"0.689768","text":"def test_validate(self):\n pass","title":""},{"docid":"e018984f2b5b11a75007869ba2dda381","score":"0.68790853","text":"def test_pre_2():","title":""},{"docid":"8f1e5285d19a66123e7b49048e7e042f","score":"0.6877008","text":"def test_error():","title":""},{"docid":"16841784da8e716b9375ad4b2b9ae223","score":"0.6875488","text":"def test_3(self):","title":""},{"docid":"4ba44bf40dd47d5dd9ca5ee9ab5cd3cd","score":"0.6860231","text":"def test_pre_1():","title":""},{"docid":"5025234664618a87d4a79943a6324df2","score":"0.685545","text":"def test_case_1(self):\r\n\r\n self.assertTrue(verifyExpectedElements())","title":""},{"docid":"0b2dab6b28c615c4bfa91265ee699775","score":"0.6847646","text":"def test_check(self):\n\n self.assertTrue(hu.checkIF())\n self.assertTrue(hu.checkIF(cfg.IF))\n self.assertTrue(hu.checkIF(TESTIF))\n self.assertFalse(hu.checkIF(BADIF))","title":""},{"docid":"ad8da9c4f89cf541e07ee70f4417550c","score":"0.683318","text":"def test(self):\n return True","title":""},{"docid":"cf446368f817e5e027032357b4c7080f","score":"0.68231785","text":"def test_validate(self):\n ranger_tool.validate()","title":""},{"docid":"9ec3280d29e58de72dd263537f5ecb4d","score":"0.6822744","text":"def test_check_errors__no_errors(self):\n self.assertFalse( True == self.sapi.check_errors() )","title":""},{"docid":"73cece719f5cb2441ce0766112a9afb3","score":"0.68171895","text":"def sanity_checks(self):\n # FIXME : What kind of sanity should be done ?\n # => testing connection to kubernetes ? testing kubevirt is OK ?\n log.info(\"Sanity checks: '{}'\".format(self._name))","title":""},{"docid":"284c81d91e45f94ce548065578e0ec19","score":"0.68157834","text":"def _check(self):\n pass","title":""},{"docid":"1fd94a9695eceed9ab7f00ef5621e48b","score":"0.68106174","text":"def check(self) -> bool:","title":""},{"docid":"a980866619d74fb8df8c63b91d8b0c8c","score":"0.6795618","text":"def test_run(self):\n self.clearOutputStream()\n runner = Runner()\n runner.setOutput(self.outputStream)\n self.assertRaises(SystemExit, runner.run, [\"--version\"])\n self.assertTrue(self.outputStream.getvalue().count(\"Python\") > 0, \\\n msg=\"failed to call pylint\")","title":""},{"docid":"a3a7ac70fb148327d0ab67d7f31fde85","score":"0.6791688","text":"def test_checkbox_6():\n assert test_main.checkbox_validation_on_success(\"ALL\") == True","title":""},{"docid":"320dcba1c969f695f0ca179a40c4c747","score":"0.67870533","text":"def test_success(self):\n self.assertEqual(find_error_line(\"\"), -1)","title":""},{"docid":"26364ea4a63a0e64004a9b5c21ab790c","score":"0.6777752","text":"def test_validation(self):","title":""},{"docid":"05faa88d86541b607f2daf9149047bab","score":"0.67658645","text":"def test_okay(self):\n\n\t\tprint(sys._getframe( ).f_code.co_name)\n\t\tself.assertTrue(True)\n\t\tprint(\"Test Framework okay.\")\n\t\tprint(\"Started from \" + os.getcwd())","title":""},{"docid":"e0fd7183cc0f40d03fb7e9540603db35","score":"0.6764888","text":"def test_checkbox_2():\n assert test_main.checkbox_validation_on_success(\"Vehicle\") == True","title":""},{"docid":"f9c82fcce3672cf836716181212cb178","score":"0.67608976","text":"def _test(self):","title":""},{"docid":"3216078368b8b1d036a81d44bb8d4511","score":"0.67568","text":"def test_first(self):\n assert(3 == 3)","title":""},{"docid":"c30b076b7efc7d552fd5e89a9c9376eb","score":"0.67560184","text":"def check_all():\n test_string_convert()\n test_string_concatenator()\n test_start_chat()\n test_add_component()\n test_find_and_return_dict()\n test_return_model_name()\n test_check_input()\n test_print_instructions()\n \n print(\"test finished\")","title":""},{"docid":"2078574fb07d87ef139f57c4887edafa","score":"0.6749363","text":"def has_a_smoke_test ():\r\n assert True == True","title":""},{"docid":"e73710781b3866985e72c0ec2be2b92b","score":"0.6736963","text":"def test_initial():\n assert True == True","title":""},{"docid":"e73710781b3866985e72c0ec2be2b92b","score":"0.6736963","text":"def test_initial():\n assert True == True","title":""},{"docid":"a8ab0c9f076fcd26b5f1f60a043fa35e","score":"0.67290723","text":"def test_condition_check_failed_requests_alarm_red():","title":""},{"docid":"3c6e620ee07c8da48e34c72585900429","score":"0.6728718","text":"def test_condition_check_failed_requests_alarm_green():","title":""},{"docid":"3ee902d380f535b230b7fb286c400dd1","score":"0.67217904","text":"def test_hello(self):\n self.assertTrue(True)","title":""},{"docid":"7f91fa53f0c11f4ebd1146de23f62324","score":"0.67214835","text":"def testCheck(self):\n # FIXME: it would be good to check if these results are right\n self.assertTrue(self.checker.Check('libc6'))\n self.assertRaises(PackageNotFoundError, self.checker.Check, 'nosuchpackage')\n self.assertTrue(self.checker.Check('perl', True))\n self.assertTrue(self.checker.Check('openjdk-6-jre-headless', True)) # missing recommended package","title":""},{"docid":"4ee44c706e3d6fbc9149b047e05b55aa","score":"0.6716576","text":"def testPass(self):\n with temp_base() as basedir:\n result = run_tests(\n basedir,\n '--mode=Release',\n '--progress=verbose',\n '--variants=default,stress',\n '--time',\n 'sweet/bananas',\n 'sweet/raspberries',\n )\n self.assertIn('Done running sweet/bananas default: pass', result.stdout, result)\n # TODO(majeski): Implement for test processors\n # self.assertIn('Total time:', result.stderr, result)\n # self.assertIn('sweet/bananas', result.stderr, result)\n self.assertEqual(0, result.returncode, result)","title":""},{"docid":"286a6a5931d6757693b9df6fc2b5bef1","score":"0.6714116","text":"def test(did_pass):\n linenum = sys._getframe(1).f_lineno\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","title":""},{"docid":"b84e9692960bb1005b33227ade8f3055","score":"0.67123175","text":"def testA(self) -> None:\n assert 1 == 1, \"This test has passed\"","title":""},{"docid":"a5822c8607a3bc7e55360975a9021faa","score":"0.6711677","text":"def test_should_run(self):\n self.assertTrue(self.installer.should_run)","title":""},{"docid":"441b95c95e17bb60541e370203edef17","score":"0.67103255","text":"def test(did_pass):\n linenum = sys._getframe(1).f_lineno\n if did_pass:\n msg = 'Test at line {0} ok.'.format(linenum)\n else:\n msg = 'Test at line {0} FAILED.'.format(linenum)\n print(msg)","title":""},{"docid":"5f86e5cca0ad5b8edb48153d3af77c96","score":"0.6702691","text":"def testit(did_pass):\r\n # This function works correctly\r\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\r\n if did_pass:\r\n msg = \"Test at line {0} ok.\".format(linenum)\r\n else:\r\n msg = (\"Test at line {0} FAILED.\".format(linenum))\r\n print(msg)","title":""},{"docid":"34e61aabf5f282bcf202f9de782faf14","score":"0.66948336","text":"def test_check_consistency_succeeds(self):\n self.configuration._check_consistency()","title":""},{"docid":"4f587422bb0f4eadd46f1c521f1c824b","score":"0.6691945","text":"def DoAfterTestCheck(self):\n pass","title":""},{"docid":"c5f22a94564086de96496f919d529460","score":"0.6690808","text":"def test_check_transaction(self):\n pass","title":""},{"docid":"35c5725d4d17fc7d3b88f122f7b5ee89","score":"0.6690794","text":"def test(did_pass):\r\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\r\n if did_pass:\r\n msg = \"Test at line {0} ok.\".format(linenum)\r\n else:\r\n msg = (\"Test at line {0} FAILED.\".format(linenum))\r\n print(msg)","title":""},{"docid":"35c5725d4d17fc7d3b88f122f7b5ee89","score":"0.6690794","text":"def test(did_pass):\r\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\r\n if did_pass:\r\n msg = \"Test at line {0} ok.\".format(linenum)\r\n else:\r\n msg = (\"Test at line {0} FAILED.\".format(linenum))\r\n print(msg)","title":""},{"docid":"50a45e9ec653016b86aef2b04ae8453a","score":"0.6685492","text":"def test_two_runs_no_clean():\n assert True","title":""},{"docid":"5fcf8cf9975c7f361b0f3dd0233b8cd7","score":"0.6680016","text":"def test_data_load_stage():\n print()\n assert True == True","title":""},{"docid":"93d634925ca4cd42ab67364dab3dcb40","score":"0.66776836","text":"def tests():","title":""},{"docid":"93d634925ca4cd42ab67364dab3dcb40","score":"0.66776836","text":"def tests():","title":""},{"docid":"90bd23d809d137de70aef50e930355a1","score":"0.6675825","text":"def verify(self):\n\n pass","title":""},{"docid":"b0d4e64518c7ba71409619cce7f3a4f0","score":"0.6674162","text":"def test(did_pass):\r\n linenum = sys._getframe(1).f_lineno # Get the caller's line number\r\n if did_pass:\r\n msg = \"Test at line {0} ok.\".format(linenum)\r\n else:\r\n msg = (\"Test at line {0} FAILED.\".format(linenum))\r\n print(msg)","title":""},{"docid":"7c71fd62ebce464ead62b0b9a932c203","score":"0.667401","text":"def test_that_fails():\n assert False","title":""},{"docid":"6ae81709679c1299bd400a4d3e53b7ec","score":"0.66585284","text":"def verify(self):\n return True","title":""},{"docid":"3295fa35a3d7fef4197acaf801f3b7f2","score":"0.6657164","text":"def test_one(self):\n assert_equals(1, 1)","title":""},{"docid":"1439e252aed41805d3acfb8779100b3a","score":"0.6654409","text":"def test_main(self):\n pass","title":""},{"docid":"94628a9fa7ee3c77f50562ecde6864d2","score":"0.6648399","text":"def test_task332(self, input_value, expected_value):\n self.assertEqual(algo.Task332.main_logic(input_value), expected_value)","title":""},{"docid":"580bd5edbb5b0b282c389aee4dba999d","score":"0.66472477","text":"def test_00(self):\n pass","title":""},{"docid":"153c44dfa04a3bc3f611dc759406f696","score":"0.6643772","text":"def test_dl_7():","title":""},{"docid":"f511b3d739dff5b721deaac30f263c73","score":"0.6637741","text":"def run_test(self, computed, expected, message = \"\"):\n self.total_tests += 1\n if computed != expected:\n msg = message + \" Computed: \" + str(computed)\n msg += \" Expected: \" + str(expected)\n print msg\n self.failures += 1","title":""},{"docid":"bb1c415bac898ec787843027ee4add78","score":"0.6637624","text":"def test1():\n check50.run(\"./question3\").stdout(\"First condition is true\\nSecond condition is false\\nThird condition is true\", regex=True).exit(0)","title":""},{"docid":"88ba45382a90a4d8eb246158767c919c","score":"0.66362286","text":"def verify(self):\n for e in self.expected:\n if e.invoke_count == 0:\n raise AssertionError(\n \"Subprocess never invoked: {0}\".format(format_command(e.command)))","title":""},{"docid":"eb14be2ece809d6bd34fe2775f729ad1","score":"0.6629225","text":"def test_is_true(self):\n self.assertEqual(True, True)","title":""},{"docid":"d088f00b1f6ad87145f0c5bb45cb17b4","score":"0.66290736","text":"def test(did_pass):\n lineum = sys._getframe(1).f_lineno\n if did_pass:\n msg = \"Test at line {0} ok\".format(lineum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(lineum))\n print(msg)","title":""}],"string":"[\n {\n \"docid\": \"b0de60d9fa8572424d4f0dea91ee446f\",\n \"score\": \"0.76596487\",\n \"text\": \"def test_check():\\n res, _ = ToolTester().check(None)\\n assert res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be4e31b3b916dfdca641c459ba608fd\",\n \"score\": \"0.7338686\",\n \"text\": \"def test_succeed():\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d66d05c927950139124fbd3014c84583\",\n \"score\": \"0.7323444\",\n \"text\": \"def test_check1(self):\\n self.task.condition = 'True'\\n\\n test, traceback = self.task.check()\\n assert test\\n assert not traceback\\n assert self.check.check_called\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2e00adcd3dde0ef4dd376332008353e\",\n \"score\": \"0.73156786\",\n \"text\": \"def test_example(self):\\n self.assertTrue(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"240b607e29d545e63c887fc8fd69b67a\",\n \"score\": \"0.731112\",\n \"text\": \"def test_assert_truth(self):\\n\\n self.assertTrue(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc50fb96c26fc0d0f9f9635566191e8\",\n \"score\": \"0.7301021\",\n \"text\": \"def test_get_check(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"362111d39535a293173504b924137678\",\n \"score\": \"0.7298853\",\n \"text\": \"def test_that_passes():\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bfa51c04773f4c74dd1eda22ef4de33\",\n \"score\": \"0.7285278\",\n \"text\": \"def test_always_pass():\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2ded7265a6e02a4a64cc6f783e4f4f\",\n \"score\": \"0.72754675\",\n \"text\": \"def test_basic(self):\\n self.assertTrue(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b57eaf216dd1448b5d511c85087c44a3\",\n \"score\": \"0.72709703\",\n \"text\": \"def test_full_cmd_success():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8fa5c85dcd1f4f41adedeee09d9bd63\",\n \"score\": \"0.7245594\",\n \"text\": \"def test_check2(self):\\n self.task.condition = '*True'\\n\\n test, traceback = self.task.check(test_instr=True)\\n assert not test\\n assert len(traceback) == 1\\n assert 'root/Test-condition' in traceback\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81600e225b6e693179fff5f6040bb240\",\n \"score\": \"0.719482\",\n \"text\": \"def verify():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0340d9a466846a2c3a0b5fdacff97f1\",\n \"score\": \"0.7166722\",\n \"text\": \"def test_post_check(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37792814f2c1071aaa51e508fcb6223b\",\n \"score\": \"0.7148263\",\n \"text\": \"def Check(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"390b1f6fc26039d74990ee4ec24f73f8\",\n \"score\": \"0.71183205\",\n \"text\": \"def test_sample():\\n\\tassert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f410d993da1efb5e1d47384281857ac4\",\n \"score\": \"0.71126956\",\n \"text\": \"def verification(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c7051d20a11cdb7f0123959f2438a79\",\n \"score\": \"0.70982695\",\n \"text\": \"def test_broken(self):\\n assert(2 == 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9533fe2971f60d336a11a7a6282ad429\",\n \"score\": \"0.7085562\",\n \"text\": \"def test1(self) -> None:\\n assert 1 == 0, \\\"Test 1 has failed\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc6bb6b914b4a888aac3bedf8a8e51bf\",\n \"score\": \"0.70767725\",\n \"text\": \"def test_check_consistency_succeeds(self):\\n self.initialization._check_consistency()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c386c30e001f4648621dbc2f68e8c0\",\n \"score\": \"0.70575374\",\n \"text\": \"def test_pre_3():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f929e5757d785c245990e120fb5043\",\n \"score\": \"0.70525193\",\n \"text\": \"def check(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0597a0a800af25d9525d41fbe295b77c\",\n \"score\": \"0.7041821\",\n \"text\": \"def test_true():\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3c87fca217dd875a683e0cc49e2e4bf\",\n \"score\": \"0.7036741\",\n \"text\": \"def sanity_check(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"796170513b66550f48ee7bbfb1e22041\",\n \"score\": \"0.7028594\",\n \"text\": \"def a_test(self):\\n self.assertTrue(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e407186a9c37da1f8268d10ae6fb1f48\",\n \"score\": \"0.70238656\",\n \"text\": \"def test_that_fails(self):\\n self.assertEqual(True, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b25f3d382c53c1ada2a1500dfe7d967\",\n \"score\": \"0.70083094\",\n \"text\": \"def test_statment():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f442fc20be7f6fff2d1f5b387fe488ad\",\n \"score\": \"0.6966077\",\n \"text\": \"def test_checkbox_4():\\n assert test_main.checkbox_validation_on_success(\\\"Cruise\\\") == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3862ecd8c6ff442021ff0181ee1ad09\",\n \"score\": \"0.69649035\",\n \"text\": \"def test_command_changes_positive() -> None:\\n collection = RulesCollection()\\n collection.register(CommandHasChangesCheckRule())\\n success = \\\"examples/playbooks/command-check-success.yml\\\"\\n good_runner = Runner(success, rules=collection)\\n assert [] == good_runner.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7968a70fb9eb73e769f7d46ecab726d7\",\n \"score\": \"0.6961945\",\n \"text\": \"def test_trivial(self):\\n self.assertTrue(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34164233450cb4c43bc1e35ac3e92aae\",\n \"score\": \"0.6960001\",\n \"text\": \"def test_canary(self):\\n self.assertEqual(2, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34164233450cb4c43bc1e35ac3e92aae\",\n \"score\": \"0.6960001\",\n \"text\": \"def test_canary(self):\\n self.assertEqual(2, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"250edae669d7c3b83e108a49c4de08de\",\n \"score\": \"0.69591653\",\n \"text\": \"def test_pre_6():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c36db7ffd82cffae66bfaaf4cc6d3a\",\n \"score\": \"0.6956748\",\n \"text\": \"def _do_unit_test(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1728cb8d614613afc3a834a39d2cfcd\",\n \"score\": \"0.69514483\",\n \"text\": \"def test_checkbox_1():\\n assert test_main.checkbox_validation_on_failure() == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"095cf8e243ec04106d36f1afa919730f\",\n \"score\": \"0.69489825\",\n \"text\": \"def test_testing(wait_for_crossbar):\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"636ce0a646b3b10092dc0c6cce55f38e\",\n \"score\": \"0.69478256\",\n \"text\": \"def check_assumptions():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59c4d891ed9e263f27cc84b4c88a781a\",\n \"score\": \"0.69239616\",\n \"text\": \"def test_checkbox_3():\\n assert test_main.checkbox_validation_on_success(\\\"Airplane\\\") == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e20aa29648f8284e67783834c5443fd\",\n \"score\": \"0.6916858\",\n \"text\": \"def check(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e20aa29648f8284e67783834c5443fd\",\n \"score\": \"0.6916858\",\n \"text\": \"def check(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e20aa29648f8284e67783834c5443fd\",\n \"score\": \"0.6916858\",\n \"text\": \"def check(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dfc847f35a4166ae8cbf36b07fca637\",\n \"score\": \"0.69100654\",\n \"text\": \"def test_pre_4():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"101b802076f24356c65cf5983693cc3d\",\n \"score\": \"0.690627\",\n \"text\": \"def test_check_if_snapshot_passed():\\n # Confirm result is True iff all values are PASSED\\n assert check_if_all_passed({\\\"a\\\": \\\"PASSED\\\", \\\"b\\\": \\\"PASSED\\\"})\\n assert not check_if_all_passed({\\\"a\\\": \\\"PASSED\\\", \\\"b\\\": \\\"FAILED\\\"})\\n assert not check_if_all_passed({\\\"a\\\": \\\"PASSED\\\", \\\"b\\\": \\\"WARNINGS\\\"})\\n assert not check_if_all_passed({\\\"a\\\": \\\"FAILED\\\", \\\"b\\\": \\\"FAILED\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a3921f97f6e011ac00ffea1c15205f1\",\n \"score\": \"0.690392\",\n \"text\": \"def test_pre5():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9798d8fe6305390af5611e551d2b11c\",\n \"score\": \"0.689768\",\n \"text\": \"def test_validate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e018984f2b5b11a75007869ba2dda381\",\n \"score\": \"0.68790853\",\n \"text\": \"def test_pre_2():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f1e5285d19a66123e7b49048e7e042f\",\n \"score\": \"0.6877008\",\n \"text\": \"def test_error():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16841784da8e716b9375ad4b2b9ae223\",\n \"score\": \"0.6875488\",\n \"text\": \"def test_3(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ba44bf40dd47d5dd9ca5ee9ab5cd3cd\",\n \"score\": \"0.6860231\",\n \"text\": \"def test_pre_1():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5025234664618a87d4a79943a6324df2\",\n \"score\": \"0.685545\",\n \"text\": \"def test_case_1(self):\\r\\n\\r\\n self.assertTrue(verifyExpectedElements())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b2dab6b28c615c4bfa91265ee699775\",\n \"score\": \"0.6847646\",\n \"text\": \"def test_check(self):\\n\\n self.assertTrue(hu.checkIF())\\n self.assertTrue(hu.checkIF(cfg.IF))\\n self.assertTrue(hu.checkIF(TESTIF))\\n self.assertFalse(hu.checkIF(BADIF))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad8da9c4f89cf541e07ee70f4417550c\",\n \"score\": \"0.683318\",\n \"text\": \"def test(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf446368f817e5e027032357b4c7080f\",\n \"score\": \"0.68231785\",\n \"text\": \"def test_validate(self):\\n ranger_tool.validate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ec3280d29e58de72dd263537f5ecb4d\",\n \"score\": \"0.6822744\",\n \"text\": \"def test_check_errors__no_errors(self):\\n self.assertFalse( True == self.sapi.check_errors() )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73cece719f5cb2441ce0766112a9afb3\",\n \"score\": \"0.68171895\",\n \"text\": \"def sanity_checks(self):\\n # FIXME : What kind of sanity should be done ?\\n # => testing connection to kubernetes ? testing kubevirt is OK ?\\n log.info(\\\"Sanity checks: '{}'\\\".format(self._name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"284c81d91e45f94ce548065578e0ec19\",\n \"score\": \"0.68157834\",\n \"text\": \"def _check(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fd94a9695eceed9ab7f00ef5621e48b\",\n \"score\": \"0.68106174\",\n \"text\": \"def check(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a980866619d74fb8df8c63b91d8b0c8c\",\n \"score\": \"0.6795618\",\n \"text\": \"def test_run(self):\\n self.clearOutputStream()\\n runner = Runner()\\n runner.setOutput(self.outputStream)\\n self.assertRaises(SystemExit, runner.run, [\\\"--version\\\"])\\n self.assertTrue(self.outputStream.getvalue().count(\\\"Python\\\") > 0, \\\\\\n msg=\\\"failed to call pylint\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a7ac70fb148327d0ab67d7f31fde85\",\n \"score\": \"0.6791688\",\n \"text\": \"def test_checkbox_6():\\n assert test_main.checkbox_validation_on_success(\\\"ALL\\\") == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"320dcba1c969f695f0ca179a40c4c747\",\n \"score\": \"0.67870533\",\n \"text\": \"def test_success(self):\\n self.assertEqual(find_error_line(\\\"\\\"), -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26364ea4a63a0e64004a9b5c21ab790c\",\n \"score\": \"0.6777752\",\n \"text\": \"def test_validation(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05faa88d86541b607f2daf9149047bab\",\n \"score\": \"0.67658645\",\n \"text\": \"def test_okay(self):\\n\\n\\t\\tprint(sys._getframe( ).f_code.co_name)\\n\\t\\tself.assertTrue(True)\\n\\t\\tprint(\\\"Test Framework okay.\\\")\\n\\t\\tprint(\\\"Started from \\\" + os.getcwd())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0fd7183cc0f40d03fb7e9540603db35\",\n \"score\": \"0.6764888\",\n \"text\": \"def test_checkbox_2():\\n assert test_main.checkbox_validation_on_success(\\\"Vehicle\\\") == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c82fcce3672cf836716181212cb178\",\n \"score\": \"0.67608976\",\n \"text\": \"def _test(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3216078368b8b1d036a81d44bb8d4511\",\n \"score\": \"0.67568\",\n \"text\": \"def test_first(self):\\n assert(3 == 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c30b076b7efc7d552fd5e89a9c9376eb\",\n \"score\": \"0.67560184\",\n \"text\": \"def check_all():\\n test_string_convert()\\n test_string_concatenator()\\n test_start_chat()\\n test_add_component()\\n test_find_and_return_dict()\\n test_return_model_name()\\n test_check_input()\\n test_print_instructions()\\n \\n print(\\\"test finished\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2078574fb07d87ef139f57c4887edafa\",\n \"score\": \"0.6749363\",\n \"text\": \"def has_a_smoke_test ():\\r\\n assert True == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73710781b3866985e72c0ec2be2b92b\",\n \"score\": \"0.6736963\",\n \"text\": \"def test_initial():\\n assert True == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73710781b3866985e72c0ec2be2b92b\",\n \"score\": \"0.6736963\",\n \"text\": \"def test_initial():\\n assert True == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8ab0c9f076fcd26b5f1f60a043fa35e\",\n \"score\": \"0.67290723\",\n \"text\": \"def test_condition_check_failed_requests_alarm_red():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c6e620ee07c8da48e34c72585900429\",\n \"score\": \"0.6728718\",\n \"text\": \"def test_condition_check_failed_requests_alarm_green():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee902d380f535b230b7fb286c400dd1\",\n \"score\": \"0.67217904\",\n \"text\": \"def test_hello(self):\\n self.assertTrue(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f91fa53f0c11f4ebd1146de23f62324\",\n \"score\": \"0.67214835\",\n \"text\": \"def testCheck(self):\\n # FIXME: it would be good to check if these results are right\\n self.assertTrue(self.checker.Check('libc6'))\\n self.assertRaises(PackageNotFoundError, self.checker.Check, 'nosuchpackage')\\n self.assertTrue(self.checker.Check('perl', True))\\n self.assertTrue(self.checker.Check('openjdk-6-jre-headless', True)) # missing recommended package\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee44c706e3d6fbc9149b047e05b55aa\",\n \"score\": \"0.6716576\",\n \"text\": \"def testPass(self):\\n with temp_base() as basedir:\\n result = run_tests(\\n basedir,\\n '--mode=Release',\\n '--progress=verbose',\\n '--variants=default,stress',\\n '--time',\\n 'sweet/bananas',\\n 'sweet/raspberries',\\n )\\n self.assertIn('Done running sweet/bananas default: pass', result.stdout, result)\\n # TODO(majeski): Implement for test processors\\n # self.assertIn('Total time:', result.stderr, result)\\n # self.assertIn('sweet/bananas', result.stderr, result)\\n self.assertEqual(0, result.returncode, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"286a6a5931d6757693b9df6fc2b5bef1\",\n \"score\": \"0.6714116\",\n \"text\": \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b84e9692960bb1005b33227ade8f3055\",\n \"score\": \"0.67123175\",\n \"text\": \"def testA(self) -> None:\\n assert 1 == 1, \\\"This test has passed\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5822c8607a3bc7e55360975a9021faa\",\n \"score\": \"0.6711677\",\n \"text\": \"def test_should_run(self):\\n self.assertTrue(self.installer.should_run)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441b95c95e17bb60541e370203edef17\",\n \"score\": \"0.67103255\",\n \"text\": \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno\\n if did_pass:\\n msg = 'Test at line {0} ok.'.format(linenum)\\n else:\\n msg = 'Test at line {0} FAILED.'.format(linenum)\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f86e5cca0ad5b8edb48153d3af77c96\",\n \"score\": \"0.6702691\",\n \"text\": \"def testit(did_pass):\\r\\n # This function works correctly\\r\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\r\\n if did_pass:\\r\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\r\\n else:\\r\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\r\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34e61aabf5f282bcf202f9de782faf14\",\n \"score\": \"0.66948336\",\n \"text\": \"def test_check_consistency_succeeds(self):\\n self.configuration._check_consistency()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f587422bb0f4eadd46f1c521f1c824b\",\n \"score\": \"0.6691945\",\n \"text\": \"def DoAfterTestCheck(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f22a94564086de96496f919d529460\",\n \"score\": \"0.6690808\",\n \"text\": \"def test_check_transaction(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c5725d4d17fc7d3b88f122f7b5ee89\",\n \"score\": \"0.6690794\",\n \"text\": \"def test(did_pass):\\r\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\r\\n if did_pass:\\r\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\r\\n else:\\r\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\r\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c5725d4d17fc7d3b88f122f7b5ee89\",\n \"score\": \"0.6690794\",\n \"text\": \"def test(did_pass):\\r\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\r\\n if did_pass:\\r\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\r\\n else:\\r\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\r\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50a45e9ec653016b86aef2b04ae8453a\",\n \"score\": \"0.6685492\",\n \"text\": \"def test_two_runs_no_clean():\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fcf8cf9975c7f361b0f3dd0233b8cd7\",\n \"score\": \"0.6680016\",\n \"text\": \"def test_data_load_stage():\\n print()\\n assert True == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93d634925ca4cd42ab67364dab3dcb40\",\n \"score\": \"0.66776836\",\n \"text\": \"def tests():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93d634925ca4cd42ab67364dab3dcb40\",\n \"score\": \"0.66776836\",\n \"text\": \"def tests():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90bd23d809d137de70aef50e930355a1\",\n \"score\": \"0.6675825\",\n \"text\": \"def verify(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0d4e64518c7ba71409619cce7f3a4f0\",\n \"score\": \"0.6674162\",\n \"text\": \"def test(did_pass):\\r\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number\\r\\n if did_pass:\\r\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\r\\n else:\\r\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\r\\n print(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c71fd62ebce464ead62b0b9a932c203\",\n \"score\": \"0.667401\",\n \"text\": \"def test_that_fails():\\n assert False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae81709679c1299bd400a4d3e53b7ec\",\n \"score\": \"0.66585284\",\n \"text\": \"def verify(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3295fa35a3d7fef4197acaf801f3b7f2\",\n \"score\": \"0.6657164\",\n \"text\": \"def test_one(self):\\n assert_equals(1, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1439e252aed41805d3acfb8779100b3a\",\n \"score\": \"0.6654409\",\n \"text\": \"def test_main(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94628a9fa7ee3c77f50562ecde6864d2\",\n \"score\": \"0.6648399\",\n \"text\": \"def test_task332(self, input_value, expected_value):\\n self.assertEqual(algo.Task332.main_logic(input_value), expected_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"580bd5edbb5b0b282c389aee4dba999d\",\n \"score\": \"0.66472477\",\n \"text\": \"def test_00(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153c44dfa04a3bc3f611dc759406f696\",\n \"score\": \"0.6643772\",\n \"text\": \"def test_dl_7():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f511b3d739dff5b721deaac30f263c73\",\n \"score\": \"0.6637741\",\n \"text\": \"def run_test(self, computed, expected, message = \\\"\\\"):\\n self.total_tests += 1\\n if computed != expected:\\n msg = message + \\\" Computed: \\\" + str(computed)\\n msg += \\\" Expected: \\\" + str(expected)\\n print msg\\n self.failures += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb1c415bac898ec787843027ee4add78\",\n \"score\": \"0.6637624\",\n \"text\": \"def test1():\\n check50.run(\\\"./question3\\\").stdout(\\\"First condition is true\\\\nSecond condition is false\\\\nThird condition is true\\\", regex=True).exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88ba45382a90a4d8eb246158767c919c\",\n \"score\": \"0.66362286\",\n \"text\": \"def verify(self):\\n for e in self.expected:\\n if e.invoke_count == 0:\\n raise AssertionError(\\n \\\"Subprocess never invoked: {0}\\\".format(format_command(e.command)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb14be2ece809d6bd34fe2775f729ad1\",\n \"score\": \"0.6629225\",\n \"text\": \"def test_is_true(self):\\n self.assertEqual(True, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d088f00b1f6ad87145f0c5bb45cb17b4\",\n \"score\": \"0.66290736\",\n \"text\": \"def test(did_pass):\\n lineum = sys._getframe(1).f_lineno\\n if did_pass:\\n msg = \\\"Test at line {0} ok\\\".format(lineum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(lineum))\\n print(msg)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1799,"cells":{"query_id":{"kind":"string","value":"43641906006d47b53e6119d47812eb63"},"query":{"kind":"string","value":"Tests scrap_for_posts returns valid post urls using a regular expression. This particular page has a content warning and needs a cookie to access to it."