2:\\n perf = np.nanmean(perf, axis=-1)\\n perf = perf[~np.isnan(perf[:, 0])]\\n perf = np.mean(perf, axis=0)\\n return perf\",\n \"def calculate_averages(data):\\n def mean(item_key):\\n all_items = [i[item_key] for i in data]\\n return sum(all_items)/float(len(all_items))\\n\\n return {\\n \\\"mean_error_count\\\": mean('error_count'),\\n \\\"mean_success_count\\\": mean('success_count'),\\n \\\"mean_mean_response_time\\\": mean('mean_respone_time'),\\n \\\"mean_data_sent_mb\\\": mean('data_sent_mb'),\\n }\",\n \"def meanOf(classObj):\\r\\n return np.mean(classObj.dataSet, axis=0)\",\n \"def predict(self, X):\\n predictions = np.asarray([model.predict(X) for model in self.estimators_])\\n return np.average(predictions, axis=0, weights=self.weights)\",\n \"def learn(self, Xtrain, ytrain):\\n self.mean = np.mean(ytrain)\",\n \"def mean(X,trimming=0):\\n \\n m = sps.trim_mean(X,trimming,0)\\n return m\",\n \"def average(data, event):\\n if len(data) == 0:\\n return 0\\n\\n score = 0\\n # scores = []\\n count = 0\\n for i in data:\\n count += 1\\n if event == 'Swim' or event == 'Run':\\n num = time_seconds(i[event])\\n #print(\\\"first if\\\")\\n #Sprint(num)\\n else:\\n num = int(i[event])\\n #print(\\\"second if\\\")\\n #print(num)\\n #scores[count] =\\n #print(\\\"end of loop count\\\" + str(count))\\n score += num\\n #print (\\\"score\\\" + str(score))\\n\\n # total = 0\\n # for x in range(0,len(scores)):\\n # total += scores[x]\\n score = float(score)\\n\\n return score / count\",\n \"def averager(feature_matrix, labels):\\n (nsamples, nfeatures) = feature_matrix.shape\\n theta_vector = np.zeros([nfeatures])\\n theta_0 = 0\\n\\n for i in xrange(0, nsamples):\\n label = labels[i]\\n sample_vector = feature_matrix[i, :]\\n theta_vector = theta_vector + label*sample_vector\\n theta_0 = theta_0 + label\\n return theta_vector, theta_0\",\n \"def gate_average(recurrent_net, sample_input):\\n length = sample_input.size\\n score, select = np.zeros(length), np.zeros(length)\\n for i in range(length):\\n select[i], score[i] = recurrent_net.activate([sample_input[i]])\\n select = sigmoid(select)\\n return np.sum(select * score) / np.sum(select)\",\n \"def mean_average_precision_built(pred_boxes, true_boxes, iou_threshold=0.5, box_format=\\\"midpoint\\\", num_classes=20):\\n\\n predictions_class = []\\n target_class = []\\n ap = []\\n\\n for i in range(num_classes):\\n predictions_class.append([])\\n target_class.append([])\\n ap.append(0.0)\\n \\n\\n for pred in pred_boxes:\\n c = int(pred[1])\\n predictions_class[c].append(pred)\\n \\n for true_b in true_boxes:\\n c = int(true_b[1])\\n target_class[c].append(true_b)\\n\\n for cl_ in range(num_classes):\\n ap_c = 0\\n # print(\\\"classnum: \\\" + str(len(target_class[cl_])))\\n # print(\\\"prednum: \\\" + str(len(predictions_class[cl_])))\\n if len(target_class[cl_])!= 0 and len(predictions_class[cl_]) != 0:\\n # print(\\\"class: \\\" + str(cl_))\\n ap_c = average_precision(predictions_class[cl_], target_class[cl_], iou_threshold)\\n ap[cl_] = ap_c\\n print(\\\"sumap: \\\" + str(sum(ap)))\\n\\n return sum(ap) / len(ap)\",\n \"def mean_run_time(self) -> float:\\n return float(self.result_array.sum(axis=0).mean())\",\n \"def average_by_time(Xs):\\n X_merged = {}\\n\\n for X in Xs:\\n for (t,x) in X:\\n if t not in X_merged: X_merged[t] = []\\n X_merged[t].append(x)\\n\\n X_avg = {}\\n\\n for t in X_merged.keys():\\n X_avg[t] = sum(X_merged[t]) / len(X_merged[t])\\n\\n return list(X_avg.items())\",\n \"def evaluate_acc(\\n model,\\n ds\\n):\\n n = 0\\n correct = 0\\n for batch_x, batch_y in ds:\\n batch_pred = get_model_prediction(model, batch_x)\\n correct += tf.math.reduce_sum(\\n tf.cast(batch_pred == batch_y, dtype=tf.int32)\\n )\\n n += batch_y.shape[0]\\n return correct / n\",\n \"def mean(x):\\n return sum(x)/len(x)\",\n \"def trimean(data):\\n p_25, p_50, p_75 = percentile(data, [25, 50, 75], axis=0)\\n\\n return (p_25 + 2 * p_50 + p_75) / 4\",\n \"def calc_mean_score(movies: List[Movie]) -> float:\\n return round(sum([m.score for m in movies]) / len(movies), 1)\",\n \"def mean_average_precision(rs):\\n return np.mean([average_precision(r) for r in rs])\",\n \"def batch_mean(cls, y, y_target):\\n tot = 0\\n for i, o in zip(y, y_target):\\n tot += cls.loss(i.reshape(-1, 1), o.reshape(-1, 1))\\n return tot / len(y)\",\n \"def get_average_MAE(true_pred_df): \\n age_group = true_pred_df.groupby('y_true')\\n \\n mae_average = []\\n for age, age_data in age_group:\\n mae_average.append(np.mean(age_data.mae))\\n \\n return mae_average\",\n \"def x_run_average_pipeline(num: int, language: str):\\n precision, recall, f1 = [], [], []\\n for i in range(num):\\n CRF = BaselineCRF(language)\\n predicted, labels = CRF.pipeline()\\n p, r, f = eval_morph_segments(predicted, labels)\\n precision.append(p)\\n recall.append(r)\\n f1.append(f)\\n\\n recall = sum(recall) / len(recall)\\n precision = sum(precision) / len(precision)\\n f1 = sum(f1) / len(f1)\\n return recall, precision, f1\",\n \"def get_mean(cls, data: tuple or list) -> float:\\n cls._data_validation(data)\\n sum_ = fsum(data)\\n n = cls.get_n(data)\\n try:\\n return float(sum_ / n)\\n except ZeroDivisionError as exc:\\n # for hyp score calculation, n = 0 for empty set is useful\\n return 0\",\n \"def calculateFeatureWeights(self):\\n instances = list(self._df_X.index)\\n ser_X = pd.Series(np.repeat(1, len(self.columns)), index=self.columns)\\n df_mean = pd.concat([ser_X for _ in self.classes], axis = 1)\\n df_mean = df_mean.transpose()\\n df_mean.index= self.classes\\n df_std = df_mean.copy()\\n dfs = [self.clf_desc.getFeatureContributions(c, self.columns, ser_X)\\n for c in self.clfs]\\n for idx in self.classes:\\n sers = pd.concat([df.loc[idx, :] for df in dfs], axis=1)\\n ser = sers.mean(axis=1)\\n df_mean.loc[idx, :] = ser\\n ser = sers.std(axis=1)\\n df_std.loc[idx, :] = ser\\n if self._class_names is not None:\\n df_mean.index = self._class_names\\n df_std.index = self._class_names\\n return df_mean, df_std\",\n \"def evaluate_accuracy(data_iter, net, ctx=[mx.cpu()]):\\n acc = nd.array([0], ctx=ctx)\\n n = 0\\n for X, y in data_iter:\\n X = X.as_in_context(ctx)\\n y = y.astype('float32').as_in_context(ctx)\\n acc += (net(X).argmax(axis=1) == y).sum()\\n n += y.size\\n return acc.asscalar() / n\",\n \"def ttest(x):\\n from ..group.onesample import stat\\n t = stat(x.T, id='student', axis=0)\\n return np.squeeze(t)\",\n \"def naive_forecasting(x_test, y_test):\\n y_pred = np.concatenate([x_test[:,-1].reshape((TESTING_BATCH_SIZE,1,N_INPUT_FEATURES)) for x in range(N_PREDICTIONS)],axis=1)\\n y_pred = y_pred[:,:,:N_OUTPUT_FEATURES]\\n return np.mean(keras.losses.mean_squared_error(y_test,y_pred)), y_pred\",\n \"def load_average(self):\\n return _favg(self.load_samples)\",\n \"def batch_stat(x):\\n\\tmean = torch.mean(x, dim=[0, 2, 3], keepdim=True)\\n\\tvar = torch.mean((x-mean)**2, dim=[0, 2, 3], keepdim=True)\\n\\treturn mean, var\",\n \"def _mean_prediction(op, g_td, y_train):\\n fl, ufl = _make_flatten_uflatten(g_td, y_train)\\n\\n mean_pred = op(fl(y_train))\\n mean_pred = np.dot(g_td, mean_pred)\\n return ufl(mean_pred)\",\n \"def label_accuracy_score(label_trues, label_preds, n_class):\\n hist = np.zeros((n_class, n_class))\\n # 一个batch里面可能有多个数据\\n # 通过迭代器将一个个数据进行计算\\n for lt, lp in zip(label_trues, label_preds):\\n # numpy.ndarray.flatten将numpy对象拉成1维\\n hist += _fast_hist(lt.flatten(), lp.flatten(), n_class)\\n\\n # np.diag(a)假如a是一个二维矩阵，那么会输出矩阵的对角线元素\\n # np.sum()可以计算出所有元素的和。如果axis=1，则表示按行相加\\n \\\"\\\"\\\"\\n acc是准确率 = 预测正确的像素点个数/总的像素点个数\\n acc_cls是预测的每一类别的准确率(比如第0行是预测的类别为0的准确率)，然后求平均\\n iu是召回率Recall，公式上面给出了\\n mean_iu就是对iu求了一个平均\\n freq是每一类被预测到的频率\\n fwavacc是频率乘以召回率，我也不知道这个指标代表什么\\n \\\"\\\"\\\"\\n acc = np.diag(hist).sum() / hist.sum()\\n acc_cls = np.diag(hist) / hist.sum(axis=1)\\n # nanmean会自动忽略nan的元素求平均\\n acc_cls = np.nanmean(acc_cls)\\n iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\\n mean_iu = np.nanmean(iu)\\n freq = hist.sum(axis=1) / hist.sum()\\n fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()\\n return acc, acc_cls, mean_iu, fwavacc\",\n \"def calculate_metric(self, distance_matrix):\\n ap_scores = []\\n for node_id in range(len(distance_matrix)):\\n sorted_nodes = np.argsort(distance_matrix[node_id]).tolist()\\n neighs = self.neighbors[node_id]\\n n_correct = 0.0\\n precisions = []\\n for i in range(1, len(sorted_nodes)):\\n if sorted_nodes[i] in neighs:\\n n_correct += 1\\n precisions.append(n_correct / i)\\n if n_correct == len(neighs):\\n break\\n\\n ap_scores.append(np.mean(precisions))\\n\\n return np.mean(ap_scores)\",\n \"def sgd(iterations):\\n for iteration in range(0,iterations):\\n error = []\\n for user_id in range(0,latent_user_preferences.shape[0]):\\n for item_id in range(0,latent_item_features.shape[0]):\\n rating = user_ratings[user_id][item_id]\\n if rating != 99:\\n err = train(user_id, item_id, rating)\\n error.append(err)\\n mse = (np.array(error) ** 2).mean() \\n if(iteration%1 == 0):#000 == 0 ):\\n print(mse)\\n return error\",\n \"def get_average(data):\\n average = sum(data) / len(data)\\n\\n return average\",\n \"def calculate_average_precision_incl_zeros(test_class, local_list='', x_data='', selected_activations='',\\n current_neuron_index=current_neuron_index,acts=acts,verbose=verbose):\\n if local_list == '':\\n # if you don't pass it in, get the data\\n local_list, selected_activations, x_data = get_local_list_for_neuron(current_neuron_index=current_neuron_index,\\n minx=0,\\n acts=acts)\\n total_no_of_points = len(x_data)\\n no_of_points_selected = len(local_list)\\n # grabs all points above 0 (special case)\\n if verbose:\\n print('Taking all points above 0.0, minimum is {}'.format(min(selected_activations)))\\n Ave_precs_x, precs_x, recall_x = calculate_ave_precs_general(\\n test_class=test_class,\\n local_list=local_list,\\n Q_stop='',\\n no_files_in_label=no_files_in_label,\\n verbose=verbose)\\n # this gives the values just above zero\\n precs_all = no_files_in_label[test_class] / total_no_of_points # a largely pointless measure\\n recall_all = 1.0\\n delta_recall_all = recall_all - recall_x # difference in recall between this point nd the next\\n weight_precs_all = precs_all * delta_recall_all # weighted precsion at point x (we do average via weighted sum)\\n Ave_precs_all = Ave_precs_x + weight_precs_all\\n if verbose:\\n print('Class {}, evaluated at Q={}:, found {}'.format(test_class, total_no_of_points, no_files_in_label[test_class] ))\\n print('Average precision={}; precision={}; recall={}'.format(Ave_precs_all,precs_all,recall_all))\\n return Ave_precs_all, precs_all, recall_all, Ave_precs_x, precs_x, recall_x\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64128476","0.6214273","0.61815697","0.6136997","0.60902673","0.6084845","0.595498","0.5950753","0.5950473","0.5936966","0.59339607","0.593313","0.5920305","0.5912948","0.59116477","0.5895597","0.58873343","0.58857715","0.5872557","0.5864992","0.5842531","0.5834908","0.58340657","0.58236194","0.5815103","0.58026725","0.5793845","0.5781943","0.5781001","0.5777389","0.5773867","0.5772066","0.57677585","0.57497","0.5749477","0.5725678","0.57189745","0.5702786","0.5701183","0.5685846","0.56764877","0.5668617","0.56587243","0.5644973","0.5629404","0.562427","0.56228757","0.56010264","0.5594926","0.5586869","0.5584204","0.5582509","0.55619496","0.5559018","0.5554754","0.55507433","0.55467266","0.5543335","0.5530003","0.5523729","0.5520663","0.5513347","0.5505141","0.55016315","0.5498386","0.54953295","0.5492211","0.5490775","0.5486078","0.5482962","0.5479895","0.547233","0.5469137","0.546667","0.54631627","0.5456805","0.5455238","0.5455195","0.54551256","0.5441655","0.54409236","0.5433991","0.54332995","0.5429532","0.5425944","0.5424991","0.5418247","0.54110247","0.5409371","0.54084426","0.540729","0.54065984","0.5404753","0.5402904","0.5402436","0.53990483","0.539707","0.53958255","0.53927386","0.53911144"],"string":"[\n \"0.64128476\",\n \"0.6214273\",\n \"0.61815697\",\n \"0.6136997\",\n \"0.60902673\",\n \"0.6084845\",\n \"0.595498\",\n \"0.5950753\",\n \"0.5950473\",\n \"0.5936966\",\n \"0.59339607\",\n \"0.593313\",\n \"0.5920305\",\n \"0.5912948\",\n \"0.59116477\",\n \"0.5895597\",\n \"0.58873343\",\n \"0.58857715\",\n \"0.5872557\",\n \"0.5864992\",\n \"0.5842531\",\n \"0.5834908\",\n \"0.58340657\",\n \"0.58236194\",\n \"0.5815103\",\n \"0.58026725\",\n \"0.5793845\",\n \"0.5781943\",\n \"0.5781001\",\n \"0.5777389\",\n \"0.5773867\",\n \"0.5772066\",\n \"0.57677585\",\n \"0.57497\",\n \"0.5749477\",\n \"0.5725678\",\n \"0.57189745\",\n \"0.5702786\",\n \"0.5701183\",\n \"0.5685846\",\n \"0.56764877\",\n \"0.5668617\",\n \"0.56587243\",\n \"0.5644973\",\n \"0.5629404\",\n \"0.562427\",\n \"0.56228757\",\n \"0.56010264\",\n \"0.5594926\",\n \"0.5586869\",\n \"0.5584204\",\n \"0.5582509\",\n \"0.55619496\",\n \"0.5559018\",\n \"0.5554754\",\n \"0.55507433\",\n \"0.55467266\",\n \"0.5543335\",\n \"0.5530003\",\n \"0.5523729\",\n \"0.5520663\",\n \"0.5513347\",\n \"0.5505141\",\n \"0.55016315\",\n \"0.5498386\",\n \"0.54953295\",\n \"0.5492211\",\n \"0.5490775\",\n \"0.5486078\",\n \"0.5482962\",\n \"0.5479895\",\n \"0.547233\",\n \"0.5469137\",\n \"0.546667\",\n \"0.54631627\",\n \"0.5456805\",\n \"0.5455238\",\n \"0.5455195\",\n \"0.54551256\",\n \"0.5441655\",\n \"0.54409236\",\n \"0.5433991\",\n \"0.54332995\",\n \"0.5429532\",\n \"0.5425944\",\n \"0.5424991\",\n \"0.5418247\",\n \"0.54110247\",\n \"0.5409371\",\n \"0.54084426\",\n \"0.540729\",\n \"0.54065984\",\n \"0.5404753\",\n \"0.5402904\",\n \"0.5402436\",\n \"0.53990483\",\n \"0.539707\",\n \"0.53958255\",\n \"0.53927386\",\n \"0.53911144\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5755531"},"document_rank":{"kind":"string","value":"33"}}},{"rowIdx":76495,"cells":{"query":{"kind":"string","value":"Calculate band according to Stavenga et al (1993)."},"document":{"kind":"string","value":"def stavenga1993_band_calculation(\n x: np.ndarray, a: Union[float, np.ndarray], b: Union[float, np.ndarray]\n) -> np.ndarray:\n return np.exp(-a * x**2 * (1 + b * x + 3 / 8 * (b * x) ** 2))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def calculate_band(value, bands):\n for band in bands:\n if band > value:\n return band","def bands(self) -> int:\n ...","def calbands( band = 0, tmo = 30 ) :\n optimizeThresholds(band,tmo)\n flattenPhases(band,tmo)\n calibrateSpectra(band=band,tmo=tmo)","def DrawBands(self, count):\n value = self.little[0]\n mobile_average = float(sum([float(self.little[i])\n for i in range(len(self.little))])) / float(self.period)\n standard_derivation = sqrt(sum([pow(self.little[i] - mobile_average, 2)\n for i in range(len(self.little))]) / self.period)\n upper_band = mobile_average + (standard_derivation * self.sd_coef)\n lower_band = mobile_average - (standard_derivation * self.sd_coef)\n self.upper.insert(0, upper_band)\n self.lower.insert(0, lower_band)\n if len(self.upper) >= self.period:\n self.upper.pop()\n if len(self.lower) >= self.period:\n self.lower.pop()\n if count >= self.period:\n for i in range(len(self.little) - 1):\n self.canvas.create_line((i * self.incr / 1.725) + self.incr * 4,\n self.height - self.incr * 4 + (self.little[i] - 1) * 5000 - 200,\n (i * self.incr / 1.725) + self.incr * 4 + self.incr / 1.725,\n self.height - self.incr * 4 + (self.little[i + 1] - 1) * 5000 - 200,\n fill = \"#FFFF00\", width = 2)\n for i in range(len(self.upper) - 1):\n self.canvas.create_line((i * self.incr / 1.635) + self.incr * 4,\n self.height - self.incr * 4 + (self.upper[i] - 1) * 5000 - 200,\n (i * self.incr / 1.635) + self.incr * 4 + self.incr / 1.635,\n self.height - self.incr * 4 + (self.upper[i + 1] - 1) * 5000 - 200,\n fill = \"#FF6600\", width = 3)\n self.canvas.create_line((i * self.incr / 1.635) + self.incr * 4,\n self.height - self.incr * 4 + (self.lower[i] - 1) * 5000 - 200,\n (i * self.incr / 1.635) + self.incr * 4 + self.incr / 1.635,\n self.height - self.incr * 4 + (self.lower[i + 1] - 1) * 5000 - 200,\n fill = \"#FF0000\", width = 3)","def band(self):\n return self._band","def get_BBands(sma, stdev):\n\tupper_band = sma + 2*stdev\n\tlower_band = sma - 2*stdev\n\treturn upper_band, lower_band","def n_band(self):\n pass","def octave_bands(fc=1000, third=False, start=0.0, n=8):\n\n div = 1\n if third:\n div = 3\n\n # Octave Bands\n fcentre = fc * (\n 2.0 ** (np.arange(start * div, (start + n) * div - (div - 1)) / div)\n )\n fd = 2 ** (0.5 / div)\n bands = np.array([[f / fd, f * fd] for f in fcentre])\n\n return bands, fcentre","def sharpen_bands(self):\n for label in self.labels:\n self.sharp_bands[label] = self.bands[label] - self.gauss_bands[\n label]","def get_bollinger_bands(rm, rstd):\n \n upper_band=rm+2*rstd\n lower_band=rm-2*rstd\n return upper_band, lower_band","def jack_SB_func(SB_array, R_array, band_str, N_sample,):\n\tband_id = band.index( band_str )\n\n\tdx_r = np.array(R_array)\n\tdy_sb = np.array(SB_array)\n\n\tn_r = dx_r.shape[1]\n\n\tLen = np.zeros( n_r, dtype = np.float32)\n\tfor nn in range( n_r ):\n\t\ttmp_I = dy_sb[:,nn]\n\t\tidnn = np.isnan(tmp_I)\n\t\tLen[nn] = N_sample - np.sum(idnn)\n\n\tStack_R = np.nanmean(dx_r, axis = 0)\n\tStack_SB = np.nanmean(dy_sb, axis = 0)\n\tstd_Stack_SB = np.nanstd(dy_sb, axis = 0)\n\n\t### only calculate r bins in which sub-sample number larger than one\n\tid_one = Len > 1\n\tStack_R = Stack_R[ id_one ]\n\tStack_SB = Stack_SB[ id_one ]\n\tstd_Stack_SB = std_Stack_SB[ id_one ]\n\tN_img = Len[ id_one ]\n\tjk_Stack_err = np.sqrt(N_img - 1) * std_Stack_SB\n\n\t### limit the radius bin contribution at least 1/3 * N_sample\n\tid_min = N_img >= np.int(N_sample / 3)\n\tlim_r = Stack_R[id_min]\n\tlim_R = np.nanmax(lim_r)\n\n\t## change flux to magnitude\n\tjk_Stack_SB = 22.5 - 2.5 * np.log10(Stack_SB) + mag_add[band_id]\n\tdSB0 = 22.5 - 2.5 * np.log10(Stack_SB + jk_Stack_err) + mag_add[band_id]\n\tdSB1 = 22.5 - 2.5 * np.log10(Stack_SB - jk_Stack_err) + mag_add[band_id]\n\terr0 = jk_Stack_SB - dSB0\n\terr1 = dSB1 - jk_Stack_SB\n\tid_nan = np.isnan(jk_Stack_SB)\n\tjk_Stack_SB, jk_Stack_R = jk_Stack_SB[id_nan == False], Stack_R[id_nan == False]\n\tjk_Stack_err0, jk_Stack_err1 = err0[id_nan == False], err1[id_nan == False]\n\tdSB0, dSB1 = dSB0[id_nan == False], dSB1[id_nan == False]\n\tidx_nan = np.isnan(dSB1)\n\tjk_Stack_err1[idx_nan] = 100.\n\n\treturn jk_Stack_SB, jk_Stack_R, jk_Stack_err0, jk_Stack_err1, Stack_R, Stack_SB, jk_Stack_err, lim_R","def calc_lc_band(table, query, band='r', type='mag'):\n\n phot_bands = ['u', 'g', 'r', 'i', 'z', 'y']\n phot_index = np.arange(0, 6, step=1)\n\n phase = table['mjd']-table['mjd'][0] # phase from first detection\n\n model_mag = models.photometricmodel(phase[table['filter']==band]).fourier_cos(*query['model_theta'][phot_index[phot_bands==band]]) # calculate the flux at each epoch\n lim_sigma = table[table['filter']==band]['5sig'] #5-sigma depth\n snr = m5snr(model_flux, lim_sigma) # SNR at that band\n sigma_err = 2.5*np.log10(1+1./snr) # magnitude error\n\n if type=='mag':\n return phase[table['filter']==band], model_mag, sigma_err\n elif type=='flux':\n return phase[table['filter']==band], model_mag, astro_tool.mag_err_2_flux_err(model_mag, sigma_err) # convert magnitude error into flux error (assume that model_mag is now in flux)","def get_bands(self, data_array_norm, baseline_array_norm, f):\n\n fmax = 50\n fidx = f < fmax\n fnum = f[fidx].size\n\n band_tot = np.zeros((fnum, fnum, data_array_norm.shape[0], data_array_norm.shape[2], data_array_norm.shape[3]))\n band_tot_bl = np.zeros((fnum, fnum, baseline_array_norm.shape[0], baseline_array_norm.shape[2], baseline_array_norm.shape[3]))\n for i in range(fnum):\n for j in range(fnum):\n if j > i:\n idx = (f >= f[i]) & (f < f[j])\n band_tot[i, j, :, :] = np.sum(data_array_norm[:, idx, :, :], axis=1) / (f[j] - f[i])\n band_tot_bl[i, j, :, :] = np.sum(baseline_array_norm[:, idx, :, :], axis=1) / (f[j] - f[i])\n\n\n band_tot_bl1 = np.mean(band_tot_bl, axis=3) # average across time bins\n band_tot_bl2 = np.repeat(band_tot_bl1[:, :, :, None, :], band_tot_bl.shape[3], axis=3) # repeat same value across time\n return band_tot, band_tot_bl2, f[fidx]","def bands(sys, args=(), momenta=65, file=None, show=True, dpi=None,\n fig_size=None, ax=None, *, params=None):\n\n if not mpl_available:\n raise RuntimeError(\"matplotlib was not found, but is required \"\n \"for bands()\")\n\n syst = sys # for naming consistency inside function bodies\n _common.ensure_isinstance(syst, system.InfiniteSystem)\n\n momenta = np.array(momenta)\n if momenta.ndim != 1:\n momenta = np.linspace(-np.pi, np.pi, momenta)\n\n # expand out the contents of 'physics.Bands' to get the H(k),\n # because 'spectrum' already does the diagonalisation.\n ham = syst.cell_hamiltonian(args, params=params)\n if not np.allclose(ham, ham.conjugate().transpose()):\n raise ValueError('The cell Hamiltonian is not Hermitian.')\n _hop = syst.inter_cell_hopping(args, params=params)\n hop = np.empty(ham.shape, dtype=complex)\n hop[:, :_hop.shape[1]] = _hop\n hop[:, _hop.shape[1]:] = 0\n\n def h_k(k):\n # H_k = H_0 + V e^-ik + V^\\dagger e^ik\n mat = hop * cmath.exp(-1j * k)\n mat += mat.conjugate().transpose() + ham\n return mat\n\n return spectrum(h_k, ('k', momenta), file=file, show=show, dpi=dpi,\n fig_size=fig_size, ax=ax)","def calc_bands(self, start: float = -pi, end: float = pi, step: float = 0.05) -> results.Bands:\n from scipy.linalg import eigh\n\n h0 = self.h0.todense()\n h1 = self.h1.todense()\n h1t = np.conj(h1.T)\n\n def eigenvalues(k):\n h = h0 + h1 * np.exp(1j * k) + h1t * np.exp(-1j * k)\n return eigh(h, eigvals_only=True)\n\n k_path = results.make_path(start, end, step=step)\n bands = [eigenvalues(k) for k in k_path.flatten()]\n return results.Bands(k_path, np.vstack(bands))","async def equalizer(\n client,\n event,\n band_00: P('float', 'band 0' , min_value = -0.25, max_value = 1.0) = 0.0,\n band_01: P('float', 'band 1' , min_value = -0.25, max_value = 1.0) = 0.0,\n band_02: P('float', 'band 2' , min_value = -0.25, max_value = 1.0) = 0.0,\n band_03: P('float', 'band 3' , min_value = -0.25, max_value = 1.0) = 0.0,\n band_04: P('float', 'band 4' , min_value = -0.25, max_value = 1.0) = 0.0,\n band_05: P('float', 'band 5' , min_value = -0.25, max_value = 1.0) = 0.0,\n band_06: P('float', 'band 6' , min_value = -0.25, max_value = 1.0) = 0.0,\n band_07: P('float', 'band 7' , min_value = -0.25, max_value = 1.0) = 0.0,\n band_08: P('float', 'band 8' , min_value = -0.25, max_value = 1.0) = 0.0,\n band_09: P('float', 'band 9' , min_value = -0.25, max_value = 1.0) = 0.0,\n band_10: P('float', 'band 10', min_value = -0.25, max_value = 1.0) = 0.0,\n band_11: P('float', 'band 11', min_value = -0.25, max_value = 1.0) = 0.0,\n band_12: P('float', 'band 12', min_value = -0.25, max_value = 1.0) = 0.0,\n band_13: P('float', 'band 13', min_value = -0.25, max_value = 1.0) = 0.0,\n band_14: P('float', 'band 14', min_value = -0.25, max_value = 1.0) = 0.0,\n):\n player = get_player_or_abort(client, event)\n \n filter = Equalizer(\n (0, band_00),\n (1, band_01),\n (2, band_02),\n (3, band_03),\n (4, band_04),\n (5, band_05),\n (6, band_06),\n (7, band_07),\n (8, band_08),\n (9, band_09),\n (10, band_10),\n (11, band_11),\n (12, band_12),\n (13, band_13),\n (14, band_14),\n )\n player.add_filter(filter)\n await player.apply_filters()\n \n return create_filter_added_embed(filter)","def bollinger_bands(self, normalize=False):\n sma = self.sma(normalize=normalize)\n rstd = self.rolling_std(normalize=normalize)\n upper_band = sma + rstd\n lower_band = sma - rstd\n return upper_band, lower_band","def waveband(self):\n return self._band","def bands(self):\n if self._bands is None:\n self._bands = self._compute_bands()\n return self._bands","def binning(S, bands):\n B = np.zeros((S.shape[0], len(bands)), dtype=S.dtype)\n for i, b in enumerate(bands):\n B[:, i] = np.mean(S[:, b[0] : b[1]], axis=1)\n\n return B","def bandpasscorrect(data):\n ret=[x for x in data]\n n=len(ret)\n ret[0]=1.083*ret[0]-0.083*ret[1]\n ret[n-1]=1.083*ret[n-1]-0.083*ret[n-2]\n for k in range(1,n-1):\n ret[k]=1.166*ret[k]-0.083*ret[k-1]-0.083*ret[k+1]\n return ret","def band(n, steps):\n return math.floor(n * steps) / steps","def _get_band_for_value(self, wss_value):\n if (wss_value > self.threshold):\n return self.positive_band\n elif (wss_value < -self.threshold):\n return self.negative_band\n else:\n return self.zero_band","def band(self, val):\n if len(val) is 2:\n self.band = tuple(val)\n \n else:\n # Should raise an error here\n pass","def get_bandstruct(\n w=[],\n atoms={},\n ef=0,\n line_density=1,\n ylabel=\"Energy (cm-1)\",\n font=22,\n var_form=None,\n filename=\"bands.png\",\n savefig=True,\n neigs=None,\n max_nk=None,\n tol=None,\n):\n info = {}\n kpoints = Kpoints().kpath(atoms, line_density=line_density)\n labels = kpoints.to_dict()[\"labels\"]\n kpts = kpoints.to_dict()[\"kpoints\"]\n print(\"kpts\", len(kpts))\n\n eigvals_q = []\n eigvals_np = []\n for ii, i in enumerate(kpts):\n if max_nk is not None and ii == max_nk:\n break\n # For reducing CI/CD time\n print(\"breaking here\", ii, max_nk)\n else:\n try:\n print(\"kp=\", ii, i)\n hk = get_hk_tb(w=w, k=i)\n HS = HermitianSolver(hk)\n vqe_vals, _ = HS.run_vqd(var_form=var_form)\n np_vals, _ = HS.run_numpy()\n print(\"np_vals\", np_vals)\n print(\"vqe_vals\", vqe_vals)\n eigvals_q.append(vqe_vals)\n eigvals_np.append(np_vals)\n # break\n if (\n neigs is not None\n and isinstance(neigs, int)\n and neigs == len(eigvals_q)\n ):\n break\n except Exception as exp:\n print(exp)\n pass\n eigvals_q = 3.14 * np.array(eigvals_q)\n eigvals_np = 3.14 * np.array(eigvals_np)\n\n for ii, i in enumerate(eigvals_q.T - ef):\n if ii == 0:\n plt.plot(i, c=\"b\", label=\"VQD\")\n else:\n plt.plot(i, c=\"b\")\n\n for ii, i in enumerate(eigvals_np.T - ef):\n if ii == 0:\n plt.plot(i, c=\"r\", label=\"Numpy\")\n else:\n plt.plot(i, c=\"r\")\n new_kp = []\n new_labels = []\n count = 0\n kp = np.arange(len(kpts))\n for i, j in zip(kp, labels):\n if j != \"\":\n if count > 1 and count < len(labels) - 1:\n if labels[count] != labels[count + 1]:\n new_kp.append(i)\n new_labels.append(\"$\" + str(j) + \"$\")\n else:\n new_kp.append(i)\n new_labels.append(\"$\" + str(j) + \"$\")\n count += 1\n info[\"eigvals_q\"] = list(eigvals_q.tolist())\n info[\"eigvals_np\"] = list(eigvals_np.tolist())\n info[\"kpts\"] = list(kpts)\n info[\"new_kp\"] = list(np.array(new_kp).tolist())\n info[\"new_labels\"] = list(new_labels)\n info[\"ef\"] = ef\n print(info)\n if tol is not None:\n plt.ylim([tol, np.max(eigvals_q)])\n plt.rcParams.update({\"font.size\": font})\n plt.xticks(new_kp, new_labels)\n plt.ylabel(ylabel)\n plt.legend()\n plt.tight_layout()\n\n if savefig:\n plt.savefig(filename)\n plt.close()\n else:\n plt.show()\n return info","def bands(self):\n\t\treturn self._bands","def norm_band(band):\n b_min, b_max = band.min(), band.max()\n return (band - b_min) / (b_max - b_min)","def band(self, name, bands, new_name=None, label=None, text_key=None):\n if not self._is_numeric(name):\n msg = \"Can only band numeric typed data! {} is {}.\"\n msg = msg.format(name, self._get_type(name))\n raise TypeError(msg)\n if not text_key: text_key = self.text_key\n if not new_name: new_name = '{}_banded'.format(name)\n if not label: label = self.text(name, False, text_key)\n franges = []\n for idx, band in enumerate(bands, start=1):\n lab = None\n if isinstance(band, dict):\n lab = list(band.keys())[0]\n band = list(band.values())[0]\n if isinstance(band, tuple):\n if band[0] < 0:\n raise ValueError('Cannot band with lower bound < 0.')\n elif band[1] < 0:\n raise ValueError('Cannot band with upper bound < 0.')\n r = '{}-{}'.format(band[0], band[1])\n franges.append([idx, lab or r, {name: frange(r)}])\n else:\n r = str(band)\n franges.append([idx, lab or r, {name: [band]}])\n\n self.derive(new_name, 'single', label, franges,\n text_key=text_key)\n\n return None","def band(E, alpha = -1.08, beta = -1.75, E0 = 189, A = 5e-3):\n if (alpha - beta)*E0 >= E:\n return A*(E/100)**alpha*np.exp(-E/E0)\n elif (alpha - beta)*E0 < E:\n return A*((alpha - beta)*E0/100)**(alpha - beta)*np.exp(beta - alpha)*(E/100)**beta","def estimate_bandpass(data):\n \n est = filter(data, params.st_bp_window_f, axis=0)\n est = filter(est, params.st_bp_window_t, axis=1)\n \n return est","def bandname(self):\n if self._properties['bandname'] is None:\n self._properties['bandname'] = \"wisew1\" if \"-w1-\" in self.filename \\\n else \"wisew2\" if \"-w2-\" in self.filename \\\n else \"wisew3\" if \"-w3-\" in self.filename \\\n else \"wisew4\" if \"-w4-\" in self.filename \\\n else \"unknown\"\n return self._properties['bandname']","def _allowed_bands():\n pass","def get_bandgap(cluster, spin, dirr=\"/home/pv278/Platinum/\"):\n outfile = open(get_path(dirr, cluster, spin)).readlines()\n line = [l for l in outfile if \"occ\" in l]\n if line:\n Eocc = line[-1].split()[-1]\n else:\n Eocc = None\n line = [l for l in outfile if \"virt\" in l]\n if line:\n Evirt = line[0].split()[4]\n else:\n Evirt = None\n \n if Eocc:\n return (float(Evirt) - float(Eocc)) * 27.211\n else:\n return","def fastbollingerSignal(price, uband, lband):\n n = len(price)\n signal = np.zeros(n)\n for i in prange(n):\n if price[i] <= lband[i]: # crossing down\n signal[i] = 1\n elif price[i] >= uband[i]: # crossing up\n signal[i] = -1\n else:\n signal[i] = 0\n return signal","def get_bollinger_bands(rm, rstd, degrees):\n\tupper_band = rm + rstd * degrees\n\tlower_band = rm - rstd * degrees\n\treturn upper_band, lower_band","def fbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,\n nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97): \n highfreq= highfreq or samplerate/2\n print \"preemph %s\"%(preemph)\n signal = sigproc.preemphasis(signal,preemph)\n frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate)\n matchframes(frames[0], frames[1])\n pspec = sigproc.powspec(frames,nfft)\n energy = pylab.sum(pspec,1) # this stores the total energy in each frame\n energy = pylab.where(energy == 0, pylab.finfo(float).eps, energy) # if energy is zero, we get problems with log\n fb = get_filterbanks(nfilt, nfft, samplerate, lowfreq, highfreq)\n print \"len(fb) %s\"%(len(fb))\n colour = \"k-\"\n for i in range(len(fb)):\n if colour == \"k-\":\n colour = \"r-\"\n else:\n colour = \"k-\"\n startedplot = False\n midpoint = 0\n for j in range(len(fb[i])):\n if fb[i][j] > 0:\n if startedplot == False:\n startedplot = j\n if j > 0:\n pylab.plot([j-1, j], [fb[i][j-1], fb[i][j]], colour)\n if fb[i][j] == 1.0:\n midpoint = j\n else:\n if not startedplot == False:\n pylab.plot([j-1, j], [fb[i][j-1], 0], colour)\n try:\n print \"slope to midpoint %.3f, slope from midpoint %.3f\"%(1.0/float(midpoint-startedplot), 1.0/float(midpoint-j+1))\n except:\n pass\n break\n pylab.show()\n feat = pylab.dot(pspec, fb.T) # compute the filterbank energies\n feat = pylab.where(feat == 0, pylab.finfo(float).eps, feat) # if feat is zero, we get problems with log\n return feat, energy","def bbands(df, price, bbands, n_bdev_up, n_bdev_dn, ma_type, n):\n\n ma_types = {0: sma, 1: ema, 2: wma, 3: dema, 4: tema}\n\n n_days_std = df[price].rolling(window=n).std(ddof=0)\n df = ma_types[ma_type](df, price, bbands + \"_middle_band\", n)\n df[bbands + \"_upper_band\"] = df[bbands + \"_middle_band\"] + (\n n_days_std * n_bdev_up\n )\n df[bbands + \"_lower_band\"] = df[bbands + \"_middle_band\"] - (\n n_days_std * n_bdev_dn\n )\n df = df.dropna().reset_index(drop=True)\n\n return df","def get_bandpass(self):\n return self.sum(axis=1)","def inst_bp(instrument,array=\"2\"):\n\n if instrument == \"MUSTANG2\" or instrument == \"MUSTANG\":\n srms = (300*u.um).to(\"m\") # surface RMS (microns)\n ### Reference: https://science.nrao.edu/facilities/gbt/proposing/GBTpg.pdf\n EA90 = 0.36 # Aperture efficiency at 90 GHz\n ### The beam efficiencies should be taken as 1.37* Aperture Efficiency\n R90 = np.exp(-4.0*np.pi*(srms/(const.c/(9.0e10*u.s**-1))).value) #\n Gnot = EA90/R90 # Unphysical, but see documentation...\n if instrument == \"MUSTANG2\":\n flow = 75.0 # GHz\n fhig = 105.0 # GHz\n else:\n flow = 82.5 # GHz\n fhig = 97.5 # GHz\n \n farr = np.arange(flow,fhig,1.0) # frequency array.\n tran = farr*0.0 + 1.0 # Let the transmission be unity everywhere.\n Larr = const.c.value/(farr*1.0e9) # Keep calm and carry on.\n ### Old formula:\n #Ruze = Gnot * np.exp(-4.0*np.pi*(srms.value)/Larr)\n ### Correct formula: (10 April 2018)\n Ruze = Gnot * np.exp(-(4.0*np.pi*srms.value/Larr)**2)\n NRuz = Ruze / np.max(Ruze) # Normalize it\n band = tran * Ruze # Bandpass, with (unnormalized) Ruze efficiency\n \n if instrument == \"NIKA2\" or instrument == \"NIKA\":\n caldir='/home/romero/NIKA2/NIKA_SVN/Processing/Pipeline/Calibration/BP/'\n bpfile=caldir+'Transmission_2017_Jan_NIKA2_v1.fits'\n hdulist = fits.open(bpfile)\n\n if array == \"1H\": # 1mm (260 GHz) array, Horizontal Polarization\n tbdata = hdulist[1].data # 1H\n freq = tbdata.field(0)\n tran = tbdata.field(1)\n erro = tbdata.field(2)\n atmt = tbdata.field(3)\n cfreq1h = np.sum(freq*tran)/np.sum(tran)\n \n if array == \"1V\": # 1mm (260 GHz) array, Vertical Polarization\n tbdata = hdulist[2].data # 1V\n freq = tbdata.field(0)\n tran = tbdata.field(1)\n erro = tbdata.field(2)\n atmt = tbdata.field(3)\n cfreq1v = np.sum(freq*tran)/np.sum(tran)\n \n if array == \"2\": # 2mm (150 GHz) array\n tbdata = hdulist[3].data # 2\n freq = tbdata.field(0)\n tran = tbdata.field(1)\n erro = tbdata.field(2)\n atmt = tbdata.field(3)\n cfreq2 = np.sum(freq*tran)/np.sum(tran)\n\n ### Trim the zero-frequency listing, if any.\n gi=np.where(freq > 0)\n freq = freq[gi]\n tran = tran[gi]\n erro = erro[gi]\n atmt = atmt[gi]\n \n### Calculate Aperture efficiencies from information found at:\n### http://www.iram.es/IRAMES/mainwiki/Iram30mEfficiencies\n Beff = 0.630 # at 210 GHz\n Aeff = Beff/1.27 # See text on webpage\n srms = (66.0*u.um).to(\"m\") # surface RMS (microns)\n R210 = np.exp(-4.0*np.pi*(srms/(const.c/(2.1e11*u.s**-1))).value) #\n Gnot = Aeff/R210 # Unphysical, but see documentation...\n\n Larr = const.c.value/(freq*1.0e9) # Keep calm and carry on. \n Ruze = Gnot * np.exp(-4.0*np.pi*(srms.value)/Larr)\n NRuz = Ruze / np.max(Ruze) # Normalize it\n band = tran * Ruze # Bandpass, with (unnormalized) Ruze efficiency\n farr = freq\n \n#########################################################################\n\n if instrument == 'ACT90':\n srms = (27.0*u.um).to(\"m\") # surface RMS (microns)\n EA90 = 0.95 # I'm making this number up...\n R90 = np.exp(-4.0*np.pi*(srms/(const.c/(9.0e10*u.s**-1))).value) #\n Gnot = EA90/R90 # Unphysical, but see documentation...\n flow = 65.0 # GHz\n fhig = 125.0 # GHz\n farr = np.arange(flow,fhig,1.0) # frequency array.\n freq_ref = 90.0 # I took EA90 to be a fictitious aperature efficiency at 90 GHz\n band = ruze_eff(farr,freq_ref,EA90,srms)\n\n if instrument == 'ACT150':\n srms = (27.0*u.um).to(\"m\") # surface RMS (microns)\n EA90 = 0.95 # I'm making this number up...\n R90 = np.exp(-4.0*np.pi*(srms/(const.c/(9.0e10*u.s**-1))).value) #\n Gnot = EA90/R90 # Unphysical, but see documentation...\n flow = 120.0 # GHz\n fhig = 180.0 # GHz\n farr = np.arange(flow,fhig,1.0) # frequency array.\n freq_ref = 90.0 # I took EA90 to be a fictitious aperature efficiency at 90 GHz\n band = ruze_eff(farr,freq_ref,EA90,srms)\n\n\n return band, farr","def calculate_flux(self, band):\n\n if (self.wavelength[0] > band.wavelength[0] or\n self.wavelength[-1] < band.wavelength[-1]):\n\n warn('Spectrum does not cover the whole bandpass, '\n 'extrapolating...')\n dw = np.median(np.diff(self.wavelength.value))\n spec_wavelength = np.arange(\n band.wavelength.value[0],\n band.wavelength.value[-1] + dw, dw) * angstrom\n spec_flux = np.interp(spec_wavelength, self.wavelength,\n self.flux.value)\n\n else:\n spec_wavelength = self.wavelength\n spec_flux = self.flux.value\n\n i, j = spec_wavelength.searchsorted(\n Quantity([band.wavelength[0], band.wavelength[-1]]))\n wavelength = spec_wavelength[i:j]\n flux = spec_flux[i:j]\n\n dw_band = np.median(np.diff(band.wavelength))\n dw_spec = np.median(np.diff(wavelength))\n\n if dw_spec.value > dw_band.value > 20:\n\n warn('Spectrum wavelength sampling interval {0:.2f}, but bandpass'\n 'sampling interval {1:.2f}'.format(dw_spec, dw_band))\n\n # Interpolate the spectrum to the passband wavelengths:\n flux = np.interp(band.wavelength, wavelength, flux)\n band_transmission = band.transmission\n wavelength = band.wavelength\n\n else:\n # Interpolate the band transmission to the spectrum wavelengths:\n band_transmission = np.interp(\n wavelength, band.wavelength, band.transmission)\n\n # Weight by the response and wavelength, appropriate when we're\n # counting the number of photons within the band:\n flux = (np.trapz(band_transmission * flux * wavelength, wavelength) /\n np.trapz(band_transmission * wavelength, wavelength))\n flux *= erg / s / cm ** 2 / angstrom\n\n return flux","def get_band_edges():\n # Vacuum level energy from LOCPOT.\n locpot = Locpot.from_file('LOCPOT')\n evac = max(locpot.get_average_along_axis(2))\n\n vasprun = Vasprun('vasprun.xml')\n bs = vasprun.get_band_structure()\n eigenvals = vasprun.eigenvalues\n efermi = vasprun.efermi - evac\n\n if bs.is_metal():\n edges = {'up_cbm': None, 'up_vbm': None, 'dn_cbm': None, 'dn_vbm': None,\n 'efermi': efermi}\n\n elif bs.is_spin_polarized:\n up_cbm = min(\n [min([e[0] for e in eigenvals[Spin.up][i] if not e[1]])\n for i in range(len(eigenvals[Spin.up]))]) - evac\n up_vbm = max(\n [max([e[0] for e in eigenvals[Spin.up][i] if e[1]])\n for i in range(len(eigenvals[Spin.up]))]) - evac\n dn_cbm = min(\n [min([e[0] for e in eigenvals[Spin.down][i] if not e[1]])\n for i in range(len(eigenvals[Spin.down]))]) - evac\n dn_vbm = max(\n [max([e[0] for e in eigenvals[Spin.down][i] if e[1]])\n for i in range(len(eigenvals[Spin.down]))]) - evac\n edges = {'up_cbm': up_cbm, 'up_vbm': up_vbm, 'dn_cbm': dn_cbm,\n 'dn_vbm': dn_vbm, 'efermi': efermi}\n\n else:\n cbm = bs.get_cbm()['energy'] - evac\n vbm = bs.get_vbm()['energy'] - evac\n edges = {'up_cbm': cbm, 'up_vbm': vbm, 'dn_cbm': cbm, 'dn_vbm': vbm,\n 'efermi': efermi}\n\n return edges","def bandlcs(self):\n lc = self.lc\n bandgroups = lc.group_by('band')\n return bandgroups","def bbands(price, length=30, numsd=2):\n ave = pd.stats.moments.rolling_mean(price,length)\n sd = pd.stats.moments.rolling_std(price,length)\n upband = ave + (sd*numsd)\n dnband = ave - (sd*numsd)\n return np.round(upband,3), np.round(dnband,3)","def make_band(f0, band_type, num):\n \n if band_type == '1Hz-spaced':\n hwidth = num//2\n step = 1\n f = np.arange(f0-hwidth, f0+hwidth+step, step)\n \n else:\n lwr = -(num-1)//2 # lower range bound for frequency calculation\n upr = (num+1)//2 # upper range bound for frequency calculation\n f = np.array(list(f0*2**(k/(12*num)) for k in range(lwr,upr)))\n \n return f","def band_hilbert(x, fs, band, N=None, axis=-1):\n x = np.asarray(x)\n Xf = np.fft.fft(x, N, axis=axis)\n w = np.fft.fftfreq(N or x.shape[0], d=1. / fs)\n Xf[(w < band[0]) | (w > band[1])] = 0\n x = np.fft.ifft(Xf, axis=axis)[:x.shape[0]]\n return 2*x","def bands(self):\n\t\treturn zip((self.primary_threshold, self.upper_earning_limit),\n\t\t\t\t self.rates)","def make_bwfull(w,minZ,maxZ,ires=1,fixw=False,m=mz0):\n cmds = []\n # coefficients for the amplitudes\n cmds.append(\"A[1,0,1000000]\")\n cmds.append(\"B[1,0,1000000]\")\n cmds.append(\"C[10000.0,0,1000000]\")\n # amplitudes\n cmds.append('m[%s,%s,%s]'%(m,minZ,maxZ))\n cmds.append('g[2.49,0,10]')\n denom = '((x^2-m^2)^2+x^4*g^2/m^2)'\n cmds.append(\"expr::z_rbw('x^2/%s',x,m,g)\"%denom)\n cmds.append(\"expr::z_int('(x^2-m^2)/%s',x,m,g)\"%denom)\n cmds.append(\"expr::z_rad('1/(x^2)',x)\")\n # resolution model\n cmds += resolutions[ires]()\n [w.factory(cmd) for cmd in cmds]\n # any parameter adjustments\n if True:\n w.var('r_m').setConstant(kTRUE) if w.var('r_m') else None\n w.var('rt_m').setConstant(kTRUE) if w.var('rt_m') else None\n w.var('g').setConstant(kTRUE) if w.var('g') and fixw else None\n # sum-of-amplitudes pdf\n lshape = RooRealSumPdf('lshape','lshape',RooArgList(w.function('z_rad'),w.function('z_int'),w.function('z_rbw')),RooArgList(w.var('A'),w.var('B'),w.var('C')))\n getattr(w,'import')(lshape)\n # convolution\n pdf = w.pdf('lshape')\n if w.pdf('res'):\n w.var('x').setBins(10000,'cache')\n cmd = 'FCONV::sum(x,lshape,res)'\n w.factory(cmd)\n pdf = w.pdf('sum')\n return pdf, kFALSE","def band_plot(N=400,a=1.0):\n foot_step=2*np.pi/N\n x=np.arange(0.0,2*np.pi/a,foot_step)\n y=band_energy(x)\n plt.plot(x,y)","def bands(self):\n return self._bands","def beam(xb,yb,zb,wx,wy,wavelen):\n\n zRx = np.pi * wx**2 / wavelen\n zRy = np.pi * wy**2 / wavelen \n \n sqrtX = np.sqrt( 1 + np.power(zb/zRx,2) ) \n sqrtY = np.sqrt( 1 + np.power(zb/zRy,2) ) \n intensity = np.exp( -2.*( np.power(xb/(wx*sqrtX ),2) \\\n + np.power(yb/(wy*sqrtY),2) )) / sqrtX / sqrtY\n return intensity","def plotallbands(_zband, _yband, _jband, _hband, _kband, _period):\n # Set pyplot style to be consisten within the program\n plt.style.use('seaborn-whitegrid')\n # Frequency = 1 / Period\n _freq = 1 / _period\n\n # Create single dataset from all bands\n _bands = [_zband, _yband, _jband, _hband, _kband]\n # Iterate through each band and plot to screen\n i = 0\n while i < 5:\n # Array to set colours for each band\n _colours = ['-b', '-g', '-r', '-c', '-m']\n # Array to set strings for graph legend\n _legend = ['Z-band', 'Y-band', 'J-band', 'H-band', 'K-band']\n # Determine the line of best fit for each band\n _xfit, _lobf = calclobf(_bands[i], _period)\n # Plot the data in the array to screen, lightly coloured and z rank behind the line of best fit\n plt.plot(_xfit, _lobf, _colours[i], lw=1, zorder=2, label=_legend[i])\n i += 1\n\n # Set x-axis limit to a single period\n plt.xlim(0, 1)\n # Set graph and axis titles\n plt.xlabel(\"Phase\")\n plt.ylabel(\"Magnitude\")\n plt.title(\"Folded light curve\")\n # Show the legend\n plt.legend()\n # Invert y-axis as convention\n plt.gca().invert_yaxis()\n # Save to current folder\n plt.savefig('curve.png')\n # Display to screen\n plt.show()","def calc_elv_bands(self, red, comp):\n # reference band\n red_V = red.data['BAND'].get_band_mag('V')\n comp_V = comp.data['BAND'].get_band_mag('V')\n\n # possible bands for the band extinction curve\n poss_bands = red.data['BAND'].get_poss_bands()\n\n waves = []\n exts = []\n uncs = []\n npts = []\n for pband_name in poss_bands.keys():\n red_mag = red.data['BAND'].get_band_mag(pband_name)\n comp_mag = comp.data['BAND'].get_band_mag(pband_name)\n if (red_mag is not None) & (comp_mag is not None):\n ext = (red_mag[0] - red_V[0]) - (comp_mag[0] - comp_V[0])\n unc = np.sqrt(red_mag[1]**2 + red_V[1]**2\n + comp_mag[1]**2 + comp_V[1]**2)\n waves.append(red.data['BAND'].band_waves[pband_name])\n exts.append(ext)\n uncs.append(unc)\n npts.append(1)\n\n if len(waves) > 0:\n self.waves['BAND'] = np.array(waves)\n self.exts['BAND'] = np.array(exts)\n self.uncs['BAND'] = np.array(uncs)\n self.npts['BAND'] = np.array(npts)","def add_bollinger_bands(self, rstd):\n self.data['upper_band'] = self.data['rolling_mean'] + 2 * rstd\n self.data['lower_band'] = self.data['rolling_mean'] - 2 * rstd","def compute_band_ratio(freqs, powers, low_band, high_band, method='mean'):\n\n # Compute the power in each band\n low_band_power = compute_absolute_power(freqs, powers, low_band, method)\n high_band_power = compute_absolute_power(freqs, powers, high_band, method)\n\n # Calculate the ratio between bands\n ratio = low_band_power / high_band_power\n\n return ratio","def bandname(self):\n if self._properties['bandname'] is None:\n self._properties['bandname'] = \"fuv\" if \"-fd-\" in self.filename else \"nuv\" if \"-nd-\" in self.filename \\\n else \"unknown\"\n return self._properties['bandname']","def get_bw(self):\n return np.array([b2 - b1 for b1, b2 in self.bands])","def get_wl_band(radar_frequency):\n return 0 if (30 < radar_frequency < 40) else 1","def calc_rsi(image):\n\n # roll axes to conventional row,col,depth\n img = np.rollaxis(image, 0, 3)\n\n # bands: Coastal(0), Blue(1), Green(2), Yellow(3), Red(4), Red-edge(5), NIR1(6), NIR2(7)) Multispectral\n COAST = img[:, :, 0]\n B = img[:, :, 1]\n G = img[:, :, 2]\n Y = img[:, :, 3]\n R = img[:, :, 4]\n RE = img[:, :, 5]\n NIR1 = img[:, :, 6]\n NIR2 = img[:, :, 7]\n\n arvi = old_div((NIR1 - (R - (B - R))), (NIR1 + (R - (B - R))))\n dd = (2 * NIR1 - R) - (G - B)\n gi2 = (B * -0.2848 + G * -0.2434 + R * -0.5436 + NIR1 * 0.7243 + NIR2 * 0.0840) * 5\n gndvi = old_div((NIR1 - G), (NIR1 + G))\n ndre = old_div((NIR1 - RE), (NIR1 + RE))\n ndvi = old_div((NIR1 - R), (NIR1 + R))\n ndvi35 = old_div((G - R), (G + R))\n ndvi84 = old_div((NIR2 - Y), (NIR2 + Y))\n nirry = old_div((NIR1), (R + Y))\n normnir = old_div(NIR1, (NIR1 + R + G))\n psri = old_div((R - B), RE)\n rey = old_div((RE - Y), (RE + Y))\n rvi = old_div(NIR1, R)\n sa = old_div(((Y + R) * 0.35), 2) + old_div((0.7 * (NIR1 + NIR2)), 2) - 0.69\n vi1 = old_div((10000 * NIR1), (RE) ** 2)\n vire = old_div(NIR1, RE)\n br = (old_div(R, B)) * (old_div(G, B)) * (old_div(RE, B)) * (old_div(NIR1, B))\n gr = old_div(G, R)\n rr = (old_div(NIR1, R)) * (old_div(G, R)) * (old_div(NIR1, RE))\n\n ###Built-Up indices\n wvbi = old_div((COAST - RE), (COAST + RE))\n wvnhfd = old_div((RE - COAST), (RE + COAST))\n\n ###SIs\n evi = old_div((2.5 * (NIR2 - R)), (NIR2 + 6 * R - 7.5 * B + 1))\n L = 0.5 # some coefficient for Soil Adjusted Vegetation Index (SAVI) DO NOT INCLUDE IN FEATURES\n savi = old_div(((1 + L) * (NIR2 - R)), (NIR2 + R + L))\n msavi = old_div((2 * NIR2 + 1 - ((2 * NIR2 + 1) ** 2 - 8 * (NIR2 - R)) ** 0.5), 2)\n bai = old_div(1.0, ((0.1 + R) ** 2 + 0.06 + NIR2))\n rgi = old_div(R, G)\n bri = old_div(B, R)\n\n rsi = np.stack(\n [arvi, dd, gi2, gndvi, ndre, ndvi, ndvi35, ndvi84, nirry, normnir, psri, rey, rvi, sa, vi1, vire, br, gr, rr,\n wvbi, wvnhfd, evi, savi, msavi, bai, rgi, bri],\n axis=2)\n\n return rsi","def bandpass(filename,f1,f2,Q,wout=True,plot=True):\n start=time.time()\n n, data, data_dB,sr,ch=inputwav(filename)\n b, a = butter(Q,Wn=(f1/sr,f2/sr),btype='bandpass')\n data_filtered=lfilter(b,a,data,axis=0)\n print('Applying FFT...')\n if wout==True:\n print('Exporting...')\n sf.write(filename[0:len(filename)-4]+'_BP.wav',data_filtered,sr,'PCM_16')\n if plot==True:\n print('Plotting...')\n py.close()\n w, h = freqz(b,a,worN=16384)\n fig, (ax1, ax2) = py.subplots(nrows=2)\n ax1.semilogx(0.5*sr*w/np.pi,abs(h),'k-')\n ax1.set_xlabel('Frequency (Hz)')\n ax1.set_ylabel('Rel. Amplitude')\n ax1.grid()\n ax1.set_ylim(0,1.1)\n ax1.set_xlim(1,20000)\n ax2.plot(data,'k-',label='Raw data')\n ax2.plot(data_filtered,'m-',lw=1,label='Filtered data')\n ax2.set_xlim(0,10000)\n ax2.set_ylim(-1,1)\n ax2.set_ylabel('Amplitude (Norm Bits)')\n ax2.set_xlabel('Samples')\n ax2.legend(loc=2,frameon=False,ncol=2)\n py.subplots_adjust(hspace=0.35) \n print('Done!')\n end=time.time()\n elapsed=int(1000*(end-start))\n print('...............................')\n print('Completed in '+str(elapsed)+' milliseconds.')\n return data_filtered","def build_signal_dataset(self):\n return np.abs(self.bandpassed).mean(axis=-2)","def waveband(self):\n return self.get(\"waveband\")","def subbandwidth(self):","def waveband(self):\n return self.get(\"waveband\", default=\"\", decode=True).split(\"#\")","def getbandlcs(lc):\n bandgroups = lc.group_by('band')\n\n return bandgroups","def __init__(self, bandit):\n self.bandit = bandit","def quality(self): \n\n subsetInt = [int(s) for s in self.subset.split() if s.isdigit()]\n columnNames = [] \n for i in range(len(subsetInt)):\n if subsetInt[i] == 1:\n columnNames.append(self.varNames[i])\n\n #qualityBand number of subset\n q = columnNames.index('Quality') \n\n if subsetInt[self.qualityBand] == 1:\n dataCount = self.subset.count('1')\n QC = np.repeat(self.DC[:,q].reshape((self.DC.shape[0],1)), dataCount-1, axis = 1)\n if self.dataset == 'MOD09A1.005' or self.dataset == 'MOD13Q1.005':\n QC = np.uint16(QC)\n else:\n QC = np.uint8(QC)\n\n QCm = QC & 1 #flips DCm mask\n DCm = np.delete(self.DC, q, 1) #looks good\n \n DCm = np.ma.masked_where(QCm == 1, DCm)\n DCm = np.ma.masked_where(DCm == 9999.0, DCm) \n \n if len(self.tiles) > 1:\n obs = self.observations/len(self.tiles)\n if len(self.tiles) == 1:\n obs = self.observations/2\n \n outArray = np.empty(shape = (self.rows*self.columns*obs, 0))\n for b in range(0, self.DC.shape[1]-1):\n cfull = DCm[:,b].reshape((self.observations, self.rows, self.columns))\n b16 = np.empty(shape = (self.rows*self.columns*obs, 0))\n for band in range(0,cfull.shape[0],2):\n c16 = np.ma.mean(cfull[band:band+1,:,:], axis=0)\n c16f = np.ma.filled(c16, 9999.0).astype(float).reshape((self.rows*self.columns))\n b16 = np.append(b16, c16f)\n outArray = np.append(outArray, b16.reshape((obs*self.rows*self.columns, 1)), axis = 1)\n \n self.finalDC = outArray\n \n np.save(str(self.directory) + '/' + self.dataset + '.npy', self.finalDC)\n del outArray, QC, DCm\n\n outfile = str(self.directory) + '/' + self.dataset + '.txt'\n f = open(outfile, 'w')\n for name in columnNames:\n if name != 'Quality':\n f.write(name + '\\n')\n var = [a for a in columnNames if not a.startswith('Quality')]\n logger.log('SUCCESS', 'The final 16-day interval quality-masked matrix was created successfully. This matrix has dimensions %d rows by %d columns. Datasets included in the matrix are %s' % (self.finalDC.shape[0], self.finalDC.shape[1], var))\n \n \n if subsetInt[self.qualityBand] != 1:\n cleanDC = np.delete(self.DC, q, 1)\n \n \n if len(self.tiles) > 1:\n obs = self.observations/len(self.tiles)\n if len(self.tiles) == 1:\n obs = self.observations/2\n \n outArray = np.empty(shape = (self.rows*self.columns*obs, 0))\n for b in range(cleanDC.shape[1]):\n cfull = cleanDC[:,b].reshape((self.observations, self.rows, self.columns))\n b16 = np.empty(shape=(self.rows*self.columns*obs))\n for band in range(cfull.shape[0]):\n c16 = np.mean(cfull[band:band+1,:,:], axis=0)\n band16 = np.append(b16, c16, axis=0)\n outArray = np.append(outArray, b16.reshape((obs*self.rows*self.columns, 1)), axis = 1)\n\n np.save(self.directory + '/' + self.dataset + '.npy', self.finalDC)\n del cleanDC, outArray\n \n outfile = self.directory + '/' + self.dataset + '.txt'\n f = open(outfile, 'w')\n for name in columnNames:\n if name != 'Quality':\n f.write(str(name) + ' \\n')\n var = [a for a in columnNames if not a.startswith('Quality')]\n logger.log('SUCCESS', 'The final 16-day interval matrix was created successfully. A quality mask was not applied, though remaining no data values are set at 9999. This matrix has dimensions %d rows by %d columns. Datasets included in the matrix are %s' % (self.finalDC.shape[0], self.finalDC.shape[1], var))","def readBandit(filename):\n\tfile = open(filename, 'r')\n\tlines = file.readlines()\n\tbandit = [float(value.strip()) for value in lines]\n\treturn bandit","def _butter_bandpass(lowcut: float, highcut: float, fs: float, order: int = 5) -> tuple:\n nyq = 0.5 * fs\n low = lowcut / nyq\n high = highcut / nyq\n b, a = butter(order, [low, high], btype=\"band\")\n return b, a","def usedbands(self):\n lc = self.lc\n return np.unique(np.asarray(lc['band']))","def band_penalty(self):\n fc_ix = np.argmin(np.abs(self.f - self.fc)) # Index to frequency array closes to center frequency\n # Number of indexes on each side of center frequency, not extending outside, only up to 10 kHz\n n = min(fc_ix, self.ix10k - fc_ix)\n if n == 0:\n return 0.0\n return np.mean(np.square(self.fr[fc_ix - n:fc_ix] - (self.gain - self.fr[fc_ix + n - 1:fc_ix - 1:-1])))","def bollinger_hband_indicator(close, n=20, ndev=2, fillna=False):\n df = pd.DataFrame([close]).transpose()\n mavg = close.rolling(n).mean()\n mstd = close.rolling(n).std()\n hband = mavg + ndev * mstd\n df['hband'] = 0.0\n df.loc[close > hband, 'hband'] = 1.0\n hband = df['hband']\n if fillna:\n hband = hband.replace([np.inf, -np.inf], np.nan).fillna(0)\n return pd.Series(hband, name='bbihband')","def bin_power(X,Band,Fs):\n\n\tC = fft(X)\n\tC = abs(C)\n\tPower =np.zeros(len(Band)-1);\n\tfor Freq_Index in range(0,len(Band)-1):\n\t\tFreq = float(Band[Freq_Index])\t\t\t\t\t\t\t\t\t\t## Xin Liu\n\t\tNext_Freq = float(Band[Freq_Index+1])\n\t\tPower[Freq_Index] = sum(C[np.floor(Freq/Fs*len(X)):np.floor(Next_Freq/Fs*len(X))])\n\tPower_Ratio = Power/sum(Power)\n\treturn Power, Power_Ratio","def recon_steer_bands(pyr, freq_resps, numlevels, numorientations):\n \n result_bands = np.zeros(pyr[0].shape)\n\n freq_hi = np.fft.fftshift(np.fft.fft2(pyr[0]))\n result_hi = np.fft.ifft2(np.fft.fftshift(np.multiply(freq_hi, np.conjugate(freq_resps[0])))).real \n \n freq_lo = np.fft.fftshift(np.fft.fft2(pyr[2]))\n result_lo = np.fft.ifft2(np.fft.fftshift(np.multiply(freq_lo, np.conjugate(freq_resps[2])))).real\n \n freq_resp_band = freq_resps[1]\n pyr_band = pyr[1] \n for i in range(numlevels):\n for j in range(numorientations): \n freq_band = np.fft.fftshift(np.fft.fft2(pyr_band[i][j]))\n result_band = np.fft.ifft2(np.fft.fftshift(np.multiply(freq_band, np.conjugate(freq_resp_band[i][j])))).real\n result_bands = result_bands + result_band \n result = result_bands + result_hi + result_lo\n return result","def Z_bandpass(C, L, R_L, f):\n return 1/(1/Xind(L, f) + 1/Xcap(C, f) + 1/R_L)","def bollinger_lband_indicator(close, n=20, ndev=2, fillna=False):\n df = pd.DataFrame([close]).transpose()\n mavg = close.rolling(n).mean()\n mstd = close.rolling(n).std()\n lband = mavg - ndev * mstd\n df['lband'] = 0.0\n df.loc[close < lband, 'lband'] = 1.0\n lband = df['lband']\n if fillna:\n lband = lband.replace([np.inf, -np.inf], np.nan).fillna(0)\n return pd.Series(lband, name='bbilband')","def power_in_bands(self, bands=None, avgaxis=None):\n if bands is None:\n bands = _third_octave_bands\n\n # center frequencies\n fcs = np.asarray([b[0] for b in bands])\n Npow2 = 2 ** (self.nt - 1).bit_length()\n f = np.fft.fftfreq(Npow2, d=1 / self.fs)\n\n shape = list(self.in_freq.shape)\n shape[-1] = len(bands)\n P = np.zeros(shape)\n for i, (fc, fl, fu) in enumerate(bands):\n if fu < self.fs / 2: # include only bands in frequency range\n iband = np.logical_and(fl <= f, f < fu)\n P[..., i] = np.sum(\n np.abs(np.fft.fft(self.in_time, n=Npow2, axis=-1)[..., iband]) ** 2\n * 2 # energy from negative and positive frequencies\n * self.dt\n / self.nt\n / self.time_length,\n axis=-1,\n )\n else:\n P[..., i] = 0\n\n if avgaxis is not None:\n P = P.mean(axis=avgaxis)\n\n return P, fcs","def make_albedo(self, x_sol, unc, nbands=7):\n\n n_doys = x_sol.shape[0] / 3\n n_bands = x_sol.shape[1]\n bhr_spectral = np.zeros((n_doys, n_bands))\n bhr_spectral_unc = np.zeros((n_doys, n_bands))\n bhr_spectral_nbar = np.zeros((n_doys, n_bands))\n bhr_spectral_nbar_unc = np.zeros((n_doys, n_bands))\n bhr_bb = np.zeros((n_doys, 3))\n bhr_bb_unc = np.zeros((n_doys, 3))\n to_vis = np.array([0.3265, 0., 0.4364, 0.2366, 0, 0, 0])\n a_to_vis = -0.0019\n to_nir = np.array([0., 0.5447, 0, 0, 0.1363, 0.0469, 0.2536])\n a_to_nir = -0.0068\n to_sw = np.array([0.3973, 0.2382, 0.3489, -0.2655, 0.1604, -0.0138, 0.0682])\n a_to_sw = 0.0036\n for band in xrange(n_bands):\n u1 = np.sqrt(unc[band].diagonal()[:n_doys])\n u2 = np.sqrt(unc[band].diagonal()[n_doys:(n_doys * 2)])\n u3 = np.sqrt(unc[band].diagonal()[2 * n_doys:])\n\n bhr_spectral[:, band] = (x_sol[:(n_doys), band] +\n 0.189184 * x_sol[(n_doys):(2 * n_doys), band] +\n 1.377622 * x_sol[(2 * n_doys):, band])\n bhr_spectral_unc[:, band] = (u1 + 0.189184 * u2 + 1.377622 * u3)\n\n bhr_bb[:, 0] = np.sum(bhr_spectral * to_vis, axis=1) + a_to_vis\n bhr_bb[:, 1] = np.sum(bhr_spectral * to_nir, axis=1) + a_to_nir\n bhr_bb[:, 2] = np.sum(bhr_spectral * to_sw, axis=1) + a_to_sw\n bhr_bb_unc[:, 0] = np.sum(bhr_spectral_unc * to_vis, axis=1) + a_to_vis\n bhr_bb_unc[:, 1] = np.sum(bhr_spectral_unc * to_nir, axis=1) + a_to_nir\n bhr_bb_unc[:, 2] = np.sum(bhr_spectral_unc * to_sw, axis=1) + a_to_sw\n\n kr = Kernels(0, 20, 0, \\\n LiType='Sparse', doIntegrals=False, \\\n normalise=1, RecipFlag=True, RossHS=False, MODISSPARSE=True, \\\n RossType='Thick')\n\n # n_doys = x_sol.shape[0] / 3\n for band in xrange(self.nbands):\n bhr_spectral_nbar[:, band] = (x_sol[:(n_doys), band] +\n kr.Ross[0] * x_sol[(n_doys):(2 * n_doys), band] +\n kr.Li[0] * x_sol[(2 * n_doys):, band])\n\n u1 = np.sqrt(unc[band].diagonal()[:n_doys])\n u2 = np.sqrt(unc[band].diagonal()[n_doys:(n_doys * 2)])\n u3 = np.sqrt(unc[band].diagonal()[2 * n_doys:])\n bhr_spectral_nbar_unc[:, band] = (u1 + kr.Ross[0] * u2 + kr.Li[0] * u3)\n\n return bhr_spectral[self.min_doy:self.max_doy], bhr_spectral_unc[self.min_doy:self.max_doy],\\\n bhr_bb[self.min_doy:self.max_doy], bhr_bb_unc[self.min_doy:self.max_doy],\\\n bhr_spectral_nbar[self.min_doy:self.max_doy], bhr_spectral_nbar_unc[self.min_doy:self.max_doy]","def band_penalty(self):\n fc_ix = np.argmin(np.abs(self.f - self.fc)) # Index to frequency array closes to center frequency\n # Number of indexes on each side of center frequency, not extending outside, only up to 10 kHz\n n = min(fc_ix, self.ix10k - fc_ix)\n if n == 0:\n return 0.0\n return np.mean(np.square(self.fr[fc_ix - n:fc_ix] - self.fr[fc_ix + n - 1:fc_ix - 1:-1]))","def l457_ts_bt_band_func(dn, lmin, lmax, qcalmin, qcalmax, k1, k2):\n ts_bt = np.copy(dn).astype(np.float64)\n ts_bt -= qcalmin\n ts_bt *= ((lmax - lmin) / (qcalmax - qcalmin))\n ts_bt += lmin\n return ts_bt_func(ts_bt, k1, k2).astype(np.float32)","def plot_power_in_bands(\n self, bands=None, use_ax=None, barkwargs={}, avgaxis=None, dbref=1, **figkwargs\n ):\n P, fc = self.power_in_bands(bands=bands, avgaxis=avgaxis)\n\n nbands = P.shape[-1]\n P = np.atleast_2d(P).reshape((-1, nbands))\n\n if use_ax is None:\n fig, ax = plt.subplots(**figkwargs)\n else:\n ax = use_ax\n fig = ax.get_figure()\n\n xticks = range(1, nbands + 1)\n for i in range(P.shape[0]):\n ax.bar(xticks, 10 * np.log10(P[i] / dbref ** 2), **barkwargs)\n ax.set_xticks(xticks)\n ax.set_xticklabels([\"{:.0f}\".format(f) for f in fc], rotation=\"vertical\")\n ax.grid(True)\n ax.set_xlabel(\"Band's center frequencies [Hz]\")\n ax.set_ylabel(\"Power [dB]\")\n\n return (P, fc, fig)","def get_bollinger_bands(rm, rstd, deviation=2):\n upper_band = rm + rstd * deviation\n lower_band = rm - rstd * deviation\n return upper_band, lower_band","def test_bands_distance(load_bands):\n from aiida_wannier90_workflows.utils.bands.distance import bands_distance\n\n pw_bands = load_bands(\"W\", \"pw.json\")\n wan_bands = load_bands(\"W\", \"w90.json\")\n\n fermi_energy = 22.753\n exclude_list_dft = [1, 2, 3, 4]\n\n dist = bands_distance(\n bands_dft=pw_bands,\n bands_wannier=wan_bands,\n fermi_energy=fermi_energy,\n exclude_list_dft=exclude_list_dft,\n )\n\n ref_dist = np.array(\n [\n [\n 2.275300000000000011e01,\n 1.036669313927612510e-02,\n 5.322753656962231350e-02,\n 5.322753656931334537e-02,\n ],\n [\n 2.375300000000000011e01,\n 1.008729713743582342e-02,\n 5.322753656993127469e-02,\n 5.322753656993126081e-02,\n ],\n [\n 2.475300000000000011e01,\n 9.774211496004188773e-03,\n 5.322753656993128857e-02,\n 5.322753656993128857e-02,\n ],\n [\n 2.575300000000000011e01,\n 9.008465047456700250e-03,\n 5.322753656993128857e-02,\n 5.322753656993128857e-02,\n ],\n [\n 2.675300000000000011e01,\n 8.588777504871471583e-03,\n 5.322753656993128857e-02,\n 5.322753656993128857e-02,\n ],\n [\n 2.775300000000000011e01,\n 8.545545737397273328e-03,\n 5.322753656993128857e-02,\n 5.322753656993128857e-02,\n ],\n ]\n )\n\n atol = 1e-8\n assert np.allclose(dist, ref_dist, atol=atol)","def butterworth_bandpass(lowcut, highcut, fs, order=5):\n nyq = 0.5 * fs # nyquist sampling rate\n low = lowcut / nyq # normalize frequency\n high = highcut / nyq # normalize frequency\n b, a = butter(order, [low, high], btype='band')\n return b, a","def bandpass_filterbank(bands, fs=1.0, order=8, output=\"sos\"):\n\n filters = []\n nyquist = fs / 2.0\n\n for band in bands:\n # remove bands above nyquist frequency\n if band[0] >= nyquist:\n raise ValueError(\"Bands should be below Nyquist frequency\")\n\n # Truncate the highest band to Nyquist frequency\n norm_band = np.minimum(0.99, np.array(band) / nyquist)\n\n # Compute coefficients\n coeffs = butter(order / 2, norm_band, \"bandpass\", output=output)\n filters.append(coeffs)\n\n return filters","def test_filt_abmag(self):\n sun = Sun.from_builtin('E490_2014')\n V = get_bandpass('johnson v')\n wave, fluxd = sun.filt(V, unit=u.ABmag)\n assert np.isclose(fluxd.value, -26.77, atol=0.007)","def correct_band(self, arr, band):\n if self.lut[band] is None:\n # No interpolation, so return NaNs\n new_arr = arr.copy()\n new_arr[:] = np.nan\n return new_arr\n else:\n return self.lut[band](arr)","def get_sn2005ek(colorplt=False):\n z = 0.016551\n ebv = 0.210\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n t_max = 53639.9\n print (\"adopt r band t_max from Drout+13\")\n \n # tb = pd.read_csv('/Users/yuhanyao/Desktop/ZTF18abfcmjw/data/Drout2013/table1', sep='\\t')\n # tb = tb.drop(columns=[\"Unnamed: 6\"])\n \n mjds = np.array([53639.3, 53640.3, 53641.3, 53642.2, 53643.2, 53645.3,\n 53646.5, 53648.0, 53649.2, 53650.4, 53651.3, 53652.5,\n 53654.2, 53655.2, 53656.2, 53657.2])\n \n Bmags = np.array([18.25, 18.38, 18.65, np.nan, 19.10, 19.71,\n 20.07, np.nan, 20.67, 20.90, 21.05, np.nan,\n 21.74, np.nan, np.nan, np.nan])\n \n Bmag_uncs = np.array([0.02, 0.03, 0.02, np.nan, 0.05, 0.07, \n 0.07, np.nan, 0.04, 0.04, 0.04, np.nan,\n 0.12, np.nan, np.nan, np.nan])\n \n Vmags = np.array([17.83, 18.03, 17.92, np.nan, 18.24, 18.66,\n 18.93, 19.48, 19.63, 19.86, 19.98, 20.35,\n 20.60, 20.74, 20.88, 21.22])\n \n Vmag_uncs = np.array([0.02, 0.03, 0.01, np.nan, 0.02, 0.02,\n 0.02, 0.06, 0.03, 0.03, 0.04, 0.05, \n 0.08, 0.10, 0.08, 0.13])\n \n Rmags = np.array([17.46, 17.41, 17.60, 17.69, 17.86, 18.18, \n np.nan, 18.83, 19.03, 19.26, 19.48, 19.75,\n 20.08, np.nan, 20.47, np.nan])\n \n Rmag_uncs = np.array([0.01, 0.02, 0.01, 0.02, 0.01, 0.01,\n np.nan, 0.03, 0.02, 0.02, 0.02, 0.04,\n 0.05, np.nan, 0.08, np.nan])\n\n Imags = np.array([17.20, 17.13, 17.18, np.nan, 17.47, 17.71, \n np.nan, 18.13, 18.26, 18.51, 18.61, 18.74, \n 19.01, np.nan, 19.47, np.nan])\n \n Imag_uncs = np.array([0.02, 0.04, 0.02, np.nan, 0.03, 0.02,\n np.nan, 0.06, 0.02, 0.02, 0.02, 0.03,\n 0.05, np.nan, 0.06, np.nan])\n \n mymjds = np.hstack([mjds, mjds, mjds, mjds])\n mymags = np.hstack([Bmags, Vmags, Rmags, Imags])\n myemags = np.hstack([Bmag_uncs, Vmag_uncs, Rmag_uncs, Imag_uncs])\n myfilts = np.hstack([ np.repeat(\"B\", len(Bmags)),\n np.repeat(\"V\", len(Bmags)),\n np.repeat(\"R\", len(Rmags)),\n np.repeat(\"I\", len(Imags)) ])\n ix = ~np.isnan(mymags)\n tb = pd.DataFrame({'mjd': mymjds[ix],\n 'mag': mymags[ix],\n 'emag': myemags[ix],\n \"filter\": myfilts[ix]})\n \n ixB = tb['filter'].values==\"B\"\n ixV = tb['filter'].values==\"V\"\n ixR = tb['filter'].values==\"R\"\n ixI = tb['filter'].values==\"I\"\n \n tb['wave'] = np.zeros(len(tb))\n tb['wave'].values[ixB] = 4359\n tb['wave'].values[ixV] = 5430\n tb['wave'].values[ixR] = 6349\n tb['wave'].values[ixI] = 8797\n \n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\n if colorplt==False:\n return tb\n else:\n tb = add_datecol(tb)\n ix = np.in1d(tb[\"filter\"].values, np.array(['B', 'R', 'I']))\n tb = tb[ix]\n\n dates = get_date_span(tb)\n datesave = []\n for i in range(len(dates)):\n x = dates[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n if len(tbsub)!=0:\n flts = tbsub['filter'].values\n if \"R\" in flts and np.sum(np.unique(flts))!=1:\n datesave.append(x)\n datesave = np.array(datesave)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"B\"]\n rtb = tbsub[tbsub[\"filter\"].values==\"R\"]\n itb = tbsub[tbsub[\"filter\"].values==\"I\"]\n if len(gtb)!=0:\n gmjds = gtb[\"mjd\"].values\n gmags = gtb[\"mag0\"].values\n gemags = gtb[\"emag\"].values\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n if len(rtb)!=0:\n rmjds = rtb[\"mjd\"].values\n rmags = rtb[\"mag0\"].values\n remags = rtb[\"emag\"].values\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n if len(itb)!=0:\n imjds = itb[\"mjd\"].values\n imags = itb[\"mag0\"].values\n iemags = itb[\"emag\"].values\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n if len(gtb)!=0 and len(rtb)!=0:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"BmR\")\n if len(rtb)!=0 and len(itb)!=0:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"RmI\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"mjd\", \"c\", \"ec\", \"cname\"])\n \n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\n ctb = ctb.to_pandas()\n return ctb","def bessel_wave(freq, alpha, beta):\n return FMFilter(SineWave(freq), SineWave(alpha), beta)","def bandname(self):\n return self._properties[\"bandname\"]","def get_sn2019bkc(colorplt = False):\n ebv = 0.06 # SFD2011\n z = 0.020\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n \n tb = _read_2019bkc_band()\n \n tb['mag0'] = tb['mag'].values- extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'].values - dis_mod\n \n tb['tmax_rf'] = tb['Phase'].values / (1+z)\n\n if colorplt==False:\n return tb\n \n else:\n #tb = add_datecol(tb)\n tb['date'] = np.floor(tb['tmax_rf'].values)\n datesave = np.array(tb['date'].values)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"g\"]\n rtb = tbsub[tbsub[\"filter\"].values==\"r\"]\n itb = tbsub[tbsub[\"filter\"].values==\"i\"]\n if len(gtb)!=0:\n gmjds = gtb[\"tmax_rf\"].values\n gmags = gtb[\"mag0\"].values\n gemags = np.ones(len(gtb)) * 0.1\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n else:\n gmag = 0\n if len(rtb)!=0:\n rmjds = rtb[\"tmax_rf\"].values\n rmags = rtb[\"mag0\"].values\n remags = np.ones(len(rtb)) * 0.1\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n else:\n rmag = 0\n if len(itb)!=0:\n imjds = itb[\"tmax_rf\"].values\n imags = itb[\"mag0\"].values\n iemags = np.ones(len(itb)) * 0.1\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n else:\n imag = 0\n if gmag and rmag:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"gmr\")\n if rmag and imag:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"rmi\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"tmax_rf\", \"c\", \"ec\", \"cname\"])\n \n ctb = ctb.to_pandas()\n return ctb","def plotblackbody(_zband, _yband, _jband, _hband, _kband, _parallax, _perr):\n # Set pyplot style to be consistent within the program\n plt.style.use('seaborn-whitegrid')\n # Import raw data to plot Hertzsprung-Russell diagram\n _hrdata = inithr('hr.dat')\n # Determine distance in parsecs\n _distance = 1 / np.tan(_parallax * 10**-3)\n _derr = (_perr * 10**-3) / ((_parallax * 10**-3)**2)\n # Create single data array with all bands\n _bands = [_zband, _yband, _jband, _hband, _kband]\n _lambda = [0.9, 1.02, 1.22, 1.63, 2.2]\n # Set up empty arrays for each star\n _largestar = np.zeros((1, 2))\n _smallstar = np.zeros((1, 2))\n\n # Determine the spectral flux density from the large star\n i = 0\n while i < 5:\n # Determine the maximum and minimum values of the observed band\n _max, _min = lightcurve.maxminvals(_bands[i])\n # The large star uses the maximum flux value (smallest magnitude)\n _largestar = np.append(_largestar, np.array([_lambda[i], (magtoflux(_min, i))], ndmin=2), axis=0)\n i += 1\n # Delete first empty row of the array\n _largestar = np.delete(_largestar, 0, axis=0)\n\n # Determine the spectral flux density from the small star\n i = 0\n while i < 5:\n # Determine the maximum and minimum values of the observed band\n _max, _min = lightcurve.maxminvals(_bands[i])\n # Smaller star flux value is combined value minus the large star\n _smallstar = np.append(_smallstar, np.array([_lambda[i], (magtoflux(_max, i) -\n magtoflux(_min, i))], ndmin=2), axis=0)\n i += 1\n # Delete the first empty row of the array\n _smallstar = np.delete(_smallstar, 0, axis=0)\n\n # Determine the luminosity and effective temperature of each star\n _luma, _lumaerr, _wiena = getwientemp(_largestar, _distance, _derr, 1)\n _lumb, _lumberr, _wienb = getwientemp(_smallstar, _distance, _derr, 2)\n\n # Calculate luminosities in solar units\n _solluma = _luma / (3.828*10**26)\n _sollumb = _lumb / (3.828*10**26)\n _lumaerr = _lumaerr / (3.828*10**26)\n _lumberr = _lumberr / (3.828*10**26)\n\n # Calculate masses using the mass/luminosity relation in solar mass units\n # N.B. only works as an approximation for main sequence stars, giants and dwarfs are not sutiable for this\n # approximation\n _solmassa = np.power(_solluma, 1/3.5)\n _solmassaerr = ((_solmassa * (1/3.5) * _lumaerr) / _solluma)**2\n _solmassb = np.power(_sollumb, 1/3.5)\n _solmassberr = ((_solmassb * (1 / 3.5) * _lumberr) / _sollumb) ** 2\n\n # Calculate stellar radius in solar radii using the relationship between luminosity, surface area and temperature\n _solrada = np.sqrt(_solluma / np.power(_wiena / 5778, 4))\n _solradb = np.sqrt(_sollumb / np.power(_wienb / 5778, 4))\n _solradaerr = ((_solrada * 0.5 * _lumaerr) / _solluma)**2\n _solradberr = ((_solradb * 0.5 * _lumberr) / _sollumb)**2\n\n # Output determined values to the screen and write to file\n print('Values for the large star:')\n print('Effective temperature: ' + str(round_sig(_wiena)))\n print('Solar luminosities: ' + str(round_sig(_solluma)) + ', error: ' + str(round_sig(_lumaerr)))\n print('Solar radii: ' + str(round_sig(_solrada)) + ', error: ' + str(round_sig(_solradaerr)))\n print('Solar masses: ' + str(round_sig(_solmassa)) + ', error: ' + str(round_sig(_solmassaerr)))\n print('-----------------------------------------------------')\n print('Values for the small star:')\n print('Effective temperature: ' + str(round_sig(_wienb)))\n print('Solar luminosities: ' + str(round_sig(_sollumb)) + ', error: ' + str(round_sig(_lumberr)))\n print('Solar radii: ' + str(round_sig(_solradb)) + ', error: ' + str(round_sig(_solradberr)))\n print('Solar masses: ' + str(round_sig(_solmassb)) + ', error: ' + str(round_sig(_solmassberr)))\n\n # Convert from luminosity to magnitude in solar units\n _luma = -2.5 * np.log10(_luma / (3.0128 * 10**28))\n _lumb = -2.5 * np.log10(_lumb / (3.0128 * 10**28))\n\n # Plot Hertzsprung-Russell diagram using provided array\n plt.scatter(_hrdata[:, 1], _hrdata[:, 0], s=0.5)\n # Plot determined values for each star\n plt.scatter(_wiena, _luma, s=16, c='red', label='Larger Star')\n plt.scatter(_wienb, _lumb, s=16, c='green', label='Smaller Star')\n # Set the x and y axis limits to sensible values\n plt.legend()\n plt.xlim(3000, 10000)\n plt.ylim(-10, 20)\n # Invert both axes as convention\n plt.gca().invert_xaxis()\n plt.gca().invert_yaxis()\n # Save figure to current folder\n plt.savefig('hr.png')\n # Display to screen\n plt.show()","def format_data(self, bands, branches):\n self.new_dict = self.__gen_dict\n order = [] # list to store high-symmetry point\n band_index = {} # dict to store band index info corresponding to its high-symmetry point e.g. \"X\": 18\n formatted_bands = []\n zero_matrix = np.zeros(np.shape(bands))\n \"\"\"\n zero_matrix is for: if one configuration does not have some high-symmetry points listed in __generic_dict\n then fill zeros in those columns \n \"\"\"\n\n for i in range(len(branches)):\n order.append(branches[i][\"name\"])\n spilt = re.split('-', order[i])\n\n band_index[spilt[0]] = branches[i]['start_index']\n band_index[spilt[1]] = branches[i]['end_index']\n\n # print('>>>>>>>>>>>>>>>>>>', band_index)\n # iterate all keys in band_index, and if exists, give value to new_dict, if not, pass\n for hs_point in band_index:\n if hs_point in self.new_dict:\n self.new_dict[hs_point] = band_index[hs_point]\n # print('>>>>>>>>>>>>>>>>>', BandsData.