},"positive_passages":{"kind":"list like","value":[{"docid":"50ce4bdf9a8fad2c1cdac3d8208a6f12","score":"0.73245084","text":"def test_scrap_for_posts_cookie(self):\n \n url = \"https://e-hentai.org/g/1263299/a42ce2eb95/\"\n \n response = self.ehen.scrap_for_posts(url, 1, 1, 1)\n \n pat = re.compile(r'https:\\/\\/e-hentai\\.org\\/s\\/[\\S]{10}\\/[\\d]+-[\\d]+')\n for post in response:\n self.assertTrue(pat.match(post))","title":""}],"string":"[\n {\n \"docid\": \"50ce4bdf9a8fad2c1cdac3d8208a6f12\",\n \"score\": \"0.73245084\",\n \"text\": \"def test_scrap_for_posts_cookie(self):\\n \\n url = \\\"https://e-hentai.org/g/1263299/a42ce2eb95/\\\"\\n \\n response = self.ehen.scrap_for_posts(url, 1, 1, 1)\\n \\n pat = re.compile(r'https:\\\\/\\\\/e-hentai\\\\.org\\\\/s\\\\/[\\\\S]{10}\\\\/[\\\\d]+-[\\\\d]+')\\n for post in response:\\n self.assertTrue(pat.match(post))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c743b4bc0e88dbd83d3d095a408f05c2","score":"0.67279756","text":"def test_scrap_for_posts(self):\n \n url = \"https://e-hentai.org/g/1087428/b240f1a9ab/\"\n \n response = self.ehen.scrap_for_posts(url, 1, 1, 1)\n \n pat = re.compile(r'https:\\/\\/e-hentai\\.org\\/s\\/[\\S]{10}\\/[\\d]+-[\\d]+')\n for post in response:\n self.assertTrue(pat.match(post))","title":""},{"docid":"a092dcf339b9edb8533d7b4b0896b07e","score":"0.5860885","text":"def get_post_urls(urls=None, page=\"http://python-history.blogspot.ca/\"):\n if urls is None:\n urls = []\n response = requests.get(page)\n soup = BeautifulSoup(response.content)\n titles = soup.find_all(\"h3\", {\"class\": \"post-title entry-title\"})\n urls.extend([title.a[\"href\"] for title in titles])\n\n try:\n # Look for an `Older Posts` button\n next_page = soup.find(\"a\", {\"class\": \"blog-pager-older-link\"})[\"href\"]\n return get_post_urls(urls=urls, page=next_page)\n except TypeError:\n return urls","title":""},{"docid":"0706388de3d7de1c796800b480fdb6b0","score":"0.5610822","text":"def process_posts(args):\n # Unpack arguments\n (reddit, post, blacklist, output_folder) = args\n\n scrape_submission(reddit, post, blacklist, output_folder)","title":""},{"docid":"9fd2556ed004934f9b5bb79323cc4285","score":"0.55120337","text":"def test_valid_posts(self):\n validate_post(\"Lorem ipsum dolor met sit amet elit.\")","title":""},{"docid":"c76ee16f146ecbfade7ab5241c884373","score":"0.54791105","text":"def test_scrap_for_posts_timed_out_or_not_found(self):\n \n url = \"invalid_url\"\n \n response = self.ehen.scrap_for_posts(url, 1, 1, 1)\n \n self.assertTrue(response == None)","title":""},{"docid":"ed9433c11f5f586bd2df1c16261effa3","score":"0.5457406","text":"def check_unhandled_posts(self):\n handled_posts = self.handled_posts.keys()\n unhandled_posts = self.candidates_backup.difference(handled_posts)\n\n for each in unhandled_posts:\n self.unhandled_posts.add(\n (extract_domain(each.url), '%s (%s)' %\n (ensure_ascii(each.title, each.url))))","title":""},{"docid":"ebbd7a79d60529869d0a1a01bee1fbe6","score":"0.5376213","text":"def get_compensation_posts(url: str) -> List[Dict[str, str]]:\n try:\n browser.get(url)\n time.sleep(5)\n post_hrefs = _get_posts(browser)\n if not post_hrefs:\n time.sleep(10)\n logger.info(f\"sleeping extra for {url}\")\n post_hrefs = _get_posts(browser)\n return post_hrefs\n except Exception as e:\n logger.error(f\"ERR: {e}\")\n raise Exception(e)\n return []","title":""},{"docid":"43ea82731f2a856a35fcbe4012c72280","score":"0.53276074","text":"def scrapePostIDs(html):\n pattern = r'<!-- post #([0-9]*) -->'\n return re.findall(pattern, html)","title":""},{"docid":"333380ad6ae8ff4adf41d4af239aaab7","score":"0.5309135","text":"def get_posts(\n account: Optional[str] = None,\n group: Union[str, int, None] = None,\n post_urls: Optional[Iterator[str]] = None,\n hashtag: Optional[str] = None,\n credentials: Optional[Credentials] = None,\n **kwargs,\n) -> Iterator[Post]:\n valid_args = sum(arg is not None for arg in (account, group, post_urls, hashtag))\n\n if valid_args != 1:\n raise ValueError(\"You need to specify either account, group, or post_urls\")\n\n _scraper.requests_kwargs['timeout'] = kwargs.pop('timeout', DEFAULT_REQUESTS_TIMEOUT)\n\n cookies = kwargs.pop('cookies', None)\n\n if cookies is not None and credentials is not None:\n raise ValueError(\"Can't use cookies and credentials arguments at the same time\")\n set_cookies(cookies)\n\n options: Union[Dict[str, Any], Set[str]] = kwargs.setdefault('options', {})\n if isinstance(options, set):\n warnings.warn(\"The options argument should be a dictionary.\", stacklevel=2)\n options = {k: True for k in options}\n options.setdefault('account', account)\n\n # TODO: Add a better throttling mechanism\n if 'sleep' in kwargs:\n warnings.warn(\n \"The sleep parameter has been removed, it won't have any effect.\", stacklevel=2\n )\n kwargs.pop('sleep')\n\n # TODO: Deprecate `pages` in favor of `page_limit` since it is less confusing\n if 'pages' in kwargs:\n kwargs['page_limit'] = kwargs.pop('pages')\n\n # TODO: Deprecate `extra_info` in favor of `options`\n if \"reactions\" not in options:\n options['reactions'] = kwargs.pop('extra_info', False)\n options['youtube_dl'] = kwargs.pop('youtube_dl', False)\n\n if credentials is not None:\n _scraper.login(*credentials)\n\n if account is not None:\n return _scraper.get_posts(account, **kwargs)\n\n elif group is not None:\n return _scraper.get_group_posts(group, **kwargs)\n\n elif hashtag is not None:\n return _scraper.get_posts_by_hashtag(hashtag, **kwargs)\n\n elif post_urls is not None:\n return _scraper.get_posts_by_url(post_urls, **kwargs)\n\n raise ValueError('No account nor group')","title":""},{"docid":"0bbe127305033fc3fce79778614f02e8","score":"0.52689415","text":"def scrapePostPermalink(html, id = ''):\n if id:\n pattern = r'<a href=\"([^\"]*)(showpost\\.php\\?)[^\"]*(p=%s)[^\"]*\"[^>]*id=\"postcount.*>' % id\n else:\n pattern = r'<a href=\"([^\"]*)(showpost\\.php\\?)[^\"]*(p=[0-9]*)[^\"]*\"[^>]*id=\"postcount.*>'\n m = re.search(pattern, html)\n if m == None:\n return ''\n else:\n return ''.join(m.groups())","title":""},{"docid":"38c1d0388977bfdcc44355b3106907d5","score":"0.52625024","text":"def scrap_for_posts(self, url, wait, retry, wait_retry):\n if url == None:\n return None\n \n response = self.get_request(url, wait, retry, wait_retry, cookies=dict(nw=\"1\"))\n \n if response['response_code'] != 200:\n return None\n\n soup = BeautifulSoup(response['payload'],\"html.parser\")\n div = soup.find(id=\"gdt\")\n tags = div.find_all(\"a\")\n links = [a.get(\"href\") for a in tags]\n return links","title":""},{"docid":"271e874e7ed4274adb936a3624325b28","score":"0.52495766","text":"def test_scrap_for_posts_none(self):\n \n url = None\n \n response = self.ehen.scrap_for_posts(url, 1, 1, 1)\n \n self.assertTrue(response == None)","title":""},{"docid":"8a9d84495d4f6334e8ec2387c6db57e0","score":"0.5245485","text":"def isPost(url):\r\n return url.find('/post/') != -1","title":""},{"docid":"24e3a2fa798f60c00e3f6c1aa13cac7f","score":"0.5160177","text":"def extract_news_url(soup_text: BeautifulSoup, url_pattern: str) -> list:\n page_urls = []\n exclude_pattern = \"https://arynews.tv/en/category\"\n articles = soup_text.find_all('article')\n for article in articles:\n # get all links in article\n for link in article.findAll(\n 'a', attrs={'href': re.compile(url_pattern)}):\n url = link.get('href')\n if exclude_pattern not in url:\n page_urls.append(url)\n if len(page_urls) > 0:\n unique_urls = list(set(page_urls))\n else:\n unique_urls = None\n return unique_urls","title":""},{"docid":"be48ff4c1bad6d4e82fc09a46670bd89","score":"0.5155859","text":"def test_parsing_posts_from_archive_page(\n archive_parsing_results_mock: Dict[str, str],\n) -> None:\n assert len(archive_parsing_results_mock) == 782\n assert (\n \"/2009/04/12/episode-1-introduction\" in list(archive_parsing_results_mock)[-1]\n )\n # Intersection of mocked pages and all links\n # intersection = set(mocked_urls) & set(all_links)\n # assert len(intersection) > 15","title":""},{"docid":"8938606167dd0dca628e6904699225bf","score":"0.5103405","text":"def get_post_urls(self, profile):\n self.browser.get('https://www.instagram.com/' + profile)\n self.browser.set_window_size(300, 1000)\n post_urls = []\n profile_end = False\n nr_posts = 0\n while len(post_urls) < self.max_posts and not profile_end:\n # Get scroll height\n last_height = self.browser.execute_script(\n 'return document.body.scrollHeight')\n # Get all URL's of all posts currently loaded\n soup = BeautifulSoup(self.browser.page_source, features=\"lxml\")\n all_as = soup.find_all('a', href=True)\n for a in all_as:\n if a['href'][:3] == '/p/' and a['href'] not in post_urls:\n post_urls.append(a['href'])\n nr_posts += 1\n if nr_posts >= self.max_posts:\n break\n # Scroll down to bottom and wait to load page\n self.browser.execute_script(\n 'window.scrollTo(0, document.body.scrollHeight);')\n time.sleep(self.timeout)\n # Calculate new scroll height and compare with last scroll height\n new_height = self.browser.execute_script(\n 'return document.body.scrollHeight')\n if new_height == last_height:\n profile_end = True\n else:\n last_height = new_height\n\n return ['https://www.instagram.com' + a for a in post_urls]","title":""},{"docid":"39fcad496776efeb19c67c715f80164f","score":"0.50660527","text":"def test_my_posts_page_logged_in(self):\n mf = add_forum('meme_forum')\n\n post_1 = add_post(mf, \"post1\", \"https://static.djangoproject.com/img/fundraising-heart.cd6bb84ffd33.svg\", self.user)\n post_2 = add_post(mf, \"post2\", \"http://thewowstyle.com/wp-content/uploads/2015/01/nature-images.jpg\", self.user)\n post_3 = add_post(mf, \"post3\", \"https://eskipaper.com/images/images-2.jpg\", self.user)\n\n request = self.request_factory.get(reverse('meme_portal:my_posts'))\n request.user = self.user.user\n\n response = my_posts(request)\n\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, 'Your Posts:')","title":""},{"docid":"d0277d2347a74cab12025a0d5bce6631","score":"0.50486696","text":"def fetch_post(driver, post_url):\n driver.get(post_url)\n try:\n email = driver.find_element_by_xpath(\"//section[@class='dateReplyBar']/a\").text\n except NoSuchElementException:\n email = ' '\n try:\n posting_title = driver.find_element_by_xpath(\"//h2[@class='postingtitle']\").text\n except:\n return\n posting_body = driver.find_element_by_xpath(\"//section[@id='postingbody']\").text.replace('\\n','\\\\n')\n timestamp = driver.find_element_by_xpath(\"//date\").get_attribute('title')\n wanted = parse_wanted(posting_title)\n if wanted:\n return False\n start, dest = parse_locs(posting_title, posting_body)\n return email, timestamp, posting_title, post_url, posting_body, start, dest","title":""},{"docid":"58504db8d570d5a3abe005a45e13fff3","score":"0.5024956","text":"def get_urls(self, tag=None, max_urls=100, sleep_seconds=1):\n assert sleep_seconds >= 1\n urls = []\n path = None\n if tag is None or (tag is not None and max_urls > 0 and max_urls <= 100):\n # use official JSON feeds\n max_json_count = 100\n if tag: # tag-specific JSON feed\n path = \"/v2/json/%s?count=%d\" % (tag, max_json_count)\n else:\n # Delicious.com hotlist\n path = \"/v2/json/?count=%d\" % (max_json_count)\n data = self._query(path, host=\"feeds.delicious.com\")\n if data:\n posts = []\n try:\n posts = simplejson.loads(data)\n except TypeError:\n pass\n\n for post in posts:\n # url\n try:\n url = post['u']\n if url:\n urls.append(url)\n except KeyError:\n pass\n else:\n # maximum number of urls/posts Delicious.com will display\n # per page on its website\n max_html_count = 20\n max_html_pages = max_urls/max_html_count\n\n \n path = \"/tag/%s\" %tag \n page_index = 1\n urls = []\n while path and page_index <= max_html_pages+1:\n data = self._query(path)\n path = None\n if data:\n # extract urls from current page\n tree = etree.HTML(data)\n print \"parsing %s page\" %page_index\n link_lists = tree.xpath(\"//div[@class='link None']\",smart_strings=False)\n for link in link_lists:\n url = link.xpath(\".//a[@class='title']/@href\",smart_strings=False)[0].split(\"url=\")[-1]\n urls.append(url)\n print 'length of urls is ', len(urls)\n\n paginations = tree.xpath(\"//div[@id='pagination']\",smart_strings=False)\n\n if paginations:\n # find next path\n nexts = tree.xpath(\"//div[@id='pagination']/span/following::a[1]/@href\",smart_strings=False)\n if nexts and (max_urls == 0 or len(urls) < max_urls) and len(urls) > 0:\n path = '/tag/%s' %tag + nexts[0]\n print 'path is', path\n page_index += 1\n time.sleep(sleep_seconds)\n if max_urls > 0:\n return urls[:max_urls]\n else:\n return urls","title":""},{"docid":"ae45aa0bc081aff2bd0be6dd29d65e50","score":"0.5023383","text":"def valid_page_url(self, host_name: str, page_url: str) -> bool:\n if page_url in self.banned_pages: return False\n if not RobotsHelper.allow_to_visit(host_name, page_url): \n self.banned_pages.add(page_url)\n return False\n return True","title":""},{"docid":"dc041a1094b4f930f3da0ccba4e07fec","score":"0.5002853","text":"def scrape(self, n, thread):\n if(thread):\n self.url += (thread+'/')\n print(\"======= scraping stared at '{}' =======\".format(self.url))\n next_base = self.url+'?after='\n next_page = \"\"\n total=0\n for i in range(n):\n # updates next page url\n next_page = next_base + next_page\n #print(next_page)\n # fetch next page\n content = self.requestHTML(next_page)\n posts, next_page = self.getAllPosts(content)\n for post in posts:\n self.data.append(self.singlePostData(post))\n total=total+1\n print(\"-> Total {} posts scraped\".format(len(posts)))\n print(\"------ page-{} Done ------\".format(i+1))\n print(\"------posts scraped: {} ------\".format(total))\n \n return self.data","title":""},{"docid":"6ee113474c2076f398ff9c233d59804a","score":"0.49790725","text":"def scrapePosts(html):\n if not (type(html) == type([])):\n s = html \n html = []\n html.append(s) \n\n post = {}\n \n for h in html:\n # Locate posts in h \n for id in scrapePostIDs(h):\n post[id] = vbpost.Post(rawhtml = scrapePostHTML(id, h))\n\n return post","title":""},{"docid":"c783b8cc2053189603e4c1ec9978550a","score":"0.49770364","text":"def parse_post(post: Dict[str, Any]) -> List[str]:\n matches: List[str] = re.findall(PATTERN, post['Message'], re.I | re.M)\n\n return matches","title":""},{"docid":"b71906fabaa72c50a5671de9336fb01e","score":"0.4975097","text":"def test_page_url(self):\n page = PageFactory(title='this? is& a! (test*)')\n self.assertEqual(page.slug, '/this-is-a-test/')","title":""},{"docid":"d636293ebd77b7069c0b6418372a2f43","score":"0.49720755","text":"def test_scrap_for_pages(self):\n \n given_url = \"https://e-hentai.org/g/1087428/b240f1a9ab/\"\n \n expected_urls = [\"https://e-hentai.org/g/1087428/b240f1a9ab/\",\n \"https://e-hentai.org/g/1087428/b240f1a9ab/?p=1\",\n \"https://e-hentai.org/g/1087428/b240f1a9ab/?p=2\",\n \"https://e-hentai.org/g/1087428/b240f1a9ab/?p=3\",\n \"https://e-hentai.org/g/1087428/b240f1a9ab/?p=4\",]\n \n url_lists = self.ehen.scrap_for_pages(given_url, 5, None)\n \n for i in range(len(expected_urls)):\n self.assertEqual(url_lists[i], expected_urls[i])","title":""},{"docid":"cef57791533ff48be2dc97522dcdce60","score":"0.49519563","text":"def test_skip_if_url_match(self):\n bot = AddTextBot(regex_skip_url='.*\\\\.com')\n\n page = _mock_page(url='wikipedia.org')\n self.assertFalse(bot.skip_page(page))\n self.assertEqual([], pywikibot.bot.ui.pop_output())\n\n page = _mock_page(url='wikipedia.com')\n self.assertTrue(bot.skip_page(page))\n self.assertEqual([\n \"Skipping mock_page because -excepturl matches ['wikipedia.com'].\"\n ], pywikibot.bot.ui.pop_output())","title":""},{"docid":"fd11bad0d7197d4605aa2b2bf9878faa","score":"0.49511975","text":"def fetch_content_page(driver, url):\n driver.get(url)\n post_urls = [e.get_attribute('href') for e in driver.find_elements_by_xpath(\"//p[@class='row']/a\")]\n return post_urls","title":""},{"docid":"bfb9d960cbed81bb4545f4a0284b3679","score":"0.49477488","text":"def isBlog(url):\r\n return url.find('.tumblr.com') + 12 >= len(url)","title":""},{"docid":"894eae2f565dfbb3d4ead68130b0c825","score":"0.49445185","text":"def extract_posts(browser, num_of_posts_to_do, is_tag = False):\n links = []\n links2 = []\n srcsetPreview_imgs = {}\n sizesPreview_img = {}\n altPreview_img = {}\n srcPreview_img = {}\n\n errMsg = ''\n\n # list links contains 30 links from the current view, as that is the maximum Instagram is showing at one time\n # list links2 contains all the links collected so far\n # preview_imgs dictionary maps link in links2 to liknk's post's preview image src\n try:\n body_elem = browser.find_element_by_tag_name('body')\n\n # load_button = body_elem.find_element_by_xpath\\\n # ('//a[contains(@class, \"_1cr2e _epyes\")]')\n # body_elem.send_keys(Keys.END)\n # sleep(3)\n\n # load_button.click()\n\n previouslen = 0\n breaking = 0\n\n num_of_posts_to_scroll = 12 * math.ceil(num_of_posts_to_do / 12)\n print(\"Getting first\", num_of_posts_to_scroll,\n \"posts but check \", num_of_posts_to_do,\n \" posts only, if you want to change this limit, change limit_amount value in crawl_profile.py\\n\")\n while (len(links2) < num_of_posts_to_do):\n links2 = []\n if is_tag:\n main = browser.find_element_by_tag_name('main')\n article_children = main.find_elements_by_xpath('./article/*')\n prev_divs = [article_children[2]]\n else:\n prev_divs = browser.find_elements_by_tag_name('main')\n links_elems = [div.find_elements_by_tag_name('a') for div in prev_divs]\n links = sum([[link_elem.get_attribute('href')\n for link_elem in elems] for elems in links_elems], [])\n for elems in links_elems:\n for link_elem in elems:\n href = link_elem.get_attribute('href')\n try:\n img = link_elem.find_element_by_tag_name('img')\n except NoSuchElementException:\n continue\n srcsetPreview_imgs[href] = img.get_attribute('srcset')\n sizesPreview_img[href] = img.get_attribute('sizes')\n altPreview_img[href] = img.get_attribute('alt')\n srcPreview_img[href] = img.get_attribute('src')\n for link in links:\n if \"/p/\" in link:\n if (altPreview_img[link] != '' and len(links2) < num_of_posts_to_do):\n links2.append(link)\n links2 = list(set(links2)) #remove duplicate elems\n print(\"Scrolling profile \", len(links2), \"/\", num_of_posts_to_scroll)\n body_elem.send_keys(Keys.END)\n sleep(Settings.sleep_time_between_post_scroll)\n\n ##remove bellow part to never break the scrolling script before reaching the num_of_posts\n if (len(links2) == previouslen):\n breaking += 1\n print(\"breaking in \", 4 - breaking,\n \"...\\nIf you believe this is only caused by slow internet, increase sleep time 'sleep_time_between_post_scroll' in settings.py\")\n else:\n breaking = 0\n if breaking > 3:\n print(\"\\nNot getting any more posts, ending scrolling.\")\n sleep(2)\n break\n previouslen = len(links2)\n ##\n\n except NoSuchElementException as err:\n print('- Something went terribly wrong\\n')\n\n post_infos = []\n\n counter = 1\n\n for link in links2:\n print(\"\\n\", counter, \"/\", len(links2))\n \n counter = counter + 1\n\n print(\"\\nScrapping link: \", link)\n web_adress_navigator(browser, link)\n try:\n caption, alt, img, srcset, sizes = extract_post_info(browser)\n\n post_infos.append({\n 'url': link,\n 'caption': caption,\n 'src': img,\n 'srcset': srcset,\n 'sizes': sizes,\n 'alt': alt,\n 'srcSmall': srcPreview_img[link],\n 'srcsetSmall': srcsetPreview_imgs[link],\n 'sizesSmall': sizesPreview_img[link],\n 'altSmall': altPreview_img[link], \n })\n msg = ''\n #if (caption == '' or caption == None): msg = msg + \"caption is null,\"\n if (alt == '' or alt == None): msg = msg + \"alt is null,\"\n if (img == '' or img == None): msg = msg + \"img is null,\"\n if (srcset == '' or img == None): msg = msg + \"srcset is null,\"\n if (msg != ''): errMsg = errMsg + ' - Link = ' + link + \" : \" + msg + '\\n'\n print('msg = ' + msg)\n except NoSuchElementException as err:\n print('- Could not get information from post: ' + link)\n print(err)\n errMsg = errMsg + '- Could not get information from post: ' + link\n\n return post_infos, errMsg","title":""},{"docid":"8f52a726bd80c0539e8b1e8683c83e4a","score":"0.4883444","text":"def testListPosts(self):\r\n\r\n response = self.client.get(reverse('list_posts'))\r\n\r\n self.assertEquals(response.status_code, 200)\r\n self.assertTemplateUsed(response, 'blog/list_posts.html')\r\n self.assertTrue(self.post in response.context['posts'])","title":""},{"docid":"75a1c9fd99636228ff634f9bac0e5621","score":"0.48579165","text":"def test_show_posts_page_dosent_exist(self):\n mf = add_forum('meme_forum')\n response = self.client.get(reverse('meme_portal:show_post', kwargs={'forum_name_slug':'meme_forum', 'post_name_slug':'nothign'}))\n\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, 'This post does not exist.')","title":""},{"docid":"7ffd8b1b88db00dbd0879d17d43332d4","score":"0.48451796","text":"def test_invalid_slug(self):\n response = self.http.get('/blog/uh~oh~$$$~invalid')\n html = response_html(response)\n\n assert response.status_code == 400\n assert 'error' in html.select_one('#page-content h1').text\n assert '400' in html.select_one('#page-content').text","title":""},{"docid":"3c4daac00e64799c31eab3b1e6b69e42","score":"0.48321187","text":"def check(self, post: dict):\n try:\n if post['score'] < 2:\n logger.debug(f\"Score filtered: {post['name']}\")\n return False\n\n # Check gif\n is_gif = False\n if 'gif' in post['url']:\n is_gif = True\n if 'mp4' in post['url']:\n is_gif = True\n\n # Delete emojis\n filtered = demoji.replace(post['title'], \"\")\n\n # Check if string with delete emojis is shorter\n if len(filtered) < len(post['title']) or \\\n ':P' in post['title'] or \\\n ';)' in post['title'] or \\\n ':)' in post['title'] or \\\n '(;' in post['title']:\n\n logger.debug(f\"Emoji filtered: {post['name']}\")\n return False\n\n if 'OC' in post['title'] or \\\n '(oc)' in post['title'] or \\\n '[oc]' in post['title'] or \\\n 'my ' in post['title'] or \\\n 'My ' in post['title'] or \\\n '?' in post['title'] or \\\n 'Self' in post['title'] or \\\n 'self' in post['title'] or \\\n 'f)' in post['title'] or \\\n 'F)' in post['title'] or \\\n 'f]' in post['title'] or \\\n 'F]' in post['title'] or \\\n 'm)' in post['title'] or \\\n 'M)' in post['title'] or \\\n 'm]' in post['title'] or \\\n 'M]' in post['title'] or \\\n 'I ' in post['title']:\n\n logger.debug(f\"Post title filtered: {post['name']}\")\n return False\n\n if post['is_self']:\n logger.debug(f\"Self filtered: {post['name']}\")\n return False\n\n if post['is_original_content']:\n logger.debug(f\"OC filtered: {post['name']}\")\n return False\n\n if 'reddit.com' in post['url']:\n # TODO browse to post\n logger.debug(f\"X-post filtered: {post['name']}\")\n return False\n\n author_id = post['author_fullname']\n # TODO check author, probly not smart with rate limiter\n\n # Check for non utf8 characters\n post_title_utf8 = post['title'].encode('utf8', 'ignore').decode('utf8')\n if len(post_title_utf8) < len(post['title']):\n logger.debug(f\"Warning post {post['name']} has non utf8 characters\")\n\n return True\n\n except:\n logger.exception(f\"Post failed: {post['name']}\")","title":""},{"docid":"37ee1bc0ab58a87a89700eec6651cbc1","score":"0.4814051","text":"def test_posts_fr(self):\n t = ThreadFactory()\n\n response = get(self.client, \"forums.posts\", args=[t.forum.slug, t.id], locale=\"fr\")\n self.assertEqual(200, response.status_code)\n self.assertEqual(\n \"{c}/fr/forums/{f}/{t}\".format(c=settings.CANONICAL_URL, f=t.forum.slug, t=t.id),\n pq(response.content)('link[rel=\"canonical\"]')[0].attrib[\"href\"],\n )","title":""},{"docid":"1b2a3add05166825e473eab2c343ed71","score":"0.48094767","text":"def __iteratePosts(self):\n try:\n posts = self.soup.findAll('li', id=re.compile ('^post_\\d+'))\n if not posts:\n log.info(self.log_msg('No posts found'))\n return False\n log.debug(self.log_msg('Total No of Posts found is %d'%len(posts)))\n posts.reverse()\n for post in posts:\n if not self.__addPost(post):\n log.info(self.log_msg('Post not added to self.pages for url\\\n %s'%self.currenturi))\n return False\n return True\n except:\n log.exception(self.log_msg('Reviews are not found for url %s'%self.currenturi))\n return False","title":""},{"docid":"95a6ce6ae457489b100ffed9fb0f6f44","score":"0.480891","text":"def crawl(self, num_urls, min_page, store=False):\n urls = []\n # Enter WaPo search\n self.driver.get('http://www.washingtonpost.com')\n self.driver.set_window_size(1920, 1080)\n self.driver.find_element_by_id('search-btn').click()\n self.driver.find_element_by_id('search-field').send_keys('the or to or of or a', Keys.ENTER)\n\n # Order search results by date\n dropdown = self.driver.find_element_by_class_name('pb-filter-sort')\n dropdown.click()\n dropdown.find_elements_by_tag_name('li')[1].click()\n self.driver.find_elements_by_id('updateResults')[0].click()\n\n page = 1\n count = 0\n while count < num_urls:\n # Get post-JavaScript\n html = self.driver.execute_script(\"return document.documentElement.innerHTML;\")\n soup = BeautifulSoup(html, 'lxml')\n news_items = soup.find_all('div', class_='pb-feed-item ng-scope')\n\n # Store news urls\n for i in range(len(news_items)):\n if count < num_urls and page > min_page:\n urls.append('http://' + news_items[i]['data-sid'])\n count += 1\n\n # Store continously while getting urls if desired\n if store:\n orig_headlines, orig_labels = self.get_labels(urls)\n if orig_labels:\n suffix = list(range(count - len(orig_labels), count))\n self.format_store(orig_headlines, orig_labels, 'data/orig/', 'O', suffix)\n\n # Sanitized data\n headlines, labels, appears = self.sanitize_labels(orig_headlines, orig_labels)\n if len(labels) > 1:\n suffix = [suffix[i] for i in range(len(orig_labels)) if appears[i]]\n self.format_store(headlines, labels, 'data/', 'W', suffix)\n elif len(labels) == 1:\n suffix = [suffix[i] for i in range(len(orig_labels)) if appears[i]]\n self.format_store(headlines, labels, 'data/', 'W1', suffix) \n urls = []\n\n # Advance to next search page\n self.driver.find_element_by_css_selector('.page-link.next').click()\n page += 1\n\n if store:\n return None\n else:\n return urls","title":""},{"docid":"f5551f938ae88ffb6e399bf406be5d92","score":"0.4806705","text":"def test_process_blog_posts_only(self):\n majestic.process_blog(\n settings=self.settings, posts=True,\n pages=False, index=False, archives=False,\n feeds=False, sitemap=False, extensions=False)\n os.chdir(str(self.outputdir))\n for dirpath, dirnames, filenames in os.walk('.'):\n self.assertTrue(dirpath in self.expected)\n self.assertEqual(\n set(self.expected[dirpath]['posts']),\n set(f for f in filenames if not f.startswith('.')))","title":""},{"docid":"468b34696ad16c809381ada7f55e7662","score":"0.4806429","text":"def scrapePostHTML(id, html):\n pattern = '<!-- post #%s -->' % id\n start = html.find(pattern)\n pattern = '<!-- / post #%s -->' % id\n end = html.find(pattern) + len(pattern)\n return html[start:end]","title":""},{"docid":"3607705245854301461b63eea4577671","score":"0.48046437","text":"def parse_post(html_string):\n root = lxml.html.fromstring(html_string)\n post_url = root.xpath(\"//link[@rel='canonical']/@href\")\n price = root.xpath(\"//span[@class='price']/text()\")\n title = root.xpath(\"//span[@id='titletextonly']/text()\")\n location = root.xpath(\"//small/text()\")\n bedrooms = root.xpath(\"(//p[@class='attrgroup'])[1]/span[starts-with(text(),'BR')]/b/text()\")\n bathrooms = root.xpath(\"(//p[@class='attrgroup'])[1]/span[text()='Ba']/b/text()\")\n sqft = root.xpath(\"(//p[@class='attrgroup'])[1]/span[text()='ft']/b/text()\")\n available_date = root.xpath(\"//span[@class='housing_movein_now property_date']/@date\")\n photos = root.xpath(\"//div[@id='thumbs']//a/@href\")\n extra_info = root.xpath(\"(//p[@class='attrgroup'])[2]//span/text()\")\n body = root.xpath(\"//section[@id='postingbody']/text()\")\n timestamps = root.xpath(\"//time/@datetime\")\n map_link = root.xpath(\"//a[text()='google map']/@href\")\n return Post(post_url[0],\n price[0],\n title[0],\n location[0][2:-1] if location else None,\n photos,\n bedrooms[0] if bedrooms else None,\n bathrooms[0] if bathrooms else None,\n sqft[0] if sqft else None,\n available_date[0] if available_date else None,\n extra_info,\n body[0].strip(),\n map_link[0] if map_link else None,\n timestamps[-1] if timestamps else None)","title":""},{"docid":"590ad9b452b65926c0d6e9041ce60e55","score":"0.47993076","text":"def test_get_post(\n client_id: Optional[str] = None,\n client_secret: Optional[str] = None,\n url: Optional[str] = None,\n) -> pd.DataFrame:\n reddit = get_reddit_api()\n if url is None:\n url = input(\"url: \")\n return get_posts(url=url, reddit=reddit)","title":""},{"docid":"be3ca32076a5ca7f009e96ef960890c8","score":"0.47926477","text":"def get_posts_from_main_page(page: str) -> dict:\n posts = {}\n\n soup = BeautifulSoup(page, \"html5lib\")\n trs = soup.select(\"table.itemlist tr.athing\")\n for ind, tr in enumerate(trs):\n p_id, p_url = '', ''\n try:\n p_id = int(tr.attrs[\"id\"])\n p_url = tr.select_one(\"td.title a.storylink\").attrs[\"href\"]\n posts[p_id] = p_url\n except (KeyError, ValueError):\n log.error(\"Error on {} post (id: {}, url: {})\".format(\n ind, p_id, p_url\n ))\n\n return posts","title":""},{"docid":"3f06e7e50f66cb7a4935f1e6a9cb2b7a","score":"0.47855508","text":"def scrapeUserNumPosts(html):\n pass","title":""},{"docid":"147417cd23746d675f49dc32caebcaaf","score":"0.4776685","text":"def get_posts(self, page_ext: str):\n try:\n url = \"{}/{}/\".format(BASE_URL, page_ext)\n self.driver.get(url)\n self.scroll()\n # ignores the sign-in panel\n try:\n self.driver.find_element_by_class_name('_62up').click()\n except:\n pass\n _time.sleep(2)\n\n soup = BeautifulSoup(self.driver.page_source, 'html.parser')\n # get publication elements\n publication_elem = soup.find_all('div', {'class': '_5pcr userContentWrapper'})\n\n data_collector = dict()\n data_collector[page_ext] = list()\n\n for elem in publication_elem:\n date = self.get_date(elem)\n post_url = BASE_URL + self.get_link(elem)\n likes = self.get_likes(elem)\n comments = self.get_comments(elem)\n shares = self.get_shares(elem)\n post_id = FacebookScraper().make_url2id(post_url),\n\n data = {\n \"id\": post_id[0],\n \"page\": page_ext,\n \"post_url\": post_url,\n \"shared_at\": date,\n \"likes\": likes.text if likes is not None else '0',\n \"comments\": comments.text.replace('commentaires', '') if comments is not None else '0',\n \"shares\": shares.text.replace('partages', '') if shares is not None else '0',\n \"created_at\": datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n }\n data_collector[page_ext].append(data)\n _logging.info(f\"Collected {len(data_collector[page_ext])} new publication\")\n finally:\n self.driver.close()\n return data_collector","title":""},{"docid":"e9128f3f2c9eb254425d8014fb076216","score":"0.4763898","text":"def find_posts(reddit):\n subreddit = reddit.subreddit(\"ACVillager\") # can be changed to any subreddit\n try:\n while True:\n for submission in subreddit.stream.submissions():\n for key in KEY_PHRASES:\n if key.lower() in submission.title.lower() and submission.created_utc > start_time:\n reddit.redditor(\"missingclubpenguin\").message(SUBJECT, submission.url) # sends user the message\n break\n except Exception as ex:\n print(ex)\n time.sleep(60)","title":""},{"docid":"11d9f2f9e7c33153365c71d542f65943","score":"0.47500277","text":"def _get_posts(browser: webdriver.Chrome) -> List[Dict[str, str]]:\n post_hrefs = []\n for e in browser.find_elements_by_class_name(\"topic-title-wrapper__27Nt\"):\n for e_url, e_time in zip(\n e.find_elements_by_class_name(\"title-link__1ay5\"),\n e.find_elements_by_class_name(\"topic-info__tdz0\"),\n ):\n post_hrefs.append(\n {\n \"post_id\": e_url.get_attribute(\"href\").split(\"/\")[-2],\n \"text\": e_url.text,\n \"href\": e_url.get_attribute(\"href\"),\n \"time\": e_time.text,\n }\n )\n return post_hrefs","title":""},{"docid":"8f63d552492077babe8e8b9fea05ea56","score":"0.4749613","text":"def shouldSkipUrl(self, url, data):\n return data.xpath('//div[@id=\"post\"]//iframe')","title":""},{"docid":"7972653179720426265cb22cc4cfb1f5","score":"0.47428045","text":"def testPost(self):\n # Test posting an empty post.\n posts = Post.objects.filter(floor=self.floor)\n count = posts.count()\n response = self.client.post(reverse(\"news_post\"), {\"post\": \"\"},\n HTTP_X_REQUESTED_WITH='XMLHttpRequest')\n self.assertContains(response, \"This should not be blank.\")\n self.assertEqual(count, posts.count(), \"Check that the number of posts did not change.\")\n \n post = \"Test post via AJAX\"\n response = self.client.post(reverse(\"news_post\"), {\"post\": post}, \n HTTP_X_REQUESTED_WITH='XMLHttpRequest')\n self.failUnlessEqual(response.status_code, 200)\n self.assertContains(response, \"Test post via AJAX\")\n \n # Check that the new post is in the news feed.\n response = self.client.get(reverse(\"news_index\"))\n self.assertContains(response, post)","title":""},{"docid":"3fb3bf721f106c1641af7ef1373b1b66","score":"0.4715013","text":"def test_parsing_invalid_html(\n requests_mock: rm_Mocker,\n archive: Archive,\n) -> None:\n markup: str = '<a class=\"entry\" id=\"post\">'\n requests_mock.get(conf.ARCHIVE_URL, text=markup)\n with pytest.raises(NotEpisodeURLError) as ex:\n archive.parser.parse_url()\n assert conf.ARCHIVE_URL in ex.value.args[0] # Trailing slash for domain URL\n assert (\n ex.value.args[1] == \"ERROR: Can't parse this page: 'article' tag was not found.\"\n )","title":""},{"docid":"4df34acd00523df4f3956550437b12e9","score":"0.47132322","text":"def test_no_posts(self):\n response = self.client.get(reverse('post_list'))\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, \"No posts here yet...\")\n self.assertQuerysetEqual(response.context['posts'], [])","title":""},{"docid":"3e5d8bf1cb5211556e192384726fcb7c","score":"0.47102448","text":"def getAllPosts(self, content):\n block = re.compile(\"[\\d\\w]{23} [-\\d\\w]{23} scrollerItem [-\\d\\w]{23} [-\\d\\w]{23} Post [\\d\\w]{9}\")\n posts = content.find_all('div', class_=block)\n return posts, posts[-1]['id']","title":""},{"docid":"3858b9890598c82518dbba4ca56cb713","score":"0.47062775","text":"def parse_posts(self, response):\n \n # approximate retrieval time\n retrieved = str(datetime.datetime.now())\n \n # parse the articles posts and \n # TODO: yield Item\n headers = response.xpath(\"/html/body/div[@class='body']/table[@summary='posts']//tr/td[@class='bold l pu']\")\n bodys = response.xpath(\"/html/body/div[@class='body']/table[@summary='posts']//tr/td[@class='l w pd']\")\n \n for h in range(len(headers)):\n \n header = headers[h]\n body = bodys[h]\n \n post = {}\n #post['url'] = response.url\n #post['title'] = response.meta['title']\n post['article_id'] = response.meta['article_id']\n post['forum'] = response.meta['forum']\n post['_id'] = header.xpath(\"a[1]/@name\").extract_first()\n post['user'] = header.xpath(\"a[@class='user']/text()\").extract_first()\n post['posted'] = \", \".join(header.xpath(\"span[@class='s']/b/text()\").extract()) \n post['retrieved'] = retrieved\n post['page_no'] = response.meta['page_number']\n \n post['links'] = len(body.xpath(\"div[@class='narrow']/a/@href\").extract())\n\n post['likes'] = 0\n likes = body.xpath(\"p[@class='s']/b[1]/text()\").extract_first(default='').strip()\n if likes:\n post['likes'] = int(likes.split()[0])\n \n post['shares'] = 0\n shares = body.xpath(\"p[@class='s']/b[2]/text()\").extract_first(default='').strip()\n if shares:\n post['shares'] = int(shares.split()[0])\n \n post['images'] = len(body.xpath(\"p/img\"))\n post['quote'] = len(body.xpath(\"div[@class='narrow']/blockquote\")) > 0\n \n yield post","title":""},{"docid":"19c2fba1cd3004504dc90c7a38fb7ef7","score":"0.47019848","text":"def test_no_posts(self):\n response = self.client.get(reverse(\"posts\"))\n self.assertEqual(response.status_code, 200)\n # self.assertContains(response, \"No polls are available.\")","title":""},{"docid":"46d658111741d006f42f86066263c778","score":"0.4700786","text":"def scrapePostID(html):\n # Try first looking in the comments\n pattern = []\n pattern.append(r'<!-- post #([0-9]*) -->')\n pattern.append(r'p=([0-9]*)')\n p = 0\n m = None\n while (m == None) and (p < len(pattern)):\n m = re.search(pattern[p], html)\n p += 1\n if m == None:\n return -1\n else:\n return m.group(1)","title":""},{"docid":"706e807cec9c1b1ab191d7ac8cb4e578","score":"0.46994996","text":"def get_posts(url: str, reddit: Any = \"praw.Reddit\") -> pd.DataFrame:\n submission = get_page(url=url, reddit=reddit)\n\n posts = []\n for top_level_comment in submission.comments[1:]:\n if isinstance(top_level_comment, MoreComments):\n continue\n posts.append(top_level_comment.body)\n\n posts = pd.DataFrame(posts, columns=[\"body\"])\n\n # get rid of removed and deleted comments\n indexNames = posts[(posts.body == \"[removed]\") | (posts.body == \"[deleted]\")].index\n posts.drop(indexNames, inplace=True)\n\n return posts","title":""},{"docid":"7938c80a1af052968473354db9dde1c0","score":"0.46782392","text":"def analyzeSite(self, db, site, usrName):\r\n # initialize the db during the first call\r\n if not db.has_key('post'):\r\n db['post'] = {}\r\n\r\n\r\n # extract the links that we want to follow some day\r\n linkPattern = re.compile(r'\\bhttp://'+usrName+'\\.ycool\\.com/post\\.(\\d+)\\.html$', re.I)\r\n archivePattern = re.compile(r'\\bhttp://'+usrName+'\\.ycool\\.com/archive(.p\\d+)?\\.html', re.I)\r\n commentPattern = re.compile(r'\\bhttp://'+usrName+'\\.ycool\\.com/followups\\.(\\d+)\\.html$', re.I)\r\n\r\n # mark the site to avoid crawling it in the future\r\n db['pages'][site.stringUrl] = site\r\n db['crawled'][site.stringUrl] = True\r\n #print(\"site.stringUrl\"+site.stringUrl)\r\n #print(db['crawled'])\r\n\r\n if archivePattern.search(site.stringUrl):\r\n for link in site.parsedContent.iterlinks():\r\n #print(\"link:\",link)\r\n if (not db['crawled'].has_key(link[2])):\r\n #db['crawled'][link[2]]=True\r\n if linkPattern.search(link[2]):\r\n #print('------1---------')\r\n #print('content link: '+link[2])\r\n self.__newOtherLinksToCrawl+=[link[2]]\r\n self.__newOtherLinksToCrawl+=[self.__getCommentLink(link[2])]\r\n elif archivePattern.search(link[2]):\r\n #print('------1---------')\r\n #print('archive link: '+link[2])\r\n self.__newArchiveLinksToCrawl+= [link[2]]\r\n #print('------5 links before remove dup---------')\r\n #print(self.__newOtherLinksToCrawl)\r\n self.__newOtherLinksToCrawl=self.__removeDup(db,self.__newOtherLinksToCrawl)\r\n #print('------5 links---------')\r\n #print(self.__newOtherLinksToCrawl)\r\n\r\n self.__newArchiveLinksToCrawl=self.__removeDup(db,self.__newArchiveLinksToCrawl)\r\n #print('------5 archives---------')\r\n #print(self.__newArchiveLinksToCrawl)\r\n # put the rawContent to db if it is a post\r\n if linkPattern.search(site.stringUrl):\r\n #print(site.stringUrl)\r\n entry =linkPattern.search(site.stringUrl).group(1)\r\n if not db['post'].has_key(entry):\r\n db['post'][entry] = {}\r\n if not db['post'][entry].has_key('postContent'):\r\n db['post'][entry]['postContent'] = {}\r\n leftFrame = self.__getLeftFrame(site.parsedContent)\r\n db['post'][entry]['postContent']['title'] = self.__getTitle(leftFrame)\r\n db['post'][entry]['postContent']['content'] = self.__getContent(leftFrame)\r\n db['post'][entry]['postContent']['creator'] = self.__getCreator(leftFrame)\r\n db['post'][entry]['postContent']['pubDate'] = self.__getpubDate(leftFrame)\r\n db['post'][entry]['postContent']['tags'] = self.__getTags(leftFrame)\r\n db['post'][entry]['postContent']['cata'] = self.__getCata(leftFrame)\r\n\r\n\r\n #db['post'][linkPattern.search(site.stringUrl).group(1)]['postContent'] = self.__getLeftFrame(site.parsedContent)\r\n #print(site.stringUrl)\r\n\r\n if commentPattern.search(site.stringUrl):\r\n #print('----1----',site.stringUrl )\r\n entry =commentPattern.search(site.stringUrl).group(1)\r\n if not db['post'].has_key(entry):\r\n db['post'][entry] = {}\r\n if not db['post'][entry].has_key('postComment'):\r\n db['post'][entry]['postComment'] = {}\r\n\r\n commentList = self.__getCommentList(self.__getLeftFrame(site.parsedContent))\r\n for e in commentList :\r\n self.__commentId+=1\r\n id = str(self.__commentId)\r\n\r\n replyEl = self.__getMyReply(e)\r\n el = self.__getCommentInfo(e)\r\n ip =self.__getCommentIp(el)[0]\r\n date = self.__getCommentDate(el)[0]\r\n if not db['post'][entry]['postComment'].has_key(id):\r\n db['post'][entry]['postComment'][id]={}\r\n print('--------a comment-----------')\r\n db['post'][entry]['postComment'][id]['author']= strip(e.getparent().getparent()[0].text_content())\r\n db['post'][entry]['postComment'][id]['ip']= ip\r\n db['post'][entry]['postComment'][id]['date']= date+u':00'\r\n db['post'][entry]['postComment'][id]['content']=tostring(e[0], encoding=unicode,method='text',pretty_print=True)\r\n #db['post'][entry]['postComment'][id]['content']=e[0].text\r\n db['post'][entry]['postComment'][id]['parent']=0\r\n db['post'][entry]['postComment'][id]['email']='x@y.com'\r\n print(\"conent %s date %s id %s \" % \\\r\n (db['post'][entry]['postComment'][id]['content'].encode('utf-8'),\\\r\n date.encode('utf-8'),id))\r\n\r\n if len(replyEl)>0:\r\n self.__commentId+=1\r\n childkey=str(self.__commentId)\r\n if not db['post'][entry]['postComment'].has_key(childkey):\r\n db['post'][entry]['postComment'][childkey]={}\r\n print('--------a reply-----------')\r\n print('reply id %s' % childkey )\r\n db['post'][entry]['postComment'][childkey]['author']=self.__userName\r\n db['post'][entry]['postComment'][childkey]['ip']='127.0.0.1'\r\n db['post'][entry]['postComment'][childkey]['date']=date+u':03'\r\n db['post'][entry]['postComment'][childkey]['email']='x@y.com'\r\n db['post'][entry]['postComment'][childkey]['content']=tostring(replyEl[0], method='text',encoding=unicode,pretty_print=True)\r\n db['post'][entry]['postComment'][childkey]['parent']=self.__commentId-1\r\n print(\"conent %s date %s parent %s\" % \\\r\n (db['post'][entry]['postComment'][childkey]['content'].encode('utf-8'), \\\r\n date.encode('utf-8'), \\\r\n db['post'][entry]['postComment'][childkey]['parent']))","title":""},{"docid":"0527a5c87e745c717dfe74c4a4297080","score":"0.467732","text":"def test_double_post_bug(self):\n feed_str = get_data_file(\"lefigaro.rss\")\n imported_feed = self.importer.import_feed(feed_str, local=True,\n force=True)\n post_count = imported_feed.post_set.count()\n imported_feed = self.importer.import_feed(feed_str, local=True,\n force=True)\n self.assertEqual(imported_feed.post_set.count(), post_count,\n \"Posts seems to be imported twice.\")","title":""},{"docid":"608943f7f9c95108a302ef688e1dfa05","score":"0.4667173","text":"def test_urls(self):\n text = txt.importer('test_files/urls.txt')\n expected = [[\"website\", \"http://foo.com/blah_blah,\", \"fun\"],\n [\"click\", \"http://foo.com/blah_blah/-\", \"awesome\"],\n [\"visit\", \"http://foo.com/blah_blah_(wikipedia)\"],\n [\"http://foo.com/blah_blah_(wikipedia)_(again)\"],\n [\"http://www.example.com/wpstyle/?p=364\"],\n [\"https://www.example.com/foo/?bar=baz&inga=42&quux\"],\n [\"http://userid:password@example.com:8080\"],\n [\"http://userid:password@example.com:8080/\"],\n [\"http://userid@example.com\"],\n [\"http://userid@example.com/\"],\n [\"http://userid@example.com:8080\"],\n [\"http://userid@example.com:8080/\"],\n [\"http://userid:password@example.com\"],\n [\"http://userid:password@example.com/\"],\n [\"http://142.42.1.1/\"],\n [\"http://142.42.1.1:8080/\"],\n [\"http://foo.com/blah_(wikipedia)#cite-1\"],\n [\"http://foo.com/blah_(wikipedia)_blah#cite-1\"],\n [\"http://foo.com/(something)?after=parens\"],\n [\"http://code.google.com/events/#&product=browser\"],\n [\"http://j.mp\"],\n [\"ftp://foo.bar/baz\"],\n [\"http://foo.bar/?q=test%20url-encoded%20stuff\"],\n [\"http://-.~_!$&'()*+,;=:%40:80%2f::::::@example.com\"],\n [\"http://1337.net\"],\n [\"http://a.b-c.de\"],\n [\"http://223.255.255.254\"],\n [\"https://foo_bar.example.com/\"]]\n actual = tokenize(text)\n self.assertEqual(expected, actual)","title":""},{"docid":"e56577b4ba4e7bf4c6c1fa47bcb54329","score":"0.46573964","text":"def test_templates_category_detail_cms_draft_content_blogposts(self):\n self._extension_cms_draft_content(BlogPostFactory, \"h3.blogpost-glimpse__title\")","title":""},{"docid":"09dddc343decfe0e3b03ea5fcac60fb9","score":"0.46514475","text":"def check_post_content(self, field, material):\n for alias in material.aliases.all():\n self.assertIn(\n alias.name, field, \"material alias is not mentioned in post content\"\n )\n self.assertTrue(\n bool(BeautifulSoup(field, \"html.parser\").find()), \"invalid HTML\"\n )\n self.assertIn(\n material.description, field, \"description is not part of post content\"\n )\n soup = BeautifulSoup(field, \"html.parser\")\n attachment_anchors = soup.find_all(\"a\", {\"target\": \"attachment\"})\n attachment_urls = [anchor[\"href\"] for anchor in attachment_anchors]\n self.assertEqual(len(attachment_anchors), material.attachments.count())\n for material_attachment in material.attachments.all():\n self.assertIn(\n f\"http://testserver{material_attachment.attachment.url}\",\n attachment_urls,\n )\n if material.lendable_stock != \"\":\n self.assertIn(material.lendable_stock, field)","title":""},{"docid":"0ef7df0db4e2cb76afbefaa93fe9920e","score":"0.46364135","text":"def test_post_contents(self):\n # Load\n topic = Topic(self.site, 'Topic:Sh6wgo5tu3qui1w2')\n post = Post(topic, 'sh6wgoagna97q0ia')\n # Wikitext\n wikitext = post.get(content_format='wikitext')\n self.assertIn('wikitext', post._content)\n self.assertNotIn('html', post._content)\n self.assertIsInstance(wikitext, str)\n self.assertNotEqual(wikitext, '')\n # HTML\n html = post.get(content_format='html')\n self.assertIn('html', post._content)\n self.assertIn('wikitext', post._content)\n self.assertIsInstance(html, str)\n self.assertNotEqual(html, '')\n # Caching (hit)\n post._content['html'] = 'something'\n html = post.get(content_format='html')\n self.assertIsInstance(html, str)\n self.assertEqual(html, 'something')\n self.assertIn('html', post._content)\n # Caching (reload)\n post._content['html'] = 'something'\n html = post.get(content_format='html', force=True)\n self.assertIsInstance(html, str)\n self.assertNotEqual(html, 'something')\n self.assertIn('html', post._content)","title":""},{"docid":"11efed318c6fee9cddd09b79a0cedce5","score":"0.46226516","text":"def collect_posts(app):\n app.logger.info('Collecting posts from past 24h for topic modeling ... ')\n posts = get_candidates(app, user_id=1)\n\n posts = [post for post in posts if post.feed_title in reference_feeds]\n\n # Remove posts with Live updates, since these cover multiple topics and thus deviate from the average post\n posts = [post for post in posts if all(keyword not in post.title for keyword in DISCARD_KEYWORDS)]\n\n # Remove advertising posts for Bloomberg products\n posts = [post for post in posts if '(Source: Bloomberg)' not in post.description]\n\n app.logger.info(f'Successfully collected {len(posts)} posts.')\n return posts","title":""},{"docid":"42d0cc6ac4983de424422c09a4d5c776","score":"0.46086076","text":"def test_list(self):\n response = self.client.get(self.posts_list_url)\n self.assertEquals(response.status_code, 200)\n self.assertContains(response, u\"A title 1\")\n self.assertContains(response, u\"A title 2\")\n self.assertContains(response, u\"A text 1\")\n self.assertContains(response, u\"A text 2\")","title":""},{"docid":"f38901494be265b6fda4c0173fbe30d5","score":"0.46057123","text":"def __addPost(self, post, is_question=False):\n try:\n unique_key_tag = post.find('a' ,'postcounter')\n if not unique_key_tag and not unique_key_tag.get('href'): \n log.info(self.log_msg('Permalink not found, ignoring the Post in the url %s'%self.currenturi))\n return True\n unique_key = unique_key_tag['href']\n if checkSessionInfo(self.__genre, self.session_info_out, unique_key,\\\n self.task.instance_data.get('update'),parent_list\\\n = [self.task.instance_data['uri']]):\n log.info(self.log_msg('Session info returns True for uri %s'%unique_key))\n return False\n page = self.__getData(post, is_question, unique_key)\n if not page:\n log.info(self.log_msg('page contains empty data, getdata \\\n returns False for uri %s'%self.currenturi))\n return True\n result = updateSessionInfo(self.__genre, self.session_info_out, unique_key, \\\n get_hash( page ),'forum', self.task.instance_data.get('update'),\\\n parent_list=[self.task.instance_data['uri']])\n if result['updated']:\n page['parent_path'] = page['path'] = [self.task.instance_data['uri']]\n page['path'].append(unique_key)\n page['uri'] = unique_key\n page['uri_domain'] = urlparse.urlparse(page['uri'])[1]\n page.update(self.__task_elements_dict)\n self.pages.append(page)\n else:\n log.info(self.log_msg('Update session info returns False for \\\n url %s'%self.currenturi))\n except:\n log.info(self.log_msg('Cannot add the post for the uri %s'%self.currenturi))\n return True","title":""},{"docid":"e8de0fb567aea2015bfe32aeaa349c6a","score":"0.46044493","text":"def test_page_requests(self):\n page = self.client.get(self.url)\n self.assertIn(self.url, page.content)","title":""},{"docid":"f44ad830c8b20ba7980edcfa01fb77c8","score":"0.46037108","text":"def test_find_urls(urlextract, text, expected):\n assert urlextract.find_urls(text) == expected","title":""},{"docid":"a1ded504ae159863418dec2d8bf68d50","score":"0.4597795","text":"def test_froum_view_with_some_posts(self):\n mf = add_forum('meme_forum')\n usr = add_userProfile()\n\n add_post(mf, \"title\", \"https://static.djangoproject.com/img/fundraising-heart.cd6bb84ffd33.svg\", usr)\n add_post(mf, \"other_title\", \"http://thewowstyle.com/wp-content/uploads/2015/01/nature-images.jpg\", usr)\n add_post(mf, \"another_title\", \"https://eskipaper.com/images/images-2.jpg\", usr)\n add_post(mf, \"title2\", \"https://hddesktopwallpapers.in/wp-content/uploads/2015/09/nice-images.jpg\", usr)\n add_post(mf, \"other_title2\", \"https://wonderfulengineering.com/wp-content/uploads/2014/01/highway-wallpapers-15.jpg\", usr)\n add_post(mf, \"other_title3\", \"http://thewowstyle.com/wp-content/uploads/2015/07/Natural-World-Wallpaper-HD-.jpg\", usr)\n\n response = self.client.get(reverse('meme_portal:show_forum', kwargs={'forum_name_slug':'meme_forum'}))\n\n self.assertEqual(response.status_code, 200)\n\n num_posts = len(response.context['posts'])\n self.assertEquals(num_posts, 6)","title":""},{"docid":"07f558bca9c2f013b621f814eed04dcf","score":"0.45939758","text":"def download_screenshots_of_reddit_posts(reddit_object, screenshot_num):\n print_step(\"Downloading Screenshots of Reddit Posts 📷\")\n\n # ! Make sure the reddit screenshots folder exists\n Path(\"assets/png\").mkdir(parents=True, exist_ok=True)\n\n with sync_playwright() as p:\n print_substep(\"Launching Headless Browser...\")\n\n browser = p.chromium.launch()\n\n # Get the thread screenshot\n page = browser.new_page()\n page.goto(reddit_object[\"thread_url\"])\n\n if page.locator('[data-testid=\"content-gate\"]').is_visible():\n # This means the post is NSFW and requires to click the proceed button.\n\n print_substep(\"Post is NSFW. You are spicy... :fire:\")\n page.locator('[data-testid=\"content-gate\"] button').click()\n\n page.locator('[data-test-id=\"post-content\"]').screenshot(\n path=\"assets/png/title.png\"\n )\n\n for idx, comment in track(\n enumerate(reddit_object[\"comments\"]), \"Downloading screenshots...\"\n ):\n\n # Stop if we have reached the screenshot_num\n if idx >= screenshot_num:\n break\n\n if page.locator('[data-testid=\"content-gate\"]').is_visible():\n page.locator('[data-testid=\"content-gate\"] button').click()\n\n page.goto(f'https://reddit.com{comment[\"comment_url\"]}')\n page.locator(f\"#t1_{comment['comment_id']}\").screenshot(\n path=f\"assets/png/comment_{idx}.png\"\n )\n print_substep(\"Screenshots downloaded Successfully.\", style=\"bold green\")","title":""},{"docid":"6dc2f7bf3c5c8f086cc6a320e346e726","score":"0.45916343","text":"def regex_parse(self, data):\r\n\r\n opening_split = data.split('<!-- CONTENT -->')\r\n fully_split = [ part.split('<!-- /CONTENT -->') for part in opening_split]\r\n # Flatten the list of lists.\r\n fully_split = list(itertools.chain.from_iterable(fully_split))\r\n\r\n content_data = ' '.join(fully_split[1::2])\r\n other_data = ' '.join(fully_split[::2])\r\n\r\n regex = '<a.*?href=[\"\\']([^\"]+[.\\s]*?)[\"\\'].*?>[^<]+[.\\s]*?</a>'\r\n content_urls = re.findall(regex, content_data)\r\n other_urls = re.findall(regex, other_data)\r\n\r\n return content_urls, other_urls","title":""},{"docid":"0f7c50e89be73319745b441ec2533cf4","score":"0.45890713","text":"def test_get_posts_returns_posts(self):\n req = self.wp_tc.get_posts()\n assert req is not None, \"GetPosts endpoint returned a null response.\"\n assert len(req) > 0, \"GetPosts endpoint not returning at least one object in JSON output.\"","title":""},{"docid":"1e8fcc8c9614f19ff0b5b8882a82b52f","score":"0.45838967","text":"def testAjaxPosts(self):\n # Generate test posts.\n for i in range(0, DEFAULT_POST_COUNT + 1):\n text = \"Testing AJAX response %d.\" % i\n post = Post(user=self.user, floor=self.floor, text=text)\n post.save()\n \n second_post = Post.objects.all().order_by(\"-pk\")[0]\n response = self.client.get(reverse(\"news_more_posts\"), HTTP_X_REQUESTED_WITH='XMLHttpRequest')\n self.failUnlessEqual(response.status_code, 200)\n self.assertNotContains(response, \"Testing AJAX response 0.\")\n self.assertContains(response, \"See more\")\n for i in range(1, DEFAULT_POST_COUNT + 1):\n self.assertContains(response, \"Testing AJAX response %d\" % i)\n \n response = self.client.get(reverse(\"news_more_posts\") + (\"?last_post=%d\" % second_post.id), HTTP_X_REQUESTED_WITH='XMLHttpRequest')\n self.assertContains(response, \"Testing AJAX response 0.\")","title":""},{"docid":"df8aa1a5aaf2f5ea26d705f0a63f1304","score":"0.45793927","text":"def scrape_buzzfeed(): \n # load the HTML content using requests and save into a variable\n buzz_request = requests.get('https://www.buzzfeednews.com/section/politics')\n buzz_homepage = buzz_request.content\n\n # create soup\n buzz_soup = BeautifulSoup(buzz_homepage, 'html.parser')\n\n # locate article URLs\n buzz_tags = buzz_soup.find_all('h2')\n\n # get article titles, content, dates, and links\n buzz_links = []\n buzz_titles = []\n buzz_dates = []\n buzz_contents = []\n\n for n in np.arange(0, min(len(buzz_tags), 30)):\n\n # get article link\n link = buzz_tags[n].find('a')['href']\n buzz_links.append(link)\n\n # get article title\n title = buzz_tags[n].find('a').get_text()\n buzz_titles.append(title)\n\n # prep article content\n article = requests.get(link)\n article_content = article.content\n soup_article = BeautifulSoup(article_content, 'html5lib')\n\n # get publication datetime\n date = soup_article.find_all('div', class_='news-article-header__timestamps')\n date = \" \".join([item.text for item in date]).replace('\\n', '')\n buzz_dates.append(date)\n\n # get article content\n body = soup_article.find_all('div', attrs={'data-module':'subbuzz-text'})\n article = \" \".join([item.text for item in body]).replace('\\n', '')\n final_article = re.sub(r' {[^}]*}', '', article)\n\n buzz_contents.append(final_article)\n\n # assembling data\n buzz_data = pd.DataFrame.from_dict({\n 'publisher': 'buzzfeed',\n 'date': buzz_dates,\n 'link': buzz_links,\n 'article_title': buzz_titles,\n 'article_text': buzz_contents \n })","title":""},{"docid":"9b2216e126de8a8e8a401356c08a1c96","score":"0.45711684","text":"def fetch(url, regex_patt):\n\ttext = request.urlopen(url)\n\tbs_text = bs4.BeautifulSoup(text, 'lxml')\n\turl_list = []\n\tfor link in bs_text.find_all('a'):\n\t\turl_list.append(str(link.get('href')))\n\t#get the cleaned list\t\n\turl_list = clean(url_list, regex_patt)\n\turl_list = list(map((lambda x : 'https://en.wikipedia.org' + x), url_list))\n\treturn set(url_list)","title":""},{"docid":"68fe6f7a1b7a645242668221654fb945","score":"0.4560216","text":"def fetch_page(URL, posts = None, last_modified = None):\n# print \"Fetching \" + URL\n\n request = urllib2.Request(URL)\n request.add_header('User-Agent','Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.19) Gecko/2010040116 Ubuntu/9.04 (jaunty) Firefox/3.0.19')\n if posts is not None:\n request.add_data(urllib.urlencode(posts))\n\n if last_modified is not None:\n request.add_header('If-Modified-Since', last_modified)\n\n try:\n response = opener.open(request)\n except urllib2.HTTPError as e:\n if e.code == 404:\n raise PageDoesNotExist(URL)\n else:\n raise e\n\n return response","title":""},{"docid":"476aa05237560eea6f01553bd0d09649","score":"0.45588604","text":"def spam_post(post_url, times, comment):\r\n driver.get(post_url)\r\n sleep(3)\r\n i = 0\r\n while times > i:\r\n driver.find_element_by_xpath('//*[@id=\"react-root\"]/section/main/div/div/article/div[2]/section[3]/ \\\r\n div/form/textarea').click()\r\n driver.find_element_by_xpath('//*[@id=\"react-root\"]/section/main/div/div/article/div[2]/section[3]/ \\\r\n div/form/textarea').send_keys(comment)\r\n driver.find_element_by_xpath('//*[@id=\"react-root\"]/section/main/div/div/article/div[2]/section \\\r\n [3]/div/form/button').click()\r\n i += 1\r\n random_num = random.randint(17, 23) # cooldown\r\n print('#{} comment: {}'.format(i, comment))\r\n print('waiting {} sec'.format(random_num))\r\n sleep(random_num)","title":""},{"docid":"e89e78e34ad286a35c52d3069d337b90","score":"0.45456493","text":"async def get_new_posts(fetcher: Fetcher, store_dir: str) -> dict:\n try:\n page = await fetcher.fetch(Y_ROOT_URL)\n except (aiohttp.ClientError, asyncio.TimeoutError):\n logging.info(\"{} is not available now\".format(Y_ROOT_URL))\n return dict()\n\n posts = get_posts_from_main_page(page)\n ready_post_ids = get_processed_posts(store_dir)\n\n not_ready_posts = {}\n for p_id, p_url in posts.items():\n if p_id not in ready_post_ids:\n not_ready_posts[p_id] = p_url\n else:\n logging.debug(\"Post {} already parsed\".format(p_id))\n\n return not_ready_posts","title":""},{"docid":"4c4a39e3e50deeab41f60306abe62ae7","score":"0.45395905","text":"def get_meduza_articles(url=\"https://meduza.io/api/w5/screens/news\") -> List[str]:\n \n content = __get_page_content(url)\n articles = re.findall('{\"key\":\"(.*?)\"', content)\n \n def contains_any(url) -> bool:\n \"\"\"\n Returns true if url contains any element of allowed\n \"\"\"\n allowed = ['shapito', 'cards', 'news', 'feature', 'episodes']\n return bool(sum(list(\n map(lambda name: name in url, \n allowed))))\n\n return list(map(lambda url: \"https://meduza.io/\" + url, \n filter(contains_any, \n articles)))","title":""},{"docid":"d782ff765bf7f4811a259a322cff241e","score":"0.45374984","text":"def _parse_response_body(resp_body: str) -> List[str]:\n base_url = \"https://news.ycombinator.com\"\n urls = []\n\n body_parser = BeautifulSoup(resp_body, 'html.parser')\n titles = body_parser.select('.storylink')\n\n for news_title in titles:\n url = news_title.get('href')\n\n if not url.startswith('http'):\n url = f'{base_url}/{url}'\n \n if not url.endswith('.pdf'):\n urls.append(url)\n\n return urls","title":""},{"docid":"eb7cc817cbcac17929a8027b8295d1b1","score":"0.45373192","text":"def test_get_existent_page(self):\n generate(total_authors=1, total_posts=1)\n post = Post.objects.first()\n request = self.factory.get('/post')\n response = views.post(request, path=post.path)\n self.assertEqual(response.status_code, 200)","title":""},{"docid":"fcfddeba1e27dc380a3bd50f25d7aea3","score":"0.45349544","text":"def find_posts_in_subreddit(subRedditName, subRedditParseConfig):\n # type: (string, dictionary) -> List of praw.Submission\n\n # Get a PRAW instance of the subreddit.\n subreddit = reddit.subreddit(subRedditName)\n\n # Retrieve various configured settings.\n sort_by = subRedditParseConfig[\"sort_by\"]\n sort_by_range = subRedditParseConfig[\"sort_by_range\"]\n search_terms = subRedditParseConfig[\"search\"]\n\n # Elligible submissions found in the subreddit.\n submissions = []\n\n # Search submissions by our configured settings for all of our various\n # search terms.\n for searchTerm in search_terms:\n searchResults = subreddit.search(searchTerm, sort=sort_by,\n time_filter=sort_by_range)\n\n # The search term must appear in the submission's title, if it's to be parsed.\n # Right now it's strict matching. This is important for things like distinguishing\n # posts with '[FRESH]' from '[FRESH VIDEO]' in /r/hiphopheads.\n for result in searchResults:\n if searchTerm in result.title:\n submissions.append(result)\n\n return submissions","title":""},{"docid":"3aa669759a7fdf36adecc64d3669d1dc","score":"0.45304048","text":"def _process_url_reddit(self, session, url, **kwargs):\n urlinfo = urlparse(url)\n if not (\n urlinfo.hostname == \"reddit.com\" or urlinfo.hostname.endswith(\".reddit.com\")\n ):\n return\n\n # Thread information\n if match := REDDIT_THREAD_PATTERN.match(urlinfo.path):\n apiurl = f\"https://reddit.com/r/{match.group('subreddit')}/comments/{match.group('thread')}.json\"\n\n def formatter(response):\n data = response[0][\"data\"][\"children\"][0][\"data\"]\n return [f\"/r/{match.group('subreddit')}:\", data.get(\"title\")]\n\n elif match := REDDIT_COMMENT_PATTERN.match(urlinfo.path):\n apiurl = f\"https://reddit.com/r/{match.group('subreddit')}/comments/{match.group('thread')}/x/{match.group('comment')}.json\"\n\n def formatter(response):\n data = response[1][\"data\"][\"children\"][0][\"data\"]\n title = response[0][\"data\"][\"children\"][0][\"data\"][\"title\"]\n return [\n f\"/r/{match.group('subreddit')} comment by {data.get('author')} on “{title}”\"\n ]\n\n elif match := REDDIT_USER_PATTERN.match(urlinfo.path):\n apiurl = f\"https://reddit.com/user/{match.group('user')}/about.json\"\n\n def formatter(response):\n data = response[\"data\"]\n return [f\"/u/{data['name']} on Reddit\"]\n\n else:\n self.log.warning(\"Unsupported reddit url: '%s'\", urlinfo.path)\n return [\"Reddit\"]\n\n with closing(session.get(apiurl, timeout=4)) as response:\n try:\n attempts = 0\n while attempts < 3:\n attempts += 1\n data = response.json()\n self.log.error(\"Response: %r\", data)\n if (\n response.status_code == 429\n or not isinstance(data, dict)\n or data.get(\"error\", 0) == 429\n ):\n time.sleep(0.5 * attempts)\n self.log.debug(\"Retrying reddit request\")\n continue\n elif response.status_code != 200:\n return [f\"Got response code {response.status_code}\"]\n else:\n return formatter(data)\n return [f\"Reddit refused to give data.