__gen_dict)\n\n # iterate all keys in new_dict, export bands (not arranged in bands dimension)\n for hs_point in self.new_dict:\n hs_value = self.new_dict[hs_point]\n if self.new_dict[hs_point] is None:\n # fill zeros in bands\n formatted_bands.append(zero_matrix[:, 0])\n else:\n formatted_bands.append(bands[:, hs_value])\n\n # transpose of formatted_bands\n formatted_bands = np.transpose(formatted_bands)\n\n return formatted_bands, self.new_dict","def calcBW(t,g,bw):\n if type(g) is not np.array:\n g = np.array(g)\n if bw>0.0:\n bw*=-1.0\n maxg = np.max(g)\n g = g-maxg\n #plt.figure('testbw')\n \n imax = np.argmax(g)\n mb = np.where(g>3.0*bw)\n # ...left\n gg = g[mb[0][0]:imax]\n tt = t[mb[0][0]:imax]\n #plt.plot(tt,gg)\n ha1 = np.interp(bw,gg,tt)\n # ...right\n gg = np.flipud(g[imax:mb[0][-1]])\n tt = np.flipud(t[imax:mb[0][-1]])\n ha2 = np.interp(bw,gg,tt)\n #plt.plot(tt,gg)\n # ...full\n FWHM = abs(ha1) + abs(ha2)\n #print 'bw: ',ha1,ha2\n return FWHM","def stack_bands(yr, roi):\n\n water_year_start = '{}-10-01'.format(yr - 1)\n\n winter_s, winter_e = '{}-01-01'.format(yr), '{}-03-01'.format(yr),\n spring_s, spring_e = '{}-03-01'.format(yr), '{}-05-01'.format(yr),\n late_spring_s, late_spring_e = '{}-05-01'.format(yr), '{}-07-01'.format(yr)\n summer_s, summer_e = '{}-07-01'.format(yr), '{}-09-01'.format(yr)\n fall_s, fall_e = '{}-09-01'.format(yr), '{}-12-31'.format(yr)\n\n periods = [('cy', winter_s, fall_e),\n ('1', spring_s, spring_e),\n ('2', late_spring_s, late_spring_e),\n ('3', summer_s, summer_e),\n ('4', fall_s, fall_e)]\n\n first = True\n for name, start, end in periods:\n bands = landsat_composites(yr, start, end, roi, name)\n if first:\n input_bands = bands\n proj = bands.select('B2_cy').projection().getInfo()\n first = False\n else:\n input_bands = input_bands.addBands(bands)\n\n for s, e, n in [(spring_s, spring_e, 'espr'),\n (late_spring_s, late_spring_e, 'lspr'),\n (summer_s, summer_e, 'smr'),\n (fall_s, fall_e, 'fl'),\n (water_year_start, spring_e, 'wy_espr'),\n (water_year_start, late_spring_e, 'wy_espr'),\n (water_year_start, summer_e, 'wy_smr'),\n (water_year_start, fall_e, 'wy')]:\n gridmet = ee.ImageCollection(\"IDAHO_EPSCOR/GRIDMET\").filterBounds(\n roi).filterDate(s, e).select('pr', 'eto', 'tmmn', 'tmmx')\n temp_reducer = ee.Reducer.mean()\n t_names = ['tmax'.format(n), 'tmin'.format(n)]\n temp_perc = gridmet.select('tmmn', 'tmmx').reduce(temp_reducer).rename(t_names).resample(\n 'bilinear').reproject(crs=proj['crs'], scale=30)\n\n precip_reducer = ee.Reducer.sum()\n precip_sum = gridmet.select('pr', 'eto').reduce(precip_reducer).rename(\n 'precip_total_{}'.format(n), 'pet_total_{}'.format(n)).resample('bilinear').reproject(crs=proj['crs'],\n scale=30)\n wd_estimate = precip_sum.select('precip_total_{}'.format(n)).subtract(precip_sum.select(\n 'pet_total_{}'.format(n))).rename('wd_est_{}'.format(n))\n input_bands = input_bands.addBands([temp_perc, precip_sum, wd_estimate])\n\n temp_reducer = ee.Reducer.percentile([10, 50, 90])\n t_names = ['tmmn_p10_cy', 'tmmn_p50_cy', 'tmmn_p90_cy', 'tmmx_p10_cy', 'tmmx_p50_cy', 'tmmx_p90_cy']\n temp_perc = gridmet.select('tmmn', 'tmmx').reduce(temp_reducer).rename(t_names).resample(\n 'bilinear').reproject(crs=proj['crs'], scale=30)\n\n precip_reducer = ee.Reducer.sum()\n precip_sum = gridmet.select('pr', 'eto').reduce(precip_reducer).rename(\n 'precip_total_cy', 'pet_total_cy').resample('bilinear').reproject(crs=proj['crs'], scale=30)\n wd_estimate = precip_sum.select('precip_total_cy').subtract(precip_sum.select(\n 'pet_total_cy')).rename('wd_est_cy')\n\n coords = ee.Image.pixelLonLat().rename(['Lon_GCS', 'LAT_GCS']).resample('bilinear').reproject(crs=proj['crs'],\n scale=30)\n ned = ee.Image('USGS/NED')\n terrain = ee.Terrain.products(ned).select('elevation', 'slope', 'aspect').reduceResolution(\n ee.Reducer.mean()).reproject(crs=proj['crs'], scale=30)\n\n world_climate = get_world_climate(proj=proj)\n elev = terrain.select('elevation')\n tpi_1250 = elev.subtract(elev.focal_mean(1250, 'circle', 'meters')).add(0.5).rename('tpi_1250')\n tpi_250 = elev.subtract(elev.focal_mean(250, 'circle', 'meters')).add(0.5).rename('tpi_250')\n tpi_150 = elev.subtract(elev.focal_mean(150, 'circle', 'meters')).add(0.5).rename('tpi_150')\n static_input_bands = coords.addBands([temp_perc, wd_estimate, terrain, tpi_1250, tpi_250, tpi_150, world_climate])\n\n nlcd = ee.Image('USGS/NLCD/NLCD2011').select('landcover').reproject(crs=proj['crs'], scale=30).rename('nlcd')\n\n cdl_cult = ee.Image('USDA/NASS/CDL/2017').select('cultivated'). \\\n remap([1, 2], [0, 1]).reproject(crs=proj['crs'], scale=30).rename('cdlclt')\n\n cdl_crop = ee.Image('USDA/NASS/CDL/2017').select('cropland').reproject(crs=proj['crs'],\n scale=30).rename('cdlcrp')\n\n gsw = ee.Image('JRC/GSW1_0/GlobalSurfaceWater')\n occ_pos = gsw.select('occurrence').gt(0)\n water = occ_pos.unmask(0).rename('gsw')\n\n static_input_bands = static_input_bands.addBands([nlcd, cdl_cult, cdl_crop, water])\n\n input_bands = input_bands.addBands(static_input_bands).clip(roi)\n\n # standardize names to match EE javascript output\n standard_names = []\n temp_ct = 1\n prec_ct = 1\n names = input_bands.bandNames().getInfo()\n for name in names:\n if 'tavg' in name and 'tavg' in standard_names:\n standard_names.append('tavg_{}'.format(temp_ct))\n temp_ct += 1\n elif 'prec' in name and 'prec' in standard_names:\n standard_names.append('prec_{}'.format(prec_ct))\n prec_ct += 1\n else:\n standard_names.append(name)\n\n input_bands = input_bands.rename(standard_names)\n return input_bands","def condensate_belowdew(Rs, Rv, Rsi, Rvi, Bo, Bg, Np, Gp):\n Btg = ((Bg * (1 - (Rs * Rvi))) + (Bo * (Rvi - Rv))) / (1 - (Rv * Rs)) # in RB/STB\n Bto = ((Bo * (1 - (Rv * Rsi))) + (Bg * (Rsi - Rs))) / (1 - (Rv * Rs)) # in RB/scf\n\n Gi = 0\n F = (Np * ((Bo - (Rs * Bg)) / (1 - (Rv * Rs)))) + ((Gp - Gi) * ((Bg - (Rv * Bo)) / (1 - (Rv * Rs))))\n Eg = Btg - Bg[0]\n return(F, Eg)","def build_steer_bands(im, freq_resps, numlevels, numorientations):\n \n dims = im.shape\n bands = []\n pyr = []\n fourier = np.fft.fftshift(np.fft.fft2(im))\n \n freq_resp_hi = freq_resps[0] \n hi = np.fft.ifft2(np.fft.fftshift(np.multiply(fourier, freq_resp_hi))).real\n \n freq_resp_lo = freq_resps[2] \n lo = np.fft.ifft2(np.fft.fftshift(np.multiply(fourier, freq_resp_lo))).real\n \n freq_resp_bands = freq_resps[1] \n for i in range(numlevels):\n for j in range(numorientations):\n freq_respband = freq_resp_bands[i][j]\n ifourier_band = np.fft.ifft2(np.fft.fftshift(np.multiply(fourier, freq_respband))).real\n bands.append(ifourier_band)\n bands = np.reshape(bands, [numlevels, numorientations, dims[0], dims[1]])\n \n pyr.append(hi)\n pyr.append(bands)\n pyr.append(lo)\n return pyr","def warm_region_cal(audio_samples, fs):\n #window the audio\n windowed_samples = timbral_util.window_audio(audio_samples)\n\n # need to define a function for the roughness stimuli, emphasising the 20 - 40 region (of the bark scale)\n min_bark_band = 10\n max_bark_band = 40\n mean_bark_band = (min_bark_band + max_bark_band) / 2.0\n array = np.arange(min_bark_band, max_bark_band)\n x = timbral_util.normal_dist(array, theta=0.01, mean=mean_bark_band)\n x -= np.min(x)\n x /= np.max(x)\n\n wr_array = np.zeros(240)\n wr_array[min_bark_band:max_bark_band] = x\n\n # need to define a second array emphasising the 20 - 40 region (of the bark scale)\n min_bark_band = 80\n max_bark_band = 240\n mean_bark_band = (min_bark_band + max_bark_band) / 2.0\n array = np.arange(min_bark_band, max_bark_band)\n x = timbral_util.normal_dist(array, theta=0.01, mean=mean_bark_band)\n x -= np.min(x)\n x /= np.max(x)\n\n hf_array = np.zeros(240)\n hf_array[min_bark_band:max_bark_band] = x\n\n windowed_loud_spec = []\n windowed_rms = []\n\n wr_vals = []\n hf_vals = []\n\n for i in range(windowed_samples.shape[0]):\n samples = windowed_samples[i, :]\n N_entire, N_single = timbral_util.specific_loudness(samples, Pref=100.0, fs=fs, Mod=0)\n\n # append the loudness spec\n windowed_loud_spec.append(N_single)\n windowed_rms.append(np.sqrt(np.mean(samples * samples)))\n\n wr_vals.append(np.sum(wr_array * N_single))\n hf_vals.append(np.sum(hf_array * N_single))\n\n mean_wr = np.mean(wr_vals)\n mean_hf = np.mean(hf_vals)\n weighted_wr = np.average(wr_vals, weights=windowed_rms)\n weighted_hf = np.average(hf_vals, weights=windowed_rms)\n\n return mean_wr, weighted_wr, mean_hf, weighted_hf","def read_frequencyBand(self):\n # PROTECTED REGION ID(CbfSubarray.frequencyBand_read) ENABLED START #\n return self._frequency_band\n # PROTECTED REGION END # // CbfSubarray.frequencyBand_read","def get_vrt_band_list():\n logger.debug('get_vrt_band_list() called')\n vrt_band_list = []\n#===============================================================================\n# sensor_dict = self.bands[tile_type_id][(dataset_info['satellite_tag'], dataset_info['sensor_name'])]\n# # log_multiline(logger.debug, sensor, 'Sensor', '\\t')\n# for file_number in sorted(sensor_dict.keys()):\n# band_info = sensor_dict[file_number]\n# if band_info['level_name'] == 'NBAR':\n# dataset_dir = dataset_info['nbar_dataset_path']\n# dataset_id = dataset_info['nbar_dataset_id']\n# processing_level = dataset_info['nbar_level_name']\n# nodata_value = dataset_info['nbar_nodata_value']\n# resampling_method = dataset_info['nbar_resampling_method']\n# elif band_info['level_name'] == 'ORTHO':\n# dataset_dir = dataset_info['l1t_dataset_path']\n# dataset_id = dataset_info['l1t_dataset_id']\n# processing_level = dataset_info['l1t_level_name']\n# nodata_value = dataset_info['l1t_nodata_value']\n# resampling_method = dataset_info['l1t_resampling_method']\n# else:\n# continue # Ignore any pan-chromatic and derived bands\n# \n# dataset_dir = os.path.join(dataset_dir, 'scene01')\n# filename = find_file(dataset_dir, band_info['file_pattern'])\n# vrt_band_list.append({'file_number': band_info['file_number'], \n# 'filename': filename, \n# 'name': band_info['band_name'],\n# 'dataset_id': dataset_id,\n# 'band_id': band_info['band_id'],\n# 'processing_level': processing_level,\n# 'nodata_value': nodata_value,\n# 'resampling_method': resampling_method,\n# 'tile_layer': band_info['tile_layer']})\n#===============================================================================\n \n #TODO: Make this able to handle multiple derived layers\n for band_level in ['FC']:\n derived_bands = self.bands[tile_type_id][('DERIVED', band_level)]\n for file_number in sorted(derived_bands.keys()):\n band_info = derived_bands[file_number]\n file_pattern = band_info['file_pattern']\n dataset_dir = os.path.join(dataset_info['fc_dataset_path'], 'scene01')\n dataset_id = dataset_info['fc_dataset_id']\n filename = find_file(dataset_dir, file_pattern) \n processing_level = dataset_info['fc_level_name']\n nodata_value = dataset_info['fc_nodata_value'] # Should be None for FC\n resampling_method = dataset_info['fc_resampling_method']\n vrt_band_list.append({'file_number': None, \n 'filename': filename, \n 'name': band_info['band_name'],\n 'dataset_id': dataset_id,\n 'band_id': band_info['band_id'],\n 'processing_level': processing_level,\n 'nodata_value': nodata_value,\n 'resampling_method': resampling_method,\n 'tile_layer': 1})\n \n log_multiline(logger.debug, vrt_band_list, 'vrt_band_list = %s', '\\t')\n return vrt_band_list","def _compute_bn(self, lvl):\n bn = [0] # number of samples crossing the left/right boundary\n for n in range(lvl):\n # 1. down-sampling of N samples by the factor scl gives (N-1)//scl + 1 samples\n # 2. bn[-1]+M-1 is the number of samples acrossing the left/right boundary, with M being the number of freqeuncies\n # => hence after the downsampling the number of boundary crossing samples is:\n bn.append((bn[-1]+self.nfreq-2)//self.scaling+1)\n bn.append(bn[-1]) # repeat the value of the coarsest scale for the approximation coefficient\n return bn[1:][::-1]"],"string":"[\n \"def calculate_band(value, bands):\\n for band in bands:\\n if band > value:\\n return band\",\n \"def bands(self) -> int:\\n ...\",\n \"def calbands( band = 0, tmo = 30 ) :\\n optimizeThresholds(band,tmo)\\n flattenPhases(band,tmo)\\n calibrateSpectra(band=band,tmo=tmo)\",\n \"def DrawBands(self, count):\\n value = self.little[0]\\n mobile_average = float(sum([float(self.little[i])\\n for i in range(len(self.little))])) / float(self.period)\\n standard_derivation = sqrt(sum([pow(self.little[i] - mobile_average, 2)\\n for i in range(len(self.little))]) / self.period)\\n upper_band = mobile_average + (standard_derivation * self.sd_coef)\\n lower_band = mobile_average - (standard_derivation * self.sd_coef)\\n self.upper.insert(0, upper_band)\\n self.lower.insert(0, lower_band)\\n if len(self.upper) >= self.period:\\n self.upper.pop()\\n if len(self.lower) >= self.period:\\n self.lower.pop()\\n if count >= self.period:\\n for i in range(len(self.little) - 1):\\n self.canvas.create_line((i * self.incr / 1.725) + self.incr * 4,\\n self.height - self.incr * 4 + (self.little[i] - 1) * 5000 - 200,\\n (i * self.incr / 1.725) + self.incr * 4 + self.incr / 1.725,\\n self.height - self.incr * 4 + (self.little[i + 1] - 1) * 5000 - 200,\\n fill = \\\"#FFFF00\\\", width = 2)\\n for i in range(len(self.upper) - 1):\\n self.canvas.create_line((i * self.incr / 1.635) + self.incr * 4,\\n self.height - self.incr * 4 + (self.upper[i] - 1) * 5000 - 200,\\n (i * self.incr / 1.635) + self.incr * 4 + self.incr / 1.635,\\n self.height - self.incr * 4 + (self.upper[i + 1] - 1) * 5000 - 200,\\n fill = \\\"#FF6600\\\", width = 3)\\n self.canvas.create_line((i * self.incr / 1.635) + self.incr * 4,\\n self.height - self.incr * 4 + (self.lower[i] - 1) * 5000 - 200,\\n (i * self.incr / 1.635) + self.incr * 4 + self.incr / 1.635,\\n self.height - self.incr * 4 + (self.lower[i + 1] - 1) * 5000 - 200,\\n fill = \\\"#FF0000\\\", width = 3)\",\n \"def band(self):\\n return self._band\",\n \"def get_BBands(sma, stdev):\\n\\tupper_band = sma + 2*stdev\\n\\tlower_band = sma - 2*stdev\\n\\treturn upper_band, lower_band\",\n \"def n_band(self):\\n pass\",\n \"def octave_bands(fc=1000, third=False, start=0.0, n=8):\\n\\n div = 1\\n if third:\\n div = 3\\n\\n # Octave Bands\\n fcentre = fc * (\\n 2.0 ** (np.arange(start * div, (start + n) * div - (div - 1)) / div)\\n )\\n fd = 2 ** (0.5 / div)\\n bands = np.array([[f / fd, f * fd] for f in fcentre])\\n\\n return bands, fcentre\",\n \"def sharpen_bands(self):\\n for label in self.labels:\\n self.sharp_bands[label] = self.bands[label] - self.gauss_bands[\\n label]\",\n \"def get_bollinger_bands(rm, rstd):\\n \\n upper_band=rm+2*rstd\\n lower_band=rm-2*rstd\\n return upper_band, lower_band\",\n \"def jack_SB_func(SB_array, R_array, band_str, N_sample,):\\n\\tband_id = band.index( band_str )\\n\\n\\tdx_r = np.array(R_array)\\n\\tdy_sb = np.array(SB_array)\\n\\n\\tn_r = dx_r.shape[1]\\n\\n\\tLen = np.zeros( n_r, dtype = np.float32)\\n\\tfor nn in range( n_r ):\\n\\t\\ttmp_I = dy_sb[:,nn]\\n\\t\\tidnn = np.isnan(tmp_I)\\n\\t\\tLen[nn] = N_sample - np.sum(idnn)\\n\\n\\tStack_R = np.nanmean(dx_r, axis = 0)\\n\\tStack_SB = np.nanmean(dy_sb, axis = 0)\\n\\tstd_Stack_SB = np.nanstd(dy_sb, axis = 0)\\n\\n\\t### only calculate r bins in which sub-sample number larger than one\\n\\tid_one = Len > 1\\n\\tStack_R = Stack_R[ id_one ]\\n\\tStack_SB = Stack_SB[ id_one ]\\n\\tstd_Stack_SB = std_Stack_SB[ id_one ]\\n\\tN_img = Len[ id_one ]\\n\\tjk_Stack_err = np.sqrt(N_img - 1) * std_Stack_SB\\n\\n\\t### limit the radius bin contribution at least 1/3 * N_sample\\n\\tid_min = N_img >= np.int(N_sample / 3)\\n\\tlim_r = Stack_R[id_min]\\n\\tlim_R = np.nanmax(lim_r)\\n\\n\\t## change flux to magnitude\\n\\tjk_Stack_SB = 22.5 - 2.5 * np.log10(Stack_SB) + mag_add[band_id]\\n\\tdSB0 = 22.5 - 2.5 * np.log10(Stack_SB + jk_Stack_err) + mag_add[band_id]\\n\\tdSB1 = 22.5 - 2.5 * np.log10(Stack_SB - jk_Stack_err) + mag_add[band_id]\\n\\terr0 = jk_Stack_SB - dSB0\\n\\terr1 = dSB1 - jk_Stack_SB\\n\\tid_nan = np.isnan(jk_Stack_SB)\\n\\tjk_Stack_SB, jk_Stack_R = jk_Stack_SB[id_nan == False], Stack_R[id_nan == False]\\n\\tjk_Stack_err0, jk_Stack_err1 = err0[id_nan == False], err1[id_nan == False]\\n\\tdSB0, dSB1 = dSB0[id_nan == False], dSB1[id_nan == False]\\n\\tidx_nan = np.isnan(dSB1)\\n\\tjk_Stack_err1[idx_nan] = 100.\\n\\n\\treturn jk_Stack_SB, jk_Stack_R, jk_Stack_err0, jk_Stack_err1, Stack_R, Stack_SB, jk_Stack_err, lim_R\",\n \"def calc_lc_band(table, query, band='r', type='mag'):\\n\\n phot_bands = ['u', 'g', 'r', 'i', 'z', 'y']\\n phot_index = np.arange(0, 6, step=1)\\n\\n phase = table['mjd']-table['mjd'][0] # phase from first detection\\n\\n model_mag = models.photometricmodel(phase[table['filter']==band]).fourier_cos(*query['model_theta'][phot_index[phot_bands==band]]) # calculate the flux at each epoch\\n lim_sigma = table[table['filter']==band]['5sig'] #5-sigma depth\\n snr = m5snr(model_flux, lim_sigma) # SNR at that band\\n sigma_err = 2.5*np.log10(1+1./snr) # magnitude error\\n\\n if type=='mag':\\n return phase[table['filter']==band], model_mag, sigma_err\\n elif type=='flux':\\n return phase[table['filter']==band], model_mag, astro_tool.mag_err_2_flux_err(model_mag, sigma_err) # convert magnitude error into flux error (assume that model_mag is now in flux)\",\n \"def get_bands(self, data_array_norm, baseline_array_norm, f):\\n\\n fmax = 50\\n fidx = f < fmax\\n fnum = f[fidx].size\\n\\n band_tot = np.zeros((fnum, fnum, data_array_norm.shape[0], data_array_norm.shape[2], data_array_norm.shape[3]))\\n band_tot_bl = np.zeros((fnum, fnum, baseline_array_norm.shape[0], baseline_array_norm.shape[2], baseline_array_norm.shape[3]))\\n for i in range(fnum):\\n for j in range(fnum):\\n if j > i:\\n idx = (f >= f[i]) & (f < f[j])\\n band_tot[i, j, :, :] = np.sum(data_array_norm[:, idx, :, :], axis=1) / (f[j] - f[i])\\n band_tot_bl[i, j, :, :] = np.sum(baseline_array_norm[:, idx, :, :], axis=1) / (f[j] - f[i])\\n\\n\\n band_tot_bl1 = np.mean(band_tot_bl, axis=3) # average across time bins\\n band_tot_bl2 = np.repeat(band_tot_bl1[:, :, :, None, :], band_tot_bl.shape[3], axis=3) # repeat same value across time\\n return band_tot, band_tot_bl2, f[fidx]\",\n \"def bands(sys, args=(), momenta=65, file=None, show=True, dpi=None,\\n fig_size=None, ax=None, *, params=None):\\n\\n if not mpl_available:\\n raise RuntimeError(\\\"matplotlib was not found, but is required \\\"\\n \\\"for bands()\\\")\\n\\n syst = sys # for naming consistency inside function bodies\\n _common.ensure_isinstance(syst, system.InfiniteSystem)\\n\\n momenta = np.array(momenta)\\n if momenta.ndim != 1:\\n momenta = np.linspace(-np.pi, np.pi, momenta)\\n\\n # expand out the contents of 'physics.Bands' to get the H(k),\\n # because 'spectrum' already does the diagonalisation.\\n ham = syst.cell_hamiltonian(args, params=params)\\n if not np.allclose(ham, ham.conjugate().transpose()):\\n raise ValueError('The cell Hamiltonian is not Hermitian.')\\n _hop = syst.inter_cell_hopping(args, params=params)\\n hop = np.empty(ham.shape, dtype=complex)\\n hop[:, :_hop.shape[1]] = _hop\\n hop[:, _hop.shape[1]:] = 0\\n\\n def h_k(k):\\n # H_k = H_0 + V e^-ik + V^\\\\dagger e^ik\\n mat = hop * cmath.exp(-1j * k)\\n mat += mat.conjugate().transpose() + ham\\n return mat\\n\\n return spectrum(h_k, ('k', momenta), file=file, show=show, dpi=dpi,\\n fig_size=fig_size, ax=ax)\",\n \"def calc_bands(self, start: float = -pi, end: float = pi, step: float = 0.05) -> results.Bands:\\n from scipy.linalg import eigh\\n\\n h0 = self.h0.todense()\\n h1 = self.h1.todense()\\n h1t = np.conj(h1.T)\\n\\n def eigenvalues(k):\\n h = h0 + h1 * np.exp(1j * k) + h1t * np.exp(-1j * k)\\n return eigh(h, eigvals_only=True)\\n\\n k_path = results.make_path(start, end, step=step)\\n bands = [eigenvalues(k) for k in k_path.flatten()]\\n return results.Bands(k_path, np.vstack(bands))\",\n \"async def equalizer(\\n client,\\n event,\\n band_00: P('float', 'band 0' , min_value = -0.25, max_value = 1.0) = 0.0,\\n band_01: P('float', 'band 1' , min_value = -0.25, max_value = 1.0) = 0.0,\\n band_02: P('float', 'band 2' , min_value = -0.25, max_value = 1.0) = 0.0,\\n band_03: P('float', 'band 3' , min_value = -0.25, max_value = 1.0) = 0.0,\\n band_04: P('float', 'band 4' , min_value = -0.25, max_value = 1.0) = 0.0,\\n band_05: P('float', 'band 5' , min_value = -0.25, max_value = 1.0) = 0.0,\\n band_06: P('float', 'band 6' , min_value = -0.25, max_value = 1.0) = 0.0,\\n band_07: P('float', 'band 7' , min_value = -0.25, max_value = 1.0) = 0.0,\\n band_08: P('float', 'band 8' , min_value = -0.25, max_value = 1.0) = 0.0,\\n band_09: P('float', 'band 9' , min_value = -0.25, max_value = 1.0) = 0.0,\\n band_10: P('float', 'band 10', min_value = -0.25, max_value = 1.0) = 0.0,\\n band_11: P('float', 'band 11', min_value = -0.25, max_value = 1.0) = 0.0,\\n band_12: P('float', 'band 12', min_value = -0.25, max_value = 1.0) = 0.0,\\n band_13: P('float', 'band 13', min_value = -0.25, max_value = 1.0) = 0.0,\\n band_14: P('float', 'band 14', min_value = -0.25, max_value = 1.0) = 0.0,\\n):\\n player = get_player_or_abort(client, event)\\n \\n filter = Equalizer(\\n (0, band_00),\\n (1, band_01),\\n (2, band_02),\\n (3, band_03),\\n (4, band_04),\\n (5, band_05),\\n (6, band_06),\\n (7, band_07),\\n (8, band_08),\\n (9, band_09),\\n (10, band_10),\\n (11, band_11),\\n (12, band_12),\\n (13, band_13),\\n (14, band_14),\\n )\\n player.add_filter(filter)\\n await player.apply_filters()\\n \\n return create_filter_added_embed(filter)\",\n \"def bollinger_bands(self, normalize=False):\\n sma = self.sma(normalize=normalize)\\n rstd = self.rolling_std(normalize=normalize)\\n upper_band = sma + rstd\\n lower_band = sma - rstd\\n return upper_band, lower_band\",\n \"def waveband(self):\\n return self._band\",\n \"def bands(self):\\n if self._bands is None:\\n self._bands = self._compute_bands()\\n return self._bands\",\n \"def binning(S, bands):\\n B = np.zeros((S.shape[0], len(bands)), dtype=S.dtype)\\n for i, b in enumerate(bands):\\n B[:, i] = np.mean(S[:, b[0] : b[1]], axis=1)\\n\\n return B\",\n \"def bandpasscorrect(data):\\n ret=[x for x in data]\\n n=len(ret)\\n ret[0]=1.083*ret[0]-0.083*ret[1]\\n ret[n-1]=1.083*ret[n-1]-0.083*ret[n-2]\\n for k in range(1,n-1):\\n ret[k]=1.166*ret[k]-0.083*ret[k-1]-0.083*ret[k+1]\\n return ret\",\n \"def band(n, steps):\\n return math.floor(n * steps) / steps\",\n \"def _get_band_for_value(self, wss_value):\\n if (wss_value > self.threshold):\\n return self.positive_band\\n elif (wss_value < -self.threshold):\\n return self.negative_band\\n else:\\n return self.zero_band\",\n \"def band(self, val):\\n if len(val) is 2:\\n self.band = tuple(val)\\n \\n else:\\n # Should raise an error here\\n pass\",\n \"def get_bandstruct(\\n w=[],\\n atoms={},\\n ef=0,\\n line_density=1,\\n ylabel=\\\"Energy (cm-1)\\\",\\n font=22,\\n var_form=None,\\n filename=\\\"bands.png\\\",\\n savefig=True,\\n neigs=None,\\n max_nk=None,\\n tol=None,\\n):\\n info = {}\\n kpoints = Kpoints().kpath(atoms, line_density=line_density)\\n labels = kpoints.to_dict()[\\\"labels\\\"]\\n kpts = kpoints.to_dict()[\\\"kpoints\\\"]\\n print(\\\"kpts\\\", len(kpts))\\n\\n eigvals_q = []\\n eigvals_np = []\\n for ii, i in enumerate(kpts):\\n if max_nk is not None and ii == max_nk:\\n break\\n # For reducing CI/CD time\\n print(\\\"breaking here\\\", ii, max_nk)\\n else:\\n try:\\n print(\\\"kp=\\\", ii, i)\\n hk = get_hk_tb(w=w, k=i)\\n HS = HermitianSolver(hk)\\n vqe_vals, _ = HS.run_vqd(var_form=var_form)\\n np_vals, _ = HS.run_numpy()\\n print(\\\"np_vals\\\", np_vals)\\n print(\\\"vqe_vals\\\", vqe_vals)\\n eigvals_q.append(vqe_vals)\\n eigvals_np.append(np_vals)\\n # break\\n if (\\n neigs is not None\\n and isinstance(neigs, int)\\n and neigs == len(eigvals_q)\\n ):\\n break\\n except Exception as exp:\\n print(exp)\\n pass\\n eigvals_q = 3.14 * np.array(eigvals_q)\\n eigvals_np = 3.14 * np.array(eigvals_np)\\n\\n for ii, i in enumerate(eigvals_q.T - ef):\\n if ii == 0:\\n plt.plot(i, c=\\\"b\\\", label=\\\"VQD\\\")\\n else:\\n plt.plot(i, c=\\\"b\\\")\\n\\n for ii, i in enumerate(eigvals_np.T - ef):\\n if ii == 0:\\n plt.plot(i, c=\\\"r\\\", label=\\\"Numpy\\\")\\n else:\\n plt.plot(i, c=\\\"r\\\")\\n new_kp = []\\n new_labels = []\\n count = 0\\n kp = np.arange(len(kpts))\\n for i, j in zip(kp, labels):\\n if j != \\\"\\\":\\n if count > 1 and count < len(labels) - 1:\\n if labels[count] != labels[count + 1]:\\n new_kp.append(i)\\n new_labels.append(\\\"$\\\" + str(j) + \\\"$\\\")\\n else:\\n new_kp.append(i)\\n new_labels.append(\\\"$\\\" + str(j) + \\\"$\\\")\\n count += 1\\n info[\\\"eigvals_q\\\"] = list(eigvals_q.tolist())\\n info[\\\"eigvals_np\\\"] = list(eigvals_np.tolist())\\n info[\\\"kpts\\\"] = list(kpts)\\n info[\\\"new_kp\\\"] = list(np.array(new_kp).tolist())\\n info[\\\"new_labels\\\"] = list(new_labels)\\n info[\\\"ef\\\"] = ef\\n print(info)\\n if tol is not None:\\n plt.ylim([tol, np.max(eigvals_q)])\\n plt.rcParams.update({\\\"font.size\\\": font})\\n plt.xticks(new_kp, new_labels)\\n plt.ylabel(ylabel)\\n plt.legend()\\n plt.tight_layout()\\n\\n if savefig:\\n plt.savefig(filename)\\n plt.close()\\n else:\\n plt.show()\\n return info\",\n \"def bands(self):\\n\\t\\treturn self._bands\",\n \"def norm_band(band):\\n b_min, b_max = band.min(), band.max()\\n return (band - b_min) / (b_max - b_min)\",\n \"def band(self, name, bands, new_name=None, label=None, text_key=None):\\n if not self._is_numeric(name):\\n msg = \\\"Can only band numeric typed data! {} is {}.\\\"\\n msg = msg.format(name, self._get_type(name))\\n raise TypeError(msg)\\n if not text_key: text_key = self.text_key\\n if not new_name: new_name = '{}_banded'.format(name)\\n if not label: label = self.text(name, False, text_key)\\n franges = []\\n for idx, band in enumerate(bands, start=1):\\n lab = None\\n if isinstance(band, dict):\\n lab = list(band.keys())[0]\\n band = list(band.values())[0]\\n if isinstance(band, tuple):\\n if band[0] < 0:\\n raise ValueError('Cannot band with lower bound < 0.')\\n elif band[1] < 0:\\n raise ValueError('Cannot band with upper bound < 0.')\\n r = '{}-{}'.format(band[0], band[1])\\n franges.append([idx, lab or r, {name: frange(r)}])\\n else:\\n r = str(band)\\n franges.append([idx, lab or r, {name: [band]}])\\n\\n self.derive(new_name, 'single', label, franges,\\n text_key=text_key)\\n\\n return None\",\n \"def band(E, alpha = -1.08, beta = -1.75, E0 = 189, A = 5e-3):\\n if (alpha - beta)*E0 >= E:\\n return A*(E/100)**alpha*np.exp(-E/E0)\\n elif (alpha - beta)*E0 < E:\\n return A*((alpha - beta)*E0/100)**(alpha - beta)*np.exp(beta - alpha)*(E/100)**beta\",\n \"def estimate_bandpass(data):\\n \\n est = filter(data, params.st_bp_window_f, axis=0)\\n est = filter(est, params.st_bp_window_t, axis=1)\\n \\n return est\",\n \"def bandname(self):\\n if self._properties['bandname'] is None:\\n self._properties['bandname'] = \\\"wisew1\\\" if \\\"-w1-\\\" in self.filename \\\\\\n else \\\"wisew2\\\" if \\\"-w2-\\\" in self.filename \\\\\\n else \\\"wisew3\\\" if \\\"-w3-\\\" in self.filename \\\\\\n else \\\"wisew4\\\" if \\\"-w4-\\\" in self.filename \\\\\\n else \\\"unknown\\\"\\n return self._properties['bandname']\",\n \"def _allowed_bands():\\n pass\",\n \"def get_bandgap(cluster, spin, dirr=\\\"/home/pv278/Platinum/\\\"):\\n outfile = open(get_path(dirr, cluster, spin)).readlines()\\n line = [l for l in outfile if \\\"occ\\\" in l]\\n if line:\\n Eocc = line[-1].split()[-1]\\n else:\\n Eocc = None\\n line = [l for l in outfile if \\\"virt\\\" in l]\\n if line:\\n Evirt = line[0].split()[4]\\n else:\\n Evirt = None\\n \\n if Eocc:\\n return (float(Evirt) - float(Eocc)) * 27.211\\n else:\\n return\",\n \"def fastbollingerSignal(price, uband, lband):\\n n = len(price)\\n signal = np.zeros(n)\\n for i in prange(n):\\n if price[i] <= lband[i]: # crossing down\\n signal[i] = 1\\n elif price[i] >= uband[i]: # crossing up\\n signal[i] = -1\\n else:\\n signal[i] = 0\\n return signal\",\n \"def get_bollinger_bands(rm, rstd, degrees):\\n\\tupper_band = rm + rstd * degrees\\n\\tlower_band = rm - rstd * degrees\\n\\treturn upper_band, lower_band\",\n \"def fbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,\\n nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97): \\n highfreq= highfreq or samplerate/2\\n print \\\"preemph %s\\\"%(preemph)\\n signal = sigproc.preemphasis(signal,preemph)\\n frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate)\\n matchframes(frames[0], frames[1])\\n pspec = sigproc.powspec(frames,nfft)\\n energy = pylab.sum(pspec,1) # this stores the total energy in each frame\\n energy = pylab.where(energy == 0, pylab.finfo(float).eps, energy) # if energy is zero, we get problems with log\\n fb = get_filterbanks(nfilt, nfft, samplerate, lowfreq, highfreq)\\n print \\\"len(fb) %s\\\"%(len(fb))\\n colour = \\\"k-\\\"\\n for i in range(len(fb)):\\n if colour == \\\"k-\\\":\\n colour = \\\"r-\\\"\\n else:\\n colour = \\\"k-\\\"\\n startedplot = False\\n midpoint = 0\\n for j in range(len(fb[i])):\\n if fb[i][j] > 0:\\n if startedplot == False:\\n startedplot = j\\n if j > 0:\\n pylab.plot([j-1, j], [fb[i][j-1], fb[i][j]], colour)\\n if fb[i][j] == 1.0:\\n midpoint = j\\n else:\\n if not startedplot == False:\\n pylab.plot([j-1, j], [fb[i][j-1], 0], colour)\\n try:\\n print \\\"slope to midpoint %.3f, slope from midpoint %.3f\\\"%(1.0/float(midpoint-startedplot), 1.0/float(midpoint-j+1))\\n except:\\n pass\\n break\\n pylab.show()\\n feat = pylab.dot(pspec, fb.T) # compute the filterbank energies\\n feat = pylab.where(feat == 0, pylab.finfo(float).eps, feat) # if feat is zero, we get problems with log\\n return feat, energy\",\n \"def bbands(df, price, bbands, n_bdev_up, n_bdev_dn, ma_type, n):\\n\\n ma_types = {0: sma, 1: ema, 2: wma, 3: dema, 4: tema}\\n\\n n_days_std = df[price].rolling(window=n).std(ddof=0)\\n df = ma_types[ma_type](df, price, bbands + \\\"_middle_band\\\", n)\\n df[bbands + \\\"_upper_band\\\"] = df[bbands + \\\"_middle_band\\\"] + (\\n n_days_std * n_bdev_up\\n )\\n df[bbands + \\\"_lower_band\\\"] = df[bbands + \\\"_middle_band\\\"] - (\\n n_days_std * n_bdev_dn\\n )\\n df = df.dropna().reset_index(drop=True)\\n\\n return df\",\n \"def get_bandpass(self):\\n return self.sum(axis=1)\",\n \"def inst_bp(instrument,array=\\\"2\\\"):\\n\\n if instrument == \\\"MUSTANG2\\\" or instrument == \\\"MUSTANG\\\":\\n srms = (300*u.um).to(\\\"m\\\") # surface RMS (microns)\\n ### Reference: https://science.nrao.edu/facilities/gbt/proposing/GBTpg.pdf\\n EA90 = 0.36 # Aperture efficiency at 90 GHz\\n ### The beam efficiencies should be taken as 1.37* Aperture Efficiency\\n R90 = np.exp(-4.0*np.pi*(srms/(const.c/(9.0e10*u.s**-1))).value) #\\n Gnot = EA90/R90 # Unphysical, but see documentation...\\n if instrument == \\\"MUSTANG2\\\":\\n flow = 75.0 # GHz\\n fhig = 105.0 # GHz\\n else:\\n flow = 82.5 # GHz\\n fhig = 97.5 # GHz\\n \\n farr = np.arange(flow,fhig,1.0) # frequency array.\\n tran = farr*0.0 + 1.0 # Let the transmission be unity everywhere.\\n Larr = const.c.value/(farr*1.0e9) # Keep calm and carry on.\\n ### Old formula:\\n #Ruze = Gnot * np.exp(-4.0*np.pi*(srms.value)/Larr)\\n ### Correct formula: (10 April 2018)\\n Ruze = Gnot * np.exp(-(4.0*np.pi*srms.value/Larr)**2)\\n NRuz = Ruze / np.max(Ruze) # Normalize it\\n band = tran * Ruze # Bandpass, with (unnormalized) Ruze efficiency\\n \\n if instrument == \\\"NIKA2\\\" or instrument == \\\"NIKA\\\":\\n caldir='/home/romero/NIKA2/NIKA_SVN/Processing/Pipeline/Calibration/BP/'\\n bpfile=caldir+'Transmission_2017_Jan_NIKA2_v1.fits'\\n hdulist = fits.open(bpfile)\\n\\n if array == \\\"1H\\\": # 1mm (260 GHz) array, Horizontal Polarization\\n tbdata = hdulist[1].data # 1H\\n freq = tbdata.field(0)\\n tran = tbdata.field(1)\\n erro = tbdata.field(2)\\n atmt = tbdata.field(3)\\n cfreq1h = np.sum(freq*tran)/np.sum(tran)\\n \\n if array == \\\"1V\\\": # 1mm (260 GHz) array, Vertical Polarization\\n tbdata = hdulist[2].data # 1V\\n freq = tbdata.field(0)\\n tran = tbdata.field(1)\\n erro = tbdata.field(2)\\n atmt = tbdata.field(3)\\n cfreq1v = np.sum(freq*tran)/np.sum(tran)\\n \\n if array == \\\"2\\\": # 2mm (150 GHz) array\\n tbdata = hdulist[3].data # 2\\n freq = tbdata.field(0)\\n tran = tbdata.field(1)\\n erro = tbdata.field(2)\\n atmt = tbdata.field(3)\\n cfreq2 = np.sum(freq*tran)/np.sum(tran)\\n\\n ### Trim the zero-frequency listing, if any.\\n gi=np.where(freq > 0)\\n freq = freq[gi]\\n tran = tran[gi]\\n erro = erro[gi]\\n atmt = atmt[gi]\\n \\n### Calculate Aperture efficiencies from information found at:\\n### http://www.iram.es/IRAMES/mainwiki/Iram30mEfficiencies\\n Beff = 0.630 # at 210 GHz\\n Aeff = Beff/1.27 # See text on webpage\\n srms = (66.0*u.um).to(\\\"m\\\") # surface RMS (microns)\\n R210 = np.exp(-4.0*np.pi*(srms/(const.c/(2.1e11*u.s**-1))).value) #\\n Gnot = Aeff/R210 # Unphysical, but see documentation...\\n\\n Larr = const.c.value/(freq*1.0e9) # Keep calm and carry on. \\n Ruze = Gnot * np.exp(-4.0*np.pi*(srms.value)/Larr)\\n NRuz = Ruze / np.max(Ruze) # Normalize it\\n band = tran * Ruze # Bandpass, with (unnormalized) Ruze efficiency\\n farr = freq\\n \\n#########################################################################\\n\\n if instrument == 'ACT90':\\n srms = (27.0*u.um).to(\\\"m\\\") # surface RMS (microns)\\n EA90 = 0.95 # I'm making this number up...\\n R90 = np.exp(-4.0*np.pi*(srms/(const.c/(9.0e10*u.s**-1))).value) #\\n Gnot = EA90/R90 # Unphysical, but see documentation...\\n flow = 65.0 # GHz\\n fhig = 125.0 # GHz\\n farr = np.arange(flow,fhig,1.0) # frequency array.\\n freq_ref = 90.0 # I took EA90 to be a fictitious aperature efficiency at 90 GHz\\n band = ruze_eff(farr,freq_ref,EA90,srms)\\n\\n if instrument == 'ACT150':\\n srms = (27.0*u.um).to(\\\"m\\\") # surface RMS (microns)\\n EA90 = 0.95 # I'm making this number up...\\n R90 = np.exp(-4.0*np.pi*(srms/(const.c/(9.0e10*u.s**-1))).value) #\\n Gnot = EA90/R90 # Unphysical, but see documentation...\\n flow = 120.0 # GHz\\n fhig = 180.0 # GHz\\n farr = np.arange(flow,fhig,1.0) # frequency array.\\n freq_ref = 90.0 # I took EA90 to be a fictitious aperature efficiency at 90 GHz\\n band = ruze_eff(farr,freq_ref,EA90,srms)\\n\\n\\n return band, farr\",\n \"def calculate_flux(self, band):\\n\\n if (self.wavelength[0] > band.wavelength[0] or\\n self.wavelength[-1] < band.wavelength[-1]):\\n\\n warn('Spectrum does not cover the whole bandpass, '\\n 'extrapolating...')\\n dw = np.median(np.diff(self.wavelength.value))\\n spec_wavelength = np.arange(\\n band.wavelength.value[0],\\n band.wavelength.value[-1] + dw, dw) * angstrom\\n spec_flux = np.interp(spec_wavelength, self.wavelength,\\n self.flux.value)\\n\\n else:\\n spec_wavelength = self.wavelength\\n spec_flux = self.flux.value\\n\\n i, j = spec_wavelength.searchsorted(\\n Quantity([band.wavelength[0], band.wavelength[-1]]))\\n wavelength = spec_wavelength[i:j]\\n flux = spec_flux[i:j]\\n\\n dw_band = np.median(np.diff(band.wavelength))\\n dw_spec = np.median(np.diff(wavelength))\\n\\n if dw_spec.value > dw_band.value > 20:\\n\\n warn('Spectrum wavelength sampling interval {0:.2f}, but bandpass'\\n 'sampling interval {1:.2f}'.format(dw_spec, dw_band))\\n\\n # Interpolate the spectrum to the passband wavelengths:\\n flux = np.interp(band.wavelength, wavelength, flux)\\n band_transmission = band.transmission\\n wavelength = band.wavelength\\n\\n else:\\n # Interpolate the band transmission to the spectrum wavelengths:\\n band_transmission = np.interp(\\n wavelength, band.wavelength, band.transmission)\\n\\n # Weight by the response and wavelength, appropriate when we're\\n # counting the number of photons within the band:\\n flux = (np.trapz(band_transmission * flux * wavelength, wavelength) /\\n np.trapz(band_transmission * wavelength, wavelength))\\n flux *= erg / s / cm ** 2 / angstrom\\n\\n return flux\",\n \"def get_band_edges():\\n # Vacuum level energy from LOCPOT.\\n locpot = Locpot.from_file('LOCPOT')\\n evac = max(locpot.get_average_along_axis(2))\\n\\n vasprun = Vasprun('vasprun.xml')\\n bs = vasprun.get_band_structure()\\n eigenvals = vasprun.eigenvalues\\n efermi = vasprun.efermi - evac\\n\\n if bs.is_metal():\\n edges = {'up_cbm': None, 'up_vbm': None, 'dn_cbm': None, 'dn_vbm': None,\\n 'efermi': efermi}\\n\\n elif bs.is_spin_polarized:\\n up_cbm = min(\\n [min([e[0] for e in eigenvals[Spin.up][i] if not e[1]])\\n for i in range(len(eigenvals[Spin.up]))]) - evac\\n up_vbm = max(\\n [max([e[0] for e in eigenvals[Spin.up][i] if e[1]])\\n for i in range(len(eigenvals[Spin.up]))]) - evac\\n dn_cbm = min(\\n [min([e[0] for e in eigenvals[Spin.down][i] if not e[1]])\\n for i in range(len(eigenvals[Spin.down]))]) - evac\\n dn_vbm = max(\\n [max([e[0] for e in eigenvals[Spin.down][i] if e[1]])\\n for i in range(len(eigenvals[Spin.down]))]) - evac\\n edges = {'up_cbm': up_cbm, 'up_vbm': up_vbm, 'dn_cbm': dn_cbm,\\n 'dn_vbm': dn_vbm, 'efermi': efermi}\\n\\n else:\\n cbm = bs.get_cbm()['energy'] - evac\\n vbm = bs.get_vbm()['energy'] - evac\\n edges = {'up_cbm': cbm, 'up_vbm': vbm, 'dn_cbm': cbm, 'dn_vbm': vbm,\\n 'efermi': efermi}\\n\\n return edges\",\n \"def bandlcs(self):\\n lc = self.lc\\n bandgroups = lc.group_by('band')\\n return bandgroups\",\n \"def bbands(price, length=30, numsd=2):\\n ave = pd.stats.moments.rolling_mean(price,length)\\n sd = pd.stats.moments.rolling_std(price,length)\\n upband = ave + (sd*numsd)\\n dnband = ave - (sd*numsd)\\n return np.round(upband,3), np.round(dnband,3)\",\n \"def make_band(f0, band_type, num):\\n \\n if band_type == '1Hz-spaced':\\n hwidth = num//2\\n step = 1\\n f = np.arange(f0-hwidth, f0+hwidth+step, step)\\n \\n else:\\n lwr = -(num-1)//2 # lower range bound for frequency calculation\\n upr = (num+1)//2 # upper range bound for frequency calculation\\n f = np.array(list(f0*2**(k/(12*num)) for k in range(lwr,upr)))\\n \\n return f\",\n \"def band_hilbert(x, fs, band, N=None, axis=-1):\\n x = np.asarray(x)\\n Xf = np.fft.fft(x, N, axis=axis)\\n w = np.fft.fftfreq(N or x.shape[0], d=1. / fs)\\n Xf[(w < band[0]) | (w > band[1])] = 0\\n x = np.fft.ifft(Xf, axis=axis)[:x.shape[0]]\\n return 2*x\",\n \"def bands(self):\\n\\t\\treturn zip((self.primary_threshold, self.upper_earning_limit),\\n\\t\\t\\t\\t self.rates)\",\n \"def make_bwfull(w,minZ,maxZ,ires=1,fixw=False,m=mz0):\\n cmds = []\\n # coefficients for the amplitudes\\n cmds.append(\\\"A[1,0,1000000]\\\")\\n cmds.append(\\\"B[1,0,1000000]\\\")\\n cmds.append(\\\"C[10000.0,0,1000000]\\\")\\n # amplitudes\\n cmds.append('m[%s,%s,%s]'%(m,minZ,maxZ))\\n cmds.append('g[2.49,0,10]')\\n denom = '((x^2-m^2)^2+x^4*g^2/m^2)'\\n cmds.append(\\\"expr::z_rbw('x^2/%s',x,m,g)\\\"%denom)\\n cmds.append(\\\"expr::z_int('(x^2-m^2)/%s',x,m,g)\\\"%denom)\\n cmds.append(\\\"expr::z_rad('1/(x^2)',x)\\\")\\n # resolution model\\n cmds += resolutions[ires]()\\n [w.factory(cmd) for cmd in cmds]\\n # any parameter adjustments\\n if True:\\n w.var('r_m').setConstant(kTRUE) if w.var('r_m') else None\\n w.var('rt_m').setConstant(kTRUE) if w.var('rt_m') else None\\n w.var('g').setConstant(kTRUE) if w.var('g') and fixw else None\\n # sum-of-amplitudes pdf\\n lshape = RooRealSumPdf('lshape','lshape',RooArgList(w.function('z_rad'),w.function('z_int'),w.function('z_rbw')),RooArgList(w.var('A'),w.var('B'),w.var('C')))\\n getattr(w,'import')(lshape)\\n # convolution\\n pdf = w.pdf('lshape')\\n if w.pdf('res'):\\n w.var('x').setBins(10000,'cache')\\n cmd = 'FCONV::sum(x,lshape,res)'\\n w.factory(cmd)\\n pdf = w.pdf('sum')\\n return pdf, kFALSE\",\n \"def band_plot(N=400,a=1.0):\\n foot_step=2*np.pi/N\\n x=np.arange(0.0,2*np.pi/a,foot_step)\\n y=band_energy(x)\\n plt.plot(x,y)\",\n \"def bands(self):\\n return self._bands\",\n \"def beam(xb,yb,zb,wx,wy,wavelen):\\n\\n zRx = np.pi * wx**2 / wavelen\\n zRy = np.pi * wy**2 / wavelen \\n \\n sqrtX = np.sqrt( 1 + np.power(zb/zRx,2) ) \\n sqrtY = np.sqrt( 1 + np.power(zb/zRy,2) ) \\n intensity = np.exp( -2.*( np.power(xb/(wx*sqrtX ),2) \\\\\\n + np.power(yb/(wy*sqrtY),2) )) / sqrtX / sqrtY\\n return intensity\",\n \"def plotallbands(_zband, _yband, _jband, _hband, _kband, _period):\\n # Set pyplot style to be consisten within the program\\n plt.style.use('seaborn-whitegrid')\\n # Frequency = 1 / Period\\n _freq = 1 / _period\\n\\n # Create single dataset from all bands\\n _bands = [_zband, _yband, _jband, _hband, _kband]\\n # Iterate through each band and plot to screen\\n i = 0\\n while i < 5:\\n # Array to set colours for each band\\n _colours = ['-b', '-g', '-r', '-c', '-m']\\n # Array to set strings for graph legend\\n _legend = ['Z-band', 'Y-band', 'J-band', 'H-band', 'K-band']\\n # Determine the line of best fit for each band\\n _xfit, _lobf = calclobf(_bands[i], _period)\\n # Plot the data in the array to screen, lightly coloured and z rank behind the line of best fit\\n plt.plot(_xfit, _lobf, _colours[i], lw=1, zorder=2, label=_legend[i])\\n i += 1\\n\\n # Set x-axis limit to a single period\\n plt.xlim(0, 1)\\n # Set graph and axis titles\\n plt.xlabel(\\\"Phase\\\")\\n plt.ylabel(\\\"Magnitude\\\")\\n plt.title(\\\"Folded light curve\\\")\\n # Show the legend\\n plt.legend()\\n # Invert y-axis as convention\\n plt.gca().invert_yaxis()\\n # Save to current folder\\n plt.savefig('curve.png')\\n # Display to screen\\n plt.show()\",\n \"def calc_elv_bands(self, red, comp):\\n # reference band\\n red_V = red.data['BAND'].get_band_mag('V')\\n comp_V = comp.data['BAND'].get_band_mag('V')\\n\\n # possible bands for the band extinction curve\\n poss_bands = red.data['BAND'].get_poss_bands()\\n\\n waves = []\\n exts = []\\n uncs = []\\n npts = []\\n for pband_name in poss_bands.keys():\\n red_mag = red.data['BAND'].get_band_mag(pband_name)\\n comp_mag = comp.data['BAND'].get_band_mag(pband_name)\\n if (red_mag is not None) & (comp_mag is not None):\\n ext = (red_mag[0] - red_V[0]) - (comp_mag[0] - comp_V[0])\\n unc = np.sqrt(red_mag[1]**2 + red_V[1]**2\\n + comp_mag[1]**2 + comp_V[1]**2)\\n waves.append(red.data['BAND'].band_waves[pband_name])\\n exts.append(ext)\\n uncs.append(unc)\\n npts.append(1)\\n\\n if len(waves) > 0:\\n self.waves['BAND'] = np.array(waves)\\n self.exts['BAND'] = np.array(exts)\\n self.uncs['BAND'] = np.array(uncs)\\n self.npts['BAND'] = np.array(npts)\",\n \"def add_bollinger_bands(self, rstd):\\n self.data['upper_band'] = self.data['rolling_mean'] + 2 * rstd\\n self.data['lower_band'] = self.data['rolling_mean'] - 2 * rstd\",\n \"def compute_band_ratio(freqs, powers, low_band, high_band, method='mean'):\\n\\n # Compute the power in each band\\n low_band_power = compute_absolute_power(freqs, powers, low_band, method)\\n high_band_power = compute_absolute_power(freqs, powers, high_band, method)\\n\\n # Calculate the ratio between bands\\n ratio = low_band_power / high_band_power\\n\\n return ratio\",\n \"def bandname(self):\\n if self._properties['bandname'] is None:\\n self._properties['bandname'] = \\\"fuv\\\" if \\\"-fd-\\\" in self.filename else \\\"nuv\\\" if \\\"-nd-\\\" in self.filename \\\\\\n else \\\"unknown\\\"\\n return self._properties['bandname']\",\n \"def get_bw(self):\\n return np.array([b2 - b1 for b1, b2 in self.bands])\",\n \"def get_wl_band(radar_frequency):\\n return 0 if (30 < radar_frequency < 40) else 1\",\n \"def calc_rsi(image):\\n\\n # roll axes to conventional row,col,depth\\n img = np.rollaxis(image, 0, 3)\\n\\n # bands: Coastal(0), Blue(1), Green(2), Yellow(3), Red(4), Red-edge(5), NIR1(6), NIR2(7)) Multispectral\\n COAST = img[:, :, 0]\\n B = img[:, :, 1]\\n G = img[:, :, 2]\\n Y = img[:, :, 3]\\n R = img[:, :, 4]\\n RE = img[:, :, 5]\\n NIR1 = img[:, :, 6]\\n NIR2 = img[:, :, 7]\\n\\n arvi = old_div((NIR1 - (R - (B - R))), (NIR1 + (R - (B - R))))\\n dd = (2 * NIR1 - R) - (G - B)\\n gi2 = (B * -0.2848 + G * -0.2434 + R * -0.5436 + NIR1 * 0.7243 + NIR2 * 0.0840) * 5\\n gndvi = old_div((NIR1 - G), (NIR1 + G))\\n ndre = old_div((NIR1 - RE), (NIR1 + RE))\\n ndvi = old_div((NIR1 - R), (NIR1 + R))\\n ndvi35 = old_div((G - R), (G + R))\\n ndvi84 = old_div((NIR2 - Y), (NIR2 + Y))\\n nirry = old_div((NIR1), (R + Y))\\n normnir = old_div(NIR1, (NIR1 + R + G))\\n psri = old_div((R - B), RE)\\n rey = old_div((RE - Y), (RE + Y))\\n rvi = old_div(NIR1, R)\\n sa = old_div(((Y + R) * 0.35), 2) + old_div((0.7 * (NIR1 + NIR2)), 2) - 0.69\\n vi1 = old_div((10000 * NIR1), (RE) ** 2)\\n vire = old_div(NIR1, RE)\\n br = (old_div(R, B)) * (old_div(G, B)) * (old_div(RE, B)) * (old_div(NIR1, B))\\n gr = old_div(G, R)\\n rr = (old_div(NIR1, R)) * (old_div(G, R)) * (old_div(NIR1, RE))\\n\\n ###Built-Up indices\\n wvbi = old_div((COAST - RE), (COAST + RE))\\n wvnhfd = old_div((RE - COAST), (RE + COAST))\\n\\n ###SIs\\n evi = old_div((2.5 * (NIR2 - R)), (NIR2 + 6 * R - 7.5 * B + 1))\\n L = 0.5 # some coefficient for Soil Adjusted Vegetation Index (SAVI) DO NOT INCLUDE IN FEATURES\\n savi = old_div(((1 + L) * (NIR2 - R)), (NIR2 + R + L))\\n msavi = old_div((2 * NIR2 + 1 - ((2 * NIR2 + 1) ** 2 - 8 * (NIR2 - R)) ** 0.5), 2)\\n bai = old_div(1.0, ((0.1 + R) ** 2 + 0.06 + NIR2))\\n rgi = old_div(R, G)\\n bri = old_div(B, R)\\n\\n rsi = np.stack(\\n [arvi, dd, gi2, gndvi, ndre, ndvi, ndvi35, ndvi84, nirry, normnir, psri, rey, rvi, sa, vi1, vire, br, gr, rr,\\n wvbi, wvnhfd, evi, savi, msavi, bai, rgi, bri],\\n axis=2)\\n\\n return rsi\",\n \"def bandpass(filename,f1,f2,Q,wout=True,plot=True):\\n start=time.time()\\n n, data, data_dB,sr,ch=inputwav(filename)\\n b, a = butter(Q,Wn=(f1/sr,f2/sr),btype='bandpass')\\n data_filtered=lfilter(b,a,data,axis=0)\\n print('Applying FFT...')\\n if wout==True:\\n print('Exporting...')\\n sf.write(filename[0:len(filename)-4]+'_BP.wav',data_filtered,sr,'PCM_16')\\n if plot==True:\\n print('Plotting...')\\n py.close()\\n w, h = freqz(b,a,worN=16384)\\n fig, (ax1, ax2) = py.subplots(nrows=2)\\n ax1.semilogx(0.5*sr*w/np.pi,abs(h),'k-')\\n ax1.set_xlabel('Frequency (Hz)')\\n ax1.set_ylabel('Rel. Amplitude')\\n ax1.grid()\\n ax1.set_ylim(0,1.1)\\n ax1.set_xlim(1,20000)\\n ax2.plot(data,'k-',label='Raw data')\\n ax2.plot(data_filtered,'m-',lw=1,label='Filtered data')\\n ax2.set_xlim(0,10000)\\n ax2.set_ylim(-1,1)\\n ax2.set_ylabel('Amplitude (Norm Bits)')\\n ax2.set_xlabel('Samples')\\n ax2.legend(loc=2,frameon=False,ncol=2)\\n py.subplots_adjust(hspace=0.35) \\n print('Done!')\\n end=time.time()\\n elapsed=int(1000*(end-start))\\n print('...............................')\\n print('Completed in '+str(elapsed)+' milliseconds.')\\n return data_filtered\",\n \"def build_signal_dataset(self):\\n return np.abs(self.bandpassed).mean(axis=-2)\",\n \"def waveband(self):\\n return self.get(\\\"waveband\\\")\",\n \"def subbandwidth(self):\",\n \"def waveband(self):\\n return self.get(\\\"waveband\\\", default=\\\"\\\", decode=True).split(\\\"#\\\")\",\n \"def getbandlcs(lc):\\n bandgroups = lc.group_by('band')\\n\\n return bandgroups\",\n \"def __init__(self, bandit):\\n self.bandit = bandit\",\n \"def quality(self): \\n\\n subsetInt = [int(s) for s in self.subset.split() if s.isdigit()]\\n columnNames = [] \\n for i in range(len(subsetInt)):\\n if subsetInt[i] == 1:\\n columnNames.append(self.varNames[i])\\n\\n #qualityBand number of subset\\n q = columnNames.index('Quality') \\n\\n if subsetInt[self.qualityBand] == 1:\\n dataCount = self.subset.count('1')\\n QC = np.repeat(self.DC[:,q].reshape((self.DC.shape[0],1)), dataCount-1, axis = 1)\\n if self.dataset == 'MOD09A1.005' or self.dataset == 'MOD13Q1.005':\\n QC = np.uint16(QC)\\n else:\\n QC = np.uint8(QC)\\n\\n QCm = QC & 1 #flips DCm mask\\n DCm = np.delete(self.DC, q, 1) #looks good\\n \\n DCm = np.ma.masked_where(QCm == 1, DCm)\\n DCm = np.ma.masked_where(DCm == 9999.0, DCm) \\n \\n if len(self.tiles) > 1:\\n obs = self.observations/len(self.tiles)\\n if len(self.tiles) == 1:\\n obs = self.observations/2\\n \\n outArray = np.empty(shape = (self.rows*self.columns*obs, 0))\\n for b in range(0, self.DC.shape[1]-1):\\n cfull = DCm[:,b].reshape((self.observations, self.rows, self.columns))\\n b16 = np.empty(shape = (self.rows*self.columns*obs, 0))\\n for band in range(0,cfull.shape[0],2):\\n c16 = np.ma.mean(cfull[band:band+1,:,:], axis=0)\\n c16f = np.ma.filled(c16, 9999.0).astype(float).reshape((self.rows*self.columns))\\n b16 = np.append(b16, c16f)\\n outArray = np.append(outArray, b16.reshape((obs*self.rows*self.columns, 1)), axis = 1)\\n \\n self.finalDC = outArray\\n \\n np.save(str(self.directory) + '/' + self.dataset + '.npy', self.finalDC)\\n del outArray, QC, DCm\\n\\n outfile = str(self.directory) + '/' + self.dataset + '.txt'\\n f = open(outfile, 'w')\\n for name in columnNames:\\n if name != 'Quality':\\n f.write(name + '\\\\n')\\n var = [a for a in columnNames if not a.startswith('Quality')]\\n logger.log('SUCCESS', 'The final 16-day interval quality-masked matrix was created successfully. This matrix has dimensions %d rows by %d columns. Datasets included in the matrix are %s' % (self.finalDC.shape[0], self.finalDC.shape[1], var))\\n \\n \\n if subsetInt[self.qualityBand] != 1:\\n cleanDC = np.delete(self.DC, q, 1)\\n \\n \\n if len(self.tiles) > 1:\\n obs = self.observations/len(self.tiles)\\n if len(self.tiles) == 1:\\n obs = self.observations/2\\n \\n outArray = np.empty(shape = (self.rows*self.columns*obs, 0))\\n for b in range(cleanDC.shape[1]):\\n cfull = cleanDC[:,b].reshape((self.observations, self.rows, self.columns))\\n b16 = np.empty(shape=(self.rows*self.columns*obs))\\n for band in range(cfull.shape[0]):\\n c16 = np.mean(cfull[band:band+1,:,:], axis=0)\\n band16 = np.append(b16, c16, axis=0)\\n outArray = np.append(outArray, b16.reshape((obs*self.rows*self.columns, 1)), axis = 1)\\n\\n np.save(self.directory + '/' + self.dataset + '.npy', self.finalDC)\\n del cleanDC, outArray\\n \\n outfile = self.directory + '/' + self.dataset + '.txt'\\n f = open(outfile, 'w')\\n for name in columnNames:\\n if name != 'Quality':\\n f.write(str(name) + ' \\\\n')\\n var = [a for a in columnNames if not a.startswith('Quality')]\\n logger.log('SUCCESS', 'The final 16-day interval matrix was created successfully. A quality mask was not applied, though remaining no data values are set at 9999. This matrix has dimensions %d rows by %d columns. Datasets included in the matrix are %s' % (self.finalDC.shape[0], self.finalDC.shape[1], var))\",\n \"def readBandit(filename):\\n\\tfile = open(filename, 'r')\\n\\tlines = file.readlines()\\n\\tbandit = [float(value.strip()) for value in lines]\\n\\treturn bandit\",\n \"def _butter_bandpass(lowcut: float, highcut: float, fs: float, order: int = 5) -> tuple:\\n nyq = 0.5 * fs\\n low = lowcut / nyq\\n high = highcut / nyq\\n b, a = butter(order, [low, high], btype=\\\"band\\\")\\n return b, a\",\n \"def usedbands(self):\\n lc = self.lc\\n return np.unique(np.asarray(lc['band']))\",\n \"def band_penalty(self):\\n fc_ix = np.argmin(np.abs(self.f - self.fc)) # Index to frequency array closes to center frequency\\n # Number of indexes on each side of center frequency, not extending outside, only up to 10 kHz\\n n = min(fc_ix, self.ix10k - fc_ix)\\n if n == 0:\\n return 0.0\\n return np.mean(np.square(self.fr[fc_ix - n:fc_ix] - (self.gain - self.fr[fc_ix + n - 1:fc_ix - 1:-1])))\",\n \"def bollinger_hband_indicator(close, n=20, ndev=2, fillna=False):\\n df = pd.DataFrame([close]).transpose()\\n mavg = close.rolling(n).mean()\\n mstd = close.rolling(n).std()\\n hband = mavg + ndev * mstd\\n df['hband'] = 0.0\\n df.loc[close > hband, 'hband'] = 1.0\\n hband = df['hband']\\n if fillna:\\n hband = hband.replace([np.inf, -np.inf], np.nan).fillna(0)\\n return pd.Series(hband, name='bbihband')\",\n \"def bin_power(X,Band,Fs):\\n\\n\\tC = fft(X)\\n\\tC = abs(C)\\n\\tPower =np.zeros(len(Band)-1);\\n\\tfor Freq_Index in range(0,len(Band)-1):\\n\\t\\tFreq = float(Band[Freq_Index])\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t## Xin Liu\\n\\t\\tNext_Freq = float(Band[Freq_Index+1])\\n\\t\\tPower[Freq_Index] = sum(C[np.floor(Freq/Fs*len(X)):np.floor(Next_Freq/Fs*len(X))])\\n\\tPower_Ratio = Power/sum(Power)\\n\\treturn Power, Power_Ratio\",\n \"def recon_steer_bands(pyr, freq_resps, numlevels, numorientations):\\n \\n result_bands = np.zeros(pyr[0].shape)\\n\\n freq_hi = np.fft.fftshift(np.fft.fft2(pyr[0]))\\n result_hi = np.fft.ifft2(np.fft.fftshift(np.multiply(freq_hi, np.conjugate(freq_resps[0])))).real \\n \\n freq_lo = np.fft.fftshift(np.fft.fft2(pyr[2]))\\n result_lo = np.fft.ifft2(np.fft.fftshift(np.multiply(freq_lo, np.conjugate(freq_resps[2])))).real\\n \\n freq_resp_band = freq_resps[1]\\n pyr_band = pyr[1] \\n for i in range(numlevels):\\n for j in range(numorientations): \\n freq_band = np.fft.fftshift(np.fft.fft2(pyr_band[i][j]))\\n result_band = np.fft.ifft2(np.fft.fftshift(np.multiply(freq_band, np.conjugate(freq_resp_band[i][j])))).real\\n result_bands = result_bands + result_band \\n result = result_bands + result_hi + result_lo\\n return result\",\n \"def Z_bandpass(C, L, R_L, f):\\n return 1/(1/Xind(L, f) + 1/Xcap(C, f) + 1/R_L)\",\n \"def bollinger_lband_indicator(close, n=20, ndev=2, fillna=False):\\n df = pd.DataFrame([close]).transpose()\\n mavg = close.rolling(n).mean()\\n mstd = close.rolling(n).std()\\n lband = mavg - ndev * mstd\\n df['lband'] = 0.0\\n df.loc[close < lband, 'lband'] = 1.0\\n lband = df['lband']\\n if fillna:\\n lband = lband.replace([np.inf, -np.inf], np.nan).fillna(0)\\n return pd.Series(lband, name='bbilband')\",\n \"def power_in_bands(self, bands=None, avgaxis=None):\\n if bands is None:\\n bands = _third_octave_bands\\n\\n # center frequencies\\n fcs = np.asarray([b[0] for b in bands])\\n Npow2 = 2 ** (self.nt - 1).bit_length()\\n f = np.fft.fftfreq(Npow2, d=1 / self.fs)\\n\\n shape = list(self.in_freq.shape)\\n shape[-1] = len(bands)\\n P = np.zeros(shape)\\n for i, (fc, fl, fu) in enumerate(bands):\\n if fu < self.fs / 2: # include only bands in frequency range\\n iband = np.logical_and(fl <= f, f < fu)\\n P[..., i] = np.sum(\\n np.abs(np.fft.fft(self.in_time, n=Npow2, axis=-1)[..., iband]) ** 2\\n * 2 # energy from negative and positive frequencies\\n * self.dt\\n / self.nt\\n / self.time_length,\\n axis=-1,\\n )\\n else:\\n P[..., i] = 0\\n\\n if avgaxis is not None:\\n P = P.mean(axis=avgaxis)\\n\\n return P, fcs\",\n \"def make_albedo(self, x_sol, unc, nbands=7):\\n\\n n_doys = x_sol.shape[0] / 3\\n n_bands = x_sol.shape[1]\\n bhr_spectral = np.zeros((n_doys, n_bands))\\n bhr_spectral_unc = np.zeros((n_doys, n_bands))\\n bhr_spectral_nbar = np.zeros((n_doys, n_bands))\\n bhr_spectral_nbar_unc = np.zeros((n_doys, n_bands))\\n bhr_bb = np.zeros((n_doys, 3))\\n bhr_bb_unc = np.zeros((n_doys, 3))\\n to_vis = np.array([0.3265, 0., 0.4364, 0.2366, 0, 0, 0])\\n a_to_vis = -0.0019\\n to_nir = np.array([0., 0.5447, 0, 0, 0.1363, 0.0469, 0.2536])\\n a_to_nir = -0.0068\\n to_sw = np.array([0.3973, 0.2382, 0.3489, -0.2655, 0.1604, -0.0138, 0.0682])\\n a_to_sw = 0.0036\\n for band in xrange(n_bands):\\n u1 = np.sqrt(unc[band].diagonal()[:n_doys])\\n u2 = np.sqrt(unc[band].diagonal()[n_doys:(n_doys * 2)])\\n u3 = np.sqrt(unc[band].diagonal()[2 * n_doys:])\\n\\n bhr_spectral[:, band] = (x_sol[:(n_doys), band] +\\n 0.189184 * x_sol[(n_doys):(2 * n_doys), band] +\\n 1.377622 * x_sol[(2 * n_doys):, band])\\n bhr_spectral_unc[:, band] = (u1 + 0.189184 * u2 + 1.377622 * u3)\\n\\n bhr_bb[:, 0] = np.sum(bhr_spectral * to_vis, axis=1) + a_to_vis\\n bhr_bb[:, 1] = np.sum(bhr_spectral * to_nir, axis=1) + a_to_nir\\n bhr_bb[:, 2] = np.sum(bhr_spectral * to_sw, axis=1) + a_to_sw\\n bhr_bb_unc[:, 0] = np.sum(bhr_spectral_unc * to_vis, axis=1) + a_to_vis\\n bhr_bb_unc[:, 1] = np.sum(bhr_spectral_unc * to_nir, axis=1) + a_to_nir\\n bhr_bb_unc[:, 2] = np.sum(bhr_spectral_unc * to_sw, axis=1) + a_to_sw\\n\\n kr = Kernels(0, 20, 0, \\\\\\n LiType='Sparse', doIntegrals=False, \\\\\\n normalise=1, RecipFlag=True, RossHS=False, MODISSPARSE=True, \\\\\\n RossType='Thick')\\n\\n # n_doys = x_sol.shape[0] / 3\\n for band in xrange(self.nbands):\\n bhr_spectral_nbar[:, band] = (x_sol[:(n_doys), band] +\\n kr.Ross[0] * x_sol[(n_doys):(2 * n_doys), band] +\\n kr.Li[0] * x_sol[(2 * n_doys):, band])\\n\\n u1 = np.sqrt(unc[band].diagonal()[:n_doys])\\n u2 = np.sqrt(unc[band].diagonal()[n_doys:(n_doys * 2)])\\n u3 = np.sqrt(unc[band].diagonal()[2 * n_doys:])\\n bhr_spectral_nbar_unc[:, band] = (u1 + kr.Ross[0] * u2 + kr.Li[0] * u3)\\n\\n return bhr_spectral[self.min_doy:self.max_doy], bhr_spectral_unc[self.min_doy:self.max_doy],\\\\\\n bhr_bb[self.min_doy:self.max_doy], bhr_bb_unc[self.min_doy:self.max_doy],\\\\\\n bhr_spectral_nbar[self.min_doy:self.max_doy], bhr_spectral_nbar_unc[self.min_doy:self.max_doy]\",\n \"def band_penalty(self):\\n fc_ix = np.argmin(np.abs(self.f - self.fc)) # Index to frequency array closes to center frequency\\n # Number of indexes on each side of center frequency, not extending outside, only up to 10 kHz\\n n = min(fc_ix, self.ix10k - fc_ix)\\n if n == 0:\\n return 0.0\\n return np.mean(np.square(self.fr[fc_ix - n:fc_ix] - self.fr[fc_ix + n - 1:fc_ix - 1:-1]))\",\n \"def l457_ts_bt_band_func(dn, lmin, lmax, qcalmin, qcalmax, k1, k2):\\n ts_bt = np.copy(dn).astype(np.float64)\\n ts_bt -= qcalmin\\n ts_bt *= ((lmax - lmin) / (qcalmax - qcalmin))\\n ts_bt += lmin\\n return ts_bt_func(ts_bt, k1, k2).astype(np.float32)\",\n \"def plot_power_in_bands(\\n self, bands=None, use_ax=None, barkwargs={}, avgaxis=None, dbref=1, **figkwargs\\n ):\\n P, fc = self.power_in_bands(bands=bands, avgaxis=avgaxis)\\n\\n nbands = P.shape[-1]\\n P = np.atleast_2d(P).reshape((-1, nbands))\\n\\n if use_ax is None:\\n fig, ax = plt.subplots(**figkwargs)\\n else:\\n ax = use_ax\\n fig = ax.get_figure()\\n\\n xticks = range(1, nbands + 1)\\n for i in range(P.shape[0]):\\n ax.bar(xticks, 10 * np.log10(P[i] / dbref ** 2), **barkwargs)\\n ax.set_xticks(xticks)\\n ax.set_xticklabels([\\\"{:.0f}\\\".format(f) for f in fc], rotation=\\\"vertical\\\")\\n ax.grid(True)\\n ax.set_xlabel(\\\"Band's center frequencies [Hz]\\\")\\n ax.set_ylabel(\\\"Power [dB]\\\")\\n\\n return (P, fc, fig)\",\n \"def get_bollinger_bands(rm, rstd, deviation=2):\\n upper_band = rm + rstd * deviation\\n lower_band = rm - rstd * deviation\\n return upper_band, lower_band\",\n \"def test_bands_distance(load_bands):\\n from aiida_wannier90_workflows.utils.bands.distance import bands_distance\\n\\n pw_bands = load_bands(\\\"W\\\", \\\"pw.json\\\")\\n wan_bands = load_bands(\\\"W\\\", \\\"w90.json\\\")\\n\\n fermi_energy = 22.753\\n exclude_list_dft = [1, 2, 3, 4]\\n\\n dist = bands_distance(\\n bands_dft=pw_bands,\\n bands_wannier=wan_bands,\\n fermi_energy=fermi_energy,\\n exclude_list_dft=exclude_list_dft,\\n )\\n\\n ref_dist = np.array(\\n [\\n [\\n 2.275300000000000011e01,\\n 1.036669313927612510e-02,\\n 5.322753656962231350e-02,\\n 5.322753656931334537e-02,\\n ],\\n [\\n 2.375300000000000011e01,\\n 1.008729713743582342e-02,\\n 5.322753656993127469e-02,\\n 5.322753656993126081e-02,\\n ],\\n [\\n 2.475300000000000011e01,\\n 9.774211496004188773e-03,\\n 5.322753656993128857e-02,\\n 5.322753656993128857e-02,\\n ],\\n [\\n 2.575300000000000011e01,\\n 9.008465047456700250e-03,\\n 5.322753656993128857e-02,\\n 5.322753656993128857e-02,\\n ],\\n [\\n 2.675300000000000011e01,\\n 8.588777504871471583e-03,\\n 5.322753656993128857e-02,\\n 5.322753656993128857e-02,\\n ],\\n [\\n 2.775300000000000011e01,\\n 8.545545737397273328e-03,\\n 5.322753656993128857e-02,\\n 5.322753656993128857e-02,\\n ],\\n ]\\n )\\n\\n atol = 1e-8\\n assert np.allclose(dist, ref_dist, atol=atol)\",\n \"def butterworth_bandpass(lowcut, highcut, fs, order=5):\\n nyq = 0.5 * fs # nyquist sampling rate\\n low = lowcut / nyq # normalize frequency\\n high = highcut / nyq # normalize frequency\\n b, a = butter(order, [low, high], btype='band')\\n return b, a\",\n \"def bandpass_filterbank(bands, fs=1.0, order=8, output=\\\"sos\\\"):\\n\\n filters = []\\n nyquist = fs / 2.0\\n\\n for band in bands:\\n # remove bands above nyquist frequency\\n if band[0] >= nyquist:\\n raise ValueError(\\\"Bands should be below Nyquist frequency\\\")\\n\\n # Truncate the highest band to Nyquist frequency\\n norm_band = np.minimum(0.99, np.array(band) / nyquist)\\n\\n # Compute coefficients\\n coeffs = butter(order / 2, norm_band, \\\"bandpass\\\", output=output)\\n filters.append(coeffs)\\n\\n return filters\",\n \"def test_filt_abmag(self):\\n sun = Sun.from_builtin('E490_2014')\\n V = get_bandpass('johnson v')\\n wave, fluxd = sun.filt(V, unit=u.ABmag)\\n assert np.isclose(fluxd.value, -26.77, atol=0.007)\",\n \"def correct_band(self, arr, band):\\n if self.lut[band] is None:\\n # No interpolation, so return NaNs\\n new_arr = arr.copy()\\n new_arr[:] = np.nan\\n return new_arr\\n else:\\n return self.lut[band](arr)\",\n \"def get_sn2005ek(colorplt=False):\\n z = 0.016551\\n ebv = 0.210\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n t_max = 53639.9\\n print (\\\"adopt r band t_max from Drout+13\\\")\\n \\n # tb = pd.read_csv('/Users/yuhanyao/Desktop/ZTF18abfcmjw/data/Drout2013/table1', sep='\\\\t')\\n # tb = tb.drop(columns=[\\\"Unnamed: 6\\\"])\\n \\n mjds = np.array([53639.3, 53640.3, 53641.3, 53642.2, 53643.2, 53645.3,\\n 53646.5, 53648.0, 53649.2, 53650.4, 53651.3, 53652.5,\\n 53654.2, 53655.2, 53656.2, 53657.2])\\n \\n Bmags = np.array([18.25, 18.38, 18.65, np.nan, 19.10, 19.71,\\n 20.07, np.nan, 20.67, 20.90, 21.05, np.nan,\\n 21.74, np.nan, np.nan, np.nan])\\n \\n Bmag_uncs = np.array([0.02, 0.03, 0.02, np.nan, 0.05, 0.07, \\n 0.07, np.nan, 0.04, 0.04, 0.04, np.nan,\\n 0.12, np.nan, np.nan, np.nan])\\n \\n Vmags = np.array([17.83, 18.03, 17.92, np.nan, 18.24, 18.66,\\n 18.93, 19.48, 19.63, 19.86, 19.98, 20.35,\\n 20.60, 20.74, 20.88, 21.22])\\n \\n Vmag_uncs = np.array([0.02, 0.03, 0.01, np.nan, 0.02, 0.02,\\n 0.02, 0.06, 0.03, 0.03, 0.04, 0.05, \\n 0.08, 0.10, 0.08, 0.13])\\n \\n Rmags = np.array([17.46, 17.41, 17.60, 17.69, 17.86, 18.18, \\n np.nan, 18.83, 19.03, 19.26, 19.48, 19.75,\\n 20.08, np.nan, 20.47, np.nan])\\n \\n Rmag_uncs = np.array([0.01, 0.02, 0.01, 0.02, 0.01, 0.01,\\n np.nan, 0.03, 0.02, 0.02, 0.02, 0.04,\\n 0.05, np.nan, 0.08, np.nan])\\n\\n Imags = np.array([17.20, 17.13, 17.18, np.nan, 17.47, 17.71, \\n np.nan, 18.13, 18.26, 18.51, 18.61, 18.74, \\n 19.01, np.nan, 19.47, np.nan])\\n \\n Imag_uncs = np.array([0.02, 0.04, 0.02, np.nan, 0.03, 0.02,\\n np.nan, 0.06, 0.02, 0.02, 0.02, 0.03,\\n 0.05, np.nan, 0.06, np.nan])\\n \\n mymjds = np.hstack([mjds, mjds, mjds, mjds])\\n mymags = np.hstack([Bmags, Vmags, Rmags, Imags])\\n myemags = np.hstack([Bmag_uncs, Vmag_uncs, Rmag_uncs, Imag_uncs])\\n myfilts = np.hstack([ np.repeat(\\\"B\\\", len(Bmags)),\\n np.repeat(\\\"V\\\", len(Bmags)),\\n np.repeat(\\\"R\\\", len(Rmags)),\\n np.repeat(\\\"I\\\", len(Imags)) ])\\n ix = ~np.isnan(mymags)\\n tb = pd.DataFrame({'mjd': mymjds[ix],\\n 'mag': mymags[ix],\\n 'emag': myemags[ix],\\n \\\"filter\\\": myfilts[ix]})\\n \\n ixB = tb['filter'].values==\\\"B\\\"\\n ixV = tb['filter'].values==\\\"V\\\"\\n ixR = tb['filter'].values==\\\"R\\\"\\n ixI = tb['filter'].values==\\\"I\\\"\\n \\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'].values[ixB] = 4359\\n tb['wave'].values[ixV] = 5430\\n tb['wave'].values[ixR] = 6349\\n tb['wave'].values[ixI] = 8797\\n \\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\\n if colorplt==False:\\n return tb\\n else:\\n tb = add_datecol(tb)\\n ix = np.in1d(tb[\\\"filter\\\"].values, np.array(['B', 'R', 'I']))\\n tb = tb[ix]\\n\\n dates = get_date_span(tb)\\n datesave = []\\n for i in range(len(dates)):\\n x = dates[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n if len(tbsub)!=0:\\n flts = tbsub['filter'].values\\n if \\\"R\\\" in flts and np.sum(np.unique(flts))!=1:\\n datesave.append(x)\\n datesave = np.array(datesave)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"B\\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"R\\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"I\\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"mjd\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = gtb[\\\"emag\\\"].values\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"mjd\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = rtb[\\\"emag\\\"].values\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n if len(itb)!=0:\\n imjds = itb[\\\"mjd\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = itb[\\\"emag\\\"].values\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n if len(gtb)!=0 and len(rtb)!=0:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"BmR\\\")\\n if len(rtb)!=0 and len(itb)!=0:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"RmI\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"mjd\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def bessel_wave(freq, alpha, beta):\\n return FMFilter(SineWave(freq), SineWave(alpha), beta)\",\n \"def bandname(self):\\n return self._properties[\\\"bandname\\\"]\",\n \"def get_sn2019bkc(colorplt = False):\\n ebv = 0.06 # SFD2011\\n z = 0.020\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n \\n tb = _read_2019bkc_band()\\n \\n tb['mag0'] = tb['mag'].values- extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'].values - dis_mod\\n \\n tb['tmax_rf'] = tb['Phase'].values / (1+z)\\n\\n if colorplt==False:\\n return tb\\n \\n else:\\n #tb = add_datecol(tb)\\n tb['date'] = np.floor(tb['tmax_rf'].values)\\n datesave = np.array(tb['date'].values)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"g\\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"r\\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"i\\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"tmax_rf\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = np.ones(len(gtb)) * 0.1\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n else:\\n gmag = 0\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"tmax_rf\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = np.ones(len(rtb)) * 0.1\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n else:\\n rmag = 0\\n if len(itb)!=0:\\n imjds = itb[\\\"tmax_rf\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = np.ones(len(itb)) * 0.1\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n else:\\n imag = 0\\n if gmag and rmag:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"gmr\\\")\\n if rmag and imag:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"rmi\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"tmax_rf\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def plotblackbody(_zband, _yband, _jband, _hband, _kband, _parallax, _perr):\\n # Set pyplot style to be consistent within the program\\n plt.style.use('seaborn-whitegrid')\\n # Import raw data to plot Hertzsprung-Russell diagram\\n _hrdata = inithr('hr.dat')\\n # Determine distance in parsecs\\n _distance = 1 / np.tan(_parallax * 10**-3)\\n _derr = (_perr * 10**-3) / ((_parallax * 10**-3)**2)\\n # Create single data array with all bands\\n _bands = [_zband, _yband, _jband, _hband, _kband]\\n _lambda = [0.9, 1.02, 1.22, 1.63, 2.2]\\n # Set up empty arrays for each star\\n _largestar = np.zeros((1, 2))\\n _smallstar = np.zeros((1, 2))\\n\\n # Determine the spectral flux density from the large star\\n i = 0\\n while i < 5:\\n # Determine the maximum and minimum values of the observed band\\n _max, _min = lightcurve.maxminvals(_bands[i])\\n # The large star uses the maximum flux value (smallest magnitude)\\n _largestar = np.append(_largestar, np.array([_lambda[i], (magtoflux(_min, i))], ndmin=2), axis=0)\\n i += 1\\n # Delete first empty row of the array\\n _largestar = np.delete(_largestar, 0, axis=0)\\n\\n # Determine the spectral flux density from the small star\\n i = 0\\n while i < 5:\\n # Determine the maximum and minimum values of the observed band\\n _max, _min = lightcurve.maxminvals(_bands[i])\\n # Smaller star flux value is combined value minus the large star\\n _smallstar = np.append(_smallstar, np.array([_lambda[i], (magtoflux(_max, i) -\\n magtoflux(_min, i))], ndmin=2), axis=0)\\n i += 1\\n # Delete the first empty row of the array\\n _smallstar = np.delete(_smallstar, 0, axis=0)\\n\\n # Determine the luminosity and effective temperature of each star\\n _luma, _lumaerr, _wiena = getwientemp(_largestar, _distance, _derr, 1)\\n _lumb, _lumberr, _wienb = getwientemp(_smallstar, _distance, _derr, 2)\\n\\n # Calculate luminosities in solar units\\n _solluma = _luma / (3.828*10**26)\\n _sollumb = _lumb / (3.828*10**26)\\n _lumaerr = _lumaerr / (3.828*10**26)\\n _lumberr = _lumberr / (3.828*10**26)\\n\\n # Calculate masses using the mass/luminosity relation in solar mass units\\n # N.B. only works as an approximation for main sequence stars, giants and dwarfs are not sutiable for this\\n # approximation\\n _solmassa = np.power(_solluma, 1/3.5)\\n _solmassaerr = ((_solmassa * (1/3.5) * _lumaerr) / _solluma)**2\\n _solmassb = np.power(_sollumb, 1/3.5)\\n _solmassberr = ((_solmassb * (1 / 3.5) * _lumberr) / _sollumb) ** 2\\n\\n # Calculate stellar radius in solar radii using the relationship between luminosity, surface area and temperature\\n _solrada = np.sqrt(_solluma / np.power(_wiena / 5778, 4))\\n _solradb = np.sqrt(_sollumb / np.power(_wienb / 5778, 4))\\n _solradaerr = ((_solrada * 0.5 * _lumaerr) / _solluma)**2\\n _solradberr = ((_solradb * 0.5 * _lumberr) / _sollumb)**2\\n\\n # Output determined values to the screen and write to file\\n print('Values for the large star:')\\n print('Effective temperature: ' + str(round_sig(_wiena)))\\n print('Solar luminosities: ' + str(round_sig(_solluma)) + ', error: ' + str(round_sig(_lumaerr)))\\n print('Solar radii: ' + str(round_sig(_solrada)) + ', error: ' + str(round_sig(_solradaerr)))\\n print('Solar masses: ' + str(round_sig(_solmassa)) + ', error: ' + str(round_sig(_solmassaerr)))\\n print('-----------------------------------------------------')\\n print('Values for the small star:')\\n print('Effective temperature: ' + str(round_sig(_wienb)))\\n print('Solar luminosities: ' + str(round_sig(_sollumb)) + ', error: ' + str(round_sig(_lumberr)))\\n print('Solar radii: ' + str(round_sig(_solradb)) + ', error: ' + str(round_sig(_solradberr)))\\n print('Solar masses: ' + str(round_sig(_solmassb)) + ', error: ' + str(round_sig(_solmassberr)))\\n\\n # Convert from luminosity to magnitude in solar units\\n _luma = -2.5 * np.log10(_luma / (3.0128 * 10**28))\\n _lumb = -2.5 * np.log10(_lumb / (3.0128 * 10**28))\\n\\n # Plot Hertzsprung-Russell diagram using provided array\\n plt.scatter(_hrdata[:, 1], _hrdata[:, 0], s=0.5)\\n # Plot determined values for each star\\n plt.scatter(_wiena, _luma, s=16, c='red', label='Larger Star')\\n plt.scatter(_wienb, _lumb, s=16, c='green', label='Smaller Star')\\n # Set the x and y axis limits to sensible values\\n plt.legend()\\n plt.xlim(3000, 10000)\\n plt.ylim(-10, 20)\\n # Invert both axes as convention\\n plt.gca().invert_xaxis()\\n plt.gca().invert_yaxis()\\n # Save figure to current folder\\n plt.savefig('hr.png')\\n # Display to screen\\n plt.show()\",\n \"def format_data(self, bands, branches):\\n self.new_dict = self.__gen_dict\\n order = [] # list to store high-symmetry point\\n band_index = {} # dict to store band index info corresponding to its high-symmetry point e.g. \\\"X\\\": 18\\n formatted_bands = []\\n zero_matrix = np.zeros(np.shape(bands))\\n \\\"\\\"\\\"\\n zero_matrix is for: if one configuration does not have some high-symmetry points listed in __generic_dict\\n then fill zeros in those columns \\n \\\"\\\"\\\"\\n\\n for i in range(len(branches)):\\n order.append(branches[i][\\\"name\\\"])\\n spilt = re.split('-', order[i])\\n\\n band_index[spilt[0]] = branches[i]['start_index']\\n band_index[spilt[1]] = branches[i]['end_index']\\n\\n # print('>>>>>>>>>>>>>>>>>>', band_index)\\n # iterate all keys in band_index, and if exists, give value to new_dict, if not, pass\\n for hs_point in band_index:\\n if hs_point in self.new_dict:\\n self.new_dict[hs_point] = band_index[hs_point]\\n # print('>>>>>>>>>>>>>>>>>', BandsData.__gen_dict)\\n\\n # iterate all keys in new_dict, export bands (not arranged in bands dimension)\\n for hs_point in self.new_dict:\\n hs_value = self.new_dict[hs_point]\\n if self.new_dict[hs_point] is None:\\n # fill zeros in bands\\n formatted_bands.append(zero_matrix[:, 0])\\n else:\\n formatted_bands.append(bands[:, hs_value])\\n\\n # transpose of formatted_bands\\n formatted_bands = np.transpose(formatted_bands)\\n\\n return formatted_bands, self.new_dict\",\n \"def calcBW(t,g,bw):\\n if type(g) is not np.array:\\n g = np.array(g)\\n if bw>0.0:\\n bw*=-1.0\\n maxg = np.max(g)\\n g = g-maxg\\n #plt.figure('testbw')\\n \\n imax = np.argmax(g)\\n mb = np.where(g>3.0*bw)\\n # ...left\\n gg = g[mb[0][0]:imax]\\n tt = t[mb[0][0]:imax]\\n #plt.plot(tt,gg)\\n ha1 = np.interp(bw,gg,tt)\\n # ...right\\n gg = np.flipud(g[imax:mb[0][-1]])\\n tt = np.flipud(t[imax:mb[0][-1]])\\n ha2 = np.interp(bw,gg,tt)\\n #plt.plot(tt,gg)\\n # ...full\\n FWHM = abs(ha1) + abs(ha2)\\n #print 'bw: ',ha1,ha2\\n return FWHM\",\n \"def stack_bands(yr, roi):\\n\\n water_year_start = '{}-10-01'.format(yr - 1)\\n\\n winter_s, winter_e = '{}-01-01'.format(yr), '{}-03-01'.format(yr),\\n spring_s, spring_e = '{}-03-01'.format(yr), '{}-05-01'.format(yr),\\n late_spring_s, late_spring_e = '{}-05-01'.format(yr), '{}-07-01'.format(yr)\\n summer_s, summer_e = '{}-07-01'.format(yr), '{}-09-01'.format(yr)\\n fall_s, fall_e = '{}-09-01'.format(yr), '{}-12-31'.format(yr)\\n\\n periods = [('cy', winter_s, fall_e),\\n ('1', spring_s, spring_e),\\n ('2', late_spring_s, late_spring_e),\\n ('3', summer_s, summer_e),\\n ('4', fall_s, fall_e)]\\n\\n first = True\\n for name, start, end in periods:\\n bands = landsat_composites(yr, start, end, roi, name)\\n if first:\\n input_bands = bands\\n proj = bands.select('B2_cy').projection().getInfo()\\n first = False\\n else:\\n input_bands = input_bands.addBands(bands)\\n\\n for s, e, n in [(spring_s, spring_e, 'espr'),\\n (late_spring_s, late_spring_e, 'lspr'),\\n (summer_s, summer_e, 'smr'),\\n (fall_s, fall_e, 'fl'),\\n (water_year_start, spring_e, 'wy_espr'),\\n (water_year_start, late_spring_e, 'wy_espr'),\\n (water_year_start, summer_e, 'wy_smr'),\\n (water_year_start, fall_e, 'wy')]:\\n gridmet = ee.ImageCollection(\\\"IDAHO_EPSCOR/GRIDMET\\\").filterBounds(\\n roi).filterDate(s, e).select('pr', 'eto', 'tmmn', 'tmmx')\\n temp_reducer = ee.Reducer.mean()\\n t_names = ['tmax'.format(n), 'tmin'.format(n)]\\n temp_perc = gridmet.select('tmmn', 'tmmx').reduce(temp_reducer).rename(t_names).resample(\\n 'bilinear').reproject(crs=proj['crs'], scale=30)\\n\\n precip_reducer = ee.Reducer.sum()\\n precip_sum = gridmet.select('pr', 'eto').reduce(precip_reducer).rename(\\n 'precip_total_{}'.format(n), 'pet_total_{}'.format(n)).resample('bilinear').reproject(crs=proj['crs'],\\n scale=30)\\n wd_estimate = precip_sum.select('precip_total_{}'.format(n)).subtract(precip_sum.select(\\n 'pet_total_{}'.format(n))).rename('wd_est_{}'.format(n))\\n input_bands = input_bands.addBands([temp_perc, precip_sum, wd_estimate])\\n\\n temp_reducer = ee.Reducer.percentile([10, 50, 90])\\n t_names = ['tmmn_p10_cy', 'tmmn_p50_cy', 'tmmn_p90_cy', 'tmmx_p10_cy', 'tmmx_p50_cy', 'tmmx_p90_cy']\\n temp_perc = gridmet.select('tmmn', 'tmmx').reduce(temp_reducer).rename(t_names).resample(\\n 'bilinear').reproject(crs=proj['crs'], scale=30)\\n\\n precip_reducer = ee.Reducer.sum()\\n precip_sum = gridmet.select('pr', 'eto').reduce(precip_reducer).rename(\\n 'precip_total_cy', 'pet_total_cy').resample('bilinear').reproject(crs=proj['crs'], scale=30)\\n wd_estimate = precip_sum.select('precip_total_cy').subtract(precip_sum.select(\\n 'pet_total_cy')).rename('wd_est_cy')\\n\\n coords = ee.Image.pixelLonLat().rename(['Lon_GCS', 'LAT_GCS']).resample('bilinear').reproject(crs=proj['crs'],\\n scale=30)\\n ned = ee.Image('USGS/NED')\\n terrain = ee.Terrain.products(ned).select('elevation', 'slope', 'aspect').reduceResolution(\\n ee.Reducer.mean()).reproject(crs=proj['crs'], scale=30)\\n\\n world_climate = get_world_climate(proj=proj)\\n elev = terrain.select('elevation')\\n tpi_1250 = elev.subtract(elev.focal_mean(1250, 'circle', 'meters')).add(0.5).rename('tpi_1250')\\n tpi_250 = elev.subtract(elev.focal_mean(250, 'circle', 'meters')).add(0.5).rename('tpi_250')\\n tpi_150 = elev.subtract(elev.focal_mean(150, 'circle', 'meters')).add(0.5).rename('tpi_150')\\n static_input_bands = coords.addBands([temp_perc, wd_estimate, terrain, tpi_1250, tpi_250, tpi_150, world_climate])\\n\\n nlcd = ee.Image('USGS/NLCD/NLCD2011').select('landcover').reproject(crs=proj['crs'], scale=30).rename('nlcd')\\n\\n cdl_cult = ee.Image('USDA/NASS/CDL/2017').select('cultivated'). \\\\\\n remap([1, 2], [0, 1]).reproject(crs=proj['crs'], scale=30).rename('cdlclt')\\n\\n cdl_crop = ee.Image('USDA/NASS/CDL/2017').select('cropland').reproject(crs=proj['crs'],\\n scale=30).rename('cdlcrp')\\n\\n gsw = ee.Image('JRC/GSW1_0/GlobalSurfaceWater')\\n occ_pos = gsw.select('occurrence').gt(0)\\n water = occ_pos.unmask(0).rename('gsw')\\n\\n static_input_bands = static_input_bands.addBands([nlcd, cdl_cult, cdl_crop, water])\\n\\n input_bands = input_bands.addBands(static_input_bands).clip(roi)\\n\\n # standardize names to match EE javascript output\\n standard_names = []\\n temp_ct = 1\\n prec_ct = 1\\n names = input_bands.bandNames().getInfo()\\n for name in names:\\n if 'tavg' in name and 'tavg' in standard_names:\\n standard_names.append('tavg_{}'.format(temp_ct))\\n temp_ct += 1\\n elif 'prec' in name and 'prec' in standard_names:\\n standard_names.append('prec_{}'.format(prec_ct))\\n prec_ct += 1\\n else:\\n standard_names.append(name)\\n\\n input_bands = input_bands.rename(standard_names)\\n return input_bands\",\n \"def condensate_belowdew(Rs, Rv, Rsi, Rvi, Bo, Bg, Np, Gp):\\n Btg = ((Bg * (1 - (Rs * Rvi))) + (Bo * (Rvi - Rv))) / (1 - (Rv * Rs)) # in RB/STB\\n Bto = ((Bo * (1 - (Rv * Rsi))) + (Bg * (Rsi - Rs))) / (1 - (Rv * Rs)) # in RB/scf\\n\\n Gi = 0\\n F = (Np * ((Bo - (Rs * Bg)) / (1 - (Rv * Rs)))) + ((Gp - Gi) * ((Bg - (Rv * Bo)) / (1 - (Rv * Rs))))\\n Eg = Btg - Bg[0]\\n return(F, Eg)\",\n \"def build_steer_bands(im, freq_resps, numlevels, numorientations):\\n \\n dims = im.shape\\n bands = []\\n pyr = []\\n fourier = np.fft.fftshift(np.fft.fft2(im))\\n \\n freq_resp_hi = freq_resps[0] \\n hi = np.fft.ifft2(np.fft.fftshift(np.multiply(fourier, freq_resp_hi))).real\\n \\n freq_resp_lo = freq_resps[2] \\n lo = np.fft.ifft2(np.fft.fftshift(np.multiply(fourier, freq_resp_lo))).real\\n \\n freq_resp_bands = freq_resps[1] \\n for i in range(numlevels):\\n for j in range(numorientations):\\n freq_respband = freq_resp_bands[i][j]\\n ifourier_band = np.fft.ifft2(np.fft.fftshift(np.multiply(fourier, freq_respband))).real\\n bands.append(ifourier_band)\\n bands = np.reshape(bands, [numlevels, numorientations, dims[0], dims[1]])\\n \\n pyr.append(hi)\\n pyr.append(bands)\\n pyr.append(lo)\\n return pyr\",\n \"def warm_region_cal(audio_samples, fs):\\n #window the audio\\n windowed_samples = timbral_util.window_audio(audio_samples)\\n\\n # need to define a function for the roughness stimuli, emphasising the 20 - 40 region (of the bark scale)\\n min_bark_band = 10\\n max_bark_band = 40\\n mean_bark_band = (min_bark_band + max_bark_band) / 2.0\\n array = np.arange(min_bark_band, max_bark_band)\\n x = timbral_util.normal_dist(array, theta=0.01, mean=mean_bark_band)\\n x -= np.min(x)\\n x /= np.max(x)\\n\\n wr_array = np.zeros(240)\\n wr_array[min_bark_band:max_bark_band] = x\\n\\n # need to define a second array emphasising the 20 - 40 region (of the bark scale)\\n min_bark_band = 80\\n max_bark_band = 240\\n mean_bark_band = (min_bark_band + max_bark_band) / 2.0\\n array = np.arange(min_bark_band, max_bark_band)\\n x = timbral_util.normal_dist(array, theta=0.01, mean=mean_bark_band)\\n x -= np.min(x)\\n x /= np.max(x)\\n\\n hf_array = np.zeros(240)\\n hf_array[min_bark_band:max_bark_band] = x\\n\\n windowed_loud_spec = []\\n windowed_rms = []\\n\\n wr_vals = []\\n hf_vals = []\\n\\n for i in range(windowed_samples.shape[0]):\\n samples = windowed_samples[i, :]\\n N_entire, N_single = timbral_util.specific_loudness(samples, Pref=100.0, fs=fs, Mod=0)\\n\\n # append the loudness spec\\n windowed_loud_spec.append(N_single)\\n windowed_rms.append(np.sqrt(np.mean(samples * samples)))\\n\\n wr_vals.append(np.sum(wr_array * N_single))\\n hf_vals.append(np.sum(hf_array * N_single))\\n\\n mean_wr = np.mean(wr_vals)\\n mean_hf = np.mean(hf_vals)\\n weighted_wr = np.average(wr_vals, weights=windowed_rms)\\n weighted_hf = np.average(hf_vals, weights=windowed_rms)\\n\\n return mean_wr, weighted_wr, mean_hf, weighted_hf\",\n \"def read_frequencyBand(self):\\n # PROTECTED REGION ID(CbfSubarray.frequencyBand_read) ENABLED START #\\n return self._frequency_band\\n # PROTECTED REGION END # // CbfSubarray.frequencyBand_read\",\n \"def get_vrt_band_list():\\n logger.debug('get_vrt_band_list() called')\\n vrt_band_list = []\\n#===============================================================================\\n# sensor_dict = self.bands[tile_type_id][(dataset_info['satellite_tag'], dataset_info['sensor_name'])]\\n# # log_multiline(logger.debug, sensor, 'Sensor', '\\\\t')\\n# for file_number in sorted(sensor_dict.keys()):\\n# band_info = sensor_dict[file_number]\\n# if band_info['level_name'] == 'NBAR':\\n# dataset_dir = dataset_info['nbar_dataset_path']\\n# dataset_id = dataset_info['nbar_dataset_id']\\n# processing_level = dataset_info['nbar_level_name']\\n# nodata_value = dataset_info['nbar_nodata_value']\\n# resampling_method = dataset_info['nbar_resampling_method']\\n# elif band_info['level_name'] == 'ORTHO':\\n# dataset_dir = dataset_info['l1t_dataset_path']\\n# dataset_id = dataset_info['l1t_dataset_id']\\n# processing_level = dataset_info['l1t_level_name']\\n# nodata_value = dataset_info['l1t_nodata_value']\\n# resampling_method = dataset_info['l1t_resampling_method']\\n# else:\\n# continue # Ignore any pan-chromatic and derived bands\\n# \\n# dataset_dir = os.path.join(dataset_dir, 'scene01')\\n# filename = find_file(dataset_dir, band_info['file_pattern'])\\n# vrt_band_list.append({'file_number': band_info['file_number'], \\n# 'filename': filename, \\n# 'name': band_info['band_name'],\\n# 'dataset_id': dataset_id,\\n# 'band_id': band_info['band_id'],\\n# 'processing_level': processing_level,\\n# 'nodata_value': nodata_value,\\n# 'resampling_method': resampling_method,\\n# 'tile_layer': band_info['tile_layer']})\\n#===============================================================================\\n \\n #TODO: Make this able to handle multiple derived layers\\n for band_level in ['FC']:\\n derived_bands = self.bands[tile_type_id][('DERIVED', band_level)]\\n for file_number in sorted(derived_bands.keys()):\\n band_info = derived_bands[file_number]\\n file_pattern = band_info['file_pattern']\\n dataset_dir = os.path.join(dataset_info['fc_dataset_path'], 'scene01')\\n dataset_id = dataset_info['fc_dataset_id']\\n filename = find_file(dataset_dir, file_pattern) \\n processing_level = dataset_info['fc_level_name']\\n nodata_value = dataset_info['fc_nodata_value'] # Should be None for FC\\n resampling_method = dataset_info['fc_resampling_method']\\n vrt_band_list.append({'file_number': None, \\n 'filename': filename, \\n 'name': band_info['band_name'],\\n 'dataset_id': dataset_id,\\n 'band_id': band_info['band_id'],\\n 'processing_level': processing_level,\\n 'nodata_value': nodata_value,\\n 'resampling_method': resampling_method,\\n 'tile_layer': 1})\\n \\n log_multiline(logger.debug, vrt_band_list, 'vrt_band_list = %s', '\\\\t')\\n return vrt_band_list\",\n \"def _compute_bn(self, lvl):\\n bn = [0] # number of samples crossing the left/right boundary\\n for n in range(lvl):\\n # 1. down-sampling of N samples by the factor scl gives (N-1)//scl + 1 samples\\n # 2. bn[-1]+M-1 is the number of samples acrossing the left/right boundary, with M being the number of freqeuncies\\n # => hence after the downsampling the number of boundary crossing samples is:\\n bn.append((bn[-1]+self.nfreq-2)//self.scaling+1)\\n bn.append(bn[-1]) # repeat the value of the coarsest scale for the approximation coefficient\\n return bn[1:][::-1]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6755474","0.6549916","0.6544884","0.62848055","0.6246133","0.62358135","0.62323934","0.61631835","0.61161864","0.6114344","0.608871","0.60167605","0.60042655","0.5974235","0.5936892","0.5935502","0.5911872","0.5887876","0.585313","0.58207893","0.58174056","0.5815267","0.5802429","0.5756662","0.5756579","0.5733492","0.57154846","0.5715437","0.5714548","0.5702648","0.5702058","0.5690709","0.5653745","0.562689","0.56163454","0.5611027","0.5609256","0.55894625","0.5588307","0.5551808","0.55507165","0.5528231","0.5516566","0.5515716","0.55116254","0.55085033","0.55077606","0.55046356","0.5503404","0.5480206","0.54792947","0.5476577","0.54737574","0.54636186","0.54603356","0.54345405","0.5431887","0.5431156","0.5430059","0.5423577","0.5422287","0.5408444","0.5402244","0.5402054","0.53809774","0.5371096","0.53605694","0.53565645","0.535359","0.5338913","0.5338383","0.5324359","0.5324325","0.53182113","0.53119546","0.53099716","0.53023446","0.52857065","0.5279766","0.5271832","0.52660626","0.5250865","0.5247835","0.52326006","0.52156097","0.52149683","0.5212051","0.52104974","0.5208776","0.5197606","0.5195176","0.51829314","0.5182901","0.5173974","0.51728","0.5168027","0.51659226","0.5156537","0.5145811","0.514141"],"string":"[\n \"0.6755474\",\n \"0.6549916\",\n \"0.6544884\",\n \"0.62848055\",\n \"0.6246133\",\n \"0.62358135\",\n \"0.62323934\",\n \"0.61631835\",\n \"0.61161864\",\n \"0.6114344\",\n \"0.608871\",\n \"0.60167605\",\n \"0.60042655\",\n \"0.5974235\",\n \"0.5936892\",\n \"0.5935502\",\n \"0.5911872\",\n \"0.5887876\",\n \"0.585313\",\n \"0.58207893\",\n \"0.58174056\",\n \"0.5815267\",\n \"0.5802429\",\n \"0.5756662\",\n \"0.5756579\",\n \"0.5733492\",\n \"0.57154846\",\n \"0.5715437\",\n \"0.5714548\",\n \"0.5702648\",\n \"0.5702058\",\n \"0.5690709\",\n \"0.5653745\",\n \"0.562689\",\n \"0.56163454\",\n \"0.5611027\",\n \"0.5609256\",\n \"0.55894625\",\n \"0.5588307\",\n \"0.5551808\",\n \"0.55507165\",\n \"0.5528231\",\n \"0.5516566\",\n \"0.5515716\",\n \"0.55116254\",\n \"0.55085033\",\n \"0.55077606\",\n \"0.55046356\",\n \"0.5503404\",\n \"0.5480206\",\n \"0.54792947\",\n \"0.5476577\",\n \"0.54737574\",\n \"0.54636186\",\n \"0.54603356\",\n \"0.54345405\",\n \"0.5431887\",\n \"0.5431156\",\n \"0.5430059\",\n \"0.5423577\",\n \"0.5422287\",\n \"0.5408444\",\n \"0.5402244\",\n \"0.5402054\",\n \"0.53809774\",\n \"0.5371096\",\n \"0.53605694\",\n \"0.53565645\",\n \"0.535359\",\n \"0.5338913\",\n \"0.5338383\",\n \"0.5324359\",\n \"0.5324325\",\n \"0.53182113\",\n \"0.53119546\",\n \"0.53099716\",\n \"0.53023446\",\n \"0.52857065\",\n \"0.5279766\",\n \"0.5271832\",\n \"0.52660626\",\n \"0.5250865\",\n \"0.5247835\",\n \"0.52326006\",\n \"0.52156097\",\n \"0.52149683\",\n \"0.5212051\",\n \"0.52104974\",\n \"0.5208776\",\n \"0.5197606\",\n \"0.5195176\",\n \"0.51829314\",\n \"0.5182901\",\n \"0.5173974\",\n \"0.51728\",\n \"0.5168027\",\n \"0.51659226\",\n \"0.5156537\",\n \"0.5145811\",\n \"0.514141\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71052694"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":76496,"cells":{"query":{"kind":"string","value":"Create Gaussian filter template normalized to the max."},"document":{"kind":"string","value":"def gaussian_template(\n wavelengths: np.ndarray,\n mean: Union[float, np.ndarray],\n std: Union[float, np.ndarray] = 30.0,\n) -> np.ndarray:\n y = norm.pdf(wavelengths, mean, std)\n return y / np.max(y, axis=-1, keepdims=True)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_gaussian_filter(size, sigma):\n h = size[0] #height of the template\n w = size[1] #width of the template \n if h % 2 == 0: h += 1 #add 1 if dimensions are even\n if w % 2 == 0: w += 1\n x = math.floor(h/2)\n y = math.floor(w/2) \n sum = 0\n #create our template\n template = np.zeros((h,w))\n #fill the template in with the numbers from Gaussian distribution\n for i in range(h):\n for j in range(w):\n template[i,j] = math.exp(-((((j-x)**2)+((i-y)**2))/(2*(sigma**2))))\n sum = sum + template[i,j]\n #normalise the numbers\n gaussian_filter = template/sum\n return gaussian_filter","def Gauss_filter(data, sigma=(0,2,2), mode='wrap'): \n import scipy.ndimage.filters as flt\n return flt.gaussian_filter(data, sigma=sigma, mode=mode)","def gaussian_filter(x):\n return _gaussian_filter(x, 3)","def create_gaussian_array(self):\n\n # Fill array of size l x w with Gaussian Noise.\n terrain_length = int(ceil(self.length/self.resolution))\n terrain_width = int(ceil(self.width/self.resolution))\n gaussian_array = np.random.normal(self.mu, self.sigma, (terrain_length,terrain_width))\n\n # Filter the array to smoothen the variation of the noise\n gaussian_array = gaussian_filter(gaussian_array, self.sigma_filter)\n\n return gaussian_array","def generate_gaussian():\n amp = 10 * numpy.random.chisquare(3)\n width = numpy.random.chisquare(3)\n mean = numpy.random.uniform(-10 + width, 10 - width)\n x = numpy.linspace(-10, 10, 500)\n y = amp * numpy.exp(- (x - mean) ** 2 / width ** 2)\n add_noise(y, 0.1)\n return x, y","def apply_gaussian(X, sigma):\n return np.array([ndimage.gaussian_filter(x, sigma) for x in X])","def gaussian_filter(stddev, array):\n\n return astropy.convolution.convolve(\n array, astropy.convolution.Gaussian2DKernel(stddev))","def fake_gaussian(img, vertical_horizontal_sigma, iter=3):\n sigma_vertical, sigma_horizontal = vertical_horizontal_sigma\n h_blured = box_filter1d(img, sigma_horizontal, horizontal=True, iter=iter)\n blured = box_filter1d(h_blured, sigma_vertical, horizontal=False, iter=iter)\n return blured","def gaussian(amp, fwhm, mean, x):\n return amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)","def _gaussian_distribution(self, x: ndarray, mu: float, sigma: float) -> ndarray:\n return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(\n -np.power(\n (x - mu) / sigma, 2) / 2)","def make_gaussian(shape, var):\n h,w = shape\n x = np.arange(w, dtype=float)\n y = np.arange(h, dtype=float)[:,np.newaxis]\n x0 = w // 2\n y0 = h // 2\n mat = np.exp(-0.5 * (pow(x-x0, 2) + pow(y-y0, 2)) / var)\n normalized_img = np.zeros((h, w))\n cv2.normalize(mat, normalized_img, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)\n return normalized_img","def gaussian_filter(size,sigma=-1):\n\n if sigma == -1:\n sigma = np.sqrt(size)\n\n filter = np.zeros((size,size))\n\n for i,j in it.product(range(size),range(size)):\n x = j-size//2\n y = i-size//2\n filter[i,j] = 1/(2*np.pi*sigma**2) * np.exp(-(x**2+y**2)/(2*sigma**2))\n\n filter = filter/filter[0,0]\n filter = filter/filter.sum()\n\n return filter","def gaussian_filter(self, sigma):\n\n mask = self.get_weighted_mask()\n mask_f = ni.gaussian_filter(mask, sigma=sigma)\n\n return SpatialReceptiveField(mask_f, self.altPos, self.aziPos, sign=self.sign,\n temporalWindow=self.temporalWindow, pixelSizeUnit=self.pixelSizeUnit,\n dataType=self.dataType, thr=self.thr, filter_sigma=sigma,\n interpolate_rate=self.interpolate_rate)","def gauss(self, X, xm, amp, w):\n return amp * np.exp(-((X - xm) / w) ** 2)","def _FSpecialGauss(size, sigma):\n radius = size // 2\n offset = 0.0\n start, stop = -radius, radius + 1\n if size % 2 == 0:\n offset = 0.5\n stop -= 1\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\n assert len(x) == size\n g = np.exp(-((x**2 + y**2)/(2.0 * sigma**2)))\n return g / g.sum()","def gaussian(t, params):\n DeprecationWarning(\"Using standard width. Better use gaussian_sigma.\")\n params['sigma'] = Qty(\n value=params['t_final'].get_value()/6,\n min_val=params['t_final'].get_value()/8,\n max_val=params['t_final'].get_value()/4,\n unit=params['t_final'].unit\n )\n return gaussian_sigma(t, params)","def _f_special_gauss(size, sigma):\n radius = size // 2\n offset = 0.0\n start, stop = -radius, radius + 1\n if size % 2 == 0:\n offset = 0.5\n stop -= 1\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\n assert len(x) == size\n g = np.exp(-((x ** 2 + y ** 2) / (2.0 * sigma ** 2)))\n g /= g.sum()\n return tf.constant(g, dtype=tf.float32)","def gaussian(amp, fwhm, mean):\n return lambda x: amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)","def _FWHMGauss(sigma, pixel=12):\n return sigma*2*np.sqrt(2*np.log(2))*pixel","def gauss_smooth(data, sigma):\n\t\t\t# make the kernel 5 sigmas wide in each direction\n\t\t\tkernel = stats.norm.pdf(np.arange(-5*sigma, (5*sigma)+1), scale=sigma)\n\t\t\t\n\t\t\treturn sp.ndimage.convolve1d(data, kernel, axis=2)","def get_mask_gauss(mask_dist_src, sigma):\n return P.Exp()(-2.772588722 * (mask_dist_src ** 2) / (sigma ** 2))","def gaussian_filter(shape=(3,3),sigma=0.5):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma))\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def boxG(x, sigma, output, mode='wrap'):\n f = nd.gaussian_filter if sigma < 5 else box3\n return f(x, sigma, output=output, mode=mode)","def build_filter(n, sigma):\n filter_ = np.zeros((n,n))\n\n begin = n//2\n\n for i in range(n):\n for j in range(n):\n val = ((i-begin)**2 + (j-begin)**2)**0.5\n filter_[i][j] = gaussian(val, sigma)\n\n return filter_","def gaussian_nonorm(t, params):\n # TODO Add zeroes for t>t_final\n t_final = tf.cast(params['t_final'].get_value(), dtype=tf.float64)\n sigma = params['sigma'].get_value()\n gauss = tf.exp(-(t - t_final / 2) ** 2 / (2 * sigma ** 2))\n return gauss","def gaussian_black(z, mu: 'normal' = 0, sigma: (0.4,1) = 0.7):\n return 1/(np.sqrt(2*np.pi)*sigma)*np.exp(-np.power((z - mu)/sigma, 2)/2)","def test_gaussian_filter():\n\n def rgb2gray(rgb):\n r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]\n gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n\n return gray\n\n img = rgb2gray(np.array(Image.open('data/graf.png')))\n gx, x = gauss_module.gauss(4)\n gx = gx.reshape(1, gx.shape[0])\n gy = gx.reshape(gx.shape[1], gx.shape[0])\n smooth_img = conv2(img, gx * np.array(gy))\n\n test_smooth_img = gauss_module.gaussianfilter(img, 4)\n\n assert np.all(smooth_img.round(5) == test_smooth_img.round(5))","def makeGaussian(height, width, sigma=3, center=None):\n x = np.arange(0, width, 1, float)\n y = np.arange(0, height, 1, float)[:, np.newaxis]\n if center is None:\n x0 = width // 2\n y0 = height // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)","def gaussian(x, amp, cen, wid):\n return amp * exp (-(x-cen)**2/(2*wid**2))","def gaussian_blur(self,img):\n return cv2.GaussianBlur(img, (self.kernel_size, self.kernel_size), 0)","def gaussian(x, peak_x=.0, sigma=1.0, name=''):\n x = x.astype(np.float)\n variables = {'function': gaussian, 'peak_x': peak_x, 'sigma': sigma}\n y = np.exp((-1 * (x - peak_x)**2) / (2 * sigma**2))\n return packer(x, y, variables, name=name)","def _fspecial_gauss_1d(self, size, sigma):\n coords = torch.arange(size).to(dtype=torch.float)\n coords -= size // 2\n g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))\n g /= g.sum()\n return g.reshape(-1)","def gaussian_white(z, mu: 'normal' = 0, sigma: (0.4, 1) = 0.7):\n return 1 - gaussian_black(z, mu, sigma)","def gs_blur(self,k,img):\n SIG = self.sigma\n sig = [SIG,k*SIG,k*k*SIG,k*k*k*SIG,k*k*k*k*SIG]\n gsArray = [0,1,2,3,4]\n scaleImages = [0,1,2,3,4]\n \n for i in range(5):\n gsArray[i] = scipy.ndimage.filters.gaussian_filter(img,sig[i])\n\n return gsArray","def model_gauss(xsigma, nx, ny=1, nz=1, ysigma=None, zsigma=None, xcenter=None, ycenter=None, zcenter=None):\n\te = EMData()\n\te.set_size(nx, ny, nz)\n\tif( ysigma == None ) : ysigma = xsigma\n\tif( zsigma == None ) : zsigma = xsigma\n\tif( xcenter == None ) : xcenter = nx//2\n\tif( ycenter == None ) : ycenter = ny//2\n\tif( zcenter == None ) : zcenter = nz//2\n\te.process_inplace(\"testimage.puregaussian\", {\"x_sigma\":xsigma,\"y_sigma\":ysigma,\"z_sigma\":zsigma,\"x_center\":xcenter,\"y_center\":ycenter,\"z_center\":zcenter} )\n\treturn e","def gaussian(dims: Tuple[int, int], cutoff_freq: float) -> np.ndarray:\n # create grid\n m, n = [(dim - 1) / 2 for dim in dims]\n yy, xx = np.ogrid[-m : m + 1, -n : n + 1]\n\n # compute transfer function\n tf = np.exp(-(np.power(xx, 2) + np.power(yy, 2)) / (2 * np.power(cutoff_freq, 2)))\n\n # normalize and return transfer func\n return (tf - np.max(tf)) / (np.max(tf) - np.min(tf))","def gaussian(mu, wid, x):\n return np.exp(-((x - mu) / (0.6005612 * wid))**2)","def _gaussian(self, c, sigma):\n d = 2*sigma*sigma\n ax = exp(-power(self._xx-self._xx.T[c], 2)/d)\n ay = exp(-power(self._yy-self._yy.T[c], 2)/d)\n return (ax * ay).T # the external product gives a matrix","def gaussian(x, amp, wid, cen):\n return amp*np.exp(-(x-cen)**2/(2*wid**2))","def _fspecial_gauss_1d(self, size, sigma):\n coords = torch.arange(size).to(dtype=torch.float)\n coords -= size // 2\n\n g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))\n g /= g.sum()\n\n return g.unsqueeze(0).unsqueeze(0)","def gauss_kernel(n_fwhm,sigma):\n\n x_length = int(n_fwhm * sigma + 0.5) #Add 0.5 to approximate to nearest integer\n y_length = x_length\n \n \n x, y = mgrid[-x_length:x_length+1, -y_length:y_length+1]\n g = numpy.exp(-(x**2/(2*(float(sigma)**2))+y**2/(2*(float(sigma)**2))))\n return g / g.sum()","def smooth_gauss(image, variance=2, kernel_size=(9, 9)):\n return cv2.GaussianBlur(image, kernel_size, variance)","def flat_top_gaussian(a1_val, a2_val, sigma1, sigma2, w1_val, w2_val, w_val):\n gauss1 = a1_val * np.exp(-(w_val - w1_val)**4/(2 * sigma1**2))\n gauss2 = a2_val * np.exp(-(w_val - w2_val)**4/(2 * sigma2**4))\n sum_gauss = gauss1 + gauss2\n return sum_gauss","def Gaussian(x, mu=0, sigma=26.4, A=1, y0=0):\r\n #width = sigma*(2*np.sqrt(2*np.log(2)))\r\n b = 1/(sigma*np.sqrt(2*np.pi))\r\n f = b*np.power(np.e, -(((x-mu)**2)/(2*sigma**2)))\r\n return A*f + y0","def gaussian2d(filter_size=5, sig=1.0):\n ax = np.arange(-filter_size // 2 + 1., filter_size // 2 + 1.)\n xx, yy = np.meshgrid(ax, ax)\n kernel = np.exp(-0.5 * (np.square(xx) + np.square(yy)) / np.square(sig))\n return kernel / np.sum(kernel)","def uni_gaussian(X, mu, sigma2):\n p = (1 / sqrt(2 * pi * sigma2))\n p = p * exp(-power(X - mu, 2) / (2 * sigma2))\n\n def prod(x, y):\n return x * y\n p = array([[reduce(prod, el)] for el in p])\n\n return p","def gaussian(x, mean, sigma):\n return np.exp(-np.square(x-mean)/(2*np.square(sigma))) / (np.sqrt(2*np.pi*sigma**2))","def set_gauss(fwhm):\n\n sigma = float(fwhm) / 2.3548\n\n op_string = \"-kernel gauss %f -fmean -mas \" % sigma + \"%s\"\n\n return op_string","def set_gauss(fwhm):\n\n sigma = float(fwhm) / 2.3548\n\n op_string = \"-kernel gauss %f -fmean -mas \" % sigma + \"%s\"\n\n return op_string","def __gaussian_blur(self, img, kernel_size=3):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)","def gaussian(x, amplitude=1.0, center=0.0, sigma=1.0):\n \n return (amplitude/(np.sqrt(2.*np.pi)*sigma)) * exp(-np.power((1.0*x-center)/(sigma), 2.)/2.)","def reduced_normal_pmf(x: np.array, mean: float, sigma: float) -> np.array:\n x = np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)\n x /= np.sqrt(2 * np.pi * sigma ** 2)\n x[mean] = 0.\n x /= x.sum()\n return x","def gaussian(size,sigma):\n a,b=np.ogrid[-size/2:size/2,-size/2:size/2]\n mask = a**2+b**2\n mask = np.exp(-mask.astype('float')/(2*float(sigma**2)))\n return mask","def makeGaussian(size, fwhm, center=None):\n\n x = sp.arange(0, size, 1, float)\n y = x[:,sp.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return sp.exp(-4*sp.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)","def Gaussian(x, mu, sigma, a):\n amplitude = a / ( sigma * np.sqrt(2 * np.pi) )\n u = (x - mu) / sigma\n return amplitude * np.exp( -0.5 * (u**2) )","def normal_pmf(x: np.array, mean: float, sigma: float) -> np.array:\n x = np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)\n x /= np.sqrt(2 * np.pi * sigma ** 2)\n x /= x.sum()\n return x","def get_gauss_kernel(sigma, samples):\n p = ny.ceil (2*ny.sqrt(2*ny.log(2))*sigma)\n r = ny.linspace(-p, p, samples)\n x,y = ny.meshgrid(r, r)\n b=bivariate_normal(x,y,sigma,sigma)\n A=(1/ny.sum(b))\n B=A*b\n return x,y,B","def gaussian(\n x: float,\n measurement: 'mstats.ValueUncertainty',\n max_sigma: float = 10.0\n) -> float:\n center = measurement.value\n width = measurement.uncertainty\n width = max(width, 1e-6)\n\n if x <= (center - max_sigma * width) or x >= (center + max_sigma * width):\n # Don't calculate values outside a \"reasonable\" 10 sigma range\n return 0.0\n\n coefficient = 1 / math.sqrt(2.0 * math.pi * width * width)\n exponent = -0.5 * ((float(x) - center) ** 2) / (width * width)\n\n return coefficient * math.exp(exponent)","def GaussianKernel(radius, std):\n size = 2 * radius + 1\n weight = torch.ones(size, size)\n weight.requires_grad = False\n for i in range(-radius, radius+1):\n for j in range(-radius, radius+1):\n dis = (i * i) + (j * j)\n weight[i+radius][j+radius] = np.exp(-dis / (2 * std * std))\n weight = weight / weight.sum()\n return weight","def filterf(self):\n from scipy.ndimage.filters import gaussian_filter as gf\n self._obj['u'] = xr.DataArray(gf(self._obj['u'],1),dims=('x','y'))\n self._obj['v'] = xr.DataArray(gf(self._obj['v'],1),dims=('x','y'))\n return self._obj","def gaussian(x, mu, sigma):\n return (np.exp(-(x - mu)**2 / 2.0 / sigma**2) /\n np.sqrt(2.0 * np.pi) / sigma)","def fmgf(array, sigma):\n x, y = np.arange(len(array)), array.copy()\n yg = ndimage.filters.gaussian_filter(y, sigma)\n y -= yg\n\n # digitizing\n m = 101\n dy = 6.0 * mad(y) / m\n ybin = np.arange(np.min(y) - 5 * dy, np.max(y) + 5 * dy + dy, dy)\n z = np.zeros([len(ybin), len(x)])\n z[np.digitize(y, ybin), x] = 1.0\n\n # filtering\n g = partial(ndimage.filters.gaussian_filter, sigma=(0, sigma))\n c = partial(ndimage.filters.convolve1d, weights=np.ones(m), axis=0)\n zf = c(c(c(g(z))))\n\n # estimates\n ym1, y0, yp1 = [ybin[np.argmax(zf, 0) + i] for i in (-1, 0, 1)]\n zm1, z0, zp1 = [zf[np.argmax(zf, 0) + i, x] for i in (-1, 0, 1)]\n t = (zm1 - z0) / (zm1 - 2 * z0 + zp1)\n\n filtered = yg + ((1 - t) ** 2) * ym1 + (2 * t * (1 - t)) * y0 + (t**2) * yp1\n return filtered","def makeGaussian(size, fwhm, sigma, center=None):\n\n x = np.arange(0, size, 1, float)\n y = x[:,np.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n \n #return (np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)) #approximation using exponenial functions\n return ((1/(2*np.pi*sigma**2))*np.exp(-((xx)**2 + (yy)**2)/(2*sigma**2))) # symmetric 2D Gaussian distribution","def gaussian(window_size, sigma):\n gauss = torch.Tensor([math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])\n return gauss/gauss.sum()","def make_gaussian(size, sigma=10, center=None):\n\n x = np.arange(0, size[1], 1, float)\n y = np.arange(0, size[0], 1, float)\n y = y[:, np.newaxis]\n\n if center is None:\n x0 = y0 = size[0] // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)","def Gaussian(x,t,sigma):\n return np.exp(-(x-t)**2/(2*sigma**2))","def fit_gaussian(array):\n\n shape = array.shape\n xmean, ymean = numpy.array(shape) / 2.\n\n xx, yy = numpy.mgrid[:shape[0], :shape[1]]\n\n g_init = astropy.modeling.models.Gaussian2D(amplitude=1., x_mean=xmean, y_mean=ymean,\n x_stddev=1., y_stddev=1.)\n\n f2 = astropy.modeling.fitting.LevMarLSQFitter()\n\n gg = f2(g_init, xx, yy, array)\n\n return gg","def gauss(self, *x, cut=None, rescaled_to_max=False):\n\n mean, std = self._meanStdCut(cut=cut)\n\n if rescaled_to_max: norm = 1\n else: norm = np.sqrt(2*np.pi*(std**2))\n\n gauss = lambda y: (\n np.exp(-((y - mean)**2)/(2*(std**2)))\n /norm)\n\n return np.array(list(map(gauss, x)))","def add_gaussian_noise(image, min_sigma, max_sigma):\r\n sigma = np.random.uniform(min_sigma, max_sigma)\r\n corrupted_im = image + np.random.normal(0, sigma, image.shape)\r\n rounded_im = (np.round(corrupted_im * MAX_PIXEL_NUMBER)) / MAX_PIXEL_NUMBER\r\n return np.clip(rounded_im, 0, 1).astype(np.float64)","def gauss_convolution(im_array, n_fwhm, fwhm) :\n \n sigma = fwhm / (2.*math.sqrt(2.*math.log(2.)))\n\t\n im_kernel_array = gauss_kernel(n_fwhm, sigma)\n conv_image = signal.convolve(im_array,im_kernel_array,mode = 'same')\n\n return (conv_image)","def build_gaussian_frame(normalized_frame, level):\n pyramid = build_gaussian_pyramid(normalized_frame, level)\n gaussian_frame = pyramid[-1]\n return gaussian_frame","def localGaussian(self, region_width, scale=0.95, down=True):\n region_width = boof_fixed_length(region_width)\n\n java_object = pbg.gateway.jvm.boofcv.factory.filter.binary.FactoryThresholdBinary.\\\n localGaussian(region_width,float(scale),down,self.boof_image_type)\n return InputToBinary(java_object)","def gaussian_filter(self, input, win, size):\n\n N, C, H, W = input.shape\n f = F.conv2d(input, win, stride=1, groups=C)\n f = F.conv2d(f, win.transpose(2, 3), stride=1, groups=C)\n\n out = torch.zeros(input.shape)\n out[:, :, self.win_size // 2:self.win_size // 2 * -1, self.win_size // 2:self.win_size // 2 * -1] = f.unsqueeze(\n 0).unsqueeze(0)\n return out\n\n # N, C, H, W = input.shape\n # out = F.conv2d(input, win, stride=1, padding=(0, self.win_size//2), groups=C)\n # out = F.conv2d(out, win.transpose(2, 3), stride=1, padding=(self.win_size//2, 0), groups=C)\n # return out","def Gaussian(x, t, sigma):\n return np.exp(-(x - t)**2 / (2 * sigma**2))","def gaussian(x, sigma):\n try: r = np.exp(-0.5*(x/sigma)**2) \n except: r = np.zeros(len(x))\n return r","def makeGaussian(size, fwhm=3, center=None):\n\n x = np.arange(0, size, 1, float)\n y = x[:, np.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4 * np.log(2) * ((x - x0)**2 + (y - y0)**2) / fwhm**2)","def gaussian_blurring(self,input_image,kernel_size,sigma):\n #Applying Gaussian Blur filter\n output_image=cv2.GaussianBlur(input_image,kernel_size,sigma)\n return output_image","def makeGaussian(size, fwhm = 3, center=None):\n\n x = np.arange(0, size, 1, float)\n y = x[:,np.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)","def makeGaussian(size, fwhm = 3, center=None):\n\n x = np.arange(0, size, 1, float)\n y = x[:,np.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)","def gaussion_smoothing(self,sigma=None):\n print(\"## Gaussian smoothing...\");\n corr_length = self.corr_length\n if sigma is None:\n corr = self.correlation\n oscillation=np.max(np.abs(corr[:-1]-corr[1:]),axis=0)\n peak=np.max(np.abs(corr),axis=0)\n oscillation /= peak\n sigma= corr_length/(5.0*oscillation*len(corr)*self.smooth_tune) # 15.0 has been tuned for many times \n print \"sigma:\"\n print sigma\n for i in np.arange(corr_length):\n self.correlation[i] *= exp(-i*i/(2*sigma*sigma))/(sigma*sqrt(2*pi))","def gaussian(x, x0=0.0, fwhm=1.0, ampl=1.0):\n return ampl * np.exp(-4 * np.log(2) * ((x - x0) / fwhm) ** 2)","def gaussian_blur(self, img):\n kernel_size = self.gaussian_blur_params[\"kernel_size\"]\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)","def _filter_gauss(image: np.array,\n axis: str,\n krnsize: int,\n krnsigma: float):\n assert(axis == 'x' or axis == 'y')\n krn = cv.getGaussianKernel(krnsize, krnsigma)\n krn = krn * krn.T\n krny, krnx = np.gradient(krn)\n\n if axis == 'x':\n return _filter_custom(image, krnx)\n elif axis == 'y':\n return _filter_custom(image, krny)","def differenceOfGausssians(image,sigma0, sigma1,window_size, roi, out = None):\n return (vigra.filters.gaussianSmoothing(image,sigma0,window_size=window_size,roi = roi)-vigra.filters.gaussianSmoothing(image,sigma1,window_size=window_size,roi = roi))","def run_gaussian_smoothing(image, kernel_size=5):\n return cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)","def set_gaussian(self, X, sigma=0):\n sigma = float(sigma)\n if sigma < 0:\n raise ValueError('sigma should be positive')\n self.set_euclidian(X)\n d = self.weights\n\n if sigma == 0:\n sigma = (d ** 2).mean()\n\n w = np.exp(- (d ** 2) / (2 * sigma))\n self.weights = w","def build_gaussian_pyramid(im, max_levels, filter_size):\n filter_vec = create_gaussian_line(filter_size)\n # creating duplicate for confy use\n temp_im = im\n pyr = [im]\n kernel = np.array([0.0625,0.25,0.375,0.25,0.0625])\n kernel = kernel.reshape((1,5))\n for i in range(max_levels - 1):\n # blurring the cur layer\n #temp_im_temp = cv2.filter2D(temp_im,-1,kernel,borderType=cv2.B)\n temp_im = scipy.signal.convolve2d(temp_im, filter_vec, mode='same')\n temp_im = scipy.signal.convolve2d(temp_im, np.transpose(filter_vec), mode='same')\n # sampling only every 2nd row and column\n temp_im = temp_im[::2, ::2]\n pyr.append(temp_im)\n\n return pyr, filter_vec","def fspecial_gauss(size, sigma, channels):\n radius = size // 2\n offset = 0.0\n start, stop = -radius, radius + 1\n if size % 2 == 0:\n offset = 0.5\n stop -= 1\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\n assert len(x) == size\n\n x = x.reshape(x.shape+(1,1))\n x = np.repeat(x, channels, axis=2)\n x = np.repeat(x, channels, axis=3)\n\n y = y.reshape(y.shape+(1,1))\n y = np.repeat(y, channels, axis=2)\n y = np.repeat(y, channels, axis=3) \n\n x = tf.constant(x, dtype=tf.float32)\n y = tf.constant(y, dtype=tf.float32)\n\n g = tf.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n return g / tf.reduce_sum(g)","def gaussianFilter(gain,BT,spSym,nTaps):\n\n a = np.sqrt(np.log(2)/2)/BT\n t = np.linspace(-.5*nTaps,.5*nTaps-1,nTaps)/spSym\n\n ft = np.sqrt(np.pi)/a *np.exp(-(np.pi**2*(t)**2)/a**2)\n ft /= np.sum(ft) * gain # normalize filter\n\n return ft","def build_gaussian_pyramid(im, max_levels, filter_size):\n\n filter_vec = gaus_1d(filter_size).reshape(1,filter_size)\n pyr = []\n pyr.append(im)\n for i in range(max_levels - 1):\n if(im.shape[0] <= 16 or im.shape[1] <= 16):\n break\n\n im = ndimage.filters.convolve(im, filter_vec.T, mode='mirror')\n im = ndimage.filters.convolve(im, filter_vec, mode='mirror')\n\n im = im[::2, ::2]\n pyr.append(im)\n\n return [pyr,filter_vec]","def gaussianBlur(img,ksize=(5,5),sigma=10):\n #kernel = cv2.getGaussianKernel(ksize,sigma)\n dst = np.zeros_like(img)\n cv2.GaussianBlur(src=img,dst=dst,ksize=ksize,sigmaX=0)\n return dst","def gaussian_blur(img, kernel=(3, 3)):\n out = cv2.GaussianBlur(img, kernel, 0)\n return out","def gaussian(x, *parameters):\n position, sigma, amplitude, background = parameters\n return amplitude * np.exp(-(x - position)**2 / (2.0 * sigma**2)) + background","def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\n U, V = fourier_meshgrid(image)\n D = fourier_distance(U, V)\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\n \n if kind == 'high' or kind == 'highpass':\n H = 1.0 - H\n \n return H","def gaussify_bands(self, sigma):\n for key, band in self.bands.items():\n self.gauss_bands[key] = gaussian_filter(input=band, sigma=sigma)","def make_gaussian(size, fwhm=3, center=None):\n\n x = np.arange(0, size, 1, float)\n y = x[:, np.newaxis]\n\n if center is None:\n x0 = y0 = size // 2\n else:\n x0 = center[0]\n y0 = center[1]\n\n k = np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / fwhm ** 2)\n return k / np.sum(k)","def Gaussian_conv(n):\n\n\t# Choose sigma and mu\n\tsigma = 1\n\tmu = 0\n\n\t# Create table\n\tb = sigma * np.random.randn(n) + mu\n\tsum = b.sum()\n\t#b = b/sum\n\tprint(b)\n\treturn b","def GaussianKernel(sigma: float = 1., width: int = 0):\n assert not ((width is None or width == 0) and\n (sigma is None or sigma == 0)), \\\n \"GaussianKernel :: both sigma ({}) & width ({}) are not valid\".format(\n sigma, width)\n\n if width is None or width == 0:\n width = int(2.0 * 3.0 * sigma + 1.0)\n if width % 2 == 0:\n width += 1\n\n if sigma is None or sigma == 0:\n sigma = (width - 1)/6.\n half = width//2\n x, y = np.meshgrid(np.linspace(-half, half, width),\n np.linspace(-half, half, width), indexing='xy')\n w = np.exp(- (x**2 + y**2) / (2.*(sigma**2)))\n w /= np.sum(w)\n return torch.from_numpy(w.astype(np.float32)).view(1, 1, width, width)","def gausspix(x, mean=0.0, sigma=1.0):\n edges = np.concatenate((x-0.5, x[-1:]+0.5))\n integrals = gaussint(edges, mean=mean, sigma=sigma)\n return integrals[1:] - integrals[0:-1]","def scipy_smooth(img, sigma=5):\n return ndimage.gaussian_filter(img, sigma=sigma)"],"string":"[\n \"def create_gaussian_filter(size, sigma):\\n h = size[0] #height of the template\\n w = size[1] #width of the template \\n if h % 2 == 0: h += 1 #add 1 if dimensions are even\\n if w % 2 == 0: w += 1\\n x = math.floor(h/2)\\n y = math.floor(w/2) \\n sum = 0\\n #create our template\\n template = np.zeros((h,w))\\n #fill the template in with the numbers from Gaussian distribution\\n for i in range(h):\\n for j in range(w):\\n template[i,j] = math.exp(-((((j-x)**2)+((i-y)**2))/(2*(sigma**2))))\\n sum = sum + template[i,j]\\n #normalise the numbers\\n gaussian_filter = template/sum\\n return gaussian_filter\",\n \"def Gauss_filter(data, sigma=(0,2,2), mode='wrap'): \\n import scipy.ndimage.filters as flt\\n return flt.gaussian_filter(data, sigma=sigma, mode=mode)\",\n \"def gaussian_filter(x):\\n return _gaussian_filter(x, 3)\",\n \"def create_gaussian_array(self):\\n\\n # Fill array of size l x w with Gaussian Noise.\\n terrain_length = int(ceil(self.length/self.resolution))\\n terrain_width = int(ceil(self.width/self.resolution))\\n gaussian_array = np.random.normal(self.mu, self.sigma, (terrain_length,terrain_width))\\n\\n # Filter the array to smoothen the variation of the noise\\n gaussian_array = gaussian_filter(gaussian_array, self.sigma_filter)\\n\\n return gaussian_array\",\n \"def generate_gaussian():\\n amp = 10 * numpy.random.chisquare(3)\\n width = numpy.random.chisquare(3)\\n mean = numpy.random.uniform(-10 + width, 10 - width)\\n x = numpy.linspace(-10, 10, 500)\\n y = amp * numpy.exp(- (x - mean) ** 2 / width ** 2)\\n add_noise(y, 0.1)\\n return x, y\",\n \"def apply_gaussian(X, sigma):\\n return np.array([ndimage.gaussian_filter(x, sigma) for x in X])\",\n \"def gaussian_filter(stddev, array):\\n\\n return astropy.convolution.convolve(\\n array, astropy.convolution.Gaussian2DKernel(stddev))\",\n \"def fake_gaussian(img, vertical_horizontal_sigma, iter=3):\\n sigma_vertical, sigma_horizontal = vertical_horizontal_sigma\\n h_blured = box_filter1d(img, sigma_horizontal, horizontal=True, iter=iter)\\n blured = box_filter1d(h_blured, sigma_vertical, horizontal=False, iter=iter)\\n return blured\",\n \"def gaussian(amp, fwhm, mean, x):\\n return amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)\",\n \"def _gaussian_distribution(self, x: ndarray, mu: float, sigma: float) -> ndarray:\\n return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(\\n -np.power(\\n (x - mu) / sigma, 2) / 2)\",\n \"def make_gaussian(shape, var):\\n h,w = shape\\n x = np.arange(w, dtype=float)\\n y = np.arange(h, dtype=float)[:,np.newaxis]\\n x0 = w // 2\\n y0 = h // 2\\n mat = np.exp(-0.5 * (pow(x-x0, 2) + pow(y-y0, 2)) / var)\\n normalized_img = np.zeros((h, w))\\n cv2.normalize(mat, normalized_img, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)\\n return normalized_img\",\n \"def gaussian_filter(size,sigma=-1):\\n\\n if sigma == -1:\\n sigma = np.sqrt(size)\\n\\n filter = np.zeros((size,size))\\n\\n for i,j in it.product(range(size),range(size)):\\n x = j-size//2\\n y = i-size//2\\n filter[i,j] = 1/(2*np.pi*sigma**2) * np.exp(-(x**2+y**2)/(2*sigma**2))\\n\\n filter = filter/filter[0,0]\\n filter = filter/filter.sum()\\n\\n return filter\",\n \"def gaussian_filter(self, sigma):\\n\\n mask = self.get_weighted_mask()\\n mask_f = ni.gaussian_filter(mask, sigma=sigma)\\n\\n return SpatialReceptiveField(mask_f, self.altPos, self.aziPos, sign=self.sign,\\n temporalWindow=self.temporalWindow, pixelSizeUnit=self.pixelSizeUnit,\\n dataType=self.dataType, thr=self.thr, filter_sigma=sigma,\\n interpolate_rate=self.interpolate_rate)\",\n \"def gauss(self, X, xm, amp, w):\\n return amp * np.exp(-((X - xm) / w) ** 2)\",\n \"def _FSpecialGauss(size, sigma):\\n radius = size // 2\\n offset = 0.0\\n start, stop = -radius, radius + 1\\n if size % 2 == 0:\\n offset = 0.5\\n stop -= 1\\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\\n assert len(x) == size\\n g = np.exp(-((x**2 + y**2)/(2.0 * sigma**2)))\\n return g / g.sum()\",\n \"def gaussian(t, params):\\n DeprecationWarning(\\\"Using standard width. Better use gaussian_sigma.\\\")\\n params['sigma'] = Qty(\\n value=params['t_final'].get_value()/6,\\n min_val=params['t_final'].get_value()/8,\\n max_val=params['t_final'].get_value()/4,\\n unit=params['t_final'].unit\\n )\\n return gaussian_sigma(t, params)\",\n \"def _f_special_gauss(size, sigma):\\n radius = size // 2\\n offset = 0.0\\n start, stop = -radius, radius + 1\\n if size % 2 == 0:\\n offset = 0.5\\n stop -= 1\\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\\n assert len(x) == size\\n g = np.exp(-((x ** 2 + y ** 2) / (2.0 * sigma ** 2)))\\n g /= g.sum()\\n return tf.constant(g, dtype=tf.float32)\",\n \"def gaussian(amp, fwhm, mean):\\n return lambda x: amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)\",\n \"def _FWHMGauss(sigma, pixel=12):\\n return sigma*2*np.sqrt(2*np.log(2))*pixel\",\n \"def gauss_smooth(data, sigma):\\n\\t\\t\\t# make the kernel 5 sigmas wide in each direction\\n\\t\\t\\tkernel = stats.norm.pdf(np.arange(-5*sigma, (5*sigma)+1), scale=sigma)\\n\\t\\t\\t\\n\\t\\t\\treturn sp.ndimage.convolve1d(data, kernel, axis=2)\",\n \"def get_mask_gauss(mask_dist_src, sigma):\\n return P.Exp()(-2.772588722 * (mask_dist_src ** 2) / (sigma ** 2))\",\n \"def gaussian_filter(shape=(3,3),sigma=0.5):\\n m,n = [(ss-1.)/2. for ss in shape]\\n y,x = np.ogrid[-m:m+1,-n:n+1]\\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma))\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def boxG(x, sigma, output, mode='wrap'):\\n f = nd.gaussian_filter if sigma < 5 else box3\\n return f(x, sigma, output=output, mode=mode)\",\n \"def build_filter(n, sigma):\\n filter_ = np.zeros((n,n))\\n\\n begin = n//2\\n\\n for i in range(n):\\n for j in range(n):\\n val = ((i-begin)**2 + (j-begin)**2)**0.5\\n filter_[i][j] = gaussian(val, sigma)\\n\\n return filter_\",\n \"def gaussian_nonorm(t, params):\\n # TODO Add zeroes for t>t_final\\n t_final = tf.cast(params['t_final'].get_value(), dtype=tf.float64)\\n sigma = params['sigma'].get_value()\\n gauss = tf.exp(-(t - t_final / 2) ** 2 / (2 * sigma ** 2))\\n return gauss\",\n \"def gaussian_black(z, mu: 'normal' = 0, sigma: (0.4,1) = 0.7):\\n return 1/(np.sqrt(2*np.pi)*sigma)*np.exp(-np.power((z - mu)/sigma, 2)/2)\",\n \"def test_gaussian_filter():\\n\\n def rgb2gray(rgb):\\n r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]\\n gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\\n\\n return gray\\n\\n img = rgb2gray(np.array(Image.open('data/graf.png')))\\n gx, x = gauss_module.gauss(4)\\n gx = gx.reshape(1, gx.shape[0])\\n gy = gx.reshape(gx.shape[1], gx.shape[0])\\n smooth_img = conv2(img, gx * np.array(gy))\\n\\n test_smooth_img = gauss_module.gaussianfilter(img, 4)\\n\\n assert np.all(smooth_img.round(5) == test_smooth_img.round(5))\",\n \"def makeGaussian(height, width, sigma=3, center=None):\\n x = np.arange(0, width, 1, float)\\n y = np.arange(0, height, 1, float)[:, np.newaxis]\\n if center is None:\\n x0 = width // 2\\n y0 = height // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)\",\n \"def gaussian(x, amp, cen, wid):\\n return amp * exp (-(x-cen)**2/(2*wid**2))\",\n \"def gaussian_blur(self,img):\\n return cv2.GaussianBlur(img, (self.kernel_size, self.kernel_size), 0)\",\n \"def gaussian(x, peak_x=.0, sigma=1.0, name=''):\\n x = x.astype(np.float)\\n variables = {'function': gaussian, 'peak_x': peak_x, 'sigma': sigma}\\n y = np.exp((-1 * (x - peak_x)**2) / (2 * sigma**2))\\n return packer(x, y, variables, name=name)\",\n \"def _fspecial_gauss_1d(self, size, sigma):\\n coords = torch.arange(size).to(dtype=torch.float)\\n coords -= size // 2\\n g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))\\n g /= g.sum()\\n return g.reshape(-1)\",\n \"def gaussian_white(z, mu: 'normal' = 0, sigma: (0.4, 1) = 0.7):\\n return 1 - gaussian_black(z, mu, sigma)\",\n \"def gs_blur(self,k,img):\\n SIG = self.sigma\\n sig = [SIG,k*SIG,k*k*SIG,k*k*k*SIG,k*k*k*k*SIG]\\n gsArray = [0,1,2,3,4]\\n scaleImages = [0,1,2,3,4]\\n \\n for i in range(5):\\n gsArray[i] = scipy.ndimage.filters.gaussian_filter(img,sig[i])\\n\\n return gsArray\",\n \"def model_gauss(xsigma, nx, ny=1, nz=1, ysigma=None, zsigma=None, xcenter=None, ycenter=None, zcenter=None):\\n\\te = EMData()\\n\\te.set_size(nx, ny, nz)\\n\\tif( ysigma == None ) : ysigma = xsigma\\n\\tif( zsigma == None ) : zsigma = xsigma\\n\\tif( xcenter == None ) : xcenter = nx//2\\n\\tif( ycenter == None ) : ycenter = ny//2\\n\\tif( zcenter == None ) : zcenter = nz//2\\n\\te.process_inplace(\\\"testimage.puregaussian\\\", {\\\"x_sigma\\\":xsigma,\\\"y_sigma\\\":ysigma,\\\"z_sigma\\\":zsigma,\\\"x_center\\\":xcenter,\\\"y_center\\\":ycenter,\\\"z_center\\\":zcenter} )\\n\\treturn e\",\n \"def gaussian(dims: Tuple[int, int], cutoff_freq: float) -> np.ndarray:\\n # create grid\\n m, n = [(dim - 1) / 2 for dim in dims]\\n yy, xx = np.ogrid[-m : m + 1, -n : n + 1]\\n\\n # compute transfer function\\n tf = np.exp(-(np.power(xx, 2) + np.power(yy, 2)) / (2 * np.power(cutoff_freq, 2)))\\n\\n # normalize and return transfer func\\n return (tf - np.max(tf)) / (np.max(tf) - np.min(tf))\",\n \"def gaussian(mu, wid, x):\\n return np.exp(-((x - mu) / (0.6005612 * wid))**2)\",\n \"def _gaussian(self, c, sigma):\\n d = 2*sigma*sigma\\n ax = exp(-power(self._xx-self._xx.T[c], 2)/d)\\n ay = exp(-power(self._yy-self._yy.T[c], 2)/d)\\n return (ax * ay).T # the external product gives a matrix\",\n \"def gaussian(x, amp, wid, cen):\\n return amp*np.exp(-(x-cen)**2/(2*wid**2))\",\n \"def _fspecial_gauss_1d(self, size, sigma):\\n coords = torch.arange(size).to(dtype=torch.float)\\n coords -= size // 2\\n\\n g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))\\n g /= g.sum()\\n\\n return g.unsqueeze(0).unsqueeze(0)\",\n \"def gauss_kernel(n_fwhm,sigma):\\n\\n x_length = int(n_fwhm * sigma + 0.5) #Add 0.5 to approximate to nearest integer\\n y_length = x_length\\n \\n \\n x, y = mgrid[-x_length:x_length+1, -y_length:y_length+1]\\n g = numpy.exp(-(x**2/(2*(float(sigma)**2))+y**2/(2*(float(sigma)**2))))\\n return g / g.sum()\",\n \"def smooth_gauss(image, variance=2, kernel_size=(9, 9)):\\n return cv2.GaussianBlur(image, kernel_size, variance)\",\n \"def flat_top_gaussian(a1_val, a2_val, sigma1, sigma2, w1_val, w2_val, w_val):\\n gauss1 = a1_val * np.exp(-(w_val - w1_val)**4/(2 * sigma1**2))\\n gauss2 = a2_val * np.exp(-(w_val - w2_val)**4/(2 * sigma2**4))\\n sum_gauss = gauss1 + gauss2\\n return sum_gauss\",\n \"def Gaussian(x, mu=0, sigma=26.4, A=1, y0=0):\\r\\n #width = sigma*(2*np.sqrt(2*np.log(2)))\\r\\n b = 1/(sigma*np.sqrt(2*np.pi))\\r\\n f = b*np.power(np.e, -(((x-mu)**2)/(2*sigma**2)))\\r\\n return A*f + y0\",\n \"def gaussian2d(filter_size=5, sig=1.0):\\n ax = np.arange(-filter_size // 2 + 1., filter_size // 2 + 1.)\\n xx, yy = np.meshgrid(ax, ax)\\n kernel = np.exp(-0.5 * (np.square(xx) + np.square(yy)) / np.square(sig))\\n return kernel / np.sum(kernel)\",\n \"def uni_gaussian(X, mu, sigma2):\\n p = (1 / sqrt(2 * pi * sigma2))\\n p = p * exp(-power(X - mu, 2) / (2 * sigma2))\\n\\n def prod(x, y):\\n return x * y\\n p = array([[reduce(prod, el)] for el in p])\\n\\n return p\",\n \"def gaussian(x, mean, sigma):\\n return np.exp(-np.square(x-mean)/(2*np.square(sigma))) / (np.sqrt(2*np.pi*sigma**2))\",\n \"def set_gauss(fwhm):\\n\\n sigma = float(fwhm) / 2.3548\\n\\n op_string = \\\"-kernel gauss %f -fmean -mas \\\" % sigma + \\\"%s\\\"\\n\\n return op_string\",\n \"def set_gauss(fwhm):\\n\\n sigma = float(fwhm) / 2.3548\\n\\n op_string = \\\"-kernel gauss %f -fmean -mas \\\" % sigma + \\\"%s\\\"\\n\\n return op_string\",\n \"def __gaussian_blur(self, img, kernel_size=3):\\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\",\n \"def gaussian(x, amplitude=1.0, center=0.0, sigma=1.0):\\n \\n return (amplitude/(np.sqrt(2.*np.pi)*sigma)) * exp(-np.power((1.0*x-center)/(sigma), 2.)/2.)\",\n \"def reduced_normal_pmf(x: np.array, mean: float, sigma: float) -> np.array:\\n x = np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)\\n x /= np.sqrt(2 * np.pi * sigma ** 2)\\n x[mean] = 0.\\n x /= x.sum()\\n return x\",\n \"def gaussian(size,sigma):\\n a,b=np.ogrid[-size/2:size/2,-size/2:size/2]\\n mask = a**2+b**2\\n mask = np.exp(-mask.astype('float')/(2*float(sigma**2)))\\n return mask\",\n \"def makeGaussian(size, fwhm, center=None):\\n\\n x = sp.arange(0, size, 1, float)\\n y = x[:,sp.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return sp.exp(-4*sp.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)\",\n \"def Gaussian(x, mu, sigma, a):\\n amplitude = a / ( sigma * np.sqrt(2 * np.pi) )\\n u = (x - mu) / sigma\\n return amplitude * np.exp( -0.5 * (u**2) )\",\n \"def normal_pmf(x: np.array, mean: float, sigma: float) -> np.array:\\n x = np.exp(-1 / 2 * ((x - mean) / sigma) ** 2)\\n x /= np.sqrt(2 * np.pi * sigma ** 2)\\n x /= x.sum()\\n return x\",\n \"def get_gauss_kernel(sigma, samples):\\n p = ny.ceil (2*ny.sqrt(2*ny.log(2))*sigma)\\n r = ny.linspace(-p, p, samples)\\n x,y = ny.meshgrid(r, r)\\n b=bivariate_normal(x,y,sigma,sigma)\\n A=(1/ny.sum(b))\\n B=A*b\\n return x,y,B\",\n \"def gaussian(\\n x: float,\\n measurement: 'mstats.ValueUncertainty',\\n max_sigma: float = 10.0\\n) -> float:\\n center = measurement.value\\n width = measurement.uncertainty\\n width = max(width, 1e-6)\\n\\n if x <= (center - max_sigma * width) or x >= (center + max_sigma * width):\\n # Don't calculate values outside a \\\"reasonable\\\" 10 sigma range\\n return 0.0\\n\\n coefficient = 1 / math.sqrt(2.0 * math.pi * width * width)\\n exponent = -0.5 * ((float(x) - center) ** 2) / (width * width)\\n\\n return coefficient * math.exp(exponent)\",\n \"def GaussianKernel(radius, std):\\n size = 2 * radius + 1\\n weight = torch.ones(size, size)\\n weight.requires_grad = False\\n for i in range(-radius, radius+1):\\n for j in range(-radius, radius+1):\\n dis = (i * i) + (j * j)\\n weight[i+radius][j+radius] = np.exp(-dis / (2 * std * std))\\n weight = weight / weight.sum()\\n return weight\",\n \"def filterf(self):\\n from scipy.ndimage.filters import gaussian_filter as gf\\n self._obj['u'] = xr.DataArray(gf(self._obj['u'],1),dims=('x','y'))\\n self._obj['v'] = xr.DataArray(gf(self._obj['v'],1),dims=('x','y'))\\n return self._obj\",\n \"def gaussian(x, mu, sigma):\\n return (np.exp(-(x - mu)**2 / 2.0 / sigma**2) /\\n np.sqrt(2.0 * np.pi) / sigma)\",\n \"def fmgf(array, sigma):\\n x, y = np.arange(len(array)), array.copy()\\n yg = ndimage.filters.gaussian_filter(y, sigma)\\n y -= yg\\n\\n # digitizing\\n m = 101\\n dy = 6.0 * mad(y) / m\\n ybin = np.arange(np.min(y) - 5 * dy, np.max(y) + 5 * dy + dy, dy)\\n z = np.zeros([len(ybin), len(x)])\\n z[np.digitize(y, ybin), x] = 1.0\\n\\n # filtering\\n g = partial(ndimage.filters.gaussian_filter, sigma=(0, sigma))\\n c = partial(ndimage.filters.convolve1d, weights=np.ones(m), axis=0)\\n zf = c(c(c(g(z))))\\n\\n # estimates\\n ym1, y0, yp1 = [ybin[np.argmax(zf, 0) + i] for i in (-1, 0, 1)]\\n zm1, z0, zp1 = [zf[np.argmax(zf, 0) + i, x] for i in (-1, 0, 1)]\\n t = (zm1 - z0) / (zm1 - 2 * z0 + zp1)\\n\\n filtered = yg + ((1 - t) ** 2) * ym1 + (2 * t * (1 - t)) * y0 + (t**2) * yp1\\n return filtered\",\n \"def makeGaussian(size, fwhm, sigma, center=None):\\n\\n x = np.arange(0, size, 1, float)\\n y = x[:,np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n \\n #return (np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)) #approximation using exponenial functions\\n return ((1/(2*np.pi*sigma**2))*np.exp(-((xx)**2 + (yy)**2)/(2*sigma**2))) # symmetric 2D Gaussian distribution\",\n \"def gaussian(window_size, sigma):\\n gauss = torch.Tensor([math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])\\n return gauss/gauss.sum()\",\n \"def make_gaussian(size, sigma=10, center=None):\\n\\n x = np.arange(0, size[1], 1, float)\\n y = np.arange(0, size[0], 1, float)\\n y = y[:, np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size[0] // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2)\",\n \"def Gaussian(x,t,sigma):\\n return np.exp(-(x-t)**2/(2*sigma**2))\",\n \"def fit_gaussian(array):\\n\\n shape = array.shape\\n xmean, ymean = numpy.array(shape) / 2.\\n\\n xx, yy = numpy.mgrid[:shape[0], :shape[1]]\\n\\n g_init = astropy.modeling.models.Gaussian2D(amplitude=1., x_mean=xmean, y_mean=ymean,\\n x_stddev=1., y_stddev=1.)\\n\\n f2 = astropy.modeling.fitting.LevMarLSQFitter()\\n\\n gg = f2(g_init, xx, yy, array)\\n\\n return gg\",\n \"def gauss(self, *x, cut=None, rescaled_to_max=False):\\n\\n mean, std = self._meanStdCut(cut=cut)\\n\\n if rescaled_to_max: norm = 1\\n else: norm = np.sqrt(2*np.pi*(std**2))\\n\\n gauss = lambda y: (\\n np.exp(-((y - mean)**2)/(2*(std**2)))\\n /norm)\\n\\n return np.array(list(map(gauss, x)))\",\n \"def add_gaussian_noise(image, min_sigma, max_sigma):\\r\\n sigma = np.random.uniform(min_sigma, max_sigma)\\r\\n corrupted_im = image + np.random.normal(0, sigma, image.shape)\\r\\n rounded_im = (np.round(corrupted_im * MAX_PIXEL_NUMBER)) / MAX_PIXEL_NUMBER\\r\\n return np.clip(rounded_im, 0, 1).astype(np.float64)\",\n \"def gauss_convolution(im_array, n_fwhm, fwhm) :\\n \\n sigma = fwhm / (2.*math.sqrt(2.*math.log(2.)))\\n\\t\\n im_kernel_array = gauss_kernel(n_fwhm, sigma)\\n conv_image = signal.convolve(im_array,im_kernel_array,mode = 'same')\\n\\n return (conv_image)\",\n \"def build_gaussian_frame(normalized_frame, level):\\n pyramid = build_gaussian_pyramid(normalized_frame, level)\\n gaussian_frame = pyramid[-1]\\n return gaussian_frame\",\n \"def localGaussian(self, region_width, scale=0.95, down=True):\\n region_width = boof_fixed_length(region_width)\\n\\n java_object = pbg.gateway.jvm.boofcv.factory.filter.binary.FactoryThresholdBinary.\\\\\\n localGaussian(region_width,float(scale),down,self.boof_image_type)\\n return InputToBinary(java_object)\",\n \"def gaussian_filter(self, input, win, size):\\n\\n N, C, H, W = input.shape\\n f = F.conv2d(input, win, stride=1, groups=C)\\n f = F.conv2d(f, win.transpose(2, 3), stride=1, groups=C)\\n\\n out = torch.zeros(input.shape)\\n out[:, :, self.win_size // 2:self.win_size // 2 * -1, self.win_size // 2:self.win_size // 2 * -1] = f.unsqueeze(\\n 0).unsqueeze(0)\\n return out\\n\\n # N, C, H, W = input.shape\\n # out = F.conv2d(input, win, stride=1, padding=(0, self.win_size//2), groups=C)\\n # out = F.conv2d(out, win.transpose(2, 3), stride=1, padding=(self.win_size//2, 0), groups=C)\\n # return out\",\n \"def Gaussian(x, t, sigma):\\n return np.exp(-(x - t)**2 / (2 * sigma**2))\",\n \"def gaussian(x, sigma):\\n try: r = np.exp(-0.5*(x/sigma)**2) \\n except: r = np.zeros(len(x))\\n return r\",\n \"def makeGaussian(size, fwhm=3, center=None):\\n\\n x = np.arange(0, size, 1, float)\\n y = x[:, np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4 * np.log(2) * ((x - x0)**2 + (y - y0)**2) / fwhm**2)\",\n \"def gaussian_blurring(self,input_image,kernel_size,sigma):\\n #Applying Gaussian Blur filter\\n output_image=cv2.GaussianBlur(input_image,kernel_size,sigma)\\n return output_image\",\n \"def makeGaussian(size, fwhm = 3, center=None):\\n\\n x = np.arange(0, size, 1, float)\\n y = x[:,np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)\",\n \"def makeGaussian(size, fwhm = 3, center=None):\\n\\n x = np.arange(0, size, 1, float)\\n y = x[:,np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)\",\n \"def gaussion_smoothing(self,sigma=None):\\n print(\\\"## Gaussian smoothing...\\\");\\n corr_length = self.corr_length\\n if sigma is None:\\n corr = self.correlation\\n oscillation=np.max(np.abs(corr[:-1]-corr[1:]),axis=0)\\n peak=np.max(np.abs(corr),axis=0)\\n oscillation /= peak\\n sigma= corr_length/(5.0*oscillation*len(corr)*self.smooth_tune) # 15.0 has been tuned for many times \\n print \\\"sigma:\\\"\\n print sigma\\n for i in np.arange(corr_length):\\n self.correlation[i] *= exp(-i*i/(2*sigma*sigma))/(sigma*sqrt(2*pi))\",\n \"def gaussian(x, x0=0.0, fwhm=1.0, ampl=1.0):\\n return ampl * np.exp(-4 * np.log(2) * ((x - x0) / fwhm) ** 2)\",\n \"def gaussian_blur(self, img):\\n kernel_size = self.gaussian_blur_params[\\\"kernel_size\\\"]\\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\",\n \"def _filter_gauss(image: np.array,\\n axis: str,\\n krnsize: int,\\n krnsigma: float):\\n assert(axis == 'x' or axis == 'y')\\n krn = cv.getGaussianKernel(krnsize, krnsigma)\\n krn = krn * krn.T\\n krny, krnx = np.gradient(krn)\\n\\n if axis == 'x':\\n return _filter_custom(image, krnx)\\n elif axis == 'y':\\n return _filter_custom(image, krny)\",\n \"def differenceOfGausssians(image,sigma0, sigma1,window_size, roi, out = None):\\n return (vigra.filters.gaussianSmoothing(image,sigma0,window_size=window_size,roi = roi)-vigra.filters.gaussianSmoothing(image,sigma1,window_size=window_size,roi = roi))\",\n \"def run_gaussian_smoothing(image, kernel_size=5):\\n return cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)\",\n \"def set_gaussian(self, X, sigma=0):\\n sigma = float(sigma)\\n if sigma < 0:\\n raise ValueError('sigma should be positive')\\n self.set_euclidian(X)\\n d = self.weights\\n\\n if sigma == 0:\\n sigma = (d ** 2).mean()\\n\\n w = np.exp(- (d ** 2) / (2 * sigma))\\n self.weights = w\",\n \"def build_gaussian_pyramid(im, max_levels, filter_size):\\n filter_vec = create_gaussian_line(filter_size)\\n # creating duplicate for confy use\\n temp_im = im\\n pyr = [im]\\n kernel = np.array([0.0625,0.25,0.375,0.25,0.0625])\\n kernel = kernel.reshape((1,5))\\n for i in range(max_levels - 1):\\n # blurring the cur layer\\n #temp_im_temp = cv2.filter2D(temp_im,-1,kernel,borderType=cv2.B)\\n temp_im = scipy.signal.convolve2d(temp_im, filter_vec, mode='same')\\n temp_im = scipy.signal.convolve2d(temp_im, np.transpose(filter_vec), mode='same')\\n # sampling only every 2nd row and column\\n temp_im = temp_im[::2, ::2]\\n pyr.append(temp_im)\\n\\n return pyr, filter_vec\",\n \"def fspecial_gauss(size, sigma, channels):\\n radius = size // 2\\n offset = 0.0\\n start, stop = -radius, radius + 1\\n if size % 2 == 0:\\n offset = 0.5\\n stop -= 1\\n x, y = np.mgrid[offset + start:stop, offset + start:stop]\\n assert len(x) == size\\n\\n x = x.reshape(x.shape+(1,1))\\n x = np.repeat(x, channels, axis=2)\\n x = np.repeat(x, channels, axis=3)\\n\\n y = y.reshape(y.shape+(1,1))\\n y = np.repeat(y, channels, axis=2)\\n y = np.repeat(y, channels, axis=3) \\n\\n x = tf.constant(x, dtype=tf.float32)\\n y = tf.constant(y, dtype=tf.float32)\\n\\n g = tf.exp(-((x**2 + y**2)/(2.0*sigma**2)))\\n return g / tf.reduce_sum(g)\",\n \"def gaussianFilter(gain,BT,spSym,nTaps):\\n\\n a = np.sqrt(np.log(2)/2)/BT\\n t = np.linspace(-.5*nTaps,.5*nTaps-1,nTaps)/spSym\\n\\n ft = np.sqrt(np.pi)/a *np.exp(-(np.pi**2*(t)**2)/a**2)\\n ft /= np.sum(ft) * gain # normalize filter\\n\\n return ft\",\n \"def build_gaussian_pyramid(im, max_levels, filter_size):\\n\\n filter_vec = gaus_1d(filter_size).reshape(1,filter_size)\\n pyr = []\\n pyr.append(im)\\n for i in range(max_levels - 1):\\n if(im.shape[0] <= 16 or im.shape[1] <= 16):\\n break\\n\\n im = ndimage.filters.convolve(im, filter_vec.T, mode='mirror')\\n im = ndimage.filters.convolve(im, filter_vec, mode='mirror')\\n\\n im = im[::2, ::2]\\n pyr.append(im)\\n\\n return [pyr,filter_vec]\",\n \"def gaussianBlur(img,ksize=(5,5),sigma=10):\\n #kernel = cv2.getGaussianKernel(ksize,sigma)\\n dst = np.zeros_like(img)\\n cv2.GaussianBlur(src=img,dst=dst,ksize=ksize,sigmaX=0)\\n return dst\",\n \"def gaussian_blur(img, kernel=(3, 3)):\\n out = cv2.GaussianBlur(img, kernel, 0)\\n return out\",\n \"def gaussian(x, *parameters):\\n position, sigma, amplitude, background = parameters\\n return amplitude * np.exp(-(x - position)**2 / (2.0 * sigma**2)) + background\",\n \"def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\\n U, V = fourier_meshgrid(image)\\n D = fourier_distance(U, V)\\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\\n \\n if kind == 'high' or kind == 'highpass':\\n H = 1.0 - H\\n \\n return H\",\n \"def gaussify_bands(self, sigma):\\n for key, band in self.bands.items():\\n self.gauss_bands[key] = gaussian_filter(input=band, sigma=sigma)\",\n \"def make_gaussian(size, fwhm=3, center=None):\\n\\n x = np.arange(0, size, 1, float)\\n y = x[:, np.newaxis]\\n\\n if center is None:\\n x0 = y0 = size // 2\\n else:\\n x0 = center[0]\\n y0 = center[1]\\n\\n k = np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / fwhm ** 2)\\n return k / np.sum(k)\",\n \"def Gaussian_conv(n):\\n\\n\\t# Choose sigma and mu\\n\\tsigma = 1\\n\\tmu = 0\\n\\n\\t# Create table\\n\\tb = sigma * np.random.randn(n) + mu\\n\\tsum = b.sum()\\n\\t#b = b/sum\\n\\tprint(b)\\n\\treturn b\",\n \"def GaussianKernel(sigma: float = 1., width: int = 0):\\n assert not ((width is None or width == 0) and\\n (sigma is None or sigma == 0)), \\\\\\n \\\"GaussianKernel :: both sigma ({}) & width ({}) are not valid\\\".format(\\n sigma, width)\\n\\n if width is None or width == 0:\\n width = int(2.0 * 3.0 * sigma + 1.0)\\n if width % 2 == 0:\\n width += 1\\n\\n if sigma is None or sigma == 0:\\n sigma = (width - 1)/6.\\n half = width//2\\n x, y = np.meshgrid(np.linspace(-half, half, width),\\n np.linspace(-half, half, width), indexing='xy')\\n w = np.exp(- (x**2 + y**2) / (2.*(sigma**2)))\\n w /= np.sum(w)\\n return torch.from_numpy(w.astype(np.float32)).view(1, 1, width, width)\",\n \"def gausspix(x, mean=0.0, sigma=1.0):\\n edges = np.concatenate((x-0.5, x[-1:]+0.5))\\n integrals = gaussint(edges, mean=mean, sigma=sigma)\\n return integrals[1:] - integrals[0:-1]\",\n \"def scipy_smooth(img, sigma=5):\\n return ndimage.gaussian_filter(img, sigma=sigma)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7409861","0.68018997","0.67854315","0.65037984","0.6479504","0.63791597","0.6300146","0.62202877","0.6173434","0.6151084","0.61396915","0.61290395","0.61198384","0.60909826","0.6076701","0.6069273","0.6066046","0.6016382","0.59799224","0.5979191","0.59706044","0.59311575","0.5923509","0.59151155","0.59149516","0.5903407","0.5900749","0.58999485","0.5880151","0.5874781","0.5870428","0.58635086","0.5846314","0.58453465","0.58278465","0.58182365","0.5801271","0.5797586","0.579168","0.57875025","0.5775125","0.5757861","0.57354677","0.57290107","0.5726956","0.5726595","0.5709291","0.57073325","0.57073325","0.57043463","0.5702321","0.5700931","0.5698195","0.56778353","0.56744874","0.56719804","0.56693834","0.5660388","0.565893","0.5656236","0.5643518","0.564043","0.5633227","0.5630489","0.5619289","0.5617497","0.5617158","0.5615084","0.56145674","0.5611908","0.56101286","0.56034166","0.55966586","0.5589809","0.55868727","0.55766207","0.55765754","0.5573512","0.5573512","0.55707633","0.5565137","0.55615216","0.5560993","0.5556988","0.55484813","0.5538138","0.5537883","0.5533003","0.5526925","0.55239177","0.5514989","0.5513085","0.5504989","0.55023736","0.55014324","0.5498636","0.54970163","0.54920584","0.5490023","0.5484706"],"string":"[\n \"0.7409861\",\n \"0.68018997\",\n \"0.67854315\",\n \"0.65037984\",\n \"0.6479504\",\n \"0.63791597\",\n \"0.6300146\",\n \"0.62202877\",\n \"0.6173434\",\n \"0.6151084\",\n \"0.61396915\",\n \"0.61290395\",\n \"0.61198384\",\n \"0.60909826\",\n \"0.6076701\",\n \"0.6069273\",\n \"0.6066046\",\n \"0.6016382\",\n \"0.59799224\",\n \"0.5979191\",\n \"0.59706044\",\n \"0.59311575\",\n \"0.5923509\",\n \"0.59151155\",\n \"0.59149516\",\n \"0.5903407\",\n \"0.5900749\",\n \"0.58999485\",\n \"0.5880151\",\n \"0.5874781\",\n \"0.5870428\",\n \"0.58635086\",\n \"0.5846314\",\n \"0.58453465\",\n \"0.58278465\",\n \"0.58182365\",\n \"0.5801271\",\n \"0.5797586\",\n \"0.579168\",\n \"0.57875025\",\n \"0.5775125\",\n \"0.5757861\",\n \"0.57354677\",\n \"0.57290107\",\n \"0.5726956\",\n \"0.5726595\",\n \"0.5709291\",\n \"0.57073325\",\n \"0.57073325\",\n \"0.57043463\",\n \"0.5702321\",\n \"0.5700931\",\n \"0.5698195\",\n \"0.56778353\",\n \"0.56744874\",\n \"0.56719804\",\n \"0.56693834\",\n \"0.5660388\",\n \"0.565893\",\n \"0.5656236\",\n \"0.5643518\",\n \"0.564043\",\n \"0.5633227\",\n \"0.5630489\",\n \"0.5619289\",\n \"0.5617497\",\n \"0.5617158\",\n \"0.5615084\",\n \"0.56145674\",\n \"0.5611908\",\n \"0.56101286\",\n \"0.56034166\",\n \"0.55966586\",\n \"0.5589809\",\n \"0.55868727\",\n \"0.55766207\",\n \"0.55765754\",\n \"0.5573512\",\n \"0.5573512\",\n \"0.55707633\",\n \"0.5565137\",\n \"0.55615216\",\n \"0.5560993\",\n \"0.5556988\",\n \"0.55484813\",\n \"0.5538138\",\n \"0.5537883\",\n \"0.5533003\",\n \"0.5526925\",\n \"0.55239177\",\n \"0.5514989\",\n \"0.5513085\",\n \"0.5504989\",\n \"0.55023736\",\n \"0.55014324\",\n \"0.5498636\",\n \"0.54970163\",\n \"0.54920584\",\n \"0.5490023\",\n \"0.5484706\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6193196"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":76497,"cells":{"query":{"kind":"string","value":"Calculate opsin template according to Stavenga et al (1993)."},"document":{"kind":"string","value":"def stavenga1993_template(\n wavelengths: np.ndarray,\n alpha_max: Union[float, np.ndarray],\n a_alpha: Union[float, np.ndarray] = 380.0,\n b_alpha: Union[float, np.ndarray] = 6.09,\n beta_max: Union[float, np.ndarray] = 350.0,\n A_beta: Union[float, np.ndarray] = 0.29,\n a_beta: Union[float, np.ndarray] = 247.0,\n b_beta: Union[float, np.ndarray] = 3.59,\n) -> np.ndarray:\n x_alpha = np.log10(wavelengths / alpha_max)\n alpha_band = stavenga1993_band_calculation(x_alpha, a_alpha, b_alpha)\n\n x_beta = np.log10(wavelengths / beta_max)\n beta_band = stavenga1993_band_calculation(x_beta, a_beta, b_beta)\n\n return alpha_band + A_beta * beta_band"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def produce_13TeV_template(tag_name=\"HKHI\"):\n num_rebin = 1\n file_name = \"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\"+tag_name+\"_Lin.root\"\n print \"Input: \", file_name\n fin = ROOT.TFile.Open(file_name, \"read\")\n h_nom = fin.Get(\"bkg_total_gg_full\").Clone(\"bkg_nominal_old\")\n h_nom.Rebin(num_rebin)\n fout = ROOT.TFile.Open(\"hists_input_\"+tag_name+\".root\", \"recreate\")\n\n h_purity_sys = fin.Get(\"bkg_purity_syst_gg_full\").Clone(\"bkg_purity_syst_gg\")\n h_reducible_sys = fin.Get(\"bkg_reducible_syst_gg_full\").Clone(\"bkg_reducible_syst_gg\")\n h_irreducible_sys = fin.Get(\"bkg_irreducible_syst_gg_full\").Clone(\"bkg_irreducible_syst_gg\")\n h_iso_sys = fin.Get(\"bkg_iso_syst_gg_full\").Clone(\"bkg_iso_syst_gg\")\n\n #file_iso = \"isolation_sys/hist.root\"\n #fin2 = ROOT.TFile.Open(file_iso, \"read\")\n #h_iso_sys = fin2.Get(\"bkg_isolation_syst_gg\")\n ## inflat irreducible uncertainty by factor of 10\n # so that it closes to stats uncertainty in data\n sf = 1\n if INFLATE_SYS:\n sf = 10\n\n # after rebinning systematic uncertainties, need to scale down,\n # otherwise the uncertainties are inflated.\n h_purity_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_irreducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_reducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_iso_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n\n ## truncate the histograms to [200, 2000] GeV\n h_nom_new = truncate_hist(h_nom, \"bkg_nominal\")\n h_purity_sys_new = truncate_hist(h_purity_sys, \"h_purity_sys_new\")\n h_irreducible_sys_new = truncate_hist(h_irreducible_sys, \"h_irreducible_sys_new\")\n h_reducible_sys_new = truncate_hist(h_reducible_sys, \"h_reducible_sys_new\")\n h_iso_sys_new = truncate_hist(h_iso_sys, \"h_iso_sys_new\")\n\n #write down sys and nominal\n fout.cd()\n h_nom_new.Write()\n h_purity_sys_new.Write()\n h_reducible_sys_new.Write()\n h_irreducible_sys_new.Write()\n h_iso_sys_new.Write()\n\n h_purity_up, h_purity_down = create_sys_hist(h_nom_new, h_purity_sys_new, \"purity_sys\")\n h_purity_up.Write()\n h_purity_down.Write()\n\n h_red_up, h_red_down = create_sys_hist(h_nom_new, h_reducible_sys_new, \"reducible_sys\")\n h_red_up.Write()\n h_red_down.Write()\n\n h_irred_up, h_irred_down = create_sys_hist(h_nom_new, h_irreducible_sys_new, \"irreducible_sys\")\n h_irred_up.Write()\n h_irred_down.Write()\n\n h_iso_up, h_iso_down = create_sys_hist(h_nom_new, h_iso_sys, \"isolation_sys\")\n h_iso_up.Write()\n h_iso_down.Write()\n\n fin.Close()\n fout.Close()","def computeTemplateFeatures(self, templates, mode=''):\n pass","def stoichiometry_func_doc(self, label):\n air = self.air_alias.val\n fuel = self.fuel_alias.val\n flue_gas = self.fuel_alias.val + '_fg'\n\n equations = ''\n for fluid in self.inl[0].fluid.val.keys():\n\n in1 = r'\\dot{m}_\\mathrm{in,1} \\cdot x_\\mathrm{fluid,in,1}'\n in2 = r'\\dot{m}_\\mathrm{in,2} \\cdot x_\\mathrm{fluid,in,2}'\n out = r'\\dot{m}_\\mathrm{out,1} \\cdot x_\\mathrm{fluid,out,1}'\n\n if fluid == air:\n latex = (\n r'0=\\Delta\\dot{m}_\\mathrm{' + fluid + r'} - '\n r'\\dot{m}_\\mathrm{' + fluid + r',stoich}'\n )\n elif fluid == fuel:\n latex = (\n r'0=\\Delta \\dot{m}_\\mathrm{' + fluid + r'} - '\n r'\\dot{m}_\\mathrm{' + fluid + r'}'\n )\n elif fluid == flue_gas:\n latex = (\n r'0=\\Delta \\dot{m}_\\mathrm{' + fluid.replace('_', ',') +\n r'} + \\dot{m}_\\mathrm{' + fuel + r'} +'\n r'\\dot{m}_\\mathrm{' + air + r',stoich}'\n )\n else:\n latex = r'0 = \\Delta \\dot{m}_\\mathrm{' + fluid + '}'\n\n if fluid == next(iter(self.inl[0].fluid.val)):\n balance = (\n r'\\Delta \\dot{m}_\\mathrm{fluid} = ' + in1 +\n '+' + in2 + '-' + out)\n m_fluid = r'\\dot{m}_\\mathrm{fluid} = ' + in1 + '+' + in2\n m_air_stoich = (\n r'\\dot{m}_\\mathrm{' + air + ',stoich}='\n r'\\dot{m}_\\mathrm{' + fuel + r'} \\cdot ' +\n str(round(self.air_min, 4)))\n latex_general_eq = (\n r'\\begin{split}' + '\\n'\n r'&' + balance + r'\\\\' + '\\n'\n r'&' + m_fluid + r'\\\\' + '\\n'\n r'&' + m_air_stoich + r'\\\\' + '\\n'\n r'\\end{split}'\n )\n equations += (\n generate_latex_eq(\n self, latex_general_eq, label + '_general_eq') + '\\n' +\n generate_latex_eq(self, latex, label + '_' + fluid) + '\\n')\n else:\n equations += (\n generate_latex_eq(self, latex, label + '_' + fluid) + '\\n')\n # remove last newline\n return equations[:-1]","def _tpl(grid, iv, ds='test'):\n #TODO: Research numpy routines which may automate this\n tax=grid._get_timelike_axis()\n def tmp(x):\n if x[tax]>0:\n return 0.0\n if x[tax]==0:\n return iv(x)\n return tmp","def fillSignalTemplates(opt):\n\n totalSig={}\n templates=[]\n\n #import signal events\n data=ROOT.TChain('data')\n data.AddFile(os.path.join(opt.input,opt.sig))\n\n #define final preselection cuts\n cuts='xangle==%d'%opt.xangle\n if len(opt.presel) : cuts += ' && ' + opt.presel\n if opt.csiacc:\n csiCuts ='csi1>%f && csi1<%f && '%opt.csiacc[opt.xangle][0]\n csiCuts+='csi2>%f && csi2<%f'%opt.csiacc[opt.xangle][1]\n cuts=csiCuts if len(cuts)==0 else '{0} && {1}'.format(cuts,csiCuts)\n\n #loop over categories build templates\n for icat in range(len(opt.categs)):\n\n #apply category cuts\n categCut=opt.categs[icat]\n categCut=cuts if len(categCut)==0 else '%s && %s'%(categCut,cuts)\n\n catName='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\n print '\\t',catName,categCut\n\n #signal modelling histograms\n histos=[]\n for name,pfix in [('sig_'+catName,''),('sig_%s_sigShape'%catName,'mix')]:\n\n templCuts=categCut.replace('csi1',pfix+'csi1')\n templCuts=templCuts.replace('csi2',pfix+'csi2')\n wgtExpr='wgt*%f'%(SIGNALXSECS[opt.xangle]*opt.lumi)\n data.Draw('{0}mmiss >> h({1},{2},{3})'.format(pfix,opt.nbins,opt.mMin,opt.mMax),\n '{0}*({1})'.format(wgtExpr,templCuts),\n 'goff')\n h=data.GetHistogram()\n histos.append( h.Clone(name) ) \n histos[-1].SetDirectory(0)\n\n if len(histos)==1:\n totalSig[icat]=h.Integral()\n\n h.Reset('ICE')\n templates += defineProcessTemplates(histos)\n \n print '\\t total signal:',totalSig\n return totalSig,templates","def cheetah_template(self, pre=False):\n if self.is_req_output:\n cht_tmpl = self.req_out_chth\n return cht_tmpl.substitute(self.xml_out)\n elif self.is_output:\n xml_out = self.xml_out\n xml_out['out_sel_name'] = self.out_sel_name\n cht_tmpl = self.file_chth\n return cht_tmpl.substitute(self.xml_out)\n elif self.is_input and not pre:\n if self.pname in self.gen_in_fmt:\n if self.gen_in_fmt[self.pname] == 'vcf,vcf_bgzip':\n cht_tmpl = self.vcf_choose\n else:\n cht_tmpl = PercentTemplate(self.reg_arg)\n elif self.pname in self.tool_data[self.tool_name]['input_fmt']:\n cht_tmpl = self.req_out_chth\n return cht_tmpl.substitute(self.xml_out)\n elif self.is_input and pre:\n cht_tmpl = self.vcf_tabix\n return cht_tmpl.substitute(self.xml_out)\n else:\n if self.xml_out['section'] not in ['required']:\n template_string = self.ext_arg\n else:\n template_string = self.reg_arg\n if self.xml_out['type'] == 'boolean':\n cht_tmpl = PercentTemplate(template_string.replace('%argument ', ''))\n else:\n cht_tmpl = PercentTemplate(template_string)\n return cht_tmpl.substitute(self.xml_out)","def calc_template(template_def, config):\n template = Template(**template_def)\n #print \"template_def:\", template_def, \"config:\", config\n try:\n retvals = process_template(template, config, target=(None, None))\n except Exception:\n print(\"==== template ====\"); pprint(template_def)\n print(\"==== config ====\"); pprint(config)\n #traceback.print_exc()\n raise\n output = {}\n for rkey, rv in retvals.items():\n module_id, terminal_id = rkey\n module_key = str(module_id)\n output.setdefault(module_key, {})\n output[module_key][terminal_id] = rv.todict()\n return output","def ti_func_doc(self, label):\n latex = (\n r'\\begin{split}' + '\\n'\n r'0 = & ti - LHV_\\mathrm{fuel} \\cdot \\left[\\sum_i \\left('\n r'\\dot{m}_{\\mathrm{in,}i}\\cdot x_{\\mathrm{fuel,in,}i}\\right)-'\n r' \\dot{m}_\\mathrm{out,1}\\cdot '\n r'x_{\\mathrm{fuel,out,1}} \\right]\\\\' + '\\n'\n r'& \\forall i \\in \\text{combustion inlets}\\\\' + '\\n'\n r'\\end{split}'\n )\n return generate_latex_eq(self, latex, label)","def computeTemplateFeatures(self, templates, mode=''):\n pass","def gen_op2(N11, N22, N12, rN11, rN22, rN12):\n raw = 2 * N12 / rN12 / (N11 / rN11 + N22 / rN22)\n print(\"=====\\n2nd method: raw = 2 * N12/rN12 / (N11/rN11 + N22/rN22)\")\n print(\"OP: 1 - raw = %.3f\\n=====\" % (1 - raw))","def gen_op4(N11, N22, N12, rN11, rN22, rN12):\n raw = 2 * N12 / rN12 / (N11 / rN11 + N22 / rN22)\n print(\"=====\\n4th method: raw = 2 * N12/rN12 / (N11/rN11 + N22/rN22)\")\n print(\"OP: 1 / raw = %.3f\\n=====\" % (1.0 / raw))","def run_template(template, config):\n all_results = {}\n for nodenum, wires in template:\n # Find the modules\n node = template.modules[nodenum]\n module_id = node['module'] #template.modules[node]\n module = lookup_module(module_id)\n inputs = _map_inputs(module, wires)\n \n # substitute values for inputs\n kwargs = dict((k, _lookup_results(all_results, v)) \n for k, v in inputs.items())\n \n # Include configuration information\n kwargs.update(node.get('config', {}))\n kwargs.update(config[nodenum])\n result = module.action(**kwargs)\n# print result\n all_results[nodenum] = result\n \n# return all_results\n# FIXXXXXXXXXXXXXXXXXXXXXX ***********************\n from .offspecular.instruments import convert_to_plottable\n return [convert_to_plottable(value['output']) if 'output' in value else {} for key, value in all_results.items()]","def gen_op1(N11, N22, N12, rN11, rN22, rN12):\n raw = N12 / (N11 + N22)\n ref = rN12 / (rN11 + rN22)\n print(\"=====\\n1st method: raw = N12/(N11+N22)\")\n print(\"raw: %.2f | ref: %.2f\" % (raw, ref) )\n print(\"1 - raw/ref = %.3f\\n=====\" % (1.0 - raw / ref))","def test_include_template(self, parse_input, tmpdir):\n template = textwrap.dedent(\n \"\"\"\n name CustomOperation\n version 0.0\n BSgate({theta}, pi/2) | [0, 1]\n Rgate({phi}) | 0\n \"\"\"\n )\n\n filename = tmpdir.join(\"test.xbb\")\n\n with open(filename, \"w\") as f:\n f.write(template)\n\n test_include = textwrap.dedent(\n \"\"\"\n name test_include\n version 0.0\n include \"{}\"\n CustomOperation(theta=0.54, phi=0.1) | [2, 1]\n \"\"\"\n ).format(filename)\n\n bb = parse_input(test_include, cwd=tmpdir)\n\n expected = [\n {\"op\": \"BSgate\", \"args\": [0.54, np.pi / 2], \"kwargs\": {}, \"modes\": [2, 1]},\n {\"op\": \"Rgate\", \"args\": [0.1], \"kwargs\": {}, \"modes\": [2]},\n ]\n\n assert bb.operations == expected","def generate_scenario_precip_inputs():\n def multiply_precip(precip_raster, multiply_factor):\n valid_mask = (precip_raster != precip_raster_nodata)\n result = numpy.empty(precip_raster.shape, dtype=numpy.float32)\n result[:] = _TARGET_NODATA\n result[valid_mask] = precip_raster[valid_mask] * multiply_factor\n return result\n\n def calc_ET0(RA, Tavg, TD, P):\n \"\"\"Modified Hargreaves from Droogers and Allen 2002.