\"]\n\n except ValueError:\n return [\"Invalid JSON response from Reddit API\"]","title":""},{"docid":"5c7a863dff235a817da841281a67e058","score":"0.4530284","text":"def scrape_posts(tag):\n\n for page_number in range(1,11):\n parameters = {\n \"tab\":\"newest\",\n \"page\":page_number,\n \"pagesize\":15\n }\n url = f\"{stackoverflow}questions/tagged/{tag}\"\n r = requests.get(url,params=parameters)\n\n if r.status_code in range(200,299):\n\n try:\n html_str = r.text\n html = HTML(html=html_str)\n \n post = html.find(\".question-summary\")\n # post_details = list(post.text.split(\"\\n\"))\n \n classes = [\".vote\",\".status\",\".views\",\".question-hyperlink\",\".excerpt\",\".tags\",\".user-details\"]\n key_names = [\"Votes\",\"Answers\",\"Views\",\"Question\",\"Summary\",\"Tags\",\"User\"]\n data = []\n\n for ques_elem in post:\n post_details = {}\n for i,search_class in enumerate(classes):\n element = ques_elem.find(search_class,first=True)\n keyname = key_names[i]\n post_details[keyname] = filter_data(element.text,keyname=keyname)\n data.append(post_details)\n \n with open(os.path.join(\"Outputs\\Multi-Scraper\\jsonFiles\",f\"post{page_number}.json\"),\"w\") as f:\n f.write(json.dumps(data,indent=2))\n\n with open(os.path.join(\"Outputs\\Multi-Scraper\\csvFiles\",f\"post{page_number}.csv\"),\"w\") as f:\n df = pd.DataFrame(data)\n df.to_csv(f,index=False)\n\n except Exception as e:\n print(\"Something went wrong: \",e)\n\n sleep(1.5)","title":""},{"docid":"3f599fc7f8f219569c06647fd95e5f29","score":"0.45220834","text":"def scrape_new_urls(request):\n startTime = datetime.datetime.now()\n unscraped_urls = Phishtank_urls.objects.filter(is_scraped=False)\n event_loop = asyncio.new_event_loop()\n browser = event_loop.run_until_complete(pyppeteer.launch(handleSIGINT=False, handleSIGTERM=False, handleSIGHUP=False , ignoreHTTPSErrors=True , args= [\"--ignore-certificate-errors-spki-list=jc7r1tE54FOO=\"]))\n for line in unscraped_urls:\n if Models_Helper.scrape_line(line, event_loop , browser=browser ):\n line.is_scraped = True\n line.save()\n event_loop.run_until_complete(browser.close())\n endTime = datetime.datetime.now()\n TotalTime = endTime - startTime\n return HttpResponse(f\"It took (in microseconds): {TotalTime.microseconds}\")","title":""},{"docid":"1f98ec454a1927385067f2a191950731","score":"0.44966224","text":"def _(\n post_limit: dt.timedelta,\n reddit_conn: praw.Reddit,\n sub_name: str = 'SuicideWatch'\n) -> Generator[RedditPost, None, None]:\n subreddit = reddit_conn.subreddit(sub_name)\n # posts: List[RedditPost] = []\n post_limit = (dt.datetime.utcnow() - post_limit)\n\n for post in subreddit.new(limit=None):\n post_utc = dt.datetime.utcfromtimestamp(post.created_utc)\n if post_utc < post_limit:\n print(f'Breaking: {post_utc} < {post_limit}')\n break\n\n yield RedditPost(post)","title":""},{"docid":"7aaa66d4505e31f9fd67a054403cef78","score":"0.4467747","text":"def test_search_urls(self):\n self.assertTrue(validate_url(self.engine._parsed_url.geturl()))","title":""},{"docid":"b31f92ed7af08e6b53c35118cd7c5f87","score":"0.44649452","text":"def can_post(acc):\n my_posts = get_my_posts(acc, acc)\n if len(my_posts) > 0:\n last_post_time = get_my_posts(acc, acc)[0][0].time\n return not last_post_time >= timezone.now() - datetime.timedelta(days=1)\n else:\n return True","title":""},{"docid":"65dc98ca5ee622afae4a837be1c1c41c","score":"0.44635552","text":"def test_urls(self):\n request = testing.DummyRequest()\n page = new_stamp_location(request)\n self.assertIn('post_url', page)\n self.assertEqual(\n page['post_url'],\n request.route_url('new-stamp-location-post')\n )","title":""},{"docid":"87df63dc6af4593f44d4a553f0b39f2e","score":"0.44599196","text":"def is_valid_post_file(basename):\n post_pattern = r'^\\d{4}-\\d{2}-\\d{2}-.*'\n markdown_extensions = ['md', 'markdown', 'mdown']\n\n if '.' not in basename:\n return False\n\n filename, ext = os.path.basename(basename).rsplit('.', 1)\n\n ignorable = filename.startswith('_')\n valid_filename = re.match(post_pattern, filename)\n valid_extension = ext in markdown_extensions\n\n return not ignorable and valid_filename and valid_extension","title":""},{"docid":"e7902904e4ac6080418318b4872ff87c","score":"0.44591194","text":"def test_parsing_non_episode_link(\n requests_mock: rm_Mocker,\n req_ses: requests.Session,\n archive: Archive,\n) -> None:\n non_episode_url = (\n \"https://teacherluke.co.uk/premium/archive-comment-section/\" # noqa: E501,B950\n )\n requests_mock.get(\n non_episode_url,\n text=\"No need to parse this page\",\n status_code=200,\n )\n link_title = \"Some title\"\n with pytest.raises(NotEpisodeURLError) as ex:\n parser.EpisodeParser(archive, non_episode_url, req_ses, link_title).parse_url()\n assert len(archive.collected_links) == 0\n assert ex.value.args[0] == non_episode_url","title":""},{"docid":"16d8e11be3ae519114c4fb4f5821c66b","score":"0.44577125","text":"def suitable(cls, url):\n urls = []\n for _VALID_URL in cls.target_website:\n urls += re.findall(_VALID_URL, url)\n else:\n return urls if urls else False","title":""},{"docid":"6d3be36e753e002f23c9783cebe44535","score":"0.4456455","text":"def test_no_spash_page():\n spider = magic_spider.MagicSpider()\n body = \"\"\"\n <html>\n <body id=\"f1d\">\n <table class=\"list\" style=\"margin-left: 20px; width: 920px;\">\n <tr class=\"odd\">\n <td>\n <a href=\"http://non_reachable_url/\">Limits to the violation of...</a>\n </td>\n </tr>\n </table>\n </body>\n </html>\n \"\"\"\n response = fake_response_from_string(body)\n node = get_node(spider, spider.itertag, text=body)\n parsed_node = spider.parse_node(response, node)\n\n response.status = 404\n response.meta[\"title\"] = parsed_node.meta[\"title\"]\n response.meta[\"urls\"] = parsed_node.meta[\"urls\"]\n parsed_item = spider.scrape_for_pdf(response).next()\n assert parsed_item\n assert parsed_item.record\n record = parsed_item.record\n\n assert isinstance(record, hepcrawl.items.HEPRecord)\n assert \"urls\" in record\n assert \"title\" in record\n assert record[\"urls\"][0][\"value\"] == \"http://non_reachable_url/\"\n assert record[\"title\"] == \"Limits to the violation of...\"","title":""},{"docid":"69d6ce91cc90ea7e2378515557473832","score":"0.44540748","text":"def page_handler(url, match):\n result = linkinfo.scrape_html_title(url)\n result.is_redundant = 'imgur: the simple image sharer' in result.text\n return result","title":""},{"docid":"ee24ea0ac766437b8d9e3bf2aa725358","score":"0.44370016","text":"def find_last_x_posts(self, sub: str, amount: int) -> List[dict]: \n new_python = self.reddit.subreddit(sub).new(limit=amount)\n posts = []\n for post in new_python:\n if not post.stickied:\n posts.append({\"title\": post.title,\n \"url\": post.url,\n \"permalink\": post.permalink,\n \"created\": post.created})\n return posts","title":""},{"docid":"6874d686d65d40f421f9c5fbee1ca424","score":"0.44317132","text":"def process(self, f):\n SCHEME_KIND = \"http://schemas.google.com/g/2005#kind\"\n VALID_KINDS = (\"http://schemas.google.com/blogger/2008/kind#post\",\n \"http://schemas.google.com/blogger/2008/kind#page\")\n NS = {'ns': 'http://www.w3.org/2005/Atom'}\n\n d = etree.parse(f)\n\n htmlckr = HTMLChecker()\n links = {}\n\n entries = d.xpath('//ns:feed/ns:entry', namespaces=NS)\n for entry in entries:\n sel = \"ns:category[@scheme='%s']\" % SCHEME_KIND\n kind = entry.find(sel, namespaces=NS)\n if kind.attrib.get('term') not in VALID_KINDS:\n continue\n sel = \"ns:link[@rel='alternate']\"\n altlink = entry.find(sel, namespaces=NS)\n if altlink is None:\n # for scheduled posts, the post URL isn't decided yet, skip such posts\n # since lnkckr wouldn't be possible to provide URL of the posts.\n continue\n post_link = altlink.attrib.get('href')\n\n content = entry.find('ns:content', namespaces=NS)\n content_text = content.text\n if isinstance(content_text, str) and hasattr(content_text, 'decode'):\n content_text = content_text.decode('utf8')\n htmlckr.links = {}\n htmlckr.process(StringIO(content_text))\n for link in htmlckr.links.keys():\n # is a blank fragment?\n if link == '#' or link == post_link + '#':\n continue\n # is a local fragment?\n if link.startswith('#'):\n link = post_link + link\n if link not in links:\n links[link] = {'status': None, 'posts': []}\n if post_link not in links[link]['posts']:\n links[link]['posts'].append(post_link)\n self.links = links","title":""},{"docid":"fd228c2764d0618fb68e7f540c951c4c","score":"0.44306654","text":"def scrape_job_posting(url, **kwargs):\n r = requests.get(url, allow_redirects=True)\n true_url = r.url\n tree = html.fromstring(r.content)\n\n # Use domain-appropriate scraper\n domain = urlparse(true_url).hostname\n if domain in ['www.indeed.ca', 'www.indeed.com']:\n scraper_cls = PSindeedCa\n elif domain in ['www.smartrecruiters.ca', 'www.smartrecruiters.com']:\n scraper_cls = PSsmartrecruiters\n else:\n scraper_cls = PageScraper\n scraper = scraper_cls(true_url, tree)\n scraper.scrape()\n return Jentry(scraper, **kwargs)","title":""},{"docid":"94eaa10a2077e71e8d540caab0f0c14c","score":"0.44255474","text":"def _extract_urls(self, text: str) -> list[str]:\n urls = self.url_re.findall(text)\n return urls","title":""},{"docid":"4fbfa54df9427352b269d2a383840cce","score":"0.44248518","text":"def test_bad_links(self):\r\n bad_links = [\r\n '<a name=\"amazonAndGoogleHaveMadeAnAudaciousGrabOfNamespaceOnTheInternetAsFarAsICanSeeTheresBeenNoMentionOfThisInTheTechPress\"> </a>',\r\n '<a href=\"#amazonAndGoogleHaveMadeAnAudaciousGrabOfNamespaceOnTheInternetAsFarAsICanSeeTheresBeenNoMentionOfThisInTheTechPress\"><img src=\"http://scripting.com/images/2001/09/20/sharpPermaLink3.gif\" class=\"imgBlogpostPermalink\" width=\"6\" height=\"9\" border=\"0\" alt=\"permalink\"></a>',\r\n '<a href=\"http://scripting.com/stories/2012/06/15/theTechPressIsOutToLunch.html#anExampleGoogleDoesntIntendToShareBlogAndItWillOnlyBeUsedToPointToBloggerSitesIfYouHaveATumblrOrWordpressBlogYouCantHaveABlogDomainHereIsTheAHrefhttpgtldresulticannorgapplicationresultapplicationstatusapplicationdetails527publicListingaOfGooglesAHrefhttpdropboxscriptingcomdavemiscgoogleblogapplicationhtmlapplicationa\"><img src=\"http://scripting.com/images/2001/09/20/sharpPermaLink3.gif\" class=\"imgBlogpostPermalink\" width=\"6\" height=\"9\" border=\"0\" alt=\"permalink\"></a>'\r\n ]\r\n\r\n for l in bad_links:\r\n link = fragment_fromstring(l)\r\n self.assertTrue(is_bad_link(link))","title":""},{"docid":"1dd0ab4ad6f234c0495842b693c83513","score":"0.44207418","text":"def get_pages(collection):\n preface = re.compile('/questions/[0-9]+')\n i = 1\n while True:\n website = \"http://stackoverflow.com/questions?page=\" + str(i) + \"&sort=votes\"\n response = urllib2.urlopen(website)\n soup = BeautifulSoup(response.read())\n links = [a_tag.get('href') for a_tag in soup.find_all('a')]\n for link in links:\n if collection.count() == MAX_QUESTIONS:\n raise DataBaseFull(\"Finish\")\n if preface.match(str(link)):\n get_questions('http://stackoverflow.com' + link, collection)\n i += 1","title":""},{"docid":"1ef69bcd1594498c7904797579a252b3","score":"0.44202402","text":"def test_normal(self):\n\n urls = (\n \"http://www.example.net/\",\n \"http://www.example.net/?test\",\n \"http://www.example.net/test?test\",\n \"http://www.example.net/test?test=val\",\n \"http://www.example.net/test?uploaded=0\",\n \"http://www.example.net:6969/\",\n \"http://www.example.net:6969/?test\",\n \"http://www.example.net:6969/test?test\",\n \"http://www.example.net:6969/test?test=val\",\n \"http://www.example.net:6969/test?uploaded=0\"\n )\n\n for url in urls:\n self.assertEqual(cheatbt.cheat(url, self.mappings), url)","title":""},{"docid":"cf721263c518bfa1236ab56d1a7fe34b","score":"0.44188613","text":"def __init__(self, postlists_getter):\n self.rgx = re.compile('([&|!() ])')\n self.get_postlists = postlists_getter","title":""},{"docid":"ff58364e3c90d8ac6b6dfc23fff25757","score":"0.44174704","text":"def test_get_posts_successful(self):\n res = self.client.get(POSTS_URL)\n self.assertEqual(res.status_code, status.HTTP_200_OK)","title":""}],"string":"[\n {\n \"docid\": \"c743b4bc0e88dbd83d3d095a408f05c2\",\n \"score\": \"0.67279756\",\n \"text\": \"def test_scrap_for_posts(self):\\n \\n url = \\\"https://e-hentai.org/g/1087428/b240f1a9ab/\\\"\\n \\n response = self.ehen.scrap_for_posts(url, 1, 1, 1)\\n \\n pat = re.compile(r'https:\\\\/\\\\/e-hentai\\\\.org\\\\/s\\\\/[\\\\S]{10}\\\\/[\\\\d]+-[\\\\d]+')\\n for post in response:\\n self.assertTrue(pat.match(post))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a092dcf339b9edb8533d7b4b0896b07e\",\n \"score\": \"0.5860885\",\n \"text\": \"def get_post_urls(urls=None, page=\\\"http://python-history.blogspot.ca/\\\"):\\n if urls is None:\\n urls = []\\n response = requests.get(page)\\n soup = BeautifulSoup(response.content)\\n titles = soup.find_all(\\\"h3\\\", {\\\"class\\\": \\\"post-title entry-title\\\"})\\n urls.extend([title.a[\\\"href\\\"] for title in titles])\\n\\n try:\\n # Look for an `Older Posts` button\\n next_page = soup.find(\\\"a\\\", {\\\"class\\\": \\\"blog-pager-older-link\\\"})[\\\"href\\\"]\\n return get_post_urls(urls=urls, page=next_page)\\n except TypeError:\\n return urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0706388de3d7de1c796800b480fdb6b0\",\n \"score\": \"0.5610822\",\n \"text\": \"def process_posts(args):\\n # Unpack arguments\\n (reddit, post, blacklist, output_folder) = args\\n\\n scrape_submission(reddit, post, blacklist, output_folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fd2556ed004934f9b5bb79323cc4285\",\n \"score\": \"0.55120337\",\n \"text\": \"def test_valid_posts(self):\\n validate_post(\\\"Lorem ipsum dolor met sit amet elit.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c76ee16f146ecbfade7ab5241c884373\",\n \"score\": \"0.54791105\",\n \"text\": \"def test_scrap_for_posts_timed_out_or_not_found(self):\\n \\n url = \\\"invalid_url\\\"\\n \\n response = self.ehen.scrap_for_posts(url, 1, 1, 1)\\n \\n self.assertTrue(response == None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed9433c11f5f586bd2df1c16261effa3\",\n \"score\": \"0.5457406\",\n \"text\": \"def check_unhandled_posts(self):\\n handled_posts = self.handled_posts.keys()\\n unhandled_posts = self.candidates_backup.difference(handled_posts)\\n\\n for each in unhandled_posts:\\n self.unhandled_posts.add(\\n (extract_domain(each.url), '%s (%s)' %\\n (ensure_ascii(each.title, each.url))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebbd7a79d60529869d0a1a01bee1fbe6\",\n \"score\": \"0.5376213\",\n \"text\": \"def get_compensation_posts(url: str) -> List[Dict[str, str]]:\\n try:\\n browser.get(url)\\n time.sleep(5)\\n post_hrefs = _get_posts(browser)\\n if not post_hrefs:\\n time.sleep(10)\\n logger.info(f\\\"sleeping extra for {url}\\\")\\n post_hrefs = _get_posts(browser)\\n return post_hrefs\\n except Exception as e:\\n logger.error(f\\\"ERR: {e}\\\")\\n raise Exception(e)\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43ea82731f2a856a35fcbe4012c72280\",\n \"score\": \"0.53276074\",\n \"text\": \"def scrapePostIDs(html):\\n pattern = r'<!-- post #([0-9]*) -->'\\n return re.findall(pattern, html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"333380ad6ae8ff4adf41d4af239aaab7\",\n \"score\": \"0.5309135\",\n \"text\": \"def get_posts(\\n account: Optional[str] = None,\\n group: Union[str, int, None] = None,\\n post_urls: Optional[Iterator[str]] = None,\\n hashtag: Optional[str] = None,\\n credentials: Optional[Credentials] = None,\\n **kwargs,\\n) -> Iterator[Post]:\\n valid_args = sum(arg is not None for arg in (account, group, post_urls, hashtag))\\n\\n if valid_args != 1:\\n raise ValueError(\\\"You need to specify either account, group, or post_urls\\\")\\n\\n _scraper.requests_kwargs['timeout'] = kwargs.pop('timeout', DEFAULT_REQUESTS_TIMEOUT)\\n\\n cookies = kwargs.pop('cookies', None)\\n\\n if cookies is not None and credentials is not None:\\n raise ValueError(\\\"Can't use cookies and credentials arguments at the same time\\\")\\n set_cookies(cookies)\\n\\n options: Union[Dict[str, Any], Set[str]] = kwargs.setdefault('options', {})\\n if isinstance(options, set):\\n warnings.warn(\\\"The options argument should be a dictionary.\\\", stacklevel=2)\\n options = {k: True for k in options}\\n options.setdefault('account', account)\\n\\n # TODO: Add a better throttling mechanism\\n if 'sleep' in kwargs:\\n warnings.warn(\\n \\\"The sleep parameter has been removed, it won't have any effect.\\\", stacklevel=2\\n )\\n kwargs.pop('sleep')\\n\\n # TODO: Deprecate `pages` in favor of `page_limit` since it is less confusing\\n if 'pages' in kwargs:\\n kwargs['page_limit'] = kwargs.pop('pages')\\n\\n # TODO: Deprecate `extra_info` in favor of `options`\\n if \\\"reactions\\\" not in options:\\n options['reactions'] = kwargs.pop('extra_info', False)\\n options['youtube_dl'] = kwargs.pop('youtube_dl', False)\\n\\n if credentials is not None:\\n _scraper.login(*credentials)\\n\\n if account is not None:\\n return _scraper.get_posts(account, **kwargs)\\n\\n elif group is not None:\\n return _scraper.get_group_posts(group, **kwargs)\\n\\n elif hashtag is not None:\\n return _scraper.get_posts_by_hashtag(hashtag, **kwargs)\\n\\n elif post_urls is not None:\\n return _scraper.get_posts_by_url(post_urls, **kwargs)\\n\\n raise ValueError('No account nor group')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bbe127305033fc3fce79778614f02e8\",\n \"score\": \"0.52689415\",\n \"text\": \"def scrapePostPermalink(html, id = ''):\\n if id:\\n pattern = r'<a href=\\\"([^\\\"]*)(showpost\\\\.php\\\\?)[^\\\"]*(p=%s)[^\\\"]*\\\"[^>]*id=\\\"postcount.*>' % id\\n else:\\n pattern = r'<a href=\\\"([^\\\"]*)(showpost\\\\.php\\\\?)[^\\\"]*(p=[0-9]*)[^\\\"]*\\\"[^>]*id=\\\"postcount.*>'\\n m = re.search(pattern, html)\\n if m == None:\\n return ''\\n else:\\n return ''.join(m.groups())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c1d0388977bfdcc44355b3106907d5\",\n \"score\": \"0.52625024\",\n \"text\": \"def scrap_for_posts(self, url, wait, retry, wait_retry):\\n if url == None:\\n return None\\n \\n response = self.get_request(url, wait, retry, wait_retry, cookies=dict(nw=\\\"1\\\"))\\n \\n if response['response_code'] != 200:\\n return None\\n\\n soup = BeautifulSoup(response['payload'],\\\"html.parser\\\")\\n div = soup.find(id=\\\"gdt\\\")\\n tags = div.find_all(\\\"a\\\")\\n links = [a.get(\\\"href\\\") for a in tags]\\n return links\",\n \"title\": \"\"\n },\n {\n \"docid\": \"271e874e7ed4274adb936a3624325b28\",\n \"score\": \"0.52495766\",\n \"text\": \"def test_scrap_for_posts_none(self):\\n \\n url = None\\n \\n response = self.ehen.scrap_for_posts(url, 1, 1, 1)\\n \\n self.assertTrue(response == None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a9d84495d4f6334e8ec2387c6db57e0\",\n \"score\": \"0.5245485\",\n \"text\": \"def isPost(url):\\r\\n return url.find('/post/') != -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e3a2fa798f60c00e3f6c1aa13cac7f\",\n \"score\": \"0.5160177\",\n \"text\": \"def extract_news_url(soup_text: BeautifulSoup, url_pattern: str) -> list:\\n page_urls = []\\n exclude_pattern = \\\"https://arynews.tv/en/category\\\"\\n articles = soup_text.find_all('article')\\n for article in articles:\\n # get all links in article\\n for link in article.findAll(\\n 'a', attrs={'href': re.compile(url_pattern)}):\\n url = link.get('href')\\n if exclude_pattern not in url:\\n page_urls.append(url)\\n if len(page_urls) > 0:\\n unique_urls = list(set(page_urls))\\n else:\\n unique_urls = None\\n return unique_urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be48ff4c1bad6d4e82fc09a46670bd89\",\n \"score\": \"0.5155859\",\n \"text\": \"def test_parsing_posts_from_archive_page(\\n archive_parsing_results_mock: Dict[str, str],\\n) -> None:\\n assert len(archive_parsing_results_mock) == 782\\n assert (\\n \\\"/2009/04/12/episode-1-introduction\\\" in list(archive_parsing_results_mock)[-1]\\n )\\n # Intersection of mocked pages and all links\\n # intersection = set(mocked_urls) & set(all_links)\\n # assert len(intersection) > 15\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8938606167dd0dca628e6904699225bf\",\n \"score\": \"0.5103405\",\n \"text\": \"def get_post_urls(self, profile):\\n self.browser.get('https://www.instagram.com/' + profile)\\n self.browser.set_window_size(300, 1000)\\n post_urls = []\\n profile_end = False\\n nr_posts = 0\\n while len(post_urls) < self.max_posts and not profile_end:\\n # Get scroll height\\n last_height = self.browser.execute_script(\\n 'return document.body.scrollHeight')\\n # Get all URL's of all posts currently loaded\\n soup = BeautifulSoup(self.browser.page_source, features=\\\"lxml\\\")\\n all_as = soup.find_all('a', href=True)\\n for a in all_as:\\n if a['href'][:3] == '/p/' and a['href'] not in post_urls:\\n post_urls.append(a['href'])\\n nr_posts += 1\\n if nr_posts >= self.max_posts:\\n break\\n # Scroll down to bottom and wait to load page\\n self.browser.execute_script(\\n 'window.scrollTo(0, document.body.scrollHeight);')\\n time.sleep(self.timeout)\\n # Calculate new scroll height and compare with last scroll height\\n new_height = self.browser.execute_script(\\n 'return document.body.scrollHeight')\\n if new_height == last_height:\\n profile_end = True\\n else:\\n last_height = new_height\\n\\n return ['https://www.instagram.com' + a for a in post_urls]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39fcad496776efeb19c67c715f80164f\",\n \"score\": \"0.50660527\",\n \"text\": \"def test_my_posts_page_logged_in(self):\\n mf = add_forum('meme_forum')\\n\\n post_1 = add_post(mf, \\\"post1\\\", \\\"https://static.djangoproject.com/img/fundraising-heart.cd6bb84ffd33.svg\\\", self.user)\\n post_2 = add_post(mf, \\\"post2\\\", \\\"http://thewowstyle.com/wp-content/uploads/2015/01/nature-images.jpg\\\", self.user)\\n post_3 = add_post(mf, \\\"post3\\\", \\\"https://eskipaper.com/images/images-2.jpg\\\", self.user)\\n\\n request = self.request_factory.get(reverse('meme_portal:my_posts'))\\n request.user = self.user.user\\n\\n response = my_posts(request)\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertContains(response, 'Your Posts:')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0277d2347a74cab12025a0d5bce6631\",\n \"score\": \"0.50486696\",\n \"text\": \"def fetch_post(driver, post_url):\\n driver.get(post_url)\\n try:\\n email = driver.find_element_by_xpath(\\\"//section[@class='dateReplyBar']/a\\\").text\\n except NoSuchElementException:\\n email = ' '\\n try:\\n posting_title = driver.find_element_by_xpath(\\\"//h2[@class='postingtitle']\\\").text\\n except:\\n return\\n posting_body = driver.find_element_by_xpath(\\\"//section[@id='postingbody']\\\").text.replace('\\\\n','\\\\\\\\n')\\n timestamp = driver.find_element_by_xpath(\\\"//date\\\").get_attribute('title')\\n wanted = parse_wanted(posting_title)\\n if wanted:\\n return False\\n start, dest = parse_locs(posting_title, posting_body)\\n return email, timestamp, posting_title, post_url, posting_body, start, dest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58504db8d570d5a3abe005a45e13fff3\",\n \"score\": \"0.5024956\",\n \"text\": \"def get_urls(self, tag=None, max_urls=100, sleep_seconds=1):\\n assert sleep_seconds >= 1\\n urls = []\\n path = None\\n if tag is None or (tag is not None and max_urls > 0 and max_urls <= 100):\\n # use official JSON feeds\\n max_json_count = 100\\n if tag: # tag-specific JSON feed\\n path = \\\"/v2/json/%s?count=%d\\\" % (tag, max_json_count)\\n else:\\n # Delicious.com hotlist\\n path = \\\"/v2/json/?count=%d\\\" % (max_json_count)\\n data = self._query(path, host=\\\"feeds.delicious.com\\\")\\n if data:\\n posts = []\\n try:\\n posts = simplejson.loads(data)\\n except TypeError:\\n pass\\n\\n for post in posts:\\n # url\\n try:\\n url = post['u']\\n if url:\\n urls.append(url)\\n except KeyError:\\n pass\\n else:\\n # maximum number of urls/posts Delicious.com will display\\n # per page on its website\\n max_html_count = 20\\n max_html_pages = max_urls/max_html_count\\n\\n \\n path = \\\"/tag/%s\\\" %tag \\n page_index = 1\\n urls = []\\n while path and page_index <= max_html_pages+1:\\n data = self._query(path)\\n path = None\\n if data:\\n # extract urls from current page\\n tree = etree.HTML(data)\\n print \\\"parsing %s page\\\" %page_index\\n link_lists = tree.xpath(\\\"//div[@class='link None']\\\",smart_strings=False)\\n for link in link_lists:\\n url = link.xpath(\\\".//a[@class='title']/@href\\\",smart_strings=False)[0].split(\\\"url=\\\")[-1]\\n urls.append(url)\\n print 'length of urls is ', len(urls)\\n\\n paginations = tree.xpath(\\\"//div[@id='pagination']\\\",smart_strings=False)\\n\\n if paginations:\\n # find next path\\n nexts = tree.xpath(\\\"//div[@id='pagination']/span/following::a[1]/@href\\\",smart_strings=False)\\n if nexts and (max_urls == 0 or len(urls) < max_urls) and len(urls) > 0:\\n path = '/tag/%s' %tag + nexts[0]\\n print 'path is', path\\n page_index += 1\\n time.sleep(sleep_seconds)\\n if max_urls > 0:\\n return urls[:max_urls]\\n else:\\n return urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae45aa0bc081aff2bd0be6dd29d65e50\",\n \"score\": \"0.5023383\",\n \"text\": \"def valid_page_url(self, host_name: str, page_url: str) -> bool:\\n if page_url in self.banned_pages: return False\\n if not RobotsHelper.allow_to_visit(host_name, page_url): \\n self.banned_pages.add(page_url)\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc041a1094b4f930f3da0ccba4e07fec\",\n \"score\": \"0.5002853\",\n \"text\": \"def scrape(self, n, thread):\\n if(thread):\\n self.url += (thread+'/')\\n print(\\\"======= scraping stared at '{}' =======\\\".format(self.url))\\n next_base = self.url+'?after='\\n next_page = \\\"\\\"\\n total=0\\n for i in range(n):\\n # updates next page url\\n next_page = next_base + next_page\\n #print(next_page)\\n # fetch next page\\n content = self.requestHTML(next_page)\\n posts, next_page = self.getAllPosts(content)\\n for post in posts:\\n self.data.append(self.singlePostData(post))\\n total=total+1\\n print(\\\"-> Total {} posts scraped\\\".format(len(posts)))\\n print(\\\"------ page-{} Done ------\\\".format(i+1))\\n print(\\\"------posts scraped: {} ------\\\".format(total))\\n \\n return self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ee113474c2076f398ff9c233d59804a\",\n \"score\": \"0.49790725\",\n \"text\": \"def scrapePosts(html):\\n if not (type(html) == type([])):\\n s = html \\n html = []\\n html.append(s) \\n\\n post = {}\\n \\n for h in html:\\n # Locate posts in h \\n for id in scrapePostIDs(h):\\n post[id] = vbpost.Post(rawhtml = scrapePostHTML(id, h))\\n\\n return post\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c783b8cc2053189603e4c1ec9978550a\",\n \"score\": \"0.49770364\",\n \"text\": \"def parse_post(post: Dict[str, Any]) -> List[str]:\\n matches: List[str] = re.findall(PATTERN, post['Message'], re.I | re.M)\\n\\n return matches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b71906fabaa72c50a5671de9336fb01e\",\n \"score\": \"0.4975097\",\n \"text\": \"def test_page_url(self):\\n page = PageFactory(title='this? is& a! (test*)')\\n self.assertEqual(page.slug, '/this-is-a-test/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d636293ebd77b7069c0b6418372a2f43\",\n \"score\": \"0.49720755\",\n \"text\": \"def test_scrap_for_pages(self):\\n \\n given_url = \\\"https://e-hentai.org/g/1087428/b240f1a9ab/\\\"\\n \\n expected_urls = [\\\"https://e-hentai.org/g/1087428/b240f1a9ab/\\\",\\n \\\"https://e-hentai.org/g/1087428/b240f1a9ab/?p=1\\\",\\n \\\"https://e-hentai.org/g/1087428/b240f1a9ab/?p=2\\\",\\n \\\"https://e-hentai.org/g/1087428/b240f1a9ab/?p=3\\\",\\n \\\"https://e-hentai.org/g/1087428/b240f1a9ab/?p=4\\\",]\\n \\n url_lists = self.ehen.scrap_for_pages(given_url, 5, None)\\n \\n for i in range(len(expected_urls)):\\n self.assertEqual(url_lists[i], expected_urls[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cef57791533ff48be2dc97522dcdce60\",\n \"score\": \"0.49519563\",\n \"text\": \"def test_skip_if_url_match(self):\\n bot = AddTextBot(regex_skip_url='.*\\\\\\\\.com')\\n\\n page = _mock_page(url='wikipedia.org')\\n self.assertFalse(bot.skip_page(page))\\n self.assertEqual([], pywikibot.bot.ui.pop_output())\\n\\n page = _mock_page(url='wikipedia.com')\\n self.assertTrue(bot.skip_page(page))\\n self.assertEqual([\\n \\\"Skipping mock_page because -excepturl matches ['wikipedia.com'].\\\"\\n ], pywikibot.bot.ui.pop_output())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd11bad0d7197d4605aa2b2bf9878faa\",\n \"score\": \"0.49511975\",\n \"text\": \"def fetch_content_page(driver, url):\\n driver.get(url)\\n post_urls = [e.get_attribute('href') for e in driver.find_elements_by_xpath(\\\"//p[@class='row']/a\\\")]\\n return post_urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfb9d960cbed81bb4545f4a0284b3679\",\n \"score\": \"0.49477488\",\n \"text\": \"def isBlog(url):\\r\\n return url.find('.tumblr.com') + 12 >= len(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"894eae2f565dfbb3d4ead68130b0c825\",\n \"score\": \"0.49445185\",\n \"text\": \"def extract_posts(browser, num_of_posts_to_do, is_tag = False):\\n links = []\\n links2 = []\\n srcsetPreview_imgs = {}\\n sizesPreview_img = {}\\n altPreview_img = {}\\n srcPreview_img = {}\\n\\n errMsg = ''\\n\\n # list links contains 30 links from the current view, as that is the maximum Instagram is showing at one time\\n # list links2 contains all the links collected so far\\n # preview_imgs dictionary maps link in links2 to liknk's post's preview image src\\n try:\\n body_elem = browser.find_element_by_tag_name('body')\\n\\n # load_button = body_elem.find_element_by_xpath\\\\\\n # ('//a[contains(@class, \\\"_1cr2e _epyes\\\")]')\\n # body_elem.send_keys(Keys.END)\\n # sleep(3)\\n\\n # load_button.click()\\n\\n previouslen = 0\\n breaking = 0\\n\\n num_of_posts_to_scroll = 12 * math.ceil(num_of_posts_to_do / 12)\\n print(\\\"Getting first\\\", num_of_posts_to_scroll,\\n \\\"posts but check \\\", num_of_posts_to_do,\\n \\\" posts only, if you want to change this limit, change limit_amount value in crawl_profile.py\\\\n\\\")\\n while (len(links2) < num_of_posts_to_do):\\n links2 = []\\n if is_tag:\\n main = browser.find_element_by_tag_name('main')\\n article_children = main.find_elements_by_xpath('./article/*')\\n prev_divs = [article_children[2]]\\n else:\\n prev_divs = browser.find_elements_by_tag_name('main')\\n links_elems = [div.find_elements_by_tag_name('a') for div in prev_divs]\\n links = sum([[link_elem.get_attribute('href')\\n for link_elem in elems] for elems in links_elems], [])\\n for elems in links_elems:\\n for link_elem in elems:\\n href = link_elem.get_attribute('href')\\n try:\\n img = link_elem.find_element_by_tag_name('img')\\n except NoSuchElementException:\\n continue\\n srcsetPreview_imgs[href] = img.get_attribute('srcset')\\n sizesPreview_img[href] = img.get_attribute('sizes')\\n altPreview_img[href] = img.get_attribute('alt')\\n srcPreview_img[href] = img.get_attribute('src')\\n for link in links:\\n if \\\"/p/\\\" in link:\\n if (altPreview_img[link] != '' and len(links2) < num_of_posts_to_do):\\n links2.append(link)\\n links2 = list(set(links2)) #remove duplicate elems\\n print(\\\"Scrolling profile \\\", len(links2), \\\"/\\\", num_of_posts_to_scroll)\\n body_elem.send_keys(Keys.END)\\n sleep(Settings.sleep_time_between_post_scroll)\\n\\n ##remove bellow part to never break the scrolling script before reaching the num_of_posts\\n if (len(links2) == previouslen):\\n breaking += 1\\n print(\\\"breaking in \\\", 4 - breaking,\\n \\\"...\\\\nIf you believe this is only caused by slow internet, increase sleep time 'sleep_time_between_post_scroll' in settings.py\\\")\\n else:\\n breaking = 0\\n if breaking > 3:\\n print(\\\"\\\\nNot getting any more posts, ending scrolling.