\n\n Parameters:\n RA (numpy.ndarray): daily extraterrestrial radiation\n Tavg (numpy.ndarray): average temperature\n TD (numpy.ndarray): difference between minimum and maximum\n temperature\n P (numpy.ndarray): monthly precipitation\n\n Returns:\n monthly reference evapotranspiration (mm)\n\n \"\"\"\n valid_mask = (\n (RA != _TARGET_NODATA) &\n (Tavg != tavg_nodata) &\n (TD != td_nodata) &\n (P != _TARGET_NODATA))\n result = numpy.empty(RA.shape, dtype=numpy.float32)\n result[:] = _TARGET_NODATA\n result[valid_mask] = (\n 0.0013 * 0.408 * RA[valid_mask] * (Tavg[valid_mask] + 17.) *\n (numpy.power((TD[valid_mask] - 0.0123 * P[valid_mask]), 0.76)) *\n 29.5)\n return result\n\n climate_dir = r\"E:\\GIS_local_3.6.19\\USFS\\NCEI_climate\"\n RA_df = pandas.read_csv(os.path.join(climate_dir, \"RA_FAO.csv\"))\n Tavg_dir = os.path.join(climate_dir, \"temp_ave_rasters\")\n TD_dir = os.path.join(climate_dir, \"tdiff_rasters\")\n events_table = os.path.join(\n climate_dir, 'rain_events_table_Palmer_creek.csv')\n\n baseline_precip_dir = r\"C:\\Users\\ginge\\Dropbox\\NatCap_backup\\USFS\\input_data\\model_inputs\\precip_NCEI\"\n outer_scenario_dir = r\"C:\\Users\\ginge\\Dropbox\\NatCap_backup\\USFS\\input_data\\model_inputs\\precip_scenarios\"\n\n example_precip_raster = os.path.join(\n baseline_precip_dir, 'precip_1.tif')\n precip_raster_nodata = pygeoprocessing.get_raster_info(\n example_precip_raster)['nodata'][0]\n\n for multiply_factor in [0.5]: # [0.7, 0.9, 1.1, 1.3]:\n scenario_dir = os.path.join(\n outer_scenario_dir, '{}x'.format(multiply_factor))\n modified_event_table = os.path.join(scenario_dir, 'rain_events.csv')\n ET0_dir = os.path.join(scenario_dir, 'ET0_rasters')\n precip_dir = os.path.join(scenario_dir, 'precip_rasters')\n if not os.path.exists(ET0_dir):\n os.makedirs(ET0_dir)\n if not os.path.exists(precip_dir):\n os.makedirs(precip_dir)\n event_df = pandas.read_csv(events_table)\n event_df.events = event_df.events * multiply_factor\n event_df.to_csv(modified_event_table)\n for month in xrange(1, 13):\n baseline_raster_path = os.path.join(\n baseline_precip_dir, 'precip_{}.tif'.format(month))\n modified_precip_path = os.path.join(\n precip_dir, 'precip_{}.tif'.format(month))\n pygeoprocessing.raster_calculator(\n [(baseline_raster_path, 1), (multiply_factor, 'raw')],\n multiply_precip, modified_precip_path,\n gdal.GDT_Float32, _TARGET_NODATA)\n\n Tavg_raster_path = os.path.join(\n Tavg_dir, 'tavg_{}.tif'.format(month))\n TD_raster_path = os.path.join(\n TD_dir, 'tdiff_{}.tif'.format(month))\n tavg_nodata = pygeoprocessing.get_raster_info(\n Tavg_raster_path)['nodata'][0]\n td_nodata = pygeoprocessing.get_raster_info(\n TD_raster_path)['nodata'][0]\n RA_val = (\n RA_df.loc[RA_df['month'] == month, 'average_RA'].values[0])\n RA_raster_path = os.path.join(climate_dir, 'RA.tif')\n pygeoprocessing.new_raster_from_base(\n modified_precip_path, RA_raster_path, gdal.GDT_Float32,\n [_TARGET_NODATA], fill_value_list=[RA_val])\n ET0_raster_path = os.path.join(ET0_dir, 'ET_{}.tif'.format(month))\n pygeoprocessing.raster_calculator(\n [(path, 1) for path in [\n RA_raster_path, Tavg_raster_path, TD_raster_path,\n modified_precip_path]],\n calc_ET0, ET0_raster_path, gdal.GDT_Float32, _TARGET_NODATA)","def _eval_templates(self, period):\n theta_best = [self._optimize(period, tmpid)\n for tmpid, _ in enumerate(self.templates)]\n chi2 = [self._chi2(theta, period, tmpid)\n for tmpid, theta in enumerate(theta_best)]\n\n return theta_best, chi2","def inhomoCoeffs(Bulk_Operators, Bdy_Operators, Bulk_second, s=1):\n\t\tRow = []\n\t\tfor Bulk_Op in Bulk_Operators:\n\t\t\t\ttable_call = table1.table[0, 0].subs({\"Delta\":Bulk_Op, \"Delta_12\":delta_12, \"Xi\":1.00})\n\t\t\t\tRow.append(table_call)\n\t\tfor index in range(len(Bulk_second)):\n\t\t\t\tRow.append(0*table1.table[0,0])\n\t\tfor Bdy_Op in Bdy_Operators:\n\t\t\tif Bdy_Op == 2.0:\n\t\t\t\ttable_call = (N-1)*table1.table[1, 0].subs({\"Delta\":Bdy_Op, \"Xi\":1.00})\n\t\t\telse:\n\t\t\t\ttable_call = table1.table[1, 0].subs({\"Delta\":Bdy_Op, \"Xi\":1.00})\n\t\t\tRow.append(table_call)\n\t\tif s==1:\n\t\t\tXi = symbols('Xi')\n\t\t\tlast_expr = Xi**((delta_1 + delta_2)/2)\n\t\t\tlast_elem = last_expr.subs({\"Xi\":1.00}).evalf()\n\t\t\tRow.append(last_elem)\n\t\tRow2 = []\n\t\tfor index in range(len(Bulk_Operators)):\n\t\t\t\tRow2.append(0*table1.table[0,0])\n\t\tfor Bulk_Op in Bulk_second:\n\t\t\t\ttable_call = table1.table[0, 0].subs({\"Delta\":Bulk_Op, \"Delta_12\":delta_12, \"Xi\":1.00})\n\t\t\t\tRow2.append(table_call)\n\t\tfor Bdy_Op in Bdy_Operators:\n\t\t\tif Bdy_Op == 2.0:\n\t\t\t\ttable_call = -1*table1.table[1,0].subs({\"Delta\":Bdy_Op, \"Xi\":1.00})\n\t\t\telse:\n\t\t\t\ttable_call = table1.table[1,0].subs({\"Delta\":Bdy_Op, \"Xi\":1.00})\n\t\t\tRow2.append(table_call)\n\t\tif s==1:\n\t\t\tXi = symbols('Xi')\n\t\t\tlast_expr = Xi**((delta_1 + delta_2)/2)\n\t\t\tlast_elem = last_expr.subs({\"Xi\":1.00}).evalf()\n\t\t\tRow2.append(last_elem)\n\t\t\t\n\t\treturn np.array(Row), np.array(Row2)","def psfTemplateModel(n, params):\n psf_template = params[\"psf_template\"]\n self.m_psf = psf_template\n print(\"PSF template shape\", np.shape(psf_template))\n dim = int(n)\n m = np.shape(psf_template)[0]\n #if m != dim:\n # raise ValueError(\"PSF template dimension not equal patch size\")\n \n if np.sum(psf_template) != 1:\n print(\"Normalizing PSF template to sum = 1\")\n psf_template = psf_template/np.sum(psf_template) \n return psf_template","def run(self, template_data, data_cont):\n\n tprops = acalib.core.transform.fits_props(template_data)\n scaled = acalib.core.transform.scale(data_cont, tprops['major'])\n rotated, angles = acalib.core.transform.rotate(scaled, tprops['angle'])\n aligned = acalib.core.transform.crop_and_align(rotated, angles)\n result = mean(aligned , axis=0)\n return result","def getTru(n,int_method,func) :\n m = np.asarray([0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,6,7,8,9,10])\n bes = np.asarray([-0.5,0,0.43675,0.47773,0.49231,0.49316,0.49280,0.50325,\n 0.51140,0.52169,0.55823,0.58086,0.60463,0.61483,0.66995])\n p = np.asarray([1,0,0.61007,0.77491,0.84071,0.87689,0.89914,0.91365,\n 0.92449,0.93279,0.94451,0.95289,0.95904,0.96385,\n 0.96731])\n h1 = np.asarray([0,0,-0.07257,-0.04963,-0.03313,-0.02282,-0.01648,\n -0.01248,-0.00970,-0.00773,-0.00522,-0.00369,-0.00272,\n -0.00206,-0.00164])\n h2 = np.asarray([0,0,-0.20048,-0.15556,-0.12070,-0.09611,-0.07919,\n -0.06747,-0.05829,-0.05106,-0.04060,-0.03311,-0.02768,\n -0.02353,-0.02053])\n h3 = np.asarray([0,0,0.01647,0.08284,0.14390,0.19680,0.24168,0.27969,\n 0.31280,0.34181,0.39002,0.42942,0.46208,0.48997,0.51325])\n \n if (func == True) :\n int_bes = splev(n,splrep(m, bes))\n int_p = splev(n,splrep(m, p))\n int_h1 = splev(n,splrep(m, h1))\n int_h2 = splev(n,splrep(m, h2))\n int_h3 = splev(n,splrep(m, h3))\n else :\n int_bes = griddata(m, bes, n, method=int_method)\n int_p = griddata(m, p, n, method=int_method)\n int_h1 = griddata(m, h1, n, method=int_method)\n int_h2 = griddata(m, h2, n, method=int_method)\n int_h3 = griddata(m, h3, n, method=int_method)\n \n return np.asarray([int_bes,int_p,int_h1,int_h2,int_h3])","def gen_op3(N11, N22, N12, rN11, rN22, rN12):\n raw = N12 / (N11 * N22)\n ref = rN12 / (rN11 * rN22)\n print(\"=====\\n3rd method: raw = N12 / (N11 * N22)\")\n print(\"raw: %.2f | reference: %.2f\" % (raw, ref))\n print(\"OP: 1 - raw = %.3f\\n=====\" % (1 - raw))","def compute_template_spectrum(self):\n b = self.setup_b_instance(1,add_ps_mask=False)\n En_min = b.CTB_en_bins[0]\n En_max = b.CTB_en_bins[1]\n dE = En_max - En_min\n self.En_center = 10**((np.log10(En_max)+np.log10(En_min))/2)\n\n flux_map = b.fixed_template_dict_nested['summed_templates'] / b.CTB_exposure_maps_masked_compressed[0]/float(hp.nside2pixarea(b.nside))\n self.spectrum = np.mean(flux_map)/dE","def Generate_BG_Template(outputSize=300, angularSize = 10, fileOut = 'BGRateMap.pickle' ):\r\n template = np.zeros((outputSize,outputSize))\r\n ppd=float(outputSize)/float(angularSize) # pixels per deg\r\n \r\n events110 = ParseFermi.Import_File('photons.txt', energyRange = (120000,140000),lonRange=(-5,5),latRange = (-5,5))\r\n events130 = ParseFermi.Import_File('photons.txt', energyRange = (100000,120000),lonRange=(-5,5),latRange = (-5,5))\r\n events150 = ParseFermi.Import_File('photons.txt', energyRange = (140000,200000),lonRange=(-5,5),latRange = (-5,5))\r\n \r\n for i in range(10000,200001,20000):\r\n if i == 130000:\r\n continue\r\n events = ParseFermi.Import_File('photons.txt', energyRange = (i-10000,i+10000),lonRange=(-5,5),latRange = (-5,5))\r\n BG = np.zeros((outputSize,outputSize)) \r\n for j in events:\r\n xIDX = int(j[1]*ppd+float(outputSize/2))\r\n yIDX = int(j[2]*ppd+float(outputSize/2))\r\n BG[yIDX][xIDX] += 1.0\r\n \r\n psfDeg = .2+float(200)/float(i)\r\n psfOut = psfDeg*ppd\r\n #print i/1e3, psfDeg, psfOut\r\n \r\n template += scipy.ndimage.filters.gaussian_filter(BG, psfOut)\r\n \r\n template = template/np.max(template)\r\n \r\n # Write to file \r\n outFile = open(fileOut, \"wb\" )\r\n pickle.dump(template, outFile)\r\n print 'Rate Map saved to ', fileOut\r\n \r\n plt.imshow(scipy.fliplr(template), 'jet',extent=[5,-5,-5,5])\r\n\r\n plt.xlabel(r'$l [^\\circ]$')\r\n plt.ylabel(r'$b [^\\circ]$')\r\n plt.xlim(5,-5)\r\n plt.ylim(-5,5)\r\n plt.colorbar()\r\n\r\n x,y = Find_Centroid(template)\r\n x,y = (x/ppd -angularSize/2.0,) ,(y/ppd -angularSize/2.0,)\r\n print x,y\r\n plt.scatter(x,y, s=10, c='r', marker = '+')\r\n \r\n X,Y = FormatEvents(events110)\r\n plt.scatter(X, Y, label = '100-120 GeV', marker = 'o' , c = 'k')\r\n \r\n X,Y = FormatEvents(events130)\r\n plt.scatter(X, Y, label = '120-140 GeV', marker = 'o' , c = 'r')\r\n \r\n X,Y = FormatEvents(events150)\r\n plt.scatter(X, Y, label = '140-200 GeV', marker = 'o' , c = 'g' )\r\n \r\n from matplotlib.font_manager import FontProperties\r\n fontP = FontProperties()\r\n fontP.set_size('small')\r\n plt.legend(loc=1, ncol=1, fancybox=True, shadow=False,prop=fontP,borderaxespad=0.,labelspacing = .2)\r\n \r\n from matplotlib.backends.backend_pdf import PdfPages\r\n if fileOut != '':\r\n pp = PdfPages(fileOut + '_sideband.pdf')\r\n plt.savefig(pp, format='pdf')\r\n print \"Figures saved to \", str(fileOut)+ '_sideband.pdf\\n',\r\n pp.close()\r\n \r\n plt.show()\r\n return template","def _add_template(self):\n template_dir = os.path.join(self.label_path, 'standard',\n 'MNI152_T1_2mm_brain.nii.gz')\n template_name = QFileDialog.getOpenFileName(\n self,\n 'Open standard file',\n template_dir,\n 'Nifti files (*.nii.gz *.nii)')\n if not template_name.isEmpty():\n template_path = str(template_name)\n self._add_img(template_path)","def vpsc_in(\r\n ## # elem, ph and vol fraction of phases.\r\n ## and ishape for flag for morphology updates\r\n nelem=1, nph=1, wph =[1.0, 0.0], ishape=[0,],\r\n\r\n #framgmentation options and critical value limit \r\n # in distortion of the inclusion \r\n # as well as ellipsoid description: orientation and \r\n # principal lengths of'em\r\n fragmentn = [0,0], crit = [25,25],\r\n ellipangl = [[0., 0., 0.],[0.,0.,0.]],\r\n ellipaxes = [[1., 1., 1.],[1.,1.,1.]],\r\n \r\n # vpsc7.in file name (fixed)\r\n # texture, singlecrystal and shape file names in list format\r\n vin_name = 'VPSC7.in',\r\n tfile= ['texture/500.tex',],\r\n sxfile=['sx/hijhiihb.sx',],\r\n fileaxes=['shape1.100', 'shape1.100'],\r\n\r\n ### precision settings for convergence prodcedure\r\n ##\r\n err=0.001, errd=0.001, errm=0.001, errso=0.01,\r\n nmxiter=100, exiter=100, initer=100,\r\n irsvar=0, xrsini=2, xrsfin=10, xrstep=2,\r\n\r\n ### post-mortem flags, texture component calc. flag.\r\n ## \r\n irecover = 0, isave=0, icubcomp = 0, nwrite = 0, \r\n\r\n ## Single crystal hardening rule (-2: dislocation, 0:Voce, 1: MTS, 2: composite)\r\n ihardlaw = 0, \r\n\r\n ## fluctuation flag\r\n iflu = 0,\r\n\r\n ## rate insensitive, interaction :(FC,aff,2:sec,3:neff,4:tan,5:SO)\r\n ## update flags for orient, grain shape, hardening, itran(youngung)\r\n ## nneigh (0 for no neighbors, 1 for pairs, etc.)\r\n iratesens = 0, interaction = 3, iupdate = [1,1,1,0], nneigh = 0, \r\n\r\n ## proce number and processes\r\n npro = None,\r\n prcs = [], #processes\r\n \r\n ishow=False\r\n ):\r\n try: os\r\n except: import os\r\n else: pass\r\n cwd = os.getcwd()\r\n\r\n # READ EXISITING 'vpsc7.in'\r\n if os.path.isfile(vin_name): #if there already exists the file!\r\n f_vin = file(vin_name, 'r')\r\n lines = f_vin.read()\r\n lines = lines.split('\\n')\r\n f_temp = file('%s.tmp'%vin_name, 'w')\r\n for i in lines:\r\n f_temp.writelines(i)\r\n f_temp.writelines('\\n')\r\n pass\r\n f_temp.close()\r\n f_vin.close()\r\n \"\"\"\r\n # DEL EXISITING 'vpsc7.in'\r\n f_vin.close()\r\n os.remove(vin_name)\r\n \"\"\"\r\n # MAKE NEW 'vpsc7.in'\r\n f_vin = file(vin_name, 'w')\r\n f_vin.writelines('%i nelem\\n'%(nelem))\r\n f_vin.writelines('%i nph\\n'%(nph))\r\n for i in range(nph):\r\n f_vin.writelines('%f '%(wph[i]))\r\n f_vin.writelines(' wph[i]\\n')\r\n for i in range(nph): \r\n f_vin.writelines('*** INFORMATION ABOUT PHASE #%i\\n'%(i+1))\r\n f_vin.writelines('%i %i %i'%(ishape[i], fragmentn[i], crit[i]))\r\n f_vin.writelines(' ishape, fragmentn, crit aspect\\n')\r\n f_vin.writelines('%f %f %f'%(ellipaxes[i][0], \r\n ellipaxes[i][1], \r\n ellipaxes[i][2]))\r\n f_vin.writelines(' initial ellipsoid ratios (dummy if ishape=4)\\n')\r\n f_vin.writelines('%f %f %f'%(ellipangl[i][0], \r\n ellipangl[i][1], \r\n ellipangl[i][2]))\r\n f_vin.writelines(' init Eul ang ellips axes(dummy if ishape=3,4)\\n')\r\n # WRITES TEXTURE FILE\r\n f_vin.writelines('----------- filetext\\n')\r\n f_vin.writelines(tfile[i]+'\\n')\r\n f_vin.writelines('----------- sxfile\\n')\r\n # WRITES SINGLE CRYSTAL FILE\r\n f_vin.writelines(sxfile[i]+'\\n')\r\n # WRITES SHAPE FILE\r\n f_vin.writelines('----------- fileaxes (dummy if ishape=0) \\n')\r\n f_vin.writelines('%s \\n'%(fileaxes[i]))\r\n\r\n\r\n #### precision control block ###\r\n f_vin.writelines('*** PRECISION SETTINGS FOR')\r\n f_vin.writelines(' CONVERGENCE PROCEDURES (default values)\\n')\r\n f_vin.writelines('%f %f %f %f'%(err, #err\r\n errd, \r\n errm,\r\n errso))\r\n f_vin.writelines(' errs,errd,errm,errso\\n')\r\n f_vin.writelines('%i %i %i %25s'%( nmxiter, exiter, initer,' '))\r\n f_vin.writelines('itmax: max # of iter, external, internal and SO loops\\n')\r\n f_vin.writelines('%i %i %i %i %30s'%(irsvar, xrsini, xrsfin, xrstep,' '))\r\n f_vin.writelines('irsvar & xrsini, xrsfin, xrstep (dummy if irsvar = 0) \\n')\r\n f_vin.writelines(\"%i ibcinv (0: don't use <Bc>**-1, 1: use \\n\"%(1))\r\n f_vin.writelines('*** INPUT/OUTPUT SETTINGS FOR THE RUN(default is zero)\\n')\r\n f_vin.writelines('%i irecover:read grain stats from POSTMORT.IN(1) OR NOT(0)\\n'%(irecover))\r\n f_vin.writelines(\"%i isave: write grain states in postmor.out at step 'isave'?\\n\"%(isave))\r\n f_vin.writelines(\"%i icubcompL calcuate fcc rolling components?\\n\"%(icubcomp))\r\n f_vin.writelines(\"%i nwrite (frequency of texture downloads)\\n\"%(nwrite))\r\n f_vin.writelines(\"*** MODELING CONDITIONS FOR THE RUN \\n\")\r\n f_vin.writelines(\"%i ihardlaw(0:VOCE, 1: MTS, 2: composite grain\\n\"%(ihardlaw))\r\n f_vin.writelines('%1i' %(iratesens))\r\n f_vin.writelines(' iratesens (0:rate insensitive, 1:rate sensitive) \\n')\r\n f_vin.writelines('%1i' %(interaction))\r\n f_vin.writelines(' interaction (0:FC,1:affine,2:secant,3:neff=10,4:tangent,5:SO) \\n')\r\n f_vin.writelines('%1i%3i%3i%3i'%(iupdate[0],iupdate[1],iupdate[2],iupdate[3]))\r\n f_vin.writelines(' iupdate: update ori, grain shape, hardening, itran \\n')\r\n f_vin.writelines('%1i'%(nneigh))\r\n f_vin.writelines(' nneigh (0 for no neighbors, 1 for pairs, etc.)\\n')\r\n f_vin.writelines('%i'%(iflu))\r\n f_vin.writelines(\" iflu(0:don't calc, 1: calc fluctuations\\n\")\r\n\r\n f_vin.writelines(\"*NUMBER OF PROCESSES\\n\")\r\n f_vin.writelines(\"%i \\n\"%(npro))\r\n f_vin.writelines(\"** (0,1: loadings, 2:pcys, 3:lankf, 4: RBR,\")\r\n f_vin.writelines(\"5:pcys_pl, 6: pysc_pl 6d(ix,iy, nprob, ang\")\r\n f_vin.writelines(\"( or if negative behave like pcys))) \\n\")\r\n for i in range(len(prcs)):\r\n f_vin.writelines(prcs[i]+'\\n')\r\n \r\n # Closure of the file\r\n f_vin.close() \r\n # -------------------\r\n\r\n # Printing-out the just-made-vpsc7.in file\r\n if ishow==True:\r\n if os.name=='nt' or os.name=='posix':\r\n print \"****** VPSC input file ******\"\r\n print \"* VPSC7 input parameters \"\r\n print \"* has been written down to \"\r\n print \"* '%8s' as below\"%vin_name\r\n print \"*****************************\\n\"\r\n if os.name=='nt':\r\n os.system('%s %s'%('type',vin_name))\r\n elif os.name=='posix':\r\n os.system('%s %s'%('cat',vin_name))\r\n else: pass","def prepare_CvD16_for_M2L_calc(templates_lam_range, verbose=False):\n import glob\n import os\n template_glob=os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*.s100')\n\n vcj_models=sorted(glob.glob(template_glob))\n temp_lamdas, x35, x3, x23, kroupa, flat=np.genfromtxt(vcj_models[-1], unpack=True)\n\n n_ages=7\n n_zs=5\n n_imfs=5\n\n \n\n\n Zs=['m1.5', 'm1.0', 'm0.5', 'p0.0', 'p0.2']\n ages=[1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.5]\n model_imfs_order=['x35', 'x3', 'x23', 'kroupa', 'flat']\n\n t_mask = ((temp_lamdas > templates_lam_range[0]) & (temp_lamdas <templates_lam_range[1]))\n\n\n\n y=x35[t_mask]\n x=temp_lamdas[t_mask]\n\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\n interp=si.interp1d(x, y, fill_value='extrapolate')\n out=interp(new_x)\n\n templates=np.empty((len(out), n_ages, n_zs, n_imfs))\n\n\n\n for a, Z in enumerate(Zs): \n for b, age in enumerate(ages):\n model=glob.glob(os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*{}*{}.ssp.s100'.format(Z, age)))[0]\n if verbose:\n print 'Loading {}'.format(model)\n data=np.genfromtxt(model)\n\n for c, counter in enumerate(reversed(range(1, data.shape[-1]))):\n \n #Interpolate templates onto a uniform wavelength grid and then log-rebin\n y=data[:, counter][t_mask] \n x=temp_lamdas[t_mask]\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\n\n interp=si.interp1d(x, y, fill_value='extrapolate')\n out=interp(new_x) \n\n templates[:, b, a, c]=out\n\n return templates, new_x","def tpl(x):\n return 3 * x","def gen_new_phiw_div_phib_arr(N_PROCESSES, phiw_div_phib_arr_new, cond_GT, fcn_D, fcn_eta, z_div_L_arr, phiw_div_phib_arr, Pi_div_DLP_arr, weight, gp_arr, gm_arr, yt_arr, phi_yt_arr, ID_yt_arr, Ieta_yt_arr):\n phi_b = cond_GT['phi_bulk']\n ed = cond_GT['epsilon_d']\n membrane_geometry = cond_GT['membrane_geometry']\n \n Ny = size(yt_arr)\n # # Python allocate the name for phi_yt_arr[0], this is the same as reference value for C++ \" y= &x\"\n phi_arr_z0 = phi_yt_arr[0]\n Ieta_arr_z0= Ieta_yt_arr[0]\n ID_arr_z0 = ID_yt_arr[0]\n\n ind_z0 = 0 #z-index at inlet\n \n z0_div_L = 0. #z-coord at inlet\n \n r0_div_R = 0. #r-coord at the centerline of pipe\n rw_div_R = 1. #r-coord at the membrane wall\n \n vw_div_vw0_z0 = get_v_conv(rw_div_R, z0_div_L, Pi_div_DLP_arr[ind_z0], cond_GT, gp_arr[ind_z0], gm_arr[ind_z0])\n gen_phi_wrt_yt(z0_div_L, phiw_div_phib_arr[ind_z0]*phi_b, fcn_D, vw_div_vw0_z0, yt_arr, phi_arr_z0, cond_GT)\n gen_INT_inv_f_wrt_yt(yt_arr, phi_arr_z0, Ieta_arr_z0, fcn_eta, cond_GT)\n Ieta_arr_z0 /= Ieta_arr_z0[-1] # CHECK\n gen_INT_inv_f_wrt_yt(yt_arr, phi_arr_z0, ID_arr_z0, fcn_D, cond_GT)\n\n uZ_z0 = get_uZ_out(z0_div_L, cond_GT['k'], cond_GT['Bp'], cond_GT['Bm'], gp_arr[ind_z0], gm_arr[ind_z0])\n F2_0 = cal_F2_Z(vw_div_vw0_z0, ed, yt_arr, Ieta_arr_z0, ID_arr_z0, uZ_z0, membrane_geometry)\n\n Nz = size(z_div_L_arr)\n if (N_PROCESSES ==1):\n # when only single-processor is allocated\n for i in range(1, Nz):\n phiw_div_phib_arr_new[i] = process_at_zi(z_div_L_arr[i], phiw_div_phib_arr[i]*phi_b, Pi_div_DLP_arr[i], cond_GT, gp_arr[i], gm_arr[i], yt_arr, phi_yt_arr[i], Ieta_yt_arr[i], fcn_eta, ID_yt_arr[i], fcn_D, F2_0)\n else:\n # this uses multiprocessing packages\n import multiprocessing as mp\n \n pool = mp.Pool(N_PROCESSES)\n args_list = [(z_div_L_arr[i], phiw_div_phib_arr[i]*phi_b, Pi_div_DLP_arr[i], cond_GT, gp_arr[i], gm_arr[i], yt_arr, phi_yt_arr[i], Ieta_yt_arr[i], fcn_eta, ID_yt_arr[i], fcn_D, F2_0)\\\n for i in range(1, Nz)]\n phiw_div_phib_arr_new[1:] = pool.starmap(process_at_zi, args_list)\n pool.close()\n pool.join()\n\n cnt_EXCEED = 0 \n for i,x in enumerate(phiw_div_phib_arr_new):\n\n x = x*cond_GT['phi_bulk']\n if x > cond_GT['phi_freeze']:\n cnt_EXCEED += 1\n phiw_div_phib_arr_new[i] = cond_GT['phi_freeze']/cond_GT['phi_bulk'] # this prevent the accidently beyond the freezing concentration\n if(cnt_EXCEED>0):\n print('Warning: exceed phi_freeze %d times out of %d\\n'%(cnt_EXCEED, cond_GT['Nz']))\n\n FPI_operator(cond_GT['weight'], phiw_div_phib_arr, phiw_div_phib_arr_new, N_skip=1) # phiw(0) must be phib.\n\n return 0","def inst_set_cpu_template_fxt(request):\n return request.param","def applyPhotoZ (self,arr):\n print \"Applying Template SED PZs\"\n\n ztrue = arr['z']\n\n #select a template\n templates = ['El_B2004a.sed']+['Sbc_B2004a.sed','Scd_B2004a.sed']\n templates = templates +['Im_B2004a.sed','SB3_B2004a.sed','SB2_B2004a.sed','ssp_25Myr_z008.sed','ssp_5Myr_z008.sed']\n\n #read in f_mod files, interpolate, get values of f_mod_b\n ngals = len(ztrue)\n\n f_mod_o = np.zeros((self.nb, ngals))\n for z in range(ngals):\n #currently templates are randomly chosen but probably should be an input with true z\n templateno = np.random.choice(range(self.nt))\n for b in range(self.nb):\n spl = InterpolatedUnivariateSpline(self.z_grid, self.f_mod[:,templateno,b])\n f_mod_o[b][z] = spl(ztrue[z])\n\n #select sigma_b - 10% for now\n sigma = 0.1*f_mod_o\n #select observed fluxes f_obs_b = f_mod_b + sigma_b*rando\n f_obs = f_mod_o+ sigma * (np.random.normal(0.,1.,self.nb*ngals).reshape((self.nb,ngals)))\n # I don't seem to be able to find a more efficient way\n arrx=np.zeros(ngals,dtype=[('pz_f_obs',float,(self.nb,)),('pz_flux_sigma',float,(self.nb,))])\n arrx['pz_f_obs']=f_obs.T\n arrx['pz_flux_sigma']=sigma.T\n arr = recfunctions.merge_arrays((arr,arrx),flatten=True,usemask=False)\n return arr","def gen_code(self, template, dec_id, i_alt):\n v = self.get_alt(dec_id, i_alt)\n\n # assuming the placeholder var is always at the end\n # which is true given how we chop up the chunks\n return template + str(v), str(v)","def hierarchical( x, output_prefix, labels_to_register=[2,3,4,5], is_test=False, verbose=True ):\n if verbose:\n print(\"Read\")\n tfn = get_data('T_template0', target_extension='.nii.gz' )\n tfnw = get_data('T_template0_WMP', target_extension='.nii.gz' )\n tlrfn = get_data('T_template0_LR', target_extension='.nii.gz' )\n bfn = antspynet.get_antsxnet_data( \"croppedMni152\" )\n\n ##### read images and do simple bxt ops\n templatea = ants.image_read( tfn )\n if verbose:\n print(\"bxt\")\n templatea = ( templatea * antspynet.brain_extraction( templatea, 't1' ) ).iMath( \"Normalize\" )\n templateawmprior = ants.image_read( tfnw )\n templatealr = ants.image_read( tlrfn )\n templateb = ants.image_read( bfn )\n templateb = ( templateb * antspynet.brain_extraction( templateb, 't1' ) ).iMath( \"Normalize\" )\n imgbxt = brain_extraction( x )\n img = x * imgbxt\n\n if verbose:\n print(\"rbp\")\n\n # this is an unbiased method for identifying predictors that can be used to\n # rank / sort data into clusters, some of which may be associated\n # with outlierness or low-quality data\n templatesmall = ants.resample_image( templateb, (91,109,91), use_voxels=True )\n rbp = random_basis_projection( img, templatesmall )\n\n if verbose:\n print(\"intensity\")\n\n ##### intensity modifications\n img = ants.iMath( img, \"Normalize\" ) * 255.0\n img = ants.denoise_image( img, imgbxt, noise_model='Gaussian')\n img = ants.n4_bias_field_correction( img ).iMath(\"Normalize\")\n\n # optional - quick look at result\n bxt_png = output_prefix + \"_brain_extraction_dnz_n4_view.png\"\n ants.plot(img,axis=2,ncol=8,nslices=24, crop=True, black_bg=False,\n filename = bxt_png )\n\n if verbose:\n print(\"hemi\")\n\n # assuming data is reasonable quality, we should proceed with the rest ...\n mylr = label_hemispheres( img, templatea, templatealr )\n\n if verbose:\n print(\"parcellation\")\n\n ##### hierarchical labeling\n myparc = deep_brain_parcellation( img, templateb,\n do_cortical_propagation = not is_test, verbose=False )\n\n ##### accumulate data into data frames\n hemi = map_segmentation_to_dataframe( \"hemisphere\", myparc['hemisphere_labels'] )\n tissue = map_segmentation_to_dataframe( \"tissues\", myparc['tissue_segmentation'] )\n dktl = map_segmentation_to_dataframe( \"lobes\", myparc['dkt_lobes'] )\n dktp = map_segmentation_to_dataframe( \"dkt\", myparc['dkt_parcellation'] )\n dktc = None\n if not is_test:\n dktc = map_segmentation_to_dataframe( \"dkt\", myparc['dkt_cortex'] )\n\n tissue_seg_png = output_prefix + \"_seg.png\"\n ants.plot( img, myparc['tissue_segmentation'], axis=2, nslices=21, ncol=7,\n alpha=0.6, filename=tissue_seg_png,\n crop=True, black_bg=False )\n\n if verbose:\n print(\"WMH\")\n\n ##### below here are more exploratory nice to have outputs\n myhypo = t1_hypointensity(\n img,\n myparc['tissue_segmentation'], # segmentation\n myparc['tissue_probabilities'][3], # wm posteriors\n templatea,\n templateawmprior )\n\n if verbose:\n print(\"registration\")\n\n ##### traditional deformable registration as a high-resolution complement to above\n wm_tractsL = None\n wm_tractsR = None\n wmtdfL = None\n wmtdfR = None\n reg = None\n if labels_to_register is not None:\n reg = hemi_reg(\n input_image = img,\n input_image_tissue_segmentation = myparc['tissue_segmentation'],\n input_image_hemisphere_segmentation = mylr,\n input_template=templatea,\n input_template_hemisphere_labels=templatealr,\n output_prefix = output_prefix + \"_SYN\",\n labels_to_register = labels_to_register,\n is_test=is_test )\n if verbose:\n print(\"wm tracts\")\n ##### how to use the hemi-reg output to generate any roi value from a template roi\n wm_tracts = ants.image_read( get_data( \"wm_major_tracts\", target_extension='.nii.gz' ) )\n wm_tractsL = ants.apply_transforms( img, wm_tracts, reg['synL']['invtransforms'],\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 1, 1 )\n wm_tractsR = ants.apply_transforms( img, wm_tracts, reg['synR']['invtransforms'],\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 2, 2 )\n wmtdfL = map_segmentation_to_dataframe( \"wm_major_tracts\", wm_tractsL )\n wmtdfR = map_segmentation_to_dataframe( \"wm_major_tracts\", wm_tractsR )\n\n if verbose:\n print(\"hippocampus\")\n\n ##### specialized labeling for hippocampus\n ntries = 10\n if is_test:\n ntries = 1\n hippLR = deep_hippo( img, templateb, ntries )\n\n mydataframes = {\n \"hemispheres\":hemi,\n \"tissues\":tissue,\n \"dktlobes\":dktl,\n \"dktregions\":dktp,\n \"dktcortex\":dktc,\n \"wmtracts_left\":wmtdfL,\n \"wmtracts_right\":wmtdfR,\n \"wmh\":myhypo['wmh_summary']\n }\n\n outputs = {\n \"brain_n4_dnz\": img,\n \"brain_n4_dnz_png\": bxt_png,\n \"brain_extraction\": imgbxt,\n \"tissue_seg_png\": tissue_seg_png,\n \"rbp\": rbp,\n \"left_right\": mylr,\n \"dkt_parc\": myparc,\n \"registration\":reg,\n \"hippLR\":hippLR,\n \"white_matter_hypointensity\":myhypo,\n \"wm_tractsL\":wm_tractsL,\n \"wm_tractsR\":wm_tractsR,\n \"dataframes\": mydataframes\n }\n\n return outputs","def _operator_generator(self, index, conj):\n pterm = PauliTerm('I', 0, 1.0)\n Zstring = PauliTerm('I', 0, 1.0)\n for j in range(index):\n Zstring = Zstring*PauliTerm('Z', j, 1.0)\n\n pterm1 = Zstring*PauliTerm('X', index, 0.5)\n scalar = 0.5 * conj * 1.0j\n pterm2 = Zstring*PauliTerm('Y', index, scalar)\n pterm = pterm * (pterm1 + pterm2)\n\n pterm = pterm.simplify()\n return pterm","def calc_ti(self):\n m = 0\n for i in self.inl:\n m += i.m.val_SI * i.fluid.val[self.fuel_alias.val]\n\n for o in self.outl:\n m -= o.m.val_SI * o.fluid.val[self.fuel_alias.val]\n\n return m * self.lhv","def calc_ST_operation(m_exhaust_GT_kgpers, T_exhaust_GT_K, T_sup_K, fuel_type):\n\n # calaulate High Pressure (HP) and Low Pressure (LP) mass flow of a double pressure steam turbine\n temp_i_K = (0.9 * ((6 / 48.2) ** (0.4 / 1.4) - 1) + 1) * (T_exhaust_GT_K - ST_DELTA_T)\n if fuel_type == 'NG':\n Mexh = 103.7 * 44E-3 + 196.2 * 18E-3 + 761.4 * 28E-3 + 200.5 * (CC_AIRRATIO - 1) * 32E-3 \\\n + 200.5 * (CC_AIRRATIO - 1) * 3.773 * 28E-3\n ncp_exh = 103.7 * 44 * 0.846 + 196.2 * 18 * 1.8723 + 761.4 * 28 * 1.039 \\\n + 200.5 * (CC_AIRRATIO - 1) * 32 * 0.918 + 200.5 * (CC_AIRRATIO - 1) * 3.773 * 28 * 1.039\n cp_exh = ncp_exh / Mexh # J/kgK\n else:\n Mexh = 98.5 * 44E-3 + 116 * 18E-3 + 436.8 * 28E-3 + 115.5 * (CC_AIRRATIO - 1) * 32E-3 \\\n + 115.5 * (CC_AIRRATIO - 1) * 3.773 * 28E-3\n ncp_exh = 98.5 * 44 * 0.846 + 116 * 18 * 1.8723 + 436.8 * 28 * 1.039 \\\n + 115.5 * (CC_AIRRATIO - 1) * 32 * 0.918 + 115.5 * (CC_AIRRATIO - 1) * 3.773 * 28 * 1.039\n cp_exh = ncp_exh / Mexh # J/kgK\n\n a = np.array([[1653E3 + HEAT_CAPACITY_OF_WATER_JPERKGK * (T_exhaust_GT_K - ST_DELTA_T - 534.5), \\\n HEAT_CAPACITY_OF_WATER_JPERKGK * (temp_i_K - 534.5)], \\\n [HEAT_CAPACITY_OF_WATER_JPERKGK * (534.5 - 431.8), \\\n 2085.8E3 + HEAT_CAPACITY_OF_WATER_JPERKGK * (534.5 - 431.8)]])\n b = np.array([m_exhaust_GT_kgpers * cp_exh * (T_exhaust_GT_K - (534.5 + ST_DELTA_T)), \\\n m_exhaust_GT_kgpers * cp_exh * (534.5 - 431.8)])\n [mdotHP_kgpers, mdotLP_kgpers] = np.linalg.solve(a, b)\n\n # calculate thermal output\n T_cond_0_K = T_sup_K + CC_DELTA_T_DH # condensation temperature constrained by the DH network temperature\n pres0 = (0.0261 * (T_cond_0_K - 273) ** 2 - 2.1394 * (T_cond_0_K - 273) + 52.893) * 1E3\n\n delta_h_evap_Jperkg = (-2.4967 * (T_cond_0_K - 273) + 2507) * 1E3\n q_output_ST_W = (mdotHP_kgpers + mdotLP_kgpers) * delta_h_evap_Jperkg # thermal output of ST\n\n # calculate electricity output\n h_HP_Jperkg = (2.5081 * (T_exhaust_GT_K - ST_DELTA_T - 273) + 2122.7) * 1E3 # J/kg\n h_LP_Jperkg = (2.3153 * (temp_i_K - 273) + 2314.7) * 1E3 # J/kg\n h_cond_Jperkg = (1.6979 * (T_cond_0_K - 273) + 2506.6) * 1E3 # J/kg\n\n el_produced_ST_W = mdotHP_kgpers * (h_HP_Jperkg - h_LP_Jperkg) + \\\n (mdotHP_kgpers + mdotLP_kgpers) * (h_LP_Jperkg - h_cond_Jperkg) # turbine electricity output\n\n el_input_compressor_W = SPEC_VOLUME_STEAM * (\n mdotLP_kgpers * (6E5 - pres0) + (mdotHP_kgpers + mdotLP_kgpers) * (48.2E5 - 6E5)) # compressor electricity use\n\n el_output_ST_W = ST_GEN_ETA * (el_produced_ST_W - el_input_compressor_W) # gross electricity production of turbine\n\n return q_output_ST_W, el_output_ST_W","def create_eqt_template(nodes, input_filename):\n output_filename = f'{input_filename[:-4]}_eqpt_sheet.txt'\n with open(output_filename, 'w', encoding='utf-8') as my_file:\n # print header similar to excel\n my_file.write('OPTIONAL\\n\\n\\n\\\n \\t\\tNode a egress amp (from a to z)\\t\\t\\t\\t\\tNode a ingress amp (from z to a) \\\n \\nNode A \\tNode Z \\tamp type \\tatt_in \\tamp gain \\ttilt \\tatt_out\\\n amp type \\tatt_in \\tamp gain \\ttilt \\tatt_out\\n')\n\n for node in nodes.values():\n if node.eqpt == 'ILA':\n my_file.write(f'{node.uid}\\t{node.to_node[0]}\\n')\n if node.eqpt == 'ROADM':\n for to_node in node.to_node:\n my_file.write(f'{node.uid}\\t{to_node}\\n')\n\n print(f'File {output_filename} successfully created with Node A - Node Z entries for Eqpt sheet in excel file.')","def __createSpecTempT(self):\n\t\tspecTempT = np.copy(self.specTemplates)\n\t\tspecTempT = np.transpose(specTempT)\n\t\tspecTempT = specTempT * self.covInv\n\t\t\t\t\n\t\treturn specTempT","def calc_CC_operation(el_output_from_GT_W, GT_size_W, fuel_type, T_sup_K):\n\n (eta0, m0_exhaust_GT_kgpers) = calc_GT_operation_fullload(GT_size_W, fuel_type)\n (eta, m_exhaust_GT_kgpers, T_exhaust_GT_K, m_fuel_kgpers) = calc_GT_operation_partload(el_output_from_GT_W,\n GT_size_W, eta0,\n m0_exhaust_GT_kgpers,\n fuel_type)\n\n (q_output_ST_W, el_output_ST_W) = calc_ST_operation(m_exhaust_GT_kgpers, T_exhaust_GT_K, T_sup_K, fuel_type)\n\n LHV = LHV_NG if fuel_type == 'NG' else LHV_BG # read LHV of NG or BG\n\n eta_el = (el_output_from_GT_W + el_output_ST_W) / (m_fuel_kgpers * LHV)\n eta_thermal = q_output_ST_W / (m_fuel_kgpers * LHV)\n eta_total = eta_el + eta_thermal\n el_output_W = el_output_ST_W + el_output_from_GT_W\n\n return {'el_output_W': el_output_W, 'q_output_ST_W': q_output_ST_W, 'eta_el': eta_el, 'eta_thermal': eta_thermal,\n 'eta_total': eta_total}","def car_template(ncomp, ra0, ra1, dec0, dec1, res):\n\n if ncomp == 3:\n pre = (3,)\n else:\n pre = ()\n\n box = get_box(ra0, ra1, dec0, dec1)\n res = res * np.pi / (180 * 60)\n temp = so_map()\n shape, wcs = enmap.geometry(box, res=res, pre=pre)\n temp.data = enmap.zeros(shape, wcs=wcs, dtype=None)\n temp.pixel = \"CAR\"\n temp.nside = None\n temp.ncomp = ncomp\n temp.geometry = temp.data.geometry[1:]\n temp.coordinate = \"equ\"\n return temp","def inst_set_cpu_template_ext_fxt(request):\n return request.param","def calc_Cinv_CCGT(CC_size_W, CCGT_cost_data):\n\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\n # capacity for the corresponding technology from the database\n if CC_size_W < CCGT_cost_data['cap_min'][0]:\n CC_size_W = CCGT_cost_data['cap_min'][0]\n CCGT_cost_data = CCGT_cost_data[\n (CCGT_cost_data['cap_min'] <= CC_size_W) & (CCGT_cost_data['cap_max'] > CC_size_W)]\n\n\n #costs of connection\n connection_costs = ngas.calc_Cinv_gas(CC_size_W)\n\n Inv_a = CCGT_cost_data.iloc[0]['a']\n Inv_b = CCGT_cost_data.iloc[0]['b']\n Inv_c = CCGT_cost_data.iloc[0]['c']\n Inv_d = CCGT_cost_data.iloc[0]['d']\n Inv_e = CCGT_cost_data.iloc[0]['e']\n Inv_IR = CCGT_cost_data.iloc[0]['IR_%']\n Inv_LT = CCGT_cost_data.iloc[0]['LT_yr']\n Inv_OM = CCGT_cost_data.iloc[0]['O&M_%'] / 100\n\n InvC = Inv_a + Inv_b * (CC_size_W) ** Inv_c + (Inv_d + Inv_e * CC_size_W) * log(CC_size_W)\n\n Capex_a_CCGT_USD = calc_capex_annualized((InvC+connection_costs), Inv_IR, Inv_LT)\n Opex_fixed_CCGT_USD = InvC * Inv_OM\n Capex_CCGT_USD = InvC\n\n return Capex_a_CCGT_USD, Opex_fixed_CCGT_USD, Capex_CCGT_USD","def _template(inlist):\n from collections import OrderedDict\n if isinstance(inlist, str):\n inlist = [inlist]\n\n templates = []\n for item in reversed(inlist):\n templates.append(output_space(item))\n\n return OrderedDict(reversed(OrderedDict(templates).items()))","def whc_tot(mukey, layers=''):\n #read appropriate soils.in content to a python list\n mukey = str(mukey)\n soil_path = \"/data/paustian/ernie/SSURGO_master_script/soil_test2/\"\n soil_fpath = soil_path+mukey[:-3]+\"/\"+mukey+\".in\"\n cont = [[]]\n data_input = open(soil_fpath, 'r')\n for line in data_input:\n cont.append(line.split())\n del cont[0]\n\n #convert all entries in the 2D list to float format where possible, or zero in the case\n #of very small numbers recorded in scientific notation\n for k in range(len(cont)):\n for l in range(len(cont[k])):\n cont[k][l] = float(cont[k][l])\n\n #loop through list and compute the water holding capacity increment represented in \n #each line\n min_h2o_evap = 0\n min_h2o = 0\n max_h2o = 0\n whc = 0\n for i in range(len(cont)):\n if not layers:\n depth = cont[i][1] - cont[i][0]\n FC = cont[i][3]\n WP = cont[i][4]\n WHC = FC - WP\n if i != 0:\n min_h2o_evap += depth*WP\n min_h2o += depth*WP\n max_h2o += depth*FC\n whc += depth*WHC\n else:\n if 1+i <= layers:\n depth = cont[i][1] - cont[i][0]\n FC = cont[i][3]\n WP = cont[i][4]\n WHC = FC - WP\n if i != 0:\n min_h2o_evap += depth*WP\n min_h2o += depth*WP\n max_h2o += depth*FC\n whc += depth*WHC\n if layers:\n if layers > len(cont):\n print \"NOTE: specified layer limit exceeds number of layers found in soils.in file\"\n\n return whc, min_h2o, max_h2o","def Build_Background_Template(numBGPhotons, bgTemplate, PSFTableFront, PSFTableBack,flatLevel = 0.0,HESS = False,outputSize=300,angularSize=10.0):\r\n \r\n numPhotons = numBGPhotons\r\n numHigh = int(round(.32 *numPhotons))\r\n numLow = numPhotons-numHigh\r\n \r\n bgEventsX = []\r\n bgEventsY = []\r\n \r\n bgTemplate = bgTemplate *(1.0-flatLevel) + flatLevel\r\n# import matplotlib.pyplot as plt\r\n# plt.imshow(bgTemplate,'jet',vmin=0, vmax=1)\r\n# plt.colorbar()\r\n# plt.show()\r\n\r\n app=float(angularSize)/float(outputSize) # angle per pixel\r\n for i in range(numPhotons):\r\n x ,y = 0, 0\r\n while True:\r\n x,y = np.random.randint(0,high = len(bgTemplate)),np.random.randint(0,high = len(bgTemplate))\r\n if (np.random.ranf() < bgTemplate[y][x]):\r\n break\r\n # Shift and scale coordinates to output map and then compute PSF modification to the position.\r\n psfMod = PSF_Spread(PSFTableFront,PSFTableBack, HESS =HESS)\r\n dx = psfMod[0]*math.cos(psfMod[1]) # PSF shift in deg\r\n dy = psfMod[0]*math.sin(psfMod[1]) # PSF shift in deg\r\n \r\n bgEventsX.append((x-outputSize/2.0)*app + dx)\r\n bgEventsY.append((y-outputSize/2.0)*app + dy)\r\n \r\n return (bgEventsX, bgEventsY)","def compute_partials(self, inputs, partials):\n partials['y', 'x'] = 2.0","def generate(self, namespace: Optional[str], template: str, func: Callable, call_args: Dict) -> str:","def make_template(size, profile, dx=0, dy=0, oversampling=10, normalized=True):\n xy = (np.arange(size * oversampling) - 0.5 * (size * oversampling - 1)) / oversampling\n z = profile(xy - dx, (xy - dy).reshape(-1, 1))\n T = downsample(z, oversampling, np.mean)\n if normalized:\n T /= T.sum()\n return T","def compute_equivalent_widths(templates, coeffs, covar, max_R=5000, Ndraw=1000, seed=0):\n \n if False:\n # Testing\n templates = t1\n chi2, coeffsx, coeffs_errx, covarx = mb.xfit_at_z(z=tfit['z'], templates=templates, fitter='nnls', fit_background=True, get_uncertainties=2)\n covar = covarx[mb.N:,mb.N:]\n coeffs = coeffsx[mb.N:]\n \n # Array versions of the templates\n wave, flux_arr, is_line = array_templates(templates, max_R=max_R)\n keys = np.array(list(templates.keys()))\n \n EWdict = OrderedDict()\n for key in keys[is_line]:\n EWdict[key] = (0., 0., 0.)\n \n # Only worry about templates with non-zero coefficients, which should\n # be accounted for in the covariance array (with get_uncertainties=2)\n clip = coeffs != 0\n # No valid lines\n if (is_line & clip).sum() == 0:\n return EWdict\n \n # Random draws from the covariance matrix\n covar_clip = covar[clip,:][:,clip]\n if seed is not None:\n np.random.seed(seed)\n \n draws = np.random.multivariate_normal(coeffs[clip], covar_clip, size=Ndraw)\n \n # Evaluate the continuum fits from the draws \n continuum = np.dot(draws*(~is_line[clip]), flux_arr[clip,:])\n \n # Compute the emission line EWs\n tidx = np.where(is_line[clip])[0]\n for ix in tidx:\n key = keys[clip][ix]\n\n # Line template\n line = np.dot(draws[:,ix][:,None], flux_arr[clip,:][ix,:][None,:])\n \n # Where line template non-zero\n mask = flux_arr[clip,:][ix,:] > 0\n ew_i = np.trapz((line/continuum)[:,mask], wave[mask], axis=1)\n \n EWdict[key] = np.percentile(ew_i, [16., 50., 84.])\n \n return EWdict","def test_render_templates():\n water_mol = Atoms(cclib.bridge.cclib2openbabel.readfile(\"data/water.xyz\", \"xyz\"))\n if not water_mol.name:\n water_mol.name = \"data/water.xyz\"\n\n main([\"-g\", \"/tmp/foo.ADF.in\"])\n main([\"/tmp/foo.ADF.in\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.in\").read().strip(),\n \"\"\"TITLE data/water.xyz\n\nCHARGE 0 0\n\nNumber of atoms\n 3\n\nATOMS Cartesian\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\nEnd\n\nBasis\nEnd\n\nGeometry\nEnd\"\"\",\n )\n\n main([\"-g\", \"/tmp/test.GAMESS.inp\"])\n main([\"/tmp/test.GAMESS.inp\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.inp\").read(),\n \"\"\" $CONTRL COORD=CART UNITS=ANGS $END\n\n $DATA\ndata/water.xyz\nC1\nO 8.0 0.0584027061 0.0584027059 0.0000000000\nH 1.0 1.0096135406 -0.0680162466 0.0000000000\nH 1.0 -0.0680162466 1.0096135407 0.0000000000\n $END\n\n\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/hello.GAMESSUK.inp\"])\n main([\"/tmp/hello.GAMESSUK.inp\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.inp\").read(), water_mol.to_string(\"gukin\"))\n\n main([\"-g\", \"/tmp/hello.world.Gaussian.gjf\"])\n main([\"/tmp/hello.world.Gaussian.gjf\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.gjf\").read(),\n \"\"\"#Put Keywords Here, check Charge and Multiplicity.\n\n data/water.xyz\n\n0 1\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\n\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/bar.Jaguar.in\"])\n main([\"/tmp/bar.Jaguar.in\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.in\").read(), water_mol.to_string(\"jin\"))\n\n main([\"-g\", \"/tmp/foo.Molpro.inp\"])\n main([\"/tmp/foo.Molpro.inp\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.inp\").read(), water_mol.to_string(\"mp\"))\n\n main([\"-g\", \"/tmp/example.MOPAC.mop\"])\n main([\"/tmp/example.MOPAC.mop\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.mop\").read(),\n \"\"\"CHARGE=0 MS=0.0\ndata/water.xyz\n\nO 0.05840 1 0.05840 1 0.00000 1\nH 1.00961 1 -0.06802 1 0.00000 1\nH -0.06802 1 1.00961 1 0.00000 1\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/bar.MPQC.in\"])\n main([\"/tmp/bar.MPQC.in\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.in\").read(), water_mol.to_string(\"mpqcin\"))\n\n main([\"-g\", \"/tmp/foo.NWChem.nw\"])\n main([\"/tmp/foo.NWChem.nw\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.nw\").read(),\n \"\"\"start molecule\n\ntitle data/water.xyz\n\ngeometry units angstroms print xyz autosym\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\nend\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/example.ORCA.inp\"])\n main([\"/tmp/example.ORCA.inp\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.inp\").read(),\n \"\"\"# data/water.xyz\n! Opt\n\n* xyz 0 1\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\n*\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/bar.Psi.dat\"])\n main([\"/tmp/bar.Psi.dat\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.dat\").read(),\n \"\"\"# data/water.xyz\n\nmolecule {\n0 1\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\nunits angstrom\n}\n\noptimize('scf')\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/example.QChem.in\"])\n main([\"/tmp/example.QChem.in\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.in\").read(), water_mol.to_string(\"qcin\"))\n\n main([\"-g\", \"/tmp/foo.ZINDO.input\"])\n main([\"/tmp/foo.ZINDO.input\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.input\").read(), water_mol.to_string(\"zin\"))","def generate():","def write_input(self):\n # load template, substitute parameters and write input file\n self.homog_core()\n self.write_mat_string()\n input_tmpl = open('base_input.txt')\n templ = Template(input_tmpl.read())\n file_string = templ.substitute(cool_frac = self.vfrac_cermet,\n r_core = self.r,\n core_z = self.z,\n r_refl = self.r + self.refl_t,\n refl_min = -self.refl_t,\n refl_max = self.z + self.refl_t,\n fuel_string = self.fuel_string,\n fuel_rho = self.rho,\n fuel_vol = self.core_vol,\n refl_vol = self.core_vol,\n thermal_power = self.Q_therm)\n # write the file\n filename = 'r_{0}_{1}.i'.format(round(self.vfrac_cermet, 3), \n round(self.r, 3))\n ifile = open(filename, 'w')\n ifile.write(file_string)\n ifile.close()\n\n return filename","def compute_code():\n for file in os.listdir('./src/Templates'):\n input.parse_template('./src/Templates/' + file, './student/' + file + '.py')\n data = input.load_input()\n return len([k for k in data['input'].keys() if '@' not in k])","def preparehspiceidvgGEO1v2(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,HFINparam,TFIN_TOPparam,TFIN_BASEparam,EOTparam,NBODYparam,NFINparam,PHIGparam,RSHSparam,RSHDparam):\n #make an aux copy of hspice file to simulate\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\n #make an aux copy of modelcard file to simulate\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\n\n #update path of model and modelcard\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\"modelcardaux.nmos\\\"')\n\n #bias update\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'HFINparam',HFINparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_TOPparam', TFIN_TOPparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_BASEparam',TFIN_BASEparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'EOTparam', EOTparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'PHIGparam', PHIGparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHSparam', RSHSparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHDparam', RSHDparam)","def ts_method(signal, peaks, template_duration: float = 0.12, fs: int = processing.FS, window: int = 10, **kwargs):\n\n t_dur = round(template_duration * fs)\n if not t_dur % 2 == 0:\n t_dur += 1\n dims = signal.shape\n # if np.max(np.abs(signal[0, :])) < np.max(np.abs(signal[1, :])):\n # r_peaks = find_qrs(signal[1, :], peak_search=peak_search)\n # r_peaks = peak_enhance(signal[1, :], peaks=r_peaks, window=0.2)\n # else:\n # processing.scatter_beautiful(r_peaks * 1000 / fs, title='peaks')\n extracted_signal = np.copy(signal)\n # print(len(r_peaks))\n # Please, rework it...\n for n in range(dims[0]):\n for i in range(0, len(peaks), window):\n\n if i + window > len(peaks):\n r_peaks = peaks[i:]\n else:\n r_peaks = peaks[i:i + window]\n\n template = np.full((len(r_peaks), t_dur), np.nan)\n for num, r_ind in enumerate(r_peaks):\n if r_ind < t_dur // 2:\n template[num, t_dur // 2 - r_ind - 1:] = extracted_signal[n, 0:r_ind + t_dur // 2 + 1]\n elif r_ind + t_dur // 2 + 1 > dims[1]:\n template[num, 0:dims[1] - r_ind + t_dur // 2] = extracted_signal[n, r_ind - t_dur // 2:]\n else:\n template[num] = extracted_signal[n, r_ind - t_dur // 2:r_ind + t_dur // 2]\n template_mean = np.nanmean(template, axis=0) # None for edge cases\n for r_ind in r_peaks:\n if r_ind < t_dur // 2:\n extracted_signal[n, 0:r_ind + t_dur // 2 + 1] -= template_mean[t_dur // 2 - r_ind - 1:]\n # processing.scatter_beautiful(components[n, :], title=' subtracted channel start ' + str(n))\n elif r_ind + t_dur // 2 + 1 > dims[1]:\n extracted_signal[n, r_ind - t_dur // 2:r_ind + t_dur // 2 + 1] -= template_mean[\n 0:dims[1] - r_ind + t_dur // 2]\n # processing.scatter_beautiful(components[n, :], title=' subtracted channel end ' + str(n))\n else:\n extracted_signal[n, r_ind - t_dur // 2:r_ind + t_dur // 2] -= template_mean\n # processing.scatter_beautiful(components[n, :], title=' subtracted channel ' + str(n))\n return extracted_signal","def preparehspiceidvgGEO1(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,HFINparam,TFIN_TOPparam,TFIN_BASEparam,EOTparam,NBODYparam,NFINparam):\n #make an aux copy of hspice file to simulate\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\n #make an aux copy of modelcard file to simulate\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\n\n #update path of model and modelcard\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\"modelcardaux.nmos\\\"')\n\n #bias update\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'HFINparam',HFINparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_TOPparam', TFIN_TOPparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_BASEparam',TFIN_BASEparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'EOTparam', EOTparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam)","def createTOFin(En):\n ftemplate = open(\"TOFtemplate.in\", \"r\")\n lines = ftemplate.readlines()\n ftofin = open(\"TOF.in\", \"w\") \n energyline = lines[12].split()\n lines[12] = \"%s %g %s\\n\"%(energyline[0], En, energyline[2])\n ftofin.writelines(lines)\n ftemplate.close()\n ftofin.close()","def load_standard_operators():\n #\n # Compute scripts\n #\n \n cscript('select' ,scriptpath+'mcdo.sh \"${operator}\" \"${out}\" \"${var}\" \"${period_iso}\" \"${domain}\" \"${alias}\" \"${units}\" \"${missing}\" ${ins} ',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('ccdo',\n scriptpath+'mcdo.sh \"${operator}\" \"${out}\" \"${var}\" \"${period_iso}\" \"${domain}\" \"${alias}\" \"${units}\" \"${missing}\" ${ins}')\n #\n cscript('minus', 'cdo sub ${in_1} ${in_2} ${out}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('plus', 'cdo add ${in_1} ${in_2} ${out}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('space_average',\n scriptpath+'mcdo.sh fldmean \"${out}\" \"${var}\" \"${period_iso}\" \"${domain}\" \"${alias}\" \"${units}\" \"${missing}\" ${ins}', \n commuteWithTimeConcatenation=True)\n #\n cscript('time_average' ,\n scriptpath+'mcdo.sh timmean \"${out}\" \"${var}\" \"${period_iso}\" \"${domain}\" \"${alias}\" \"${units}\" \"${missing}\" ${ins}' ,\n commuteWithSpaceConcatenation=True)\n #\n cscript('llbox' ,\n scriptpath+'mcdo.sh \"\" \"${out}\" \"${var}\" \"${period_iso}\" \"${latmin},${latmax},${lonmin},${lonmax}\" \"${alias}\" \"${units}\" \"${missing}\" ${ins}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('regrid' ,\n scriptpath+'regrid.sh ${in} ${in_2} ${out} ${option}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('regridn' ,\n scriptpath+'regrid.sh ${in} ${cdogrid} ${out} ${option}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('rescale' ,\n 'cdo expr,\\\"${var}=${scale}*${var}+${offset};\\\" ${in} ${out}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('mean_and_std',\n scriptpath+'mean_and_std.sh ${in} ${var} ${out} ${out_sdev}', \n # This tells CliMAF how to compute varname for name output 'sdev' \n\t # using input varname\n sdev_var=\"std(%s)\" , \n commuteWithTimeConcatenation=True)\n #\n # Declare plot scripts\n cscript('ncview' ,'ncview ${in} 1>/dev/null 2>&1&' )\n #\n cscript('timeplot', 'ncl '+scriptpath+'timeplot.ncl infile=\\'\\\"${in}\\\"\\' outfile=\\'\\\"${out}\\\"\\' '\n 'var=\\'\\\"${var}\\\"\\' title=\\'\\\"${title}\\\"\\'',format=\"png\")\n #\n # plot: main field (main_file) + auxiliary field (aux_file, optional) + vectors (u_file & v_file, optionals)\n #\n cscript('plot' , '(ncl -Q '+ scriptpath +'gplot.ncl main_file=\\'\\\"${in}\\\"\\' aux_file=\\'\\\"${in_2}\\\"\\' '\n 'u_file=\\'\\\"${in_3}\\\"\\' v_file=\\'\\\"${in_4}\\\"\\' rotation=${rotation} '\n 'plotname=\\'\\\"${out}\\\"\\' cmap=\\'\\\"${color}\\\"\\' vmin=${min} vmax=${max} vdelta=${delta} '\n 'main_var=\\'\\\"${var}\\\"\\' aux_var=\\'\\\"${var_2}\\\"\\' u_var=\\'\\\"${var_3}\\\"\\' v_var=\\'\\\"${var_4}\\\"\\' '\n 'title=\\'\\\"${title}\\\"\\' scale=${scale} offset=${offset} mpCenterLonF=${mpCenterLonF} '\n 'vcRefMagnitudeF=${vcRefMagnitudeF} vcRefLengthF=${vcRefLengthF} vcMinDistanceF=${vcMinDistanceF} '\n 'vcGlyphStyle=\\'\\\"${vcGlyphStyle}\\\"\\' vcLineArrowColor=\\'\\\"${vcLineArrowColor}\\\"\\' '\n 'units=\\'\\\"${units}\\\"\\' linp=${linp} colors=\\'\\\"${colors}\\\"\\' level=${level} time=${time} '\n 'proj=\\'\\\"${proj}\\\"\\' contours=\\'\\\"${contours}\\\"\\' focus=\\'\\\"${focus}\\\"\\' '\n 'type=\\'\\\"${format}\\\"\\' resolution=\\'\\\"${resolution}\\\"\\' trim=${trim} '\n 'vcb=${vcb} lbLabelFontHeightF=${lbLabelFontHeightF} invXY=${invXY} '\n 'tmYLLabelFontHeightF=${tmYLLabelFontHeightF} tmXBLabelFontHeightF=${tmXBLabelFontHeightF} '\n 'tmYRLabelFontHeightF=${tmYRLabelFontHeightF} tiXAxisFontHeightF=${tiXAxisFontHeightF} '\n 'tiYAxisFontHeightF=${tiYAxisFontHeightF} gsnPolarLabelFontHeightF=${gsnPolarLabelFontHeightF} '\n 'tiMainFont=\\'\\\"${tiMainFont}\\\"\\' tiMainFontHeightF=${tiMainFontHeightF} '\n 'tiMainPosition=\\'\\\"${tiMainPosition}\\\"\\' gsnLeftString=\\'\\\"${gsnLeftString}\\\"\\' '\n 'gsnRightString=\\'\\\"${gsnRightString}\\\"\\' gsnCenterString=\\'\\\"${gsnCenterString}\\\"\\' '\n 'gsnStringFont=\\'\\\"${gsnStringFont}\\\"\\' gsnStringFontHeightF=${gsnStringFontHeightF} )', format=\"graph\") \n # \n cscript('lines' , '(ncl -Q '+ scriptpath +'lineplot.ncl infile=\\'\\\"${mmin}\\\"\\' '\n 'plotname=\\'\\\"${out}\\\"\\' var=\\'\\\"${var}\\\"\\' title=\\'\\\"${title}\\\"\\' '\n 'linp=${linp} labels=\\'\\\"${labels}\\\"\\' colors=\\'\\\"${colors}\\\"\\' thickness=${thickness}'\n 'T_axis=\\'\\\"${T_axis}\\\"\\' fmt=\\'\\\"${fmt}\\\"\\' && '\n 'convert ${out} -trim ${out}) ', format=\"png\")\n #\n cscript('curves' , '(ncl -Q '+ scriptpath +'curves.ncl infile=\\'\\\"${mmin}\\\"\\' '\n 'plotname=\\'\\\"${out}\\\"\\' var=\\'\\\"${var}\\\"\\' title=\\'\\\"${title}\\\"\\' '\n 'labels=\\'\\\"${labels}\\\"\\' colors=\\'\\\"${colors}\\\"\\' thickness=${thickness} && '\n 'convert ${out} -trim ${out}) ', format=\"png\")\n #\n # cpdfcrop : pdfcrop by preserving metadata\n #\n cscript('cpdfcrop' , 'pdfcrop ${in} ${out} ', format=\"pdf\")\n # \n cscript('ncdump' , 'ncdump -h ${in} ', format=\"txt\")\n #\n if (os.system(\"type cdfmean >/dev/null 2>&1\")== 0 ) :\n load_cdftools_operators()\n else :\n clogger.warning(\"No Cdftool available\")","def calc_raws(df, unit='leader'):\n\n df['distrust'] = df['HDIS']/(df['HDIS']+df['LDIS'])\n df['task'] = df['HTASK']/(df['HTASK']+df['LTASK'])\n df['bace'] = df['IC']/(df['IC']+df['EC'])\n df['igb'] = df['HBIAS']/(df['HBIAS']+df['LBIAS'])\n df['sc'] = df['HSC']/(df['HSC']+df['LSC'])\n df['cc'] = df['HCC']/(df['HCC']+df['LCC'])\n df['power'] = df['HPWR']/(df['HPWR']+df['LPWR'])\n\n df['i1'] = df.apply(i1_func, axis=1)\n df['i2'] = df.apply(i2_func, axis=1)\n df['i3'] = df.apply(i3_func, axis=1)\n\n df['i4a'] = df.apply(i4a_func, axis=1)\n df['i4b'] = df.apply(i4b_func, axis=1)\n df['i5ap'] = df.apply(i5ap_func, axis=1)\n df['i5pr'] = df.apply(i5pr_func, axis=1)\n df['i5re'] = df.apply(i5re_func, axis=1)\n df['i5op'] = df.apply(i5op_func, axis=1)\n df['i5th'] = df.apply(i5th_func, axis=1)\n df['i5pu'] = df.apply(i5pu_func, axis=1)\n\n df['p1'] = df.apply(p1_func, axis=1)\n df['p2'] = df.apply(p2_func, axis=1)\n df['p3'] = df.apply(p3_func, axis=1)\n df['p4'] = df.apply(p4_func, axis=1)\n df['p5'] = df.apply(p5_func, axis=1)\n\n if unit == 'leader':\n\n keep = ['firstname', 'lastname', 'name', 'Ccode', 'vcount', 'distrust', 'task',\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\n 'p5']\n\n df = df[keep]\n\n elif unit == 'year':\n\n keep = ['firstname', 'lastname', 'name', 'year', 'Ccode', 'vcount', 'distrust', 'task',\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\n 'p5']\n\n df = df[keep]\n\n elif unit == 'month':\n\n keep = ['firstname', 'lastname', 'name', 'yr_month','Ccode', 'vcount', 'distrust', 'task',\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\n 'p5']\n\n df = df[keep]\n\n df['year'] = df['yr_month'].apply(lambda x: x.split('-')[0])\n df['month'] = df['yr_month'].apply(lambda x: x.split('-')[1])\n\n elif unit == 'quarter':\n\n keep = ['firstname', 'lastname', 'name', 'yr_quarter', 'Ccode', 'vcount', 'distrust', 'task',\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\n 'p5']\n\n df = df[keep]\n\n df['year'] = df['yr_quarter'].apply(lambda x: x.split('-')[0])\n df['quarter'] = df['yr_quarter'].apply(lambda x: x.split('-')[1])\n\n return df","def calculate_output(self):","def calc(s,tnx,i0s,ies):\n\n # round down\n tile0 = s.start // tnx\n # round up\n tilee = -(-s.stop // tnx)\n\n tiles = []\n srcslices = []\n tgtslices = []\n for tile in range(tile0,tilee):\n ii0 = max(0, -((s.start - i0s[tile]) // s.step))\n iie = -((s.start - min(s.stop,ies[tile])) // s.step)\n if iie > ii0:\n tiles.append(tile)\n myi0 = s.start + ii0*s.step - i0s[tile]\n myie = s.start + iie*s.step - i0s[tile]\n srcslices.append(slice(myi0,myie,s.step))\n tgtslices.append(slice(ii0,iie))\n\n return tiles, srcslices, tgtslices","def _modify_template_nics_according_to_input(template_nics, input_nics, cmd, client,\n resource_group_name, vm_name,\n location, private_cloud):\n # Populating the nic names of vm-template in a dictionary,\n # and mapping them to their index in template_nics list\n vm_template_nic_names = {}\n for (i, nic) in enumerate(template_nics):\n vm_template_nic_names[nic.virtual_nic_name] = i\n\n from .vendored_sdks.models import VirtualNic\n from .vendored_sdks.models import VirtualNetwork\n from ._validators import virtual_network_name_or_id_validator\n\n # Check if nics entered by a user exist in vm-template,\n # then override the properties specified. Else create a new nic.\n for nic in input_nics:\n if nic['name'] in vm_template_nic_names.keys():\n index = vm_template_nic_names[nic['name']]\n if 'virtual-network' in nic.keys():\n template_nics[index].network.id = nic['virtual-network']\n if 'adapter' in nic.keys():\n template_nics[index].nic_type = nic['adapter']\n if 'power-on-boot' in nic.keys():\n template_nics[index].power_on_boot = nic['power-on-boot']\n template_nics[index].virtual_nic_id = None\n\n else:\n nic_name = nic['name']\n if 'virtual-network' in nic.keys():\n vnet = nic['virtual-network']\n else:\n raise CLIError('virtual-network parameter not specified for nic ' +\n nic_name + \".\")\n if 'adapter' in nic.keys():\n adapter = nic['adapter']\n else:\n raise CLIError('adapter parameter not specified for nic ' +\n nic_name + \".\")\n if 'power-on-boot' in nic.keys():\n power_on_boot = nic['power-on-boot']\n else:\n raise CLIError('power-on-boot parameter not specified for nic ' +\n nic_name + \".\")\n\n vnet = virtual_network_name_or_id_validator(cmd, client, vnet,\n resource_group_name, vm_name,\n location, private_cloud)\n network = VirtualNetwork(id=vnet)\n nic_object = VirtualNic(network=network,\n nic_type=adapter,\n power_on_boot=power_on_boot)\n template_nics.append(nic_object)\n return template_nics","def _model(self, t, theta, period, tmpid):\n template = self.templates[tmpid]\n phase = (t / period - theta[2]) % 1\n return theta[0] + theta[1] * template(phase)","def make_vrt(self):\n for index, i in enumerate(self.months):\n month = str(index + 1)\n if len(month) < 2:\n month = '0' + month\n txt_file = i.joinpath('subnational/tiffs.txt')\n outfile = i.joinpath(f'{self.country}_{month}_normalised.vrt')\n if not outfile.exists():\n gdal_cmd = f'gdalbuildvrt -input_file_list {str(txt_file)} {str(outfile)}'\n subprocess.call(gdal_cmd, shell=True)","def fillBackgroundTemplates(opt):\n\n totalBkg={}\n templates=[]\n\n #import signal events\n data=ROOT.TChain('data')\n for f in [os.path.join(opt.input,x) for x in os.listdir(opt.input) if 'Data13TeV' in x]:\n if 'MuonEG' in f : continue\n data.AddFile(f)\n\n #define final preselection cuts\n cuts='xangle==%d'%opt.xangle\n if len(opt.presel) : cuts += ' && ' + opt.presel \n if opt.csiacc:\n csiCuts ='csi1>%f && csi1<%f && '%opt.csiacc[opt.xangle][0]\n csiCuts+='csi2>%f && csi2<%f'%opt.csiacc[opt.xangle][1]\n cuts=csiCuts if len(cuts)==0 else '{0} && {1}'.format(cuts,csiCuts)\n\n #loop over categories build templates\n for icat in range(len(opt.categs)):\n\n #apply category cuts\n categCut=opt.categs[icat]\n categCut=cuts if len(categCut)==0 else '%s && %s'%(categCut,cuts)\n catName='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\n\n print '\\t',catName,categCut\n\n #background modelling histos\n histos=[]\n data_obs=None\n for name,pfix in [('bkg_'+catName,'mix'),('bkg_%s_bkgShape'%catName,'mixem')]:\n\n templCuts=categCut.replace('csi1',pfix+'csi1')\n templCuts=templCuts.replace('csi2',pfix+'csi2')\n data.Draw('{0}mmiss >> h({1},{2},{3})'.format(pfix,opt.nbins,opt.mMin,opt.mMax),templCuts,'goff')\n h=data.GetHistogram()\n histos.append(h.Clone(name))\n histos[-1].SetDirectory(0)\n\n if len(histos)==1:\n totalBkg[icat]=h.Integral()\n if not opt.unblind :\n data_obs=h.Clone('data_obs_'+catName)\n data_obs.SetDirectory(0)\n\n h.Reset('ICE')\n templates += defineProcessTemplates(histos)\n\n #observed data in this category if unblinding\n if opt.unblind:\n data.Draw('mmiss >> h({1},{2},{3})'.format(opt.nbins,opt.mMin,opt.mMax),categCut,'goff')\n data_obs=data.GetHistogram().Clone('data_obs_'+catName)\n data_obs.SetDirectory(0)\n\n templates.append(data_obs)\n\n print '\\t total background:',totalBkg\n return totalBkg,templates","def nc2gtiff(input_name, epsg_in=3413, epsg_out=3857):\n sub_name=[\"dX\",\"dY\"]\n \n for sub in sub_name:\n result = os.system(\"echo ''\")\n #netCDF to geotiff (EPSG code is not changed) \n out_name = \"./data/tiff_raw/{}_{}_tmp.tiff\".format(os.path.basename(input_name)[:-3],sub)\n #c is shell command\n #options\n #-a_srs:EPSGcode of input files\n #-of:output format\n #\n print(\"nc2gtiff\")\n c = \"gdal_translate -a_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' NETCDF:'{in_name}':{sub} \\\n -of 'Gtiff' \\\n '{out_name}'\".format(epsg=epsg_in,sub=sub, \\\n in_name=input_name,out_name=out_name) \n result = os.system(c) #run shell command\n #print(c) #if you want to see the filled c, remove '#' in the head of this line\n if result!=0: # if it raises error, return filename \n print(input_name, result, \"translate\")\n \n result = os.system(\"echo ''\")\n\n #geotiff EPSG_in to EPCG_out\n print(\"geotiff2geotiff\")\n target_name=\"./data/tiff_target/{}_{}.tiff\".format(os.path.basename(input_name)[:-3],sub)\n c = \"gdalwarp -overwrite -s_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' {out_name} -r cubic\\\n {target_name} -t_srs EPSG:{epsg_out} -of \\\n 'GTIFF'\".format(out_name=out_name, target_name=target_name, \n epsg_in=epsg_in, epsg_out=epsg_out)\n result = os.system(c) # run shell command\n if result!=0:\n print(input_name, result, \"warp\")","def hexapodZernikeLinearModel_hexapodcoordinate():\n Tfile='/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_training.cp'\n b=p.load(open(Tfile))\n nobs = len(b)\n x = b[:,0]\n y = b[:,1]\n z = b[:,2]\n theta = b[:,3]\n phi = b[:,4]\n fwhm = b[:,5]\n e1 = b[:,6]\n e2 = b[:,7]\n thetax = theta*np.cos(np.deg2rad(phi))\n thetay = theta*np.sin(np.deg2rad(phi))\n xh = x*1000 # convert to hexapod coordinate\n yh = -y*1000\n zh = -z*1000\n xtilth = - thetay\n ytilth = - thetax\n\n M22realTrefoil2 = b[:,37] # for x decenter\n M22imagTrefoil1 = b[:,54] \n M22TrefoilXshift = 0.5*(M22realTrefoil2+M22imagTrefoil1)\n\n M22realTrefoil1 = b[:,34] # for y decenter\n M22imagTrefoil2 = b[:,57] \n M22TrefoilYshift = 0.5*(M22realTrefoil1 - M22imagTrefoil2)\n\n M20defocus = b[:,12] # for defocus\n\n M22realComa2 = b[:,36] # for x-tilt\n M22imagComa1 = b[:,55]\n M22ComaXtilt = 0.5*(M22realComa2+M22imagComa1)\n\n M22realComa1 = b[:,35] # for y-tilt\n M22imagComa2 = b[:,56]\n M22ComaYtilt = 0.5*(M22realComa1 - M22imagComa2)\n \n pl.figure(figsize=(21,12))\n pl.subplot(2,3,1)\n t=bp.bin_scatter(M22TrefoilXshift,xh,nbins=20,fmt='bo',scatter=True)\n res = linefit(M22TrefoilXshift,xh)\n pl.plot(M22TrefoilXshift,M22TrefoilXshift*res[1]+res[0],'r,')\n pl.ylabel('x-decenter [micron]')\n pl.xlabel('(M22realTrefoil2+M22imagTrefoil1)/2')\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\n pl.grid()\n pl.subplot(2,3,2)\n t=bp.bin_scatter(M22TrefoilYshift,yh,nbins=20,fmt='bo',scatter=True)\n res = linefit(M22TrefoilYshift,yh)\n pl.plot(M22TrefoilYshift,M22TrefoilYshift*res[1]+res[0],'r,')\n pl.ylabel('y-decenter [micron]')\n pl.xlabel('(M22realTrefoil1 - M22imagTrefoil2)/2')\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\n pl.grid()\n pl.subplot(2,3,3)\n t=bp.bin_scatter(M20defocus,zh,nbins=20,fmt='bo',scatter=True)\n res = linefit(M20defocus,zh)\n pl.plot(M20defocus,M20defocus*res[1]+res[0],'r,')\n pl.ylabel('z-defocus [micron]')\n pl.xlabel('M20defocus')\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\n pl.grid()\n pl.subplot(2,3,4)\n t=bp.bin_scatter(M22ComaXtilt,ytilth,nbins=20,fmt='bo',scatter=True)\n res = linefit(M22ComaXtilt,ytilth)\n pl.plot(M22ComaXtilt,M22ComaXtilt*res[1]+res[0],'r,')\n pl.ylabel('y-tilt [arcsec]') # in hexapod coordiate, xtilt and y tilt is switched from the CRAY coordiante\n pl.xlabel('(M22realComa2+M22imagComa1)/2')\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\n pl.grid()\n pl.subplot(2,3,5)\n t=bp.bin_scatter(M22ComaYtilt,xtilth,nbins=20,fmt='bo',scatter=True)\n res = linefit(M22ComaYtilt,xtilth)\n pl.plot(M22ComaYtilt,M22ComaYtilt*res[1]+res[0],'r,')\n pl.ylabel('x-tilt [arcsec]')\n pl.xlabel('(M22realComa1 - M22imagComa2)/2')\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\n pl.grid()\n pl.savefig('linearModel_hexapod_coordinate.png')\n pl.close()","def test7():\n from wpylib.text_tools import str_snippet as snippet\n from wpylib.params.params_flat import ActiveReadValue as Act\n defaults = {\n 'nbasis': 320,\n 'npart': 37,\n 'deltau': 0.025,\n }\n print \"test7()\"\n p = params(defaults, nbasis=332)\n p.input_template = Act(lambda P: snippet(\"\"\"\\\n input = {\n nbasis = %(nbasis)d\n npart = %(npart)d\n deltau = %(deltau)s\n };\n code = {\n %(code_template)s\n };\n \"\"\") % P)\n p.code_template = Act(lambda P: snippet(\"\"\"\\\n for (i = 1; %(nbasis)d; ++i) {\n print i, %(deltau)g\n }\n \"\"\") % P)\n print \"\\nInput template 1:\"\n print p.input_template\n print \"\\n--- The case above was OK. ---\\n\"\n\n print \"\\n--- The case below was not OK. ---\\nThe nbasis and deltau args were not updated in two printouts below\"\n Q = params(p)\n Q.nbasis = 327\n print \"\\nInput template 2 after updating nbasis:\"\n print Q.input_template\n\n\n print \"\\n--- The case below was OK, but it requires flattening of the search dicts. ---\\n\"\n\n R = params(p, _flatten_=True)\n R.nbasis = 327\n pprint(R)\n print len(R._list_)\n print \"\\nInput template 2 after flattening and updating nbasis:\"\n print R.input_template\n\n print \"\\nHere is the generator script:\\n*%(input_template)s*\" % p._create_(_flatten_=True)._update_(dict(deltau=2.775))","def integ_exact(model,func_params):\n\n\n if (model=='genz_osc'):\n gcf=func_params\n dim=gcf.shape[0]-1\n integ_ex=cos(2.*pi*gcf[0]+0.5*sum(gcf[1:]))\n for i in range(1,dim+1):\n integ_ex*=(2.*sin(gcf[i]/2.)/gcf[i])\n elif (model=='genz_exp'):\n gcf=func_params\n dim=gcf.shape[0]-1\n integ_ex=1.\n for i in range(1,dim+1):\n at1=exp(-gcf[i]*gcf[0])\n at2=exp(gcf[i]*(1.-gcf[0]))\n integ_ex*=((at2-at1)/(gcf[i]))\n elif (model=='genz_cont'):\n gcf=func_params\n dim=gcf.shape[0]-1\n integ_ex=1.\n for i in range(1,dim+1):\n integ_ex*= ((2.-exp(gcf[i]*(-gcf[0]))-exp(gcf[i]*(gcf[0]-1.)))/gcf[i])\n elif (model=='genz_gaus'):\n gcf=func_params\n dim=gcf.shape[0]-1\n integ_ex=1.\n for i in range(1,dim+1):\n at1=erf(-gcf[i]*gcf[0])\n at2=erf(gcf[i]*(1.-gcf[0]))\n integ_ex*=((at2-at1)*sqrt(pi)/(2.*gcf[i]))\n elif (model=='genz_cpeak'):\n gcf=func_params\n dim=gcf.shape[0]-1\n numer=0.0\n count=1\n denom=1.\n for i in range(1,dim+1):\n comb=list(itertools.combinations(range(1,dim+1),i))\n for j in range(len(comb)):\n assert(i==len(comb[j]))\n #print i,j,pow(-1,i)\n numer+=(pow(-1,i)/(1.+sum(gcf[list(comb[j])])))\n count+=1\n denom*=(i*gcf[i])\n #print count, numer\n integ_ex=(1.+numer)/denom\n elif (model=='genz_ppeak'):\n gcf=func_params\n dim=gcf.shape[0]-1\n integ_ex=1.\n for i in range(1,dim+1):\n at1=np.arctan(-gcf[i]*gcf[0])\n at2=np.arctan(gcf[i]*(1.-gcf[0]))\n integ_ex*=(gcf[i]*(at2-at1))\n\n return integ_ex","def calc_T_sys(nu_obs):\n return 100 * u.K + 120 * (nu_obs / (150 * u.MHz))**(-2.55) * u.K","def generate_ta(data):\n raise NotImplementedError","def New(*args, **kargs):\n obj = itkIsoDataThresholdCalculatorHDUS.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def supgen(f, INTER):\n\n A,Bc = INTER\n y = intersec(erode(f,A),\n erode(neg(f),Bc))\n return y","def diffeo_apply(diffeo, template):\n M, N = diffeo.shape[0], diffeo.shape[1]\n result = np.empty_like(template)\n for i, j in coords_iterate((M, N)):\n i1 = diffeo[i, j, 0]\n j1 = diffeo[i, j, 1]\n result[i, j, ...] = template[i1, j1, ...]\n return result","def __calc_t_i(P0, P1, lat, lon, proj):\n\n # projected coordinates are in km\n p0x, p0y = proj(P0.x, P0.y)\n p1x, p1y = proj(P1.x, P1.y)\n\n # Unit vector pointing normal to strike\n t_i_hat = Vector(p1y - p0y, -(p1x - p0x), 0).norm()\n\n # Convert sites to Cartesian\n sx, sy = proj(lon, lat)\n sx1d = np.reshape(sx, (-1,))\n sy1d = np.reshape(sy, (-1,))\n\n # Vectors from P0 to sites\n r = np.zeros([len(sx1d), 2])\n r[:, 0] = sx1d - p0x\n r[:, 1] = sy1d - p0y\n\n # Dot product gives t_i\n t_i = np.sum(t_i_hat.getArray()[0:2] * r, axis=1)\n shp = t_i.shape\n if len(shp) == 1:\n t_i.shape = (shp[0], 1)\n t_i = np.fliplr(t_i)\n return t_i","def __calc_t_i(P0, P1, lat, lon, proj):\n\n # projected coordinates are in km\n p0x, p0y = proj(P0.x, P0.y)\n p1x, p1y = proj(P1.x, P1.y)\n\n # Unit vector pointing normal to strike\n t_i_hat = Vector(p1y - p0y, -(p1x - p0x), 0).norm()\n\n # Convert sites to Cartesian\n sx, sy = proj(lon, lat)\n sx1d = np.reshape(sx, (-1,))\n sy1d = np.reshape(sy, (-1,))\n\n # Vectors from P0 to sites\n r = np.zeros([len(sx1d), 2])\n r[:, 0] = sx1d - p0x\n r[:, 1] = sy1d - p0y\n\n # Dot product gives t_i\n t_i = np.sum(t_i_hat.getArray()[0:2] * r, axis=1)\n shp = t_i.shape\n if len(shp) == 1:\n t_i.shape = (shp[0], 1)\n t_i = np.fliplr(t_i)\n return t_i","def preparehspiceidvgGEO4(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,Ach_UFCMparam,Cins_UFCMparam,W_UFCMparam,NBODYparam,NFINparam):\n#L=Lparam Ach_UFCM=Ach_UFCMparam Cins_UFCM=Cins_UFCMparam W_UFCM=W_UFCMparam NBODY=NBODYparam NFIN=NFINparam\n #make an aux copy of hspice file to simulate\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\n #make an aux copy of modelcard file to simulate\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\n\n #update path of model and modelcard\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\"modelcardaux.nmos\\\"')\n\n #bias update\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Ach_UFCMparam',Ach_UFCMparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Cins_UFCMparam', Cins_UFCMparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'W_UFCMparam',W_UFCMparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam)","def calc_population_02_state(U):\n if U.type=='oper':\n sump = 0\n for i_list in [[0,2]]:\n for j_list in [[1,1]]:\n bra_i = qtp.tensor(qtp.ket([i_list[0]], dim=[3]),\n qtp.ket([i_list[1]], dim=[3])).dag()\n ket_j = qtp.tensor(qtp.ket([j_list[0]], dim=[3]),\n qtp.ket([j_list[1]], dim=[3]))\n p = np.abs((bra_i*U*ket_j).data[0, 0])**2\n sump += p\n return np.real(sump)\n elif U.type=='super':\n sump = 0\n for i_list in [[0,2]]:\n for j_list in [[1,1]]:\n ket_i = qtp.tensor(qtp.ket([i_list[0]], dim=[3]),\n qtp.ket([i_list[1]], dim=[3]))\n rho_i=qtp.operator_to_vector(qtp.ket2dm(ket_i))\n ket_j = qtp.tensor(qtp.ket([j_list[0]], dim=[3]),\n qtp.ket([j_list[1]], dim=[3]))\n rho_j=qtp.operator_to_vector(qtp.ket2dm(ket_j))\n p = (rho_i.dag()*U*rho_j).data[0, 0]\n sump += p\n return np.real(sump)","def format_template(commit_info, nb):\n\n nb_path = os.path.abspath(nb).replace('ipynb', 'md')\n with open(nb_path, 'r+') as file:\n template = NbTemplate(file.read())\n updated = template.substitute(commit_info)\n file.seek(0)\n file.write(updated)\n file.truncate()","def calculate_T(func, a, b, N):\n # Trapezoid width \n\n h = (b - a)/N\n\n # Every odd slice\n new_part = 0\n for i in range(1, N//2 + 1):\n new_part += func(a + (2*i - 1)*h) \n \n return 2/3. * new_part","def template_analysis_function(self, system, wave_idx, config, surface, *args, **kwargs):\n\n # Write your function operations here\n\n # This will typically contain a lot of ZOS API commands such as:\n # - Setting the Current configuration: system.MCE.SetCurrentConfiguration(config)\n # - Reading the Field Points: sysField = system.SystemData.Fields\n # fx = sysField.GetField(1).X\n # fy = sysField.GetField(1).Y\n # - Running some Ray Trace: raytrace = system.Tools.OpenBatchRayTrace()\n # normUnPolData = raytrace.CreateNormUnpol(N_rays,\n # constants.RaysType_Real, toSurface)\n\n # You should return a list of arrays\n\n return","def nd_2_nz_compute(self):\n tik_instance = self.set_tik_instance()\n format_transfer_case = self.set_format_transfer_case()\n if format_transfer_case == 0:\n tik_instance = self.format_transfer_case_zero(tik_instance)\n elif format_transfer_case == 1:\n tik_instance = self.format_transfer_case_one(tik_instance)\n elif format_transfer_case == 2:\n tik_instance = self.format_transfer_case_two(tik_instance)\n elif format_transfer_case == 3:\n tik_instance = self.format_transfer_case_three(tik_instance)\n elif format_transfer_case == 4:\n tik_instance = self.format_transfer_case_four(tik_instance)\n elif format_transfer_case == 5:\n tik_instance = self.format_transfer_case_five(tik_instance)\n elif format_transfer_case == 6:\n tik_instance = self.format_transfer_case_six(tik_instance)\n elif format_transfer_case == 7:\n tik_instance = self.format_transfer_case_seven(tik_instance)\n elif format_transfer_case == 8:\n tik_instance = self.format_transfer_case_eight(tik_instance)\n return tik_instance","def IPTinput_gen(Nom,beta,ef,mutld):\n\tV,D =0.01,1\n\tf1 = open(\"Delta.inp\", 'w')\n\tf2 = open(\"G0.inp\", 'w')\n\tDelta=[]\n\tfor n in range(Nom):\n\t iom = (2.*n+1)*pi/beta\n\t Redelta=-V**2/(2.0*D)*log( ((D/2.-mutld)**2+iom**2)/((D/2.+mutld)**2+iom**2))\n\t Imdelta=-V**2/D*(arctan((D/2.-mutld)/iom)-arctan((-D/2.-mutld)/iom))\n\t print>>f1,iom,Redelta,Imdelta\n\t G0= 1./( 1.0j*iom-(ef-mutld)-(Redelta+1.0j*Imdelta) )\n\t print>>f2,iom,G0.real,G0.imag\n\tf1.close()\n\tf2.close()","def defineProcessTemplates(histos):\n\n templates=[]\n\n #nominal\n templates.append( histos[0] )\n nomStats=templates[-1].Integral()\n\n #systematic variations\n #if Up/Down already in the name store directly updating the name\n #if not, mirror the variation given \n for i in xrange(1,len(histos)): \n templates.append( histos[i] )\n key=templates[-1].GetName()\n if not 'Up' in key and not 'Down' in key :\n templates[-1].SetName(key+'Up')\n templates.append( histos[i].Clone(key+'Down') )\n for xbin in range(templates[0].GetNbinsX()):\n templates[-1].SetBinContent(xbin+1,2*templates[0].GetBinContent(xbin+1)-templates[-2].GetBinContent(xbin+1))\n \n #don't leave bins with 0's\n for h in templates:\n h.SetDirectory(0)\n iStats=h.Integral()\n if iStats>0: h.Scale(nomStats/iStats)\n for xbin in range(h.GetNbinsX()):\n if h.GetBinContent(xbin+1)>0: continue\n h.SetBinContent(xbin+1,1e-6)\n \n return templates","def calcT1(g2, g1):\n idop = FermiOp(g2.orbs, 3, 3)\n idop.data = np.eye(int(binom(g2.orbs, 3)))\n\n return p2N(g2, 3) - p2N(g1, 3) + idop","def mseed_2_templates(wav_dirs, cat, outdir, length, prepick,\n highcut=None, lowcut=None, f_order=None,\n samp_rate=None, min_snr=2.,\n start=None, end=None, miniseed=True,\n asdf_file=False, debug=1):\n\n # Establish date range for template creation\n cat.events.sort(key=lambda x: x.origins[-1].time)\n if start:\n cat_start = datetime.datetime.strptime(start, '%d/%m/%Y')\n cat_end = datetime.datetime.strptime(end, '%d/%m/%Y')\n else:\n cat_start = cat[0].origins[-1].time.date\n cat_end = cat[-1].origins[-1].time.date\n for date in date_generator(cat_start, cat_end):\n dto = UTCDateTime(date)\n print('Processing templates for: %s' % str(dto))\n q_start = dto - 10\n q_end = dto + 86410\n # Establish which events are in this day\n sch_str_start = 'time >= %s' % str(dto)\n sch_str_end = 'time <= %s' % str(dto + 86400)\n tmp_cat = cat.filter(sch_str_start, sch_str_end)\n if len(tmp_cat) == 0:\n print('No events on: %s' % str(dto))\n continue\n # Which stachans we got?\n stachans = {pk.waveform_id.station_code: [] for ev in tmp_cat\n for pk in ev.picks}\n for ev in tmp_cat:\n for pk in ev.picks:\n chan_code = pk.waveform_id.channel_code\n if chan_code not in stachans[pk.waveform_id.station_code]:\n stachans[pk.waveform_id.station_code].append(chan_code)\n wav_read_start = timer()\n # Be sure to go +/- 10 sec to account for GeoNet shit timing\n if asdf_file:\n with pyasdf.ASDFDataSet(asdf_file) as ds:\n st = Stream()\n for sta, chans in iter(stachans.items()):\n for station in ds.ifilter(ds.q.station == sta,\n ds.q.channel == chans,\n ds.q.starttime >= q_start,\n ds.q.endtime <= q_end):\n st += station.raw_recording\n elif miniseed:\n wav_ds = ['%s%d' % (d, dto.year) for d in wav_dirs]\n st = grab_day_wavs(wav_ds, dto, stachans)\n wav_read_stop = timer()\n print('Reading waveforms took %.3f seconds' % (wav_read_stop\n - wav_read_start))\n print('Looping through stachans to merge/resamp')\n stachans = [(tr.stats.station, tr.stats.channel) for tr in st]\n for stachan in list(set(stachans)):\n tmp_st = st.select(station=stachan[0], channel=stachan[1])\n if len(tmp_st) > 1 and len(set([tr.stats.sampling_rate for tr in tmp_st])) > 1:\n print('Traces from %s.%s have differing samp rates' % (stachan[0], stachan[1]))\n for tr in tmp_st:\n st.remove(tr)\n tmp_st.resample(sampling_rate=samp_rate)\n st += tmp_st\n st.merge(fill_value='interpolate')\n resamp_stop = timer()\n print('Resample/merge took %s secs' % str(resamp_stop - wav_read_stop))\n print('Preprocessing...')\n # Process the stream\n try:\n st1 = pre_processing.dayproc(st, lowcut=lowcut, highcut=highcut,\n filt_order=f_order, samp_rate=samp_rate,\n starttime=dto, debug=debug, ignore_length=True,\n num_cores=4)\n except NotImplementedError or Exception as e:\n print('Found error in dayproc, noting date and continuing')\n print(e)\n with open('%s/dayproc_errors.txt' % outdir, mode='a') as fo:\n fo.write('%s\\n%s\\n' % (str(date), e))\n continue\n print('Feeding stream to template_gen...')\n for event in tmp_cat:\n print('Copying stream to keep away from the trim...')\n trim_st = copy.deepcopy(st1)\n ev_name = str(event.resource_id).split('/')[-1]\n pk_stachans = ['%s.%s' % (pk.waveform_id.station_code,\n pk.waveform_id.channel_code)\n for pk in event.picks]\n # Run check to ensure that there is only one pick for each channel\n dups = [pk for pk, count\n in collections.Counter(pk_stachans).items() if count > 1]\n if len(dups) > 0:\n print('Event %s still has dup picks. Skipping' % ev_name)\n continue\n template = template_gen(event.picks, trim_st, length=length,\n prepick=prepick, min_snr=min_snr)\n if len([tr for tr in template\n if tr.stats.channel[-1] == 'Z']) < 6:\n print('Skipping template with fewer than 6 Z-comp traces')\n continue\n # temp_list.append(template)\n print('Writing event %s to file...' % ev_name)\n template.write('%s/%s.mseed' % (outdir, ev_name),\n format=\"MSEED\")\n del trim_st\n del tmp_cat, st1, st","def gen_compute_template(self):\n\n print \"\\t* Generating compute template in file %s\" % self.compute_filename\n if self.cmdline:\n with open(self.compute_filename, 'w') as f:\n f.write(yaml.safe_dump(self.compute_template))\n\n try:\n self.heatclient.stacks.validate(template=yaml.safe_dump(self.compute_template))\n except Exception as e:\n print \"Unfortunately your file is malformed. Received error: (%s)\" % str(e)\n print \"Exiting ...\"\n sys.exit(1)\n\n return self.compute_template","def build_template_meas(name='example'):\n\n meas = templates.TemplateMeasurement(name)\n meas.set_lumi(1, 0.02)\n\n # Base shape for the signal, triangular distribution\n sig = np.array([10000, 12500, 15000, 12500, 10000], dtype=float)\n # Add a source for the signal\n src_sig = meas.new_source('sig', sig)\n src_sig.use_lumi() # impacted by luminosity\n src_sig.use_stats(.1*(10*sig)**0.5) # stat unc. from 10x MC\n src_sig.set_xsec(1, 0.95, 1.05) # cross section constrained to +/-5%\n # Add a template: under the influence of parameter p, a linear slope is\n # added to the signal\n src_sig.add_template('p', sig*[-.2, -.1, 0, +.1, +.2])\n # Add highly asymmetric systematic uncertainty which looks a lot like the\n # signal. This is a challenging model to fit.\n src_sig.add_syst('s1', sig*[-.06, -.02, 0, +.02, +.06], polarity='up')\n src_sig.add_syst('s1', sig*[-.03, -.01, 0, +.01, +.03], polarity='down')\n # Add another systematic which doesn't look like the signal or the data\n # (should be constrained)\n src_sig.add_syst('s2', sig*[+.02, +.01, 0, +.01, +.02])\n\n # Add a flat-ish background (different shape from signal)\n bg1 = np.array([1600, 1300, 1000, 1000, 1000], dtype=float)\n src_bg1 = meas.new_source('bg1', bg1)\n src_bg1.use_lumi()\n src_bg1.use_stats(.1*(10*bg1)**0.5)\n src_bg1.set_xsec(1, 0.8, 1.1)\n # It is also impacted by systematic 2\n src_bg1.add_syst('s2', bg1*[+.02, +.01, 0, +.01, +.02])\n\n # Add a background not impacted by lumi or stats (e.g. data driven)\n bg2 = np.array([1000, 1000, 1000, 1300, 1600], dtype=float)\n src_bg2 = meas.new_source('bg2', bg2)\n src_bg2.set_xsec(1, 0.9, 1.1)\n\n # Build the spectrum object\n meas.build()\n\n return meas","def healpix_template(ncomp, nside, coordinate=None):\n\n temp = so_map()\n\n if ncomp == 3:\n temp.data = np.zeros((3, 12 * nside ** 2))\n else:\n temp.data = np.zeros((12 * nside ** 2))\n\n temp.pixel = \"HEALPIX\"\n temp.ncomp = ncomp\n temp.nside = nside\n temp.geometry = \"healpix geometry\"\n temp.coordinate = coordinate\n return temp","def cubic_template(template, spacing=1, connectivity=6,\n node_prefix='node', edge_prefix='edge'):\n template = np.atleast_3d(template).astype(bool)\n # Generate a full cubic network\n temp = cubic(shape=template.shape, spacing=spacing,\n connectivity=connectivity,\n node_prefix=node_prefix, edge_prefix=edge_prefix)\n # Store some info about template\n coords = np.unravel_index(range(template.size), template.shape)\n coords = np.vstack(coords).T\n Np = coords.shape[0]\n temp[node_prefix+'.template_coords'] = coords\n temp[node_prefix+'.template_indices'] = np.arange(Np)\n # Trim pores not present in template\n temp = trim_nodes(network=temp, inds=~template.flatten())\n return temp","def make_overall_vignetting(energy=7.2, *args,\n subgrid=10, urdweights={},\n **kwargs):\n if subgrid < 1:\n print(\"ahtung! subgrid defines splines of the translation of multiple vigneting file into one map\")\n print(\"set subgrid to 2\")\n subgrid = 2\n #x, y = np.meshgrid(np.linspace(-24., 24., 48*subgrid), np.np.linspace(-24., 24., 48*subgrid))\n xmin, xmax = -24.*DL, 24.*DL\n ymin, ymax = -24.*DL, 24.*DL\n\n vecs = offset_to_vec(np.array([xmin, xmax, xmax, xmin]),\n np.array([ymin, ymin, ymax, ymax]))\n\n vmaps = {}\n for urdn in URDNS:\n quat = ARTQUATS[urdn]\n xlim, ylim = vec_to_offset(quat.apply(vecs))\n xmin, xmax = min(xmin, xlim.min()), max(xmax, xlim.max())\n ymin, ymax = min(ymin, ylim.min()), max(ymax, ylim.max())\n\n dd = DL/subgrid\n dx = dd - (xmax - xmin)%dd\n xmin, xmax = xmin - dx/2., xmax + dx\n dy = dd - (ymax - ymin)%dd\n ymin, ymax = ymin - dy/2., ymax + dy\n\n x, y = np.mgrid[xmin:xmax:dd, ymin:ymax:dd]\n shape = x.shape\n newvmap = np.zeros(shape, np.double)\n vecs = offset_to_vec(np.ravel(x), np.ravel(y))\n\n for urdn in URDNS:\n vmap = make_vignetting_for_urdn(urdn, energy, *args, **kwargs)\n quat = ARTQUATS[urdn]\n newvmap += vmap(vec_to_offset_pairs(quat.apply(vecs, inverse=True))).reshape(shape)*urdweights.get(urdn, 1.)\n\n vmap = RegularGridInterpolator((x[:, 0], y[0]), newvmap, bounds_error=False, fill_value=0)\n return vmap","def label_hemispheres( x, template, templateLR, reg_iterations=[200,50,2,0] ):\n reg = ants.registration(\n ants.rank_intensity(x),\n ants.rank_intensity(template),\n 'SyN',\n aff_metric='GC',\n syn_metric='CC',\n syn_sampling=2,\n reg_iterations=reg_iterations,\n random_seed = 1 )\n return( ants.apply_transforms( x, templateLR, reg['fwdtransforms'],\n interpolator='genericLabel') )","def _ProcessTemplate(self,topdir):\n self.dicomdir = \"%s/anatomicals\" % self.topdir\n self.rawdir = \"%s/raw\" % topdir\n self.rawdirs = {}\n tmplt = self._GetTemplate()\n if self.opts.outdir is not None:\n# Override template output directory.\n tmplt['top_outdir'] = self.opts.outdir\n self.tmplt = tmplt\n if len(tmplt['top_outdir']) == 0:\n tmplt['top_outdir'] = os.path.realpath(self.topdir)\n raise RuntimeError('Template file must specify an output directory.')\n tmplt['top_outdir'] = os.path.realpath(tmplt['top_outdir'])\n if '/home' in tmplt['top_outdir'][:7]:\n raise RuntimeError('Image data cannot be stored in the /home partition. Change the \"top_outdir\" entry in the template file: %s.' % (' '.join(self.templates)))\n# tmplt['subject'] = 'orig'\n self.procdir = os.path.abspath(\"%s/%s\" % \\\n (tmplt['top_outdir'],tmplt['subject']))\n target = os.path.abspath('%s/../..' % tmplt['top_outdir'])\n if not ismounted(target):\n raise RuntimeError('Could not access partition at %s' % target)\n\n self.anatdir = \"%s/anat\" % self.procdir\n self.fmapdir = \"%s/%s\" % (self.procdir,tmplt['fmap']['outdir'])\n self.dtidir = \"%s/%s\" % (self.procdir,tmplt['dti']['outdir'])\n self.logdir = \"%s/%s\" % (self.procdir,tmplt['logdir'])\n self.skip = tmplt.get('skip', DEFAULT_SKIP)\n self.acq_tr = tmplt.get('acq_tr',None)\n self.episetup_dir = \"%s/%s\" % (self.procdir,tmplt['first_epi'])\n self.fsl_cmpblty = tmplt.get('fsl_compatibility',False)\n self.epi_file_format = self.tmplt['epi_file_format']\n self.censor_thresh = tmplt.get('censor_threshold', 2.)\n self.censor_interleave = tmplt.get('censor_interleave', True)\n# self.server_userid = self.tmplt.get('server_userid','default')\n\n# Overide flags for aligning EPIs and skull-stripping with command-\n# line options.\n if self.opts.align_fmaps:\n self.align_fmaps = True\n else:\n self.align_fmaps = self.tmplt.get('epi_align', False)\n\n if self.opts.no_align_fmaps:\n self.no_align_fmaps = True\n else:\n self.no_align_fmaps = self.tmplt.get('no_epi_align', False)\n\n if self.opts.skull_strip:\n self.skull_strip = True\n else:\n self.skull_strip = self.tmplt.get('skull_strip', False)\n\n# Create log file now so it can be used immediately.\n if not os.path.exists(self.logdir):\n if self.verbose:\n print 'mkdir %s' % self.logdir\n if not self.opts.fake_opts:\n self.MakeDir(self.logdir)\n\n self._ProcessTemplateEpiInfo()","def no_icp(source, templates, home=None, use_2d=False):\n if source.shape[0] == 0:\n return create_config(0,0,0, fitness=0)\n origorig = source.copy()\n source, xinit, yinit, theta = init_transform(source)\n\n remove_out = False\n if remove_out:\n iq_range=0.5\n pcnt = (1 - iq_range) / 2\n xqlow, xqhigh = np.quantile(source[:,0], [pcnt, 1-pcnt])\n yqlow, yqhigh = np.quantile(source[:,1], [pcnt, 1-pcnt])\n xiqr, yiqr = xqhigh-xqlow, yqhigh-yqlow\n no_out = (source[:,0]<xqhigh+1.5*xiqr) & (source[:,0]>xqlow-1.5*xiqr) &\\\n (source[:,1]<yqhigh+1.5*yiqr) & (source[:,1]>yqlow-1.5*yiqr)\n source = source[no_out,:]\n\n remove_low = False\n if remove_low:\n not_low = (source[:,-1]>0.15) & (source[:,-1]<1.4)\n source = source[not_low,:]\n\n if source.shape[0] == 0:\n return create_config(0,0,0,fitness=0)\n\n\n # Recenter the data independently from point density\n xmin, xmax = np.min(source[:,0]), np.max(source[:,0])\n ymin, ymax = np.min(source[:,1]), np.max(source[:,1])\n x_center, y_center = xmin+(xmax-xmin)/2, ymin+(ymax-ymin)/2\n center2 = rotate(-theta, np.array([[x_center, y_center, 0]]), axis=\"z\")\n xinit += center2[0][0]\n yinit += center2[0][1]\n source = source - [x_center, y_center, 0]\n\n angle_diffs = [-5.,-2.,-1.,0.,1.,2.,5.]\n center_diffs = [-0.2,-0.15,-0.10,-0.07,-0.05,-0.01,0,0.01,0.05,0.07,0.10,0.15,0.2]\n lengths = [2.,2.1]\n widths = [1.]\n trials = list(product(center_diffs, center_diffs, angle_diffs, lengths, widths))\n\n trialmap = np.array([np.array([xinit, yinit, np.degrees(theta)-90, 0.0, 0.0])+np.array(t) for t in trials])\n results = multi_grid_iou(origorig[:, :2], trialmap, 0.05)\n best = np.argmax(results)\n cfg = create_config(*trialmap[best])\n cfg[\"bed\"][\"fitness\"] = np.max(results)\n return cfg","def initialize_nick_templates(in_template, out_template):\n\n # create the regex for in_template\n regex_string = fnmatch.translate(in_template)\n # we must account for a possible line break coming over the wire\n\n # NOTE-PYTHON3: fnmatch.translate format changed since Python2\n regex_string = regex_string[:-2] + r\"(?:[\\n\\r]*?)\\Z\"\n\n # validate the templates\n regex_args = [match.group(2) for match in _RE_NICK_ARG.finditer(regex_string)]\n temp_args = [match.group(2) for match in _RE_NICK_TEMPLATE_ARG.finditer(out_template)]\n if set(regex_args) != set(temp_args):\n # We don't have the same $-tags in input/output.\n raise NickTemplateInvalid\n\n regex_string = _RE_NICK_SPACE.sub(r\"\\\\s+\", regex_string)\n regex_string = _RE_NICK_ARG.sub(lambda m: \"(?P<arg%s>.+?)\" % m.group(2), regex_string)\n template_string = _RE_NICK_TEMPLATE_ARG.sub(lambda m: \"{arg%s}\" % m.group(2), out_template)\n\n return regex_string, template_string","def generate_latex_output(self, calc_spec_tex_file_handle, sim, ui=None,\r\n res_dir=None):\r\n average_pressure = np.mean(self.sun.press[self.sun.press > 0])\r\n numeric_output_data = [\r\n ('pressure, max.', to_preci(np.amax(self.sun.press), 4),\r\n UnitTex.pressure.value, 'unverified'),\r\n ('pressure, av.', to_preci(average_pressure, 4),\r\n UnitTex.pressure.value, 'unverified'),\r\n ('e_a,kin sun, max.', to_preci(np.amax(self.sun.e_akin), 4),\r\n UnitTex.eakin.value, 'unverified'),\r\n ('e_a,kin planet, max.', to_preci(np.amax(self.planet.e_akin), 4),\r\n UnitTex.eakin.value, 'unverified'),\r\n ('pv_rel, max.', to_preci(np.amax(self.pv), 4),\r\n UnitTex.pvrel.value, 'unverified'),\r\n ('contact area', to_preci(self.sun.get_area(self.sun.press), 4),\r\n UnitTex.area.value, 'unverified')]\r\n table_calc_summary = []\r\n for key, value, unit, status in sorted(numeric_output_data):\r\n table_calc_summary.append(\r\n (re.sub('_', '\\_', key), value, unit, status))\r\n\r\n latex_variables = {'table_calc_summary': table_calc_summary,\r\n 'contact_plot1': '{}{}contact1.png'.format(\r\n SubDir.tex_figs_rel_to_tex_file.value, '/'),\r\n 'pressure_plot1': '{}{}pressure1.png'.format(\r\n SubDir.tex_figs_rel_to_tex_file.value, '/'),\r\n 'energy_plot1': '{}{}energy1.png'.format(\r\n SubDir.tex_figs_rel_to_tex_file.value, '/')}\r\n template_calc_specific = get_calc_specific_latex_template(\r\n TexTempl.RingOnRing.value, sim)\r\n with open(calc_spec_tex_file_handle, 'w') as f:\r\n f.write(template_calc_specific.render(latex_variables))","def t1_hypointensity( x, xsegmentation, xWMProbability, template, templateWMPrior, wmh_thresh=0.1 ):\n mybig = [88,128,128]\n templatesmall = ants.resample_image( template, mybig, use_voxels=True )\n qaff = ants.registration(\n ants.rank_intensity(x),\n ants.rank_intensity(templatesmall), 'SyN',\n syn_sampling=2,\n syn_metric='CC',\n reg_iterations = [25,15,0,0],\n aff_metric='GC', random_seed=1 )\n afftx = qaff['fwdtransforms'][1]\n templateWMPrior2x = ants.apply_transforms( x, templateWMPrior, qaff['fwdtransforms'] )\n cerebrum = ants.threshold_image( xsegmentation, 2, 4 )\n realWM = ants.threshold_image( templateWMPrior2x , 0.1, math.inf )\n inimg = ants.rank_intensity( x )\n parcellateWMdnz = ants.kmeans_segmentation( inimg, 2, realWM, mrf=0.3 )['probabilityimages'][0]\n x2template = ants.apply_transforms( templatesmall, x, afftx, whichtoinvert=[True] )\n parcellateWMdnz2template = ants.apply_transforms( templatesmall,\n cerebrum * parcellateWMdnz, afftx, whichtoinvert=[True] )\n # features = rank+dnz-image, lprob, wprob, wprior at mybig resolution\n f1 = x2template.numpy()\n f2 = parcellateWMdnz2template.numpy()\n f3 = ants.apply_transforms( templatesmall, xWMProbability, afftx, whichtoinvert=[True] ).numpy()\n f4 = ants.apply_transforms( templatesmall, templateWMPrior, qaff['fwdtransforms'][0] ).numpy()\n myfeatures = np.stack( (f1,f2,f3,f4), axis=3 )\n newshape = np.concatenate( [ [1],np.asarray( myfeatures.shape )] )\n myfeatures = myfeatures.reshape( newshape )\n\n inshape = [None,None,None,4]\n wmhunet = antspynet.create_unet_model_3d( inshape,\n number_of_outputs = 1,\n number_of_layers = 4,\n mode = 'sigmoid' )\n\n wmhunet.load_weights( get_data(\"simwmhseg\", target_extension='.h5') )\n\n pp = wmhunet.predict( myfeatures )\n\n limg = ants.from_numpy( tf.squeeze( pp[0] ).numpy( ) )\n limg = ants.copy_image_info( templatesmall, limg )\n lesresam = ants.apply_transforms( x, limg, afftx, whichtoinvert=[False] )\n # lesresam = lesresam * cerebrum\n rnmdl = antspynet.create_resnet_model_3d( inshape,\n number_of_classification_labels = 1,\n layers = (1,2,3),\n residual_block_schedule = (3,4,6,3), squeeze_and_excite = True,\n lowest_resolution = 32, cardinality = 1, mode = \"regression\" )\n rnmdl.load_weights( get_data(\"simwmdisc\", target_extension='.h5' ) )\n qq = rnmdl.predict( myfeatures )\n\n lesresamb = ants.threshold_image( lesresam, wmh_thresh, 1.0 )\n lgo=ants.label_geometry_measures( lesresamb, lesresam )\n wmhsummary = pd.read_csv( get_data(\"wmh_evidence\", target_extension='.csv' ) )\n wmhsummary.at[0,'Value']=lgo.at[0,'VolumeInMillimeters']\n wmhsummary.at[1,'Value']=lgo.at[0,'IntegratedIntensity']\n wmhsummary.at[2,'Value']=float(qq)\n\n return {\n \"wmh_summary\":wmhsummary,\n \"wmh_probability_image\":lesresam,\n \"wmh_evidence_of_existence\":float(qq),\n \"wmh_max_prob\":lesresam.max(),\n \"features\":myfeatures }","def nb_clementsbekkers(data, template):\n ## Prepare a bunch of arrays we'll need later\n n_template = len(template)\n # if n_template <= 1:\n # raise ValueError(\"nb_clementsbekkers: Length of template must be useful, and > 1\")\n n_data = data.shape[0]\n n_dt = n_data - n_template\n # if n_dt < 10:\n # raise ValueError(\"nb_clementsbekkers: n_dt, n_template\", n_dt, n_template)\n #\n sum_template = template.sum()\n sum_template_2 = (template*template).sum()\n\n data_2 = data*data\n sum_data = np.sum(data[:n_template])\n sum_data_2 = data_2[:n_template].sum()\n scale = np.zeros(n_dt)\n offset = np.zeros(n_dt)\n crit = np.zeros(n_dt)\n for i in range(n_dt):\n if i > 0:\n sum_data = sum_data + data[i+n_template] - data[i-1]\n sum_data_2 = sum_data_2 + data_2[i+n_template] - data_2[i-1]\n sum_data_template_prod = np.multiply(data[i:i+n_template], template).sum()\n scale[i] = (\n (sum_data_template_prod - sum_data * sum_template/n_template)/\n (sum_template_2 - sum_template*sum_template/n_template)\n )\n offset[i] = (sum_data - scale[i]*sum_template)/n_template\n fitted_template = template * scale[i] + offset[i]\n sse = ((data[i:i+n_template] - fitted_template)**2).sum()\n crit[i] = scale[i]/np.sqrt(sse/(n_template-1))\n DC = scale/crit\n return(DC, scale, crit)","def zernikeHexapodTrend(mnts='M20'):\n Tfile='/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_training.cp'\n b=p.load(open(Tfile))\n nobs = len(b)\n x = b[:,0]\n y = b[:,1]\n z = b[:,2]\n theta = b[:,3]\n phi = b[:,4]\n fwhm = b[:,5]\n e1 = b[:,6]\n e2 = b[:,7]\n thetax = theta*np.cos(np.deg2rad(phi))\n thetay = theta*np.sin(np.deg2rad(phi))\n if mnts == 'M20':\n idxBase = 9\n if mnts == 'M22real':\n idxBase = 29\n if mnts == 'M22imag':\n idxBase = 49\n idx = np.arange(14)\n zernikeName=('Piston','Tip','Tilt','Astignism','Defocus','Astignism','Trefoil','Coma','Coma','Trefoil','Ashtray','Astigm.5th','Spherical','Astigm.5th','Ashtray','16','17','18','19','20')\n for i in range(14):\n pl.figure(figsize=(21,10))\n pl.subplot(2,3,1)\n bp.bin_scatter(x,b[:,idxBase+idx[i]],binsize=0.01,fmt='bo',scatter=True)\n pl.xlabel('x decenter')\n pl.ylabel(zernikeName[i+1])\n pl.title(mnts)\n pl.subplot(2,3,2)\n bp.bin_scatter(y,b[:,idxBase+idx[i]],binsize=0.01,fmt='bo',scatter=True)\n pl.xlabel('y decenter')\n pl.ylabel(zernikeName[i+1])\n pl.title(mnts)\n pl.subplot(2,3,3)\n bp.bin_scatter(z,b[:,idxBase+idx[i]],binsize=0.01,fmt='bo',scatter=True)\n pl.xlabel('z-defocus')\n pl.ylabel(zernikeName[i+1])\n pl.title(mnts)\n pl.subplot(2,3,4)\n bp.bin_scatter(thetax,b[:,idxBase+idx[i]],binsize=5,fmt='bo',scatter=True)\n pl.xlabel('x-tilt')\n pl.ylabel(zernikeName[i+1])\n pl.title(mnts)\n pl.subplot(2,3,5)\n bp.bin_scatter(thetay,b[:,idxBase+idx[i]],binsize=5,fmt='bo',scatter=True)\n pl.xlabel('y-tilt')\n pl.ylabel(zernikeName[i+1])\n pl.title(mnts)\n pl.savefig(mnts+'_'+str(i+1)+'_'+zernikeName[i+1]+'.png')\n pl.close()","def calculate_identity(target, template):\n\n identical_residues = 0\n length_of_target = 0\n for i in range(len(target)):\n if target[i] != '-':\n length_of_target += 1\n if target[i] == template[i]:\n identical_residues += 1\n identity_percent = round(identical_residues / length_of_target * 100, 2)\n return identity_percent","def gittins_index(n=100, grid=20000, discount=0.9):\n g, v = recursion_loop(n, discount, grid)\n g_reformat = reformat_gittins(g) \n return g_reformat","def fig16():\n # fmt: off\n tpm = np.array([\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0],\n [1, 0, 1, 0, 0, 0, 0],\n [1, 0, 0, 0, 1, 0, 0],\n [1, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 1, 0, 0],\n [1, 0, 1, 0, 0, 0, 0],\n [1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 0, 0],\n [1, 0, 1, 1, 1, 0, 0],\n [1, 0, 0, 1, 1, 0, 0],\n [1, 0, 0, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 0, 0],\n [1, 0, 1, 1, 1, 0, 0],\n [1, 1, 0, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 0, 0],\n [1, 0, 1, 1, 1, 0, 0],\n [1, 0, 0, 1, 1, 0, 0],\n [1, 0, 0, 1, 1, 0, 0],\n [1, 0, 1, 1, 1, 0, 0],\n [1, 0, 1, 1, 1, 0, 0],\n [1, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 0, 0],\n [1, 0, 1, 1, 1, 0, 0],\n [1, 0, 0, 1, 1, 0, 0],\n [1, 0, 0, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 0, 0],\n [1, 0, 1, 1, 1, 0, 0],\n [1, 1, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 1, 1, 0],\n [1, 0, 1, 0, 0, 1, 0],\n [1, 0, 0, 0, 1, 1, 0],\n [1, 0, 0, 0, 0, 1, 0],\n [1, 0, 1, 0, 1, 1, 0],\n [1, 0, 1, 0, 0, 1, 0],\n [1, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 1, 1, 0],\n [1, 0, 1, 0, 0, 1, 0],\n [1, 0, 0, 0, 1, 1, 0],\n [1, 0, 0, 0, 0, 1, 0],\n [1, 0, 1, 0, 1, 1, 0],\n [1, 0, 1, 0, 0, 1, 0],\n [1, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 0, 0, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 0],\n [1, 0, 1, 1, 1, 1, 0],\n [1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 1],\n [0, 0, 1, 0, 1, 1, 1],\n [1, 0, 1, 0, 0, 1, 1],\n [1, 0, 0, 0, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1],\n [1, 0, 1, 0, 1, 1, 1],\n [1, 0, 1, 0, 0, 1, 1],\n [1, 0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1],\n [1, 1, 0, 1, 1, 1, 1],\n [0, 0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1],\n [1, 1, 0, 1, 1, 1, 1],\n ])\n cm = np.array([\n [0, 1, 1, 0, 1, 0, 0],\n [1, 0, 1, 0, 0, 0, 0],\n [1, 1, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 1],\n [0, 0, 0, 0, 0, 1, 1],\n ])\n # fmt: on\n return Network(tpm, cm=cm, node_labels=LABELS[:tpm.shape[1]])"],"string":"[\n \"def produce_13TeV_template(tag_name=\\\"HKHI\\\"):\\n num_rebin = 1\\n file_name = \\\"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\\\"+tag_name+\\\"_Lin.root\\\"\\n print \\\"Input: \\\", file_name\\n fin = ROOT.TFile.Open(file_name, \\\"read\\\")\\n h_nom = fin.Get(\\\"bkg_total_gg_full\\\").Clone(\\\"bkg_nominal_old\\\")\\n h_nom.Rebin(num_rebin)\\n fout = ROOT.TFile.Open(\\\"hists_input_\\\"+tag_name+\\\".root\\\", \\\"recreate\\\")\\n\\n h_purity_sys = fin.Get(\\\"bkg_purity_syst_gg_full\\\").Clone(\\\"bkg_purity_syst_gg\\\")\\n h_reducible_sys = fin.Get(\\\"bkg_reducible_syst_gg_full\\\").Clone(\\\"bkg_reducible_syst_gg\\\")\\n h_irreducible_sys = fin.Get(\\\"bkg_irreducible_syst_gg_full\\\").Clone(\\\"bkg_irreducible_syst_gg\\\")\\n h_iso_sys = fin.Get(\\\"bkg_iso_syst_gg_full\\\").Clone(\\\"bkg_iso_syst_gg\\\")\\n\\n #file_iso = \\\"isolation_sys/hist.root\\\"\\n #fin2 = ROOT.TFile.Open(file_iso, \\\"read\\\")\\n #h_iso_sys = fin2.Get(\\\"bkg_isolation_syst_gg\\\")\\n ## inflat irreducible uncertainty by factor of 10\\n # so that it closes to stats uncertainty in data\\n sf = 1\\n if INFLATE_SYS:\\n sf = 10\\n\\n # after rebinning systematic uncertainties, need to scale down,\\n # otherwise the uncertainties are inflated.\\n h_purity_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_irreducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_reducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_iso_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n\\n ## truncate the histograms to [200, 2000] GeV\\n h_nom_new = truncate_hist(h_nom, \\\"bkg_nominal\\\")\\n h_purity_sys_new = truncate_hist(h_purity_sys, \\\"h_purity_sys_new\\\")\\n h_irreducible_sys_new = truncate_hist(h_irreducible_sys, \\\"h_irreducible_sys_new\\\")\\n h_reducible_sys_new = truncate_hist(h_reducible_sys, \\\"h_reducible_sys_new\\\")\\n h_iso_sys_new = truncate_hist(h_iso_sys, \\\"h_iso_sys_new\\\")\\n\\n #write down sys and nominal\\n fout.cd()\\n h_nom_new.Write()\\n h_purity_sys_new.Write()\\n h_reducible_sys_new.Write()\\n h_irreducible_sys_new.Write()\\n h_iso_sys_new.Write()\\n\\n h_purity_up, h_purity_down = create_sys_hist(h_nom_new, h_purity_sys_new, \\\"purity_sys\\\")\\n h_purity_up.Write()\\n h_purity_down.Write()\\n\\n h_red_up, h_red_down = create_sys_hist(h_nom_new, h_reducible_sys_new, \\\"reducible_sys\\\")\\n h_red_up.Write()\\n h_red_down.Write()\\n\\n h_irred_up, h_irred_down = create_sys_hist(h_nom_new, h_irreducible_sys_new, \\\"irreducible_sys\\\")\\n h_irred_up.Write()\\n h_irred_down.Write()\\n\\n h_iso_up, h_iso_down = create_sys_hist(h_nom_new, h_iso_sys, \\\"isolation_sys\\\")\\n h_iso_up.Write()\\n h_iso_down.Write()\\n\\n fin.Close()\\n fout.Close()\",\n \"def computeTemplateFeatures(self, templates, mode=''):\\n pass\",\n \"def stoichiometry_func_doc(self, label):\\n air = self.air_alias.val\\n fuel = self.fuel_alias.val\\n flue_gas = self.fuel_alias.val + '_fg'\\n\\n equations = ''\\n for fluid in self.inl[0].fluid.val.keys():\\n\\n in1 = r'\\\\dot{m}_\\\\mathrm{in,1} \\\\cdot x_\\\\mathrm{fluid,in,1}'\\n in2 = r'\\\\dot{m}_\\\\mathrm{in,2} \\\\cdot x_\\\\mathrm{fluid,in,2}'\\n out = r'\\\\dot{m}_\\\\mathrm{out,1} \\\\cdot x_\\\\mathrm{fluid,out,1}'\\n\\n if fluid == air:\\n latex = (\\n r'0=\\\\Delta\\\\dot{m}_\\\\mathrm{' + fluid + r'} - '\\n r'\\\\dot{m}_\\\\mathrm{' + fluid + r',stoich}'\\n )\\n elif fluid == fuel:\\n latex = (\\n r'0=\\\\Delta \\\\dot{m}_\\\\mathrm{' + fluid + r'} - '\\n r'\\\\dot{m}_\\\\mathrm{' + fluid + r'}'\\n )\\n elif fluid == flue_gas:\\n latex = (\\n r'0=\\\\Delta \\\\dot{m}_\\\\mathrm{' + fluid.replace('_', ',') +\\n r'} + \\\\dot{m}_\\\\mathrm{' + fuel + r'} +'\\n r'\\\\dot{m}_\\\\mathrm{' + air + r',stoich}'\\n )\\n else:\\n latex = r'0 = \\\\Delta \\\\dot{m}_\\\\mathrm{' + fluid + '}'\\n\\n if fluid == next(iter(self.inl[0].fluid.val)):\\n balance = (\\n r'\\\\Delta \\\\dot{m}_\\\\mathrm{fluid} = ' + in1 +\\n '+' + in2 + '-' + out)\\n m_fluid = r'\\\\dot{m}_\\\\mathrm{fluid} = ' + in1 + '+' + in2\\n m_air_stoich = (\\n r'\\\\dot{m}_\\\\mathrm{' + air + ',stoich}='\\n r'\\\\dot{m}_\\\\mathrm{' + fuel + r'} \\\\cdot ' +\\n str(round(self.air_min, 4)))\\n latex_general_eq = (\\n r'\\\\begin{split}' + '\\\\n'\\n r'&' + balance + r'\\\\\\\\' + '\\\\n'\\n r'&' + m_fluid + r'\\\\\\\\' + '\\\\n'\\n r'&' + m_air_stoich + r'\\\\\\\\' + '\\\\n'\\n r'\\\\end{split}'\\n )\\n equations += (\\n generate_latex_eq(\\n self, latex_general_eq, label + '_general_eq') + '\\\\n' +\\n generate_latex_eq(self, latex, label + '_' + fluid) + '\\\\n')\\n else:\\n equations += (\\n generate_latex_eq(self, latex, label + '_' + fluid) + '\\\\n')\\n # remove last newline\\n return equations[:-1]\",\n \"def _tpl(grid, iv, ds='test'):\\n #TODO: Research numpy routines which may automate this\\n tax=grid._get_timelike_axis()\\n def tmp(x):\\n if x[tax]>0:\\n return 0.0\\n if x[tax]==0:\\n return iv(x)\\n return tmp\",\n \"def fillSignalTemplates(opt):\\n\\n totalSig={}\\n templates=[]\\n\\n #import signal events\\n data=ROOT.TChain('data')\\n data.AddFile(os.path.join(opt.input,opt.sig))\\n\\n #define final preselection cuts\\n cuts='xangle==%d'%opt.xangle\\n if len(opt.presel) : cuts += ' && ' + opt.presel\\n if opt.csiacc:\\n csiCuts ='csi1>%f && csi1<%f && '%opt.csiacc[opt.xangle][0]\\n csiCuts+='csi2>%f && csi2<%f'%opt.csiacc[opt.xangle][1]\\n cuts=csiCuts if len(cuts)==0 else '{0} && {1}'.format(cuts,csiCuts)\\n\\n #loop over categories build templates\\n for icat in range(len(opt.categs)):\\n\\n #apply category cuts\\n categCut=opt.categs[icat]\\n categCut=cuts if len(categCut)==0 else '%s && %s'%(categCut,cuts)\\n\\n catName='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\\n print '\\\\t',catName,categCut\\n\\n #signal modelling histograms\\n histos=[]\\n for name,pfix in [('sig_'+catName,''),('sig_%s_sigShape'%catName,'mix')]:\\n\\n templCuts=categCut.replace('csi1',pfix+'csi1')\\n templCuts=templCuts.replace('csi2',pfix+'csi2')\\n wgtExpr='wgt*%f'%(SIGNALXSECS[opt.xangle]*opt.lumi)\\n data.Draw('{0}mmiss >> h({1},{2},{3})'.format(pfix,opt.nbins,opt.mMin,opt.mMax),\\n '{0}*({1})'.format(wgtExpr,templCuts),\\n 'goff')\\n h=data.GetHistogram()\\n histos.append( h.Clone(name) ) \\n histos[-1].SetDirectory(0)\\n\\n if len(histos)==1:\\n totalSig[icat]=h.Integral()\\n\\n h.Reset('ICE')\\n templates += defineProcessTemplates(histos)\\n \\n print '\\\\t total signal:',totalSig\\n return totalSig,templates\",\n \"def cheetah_template(self, pre=False):\\n if self.is_req_output:\\n cht_tmpl = self.req_out_chth\\n return cht_tmpl.substitute(self.xml_out)\\n elif self.is_output:\\n xml_out = self.xml_out\\n xml_out['out_sel_name'] = self.out_sel_name\\n cht_tmpl = self.file_chth\\n return cht_tmpl.substitute(self.xml_out)\\n elif self.is_input and not pre:\\n if self.pname in self.gen_in_fmt:\\n if self.gen_in_fmt[self.pname] == 'vcf,vcf_bgzip':\\n cht_tmpl = self.vcf_choose\\n else:\\n cht_tmpl = PercentTemplate(self.reg_arg)\\n elif self.pname in self.tool_data[self.tool_name]['input_fmt']:\\n cht_tmpl = self.req_out_chth\\n return cht_tmpl.substitute(self.xml_out)\\n elif self.is_input and pre:\\n cht_tmpl = self.vcf_tabix\\n return cht_tmpl.substitute(self.xml_out)\\n else:\\n if self.xml_out['section'] not in ['required']:\\n template_string = self.ext_arg\\n else:\\n template_string = self.reg_arg\\n if self.xml_out['type'] == 'boolean':\\n cht_tmpl = PercentTemplate(template_string.replace('%argument ', ''))\\n else:\\n cht_tmpl = PercentTemplate(template_string)\\n return cht_tmpl.substitute(self.xml_out)\",\n \"def calc_template(template_def, config):\\n template = Template(**template_def)\\n #print \\\"template_def:\\\", template_def, \\\"config:\\\", config\\n try:\\n retvals = process_template(template, config, target=(None, None))\\n except Exception:\\n print(\\\"==== template ====\\\"); pprint(template_def)\\n print(\\\"==== config ====\\\"); pprint(config)\\n #traceback.print_exc()\\n raise\\n output = {}\\n for rkey, rv in retvals.items():\\n module_id, terminal_id = rkey\\n module_key = str(module_id)\\n output.setdefault(module_key, {})\\n output[module_key][terminal_id] = rv.todict()\\n return output\",\n \"def ti_func_doc(self, label):\\n latex = (\\n r'\\\\begin{split}' + '\\\\n'\\n r'0 = & ti - LHV_\\\\mathrm{fuel} \\\\cdot \\\\left[\\\\sum_i \\\\left('\\n r'\\\\dot{m}_{\\\\mathrm{in,}i}\\\\cdot x_{\\\\mathrm{fuel,in,}i}\\\\right)-'\\n r' \\\\dot{m}_\\\\mathrm{out,1}\\\\cdot '\\n r'x_{\\\\mathrm{fuel,out,1}} \\\\right]\\\\\\\\' + '\\\\n'\\n r'& \\\\forall i \\\\in \\\\text{combustion inlets}\\\\\\\\' + '\\\\n'\\n r'\\\\end{split}'\\n )\\n return generate_latex_eq(self, latex, label)\",\n \"def computeTemplateFeatures(self, templates, mode=''):\\n pass\",\n \"def gen_op2(N11, N22, N12, rN11, rN22, rN12):\\n raw = 2 * N12 / rN12 / (N11 / rN11 + N22 / rN22)\\n print(\\\"=====\\\\n2nd method: raw = 2 * N12/rN12 / (N11/rN11 + N22/rN22)\\\")\\n print(\\\"OP: 1 - raw = %.3f\\\\n=====\\\" % (1 - raw))\",\n \"def gen_op4(N11, N22, N12, rN11, rN22, rN12):\\n raw = 2 * N12 / rN12 / (N11 / rN11 + N22 / rN22)\\n print(\\\"=====\\\\n4th method: raw = 2 * N12/rN12 / (N11/rN11 + N22/rN22)\\\")\\n print(\\\"OP: 1 / raw = %.3f\\\\n=====\\\" % (1.0 / raw))\",\n \"def run_template(template, config):\\n all_results = {}\\n for nodenum, wires in template:\\n # Find the modules\\n node = template.modules[nodenum]\\n module_id = node['module'] #template.modules[node]\\n module = lookup_module(module_id)\\n inputs = _map_inputs(module, wires)\\n \\n # substitute values for inputs\\n kwargs = dict((k, _lookup_results(all_results, v)) \\n for k, v in inputs.items())\\n \\n # Include configuration information\\n kwargs.update(node.get('config', {}))\\n kwargs.update(config[nodenum])\\n result = module.action(**kwargs)\\n# print result\\n all_results[nodenum] = result\\n \\n# return all_results\\n# FIXXXXXXXXXXXXXXXXXXXXXX ***********************\\n from .offspecular.instruments import convert_to_plottable\\n return [convert_to_plottable(value['output']) if 'output' in value else {} for key, value in all_results.items()]\",\n \"def gen_op1(N11, N22, N12, rN11, rN22, rN12):\\n raw = N12 / (N11 + N22)\\n ref = rN12 / (rN11 + rN22)\\n print(\\\"=====\\\\n1st method: raw = N12/(N11+N22)\\\")\\n print(\\\"raw: %.2f | ref: %.2f\\\" % (raw, ref) )\\n print(\\\"1 - raw/ref = %.3f\\\\n=====\\\" % (1.0 - raw / ref))\",\n \"def test_include_template(self, parse_input, tmpdir):\\n template = textwrap.dedent(\\n \\\"\\\"\\\"\\n name CustomOperation\\n version 0.0\\n BSgate({theta}, pi/2) | [0, 1]\\n Rgate({phi}) | 0\\n \\\"\\\"\\\"\\n )\\n\\n filename = tmpdir.join(\\\"test.xbb\\\")\\n\\n with open(filename, \\\"w\\\") as f:\\n f.write(template)\\n\\n test_include = textwrap.dedent(\\n \\\"\\\"\\\"\\n name test_include\\n version 0.0\\n include \\\"{}\\\"\\n CustomOperation(theta=0.54, phi=0.1) | [2, 1]\\n \\\"\\\"\\\"\\n ).format(filename)\\n\\n bb = parse_input(test_include, cwd=tmpdir)\\n\\n expected = [\\n {\\\"op\\\": \\\"BSgate\\\", \\\"args\\\": [0.54, np.pi / 2], \\\"kwargs\\\": {}, \\\"modes\\\": [2, 1]},\\n {\\\"op\\\": \\\"Rgate\\\", \\\"args\\\": [0.1], \\\"kwargs\\\": {}, \\\"modes\\\": [2]},\\n ]\\n\\n assert bb.operations == expected\",\n \"def generate_scenario_precip_inputs():\\n def multiply_precip(precip_raster, multiply_factor):\\n valid_mask = (precip_raster != precip_raster_nodata)\\n result = numpy.empty(precip_raster.shape, dtype=numpy.float32)\\n result[:] = _TARGET_NODATA\\n result[valid_mask] = precip_raster[valid_mask] * multiply_factor\\n return result\\n\\n def calc_ET0(RA, Tavg, TD, P):\\n \\\"\\\"\\\"Modified Hargreaves from Droogers and Allen 2002.\\n\\n Parameters:\\n RA (numpy.ndarray): daily extraterrestrial radiation\\n Tavg (numpy.ndarray): average temperature\\n TD (numpy.ndarray): difference between minimum and maximum\\n temperature\\n P (numpy.ndarray): monthly precipitation\\n\\n Returns:\\n monthly reference evapotranspiration (mm)\\n\\n \\\"\\\"\\\"\\n valid_mask = (\\n (RA != _TARGET_NODATA) &\\n (Tavg != tavg_nodata) &\\n (TD != td_nodata) &\\n (P != _TARGET_NODATA))\\n result = numpy.empty(RA.shape, dtype=numpy.float32)\\n result[:] = _TARGET_NODATA\\n result[valid_mask] = (\\n 0.0013 * 0.408 * RA[valid_mask] * (Tavg[valid_mask] + 17.) *\\n (numpy.power((TD[valid_mask] - 0.0123 * P[valid_mask]), 0.76)) *\\n 29.5)\\n return result\\n\\n climate_dir = r\\\"E:\\\\GIS_local_3.6.19\\\\USFS\\\\NCEI_climate\\\"\\n RA_df = pandas.read_csv(os.path.join(climate_dir, \\\"RA_FAO.csv\\\"))\\n Tavg_dir = os.path.join(climate_dir, \\\"temp_ave_rasters\\\")\\n TD_dir = os.path.join(climate_dir, \\\"tdiff_rasters\\\")\\n events_table = os.path.join(\\n climate_dir, 'rain_events_table_Palmer_creek.csv')\\n\\n baseline_precip_dir = r\\\"C:\\\\Users\\\\ginge\\\\Dropbox\\\\NatCap_backup\\\\USFS\\\\input_data\\\\model_inputs\\\\precip_NCEI\\\"\\n outer_scenario_dir = r\\\"C:\\\\Users\\\\ginge\\\\Dropbox\\\\NatCap_backup\\\\USFS\\\\input_data\\\\model_inputs\\\\precip_scenarios\\\"\\n\\n example_precip_raster = os.path.join(\\n baseline_precip_dir, 'precip_1.tif')\\n precip_raster_nodata = pygeoprocessing.get_raster_info(\\n example_precip_raster)['nodata'][0]\\n\\n for multiply_factor in [0.5]: # [0.7, 0.9, 1.1, 1.3]:\\n scenario_dir = os.path.join(\\n outer_scenario_dir, '{}x'.format(multiply_factor))\\n modified_event_table = os.path.join(scenario_dir, 'rain_events.csv')\\n ET0_dir = os.path.join(scenario_dir, 'ET0_rasters')\\n precip_dir = os.path.join(scenario_dir, 'precip_rasters')\\n if not os.path.exists(ET0_dir):\\n os.makedirs(ET0_dir)\\n if not os.path.exists(precip_dir):\\n os.makedirs(precip_dir)\\n event_df = pandas.read_csv(events_table)\\n event_df.events = event_df.events * multiply_factor\\n event_df.to_csv(modified_event_table)\\n for month in xrange(1, 13):\\n baseline_raster_path = os.path.join(\\n baseline_precip_dir, 'precip_{}.tif'.format(month))\\n modified_precip_path = os.path.join(\\n precip_dir, 'precip_{}.tif'.format(month))\\n pygeoprocessing.raster_calculator(\\n [(baseline_raster_path, 1), (multiply_factor, 'raw')],\\n multiply_precip, modified_precip_path,\\n gdal.GDT_Float32, _TARGET_NODATA)\\n\\n Tavg_raster_path = os.path.join(\\n Tavg_dir, 'tavg_{}.tif'.format(month))\\n TD_raster_path = os.path.join(\\n TD_dir, 'tdiff_{}.tif'.format(month))\\n tavg_nodata = pygeoprocessing.get_raster_info(\\n Tavg_raster_path)['nodata'][0]\\n td_nodata = pygeoprocessing.get_raster_info(\\n TD_raster_path)['nodata'][0]\\n RA_val = (\\n RA_df.loc[RA_df['month'] == month, 'average_RA'].values[0])\\n RA_raster_path = os.path.join(climate_dir, 'RA.tif')\\n pygeoprocessing.new_raster_from_base(\\n modified_precip_path, RA_raster_path, gdal.GDT_Float32,\\n [_TARGET_NODATA], fill_value_list=[RA_val])\\n ET0_raster_path = os.path.join(ET0_dir, 'ET_{}.tif'.format(month))\\n pygeoprocessing.raster_calculator(\\n [(path, 1) for path in [\\n RA_raster_path, Tavg_raster_path, TD_raster_path,\\n modified_precip_path]],\\n calc_ET0, ET0_raster_path, gdal.GDT_Float32, _TARGET_NODATA)\",\n \"def _eval_templates(self, period):\\n theta_best = [self._optimize(period, tmpid)\\n for tmpid, _ in enumerate(self.templates)]\\n chi2 = [self._chi2(theta, period, tmpid)\\n for tmpid, theta in enumerate(theta_best)]\\n\\n return theta_best, chi2\",\n \"def inhomoCoeffs(Bulk_Operators, Bdy_Operators, Bulk_second, s=1):\\n\\t\\tRow = []\\n\\t\\tfor Bulk_Op in Bulk_Operators:\\n\\t\\t\\t\\ttable_call = table1.table[0, 0].subs({\\\"Delta\\\":Bulk_Op, \\\"Delta_12\\\":delta_12, \\\"Xi\\\":1.00})\\n\\t\\t\\t\\tRow.append(table_call)\\n\\t\\tfor index in range(len(Bulk_second)):\\n\\t\\t\\t\\tRow.append(0*table1.table[0,0])\\n\\t\\tfor Bdy_Op in Bdy_Operators:\\n\\t\\t\\tif Bdy_Op == 2.0:\\n\\t\\t\\t\\ttable_call = (N-1)*table1.table[1, 0].subs({\\\"Delta\\\":Bdy_Op, \\\"Xi\\\":1.00})\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttable_call = table1.table[1, 0].subs({\\\"Delta\\\":Bdy_Op, \\\"Xi\\\":1.00})\\n\\t\\t\\tRow.append(table_call)\\n\\t\\tif s==1:\\n\\t\\t\\tXi = symbols('Xi')\\n\\t\\t\\tlast_expr = Xi**((delta_1 + delta_2)/2)\\n\\t\\t\\tlast_elem = last_expr.subs({\\\"Xi\\\":1.00}).evalf()\\n\\t\\t\\tRow.append(last_elem)\\n\\t\\tRow2 = []\\n\\t\\tfor index in range(len(Bulk_Operators)):\\n\\t\\t\\t\\tRow2.append(0*table1.table[0,0])\\n\\t\\tfor Bulk_Op in Bulk_second:\\n\\t\\t\\t\\ttable_call = table1.table[0, 0].subs({\\\"Delta\\\":Bulk_Op, \\\"Delta_12\\\":delta_12, \\\"Xi\\\":1.00})\\n\\t\\t\\t\\tRow2.append(table_call)\\n\\t\\tfor Bdy_Op in Bdy_Operators:\\n\\t\\t\\tif Bdy_Op == 2.0:\\n\\t\\t\\t\\ttable_call = -1*table1.table[1,0].subs({\\\"Delta\\\":Bdy_Op, \\\"Xi\\\":1.00})\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttable_call = table1.table[1,0].subs({\\\"Delta\\\":Bdy_Op, \\\"Xi\\\":1.00})\\n\\t\\t\\tRow2.append(table_call)\\n\\t\\tif s==1:\\n\\t\\t\\tXi = symbols('Xi')\\n\\t\\t\\tlast_expr = Xi**((delta_1 + delta_2)/2)\\n\\t\\t\\tlast_elem = last_expr.subs({\\\"Xi\\\":1.00}).evalf()\\n\\t\\t\\tRow2.append(last_elem)\\n\\t\\t\\t\\n\\t\\treturn np.array(Row), np.array(Row2)\",\n \"def psfTemplateModel(n, params):\\n psf_template = params[\\\"psf_template\\\"]\\n self.m_psf = psf_template\\n print(\\\"PSF template shape\\\", np.shape(psf_template))\\n dim = int(n)\\n m = np.shape(psf_template)[0]\\n #if m != dim:\\n # raise ValueError(\\\"PSF template dimension not equal patch size\\\")\\n \\n if np.sum(psf_template) != 1:\\n print(\\\"Normalizing PSF template to sum = 1\\\")\\n psf_template = psf_template/np.sum(psf_template) \\n return psf_template\",\n \"def run(self, template_data, data_cont):\\n\\n tprops = acalib.core.transform.fits_props(template_data)\\n scaled = acalib.core.transform.scale(data_cont, tprops['major'])\\n rotated, angles = acalib.core.transform.rotate(scaled, tprops['angle'])\\n aligned = acalib.core.transform.crop_and_align(rotated, angles)\\n result = mean(aligned , axis=0)\\n return result\",\n \"def getTru(n,int_method,func) :\\n m = np.asarray([0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,6,7,8,9,10])\\n bes = np.asarray([-0.5,0,0.43675,0.47773,0.49231,0.49316,0.49280,0.50325,\\n 0.51140,0.52169,0.55823,0.58086,0.60463,0.61483,0.66995])\\n p = np.asarray([1,0,0.61007,0.77491,0.84071,0.87689,0.89914,0.91365,\\n 0.92449,0.93279,0.94451,0.95289,0.95904,0.96385,\\n 0.96731])\\n h1 = np.asarray([0,0,-0.07257,-0.04963,-0.03313,-0.02282,-0.01648,\\n -0.01248,-0.00970,-0.00773,-0.00522,-0.00369,-0.00272,\\n -0.00206,-0.00164])\\n h2 = np.asarray([0,0,-0.20048,-0.15556,-0.12070,-0.09611,-0.07919,\\n -0.06747,-0.05829,-0.05106,-0.04060,-0.03311,-0.02768,\\n -0.02353,-0.02053])\\n h3 = np.asarray([0,0,0.01647,0.08284,0.14390,0.19680,0.24168,0.27969,\\n 0.31280,0.34181,0.39002,0.42942,0.46208,0.48997,0.51325])\\n \\n if (func == True) :\\n int_bes = splev(n,splrep(m, bes))\\n int_p = splev(n,splrep(m, p))\\n int_h1 = splev(n,splrep(m, h1))\\n int_h2 = splev(n,splrep(m, h2))\\n int_h3 = splev(n,splrep(m, h3))\\n else :\\n int_bes = griddata(m, bes, n, method=int_method)\\n int_p = griddata(m, p, n, method=int_method)\\n int_h1 = griddata(m, h1, n, method=int_method)\\n int_h2 = griddata(m, h2, n, method=int_method)\\n int_h3 = griddata(m, h3, n, method=int_method)\\n \\n return np.asarray([int_bes,int_p,int_h1,int_h2,int_h3])\",\n \"def gen_op3(N11, N22, N12, rN11, rN22, rN12):\\n raw = N12 / (N11 * N22)\\n ref = rN12 / (rN11 * rN22)\\n print(\\\"=====\\\\n3rd method: raw = N12 / (N11 * N22)\\\")\\n print(\\\"raw: %.2f | reference: %.2f\\\" % (raw, ref))\\n print(\\\"OP: 1 - raw = %.3f\\\\n=====\\\" % (1 - raw))\",\n \"def compute_template_spectrum(self):\\n b = self.setup_b_instance(1,add_ps_mask=False)\\n En_min = b.CTB_en_bins[0]\\n En_max = b.CTB_en_bins[1]\\n dE = En_max - En_min\\n self.En_center = 10**((np.log10(En_max)+np.log10(En_min))/2)\\n\\n flux_map = b.fixed_template_dict_nested['summed_templates'] / b.CTB_exposure_maps_masked_compressed[0]/float(hp.nside2pixarea(b.nside))\\n self.spectrum = np.mean(flux_map)/dE\",\n \"def Generate_BG_Template(outputSize=300, angularSize = 10, fileOut = 'BGRateMap.pickle' ):\\r\\n template = np.zeros((outputSize,outputSize))\\r\\n ppd=float(outputSize)/float(angularSize) # pixels per deg\\r\\n \\r\\n events110 = ParseFermi.Import_File('photons.txt', energyRange = (120000,140000),lonRange=(-5,5),latRange = (-5,5))\\r\\n events130 = ParseFermi.Import_File('photons.txt', energyRange = (100000,120000),lonRange=(-5,5),latRange = (-5,5))\\r\\n events150 = ParseFermi.Import_File('photons.txt', energyRange = (140000,200000),lonRange=(-5,5),latRange = (-5,5))\\r\\n \\r\\n for i in range(10000,200001,20000):\\r\\n if i == 130000:\\r\\n continue\\r\\n events = ParseFermi.Import_File('photons.txt', energyRange = (i-10000,i+10000),lonRange=(-5,5),latRange = (-5,5))\\r\\n BG = np.zeros((outputSize,outputSize)) \\r\\n for j in events:\\r\\n xIDX = int(j[1]*ppd+float(outputSize/2))\\r\\n yIDX = int(j[2]*ppd+float(outputSize/2))\\r\\n BG[yIDX][xIDX] += 1.0\\r\\n \\r\\n psfDeg = .2+float(200)/float(i)\\r\\n psfOut = psfDeg*ppd\\r\\n #print i/1e3, psfDeg, psfOut\\r\\n \\r\\n template += scipy.ndimage.filters.gaussian_filter(BG, psfOut)\\r\\n \\r\\n template = template/np.max(template)\\r\\n \\r\\n # Write to file \\r\\n outFile = open(fileOut, \\\"wb\\\" )\\r\\n pickle.dump(template, outFile)\\r\\n print 'Rate Map saved to ', fileOut\\r\\n \\r\\n plt.imshow(scipy.fliplr(template), 'jet',extent=[5,-5,-5,5])\\r\\n\\r\\n plt.xlabel(r'$l [^\\\\circ]$')\\r\\n plt.ylabel(r'$b [^\\\\circ]$')\\r\\n plt.xlim(5,-5)\\r\\n plt.ylim(-5,5)\\r\\n plt.colorbar()\\r\\n\\r\\n x,y = Find_Centroid(template)\\r\\n x,y = (x/ppd -angularSize/2.0,) ,(y/ppd -angularSize/2.0,)\\r\\n print x,y\\r\\n plt.scatter(x,y, s=10, c='r', marker = '+')\\r\\n \\r\\n X,Y = FormatEvents(events110)\\r\\n plt.scatter(X, Y, label = '100-120 GeV', marker = 'o' , c = 'k')\\r\\n \\r\\n X,Y = FormatEvents(events130)\\r\\n plt.scatter(X, Y, label = '120-140 GeV', marker = 'o' , c = 'r')\\r\\n \\r\\n X,Y = FormatEvents(events150)\\r\\n plt.scatter(X, Y, label = '140-200 GeV', marker = 'o' , c = 'g' )\\r\\n \\r\\n from matplotlib.font_manager import FontProperties\\r\\n fontP = FontProperties()\\r\\n fontP.set_size('small')\\r\\n plt.legend(loc=1, ncol=1, fancybox=True, shadow=False,prop=fontP,borderaxespad=0.,labelspacing = .2)\\r\\n \\r\\n from matplotlib.backends.backend_pdf import PdfPages\\r\\n if fileOut != '':\\r\\n pp = PdfPages(fileOut + '_sideband.pdf')\\r\\n plt.savefig(pp, format='pdf')\\r\\n print \\\"Figures saved to \\\", str(fileOut)+ '_sideband.pdf\\\\n',\\r\\n pp.close()\\r\\n \\r\\n plt.show()\\r\\n return template\",\n \"def _add_template(self):\\n template_dir = os.path.join(self.label_path, 'standard',\\n 'MNI152_T1_2mm_brain.nii.gz')\\n template_name = QFileDialog.getOpenFileName(\\n self,\\n 'Open standard file',\\n template_dir,\\n 'Nifti files (*.nii.gz *.nii)')\\n if not template_name.isEmpty():\\n template_path = str(template_name)\\n self._add_img(template_path)\",\n \"def vpsc_in(\\r\\n ## # elem, ph and vol fraction of phases.\\r\\n ## and ishape for flag for morphology updates\\r\\n nelem=1, nph=1, wph =[1.0, 0.0], ishape=[0,],\\r\\n\\r\\n #framgmentation options and critical value limit \\r\\n # in distortion of the inclusion \\r\\n # as well as ellipsoid description: orientation and \\r\\n # principal lengths of'em\\r\\n fragmentn = [0,0], crit = [25,25],\\r\\n ellipangl = [[0., 0., 0.],[0.,0.,0.]],\\r\\n ellipaxes = [[1., 1., 1.],[1.,1.,1.]],\\r\\n \\r\\n # vpsc7.in file name (fixed)\\r\\n # texture, singlecrystal and shape file names in list format\\r\\n vin_name = 'VPSC7.in',\\r\\n tfile= ['texture/500.tex',],\\r\\n sxfile=['sx/hijhiihb.sx',],\\r\\n fileaxes=['shape1.100', 'shape1.100'],\\r\\n\\r\\n ### precision settings for convergence prodcedure\\r\\n ##\\r\\n err=0.001, errd=0.001, errm=0.001, errso=0.01,\\r\\n nmxiter=100, exiter=100, initer=100,\\r\\n irsvar=0, xrsini=2, xrsfin=10, xrstep=2,\\r\\n\\r\\n ### post-mortem flags, texture component calc. flag.\\r\\n ## \\r\\n irecover = 0, isave=0, icubcomp = 0, nwrite = 0, \\r\\n\\r\\n ## Single crystal hardening rule (-2: dislocation, 0:Voce, 1: MTS, 2: composite)\\r\\n ihardlaw = 0, \\r\\n\\r\\n ## fluctuation flag\\r\\n iflu = 0,\\r\\n\\r\\n ## rate insensitive, interaction :(FC,aff,2:sec,3:neff,4:tan,5:SO)\\r\\n ## update flags for orient, grain shape, hardening, itran(youngung)\\r\\n ## nneigh (0 for no neighbors, 1 for pairs, etc.)\\r\\n iratesens = 0, interaction = 3, iupdate = [1,1,1,0], nneigh = 0, \\r\\n\\r\\n ## proce number and processes\\r\\n npro = None,\\r\\n prcs = [], #processes\\r\\n \\r\\n ishow=False\\r\\n ):\\r\\n try: os\\r\\n except: import os\\r\\n else: pass\\r\\n cwd = os.getcwd()\\r\\n\\r\\n # READ EXISITING 'vpsc7.in'\\r\\n if os.path.isfile(vin_name): #if there already exists the file!\\r\\n f_vin = file(vin_name, 'r')\\r\\n lines = f_vin.read()\\r\\n lines = lines.split('\\\\n')\\r\\n f_temp = file('%s.tmp'%vin_name, 'w')\\r\\n for i in lines:\\r\\n f_temp.writelines(i)\\r\\n f_temp.writelines('\\\\n')\\r\\n pass\\r\\n f_temp.close()\\r\\n f_vin.close()\\r\\n \\\"\\\"\\\"\\r\\n # DEL EXISITING 'vpsc7.in'\\r\\n f_vin.close()\\r\\n os.remove(vin_name)\\r\\n \\\"\\\"\\\"\\r\\n # MAKE NEW 'vpsc7.in'\\r\\n f_vin = file(vin_name, 'w')\\r\\n f_vin.writelines('%i nelem\\\\n'%(nelem))\\r\\n f_vin.writelines('%i nph\\\\n'%(nph))\\r\\n for i in range(nph):\\r\\n f_vin.writelines('%f '%(wph[i]))\\r\\n f_vin.writelines(' wph[i]\\\\n')\\r\\n for i in range(nph): \\r\\n f_vin.writelines('*** INFORMATION ABOUT PHASE #%i\\\\n'%(i+1))\\r\\n f_vin.writelines('%i %i %i'%(ishape[i], fragmentn[i], crit[i]))\\r\\n f_vin.writelines(' ishape, fragmentn, crit aspect\\\\n')\\r\\n f_vin.writelines('%f %f %f'%(ellipaxes[i][0], \\r\\n ellipaxes[i][1], \\r\\n ellipaxes[i][2]))\\r\\n f_vin.writelines(' initial ellipsoid ratios (dummy if ishape=4)\\\\n')\\r\\n f_vin.writelines('%f %f %f'%(ellipangl[i][0], \\r\\n ellipangl[i][1], \\r\\n ellipangl[i][2]))\\r\\n f_vin.writelines(' init Eul ang ellips axes(dummy if ishape=3,4)\\\\n')\\r\\n # WRITES TEXTURE FILE\\r\\n f_vin.writelines('----------- filetext\\\\n')\\r\\n f_vin.writelines(tfile[i]+'\\\\n')\\r\\n f_vin.writelines('----------- sxfile\\\\n')\\r\\n # WRITES SINGLE CRYSTAL FILE\\r\\n f_vin.writelines(sxfile[i]+'\\\\n')\\r\\n # WRITES SHAPE FILE\\r\\n f_vin.writelines('----------- fileaxes (dummy if ishape=0) \\\\n')\\r\\n f_vin.writelines('%s \\\\n'%(fileaxes[i]))\\r\\n\\r\\n\\r\\n #### precision control block ###\\r\\n f_vin.writelines('*** PRECISION SETTINGS FOR')\\r\\n f_vin.writelines(' CONVERGENCE PROCEDURES (default values)\\\\n')\\r\\n f_vin.writelines('%f %f %f %f'%(err, #err\\r\\n errd, \\r\\n errm,\\r\\n errso))\\r\\n f_vin.writelines(' errs,errd,errm,errso\\\\n')\\r\\n f_vin.writelines('%i %i %i %25s'%( nmxiter, exiter, initer,' '))\\r\\n f_vin.writelines('itmax: max # of iter, external, internal and SO loops\\\\n')\\r\\n f_vin.writelines('%i %i %i %i %30s'%(irsvar, xrsini, xrsfin, xrstep,' '))\\r\\n f_vin.writelines('irsvar & xrsini, xrsfin, xrstep (dummy if irsvar = 0) \\\\n')\\r\\n f_vin.writelines(\\\"%i ibcinv (0: don't use <Bc>**-1, 1: use \\\\n\\\"%(1))\\r\\n f_vin.writelines('*** INPUT/OUTPUT SETTINGS FOR THE RUN(default is zero)\\\\n')\\r\\n f_vin.writelines('%i irecover:read grain stats from POSTMORT.IN(1) OR NOT(0)\\\\n'%(irecover))\\r\\n f_vin.writelines(\\\"%i isave: write grain states in postmor.out at step 'isave'?\\\\n\\\"%(isave))\\r\\n f_vin.writelines(\\\"%i icubcompL calcuate fcc rolling components?\\\\n\\\"%(icubcomp))\\r\\n f_vin.writelines(\\\"%i nwrite (frequency of texture downloads)\\\\n\\\"%(nwrite))\\r\\n f_vin.writelines(\\\"*** MODELING CONDITIONS FOR THE RUN \\\\n\\\")\\r\\n f_vin.writelines(\\\"%i ihardlaw(0:VOCE, 1: MTS, 2: composite grain\\\\n\\\"%(ihardlaw))\\r\\n f_vin.writelines('%1i' %(iratesens))\\r\\n f_vin.writelines(' iratesens (0:rate insensitive, 1:rate sensitive) \\\\n')\\r\\n f_vin.writelines('%1i' %(interaction))\\r\\n f_vin.writelines(' interaction (0:FC,1:affine,2:secant,3:neff=10,4:tangent,5:SO) \\\\n')\\r\\n f_vin.writelines('%1i%3i%3i%3i'%(iupdate[0],iupdate[1],iupdate[2],iupdate[3]))\\r\\n f_vin.writelines(' iupdate: update ori, grain shape, hardening, itran \\\\n')\\r\\n f_vin.writelines('%1i'%(nneigh))\\r\\n f_vin.writelines(' nneigh (0 for no neighbors, 1 for pairs, etc.)\\\\n')\\r\\n f_vin.writelines('%i'%(iflu))\\r\\n f_vin.writelines(\\\" iflu(0:don't calc, 1: calc fluctuations\\\\n\\\")\\r\\n\\r\\n f_vin.writelines(\\\"*NUMBER OF PROCESSES\\\\n\\\")\\r\\n f_vin.writelines(\\\"%i \\\\n\\\"%(npro))\\r\\n f_vin.writelines(\\\"** (0,1: loadings, 2:pcys, 3:lankf, 4: RBR,\\\")\\r\\n f_vin.writelines(\\\"5:pcys_pl, 6: pysc_pl 6d(ix,iy, nprob, ang\\\")\\r\\n f_vin.writelines(\\\"( or if negative behave like pcys))) \\\\n\\\")\\r\\n for i in range(len(prcs)):\\r\\n f_vin.writelines(prcs[i]+'\\\\n')\\r\\n \\r\\n # Closure of the file\\r\\n f_vin.close() \\r\\n # -------------------\\r\\n\\r\\n # Printing-out the just-made-vpsc7.in file\\r\\n if ishow==True:\\r\\n if os.name=='nt' or os.name=='posix':\\r\\n print \\\"****** VPSC input file ******\\\"\\r\\n print \\\"* VPSC7 input parameters \\\"\\r\\n print \\\"* has been written down to \\\"\\r\\n print \\\"* '%8s' as below\\\"%vin_name\\r\\n print \\\"*****************************\\\\n\\\"\\r\\n if os.name=='nt':\\r\\n os.system('%s %s'%('type',vin_name))\\r\\n elif os.name=='posix':\\r\\n os.system('%s %s'%('cat',vin_name))\\r\\n else: pass\",\n \"def prepare_CvD16_for_M2L_calc(templates_lam_range, verbose=False):\\n import glob\\n import os\\n template_glob=os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*.s100')\\n\\n vcj_models=sorted(glob.glob(template_glob))\\n temp_lamdas, x35, x3, x23, kroupa, flat=np.genfromtxt(vcj_models[-1], unpack=True)\\n\\n n_ages=7\\n n_zs=5\\n n_imfs=5\\n\\n \\n\\n\\n Zs=['m1.5', 'm1.0', 'm0.5', 'p0.0', 'p0.2']\\n ages=[1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.5]\\n model_imfs_order=['x35', 'x3', 'x23', 'kroupa', 'flat']\\n\\n t_mask = ((temp_lamdas > templates_lam_range[0]) & (temp_lamdas <templates_lam_range[1]))\\n\\n\\n\\n y=x35[t_mask]\\n x=temp_lamdas[t_mask]\\n\\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\\n interp=si.interp1d(x, y, fill_value='extrapolate')\\n out=interp(new_x)\\n\\n templates=np.empty((len(out), n_ages, n_zs, n_imfs))\\n\\n\\n\\n for a, Z in enumerate(Zs): \\n for b, age in enumerate(ages):\\n model=glob.glob(os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*{}*{}.ssp.s100'.format(Z, age)))[0]\\n if verbose:\\n print 'Loading {}'.format(model)\\n data=np.genfromtxt(model)\\n\\n for c, counter in enumerate(reversed(range(1, data.shape[-1]))):\\n \\n #Interpolate templates onto a uniform wavelength grid and then log-rebin\\n y=data[:, counter][t_mask] \\n x=temp_lamdas[t_mask]\\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\\n\\n interp=si.interp1d(x, y, fill_value='extrapolate')\\n out=interp(new_x) \\n\\n templates[:, b, a, c]=out\\n\\n return templates, new_x\",\n \"def tpl(x):\\n return 3 * x\",\n \"def gen_new_phiw_div_phib_arr(N_PROCESSES, phiw_div_phib_arr_new, cond_GT, fcn_D, fcn_eta, z_div_L_arr, phiw_div_phib_arr, Pi_div_DLP_arr, weight, gp_arr, gm_arr, yt_arr, phi_yt_arr, ID_yt_arr, Ieta_yt_arr):\\n phi_b = cond_GT['phi_bulk']\\n ed = cond_GT['epsilon_d']\\n membrane_geometry = cond_GT['membrane_geometry']\\n \\n Ny = size(yt_arr)\\n # # Python allocate the name for phi_yt_arr[0], this is the same as reference value for C++ \\\" y= &x\\\"\\n phi_arr_z0 = phi_yt_arr[0]\\n Ieta_arr_z0= Ieta_yt_arr[0]\\n ID_arr_z0 = ID_yt_arr[0]\\n\\n ind_z0 = 0 #z-index at inlet\\n \\n z0_div_L = 0. #z-coord at inlet\\n \\n r0_div_R = 0. #r-coord at the centerline of pipe\\n rw_div_R = 1. #r-coord at the membrane wall\\n \\n vw_div_vw0_z0 = get_v_conv(rw_div_R, z0_div_L, Pi_div_DLP_arr[ind_z0], cond_GT, gp_arr[ind_z0], gm_arr[ind_z0])\\n gen_phi_wrt_yt(z0_div_L, phiw_div_phib_arr[ind_z0]*phi_b, fcn_D, vw_div_vw0_z0, yt_arr, phi_arr_z0, cond_GT)\\n gen_INT_inv_f_wrt_yt(yt_arr, phi_arr_z0, Ieta_arr_z0, fcn_eta, cond_GT)\\n Ieta_arr_z0 /= Ieta_arr_z0[-1] # CHECK\\n gen_INT_inv_f_wrt_yt(yt_arr, phi_arr_z0, ID_arr_z0, fcn_D, cond_GT)\\n\\n uZ_z0 = get_uZ_out(z0_div_L, cond_GT['k'], cond_GT['Bp'], cond_GT['Bm'], gp_arr[ind_z0], gm_arr[ind_z0])\\n F2_0 = cal_F2_Z(vw_div_vw0_z0, ed, yt_arr, Ieta_arr_z0, ID_arr_z0, uZ_z0, membrane_geometry)\\n\\n Nz = size(z_div_L_arr)\\n if (N_PROCESSES ==1):\\n # when only single-processor is allocated\\n for i in range(1, Nz):\\n phiw_div_phib_arr_new[i] = process_at_zi(z_div_L_arr[i], phiw_div_phib_arr[i]*phi_b, Pi_div_DLP_arr[i], cond_GT, gp_arr[i], gm_arr[i], yt_arr, phi_yt_arr[i], Ieta_yt_arr[i], fcn_eta, ID_yt_arr[i], fcn_D, F2_0)\\n else:\\n # this uses multiprocessing packages\\n import multiprocessing as mp\\n \\n pool = mp.Pool(N_PROCESSES)\\n args_list = [(z_div_L_arr[i], phiw_div_phib_arr[i]*phi_b, Pi_div_DLP_arr[i], cond_GT, gp_arr[i], gm_arr[i], yt_arr, phi_yt_arr[i], Ieta_yt_arr[i], fcn_eta, ID_yt_arr[i], fcn_D, F2_0)\\\\\\n for i in range(1, Nz)]\\n phiw_div_phib_arr_new[1:] = pool.starmap(process_at_zi, args_list)\\n pool.close()\\n pool.join()\\n\\n cnt_EXCEED = 0 \\n for i,x in enumerate(phiw_div_phib_arr_new):\\n\\n x = x*cond_GT['phi_bulk']\\n if x > cond_GT['phi_freeze']:\\n cnt_EXCEED += 1\\n phiw_div_phib_arr_new[i] = cond_GT['phi_freeze']/cond_GT['phi_bulk'] # this prevent the accidently beyond the freezing concentration\\n if(cnt_EXCEED>0):\\n print('Warning: exceed phi_freeze %d times out of %d\\\\n'%(cnt_EXCEED, cond_GT['Nz']))\\n\\n FPI_operator(cond_GT['weight'], phiw_div_phib_arr, phiw_div_phib_arr_new, N_skip=1) # phiw(0) must be phib.\\n\\n return 0\",\n \"def inst_set_cpu_template_fxt(request):\\n return request.param\",\n \"def applyPhotoZ (self,arr):\\n print \\\"Applying Template SED PZs\\\"\\n\\n ztrue = arr['z']\\n\\n #select a template\\n templates = ['El_B2004a.sed']+['Sbc_B2004a.sed','Scd_B2004a.sed']\\n templates = templates +['Im_B2004a.sed','SB3_B2004a.sed','SB2_B2004a.sed','ssp_25Myr_z008.sed','ssp_5Myr_z008.sed']\\n\\n #read in f_mod files, interpolate, get values of f_mod_b\\n ngals = len(ztrue)\\n\\n f_mod_o = np.zeros((self.nb, ngals))\\n for z in range(ngals):\\n #currently templates are randomly chosen but probably should be an input with true z\\n templateno = np.random.choice(range(self.nt))\\n for b in range(self.nb):\\n spl = InterpolatedUnivariateSpline(self.z_grid, self.f_mod[:,templateno,b])\\n f_mod_o[b][z] = spl(ztrue[z])\\n\\n #select sigma_b - 10% for now\\n sigma = 0.1*f_mod_o\\n #select observed fluxes f_obs_b = f_mod_b + sigma_b*rando\\n f_obs = f_mod_o+ sigma * (np.random.normal(0.,1.,self.nb*ngals).reshape((self.nb,ngals)))\\n # I don't seem to be able to find a more efficient way\\n arrx=np.zeros(ngals,dtype=[('pz_f_obs',float,(self.nb,)),('pz_flux_sigma',float,(self.nb,))])\\n arrx['pz_f_obs']=f_obs.T\\n arrx['pz_flux_sigma']=sigma.T\\n arr = recfunctions.merge_arrays((arr,arrx),flatten=True,usemask=False)\\n return arr\",\n \"def gen_code(self, template, dec_id, i_alt):\\n v = self.get_alt(dec_id, i_alt)\\n\\n # assuming the placeholder var is always at the end\\n # which is true given how we chop up the chunks\\n return template + str(v), str(v)\",\n \"def hierarchical( x, output_prefix, labels_to_register=[2,3,4,5], is_test=False, verbose=True ):\\n if verbose:\\n print(\\\"Read\\\")\\n tfn = get_data('T_template0', target_extension='.nii.gz' )\\n tfnw = get_data('T_template0_WMP', target_extension='.nii.gz' )\\n tlrfn = get_data('T_template0_LR', target_extension='.nii.gz' )\\n bfn = antspynet.get_antsxnet_data( \\\"croppedMni152\\\" )\\n\\n ##### read images and do simple bxt ops\\n templatea = ants.image_read( tfn )\\n if verbose:\\n print(\\\"bxt\\\")\\n templatea = ( templatea * antspynet.brain_extraction( templatea, 't1' ) ).iMath( \\\"Normalize\\\" )\\n templateawmprior = ants.image_read( tfnw )\\n templatealr = ants.image_read( tlrfn )\\n templateb = ants.image_read( bfn )\\n templateb = ( templateb * antspynet.brain_extraction( templateb, 't1' ) ).iMath( \\\"Normalize\\\" )\\n imgbxt = brain_extraction( x )\\n img = x * imgbxt\\n\\n if verbose:\\n print(\\\"rbp\\\")\\n\\n # this is an unbiased method for identifying predictors that can be used to\\n # rank / sort data into clusters, some of which may be associated\\n # with outlierness or low-quality data\\n templatesmall = ants.resample_image( templateb, (91,109,91), use_voxels=True )\\n rbp = random_basis_projection( img, templatesmall )\\n\\n if verbose:\\n print(\\\"intensity\\\")\\n\\n ##### intensity modifications\\n img = ants.iMath( img, \\\"Normalize\\\" ) * 255.0\\n img = ants.denoise_image( img, imgbxt, noise_model='Gaussian')\\n img = ants.n4_bias_field_correction( img ).iMath(\\\"Normalize\\\")\\n\\n # optional - quick look at result\\n bxt_png = output_prefix + \\\"_brain_extraction_dnz_n4_view.png\\\"\\n ants.plot(img,axis=2,ncol=8,nslices=24, crop=True, black_bg=False,\\n filename = bxt_png )\\n\\n if verbose:\\n print(\\\"hemi\\\")\\n\\n # assuming data is reasonable quality, we should proceed with the rest ...\\n mylr = label_hemispheres( img, templatea, templatealr )\\n\\n if verbose:\\n print(\\\"parcellation\\\")\\n\\n ##### hierarchical labeling\\n myparc = deep_brain_parcellation( img, templateb,\\n do_cortical_propagation = not is_test, verbose=False )\\n\\n ##### accumulate data into data frames\\n hemi = map_segmentation_to_dataframe( \\\"hemisphere\\\", myparc['hemisphere_labels'] )\\n tissue = map_segmentation_to_dataframe( \\\"tissues\\\", myparc['tissue_segmentation'] )\\n dktl = map_segmentation_to_dataframe( \\\"lobes\\\", myparc['dkt_lobes'] )\\n dktp = map_segmentation_to_dataframe( \\\"dkt\\\", myparc['dkt_parcellation'] )\\n dktc = None\\n if not is_test:\\n dktc = map_segmentation_to_dataframe( \\\"dkt\\\", myparc['dkt_cortex'] )\\n\\n tissue_seg_png = output_prefix + \\\"_seg.png\\\"\\n ants.plot( img, myparc['tissue_segmentation'], axis=2, nslices=21, ncol=7,\\n alpha=0.6, filename=tissue_seg_png,\\n crop=True, black_bg=False )\\n\\n if verbose:\\n print(\\\"WMH\\\")\\n\\n ##### below here are more exploratory nice to have outputs\\n myhypo = t1_hypointensity(\\n img,\\n myparc['tissue_segmentation'], # segmentation\\n myparc['tissue_probabilities'][3], # wm posteriors\\n templatea,\\n templateawmprior )\\n\\n if verbose:\\n print(\\\"registration\\\")\\n\\n ##### traditional deformable registration as a high-resolution complement to above\\n wm_tractsL = None\\n wm_tractsR = None\\n wmtdfL = None\\n wmtdfR = None\\n reg = None\\n if labels_to_register is not None:\\n reg = hemi_reg(\\n input_image = img,\\n input_image_tissue_segmentation = myparc['tissue_segmentation'],\\n input_image_hemisphere_segmentation = mylr,\\n input_template=templatea,\\n input_template_hemisphere_labels=templatealr,\\n output_prefix = output_prefix + \\\"_SYN\\\",\\n labels_to_register = labels_to_register,\\n is_test=is_test )\\n if verbose:\\n print(\\\"wm tracts\\\")\\n ##### how to use the hemi-reg output to generate any roi value from a template roi\\n wm_tracts = ants.image_read( get_data( \\\"wm_major_tracts\\\", target_extension='.nii.gz' ) )\\n wm_tractsL = ants.apply_transforms( img, wm_tracts, reg['synL']['invtransforms'],\\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 1, 1 )\\n wm_tractsR = ants.apply_transforms( img, wm_tracts, reg['synR']['invtransforms'],\\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 2, 2 )\\n wmtdfL = map_segmentation_to_dataframe( \\\"wm_major_tracts\\\", wm_tractsL )\\n wmtdfR = map_segmentation_to_dataframe( \\\"wm_major_tracts\\\", wm_tractsR )\\n\\n if verbose:\\n print(\\\"hippocampus\\\")\\n\\n ##### specialized labeling for hippocampus\\n ntries = 10\\n if is_test:\\n ntries = 1\\n hippLR = deep_hippo( img, templateb, ntries )\\n\\n mydataframes = {\\n \\\"hemispheres\\\":hemi,\\n \\\"tissues\\\":tissue,\\n \\\"dktlobes\\\":dktl,\\n \\\"dktregions\\\":dktp,\\n \\\"dktcortex\\\":dktc,\\n \\\"wmtracts_left\\\":wmtdfL,\\n \\\"wmtracts_right\\\":wmtdfR,\\n \\\"wmh\\\":myhypo['wmh_summary']\\n }\\n\\n outputs = {\\n \\\"brain_n4_dnz\\\": img,\\n \\\"brain_n4_dnz_png\\\": bxt_png,\\n \\\"brain_extraction\\\": imgbxt,\\n \\\"tissue_seg_png\\\": tissue_seg_png,\\n \\\"rbp\\\": rbp,\\n \\\"left_right\\\": mylr,\\n \\\"dkt_parc\\\": myparc,\\n \\\"registration\\\":reg,\\n \\\"hippLR\\\":hippLR,\\n \\\"white_matter_hypointensity\\\":myhypo,\\n \\\"wm_tractsL\\\":wm_tractsL,\\n \\\"wm_tractsR\\\":wm_tractsR,\\n \\\"dataframes\\\": mydataframes\\n }\\n\\n return outputs\",\n \"def _operator_generator(self, index, conj):\\n pterm = PauliTerm('I', 0, 1.0)\\n Zstring = PauliTerm('I', 0, 1.0)\\n for j in range(index):\\n Zstring = Zstring*PauliTerm('Z', j, 1.0)\\n\\n pterm1 = Zstring*PauliTerm('X', index, 0.5)\\n scalar = 0.5 * conj * 1.0j\\n pterm2 = Zstring*PauliTerm('Y', index, scalar)\\n pterm = pterm * (pterm1 + pterm2)\\n\\n pterm = pterm.simplify()\\n return pterm\",\n \"def calc_ti(self):\\n m = 0\\n for i in self.inl:\\n m += i.m.val_SI * i.fluid.val[self.fuel_alias.val]\\n\\n for o in self.outl:\\n m -= o.m.val_SI * o.fluid.val[self.fuel_alias.val]\\n\\n return m * self.lhv\",\n \"def calc_ST_operation(m_exhaust_GT_kgpers, T_exhaust_GT_K, T_sup_K, fuel_type):\\n\\n # calaulate High Pressure (HP) and Low Pressure (LP) mass flow of a double pressure steam turbine\\n temp_i_K = (0.9 * ((6 / 48.2) ** (0.4 / 1.4) - 1) + 1) * (T_exhaust_GT_K - ST_DELTA_T)\\n if fuel_type == 'NG':\\n Mexh = 103.7 * 44E-3 + 196.2 * 18E-3 + 761.4 * 28E-3 + 200.5 * (CC_AIRRATIO - 1) * 32E-3 \\\\\\n + 200.5 * (CC_AIRRATIO - 1) * 3.773 * 28E-3\\n ncp_exh = 103.7 * 44 * 0.846 + 196.2 * 18 * 1.8723 + 761.4 * 28 * 1.039 \\\\\\n + 200.5 * (CC_AIRRATIO - 1) * 32 * 0.918 + 200.5 * (CC_AIRRATIO - 1) * 3.773 * 28 * 1.039\\n cp_exh = ncp_exh / Mexh # J/kgK\\n else:\\n Mexh = 98.5 * 44E-3 + 116 * 18E-3 + 436.8 * 28E-3 + 115.5 * (CC_AIRRATIO - 1) * 32E-3 \\\\\\n + 115.5 * (CC_AIRRATIO - 1) * 3.773 * 28E-3\\n ncp_exh = 98.5 * 44 * 0.846 + 116 * 18 * 1.8723 + 436.8 * 28 * 1.039 \\\\\\n + 115.5 * (CC_AIRRATIO - 1) * 32 * 0.918 + 115.5 * (CC_AIRRATIO - 1) * 3.773 * 28 * 1.039\\n cp_exh = ncp_exh / Mexh # J/kgK\\n\\n a = np.array([[1653E3 + HEAT_CAPACITY_OF_WATER_JPERKGK * (T_exhaust_GT_K - ST_DELTA_T - 534.5), \\\\\\n HEAT_CAPACITY_OF_WATER_JPERKGK * (temp_i_K - 534.5)], \\\\\\n [HEAT_CAPACITY_OF_WATER_JPERKGK * (534.5 - 431.8), \\\\\\n 2085.8E3 + HEAT_CAPACITY_OF_WATER_JPERKGK * (534.5 - 431.8)]])\\n b = np.array([m_exhaust_GT_kgpers * cp_exh * (T_exhaust_GT_K - (534.5 + ST_DELTA_T)), \\\\\\n m_exhaust_GT_kgpers * cp_exh * (534.5 - 431.8)])\\n [mdotHP_kgpers, mdotLP_kgpers] = np.linalg.solve(a, b)\\n\\n # calculate thermal output\\n T_cond_0_K = T_sup_K + CC_DELTA_T_DH # condensation temperature constrained by the DH network temperature\\n pres0 = (0.0261 * (T_cond_0_K - 273) ** 2 - 2.1394 * (T_cond_0_K - 273) + 52.893) * 1E3\\n\\n delta_h_evap_Jperkg = (-2.4967 * (T_cond_0_K - 273) + 2507) * 1E3\\n q_output_ST_W = (mdotHP_kgpers + mdotLP_kgpers) * delta_h_evap_Jperkg # thermal output of ST\\n\\n # calculate electricity output\\n h_HP_Jperkg = (2.5081 * (T_exhaust_GT_K - ST_DELTA_T - 273) + 2122.7) * 1E3 # J/kg\\n h_LP_Jperkg = (2.3153 * (temp_i_K - 273) + 2314.7) * 1E3 # J/kg\\n h_cond_Jperkg = (1.6979 * (T_cond_0_K - 273) + 2506.6) * 1E3 # J/kg\\n\\n el_produced_ST_W = mdotHP_kgpers * (h_HP_Jperkg - h_LP_Jperkg) + \\\\\\n (mdotHP_kgpers + mdotLP_kgpers) * (h_LP_Jperkg - h_cond_Jperkg) # turbine electricity output\\n\\n el_input_compressor_W = SPEC_VOLUME_STEAM * (\\n mdotLP_kgpers * (6E5 - pres0) + (mdotHP_kgpers + mdotLP_kgpers) * (48.2E5 - 6E5)) # compressor electricity use\\n\\n el_output_ST_W = ST_GEN_ETA * (el_produced_ST_W - el_input_compressor_W) # gross electricity production of turbine\\n\\n return q_output_ST_W, el_output_ST_W\",\n \"def create_eqt_template(nodes, input_filename):\\n output_filename = f'{input_filename[:-4]}_eqpt_sheet.txt'\\n with open(output_filename, 'w', encoding='utf-8') as my_file:\\n # print header similar to excel\\n my_file.write('OPTIONAL\\\\n\\\\n\\\\n\\\\\\n \\\\t\\\\tNode a egress amp (from a to z)\\\\t\\\\t\\\\t\\\\t\\\\tNode a ingress amp (from z to a) \\\\\\n \\\\nNode A \\\\tNode Z \\\\tamp type \\\\tatt_in \\\\tamp gain \\\\ttilt \\\\tatt_out\\\\\\n amp type \\\\tatt_in \\\\tamp gain \\\\ttilt \\\\tatt_out\\\\n')\\n\\n for node in nodes.values():\\n if node.eqpt == 'ILA':\\n my_file.write(f'{node.uid}\\\\t{node.to_node[0]}\\\\n')\\n if node.eqpt == 'ROADM':\\n for to_node in node.to_node:\\n my_file.write(f'{node.uid}\\\\t{to_node}\\\\n')\\n\\n print(f'File {output_filename} successfully created with Node A - Node Z entries for Eqpt sheet in excel file.')\",\n \"def __createSpecTempT(self):\\n\\t\\tspecTempT = np.copy(self.specTemplates)\\n\\t\\tspecTempT = np.transpose(specTempT)\\n\\t\\tspecTempT = specTempT * self.covInv\\n\\t\\t\\t\\t\\n\\t\\treturn specTempT\",\n \"def calc_CC_operation(el_output_from_GT_W, GT_size_W, fuel_type, T_sup_K):\\n\\n (eta0, m0_exhaust_GT_kgpers) = calc_GT_operation_fullload(GT_size_W, fuel_type)\\n (eta, m_exhaust_GT_kgpers, T_exhaust_GT_K, m_fuel_kgpers) = calc_GT_operation_partload(el_output_from_GT_W,\\n GT_size_W, eta0,\\n m0_exhaust_GT_kgpers,\\n fuel_type)\\n\\n (q_output_ST_W, el_output_ST_W) = calc_ST_operation(m_exhaust_GT_kgpers, T_exhaust_GT_K, T_sup_K, fuel_type)\\n\\n LHV = LHV_NG if fuel_type == 'NG' else LHV_BG # read LHV of NG or BG\\n\\n eta_el = (el_output_from_GT_W + el_output_ST_W) / (m_fuel_kgpers * LHV)\\n eta_thermal = q_output_ST_W / (m_fuel_kgpers * LHV)\\n eta_total = eta_el + eta_thermal\\n el_output_W = el_output_ST_W + el_output_from_GT_W\\n\\n return {'el_output_W': el_output_W, 'q_output_ST_W': q_output_ST_W, 'eta_el': eta_el, 'eta_thermal': eta_thermal,\\n 'eta_total': eta_total}\",\n \"def car_template(ncomp, ra0, ra1, dec0, dec1, res):\\n\\n if ncomp == 3:\\n pre = (3,)\\n else:\\n pre = ()\\n\\n box = get_box(ra0, ra1, dec0, dec1)\\n res = res * np.pi / (180 * 60)\\n temp = so_map()\\n shape, wcs = enmap.geometry(box, res=res, pre=pre)\\n temp.data = enmap.zeros(shape, wcs=wcs, dtype=None)\\n temp.pixel = \\\"CAR\\\"\\n temp.nside = None\\n temp.ncomp = ncomp\\n temp.geometry = temp.data.geometry[1:]\\n temp.coordinate = \\\"equ\\\"\\n return temp\",\n \"def inst_set_cpu_template_ext_fxt(request):\\n return request.param\",\n \"def calc_Cinv_CCGT(CC_size_W, CCGT_cost_data):\\n\\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\\n # capacity for the corresponding technology from the database\\n if CC_size_W < CCGT_cost_data['cap_min'][0]:\\n CC_size_W = CCGT_cost_data['cap_min'][0]\\n CCGT_cost_data = CCGT_cost_data[\\n (CCGT_cost_data['cap_min'] <= CC_size_W) & (CCGT_cost_data['cap_max'] > CC_size_W)]\\n\\n\\n #costs of connection\\n connection_costs = ngas.calc_Cinv_gas(CC_size_W)\\n\\n Inv_a = CCGT_cost_data.iloc[0]['a']\\n Inv_b = CCGT_cost_data.iloc[0]['b']\\n Inv_c = CCGT_cost_data.iloc[0]['c']\\n Inv_d = CCGT_cost_data.iloc[0]['d']\\n Inv_e = CCGT_cost_data.iloc[0]['e']\\n Inv_IR = CCGT_cost_data.iloc[0]['IR_%']\\n Inv_LT = CCGT_cost_data.iloc[0]['LT_yr']\\n Inv_OM = CCGT_cost_data.iloc[0]['O&M_%'] / 100\\n\\n InvC = Inv_a + Inv_b * (CC_size_W) ** Inv_c + (Inv_d + Inv_e * CC_size_W) * log(CC_size_W)\\n\\n Capex_a_CCGT_USD = calc_capex_annualized((InvC+connection_costs), Inv_IR, Inv_LT)\\n Opex_fixed_CCGT_USD = InvC * Inv_OM\\n Capex_CCGT_USD = InvC\\n\\n return Capex_a_CCGT_USD, Opex_fixed_CCGT_USD, Capex_CCGT_USD\",\n \"def _template(inlist):\\n from collections import OrderedDict\\n if isinstance(inlist, str):\\n inlist = [inlist]\\n\\n templates = []\\n for item in reversed(inlist):\\n templates.append(output_space(item))\\n\\n return OrderedDict(reversed(OrderedDict(templates).items()))\",\n \"def whc_tot(mukey, layers=''):\\n #read appropriate soils.in content to a python list\\n mukey = str(mukey)\\n soil_path = \\\"/data/paustian/ernie/SSURGO_master_script/soil_test2/\\\"\\n soil_fpath = soil_path+mukey[:-3]+\\\"/\\\"+mukey+\\\".in\\\"\\n cont = [[]]\\n data_input = open(soil_fpath, 'r')\\n for line in data_input:\\n cont.append(line.split())\\n del cont[0]\\n\\n #convert all entries in the 2D list to float format where possible, or zero in the case\\n #of very small numbers recorded in scientific notation\\n for k in range(len(cont)):\\n for l in range(len(cont[k])):\\n cont[k][l] = float(cont[k][l])\\n\\n #loop through list and compute the water holding capacity increment represented in \\n #each line\\n min_h2o_evap = 0\\n min_h2o = 0\\n max_h2o = 0\\n whc = 0\\n for i in range(len(cont)):\\n if not layers:\\n depth = cont[i][1] - cont[i][0]\\n FC = cont[i][3]\\n WP = cont[i][4]\\n WHC = FC - WP\\n if i != 0:\\n min_h2o_evap += depth*WP\\n min_h2o += depth*WP\\n max_h2o += depth*FC\\n whc += depth*WHC\\n else:\\n if 1+i <= layers:\\n depth = cont[i][1] - cont[i][0]\\n FC = cont[i][3]\\n WP = cont[i][4]\\n WHC = FC - WP\\n if i != 0:\\n min_h2o_evap += depth*WP\\n min_h2o += depth*WP\\n max_h2o += depth*FC\\n whc += depth*WHC\\n if layers:\\n if layers > len(cont):\\n print \\\"NOTE: specified layer limit exceeds number of layers found in soils.in file\\\"\\n\\n return whc, min_h2o, max_h2o\",\n \"def Build_Background_Template(numBGPhotons, bgTemplate, PSFTableFront, PSFTableBack,flatLevel = 0.0,HESS = False,outputSize=300,angularSize=10.0):\\r\\n \\r\\n numPhotons = numBGPhotons\\r\\n numHigh = int(round(.32 *numPhotons))\\r\\n numLow = numPhotons-numHigh\\r\\n \\r\\n bgEventsX = []\\r\\n bgEventsY = []\\r\\n \\r\\n bgTemplate = bgTemplate *(1.0-flatLevel) + flatLevel\\r\\n# import matplotlib.pyplot as plt\\r\\n# plt.imshow(bgTemplate,'jet',vmin=0, vmax=1)\\r\\n# plt.colorbar()\\r\\n# plt.show()\\r\\n\\r\\n app=float(angularSize)/float(outputSize) # angle per pixel\\r\\n for i in range(numPhotons):\\r\\n x ,y = 0, 0\\r\\n while True:\\r\\n x,y = np.random.randint(0,high = len(bgTemplate)),np.random.randint(0,high = len(bgTemplate))\\r\\n if (np.random.ranf() < bgTemplate[y][x]):\\r\\n break\\r\\n # Shift and scale coordinates to output map and then compute PSF modification to the position.\\r\\n psfMod = PSF_Spread(PSFTableFront,PSFTableBack, HESS =HESS)\\r\\n dx = psfMod[0]*math.cos(psfMod[1]) # PSF shift in deg\\r\\n dy = psfMod[0]*math.sin(psfMod[1]) # PSF shift in deg\\r\\n \\r\\n bgEventsX.append((x-outputSize/2.0)*app + dx)\\r\\n bgEventsY.append((y-outputSize/2.0)*app + dy)\\r\\n \\r\\n return (bgEventsX, bgEventsY)\",\n \"def compute_partials(self, inputs, partials):\\n partials['y', 'x'] = 2.0\",\n \"def generate(self, namespace: Optional[str], template: str, func: Callable, call_args: Dict) -> str:\",\n \"def make_template(size, profile, dx=0, dy=0, oversampling=10, normalized=True):\\n xy = (np.arange(size * oversampling) - 0.5 * (size * oversampling - 1)) / oversampling\\n z = profile(xy - dx, (xy - dy).reshape(-1, 1))\\n T = downsample(z, oversampling, np.mean)\\n if normalized:\\n T /= T.sum()\\n return T\",\n \"def compute_equivalent_widths(templates, coeffs, covar, max_R=5000, Ndraw=1000, seed=0):\\n \\n if False:\\n # Testing\\n templates = t1\\n chi2, coeffsx, coeffs_errx, covarx = mb.xfit_at_z(z=tfit['z'], templates=templates, fitter='nnls', fit_background=True, get_uncertainties=2)\\n covar = covarx[mb.N:,mb.N:]\\n coeffs = coeffsx[mb.N:]\\n \\n # Array versions of the templates\\n wave, flux_arr, is_line = array_templates(templates, max_R=max_R)\\n keys = np.array(list(templates.keys()))\\n \\n EWdict = OrderedDict()\\n for key in keys[is_line]:\\n EWdict[key] = (0., 0., 0.)