\\\")\\n sleep(2)\\n break\\n previouslen = len(links2)\\n ##\\n\\n except NoSuchElementException as err:\\n print('- Something went terribly wrong\\\\n')\\n\\n post_infos = []\\n\\n counter = 1\\n\\n for link in links2:\\n print(\\\"\\\\n\\\", counter, \\\"/\\\", len(links2))\\n \\n counter = counter + 1\\n\\n print(\\\"\\\\nScrapping link: \\\", link)\\n web_adress_navigator(browser, link)\\n try:\\n caption, alt, img, srcset, sizes = extract_post_info(browser)\\n\\n post_infos.append({\\n 'url': link,\\n 'caption': caption,\\n 'src': img,\\n 'srcset': srcset,\\n 'sizes': sizes,\\n 'alt': alt,\\n 'srcSmall': srcPreview_img[link],\\n 'srcsetSmall': srcsetPreview_imgs[link],\\n 'sizesSmall': sizesPreview_img[link],\\n 'altSmall': altPreview_img[link], \\n })\\n msg = ''\\n #if (caption == '' or caption == None): msg = msg + \\\"caption is null,\\\"\\n if (alt == '' or alt == None): msg = msg + \\\"alt is null,\\\"\\n if (img == '' or img == None): msg = msg + \\\"img is null,\\\"\\n if (srcset == '' or img == None): msg = msg + \\\"srcset is null,\\\"\\n if (msg != ''): errMsg = errMsg + ' - Link = ' + link + \\\" : \\\" + msg + '\\\\n'\\n print('msg = ' + msg)\\n except NoSuchElementException as err:\\n print('- Could not get information from post: ' + link)\\n print(err)\\n errMsg = errMsg + '- Could not get information from post: ' + link\\n\\n return post_infos, errMsg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f52a726bd80c0539e8b1e8683c83e4a\",\n \"score\": \"0.4883444\",\n \"text\": \"def testListPosts(self):\\r\\n\\r\\n response = self.client.get(reverse('list_posts'))\\r\\n\\r\\n self.assertEquals(response.status_code, 200)\\r\\n self.assertTemplateUsed(response, 'blog/list_posts.html')\\r\\n self.assertTrue(self.post in response.context['posts'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75a1c9fd99636228ff634f9bac0e5621\",\n \"score\": \"0.48579165\",\n \"text\": \"def test_show_posts_page_dosent_exist(self):\\n mf = add_forum('meme_forum')\\n response = self.client.get(reverse('meme_portal:show_post', kwargs={'forum_name_slug':'meme_forum', 'post_name_slug':'nothign'}))\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertContains(response, 'This post does not exist.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ffd8b1b88db00dbd0879d17d43332d4\",\n \"score\": \"0.48451796\",\n \"text\": \"def test_invalid_slug(self):\\n response = self.http.get('/blog/uh~oh~$$$~invalid')\\n html = response_html(response)\\n\\n assert response.status_code == 400\\n assert 'error' in html.select_one('#page-content h1').text\\n assert '400' in html.select_one('#page-content').text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c4daac00e64799c31eab3b1e6b69e42\",\n \"score\": \"0.48321187\",\n \"text\": \"def check(self, post: dict):\\n try:\\n if post['score'] < 2:\\n logger.debug(f\\\"Score filtered: {post['name']}\\\")\\n return False\\n\\n # Check gif\\n is_gif = False\\n if 'gif' in post['url']:\\n is_gif = True\\n if 'mp4' in post['url']:\\n is_gif = True\\n\\n # Delete emojis\\n filtered = demoji.replace(post['title'], \\\"\\\")\\n\\n # Check if string with delete emojis is shorter\\n if len(filtered) < len(post['title']) or \\\\\\n ':P' in post['title'] or \\\\\\n ';)' in post['title'] or \\\\\\n ':)' in post['title'] or \\\\\\n '(;' in post['title']:\\n\\n logger.debug(f\\\"Emoji filtered: {post['name']}\\\")\\n return False\\n\\n if 'OC' in post['title'] or \\\\\\n '(oc)' in post['title'] or \\\\\\n '[oc]' in post['title'] or \\\\\\n 'my ' in post['title'] or \\\\\\n 'My ' in post['title'] or \\\\\\n '?' in post['title'] or \\\\\\n 'Self' in post['title'] or \\\\\\n 'self' in post['title'] or \\\\\\n 'f)' in post['title'] or \\\\\\n 'F)' in post['title'] or \\\\\\n 'f]' in post['title'] or \\\\\\n 'F]' in post['title'] or \\\\\\n 'm)' in post['title'] or \\\\\\n 'M)' in post['title'] or \\\\\\n 'm]' in post['title'] or \\\\\\n 'M]' in post['title'] or \\\\\\n 'I ' in post['title']:\\n\\n logger.debug(f\\\"Post title filtered: {post['name']}\\\")\\n return False\\n\\n if post['is_self']:\\n logger.debug(f\\\"Self filtered: {post['name']}\\\")\\n return False\\n\\n if post['is_original_content']:\\n logger.debug(f\\\"OC filtered: {post['name']}\\\")\\n return False\\n\\n if 'reddit.com' in post['url']:\\n # TODO browse to post\\n logger.debug(f\\\"X-post filtered: {post['name']}\\\")\\n return False\\n\\n author_id = post['author_fullname']\\n # TODO check author, probly not smart with rate limiter\\n\\n # Check for non utf8 characters\\n post_title_utf8 = post['title'].encode('utf8', 'ignore').decode('utf8')\\n if len(post_title_utf8) < len(post['title']):\\n logger.debug(f\\\"Warning post {post['name']} has non utf8 characters\\\")\\n\\n return True\\n\\n except:\\n logger.exception(f\\\"Post failed: {post['name']}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37ee1bc0ab58a87a89700eec6651cbc1\",\n \"score\": \"0.4814051\",\n \"text\": \"def test_posts_fr(self):\\n t = ThreadFactory()\\n\\n response = get(self.client, \\\"forums.posts\\\", args=[t.forum.slug, t.id], locale=\\\"fr\\\")\\n self.assertEqual(200, response.status_code)\\n self.assertEqual(\\n \\\"{c}/fr/forums/{f}/{t}\\\".format(c=settings.CANONICAL_URL, f=t.forum.slug, t=t.id),\\n pq(response.content)('link[rel=\\\"canonical\\\"]')[0].attrib[\\\"href\\\"],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b2a3add05166825e473eab2c343ed71\",\n \"score\": \"0.48094767\",\n \"text\": \"def __iteratePosts(self):\\n try:\\n posts = self.soup.findAll('li', id=re.compile ('^post_\\\\d+'))\\n if not posts:\\n log.info(self.log_msg('No posts found'))\\n return False\\n log.debug(self.log_msg('Total No of Posts found is %d'%len(posts)))\\n posts.reverse()\\n for post in posts:\\n if not self.__addPost(post):\\n log.info(self.log_msg('Post not added to self.pages for url\\\\\\n %s'%self.currenturi))\\n return False\\n return True\\n except:\\n log.exception(self.log_msg('Reviews are not found for url %s'%self.currenturi))\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95a6ce6ae457489b100ffed9fb0f6f44\",\n \"score\": \"0.480891\",\n \"text\": \"def crawl(self, num_urls, min_page, store=False):\\n urls = []\\n # Enter WaPo search\\n self.driver.get('http://www.washingtonpost.com')\\n self.driver.set_window_size(1920, 1080)\\n self.driver.find_element_by_id('search-btn').click()\\n self.driver.find_element_by_id('search-field').send_keys('the or to or of or a', Keys.ENTER)\\n\\n # Order search results by date\\n dropdown = self.driver.find_element_by_class_name('pb-filter-sort')\\n dropdown.click()\\n dropdown.find_elements_by_tag_name('li')[1].click()\\n self.driver.find_elements_by_id('updateResults')[0].click()\\n\\n page = 1\\n count = 0\\n while count < num_urls:\\n # Get post-JavaScript\\n html = self.driver.execute_script(\\\"return document.documentElement.innerHTML;\\\")\\n soup = BeautifulSoup(html, 'lxml')\\n news_items = soup.find_all('div', class_='pb-feed-item ng-scope')\\n\\n # Store news urls\\n for i in range(len(news_items)):\\n if count < num_urls and page > min_page:\\n urls.append('http://' + news_items[i]['data-sid'])\\n count += 1\\n\\n # Store continously while getting urls if desired\\n if store:\\n orig_headlines, orig_labels = self.get_labels(urls)\\n if orig_labels:\\n suffix = list(range(count - len(orig_labels), count))\\n self.format_store(orig_headlines, orig_labels, 'data/orig/', 'O', suffix)\\n\\n # Sanitized data\\n headlines, labels, appears = self.sanitize_labels(orig_headlines, orig_labels)\\n if len(labels) > 1:\\n suffix = [suffix[i] for i in range(len(orig_labels)) if appears[i]]\\n self.format_store(headlines, labels, 'data/', 'W', suffix)\\n elif len(labels) == 1:\\n suffix = [suffix[i] for i in range(len(orig_labels)) if appears[i]]\\n self.format_store(headlines, labels, 'data/', 'W1', suffix) \\n urls = []\\n\\n # Advance to next search page\\n self.driver.find_element_by_css_selector('.page-link.next').click()\\n page += 1\\n\\n if store:\\n return None\\n else:\\n return urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5551f938ae88ffb6e399bf406be5d92\",\n \"score\": \"0.4806705\",\n \"text\": \"def test_process_blog_posts_only(self):\\n majestic.process_blog(\\n settings=self.settings, posts=True,\\n pages=False, index=False, archives=False,\\n feeds=False, sitemap=False, extensions=False)\\n os.chdir(str(self.outputdir))\\n for dirpath, dirnames, filenames in os.walk('.'):\\n self.assertTrue(dirpath in self.expected)\\n self.assertEqual(\\n set(self.expected[dirpath]['posts']),\\n set(f for f in filenames if not f.startswith('.')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"468b34696ad16c809381ada7f55e7662\",\n \"score\": \"0.4806429\",\n \"text\": \"def scrapePostHTML(id, html):\\n pattern = '<!-- post #%s -->' % id\\n start = html.find(pattern)\\n pattern = '<!-- / post #%s -->' % id\\n end = html.find(pattern) + len(pattern)\\n return html[start:end]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3607705245854301461b63eea4577671\",\n \"score\": \"0.48046437\",\n \"text\": \"def parse_post(html_string):\\n root = lxml.html.fromstring(html_string)\\n post_url = root.xpath(\\\"//link[@rel='canonical']/@href\\\")\\n price = root.xpath(\\\"//span[@class='price']/text()\\\")\\n title = root.xpath(\\\"//span[@id='titletextonly']/text()\\\")\\n location = root.xpath(\\\"//small/text()\\\")\\n bedrooms = root.xpath(\\\"(//p[@class='attrgroup'])[1]/span[starts-with(text(),'BR')]/b/text()\\\")\\n bathrooms = root.xpath(\\\"(//p[@class='attrgroup'])[1]/span[text()='Ba']/b/text()\\\")\\n sqft = root.xpath(\\\"(//p[@class='attrgroup'])[1]/span[text()='ft']/b/text()\\\")\\n available_date = root.xpath(\\\"//span[@class='housing_movein_now property_date']/@date\\\")\\n photos = root.xpath(\\\"//div[@id='thumbs']//a/@href\\\")\\n extra_info = root.xpath(\\\"(//p[@class='attrgroup'])[2]//span/text()\\\")\\n body = root.xpath(\\\"//section[@id='postingbody']/text()\\\")\\n timestamps = root.xpath(\\\"//time/@datetime\\\")\\n map_link = root.xpath(\\\"//a[text()='google map']/@href\\\")\\n return Post(post_url[0],\\n price[0],\\n title[0],\\n location[0][2:-1] if location else None,\\n photos,\\n bedrooms[0] if bedrooms else None,\\n bathrooms[0] if bathrooms else None,\\n sqft[0] if sqft else None,\\n available_date[0] if available_date else None,\\n extra_info,\\n body[0].strip(),\\n map_link[0] if map_link else None,\\n timestamps[-1] if timestamps else None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"590ad9b452b65926c0d6e9041ce60e55\",\n \"score\": \"0.47993076\",\n \"text\": \"def test_get_post(\\n client_id: Optional[str] = None,\\n client_secret: Optional[str] = None,\\n url: Optional[str] = None,\\n) -> pd.DataFrame:\\n reddit = get_reddit_api()\\n if url is None:\\n url = input(\\\"url: \\\")\\n return get_posts(url=url, reddit=reddit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be3ca32076a5ca7f009e96ef960890c8\",\n \"score\": \"0.47926477\",\n \"text\": \"def get_posts_from_main_page(page: str) -> dict:\\n posts = {}\\n\\n soup = BeautifulSoup(page, \\\"html5lib\\\")\\n trs = soup.select(\\\"table.itemlist tr.athing\\\")\\n for ind, tr in enumerate(trs):\\n p_id, p_url = '', ''\\n try:\\n p_id = int(tr.attrs[\\\"id\\\"])\\n p_url = tr.select_one(\\\"td.title a.storylink\\\").attrs[\\\"href\\\"]\\n posts[p_id] = p_url\\n except (KeyError, ValueError):\\n log.error(\\\"Error on {} post (id: {}, url: {})\\\".format(\\n ind, p_id, p_url\\n ))\\n\\n return posts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f06e7e50f66cb7a4935f1e6a9cb2b7a\",\n \"score\": \"0.47855508\",\n \"text\": \"def scrapeUserNumPosts(html):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147417cd23746d675f49dc32caebcaaf\",\n \"score\": \"0.4776685\",\n \"text\": \"def get_posts(self, page_ext: str):\\n try:\\n url = \\\"{}/{}/\\\".format(BASE_URL, page_ext)\\n self.driver.get(url)\\n self.scroll()\\n # ignores the sign-in panel\\n try:\\n self.driver.find_element_by_class_name('_62up').click()\\n except:\\n pass\\n _time.sleep(2)\\n\\n soup = BeautifulSoup(self.driver.page_source, 'html.parser')\\n # get publication elements\\n publication_elem = soup.find_all('div', {'class': '_5pcr userContentWrapper'})\\n\\n data_collector = dict()\\n data_collector[page_ext] = list()\\n\\n for elem in publication_elem:\\n date = self.get_date(elem)\\n post_url = BASE_URL + self.get_link(elem)\\n likes = self.get_likes(elem)\\n comments = self.get_comments(elem)\\n shares = self.get_shares(elem)\\n post_id = FacebookScraper().make_url2id(post_url),\\n\\n data = {\\n \\\"id\\\": post_id[0],\\n \\\"page\\\": page_ext,\\n \\\"post_url\\\": post_url,\\n \\\"shared_at\\\": date,\\n \\\"likes\\\": likes.text if likes is not None else '0',\\n \\\"comments\\\": comments.text.replace('commentaires', '') if comments is not None else '0',\\n \\\"shares\\\": shares.text.replace('partages', '') if shares is not None else '0',\\n \\\"created_at\\\": datetime.now().strftime(\\\"%d/%m/%Y %H:%M:%S\\\")\\n }\\n data_collector[page_ext].append(data)\\n _logging.info(f\\\"Collected {len(data_collector[page_ext])} new publication\\\")\\n finally:\\n self.driver.close()\\n return data_collector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9128f3f2c9eb254425d8014fb076216\",\n \"score\": \"0.4763898\",\n \"text\": \"def find_posts(reddit):\\n subreddit = reddit.subreddit(\\\"ACVillager\\\") # can be changed to any subreddit\\n try:\\n while True:\\n for submission in subreddit.stream.submissions():\\n for key in KEY_PHRASES:\\n if key.lower() in submission.title.lower() and submission.created_utc > start_time:\\n reddit.redditor(\\\"missingclubpenguin\\\").message(SUBJECT, submission.url) # sends user the message\\n break\\n except Exception as ex:\\n print(ex)\\n time.sleep(60)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11d9f2f9e7c33153365c71d542f65943\",\n \"score\": \"0.47500277\",\n \"text\": \"def _get_posts(browser: webdriver.Chrome) -> List[Dict[str, str]]:\\n post_hrefs = []\\n for e in browser.find_elements_by_class_name(\\\"topic-title-wrapper__27Nt\\\"):\\n for e_url, e_time in zip(\\n e.find_elements_by_class_name(\\\"title-link__1ay5\\\"),\\n e.find_elements_by_class_name(\\\"topic-info__tdz0\\\"),\\n ):\\n post_hrefs.append(\\n {\\n \\\"post_id\\\": e_url.get_attribute(\\\"href\\\").split(\\\"/\\\")[-2],\\n \\\"text\\\": e_url.text,\\n \\\"href\\\": e_url.get_attribute(\\\"href\\\"),\\n \\\"time\\\": e_time.text,\\n }\\n )\\n return post_hrefs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f63d552492077babe8e8b9fea05ea56\",\n \"score\": \"0.4749613\",\n \"text\": \"def shouldSkipUrl(self, url, data):\\n return data.xpath('//div[@id=\\\"post\\\"]//iframe')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7972653179720426265cb22cc4cfb1f5\",\n \"score\": \"0.47428045\",\n \"text\": \"def testPost(self):\\n # Test posting an empty post.\\n posts = Post.objects.filter(floor=self.floor)\\n count = posts.count()\\n response = self.client.post(reverse(\\\"news_post\\\"), {\\\"post\\\": \\\"\\\"},\\n HTTP_X_REQUESTED_WITH='XMLHttpRequest')\\n self.assertContains(response, \\\"This should not be blank.\\\")\\n self.assertEqual(count, posts.count(), \\\"Check that the number of posts did not change.\\\")\\n \\n post = \\\"Test post via AJAX\\\"\\n response = self.client.post(reverse(\\\"news_post\\\"), {\\\"post\\\": post}, \\n HTTP_X_REQUESTED_WITH='XMLHttpRequest')\\n self.failUnlessEqual(response.status_code, 200)\\n self.assertContains(response, \\\"Test post via AJAX\\\")\\n \\n # Check that the new post is in the news feed.\\n response = self.client.get(reverse(\\\"news_index\\\"))\\n self.assertContains(response, post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fb3bf721f106c1641af7ef1373b1b66\",\n \"score\": \"0.4715013\",\n \"text\": \"def test_parsing_invalid_html(\\n requests_mock: rm_Mocker,\\n archive: Archive,\\n) -> None:\\n markup: str = '<a class=\\\"entry\\\" id=\\\"post\\\">'\\n requests_mock.get(conf.ARCHIVE_URL, text=markup)\\n with pytest.raises(NotEpisodeURLError) as ex:\\n archive.parser.parse_url()\\n assert conf.ARCHIVE_URL in ex.value.args[0] # Trailing slash for domain URL\\n assert (\\n ex.value.args[1] == \\\"ERROR: Can't parse this page: 'article' tag was not found.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4df34acd00523df4f3956550437b12e9\",\n \"score\": \"0.47132322\",\n \"text\": \"def test_no_posts(self):\\n response = self.client.get(reverse('post_list'))\\n self.assertEqual(response.status_code, 200)\\n self.assertContains(response, \\\"No posts here yet...\\\")\\n self.assertQuerysetEqual(response.context['posts'], [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e5d8bf1cb5211556e192384726fcb7c\",\n \"score\": \"0.47102448\",\n \"text\": \"def getAllPosts(self, content):\\n block = re.compile(\\\"[\\\\d\\\\w]{23} [-\\\\d\\\\w]{23} scrollerItem [-\\\\d\\\\w]{23} [-\\\\d\\\\w]{23} Post [\\\\d\\\\w]{9}\\\")\\n posts = content.find_all('div', class_=block)\\n return posts, posts[-1]['id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3858b9890598c82518dbba4ca56cb713\",\n \"score\": \"0.47062775\",\n \"text\": \"def parse_posts(self, response):\\n \\n # approximate retrieval time\\n retrieved = str(datetime.datetime.now())\\n \\n # parse the articles posts and \\n # TODO: yield Item\\n headers = response.xpath(\\\"/html/body/div[@class='body']/table[@summary='posts']//tr/td[@class='bold l pu']\\\")\\n bodys = response.xpath(\\\"/html/body/div[@class='body']/table[@summary='posts']//tr/td[@class='l w pd']\\\")\\n \\n for h in range(len(headers)):\\n \\n header = headers[h]\\n body = bodys[h]\\n \\n post = {}\\n #post['url'] = response.url\\n #post['title'] = response.meta['title']\\n post['article_id'] = response.meta['article_id']\\n post['forum'] = response.meta['forum']\\n post['_id'] = header.xpath(\\\"a[1]/@name\\\").extract_first()\\n post['user'] = header.xpath(\\\"a[@class='user']/text()\\\").extract_first()\\n post['posted'] = \\\", \\\".join(header.xpath(\\\"span[@class='s']/b/text()\\\").extract()) \\n post['retrieved'] = retrieved\\n post['page_no'] = response.meta['page_number']\\n \\n post['links'] = len(body.xpath(\\\"div[@class='narrow']/a/@href\\\").extract())\\n\\n post['likes'] = 0\\n likes = body.xpath(\\\"p[@class='s']/b[1]/text()\\\").extract_first(default='').strip()\\n if likes:\\n post['likes'] = int(likes.split()[0])\\n \\n post['shares'] = 0\\n shares = body.xpath(\\\"p[@class='s']/b[2]/text()\\\").extract_first(default='').strip()\\n if shares:\\n post['shares'] = int(shares.split()[0])\\n \\n post['images'] = len(body.xpath(\\\"p/img\\\"))\\n post['quote'] = len(body.xpath(\\\"div[@class='narrow']/blockquote\\\")) > 0\\n \\n yield post\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c2fba1cd3004504dc90c7a38fb7ef7\",\n \"score\": \"0.47019848\",\n \"text\": \"def test_no_posts(self):\\n response = self.client.get(reverse(\\\"posts\\\"))\\n self.assertEqual(response.status_code, 200)\\n # self.assertContains(response, \\\"No polls are available.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46d658111741d006f42f86066263c778\",\n \"score\": \"0.4700786\",\n \"text\": \"def scrapePostID(html):\\n # Try first looking in the comments\\n pattern = []\\n pattern.append(r'<!-- post #([0-9]*) -->')\\n pattern.append(r'p=([0-9]*)')\\n p = 0\\n m = None\\n while (m == None) and (p < len(pattern)):\\n m = re.search(pattern[p], html)\\n p += 1\\n if m == None:\\n return -1\\n else:\\n return m.group(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"706e807cec9c1b1ab191d7ac8cb4e578\",\n \"score\": \"0.46994996\",\n \"text\": \"def get_posts(url: str, reddit: Any = \\\"praw.Reddit\\\") -> pd.DataFrame:\\n submission = get_page(url=url, reddit=reddit)\\n\\n posts = []\\n for top_level_comment in submission.comments[1:]:\\n if isinstance(top_level_comment, MoreComments):\\n continue\\n posts.append(top_level_comment.body)\\n\\n posts = pd.DataFrame(posts, columns=[\\\"body\\\"])\\n\\n # get rid of removed and deleted comments\\n indexNames = posts[(posts.body == \\\"[removed]\\\") | (posts.body == \\\"[deleted]\\\")].index\\n posts.drop(indexNames, inplace=True)\\n\\n return posts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7938c80a1af052968473354db9dde1c0\",\n \"score\": \"0.46782392\",\n \"text\": \"def analyzeSite(self, db, site, usrName):\\r\\n # initialize the db during the first call\\r\\n if not db.has_key('post'):\\r\\n db['post'] = {}\\r\\n\\r\\n\\r\\n # extract the links that we want to follow some day\\r\\n linkPattern = re.compile(r'\\\\bhttp://'+usrName+'\\\\.ycool\\\\.com/post\\\\.(\\\\d+)\\\\.html$', re.I)\\r\\n archivePattern = re.compile(r'\\\\bhttp://'+usrName+'\\\\.ycool\\\\.com/archive(.p\\\\d+)?\\\\.html', re.I)\\r\\n commentPattern = re.compile(r'\\\\bhttp://'+usrName+'\\\\.ycool\\\\.com/followups\\\\.(\\\\d+)\\\\.html$', re.I)\\r\\n\\r\\n # mark the site to avoid crawling it in the future\\r\\n db['pages'][site.stringUrl] = site\\r\\n db['crawled'][site.stringUrl] = True\\r\\n #print(\\\"site.stringUrl\\\"+site.stringUrl)\\r\\n #print(db['crawled'])\\r\\n\\r\\n if archivePattern.search(site.stringUrl):\\r\\n for link in site.parsedContent.iterlinks():\\r\\n #print(\\\"link:\\\",link)\\r\\n if (not db['crawled'].has_key(link[2])):\\r\\n #db['crawled'][link[2]]=True\\r\\n if linkPattern.search(link[2]):\\r\\n #print('------1---------')\\r\\n #print('content link: '+link[2])\\r\\n self.__newOtherLinksToCrawl+=[link[2]]\\r\\n self.__newOtherLinksToCrawl+=[self.__getCommentLink(link[2])]\\r\\n elif archivePattern.search(link[2]):\\r\\n #print('------1---------')\\r\\n #print('archive link: '+link[2])\\r\\n self.__newArchiveLinksToCrawl+= [link[2]]\\r\\n #print('------5 links before remove dup---------')\\r\\n #print(self.__newOtherLinksToCrawl)\\r\\n self.__newOtherLinksToCrawl=self.__removeDup(db,self.__newOtherLinksToCrawl)\\r\\n #print('------5 links---------')\\r\\n #print(self.__newOtherLinksToCrawl)\\r\\n\\r\\n self.__newArchiveLinksToCrawl=self.__removeDup(db,self.__newArchiveLinksToCrawl)\\r\\n #print('------5 archives---------')\\r\\n #print(self.__newArchiveLinksToCrawl)\\r\\n # put the rawContent to db if it is a post\\r\\n if linkPattern.search(site.stringUrl):\\r\\n #print(site.stringUrl)\\r\\n entry =linkPattern.search(site.stringUrl).group(1)\\r\\n if not db['post'].has_key(entry):\\r\\n db['post'][entry] = {}\\r\\n if not db['post'][entry].has_key('postContent'):\\r\\n db['post'][entry]['postContent'] = {}\\r\\n leftFrame = self.__getLeftFrame(site.parsedContent)\\r\\n db['post'][entry]['postContent']['title'] = self.__getTitle(leftFrame)\\r\\n db['post'][entry]['postContent']['content'] = self.__getContent(leftFrame)\\r\\n db['post'][entry]['postContent']['creator'] = self.__getCreator(leftFrame)\\r\\n db['post'][entry]['postContent']['pubDate'] = self.__getpubDate(leftFrame)\\r\\n db['post'][entry]['postContent']['tags'] = self.__getTags(leftFrame)\\r\\n db['post'][entry]['postContent']['cata'] = self.__getCata(leftFrame)\\r\\n\\r\\n\\r\\n #db['post'][linkPattern.search(site.stringUrl).group(1)]['postContent'] = self.__getLeftFrame(site.parsedContent)\\r\\n #print(site.stringUrl)\\r\\n\\r\\n if commentPattern.search(site.stringUrl):\\r\\n #print('----1----',site.stringUrl )\\r\\n entry =commentPattern.search(site.stringUrl).group(1)\\r\\n if not db['post'].has_key(entry):\\r\\n db['post'][entry] = {}\\r\\n if not db['post'][entry].has_key('postComment'):\\r\\n db['post'][entry]['postComment'] = {}\\r\\n\\r\\n commentList = self.__getCommentList(self.__getLeftFrame(site.parsedContent))\\r\\n for e in commentList :\\r\\n self.__commentId+=1\\r\\n id = str(self.__commentId)\\r\\n\\r\\n replyEl = self.__getMyReply(e)\\r\\n el = self.__getCommentInfo(e)\\r\\n ip =self.__getCommentIp(el)[0]\\r\\n date = self.__getCommentDate(el)[0]\\r\\n if not db['post'][entry]['postComment'].has_key(id):\\r\\n db['post'][entry]['postComment'][id]={}\\r\\n print('--------a comment-----------')\\r\\n db['post'][entry]['postComment'][id]['author']= strip(e.getparent().getparent()[0].text_content())\\r\\n db['post'][entry]['postComment'][id]['ip']= ip\\r\\n db['post'][entry]['postComment'][id]['date']= date+u':00'\\r\\n db['post'][entry]['postComment'][id]['content']=tostring(e[0], encoding=unicode,method='text',pretty_print=True)\\r\\n #db['post'][entry]['postComment'][id]['content']=e[0].text\\r\\n db['post'][entry]['postComment'][id]['parent']=0\\r\\n db['post'][entry]['postComment'][id]['email']='x@y.com'\\r\\n print(\\\"conent %s date %s id %s \\\" % \\\\\\r\\n (db['post'][entry]['postComment'][id]['content'].encode('utf-8'),\\\\\\r\\n date.encode('utf-8'),id))\\r\\n\\r\\n if len(replyEl)>0:\\r\\n self.__commentId+=1\\r\\n childkey=str(self.__commentId)\\r\\n if not db['post'][entry]['postComment'].has_key(childkey):\\r\\n db['post'][entry]['postComment'][childkey]={}\\r\\n print('--------a reply-----------')\\r\\n print('reply id %s' % childkey )\\r\\n db['post'][entry]['postComment'][childkey]['author']=self.__userName\\r\\n db['post'][entry]['postComment'][childkey]['ip']='127.0.0.1'\\r\\n db['post'][entry]['postComment'][childkey]['date']=date+u':03'\\r\\n db['post'][entry]['postComment'][childkey]['email']='x@y.com'\\r\\n db['post'][entry]['postComment'][childkey]['content']=tostring(replyEl[0], method='text',encoding=unicode,pretty_print=True)\\r\\n db['post'][entry]['postComment'][childkey]['parent']=self.__commentId-1\\r\\n print(\\\"conent %s date %s parent %s\\\" % \\\\\\r\\n (db['post'][entry]['postComment'][childkey]['content'].encode('utf-8'), \\\\\\r\\n date.encode('utf-8'), \\\\\\r\\n db['post'][entry]['postComment'][childkey]['parent']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0527a5c87e745c717dfe74c4a4297080\",\n \"score\": \"0.467732\",\n \"text\": \"def test_double_post_bug(self):\\n feed_str = get_data_file(\\\"lefigaro.rss\\\")\\n imported_feed = self.importer.import_feed(feed_str, local=True,\\n force=True)\\n post_count = imported_feed.post_set.count()\\n imported_feed = self.importer.import_feed(feed_str, local=True,\\n force=True)\\n self.assertEqual(imported_feed.post_set.count(), post_count,\\n \\\"Posts seems to be imported twice.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"608943f7f9c95108a302ef688e1dfa05\",\n \"score\": \"0.4667173\",\n \"text\": \"def test_urls(self):\\n text = txt.importer('test_files/urls.txt')\\n expected = [[\\\"website\\\", \\\"http://foo.com/blah_blah,\\\", \\\"fun\\\"],\\n [\\\"click\\\", \\\"http://foo.com/blah_blah/-\\\", \\\"awesome\\\"],\\n [\\\"visit\\\", \\\"http://foo.com/blah_blah_(wikipedia)\\\"],\\n [\\\"http://foo.com/blah_blah_(wikipedia)_(again)\\\"],\\n [\\\"http://www.example.com/wpstyle/?p=364\\\"],\\n [\\\"https://www.example.com/foo/?bar=baz&inga=42&quux\\\"],\\n [\\\"http://userid:password@example.com:8080\\\"],\\n [\\\"http://userid:password@example.com:8080/\\\"],\\n [\\\"http://userid@example.com\\\"],\\n [\\\"http://userid@example.com/\\\"],\\n [\\\"http://userid@example.com:8080\\\"],\\n [\\\"http://userid@example.com:8080/\\\"],\\n [\\\"http://userid:password@example.com\\\"],\\n [\\\"http://userid:password@example.com/\\\"],\\n [\\\"http://142.42.1.1/\\\"],\\n [\\\"http://142.42.1.1:8080/\\\"],\\n [\\\"http://foo.com/blah_(wikipedia)#cite-1\\\"],\\n [\\\"http://foo.com/blah_(wikipedia)_blah#cite-1\\\"],\\n [\\\"http://foo.com/(something)?after=parens\\\"],\\n [\\\"http://code.google.com/events/#&product=browser\\\"],\\n [\\\"http://j.mp\\\"],\\n [\\\"ftp://foo.bar/baz\\\"],\\n [\\\"http://foo.bar/?q=test%20url-encoded%20stuff\\\"],\\n [\\\"http://-.~_!$&'()*+,;=:%40:80%2f::::::@example.com\\\"],\\n [\\\"http://1337.net\\\"],\\n [\\\"http://a.b-c.de\\\"],\\n [\\\"http://223.255.255.254\\\"],\\n [\\\"https://foo_bar.example.com/\\\"]]\\n actual = tokenize(text)\\n self.assertEqual(expected, actual)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e56577b4ba4e7bf4c6c1fa47bcb54329\",\n \"score\": \"0.46573964\",\n \"text\": \"def test_templates_category_detail_cms_draft_content_blogposts(self):\\n self._extension_cms_draft_content(BlogPostFactory, \\\"h3.blogpost-glimpse__title\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09dddc343decfe0e3b03ea5fcac60fb9\",\n \"score\": \"0.46514475\",\n \"text\": \"def check_post_content(self, field, material):\\n for alias in material.aliases.all():\\n self.assertIn(\\n alias.name, field, \\\"material alias is not mentioned in post content\\\"\\n )\\n self.assertTrue(\\n bool(BeautifulSoup(field, \\\"html.parser\\\").find()), \\\"invalid HTML\\\"\\n )\\n self.assertIn(\\n material.description, field, \\\"description is not part of post content\\\"\\n )\\n soup = BeautifulSoup(field, \\\"html.parser\\\")\\n attachment_anchors = soup.find_all(\\\"a\\\", {\\\"target\\\": \\\"attachment\\\"})\\n attachment_urls = [anchor[\\\"href\\\"] for anchor in attachment_anchors]\\n self.assertEqual(len(attachment_anchors), material.attachments.count())\\n for material_attachment in material.attachments.all():\\n self.assertIn(\\n f\\\"http://testserver{material_attachment.attachment.url}\\\",\\n attachment_urls,\\n )\\n if material.lendable_stock != \\\"\\\":\\n self.assertIn(material.lendable_stock, field)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef7df0db4e2cb76afbefaa93fe9920e\",\n \"score\": \"0.46364135\",\n \"text\": \"def test_post_contents(self):\\n # Load\\n topic = Topic(self.site, 'Topic:Sh6wgo5tu3qui1w2')\\n post = Post(topic, 'sh6wgoagna97q0ia')\\n # Wikitext\\n wikitext = post.get(content_format='wikitext')\\n self.assertIn('wikitext', post._content)\\n self.assertNotIn('html', post._content)\\n self.assertIsInstance(wikitext, str)\\n self.assertNotEqual(wikitext, '')\\n # HTML\\n html = post.get(content_format='html')\\n self.assertIn('html', post._content)\\n self.assertIn('wikitext', post._content)\\n self.assertIsInstance(html, str)\\n self.assertNotEqual(html, '')\\n # Caching (hit)\\n post._content['html'] = 'something'\\n html = post.get(content_format='html')\\n self.assertIsInstance(html, str)\\n self.assertEqual(html, 'something')\\n self.assertIn('html', post._content)\\n # Caching (reload)\\n post._content['html'] = 'something'\\n html = post.get(content_format='html', force=True)\\n self.assertIsInstance(html, str)\\n self.assertNotEqual(html, 'something')\\n self.assertIn('html', post._content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11efed318c6fee9cddd09b79a0cedce5\",\n \"score\": \"0.46226516\",\n \"text\": \"def collect_posts(app):\\n app.logger.info('Collecting posts from past 24h for topic modeling ... ')\\n posts = get_candidates(app, user_id=1)\\n\\n posts = [post for post in posts if post.feed_title in reference_feeds]\\n\\n # Remove posts with Live updates, since these cover multiple topics and thus deviate from the average post\\n posts = [post for post in posts if all(keyword not in post.title for keyword in DISCARD_KEYWORDS)]\\n\\n # Remove advertising posts for Bloomberg products\\n posts = [post for post in posts if '(Source: Bloomberg)' not in post.description]\\n\\n app.logger.info(f'Successfully collected {len(posts)} posts.')\\n return posts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42d0cc6ac4983de424422c09a4d5c776\",\n \"score\": \"0.46086076\",\n \"text\": \"def test_list(self):\\n response = self.client.get(self.posts_list_url)\\n self.assertEquals(response.status_code, 200)\\n self.assertContains(response, u\\\"A title 1\\\")\\n self.assertContains(response, u\\\"A title 2\\\")\\n self.assertContains(response, u\\\"A text 1\\\")\\n self.assertContains(response, u\\\"A text 2\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f38901494be265b6fda4c0173fbe30d5\",\n \"score\": \"0.46057123\",\n \"text\": \"def __addPost(self, post, is_question=False):\\n try:\\n unique_key_tag = post.find('a' ,'postcounter')\\n if not unique_key_tag and not unique_key_tag.get('href'): \\n log.info(self.log_msg('Permalink not found, ignoring the Post in the url %s'%self.currenturi))\\n return True\\n unique_key = unique_key_tag['href']\\n if checkSessionInfo(self.__genre, self.session_info_out, unique_key,\\\\\\n self.task.instance_data.get('update'),parent_list\\\\\\n = [self.task.instance_data['uri']]):\\n log.info(self.log_msg('Session info returns True for uri %s'%unique_key))\\n return False\\n page = self.