\\n \\n # Only worry about templates with non-zero coefficients, which should\\n # be accounted for in the covariance array (with get_uncertainties=2)\\n clip = coeffs != 0\\n # No valid lines\\n if (is_line & clip).sum() == 0:\\n return EWdict\\n \\n # Random draws from the covariance matrix\\n covar_clip = covar[clip,:][:,clip]\\n if seed is not None:\\n np.random.seed(seed)\\n \\n draws = np.random.multivariate_normal(coeffs[clip], covar_clip, size=Ndraw)\\n \\n # Evaluate the continuum fits from the draws \\n continuum = np.dot(draws*(~is_line[clip]), flux_arr[clip,:])\\n \\n # Compute the emission line EWs\\n tidx = np.where(is_line[clip])[0]\\n for ix in tidx:\\n key = keys[clip][ix]\\n\\n # Line template\\n line = np.dot(draws[:,ix][:,None], flux_arr[clip,:][ix,:][None,:])\\n \\n # Where line template non-zero\\n mask = flux_arr[clip,:][ix,:] > 0\\n ew_i = np.trapz((line/continuum)[:,mask], wave[mask], axis=1)\\n \\n EWdict[key] = np.percentile(ew_i, [16., 50., 84.])\\n \\n return EWdict\",\n \"def test_render_templates():\\n water_mol = Atoms(cclib.bridge.cclib2openbabel.readfile(\\\"data/water.xyz\\\", \\\"xyz\\\"))\\n if not water_mol.name:\\n water_mol.name = \\\"data/water.xyz\\\"\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.ADF.in\\\"])\\n main([\\\"/tmp/foo.ADF.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.in\\\").read().strip(),\\n \\\"\\\"\\\"TITLE data/water.xyz\\n\\nCHARGE 0 0\\n\\nNumber of atoms\\n 3\\n\\nATOMS Cartesian\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\nEnd\\n\\nBasis\\nEnd\\n\\nGeometry\\nEnd\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/test.GAMESS.inp\\\"])\\n main([\\\"/tmp/test.GAMESS.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.inp\\\").read(),\\n \\\"\\\"\\\" $CONTRL COORD=CART UNITS=ANGS $END\\n\\n $DATA\\ndata/water.xyz\\nC1\\nO 8.0 0.0584027061 0.0584027059 0.0000000000\\nH 1.0 1.0096135406 -0.0680162466 0.0000000000\\nH 1.0 -0.0680162466 1.0096135407 0.0000000000\\n $END\\n\\n\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/hello.GAMESSUK.inp\\\"])\\n main([\\\"/tmp/hello.GAMESSUK.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.inp\\\").read(), water_mol.to_string(\\\"gukin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/hello.world.Gaussian.gjf\\\"])\\n main([\\\"/tmp/hello.world.Gaussian.gjf\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.gjf\\\").read(),\\n \\\"\\\"\\\"#Put Keywords Here, check Charge and Multiplicity.\\n\\n data/water.xyz\\n\\n0 1\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\n\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/bar.Jaguar.in\\\"])\\n main([\\\"/tmp/bar.Jaguar.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.in\\\").read(), water_mol.to_string(\\\"jin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.Molpro.inp\\\"])\\n main([\\\"/tmp/foo.Molpro.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.inp\\\").read(), water_mol.to_string(\\\"mp\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/example.MOPAC.mop\\\"])\\n main([\\\"/tmp/example.MOPAC.mop\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.mop\\\").read(),\\n \\\"\\\"\\\"CHARGE=0 MS=0.0\\ndata/water.xyz\\n\\nO 0.05840 1 0.05840 1 0.00000 1\\nH 1.00961 1 -0.06802 1 0.00000 1\\nH -0.06802 1 1.00961 1 0.00000 1\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/bar.MPQC.in\\\"])\\n main([\\\"/tmp/bar.MPQC.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.in\\\").read(), water_mol.to_string(\\\"mpqcin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.NWChem.nw\\\"])\\n main([\\\"/tmp/foo.NWChem.nw\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.nw\\\").read(),\\n \\\"\\\"\\\"start molecule\\n\\ntitle data/water.xyz\\n\\ngeometry units angstroms print xyz autosym\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\nend\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/example.ORCA.inp\\\"])\\n main([\\\"/tmp/example.ORCA.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.inp\\\").read(),\\n \\\"\\\"\\\"# data/water.xyz\\n! Opt\\n\\n* xyz 0 1\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\n*\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/bar.Psi.dat\\\"])\\n main([\\\"/tmp/bar.Psi.dat\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.dat\\\").read(),\\n \\\"\\\"\\\"# data/water.xyz\\n\\nmolecule {\\n0 1\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\nunits angstrom\\n}\\n\\noptimize('scf')\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/example.QChem.in\\\"])\\n main([\\\"/tmp/example.QChem.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.in\\\").read(), water_mol.to_string(\\\"qcin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.ZINDO.input\\\"])\\n main([\\\"/tmp/foo.ZINDO.input\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.input\\\").read(), water_mol.to_string(\\\"zin\\\"))\",\n \"def generate():\",\n \"def write_input(self):\\n # load template, substitute parameters and write input file\\n self.homog_core()\\n self.write_mat_string()\\n input_tmpl = open('base_input.txt')\\n templ = Template(input_tmpl.read())\\n file_string = templ.substitute(cool_frac = self.vfrac_cermet,\\n r_core = self.r,\\n core_z = self.z,\\n r_refl = self.r + self.refl_t,\\n refl_min = -self.refl_t,\\n refl_max = self.z + self.refl_t,\\n fuel_string = self.fuel_string,\\n fuel_rho = self.rho,\\n fuel_vol = self.core_vol,\\n refl_vol = self.core_vol,\\n thermal_power = self.Q_therm)\\n # write the file\\n filename = 'r_{0}_{1}.i'.format(round(self.vfrac_cermet, 3), \\n round(self.r, 3))\\n ifile = open(filename, 'w')\\n ifile.write(file_string)\\n ifile.close()\\n\\n return filename\",\n \"def compute_code():\\n for file in os.listdir('./src/Templates'):\\n input.parse_template('./src/Templates/' + file, './student/' + file + '.py')\\n data = input.load_input()\\n return len([k for k in data['input'].keys() if '@' not in k])\",\n \"def preparehspiceidvgGEO1v2(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,HFINparam,TFIN_TOPparam,TFIN_BASEparam,EOTparam,NBODYparam,NFINparam,PHIGparam,RSHSparam,RSHDparam):\\n #make an aux copy of hspice file to simulate\\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\\n #make an aux copy of modelcard file to simulate\\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\\n\\n #update path of model and modelcard\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\\\\\"modelcardaux.nmos\\\\\\\"')\\n\\n #bias update\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'HFINparam',HFINparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_TOPparam', TFIN_TOPparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_BASEparam',TFIN_BASEparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'EOTparam', EOTparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'PHIGparam', PHIGparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHSparam', RSHSparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHDparam', RSHDparam)\",\n \"def ts_method(signal, peaks, template_duration: float = 0.12, fs: int = processing.FS, window: int = 10, **kwargs):\\n\\n t_dur = round(template_duration * fs)\\n if not t_dur % 2 == 0:\\n t_dur += 1\\n dims = signal.shape\\n # if np.max(np.abs(signal[0, :])) < np.max(np.abs(signal[1, :])):\\n # r_peaks = find_qrs(signal[1, :], peak_search=peak_search)\\n # r_peaks = peak_enhance(signal[1, :], peaks=r_peaks, window=0.2)\\n # else:\\n # processing.scatter_beautiful(r_peaks * 1000 / fs, title='peaks')\\n extracted_signal = np.copy(signal)\\n # print(len(r_peaks))\\n # Please, rework it...\\n for n in range(dims[0]):\\n for i in range(0, len(peaks), window):\\n\\n if i + window > len(peaks):\\n r_peaks = peaks[i:]\\n else:\\n r_peaks = peaks[i:i + window]\\n\\n template = np.full((len(r_peaks), t_dur), np.nan)\\n for num, r_ind in enumerate(r_peaks):\\n if r_ind < t_dur // 2:\\n template[num, t_dur // 2 - r_ind - 1:] = extracted_signal[n, 0:r_ind + t_dur // 2 + 1]\\n elif r_ind + t_dur // 2 + 1 > dims[1]:\\n template[num, 0:dims[1] - r_ind + t_dur // 2] = extracted_signal[n, r_ind - t_dur // 2:]\\n else:\\n template[num] = extracted_signal[n, r_ind - t_dur // 2:r_ind + t_dur // 2]\\n template_mean = np.nanmean(template, axis=0) # None for edge cases\\n for r_ind in r_peaks:\\n if r_ind < t_dur // 2:\\n extracted_signal[n, 0:r_ind + t_dur // 2 + 1] -= template_mean[t_dur // 2 - r_ind - 1:]\\n # processing.scatter_beautiful(components[n, :], title=' subtracted channel start ' + str(n))\\n elif r_ind + t_dur // 2 + 1 > dims[1]:\\n extracted_signal[n, r_ind - t_dur // 2:r_ind + t_dur // 2 + 1] -= template_mean[\\n 0:dims[1] - r_ind + t_dur // 2]\\n # processing.scatter_beautiful(components[n, :], title=' subtracted channel end ' + str(n))\\n else:\\n extracted_signal[n, r_ind - t_dur // 2:r_ind + t_dur // 2] -= template_mean\\n # processing.scatter_beautiful(components[n, :], title=' subtracted channel ' + str(n))\\n return extracted_signal\",\n \"def preparehspiceidvgGEO1(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,HFINparam,TFIN_TOPparam,TFIN_BASEparam,EOTparam,NBODYparam,NFINparam):\\n #make an aux copy of hspice file to simulate\\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\\n #make an aux copy of modelcard file to simulate\\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\\n\\n #update path of model and modelcard\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\\\\\"modelcardaux.nmos\\\\\\\"')\\n\\n #bias update\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'HFINparam',HFINparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_TOPparam', TFIN_TOPparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_BASEparam',TFIN_BASEparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'EOTparam', EOTparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam)\",\n \"def createTOFin(En):\\n ftemplate = open(\\\"TOFtemplate.in\\\", \\\"r\\\")\\n lines = ftemplate.readlines()\\n ftofin = open(\\\"TOF.in\\\", \\\"w\\\") \\n energyline = lines[12].split()\\n lines[12] = \\\"%s %g %s\\\\n\\\"%(energyline[0], En, energyline[2])\\n ftofin.writelines(lines)\\n ftemplate.close()\\n ftofin.close()\",\n \"def load_standard_operators():\\n #\\n # Compute scripts\\n #\\n \\n cscript('select' ,scriptpath+'mcdo.sh \\\"${operator}\\\" \\\"${out}\\\" \\\"${var}\\\" \\\"${period_iso}\\\" \\\"${domain}\\\" \\\"${alias}\\\" \\\"${units}\\\" \\\"${missing}\\\" ${ins} ',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('ccdo',\\n scriptpath+'mcdo.sh \\\"${operator}\\\" \\\"${out}\\\" \\\"${var}\\\" \\\"${period_iso}\\\" \\\"${domain}\\\" \\\"${alias}\\\" \\\"${units}\\\" \\\"${missing}\\\" ${ins}')\\n #\\n cscript('minus', 'cdo sub ${in_1} ${in_2} ${out}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('plus', 'cdo add ${in_1} ${in_2} ${out}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('space_average',\\n scriptpath+'mcdo.sh fldmean \\\"${out}\\\" \\\"${var}\\\" \\\"${period_iso}\\\" \\\"${domain}\\\" \\\"${alias}\\\" \\\"${units}\\\" \\\"${missing}\\\" ${ins}', \\n commuteWithTimeConcatenation=True)\\n #\\n cscript('time_average' ,\\n scriptpath+'mcdo.sh timmean \\\"${out}\\\" \\\"${var}\\\" \\\"${period_iso}\\\" \\\"${domain}\\\" \\\"${alias}\\\" \\\"${units}\\\" \\\"${missing}\\\" ${ins}' ,\\n commuteWithSpaceConcatenation=True)\\n #\\n cscript('llbox' ,\\n scriptpath+'mcdo.sh \\\"\\\" \\\"${out}\\\" \\\"${var}\\\" \\\"${period_iso}\\\" \\\"${latmin},${latmax},${lonmin},${lonmax}\\\" \\\"${alias}\\\" \\\"${units}\\\" \\\"${missing}\\\" ${ins}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('regrid' ,\\n scriptpath+'regrid.sh ${in} ${in_2} ${out} ${option}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('regridn' ,\\n scriptpath+'regrid.sh ${in} ${cdogrid} ${out} ${option}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('rescale' ,\\n 'cdo expr,\\\\\\\"${var}=${scale}*${var}+${offset};\\\\\\\" ${in} ${out}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('mean_and_std',\\n scriptpath+'mean_and_std.sh ${in} ${var} ${out} ${out_sdev}', \\n # This tells CliMAF how to compute varname for name output 'sdev' \\n\\t # using input varname\\n sdev_var=\\\"std(%s)\\\" , \\n commuteWithTimeConcatenation=True)\\n #\\n # Declare plot scripts\\n cscript('ncview' ,'ncview ${in} 1>/dev/null 2>&1&' )\\n #\\n cscript('timeplot', 'ncl '+scriptpath+'timeplot.ncl infile=\\\\'\\\\\\\"${in}\\\\\\\"\\\\' outfile=\\\\'\\\\\\\"${out}\\\\\\\"\\\\' '\\n 'var=\\\\'\\\\\\\"${var}\\\\\\\"\\\\' title=\\\\'\\\\\\\"${title}\\\\\\\"\\\\'',format=\\\"png\\\")\\n #\\n # plot: main field (main_file) + auxiliary field (aux_file, optional) + vectors (u_file & v_file, optionals)\\n #\\n cscript('plot' , '(ncl -Q '+ scriptpath +'gplot.ncl main_file=\\\\'\\\\\\\"${in}\\\\\\\"\\\\' aux_file=\\\\'\\\\\\\"${in_2}\\\\\\\"\\\\' '\\n 'u_file=\\\\'\\\\\\\"${in_3}\\\\\\\"\\\\' v_file=\\\\'\\\\\\\"${in_4}\\\\\\\"\\\\' rotation=${rotation} '\\n 'plotname=\\\\'\\\\\\\"${out}\\\\\\\"\\\\' cmap=\\\\'\\\\\\\"${color}\\\\\\\"\\\\' vmin=${min} vmax=${max} vdelta=${delta} '\\n 'main_var=\\\\'\\\\\\\"${var}\\\\\\\"\\\\' aux_var=\\\\'\\\\\\\"${var_2}\\\\\\\"\\\\' u_var=\\\\'\\\\\\\"${var_3}\\\\\\\"\\\\' v_var=\\\\'\\\\\\\"${var_4}\\\\\\\"\\\\' '\\n 'title=\\\\'\\\\\\\"${title}\\\\\\\"\\\\' scale=${scale} offset=${offset} mpCenterLonF=${mpCenterLonF} '\\n 'vcRefMagnitudeF=${vcRefMagnitudeF} vcRefLengthF=${vcRefLengthF} vcMinDistanceF=${vcMinDistanceF} '\\n 'vcGlyphStyle=\\\\'\\\\\\\"${vcGlyphStyle}\\\\\\\"\\\\' vcLineArrowColor=\\\\'\\\\\\\"${vcLineArrowColor}\\\\\\\"\\\\' '\\n 'units=\\\\'\\\\\\\"${units}\\\\\\\"\\\\' linp=${linp} colors=\\\\'\\\\\\\"${colors}\\\\\\\"\\\\' level=${level} time=${time} '\\n 'proj=\\\\'\\\\\\\"${proj}\\\\\\\"\\\\' contours=\\\\'\\\\\\\"${contours}\\\\\\\"\\\\' focus=\\\\'\\\\\\\"${focus}\\\\\\\"\\\\' '\\n 'type=\\\\'\\\\\\\"${format}\\\\\\\"\\\\' resolution=\\\\'\\\\\\\"${resolution}\\\\\\\"\\\\' trim=${trim} '\\n 'vcb=${vcb} lbLabelFontHeightF=${lbLabelFontHeightF} invXY=${invXY} '\\n 'tmYLLabelFontHeightF=${tmYLLabelFontHeightF} tmXBLabelFontHeightF=${tmXBLabelFontHeightF} '\\n 'tmYRLabelFontHeightF=${tmYRLabelFontHeightF} tiXAxisFontHeightF=${tiXAxisFontHeightF} '\\n 'tiYAxisFontHeightF=${tiYAxisFontHeightF} gsnPolarLabelFontHeightF=${gsnPolarLabelFontHeightF} '\\n 'tiMainFont=\\\\'\\\\\\\"${tiMainFont}\\\\\\\"\\\\' tiMainFontHeightF=${tiMainFontHeightF} '\\n 'tiMainPosition=\\\\'\\\\\\\"${tiMainPosition}\\\\\\\"\\\\' gsnLeftString=\\\\'\\\\\\\"${gsnLeftString}\\\\\\\"\\\\' '\\n 'gsnRightString=\\\\'\\\\\\\"${gsnRightString}\\\\\\\"\\\\' gsnCenterString=\\\\'\\\\\\\"${gsnCenterString}\\\\\\\"\\\\' '\\n 'gsnStringFont=\\\\'\\\\\\\"${gsnStringFont}\\\\\\\"\\\\' gsnStringFontHeightF=${gsnStringFontHeightF} )', format=\\\"graph\\\") \\n # \\n cscript('lines' , '(ncl -Q '+ scriptpath +'lineplot.ncl infile=\\\\'\\\\\\\"${mmin}\\\\\\\"\\\\' '\\n 'plotname=\\\\'\\\\\\\"${out}\\\\\\\"\\\\' var=\\\\'\\\\\\\"${var}\\\\\\\"\\\\' title=\\\\'\\\\\\\"${title}\\\\\\\"\\\\' '\\n 'linp=${linp} labels=\\\\'\\\\\\\"${labels}\\\\\\\"\\\\' colors=\\\\'\\\\\\\"${colors}\\\\\\\"\\\\' thickness=${thickness}'\\n 'T_axis=\\\\'\\\\\\\"${T_axis}\\\\\\\"\\\\' fmt=\\\\'\\\\\\\"${fmt}\\\\\\\"\\\\' && '\\n 'convert ${out} -trim ${out}) ', format=\\\"png\\\")\\n #\\n cscript('curves' , '(ncl -Q '+ scriptpath +'curves.ncl infile=\\\\'\\\\\\\"${mmin}\\\\\\\"\\\\' '\\n 'plotname=\\\\'\\\\\\\"${out}\\\\\\\"\\\\' var=\\\\'\\\\\\\"${var}\\\\\\\"\\\\' title=\\\\'\\\\\\\"${title}\\\\\\\"\\\\' '\\n 'labels=\\\\'\\\\\\\"${labels}\\\\\\\"\\\\' colors=\\\\'\\\\\\\"${colors}\\\\\\\"\\\\' thickness=${thickness} && '\\n 'convert ${out} -trim ${out}) ', format=\\\"png\\\")\\n #\\n # cpdfcrop : pdfcrop by preserving metadata\\n #\\n cscript('cpdfcrop' , 'pdfcrop ${in} ${out} ', format=\\\"pdf\\\")\\n # \\n cscript('ncdump' , 'ncdump -h ${in} ', format=\\\"txt\\\")\\n #\\n if (os.system(\\\"type cdfmean >/dev/null 2>&1\\\")== 0 ) :\\n load_cdftools_operators()\\n else :\\n clogger.warning(\\\"No Cdftool available\\\")\",\n \"def calc_raws(df, unit='leader'):\\n\\n df['distrust'] = df['HDIS']/(df['HDIS']+df['LDIS'])\\n df['task'] = df['HTASK']/(df['HTASK']+df['LTASK'])\\n df['bace'] = df['IC']/(df['IC']+df['EC'])\\n df['igb'] = df['HBIAS']/(df['HBIAS']+df['LBIAS'])\\n df['sc'] = df['HSC']/(df['HSC']+df['LSC'])\\n df['cc'] = df['HCC']/(df['HCC']+df['LCC'])\\n df['power'] = df['HPWR']/(df['HPWR']+df['LPWR'])\\n\\n df['i1'] = df.apply(i1_func, axis=1)\\n df['i2'] = df.apply(i2_func, axis=1)\\n df['i3'] = df.apply(i3_func, axis=1)\\n\\n df['i4a'] = df.apply(i4a_func, axis=1)\\n df['i4b'] = df.apply(i4b_func, axis=1)\\n df['i5ap'] = df.apply(i5ap_func, axis=1)\\n df['i5pr'] = df.apply(i5pr_func, axis=1)\\n df['i5re'] = df.apply(i5re_func, axis=1)\\n df['i5op'] = df.apply(i5op_func, axis=1)\\n df['i5th'] = df.apply(i5th_func, axis=1)\\n df['i5pu'] = df.apply(i5pu_func, axis=1)\\n\\n df['p1'] = df.apply(p1_func, axis=1)\\n df['p2'] = df.apply(p2_func, axis=1)\\n df['p3'] = df.apply(p3_func, axis=1)\\n df['p4'] = df.apply(p4_func, axis=1)\\n df['p5'] = df.apply(p5_func, axis=1)\\n\\n if unit == 'leader':\\n\\n keep = ['firstname', 'lastname', 'name', 'Ccode', 'vcount', 'distrust', 'task',\\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\\n 'p5']\\n\\n df = df[keep]\\n\\n elif unit == 'year':\\n\\n keep = ['firstname', 'lastname', 'name', 'year', 'Ccode', 'vcount', 'distrust', 'task',\\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\\n 'p5']\\n\\n df = df[keep]\\n\\n elif unit == 'month':\\n\\n keep = ['firstname', 'lastname', 'name', 'yr_month','Ccode', 'vcount', 'distrust', 'task',\\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\\n 'p5']\\n\\n df = df[keep]\\n\\n df['year'] = df['yr_month'].apply(lambda x: x.split('-')[0])\\n df['month'] = df['yr_month'].apply(lambda x: x.split('-')[1])\\n\\n elif unit == 'quarter':\\n\\n keep = ['firstname', 'lastname', 'name', 'yr_quarter', 'Ccode', 'vcount', 'distrust', 'task',\\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\\n 'p5']\\n\\n df = df[keep]\\n\\n df['year'] = df['yr_quarter'].apply(lambda x: x.split('-')[0])\\n df['quarter'] = df['yr_quarter'].apply(lambda x: x.split('-')[1])\\n\\n return df\",\n \"def calculate_output(self):\",\n \"def calc(s,tnx,i0s,ies):\\n\\n # round down\\n tile0 = s.start // tnx\\n # round up\\n tilee = -(-s.stop // tnx)\\n\\n tiles = []\\n srcslices = []\\n tgtslices = []\\n for tile in range(tile0,tilee):\\n ii0 = max(0, -((s.start - i0s[tile]) // s.step))\\n iie = -((s.start - min(s.stop,ies[tile])) // s.step)\\n if iie > ii0:\\n tiles.append(tile)\\n myi0 = s.start + ii0*s.step - i0s[tile]\\n myie = s.start + iie*s.step - i0s[tile]\\n srcslices.append(slice(myi0,myie,s.step))\\n tgtslices.append(slice(ii0,iie))\\n\\n return tiles, srcslices, tgtslices\",\n \"def _modify_template_nics_according_to_input(template_nics, input_nics, cmd, client,\\n resource_group_name, vm_name,\\n location, private_cloud):\\n # Populating the nic names of vm-template in a dictionary,\\n # and mapping them to their index in template_nics list\\n vm_template_nic_names = {}\\n for (i, nic) in enumerate(template_nics):\\n vm_template_nic_names[nic.virtual_nic_name] = i\\n\\n from .vendored_sdks.models import VirtualNic\\n from .vendored_sdks.models import VirtualNetwork\\n from ._validators import virtual_network_name_or_id_validator\\n\\n # Check if nics entered by a user exist in vm-template,\\n # then override the properties specified. Else create a new nic.\\n for nic in input_nics:\\n if nic['name'] in vm_template_nic_names.keys():\\n index = vm_template_nic_names[nic['name']]\\n if 'virtual-network' in nic.keys():\\n template_nics[index].network.id = nic['virtual-network']\\n if 'adapter' in nic.keys():\\n template_nics[index].nic_type = nic['adapter']\\n if 'power-on-boot' in nic.keys():\\n template_nics[index].power_on_boot = nic['power-on-boot']\\n template_nics[index].virtual_nic_id = None\\n\\n else:\\n nic_name = nic['name']\\n if 'virtual-network' in nic.keys():\\n vnet = nic['virtual-network']\\n else:\\n raise CLIError('virtual-network parameter not specified for nic ' +\\n nic_name + \\\".\\\")\\n if 'adapter' in nic.keys():\\n adapter = nic['adapter']\\n else:\\n raise CLIError('adapter parameter not specified for nic ' +\\n nic_name + \\\".\\\")\\n if 'power-on-boot' in nic.keys():\\n power_on_boot = nic['power-on-boot']\\n else:\\n raise CLIError('power-on-boot parameter not specified for nic ' +\\n nic_name + \\\".\\\")\\n\\n vnet = virtual_network_name_or_id_validator(cmd, client, vnet,\\n resource_group_name, vm_name,\\n location, private_cloud)\\n network = VirtualNetwork(id=vnet)\\n nic_object = VirtualNic(network=network,\\n nic_type=adapter,\\n power_on_boot=power_on_boot)\\n template_nics.append(nic_object)\\n return template_nics\",\n \"def _model(self, t, theta, period, tmpid):\\n template = self.templates[tmpid]\\n phase = (t / period - theta[2]) % 1\\n return theta[0] + theta[1] * template(phase)\",\n \"def make_vrt(self):\\n for index, i in enumerate(self.months):\\n month = str(index + 1)\\n if len(month) < 2:\\n month = '0' + month\\n txt_file = i.joinpath('subnational/tiffs.txt')\\n outfile = i.joinpath(f'{self.country}_{month}_normalised.vrt')\\n if not outfile.exists():\\n gdal_cmd = f'gdalbuildvrt -input_file_list {str(txt_file)} {str(outfile)}'\\n subprocess.call(gdal_cmd, shell=True)\",\n \"def fillBackgroundTemplates(opt):\\n\\n totalBkg={}\\n templates=[]\\n\\n #import signal events\\n data=ROOT.TChain('data')\\n for f in [os.path.join(opt.input,x) for x in os.listdir(opt.input) if 'Data13TeV' in x]:\\n if 'MuonEG' in f : continue\\n data.AddFile(f)\\n\\n #define final preselection cuts\\n cuts='xangle==%d'%opt.xangle\\n if len(opt.presel) : cuts += ' && ' + opt.presel \\n if opt.csiacc:\\n csiCuts ='csi1>%f && csi1<%f && '%opt.csiacc[opt.xangle][0]\\n csiCuts+='csi2>%f && csi2<%f'%opt.csiacc[opt.xangle][1]\\n cuts=csiCuts if len(cuts)==0 else '{0} && {1}'.format(cuts,csiCuts)\\n\\n #loop over categories build templates\\n for icat in range(len(opt.categs)):\\n\\n #apply category cuts\\n categCut=opt.categs[icat]\\n categCut=cuts if len(categCut)==0 else '%s && %s'%(categCut,cuts)\\n catName='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\\n\\n print '\\\\t',catName,categCut\\n\\n #background modelling histos\\n histos=[]\\n data_obs=None\\n for name,pfix in [('bkg_'+catName,'mix'),('bkg_%s_bkgShape'%catName,'mixem')]:\\n\\n templCuts=categCut.replace('csi1',pfix+'csi1')\\n templCuts=templCuts.replace('csi2',pfix+'csi2')\\n data.Draw('{0}mmiss >> h({1},{2},{3})'.format(pfix,opt.nbins,opt.mMin,opt.mMax),templCuts,'goff')\\n h=data.GetHistogram()\\n histos.append(h.Clone(name))\\n histos[-1].SetDirectory(0)\\n\\n if len(histos)==1:\\n totalBkg[icat]=h.Integral()\\n if not opt.unblind :\\n data_obs=h.Clone('data_obs_'+catName)\\n data_obs.SetDirectory(0)\\n\\n h.Reset('ICE')\\n templates += defineProcessTemplates(histos)\\n\\n #observed data in this category if unblinding\\n if opt.unblind:\\n data.Draw('mmiss >> h({1},{2},{3})'.format(opt.nbins,opt.mMin,opt.mMax),categCut,'goff')\\n data_obs=data.GetHistogram().Clone('data_obs_'+catName)\\n data_obs.SetDirectory(0)\\n\\n templates.append(data_obs)\\n\\n print '\\\\t total background:',totalBkg\\n return totalBkg,templates\",\n \"def nc2gtiff(input_name, epsg_in=3413, epsg_out=3857):\\n sub_name=[\\\"dX\\\",\\\"dY\\\"]\\n \\n for sub in sub_name:\\n result = os.system(\\\"echo ''\\\")\\n #netCDF to geotiff (EPSG code is not changed) \\n out_name = \\\"./data/tiff_raw/{}_{}_tmp.tiff\\\".format(os.path.basename(input_name)[:-3],sub)\\n #c is shell command\\n #options\\n #-a_srs:EPSGcode of input files\\n #-of:output format\\n #\\n print(\\\"nc2gtiff\\\")\\n c = \\\"gdal_translate -a_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' NETCDF:'{in_name}':{sub} \\\\\\n -of 'Gtiff' \\\\\\n '{out_name}'\\\".format(epsg=epsg_in,sub=sub, \\\\\\n in_name=input_name,out_name=out_name) \\n result = os.system(c) #run shell command\\n #print(c) #if you want to see the filled c, remove '#' in the head of this line\\n if result!=0: # if it raises error, return filename \\n print(input_name, result, \\\"translate\\\")\\n \\n result = os.system(\\\"echo ''\\\")\\n\\n #geotiff EPSG_in to EPCG_out\\n print(\\\"geotiff2geotiff\\\")\\n target_name=\\\"./data/tiff_target/{}_{}.tiff\\\".format(os.path.basename(input_name)[:-3],sub)\\n c = \\\"gdalwarp -overwrite -s_srs '+proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45' {out_name} -r cubic\\\\\\n {target_name} -t_srs EPSG:{epsg_out} -of \\\\\\n 'GTIFF'\\\".format(out_name=out_name, target_name=target_name, \\n epsg_in=epsg_in, epsg_out=epsg_out)\\n result = os.system(c) # run shell command\\n if result!=0:\\n print(input_name, result, \\\"warp\\\")\",\n \"def hexapodZernikeLinearModel_hexapodcoordinate():\\n Tfile='/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_training.cp'\\n b=p.load(open(Tfile))\\n nobs = len(b)\\n x = b[:,0]\\n y = b[:,1]\\n z = b[:,2]\\n theta = b[:,3]\\n phi = b[:,4]\\n fwhm = b[:,5]\\n e1 = b[:,6]\\n e2 = b[:,7]\\n thetax = theta*np.cos(np.deg2rad(phi))\\n thetay = theta*np.sin(np.deg2rad(phi))\\n xh = x*1000 # convert to hexapod coordinate\\n yh = -y*1000\\n zh = -z*1000\\n xtilth = - thetay\\n ytilth = - thetax\\n\\n M22realTrefoil2 = b[:,37] # for x decenter\\n M22imagTrefoil1 = b[:,54] \\n M22TrefoilXshift = 0.5*(M22realTrefoil2+M22imagTrefoil1)\\n\\n M22realTrefoil1 = b[:,34] # for y decenter\\n M22imagTrefoil2 = b[:,57] \\n M22TrefoilYshift = 0.5*(M22realTrefoil1 - M22imagTrefoil2)\\n\\n M20defocus = b[:,12] # for defocus\\n\\n M22realComa2 = b[:,36] # for x-tilt\\n M22imagComa1 = b[:,55]\\n M22ComaXtilt = 0.5*(M22realComa2+M22imagComa1)\\n\\n M22realComa1 = b[:,35] # for y-tilt\\n M22imagComa2 = b[:,56]\\n M22ComaYtilt = 0.5*(M22realComa1 - M22imagComa2)\\n \\n pl.figure(figsize=(21,12))\\n pl.subplot(2,3,1)\\n t=bp.bin_scatter(M22TrefoilXshift,xh,nbins=20,fmt='bo',scatter=True)\\n res = linefit(M22TrefoilXshift,xh)\\n pl.plot(M22TrefoilXshift,M22TrefoilXshift*res[1]+res[0],'r,')\\n pl.ylabel('x-decenter [micron]')\\n pl.xlabel('(M22realTrefoil2+M22imagTrefoil1)/2')\\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\\n pl.grid()\\n pl.subplot(2,3,2)\\n t=bp.bin_scatter(M22TrefoilYshift,yh,nbins=20,fmt='bo',scatter=True)\\n res = linefit(M22TrefoilYshift,yh)\\n pl.plot(M22TrefoilYshift,M22TrefoilYshift*res[1]+res[0],'r,')\\n pl.ylabel('y-decenter [micron]')\\n pl.xlabel('(M22realTrefoil1 - M22imagTrefoil2)/2')\\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\\n pl.grid()\\n pl.subplot(2,3,3)\\n t=bp.bin_scatter(M20defocus,zh,nbins=20,fmt='bo',scatter=True)\\n res = linefit(M20defocus,zh)\\n pl.plot(M20defocus,M20defocus*res[1]+res[0],'r,')\\n pl.ylabel('z-defocus [micron]')\\n pl.xlabel('M20defocus')\\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\\n pl.grid()\\n pl.subplot(2,3,4)\\n t=bp.bin_scatter(M22ComaXtilt,ytilth,nbins=20,fmt='bo',scatter=True)\\n res = linefit(M22ComaXtilt,ytilth)\\n pl.plot(M22ComaXtilt,M22ComaXtilt*res[1]+res[0],'r,')\\n pl.ylabel('y-tilt [arcsec]') # in hexapod coordiate, xtilt and y tilt is switched from the CRAY coordiante\\n pl.xlabel('(M22realComa2+M22imagComa1)/2')\\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\\n pl.grid()\\n pl.subplot(2,3,5)\\n t=bp.bin_scatter(M22ComaYtilt,xtilth,nbins=20,fmt='bo',scatter=True)\\n res = linefit(M22ComaYtilt,xtilth)\\n pl.plot(M22ComaYtilt,M22ComaYtilt*res[1]+res[0],'r,')\\n pl.ylabel('x-tilt [arcsec]')\\n pl.xlabel('(M22realComa1 - M22imagComa2)/2')\\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\\n pl.grid()\\n pl.savefig('linearModel_hexapod_coordinate.png')\\n pl.close()\",\n \"def test7():\\n from wpylib.text_tools import str_snippet as snippet\\n from wpylib.params.params_flat import ActiveReadValue as Act\\n defaults = {\\n 'nbasis': 320,\\n 'npart': 37,\\n 'deltau': 0.025,\\n }\\n print \\\"test7()\\\"\\n p = params(defaults, nbasis=332)\\n p.input_template = Act(lambda P: snippet(\\\"\\\"\\\"\\\\\\n input = {\\n nbasis = %(nbasis)d\\n npart = %(npart)d\\n deltau = %(deltau)s\\n };\\n code = {\\n %(code_template)s\\n };\\n \\\"\\\"\\\") % P)\\n p.code_template = Act(lambda P: snippet(\\\"\\\"\\\"\\\\\\n for (i = 1; %(nbasis)d; ++i) {\\n print i, %(deltau)g\\n }\\n \\\"\\\"\\\") % P)\\n print \\\"\\\\nInput template 1:\\\"\\n print p.input_template\\n print \\\"\\\\n--- The case above was OK. ---\\\\n\\\"\\n\\n print \\\"\\\\n--- The case below was not OK. ---\\\\nThe nbasis and deltau args were not updated in two printouts below\\\"\\n Q = params(p)\\n Q.nbasis = 327\\n print \\\"\\\\nInput template 2 after updating nbasis:\\\"\\n print Q.input_template\\n\\n\\n print \\\"\\\\n--- The case below was OK, but it requires flattening of the search dicts. ---\\\\n\\\"\\n\\n R = params(p, _flatten_=True)\\n R.nbasis = 327\\n pprint(R)\\n print len(R._list_)\\n print \\\"\\\\nInput template 2 after flattening and updating nbasis:\\\"\\n print R.input_template\\n\\n print \\\"\\\\nHere is the generator script:\\\\n*%(input_template)s*\\\" % p._create_(_flatten_=True)._update_(dict(deltau=2.775))\",\n \"def integ_exact(model,func_params):\\n\\n\\n if (model=='genz_osc'):\\n gcf=func_params\\n dim=gcf.shape[0]-1\\n integ_ex=cos(2.*pi*gcf[0]+0.5*sum(gcf[1:]))\\n for i in range(1,dim+1):\\n integ_ex*=(2.*sin(gcf[i]/2.)/gcf[i])\\n elif (model=='genz_exp'):\\n gcf=func_params\\n dim=gcf.shape[0]-1\\n integ_ex=1.\\n for i in range(1,dim+1):\\n at1=exp(-gcf[i]*gcf[0])\\n at2=exp(gcf[i]*(1.-gcf[0]))\\n integ_ex*=((at2-at1)/(gcf[i]))\\n elif (model=='genz_cont'):\\n gcf=func_params\\n dim=gcf.shape[0]-1\\n integ_ex=1.\\n for i in range(1,dim+1):\\n integ_ex*= ((2.-exp(gcf[i]*(-gcf[0]))-exp(gcf[i]*(gcf[0]-1.)))/gcf[i])\\n elif (model=='genz_gaus'):\\n gcf=func_params\\n dim=gcf.shape[0]-1\\n integ_ex=1.\\n for i in range(1,dim+1):\\n at1=erf(-gcf[i]*gcf[0])\\n at2=erf(gcf[i]*(1.-gcf[0]))\\n integ_ex*=((at2-at1)*sqrt(pi)/(2.*gcf[i]))\\n elif (model=='genz_cpeak'):\\n gcf=func_params\\n dim=gcf.shape[0]-1\\n numer=0.0\\n count=1\\n denom=1.\\n for i in range(1,dim+1):\\n comb=list(itertools.combinations(range(1,dim+1),i))\\n for j in range(len(comb)):\\n assert(i==len(comb[j]))\\n #print i,j,pow(-1,i)\\n numer+=(pow(-1,i)/(1.+sum(gcf[list(comb[j])])))\\n count+=1\\n denom*=(i*gcf[i])\\n #print count, numer\\n integ_ex=(1.+numer)/denom\\n elif (model=='genz_ppeak'):\\n gcf=func_params\\n dim=gcf.shape[0]-1\\n integ_ex=1.\\n for i in range(1,dim+1):\\n at1=np.arctan(-gcf[i]*gcf[0])\\n at2=np.arctan(gcf[i]*(1.-gcf[0]))\\n integ_ex*=(gcf[i]*(at2-at1))\\n\\n return integ_ex\",\n \"def calc_T_sys(nu_obs):\\n return 100 * u.K + 120 * (nu_obs / (150 * u.MHz))**(-2.55) * u.K\",\n \"def generate_ta(data):\\n raise NotImplementedError\",\n \"def New(*args, **kargs):\\n obj = itkIsoDataThresholdCalculatorHDUS.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def supgen(f, INTER):\\n\\n A,Bc = INTER\\n y = intersec(erode(f,A),\\n erode(neg(f),Bc))\\n return y\",\n \"def diffeo_apply(diffeo, template):\\n M, N = diffeo.shape[0], diffeo.shape[1]\\n result = np.empty_like(template)\\n for i, j in coords_iterate((M, N)):\\n i1 = diffeo[i, j, 0]\\n j1 = diffeo[i, j, 1]\\n result[i, j, ...] = template[i1, j1, ...]\\n return result\",\n \"def __calc_t_i(P0, P1, lat, lon, proj):\\n\\n # projected coordinates are in km\\n p0x, p0y = proj(P0.x, P0.y)\\n p1x, p1y = proj(P1.x, P1.y)\\n\\n # Unit vector pointing normal to strike\\n t_i_hat = Vector(p1y - p0y, -(p1x - p0x), 0).norm()\\n\\n # Convert sites to Cartesian\\n sx, sy = proj(lon, lat)\\n sx1d = np.reshape(sx, (-1,))\\n sy1d = np.reshape(sy, (-1,))\\n\\n # Vectors from P0 to sites\\n r = np.zeros([len(sx1d), 2])\\n r[:, 0] = sx1d - p0x\\n r[:, 1] = sy1d - p0y\\n\\n # Dot product gives t_i\\n t_i = np.sum(t_i_hat.getArray()[0:2] * r, axis=1)\\n shp = t_i.shape\\n if len(shp) == 1:\\n t_i.shape = (shp[0], 1)\\n t_i = np.fliplr(t_i)\\n return t_i\",\n \"def __calc_t_i(P0, P1, lat, lon, proj):\\n\\n # projected coordinates are in km\\n p0x, p0y = proj(P0.x, P0.y)\\n p1x, p1y = proj(P1.x, P1.y)\\n\\n # Unit vector pointing normal to strike\\n t_i_hat = Vector(p1y - p0y, -(p1x - p0x), 0).norm()\\n\\n # Convert sites to Cartesian\\n sx, sy = proj(lon, lat)\\n sx1d = np.reshape(sx, (-1,))\\n sy1d = np.reshape(sy, (-1,))\\n\\n # Vectors from P0 to sites\\n r = np.zeros([len(sx1d), 2])\\n r[:, 0] = sx1d - p0x\\n r[:, 1] = sy1d - p0y\\n\\n # Dot product gives t_i\\n t_i = np.sum(t_i_hat.getArray()[0:2] * r, axis=1)\\n shp = t_i.shape\\n if len(shp) == 1:\\n t_i.shape = (shp[0], 1)\\n t_i = np.fliplr(t_i)\\n return t_i\",\n \"def preparehspiceidvgGEO4(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,Ach_UFCMparam,Cins_UFCMparam,W_UFCMparam,NBODYparam,NFINparam):\\n#L=Lparam Ach_UFCM=Ach_UFCMparam Cins_UFCM=Cins_UFCMparam W_UFCM=W_UFCMparam NBODY=NBODYparam NFIN=NFINparam\\n #make an aux copy of hspice file to simulate\\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\\n #make an aux copy of modelcard file to simulate\\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\\n\\n #update path of model and modelcard\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\\\\\"modelcardaux.nmos\\\\\\\"')\\n\\n #bias update\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Ach_UFCMparam',Ach_UFCMparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Cins_UFCMparam', Cins_UFCMparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'W_UFCMparam',W_UFCMparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam)\",\n \"def calc_population_02_state(U):\\n if U.type=='oper':\\n sump = 0\\n for i_list in [[0,2]]:\\n for j_list in [[1,1]]:\\n bra_i = qtp.tensor(qtp.ket([i_list[0]], dim=[3]),\\n qtp.ket([i_list[1]], dim=[3])).dag()\\n ket_j = qtp.tensor(qtp.ket([j_list[0]], dim=[3]),\\n qtp.ket([j_list[1]], dim=[3]))\\n p = np.abs((bra_i*U*ket_j).data[0, 0])**2\\n sump += p\\n return np.real(sump)\\n elif U.type=='super':\\n sump = 0\\n for i_list in [[0,2]]:\\n for j_list in [[1,1]]:\\n ket_i = qtp.tensor(qtp.ket([i_list[0]], dim=[3]),\\n qtp.ket([i_list[1]], dim=[3]))\\n rho_i=qtp.operator_to_vector(qtp.ket2dm(ket_i))\\n ket_j = qtp.tensor(qtp.ket([j_list[0]], dim=[3]),\\n qtp.ket([j_list[1]], dim=[3]))\\n rho_j=qtp.operator_to_vector(qtp.ket2dm(ket_j))\\n p = (rho_i.dag()*U*rho_j).data[0, 0]\\n sump += p\\n return np.real(sump)\",\n \"def format_template(commit_info, nb):\\n\\n nb_path = os.path.abspath(nb).replace('ipynb', 'md')\\n with open(nb_path, 'r+') as file:\\n template = NbTemplate(file.read())\\n updated = template.substitute(commit_info)\\n file.seek(0)\\n file.write(updated)\\n file.truncate()\",\n \"def calculate_T(func, a, b, N):\\n # Trapezoid width \\n\\n h = (b - a)/N\\n\\n # Every odd slice\\n new_part = 0\\n for i in range(1, N//2 + 1):\\n new_part += func(a + (2*i - 1)*h) \\n \\n return 2/3. * new_part\",\n \"def template_analysis_function(self, system, wave_idx, config, surface, *args, **kwargs):\\n\\n # Write your function operations here\\n\\n # This will typically contain a lot of ZOS API commands such as:\\n # - Setting the Current configuration: system.MCE.SetCurrentConfiguration(config)\\n # - Reading the Field Points: sysField = system.SystemData.Fields\\n # fx = sysField.GetField(1).X\\n # fy = sysField.GetField(1).Y\\n # - Running some Ray Trace: raytrace = system.Tools.OpenBatchRayTrace()\\n # normUnPolData = raytrace.CreateNormUnpol(N_rays,\\n # constants.RaysType_Real, toSurface)\\n\\n # You should return a list of arrays\\n\\n return\",\n \"def nd_2_nz_compute(self):\\n tik_instance = self.set_tik_instance()\\n format_transfer_case = self.set_format_transfer_case()\\n if format_transfer_case == 0:\\n tik_instance = self.format_transfer_case_zero(tik_instance)\\n elif format_transfer_case == 1:\\n tik_instance = self.format_transfer_case_one(tik_instance)\\n elif format_transfer_case == 2:\\n tik_instance = self.format_transfer_case_two(tik_instance)\\n elif format_transfer_case == 3:\\n tik_instance = self.format_transfer_case_three(tik_instance)\\n elif format_transfer_case == 4:\\n tik_instance = self.format_transfer_case_four(tik_instance)\\n elif format_transfer_case == 5:\\n tik_instance = self.format_transfer_case_five(tik_instance)\\n elif format_transfer_case == 6:\\n tik_instance = self.format_transfer_case_six(tik_instance)\\n elif format_transfer_case == 7:\\n tik_instance = self.format_transfer_case_seven(tik_instance)\\n elif format_transfer_case == 8:\\n tik_instance = self.format_transfer_case_eight(tik_instance)\\n return tik_instance\",\n \"def IPTinput_gen(Nom,beta,ef,mutld):\\n\\tV,D =0.01,1\\n\\tf1 = open(\\\"Delta.inp\\\", 'w')\\n\\tf2 = open(\\\"G0.inp\\\", 'w')\\n\\tDelta=[]\\n\\tfor n in range(Nom):\\n\\t iom = (2.*n+1)*pi/beta\\n\\t Redelta=-V**2/(2.0*D)*log( ((D/2.-mutld)**2+iom**2)/((D/2.+mutld)**2+iom**2))\\n\\t Imdelta=-V**2/D*(arctan((D/2.-mutld)/iom)-arctan((-D/2.-mutld)/iom))\\n\\t print>>f1,iom,Redelta,Imdelta\\n\\t G0= 1./( 1.0j*iom-(ef-mutld)-(Redelta+1.0j*Imdelta) )\\n\\t print>>f2,iom,G0.real,G0.imag\\n\\tf1.close()\\n\\tf2.close()\",\n \"def defineProcessTemplates(histos):\\n\\n templates=[]\\n\\n #nominal\\n templates.append( histos[0] )\\n nomStats=templates[-1].Integral()\\n\\n #systematic variations\\n #if Up/Down already in the name store directly updating the name\\n #if not, mirror the variation given \\n for i in xrange(1,len(histos)): \\n templates.append( histos[i] )\\n key=templates[-1].GetName()\\n if not 'Up' in key and not 'Down' in key :\\n templates[-1].SetName(key+'Up')\\n templates.append( histos[i].Clone(key+'Down') )\\n for xbin in range(templates[0].GetNbinsX()):\\n templates[-1].SetBinContent(xbin+1,2*templates[0].GetBinContent(xbin+1)-templates[-2].GetBinContent(xbin+1))\\n \\n #don't leave bins with 0's\\n for h in templates:\\n h.SetDirectory(0)\\n iStats=h.Integral()\\n if iStats>0: h.Scale(nomStats/iStats)\\n for xbin in range(h.GetNbinsX()):\\n if h.GetBinContent(xbin+1)>0: continue\\n h.SetBinContent(xbin+1,1e-6)\\n \\n return templates\",\n \"def calcT1(g2, g1):\\n idop = FermiOp(g2.orbs, 3, 3)\\n idop.data = np.eye(int(binom(g2.orbs, 3)))\\n\\n return p2N(g2, 3) - p2N(g1, 3) + idop\",\n \"def mseed_2_templates(wav_dirs, cat, outdir, length, prepick,\\n highcut=None, lowcut=None, f_order=None,\\n samp_rate=None, min_snr=2.,\\n start=None, end=None, miniseed=True,\\n asdf_file=False, debug=1):\\n\\n # Establish date range for template creation\\n cat.events.sort(key=lambda x: x.origins[-1].time)\\n if start:\\n cat_start = datetime.datetime.strptime(start, '%d/%m/%Y')\\n cat_end = datetime.datetime.strptime(end, '%d/%m/%Y')\\n else:\\n cat_start = cat[0].origins[-1].time.date\\n cat_end = cat[-1].origins[-1].time.date\\n for date in date_generator(cat_start, cat_end):\\n dto = UTCDateTime(date)\\n print('Processing templates for: %s' % str(dto))\\n q_start = dto - 10\\n q_end = dto + 86410\\n # Establish which events are in this day\\n sch_str_start = 'time >= %s' % str(dto)\\n sch_str_end = 'time <= %s' % str(dto + 86400)\\n tmp_cat = cat.filter(sch_str_start, sch_str_end)\\n if len(tmp_cat) == 0:\\n print('No events on: %s' % str(dto))\\n continue\\n # Which stachans we got?\\n stachans = {pk.waveform_id.station_code: [] for ev in tmp_cat\\n for pk in ev.picks}\\n for ev in tmp_cat:\\n for pk in ev.picks:\\n chan_code = pk.waveform_id.channel_code\\n if chan_code not in stachans[pk.waveform_id.station_code]:\\n stachans[pk.waveform_id.station_code].append(chan_code)\\n wav_read_start = timer()\\n # Be sure to go +/- 10 sec to account for GeoNet shit timing\\n if asdf_file:\\n with pyasdf.ASDFDataSet(asdf_file) as ds:\\n st = Stream()\\n for sta, chans in iter(stachans.items()):\\n for station in ds.ifilter(ds.q.station == sta,\\n ds.q.channel == chans,\\n ds.q.starttime >= q_start,\\n ds.q.endtime <= q_end):\\n st += station.raw_recording\\n elif miniseed:\\n wav_ds = ['%s%d' % (d, dto.year) for d in wav_dirs]\\n st = grab_day_wavs(wav_ds, dto, stachans)\\n wav_read_stop = timer()\\n print('Reading waveforms took %.3f seconds' % (wav_read_stop\\n - wav_read_start))\\n print('Looping through stachans to merge/resamp')\\n stachans = [(tr.stats.station, tr.stats.channel) for tr in st]\\n for stachan in list(set(stachans)):\\n tmp_st = st.select(station=stachan[0], channel=stachan[1])\\n if len(tmp_st) > 1 and len(set([tr.stats.sampling_rate for tr in tmp_st])) > 1:\\n print('Traces from %s.%s have differing samp rates' % (stachan[0], stachan[1]))\\n for tr in tmp_st:\\n st.remove(tr)\\n tmp_st.resample(sampling_rate=samp_rate)\\n st += tmp_st\\n st.merge(fill_value='interpolate')\\n resamp_stop = timer()\\n print('Resample/merge took %s secs' % str(resamp_stop - wav_read_stop))\\n print('Preprocessing...')\\n # Process the stream\\n try:\\n st1 = pre_processing.dayproc(st, lowcut=lowcut, highcut=highcut,\\n filt_order=f_order, samp_rate=samp_rate,\\n starttime=dto, debug=debug, ignore_length=True,\\n num_cores=4)\\n except NotImplementedError or Exception as e:\\n print('Found error in dayproc, noting date and continuing')\\n print(e)\\n with open('%s/dayproc_errors.txt' % outdir, mode='a') as fo:\\n fo.write('%s\\\\n%s\\\\n' % (str(date), e))\\n continue\\n print('Feeding stream to template_gen...')\\n for event in tmp_cat:\\n print('Copying stream to keep away from the trim...')\\n trim_st = copy.deepcopy(st1)\\n ev_name = str(event.resource_id).split('/')[-1]\\n pk_stachans = ['%s.%s' % (pk.waveform_id.station_code,\\n pk.waveform_id.channel_code)\\n for pk in event.picks]\\n # Run check to ensure that there is only one pick for each channel\\n dups = [pk for pk, count\\n in collections.Counter(pk_stachans).items() if count > 1]\\n if len(dups) > 0:\\n print('Event %s still has dup picks. Skipping' % ev_name)\\n continue\\n template = template_gen(event.picks, trim_st, length=length,\\n prepick=prepick, min_snr=min_snr)\\n if len([tr for tr in template\\n if tr.stats.channel[-1] == 'Z']) < 6:\\n print('Skipping template with fewer than 6 Z-comp traces')\\n continue\\n # temp_list.append(template)\\n print('Writing event %s to file...' % ev_name)\\n template.write('%s/%s.mseed' % (outdir, ev_name),\\n format=\\\"MSEED\\\")\\n del trim_st\\n del tmp_cat, st1, st\",\n \"def gen_compute_template(self):\\n\\n print \\\"\\\\t* Generating compute template in file %s\\\" % self.compute_filename\\n if self.cmdline:\\n with open(self.compute_filename, 'w') as f:\\n f.write(yaml.safe_dump(self.compute_template))\\n\\n try:\\n self.heatclient.stacks.validate(template=yaml.safe_dump(self.compute_template))\\n except Exception as e:\\n print \\\"Unfortunately your file is malformed. Received error: (%s)\\\" % str(e)\\n print \\\"Exiting ...\\\"\\n sys.exit(1)\\n\\n return self.compute_template\",\n \"def build_template_meas(name='example'):\\n\\n meas = templates.TemplateMeasurement(name)\\n meas.set_lumi(1, 0.02)\\n\\n # Base shape for the signal, triangular distribution\\n sig = np.array([10000, 12500, 15000, 12500, 10000], dtype=float)\\n # Add a source for the signal\\n src_sig = meas.new_source('sig', sig)\\n src_sig.use_lumi() # impacted by luminosity\\n src_sig.use_stats(.1*(10*sig)**0.5) # stat unc. from 10x MC\\n src_sig.set_xsec(1, 0.95, 1.05) # cross section constrained to +/-5%\\n # Add a template: under the influence of parameter p, a linear slope is\\n # added to the signal\\n src_sig.add_template('p', sig*[-.2, -.1, 0, +.1, +.2])\\n # Add highly asymmetric systematic uncertainty which looks a lot like the\\n # signal. This is a challenging model to fit.\\n src_sig.add_syst('s1', sig*[-.06, -.02, 0, +.02, +.06], polarity='up')\\n src_sig.add_syst('s1', sig*[-.03, -.01, 0, +.01, +.03], polarity='down')\\n # Add another systematic which doesn't look like the signal or the data\\n # (should be constrained)\\n src_sig.add_syst('s2', sig*[+.02, +.01, 0, +.01, +.02])\\n\\n # Add a flat-ish background (different shape from signal)\\n bg1 = np.array([1600, 1300, 1000, 1000, 1000], dtype=float)\\n src_bg1 = meas.new_source('bg1', bg1)\\n src_bg1.use_lumi()\\n src_bg1.use_stats(.1*(10*bg1)**0.5)\\n src_bg1.set_xsec(1, 0.8, 1.1)\\n # It is also impacted by systematic 2\\n src_bg1.add_syst('s2', bg1*[+.02, +.01, 0, +.01, +.02])\\n\\n # Add a background not impacted by lumi or stats (e.g. data driven)\\n bg2 = np.array([1000, 1000, 1000, 1300, 1600], dtype=float)\\n src_bg2 = meas.new_source('bg2', bg2)\\n src_bg2.set_xsec(1, 0.9, 1.1)\\n\\n # Build the spectrum object\\n meas.build()\\n\\n return meas\",\n \"def healpix_template(ncomp, nside, coordinate=None):\\n\\n temp = so_map()\\n\\n if ncomp == 3:\\n temp.data = np.zeros((3, 12 * nside ** 2))\\n else:\\n temp.data = np.zeros((12 * nside ** 2))\\n\\n temp.pixel = \\\"HEALPIX\\\"\\n temp.ncomp = ncomp\\n temp.nside = nside\\n temp.geometry = \\\"healpix geometry\\\"\\n temp.coordinate = coordinate\\n return temp\",\n \"def cubic_template(template, spacing=1, connectivity=6,\\n node_prefix='node', edge_prefix='edge'):\\n template = np.atleast_3d(template).astype(bool)\\n # Generate a full cubic network\\n temp = cubic(shape=template.shape, spacing=spacing,\\n connectivity=connectivity,\\n node_prefix=node_prefix, edge_prefix=edge_prefix)\\n # Store some info about template\\n coords = np.unravel_index(range(template.size), template.shape)\\n coords = np.vstack(coords).T\\n Np = coords.shape[0]\\n temp[node_prefix+'.template_coords'] = coords\\n temp[node_prefix+'.template_indices'] = np.arange(Np)\\n # Trim pores not present in template\\n temp = trim_nodes(network=temp, inds=~template.flatten())\\n return temp\",\n \"def make_overall_vignetting(energy=7.2, *args,\\n subgrid=10, urdweights={},\\n **kwargs):\\n if subgrid < 1:\\n print(\\\"ahtung! subgrid defines splines of the translation of multiple vigneting file into one map\\\")\\n print(\\\"set subgrid to 2\\\")\\n subgrid = 2\\n #x, y = np.meshgrid(np.linspace(-24., 24., 48*subgrid), np.np.linspace(-24., 24., 48*subgrid))\\n xmin, xmax = -24.*DL, 24.*DL\\n ymin, ymax = -24.*DL, 24.*DL\\n\\n vecs = offset_to_vec(np.array([xmin, xmax, xmax, xmin]),\\n np.array([ymin, ymin, ymax, ymax]))\\n\\n vmaps = {}\\n for urdn in URDNS:\\n quat = ARTQUATS[urdn]\\n xlim, ylim = vec_to_offset(quat.apply(vecs))\\n xmin, xmax = min(xmin, xlim.min()), max(xmax, xlim.max())\\n ymin, ymax = min(ymin, ylim.min()), max(ymax, ylim.max())\\n\\n dd = DL/subgrid\\n dx = dd - (xmax - xmin)%dd\\n xmin, xmax = xmin - dx/2., xmax + dx\\n dy = dd - (ymax - ymin)%dd\\n ymin, ymax = ymin - dy/2., ymax + dy\\n\\n x, y = np.mgrid[xmin:xmax:dd, ymin:ymax:dd]\\n shape = x.shape\\n newvmap = np.zeros(shape, np.double)\\n vecs = offset_to_vec(np.ravel(x), np.ravel(y))\\n\\n for urdn in URDNS:\\n vmap = make_vignetting_for_urdn(urdn, energy, *args, **kwargs)\\n quat = ARTQUATS[urdn]\\n newvmap += vmap(vec_to_offset_pairs(quat.apply(vecs, inverse=True))).reshape(shape)*urdweights.get(urdn, 1.)\\n\\n vmap = RegularGridInterpolator((x[:, 0], y[0]), newvmap, bounds_error=False, fill_value=0)\\n return vmap\",\n \"def label_hemispheres( x, template, templateLR, reg_iterations=[200,50,2,0] ):\\n reg = ants.registration(\\n ants.rank_intensity(x),\\n ants.rank_intensity(template),\\n 'SyN',\\n aff_metric='GC',\\n syn_metric='CC',\\n syn_sampling=2,\\n reg_iterations=reg_iterations,\\n random_seed = 1 )\\n return( ants.apply_transforms( x, templateLR, reg['fwdtransforms'],\\n interpolator='genericLabel') )\",\n \"def _ProcessTemplate(self,topdir):\\n self.dicomdir = \\\"%s/anatomicals\\\" % self.topdir\\n self.rawdir = \\\"%s/raw\\\" % topdir\\n self.rawdirs = {}\\n tmplt = self._GetTemplate()\\n if self.opts.outdir is not None:\\n# Override template output directory.\\n tmplt['top_outdir'] = self.opts.outdir\\n self.tmplt = tmplt\\n if len(tmplt['top_outdir']) == 0:\\n tmplt['top_outdir'] = os.path.realpath(self.topdir)\\n raise RuntimeError('Template file must specify an output directory.')\\n tmplt['top_outdir'] = os.path.realpath(tmplt['top_outdir'])\\n if '/home' in tmplt['top_outdir'][:7]:\\n raise RuntimeError('Image data cannot be stored in the /home partition. Change the \\\"top_outdir\\\" entry in the template file: %s.' % (' '.join(self.templates)))\\n# tmplt['subject'] = 'orig'\\n self.procdir = os.path.abspath(\\\"%s/%s\\\" % \\\\\\n (tmplt['top_outdir'],tmplt['subject']))\\n target = os.path.abspath('%s/../..' % tmplt['top_outdir'])\\n if not ismounted(target):\\n raise RuntimeError('Could not access partition at %s' % target)\\n\\n self.anatdir = \\\"%s/anat\\\" % self.procdir\\n self.fmapdir = \\\"%s/%s\\\" % (self.procdir,tmplt['fmap']['outdir'])\\n self.dtidir = \\\"%s/%s\\\" % (self.procdir,tmplt['dti']['outdir'])\\n self.logdir = \\\"%s/%s\\\" % (self.procdir,tmplt['logdir'])\\n self.skip = tmplt.get('skip', DEFAULT_SKIP)\\n self.acq_tr = tmplt.get('acq_tr',None)\\n self.episetup_dir = \\\"%s/%s\\\" % (self.procdir,tmplt['first_epi'])\\n self.fsl_cmpblty = tmplt.get('fsl_compatibility',False)\\n self.epi_file_format = self.tmplt['epi_file_format']\\n self.censor_thresh = tmplt.get('censor_threshold', 2.)\\n self.censor_interleave = tmplt.get('censor_interleave', True)\\n# self.server_userid = self.tmplt.get('server_userid','default')\\n\\n# Overide flags for aligning EPIs and skull-stripping with command-\\n# line options.\\n if self.opts.align_fmaps:\\n self.align_fmaps = True\\n else:\\n self.align_fmaps = self.tmplt.get('epi_align', False)\\n\\n if self.opts.no_align_fmaps:\\n self.no_align_fmaps = True\\n else:\\n self.no_align_fmaps = self.tmplt.get('no_epi_align', False)\\n\\n if self.opts.skull_strip:\\n self.skull_strip = True\\n else:\\n self.skull_strip = self.tmplt.get('skull_strip', False)\\n\\n# Create log file now so it can be used immediately.\\n if not os.path.exists(self.logdir):\\n if self.verbose:\\n print 'mkdir %s' % self.logdir\\n if not self.opts.fake_opts:\\n self.MakeDir(self.logdir)\\n\\n self._ProcessTemplateEpiInfo()\",\n \"def no_icp(source, templates, home=None, use_2d=False):\\n if source.shape[0] == 0:\\n return create_config(0,0,0, fitness=0)\\n origorig = source.copy()\\n source, xinit, yinit, theta = init_transform(source)\\n\\n remove_out = False\\n if remove_out:\\n iq_range=0.5\\n pcnt = (1 - iq_range) / 2\\n xqlow, xqhigh = np.quantile(source[:,0], [pcnt, 1-pcnt])\\n yqlow, yqhigh = np.quantile(source[:,1], [pcnt, 1-pcnt])\\n xiqr, yiqr = xqhigh-xqlow, yqhigh-yqlow\\n no_out = (source[:,0]<xqhigh+1.5*xiqr) & (source[:,0]>xqlow-1.5*xiqr) &\\\\\\n (source[:,1]<yqhigh+1.5*yiqr) & (source[:,1]>yqlow-1.5*yiqr)\\n source = source[no_out,:]\\n\\n remove_low = False\\n if remove_low:\\n not_low = (source[:,-1]>0.15) & (source[:,-1]<1.4)\\n source = source[not_low,:]\\n\\n if source.shape[0] == 0:\\n return create_config(0,0,0,fitness=0)\\n\\n\\n # Recenter the data independently from point density\\n xmin, xmax = np.min(source[:,0]), np.max(source[:,0])\\n ymin, ymax = np.min(source[:,1]), np.max(source[:,1])\\n x_center, y_center = xmin+(xmax-xmin)/2, ymin+(ymax-ymin)/2\\n center2 = rotate(-theta, np.array([[x_center, y_center, 0]]), axis=\\\"z\\\")\\n xinit += center2[0][0]\\n yinit += center2[0][1]\\n source = source - [x_center, y_center, 0]\\n\\n angle_diffs = [-5.,-2.,-1.,0.,1.,2.,5.]\\n center_diffs = [-0.2,-0.15,-0.10,-0.07,-0.05,-0.01,0,0.01,0.05,0.07,0.10,0.15,0.2]\\n lengths = [2.,2.1]\\n widths = [1.]\\n trials = list(product(center_diffs, center_diffs, angle_diffs, lengths, widths))\\n\\n trialmap = np.array([np.array([xinit, yinit, np.degrees(theta)-90, 0.0, 0.0])+np.array(t) for t in trials])\\n results = multi_grid_iou(origorig[:, :2], trialmap, 0.05)\\n best = np.argmax(results)\\n cfg = create_config(*trialmap[best])\\n cfg[\\\"bed\\\"][\\\"fitness\\\"] = np.max(results)\\n return cfg\",\n \"def initialize_nick_templates(in_template, out_template):\\n\\n # create the regex for in_template\\n regex_string = fnmatch.translate(in_template)\\n # we must account for a possible line break coming over the wire\\n\\n # NOTE-PYTHON3: fnmatch.translate format changed since Python2\\n regex_string = regex_string[:-2] + r\\\"(?:[\\\\n\\\\r]*?)\\\\Z\\\"\\n\\n # validate the templates\\n regex_args = [match.group(2) for match in _RE_NICK_ARG.finditer(regex_string)]\\n temp_args = [match.group(2) for match in _RE_NICK_TEMPLATE_ARG.finditer(out_template)]\\n if set(regex_args) != set(temp_args):\\n # We don't have the same $-tags in input/output.\\n raise NickTemplateInvalid\\n\\n regex_string = _RE_NICK_SPACE.sub(r\\\"\\\\\\\\s+\\\", regex_string)\\n regex_string = _RE_NICK_ARG.sub(lambda m: \\\"(?P<arg%s>.+?)\\\" % m.group(2), regex_string)\\n template_string = _RE_NICK_TEMPLATE_ARG.sub(lambda m: \\\"{arg%s}\\\" % m.group(2), out_template)\\n\\n return regex_string, template_string\",\n \"def generate_latex_output(self, calc_spec_tex_file_handle, sim, ui=None,\\r\\n res_dir=None):\\r\\n average_pressure = np.mean(self.sun.press[self.sun.press > 0])\\r\\n numeric_output_data = [\\r\\n ('pressure, max.', to_preci(np.amax(self.sun.press), 4),\\r\\n UnitTex.pressure.value, 'unverified'),\\r\\n ('pressure, av.', to_preci(average_pressure, 4),\\r\\n UnitTex.pressure.value, 'unverified'),\\r\\n ('e_a,kin sun, max.', to_preci(np.amax(self.sun.e_akin), 4),\\r\\n UnitTex.eakin.value, 'unverified'),\\r\\n ('e_a,kin planet, max.', to_preci(np.amax(self.planet.e_akin), 4),\\r\\n UnitTex.eakin.value, 'unverified'),\\r\\n ('pv_rel, max.', to_preci(np.amax(self.pv), 4),\\r\\n UnitTex.pvrel.value, 'unverified'),\\r\\n ('contact area', to_preci(self.sun.get_area(self.sun.press), 4),\\r\\n UnitTex.area.value, 'unverified')]\\r\\n table_calc_summary = []\\r\\n for key, value, unit, status in sorted(numeric_output_data):\\r\\n table_calc_summary.append(\\r\\n (re.sub('_', '\\\\_', key), value, unit, status))\\r\\n\\r\\n latex_variables = {'table_calc_summary': table_calc_summary,\\r\\n 'contact_plot1': '{}{}contact1.png'.format(\\r\\n SubDir.tex_figs_rel_to_tex_file.value, '/'),\\r\\n 'pressure_plot1': '{}{}pressure1.png'.format(\\r\\n SubDir.tex_figs_rel_to_tex_file.value, '/'),\\r\\n 'energy_plot1': '{}{}energy1.png'.format(\\r\\n SubDir.tex_figs_rel_to_tex_file.value, '/')}\\r\\n template_calc_specific = get_calc_specific_latex_template(\\r\\n TexTempl.RingOnRing.value, sim)\\r\\n with open(calc_spec_tex_file_handle, 'w') as f:\\r\\n f.write(template_calc_specific.render(latex_variables))\",\n \"def t1_hypointensity( x, xsegmentation, xWMProbability, template, templateWMPrior, wmh_thresh=0.1 ):\\n mybig = [88,128,128]\\n templatesmall = ants.resample_image( template, mybig, use_voxels=True )\\n qaff = ants.registration(\\n ants.rank_intensity(x),\\n ants.rank_intensity(templatesmall), 'SyN',\\n syn_sampling=2,\\n syn_metric='CC',\\n reg_iterations = [25,15,0,0],\\n aff_metric='GC', random_seed=1 )\\n afftx = qaff['fwdtransforms'][1]\\n templateWMPrior2x = ants.apply_transforms( x, templateWMPrior, qaff['fwdtransforms'] )\\n cerebrum = ants.threshold_image( xsegmentation, 2, 4 )\\n realWM = ants.threshold_image( templateWMPrior2x , 0.1, math.inf )\\n inimg = ants.rank_intensity( x )\\n parcellateWMdnz = ants.kmeans_segmentation( inimg, 2, realWM, mrf=0.3 )['probabilityimages'][0]\\n x2template = ants.apply_transforms( templatesmall, x, afftx, whichtoinvert=[True] )\\n parcellateWMdnz2template = ants.apply_transforms( templatesmall,\\n cerebrum * parcellateWMdnz, afftx, whichtoinvert=[True] )\\n # features = rank+dnz-image, lprob, wprob, wprior at mybig resolution\\n f1 = x2template.numpy()\\n f2 = parcellateWMdnz2template.numpy()\\n f3 = ants.apply_transforms( templatesmall, xWMProbability, afftx, whichtoinvert=[True] ).numpy()\\n f4 = ants.apply_transforms( templatesmall, templateWMPrior, qaff['fwdtransforms'][0] ).numpy()\\n myfeatures = np.stack( (f1,f2,f3,f4), axis=3 )\\n newshape = np.concatenate( [ [1],np.asarray( myfeatures.shape )] )\\n myfeatures = myfeatures.reshape( newshape )\\n\\n inshape = [None,None,None,4]\\n wmhunet = antspynet.create_unet_model_3d( inshape,\\n number_of_outputs = 1,\\n number_of_layers = 4,\\n mode = 'sigmoid' )\\n\\n wmhunet.load_weights( get_data(\\\"simwmhseg\\\", target_extension='.h5') )\\n\\n pp = wmhunet.predict( myfeatures )\\n\\n limg = ants.from_numpy( tf.squeeze( pp[0] ).numpy( ) )\\n limg = ants.copy_image_info( templatesmall, limg )\\n lesresam = ants.apply_transforms( x, limg, afftx, whichtoinvert=[False] )\\n # lesresam = lesresam * cerebrum\\n rnmdl = antspynet.create_resnet_model_3d( inshape,\\n number_of_classification_labels = 1,\\n layers = (1,2,3),\\n residual_block_schedule = (3,4,6,3), squeeze_and_excite = True,\\n lowest_resolution = 32, cardinality = 1, mode = \\\"regression\\\" )\\n rnmdl.load_weights( get_data(\\\"simwmdisc\\\", target_extension='.h5' ) )\\n qq = rnmdl.predict( myfeatures )\\n\\n lesresamb = ants.threshold_image( lesresam, wmh_thresh, 1.0 )\\n lgo=ants.label_geometry_measures( lesresamb, lesresam )\\n wmhsummary = pd.read_csv( get_data(\\\"wmh_evidence\\\", target_extension='.csv' ) )\\n wmhsummary.at[0,'Value']=lgo.at[0,'VolumeInMillimeters']\\n wmhsummary.at[1,'Value']=lgo.at[0,'IntegratedIntensity']\\n wmhsummary.at[2,'Value']=float(qq)\\n\\n return {\\n \\\"wmh_summary\\\":wmhsummary,\\n \\\"wmh_probability_image\\\":lesresam,\\n \\\"wmh_evidence_of_existence\\\":float(qq),\\n \\\"wmh_max_prob\\\":lesresam.max(),\\n \\\"features\\\":myfeatures }\",\n \"def nb_clementsbekkers(data, template):\\n ## Prepare a bunch of arrays we'll need later\\n n_template = len(template)\\n # if n_template <= 1:\\n # raise ValueError(\\\"nb_clementsbekkers: Length of template must be useful, and > 1\\\")\\n n_data = data.shape[0]\\n n_dt = n_data - n_template\\n # if n_dt < 10:\\n # raise ValueError(\\\"nb_clementsbekkers: n_dt, n_template\\\", n_dt, n_template)\\n #\\n sum_template = template.sum()\\n sum_template_2 = (template*template).sum()\\n\\n data_2 = data*data\\n sum_data = np.sum(data[:n_template])\\n sum_data_2 = data_2[:n_template].sum()\\n scale = np.zeros(n_dt)\\n offset = np.zeros(n_dt)\\n crit = np.zeros(n_dt)\\n for i in range(n_dt):\\n if i > 0:\\n sum_data = sum_data + data[i+n_template] - data[i-1]\\n sum_data_2 = sum_data_2 + data_2[i+n_template] - data_2[i-1]\\n sum_data_template_prod = np.multiply(data[i:i+n_template], template).sum()\\n scale[i] = (\\n (sum_data_template_prod - sum_data * sum_template/n_template)/\\n (sum_template_2 - sum_template*sum_template/n_template)\\n )\\n offset[i] = (sum_data - scale[i]*sum_template)/n_template\\n fitted_template = template * scale[i] + offset[i]\\n sse = ((data[i:i+n_template] - fitted_template)**2).sum()\\n crit[i] = scale[i]/np.sqrt(sse/(n_template-1))\\n DC = scale/crit\\n return(DC, scale, crit)\",\n \"def zernikeHexapodTrend(mnts='M20'):\\n Tfile='/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_training.cp'\\n b=p.load(open(Tfile))\\n nobs = len(b)\\n x = b[:,0]\\n y = b[:,1]\\n z = b[:,2]\\n theta = b[:,3]\\n phi = b[:,4]\\n fwhm = b[:,5]\\n e1 = b[:,6]\\n e2 = b[:,7]\\n thetax = theta*np.cos(np.deg2rad(phi))\\n thetay = theta*np.sin(np.deg2rad(phi))\\n if mnts == 'M20':\\n idxBase = 9\\n if mnts == 'M22real':\\n idxBase = 29\\n if mnts == 'M22imag':\\n idxBase = 49\\n idx = np.arange(14)\\n zernikeName=('Piston','Tip','Tilt','Astignism','Defocus','Astignism','Trefoil','Coma','Coma','Trefoil','Ashtray','Astigm.5th','Spherical','Astigm.5th','Ashtray','16','17','18','19','20')\\n for i in range(14):\\n pl.figure(figsize=(21,10))\\n pl.subplot(2,3,1)\\n bp.bin_scatter(x,b[:,idxBase+idx[i]],binsize=0.01,fmt='bo',scatter=True)\\n pl.xlabel('x decenter')\\n pl.ylabel(zernikeName[i+1])\\n pl.title(mnts)\\n pl.subplot(2,3,2)\\n bp.bin_scatter(y,b[:,idxBase+idx[i]],binsize=0.01,fmt='bo',scatter=True)\\n pl.xlabel('y decenter')\\n pl.ylabel(zernikeName[i+1])\\n pl.title(mnts)\\n pl.subplot(2,3,3)\\n bp.bin_scatter(z,b[:,idxBase+idx[i]],binsize=0.01,fmt='bo',scatter=True)\\n pl.xlabel('z-defocus')\\n pl.ylabel(zernikeName[i+1])\\n pl.title(mnts)\\n pl.subplot(2,3,4)\\n bp.bin_scatter(thetax,b[:,idxBase+idx[i]],binsize=5,fmt='bo',scatter=True)\\n pl.xlabel('x-tilt')\\n pl.ylabel(zernikeName[i+1])\\n pl.title(mnts)\\n pl.subplot(2,3,5)\\n bp.bin_scatter(thetay,b[:,idxBase+idx[i]],binsize=5,fmt='bo',scatter=True)\\n pl.xlabel('y-tilt')\\n pl.ylabel(zernikeName[i+1])\\n pl.title(mnts)\\n pl.savefig(mnts+'_'+str(i+1)+'_'+zernikeName[i+1]+'.png')\\n pl.close()\",\n \"def calculate_identity(target, template):\\n\\n identical_residues = 0\\n length_of_target = 0\\n for i in range(len(target)):\\n if target[i] != '-':\\n length_of_target += 1\\n if target[i] == template[i]:\\n identical_residues += 1\\n identity_percent = round(identical_residues / length_of_target * 100, 2)\\n return identity_percent\",\n \"def gittins_index(n=100, grid=20000, discount=0.9):\\n g, v = recursion_loop(n, discount, grid)\\n g_reformat = reformat_gittins(g) \\n return g_reformat\",\n \"def fig16():\\n # fmt: off\\n tpm = np.array([\\n [0, 0, 0, 0, 0, 0, 0],\\n [0, 0, 1, 0, 1, 0, 0],\\n [1, 0, 1, 0, 0, 0, 0],\\n [1, 0, 0, 0, 1, 0, 0],\\n [1, 0, 0, 0, 0, 0, 0],\\n [1, 0, 1, 0, 1, 0, 0],\\n [1, 0, 1, 0, 0, 0, 0],\\n [1, 0, 0, 0, 1, 0, 0],\\n [0, 0, 0, 1, 1, 0, 0],\\n [0, 0, 1, 1, 1, 0, 0],\\n [1, 0, 1, 1, 1, 0, 0],\\n [1, 0, 0, 1, 1, 0, 0],\\n [1, 0, 0, 1, 1, 0, 0],\\n [1, 1, 1, 1, 1, 0, 0],\\n [1, 0, 1, 1, 1, 0, 0],\\n [1, 1, 0, 1, 1, 0, 0],\\n [0, 0, 0, 1, 1, 0, 0],\\n [0, 0, 1, 1, 1, 0, 0],\\n [1, 0, 1, 1, 1, 0, 0],\\n [1, 0, 0, 1, 1, 0, 0],\\n [1, 0, 0, 1, 1, 0, 0],\\n [1, 0, 1, 1, 1, 0, 0],\\n [1, 0, 1, 1, 1, 0, 0],\\n [1, 0, 0, 1, 1, 0, 0],\\n [0, 0, 0, 1, 1, 0, 0],\\n [0, 0, 1, 1, 1, 0, 0],\\n [1, 0, 1, 1, 1, 0, 0],\\n [1, 0, 0, 1, 1, 0, 0],\\n [1, 0, 0, 1, 1, 0, 0],\\n [1, 1, 1, 1, 1, 0, 0],\\n [1, 0, 1, 1, 1, 0, 0],\\n [1, 1, 0, 1, 1, 0, 0],\\n [0, 0, 0, 0, 0, 1, 0],\\n [0, 0, 1, 0, 1, 1, 0],\\n [1, 0, 1, 0, 0, 1, 0],\\n [1, 0, 0, 0, 1, 1, 0],\\n [1, 0, 0, 0, 0, 1, 0],\\n [1, 0, 1, 0, 1, 1, 0],\\n [1, 0, 1, 0, 0, 1, 0],\\n [1, 0, 0, 0, 1, 1, 0],\\n [0, 0, 0, 1, 1, 1, 0],\\n [0, 0, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 1, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 1, 0, 1, 1, 1, 0],\\n [0, 0, 0, 1, 1, 1, 0],\\n [0, 0, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [0, 0, 0, 1, 1, 1, 0],\\n [0, 0, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 1, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 1, 0, 1, 1, 1, 0],\\n [0, 0, 0, 0, 0, 1, 0],\\n [0, 0, 1, 0, 1, 1, 0],\\n [1, 0, 1, 0, 0, 1, 0],\\n [1, 0, 0, 0, 1, 1, 0],\\n [1, 0, 0, 0, 0, 1, 0],\\n [1, 0, 1, 0, 1, 1, 0],\\n [1, 0, 1, 0, 0, 1, 0],\\n [1, 0, 0, 0, 1, 1, 0],\\n [0, 0, 0, 1, 1, 1, 0],\\n [0, 0, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 1, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 1, 0, 1, 1, 1, 0],\\n [0, 0, 0, 1, 1, 1, 0],\\n [0, 0, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [0, 0, 0, 1, 1, 1, 0],\\n [0, 0, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 0, 0, 1, 1, 1, 0],\\n [1, 1, 1, 1, 1, 1, 0],\\n [1, 0, 1, 1, 1, 1, 0],\\n [1, 1, 0, 1, 1, 1, 0],\\n [0, 0, 0, 0, 0, 1, 1],\\n [0, 0, 1, 0, 1, 1, 1],\\n [1, 0, 1, 0, 0, 1, 1],\\n [1, 0, 0, 0, 1, 1, 1],\\n [1, 0, 0, 0, 0, 1, 1],\\n [1, 0, 1, 0, 1, 1, 1],\\n [1, 0, 1, 0, 0, 1, 1],\\n [1, 0, 0, 0, 1, 1, 1],\\n [0, 0, 0, 1, 1, 1, 1],\\n [0, 0, 1, 1, 1, 1, 1],\\n [1, 0, 1, 1, 1, 1, 1],\\n [1, 0, 0, 1, 1, 1, 1],\\n [1, 0, 0, 1, 1, 1, 1],\\n [1, 1, 1, 1, 1, 1, 1],\\n [1, 0, 1, 1, 1, 1, 1],\\n [1, 1, 0, 1, 1, 1, 1],\\n [0, 0, 0, 1, 1, 1, 1],\\n [0, 0, 1, 1, 1, 1, 1],\\n [1, 0, 1, 1, 1, 1, 1],\\n [1, 0, 0, 1, 1, 1, 1],\\n [1, 0, 0, 1, 1, 1, 1],\\n [1, 0, 1, 1, 1, 1, 1],\\n [1, 0, 1, 1, 1, 1, 1],\\n [1, 0, 0, 1, 1, 1, 1],\\n [0, 0, 0, 1, 1, 1, 1],\\n [0, 0, 1, 1, 1, 1, 1],\\n [1, 0, 1, 1, 1, 1, 1],\\n [1, 0, 0, 1, 1, 1, 1],\\n [1, 0, 0, 1, 1, 1, 1],\\n [1, 1, 1, 1, 1, 1, 1],\\n [1, 0, 1, 1, 1, 1, 1],\\n [1, 1, 0, 1, 1, 1, 1],\\n ])\\n cm = np.array([\\n [0, 1, 1, 0, 1, 0, 0],\\n [1, 0, 1, 0, 0, 0, 0],\\n [1, 1, 0, 0, 0, 0, 0],\\n [0, 1, 0, 1, 1, 0, 0],\\n [0, 0, 0, 1, 1, 0, 0],\\n [0, 0, 0, 0, 0, 1, 1],\\n [0, 0, 0, 0, 0, 1, 1],\\n ])\\n # fmt: on\\n return Network(tpm, cm=cm, node_labels=LABELS[:tpm.shape[1]])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63073283","0.56289643","0.5594479","0.5521098","0.54792696","0.54278696","0.53754073","0.5372962","0.5349426","0.53242576","0.52706194","0.52683187","0.5263925","0.52538013","0.52045524","0.516755","0.5156881","0.51513654","0.5134924","0.5134404","0.51318544","0.51079917","0.51078975","0.50980085","0.5096643","0.50715524","0.5063995","0.5061838","0.504944","0.50400215","0.502056","0.49953157","0.49952257","0.4986656","0.4985856","0.49789116","0.4962792","0.4954358","0.49378788","0.4913363","0.49101862","0.49058542","0.48955297","0.4889044","0.4862595","0.4857648","0.48513865","0.48507184","0.48472208","0.48467377","0.4832096","0.4830552","0.48222673","0.48106104","0.48077875","0.48039663","0.47986418","0.4790062","0.47888526","0.47863564","0.47833583","0.4780756","0.47767922","0.47661847","0.47641847","0.4763286","0.47535416","0.47502142","0.47489274","0.47488302","0.47421497","0.47410604","0.47403008","0.473975","0.473975","0.47394985","0.4735481","0.47338665","0.47330564","0.4726958","0.4726693","0.47212878","0.47166538","0.47128847","0.47080815","0.47074258","0.47068867","0.47026107","0.46987936","0.46966845","0.46915665","0.46892652","0.46883538","0.46882096","0.46852094","0.4684167","0.46817106","0.46772507","0.46756655","0.46733922","0.4671526"],"string":"[\n \"0.63073283\",\n \"0.56289643\",\n \"0.5594479\",\n \"0.5521098\",\n \"0.54792696\",\n \"0.54278696\",\n \"0.53754073\",\n \"0.5372962\",\n \"0.5349426\",\n \"0.53242576\",\n \"0.52706194\",\n \"0.52683187\",\n \"0.5263925\",\n \"0.52538013\",\n \"0.52045524\",\n \"0.516755\",\n \"0.5156881\",\n \"0.51513654\",\n \"0.5134924\",\n \"0.5134404\",\n \"0.51318544\",\n \"0.51079917\",\n \"0.51078975\",\n \"0.50980085\",\n \"0.5096643\",\n \"0.50715524\",\n \"0.5063995\",\n \"0.5061838\",\n \"0.504944\",\n \"0.50400215\",\n \"0.502056\",\n \"0.49953157\",\n \"0.49952257\",\n \"0.4986656\",\n \"0.4985856\",\n \"0.49789116\",\n \"0.4962792\",\n \"0.4954358\",\n \"0.49378788\",\n \"0.4913363\",\n \"0.49101862\",\n \"0.49058542\",\n \"0.48955297\",\n \"0.4889044\",\n \"0.4862595\",\n \"0.4857648\",\n \"0.48513865\",\n \"0.48507184\",\n \"0.48472208\",\n \"0.48467377\",\n \"0.4832096\",\n \"0.4830552\",\n \"0.48222673\",\n \"0.48106104\",\n \"0.48077875\",\n \"0.48039663\",\n \"0.47986418\",\n \"0.4790062\",\n \"0.47888526\",\n \"0.47863564\",\n \"0.47833583\",\n \"0.4780756\",\n \"0.47767922\",\n \"0.47661847\",\n \"0.47641847\",\n \"0.4763286\",\n \"0.47535416\",\n \"0.47502142\",\n \"0.47489274\",\n \"0.47488302\",\n \"0.47421497\",\n \"0.47410604\",\n \"0.47403008\",\n \"0.473975\",\n \"0.473975\",\n \"0.47394985\",\n \"0.4735481\",\n \"0.47338665\",\n \"0.47330564\",\n \"0.4726958\",\n \"0.4726693\",\n \"0.47212878\",\n \"0.47166538\",\n \"0.47128847\",\n \"0.47080815\",\n \"0.47074258\",\n \"0.47068867\",\n \"0.47026107\",\n \"0.46987936\",\n \"0.46966845\",\n \"0.46915665\",\n \"0.46892652\",\n \"0.46883538\",\n \"0.46882096\",\n \"0.46852094\",\n \"0.4684167\",\n \"0.46817106\",\n \"0.46772507\",\n \"0.46756655\",\n \"0.46733922\",\n \"0.4671526\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76498,"cells":{"query":{"kind":"string","value":"Calculate Opsin template according to Govardovskii et al (2000)."},"document":{"kind":"string","value":"def govardovskii2000_template(\n wavelengths: np.ndarray,\n alpha_max: Union[float, np.ndarray],\n A_alpha: Union[float, np.ndarray] = 69.7,\n a_alpha1: Union[float, np.ndarray] = 0.8795,\n a_alpha2: Union[float, np.ndarray] = 0.0459,\n a_alpha3: Union[float, np.ndarray] = 300.0,\n a_alpha4: Union[float, np.ndarray] = 11940.0,\n B_alpha: Union[float, np.ndarray] = 28.0,\n b_alpha: Union[float, np.ndarray] = 0.922,\n C_alpha: Union[float, np.ndarray] = -14.9,\n c_alpha: Union[float, np.ndarray] = 1.104,\n D_alpha: Union[float, np.ndarray] = 0.674,\n A_beta: Union[float, np.ndarray] = 0.26,\n beta_max1: Union[float, np.ndarray] = 189.0,\n beta_max2: Union[float, np.ndarray] = 0.315,\n d_beta1: Union[float, np.ndarray] = -40.5,\n d_beta2: Union[float, np.ndarray] = 0.195,\n) -> np.ndarray:\n x_alpha = (wavelengths / alpha_max) ** -1\n a_alpha = a_alpha1 + a_alpha2 * np.exp(-((alpha_max - a_alpha3) ** 2) / a_alpha4)\n\n alpha_band = (\n np.exp(A_alpha * (a_alpha - x_alpha))\n + np.exp(B_alpha * (b_alpha - x_alpha))\n + np.exp(C_alpha * (c_alpha - x_alpha))\n + D_alpha\n ) ** -1\n\n beta_max = beta_max1 + beta_max2 * alpha_max\n d_beta = d_beta1 + d_beta2 * alpha_max\n beta_band = np.exp(-(((wavelengths - beta_max) / d_beta) ** 2))\n\n return alpha_band + A_beta * beta_band"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def produce_13TeV_template(tag_name=\"HKHI\"):\n num_rebin = 1\n file_name = \"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\"+tag_name+\"_Lin.root\"\n print \"Input: \", file_name\n fin = ROOT.TFile.Open(file_name, \"read\")\n h_nom = fin.Get(\"bkg_total_gg_full\").Clone(\"bkg_nominal_old\")\n h_nom.Rebin(num_rebin)\n fout = ROOT.TFile.Open(\"hists_input_\"+tag_name+\".root\", \"recreate\")\n\n h_purity_sys = fin.Get(\"bkg_purity_syst_gg_full\").Clone(\"bkg_purity_syst_gg\")\n h_reducible_sys = fin.Get(\"bkg_reducible_syst_gg_full\").Clone(\"bkg_reducible_syst_gg\")\n h_irreducible_sys = fin.Get(\"bkg_irreducible_syst_gg_full\").Clone(\"bkg_irreducible_syst_gg\")\n h_iso_sys = fin.Get(\"bkg_iso_syst_gg_full\").Clone(\"bkg_iso_syst_gg\")\n\n #file_iso = \"isolation_sys/hist.root\"\n #fin2 = ROOT.TFile.Open(file_iso, \"read\")\n #h_iso_sys = fin2.Get(\"bkg_isolation_syst_gg\")\n ## inflat irreducible uncertainty by factor of 10\n # so that it closes to stats uncertainty in data\n sf = 1\n if INFLATE_SYS:\n sf = 10\n\n # after rebinning systematic uncertainties, need to scale down,\n # otherwise the uncertainties are inflated.\n h_purity_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_irreducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_reducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_iso_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n\n ## truncate the histograms to [200, 2000] GeV\n h_nom_new = truncate_hist(h_nom, \"bkg_nominal\")\n h_purity_sys_new = truncate_hist(h_purity_sys, \"h_purity_sys_new\")\n h_irreducible_sys_new = truncate_hist(h_irreducible_sys, \"h_irreducible_sys_new\")\n h_reducible_sys_new = truncate_hist(h_reducible_sys, \"h_reducible_sys_new\")\n h_iso_sys_new = truncate_hist(h_iso_sys, \"h_iso_sys_new\")\n\n #write down sys and nominal\n fout.cd()\n h_nom_new.Write()\n h_purity_sys_new.Write()\n h_reducible_sys_new.Write()\n h_irreducible_sys_new.Write()\n h_iso_sys_new.Write()\n\n h_purity_up, h_purity_down = create_sys_hist(h_nom_new, h_purity_sys_new, \"purity_sys\")\n h_purity_up.Write()\n h_purity_down.Write()\n\n h_red_up, h_red_down = create_sys_hist(h_nom_new, h_reducible_sys_new, \"reducible_sys\")\n h_red_up.Write()\n h_red_down.Write()\n\n h_irred_up, h_irred_down = create_sys_hist(h_nom_new, h_irreducible_sys_new, \"irreducible_sys\")\n h_irred_up.Write()\n h_irred_down.Write()\n\n h_iso_up, h_iso_down = create_sys_hist(h_nom_new, h_iso_sys, \"isolation_sys\")\n h_iso_up.Write()\n h_iso_down.Write()\n\n fin.Close()\n fout.Close()","def cheetah_template(self, pre=False):\n if self.is_req_output:\n cht_tmpl = self.req_out_chth\n return cht_tmpl.substitute(self.xml_out)\n elif self.is_output:\n xml_out = self.xml_out\n xml_out['out_sel_name'] = self.out_sel_name\n cht_tmpl = self.file_chth\n return cht_tmpl.substitute(self.xml_out)\n elif self.is_input and not pre:\n if self.pname in self.gen_in_fmt:\n if self.gen_in_fmt[self.pname] == 'vcf,vcf_bgzip':\n cht_tmpl = self.vcf_choose\n else:\n cht_tmpl = PercentTemplate(self.reg_arg)\n elif self.pname in self.tool_data[self.tool_name]['input_fmt']:\n cht_tmpl = self.req_out_chth\n return cht_tmpl.substitute(self.xml_out)\n elif self.is_input and pre:\n cht_tmpl = self.vcf_tabix\n return cht_tmpl.substitute(self.xml_out)\n else:\n if self.xml_out['section'] not in ['required']:\n template_string = self.ext_arg\n else:\n template_string = self.reg_arg\n if self.xml_out['type'] == 'boolean':\n cht_tmpl = PercentTemplate(template_string.replace('%argument ', ''))\n else:\n cht_tmpl = PercentTemplate(template_string)\n return cht_tmpl.substitute(self.xml_out)","def stoichiometry_func_doc(self, label):\n air = self.air_alias.val\n fuel = self.fuel_alias.val\n flue_gas = self.fuel_alias.val + '_fg'\n\n equations = ''\n for fluid in self.inl[0].fluid.val.keys():\n\n in1 = r'\\dot{m}_\\mathrm{in,1} \\cdot x_\\mathrm{fluid,in,1}'\n in2 = r'\\dot{m}_\\mathrm{in,2} \\cdot x_\\mathrm{fluid,in,2}'\n out = r'\\dot{m}_\\mathrm{out,1} \\cdot x_\\mathrm{fluid,out,1}'\n\n if fluid == air:\n latex = (\n r'0=\\Delta\\dot{m}_\\mathrm{' + fluid + r'} - '\n r'\\dot{m}_\\mathrm{' + fluid + r',stoich}'\n )\n elif fluid == fuel:\n latex = (\n r'0=\\Delta \\dot{m}_\\mathrm{' + fluid + r'} - '\n r'\\dot{m}_\\mathrm{' + fluid + r'}'\n )\n elif fluid == flue_gas:\n latex = (\n r'0=\\Delta \\dot{m}_\\mathrm{' + fluid.replace('_', ',') +\n r'} + \\dot{m}_\\mathrm{' + fuel + r'} +'\n r'\\dot{m}_\\mathrm{' + air + r',stoich}'\n )\n else:\n latex = r'0 = \\Delta \\dot{m}_\\mathrm{' + fluid + '}'\n\n if fluid == next(iter(self.inl[0].fluid.val)):\n balance = (\n r'\\Delta \\dot{m}_\\mathrm{fluid} = ' + in1 +\n '+' + in2 + '-' + out)\n m_fluid = r'\\dot{m}_\\mathrm{fluid} = ' + in1 + '+' + in2\n m_air_stoich = (\n r'\\dot{m}_\\mathrm{' + air + ',stoich}='\n r'\\dot{m}_\\mathrm{' + fuel + r'} \\cdot ' +\n str(round(self.air_min, 4)))\n latex_general_eq = (\n r'\\begin{split}' + '\\n'\n r'&' + balance + r'\\\\' + '\\n'\n r'&' + m_fluid + r'\\\\' + '\\n'\n r'&' + m_air_stoich + r'\\\\' + '\\n'\n r'\\end{split}'\n )\n equations += (\n generate_latex_eq(\n self, latex_general_eq, label + '_general_eq') + '\\n' +\n generate_latex_eq(self, latex, label + '_' + fluid) + '\\n')\n else:\n equations += (\n generate_latex_eq(self, latex, label + '_' + fluid) + '\\n')\n # remove last newline\n return equations[:-1]","def run_template(template, config):\n all_results = {}\n for nodenum, wires in template:\n # Find the modules\n node = template.modules[nodenum]\n module_id = node['module'] #template.modules[node]\n module = lookup_module(module_id)\n inputs = _map_inputs(module, wires)\n \n # substitute values for inputs\n kwargs = dict((k, _lookup_results(all_results, v)) \n for k, v in inputs.items())\n \n # Include configuration information\n kwargs.update(node.get('config', {}))\n kwargs.update(config[nodenum])\n result = module.action(**kwargs)\n# print result\n all_results[nodenum] = result\n \n# return all_results\n# FIXXXXXXXXXXXXXXXXXXXXXX ***********************\n from .offspecular.instruments import convert_to_plottable\n return [convert_to_plottable(value['output']) if 'output' in value else {} for key, value in all_results.items()]","def calc_template(template_def, config):\n template = Template(**template_def)\n #print \"template_def:\", template_def, \"config:\", config\n try:\n retvals = process_template(template, config, target=(None, None))\n except Exception:\n print(\"==== template ====\"); pprint(template_def)\n print(\"==== config ====\"); pprint(config)\n #traceback.print_exc()\n raise\n output = {}\n for rkey, rv in retvals.items():\n module_id, terminal_id = rkey\n module_key = str(module_id)\n output.setdefault(module_key, {})\n output[module_key][terminal_id] = rv.todict()\n return output","def computeTemplateFeatures(self, templates, mode=''):\n pass","def ti_func_doc(self, label):\n latex = (\n r'\\begin{split}' + '\\n'\n r'0 = & ti - LHV_\\mathrm{fuel} \\cdot \\left[\\sum_i \\left('\n r'\\dot{m}_{\\mathrm{in,}i}\\cdot x_{\\mathrm{fuel,in,}i}\\right)-'\n r' \\dot{m}_\\mathrm{out,1}\\cdot '\n r'x_{\\mathrm{fuel,out,1}} \\right]\\\\' + '\\n'\n r'& \\forall i \\in \\text{combustion inlets}\\\\' + '\\n'\n r'\\end{split}'\n )\n return generate_latex_eq(self, latex, label)","def fillSignalTemplates(opt):\n\n totalSig={}\n templates=[]\n\n #import signal events\n data=ROOT.TChain('data')\n data.AddFile(os.path.join(opt.input,opt.sig))\n\n #define final preselection cuts\n cuts='xangle==%d'%opt.xangle\n if len(opt.presel) : cuts += ' && ' + opt.presel\n if opt.csiacc:\n csiCuts ='csi1>%f && csi1<%f && '%opt.csiacc[opt.xangle][0]\n csiCuts+='csi2>%f && csi2<%f'%opt.csiacc[opt.xangle][1]\n cuts=csiCuts if len(cuts)==0 else '{0} && {1}'.format(cuts,csiCuts)\n\n #loop over categories build templates\n for icat in range(len(opt.categs)):\n\n #apply category cuts\n categCut=opt.categs[icat]\n categCut=cuts if len(categCut)==0 else '%s && %s'%(categCut,cuts)\n\n catName='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\n print '\\t',catName,categCut\n\n #signal modelling histograms\n histos=[]\n for name,pfix in [('sig_'+catName,''),('sig_%s_sigShape'%catName,'mix')]:\n\n templCuts=categCut.replace('csi1',pfix+'csi1')\n templCuts=templCuts.replace('csi2',pfix+'csi2')\n wgtExpr='wgt*%f'%(SIGNALXSECS[opt.xangle]*opt.lumi)\n data.Draw('{0}mmiss >> h({1},{2},{3})'.format(pfix,opt.nbins,opt.mMin,opt.mMax),\n '{0}*({1})'.format(wgtExpr,templCuts),\n 'goff')\n h=data.GetHistogram()\n histos.append( h.Clone(name) ) \n histos[-1].SetDirectory(0)\n\n if len(histos)==1:\n totalSig[icat]=h.Integral()\n\n h.Reset('ICE')\n templates += defineProcessTemplates(histos)\n \n print '\\t total signal:',totalSig\n return totalSig,templates","def _tpl(grid, iv, ds='test'):\n #TODO: Research numpy routines which may automate this\n tax=grid._get_timelike_axis()\n def tmp(x):\n if x[tax]>0:\n return 0.0\n if x[tax]==0:\n return iv(x)\n return tmp","def inst_set_cpu_template_fxt(request):\n return request.param","def _eval_templates(self, period):\n theta_best = [self._optimize(period, tmpid)\n for tmpid, _ in enumerate(self.templates)]\n chi2 = [self._chi2(theta, period, tmpid)\n for tmpid, theta in enumerate(theta_best)]\n\n return theta_best, chi2","def gen_op2(N11, N22, N12, rN11, rN22, rN12):\n raw = 2 * N12 / rN12 / (N11 / rN11 + N22 / rN22)\n print(\"=====\\n2nd method: raw = 2 * N12/rN12 / (N11/rN11 + N22/rN22)\")\n print(\"OP: 1 - raw = %.3f\\n=====\" % (1 - raw))","def _add_template(self):\n template_dir = os.path.join(self.label_path, 'standard',\n 'MNI152_T1_2mm_brain.nii.gz')\n template_name = QFileDialog.getOpenFileName(\n self,\n 'Open standard file',\n template_dir,\n 'Nifti files (*.nii.gz *.nii)')\n if not template_name.isEmpty():\n template_path = str(template_name)\n self._add_img(template_path)","def computeTemplateFeatures(self, templates, mode=''):\n pass","def _modify_template_nics_according_to_input(template_nics, input_nics, cmd, client,\n resource_group_name, vm_name,\n location, private_cloud):\n # Populating the nic names of vm-template in a dictionary,\n # and mapping them to their index in template_nics list\n vm_template_nic_names = {}\n for (i, nic) in enumerate(template_nics):\n vm_template_nic_names[nic.virtual_nic_name] = i\n\n from .vendored_sdks.models import VirtualNic\n from .vendored_sdks.models import VirtualNetwork\n from ._validators import virtual_network_name_or_id_validator\n\n # Check if nics entered by a user exist in vm-template,\n # then override the properties specified. Else create a new nic.