__getData(post, is_question, unique_key)\\n if not page:\\n log.info(self.log_msg('page contains empty data, getdata \\\\\\n returns False for uri %s'%self.currenturi))\\n return True\\n result = updateSessionInfo(self.__genre, self.session_info_out, unique_key, \\\\\\n get_hash( page ),'forum', self.task.instance_data.get('update'),\\\\\\n parent_list=[self.task.instance_data['uri']])\\n if result['updated']:\\n page['parent_path'] = page['path'] = [self.task.instance_data['uri']]\\n page['path'].append(unique_key)\\n page['uri'] = unique_key\\n page['uri_domain'] = urlparse.urlparse(page['uri'])[1]\\n page.update(self.__task_elements_dict)\\n self.pages.append(page)\\n else:\\n log.info(self.log_msg('Update session info returns False for \\\\\\n url %s'%self.currenturi))\\n except:\\n log.info(self.log_msg('Cannot add the post for the uri %s'%self.currenturi))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8de0fb567aea2015bfe32aeaa349c6a\",\n \"score\": \"0.46044493\",\n \"text\": \"def test_page_requests(self):\\n page = self.client.get(self.url)\\n self.assertIn(self.url, page.content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f44ad830c8b20ba7980edcfa01fb77c8\",\n \"score\": \"0.46037108\",\n \"text\": \"def test_find_urls(urlextract, text, expected):\\n assert urlextract.find_urls(text) == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ded504ae159863418dec2d8bf68d50\",\n \"score\": \"0.4597795\",\n \"text\": \"def test_froum_view_with_some_posts(self):\\n mf = add_forum('meme_forum')\\n usr = add_userProfile()\\n\\n add_post(mf, \\\"title\\\", \\\"https://static.djangoproject.com/img/fundraising-heart.cd6bb84ffd33.svg\\\", usr)\\n add_post(mf, \\\"other_title\\\", \\\"http://thewowstyle.com/wp-content/uploads/2015/01/nature-images.jpg\\\", usr)\\n add_post(mf, \\\"another_title\\\", \\\"https://eskipaper.com/images/images-2.jpg\\\", usr)\\n add_post(mf, \\\"title2\\\", \\\"https://hddesktopwallpapers.in/wp-content/uploads/2015/09/nice-images.jpg\\\", usr)\\n add_post(mf, \\\"other_title2\\\", \\\"https://wonderfulengineering.com/wp-content/uploads/2014/01/highway-wallpapers-15.jpg\\\", usr)\\n add_post(mf, \\\"other_title3\\\", \\\"http://thewowstyle.com/wp-content/uploads/2015/07/Natural-World-Wallpaper-HD-.jpg\\\", usr)\\n\\n response = self.client.get(reverse('meme_portal:show_forum', kwargs={'forum_name_slug':'meme_forum'}))\\n\\n self.assertEqual(response.status_code, 200)\\n\\n num_posts = len(response.context['posts'])\\n self.assertEquals(num_posts, 6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07f558bca9c2f013b621f814eed04dcf\",\n \"score\": \"0.45939758\",\n \"text\": \"def download_screenshots_of_reddit_posts(reddit_object, screenshot_num):\\n print_step(\\\"Downloading Screenshots of Reddit Posts 📷\\\")\\n\\n # ! Make sure the reddit screenshots folder exists\\n Path(\\\"assets/png\\\").mkdir(parents=True, exist_ok=True)\\n\\n with sync_playwright() as p:\\n print_substep(\\\"Launching Headless Browser...\\\")\\n\\n browser = p.chromium.launch()\\n\\n # Get the thread screenshot\\n page = browser.new_page()\\n page.goto(reddit_object[\\\"thread_url\\\"])\\n\\n if page.locator('[data-testid=\\\"content-gate\\\"]').is_visible():\\n # This means the post is NSFW and requires to click the proceed button.\\n\\n print_substep(\\\"Post is NSFW. You are spicy... :fire:\\\")\\n page.locator('[data-testid=\\\"content-gate\\\"] button').click()\\n\\n page.locator('[data-test-id=\\\"post-content\\\"]').screenshot(\\n path=\\\"assets/png/title.png\\\"\\n )\\n\\n for idx, comment in track(\\n enumerate(reddit_object[\\\"comments\\\"]), \\\"Downloading screenshots...\\\"\\n ):\\n\\n # Stop if we have reached the screenshot_num\\n if idx >= screenshot_num:\\n break\\n\\n if page.locator('[data-testid=\\\"content-gate\\\"]').is_visible():\\n page.locator('[data-testid=\\\"content-gate\\\"] button').click()\\n\\n page.goto(f'https://reddit.com{comment[\\\"comment_url\\\"]}')\\n page.locator(f\\\"#t1_{comment['comment_id']}\\\").screenshot(\\n path=f\\\"assets/png/comment_{idx}.png\\\"\\n )\\n print_substep(\\\"Screenshots downloaded Successfully.\\\", style=\\\"bold green\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dc2f7bf3c5c8f086cc6a320e346e726\",\n \"score\": \"0.45916343\",\n \"text\": \"def regex_parse(self, data):\\r\\n\\r\\n opening_split = data.split('<!-- CONTENT -->')\\r\\n fully_split = [ part.split('<!-- /CONTENT -->') for part in opening_split]\\r\\n # Flatten the list of lists.\\r\\n fully_split = list(itertools.chain.from_iterable(fully_split))\\r\\n\\r\\n content_data = ' '.join(fully_split[1::2])\\r\\n other_data = ' '.join(fully_split[::2])\\r\\n\\r\\n regex = '<a.*?href=[\\\"\\\\']([^\\\"]+[.\\\\s]*?)[\\\"\\\\'].*?>[^<]+[.\\\\s]*?</a>'\\r\\n content_urls = re.findall(regex, content_data)\\r\\n other_urls = re.findall(regex, other_data)\\r\\n\\r\\n return content_urls, other_urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f7c50e89be73319745b441ec2533cf4\",\n \"score\": \"0.45890713\",\n \"text\": \"def test_get_posts_returns_posts(self):\\n req = self.wp_tc.get_posts()\\n assert req is not None, \\\"GetPosts endpoint returned a null response.\\\"\\n assert len(req) > 0, \\\"GetPosts endpoint not returning at least one object in JSON output.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8fcc8c9614f19ff0b5b8882a82b52f\",\n \"score\": \"0.45838967\",\n \"text\": \"def testAjaxPosts(self):\\n # Generate test posts.\\n for i in range(0, DEFAULT_POST_COUNT + 1):\\n text = \\\"Testing AJAX response %d.\\\" % i\\n post = Post(user=self.user, floor=self.floor, text=text)\\n post.save()\\n \\n second_post = Post.objects.all().order_by(\\\"-pk\\\")[0]\\n response = self.client.get(reverse(\\\"news_more_posts\\\"), HTTP_X_REQUESTED_WITH='XMLHttpRequest')\\n self.failUnlessEqual(response.status_code, 200)\\n self.assertNotContains(response, \\\"Testing AJAX response 0.\\\")\\n self.assertContains(response, \\\"See more\\\")\\n for i in range(1, DEFAULT_POST_COUNT + 1):\\n self.assertContains(response, \\\"Testing AJAX response %d\\\" % i)\\n \\n response = self.client.get(reverse(\\\"news_more_posts\\\") + (\\\"?last_post=%d\\\" % second_post.id), HTTP_X_REQUESTED_WITH='XMLHttpRequest')\\n self.assertContains(response, \\\"Testing AJAX response 0.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df8aa1a5aaf2f5ea26d705f0a63f1304\",\n \"score\": \"0.45793927\",\n \"text\": \"def scrape_buzzfeed(): \\n # load the HTML content using requests and save into a variable\\n buzz_request = requests.get('https://www.buzzfeednews.com/section/politics')\\n buzz_homepage = buzz_request.content\\n\\n # create soup\\n buzz_soup = BeautifulSoup(buzz_homepage, 'html.parser')\\n\\n # locate article URLs\\n buzz_tags = buzz_soup.find_all('h2')\\n\\n # get article titles, content, dates, and links\\n buzz_links = []\\n buzz_titles = []\\n buzz_dates = []\\n buzz_contents = []\\n\\n for n in np.arange(0, min(len(buzz_tags), 30)):\\n\\n # get article link\\n link = buzz_tags[n].find('a')['href']\\n buzz_links.append(link)\\n\\n # get article title\\n title = buzz_tags[n].find('a').get_text()\\n buzz_titles.append(title)\\n\\n # prep article content\\n article = requests.get(link)\\n article_content = article.content\\n soup_article = BeautifulSoup(article_content, 'html5lib')\\n\\n # get publication datetime\\n date = soup_article.find_all('div', class_='news-article-header__timestamps')\\n date = \\\" \\\".join([item.text for item in date]).replace('\\\\n', '')\\n buzz_dates.append(date)\\n\\n # get article content\\n body = soup_article.find_all('div', attrs={'data-module':'subbuzz-text'})\\n article = \\\" \\\".join([item.text for item in body]).replace('\\\\n', '')\\n final_article = re.sub(r' {[^}]*}', '', article)\\n\\n buzz_contents.append(final_article)\\n\\n # assembling data\\n buzz_data = pd.DataFrame.from_dict({\\n 'publisher': 'buzzfeed',\\n 'date': buzz_dates,\\n 'link': buzz_links,\\n 'article_title': buzz_titles,\\n 'article_text': buzz_contents \\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b2216e126de8a8e8a401356c08a1c96\",\n \"score\": \"0.45711684\",\n \"text\": \"def fetch(url, regex_patt):\\n\\ttext = request.urlopen(url)\\n\\tbs_text = bs4.BeautifulSoup(text, 'lxml')\\n\\turl_list = []\\n\\tfor link in bs_text.find_all('a'):\\n\\t\\turl_list.append(str(link.get('href')))\\n\\t#get the cleaned list\\t\\n\\turl_list = clean(url_list, regex_patt)\\n\\turl_list = list(map((lambda x : 'https://en.wikipedia.org' + x), url_list))\\n\\treturn set(url_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68fe6f7a1b7a645242668221654fb945\",\n \"score\": \"0.4560216\",\n \"text\": \"def fetch_page(URL, posts = None, last_modified = None):\\n# print \\\"Fetching \\\" + URL\\n\\n request = urllib2.Request(URL)\\n request.add_header('User-Agent','Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.19) Gecko/2010040116 Ubuntu/9.04 (jaunty) Firefox/3.0.19')\\n if posts is not None:\\n request.add_data(urllib.urlencode(posts))\\n\\n if last_modified is not None:\\n request.add_header('If-Modified-Since', last_modified)\\n\\n try:\\n response = opener.open(request)\\n except urllib2.HTTPError as e:\\n if e.code == 404:\\n raise PageDoesNotExist(URL)\\n else:\\n raise e\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"476aa05237560eea6f01553bd0d09649\",\n \"score\": \"0.45588604\",\n \"text\": \"def spam_post(post_url, times, comment):\\r\\n driver.get(post_url)\\r\\n sleep(3)\\r\\n i = 0\\r\\n while times > i:\\r\\n driver.find_element_by_xpath('//*[@id=\\\"react-root\\\"]/section/main/div/div/article/div[2]/section[3]/ \\\\\\r\\n div/form/textarea').click()\\r\\n driver.find_element_by_xpath('//*[@id=\\\"react-root\\\"]/section/main/div/div/article/div[2]/section[3]/ \\\\\\r\\n div/form/textarea').send_keys(comment)\\r\\n driver.find_element_by_xpath('//*[@id=\\\"react-root\\\"]/section/main/div/div/article/div[2]/section \\\\\\r\\n [3]/div/form/button').click()\\r\\n i += 1\\r\\n random_num = random.randint(17, 23) # cooldown\\r\\n print('#{} comment: {}'.format(i, comment))\\r\\n print('waiting {} sec'.format(random_num))\\r\\n sleep(random_num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e89e78e34ad286a35c52d3069d337b90\",\n \"score\": \"0.45456493\",\n \"text\": \"async def get_new_posts(fetcher: Fetcher, store_dir: str) -> dict:\\n try:\\n page = await fetcher.fetch(Y_ROOT_URL)\\n except (aiohttp.ClientError, asyncio.TimeoutError):\\n logging.info(\\\"{} is not available now\\\".format(Y_ROOT_URL))\\n return dict()\\n\\n posts = get_posts_from_main_page(page)\\n ready_post_ids = get_processed_posts(store_dir)\\n\\n not_ready_posts = {}\\n for p_id, p_url in posts.items():\\n if p_id not in ready_post_ids:\\n not_ready_posts[p_id] = p_url\\n else:\\n logging.debug(\\\"Post {} already parsed\\\".format(p_id))\\n\\n return not_ready_posts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4a39e3e50deeab41f60306abe62ae7\",\n \"score\": \"0.45395905\",\n \"text\": \"def get_meduza_articles(url=\\\"https://meduza.io/api/w5/screens/news\\\") -> List[str]:\\n \\n content = __get_page_content(url)\\n articles = re.findall('{\\\"key\\\":\\\"(.*?)\\\"', content)\\n \\n def contains_any(url) -> bool:\\n \\\"\\\"\\\"\\n Returns true if url contains any element of allowed\\n \\\"\\\"\\\"\\n allowed = ['shapito', 'cards', 'news', 'feature', 'episodes']\\n return bool(sum(list(\\n map(lambda name: name in url, \\n allowed))))\\n\\n return list(map(lambda url: \\\"https://meduza.io/\\\" + url, \\n filter(contains_any, \\n articles)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d782ff765bf7f4811a259a322cff241e\",\n \"score\": \"0.45374984\",\n \"text\": \"def _parse_response_body(resp_body: str) -> List[str]:\\n base_url = \\\"https://news.ycombinator.com\\\"\\n urls = []\\n\\n body_parser = BeautifulSoup(resp_body, 'html.parser')\\n titles = body_parser.select('.storylink')\\n\\n for news_title in titles:\\n url = news_title.get('href')\\n\\n if not url.startswith('http'):\\n url = f'{base_url}/{url}'\\n \\n if not url.endswith('.pdf'):\\n urls.append(url)\\n\\n return urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb7cc817cbcac17929a8027b8295d1b1\",\n \"score\": \"0.45373192\",\n \"text\": \"def test_get_existent_page(self):\\n generate(total_authors=1, total_posts=1)\\n post = Post.objects.first()\\n request = self.factory.get('/post')\\n response = views.post(request, path=post.path)\\n self.assertEqual(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcfddeba1e27dc380a3bd50f25d7aea3\",\n \"score\": \"0.45349544\",\n \"text\": \"def find_posts_in_subreddit(subRedditName, subRedditParseConfig):\\n # type: (string, dictionary) -> List of praw.Submission\\n\\n # Get a PRAW instance of the subreddit.\\n subreddit = reddit.subreddit(subRedditName)\\n\\n # Retrieve various configured settings.\\n sort_by = subRedditParseConfig[\\\"sort_by\\\"]\\n sort_by_range = subRedditParseConfig[\\\"sort_by_range\\\"]\\n search_terms = subRedditParseConfig[\\\"search\\\"]\\n\\n # Elligible submissions found in the subreddit.\\n submissions = []\\n\\n # Search submissions by our configured settings for all of our various\\n # search terms.\\n for searchTerm in search_terms:\\n searchResults = subreddit.search(searchTerm, sort=sort_by,\\n time_filter=sort_by_range)\\n\\n # The search term must appear in the submission's title, if it's to be parsed.\\n # Right now it's strict matching. This is important for things like distinguishing\\n # posts with '[FRESH]' from '[FRESH VIDEO]' in /r/hiphopheads.\\n for result in searchResults:\\n if searchTerm in result.title:\\n submissions.append(result)\\n\\n return submissions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa669759a7fdf36adecc64d3669d1dc\",\n \"score\": \"0.45304048\",\n \"text\": \"def _process_url_reddit(self, session, url, **kwargs):\\n urlinfo = urlparse(url)\\n if not (\\n urlinfo.hostname == \\\"reddit.com\\\" or urlinfo.hostname.endswith(\\\".reddit.com\\\")\\n ):\\n return\\n\\n # Thread information\\n if match := REDDIT_THREAD_PATTERN.match(urlinfo.path):\\n apiurl = f\\\"https://reddit.com/r/{match.group('subreddit')}/comments/{match.group('thread')}.json\\\"\\n\\n def formatter(response):\\n data = response[0][\\\"data\\\"][\\\"children\\\"][0][\\\"data\\\"]\\n return [f\\\"/r/{match.group('subreddit')}:\\\", data.get(\\\"title\\\")]\\n\\n elif match := REDDIT_COMMENT_PATTERN.match(urlinfo.path):\\n apiurl = f\\\"https://reddit.com/r/{match.group('subreddit')}/comments/{match.group('thread')}/x/{match.group('comment')}.json\\\"\\n\\n def formatter(response):\\n data = response[1][\\\"data\\\"][\\\"children\\\"][0][\\\"data\\\"]\\n title = response[0][\\\"data\\\"][\\\"children\\\"][0][\\\"data\\\"][\\\"title\\\"]\\n return [\\n f\\\"/r/{match.group('subreddit')} comment by {data.get('author')} on “{title}”\\\"\\n ]\\n\\n elif match := REDDIT_USER_PATTERN.match(urlinfo.path):\\n apiurl = f\\\"https://reddit.com/user/{match.group('user')}/about.json\\\"\\n\\n def formatter(response):\\n data = response[\\\"data\\\"]\\n return [f\\\"/u/{data['name']} on Reddit\\\"]\\n\\n else:\\n self.log.warning(\\\"Unsupported reddit url: '%s'\\\", urlinfo.path)\\n return [\\\"Reddit\\\"]\\n\\n with closing(session.get(apiurl, timeout=4)) as response:\\n try:\\n attempts = 0\\n while attempts < 3:\\n attempts += 1\\n data = response.json()\\n self.log.error(\\\"Response: %r\\\", data)\\n if (\\n response.status_code == 429\\n or not isinstance(data, dict)\\n or data.get(\\\"error\\\", 0) == 429\\n ):\\n time.sleep(0.5 * attempts)\\n self.log.debug(\\\"Retrying reddit request\\\")\\n continue\\n elif response.status_code != 200:\\n return [f\\\"Got response code {response.status_code}\\\"]\\n else:\\n return formatter(data)\\n return [f\\\"Reddit refused to give data.\\\"]\\n\\n except ValueError:\\n return [\\\"Invalid JSON response from Reddit API\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c7a863dff235a817da841281a67e058\",\n \"score\": \"0.4530284\",\n \"text\": \"def scrape_posts(tag):\\n\\n for page_number in range(1,11):\\n parameters = {\\n \\\"tab\\\":\\\"newest\\\",\\n \\\"page\\\":page_number,\\n \\\"pagesize\\\":15\\n }\\n url = f\\\"{stackoverflow}questions/tagged/{tag}\\\"\\n r = requests.get(url,params=parameters)\\n\\n if r.status_code in range(200,299):\\n\\n try:\\n html_str = r.text\\n html = HTML(html=html_str)\\n \\n post = html.find(\\\".question-summary\\\")\\n # post_details = list(post.text.split(\\\"\\\\n\\\"))\\n \\n classes = [\\\".vote\\\",\\\".status\\\",\\\".views\\\",\\\".question-hyperlink\\\",\\\".excerpt\\\",\\\".tags\\\",\\\".user-details\\\"]\\n key_names = [\\\"Votes\\\",\\\"Answers\\\",\\\"Views\\\",\\\"Question\\\",\\\"Summary\\\",\\\"Tags\\\",\\\"User\\\"]\\n data = []\\n\\n for ques_elem in post:\\n post_details = {}\\n for i,search_class in enumerate(classes):\\n element = ques_elem.find(search_class,first=True)\\n keyname = key_names[i]\\n post_details[keyname] = filter_data(element.text,keyname=keyname)\\n data.append(post_details)\\n \\n with open(os.path.join(\\\"Outputs\\\\Multi-Scraper\\\\jsonFiles\\\",f\\\"post{page_number}.json\\\"),\\\"w\\\") as f:\\n f.write(json.dumps(data,indent=2))\\n\\n with open(os.path.join(\\\"Outputs\\\\Multi-Scraper\\\\csvFiles\\\",f\\\"post{page_number}.csv\\\"),\\\"w\\\") as f:\\n df = pd.DataFrame(data)\\n df.to_csv(f,index=False)\\n\\n except Exception as e:\\n print(\\\"Something went wrong: \\\",e)\\n\\n sleep(1.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f599fc7f8f219569c06647fd95e5f29\",\n \"score\": \"0.45220834\",\n \"text\": \"def scrape_new_urls(request):\\n startTime = datetime.datetime.now()\\n unscraped_urls = Phishtank_urls.objects.filter(is_scraped=False)\\n event_loop = asyncio.new_event_loop()\\n browser = event_loop.run_until_complete(pyppeteer.launch(handleSIGINT=False, handleSIGTERM=False, handleSIGHUP=False , ignoreHTTPSErrors=True , args= [\\\"--ignore-certificate-errors-spki-list=jc7r1tE54FOO=\\\"]))\\n for line in unscraped_urls:\\n if Models_Helper.scrape_line(line, event_loop , browser=browser ):\\n line.is_scraped = True\\n line.save()\\n event_loop.run_until_complete(browser.close())\\n endTime = datetime.datetime.now()\\n TotalTime = endTime - startTime\\n return HttpResponse(f\\\"It took (in microseconds): {TotalTime.microseconds}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f98ec454a1927385067f2a191950731\",\n \"score\": \"0.44966224\",\n \"text\": \"def _(\\n post_limit: dt.timedelta,\\n reddit_conn: praw.Reddit,\\n sub_name: str = 'SuicideWatch'\\n) -> Generator[RedditPost, None, None]:\\n subreddit = reddit_conn.subreddit(sub_name)\\n # posts: List[RedditPost] = []\\n post_limit = (dt.datetime.utcnow() - post_limit)\\n\\n for post in subreddit.new(limit=None):\\n post_utc = dt.datetime.utcfromtimestamp(post.created_utc)\\n if post_utc < post_limit:\\n print(f'Breaking: {post_utc} < {post_limit}')\\n break\\n\\n yield RedditPost(post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aaa66d4505e31f9fd67a054403cef78\",\n \"score\": \"0.4467747\",\n \"text\": \"def test_search_urls(self):\\n self.assertTrue(validate_url(self.engine._parsed_url.geturl()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b31f92ed7af08e6b53c35118cd7c5f87\",\n \"score\": \"0.44649452\",\n \"text\": \"def can_post(acc):\\n my_posts = get_my_posts(acc, acc)\\n if len(my_posts) > 0:\\n last_post_time = get_my_posts(acc, acc)[0][0].time\\n return not last_post_time >= timezone.now() - datetime.timedelta(days=1)\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65dc98ca5ee622afae4a837be1c1c41c\",\n \"score\": \"0.44635552\",\n \"text\": \"def test_urls(self):\\n request = testing.DummyRequest()\\n page = new_stamp_location(request)\\n self.assertIn('post_url', page)\\n self.assertEqual(\\n page['post_url'],\\n request.route_url('new-stamp-location-post')\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87df63dc6af4593f44d4a553f0b39f2e\",\n \"score\": \"0.44599196\",\n \"text\": \"def is_valid_post_file(basename):\\n post_pattern = r'^\\\\d{4}-\\\\d{2}-\\\\d{2}-.*'\\n markdown_extensions = ['md', 'markdown', 'mdown']\\n\\n if '.' not in basename:\\n return False\\n\\n filename, ext = os.path.basename(basename).rsplit('.', 1)\\n\\n ignorable = filename.startswith('_')\\n valid_filename = re.match(post_pattern, filename)\\n valid_extension = ext in markdown_extensions\\n\\n return not ignorable and valid_filename and valid_extension\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7902904e4ac6080418318b4872ff87c\",\n \"score\": \"0.44591194\",\n \"text\": \"def test_parsing_non_episode_link(\\n requests_mock: rm_Mocker,\\n req_ses: requests.Session,\\n archive: Archive,\\n) -> None:\\n non_episode_url = (\\n \\\"https://teacherluke.co.uk/premium/archive-comment-section/\\\" # noqa: E501,B950\\n )\\n requests_mock.get(\\n non_episode_url,\\n text=\\\"No need to parse this page\\\",\\n status_code=200,\\n )\\n link_title = \\\"Some title\\\"\\n with pytest.raises(NotEpisodeURLError) as ex:\\n parser.EpisodeParser(archive, non_episode_url, req_ses, link_title).parse_url()\\n assert len(archive.collected_links) == 0\\n assert ex.value.args[0] == non_episode_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d8e11be3ae519114c4fb4f5821c66b\",\n \"score\": \"0.44577125\",\n \"text\": \"def suitable(cls, url):\\n urls = []\\n for _VALID_URL in cls.target_website:\\n urls += re.findall(_VALID_URL, url)\\n else:\\n return urls if urls else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d3be36e753e002f23c9783cebe44535\",\n \"score\": \"0.4456455\",\n \"text\": \"def test_no_spash_page():\\n spider = magic_spider.MagicSpider()\\n body = \\\"\\\"\\\"\\n <html>\\n <body id=\\\"f1d\\\">\\n <table class=\\\"list\\\" style=\\\"margin-left: 20px; width: 920px;\\\">\\n <tr class=\\\"odd\\\">\\n <td>\\n <a href=\\\"http://non_reachable_url/\\\">Limits to the violation of...</a>\\n </td>\\n </tr>\\n </table>\\n </body>\\n </html>\\n \\\"\\\"\\\"\\n response = fake_response_from_string(body)\\n node = get_node(spider, spider.itertag, text=body)\\n parsed_node = spider.parse_node(response, node)\\n\\n response.status = 404\\n response.meta[\\\"title\\\"] = parsed_node.meta[\\\"title\\\"]\\n response.meta[\\\"urls\\\"] = parsed_node.meta[\\\"urls\\\"]\\n parsed_item = spider.scrape_for_pdf(response).next()\\n assert parsed_item\\n assert parsed_item.record\\n record = parsed_item.record\\n\\n assert isinstance(record, hepcrawl.items.HEPRecord)\\n assert \\\"urls\\\" in record\\n assert \\\"title\\\" in record\\n assert record[\\\"urls\\\"][0][\\\"value\\\"] == \\\"http://non_reachable_url/\\\"\\n assert record[\\\"title\\\"] == \\\"Limits to the violation of...\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69d6ce91cc90ea7e2378515557473832\",\n \"score\": \"0.44540748\",\n \"text\": \"def page_handler(url, match):\\n result = linkinfo.scrape_html_title(url)\\n result.is_redundant = 'imgur: the simple image sharer' in result.text\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee24ea0ac766437b8d9e3bf2aa725358\",\n \"score\": \"0.44370016\",\n \"text\": \"def find_last_x_posts(self, sub: str, amount: int) -> List[dict]: \\n new_python = self.reddit.subreddit(sub).new(limit=amount)\\n posts = []\\n for post in new_python:\\n if not post.stickied:\\n posts.append({\\\"title\\\": post.title,\\n \\\"url\\\": post.url,\\n \\\"permalink\\\": post.permalink,\\n \\\"created\\\": post.created})\\n return posts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6874d686d65d40f421f9c5fbee1ca424\",\n \"score\": \"0.44317132\",\n \"text\": \"def process(self, f):\\n SCHEME_KIND = \\\"http://schemas.google.com/g/2005#kind\\\"\\n VALID_KINDS = (\\\"http://schemas.google.com/blogger/2008/kind#post\\\",\\n \\\"http://schemas.google.com/blogger/2008/kind#page\\\")\\n NS = {'ns': 'http://www.w3.org/2005/Atom'}\\n\\n d = etree.parse(f)\\n\\n htmlckr = HTMLChecker()\\n links = {}\\n\\n entries = d.xpath('//ns:feed/ns:entry', namespaces=NS)\\n for entry in entries:\\n sel = \\\"ns:category[@scheme='%s']\\\" % SCHEME_KIND\\n kind = entry.find(sel, namespaces=NS)\\n if kind.attrib.get('term') not in VALID_KINDS:\\n continue\\n sel = \\\"ns:link[@rel='alternate']\\\"\\n altlink = entry.find(sel, namespaces=NS)\\n if altlink is None:\\n # for scheduled posts, the post URL isn't decided yet, skip such posts\\n # since lnkckr wouldn't be possible to provide URL of the posts.\\n continue\\n post_link = altlink.attrib.get('href')\\n\\n content = entry.find('ns:content', namespaces=NS)\\n content_text = content.text\\n if isinstance(content_text, str) and hasattr(content_text, 'decode'):\\n content_text = content_text.decode('utf8')\\n htmlckr.links = {}\\n htmlckr.process(StringIO(content_text))\\n for link in htmlckr.links.keys():\\n # is a blank fragment?\\n if link == '#' or link == post_link + '#':\\n continue\\n # is a local fragment?\\n if link.startswith('#'):\\n link = post_link + link\\n if link not in links:\\n links[link] = {'status': None, 'posts': []}\\n if post_link not in links[link]['posts']:\\n links[link]['posts'].append(post_link)\\n self.links = links\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd228c2764d0618fb68e7f540c951c4c\",\n \"score\": \"0.44306654\",\n \"text\": \"def scrape_job_posting(url, **kwargs):\\n r = requests.get(url, allow_redirects=True)\\n true_url = r.url\\n tree = html.fromstring(r.content)\\n\\n # Use domain-appropriate scraper\\n domain = urlparse(true_url).hostname\\n if domain in ['www.indeed.ca', 'www.indeed.com']:\\n scraper_cls = PSindeedCa\\n elif domain in ['www.smartrecruiters.ca', 'www.smartrecruiters.com']:\\n scraper_cls = PSsmartrecruiters\\n else:\\n scraper_cls = PageScraper\\n scraper = scraper_cls(true_url, tree)\\n scraper.scrape()\\n return Jentry(scraper, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94eaa10a2077e71e8d540caab0f0c14c\",\n \"score\": \"0.44255474\",\n \"text\": \"def _extract_urls(self, text: str) -> list[str]:\\n urls = self.url_re.findall(text)\\n return urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fbfa54df9427352b269d2a383840cce\",\n \"score\": \"0.44248518\",\n \"text\": \"def test_bad_links(self):\\r\\n bad_links = [\\r\\n '<a name=\\\"amazonAndGoogleHaveMadeAnAudaciousGrabOfNamespaceOnTheInternetAsFarAsICanSeeTheresBeenNoMentionOfThisInTheTechPress\\\"> </a>',\\r\\n '<a href=\\\"#amazonAndGoogleHaveMadeAnAudaciousGrabOfNamespaceOnTheInternetAsFarAsICanSeeTheresBeenNoMentionOfThisInTheTechPress\\\"><img src=\\\"http://scripting.com/images/2001/09/20/sharpPermaLink3.gif\\\" class=\\\"imgBlogpostPermalink\\\" width=\\\"6\\\" height=\\\"9\\\" border=\\\"0\\\" alt=\\\"permalink\\\"></a>',\\r\\n '<a href=\\\"http://scripting.com/stories/2012/06/15/theTechPressIsOutToLunch.html#anExampleGoogleDoesntIntendToShareBlogAndItWillOnlyBeUsedToPointToBloggerSitesIfYouHaveATumblrOrWordpressBlogYouCantHaveABlogDomainHereIsTheAHrefhttpgtldresulticannorgapplicationresultapplicationstatusapplicationdetails527publicListingaOfGooglesAHrefhttpdropboxscriptingcomdavemiscgoogleblogapplicationhtmlapplicationa\\\"><img src=\\\"http://scripting.com/images/2001/09/20/sharpPermaLink3.gif\\\" class=\\\"imgBlogpostPermalink\\\" width=\\\"6\\\" height=\\\"9\\\" border=\\\"0\\\" alt=\\\"permalink\\\"></a>'\\r\\n ]\\r\\n\\r\\n for l in bad_links:\\r\\n link = fragment_fromstring(l)\\r\\n self.assertTrue(is_bad_link(link))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd0ab4ad6f234c0495842b693c83513\",\n \"score\": \"0.44207418\",\n \"text\": \"def get_pages(collection):\\n preface = re.compile('/questions/[0-9]+')\\n i = 1\\n while True:\\n website = \\\"http://stackoverflow.com/questions?page=\\\" + str(i) + \\\"&sort=votes\\\"\\n response = urllib2.urlopen(website)\\n soup = BeautifulSoup(response.read())\\n links = [a_tag.get('href') for a_tag in soup.find_all('a')]\\n for link in links:\\n if collection.count() == MAX_QUESTIONS:\\n raise DataBaseFull(\\\"Finish\\\")\\n if preface.match(str(link)):\\n get_questions('http://stackoverflow.com' + link, collection)\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef69bcd1594498c7904797579a252b3\",\n \"score\": \"0.44202402\",\n \"text\": \"def test_normal(self):\\n\\n urls = (\\n \\\"http://www.example.net/\\\",\\n \\\"http://www.example.net/?test\\\",\\n \\\"http://www.example.net/test?test\\\",\\n \\\"http://www.example.net/test?test=val\\\",\\n \\\"http://www.example.net/test?uploaded=0\\\",\\n \\\"http://www.example.net:6969/\\\",\\n \\\"http://www.example.net:6969/?test\\\",\\n \\\"http://www.example.net:6969/test?test\\\",\\n \\\"http://www.example.net:6969/test?test=val\\\",\\n \\\"http://www.example.net:6969/test?uploaded=0\\\"\\n )\\n\\n for url in urls:\\n self.assertEqual(cheatbt.cheat(url, self.mappings), url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf721263c518bfa1236ab56d1a7fe34b\",\n \"score\": \"0.44188613\",\n \"text\": \"def __init__(self, postlists_getter):\\n self.rgx = re.compile('([&|!() ])')\\n self.get_postlists = postlists_getter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff58364e3c90d8ac6b6dfc23fff25757\",\n \"score\": \"0.44174704\",\n \"text\": \"def test_get_posts_successful(self):\\n res = self.client.get(POSTS_URL)\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":17,"numItemsPerPage":100,"numTotalItems":994482,"offset":1700,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sInJlcG9JZHMiOlsiNjgzOGE5MTA0YTNhNzFhOTE3OTU0Y2YwIl0sImlhdCI6MTc5MTc5Mzc5MSwianRpIjoiMDY3MzZlZjctMzI1Zi00ZmRhLTkyZWItNWY4N2M3YjBhOTdhIiwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc5MTc5NzM5MSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.bPt-j1wahc1nwQPxWF0k0BlvbWbmsuaujIcSS1jWmN1-8YOhXGIsfOjeibPIzpPWYCL_qPC5yHNQbW9RrST3CA","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 |
|---|---|---|---|
1c5a069be26f7f0e91e59148c802ad17 | Create a new DomainAssociation on an App | [
{
"docid": "81b9f2113a1ba457c6dd65ad42b485cc",
"score": "0.54804415",
"text": "def update_domain_association(self, appId: str, domainName: str, subDomainSettings: List, enableAutoSubDomain: bool = None) -> Dict:\n pass",
"title": ""
}
] | [
{
"docid": "87f84e2f292e546be471ab59a0b01eab",
"score": "0.7183197",
"text": "def create_domain_association(self, appId: str, domainName: str, subDomainSettings: List, enableAutoSubDomain: bool = None) -> Dict:\n pass",
"title": ""
},
{
"docid": "cbcd1d9b3813db060204d9dc0571a2c4",
... |
f3ff2c7a040e8c2ebd09411de0fa3bd1 | Creates box and classification targets for a batch. | [
{
"docid": "e0e7fa272de84ab3bea994b25e41282e",
"score": "0.53381574",
"text": "def encode_batch(self, batch_images, gt_boxes, cls_ids):\n # print(\"================== LabelEncoder.encode_batch() ================\")\n images_shape = tf.shape(batch_images) # Tensor(\"Shape:0\", shape=(4,),... | [
{
"docid": "d7326264dd15f2259f4ae7b4bd19fc21",
"score": "0.6387346",
"text": "def multi_gt_creator(input_size, strides, scale_thresholds, num_classes, label_lists=[], use_anchor=False, name='VOC'):\n if not use_anchor:\n assert len(strides) == len(scale_thresholds)\n # prepare the all empty... |
97d70b040208a5510b0aed2813ef30e1 | Removes the given tag from this function. name the tag name to be removed. | [
{
"docid": "c21b0a0f5fb1cbebd40502d60c80d8f1",
"score": "0.8454357",
"text": "def removeTag(self, name: unicode) -> None:\n ...",
"title": ""
}
] | [
{
"docid": "9c673493ead30e0c899b0d2d8223be54",
"score": "0.7143746",
"text": "def remove_tag(self, tag_name):\n if self.id is None:\n raise Exception(\"Missing id: This API requires a load test ID be supplied.\")\n tag_name = urllib.quote_plus(tag_name)\n return self.conn... |
a8cbd374234aac6ec71d5b9d14526065 | Manipulate command aliases. An Alias is an alternative invocation for a command. If a command is not found under the invocation given, the list of aliases is checked, and if the invocation is found to be an alias, the request is instead forwarded to the command word set under the alias. Due to this approach to resoluti... | [
{
"docid": "fed54708c6d83e86e57e4804052f98a4",
"score": "0.6096765",
"text": "async def cmd_calias(self, args, **_):\n if not args:\n raise CommandInputError(\"This command requires a subcommand.\")\n\n # The first argument passed is a subcommand; What action should be taken.\n ... | [
{
"docid": "1afb14ecd0a128b0ac2149e8925c1284",
"score": "0.6841163",
"text": "def register_alias(self, alias_name=None, command_name=None):\n\n if alias_name == command_name or None in [alias_name, command_name]:\n raise ValueError(\n \"alias_name and command_name must b... |
e51c91ba31411065e95abd67208532b6 | Return the percentiles of a datasets | [
{
"docid": "73b2683454887099baf3ef5ded9d38ff",
"score": "0.5939454",
"text": "def api_get_percentile(id=None):\n auth_func()\n if not id:\n return jsonify({})\n query = db.session.query(Percentile).filter_by(dataset_id=id)\n result = []\n for elem in query:\n result.append(e... | [
{
"docid": "934fc11abbcd3d6495741e96b93a1737",
"score": "0.7504458",
"text": "def percentiles(self):\n return self._percentiles",
"title": ""
},
{
"docid": "89e6cab31adfad66b50252e6fd021668",
"score": "0.72433335",
"text": "def Percentile(data, percentile=(25.0, 75.0), N=100):... |
f2cb9dfa96ebc1153e4e828b62bbdcf7 | Read ahead a number of buffers to fill the window. | [
{
"docid": "8a63e3e8fd1a648a83a73810cd461cc7",
"score": "0.6799353",
"text": "def FetchWindow(self):\n # Read the first lot of chunks to save on round trips\n number_of_chunks_to_readahead = min(\n self.state.file_size/self.CHUNK_SIZE + 1 -\n self.state.current_chunk_number,\n ... | [
{
"docid": "ad4889c8e2f38472c9fae868bca9b2d9",
"score": "0.71676314",
"text": "def read_next_buffer(self, n):\n\t\tx = 0\n\t\td = 0\n\t\tif self.seekable:\n\t\t\tself.rw.seek(self.r_pointer)\n\t\twhile n > x:\n\n\t\t\tbuff = self.rw.read(n)\n\t\t\td = len(buff)\n\t\t\tif d == 0:\n\t\t\t\tif self.wait_fu... |
71db05b56e300b005ff3604e4c59f280 | Retuens Proxy list assocaited witht hat VSInstance . Read only attribute | [
{
"docid": "b739ea08821c6e0d0353cbcaf007c9f8",
"score": "0.0",
"text": "def co_ordinator(self):\n if self.vsa_co_ordinator is None:\n self.vsa_co_ordinator = self.vsa_instance.co_ordinator\n\n return self.vsa_co_ordinator",
"title": ""
}
] | [
{
"docid": "5f7589fc1c973fc158a7f1720636077b",
"score": "0.71082884",
"text": "def proxy_list(self):\n return self.vsa_proxy_list",
"title": ""
},
{
"docid": "7313e1bcdc6e0e3de7c4ad2018a24dfb",
"score": "0.6473861",
"text": "def get_instances(self):",
"title": ""
},
{
... |
29e766762c19e733d8f684006d27abe1 | Sets the active item to the one indicated by index if the tablelist"s state is not disabled. If index is outside the range of items in the tablelist then the closest item is activated. The active item is drawn as specified by the activestyle configuration option when the widget has the input focus and the selection typ... | [
{
"docid": "597b90e1e2cc25ad2c0f484241fb8c67",
"score": "0.61238855",
"text": "def activate(self, index):\n return self.tk.call((self._w, \"activate\", index))",
"title": ""
}
] | [
{
"docid": "c9951b1416f1748c2969436c80feda19",
"score": "0.6596948",
"text": "def set_active(self, index):\n self.combo.set_active(index)",
"title": ""
},
{
"docid": "b59bdd3098a686a0168448851a506590",
"score": "0.6454382",
"text": "def activatecell(self, index):\n retu... |
a6f1c8cd804a84d928070259bcb3a192 | Function for getting data from MySQL Database Output url/ when you not provide id, it will fetch all the data url// when id provided, it will fetch specific data | [
{
"docid": "d619e072b5d11a9ce8d6a07e098ad576",
"score": "0.5017003",
"text": "def get(self, data_id = False):\n sql_builder = SQLBuilder()\n\n if not data_id:\n sql_builder.select(\"*\", self.database_name)\n self.internal_data = self.query(sql_builder.build()).data()... | [
{
"docid": "b9eda5d1433bf345ca146e7ebf0f5c96",
"score": "0.60246813",
"text": "def get(id, destination):\n api = rest.DataSetApi(configuration.get_api_client())\n if destination is None:\n result = api.get_dataset(id)\n pprint.pp_dict(util.to_dict(result))\n else:\n with ut... |
d60a3077a56ef51bef938d323f601b54 | Installs all Dependencies and Setups the Backend | [
{
"docid": "1bed93428431c5668c3afeeafe32e04c",
"score": "0.0",
"text": "def setup_everything():\n pass",
"title": ""
}
] | [
{
"docid": "20714e5f274a6fe34871e4edfa530399",
"score": "0.72239935",
"text": "def init():\r\n _install_requirements()\r\n _install_django()\r\n _install_starspan()",
"title": ""
},
{
"docid": "cce4e97f681a2fa643cfef2d66aa3410",
"score": "0.7172018",
"text": "def setup():\n ... |
5c43fb9bb7c259cb18dc032d80ed94c1 | Player is created with no inventory (empty list). | [
{
"docid": "ded207afbbd6fe4c9af648104b52cca1",
"score": "0.7656606",
"text": "def test_player_created_with_empty_inventory(self):\n player = Player('Dennis')\n\n self.assertEqual([], player.inventory)",
"title": ""
}
] | [
{
"docid": "87120eb5ae3137ee50b695b13bf75881",
"score": "0.7164654",
"text": "def test_create_player_no_name(self):\n player = PlayerFactory.create_player()\n\n self.assertTrue(player.name in DEFAULT_NAMES)\n self.assertEqual(0, player.points)\n self.assertEqual([], player.in... |
3d9ff75d718750b89e118d43a4cbf264 | Returns list of article text where each element is the concatenation of 3 sentences. | [
{
"docid": "2fdb8966bf888c26bf717b209ff6c780",
"score": "0.71108973",
"text": "def get_article_text(url: str) -> List[str]:\n try:\n article = art.Article(url, language=\"en\")\n article.download()\n article.parse()\n article.nlp()\n text = a... | [
{
"docid": "10e5d7cc19acdb06053acdab7934bf7f",
"score": "0.7031593",
"text": "def get_articles(self, text):\n articles = []\n \n if self.re_abstract.search(text):\n reduced_text = self.re_abstract.search(text).group(1)\n else:\n reduced_text = text\n\n ... |
e42f8484c01ca9b17e042e09e1c3b9af | Construct. Should only be called through derived class. | [
{
"docid": "994d27841eeaba4574e25e282331ccef",
"score": "0.0",
"text": "def __init__(self, domain, range_, dual_to_range, parameters):\n self._domain = domain\n self._range = range_\n self._dual_to_range = dual_to_range\n self._parameters = parameters\n self._cached = ... | [
{
"docid": "f030941ab920cb6ca92c5d65c14e1318",
"score": "0.8816562",
"text": "def construct(self):\n pass # override this method",
"title": ""
},
{
"docid": "d62d9fb8a3dca691fa04ad99f64d23e1",
"score": "0.87089986",
"text": "def construct(self):\r\n\r\n raise NotImpleme... |
d33952a7ecceaab9cc3e3a3670f0f12d | Set up the tag action table to send spikes to an individual synapse | [
{
"docid": "ac673a6fbce0743d0e66cf6b78a34c75",
"score": "0.0",
"text": "def set_tat(syn_idx):\n addr = HAL.driver.BDPars.GetSynAERAddr(syn_idx)\n tat_entry = bd.PackWord([\n (bd.TATSpikeWord.STOP, TAT_STOP_BIT),\n (bd.TATSpikeWord.SYNAPSE_ADDRESS_0, addr),\n (bd.TATSpikeWord.S... | [
{
"docid": "3e7b161710444eb0d60dcd7c9e8017af",
"score": "0.51474947",
"text": "def tagAPost( self, action_param ):\n self.doc['tags'] = self.doc.get('tags', [])\n self.doc['tags'].append(action_param)",
"title": ""
},
{
"docid": "d23c952b327ee8c2d9d7573c8e36d212",
"score": ... |
14d8ecb64bee6c136662b3029e9fe8bb | Delete the current course and create a new equivalent course with no content, but all sections and users moved over. | [
{
"docid": "02f8924edfaaeec21afe3edac0779b1c",
"score": "0.5319089",
"text": "def reset(self, **kwargs):\n response = self._requester.request(\n \"POST\",\n \"courses/{}/reset_content\".format(self.id),\n _kwargs=combine_kwargs(**kwargs),\n )\n retur... | [
{
"docid": "5647f898e66f87e4087e8025ea2b9524",
"score": "0.6613592",
"text": "def delete_test_course(self):\n self.cursor = self.connection.cursor()\n self.sql = \"DELETE FROM appsrc_courses WHERE coursename = 'Test_subscribe_course'\"\n self.cursor.execute(self.sql)\n self.c... |
f2fa33105a44384f4608b7b25e01ec20 | Deployment request that is HTTP Triggered. | [
{
"docid": "7b4b7adaf44775b566e9a5bf58591485",
"score": "0.5673682",
"text": "def main(req: func.HttpRequest):\n\n # Get the POST request body\n req_body = req.get_json()\n\n configuration = ubiops.Configuration()\n configuration.api_key['Authorization'] = f\"Token {TOKEN}\"\n\n client = ... | [
{
"docid": "b6c13dd6c464f078f32c85e45bc38b6b",
"score": "0.61837566",
"text": "def post(self):\n super(DeployAPI, self).post()\n\n task_id = request.form['task_id']\n if not task_id or not task_id.isdigit():\n return self.render_json(code=-1)\n wi = Deployer(task_i... |
96f3e068d750211eadc1772475c691a2 | CVSpilsGetWorkSpace returns the CVSpils real and integer workspaces as a tuple (integer, real) cvodememobj (CVodeMemObj)a CVodeMemObj as returned by CVodeCreate() | [
{
"docid": "29568da17da9d6a10df7a2b55bf47a6e",
"score": "0.8580142",
"text": "def CVSpilsGetWorkSpace(cvodememobj):\n\tlenrwLS = ctypes.c_long(0)\n\tleniwLS = ctypes.c_long(0)\n\tret = cvode.CVSpilsGetWorkSpace(cvodememobj.obj, ctypes.byref(lenrwLS), ctypes.byref(leniwLS))\n\tif ret < 0:\n\t\traise Asse... | [
{
"docid": "84328137a3311d7968e4c2af33e19086",
"score": "0.81833005",
"text": "def CVodeGetWorkSpace(cvodememobj):\n\tlenrw = ctypes.c_long()\n\tleniw = ctypes.c_long()\n\tret = cvode.CVodeGetWorkSpace(cvodememobj.obj, ctypes.byref(lenrw), ctypes.byref(leniw))\n\tif ret < 0:\n\t\traise AssertionError(\"... |
edf4a47c644b2e1bdc817dc25a924fda | Creating an AgentChannelKind with a wrong value should result in an exception being raised. | [
{
"docid": "186328c92ae9cf2b9a3ec32f768967c6",
"score": "0.6798634",
"text": "def test_agentchannelkind_value_error():\n # Value error exceptions\n with pytest.raises(ValueError):\n AgentChannelKind('FOO')",
"title": ""
}
] | [
{
"docid": "cf3d3c03e05e19f8736891fceb76c2ee",
"score": "0.560062",
"text": "def test_kind_value_error():\n # Value error exceptions\n with pytest.raises(ValueError):\n AgentAddressKind('FOO')",
"title": ""
},
{
"docid": "9188f9fc335a55df40baf605db363c9f",
"score": "0.537666... |
1617a027acaff9b58729d8fd3e8bd6da | Given 2 endpoints, return the (new or cached) LineSegment3D inst. | [
{
"docid": "d55ccdf361945f1ea8cb09f80f5cd51b",
"score": "0.67938554",
"text": "def add(self, p1, p2):\n key = (p1, p2) if p1 < p2 else (p2, p1)\n if key in self.seghash:\n seg = self.seghash[key]\n seg.count += 1\n return seg\n seg = LineSegment3D(p1... | [
{
"docid": "7e0a3749191aea6fe61f399996bc0efb",
"score": "0.6908455",
"text": "def line(self):\n vector = Vector3D.by_points(self.first_point, self.second_point)\n return Line3D(self.first_point, vector)",
"title": ""
},
{
"docid": "c150647dcdd6f08a05156c08e67c9120",
"score": "0.584... |
44a75547c75bf3c2f54ec3dff87b2b93 | Sentence Segmentation using the Punkt Tokenizer | [
{
"docid": "4cce3030a6a05ba5a0fce9131556aa1b",
"score": "0.78199977",
"text": "def punkt(self, text):\n self.tokenizer = nltk.tokenize.punkt.PunktSentenceTokenizer()\n segmentedText = self.tokenizer.tokenize(text)\n\n return segmentedText",
"title": ""
}
] | [
{
"docid": "f58079d75f5e0f212c388fc5f29bbb48",
"score": "0.80314946",
"text": "def punkt(self, text):\n\n\t\tsegmentedText = None\n\t\ttokenizer = nltk.tokenize.punkt.PunktSentenceTokenizer()\n\t\tsegmentedText = tokenizer.tokenize(text)\n\t\t#Fill in code here\n\t\t\n\t\treturn segmentedText",
"tit... |
d5a74d8de3c200a4dfc410fd620f4b41 | Get/set linkage limit for LG parser | [
{
"docid": "71eadf45a99b85fa9e54b254d3ef2816",
"score": "0.7665177",
"text": "def linkage_limit(self):\n return self._linkage_limit",
"title": ""
}
] | [
{
"docid": "eb9d2f669a3ec84c05d32b99c8ba844e",
"score": "0.57999986",
"text": "def get_limit(self):\n return # cardinal",
"title": ""
},
{
"docid": "1bb55988decf005466e0f44c966fca62",
"score": "0.5763379",
"text": "def set_cvar_limit(_name, _value, _min, _max, _flags):",
... |
d997c3fbd7a65c69194ab3c5bd023ee5 | Turns the User entity into a ProtoRPC object. This is necessary so the entity can be returned in an API request. | [
{
"docid": "e01b7cc434be849d9a2c38310d06914b",
"score": "0.0",
"text": "def to_message(self):\n return TokenResponseMessage(status='ok')",
"title": ""
}
] | [
{
"docid": "4b4f2fe2559cc629ce9e25319fba2353",
"score": "0.68809533",
"text": "def get_user(self):\n user_dict = MessageToDict(\n self.user,\n use_integers_for_enums=True,\n preserving_proto_field_name=True,\n including_default_value_fields=True\n ... |
756ef74f66ba95bb89e1df7f8960ac7a | Multiplies a polynomial to a polynomial or an integer. | [
{
"docid": "b6a58afb898515fe72686f1120cb0f1c",
"score": "0.68666255",
"text": "def __mul__(self, other):\n\n # if self == 0, return it\n if not self.__terms:\n return self\n\n # if other is an integer, create a constant polynomial\n if isinstance(other, int):\n ... | [
{
"docid": "e4b3c28709fdf72f5a2aed4a0ebb6e9d",
"score": "0.70012236",
"text": "def multiply(self,poly):\n new_p = []\n for value in poly.__polynom:\n for p in self.__polynom:\n base = value[0] * p[0]\n exponent = value[1] + p[1]\n new... |
52b82a137c060fcb03409346c9bbe686 | delete() deletes an existing model from the database | [
{
"docid": "191def5ec5d98a9e78473ece48080770",
"score": "0.75252324",
"text": "def delete(self):\n db.session.delete(self)\n db.session.commit()",
"title": ""
}
] | [
{
"docid": "28448c747b76f0b517b3d8efa3ac4bdf",
"score": "0.85271007",
"text": "def model_delete(self, db):\n db.session.delete(self)\n db.session.commit()",
"title": ""
},
{
"docid": "e4a20f3817b6c4b23893d63060d78974",
"score": "0.84566283",
"text": "def delete(self, mo... |
a324f530edbacc6422d4021593f8edf0 | Random Noise from Power Spectrum Returns a noised array where the instrinsic distribution follows that of the input power spectrum. The output has an instrinsic standard deviation scaled to 1.0. | [
{
"docid": "2776518dad6b4f04f2c0a4367c4516ab",
"score": "0.6511499",
"text": "def fft_noise(pow_spec, nstep_out=None, fmin=None, f=None, \n pad_mode='edge', rand_seed=None, **kwargs):\n \n rng = np.random.default_rng(rand_seed)\n\n if nstep_out is None:\n nstep_out = 2 * (le... | [
{
"docid": "c783546059e5eaf25438ae96ca38a13b",
"score": "0.7136442",
"text": "def samples_noise(self, n_samples):\n\t\treturn np.random.lognormal(mean=0.,\n\t\t\t\t\t\t\t sigma=1,\n\t\t\t\t\t\t\t size=n_samples)",
"title": ""
},
{
"docid": "fd9b7b563f8bf572ea5a8075c38057e3",
"score":... |
8a817b83009d6a9ab018750064a042b3 | Calculate the optimal control input for the given state. | [
{
"docid": "69414488f76f30f8cd4ef4a947d1e906",
"score": "0.0",
"text": "def calc_ilqr_input(env, sim_env, tN=50, max_iter=1e6, U=None):\n\n #start state\n x_0 = env.state.copy()\n\n #we start with an empty control\n #U = U[:tN,:].T\n\n U = np.zeros((env.DOF,tN)) if U is None else U\n c... | [
{
"docid": "82645842ed0150115cd4c022fdcb6ef1",
"score": "0.6532401",
"text": "def greedy_action(self, state):\n return np.argmax(self.get_value(state), axis=0)",
"title": ""
},
{
"docid": "578bc02054104fdbcdd646be189a6066",
"score": "0.63869345",
"text": "def computeValueFromQ... |
4fe192de26196cca0dc92ca1e5a03b13 | Remove an observer from the observer list | [
{
"docid": "d23894dac0e7cb5ee4bd1fd13867efbe",
"score": "0.8020863",
"text": "def unsubscribe(self, observer):\n self._observers.remove(observer)",
"title": ""
}
] | [
{
"docid": "b2b62f02e39cb4807e9c5f46955d6eec",
"score": "0.8981121",
"text": "def removeObserver(self, observer):\n self._observers.remove(observer)",
"title": ""
},
{
"docid": "8ca6223313c73e1b83fa2769c0c2c315",
"score": "0.8769711",
"text": "def remove_observer(self, observer):\... |
807b42158f62214474c044ef1ad31dbd | Builds environment variables for master IP hooks. | [
{
"docid": "fa319050f111d9ac4f6b041fa2f2e55d",
"score": "0.7472541",
"text": "def _BuildMasterIpEnv(master_params, use_external_mip_script=None):\n # pylint: disable=W0613\n ver = netutils.IPAddress.GetVersionFromAddressFamily(master_params.ip_family)\n env = {\n \"MASTER_NETDEV\": master_params.n... | [
{
"docid": "1c6a21abb5752927f8c3a854bd78bb1b",
"score": "0.6393331",
"text": "def export_env_vars(_):\n data = {}\n data[\"DJANGO_BASE_URL\"] = os.environ[\"DJANGO_BASE_URL\"]\n data[\"FLOWER_URL\"] = os.environ[\"FLOWER_URL\"]\n data[\"PROJECT_NAME\"] = os.environ[\"PROJECT_NAME\"]\n dat... |
fadecfb0046adaed16a466d19bdf8190 | Get the given environment in the given project. The authenticated user should have access to the project. | [
{
"docid": "3db610a1833cfced8564174161772e1d",
"score": "0.7150934",
"text": "def retrieve(self, env, options={}):\n body = options['query'] if 'query' in options else {}\n\n response = self.client.get('/orgs/' + self.org + '/projects/' + self.project + '/envs/' + env + '', body, options)\... | [
{
"docid": "27c98452d3e7f676662ebb70eb40b756",
"score": "0.6868601",
"text": "def get_environment():\n return settings.environment",
"title": ""
},
{
"docid": "b090213696c9188f1346fd8bed55ca13",
"score": "0.6650112",
"text": "def user_project_env_show(api_token, app_token, project... |
76c34ef3df353bcbfffad738239eef93 | We'll create the translators dynamically as needed for this class. This is probably woefully inefficient, but we'll try to fix that later. | [
{
"docid": "1b3b7d8622852cd88016ba0a8b66928a",
"score": "0.57577264",
"text": "def _getTranslator(self, idx):\n if idx >= self._count:\n raise IndexError('Index {0} be less than {1}'.format(idx, self._count))\n offset = self._elementsize * idx\n return _Translator(self._b... | [
{
"docid": "0717655d6203d83032f037c905b3d146",
"score": "0.7184714",
"text": "def get_translations():",
"title": ""
},
{
"docid": "ed720aac3763ade0fbb29b9ae54ea1fd",
"score": "0.68003803",
"text": "def translate():",
"title": ""
},
{
"docid": "567c635345b37f77623871dda2a3... |
4621d7fdb70ffa3a63c0c811750402e3 | Takes a list and converts it to a dictionary using the specified column, the column is not included in the values and the entries must be unique | [
{
"docid": "767c7450c6e4338e0166559c4c44deef",
"score": "0.7129954",
"text": "def list2dict(l, key_col):\n d = OrderedDict()\n for row in l: \n d[row[key_col]] = row[:key_col] + row[key_col+1:]\n return d",
"title": ""
}
] | [
{
"docid": "79469bd52f773ef161ef1767f0abe3fb",
"score": "0.6960906",
"text": "def getUniqueValuesDictionary(inputDataFrame, colslist = []):\n\n dictUniqueValues = {}\n\n try:\n \n if len(colslist) != 0:\n for i in range(len(colslist)):\n dictUniqueValues[col... |
3eeabde4d695507ea878f0f3eca102d2 | Perform a check to see if an [equals[ multiline comment or [equals[ longform string has been closed as it should have been We do this by adding a comment of the form [other[ ]equals] ]other] (where "other" is some other amount of equals) If [equals[ had been properly closed, this is a perfectly valid comment. If not, i... | [
{
"docid": "6abdf3d0c21f710ef5c369b66aa6893a",
"score": "0.65845835",
"text": "def check_closed(equals):\n other = '==' if equals == '====' else '===='\n return f' --[{other}[ ]{equals}] ]{other}]'",
"title": ""
}
] | [
{
"docid": "37278526b120da9e1c5c701a300cb69f",
"score": "0.7052073",
"text": "def helper_test_multiline_comment_or_long_string(prefix):\n\n def check_closed(equals):\n \"\"\"\n Perform a check to see if an --[equals[ multiline comment or\n [equals[ long-form string has been close... |
6a26762df83dc122d294ac2a17f2d969 | sum hours from time string | [
{
"docid": "1f316d8ee7355da24bfd54f1c1a1ade4",
"score": "0.8299522",
"text": "def parse_and_sum_hours(self, time_string: str) -> int:\n hours_string_list = re.findall(self.HOUR_REGEX, time_string)\n\n if hours_string_list:\n hours_string = \" \".join(hours_string_list)\n ... | [
{
"docid": "c130e8a9f684564af44e2b9d9f5139a1",
"score": "0.7645484",
"text": "def get_hours(time_str : str) -> float:\n h, m, s = time_str.split(':')\n s = s.split('.')[0]\n return int(h) + int(m) / 60 + int(s) / 3600",
"title": ""
},
{
"docid": "de1d7f34b8d5834254ef18c8179a6cb9",
... |
2c79b41f304666ec8fd6fdacd7db5870 | Moves the blank tile one row down. | [
{
"docid": "c355719f5edd67f840e75c0ffff1f34b",
"score": "0.5803637",
"text": "def move_down(self):\n pos = self.config.index(0)\n if pos in (6, 7, 8):\n return None\n else:\n new_config = list(self.config)\n new_config[pos], new_config[pos + 3] = new... | [
{
"docid": "d68516748ff56650d1ea24895c73bb97",
"score": "0.6547004",
"text": "def __down(self):\n new_board = self.copy()\n for i in range(4):\n col = self.__get_column(i)\n\n col.reverse()\n col = fold_4_list(col)\n col = col + [None] * (4 - len... |
269dcae555b3c86c05f20e0f7a788c6c | Sets the data and name members. | [
{
"docid": "98775ef650cae3f5bf7dfb26ae19b78c",
"score": "0.0",
"text": "def __init__(self, v=None, n=None):\n self._values = v\n self._name = n",
"title": ""
}
] | [
{
"docid": "5cffa65cd0eaf151ce496d65e1f082f6",
"score": "0.766992",
"text": "def set_data(self, data):\n\t\tself.data.update(data)\n\t\tif 'name' in data:\n\t\t\tself.set_name(self.data.get('name'))\n\t\tif 'tags' in data:\n\t\t\tself.set_tags(self.data.get('tags'))",
"title": ""
},
{
"docid... |
5101562e2122b4eac207fee6728cd909 | TODO 1. open and read question_file and response_file, make them as response dictionary 2. read conversation_file and make all pairs of lines in a row as reasponse dictionary 1 and 2 must be one dictionary | [
{
"docid": "93e5f59d6b6672676324b42ad777862e",
"score": "0.784545",
"text": "def load_canned_responses(question_file, response_file, conversation_file):\n #IMPLEMENT YOUR CODE\n canned_response_dic = {}\n A = []\n B = []\n C = []\n \n with open(question_file , 'r') as question:\n ... | [
{
"docid": "6f0a941a2d580a5373bc6b930c7fa6eb",
"score": "0.6792521",
"text": "def readAnswers(answerFile):\n \"\"\"\n Load answers into dict for analysis\n \"\"\"\n \n AllInfo, AllData = None, None\n CurrentInfo, CurrentData = None, None\n lineNum = 0\n \n with open(answerFile, 'r') as fIn:\n ... |
3babe2b28bf0a51664a48d7230909d13 | Show message in main window's status bar | [
{
"docid": "abc0a15688601a7d7d8e37f0e41b4900",
"score": "0.7901596",
"text": "def show_message(self, message, timeout=0):\r\n self.main.statusBar().showMessage(message, timeout)",
"title": ""
}
] | [
{
"docid": "7bbf89293bee7c4ee486613b33fbbf6e",
"score": "0.7645076",
"text": "def setStatusBarText(self,text=\"\"):\n self.statusBar().showMessage(text)\n QtCore.QCoreApplication.processEvents()",
"title": ""
},
{
"docid": "54c32148033c5612a713fdf45a4ac750",
"score": "0.761... |
5b95947e83a6fab636254a73d11bdd7c | Filters all the tweets and retains only the data that is required and puts it to the file with the name TWEETS_FILE_NAME | [
{
"docid": "c387c47a6ec5424a0f543cbff7c8fb30",
"score": "0.5975242",
"text": "def saveData(tweetLst, tweetFileName=TWEETS_FILE_NAME,userFileName=USER_FILE_NAME):\n\tglobal unique_userCounts, tweet_count\n\n\tunique_users = []\n\tunique_users_set = set()\n\tnewTweets = []\n\ttweetIds = []\n\n\tfor tweet ... | [
{
"docid": "a9b33e3176f36f9e0dc0585793f20670",
"score": "0.7059614",
"text": "def write_tweets(tweets, filename):\n \n with open(filename, 'w') as out_file:\n for tweet in tweets:\n out_file.write('TEXT: ' + tweet.text.with_headings() + '\\n')\n out_file.write('HASHTAG... |
0d3336988d64e1171efcbd30bfbedac9 | dumps the local bloom to a hexadicimal string representation | [
{
"docid": "a72b82789697303bfd0d6a09404287f7",
"score": "0.69733566",
"text": "def saveBF(bloom=LOCAL_BLOOM):\n try:\n hex_string = bfs[bloom].export_hex()\n return hex_string\n except KeyError:\n # if we have no bloom of this name we create an empty one\n # and we log ... | [
{
"docid": "3fb79fe622116980443a47259ffd713e",
"score": "0.64312583",
"text": "def hex(self):\r\n # A possible faster, all-python, implementation:\r\n # str(self.wkb).encode('hex')\r\n return wkb_w(3 if self.hasz else 2).write_hex(self)",
"title": ""
},
{
"docid": "3e00... |
0689b84d286d733d35154c149f4f8fd4 | return the number of elements in the forest | [
{
"docid": "a37d8703cc967ddaa817d9771484f315",
"score": "0.0",
"text": "def __len__(self):\n return self.__size",
"title": ""
}
] | [
{
"docid": "4a5532527fc756908a881034eb4a9e9f",
"score": "0.7763599",
"text": "def num_trees(self):\n return _regression.fanova_forest_prototype_num_trees(self)",
"title": ""
},
{
"docid": "e0b3cac74e7db2576d35ed8e83f63546",
"score": "0.7652106",
"text": "def num_trees(self):\n... |
85297c5b92a17ef0a8da05bee13c3a86 | Get an attribute from this object | [
{
"docid": "07234598e0cb5ec3ccb82881534f4c81",
"score": "0.0",
"text": "def __getattr__(self, name):\n self.assert_valid()\n value = _swig_getattr(self, self.__class__, name)\n try:\n old_dict = self.__dict__[\"_member_dicts\"][name]\n value.__dict__[\"_parent_pool\"] = old_... | [
{
"docid": "aecaddc4a907dedb31781e4dd4e85003",
"score": "0.84407556",
"text": "def get_attribute(self, attr):\n if attr in self.data:\n return self.data[attr]",
"title": ""
},
{
"docid": "d7c746763914fc80f0de465b83f4a3a9",
"score": "0.83231586",
"text": "def __getat... |
a2df1d797974e0e897cf86a44160f463 | Return a first estimation on the parameter from the data. | [
{
"docid": "270e7c275de89d288b9c8ed7dbc076ba",
"score": "0.0",
"text": "def calc_estimate(data):\n xc0, yc0 = data.x.mean(axis=1)\n r0 = np.sqrt((data.x[0] - xc0) ** 2 + (data.x[1] - yc0) ** 2).mean()\n return xc0, yc0, r0",
"title": ""
}
] | [
{
"docid": "9f1dc23f54c0c1297937925da7af35c9",
"score": "0.64182067",
"text": "def first_parameter(self, singular_value):\n self.log(\"calc first term parameter @\" + str(singular_value))\n first_term = self.I_minus_R(singular_value) * self.parcov * \\\n self.I_minus_R(... |
62a4e85e6afa8226c2691f6462640c97 | Select one individual according to the Birth Fitness. | [
{
"docid": "8b935000557a23489c345a19f20f9852",
"score": "0.54387355",
"text": "def _roulette_wheel_selection_Birth(self):\n return self.__roulette_wheel_selection(attr=\"BirthFitness\")",
"title": ""
}
] | [
{
"docid": "f19d256d620b1aff0896345f08a7d6e0",
"score": "0.5910112",
"text": "def Selection(self):\n fitness_1, individual_1 = self.pb.Load_from_Disk()\n fitness_2, individual_2 = self.pb.Load_from_Disk()\n if fitness_1>fitness_2:\n self.pb.Killer(individual_2.ID)\n ... |
76280cf3e4e7da292043754d4c2071c3 | Called when a job is submitted | [
{
"docid": "54698137952fc695fdb32ce9901ba2ed",
"score": "0.6712782",
"text": "def submit(self, job_parameters):\n # FIX: Don't pass through the real job name. Bilby outputs the job files by whatever this parameter is, that\n # means that names containing special characters will break. Uniq... | [
{
"docid": "1999990f3e23260083a967d5cd9a493a",
"score": "0.7461958",
"text": "def submit(self):\n self.keep_data = False\n ManagedJob.submit(self)",
"title": ""
},
{
"docid": "2eb00eaf94ac2887b81a0a999c7949c8",
"score": "0.7376575",
"text": "def submit(self, data):\n ... |
77fad2190741e52ffa31c26da0f81972 | Test album route status code. | [
{
"docid": "8c64338a5c0c2163366393c3e6736031",
"score": "0.74703544",
"text": "def test_album_status(self):\n response = self.client.get('/images/albums/')\n self.assertEqual(response.status_code, 200)",
"title": ""
}
] | [
{
"docid": "aa6d6d3c967cd9242dd3db84704ed00f",
"score": "0.7424221",
"text": "def test_album_detail_status(self):\n response = self.client.get('/images/albums/1')\n self.assertEqual(response.status_code, 200)",
"title": ""
},
{
"docid": "7d35a43eba9ba797374522532f9497b1",
"... |
30a4bec1e3ac0001499b43d62e0f941d | Wait up to max_delay seconds then returns length of delay | [
{
"docid": "ebe7bb23570ee99e9b9d6d9ae3507d30",
"score": "0.7474672",
"text": "async def wait_random(max_delay: int = 10) -> float:\n delay = random.uniform(0, max_delay)\n await asyncio.sleep(delay)\n return delay",
"title": ""
}
] | [
{
"docid": "89ad7c7d0920fadb4f3b3f4e065a63d3",
"score": "0.7511554",
"text": "async def wait_random(max_delay: int = 10) -> float:\n actual_delay: float = random.uniform(0, max_delay)\n await asyncio.sleep(actual_delay)\n return actual_delay",
"title": ""
},
{
"docid": "ef17c0468ed6... |
ed70989948b0a3528da56a469822b590 | Load data from filename into table | [
{
"docid": "9695f359edda9e0b866746ab9f3f96cc",
"score": "0.0",
"text": "def bcpin (table, filename):\n\n # disable indices for performance\n db.disableIndices(table)\n\n filepath = os.path.join(OUTPUTDIR, filename)\n db.bcp(filepath, table)\n\n # re-enable indices\n ... | [
{
"docid": "de2fc79571e851605b3687f4a3c9b4b9",
"score": "0.7550051",
"text": "def read_file(file_name, table):\r\n table.clear() # this clears existing data and allows to load data from file\r\n try:\r\n objFile = open(file_name, 'r')\r\n except FileNotFoundError as e:\r... |
823eb8f1f331790e5fc7385c2a75b535 | Linear regression loss without regularization terms; 0.5 to be consistent with sklearn Lasso (the global problem we try to solve) | [
{
"docid": "237d65d75610d1befc37d1b67f8c1947",
"score": "0.60907257",
"text": "def loss(self,X,y,beta):\n ret = ''\n for k,v in X.items():\n ret += 'y = %s \\n v = %s \\n beta = %s \\n SparseDotProduct(v,beta) = %s \\n'%\\\n (str(y),str(v),str(beta),str(Sparse... | [
{
"docid": "16086d5b054f53ccd1f915771c34db93",
"score": "0.75056636",
"text": "def linearSquaredLoss(x, y, w):\n # Sum along the features and take average across data points\n ypred = np.dot(x, w)\n L = 0.\n L = ((ypred - y)**2).sum(1).mean()\n return L",
"title": ""
},
{
"doc... |
3e8567946eb860f67fc1370a8afcf82e | Cancel a running query | [
{
"docid": "7ed588b91b25e39a5df6533abff1a773",
"score": "0.8155592",
"text": "def cancel(self):\n if self.status not in RUNNING_STATUSES:\n raise QueryError(\"Query is not running\")\n self.session.delete(self.url)",
"title": ""
}
] | [
{
"docid": "35e3e66f16f88935b41110e9d6cfd275",
"score": "0.78888893",
"text": "def cancel_job(self,query_data):\n self.cur.execute(query_data)\n self.conn.commit()",
"title": ""
},
{
"docid": "37fb0fe263dfb1afbb22fc2a9435dec8",
"score": "0.78090525",
"text": "def cancel... |
9e9e231dbd214ba2bb9cfa24d480c12b | Initialize the mnist data set as a one hot vector and labelled data | [
{
"docid": "e94c76edf8d65a50e0f25605a39a16b9",
"score": "0.66478354",
"text": "def __init__(self):\n self.mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n self.mnist_model = MNISTNeuralModel()\n self.mnist_model.setup_layers()",
"title": ""
}
] | [
{
"docid": "3448d31cef8cfa9526a14a13a479adac",
"score": "0.7462869",
"text": "def one_hot(labels):\n nClasses = 10\n oneHot_labs = np.zeros((labels.shape[0], nClasses))\n for i in range(len(labels)):\n oneHot_labs[i][labels[i]] = 1.\n return oneHot_labs",
"title": ""
},
{
... |
c6d17ee64b7116af6e4d0130f849f3aa | Get all clusters with Cloud Run enabled. | [
{
"docid": "57658d2d7d22bba45237347a77f9819e",
"score": "0.65372664",
"text": "def ListClusters(location=None):\n container_api = container_api_adapter.NewAPIAdapter(CONTAINER_API_VERSION)\n project = properties.VALUES.core.project.Get(required=True)\n\n response = container_api.ListClusters(project,... | [
{
"docid": "a171c4ee0820b69c03240fa1040c32be",
"score": "0.6965474",
"text": "def get_all_clusters(self):\n if self.mInitialized:\n return self.mApi.get_all_clusters(view='full')\n else:\n raise Exception(\"this CmApi instance isn't properly initialized\")",
"titl... |
f04ccaef60ed4799232cfde8be87d7d9 | Store a single response to the survey | [
{
"docid": "6965ba1af4bf2b23f38fee618a881e76",
"score": "0.6810325",
"text": "def storeResponse(self, newResponse):\n self.responses.append(newResponse)",
"title": ""
}
] | [
{
"docid": "2c488367bcfc28e49f1b9186e3fd6da2",
"score": "0.7780322",