\n for nic in input_nics:\n if nic['name'] in vm_template_nic_names.keys():\n index = vm_template_nic_names[nic['name']]\n if 'virtual-network' in nic.keys():\n template_nics[index].network.id = nic['virtual-network']\n if 'adapter' in nic.keys():\n template_nics[index].nic_type = nic['adapter']\n if 'power-on-boot' in nic.keys():\n template_nics[index].power_on_boot = nic['power-on-boot']\n template_nics[index].virtual_nic_id = None\n\n else:\n nic_name = nic['name']\n if 'virtual-network' in nic.keys():\n vnet = nic['virtual-network']\n else:\n raise CLIError('virtual-network parameter not specified for nic ' +\n nic_name + \".\")\n if 'adapter' in nic.keys():\n adapter = nic['adapter']\n else:\n raise CLIError('adapter parameter not specified for nic ' +\n nic_name + \".\")\n if 'power-on-boot' in nic.keys():\n power_on_boot = nic['power-on-boot']\n else:\n raise CLIError('power-on-boot parameter not specified for nic ' +\n nic_name + \".\")\n\n vnet = virtual_network_name_or_id_validator(cmd, client, vnet,\n resource_group_name, vm_name,\n location, private_cloud)\n network = VirtualNetwork(id=vnet)\n nic_object = VirtualNic(network=network,\n nic_type=adapter,\n power_on_boot=power_on_boot)\n template_nics.append(nic_object)\n return template_nics","def run(self, template_data, data_cont):\n\n tprops = acalib.core.transform.fits_props(template_data)\n scaled = acalib.core.transform.scale(data_cont, tprops['major'])\n rotated, angles = acalib.core.transform.rotate(scaled, tprops['angle'])\n aligned = acalib.core.transform.crop_and_align(rotated, angles)\n result = mean(aligned , axis=0)\n return result","def gen_op4(N11, N22, N12, rN11, rN22, rN12):\n raw = 2 * N12 / rN12 / (N11 / rN11 + N22 / rN22)\n print(\"=====\\n4th method: raw = 2 * N12/rN12 / (N11/rN11 + N22/rN22)\")\n print(\"OP: 1 / raw = %.3f\\n=====\" % (1.0 / raw))","def test_include_template(self, parse_input, tmpdir):\n template = textwrap.dedent(\n \"\"\"\n name CustomOperation\n version 0.0\n BSgate({theta}, pi/2) | [0, 1]\n Rgate({phi}) | 0\n \"\"\"\n )\n\n filename = tmpdir.join(\"test.xbb\")\n\n with open(filename, \"w\") as f:\n f.write(template)\n\n test_include = textwrap.dedent(\n \"\"\"\n name test_include\n version 0.0\n include \"{}\"\n CustomOperation(theta=0.54, phi=0.1) | [2, 1]\n \"\"\"\n ).format(filename)\n\n bb = parse_input(test_include, cwd=tmpdir)\n\n expected = [\n {\"op\": \"BSgate\", \"args\": [0.54, np.pi / 2], \"kwargs\": {}, \"modes\": [2, 1]},\n {\"op\": \"Rgate\", \"args\": [0.1], \"kwargs\": {}, \"modes\": [2]},\n ]\n\n assert bb.operations == expected","def comando_nfce(self):\r\n return self.template_nfce()","def gen_code(self, template, dec_id, i_alt):\n v = self.get_alt(dec_id, i_alt)\n\n # assuming the placeholder var is always at the end\n # which is true given how we chop up the chunks\n return template + str(v), str(v)","def inst_set_cpu_template_ext_fxt(request):\n return request.param","def gen_op1(N11, N22, N12, rN11, rN22, rN12):\n raw = N12 / (N11 + N22)\n ref = rN12 / (rN11 + rN22)\n print(\"=====\\n1st method: raw = N12/(N11+N22)\")\n print(\"raw: %.2f | ref: %.2f\" % (raw, ref) )\n print(\"1 - raw/ref = %.3f\\n=====\" % (1.0 - raw / ref))","def tpl(x):\n return 3 * x","def _operator_generator(self, index, conj):\n pterm = PauliTerm('I', 0, 1.0)\n Zstring = PauliTerm('I', 0, 1.0)\n for j in range(index):\n Zstring = Zstring*PauliTerm('Z', j, 1.0)\n\n pterm1 = Zstring*PauliTerm('X', index, 0.5)\n scalar = 0.5 * conj * 1.0j\n pterm2 = Zstring*PauliTerm('Y', index, scalar)\n pterm = pterm * (pterm1 + pterm2)\n\n pterm = pterm.simplify()\n return pterm","def compute_code():\n for file in os.listdir('./src/Templates'):\n input.parse_template('./src/Templates/' + file, './student/' + file + '.py')\n data = input.load_input()\n return len([k for k in data['input'].keys() if '@' not in k])","def compute_partials(self, inputs, partials):\n partials['y', 'x'] = 2.0","def generate_scenario_precip_inputs():\n def multiply_precip(precip_raster, multiply_factor):\n valid_mask = (precip_raster != precip_raster_nodata)\n result = numpy.empty(precip_raster.shape, dtype=numpy.float32)\n result[:] = _TARGET_NODATA\n result[valid_mask] = precip_raster[valid_mask] * multiply_factor\n return result\n\n def calc_ET0(RA, Tavg, TD, P):\n \"\"\"Modified Hargreaves from Droogers and Allen 2002.\n\n Parameters:\n RA (numpy.ndarray): daily extraterrestrial radiation\n Tavg (numpy.ndarray): average temperature\n TD (numpy.ndarray): difference between minimum and maximum\n temperature\n P (numpy.ndarray): monthly precipitation\n\n Returns:\n monthly reference evapotranspiration (mm)\n\n \"\"\"\n valid_mask = (\n (RA != _TARGET_NODATA) &\n (Tavg != tavg_nodata) &\n (TD != td_nodata) &\n (P != _TARGET_NODATA))\n result = numpy.empty(RA.shape, dtype=numpy.float32)\n result[:] = _TARGET_NODATA\n result[valid_mask] = (\n 0.0013 * 0.408 * RA[valid_mask] * (Tavg[valid_mask] + 17.) *\n (numpy.power((TD[valid_mask] - 0.0123 * P[valid_mask]), 0.76)) *\n 29.5)\n return result\n\n climate_dir = r\"E:\\GIS_local_3.6.19\\USFS\\NCEI_climate\"\n RA_df = pandas.read_csv(os.path.join(climate_dir, \"RA_FAO.csv\"))\n Tavg_dir = os.path.join(climate_dir, \"temp_ave_rasters\")\n TD_dir = os.path.join(climate_dir, \"tdiff_rasters\")\n events_table = os.path.join(\n climate_dir, 'rain_events_table_Palmer_creek.csv')\n\n baseline_precip_dir = r\"C:\\Users\\ginge\\Dropbox\\NatCap_backup\\USFS\\input_data\\model_inputs\\precip_NCEI\"\n outer_scenario_dir = r\"C:\\Users\\ginge\\Dropbox\\NatCap_backup\\USFS\\input_data\\model_inputs\\precip_scenarios\"\n\n example_precip_raster = os.path.join(\n baseline_precip_dir, 'precip_1.tif')\n precip_raster_nodata = pygeoprocessing.get_raster_info(\n example_precip_raster)['nodata'][0]\n\n for multiply_factor in [0.5]: # [0.7, 0.9, 1.1, 1.3]:\n scenario_dir = os.path.join(\n outer_scenario_dir, '{}x'.format(multiply_factor))\n modified_event_table = os.path.join(scenario_dir, 'rain_events.csv')\n ET0_dir = os.path.join(scenario_dir, 'ET0_rasters')\n precip_dir = os.path.join(scenario_dir, 'precip_rasters')\n if not os.path.exists(ET0_dir):\n os.makedirs(ET0_dir)\n if not os.path.exists(precip_dir):\n os.makedirs(precip_dir)\n event_df = pandas.read_csv(events_table)\n event_df.events = event_df.events * multiply_factor\n event_df.to_csv(modified_event_table)\n for month in xrange(1, 13):\n baseline_raster_path = os.path.join(\n baseline_precip_dir, 'precip_{}.tif'.format(month))\n modified_precip_path = os.path.join(\n precip_dir, 'precip_{}.tif'.format(month))\n pygeoprocessing.raster_calculator(\n [(baseline_raster_path, 1), (multiply_factor, 'raw')],\n multiply_precip, modified_precip_path,\n gdal.GDT_Float32, _TARGET_NODATA)\n\n Tavg_raster_path = os.path.join(\n Tavg_dir, 'tavg_{}.tif'.format(month))\n TD_raster_path = os.path.join(\n TD_dir, 'tdiff_{}.tif'.format(month))\n tavg_nodata = pygeoprocessing.get_raster_info(\n Tavg_raster_path)['nodata'][0]\n td_nodata = pygeoprocessing.get_raster_info(\n TD_raster_path)['nodata'][0]\n RA_val = (\n RA_df.loc[RA_df['month'] == month, 'average_RA'].values[0])\n RA_raster_path = os.path.join(climate_dir, 'RA.tif')\n pygeoprocessing.new_raster_from_base(\n modified_precip_path, RA_raster_path, gdal.GDT_Float32,\n [_TARGET_NODATA], fill_value_list=[RA_val])\n ET0_raster_path = os.path.join(ET0_dir, 'ET_{}.tif'.format(month))\n pygeoprocessing.raster_calculator(\n [(path, 1) for path in [\n RA_raster_path, Tavg_raster_path, TD_raster_path,\n modified_precip_path]],\n calc_ET0, ET0_raster_path, gdal.GDT_Float32, _TARGET_NODATA)","def generate(self, namespace: Optional[str], template: str, func: Callable, call_args: Dict) -> str:","def gen_compute_template(self):\n\n print \"\\t* Generating compute template in file %s\" % self.compute_filename\n if self.cmdline:\n with open(self.compute_filename, 'w') as f:\n f.write(yaml.safe_dump(self.compute_template))\n\n try:\n self.heatclient.stacks.validate(template=yaml.safe_dump(self.compute_template))\n except Exception as e:\n print \"Unfortunately your file is malformed. Received error: (%s)\" % str(e)\n print \"Exiting ...\"\n sys.exit(1)\n\n return self.compute_template","def psfTemplateModel(n, params):\n psf_template = params[\"psf_template\"]\n self.m_psf = psf_template\n print(\"PSF template shape\", np.shape(psf_template))\n dim = int(n)\n m = np.shape(psf_template)[0]\n #if m != dim:\n # raise ValueError(\"PSF template dimension not equal patch size\")\n \n if np.sum(psf_template) != 1:\n print(\"Normalizing PSF template to sum = 1\")\n psf_template = psf_template/np.sum(psf_template) \n return psf_template","def inhomoCoeffs(Bulk_Operators, Bdy_Operators, Bulk_second, s=1):\n\t\tRow = []\n\t\tfor Bulk_Op in Bulk_Operators:\n\t\t\t\ttable_call = table1.table[0, 0].subs({\"Delta\":Bulk_Op, \"Delta_12\":delta_12, \"Xi\":1.00})\n\t\t\t\tRow.append(table_call)\n\t\tfor index in range(len(Bulk_second)):\n\t\t\t\tRow.append(0*table1.table[0,0])\n\t\tfor Bdy_Op in Bdy_Operators:\n\t\t\tif Bdy_Op == 2.0:\n\t\t\t\ttable_call = (N-1)*table1.table[1, 0].subs({\"Delta\":Bdy_Op, \"Xi\":1.00})\n\t\t\telse:\n\t\t\t\ttable_call = table1.table[1, 0].subs({\"Delta\":Bdy_Op, \"Xi\":1.00})\n\t\t\tRow.append(table_call)\n\t\tif s==1:\n\t\t\tXi = symbols('Xi')\n\t\t\tlast_expr = Xi**((delta_1 + delta_2)/2)\n\t\t\tlast_elem = last_expr.subs({\"Xi\":1.00}).evalf()\n\t\t\tRow.append(last_elem)\n\t\tRow2 = []\n\t\tfor index in range(len(Bulk_Operators)):\n\t\t\t\tRow2.append(0*table1.table[0,0])\n\t\tfor Bulk_Op in Bulk_second:\n\t\t\t\ttable_call = table1.table[0, 0].subs({\"Delta\":Bulk_Op, \"Delta_12\":delta_12, \"Xi\":1.00})\n\t\t\t\tRow2.append(table_call)\n\t\tfor Bdy_Op in Bdy_Operators:\n\t\t\tif Bdy_Op == 2.0:\n\t\t\t\ttable_call = -1*table1.table[1,0].subs({\"Delta\":Bdy_Op, \"Xi\":1.00})\n\t\t\telse:\n\t\t\t\ttable_call = table1.table[1,0].subs({\"Delta\":Bdy_Op, \"Xi\":1.00})\n\t\t\tRow2.append(table_call)\n\t\tif s==1:\n\t\t\tXi = symbols('Xi')\n\t\t\tlast_expr = Xi**((delta_1 + delta_2)/2)\n\t\t\tlast_elem = last_expr.subs({\"Xi\":1.00}).evalf()\n\t\t\tRow2.append(last_elem)\n\t\t\t\n\t\treturn np.array(Row), np.array(Row2)","def gen_op3(N11, N22, N12, rN11, rN22, rN12):\n raw = N12 / (N11 * N22)\n ref = rN12 / (rN11 * rN22)\n print(\"=====\\n3rd method: raw = N12 / (N11 * N22)\")\n print(\"raw: %.2f | reference: %.2f\" % (raw, ref))\n print(\"OP: 1 - raw = %.3f\\n=====\" % (1 - raw))","def hierarchical( x, output_prefix, labels_to_register=[2,3,4,5], is_test=False, verbose=True ):\n if verbose:\n print(\"Read\")\n tfn = get_data('T_template0', target_extension='.nii.gz' )\n tfnw = get_data('T_template0_WMP', target_extension='.nii.gz' )\n tlrfn = get_data('T_template0_LR', target_extension='.nii.gz' )\n bfn = antspynet.get_antsxnet_data( \"croppedMni152\" )\n\n ##### read images and do simple bxt ops\n templatea = ants.image_read( tfn )\n if verbose:\n print(\"bxt\")\n templatea = ( templatea * antspynet.brain_extraction( templatea, 't1' ) ).iMath( \"Normalize\" )\n templateawmprior = ants.image_read( tfnw )\n templatealr = ants.image_read( tlrfn )\n templateb = ants.image_read( bfn )\n templateb = ( templateb * antspynet.brain_extraction( templateb, 't1' ) ).iMath( \"Normalize\" )\n imgbxt = brain_extraction( x )\n img = x * imgbxt\n\n if verbose:\n print(\"rbp\")\n\n # this is an unbiased method for identifying predictors that can be used to\n # rank / sort data into clusters, some of which may be associated\n # with outlierness or low-quality data\n templatesmall = ants.resample_image( templateb, (91,109,91), use_voxels=True )\n rbp = random_basis_projection( img, templatesmall )\n\n if verbose:\n print(\"intensity\")\n\n ##### intensity modifications\n img = ants.iMath( img, \"Normalize\" ) * 255.0\n img = ants.denoise_image( img, imgbxt, noise_model='Gaussian')\n img = ants.n4_bias_field_correction( img ).iMath(\"Normalize\")\n\n # optional - quick look at result\n bxt_png = output_prefix + \"_brain_extraction_dnz_n4_view.png\"\n ants.plot(img,axis=2,ncol=8,nslices=24, crop=True, black_bg=False,\n filename = bxt_png )\n\n if verbose:\n print(\"hemi\")\n\n # assuming data is reasonable quality, we should proceed with the rest ...\n mylr = label_hemispheres( img, templatea, templatealr )\n\n if verbose:\n print(\"parcellation\")\n\n ##### hierarchical labeling\n myparc = deep_brain_parcellation( img, templateb,\n do_cortical_propagation = not is_test, verbose=False )\n\n ##### accumulate data into data frames\n hemi = map_segmentation_to_dataframe( \"hemisphere\", myparc['hemisphere_labels'] )\n tissue = map_segmentation_to_dataframe( \"tissues\", myparc['tissue_segmentation'] )\n dktl = map_segmentation_to_dataframe( \"lobes\", myparc['dkt_lobes'] )\n dktp = map_segmentation_to_dataframe( \"dkt\", myparc['dkt_parcellation'] )\n dktc = None\n if not is_test:\n dktc = map_segmentation_to_dataframe( \"dkt\", myparc['dkt_cortex'] )\n\n tissue_seg_png = output_prefix + \"_seg.png\"\n ants.plot( img, myparc['tissue_segmentation'], axis=2, nslices=21, ncol=7,\n alpha=0.6, filename=tissue_seg_png,\n crop=True, black_bg=False )\n\n if verbose:\n print(\"WMH\")\n\n ##### below here are more exploratory nice to have outputs\n myhypo = t1_hypointensity(\n img,\n myparc['tissue_segmentation'], # segmentation\n myparc['tissue_probabilities'][3], # wm posteriors\n templatea,\n templateawmprior )\n\n if verbose:\n print(\"registration\")\n\n ##### traditional deformable registration as a high-resolution complement to above\n wm_tractsL = None\n wm_tractsR = None\n wmtdfL = None\n wmtdfR = None\n reg = None\n if labels_to_register is not None:\n reg = hemi_reg(\n input_image = img,\n input_image_tissue_segmentation = myparc['tissue_segmentation'],\n input_image_hemisphere_segmentation = mylr,\n input_template=templatea,\n input_template_hemisphere_labels=templatealr,\n output_prefix = output_prefix + \"_SYN\",\n labels_to_register = labels_to_register,\n is_test=is_test )\n if verbose:\n print(\"wm tracts\")\n ##### how to use the hemi-reg output to generate any roi value from a template roi\n wm_tracts = ants.image_read( get_data( \"wm_major_tracts\", target_extension='.nii.gz' ) )\n wm_tractsL = ants.apply_transforms( img, wm_tracts, reg['synL']['invtransforms'],\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 1, 1 )\n wm_tractsR = ants.apply_transforms( img, wm_tracts, reg['synR']['invtransforms'],\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 2, 2 )\n wmtdfL = map_segmentation_to_dataframe( \"wm_major_tracts\", wm_tractsL )\n wmtdfR = map_segmentation_to_dataframe( \"wm_major_tracts\", wm_tractsR )\n\n if verbose:\n print(\"hippocampus\")\n\n ##### specialized labeling for hippocampus\n ntries = 10\n if is_test:\n ntries = 1\n hippLR = deep_hippo( img, templateb, ntries )\n\n mydataframes = {\n \"hemispheres\":hemi,\n \"tissues\":tissue,\n \"dktlobes\":dktl,\n \"dktregions\":dktp,\n \"dktcortex\":dktc,\n \"wmtracts_left\":wmtdfL,\n \"wmtracts_right\":wmtdfR,\n \"wmh\":myhypo['wmh_summary']\n }\n\n outputs = {\n \"brain_n4_dnz\": img,\n \"brain_n4_dnz_png\": bxt_png,\n \"brain_extraction\": imgbxt,\n \"tissue_seg_png\": tissue_seg_png,\n \"rbp\": rbp,\n \"left_right\": mylr,\n \"dkt_parc\": myparc,\n \"registration\":reg,\n \"hippLR\":hippLR,\n \"white_matter_hypointensity\":myhypo,\n \"wm_tractsL\":wm_tractsL,\n \"wm_tractsR\":wm_tractsR,\n \"dataframes\": mydataframes\n }\n\n return outputs","def calc_ti(self):\n m = 0\n for i in self.inl:\n m += i.m.val_SI * i.fluid.val[self.fuel_alias.val]\n\n for o in self.outl:\n m -= o.m.val_SI * o.fluid.val[self.fuel_alias.val]\n\n return m * self.lhv","def generate_registry_operations(data):\n data = render_to_string(TEMPLATE_VC, {\"data\": data})\n open(os.path.join(PLUGINSPACE_TD_VC_DIR, FILENAME_VC), \"w+\").write(data)\n return","def prepare_CvD16_for_M2L_calc(templates_lam_range, verbose=False):\n import glob\n import os\n template_glob=os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*.s100')\n\n vcj_models=sorted(glob.glob(template_glob))\n temp_lamdas, x35, x3, x23, kroupa, flat=np.genfromtxt(vcj_models[-1], unpack=True)\n\n n_ages=7\n n_zs=5\n n_imfs=5\n\n \n\n\n Zs=['m1.5', 'm1.0', 'm0.5', 'p0.0', 'p0.2']\n ages=[1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.5]\n model_imfs_order=['x35', 'x3', 'x23', 'kroupa', 'flat']\n\n t_mask = ((temp_lamdas > templates_lam_range[0]) & (temp_lamdas <templates_lam_range[1]))\n\n\n\n y=x35[t_mask]\n x=temp_lamdas[t_mask]\n\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\n interp=si.interp1d(x, y, fill_value='extrapolate')\n out=interp(new_x)\n\n templates=np.empty((len(out), n_ages, n_zs, n_imfs))\n\n\n\n for a, Z in enumerate(Zs): \n for b, age in enumerate(ages):\n model=glob.glob(os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*{}*{}.ssp.s100'.format(Z, age)))[0]\n if verbose:\n print 'Loading {}'.format(model)\n data=np.genfromtxt(model)\n\n for c, counter in enumerate(reversed(range(1, data.shape[-1]))):\n \n #Interpolate templates onto a uniform wavelength grid and then log-rebin\n y=data[:, counter][t_mask] \n x=temp_lamdas[t_mask]\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\n\n interp=si.interp1d(x, y, fill_value='extrapolate')\n out=interp(new_x) \n\n templates[:, b, a, c]=out\n\n return templates, new_x","def __createSpecTempT(self):\n\t\tspecTempT = np.copy(self.specTemplates)\n\t\tspecTempT = np.transpose(specTempT)\n\t\tspecTempT = specTempT * self.covInv\n\t\t\t\t\n\t\treturn specTempT","def diffeo_apply(diffeo, template):\n M, N = diffeo.shape[0], diffeo.shape[1]\n result = np.empty_like(template)\n for i, j in coords_iterate((M, N)):\n i1 = diffeo[i, j, 0]\n j1 = diffeo[i, j, 1]\n result[i, j, ...] = template[i1, j1, ...]\n return result","def _template_formatting(field, inputs, inputs_dict_st):\n from .specs import MultiInputObj, MultiOutputFile\n\n # if a template is a function it has to be run first with the inputs as the only arg\n template = field.metadata[\"output_file_template\"]\n if callable(template):\n template = template(inputs)\n\n # as default, we assume that keep_extension is True\n keep_extension = field.metadata.get(\"keep_extension\", True)\n\n inp_fields = re.findall(r\"{\\w+}\", template)\n inp_fields_fl = re.findall(r\"{\\w+:[0-9.]+f}\", template)\n inp_fields += [re.sub(\":[0-9.]+f\", \"\", el) for el in inp_fields_fl]\n if len(inp_fields) == 0:\n return template\n\n val_dict = {}\n file_template = None\n\n for fld in inp_fields:\n fld_name = fld[1:-1] # extracting the name form {field_name}\n if fld_name not in inputs_dict_st:\n raise AttributeError(f\"{fld_name} is not provided in the input\")\n fld_value = inputs_dict_st[fld_name]\n if fld_value is attr.NOTHING:\n # if value is NOTHING, nothing should be added to the command\n return attr.NOTHING\n else:\n # checking for fields that can be treated as a file:\n # have type File, or value that is path like (including str with extensions)\n if isinstance(fld_value, os.PathLike) or (\n isinstance(fld_value, str) and \".\" in fld_value\n ):\n if file_template:\n raise Exception(\n f\"can't have multiple paths in {field.name} template,\"\n f\" but {template} provided\"\n )\n else:\n file_template = (fld_name, fld_value)\n else:\n val_dict[fld_name] = fld_value\n\n # if field is MultiOutputFile and some elements from val_dict are lists,\n # each element of the list should be used separately in the template\n # and return a list with formatted values\n if field.type is MultiOutputFile and any(\n [isinstance(el, (list, MultiInputObj)) for el in val_dict.values()]\n ):\n # all fields that are lists\n keys_list = [\n k for k, el in val_dict.items() if isinstance(el, (list, MultiInputObj))\n ]\n if any(\n [len(val_dict[key]) != len(val_dict[keys_list[0]]) for key in keys_list[1:]]\n ):\n raise Exception(\n f\"all fields used in {field.name} template have to have the same length\"\n f\" or be a single value\"\n )\n formatted_value = []\n for ii in range(len(val_dict[keys_list[0]])):\n val_dict_el = copy(val_dict)\n # updating values to a single element from the list\n for key in keys_list:\n val_dict_el[key] = val_dict[key][ii]\n\n formatted_value.append(\n _element_formatting(\n template, val_dict_el, file_template, keep_extension=keep_extension\n )\n )\n else:\n formatted_value = _element_formatting(\n template, val_dict, file_template, keep_extension=keep_extension\n )\n return formatted_value","def calculate_output(self):","def _fixPODTemplatesInstructions(self):\n # delete the filtered_groups_in_charge script in portal_skins/custom\n if \"filtered_groups_in_charge\" in self.portal.portal_skins.custom.objectIds():\n self.portal.portal_skins.custom.manage_delObjects(ids=[\"filtered_groups_in_charge\"])\n # for every POD templates\n replacements = {\n 'self.filtered_groups_in_charge':\n 'self.get_representatives_in_charge',\n }\n self.updatePODTemplatesCode(replacements=replacements)","def write_input(self):\n # load template, substitute parameters and write input file\n self.homog_core()\n self.write_mat_string()\n input_tmpl = open('base_input.txt')\n templ = Template(input_tmpl.read())\n file_string = templ.substitute(cool_frac = self.vfrac_cermet,\n r_core = self.r,\n core_z = self.z,\n r_refl = self.r + self.refl_t,\n refl_min = -self.refl_t,\n refl_max = self.z + self.refl_t,\n fuel_string = self.fuel_string,\n fuel_rho = self.rho,\n fuel_vol = self.core_vol,\n refl_vol = self.core_vol,\n thermal_power = self.Q_therm)\n # write the file\n filename = 'r_{0}_{1}.i'.format(round(self.vfrac_cermet, 3), \n round(self.r, 3))\n ifile = open(filename, 'w')\n ifile.write(file_string)\n ifile.close()\n\n return filename","def getTru(n,int_method,func) :\n m = np.asarray([0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,6,7,8,9,10])\n bes = np.asarray([-0.5,0,0.43675,0.47773,0.49231,0.49316,0.49280,0.50325,\n 0.51140,0.52169,0.55823,0.58086,0.60463,0.61483,0.66995])\n p = np.asarray([1,0,0.61007,0.77491,0.84071,0.87689,0.89914,0.91365,\n 0.92449,0.93279,0.94451,0.95289,0.95904,0.96385,\n 0.96731])\n h1 = np.asarray([0,0,-0.07257,-0.04963,-0.03313,-0.02282,-0.01648,\n -0.01248,-0.00970,-0.00773,-0.00522,-0.00369,-0.00272,\n -0.00206,-0.00164])\n h2 = np.asarray([0,0,-0.20048,-0.15556,-0.12070,-0.09611,-0.07919,\n -0.06747,-0.05829,-0.05106,-0.04060,-0.03311,-0.02768,\n -0.02353,-0.02053])\n h3 = np.asarray([0,0,0.01647,0.08284,0.14390,0.19680,0.24168,0.27969,\n 0.31280,0.34181,0.39002,0.42942,0.46208,0.48997,0.51325])\n \n if (func == True) :\n int_bes = splev(n,splrep(m, bes))\n int_p = splev(n,splrep(m, p))\n int_h1 = splev(n,splrep(m, h1))\n int_h2 = splev(n,splrep(m, h2))\n int_h3 = splev(n,splrep(m, h3))\n else :\n int_bes = griddata(m, bes, n, method=int_method)\n int_p = griddata(m, p, n, method=int_method)\n int_h1 = griddata(m, h1, n, method=int_method)\n int_h2 = griddata(m, h2, n, method=int_method)\n int_h3 = griddata(m, h3, n, method=int_method)\n \n return np.asarray([int_bes,int_p,int_h1,int_h2,int_h3])","def New(*args, **kargs):\n obj = itkTernaryAddImageFilterIUS2IUS2IUS2IUS2.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def Generate_BG_Template(outputSize=300, angularSize = 10, fileOut = 'BGRateMap.pickle' ):\r\n template = np.zeros((outputSize,outputSize))\r\n ppd=float(outputSize)/float(angularSize) # pixels per deg\r\n \r\n events110 = ParseFermi.Import_File('photons.txt', energyRange = (120000,140000),lonRange=(-5,5),latRange = (-5,5))\r\n events130 = ParseFermi.Import_File('photons.txt', energyRange = (100000,120000),lonRange=(-5,5),latRange = (-5,5))\r\n events150 = ParseFermi.Import_File('photons.txt', energyRange = (140000,200000),lonRange=(-5,5),latRange = (-5,5))\r\n \r\n for i in range(10000,200001,20000):\r\n if i == 130000:\r\n continue\r\n events = ParseFermi.Import_File('photons.txt', energyRange = (i-10000,i+10000),lonRange=(-5,5),latRange = (-5,5))\r\n BG = np.zeros((outputSize,outputSize)) \r\n for j in events:\r\n xIDX = int(j[1]*ppd+float(outputSize/2))\r\n yIDX = int(j[2]*ppd+float(outputSize/2))\r\n BG[yIDX][xIDX] += 1.0\r\n \r\n psfDeg = .2+float(200)/float(i)\r\n psfOut = psfDeg*ppd\r\n #print i/1e3, psfDeg, psfOut\r\n \r\n template += scipy.ndimage.filters.gaussian_filter(BG, psfOut)\r\n \r\n template = template/np.max(template)\r\n \r\n # Write to file \r\n outFile = open(fileOut, \"wb\" )\r\n pickle.dump(template, outFile)\r\n print 'Rate Map saved to ', fileOut\r\n \r\n plt.imshow(scipy.fliplr(template), 'jet',extent=[5,-5,-5,5])\r\n\r\n plt.xlabel(r'$l [^\\circ]$')\r\n plt.ylabel(r'$b [^\\circ]$')\r\n plt.xlim(5,-5)\r\n plt.ylim(-5,5)\r\n plt.colorbar()\r\n\r\n x,y = Find_Centroid(template)\r\n x,y = (x/ppd -angularSize/2.0,) ,(y/ppd -angularSize/2.0,)\r\n print x,y\r\n plt.scatter(x,y, s=10, c='r', marker = '+')\r\n \r\n X,Y = FormatEvents(events110)\r\n plt.scatter(X, Y, label = '100-120 GeV', marker = 'o' , c = 'k')\r\n \r\n X,Y = FormatEvents(events130)\r\n plt.scatter(X, Y, label = '120-140 GeV', marker = 'o' , c = 'r')\r\n \r\n X,Y = FormatEvents(events150)\r\n plt.scatter(X, Y, label = '140-200 GeV', marker = 'o' , c = 'g' )\r\n \r\n from matplotlib.font_manager import FontProperties\r\n fontP = FontProperties()\r\n fontP.set_size('small')\r\n plt.legend(loc=1, ncol=1, fancybox=True, shadow=False,prop=fontP,borderaxespad=0.,labelspacing = .2)\r\n \r\n from matplotlib.backends.backend_pdf import PdfPages\r\n if fileOut != '':\r\n pp = PdfPages(fileOut + '_sideband.pdf')\r\n plt.savefig(pp, format='pdf')\r\n print \"Figures saved to \", str(fileOut)+ '_sideband.pdf\\n',\r\n pp.close()\r\n \r\n plt.show()\r\n return template","def defineProcessTemplates(histos):\n\n templates=[]\n\n #nominal\n templates.append( histos[0] )\n nomStats=templates[-1].Integral()\n\n #systematic variations\n #if Up/Down already in the name store directly updating the name\n #if not, mirror the variation given \n for i in xrange(1,len(histos)): \n templates.append( histos[i] )\n key=templates[-1].GetName()\n if not 'Up' in key and not 'Down' in key :\n templates[-1].SetName(key+'Up')\n templates.append( histos[i].Clone(key+'Down') )\n for xbin in range(templates[0].GetNbinsX()):\n templates[-1].SetBinContent(xbin+1,2*templates[0].GetBinContent(xbin+1)-templates[-2].GetBinContent(xbin+1))\n \n #don't leave bins with 0's\n for h in templates:\n h.SetDirectory(0)\n iStats=h.Integral()\n if iStats>0: h.Scale(nomStats/iStats)\n for xbin in range(h.GetNbinsX()):\n if h.GetBinContent(xbin+1)>0: continue\n h.SetBinContent(xbin+1,1e-6)\n \n return templates","def generate():","def _template(inlist):\n from collections import OrderedDict\n if isinstance(inlist, str):\n inlist = [inlist]\n\n templates = []\n for item in reversed(inlist):\n templates.append(output_space(item))\n\n return OrderedDict(reversed(OrderedDict(templates).items()))","def compute_template_spectrum(self):\n b = self.setup_b_instance(1,add_ps_mask=False)\n En_min = b.CTB_en_bins[0]\n En_max = b.CTB_en_bins[1]\n dE = En_max - En_min\n self.En_center = 10**((np.log10(En_max)+np.log10(En_min))/2)\n\n flux_map = b.fixed_template_dict_nested['summed_templates'] / b.CTB_exposure_maps_masked_compressed[0]/float(hp.nside2pixarea(b.nside))\n self.spectrum = np.mean(flux_map)/dE","def generate(self):\n\n letter = ''\n\n for template_name in self.templates:\n template = self.templates[template_name]\n\n # Process all replacements ({...} syntax).\n replacements = re.finditer(REPLACEMENT_REGEX, template)\n for replacement in replacements:\n match = replacement.group()\n key = replacement.group(1)\n\n template = template.replace(match, self._lookup(key))\n\n # Process all conditionals (<...> syntax).\n conditionals = re.finditer(CONDITIONAL_REGEX, template)\n for conditional in conditionals:\n match = conditional.group()\n\n # Process each condition within the conditional ([...]... syntax).\n conditions = re.finditer(CONDITION_REGEX, match)\n for index, condition in enumerate(conditions):\n skill_type = condition.group(2)\n skill = condition.group(3)\n text = condition.group(4)\n\n # If the skill is empty, treat it as a catch all case.\n if not skill or skill in self._lookup(skill_type):\n template = template.replace(match, text)\n break\n\n letter += template\n\n return letter","def Generate(self,the_scf):\n self.dip_ints = the_scf.mol.intor('cint1e_r_sph', comp=3) # component,ao,ao.\n charges = the_scf.mol.atom_charges()\n coords = the_scf.mol.atom_coords()\n self.nuc_dip = np.einsum('i,ix->x', charges, coords)\n return","def New(*args, **kargs):\n obj = itkIsoDataThresholdCalculatorHDUS.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def Template(Fenetre_largeur,Fenetre_hauteur):\r\n li= Select_ligne(\"Nombre de lignes: \",Fenetre_largeur,Fenetre_hauteur)\r\n nom=\"Template\"\r\n fich=\"Template\"\r\n version=0\r\n while Path(\"stages/\"+fich+\".txt\").is_file() == True:\r\n version+=1\r\n fich=nom+str(version)\r\n fichier=open(\"stages/\"+fich+\".txt\",'w')\r\n fichier.write(str(li))\r\n fichier.write(\"\\n\")\r\n fichier.write(\"\\n\")\r\n for i in range(li):\r\n for j in range(10):\r\n fichier.write(\"0,0|\")\r\n fichier.write(\"\\n\")\r\n fichier.write(\"\\n\")\r\n fichier.write(\"gauche: resistance, droite: bonus\")\r\n fichier.write(\"\\n\")\r\n fichier.write(\"resistance max: 3\")\r\n fichier.write(\"\\n\")\r\n fichier.write(\"6=barre+\")\r\n fichier.write(\"\\n\")\r\n fichier.write(\"7=score+\")\r\n fichier.close()","def create_eqt_template(nodes, input_filename):\n output_filename = f'{input_filename[:-4]}_eqpt_sheet.txt'\n with open(output_filename, 'w', encoding='utf-8') as my_file:\n # print header similar to excel\n my_file.write('OPTIONAL\\n\\n\\n\\\n \\t\\tNode a egress amp (from a to z)\\t\\t\\t\\t\\tNode a ingress amp (from z to a) \\\n \\nNode A \\tNode Z \\tamp type \\tatt_in \\tamp gain \\ttilt \\tatt_out\\\n amp type \\tatt_in \\tamp gain \\ttilt \\tatt_out\\n')\n\n for node in nodes.values():\n if node.eqpt == 'ILA':\n my_file.write(f'{node.uid}\\t{node.to_node[0]}\\n')\n if node.eqpt == 'ROADM':\n for to_node in node.to_node:\n my_file.write(f'{node.uid}\\t{to_node}\\n')\n\n print(f'File {output_filename} successfully created with Node A - Node Z entries for Eqpt sheet in excel file.')","def wrapper(*args, **kwargs):\n assert not kwargs, \"Do not support kwargs in template function call\"\n task_env = TaskExtractEnv.current\n if task_env is not None and task_env.tracing:\n task_env.add_task(task_name, args)\n workload = args_to_workload(args, task_name)\n tgt = Target.current()\n cfg = DispatchContext.current.query(tgt, workload)\n node = topi_compute(cfg, *args)\n\n # attach workload to return op\n op = node.op\n attrs = {}\n for k, v in node.op.attrs.items():\n attrs[k] = v\n attrs[\"workload\"] = workload\n if isinstance(op, tensor.ComputeOp):\n op = tvm.te._ffi_api.ComputeOp(op.name, op.tag, attrs, op.axis, op.body)\n elif isinstance(op, tensor.ExternOp):\n op = tvm.te._ffi_api.ExternOp(\n op.name,\n op.tag,\n attrs,\n op.inputs,\n op.input_placeholders,\n op.output_placeholders,\n op.body,\n )\n else:\n raise RuntimeError(\"Unsupported op type: \" + str(type(op)))\n\n if isinstance(node, tensor.Tensor):\n return op.output(0)\n return [op.output(i) for i in range(len(node))]","def New(*args, **kargs):\n obj = itkTernaryAddImageFilterIUC2IUC2IUC2IUC2.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def calc_raws(df, unit='leader'):\n\n df['distrust'] = df['HDIS']/(df['HDIS']+df['LDIS'])\n df['task'] = df['HTASK']/(df['HTASK']+df['LTASK'])\n df['bace'] = df['IC']/(df['IC']+df['EC'])\n df['igb'] = df['HBIAS']/(df['HBIAS']+df['LBIAS'])\n df['sc'] = df['HSC']/(df['HSC']+df['LSC'])\n df['cc'] = df['HCC']/(df['HCC']+df['LCC'])\n df['power'] = df['HPWR']/(df['HPWR']+df['LPWR'])\n\n df['i1'] = df.apply(i1_func, axis=1)\n df['i2'] = df.apply(i2_func, axis=1)\n df['i3'] = df.apply(i3_func, axis=1)\n\n df['i4a'] = df.apply(i4a_func, axis=1)\n df['i4b'] = df.apply(i4b_func, axis=1)\n df['i5ap'] = df.apply(i5ap_func, axis=1)\n df['i5pr'] = df.apply(i5pr_func, axis=1)\n df['i5re'] = df.apply(i5re_func, axis=1)\n df['i5op'] = df.apply(i5op_func, axis=1)\n df['i5th'] = df.apply(i5th_func, axis=1)\n df['i5pu'] = df.apply(i5pu_func, axis=1)\n\n df['p1'] = df.apply(p1_func, axis=1)\n df['p2'] = df.apply(p2_func, axis=1)\n df['p3'] = df.apply(p3_func, axis=1)\n df['p4'] = df.apply(p4_func, axis=1)\n df['p5'] = df.apply(p5_func, axis=1)\n\n if unit == 'leader':\n\n keep = ['firstname', 'lastname', 'name', 'Ccode', 'vcount', 'distrust', 'task',\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\n 'p5']\n\n df = df[keep]\n\n elif unit == 'year':\n\n keep = ['firstname', 'lastname', 'name', 'year', 'Ccode', 'vcount', 'distrust', 'task',\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\n 'p5']\n\n df = df[keep]\n\n elif unit == 'month':\n\n keep = ['firstname', 'lastname', 'name', 'yr_month','Ccode', 'vcount', 'distrust', 'task',\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\n 'p5']\n\n df = df[keep]\n\n df['year'] = df['yr_month'].apply(lambda x: x.split('-')[0])\n df['month'] = df['yr_month'].apply(lambda x: x.split('-')[1])\n\n elif unit == 'quarter':\n\n keep = ['firstname', 'lastname', 'name', 'yr_quarter', 'Ccode', 'vcount', 'distrust', 'task',\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\n 'p5']\n\n df = df[keep]\n\n df['year'] = df['yr_quarter'].apply(lambda x: x.split('-')[0])\n df['quarter'] = df['yr_quarter'].apply(lambda x: x.split('-')[1])\n\n return df","def car_template(ncomp, ra0, ra1, dec0, dec1, res):\n\n if ncomp == 3:\n pre = (3,)\n else:\n pre = ()\n\n box = get_box(ra0, ra1, dec0, dec1)\n res = res * np.pi / (180 * 60)\n temp = so_map()\n shape, wcs = enmap.geometry(box, res=res, pre=pre)\n temp.data = enmap.zeros(shape, wcs=wcs, dtype=None)\n temp.pixel = \"CAR\"\n temp.nside = None\n temp.ncomp = ncomp\n temp.geometry = temp.data.geometry[1:]\n temp.coordinate = \"equ\"\n return temp","def New(*args, **kargs):\n obj = itkTernaryAddImageFilterIUL2IUL2IUL2IUL2.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def get_template_script(output_dir='.', keep_schrodinger=False, keep_openeye=False,\n systems='all'):\n paths = examples_paths()\n template_script = \"\"\"\n ---\n options:\n output_dir: {output_dir}\n default_number_of_iterations: 0\n temperature: 300*kelvin\n pressure: 1*atmosphere\n minimize: no\n verbose: no\n default_nsteps_per_iteration: 1\n molecules:\n benzene:\n filepath: {benzene_path}\n antechamber: {{charge_method: bcc}}\n benzene-epik0:\n filepath: {benzene_path}\n epik:\n select: 0\n antechamber: {{charge_method: bcc}}\n benzene-epikcustom:\n filepath: {benzene_path}\n epik:\n select: 0\n ph: 7.0\n tautomerize: yes\n antechamber: {{charge_method: bcc}}\n p-xylene:\n filepath: {pxylene_path}\n antechamber: {{charge_method: bcc}}\n p-xylene-name:\n name: p-xylene\n openeye: {{quacpac: am1-bcc}}\n antechamber: {{charge_method: null}}\n toluene:\n filepath: {toluene_path}\n antechamber: {{charge_method: bcc}}\n toluene-smiles:\n smiles: Cc1ccccc1\n antechamber: {{charge_method: bcc}}\n toluene-name:\n name: toluene\n antechamber: {{charge_method: bcc}}\n Abl:\n filepath: {abl_path}\n T4Lysozyme:\n filepath: {lysozyme_path}\n solvents:\n vacuum:\n nonbonded_method: NoCutoff\n GBSA-OBC2:\n nonbonded_method: NoCutoff\n implicit_solvent: OBC2\n PME:\n nonbonded_method: PME\n nonbonded_cutoff: 1*nanometer\n clearance: 10*angstroms\n positive_ion: Na+\n negative_ion: Cl-\n leap:\n parameters: [leaprc.water.tip4pew]\n systems:\n explicit-system:\n receptor: benzene\n ligand: toluene\n solvent: PME\n leap:\n parameters: [leaprc.protein.ff14SB, leaprc.gaff]\n implicit-system:\n receptor: T4Lysozyme\n ligand: p-xylene\n solvent: GBSA-OBC2\n leap:\n parameters: [leaprc.protein.ff14SB, leaprc.gaff]\n hydration-system:\n solute: toluene\n solvent1: PME\n solvent2: vacuum\n leap:\n parameters: [leaprc.protein.ff14SB, leaprc.gaff]\n mcmc_moves:\n single:\n type: LangevinSplittingDynamicsMove\n sequence:\n type: SequenceMove\n move_list:\n - type: MCDisplacementMove\n - type: LangevinDynamicsMove\n samplers:\n repex:\n type: ReplicaExchangeSampler\n sams:\n type: SAMSSampler\n protocols:\n absolute-binding:\n complex:\n alchemical_path:\n lambda_electrostatics: [1.0, 0.5, 0.0]\n lambda_sterics: [1.0, 0.5, 0.0]\n solvent:\n alchemical_path:\n lambda_electrostatics: [1.0, 0.5, 0.0]\n lambda_sterics: [1.0, 0.5, 0.0]\n hydration-protocol:\n solvent1:\n alchemical_path:\n lambda_electrostatics: [1.0, 0.0]\n lambda_sterics: [1.0, 0.0]\n solvent2:\n alchemical_path:\n lambda_electrostatics: [1.0, 0.0]\n lambda_sterics: [1.0, 1.0]\n experiments:\n system: explicit-system\n protocol: absolute-binding\n \"\"\".format(output_dir=output_dir, benzene_path=paths['benzene'],\n pxylene_path=paths['p-xylene'], toluene_path=paths['toluene'],\n abl_path=paths['abl'], lysozyme_path=paths['lysozyme'])\n\n # Load script as dictionary.\n script_dict = yank_load(template_script)\n\n # Find all molecules that require optional tools.\n molecules_to_remove = []\n for molecule_id, molecule_description in script_dict['molecules'].items():\n need_schrodinger = 'epik' in molecule_description\n need_openeye = any([k in molecule_description for k in ['name', 'smiles', 'openeye']])\n if ((need_schrodinger and not keep_schrodinger) or\n (need_openeye and not keep_openeye)):\n molecules_to_remove.append(molecule_id)\n\n # Remove molecules.\n for molecule_id in molecules_to_remove:\n del script_dict['molecules'][molecule_id]\n\n # Remove systems.\n if systems != 'all':\n systems_to_remove = [s for s in script_dict['systems'] if s not in systems]\n for system_id in systems_to_remove:\n del script_dict['systems'][system_id]\n\n return script_dict","def template(self) -> Dict[Union[int, str], int]:\r\n if not hasattr(self, '_template'):\r\n index = 0\r\n self._template = {}\r\n n = sum(self.sequence)\r\n for i, step in enumerate(self):\r\n max = index + step - 1\r\n max_next = max + self.sequence[i + 1] if i < len(self.sequence) - 1 else 0\r\n for j in range(index, index + step):\r\n if j < max:\r\n self._template[j] = j + 1\r\n if j + step < n and j + step <= max_next:\r\n self._template[str(j)] = j + step\r\n index += step\r\n return self._template","def calc_population_02_state(U):\n if U.type=='oper':\n sump = 0\n for i_list in [[0,2]]:\n for j_list in [[1,1]]:\n bra_i = qtp.tensor(qtp.ket([i_list[0]], dim=[3]),\n qtp.ket([i_list[1]], dim=[3])).dag()\n ket_j = qtp.tensor(qtp.ket([j_list[0]], dim=[3]),\n qtp.ket([j_list[1]], dim=[3]))\n p = np.abs((bra_i*U*ket_j).data[0, 0])**2\n sump += p\n return np.real(sump)\n elif U.type=='super':\n sump = 0\n for i_list in [[0,2]]:\n for j_list in [[1,1]]:\n ket_i = qtp.tensor(qtp.ket([i_list[0]], dim=[3]),\n qtp.ket([i_list[1]], dim=[3]))\n rho_i=qtp.operator_to_vector(qtp.ket2dm(ket_i))\n ket_j = qtp.tensor(qtp.ket([j_list[0]], dim=[3]),\n qtp.ket([j_list[1]], dim=[3]))\n rho_j=qtp.operator_to_vector(qtp.ket2dm(ket_j))\n p = (rho_i.dag()*U*rho_j).data[0, 0]\n sump += p\n return np.real(sump)","def calc_Cinv_CCGT(CC_size_W, CCGT_cost_data):\n\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\n # capacity for the corresponding technology from the database\n if CC_size_W < CCGT_cost_data['cap_min'][0]:\n CC_size_W = CCGT_cost_data['cap_min'][0]\n CCGT_cost_data = CCGT_cost_data[\n (CCGT_cost_data['cap_min'] <= CC_size_W) & (CCGT_cost_data['cap_max'] > CC_size_W)]\n\n\n #costs of connection\n connection_costs = ngas.calc_Cinv_gas(CC_size_W)\n\n Inv_a = CCGT_cost_data.iloc[0]['a']\n Inv_b = CCGT_cost_data.iloc[0]['b']\n Inv_c = CCGT_cost_data.iloc[0]['c']\n Inv_d = CCGT_cost_data.iloc[0]['d']\n Inv_e = CCGT_cost_data.iloc[0]['e']\n Inv_IR = CCGT_cost_data.iloc[0]['IR_%']\n Inv_LT = CCGT_cost_data.iloc[0]['LT_yr']\n Inv_OM = CCGT_cost_data.iloc[0]['O&M_%'] / 100\n\n InvC = Inv_a + Inv_b * (CC_size_W) ** Inv_c + (Inv_d + Inv_e * CC_size_W) * log(CC_size_W)\n\n Capex_a_CCGT_USD = calc_capex_annualized((InvC+connection_costs), Inv_IR, Inv_LT)\n Opex_fixed_CCGT_USD = InvC * Inv_OM\n Capex_CCGT_USD = InvC\n\n return Capex_a_CCGT_USD, Opex_fixed_CCGT_USD, Capex_CCGT_USD","def _t_at_interface(self, polarization, n_1, n_2):\n if polarization == 's':\n return 2*n_1/(n_1 + n_2)\n elif polarization == 'p':\n return 2*n_1/(n_1 + n_2)\n else:\n raise ValueError(\"Polarization must be 's' or 'p'\")","def test_operator_to_template(self):\n\n from kubernetes import client as k8s_client\n\n with dsl.Pipeline('somename') as p:\n msg1 = dsl.PipelineParam('msg1')\n msg2 = dsl.PipelineParam('msg2', value='value2')\n op = dsl.ContainerOp(name='echo', image='image', command=['sh', '-c'],\n arguments=['echo %s %s | tee /tmp/message.txt' % (msg1, msg2)],\n file_outputs={'merged': '/tmp/message.txt'}) \\\n .add_volume_mount(k8s_client.V1VolumeMount(\n mount_path='/secret/gcp-credentials',\n name='gcp-credentials')) \\\n .add_env_variable(k8s_client.V1EnvVar(\n name='GOOGLE_APPLICATION_CREDENTIALS',\n value='/secret/gcp-credentials/user-gcp-sa.json'))\n golden_output = {\n 'container': {\n 'image': 'image',\n 'args': [\n 'echo {{inputs.parameters.msg1}} {{inputs.parameters.msg2}} | tee /tmp/message.txt'\n ],\n 'command': ['sh', '-c'],\n 'env': [\n {\n 'name': 'GOOGLE_APPLICATION_CREDENTIALS',\n 'value': '/secret/gcp-credentials/user-gcp-sa.json'\n }\n ],\n 'volumeMounts':[\n {\n 'mountPath': '/secret/gcp-credentials',\n 'name': 'gcp-credentials',\n }\n ]\n },\n 'inputs': {'parameters':\n [\n {'name': 'msg1'},\n {'name': 'msg2', 'value': 'value2'},\n ]},\n 'name': 'echo',\n 'outputs': {\n 'parameters': [\n {'name': 'echo-merged',\n 'valueFrom': {'path': '/tmp/message.txt'}\n }],\n 'artifacts': [{\n 'name': 'mlpipeline-ui-metadata',\n 'path': '/mlpipeline-ui-metadata.json',\n 's3': {\n 'accessKeySecret': {\n 'key': 'accesskey',\n 'name': 'mlpipeline-minio-artifact',\n },\n 'bucket': 'mlpipeline',\n 'endpoint': 'minio-service.kubeflow:9000',\n 'insecure': True,\n 'key': 'runs/{{workflow.uid}}/{{pod.name}}/mlpipeline-ui-metadata.tgz',\n 'secretKeySecret': {\n 'key': 'secretkey',\n 'name': 'mlpipeline-minio-artifact',\n }\n }\n },{\n 'name': 'mlpipeline-metrics',\n 'path': '/mlpipeline-metrics.json',\n 's3': {\n 'accessKeySecret': {\n 'key': 'accesskey',\n 'name': 'mlpipeline-minio-artifact',\n },\n 'bucket': 'mlpipeline',\n 'endpoint': 'minio-service.kubeflow:9000',\n 'insecure': True,\n 'key': 'runs/{{workflow.uid}}/{{pod.name}}/mlpipeline-metrics.tgz',\n 'secretKeySecret': {\n 'key': 'secretkey',\n 'name': 'mlpipeline-minio-artifact',\n }\n }\n }]\n }\n }\n\n self.maxDiff = None\n self.assertEqual(golden_output, compiler.Compiler()._op_to_template(op))","def gen_compute_data(self):\n\n print \"\\t* Generating combined nova and neutron data\"\n self.init_compute_clients()\n self.compute_data[\"heat_template_version\"] = \"2013-05-23\"\n self.compute_data[\"description\"] = \"Generated Template %s on Project %s\" % \\\n (str(datetime.datetime.now().strftime(\"%A, %d. %B %Y %I:%M%p\")), str(self.tenant_name))\n self.compute_data[\"parameters\"] = {}\n self.compute_data[\"resources\"] = {}\n self.gen_parameters()\n self.gen_resources()\n self.compute_template = self.compute_data","def calc_CC_operation(el_output_from_GT_W, GT_size_W, fuel_type, T_sup_K):\n\n (eta0, m0_exhaust_GT_kgpers) = calc_GT_operation_fullload(GT_size_W, fuel_type)\n (eta, m_exhaust_GT_kgpers, T_exhaust_GT_K, m_fuel_kgpers) = calc_GT_operation_partload(el_output_from_GT_W,\n GT_size_W, eta0,\n m0_exhaust_GT_kgpers,\n fuel_type)\n\n (q_output_ST_W, el_output_ST_W) = calc_ST_operation(m_exhaust_GT_kgpers, T_exhaust_GT_K, T_sup_K, fuel_type)\n\n LHV = LHV_NG if fuel_type == 'NG' else LHV_BG # read LHV of NG or BG\n\n eta_el = (el_output_from_GT_W + el_output_ST_W) / (m_fuel_kgpers * LHV)\n eta_thermal = q_output_ST_W / (m_fuel_kgpers * LHV)\n eta_total = eta_el + eta_thermal\n el_output_W = el_output_ST_W + el_output_from_GT_W\n\n return {'el_output_W': el_output_W, 'q_output_ST_W': q_output_ST_W, 'eta_el': eta_el, 'eta_thermal': eta_thermal,\n 'eta_total': eta_total}","def nlg(state, slots=[]):\r\n output = get_template_res(state, slots) # get the generated output/response\r\n return output # return the appropriate response to the user\r","def nb_clementsbekkers(data, template):\n ## Prepare a bunch of arrays we'll need later\n n_template = len(template)\n # if n_template <= 1:\n # raise ValueError(\"nb_clementsbekkers: Length of template must be useful, and > 1\")\n n_data = data.shape[0]\n n_dt = n_data - n_template\n # if n_dt < 10:\n # raise ValueError(\"nb_clementsbekkers: n_dt, n_template\", n_dt, n_template)\n #\n sum_template = template.sum()\n sum_template_2 = (template*template).sum()\n\n data_2 = data*data\n sum_data = np.sum(data[:n_template])\n sum_data_2 = data_2[:n_template].sum()\n scale = np.zeros(n_dt)\n offset = np.zeros(n_dt)\n crit = np.zeros(n_dt)\n for i in range(n_dt):\n if i > 0:\n sum_data = sum_data + data[i+n_template] - data[i-1]\n sum_data_2 = sum_data_2 + data_2[i+n_template] - data_2[i-1]\n sum_data_template_prod = np.multiply(data[i:i+n_template], template).sum()\n scale[i] = (\n (sum_data_template_prod - sum_data * sum_template/n_template)/\n (sum_template_2 - sum_template*sum_template/n_template)\n )\n offset[i] = (sum_data - scale[i]*sum_template)/n_template\n fitted_template = template * scale[i] + offset[i]\n sse = ((data[i:i+n_template] - fitted_template)**2).sum()\n crit[i] = scale[i]/np.sqrt(sse/(n_template-1))\n DC = scale/crit\n return(DC, scale, crit)","def New(*args, **kargs):\n obj = itkSubtractImageFilterIUS2IUS2IUS2.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def tmpl_to_str(self, template_idx, o1_id, o2_id):\r\n template_str = self.template_generator.templates[template_idx]\r\n holes = template_str.count('OBJ')\r\n assert holes <= 2\r\n if holes <= 0:\r\n return template_str\r\n elif holes == 1:\r\n return template_str.replace('OBJ', self.vocab_act[o1_id])\r\n else:\r\n return template_str.replace('OBJ', self.vocab_act[o1_id], 1)\\\r\n .replace('OBJ', self.vocab_act[o2_id], 1)","def load_standard_operators():\n #\n # Compute scripts\n #\n \n cscript('select' ,scriptpath+'mcdo.sh \"${operator}\" \"${out}\" \"${var}\" \"${period_iso}\" \"${domain}\" \"${alias}\" \"${units}\" \"${missing}\" ${ins} ',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('ccdo',\n scriptpath+'mcdo.sh \"${operator}\" \"${out}\" \"${var}\" \"${period_iso}\" \"${domain}\" \"${alias}\" \"${units}\" \"${missing}\" ${ins}')\n #\n cscript('minus', 'cdo sub ${in_1} ${in_2} ${out}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('plus', 'cdo add ${in_1} ${in_2} ${out}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('space_average',\n scriptpath+'mcdo.sh fldmean \"${out}\" \"${var}\" \"${period_iso}\" \"${domain}\" \"${alias}\" \"${units}\" \"${missing}\" ${ins}', \n commuteWithTimeConcatenation=True)\n #\n cscript('time_average' ,\n scriptpath+'mcdo.sh timmean \"${out}\" \"${var}\" \"${period_iso}\" \"${domain}\" \"${alias}\" \"${units}\" \"${missing}\" ${ins}' ,\n commuteWithSpaceConcatenation=True)\n #\n cscript('llbox' ,\n scriptpath+'mcdo.sh \"\" \"${out}\" \"${var}\" \"${period_iso}\" \"${latmin},${latmax},${lonmin},${lonmax}\" \"${alias}\" \"${units}\" \"${missing}\" ${ins}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('regrid' ,\n scriptpath+'regrid.sh ${in} ${in_2} ${out} ${option}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('regridn' ,\n scriptpath+'regrid.sh ${in} ${cdogrid} ${out} ${option}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('rescale' ,\n 'cdo expr,\\\"${var}=${scale}*${var}+${offset};\\\" ${in} ${out}',\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\n #\n cscript('mean_and_std',\n scriptpath+'mean_and_std.sh ${in} ${var} ${out} ${out_sdev}', \n # This tells CliMAF how to compute varname for name output 'sdev' \n\t # using input varname\n sdev_var=\"std(%s)\" , \n commuteWithTimeConcatenation=True)\n #\n # Declare plot scripts\n cscript('ncview' ,'ncview ${in} 1>/dev/null 2>&1&' )\n #\n cscript('timeplot', 'ncl '+scriptpath+'timeplot.ncl infile=\\'\\\"${in}\\\"\\' outfile=\\'\\\"${out}\\\"\\' '\n 'var=\\'\\\"${var}\\\"\\' title=\\'\\\"${title}\\\"\\'',format=\"png\")\n #\n # plot: main field (main_file) + auxiliary field (aux_file, optional) + vectors (u_file & v_file, optionals)\n #\n cscript('plot' , '(ncl -Q '+ scriptpath +'gplot.ncl main_file=\\'\\\"${in}\\\"\\' aux_file=\\'\\\"${in_2}\\\"\\' '\n 'u_file=\\'\\\"${in_3}\\\"\\' v_file=\\'\\\"${in_4}\\\"\\' rotation=${rotation} '\n 'plotname=\\'\\\"${out}\\\"\\' cmap=\\'\\\"${color}\\\"\\' vmin=${min} vmax=${max} vdelta=${delta} '\n 'main_var=\\'\\\"${var}\\\"\\' aux_var=\\'\\\"${var_2}\\\"\\' u_var=\\'\\\"${var_3}\\\"\\' v_var=\\'\\\"${var_4}\\\"\\' '\n 'title=\\'\\\"${title}\\\"\\' scale=${scale} offset=${offset} mpCenterLonF=${mpCenterLonF} '\n 'vcRefMagnitudeF=${vcRefMagnitudeF} vcRefLengthF=${vcRefLengthF} vcMinDistanceF=${vcMinDistanceF} '\n 'vcGlyphStyle=\\'\\\"${vcGlyphStyle}\\\"\\' vcLineArrowColor=\\'\\\"${vcLineArrowColor}\\\"\\' '\n 'units=\\'\\\"${units}\\\"\\' linp=${linp} colors=\\'\\\"${colors}\\\"\\' level=${level} time=${time} '\n 'proj=\\'\\\"${proj}\\\"\\' contours=\\'\\\"${contours}\\\"\\' focus=\\'\\\"${focus}\\\"\\' '\n 'type=\\'\\\"${format}\\\"\\' resolution=\\'\\\"${resolution}\\\"\\' trim=${trim} '\n 'vcb=${vcb} lbLabelFontHeightF=${lbLabelFontHeightF} invXY=${invXY} '\n 'tmYLLabelFontHeightF=${tmYLLabelFontHeightF} tmXBLabelFontHeightF=${tmXBLabelFontHeightF} '\n 'tmYRLabelFontHeightF=${tmYRLabelFontHeightF} tiXAxisFontHeightF=${tiXAxisFontHeightF} '\n 'tiYAxisFontHeightF=${tiYAxisFontHeightF} gsnPolarLabelFontHeightF=${gsnPolarLabelFontHeightF} '\n 'tiMainFont=\\'\\\"${tiMainFont}\\\"\\' tiMainFontHeightF=${tiMainFontHeightF} '\n 'tiMainPosition=\\'\\\"${tiMainPosition}\\\"\\' gsnLeftString=\\'\\\"${gsnLeftString}\\\"\\' '\n 'gsnRightString=\\'\\\"${gsnRightString}\\\"\\' gsnCenterString=\\'\\\"${gsnCenterString}\\\"\\' '\n 'gsnStringFont=\\'\\\"${gsnStringFont}\\\"\\' gsnStringFontHeightF=${gsnStringFontHeightF} )', format=\"graph\") \n # \n cscript('lines' , '(ncl -Q '+ scriptpath +'lineplot.ncl infile=\\'\\\"${mmin}\\\"\\' '\n 'plotname=\\'\\\"${out}\\\"\\' var=\\'\\\"${var}\\\"\\' title=\\'\\\"${title}\\\"\\' '\n 'linp=${linp} labels=\\'\\\"${labels}\\\"\\' colors=\\'\\\"${colors}\\\"\\' thickness=${thickness}'\n 'T_axis=\\'\\\"${T_axis}\\\"\\' fmt=\\'\\\"${fmt}\\\"\\' && '\n 'convert ${out} -trim ${out}) ', format=\"png\")\n #\n cscript('curves' , '(ncl -Q '+ scriptpath +'curves.ncl infile=\\'\\\"${mmin}\\\"\\' '\n 'plotname=\\'\\\"${out}\\\"\\' var=\\'\\\"${var}\\\"\\' title=\\'\\\"${title}\\\"\\' '\n 'labels=\\'\\\"${labels}\\\"\\' colors=\\'\\\"${colors}\\\"\\' thickness=${thickness} && '\n 'convert ${out} -trim ${out}) ', format=\"png\")\n #\n # cpdfcrop : pdfcrop by preserving metadata\n #\n cscript('cpdfcrop' , 'pdfcrop ${in} ${out} ', format=\"pdf\")\n # \n cscript('ncdump' , 'ncdump -h ${in} ', format=\"txt\")\n #\n if (os.system(\"type cdfmean >/dev/null 2>&1\")== 0 ) :\n load_cdftools_operators()\n else :\n clogger.warning(\"No Cdftool available\")","def _ProcessTemplate(self,topdir):\n self.dicomdir = \"%s/anatomicals\" % self.topdir\n self.rawdir = \"%s/raw\" % topdir\n self.rawdirs = {}\n tmplt = self._GetTemplate()\n if self.opts.outdir is not None:\n# Override template output directory.\n tmplt['top_outdir'] = self.opts.outdir\n self.tmplt = tmplt\n if len(tmplt['top_outdir']) == 0:\n tmplt['top_outdir'] = os.path.realpath(self.topdir)\n raise RuntimeError('Template file must specify an output directory.')\n tmplt['top_outdir'] = os.path.realpath(tmplt['top_outdir'])\n if '/home' in tmplt['top_outdir'][:7]:\n raise RuntimeError('Image data cannot be stored in the /home partition. Change the \"top_outdir\" entry in the template file: %s.' % (' '.join(self.templates)))\n# tmplt['subject'] = 'orig'\n self.procdir = os.path.abspath(\"%s/%s\" % \\\n (tmplt['top_outdir'],tmplt['subject']))\n target = os.path.abspath('%s/../..' % tmplt['top_outdir'])\n if not ismounted(target):\n raise RuntimeError('Could not access partition at %s' % target)\n\n self.anatdir = \"%s/anat\" % self.procdir\n self.fmapdir = \"%s/%s\" % (self.procdir,tmplt['fmap']['outdir'])\n self.dtidir = \"%s/%s\" % (self.procdir,tmplt['dti']['outdir'])\n self.logdir = \"%s/%s\" % (self.procdir,tmplt['logdir'])\n self.skip = tmplt.get('skip', DEFAULT_SKIP)\n self.acq_tr = tmplt.get('acq_tr',None)\n self.episetup_dir = \"%s/%s\" % (self.procdir,tmplt['first_epi'])\n self.fsl_cmpblty = tmplt.get('fsl_compatibility',False)\n self.epi_file_format = self.tmplt['epi_file_format']\n self.censor_thresh = tmplt.get('censor_threshold', 2.)\n self.censor_interleave = tmplt.get('censor_interleave', True)\n# self.server_userid = self.tmplt.get('server_userid','default')\n\n# Overide flags for aligning EPIs and skull-stripping with command-\n# line options.\n if self.opts.align_fmaps:\n self.align_fmaps = True\n else:\n self.align_fmaps = self.tmplt.get('epi_align', False)\n\n if self.opts.no_align_fmaps:\n self.no_align_fmaps = True\n else:\n self.no_align_fmaps = self.tmplt.get('no_epi_align', False)\n\n if self.opts.skull_strip:\n self.skull_strip = True\n else:\n self.skull_strip = self.tmplt.get('skull_strip', False)\n\n# Create log file now so it can be used immediately.\n if not os.path.exists(self.logdir):\n if self.verbose:\n print 'mkdir %s' % self.logdir\n if not self.opts.fake_opts:\n self.MakeDir(self.logdir)\n\n self._ProcessTemplateEpiInfo()","def edit_template(self, template):\n macro = []\n for line in template:\n search_result = re.search(line_to_edit, line)\n if search_result != None:\n line = line[(search_result.start()+1):]\n if line.find(\"/rat/procset\") >= 0:\n parts = line.split(\" \")\n new_line = parts[0] + \" \" + parts[1] + \" \\\"\"\n new_line += self._path\n new_line += \"\\\"\\n\"\n line = new_line\n if line.find(\"/generator/add\") >= 0:\n parts = line.split(\" \")\n new_line = parts[0] + \" \" + parts[1] + \" \"\n parts2 = parts[2].split(\":\")\n new_line += self._generator.get_generator() + \":\" + parts2[1]\n new_line += \"\\n\"\n line = new_line\n if line.find(\"/generator/vtx/set\") >= 0:\n parts = line.split(\" \")\n new_line = parts[0] + \" \"\n new_line += self._generator.get_type() + \" \"\n new_line += self._generator.get_isotope().get_name() + \" \"\n new_line += str(int(self._generator.get_level())) + \" \"\n new_line += str(int(self._generator.get_mode())) + \" \"\n new_line += str(self._generator.get_e_lo()) + \" \"\n new_line += str(self._generator.get_e_hi())\n new_line += \"\\n\"\n line = new_line\n try:\n assert(re.match\\\n (r\"^.*\\s[0-9]{1,2}?\\s[0-9]{1,2}?\\s\"\n \"[0-9]+\\.[0-9]+\\s[0-9]+\\.[0-9]+$\", line)\\\n != None),\\\n \"/generator/vtx/set line has incorrect format\"\n except AssertionError as detail:\n print \"generate_spectrum.edit_template: error,\", detail\n sys.exit(1)\n if line.find(\"/rat/run/start\") >= 0:\n parts = line.split(\" \")\n new_line = parts[0] + \" \" + str(self._n_events)\n new_line += \"\\n\"\n line = new_line\n macro.append(line)\n return macro","def IPTinput_gen(Nom,beta,ef,mutld):\n\tV,D =0.01,1\n\tf1 = open(\"Delta.inp\", 'w')\n\tf2 = open(\"G0.inp\", 'w')\n\tDelta=[]\n\tfor n in range(Nom):\n\t iom = (2.*n+1)*pi/beta\n\t Redelta=-V**2/(2.0*D)*log( ((D/2.-mutld)**2+iom**2)/((D/2.+mutld)**2+iom**2))\n\t Imdelta=-V**2/D*(arctan((D/2.-mutld)/iom)-arctan((-D/2.-mutld)/iom))\n\t print>>f1,iom,Redelta,Imdelta\n\t G0= 1./( 1.0j*iom-(ef-mutld)-(Redelta+1.0j*Imdelta) )\n\t print>>f2,iom,G0.real,G0.imag\n\tf1.close()\n\tf2.close()","def calc_resources(self):\n self.popula = self.energy = self.popula_used = self.energy_used = 0\n self.cnt_public = self.cnt_shop = self.cnt_1 = self.cnt_2 = self.cnt_3 = self.cnt_4 = self.cnt_5 = self.cnt_office = 0\n self.popula += self.extra_pop\n for i in range(20):\n b = self.b[i]\n if b == 'T':\n self.popula += self.f[i] * 2\n self.energy_used += 1\n elif b == 'O':\n self.popula_used += 1\n self.energy_used += 1\n self.cnt_office += self.f[i]\n elif b == 'U':\n self.popula_used += 1\n self.cnt_public += 1\n elif b == 'S':\n self.energy_used += 1\n self.cnt_shop += 1\n elif b == '1':\n self.popula += 1\n self.energy += 1\n self.popula_used += 1\n self.cnt_1 += 1\n elif b == '2':\n self.popula_used += 1\n self.cnt_2 += 1\n elif b == '3':\n self.popula_used += 1\n self.cnt_3 += 1\n elif b == '4':\n self.popula += 2\n self.popula_used += 1\n self.cnt_4 += 1\n elif b == '5':\n self.energy += 2\n self.popula_used += 1\n self.cnt_5 += 1\n elif b == 'A':\n self.energy += 2\n self.popula_used += 1\n elif b == 'F':\n self.energy += 3\n self.popula_used += 1\n elif b == 'G':\n self.popula += 1\n if 'tvst' in args.exp:\n self.popula += self.cnt_shop\n if 'ward' in args.exp:\n self.popula += 3\n if 'elec' in args.exp:\n self.energy += 3\n if 'capi' in args.exp:\n self.popula_used += 2\n if 'fire' in args.exp:\n self.popula_used += 1\n if 'park' in args.exp:\n self.popula_used += 1","def isolate_relevant_information(self):\n\n def get_formula(oncat_formula):\n \"\"\"will need to go from something like\n \"${value/10e11}`\"\n to something more pythonic\n \"{value/10e11}\"\"\"\n regular_expression = r'\\$(?P<formula>.+)\\`'\n m = re.search(regular_expression, oncat_formula)\n if m:\n return m.group('formula')\n else:\n return \"\"\n\n template_information = {}\n for _index, _element in enumerate(self._oncat_default_template):\n _title = _element[\"name\"]\n _path = _element[\"path\"]\n if \"units\" in _element:\n _units = _element[\"units\"]\n else:\n _units = \"\"\n if \"transform\" in _element:\n _formula = get_formula(_element[\"transform\"])\n else:\n _formula = \"\"\n template_information[_index] = {'title': _title,\n 'path': _path,\n 'units': _units,\n 'formula': _formula}\n self.template_information = template_information","def compute_partials(self, inputs, partials):\n t = inputs['t']\n\n nn = self.options['num_nodes']\n\n omega = self.options['omega']\n gha = self.options['gha0'] + omega * t\n\n partials['q_IE', 't'][:nn] = -np.sin(gha) * omega\n partials['q_IE', 't'][nn:2*nn] = -np.cos(gha) * omega","def apply_to(self, template):\n pass","def format_template(commit_info, nb):\n\n nb_path = os.path.abspath(nb).replace('ipynb', 'md')\n with open(nb_path, 'r+') as file:\n template = NbTemplate(file.read())\n updated = template.substitute(commit_info)\n file.seek(0)\n file.write(updated)\n file.truncate()","def applyPhotoZ (self,arr):\n print \"Applying Template SED PZs\"\n\n ztrue = arr['z']\n\n #select a template\n templates = ['El_B2004a.sed']+['Sbc_B2004a.sed','Scd_B2004a.sed']\n templates = templates +['Im_B2004a.sed','SB3_B2004a.sed','SB2_B2004a.sed','ssp_25Myr_z008.sed','ssp_5Myr_z008.sed']\n\n #read in f_mod files, interpolate, get values of f_mod_b\n ngals = len(ztrue)\n\n f_mod_o = np.zeros((self.nb, ngals))\n for z in range(ngals):\n #currently templates are randomly chosen but probably should be an input with true z\n templateno = np.random.choice(range(self.nt))\n for b in range(self.nb):\n spl = InterpolatedUnivariateSpline(self.z_grid, self.f_mod[:,templateno,b])\n f_mod_o[b][z] = spl(ztrue[z])\n\n #select sigma_b - 10% for now\n sigma = 0.1*f_mod_o\n #select observed fluxes f_obs_b = f_mod_b + sigma_b*rando\n f_obs = f_mod_o+ sigma * (np.random.normal(0.,1.,self.nb*ngals).reshape((self.nb,ngals)))\n # I don't seem to be able to find a more efficient way\n arrx=np.zeros(ngals,dtype=[('pz_f_obs',float,(self.nb,)),('pz_flux_sigma',float,(self.nb,))])\n arrx['pz_f_obs']=f_obs.T\n arrx['pz_flux_sigma']=sigma.T\n arr = recfunctions.merge_arrays((arr,arrx),flatten=True,usemask=False)\n return arr","def buildInstruction(ops: List[ghidra.program.model.pcode.PcodeOp], fallthruoffset: int, paramshift: int, addrFactory: ghidra.program.model.address.AddressFactory) -> unicode:\n ...","def fillBackgroundTemplates(opt):\n\n totalBkg={}\n templates=[]\n\n #import signal events\n data=ROOT.TChain('data')\n for f in [os.path.join(opt.input,x) for x in os.listdir(opt.input) if 'Data13TeV' in x]:\n if 'MuonEG' in f : continue\n data.AddFile(f)\n\n #define final preselection cuts\n cuts='xangle==%d'%opt.xangle\n if len(opt.presel) : cuts += ' && ' + opt.presel \n if opt.csiacc:\n csiCuts ='csi1>%f && csi1<%f && '%opt.csiacc[opt.xangle][0]\n csiCuts+='csi2>%f && csi2<%f'%opt.csiacc[opt.xangle][1]\n cuts=csiCuts if len(cuts)==0 else '{0} && {1}'.format(cuts,csiCuts)\n\n #loop over categories build templates\n for icat in range(len(opt.categs)):\n\n #apply category cuts\n categCut=opt.categs[icat]\n categCut=cuts if len(categCut)==0 else '%s && %s'%(categCut,cuts)\n catName='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\n\n print '\\t',catName,categCut\n\n #background modelling histos\n histos=[]\n data_obs=None\n for name,pfix in [('bkg_'+catName,'mix'),('bkg_%s_bkgShape'%catName,'mixem')]:\n\n templCuts=categCut.replace('csi1',pfix+'csi1')\n templCuts=templCuts.replace('csi2',pfix+'csi2')\n data.Draw('{0}mmiss >> h({1},{2},{3})'.format(pfix,opt.nbins,opt.mMin,opt.mMax),templCuts,'goff')\n h=data.GetHistogram()\n histos.append(h.Clone(name))\n histos[-1].SetDirectory(0)\n\n if len(histos)==1:\n totalBkg[icat]=h.Integral()\n if not opt.unblind :\n data_obs=h.Clone('data_obs_'+catName)\n data_obs.SetDirectory(0)\n\n h.Reset('ICE')\n templates += defineProcessTemplates(histos)\n\n #observed data in this category if unblinding\n if opt.unblind:\n data.Draw('mmiss >> h({1},{2},{3})'.format(opt.nbins,opt.mMin,opt.mMax),categCut,'goff')\n data_obs=data.GetHistogram().Clone('data_obs_'+catName)\n data_obs.SetDirectory(0)\n\n templates.append(data_obs)\n\n print '\\t total background:',totalBkg\n return totalBkg,templates","def healpix_template(ncomp, nside, coordinate=None):\n\n temp = so_map()\n\n if ncomp == 3:\n temp.data = np.zeros((3, 12 * nside ** 2))\n else:\n temp.data = np.zeros((12 * nside ** 2))\n\n temp.pixel = \"HEALPIX\"\n temp.ncomp = ncomp\n temp.nside = nside\n temp.geometry = \"healpix geometry\"\n temp.coordinate = coordinate\n return temp","def _get_template_data(vm_data: Dict[str, Any], span: Span) -> Optional[Dict[str, Any]]:\n vm_id = vm_data['id']\n Windows.logger.debug(f'Compiling template data for VM #{vm_id}')\n data: Dict[str, Any] = {key: None for key in Windows.template_keys}\n\n data['vm_identifier'] = f'{vm_data[\"project\"][\"id\"]}_{vm_id}'\n data['image_answer_file_name'] = vm_data['image']['answer_file_name']\n\n data['image_filename'] = vm_data['image']['filename']\n # check if file exists at /mnt/images/HyperV/VHDXs/\n path = '/mnt/images/HyperV/VHDXs/'\n child_span = opentracing.tracer.start_span('vm_image_file_download', child_of=span)\n if not Windows.check_image(data['image_filename'], path):\n # download the file\n downloaded, errors = Windows.download_image(data['image_filename'], path)\n if not downloaded:\n for error in errors:\n Windows.logger.error(error)\n vm_data['errors'].append(error)\n return None\n child_span.finish()\n\n # RAM is needed in MB for the builder but we take it in in GB (1024, not 1000)\n data['ram'] = vm_data['ram'] * 1024\n data['cpu'] = vm_data['cpu']\n data['dns'] = vm_data['dns']\n\n # Generate encrypted passwords\n data['admin_password'] = Windows._password_generator(size=12)\n # Also save the password back to the VM data dict\n vm_data['admin_password'] = data['admin_password']\n\n # Check for the primary storage\n if not any(storage['primary'] for storage in vm_data['storages']):\n error = 'No primary storage drive found. Expected one primary storage drive'\n Windows.logger.error(error)\n vm_data['errors'].append(error)\n return None\n\n data['storages'] = vm_data['storages']\n data['storage_type'] = vm_data['storage_type']\n\n # Get the Networking details\n data['vlans'] = []\n data['ip_addresses'] = []\n data['default_ips'] = []\n data['default_gateway'] = ''\n data['default_netmask_int'] = ''\n data['default_vlan'] = ''\n\n # The private IPs for the VM will be the one we need to pass to the template\n vm_data['ip_addresses'].reverse()\n ip_addresses = []\n subnets = []\n for ip in vm_data['ip_addresses']:\n if IPAddress(ip['address']).is_private():\n ip_addresses.append(ip)\n subnets.append({\n 'address_range': ip['subnet']['address_range'],\n 'vlan': ip['subnet']['vlan'],\n 'id': ip['subnet']['id'],\n })\n # Removing duplicates\n subnets = [dict(tuple_item) for tuple_item in {tuple(subnet.items()) for subnet in subnets}]\n # sorting nics (each subnet is one nic)\n for subnet in subnets:\n non_default_ips = []\n gateway, netmask_int = subnet['address_range'].split('/')\n vlan = str(subnet['vlan'])\n data['vlans'].append(vlan)\n\n for ip_address in ip_addresses:\n address = ip_address['address']\n if ip_address['subnet']['id'] == subnet['id']:\n # Pick the default ips if any\n if vm_data['gateway_subnet'] is not None:\n if subnet['id'] == vm_data['gateway_subnet']['id']:\n data['default_ips'].append(address)\n data['default_gateway'] = gateway\n data['default_netmask_int'] = netmask_int\n data['default_vlan'] = vlan\n continue\n # else store the non gateway subnet ips\n non_default_ips.append(address)\n\n if len(non_default_ips) > 0:\n data['ip_addresses'].append({\n 'ips': non_default_ips,\n 'gateway': gateway,\n 'netmask_int': netmask_int,\n 'vlan': vlan,\n })\n\n # Add locale data to the VM\n data['language'] = 'en_IE'\n data['timezone'] = 'GMT Standard Time'\n\n # Get the host name of the server\n host_name = None\n for interface in vm_data['server_data']['interfaces']:\n if interface['enabled'] is True and interface['ip_address'] is not None:\n if IPAddress(str(interface['ip_address'])).version == 6:\n host_name = interface['hostname']\n break\n if host_name is None:\n error = f'Host name is not found for the server # {vm_data[\"server_id\"]}'\n Windows.logger.error(error)\n vm_data['errors'].append(error)\n return None\n\n # Add the host information to the data\n data['host_name'] = host_name\n data['network_drive_url'] = settings.NETWORK_DRIVE_URL\n data['vms_path'] = settings.HYPERV_VMS_PATH\n\n return data","def New(*args, **kargs):\n obj = itkIsoDataThresholdCalculatorHDSS.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def generate_latex_output(self, calc_spec_tex_file_handle, sim, ui=None,\r\n res_dir=None):\r\n average_pressure = np.mean(self.sun.press[self.sun.press > 0])\r\n numeric_output_data = [\r\n ('pressure, max.', to_preci(np.amax(self.sun.press), 4),\r\n UnitTex.pressure.value, 'unverified'),\r\n ('pressure, av.', to_preci(average_pressure, 4),\r\n UnitTex.pressure.value, 'unverified'),\r\n ('e_a,kin sun, max.', to_preci(np.amax(self.sun.e_akin), 4),\r\n UnitTex.eakin.value, 'unverified'),\r\n ('e_a,kin planet, max.', to_preci(np.amax(self.planet.e_akin), 4),\r\n UnitTex.eakin.value, 'unverified'),\r\n ('pv_rel, max.', to_preci(np.amax(self.pv), 4),\r\n UnitTex.pvrel.value, 'unverified'),\r\n ('contact area', to_preci(self.sun.get_area(self.sun.press), 4),\r\n UnitTex.area.value, 'unverified')]\r\n table_calc_summary = []\r\n for key, value, unit, status in sorted(numeric_output_data):\r\n table_calc_summary.append(\r\n (re.sub('_', '\\_', key), value, unit, status))\r\n\r\n latex_variables = {'table_calc_summary': table_calc_summary,\r\n 'contact_plot1': '{}{}contact1.png'.format(\r\n SubDir.tex_figs_rel_to_tex_file.value, '/'),\r\n 'pressure_plot1': '{}{}pressure1.png'.format(\r\n SubDir.tex_figs_rel_to_tex_file.value, '/'),\r\n 'energy_plot1': '{}{}energy1.png'.format(\r\n SubDir.tex_figs_rel_to_tex_file.value, '/')}\r\n template_calc_specific = get_calc_specific_latex_template(\r\n TexTempl.RingOnRing.value, sim)\r\n with open(calc_spec_tex_file_handle, 'w') as f:\r\n f.write(template_calc_specific.render(latex_variables))","def no_icp(source, templates, home=None, use_2d=False):\n if source.shape[0] == 0:\n return create_config(0,0,0, fitness=0)\n origorig = source.copy()\n source, xinit, yinit, theta = init_transform(source)\n\n remove_out = False\n if remove_out:\n iq_range=0.5\n pcnt = (1 - iq_range) / 2\n xqlow, xqhigh = np.quantile(source[:,0], [pcnt, 1-pcnt])\n yqlow, yqhigh = np.quantile(source[:,1], [pcnt, 1-pcnt])\n xiqr, yiqr = xqhigh-xqlow, yqhigh-yqlow\n no_out = (source[:,0]<xqhigh+1.5*xiqr) & (source[:,0]>xqlow-1.5*xiqr) &\\\n (source[:,1]<yqhigh+1.5*yiqr) & (source[:,1]>yqlow-1.5*yiqr)\n source = source[no_out,:]\n\n remove_low = False\n if remove_low:\n not_low = (source[:,-1]>0.15) & (source[:,-1]<1.4)\n source = source[not_low,:]\n\n if source.shape[0] == 0:\n return create_config(0,0,0,fitness=0)\n\n\n # Recenter the data independently from point density\n xmin, xmax = np.min(source[:,0]), np.max(source[:,0])\n ymin, ymax = np.min(source[:,1]), np.max(source[:,1])\n x_center, y_center = xmin+(xmax-xmin)/2, ymin+(ymax-ymin)/2\n center2 = rotate(-theta, np.array([[x_center, y_center, 0]]), axis=\"z\")\n xinit += center2[0][0]\n yinit += center2[0][1]\n source = source - [x_center, y_center, 0]\n\n angle_diffs = [-5.,-2.,-1.,0.,1.,2.,5.]\n center_diffs = [-0.2,-0.15,-0.10,-0.07,-0.05,-0.01,0,0.01,0.05,0.07,0.10,0.15,0.2]\n lengths = [2.,2.1]\n widths = [1.]\n trials = list(product(center_diffs, center_diffs, angle_diffs, lengths, widths))\n\n trialmap = np.array([np.array([xinit, yinit, np.degrees(theta)-90, 0.0, 0.0])+np.array(t) for t in trials])\n results = multi_grid_iou(origorig[:, :2], trialmap, 0.05)\n best = np.argmax(results)\n cfg = create_config(*trialmap[best])\n cfg[\"bed\"][\"fitness\"] = np.max(results)\n return cfg","def New(*args, **kargs):\n obj = itkIsoDataThresholdCalculatorHFUS.