"text": "def test_store_single_response(self):\n\t\tself.my_survey.store_response(self.responses[0])\n\t\tself.assertIn(self.responses[0], self.my_survey.responses)",
"title": ""
},
{
"docid": "a1352f01764da735d6caa4a732072... |
47c5c6a4cd55fdc0687357fadfa35227 | plot current playfield into a surface with given 'dim'ensions. If dim is (0,0) zoom = 1 (16x12 each block) will be used. Resulting surface may exceed screen or dim, Draw a 'border' (scaled as well). Draw playfield with at least 'minsize' blocks size and center it in dim. If floatzoom is false, only integer zoomlevels a... | [
{
"docid": "c9323c06859d7672c77093f5b1ca52e8",
"score": "0.68816406",
"text": "def plot(self, dim=(0,0), minfield=(20,17), border=2, floatzoom=False):\n # grid size\n gw = self.soko.currentgrid.width\n gh = self.soko.currentgrid.height\n # border offset left and top\n ... | [
{
"docid": "d5b91ce8bbe9e797b816b8fc8f91e28f",
"score": "0.54572695",
"text": "def drawGrid(surface,playfieldWidth,playfieldHeight,camera):\r\n GRIDSIZE = 50\r\n BLACK = (0,0,0)\r\n H_Start = 0\r\n H_End = playfieldWidth\r\n V_Start = 0\r\n V_End = playfieldHeight\r\n for i in range... |
646cd874a9a7ce1085e9b2d50404b7c9 | Greps last pressure from OUTCAR | [
{
"docid": "38e3f0e410fcc87bf62341683e24b6a3",
"score": "0.6180783",
"text": "def pressure(self):\n if not self.is_dft: raise AttributeError('not a DFT calculation.')\n from quantities import kbar as kB\n regex = r\"\"\"external\\s+pressure\\s*=\\s*(\\S+)\\s*kB\\s+Pullay\\s+stress\\s*=\\s*(\\S+... | [
{
"docid": "3dbee71bc4fb43e68e95a434192cca33",
"score": "0.6321372",
"text": "def pulay_pressure(self):\n if not self.is_dft: raise AttributeError('not a DFT calculation.')\n from quantities import kbar as kB\n regex = r\"external\\s+pressure\\s*=\\s*(\\S+)\\s*kB\\s+\" \\... |
eb048e99621e54472dc93b34ae43ba4d | Put code that handles mouse events here. | [
{
"docid": "ec42501ba8b03f232c4b2d4bc085afba",
"score": "0.0",
"text": "def process_mouse_event(self, pos, down, double_click):\n if down and self.deck.is_clicked(pos):\n card_ = self.deck.pop_top_card()\n if isinstance(card_, card.Card):\n card_.flip()\n ... | [
{
"docid": "0724e508b45727794ef2255b06046ca7",
"score": "0.79453975",
"text": "def handle_event(self, event):\n self.mouse.handle_event(event)",
"title": ""
},
{
"docid": "aa8cf9eb98802b97682e63f392e1705f",
"score": "0.70289916",
"text": "def on_mouse_over(self):\n pass... |
859bab55c50e10538a32b4d02375e395 | Query the SLURM host for all jobs in /[controldir]/processing with ``squeue``. If the job has stopped running, more detailed information is fetched with ``scontrol``, and the diagnostics and comments files are updated. Finally ``gmkick`` is executed on all jobs with an exit code. | [
{
"docid": "a81619da127c58014d34b0d13367b136",
"score": "0.64918053",
"text": "def Scan(config, ctr_dirs):\n \n configure(config, set_slurm)\n if cconfig.Config.scanscriptlog:\n scanlogfile = arc.common.LogFile(cconfig.Config.scanscriptlog)\n arc.common.Logger.getRootLogger().addD... | [
{
"docid": "bf5f5609e07e6a6a386c7ed8929ff8a0",
"score": "0.6614079",
"text": "def jobs(self, status=None):\n #TODO: list completed but not deactivated as well as completed\n #print \"queue\"\n output,_ = _slurm_exec('squeue','-o', '%i %M %j')\n #print \"output\",output\n ... |
68e042ee1dc76e2e2d26046d63f7ec94 | Gets scalar, vector and tensor information from the given data (either cell or point data). | [
{
"docid": "6a811de2fefe8fe89ec07d752fe488b7",
"score": "0.0",
"text": "def get_attribute_list(data):\r\n attr = {'scalars':[], 'vectors':[], 'tensors':[]}\r\n if data is not None:\r\n n = data.number_of_arrays\r\n for i in range(n):\r\n name = data.get_array_name(i)\r\n ... | [
{
"docid": "97cb70485abb8f4c4c5c795aac78857d",
"score": "0.68607485",
"text": "def get_data_tensor(self, data: Any) -> Any:\n pass # noqa",
"title": ""
},
{
"docid": "95e5f66b992ebe1010f039f5b4de8e26",
"score": "0.6672319",
"text": "def get_data_tensor(self, data: np.ndarray ... |
e05ee522a1f9eff4ea434a6863b27081 | Parses a firebase_link path into its component segments. | [
{
"docid": "caba7b409b528d06516c3b3b6559c4e6",
"score": "0.73900986",
"text": "def parse_firebase_link_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^properties/(?P<property>.+?)/firebaseLinks/(?P<firebase_link>.+?)$\", path\n )\n return m.groupdict() if m else ... | [
{
"docid": "1a69cb30c3dd4d7ddd77501094ceeb06",
"score": "0.58743846",
"text": "def parse_google_ads_link_path(path: str) -> Dict[str, str]:\n m = re.match(\n r\"^properties/(?P<property>.+?)/googleAdsLinks/(?P<google_ads_link>.+?)$\",\n path,\n )\n return m.gro... |
1ab7be6dbc7809d5fe7aff85d6449907 | Parse and apply coordinate related keywords from the FITS header. | [
{
"docid": "d5be3d4f5071bc8a1993d5cfaeb960aa",
"score": "0.0",
"text": "def parse_astrometry(self):\n options = self.options\n if options is None:\n return\n if \"EQUINOX\" in options:\n self.epoch = Epoch.get_epoch(options.get_float(\"EQUINOX\"))\n\n if... | [
{
"docid": "37e08d9dad3596d043812179c1fa26a2",
"score": "0.61663365",
"text": "def parseheader(self, s):\n\n i = s.find('rEL3X')\n if i>0:\n xy = 'x'\n i = s.find('rEL3Y')\n if i>0:\n xy = 'y'\n\n i1 = s.find('Freq=')\n i2 = s.find('Phi=')\... |
fbb18c1ee65dd63e7ffed74958fd0b78 | This function is responsible to parsing the users file | [
{
"docid": "bf7dc5ce4bebed91c7c84ba5782cfec7",
"score": "0.678132",
"text": "def get_users(users_file):\n if not os.path.exists(users_file):\n LOGGER.info(\"Users file not found\")\n return\n with open(users_file) as users:\n for f in users:\n yield f.strip()",
... | [
{
"docid": "21d1b3383c40c6641131db6239e179e7",
"score": "0.7200983",
"text": "def parseUserDataFile(_userFile):\n users = {}\n f = open(_userFile, 'r')\n lines = f.readlines()\n for line in lines:\n features = []\n tokens = line.split(\"::\")\n\n # Encode gender variable, 1 is female, 0 is ... |
2bb187a8f728d50e37e56ddc3e5b4b94 | return the string representation of the minheap | [
{
"docid": "51ae14e7ed7d005e727a0638c6001487",
"score": "0.0",
"text": "def __str__(self):\n\t\treturn str (self.__h[:self.__len])",
"title": ""
}
] | [
{
"docid": "4cbc3a7fa477e7e4d787b8f31495e158",
"score": "0.7949308",
"text": "def __str__(self) -> str:\r\n return str(self._heap)",
"title": ""
},
{
"docid": "71cbb90c8cdec1e025fb2cf874820806",
"score": "0.78326654",
"text": "def __repr__(self):\n return 'BinaryMinHeap... |
2e5e76f8c98a86efe13e21d7d08333a3 | Computes the precision for the specified values of k | [
{
"docid": "95eaf79f5561e4f222a41d35de98e8dd",
"score": "0.0",
"text": "def accuracy(output, target, topk=(1,)):\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n ... | [
{
"docid": "15d8595b17755acee4fd1baadb8ec6e4",
"score": "0.82579345",
"text": "def precision_at_k(scores, expected, k):\n first_k = reorder(scores, expected)[0:k]\n targets = [t for s, t in first_k]\n return sum(targets) / len(targets)",
"title": ""
},
{
"docid": "a594b0ff377cc7e98b... |
7040654ca84b61d83e211eedb657fb1d | Create dictionary of transient variables for carbon pools. | [
{
"docid": "8713d37db3a67417f2d96e0e66dd6b67",
"score": "0.702766",
"text": "def _create_transient_dict(carbon_pool_transient_uri):\n transient_dict = utils.build_lookup_from_csv(\n carbon_pool_transient_uri, 'code')\n\n def _filter_dict_by_header(header_prefix):\n \"\"\"Retrieve soi... | [
{
"docid": "4225b5576ad60e2d7f5af9cb7bf73256",
"score": "0.64676",
"text": "def variable_map(self):\n return {}",
"title": ""
},
{
"docid": "233fb855217e799f74cb30083bcaa293",
"score": "0.64284384",
"text": "def getCarbonDict(self):\n transDict = self.getTransitionDict(... |
28bb051accd2de56c55363aa402655f1 | This function asserts that the answers positions is correct. | [
{
"docid": "ac2df4cda3f04ed77003e0cb83de7268",
"score": "0.7074101",
"text": "def assert_answer_position(track_variables, group):\n\n ans = track_variables['answers'][0].tolist()\n x = track_variables['x'][0].tolist()\n ans = array(ans)\n x = array(x)\n unknown_error = 0\n file_error =... | [
{
"docid": "99902007de83b22b4643e44d94cfcc42",
"score": "0.6440867",
"text": "def test_anyOfTheAbove(self):\n qns = [x for x in self.createSource(\"\"\"\n%ID\tq1\n%title\tQuestion with no right answer\n%format\tlatex\n\nWhat is the winning move?\n\na) LEFT 5\nb) FORWARD 10\nc) Jump!\nd.true) None... |
74857d7a0373d6a7d36b123660da00b5 | Adds text strings to an existing plot and returns a PlotId representing the text strings added. txt = Ngl.add_text(wks, plot, text, x, y, res=None) wks The identifier returned from calling Ngl.open_wks. plot The id of the plot which you want to add the text strings to. text An array of text strings to add. x, y Onedime... | [
{
"docid": "22421d8ce2e322d93209955441385101",
"score": "0.72810423",
"text": "def add_text(wks,plot,text,x,y,rlistc=None):\n rlist = _crt_dict(rlistc)\n \n#\n# Separate the resource dictionary into those resources\n# that apply to am or tx.\n#\n am_rlist = {}\n tx_rlist = {}\n for key in list(r... | [
{
"docid": "b39a879f7a39566b5b58bfcf887d41e6",
"score": "0.68749857",
"text": "def addText(self, text, x_coord, y_coord):\n label = self.font.render(text, True, self.font_color)\n self.stuff_to_draw.append((x_coord, y_coord, label))\n self.render()",
"title": ""
},
{
"do... |
85a5ac847daea2bdbfb48d5de2f0a377 | Extract the way (w) if its type matches extract_type (highway, footway, or cycleway). | [
{
"docid": "c0a3fec820b17b88432841a4a86e27a4",
"score": "0.81712985",
"text": "def extract_way_type(self, w):\n should_extract = False\n way_type = None\n for tag in w.tags:\n if tag.k == self.extract_type:\n way_type = tag.v\n should_extract... | [
{
"docid": "1ed8c0a4d8bbd070e680b3435b77f234",
"score": "0.8201045",
"text": "def way(self, w):\n if self.extract_type == 'tennis':\n self.extract_if_tennis_court(w)\n else:\n self.extract_way_type(w)",
"title": ""
},
{
"docid": "4fe44326e76870c0e345d867ce... |
10f01af8b17147195aef087efee5ef1b | Retrieves the trophy groups for a title and their respective trophy count. This is most commonly seen in games which have expansions where additional trophies are added. | [
{
"docid": "8491b643a9cd4ccb1c69a313985129f6",
"score": "0.6149653",
"text": "def trophy_groups_summary(self, platform: Literal[\"PS Vita\", \"PS3\", \"PS4\", \"PS5\"]) -> TrophyGroupsSummary:\n return TrophyGroupsSummaryBuilder(\n request_builder=self._request_builder,\n np... | [
{
"docid": "8ea6aeae0e267090522c225821af6510",
"score": "0.5491638",
"text": "def tgroups():",
"title": ""
},
{
"docid": "b20ff0f1eee8541dd9b22e0708e29e3e",
"score": "0.5440492",
"text": "async def get_groups():\n ont_r = OntologyResource(url=get_sparql_endpoint())\n si = Sparq... |
078cdbb2f2bead0530a4915070b27e86 | enable workflow interrupt handler while solver is running | [
{
"docid": "80a620060d8ce8c30389ddc42c2dbfc3",
"score": "0.5286894",
"text": "def enable_signal_handler(self):#, callback='*'):\n \"\"\" #XXX: disabled, as would add state to solver\ninput::\n - if a callback function is provided, generate a new handler with\n the given callback. If call... | [
{
"docid": "0d9537b29c780fcf5340a6f9e2e91cd8",
"score": "0.687691",
"text": "def interrupt_handler(self):\n pass",
"title": ""
},
{
"docid": "27d87ac90a5a3cd21ac1c4f8afd24551",
"score": "0.6475221",
"text": "def setup_interrupt(self):\n raise NotImplementedError",
"... |
3ee2a6fc478543dde1367c6055626fdf | SetGamma(itkHessianToObjectnessMeasureImageFilterISSRTD33IUS3 self, double const _arg) | [
{
"docid": "5b0306420ecbc948e7a7b75aa631eac2",
"score": "0.90032715",
"text": "def SetGamma(self, _arg: 'double const') -> \"void\":\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD33IUS3_SetGamma(self, _arg)",
"title": ""
}
] | [
{
"docid": "996a04245fc6dd2dad4b828b2542ba67",
"score": "0.8934521",
"text": "def SetGamma(self, _arg: 'double const') -> \"void\":\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD33ISS3_SetGamma(self, _arg)",
"title": ""
},
{
"do... |
ba48b334b541d2f8be19506ea61d883c | Retrieve all the interfaces of the system including bridges, virtual ethernet, and loopback. | [
{
"docid": "bc4350f75f02416745267599b3905812",
"score": "0.7531896",
"text": "def GetInterfaces(\n self, request: core_pb2.GetInterfacesRequest, context: ServicerContext\n ) -> core_pb2.GetInterfacesResponse:\n ifaces = []\n for iface in os.listdir(\"/sys/class/net\"):\n ... | [
{
"docid": "96d45b924bdcad9a2543f83c0ff865cc",
"score": "0.7642081",
"text": "def show_all_interfaces(self):\n result = self.run_show_command(\"show interface brief\")\n interfaces = result['ins_api']['outputs']['output']['body']['TABLE_interface']['ROW_interface']\n return {interfa... |
5198b2fd1d8f4891fc48693cee1a4477 | Computes the source term in a humidity equation. Supported types of humidity are `q_t` and `q_r`. | [
{
"docid": "c96e153456289cb12ad78bd35c8a39ac",
"score": "0.6919277",
"text": "def humidity_source_fn(\n self,\n varname: str,\n states: water.FlowFieldMap,\n additional_states: water.FlowFieldMap,\n thermo_states: water.FlowFieldMap,\n ) -> water.FlowFieldVal:\n q_r = thermo... | [
{
"docid": "9b3fc5d30208f07def27adc16b99b686",
"score": "0.61881125",
"text": "def potential_temperature_source_fn(\n self,\n varname: str,\n states: water.FlowFieldMap,\n additional_states: water.FlowFieldMap,\n thermo_states: water.FlowFieldMap,\n ) -> water.FlowFieldVal:\n ... |
bbf63c3e14cc1fabd30e0ca2b5e7c3b2 | Performs ising updates on the array. | [
{
"docid": "683bd0a856dc0f019ade9292aae39b51",
"score": "0.0",
"text": "def ising(updates, universe):\n if updates == 0:\n return\n cost = np.zeros(3, np.float32)\n cost[1] = np.exp(-4 * universe.beta)\n cost[2] = cost[1] ** 2\n N = universe.size\n D = universe.width\n univer... | [
{
"docid": "9ad33f32f0601f4d846df57b664a452a",
"score": "0.6835648",
"text": "def update_array ( self, value ):\r\n self._busy = True\r\n self.value = value\r\n self._busy = False",
"title": ""
},
{
"docid": "07e970d206b1518e4d307cc25073789f",
"score": "0.6616204",
... |
6bced873298c752c936a265b7ea06cc1 | Set pixel clock in MHz. | [
{
"docid": "edec358d929831671f9a869a877d7a6a",
"score": "0.6940596",
"text": "def set_pixelclock(self, exp):\n pParam = ctypes.c_uint(exp)\n self.call(\"is_PixelClock\", self._camID, IS_PIXELCLOCK_CMD_SET, ptr(pParam), ctypes.sizeof(pParam))",
"title": ""
}
] | [
{
"docid": "3e25385c47590f689921102fd5770292",
"score": "0.6754697",
"text": "def set_clock(self, freq, line_rate):\n self._set_clock(IDT_8T49N24X_XTAL_FREQ, freq, True)",
"title": ""
},
{
"docid": "31fbac39a43be59b8a8603efe939e90f",
"score": "0.6615966",
"text": "def set_SpiC... |
be7e5a54a675afab9e94b3f62743efc6 | Triplet loss for speaker identification. | [
{
"docid": "7c14f98e72171ee08a67b8145f319676",
"score": "0.0",
"text": "def triplet_loss_tf(y_true: tf.Tensor, y_pred: tf.Tensor, **kwargs) -> tf.Tensor:\n \n # get hyperparameters from kwargs\n alpha = float(kwargs['alpha'])\n batch_size = int(kwargs['batch_size'])\n \n # compute simi... | [
{
"docid": "8e190672059a1d2b6f01a6ccbd1700e8",
"score": "0.6371815",
"text": "def TripletLoss(anchor, positive, negatives):",
"title": ""
},
{
"docid": "814f384d3b954a793c0a58101d0b99c9",
"score": "0.6268254",
"text": "def loss(self):\n pass",
"title": ""
},
{
"doc... |
8a5ef99b34473b9e864db1e6058b3504 | Check to see if provided argument is a depth | [
{
"docid": "21ac04e5564d16d78dc6bcdc124319e7",
"score": "0.8345771",
"text": "def is_depth(arg):\n depth = False\n if re.search(r'depth:', str(arg).lower()):\n depth = True\n return depth",
"title": ""
}
] | [
{
"docid": "df7d5622d4a3a3db8b577fd643a968dd",
"score": "0.70123684",
"text": "def presentDepth(*args, **kwargs):\n \n pass",
"title": ""
},
{
"docid": "2edf15ed9a0f53d0cafa9578b50a945a",
"score": "0.66973084",
"text": "def get_depth(arg):\n depth = re.sub(r'depth:', \"\... |
bb9bb62a21295d38d36dbdbe233393cc | Get the initial blocks file for a leaf genome. | [
{
"docid": "d87e1c538d85d84f639e718dc9a97195",
"score": "0.643224",
"text": "def get_leaf_blocks(hal_path, genome, output_path):\n seqs = get_sequence(hal_path, genome)\n lengths = get_chrom_sizes(hal_path, genome)\n with open(output_path, 'w') as out:\n for chrom in sorted(lengths.keys(... | [
{
"docid": "358aba9b0407df8c4fb14b8ea5ad3082",
"score": "0.5765065",
"text": "def genesis():\n\n prevHash = \"0\"*64\n data = \"genesis\"\n return createBlock(data, prevHash)",
"title": ""
},
{
"docid": "fd966cfa011bcfe6b8fec217d50269c1",
"score": "0.5304925",
"text": "def g... |
e983f086a6c8457f2aa4f7c71ce5a80e | Getter method for id, mapped from YANG variable /nsd_catalog/nsd/vnffgd/rsp/id (string) | [
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.0",
"text": "def _get_id(self):\n return self.__id",
"title": ""
}
] | [
{
"docid": "ffcfa10b0baa4a9a52f8f9152b406e45",
"score": "0.55626786",
"text": "def get_id(cls, id): # pragma: no cover\n pass",
"title": ""
},
{
"docid": "e1c4d7b0e0aae331586a0eb8d350a3c3",
"score": "0.55282295",
"text": "def _get_rsp_id_ref(self):\n return self.__rsp_id_r... |
6cc2da6071e59ac67a2940771fb643a7 | Create a handle to a Krigrid object | [
{
"docid": "c171116e2c69b716018a212379b0b73b",
"score": "0.0",
"text": "def create(cls):\n ret_val = gxapi_cy.WrapKGRD._create(GXContext._get_tls_geo())\n return GXKGRD(ret_val)",
"title": ""
}
] | [
{
"docid": "b9939eb5b27343ff163039b76c2f9bbb",
"score": "0.53088",
"text": "def New(*args, **kargs):\n obj = itkPathDCID33.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj",
"title": ""
},
{
"docid": "1c06c617c5ba50115c5e1b2c... |
b07b44514ff12511414c931c30f2022c | Populates self.ch and returns either the character or None for end of input. | [
{
"docid": "9b5acf22aacc91bfe894293a3907eb0d",
"score": "0.76046914",
"text": "def next_char(self):\n if not self.at_end:\n try:\n self.ch = self.gen.next()\n except StopIteration:\n self.at_end = True\n self.ch = None\n re... | [
{
"docid": "f497451072452ec8561d2cb2d2e41c59",
"score": "0.7178319",
"text": "def __call__(self, raw=False):\n # get one or more char here.\n first_char = self._do_getch()\n # future logic for encoding issues here.\n # future logic for remapping to appropriate keypress here.\... |
95e0f3b83797c1c161d790712e94615d | Gets the subsystems of this EdgeMetrics. | [
{
"docid": "6fa33d5326e7907c3571d876f737692b",
"score": "0.79742545",
"text": "def subsystems(self):\n return self._subsystems",
"title": ""
}
] | [
{
"docid": "e2d74396aa001f9d735412ba0be4f14a",
"score": "0.58958477",
"text": "def subsystems(self):\n ret = set()\n for task_type in self.task_types():\n ret.update([dep.subsystem_cls for dep in task_type.subsystem_dependencies_iter()])\n return ret",
"title": ""
},
{
"docid":... |
360354efab565d834b96e5e642b3d6eb | Try to find the a value in a sorted list of elements | [
{
"docid": "79924a620c605bafc44c545129d55c3e",
"score": "0.608462",
"text": "def dichotomic_find(l,v):\n indexm = None\n begin = 0\n end = len(l) -1\n found = False\n\n while not(found) and begin <= end:\n indexm = (begin + end) // 2\n if l[indexm] == v:\n found =... | [
{
"docid": "5d7ff3b4315ad0735d531aa4ba56c7d2",
"score": "0.70573443",
"text": "def find(elements, value, key=lambda x: x):\r\n index = find_index(elements, value, key)\r\n return None if index is None else elements[index]",
"title": ""
},
{
"docid": "3d4923d0893d1a30cad0f31a0627c05d",
... |
4f86189bb7386881d786f306e02ce5b2 | transcription of DNA strand to RNA | [
{
"docid": "0070f3fd22b44d1c9799469fc7dac1d8",
"score": "0.7223157",
"text": "def DNA_to_RNA(input_DNA):\n for letter in input_DNA:\n if letter == \"T\":\n RNA_seq = input_DNA.replace (\"T\", \"U\")\n return RNA_seq",
"title": ""
}
] | [
{
"docid": "c9b8a07b198e4d9e47e06f135b70874e",
"score": "0.8091711",
"text": "def to_rna(dna_strand):\n\n return dna_strand.translate(DNA_TO_RNA)",
"title": ""
},
{
"docid": "96effa74679c587ea2ba5cfe061e2dff",
"score": "0.8041995",
"text": "def to_rna(dna_strand: str) -> str:\n ... |
af09ec2c0e41760d99edb500f65bfbb8 | Conducts ONE TOO FEW iterations of BellmanFord | [
{
"docid": "5f292681be62640ae1f5a7fe4e42e67e",
"score": "0.5418772",
"text": "def defective_bellman_ford(G, src):\n from ch07.single_source_sp import WEIGHT\n inf = float('inf')\n dist_to = {v:inf for v in G.nodes()}\n dist_to[src] = 0\n edge_to = {}\n\n def relax(e):\n u, v, we... | [
{
"docid": "af3a903b1142e01732e6a898e55d4ef1",
"score": "0.6557465",
"text": "def challenge_bellman_ford():\n from ch07.single_source_sp import bellman_ford\n DG = nx.DiGraph()\n DG.add_edge('d', 'e', weight=1)\n DG.add_edge('c', 'd', weight=1)\n DG.add_edge('b', 'c', weight=1)\n DG.ad... |
260535276547a50ec4d4b9c711855ac5 | Pads a sequences of sequences so that they're all the same length. Inplace. | [
{
"docid": "f84708de8511a0ff35a99d7162a72692",
"score": "0.6910745",
"text": "def pad_sequences(sequences, token=0, max_length=None):\n\n if max_length is not None:\n for s in sequences:\n while len(s) > max_length:\n s.pop()\n\n mx = max([len(s) for s in sequences... | [
{
"docid": "eed944a1d27bddfe7f93a8af99bfc0bc",
"score": "0.7702567",
"text": "def _pad_sequences(sequences, max_steps):\n if isinstance(sequences, np.ndarray): return sequences\n assert isinstance(sequences, list)\n checker.check_positive_integer(max_steps)\n checker.check_type(sequences, np... |
6aa881b9a2f30de1ffe583b1ffce1eb5 | Put relevant metrics data of class into dict | [
{
"docid": "5afb20a90d6603e24821ab4cd00afd40",
"score": "0.64603966",
"text": "def metrics_dict(self):\n return {\"num_msgs\" : self.num_msg,\n \"throughput\" : self.throughput, \n \"queue_length\" : self.qlength,\n \"latency\" : self.latency,\n ... | [
{
"docid": "6d0d663c48cf8e46387df0b963c73172",
"score": "0.69854665",
"text": "def stats_by_class(dataset):\n\n splitted = split_by_class(dataset)\n stats = {}\n for c, values in splitted.items():\n stats[c] = get_stats(values)\n return stats",
"title": ""
},
{
"docid": "f... |
112830f08bad1cb2397356518037ea65 | Stop EC2 instance by ID | [
{
"docid": "a2b50f25f8f1f38e24fc4166ab3f75f9",
"score": "0.77420604",
"text": "def stop_instance(self, arg_instance_id):\n try:\n print(f'Instance \"{arg_instance_id}\" stopping begins.')\n instance = self.ec2_resource.Instance(arg_instance_id)\n instance.stop()\n... | [
{
"docid": "cd1761b40bdc3960e6b73585e5a48752",
"score": "0.8403922",
"text": "def aws_stop(self, msg, args):\n id = args\n instance = self._get_instance_by_id(id)\n response = \"\"\n if self._is_instance_permitted(id):\n i = self.ec2.instances.filter(InstanceIds=[i... |
3595cadc708f0a78b7a0b73784621f8f | Ensures correctness of _primary_and_secondary_sources_to_table. | [
{
"docid": "216133a72349cc172d2720c5c0bac587",
"score": "0.78826684",
"text": "def test_primary_and_secondary_sources_to_table(self):\n\n this_actual_table = raw_wind_io._primary_and_secondary_sources_to_table()\n\n this_actual_table.sort_values(\n raw_wind_io.PRIMARY_SOURCE_COL... | [
{
"docid": "899c520089ce88e397c2dfe17e9130c9",
"score": "0.5935422",
"text": "def test_check_data_sources_fake_secondary(self):\n\n with self.assertRaises(ValueError):\n raw_wind_io.check_data_sources(\n primary_source=raw_wind_io.MADIS_DATA_SOURCE,\n seco... |
35cdb17813597bb09f951742cda5a40e | Return the type a pointer points to. | [
{
"docid": "dcfc041fc41b2a95998a5c16d6c490ab",
"score": "0.692451",
"text": "def get_ptr_type(structure, member):\n if len(member) > 1:\n _, tp = get_obj_offset(types, [structure, member[0]])\n if tp == 'array':\n return types[structure][1][member[0]][1][2][1]\n else:\... | [
{
"docid": "4230d3cdb70d0eae25061363bec4071a",
"score": "0.8147374",
"text": "def get_pointer_type(self):\n return self.get_c_type() + ' *'",
"title": ""
},
{
"docid": "26bfe339aabfbf2261fa45d63d1e0261",
"score": "0.6892176",
"text": "def get_type_declaration(self):\n c... |
321b679eff58416edcbfb834701f2f07 | Return a dict of keyword arguments. | [
{
"docid": "e04521da7751862a8690f24947faa438",
"score": "0.60982734",
"text": "def get_kwd_args(func):\n try:\n sig = inspect.signature(func)\n except AttributeError:\n args, _, _, defaults = inspect.getargspec(func)\n if defaults:\n kwonlyargs = args[-len(defaults)... | [
{
"docid": "a8bb33e673ce46d959e05908a59bb319",
"score": "0.76821774",
"text": "def get_kwargs(self):\n return {}",
"title": ""
},
{
"docid": "b212d19548ebe4a7d81bd1ead37b7578",
"score": "0.7656699",
"text": "def as_kwargs(self) -> Dict[str, Any]:\n ret = {}\n for... |
b212fc884b3873a73d7366dea6bc607c | Create 'Submit' tab and add to UI frame. | [
{
"docid": "1003d833f4bf399f5e9c94f583707d2a",
"score": "0.5240085",
"text": "def __init__(self, base, frame):\n self.base = base\n self.label = \"Submit\"\n self.page = maya.form_layout(enableBackground=True) \n self.select_pool_type = 1\n self.select_instances = 1\n ... | [
{
"docid": "49a8b321adaf67148f61d57eef97bcc1",
"score": "0.62406707",
"text": "def create_new_tab(self, page=-1):\n new_tab = STATDotWindow.Tab()\n tab_label_box = gtk.EventBox()\n tab_label_box.add(new_tab.label)\n tab_label_box.connect('event', self.on_tab_clicked, new_tab.... |
a17cbb0188b345190034c00e7d3bf5c9 | Boolean indicating if the share is pending. | [
{
"docid": "a2afe99d58c91477255e17e25d0b592d",
"score": "0.77301145",
"text": "def is_pending(self):\n return self.status == Status.PENDING",
"title": ""
}
] | [
{
"docid": "98c322aa75b28d401d968997b511a834",
"score": "0.7388369",
"text": "def has_pending(self):\n return self._pending_count > 0",
"title": ""
},
{
"docid": "523f2808a5cb4de9818b788829d25604",
"score": "0.71935004",
"text": "def is_pending(self):\n\n return self._s... |
de54d78ec6bc27371d496d58a3050204 | Get the dictionary of a dataclass | [
{
"docid": "8b8293686f3040d330353c83f7839383",
"score": "0.0",
"text": "def default(self, o: Any) -> Any:\n if dataclassy.functions.is_dataclass_instance(o):\n return dataclassy.asdict(o)\n if isinstance(o, set):\n return list(o)\n return dataclassy.asdict(o)",... | [
{
"docid": "14f4e99b24c54dbbdea1cfb5357aa462",
"score": "0.8139149",
"text": "def dataclass_to_dict(dataklass: C) -> Dict[str, Any]:\n data = {}\n if is_dataclass(dataklass):\n for field in fields(dataklass):\n value = getattr(dataklass, field.name)\n if is_dataclass(v... |
4960b8336d863a9cccb69e67c9ac431c | Calculate the covariance matrix between orbital elements, and initial state vector using all Monte Carlo trajectories. The covariance matrix is weighted by the timing residuals. The orbital covariance matrix is calculated for radians and the inital state vector matrix in meters and meters per second. | [
{
"docid": "6a93c0efd13f0123b7c5d3e5e7e2d430",
"score": "0.70054585",
"text": "def calcCovMatrices(mc_traj_list):\n\n # Filter out those trajectories for which the last perihelion time could not be estimated\n mc_traj_list = [traj for traj in mc_traj_list if traj.orbit.last_perihelion is not None]... | [
{
"docid": "bc9b754996a3685fcb12d5c38d26cd34",
"score": "0.6350269",
"text": "def covariance(self):\n c,r = self.size(); float(c); float(r)\n mean_vector = self.mean()\n covariance_mat = mmatrix((c,r))\n for index_c in range(c):\n tally = float(0)\n for ... |
e61d5d6da4a061cda9ed8338be837c73 | This function will post a message to the room based on provided room ID | [
{
"docid": "b8b411438b860ac6461f8d82518cf229",
"score": "0.7153023",
"text": "def post_message(room_id):\n text = input(\"test from Sam Tian \")\n URL = \"https://api.ciscospark.com/v1/messages\"\n PAYLOAD = {\"roomId\": room_id, \"text\": text}\n\n response = requests.post(url=URL, json=PAY... | [
{
"docid": "c926d331d105eb3c95f82d1c5ff3d7e5",
"score": "0.7540419",
"text": "def on_post(self, req, resp, room_id, message_id=None):\n\n room = Room.objects.get(id=room_id)\n\n message = Message(message=req.json[\"message\"])\n message.room = room\n message.author = req.sess... |
5fb0fc04c86be50925b545dac8c3f637 | Creates an acceptor from a string. This function creates a FST which accepts its input with a fixed weight (defaulting to semiring One). | [
{
"docid": "64c1810bd7aac41df103f0b285e4ece3",
"score": "0.76613843",
"text": "def acceptor(\n astring: str, symbol_table: SymbolTable, weight: Optional[float] = None\n) -> VectorFst:\n if weight is None:\n weight = weight_one()\n\n acceptor_fst_ptr = ctypes.pointer(ctypes.c_void_p())\n ... | [
{
"docid": "3a11123790342656c29964c9455581a1",
"score": "0.5958145",
"text": "def from_string(factory, str_graph):\n\n builder = Builder(factory)\n node_sequences = [s.split('->') for s in str_graph.split(\",\")]\n\n for seq in node_sequences:\n last_id = None\n for s in seq:\n ... |
cc834995374349d6489a52ff4e17e95b | Private slot to show the contents of a table. table name of the table for which to show the contents (string) | [
{
"docid": "7071ae00f36e28ee0871ca3d2f085a5b",
"score": "0.62225384",
"text": "def on_connections_tableActivated(self, table):\n self.showTable(table)",
"title": ""
}
] | [
{
"docid": "3f313076ed2959f209cda71176c10ca1",
"score": "0.7463752",
"text": "def show_table(table):\n ui.print_table(table, table_structure)",
"title": ""
},
{
"docid": "fa6e6a9c9248556684c25af17ed59ffc",
"score": "0.74433726",
"text": "def show_table(table):\n ui.clear_scr()\... |
cd3a0cfa9f2722cf570fbbfd381ffaff | Uniform log prior for the 2D Rosenbrock likelihood following Wang & Li (2017) where the prior pi(x) is a uniform distribution over [5, 5] x [5, 5] x ... for however many dimensions (dim = x.shape[1]) | [
{
"docid": "c95cb3578239edfe8a7cd908978e8fd8",
"score": "0.0",
"text": "def rosenbrockLnprior(theta):\n\n if np.any(np.fabs(theta) > 5):\n return -np.inf\n else:\n return 0.0",
"title": ""
}
] | [
{
"docid": "3c5b98dc9d700b0e2e1aa1542cf74ec7",
"score": "0.7165135",
"text": "def log_prior(mu, log_sigma):\n return 0.5 * T.sum(1 + 2 * log_sigma - mu ** 2 - T.exp(2 * log_sigma))",
"title": ""
},
{
"docid": "0f64d33d9ca8e1c719d82e088ca4cd88",
"score": "0.7152979",
"text": "def l... |
8b57b40b506a65569c2bf2211d30b5ce | Deletes a specified link. | [
{
"docid": "817159d0d23e3ebac410c02f312e746c",
"score": "0.6469105",
"text": "def delete_link(\n self, drawer, id, custom_headers=None, raw=False, **operation_config):\n # Construct URL\n url = '/v1/content/links/{drawer}/{id}'\n path_format_arguments = {\n 'dr... | [
{
"docid": "75d04a8e3c7382b57f67bc652bebb46f",
"score": "0.8691151",
"text": "def delete_link(self, link):",
"title": ""
},
{
"docid": "330e382b80fa4d675a239b8fc57303d7",
"score": "0.7785142",
"text": "async def delete_link(self, player_tag: str):\n return await self._request(... |
a7b1a69abe3a81bef0ab8c3bf77ea64d | Test that the check ran as expected. | [
{
"docid": "24c0a765979399b2f26022a1bca2dcce",
"score": "0.0",
"text": "async def test_did_run(coresys: CoreSys):\n core_security = CheckCoreSecurity(coresys)\n should_run = core_security.states\n should_not_run = [state for state in CoreState if state not in should_run]\n assert len(should_... | [
{
"docid": "b0de60d9fa8572424d4f0dea91ee446f",
"score": "0.76596487",
"text": "def test_check():\n res, _ = ToolTester().check(None)\n assert res",
"title": ""
},
{
"docid": "0be4e31b3b916dfdca641c459ba608fd",
"score": "0.7338686",
"text": "def test_succeed():\n assert True",
... |
43641906006d47b53e6119d47812eb63 | Tests scrap_for_posts returns valid post urls using a regular expression. This particular page has a content warning and needs a cookie to access to it. | [
{
"docid": "50ce4bdf9a8fad2c1cdac3d8208a6f12",
"score": "0.73245084",
"text": "def test_scrap_for_posts_cookie(self):\n \n url = \"https://e-hentai.org/g/1263299/a42ce2eb95/\"\n \n response = self.ehen.scrap_for_posts(url, 1, 1, 1)\n \n pat = re.compile(r'https:... | [
{
"docid": "c743b4bc0e88dbd83d3d095a408f05c2",
"score": "0.67279756",
"text": "def test_scrap_for_posts(self):\n \n url = \"https://e-hentai.org/g/1087428/b240f1a9ab/\"\n \n response = self.ehen.scrap_for_posts(url, 1, 1, 1)\n \n pat = re.compile(r'https:\\/\\/e... |