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj","def calc_ST_operation(m_exhaust_GT_kgpers, T_exhaust_GT_K, T_sup_K, fuel_type):\n\n # calaulate High Pressure (HP) and Low Pressure (LP) mass flow of a double pressure steam turbine\n temp_i_K = (0.9 * ((6 / 48.2) ** (0.4 / 1.4) - 1) + 1) * (T_exhaust_GT_K - ST_DELTA_T)\n if fuel_type == 'NG':\n Mexh = 103.7 * 44E-3 + 196.2 * 18E-3 + 761.4 * 28E-3 + 200.5 * (CC_AIRRATIO - 1) * 32E-3 \\\n + 200.5 * (CC_AIRRATIO - 1) * 3.773 * 28E-3\n ncp_exh = 103.7 * 44 * 0.846 + 196.2 * 18 * 1.8723 + 761.4 * 28 * 1.039 \\\n + 200.5 * (CC_AIRRATIO - 1) * 32 * 0.918 + 200.5 * (CC_AIRRATIO - 1) * 3.773 * 28 * 1.039\n cp_exh = ncp_exh / Mexh # J/kgK\n else:\n Mexh = 98.5 * 44E-3 + 116 * 18E-3 + 436.8 * 28E-3 + 115.5 * (CC_AIRRATIO - 1) * 32E-3 \\\n + 115.5 * (CC_AIRRATIO - 1) * 3.773 * 28E-3\n ncp_exh = 98.5 * 44 * 0.846 + 116 * 18 * 1.8723 + 436.8 * 28 * 1.039 \\\n + 115.5 * (CC_AIRRATIO - 1) * 32 * 0.918 + 115.5 * (CC_AIRRATIO - 1) * 3.773 * 28 * 1.039\n cp_exh = ncp_exh / Mexh # J/kgK\n\n a = np.array([[1653E3 + HEAT_CAPACITY_OF_WATER_JPERKGK * (T_exhaust_GT_K - ST_DELTA_T - 534.5), \\\n HEAT_CAPACITY_OF_WATER_JPERKGK * (temp_i_K - 534.5)], \\\n [HEAT_CAPACITY_OF_WATER_JPERKGK * (534.5 - 431.8), \\\n 2085.8E3 + HEAT_CAPACITY_OF_WATER_JPERKGK * (534.5 - 431.8)]])\n b = np.array([m_exhaust_GT_kgpers * cp_exh * (T_exhaust_GT_K - (534.5 + ST_DELTA_T)), \\\n m_exhaust_GT_kgpers * cp_exh * (534.5 - 431.8)])\n [mdotHP_kgpers, mdotLP_kgpers] = np.linalg.solve(a, b)\n\n # calculate thermal output\n T_cond_0_K = T_sup_K + CC_DELTA_T_DH # condensation temperature constrained by the DH network temperature\n pres0 = (0.0261 * (T_cond_0_K - 273) ** 2 - 2.1394 * (T_cond_0_K - 273) + 52.893) * 1E3\n\n delta_h_evap_Jperkg = (-2.4967 * (T_cond_0_K - 273) + 2507) * 1E3\n q_output_ST_W = (mdotHP_kgpers + mdotLP_kgpers) * delta_h_evap_Jperkg # thermal output of ST\n\n # calculate electricity output\n h_HP_Jperkg = (2.5081 * (T_exhaust_GT_K - ST_DELTA_T - 273) + 2122.7) * 1E3 # J/kg\n h_LP_Jperkg = (2.3153 * (temp_i_K - 273) + 2314.7) * 1E3 # J/kg\n h_cond_Jperkg = (1.6979 * (T_cond_0_K - 273) + 2506.6) * 1E3 # J/kg\n\n el_produced_ST_W = mdotHP_kgpers * (h_HP_Jperkg - h_LP_Jperkg) + \\\n (mdotHP_kgpers + mdotLP_kgpers) * (h_LP_Jperkg - h_cond_Jperkg) # turbine electricity output\n\n el_input_compressor_W = SPEC_VOLUME_STEAM * (\n mdotLP_kgpers * (6E5 - pres0) + (mdotHP_kgpers + mdotLP_kgpers) * (48.2E5 - 6E5)) # compressor electricity use\n\n el_output_ST_W = ST_GEN_ETA * (el_produced_ST_W - el_input_compressor_W) # gross electricity production of turbine\n\n return q_output_ST_W, el_output_ST_W","def _model(self, t, theta, period, tmpid):\n template = self.templates[tmpid]\n phase = (t / period - theta[2]) % 1\n return theta[0] + theta[1] * template(phase)","def preparehspiceidvgGEO1v2(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,HFINparam,TFIN_TOPparam,TFIN_BASEparam,EOTparam,NBODYparam,NFINparam,PHIGparam,RSHSparam,RSHDparam):\n #make an aux copy of hspice file to simulate\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\n #make an aux copy of modelcard file to simulate\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\n\n #update path of model and modelcard\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\"modelcardaux.nmos\\\"')\n\n #bias update\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'HFINparam',HFINparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_TOPparam', TFIN_TOPparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_BASEparam',TFIN_BASEparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'EOTparam', EOTparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'PHIGparam', PHIGparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHSparam', RSHSparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHDparam', RSHDparam)","def generate_template():\n date = input(\"What day is the template for? dd/mm/yy: \")\n day = input(\"What day of the week is this?: \")\n if \"/\" not in date:\n date_components = [date[0:2], date[2:4], date[4:]]\n else:\n date_components = date.split(\"/\")\n file_name = \"{}{}{}.table\".format(date_components[0], date_components[1], date_components[2])\n try:\n template_file = open(\"templates/{}_template.table\".format(day), \"r\")\n except IOError:\n print(\"source template not found\")\n else:\n source_lines = []\n output_file = open(file_name, \"w\")\n for line in template_file:\n source_lines.append(line)\n output_file.write(line)\n print(\"template generated successfully for: {} on date: {}\".format(day, date))","def compute(self, inputs, outputs):\n\toutputs['TOW'] = inputs['W_battery'] + inputs['W_payload'] + inputs['W_empty']","def _base_troposphere_template(self):\n template = troposphere.Template()\n template.add_parameter(\n troposphere.Parameter(\n \"Stage\",\n Default=\"dev\",\n Description=\"Name of the Stage\",\n Type=\"String\",\n )\n )\n\n template.add_parameter(\n troposphere.Parameter(\n \"Region\",\n Description=\"AWS Region\",\n Type=\"String\",\n )\n )\n return template","def test7():\n from wpylib.text_tools import str_snippet as snippet\n from wpylib.params.params_flat import ActiveReadValue as Act\n defaults = {\n 'nbasis': 320,\n 'npart': 37,\n 'deltau': 0.025,\n }\n print \"test7()\"\n p = params(defaults, nbasis=332)\n p.input_template = Act(lambda P: snippet(\"\"\"\\\n input = {\n nbasis = %(nbasis)d\n npart = %(npart)d\n deltau = %(deltau)s\n };\n code = {\n %(code_template)s\n };\n \"\"\") % P)\n p.code_template = Act(lambda P: snippet(\"\"\"\\\n for (i = 1; %(nbasis)d; ++i) {\n print i, %(deltau)g\n }\n \"\"\") % P)\n print \"\\nInput template 1:\"\n print p.input_template\n print \"\\n--- The case above was OK. ---\\n\"\n\n print \"\\n--- The case below was not OK. ---\\nThe nbasis and deltau args were not updated in two printouts below\"\n Q = params(p)\n Q.nbasis = 327\n print \"\\nInput template 2 after updating nbasis:\"\n print Q.input_template\n\n\n print \"\\n--- The case below was OK, but it requires flattening of the search dicts. ---\\n\"\n\n R = params(p, _flatten_=True)\n R.nbasis = 327\n pprint(R)\n print len(R._list_)\n print \"\\nInput template 2 after flattening and updating nbasis:\"\n print R.input_template\n\n print \"\\nHere is the generator script:\\n*%(input_template)s*\" % p._create_(_flatten_=True)._update_(dict(deltau=2.775))","def nd_2_nz_compute(self):\n tik_instance = self.set_tik_instance()\n format_transfer_case = self.set_format_transfer_case()\n if format_transfer_case == 0:\n tik_instance = self.format_transfer_case_zero(tik_instance)\n elif format_transfer_case == 1:\n tik_instance = self.format_transfer_case_one(tik_instance)\n elif format_transfer_case == 2:\n tik_instance = self.format_transfer_case_two(tik_instance)\n elif format_transfer_case == 3:\n tik_instance = self.format_transfer_case_three(tik_instance)\n elif format_transfer_case == 4:\n tik_instance = self.format_transfer_case_four(tik_instance)\n elif format_transfer_case == 5:\n tik_instance = self.format_transfer_case_five(tik_instance)\n elif format_transfer_case == 6:\n tik_instance = self.format_transfer_case_six(tik_instance)\n elif format_transfer_case == 7:\n tik_instance = self.format_transfer_case_seven(tik_instance)\n elif format_transfer_case == 8:\n tik_instance = self.format_transfer_case_eight(tik_instance)\n return tik_instance","def compute_equivalent_widths(templates, coeffs, covar, max_R=5000, Ndraw=1000, seed=0):\n \n if False:\n # Testing\n templates = t1\n chi2, coeffsx, coeffs_errx, covarx = mb.xfit_at_z(z=tfit['z'], templates=templates, fitter='nnls', fit_background=True, get_uncertainties=2)\n covar = covarx[mb.N:,mb.N:]\n coeffs = coeffsx[mb.N:]\n \n # Array versions of the templates\n wave, flux_arr, is_line = array_templates(templates, max_R=max_R)\n keys = np.array(list(templates.keys()))\n \n EWdict = OrderedDict()\n for key in keys[is_line]:\n EWdict[key] = (0., 0., 0.)\n \n # Only worry about templates with non-zero coefficients, which should\n # be accounted for in the covariance array (with get_uncertainties=2)\n clip = coeffs != 0\n # No valid lines\n if (is_line & clip).sum() == 0:\n return EWdict\n \n # Random draws from the covariance matrix\n covar_clip = covar[clip,:][:,clip]\n if seed is not None:\n np.random.seed(seed)\n \n draws = np.random.multivariate_normal(coeffs[clip], covar_clip, size=Ndraw)\n \n # Evaluate the continuum fits from the draws \n continuum = np.dot(draws*(~is_line[clip]), flux_arr[clip,:])\n \n # Compute the emission line EWs\n tidx = np.where(is_line[clip])[0]\n for ix in tidx:\n key = keys[clip][ix]\n\n # Line template\n line = np.dot(draws[:,ix][:,None], flux_arr[clip,:][ix,:][None,:])\n \n # Where line template non-zero\n mask = flux_arr[clip,:][ix,:] > 0\n ew_i = np.trapz((line/continuum)[:,mask], wave[mask], axis=1)\n \n EWdict[key] = np.percentile(ew_i, [16., 50., 84.])\n \n return EWdict","def generate_template_std(classname, outfile, doc='', MathJax=False, login=False):\n code = '''\\\n<!DOCTYPE html>\n<html lang=\"en\">\n <head>\n <meta charset=\"utf-8\" />\n <title>Flask %(classname)s app</title>\n </head>\n <body>\n''' % vars()\n if MathJax:\n code += '''\n<script type=\"text/x-mathjax-config\">\nMathJax.Hub.Config({\n TeX: {\n equationNumbers: { autoNumber: \"AMS\" },\n extensions: [\"AMSmath.js\", \"AMSsymbols.js\", \"autobold.js\"]\n }\n});\n</script>\n<script type=\"text/javascript\"\n src=\"http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n</script>\n<!-- Fix slow MathJax rendering in IE8 -->\n<meta http-equiv=\"X-UA-Compatible\" content=\"IE=EmulateIE7\">\n'''\n if login:\n code += '''\n {% if is_anonymous %}\n <p align=\"right\"><a href=\"/login\">Login</a>\n / <a href=\"/reg\">Register</a></p>\n {% else %}\n <p align=\"right\">Logged in as {{user.username}}<br>\n <a href=\"/old\">Previous simulations<a/><br>\n <a href=\"/logout\">Logout</a></p>\n {% endif %}\n'''\n code += '''\\\n %(doc)s\n\n <!-- Input and Results are typeset as a two-column table -->\n <table>\n <tr>\n <td valign=\"top\">\n <h2>Input:</h2>\n\n <form method=post action=\"\" enctype=\"multipart/form-data\">\n <table>\n {%% for field in form %%}\n <tr><td>{{ field.name }}</td>\n <td>{{ field(size=20) }}</td>\n <td>{%% if field.errors %%}\n <ul class=errors>\n {%% for error in field.errors %%}\n <li>{{ error }}</li>\n {%% endfor %%}</ul>\n {%% endif %%}</td></tr>\n {%% endfor %%}\n </table>\n <p><input type=\"submit\" value=\"Compute\">\n </form></p>\n </td>\n\n <td valign=\"top\">\n {%% if result != None %%}\n <h2>Results:</h2>\n {{ result|safe }}\n''' % vars()\n\n if login:\n code +='''\n {% if not is_anonymous %}\n <h3>Comments:</h3>\n <form method=post action=\"/add_comment\">\n <textarea name=\"comments\" rows=\"4\" cols=\"40\"></textarea>\n <p><input type=\"submit\" value=\"Add\">\n </form>\n {% endif %}\n'''\n code += '''\n {% endif %}\n </td>\n </tr>\n </table>\n </body>\n</html>'''\n\n if outfile is None:\n return code\n else:\n f = open(outfile, 'w')\n f.write(code)\n f.close()","def evaluate_template(template: str, prefix=\"$\", sufix=\"$\"):\n return get_context().evaluate_template(template, prefix=prefix, sufix=sufix)","def energy_balance_func_doc(self, label):\n latex = (\n r'0= T_\\mathrm{in} - T_{\\mathrm{out,}j}'\n r'\\; \\forall j \\in \\text{outlets}'\n )\n return generate_latex_eq(self, latex, label)"],"string":"[\n \"def produce_13TeV_template(tag_name=\\\"HKHI\\\"):\\n num_rebin = 1\\n file_name = \\\"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\\\"+tag_name+\\\"_Lin.root\\\"\\n print \\\"Input: \\\", file_name\\n fin = ROOT.TFile.Open(file_name, \\\"read\\\")\\n h_nom = fin.Get(\\\"bkg_total_gg_full\\\").Clone(\\\"bkg_nominal_old\\\")\\n h_nom.Rebin(num_rebin)\\n fout = ROOT.TFile.Open(\\\"hists_input_\\\"+tag_name+\\\".root\\\", \\\"recreate\\\")\\n\\n h_purity_sys = fin.Get(\\\"bkg_purity_syst_gg_full\\\").Clone(\\\"bkg_purity_syst_gg\\\")\\n h_reducible_sys = fin.Get(\\\"bkg_reducible_syst_gg_full\\\").Clone(\\\"bkg_reducible_syst_gg\\\")\\n h_irreducible_sys = fin.Get(\\\"bkg_irreducible_syst_gg_full\\\").Clone(\\\"bkg_irreducible_syst_gg\\\")\\n h_iso_sys = fin.Get(\\\"bkg_iso_syst_gg_full\\\").Clone(\\\"bkg_iso_syst_gg\\\")\\n\\n #file_iso = \\\"isolation_sys/hist.root\\\"\\n #fin2 = ROOT.TFile.Open(file_iso, \\\"read\\\")\\n #h_iso_sys = fin2.Get(\\\"bkg_isolation_syst_gg\\\")\\n ## inflat irreducible uncertainty by factor of 10\\n # so that it closes to stats uncertainty in data\\n sf = 1\\n if INFLATE_SYS:\\n sf = 10\\n\\n # after rebinning systematic uncertainties, need to scale down,\\n # otherwise the uncertainties are inflated.\\n h_purity_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_irreducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_reducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_iso_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n\\n ## truncate the histograms to [200, 2000] GeV\\n h_nom_new = truncate_hist(h_nom, \\\"bkg_nominal\\\")\\n h_purity_sys_new = truncate_hist(h_purity_sys, \\\"h_purity_sys_new\\\")\\n h_irreducible_sys_new = truncate_hist(h_irreducible_sys, \\\"h_irreducible_sys_new\\\")\\n h_reducible_sys_new = truncate_hist(h_reducible_sys, \\\"h_reducible_sys_new\\\")\\n h_iso_sys_new = truncate_hist(h_iso_sys, \\\"h_iso_sys_new\\\")\\n\\n #write down sys and nominal\\n fout.cd()\\n h_nom_new.Write()\\n h_purity_sys_new.Write()\\n h_reducible_sys_new.Write()\\n h_irreducible_sys_new.Write()\\n h_iso_sys_new.Write()\\n\\n h_purity_up, h_purity_down = create_sys_hist(h_nom_new, h_purity_sys_new, \\\"purity_sys\\\")\\n h_purity_up.Write()\\n h_purity_down.Write()\\n\\n h_red_up, h_red_down = create_sys_hist(h_nom_new, h_reducible_sys_new, \\\"reducible_sys\\\")\\n h_red_up.Write()\\n h_red_down.Write()\\n\\n h_irred_up, h_irred_down = create_sys_hist(h_nom_new, h_irreducible_sys_new, \\\"irreducible_sys\\\")\\n h_irred_up.Write()\\n h_irred_down.Write()\\n\\n h_iso_up, h_iso_down = create_sys_hist(h_nom_new, h_iso_sys, \\\"isolation_sys\\\")\\n h_iso_up.Write()\\n h_iso_down.Write()\\n\\n fin.Close()\\n fout.Close()\",\n \"def cheetah_template(self, pre=False):\\n if self.is_req_output:\\n cht_tmpl = self.req_out_chth\\n return cht_tmpl.substitute(self.xml_out)\\n elif self.is_output:\\n xml_out = self.xml_out\\n xml_out['out_sel_name'] = self.out_sel_name\\n cht_tmpl = self.file_chth\\n return cht_tmpl.substitute(self.xml_out)\\n elif self.is_input and not pre:\\n if self.pname in self.gen_in_fmt:\\n if self.gen_in_fmt[self.pname] == 'vcf,vcf_bgzip':\\n cht_tmpl = self.vcf_choose\\n else:\\n cht_tmpl = PercentTemplate(self.reg_arg)\\n elif self.pname in self.tool_data[self.tool_name]['input_fmt']:\\n cht_tmpl = self.req_out_chth\\n return cht_tmpl.substitute(self.xml_out)\\n elif self.is_input and pre:\\n cht_tmpl = self.vcf_tabix\\n return cht_tmpl.substitute(self.xml_out)\\n else:\\n if self.xml_out['section'] not in ['required']:\\n template_string = self.ext_arg\\n else:\\n template_string = self.reg_arg\\n if self.xml_out['type'] == 'boolean':\\n cht_tmpl = PercentTemplate(template_string.replace('%argument ', ''))\\n else:\\n cht_tmpl = PercentTemplate(template_string)\\n return cht_tmpl.substitute(self.xml_out)\",\n \"def stoichiometry_func_doc(self, label):\\n air = self.air_alias.val\\n fuel = self.fuel_alias.val\\n flue_gas = self.fuel_alias.val + '_fg'\\n\\n equations = ''\\n for fluid in self.inl[0].fluid.val.keys():\\n\\n in1 = r'\\\\dot{m}_\\\\mathrm{in,1} \\\\cdot x_\\\\mathrm{fluid,in,1}'\\n in2 = r'\\\\dot{m}_\\\\mathrm{in,2} \\\\cdot x_\\\\mathrm{fluid,in,2}'\\n out = r'\\\\dot{m}_\\\\mathrm{out,1} \\\\cdot x_\\\\mathrm{fluid,out,1}'\\n\\n if fluid == air:\\n latex = (\\n r'0=\\\\Delta\\\\dot{m}_\\\\mathrm{' + fluid + r'} - '\\n r'\\\\dot{m}_\\\\mathrm{' + fluid + r',stoich}'\\n )\\n elif fluid == fuel:\\n latex = (\\n r'0=\\\\Delta \\\\dot{m}_\\\\mathrm{' + fluid + r'} - '\\n r'\\\\dot{m}_\\\\mathrm{' + fluid + r'}'\\n )\\n elif fluid == flue_gas:\\n latex = (\\n r'0=\\\\Delta \\\\dot{m}_\\\\mathrm{' + fluid.replace('_', ',') +\\n r'} + \\\\dot{m}_\\\\mathrm{' + fuel + r'} +'\\n r'\\\\dot{m}_\\\\mathrm{' + air + r',stoich}'\\n )\\n else:\\n latex = r'0 = \\\\Delta \\\\dot{m}_\\\\mathrm{' + fluid + '}'\\n\\n if fluid == next(iter(self.inl[0].fluid.val)):\\n balance = (\\n r'\\\\Delta \\\\dot{m}_\\\\mathrm{fluid} = ' + in1 +\\n '+' + in2 + '-' + out)\\n m_fluid = r'\\\\dot{m}_\\\\mathrm{fluid} = ' + in1 + '+' + in2\\n m_air_stoich = (\\n r'\\\\dot{m}_\\\\mathrm{' + air + ',stoich}='\\n r'\\\\dot{m}_\\\\mathrm{' + fuel + r'} \\\\cdot ' +\\n str(round(self.air_min, 4)))\\n latex_general_eq = (\\n r'\\\\begin{split}' + '\\\\n'\\n r'&' + balance + r'\\\\\\\\' + '\\\\n'\\n r'&' + m_fluid + r'\\\\\\\\' + '\\\\n'\\n r'&' + m_air_stoich + r'\\\\\\\\' + '\\\\n'\\n r'\\\\end{split}'\\n )\\n equations += (\\n generate_latex_eq(\\n self, latex_general_eq, label + '_general_eq') + '\\\\n' +\\n generate_latex_eq(self, latex, label + '_' + fluid) + '\\\\n')\\n else:\\n equations += (\\n generate_latex_eq(self, latex, label + '_' + fluid) + '\\\\n')\\n # remove last newline\\n return equations[:-1]\",\n \"def run_template(template, config):\\n all_results = {}\\n for nodenum, wires in template:\\n # Find the modules\\n node = template.modules[nodenum]\\n module_id = node['module'] #template.modules[node]\\n module = lookup_module(module_id)\\n inputs = _map_inputs(module, wires)\\n \\n # substitute values for inputs\\n kwargs = dict((k, _lookup_results(all_results, v)) \\n for k, v in inputs.items())\\n \\n # Include configuration information\\n kwargs.update(node.get('config', {}))\\n kwargs.update(config[nodenum])\\n result = module.action(**kwargs)\\n# print result\\n all_results[nodenum] = result\\n \\n# return all_results\\n# FIXXXXXXXXXXXXXXXXXXXXXX ***********************\\n from .offspecular.instruments import convert_to_plottable\\n return [convert_to_plottable(value['output']) if 'output' in value else {} for key, value in all_results.items()]\",\n \"def calc_template(template_def, config):\\n template = Template(**template_def)\\n #print \\\"template_def:\\\", template_def, \\\"config:\\\", config\\n try:\\n retvals = process_template(template, config, target=(None, None))\\n except Exception:\\n print(\\\"==== template ====\\\"); pprint(template_def)\\n print(\\\"==== config ====\\\"); pprint(config)\\n #traceback.print_exc()\\n raise\\n output = {}\\n for rkey, rv in retvals.items():\\n module_id, terminal_id = rkey\\n module_key = str(module_id)\\n output.setdefault(module_key, {})\\n output[module_key][terminal_id] = rv.todict()\\n return output\",\n \"def computeTemplateFeatures(self, templates, mode=''):\\n pass\",\n \"def ti_func_doc(self, label):\\n latex = (\\n r'\\\\begin{split}' + '\\\\n'\\n r'0 = & ti - LHV_\\\\mathrm{fuel} \\\\cdot \\\\left[\\\\sum_i \\\\left('\\n r'\\\\dot{m}_{\\\\mathrm{in,}i}\\\\cdot x_{\\\\mathrm{fuel,in,}i}\\\\right)-'\\n r' \\\\dot{m}_\\\\mathrm{out,1}\\\\cdot '\\n r'x_{\\\\mathrm{fuel,out,1}} \\\\right]\\\\\\\\' + '\\\\n'\\n r'& \\\\forall i \\\\in \\\\text{combustion inlets}\\\\\\\\' + '\\\\n'\\n r'\\\\end{split}'\\n )\\n return generate_latex_eq(self, latex, label)\",\n \"def fillSignalTemplates(opt):\\n\\n totalSig={}\\n templates=[]\\n\\n #import signal events\\n data=ROOT.TChain('data')\\n data.AddFile(os.path.join(opt.input,opt.sig))\\n\\n #define final preselection cuts\\n cuts='xangle==%d'%opt.xangle\\n if len(opt.presel) : cuts += ' && ' + opt.presel\\n if opt.csiacc:\\n csiCuts ='csi1>%f && csi1<%f && '%opt.csiacc[opt.xangle][0]\\n csiCuts+='csi2>%f && csi2<%f'%opt.csiacc[opt.xangle][1]\\n cuts=csiCuts if len(cuts)==0 else '{0} && {1}'.format(cuts,csiCuts)\\n\\n #loop over categories build templates\\n for icat in range(len(opt.categs)):\\n\\n #apply category cuts\\n categCut=opt.categs[icat]\\n categCut=cuts if len(categCut)==0 else '%s && %s'%(categCut,cuts)\\n\\n catName='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\\n print '\\\\t',catName,categCut\\n\\n #signal modelling histograms\\n histos=[]\\n for name,pfix in [('sig_'+catName,''),('sig_%s_sigShape'%catName,'mix')]:\\n\\n templCuts=categCut.replace('csi1',pfix+'csi1')\\n templCuts=templCuts.replace('csi2',pfix+'csi2')\\n wgtExpr='wgt*%f'%(SIGNALXSECS[opt.xangle]*opt.lumi)\\n data.Draw('{0}mmiss >> h({1},{2},{3})'.format(pfix,opt.nbins,opt.mMin,opt.mMax),\\n '{0}*({1})'.format(wgtExpr,templCuts),\\n 'goff')\\n h=data.GetHistogram()\\n histos.append( h.Clone(name) ) \\n histos[-1].SetDirectory(0)\\n\\n if len(histos)==1:\\n totalSig[icat]=h.Integral()\\n\\n h.Reset('ICE')\\n templates += defineProcessTemplates(histos)\\n \\n print '\\\\t total signal:',totalSig\\n return totalSig,templates\",\n \"def _tpl(grid, iv, ds='test'):\\n #TODO: Research numpy routines which may automate this\\n tax=grid._get_timelike_axis()\\n def tmp(x):\\n if x[tax]>0:\\n return 0.0\\n if x[tax]==0:\\n return iv(x)\\n return tmp\",\n \"def inst_set_cpu_template_fxt(request):\\n return request.param\",\n \"def _eval_templates(self, period):\\n theta_best = [self._optimize(period, tmpid)\\n for tmpid, _ in enumerate(self.templates)]\\n chi2 = [self._chi2(theta, period, tmpid)\\n for tmpid, theta in enumerate(theta_best)]\\n\\n return theta_best, chi2\",\n \"def gen_op2(N11, N22, N12, rN11, rN22, rN12):\\n raw = 2 * N12 / rN12 / (N11 / rN11 + N22 / rN22)\\n print(\\\"=====\\\\n2nd method: raw = 2 * N12/rN12 / (N11/rN11 + N22/rN22)\\\")\\n print(\\\"OP: 1 - raw = %.3f\\\\n=====\\\" % (1 - raw))\",\n \"def _add_template(self):\\n template_dir = os.path.join(self.label_path, 'standard',\\n 'MNI152_T1_2mm_brain.nii.gz')\\n template_name = QFileDialog.getOpenFileName(\\n self,\\n 'Open standard file',\\n template_dir,\\n 'Nifti files (*.nii.gz *.nii)')\\n if not template_name.isEmpty():\\n template_path = str(template_name)\\n self._add_img(template_path)\",\n \"def computeTemplateFeatures(self, templates, mode=''):\\n pass\",\n \"def _modify_template_nics_according_to_input(template_nics, input_nics, cmd, client,\\n resource_group_name, vm_name,\\n location, private_cloud):\\n # Populating the nic names of vm-template in a dictionary,\\n # and mapping them to their index in template_nics list\\n vm_template_nic_names = {}\\n for (i, nic) in enumerate(template_nics):\\n vm_template_nic_names[nic.virtual_nic_name] = i\\n\\n from .vendored_sdks.models import VirtualNic\\n from .vendored_sdks.models import VirtualNetwork\\n from ._validators import virtual_network_name_or_id_validator\\n\\n # Check if nics entered by a user exist in vm-template,\\n # then override the properties specified. Else create a new nic.\\n for nic in input_nics:\\n if nic['name'] in vm_template_nic_names.keys():\\n index = vm_template_nic_names[nic['name']]\\n if 'virtual-network' in nic.keys():\\n template_nics[index].network.id = nic['virtual-network']\\n if 'adapter' in nic.keys():\\n template_nics[index].nic_type = nic['adapter']\\n if 'power-on-boot' in nic.keys():\\n template_nics[index].power_on_boot = nic['power-on-boot']\\n template_nics[index].virtual_nic_id = None\\n\\n else:\\n nic_name = nic['name']\\n if 'virtual-network' in nic.keys():\\n vnet = nic['virtual-network']\\n else:\\n raise CLIError('virtual-network parameter not specified for nic ' +\\n nic_name + \\\".\\\")\\n if 'adapter' in nic.keys():\\n adapter = nic['adapter']\\n else:\\n raise CLIError('adapter parameter not specified for nic ' +\\n nic_name + \\\".\\\")\\n if 'power-on-boot' in nic.keys():\\n power_on_boot = nic['power-on-boot']\\n else:\\n raise CLIError('power-on-boot parameter not specified for nic ' +\\n nic_name + \\\".\\\")\\n\\n vnet = virtual_network_name_or_id_validator(cmd, client, vnet,\\n resource_group_name, vm_name,\\n location, private_cloud)\\n network = VirtualNetwork(id=vnet)\\n nic_object = VirtualNic(network=network,\\n nic_type=adapter,\\n power_on_boot=power_on_boot)\\n template_nics.append(nic_object)\\n return template_nics\",\n \"def run(self, template_data, data_cont):\\n\\n tprops = acalib.core.transform.fits_props(template_data)\\n scaled = acalib.core.transform.scale(data_cont, tprops['major'])\\n rotated, angles = acalib.core.transform.rotate(scaled, tprops['angle'])\\n aligned = acalib.core.transform.crop_and_align(rotated, angles)\\n result = mean(aligned , axis=0)\\n return result\",\n \"def gen_op4(N11, N22, N12, rN11, rN22, rN12):\\n raw = 2 * N12 / rN12 / (N11 / rN11 + N22 / rN22)\\n print(\\\"=====\\\\n4th method: raw = 2 * N12/rN12 / (N11/rN11 + N22/rN22)\\\")\\n print(\\\"OP: 1 / raw = %.3f\\\\n=====\\\" % (1.0 / raw))\",\n \"def test_include_template(self, parse_input, tmpdir):\\n template = textwrap.dedent(\\n \\\"\\\"\\\"\\n name CustomOperation\\n version 0.0\\n BSgate({theta}, pi/2) | [0, 1]\\n Rgate({phi}) | 0\\n \\\"\\\"\\\"\\n )\\n\\n filename = tmpdir.join(\\\"test.xbb\\\")\\n\\n with open(filename, \\\"w\\\") as f:\\n f.write(template)\\n\\n test_include = textwrap.dedent(\\n \\\"\\\"\\\"\\n name test_include\\n version 0.0\\n include \\\"{}\\\"\\n CustomOperation(theta=0.54, phi=0.1) | [2, 1]\\n \\\"\\\"\\\"\\n ).format(filename)\\n\\n bb = parse_input(test_include, cwd=tmpdir)\\n\\n expected = [\\n {\\\"op\\\": \\\"BSgate\\\", \\\"args\\\": [0.54, np.pi / 2], \\\"kwargs\\\": {}, \\\"modes\\\": [2, 1]},\\n {\\\"op\\\": \\\"Rgate\\\", \\\"args\\\": [0.1], \\\"kwargs\\\": {}, \\\"modes\\\": [2]},\\n ]\\n\\n assert bb.operations == expected\",\n \"def comando_nfce(self):\\r\\n return self.template_nfce()\",\n \"def gen_code(self, template, dec_id, i_alt):\\n v = self.get_alt(dec_id, i_alt)\\n\\n # assuming the placeholder var is always at the end\\n # which is true given how we chop up the chunks\\n return template + str(v), str(v)\",\n \"def inst_set_cpu_template_ext_fxt(request):\\n return request.param\",\n \"def gen_op1(N11, N22, N12, rN11, rN22, rN12):\\n raw = N12 / (N11 + N22)\\n ref = rN12 / (rN11 + rN22)\\n print(\\\"=====\\\\n1st method: raw = N12/(N11+N22)\\\")\\n print(\\\"raw: %.2f | ref: %.2f\\\" % (raw, ref) )\\n print(\\\"1 - raw/ref = %.3f\\\\n=====\\\" % (1.0 - raw / ref))\",\n \"def tpl(x):\\n return 3 * x\",\n \"def _operator_generator(self, index, conj):\\n pterm = PauliTerm('I', 0, 1.0)\\n Zstring = PauliTerm('I', 0, 1.0)\\n for j in range(index):\\n Zstring = Zstring*PauliTerm('Z', j, 1.0)\\n\\n pterm1 = Zstring*PauliTerm('X', index, 0.5)\\n scalar = 0.5 * conj * 1.0j\\n pterm2 = Zstring*PauliTerm('Y', index, scalar)\\n pterm = pterm * (pterm1 + pterm2)\\n\\n pterm = pterm.simplify()\\n return pterm\",\n \"def compute_code():\\n for file in os.listdir('./src/Templates'):\\n input.parse_template('./src/Templates/' + file, './student/' + file + '.py')\\n data = input.load_input()\\n return len([k for k in data['input'].keys() if '@' not in k])\",\n \"def compute_partials(self, inputs, partials):\\n partials['y', 'x'] = 2.0\",\n \"def generate_scenario_precip_inputs():\\n def multiply_precip(precip_raster, multiply_factor):\\n valid_mask = (precip_raster != precip_raster_nodata)\\n result = numpy.empty(precip_raster.shape, dtype=numpy.float32)\\n result[:] = _TARGET_NODATA\\n result[valid_mask] = precip_raster[valid_mask] * multiply_factor\\n return result\\n\\n def calc_ET0(RA, Tavg, TD, P):\\n \\\"\\\"\\\"Modified Hargreaves from Droogers and Allen 2002.\\n\\n Parameters:\\n RA (numpy.ndarray): daily extraterrestrial radiation\\n Tavg (numpy.ndarray): average temperature\\n TD (numpy.ndarray): difference between minimum and maximum\\n temperature\\n P (numpy.ndarray): monthly precipitation\\n\\n Returns:\\n monthly reference evapotranspiration (mm)\\n\\n \\\"\\\"\\\"\\n valid_mask = (\\n (RA != _TARGET_NODATA) &\\n (Tavg != tavg_nodata) &\\n (TD != td_nodata) &\\n (P != _TARGET_NODATA))\\n result = numpy.empty(RA.shape, dtype=numpy.float32)\\n result[:] = _TARGET_NODATA\\n result[valid_mask] = (\\n 0.0013 * 0.408 * RA[valid_mask] * (Tavg[valid_mask] + 17.) *\\n (numpy.power((TD[valid_mask] - 0.0123 * P[valid_mask]), 0.76)) *\\n 29.5)\\n return result\\n\\n climate_dir = r\\\"E:\\\\GIS_local_3.6.19\\\\USFS\\\\NCEI_climate\\\"\\n RA_df = pandas.read_csv(os.path.join(climate_dir, \\\"RA_FAO.csv\\\"))\\n Tavg_dir = os.path.join(climate_dir, \\\"temp_ave_rasters\\\")\\n TD_dir = os.path.join(climate_dir, \\\"tdiff_rasters\\\")\\n events_table = os.path.join(\\n climate_dir, 'rain_events_table_Palmer_creek.csv')\\n\\n baseline_precip_dir = r\\\"C:\\\\Users\\\\ginge\\\\Dropbox\\\\NatCap_backup\\\\USFS\\\\input_data\\\\model_inputs\\\\precip_NCEI\\\"\\n outer_scenario_dir = r\\\"C:\\\\Users\\\\ginge\\\\Dropbox\\\\NatCap_backup\\\\USFS\\\\input_data\\\\model_inputs\\\\precip_scenarios\\\"\\n\\n example_precip_raster = os.path.join(\\n baseline_precip_dir, 'precip_1.tif')\\n precip_raster_nodata = pygeoprocessing.get_raster_info(\\n example_precip_raster)['nodata'][0]\\n\\n for multiply_factor in [0.5]: # [0.7, 0.9, 1.1, 1.3]:\\n scenario_dir = os.path.join(\\n outer_scenario_dir, '{}x'.format(multiply_factor))\\n modified_event_table = os.path.join(scenario_dir, 'rain_events.csv')\\n ET0_dir = os.path.join(scenario_dir, 'ET0_rasters')\\n precip_dir = os.path.join(scenario_dir, 'precip_rasters')\\n if not os.path.exists(ET0_dir):\\n os.makedirs(ET0_dir)\\n if not os.path.exists(precip_dir):\\n os.makedirs(precip_dir)\\n event_df = pandas.read_csv(events_table)\\n event_df.events = event_df.events * multiply_factor\\n event_df.to_csv(modified_event_table)\\n for month in xrange(1, 13):\\n baseline_raster_path = os.path.join(\\n baseline_precip_dir, 'precip_{}.tif'.format(month))\\n modified_precip_path = os.path.join(\\n precip_dir, 'precip_{}.tif'.format(month))\\n pygeoprocessing.raster_calculator(\\n [(baseline_raster_path, 1), (multiply_factor, 'raw')],\\n multiply_precip, modified_precip_path,\\n gdal.GDT_Float32, _TARGET_NODATA)\\n\\n Tavg_raster_path = os.path.join(\\n Tavg_dir, 'tavg_{}.tif'.format(month))\\n TD_raster_path = os.path.join(\\n TD_dir, 'tdiff_{}.tif'.format(month))\\n tavg_nodata = pygeoprocessing.get_raster_info(\\n Tavg_raster_path)['nodata'][0]\\n td_nodata = pygeoprocessing.get_raster_info(\\n TD_raster_path)['nodata'][0]\\n RA_val = (\\n RA_df.loc[RA_df['month'] == month, 'average_RA'].values[0])\\n RA_raster_path = os.path.join(climate_dir, 'RA.tif')\\n pygeoprocessing.new_raster_from_base(\\n modified_precip_path, RA_raster_path, gdal.GDT_Float32,\\n [_TARGET_NODATA], fill_value_list=[RA_val])\\n ET0_raster_path = os.path.join(ET0_dir, 'ET_{}.tif'.format(month))\\n pygeoprocessing.raster_calculator(\\n [(path, 1) for path in [\\n RA_raster_path, Tavg_raster_path, TD_raster_path,\\n modified_precip_path]],\\n calc_ET0, ET0_raster_path, gdal.GDT_Float32, _TARGET_NODATA)\",\n \"def generate(self, namespace: Optional[str], template: str, func: Callable, call_args: Dict) -> str:\",\n \"def gen_compute_template(self):\\n\\n print \\\"\\\\t* Generating compute template in file %s\\\" % self.compute_filename\\n if self.cmdline:\\n with open(self.compute_filename, 'w') as f:\\n f.write(yaml.safe_dump(self.compute_template))\\n\\n try:\\n self.heatclient.stacks.validate(template=yaml.safe_dump(self.compute_template))\\n except Exception as e:\\n print \\\"Unfortunately your file is malformed. Received error: (%s)\\\" % str(e)\\n print \\\"Exiting ...\\\"\\n sys.exit(1)\\n\\n return self.compute_template\",\n \"def psfTemplateModel(n, params):\\n psf_template = params[\\\"psf_template\\\"]\\n self.m_psf = psf_template\\n print(\\\"PSF template shape\\\", np.shape(psf_template))\\n dim = int(n)\\n m = np.shape(psf_template)[0]\\n #if m != dim:\\n # raise ValueError(\\\"PSF template dimension not equal patch size\\\")\\n \\n if np.sum(psf_template) != 1:\\n print(\\\"Normalizing PSF template to sum = 1\\\")\\n psf_template = psf_template/np.sum(psf_template) \\n return psf_template\",\n \"def inhomoCoeffs(Bulk_Operators, Bdy_Operators, Bulk_second, s=1):\\n\\t\\tRow = []\\n\\t\\tfor Bulk_Op in Bulk_Operators:\\n\\t\\t\\t\\ttable_call = table1.table[0, 0].subs({\\\"Delta\\\":Bulk_Op, \\\"Delta_12\\\":delta_12, \\\"Xi\\\":1.00})\\n\\t\\t\\t\\tRow.append(table_call)\\n\\t\\tfor index in range(len(Bulk_second)):\\n\\t\\t\\t\\tRow.append(0*table1.table[0,0])\\n\\t\\tfor Bdy_Op in Bdy_Operators:\\n\\t\\t\\tif Bdy_Op == 2.0:\\n\\t\\t\\t\\ttable_call = (N-1)*table1.table[1, 0].subs({\\\"Delta\\\":Bdy_Op, \\\"Xi\\\":1.00})\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttable_call = table1.table[1, 0].subs({\\\"Delta\\\":Bdy_Op, \\\"Xi\\\":1.00})\\n\\t\\t\\tRow.append(table_call)\\n\\t\\tif s==1:\\n\\t\\t\\tXi = symbols('Xi')\\n\\t\\t\\tlast_expr = Xi**((delta_1 + delta_2)/2)\\n\\t\\t\\tlast_elem = last_expr.subs({\\\"Xi\\\":1.00}).evalf()\\n\\t\\t\\tRow.append(last_elem)\\n\\t\\tRow2 = []\\n\\t\\tfor index in range(len(Bulk_Operators)):\\n\\t\\t\\t\\tRow2.append(0*table1.table[0,0])\\n\\t\\tfor Bulk_Op in Bulk_second:\\n\\t\\t\\t\\ttable_call = table1.table[0, 0].subs({\\\"Delta\\\":Bulk_Op, \\\"Delta_12\\\":delta_12, \\\"Xi\\\":1.00})\\n\\t\\t\\t\\tRow2.append(table_call)\\n\\t\\tfor Bdy_Op in Bdy_Operators:\\n\\t\\t\\tif Bdy_Op == 2.0:\\n\\t\\t\\t\\ttable_call = -1*table1.table[1,0].subs({\\\"Delta\\\":Bdy_Op, \\\"Xi\\\":1.00})\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttable_call = table1.table[1,0].subs({\\\"Delta\\\":Bdy_Op, \\\"Xi\\\":1.00})\\n\\t\\t\\tRow2.append(table_call)\\n\\t\\tif s==1:\\n\\t\\t\\tXi = symbols('Xi')\\n\\t\\t\\tlast_expr = Xi**((delta_1 + delta_2)/2)\\n\\t\\t\\tlast_elem = last_expr.subs({\\\"Xi\\\":1.00}).evalf()\\n\\t\\t\\tRow2.append(last_elem)\\n\\t\\t\\t\\n\\t\\treturn np.array(Row), np.array(Row2)\",\n \"def gen_op3(N11, N22, N12, rN11, rN22, rN12):\\n raw = N12 / (N11 * N22)\\n ref = rN12 / (rN11 * rN22)\\n print(\\\"=====\\\\n3rd method: raw = N12 / (N11 * N22)\\\")\\n print(\\\"raw: %.2f | reference: %.2f\\\" % (raw, ref))\\n print(\\\"OP: 1 - raw = %.3f\\\\n=====\\\" % (1 - raw))\",\n \"def hierarchical( x, output_prefix, labels_to_register=[2,3,4,5], is_test=False, verbose=True ):\\n if verbose:\\n print(\\\"Read\\\")\\n tfn = get_data('T_template0', target_extension='.nii.gz' )\\n tfnw = get_data('T_template0_WMP', target_extension='.nii.gz' )\\n tlrfn = get_data('T_template0_LR', target_extension='.nii.gz' )\\n bfn = antspynet.get_antsxnet_data( \\\"croppedMni152\\\" )\\n\\n ##### read images and do simple bxt ops\\n templatea = ants.image_read( tfn )\\n if verbose:\\n print(\\\"bxt\\\")\\n templatea = ( templatea * antspynet.brain_extraction( templatea, 't1' ) ).iMath( \\\"Normalize\\\" )\\n templateawmprior = ants.image_read( tfnw )\\n templatealr = ants.image_read( tlrfn )\\n templateb = ants.image_read( bfn )\\n templateb = ( templateb * antspynet.brain_extraction( templateb, 't1' ) ).iMath( \\\"Normalize\\\" )\\n imgbxt = brain_extraction( x )\\n img = x * imgbxt\\n\\n if verbose:\\n print(\\\"rbp\\\")\\n\\n # this is an unbiased method for identifying predictors that can be used to\\n # rank / sort data into clusters, some of which may be associated\\n # with outlierness or low-quality data\\n templatesmall = ants.resample_image( templateb, (91,109,91), use_voxels=True )\\n rbp = random_basis_projection( img, templatesmall )\\n\\n if verbose:\\n print(\\\"intensity\\\")\\n\\n ##### intensity modifications\\n img = ants.iMath( img, \\\"Normalize\\\" ) * 255.0\\n img = ants.denoise_image( img, imgbxt, noise_model='Gaussian')\\n img = ants.n4_bias_field_correction( img ).iMath(\\\"Normalize\\\")\\n\\n # optional - quick look at result\\n bxt_png = output_prefix + \\\"_brain_extraction_dnz_n4_view.png\\\"\\n ants.plot(img,axis=2,ncol=8,nslices=24, crop=True, black_bg=False,\\n filename = bxt_png )\\n\\n if verbose:\\n print(\\\"hemi\\\")\\n\\n # assuming data is reasonable quality, we should proceed with the rest ...\\n mylr = label_hemispheres( img, templatea, templatealr )\\n\\n if verbose:\\n print(\\\"parcellation\\\")\\n\\n ##### hierarchical labeling\\n myparc = deep_brain_parcellation( img, templateb,\\n do_cortical_propagation = not is_test, verbose=False )\\n\\n ##### accumulate data into data frames\\n hemi = map_segmentation_to_dataframe( \\\"hemisphere\\\", myparc['hemisphere_labels'] )\\n tissue = map_segmentation_to_dataframe( \\\"tissues\\\", myparc['tissue_segmentation'] )\\n dktl = map_segmentation_to_dataframe( \\\"lobes\\\", myparc['dkt_lobes'] )\\n dktp = map_segmentation_to_dataframe( \\\"dkt\\\", myparc['dkt_parcellation'] )\\n dktc = None\\n if not is_test:\\n dktc = map_segmentation_to_dataframe( \\\"dkt\\\", myparc['dkt_cortex'] )\\n\\n tissue_seg_png = output_prefix + \\\"_seg.png\\\"\\n ants.plot( img, myparc['tissue_segmentation'], axis=2, nslices=21, ncol=7,\\n alpha=0.6, filename=tissue_seg_png,\\n crop=True, black_bg=False )\\n\\n if verbose:\\n print(\\\"WMH\\\")\\n\\n ##### below here are more exploratory nice to have outputs\\n myhypo = t1_hypointensity(\\n img,\\n myparc['tissue_segmentation'], # segmentation\\n myparc['tissue_probabilities'][3], # wm posteriors\\n templatea,\\n templateawmprior )\\n\\n if verbose:\\n print(\\\"registration\\\")\\n\\n ##### traditional deformable registration as a high-resolution complement to above\\n wm_tractsL = None\\n wm_tractsR = None\\n wmtdfL = None\\n wmtdfR = None\\n reg = None\\n if labels_to_register is not None:\\n reg = hemi_reg(\\n input_image = img,\\n input_image_tissue_segmentation = myparc['tissue_segmentation'],\\n input_image_hemisphere_segmentation = mylr,\\n input_template=templatea,\\n input_template_hemisphere_labels=templatealr,\\n output_prefix = output_prefix + \\\"_SYN\\\",\\n labels_to_register = labels_to_register,\\n is_test=is_test )\\n if verbose:\\n print(\\\"wm tracts\\\")\\n ##### how to use the hemi-reg output to generate any roi value from a template roi\\n wm_tracts = ants.image_read( get_data( \\\"wm_major_tracts\\\", target_extension='.nii.gz' ) )\\n wm_tractsL = ants.apply_transforms( img, wm_tracts, reg['synL']['invtransforms'],\\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 1, 1 )\\n wm_tractsR = ants.apply_transforms( img, wm_tracts, reg['synR']['invtransforms'],\\n interpolator='genericLabel' ) * ants.threshold_image( mylr, 2, 2 )\\n wmtdfL = map_segmentation_to_dataframe( \\\"wm_major_tracts\\\", wm_tractsL )\\n wmtdfR = map_segmentation_to_dataframe( \\\"wm_major_tracts\\\", wm_tractsR )\\n\\n if verbose:\\n print(\\\"hippocampus\\\")\\n\\n ##### specialized labeling for hippocampus\\n ntries = 10\\n if is_test:\\n ntries = 1\\n hippLR = deep_hippo( img, templateb, ntries )\\n\\n mydataframes = {\\n \\\"hemispheres\\\":hemi,\\n \\\"tissues\\\":tissue,\\n \\\"dktlobes\\\":dktl,\\n \\\"dktregions\\\":dktp,\\n \\\"dktcortex\\\":dktc,\\n \\\"wmtracts_left\\\":wmtdfL,\\n \\\"wmtracts_right\\\":wmtdfR,\\n \\\"wmh\\\":myhypo['wmh_summary']\\n }\\n\\n outputs = {\\n \\\"brain_n4_dnz\\\": img,\\n \\\"brain_n4_dnz_png\\\": bxt_png,\\n \\\"brain_extraction\\\": imgbxt,\\n \\\"tissue_seg_png\\\": tissue_seg_png,\\n \\\"rbp\\\": rbp,\\n \\\"left_right\\\": mylr,\\n \\\"dkt_parc\\\": myparc,\\n \\\"registration\\\":reg,\\n \\\"hippLR\\\":hippLR,\\n \\\"white_matter_hypointensity\\\":myhypo,\\n \\\"wm_tractsL\\\":wm_tractsL,\\n \\\"wm_tractsR\\\":wm_tractsR,\\n \\\"dataframes\\\": mydataframes\\n }\\n\\n return outputs\",\n \"def calc_ti(self):\\n m = 0\\n for i in self.inl:\\n m += i.m.val_SI * i.fluid.val[self.fuel_alias.val]\\n\\n for o in self.outl:\\n m -= o.m.val_SI * o.fluid.val[self.fuel_alias.val]\\n\\n return m * self.lhv\",\n \"def generate_registry_operations(data):\\n data = render_to_string(TEMPLATE_VC, {\\\"data\\\": data})\\n open(os.path.join(PLUGINSPACE_TD_VC_DIR, FILENAME_VC), \\\"w+\\\").write(data)\\n return\",\n \"def prepare_CvD16_for_M2L_calc(templates_lam_range, verbose=False):\\n import glob\\n import os\\n template_glob=os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*.s100')\\n\\n vcj_models=sorted(glob.glob(template_glob))\\n temp_lamdas, x35, x3, x23, kroupa, flat=np.genfromtxt(vcj_models[-1], unpack=True)\\n\\n n_ages=7\\n n_zs=5\\n n_imfs=5\\n\\n \\n\\n\\n Zs=['m1.5', 'm1.0', 'm0.5', 'p0.0', 'p0.2']\\n ages=[1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.5]\\n model_imfs_order=['x35', 'x3', 'x23', 'kroupa', 'flat']\\n\\n t_mask = ((temp_lamdas > templates_lam_range[0]) & (temp_lamdas <templates_lam_range[1]))\\n\\n\\n\\n y=x35[t_mask]\\n x=temp_lamdas[t_mask]\\n\\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\\n interp=si.interp1d(x, y, fill_value='extrapolate')\\n out=interp(new_x)\\n\\n templates=np.empty((len(out), n_ages, n_zs, n_imfs))\\n\\n\\n\\n for a, Z in enumerate(Zs): \\n for b, age in enumerate(ages):\\n model=glob.glob(os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*{}*{}.ssp.s100'.format(Z, age)))[0]\\n if verbose:\\n print 'Loading {}'.format(model)\\n data=np.genfromtxt(model)\\n\\n for c, counter in enumerate(reversed(range(1, data.shape[-1]))):\\n \\n #Interpolate templates onto a uniform wavelength grid and then log-rebin\\n y=data[:, counter][t_mask] \\n x=temp_lamdas[t_mask]\\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\\n\\n interp=si.interp1d(x, y, fill_value='extrapolate')\\n out=interp(new_x) \\n\\n templates[:, b, a, c]=out\\n\\n return templates, new_x\",\n \"def __createSpecTempT(self):\\n\\t\\tspecTempT = np.copy(self.specTemplates)\\n\\t\\tspecTempT = np.transpose(specTempT)\\n\\t\\tspecTempT = specTempT * self.covInv\\n\\t\\t\\t\\t\\n\\t\\treturn specTempT\",\n \"def diffeo_apply(diffeo, template):\\n M, N = diffeo.shape[0], diffeo.shape[1]\\n result = np.empty_like(template)\\n for i, j in coords_iterate((M, N)):\\n i1 = diffeo[i, j, 0]\\n j1 = diffeo[i, j, 1]\\n result[i, j, ...] = template[i1, j1, ...]\\n return result\",\n \"def _template_formatting(field, inputs, inputs_dict_st):\\n from .specs import MultiInputObj, MultiOutputFile\\n\\n # if a template is a function it has to be run first with the inputs as the only arg\\n template = field.metadata[\\\"output_file_template\\\"]\\n if callable(template):\\n template = template(inputs)\\n\\n # as default, we assume that keep_extension is True\\n keep_extension = field.metadata.get(\\\"keep_extension\\\", True)\\n\\n inp_fields = re.findall(r\\\"{\\\\w+}\\\", template)\\n inp_fields_fl = re.findall(r\\\"{\\\\w+:[0-9.]+f}\\\", template)\\n inp_fields += [re.sub(\\\":[0-9.]+f\\\", \\\"\\\", el) for el in inp_fields_fl]\\n if len(inp_fields) == 0:\\n return template\\n\\n val_dict = {}\\n file_template = None\\n\\n for fld in inp_fields:\\n fld_name = fld[1:-1] # extracting the name form {field_name}\\n if fld_name not in inputs_dict_st:\\n raise AttributeError(f\\\"{fld_name} is not provided in the input\\\")\\n fld_value = inputs_dict_st[fld_name]\\n if fld_value is attr.NOTHING:\\n # if value is NOTHING, nothing should be added to the command\\n return attr.NOTHING\\n else:\\n # checking for fields that can be treated as a file:\\n # have type File, or value that is path like (including str with extensions)\\n if isinstance(fld_value, os.PathLike) or (\\n isinstance(fld_value, str) and \\\".\\\" in fld_value\\n ):\\n if file_template:\\n raise Exception(\\n f\\\"can't have multiple paths in {field.name} template,\\\"\\n f\\\" but {template} provided\\\"\\n )\\n else:\\n file_template = (fld_name, fld_value)\\n else:\\n val_dict[fld_name] = fld_value\\n\\n # if field is MultiOutputFile and some elements from val_dict are lists,\\n # each element of the list should be used separately in the template\\n # and return a list with formatted values\\n if field.type is MultiOutputFile and any(\\n [isinstance(el, (list, MultiInputObj)) for el in val_dict.values()]\\n ):\\n # all fields that are lists\\n keys_list = [\\n k for k, el in val_dict.items() if isinstance(el, (list, MultiInputObj))\\n ]\\n if any(\\n [len(val_dict[key]) != len(val_dict[keys_list[0]]) for key in keys_list[1:]]\\n ):\\n raise Exception(\\n f\\\"all fields used in {field.name} template have to have the same length\\\"\\n f\\\" or be a single value\\\"\\n )\\n formatted_value = []\\n for ii in range(len(val_dict[keys_list[0]])):\\n val_dict_el = copy(val_dict)\\n # updating values to a single element from the list\\n for key in keys_list:\\n val_dict_el[key] = val_dict[key][ii]\\n\\n formatted_value.append(\\n _element_formatting(\\n template, val_dict_el, file_template, keep_extension=keep_extension\\n )\\n )\\n else:\\n formatted_value = _element_formatting(\\n template, val_dict, file_template, keep_extension=keep_extension\\n )\\n return formatted_value\",\n \"def calculate_output(self):\",\n \"def _fixPODTemplatesInstructions(self):\\n # delete the filtered_groups_in_charge script in portal_skins/custom\\n if \\\"filtered_groups_in_charge\\\" in self.portal.portal_skins.custom.objectIds():\\n self.portal.portal_skins.custom.manage_delObjects(ids=[\\\"filtered_groups_in_charge\\\"])\\n # for every POD templates\\n replacements = {\\n 'self.filtered_groups_in_charge':\\n 'self.get_representatives_in_charge',\\n }\\n self.updatePODTemplatesCode(replacements=replacements)\",\n \"def write_input(self):\\n # load template, substitute parameters and write input file\\n self.homog_core()\\n self.write_mat_string()\\n input_tmpl = open('base_input.txt')\\n templ = Template(input_tmpl.read())\\n file_string = templ.substitute(cool_frac = self.vfrac_cermet,\\n r_core = self.r,\\n core_z = self.z,\\n r_refl = self.r + self.refl_t,\\n refl_min = -self.refl_t,\\n refl_max = self.z + self.refl_t,\\n fuel_string = self.fuel_string,\\n fuel_rho = self.rho,\\n fuel_vol = self.core_vol,\\n refl_vol = self.core_vol,\\n thermal_power = self.Q_therm)\\n # write the file\\n filename = 'r_{0}_{1}.i'.format(round(self.vfrac_cermet, 3), \\n round(self.r, 3))\\n ifile = open(filename, 'w')\\n ifile.write(file_string)\\n ifile.close()\\n\\n return filename\",\n \"def getTru(n,int_method,func) :\\n m = np.asarray([0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,6,7,8,9,10])\\n bes = np.asarray([-0.5,0,0.43675,0.47773,0.49231,0.49316,0.49280,0.50325,\\n 0.51140,0.52169,0.55823,0.58086,0.60463,0.61483,0.66995])\\n p = np.asarray([1,0,0.61007,0.77491,0.84071,0.87689,0.89914,0.91365,\\n 0.92449,0.93279,0.94451,0.95289,0.95904,0.96385,\\n 0.96731])\\n h1 = np.asarray([0,0,-0.07257,-0.04963,-0.03313,-0.02282,-0.01648,\\n -0.01248,-0.00970,-0.00773,-0.00522,-0.00369,-0.00272,\\n -0.00206,-0.00164])\\n h2 = np.asarray([0,0,-0.20048,-0.15556,-0.12070,-0.09611,-0.07919,\\n -0.06747,-0.05829,-0.05106,-0.04060,-0.03311,-0.02768,\\n -0.02353,-0.02053])\\n h3 = np.asarray([0,0,0.01647,0.08284,0.14390,0.19680,0.24168,0.27969,\\n 0.31280,0.34181,0.39002,0.42942,0.46208,0.48997,0.51325])\\n \\n if (func == True) :\\n int_bes = splev(n,splrep(m, bes))\\n int_p = splev(n,splrep(m, p))\\n int_h1 = splev(n,splrep(m, h1))\\n int_h2 = splev(n,splrep(m, h2))\\n int_h3 = splev(n,splrep(m, h3))\\n else :\\n int_bes = griddata(m, bes, n, method=int_method)\\n int_p = griddata(m, p, n, method=int_method)\\n int_h1 = griddata(m, h1, n, method=int_method)\\n int_h2 = griddata(m, h2, n, method=int_method)\\n int_h3 = griddata(m, h3, n, method=int_method)\\n \\n return np.asarray([int_bes,int_p,int_h1,int_h2,int_h3])\",\n \"def New(*args, **kargs):\\n obj = itkTernaryAddImageFilterIUS2IUS2IUS2IUS2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def Generate_BG_Template(outputSize=300, angularSize = 10, fileOut = 'BGRateMap.pickle' ):\\r\\n template = np.zeros((outputSize,outputSize))\\r\\n ppd=float(outputSize)/float(angularSize) # pixels per deg\\r\\n \\r\\n events110 = ParseFermi.Import_File('photons.txt', energyRange = (120000,140000),lonRange=(-5,5),latRange = (-5,5))\\r\\n events130 = ParseFermi.Import_File('photons.txt', energyRange = (100000,120000),lonRange=(-5,5),latRange = (-5,5))\\r\\n events150 = ParseFermi.Import_File('photons.txt', energyRange = (140000,200000),lonRange=(-5,5),latRange = (-5,5))\\r\\n \\r\\n for i in range(10000,200001,20000):\\r\\n if i == 130000:\\r\\n continue\\r\\n events = ParseFermi.Import_File('photons.txt', energyRange = (i-10000,i+10000),lonRange=(-5,5),latRange = (-5,5))\\r\\n BG = np.zeros((outputSize,outputSize)) \\r\\n for j in events:\\r\\n xIDX = int(j[1]*ppd+float(outputSize/2))\\r\\n yIDX = int(j[2]*ppd+float(outputSize/2))\\r\\n BG[yIDX][xIDX] += 1.0\\r\\n \\r\\n psfDeg = .2+float(200)/float(i)\\r\\n psfOut = psfDeg*ppd\\r\\n #print i/1e3, psfDeg, psfOut\\r\\n \\r\\n template += scipy.ndimage.filters.gaussian_filter(BG, psfOut)\\r\\n \\r\\n template = template/np.max(template)\\r\\n \\r\\n # Write to file \\r\\n outFile = open(fileOut, \\\"wb\\\" )\\r\\n pickle.dump(template, outFile)\\r\\n print 'Rate Map saved to ', fileOut\\r\\n \\r\\n plt.imshow(scipy.fliplr(template), 'jet',extent=[5,-5,-5,5])\\r\\n\\r\\n plt.xlabel(r'$l [^\\\\circ]$')\\r\\n plt.ylabel(r'$b [^\\\\circ]$')\\r\\n plt.xlim(5,-5)\\r\\n plt.ylim(-5,5)\\r\\n plt.colorbar()\\r\\n\\r\\n x,y = Find_Centroid(template)\\r\\n x,y = (x/ppd -angularSize/2.0,) ,(y/ppd -angularSize/2.0,)\\r\\n print x,y\\r\\n plt.scatter(x,y, s=10, c='r', marker = '+')\\r\\n \\r\\n X,Y = FormatEvents(events110)\\r\\n plt.scatter(X, Y, label = '100-120 GeV', marker = 'o' , c = 'k')\\r\\n \\r\\n X,Y = FormatEvents(events130)\\r\\n plt.scatter(X, Y, label = '120-140 GeV', marker = 'o' , c = 'r')\\r\\n \\r\\n X,Y = FormatEvents(events150)\\r\\n plt.scatter(X, Y, label = '140-200 GeV', marker = 'o' , c = 'g' )\\r\\n \\r\\n from matplotlib.font_manager import FontProperties\\r\\n fontP = FontProperties()\\r\\n fontP.set_size('small')\\r\\n plt.legend(loc=1, ncol=1, fancybox=True, shadow=False,prop=fontP,borderaxespad=0.,labelspacing = .2)\\r\\n \\r\\n from matplotlib.backends.backend_pdf import PdfPages\\r\\n if fileOut != '':\\r\\n pp = PdfPages(fileOut + '_sideband.pdf')\\r\\n plt.savefig(pp, format='pdf')\\r\\n print \\\"Figures saved to \\\", str(fileOut)+ '_sideband.pdf\\\\n',\\r\\n pp.close()\\r\\n \\r\\n plt.show()\\r\\n return template\",\n \"def defineProcessTemplates(histos):\\n\\n templates=[]\\n\\n #nominal\\n templates.append( histos[0] )\\n nomStats=templates[-1].Integral()\\n\\n #systematic variations\\n #if Up/Down already in the name store directly updating the name\\n #if not, mirror the variation given \\n for i in xrange(1,len(histos)): \\n templates.append( histos[i] )\\n key=templates[-1].GetName()\\n if not 'Up' in key and not 'Down' in key :\\n templates[-1].SetName(key+'Up')\\n templates.append( histos[i].Clone(key+'Down') )\\n for xbin in range(templates[0].GetNbinsX()):\\n templates[-1].SetBinContent(xbin+1,2*templates[0].GetBinContent(xbin+1)-templates[-2].GetBinContent(xbin+1))\\n \\n #don't leave bins with 0's\\n for h in templates:\\n h.SetDirectory(0)\\n iStats=h.Integral()\\n if iStats>0: h.Scale(nomStats/iStats)\\n for xbin in range(h.GetNbinsX()):\\n if h.GetBinContent(xbin+1)>0: continue\\n h.SetBinContent(xbin+1,1e-6)\\n \\n return templates\",\n \"def generate():\",\n \"def _template(inlist):\\n from collections import OrderedDict\\n if isinstance(inlist, str):\\n inlist = [inlist]\\n\\n templates = []\\n for item in reversed(inlist):\\n templates.append(output_space(item))\\n\\n return OrderedDict(reversed(OrderedDict(templates).items()))\",\n \"def compute_template_spectrum(self):\\n b = self.setup_b_instance(1,add_ps_mask=False)\\n En_min = b.CTB_en_bins[0]\\n En_max = b.CTB_en_bins[1]\\n dE = En_max - En_min\\n self.En_center = 10**((np.log10(En_max)+np.log10(En_min))/2)\\n\\n flux_map = b.fixed_template_dict_nested['summed_templates'] / b.CTB_exposure_maps_masked_compressed[0]/float(hp.nside2pixarea(b.nside))\\n self.spectrum = np.mean(flux_map)/dE\",\n \"def generate(self):\\n\\n letter = ''\\n\\n for template_name in self.templates:\\n template = self.templates[template_name]\\n\\n # Process all replacements ({...} syntax).\\n replacements = re.finditer(REPLACEMENT_REGEX, template)\\n for replacement in replacements:\\n match = replacement.group()\\n key = replacement.group(1)\\n\\n template = template.replace(match, self._lookup(key))\\n\\n # Process all conditionals (<...> syntax).\\n conditionals = re.finditer(CONDITIONAL_REGEX, template)\\n for conditional in conditionals:\\n match = conditional.group()\\n\\n # Process each condition within the conditional ([...]... syntax).\\n conditions = re.finditer(CONDITION_REGEX, match)\\n for index, condition in enumerate(conditions):\\n skill_type = condition.group(2)\\n skill = condition.group(3)\\n text = condition.group(4)\\n\\n # If the skill is empty, treat it as a catch all case.\\n if not skill or skill in self._lookup(skill_type):\\n template = template.replace(match, text)\\n break\\n\\n letter += template\\n\\n return letter\",\n \"def Generate(self,the_scf):\\n self.dip_ints = the_scf.mol.intor('cint1e_r_sph', comp=3) # component,ao,ao.\\n charges = the_scf.mol.atom_charges()\\n coords = the_scf.mol.atom_coords()\\n self.nuc_dip = np.einsum('i,ix->x', charges, coords)\\n return\",\n \"def New(*args, **kargs):\\n obj = itkIsoDataThresholdCalculatorHDUS.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def Template(Fenetre_largeur,Fenetre_hauteur):\\r\\n li= Select_ligne(\\\"Nombre de lignes: \\\",Fenetre_largeur,Fenetre_hauteur)\\r\\n nom=\\\"Template\\\"\\r\\n fich=\\\"Template\\\"\\r\\n version=0\\r\\n while Path(\\\"stages/\\\"+fich+\\\".txt\\\").is_file() == True:\\r\\n version+=1\\r\\n fich=nom+str(version)\\r\\n fichier=open(\\\"stages/\\\"+fich+\\\".txt\\\",'w')\\r\\n fichier.write(str(li))\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n for i in range(li):\\r\\n for j in range(10):\\r\\n fichier.write(\\\"0,0|\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"gauche: resistance, droite: bonus\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"resistance max: 3\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"6=barre+\\\")\\r\\n fichier.write(\\\"\\\\n\\\")\\r\\n fichier.write(\\\"7=score+\\\")\\r\\n fichier.close()\",\n \"def create_eqt_template(nodes, input_filename):\\n output_filename = f'{input_filename[:-4]}_eqpt_sheet.txt'\\n with open(output_filename, 'w', encoding='utf-8') as my_file:\\n # print header similar to excel\\n my_file.write('OPTIONAL\\\\n\\\\n\\\\n\\\\\\n \\\\t\\\\tNode a egress amp (from a to z)\\\\t\\\\t\\\\t\\\\t\\\\tNode a ingress amp (from z to a) \\\\\\n \\\\nNode A \\\\tNode Z \\\\tamp type \\\\tatt_in \\\\tamp gain \\\\ttilt \\\\tatt_out\\\\\\n amp type \\\\tatt_in \\\\tamp gain \\\\ttilt \\\\tatt_out\\\\n')\\n\\n for node in nodes.values():\\n if node.eqpt == 'ILA':\\n my_file.write(f'{node.uid}\\\\t{node.to_node[0]}\\\\n')\\n if node.eqpt == 'ROADM':\\n for to_node in node.to_node:\\n my_file.write(f'{node.uid}\\\\t{to_node}\\\\n')\\n\\n print(f'File {output_filename} successfully created with Node A - Node Z entries for Eqpt sheet in excel file.')\",\n \"def wrapper(*args, **kwargs):\\n assert not kwargs, \\\"Do not support kwargs in template function call\\\"\\n task_env = TaskExtractEnv.current\\n if task_env is not None and task_env.tracing:\\n task_env.add_task(task_name, args)\\n workload = args_to_workload(args, task_name)\\n tgt = Target.current()\\n cfg = DispatchContext.current.query(tgt, workload)\\n node = topi_compute(cfg, *args)\\n\\n # attach workload to return op\\n op = node.op\\n attrs = {}\\n for k, v in node.op.attrs.items():\\n attrs[k] = v\\n attrs[\\\"workload\\\"] = workload\\n if isinstance(op, tensor.ComputeOp):\\n op = tvm.te._ffi_api.ComputeOp(op.name, op.tag, attrs, op.axis, op.body)\\n elif isinstance(op, tensor.ExternOp):\\n op = tvm.te._ffi_api.ExternOp(\\n op.name,\\n op.tag,\\n attrs,\\n op.inputs,\\n op.input_placeholders,\\n op.output_placeholders,\\n op.body,\\n )\\n else:\\n raise RuntimeError(\\\"Unsupported op type: \\\" + str(type(op)))\\n\\n if isinstance(node, tensor.Tensor):\\n return op.output(0)\\n return [op.output(i) for i in range(len(node))]\",\n \"def New(*args, **kargs):\\n obj = itkTernaryAddImageFilterIUC2IUC2IUC2IUC2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def calc_raws(df, unit='leader'):\\n\\n df['distrust'] = df['HDIS']/(df['HDIS']+df['LDIS'])\\n df['task'] = df['HTASK']/(df['HTASK']+df['LTASK'])\\n df['bace'] = df['IC']/(df['IC']+df['EC'])\\n df['igb'] = df['HBIAS']/(df['HBIAS']+df['LBIAS'])\\n df['sc'] = df['HSC']/(df['HSC']+df['LSC'])\\n df['cc'] = df['HCC']/(df['HCC']+df['LCC'])\\n df['power'] = df['HPWR']/(df['HPWR']+df['LPWR'])\\n\\n df['i1'] = df.apply(i1_func, axis=1)\\n df['i2'] = df.apply(i2_func, axis=1)\\n df['i3'] = df.apply(i3_func, axis=1)\\n\\n df['i4a'] = df.apply(i4a_func, axis=1)\\n df['i4b'] = df.apply(i4b_func, axis=1)\\n df['i5ap'] = df.apply(i5ap_func, axis=1)\\n df['i5pr'] = df.apply(i5pr_func, axis=1)\\n df['i5re'] = df.apply(i5re_func, axis=1)\\n df['i5op'] = df.apply(i5op_func, axis=1)\\n df['i5th'] = df.apply(i5th_func, axis=1)\\n df['i5pu'] = df.apply(i5pu_func, axis=1)\\n\\n df['p1'] = df.apply(p1_func, axis=1)\\n df['p2'] = df.apply(p2_func, axis=1)\\n df['p3'] = df.apply(p3_func, axis=1)\\n df['p4'] = df.apply(p4_func, axis=1)\\n df['p5'] = df.apply(p5_func, axis=1)\\n\\n if unit == 'leader':\\n\\n keep = ['firstname', 'lastname', 'name', 'Ccode', 'vcount', 'distrust', 'task',\\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\\n 'p5']\\n\\n df = df[keep]\\n\\n elif unit == 'year':\\n\\n keep = ['firstname', 'lastname', 'name', 'year', 'Ccode', 'vcount', 'distrust', 'task',\\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\\n 'p5']\\n\\n df = df[keep]\\n\\n elif unit == 'month':\\n\\n keep = ['firstname', 'lastname', 'name', 'yr_month','Ccode', 'vcount', 'distrust', 'task',\\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\\n 'p5']\\n\\n df = df[keep]\\n\\n df['year'] = df['yr_month'].apply(lambda x: x.split('-')[0])\\n df['month'] = df['yr_month'].apply(lambda x: x.split('-')[1])\\n\\n elif unit == 'quarter':\\n\\n keep = ['firstname', 'lastname', 'name', 'yr_quarter', 'Ccode', 'vcount', 'distrust', 'task',\\n 'bace', 'igb', 'sc', 'cc', 'power', 'i1', 'i2', 'i3', 'i4a', 'i4b',\\n 'i5ap', 'i5pr', 'i5re', 'i5op', 'i5th', 'i5pu', 'p1', 'p2', 'p3', 'p4',\\n 'p5']\\n\\n df = df[keep]\\n\\n df['year'] = df['yr_quarter'].apply(lambda x: x.split('-')[0])\\n df['quarter'] = df['yr_quarter'].apply(lambda x: x.split('-')[1])\\n\\n return df\",\n \"def car_template(ncomp, ra0, ra1, dec0, dec1, res):\\n\\n if ncomp == 3:\\n pre = (3,)\\n else:\\n pre = ()\\n\\n box = get_box(ra0, ra1, dec0, dec1)\\n res = res * np.pi / (180 * 60)\\n temp = so_map()\\n shape, wcs = enmap.geometry(box, res=res, pre=pre)\\n temp.data = enmap.zeros(shape, wcs=wcs, dtype=None)\\n temp.pixel = \\\"CAR\\\"\\n temp.nside = None\\n temp.ncomp = ncomp\\n temp.geometry = temp.data.geometry[1:]\\n temp.coordinate = \\\"equ\\\"\\n return temp\",\n \"def New(*args, **kargs):\\n obj = itkTernaryAddImageFilterIUL2IUL2IUL2IUL2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def get_template_script(output_dir='.', keep_schrodinger=False, keep_openeye=False,\\n systems='all'):\\n paths = examples_paths()\\n template_script = \\\"\\\"\\\"\\n ---\\n options:\\n output_dir: {output_dir}\\n default_number_of_iterations: 0\\n temperature: 300*kelvin\\n pressure: 1*atmosphere\\n minimize: no\\n verbose: no\\n default_nsteps_per_iteration: 1\\n molecules:\\n benzene:\\n filepath: {benzene_path}\\n antechamber: {{charge_method: bcc}}\\n benzene-epik0:\\n filepath: {benzene_path}\\n epik:\\n select: 0\\n antechamber: {{charge_method: bcc}}\\n benzene-epikcustom:\\n filepath: {benzene_path}\\n epik:\\n select: 0\\n ph: 7.0\\n tautomerize: yes\\n antechamber: {{charge_method: bcc}}\\n p-xylene:\\n filepath: {pxylene_path}\\n antechamber: {{charge_method: bcc}}\\n p-xylene-name:\\n name: p-xylene\\n openeye: {{quacpac: am1-bcc}}\\n antechamber: {{charge_method: null}}\\n toluene:\\n filepath: {toluene_path}\\n antechamber: {{charge_method: bcc}}\\n toluene-smiles:\\n smiles: Cc1ccccc1\\n antechamber: {{charge_method: bcc}}\\n toluene-name:\\n name: toluene\\n antechamber: {{charge_method: bcc}}\\n Abl:\\n filepath: {abl_path}\\n T4Lysozyme:\\n filepath: {lysozyme_path}\\n solvents:\\n vacuum:\\n nonbonded_method: NoCutoff\\n GBSA-OBC2:\\n nonbonded_method: NoCutoff\\n implicit_solvent: OBC2\\n PME:\\n nonbonded_method: PME\\n nonbonded_cutoff: 1*nanometer\\n clearance: 10*angstroms\\n positive_ion: Na+\\n negative_ion: Cl-\\n leap:\\n parameters: [leaprc.water.tip4pew]\\n systems:\\n explicit-system:\\n receptor: benzene\\n ligand: toluene\\n solvent: PME\\n leap:\\n parameters: [leaprc.protein.ff14SB, leaprc.gaff]\\n implicit-system:\\n receptor: T4Lysozyme\\n ligand: p-xylene\\n solvent: GBSA-OBC2\\n leap:\\n parameters: [leaprc.protein.ff14SB, leaprc.gaff]\\n hydration-system:\\n solute: toluene\\n solvent1: PME\\n solvent2: vacuum\\n leap:\\n parameters: [leaprc.protein.ff14SB, leaprc.gaff]\\n mcmc_moves:\\n single:\\n type: LangevinSplittingDynamicsMove\\n sequence:\\n type: SequenceMove\\n move_list:\\n - type: MCDisplacementMove\\n - type: LangevinDynamicsMove\\n samplers:\\n repex:\\n type: ReplicaExchangeSampler\\n sams:\\n type: SAMSSampler\\n protocols:\\n absolute-binding:\\n complex:\\n alchemical_path:\\n lambda_electrostatics: [1.0, 0.5, 0.0]\\n lambda_sterics: [1.0, 0.5, 0.0]\\n solvent:\\n alchemical_path:\\n lambda_electrostatics: [1.0, 0.5, 0.0]\\n lambda_sterics: [1.0, 0.5, 0.0]\\n hydration-protocol:\\n solvent1:\\n alchemical_path:\\n lambda_electrostatics: [1.0, 0.0]\\n lambda_sterics: [1.0, 0.0]\\n solvent2:\\n alchemical_path:\\n lambda_electrostatics: [1.0, 0.0]\\n lambda_sterics: [1.0, 1.0]\\n experiments:\\n system: explicit-system\\n protocol: absolute-binding\\n \\\"\\\"\\\".format(output_dir=output_dir, benzene_path=paths['benzene'],\\n pxylene_path=paths['p-xylene'], toluene_path=paths['toluene'],\\n abl_path=paths['abl'], lysozyme_path=paths['lysozyme'])\\n\\n # Load script as dictionary.\\n script_dict = yank_load(template_script)\\n\\n # Find all molecules that require optional tools.\\n molecules_to_remove = []\\n for molecule_id, molecule_description in script_dict['molecules'].items():\\n need_schrodinger = 'epik' in molecule_description\\n need_openeye = any([k in molecule_description for k in ['name', 'smiles', 'openeye']])\\n if ((need_schrodinger and not keep_schrodinger) or\\n (need_openeye and not keep_openeye)):\\n molecules_to_remove.append(molecule_id)\\n\\n # Remove molecules.\\n for molecule_id in molecules_to_remove:\\n del script_dict['molecules'][molecule_id]\\n\\n # Remove systems.\\n if systems != 'all':\\n systems_to_remove = [s for s in script_dict['systems'] if s not in systems]\\n for system_id in systems_to_remove:\\n del script_dict['systems'][system_id]\\n\\n return script_dict\",\n \"def template(self) -> Dict[Union[int, str], int]:\\r\\n if not hasattr(self, '_template'):\\r\\n index = 0\\r\\n self._template = {}\\r\\n n = sum(self.sequence)\\r\\n for i, step in enumerate(self):\\r\\n max = index + step - 1\\r\\n max_next = max + self.sequence[i + 1] if i < len(self.sequence) - 1 else 0\\r\\n for j in range(index, index + step):\\r\\n if j < max:\\r\\n self._template[j] = j + 1\\r\\n if j + step < n and j + step <= max_next:\\r\\n self._template[str(j)] = j + step\\r\\n index += step\\r\\n return self._template\",\n \"def calc_population_02_state(U):\\n if U.type=='oper':\\n sump = 0\\n for i_list in [[0,2]]:\\n for j_list in [[1,1]]:\\n bra_i = qtp.tensor(qtp.ket([i_list[0]], dim=[3]),\\n qtp.ket([i_list[1]], dim=[3])).dag()\\n ket_j = qtp.tensor(qtp.ket([j_list[0]], dim=[3]),\\n qtp.ket([j_list[1]], dim=[3]))\\n p = np.abs((bra_i*U*ket_j).data[0, 0])**2\\n sump += p\\n return np.real(sump)\\n elif U.type=='super':\\n sump = 0\\n for i_list in [[0,2]]:\\n for j_list in [[1,1]]:\\n ket_i = qtp.tensor(qtp.ket([i_list[0]], dim=[3]),\\n qtp.ket([i_list[1]], dim=[3]))\\n rho_i=qtp.operator_to_vector(qtp.ket2dm(ket_i))\\n ket_j = qtp.tensor(qtp.ket([j_list[0]], dim=[3]),\\n qtp.ket([j_list[1]], dim=[3]))\\n rho_j=qtp.operator_to_vector(qtp.ket2dm(ket_j))\\n p = (rho_i.dag()*U*rho_j).data[0, 0]\\n sump += p\\n return np.real(sump)\",\n \"def calc_Cinv_CCGT(CC_size_W, CCGT_cost_data):\\n\\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\\n # capacity for the corresponding technology from the database\\n if CC_size_W < CCGT_cost_data['cap_min'][0]:\\n CC_size_W = CCGT_cost_data['cap_min'][0]\\n CCGT_cost_data = CCGT_cost_data[\\n (CCGT_cost_data['cap_min'] <= CC_size_W) & (CCGT_cost_data['cap_max'] > CC_size_W)]\\n\\n\\n #costs of connection\\n connection_costs = ngas.calc_Cinv_gas(CC_size_W)\\n\\n Inv_a = CCGT_cost_data.iloc[0]['a']\\n Inv_b = CCGT_cost_data.iloc[0]['b']\\n Inv_c = CCGT_cost_data.iloc[0]['c']\\n Inv_d = CCGT_cost_data.iloc[0]['d']\\n Inv_e = CCGT_cost_data.iloc[0]['e']\\n Inv_IR = CCGT_cost_data.iloc[0]['IR_%']\\n Inv_LT = CCGT_cost_data.iloc[0]['LT_yr']\\n Inv_OM = CCGT_cost_data.iloc[0]['O&M_%'] / 100\\n\\n InvC = Inv_a + Inv_b * (CC_size_W) ** Inv_c + (Inv_d + Inv_e * CC_size_W) * log(CC_size_W)\\n\\n Capex_a_CCGT_USD = calc_capex_annualized((InvC+connection_costs), Inv_IR, Inv_LT)\\n Opex_fixed_CCGT_USD = InvC * Inv_OM\\n Capex_CCGT_USD = InvC\\n\\n return Capex_a_CCGT_USD, Opex_fixed_CCGT_USD, Capex_CCGT_USD\",\n \"def _t_at_interface(self, polarization, n_1, n_2):\\n if polarization == 's':\\n return 2*n_1/(n_1 + n_2)\\n elif polarization == 'p':\\n return 2*n_1/(n_1 + n_2)\\n else:\\n raise ValueError(\\\"Polarization must be 's' or 'p'\\\")\",\n \"def test_operator_to_template(self):\\n\\n from kubernetes import client as k8s_client\\n\\n with dsl.Pipeline('somename') as p:\\n msg1 = dsl.PipelineParam('msg1')\\n msg2 = dsl.PipelineParam('msg2', value='value2')\\n op = dsl.ContainerOp(name='echo', image='image', command=['sh', '-c'],\\n arguments=['echo %s %s | tee /tmp/message.txt' % (msg1, msg2)],\\n file_outputs={'merged': '/tmp/message.txt'}) \\\\\\n .add_volume_mount(k8s_client.V1VolumeMount(\\n mount_path='/secret/gcp-credentials',\\n name='gcp-credentials')) \\\\\\n .add_env_variable(k8s_client.V1EnvVar(\\n name='GOOGLE_APPLICATION_CREDENTIALS',\\n value='/secret/gcp-credentials/user-gcp-sa.json'))\\n golden_output = {\\n 'container': {\\n 'image': 'image',\\n 'args': [\\n 'echo {{inputs.parameters.msg1}} {{inputs.parameters.msg2}} | tee /tmp/message.txt'\\n ],\\n 'command': ['sh', '-c'],\\n 'env': [\\n {\\n 'name': 'GOOGLE_APPLICATION_CREDENTIALS',\\n 'value': '/secret/gcp-credentials/user-gcp-sa.json'\\n }\\n ],\\n 'volumeMounts':[\\n {\\n 'mountPath': '/secret/gcp-credentials',\\n 'name': 'gcp-credentials',\\n }\\n ]\\n },\\n 'inputs': {'parameters':\\n [\\n {'name': 'msg1'},\\n {'name': 'msg2', 'value': 'value2'},\\n ]},\\n 'name': 'echo',\\n 'outputs': {\\n 'parameters': [\\n {'name': 'echo-merged',\\n 'valueFrom': {'path': '/tmp/message.txt'}\\n }],\\n 'artifacts': [{\\n 'name': 'mlpipeline-ui-metadata',\\n 'path': '/mlpipeline-ui-metadata.json',\\n 's3': {\\n 'accessKeySecret': {\\n 'key': 'accesskey',\\n 'name': 'mlpipeline-minio-artifact',\\n },\\n 'bucket': 'mlpipeline',\\n 'endpoint': 'minio-service.kubeflow:9000',\\n 'insecure': True,\\n 'key': 'runs/{{workflow.uid}}/{{pod.name}}/mlpipeline-ui-metadata.tgz',\\n 'secretKeySecret': {\\n 'key': 'secretkey',\\n 'name': 'mlpipeline-minio-artifact',\\n }\\n }\\n },{\\n 'name': 'mlpipeline-metrics',\\n 'path': '/mlpipeline-metrics.json',\\n 's3': {\\n 'accessKeySecret': {\\n 'key': 'accesskey',\\n 'name': 'mlpipeline-minio-artifact',\\n },\\n 'bucket': 'mlpipeline',\\n 'endpoint': 'minio-service.kubeflow:9000',\\n 'insecure': True,\\n 'key': 'runs/{{workflow.uid}}/{{pod.name}}/mlpipeline-metrics.tgz',\\n 'secretKeySecret': {\\n 'key': 'secretkey',\\n 'name': 'mlpipeline-minio-artifact',\\n }\\n }\\n }]\\n }\\n }\\n\\n self.maxDiff = None\\n self.assertEqual(golden_output, compiler.Compiler()._op_to_template(op))\",\n \"def gen_compute_data(self):\\n\\n print \\\"\\\\t* Generating combined nova and neutron data\\\"\\n self.init_compute_clients()\\n self.compute_data[\\\"heat_template_version\\\"] = \\\"2013-05-23\\\"\\n self.compute_data[\\\"description\\\"] = \\\"Generated Template %s on Project %s\\\" % \\\\\\n (str(datetime.datetime.now().strftime(\\\"%A, %d. %B %Y %I:%M%p\\\")), str(self.tenant_name))\\n self.compute_data[\\\"parameters\\\"] = {}\\n self.compute_data[\\\"resources\\\"] = {}\\n self.gen_parameters()\\n self.gen_resources()\\n self.compute_template = self.compute_data\",\n \"def calc_CC_operation(el_output_from_GT_W, GT_size_W, fuel_type, T_sup_K):\\n\\n (eta0, m0_exhaust_GT_kgpers) = calc_GT_operation_fullload(GT_size_W, fuel_type)\\n (eta, m_exhaust_GT_kgpers, T_exhaust_GT_K, m_fuel_kgpers) = calc_GT_operation_partload(el_output_from_GT_W,\\n GT_size_W, eta0,\\n m0_exhaust_GT_kgpers,\\n fuel_type)\\n\\n (q_output_ST_W, el_output_ST_W) = calc_ST_operation(m_exhaust_GT_kgpers, T_exhaust_GT_K, T_sup_K, fuel_type)\\n\\n LHV = LHV_NG if fuel_type == 'NG' else LHV_BG # read LHV of NG or BG\\n\\n eta_el = (el_output_from_GT_W + el_output_ST_W) / (m_fuel_kgpers * LHV)\\n eta_thermal = q_output_ST_W / (m_fuel_kgpers * LHV)\\n eta_total = eta_el + eta_thermal\\n el_output_W = el_output_ST_W + el_output_from_GT_W\\n\\n return {'el_output_W': el_output_W, 'q_output_ST_W': q_output_ST_W, 'eta_el': eta_el, 'eta_thermal': eta_thermal,\\n 'eta_total': eta_total}\",\n \"def nlg(state, slots=[]):\\r\\n output = get_template_res(state, slots) # get the generated output/response\\r\\n return output # return the appropriate response to the user\\r\",\n \"def nb_clementsbekkers(data, template):\\n ## Prepare a bunch of arrays we'll need later\\n n_template = len(template)\\n # if n_template <= 1:\\n # raise ValueError(\\\"nb_clementsbekkers: Length of template must be useful, and > 1\\\")\\n n_data = data.shape[0]\\n n_dt = n_data - n_template\\n # if n_dt < 10:\\n # raise ValueError(\\\"nb_clementsbekkers: n_dt, n_template\\\", n_dt, n_template)\\n #\\n sum_template = template.sum()\\n sum_template_2 = (template*template).sum()\\n\\n data_2 = data*data\\n sum_data = np.sum(data[:n_template])\\n sum_data_2 = data_2[:n_template].sum()\\n scale = np.zeros(n_dt)\\n offset = np.zeros(n_dt)\\n crit = np.zeros(n_dt)\\n for i in range(n_dt):\\n if i > 0:\\n sum_data = sum_data + data[i+n_template] - data[i-1]\\n sum_data_2 = sum_data_2 + data_2[i+n_template] - data_2[i-1]\\n sum_data_template_prod = np.multiply(data[i:i+n_template], template).sum()\\n scale[i] = (\\n (sum_data_template_prod - sum_data * sum_template/n_template)/\\n (sum_template_2 - sum_template*sum_template/n_template)\\n )\\n offset[i] = (sum_data - scale[i]*sum_template)/n_template\\n fitted_template = template * scale[i] + offset[i]\\n sse = ((data[i:i+n_template] - fitted_template)**2).sum()\\n crit[i] = scale[i]/np.sqrt(sse/(n_template-1))\\n DC = scale/crit\\n return(DC, scale, crit)\",\n \"def New(*args, **kargs):\\n obj = itkSubtractImageFilterIUS2IUS2IUS2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def tmpl_to_str(self, template_idx, o1_id, o2_id):\\r\\n template_str = self.template_generator.templates[template_idx]\\r\\n holes = template_str.count('OBJ')\\r\\n assert holes <= 2\\r\\n if holes <= 0:\\r\\n return template_str\\r\\n elif holes == 1:\\r\\n return template_str.replace('OBJ', self.vocab_act[o1_id])\\r\\n else:\\r\\n return template_str.replace('OBJ', self.vocab_act[o1_id], 1)\\\\\\r\\n .replace('OBJ', self.vocab_act[o2_id], 1)\",\n \"def load_standard_operators():\\n #\\n # Compute scripts\\n #\\n \\n cscript('select' ,scriptpath+'mcdo.sh \\\"${operator}\\\" \\\"${out}\\\" \\\"${var}\\\" \\\"${period_iso}\\\" \\\"${domain}\\\" \\\"${alias}\\\" \\\"${units}\\\" \\\"${missing}\\\" ${ins} ',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('ccdo',\\n scriptpath+'mcdo.sh \\\"${operator}\\\" \\\"${out}\\\" \\\"${var}\\\" \\\"${period_iso}\\\" \\\"${domain}\\\" \\\"${alias}\\\" \\\"${units}\\\" \\\"${missing}\\\" ${ins}')\\n #\\n cscript('minus', 'cdo sub ${in_1} ${in_2} ${out}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('plus', 'cdo add ${in_1} ${in_2} ${out}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('space_average',\\n scriptpath+'mcdo.sh fldmean \\\"${out}\\\" \\\"${var}\\\" \\\"${period_iso}\\\" \\\"${domain}\\\" \\\"${alias}\\\" \\\"${units}\\\" \\\"${missing}\\\" ${ins}', \\n commuteWithTimeConcatenation=True)\\n #\\n cscript('time_average' ,\\n scriptpath+'mcdo.sh timmean \\\"${out}\\\" \\\"${var}\\\" \\\"${period_iso}\\\" \\\"${domain}\\\" \\\"${alias}\\\" \\\"${units}\\\" \\\"${missing}\\\" ${ins}' ,\\n commuteWithSpaceConcatenation=True)\\n #\\n cscript('llbox' ,\\n scriptpath+'mcdo.sh \\\"\\\" \\\"${out}\\\" \\\"${var}\\\" \\\"${period_iso}\\\" \\\"${latmin},${latmax},${lonmin},${lonmax}\\\" \\\"${alias}\\\" \\\"${units}\\\" \\\"${missing}\\\" ${ins}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('regrid' ,\\n scriptpath+'regrid.sh ${in} ${in_2} ${out} ${option}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('regridn' ,\\n scriptpath+'regrid.sh ${in} ${cdogrid} ${out} ${option}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('rescale' ,\\n 'cdo expr,\\\\\\\"${var}=${scale}*${var}+${offset};\\\\\\\" ${in} ${out}',\\n commuteWithTimeConcatenation=True, commuteWithSpaceConcatenation=True)\\n #\\n cscript('mean_and_std',\\n scriptpath+'mean_and_std.sh ${in} ${var} ${out} ${out_sdev}', \\n # This tells CliMAF how to compute varname for name output 'sdev' \\n\\t # using input varname\\n sdev_var=\\\"std(%s)\\\" , \\n commuteWithTimeConcatenation=True)\\n #\\n # Declare plot scripts\\n cscript('ncview' ,'ncview ${in} 1>/dev/null 2>&1&' )\\n #\\n cscript('timeplot', 'ncl '+scriptpath+'timeplot.ncl infile=\\\\'\\\\\\\"${in}\\\\\\\"\\\\' outfile=\\\\'\\\\\\\"${out}\\\\\\\"\\\\' '\\n 'var=\\\\'\\\\\\\"${var}\\\\\\\"\\\\' title=\\\\'\\\\\\\"${title}\\\\\\\"\\\\'',format=\\\"png\\\")\\n #\\n # plot: main field (main_file) + auxiliary field (aux_file, optional) + vectors (u_file & v_file, optionals)\\n #\\n cscript('plot' , '(ncl -Q '+ scriptpath +'gplot.ncl main_file=\\\\'\\\\\\\"${in}\\\\\\\"\\\\' aux_file=\\\\'\\\\\\\"${in_2}\\\\\\\"\\\\' '\\n 'u_file=\\\\'\\\\\\\"${in_3}\\\\\\\"\\\\' v_file=\\\\'\\\\\\\"${in_4}\\\\\\\"\\\\' rotation=${rotation} '\\n 'plotname=\\\\'\\\\\\\"${out}\\\\\\\"\\\\' cmap=\\\\'\\\\\\\"${color}\\\\\\\"\\\\' vmin=${min} vmax=${max} vdelta=${delta} '\\n 'main_var=\\\\'\\\\\\\"${var}\\\\\\\"\\\\' aux_var=\\\\'\\\\\\\"${var_2}\\\\\\\"\\\\' u_var=\\\\'\\\\\\\"${var_3}\\\\\\\"\\\\' v_var=\\\\'\\\\\\\"${var_4}\\\\\\\"\\\\' '\\n 'title=\\\\'\\\\\\\"${title}\\\\\\\"\\\\' scale=${scale} offset=${offset} mpCenterLonF=${mpCenterLonF} '\\n 'vcRefMagnitudeF=${vcRefMagnitudeF} vcRefLengthF=${vcRefLengthF} vcMinDistanceF=${vcMinDistanceF} '\\n 'vcGlyphStyle=\\\\'\\\\\\\"${vcGlyphStyle}\\\\\\\"\\\\' vcLineArrowColor=\\\\'\\\\\\\"${vcLineArrowColor}\\\\\\\"\\\\' '\\n 'units=\\\\'\\\\\\\"${units}\\\\\\\"\\\\' linp=${linp} colors=\\\\'\\\\\\\"${colors}\\\\\\\"\\\\' level=${level} time=${time} '\\n 'proj=\\\\'\\\\\\\"${proj}\\\\\\\"\\\\' contours=\\\\'\\\\\\\"${contours}\\\\\\\"\\\\' focus=\\\\'\\\\\\\"${focus}\\\\\\\"\\\\' '\\n 'type=\\\\'\\\\\\\"${format}\\\\\\\"\\\\' resolution=\\\\'\\\\\\\"${resolution}\\\\\\\"\\\\' trim=${trim} '\\n 'vcb=${vcb} lbLabelFontHeightF=${lbLabelFontHeightF} invXY=${invXY} '\\n 'tmYLLabelFontHeightF=${tmYLLabelFontHeightF} tmXBLabelFontHeightF=${tmXBLabelFontHeightF} '\\n 'tmYRLabelFontHeightF=${tmYRLabelFontHeightF} tiXAxisFontHeightF=${tiXAxisFontHeightF} '\\n 'tiYAxisFontHeightF=${tiYAxisFontHeightF} gsnPolarLabelFontHeightF=${gsnPolarLabelFontHeightF} '\\n 'tiMainFont=\\\\'\\\\\\\"${tiMainFont}\\\\\\\"\\\\' tiMainFontHeightF=${tiMainFontHeightF} '\\n 'tiMainPosition=\\\\'\\\\\\\"${tiMainPosition}\\\\\\\"\\\\' gsnLeftString=\\\\'\\\\\\\"${gsnLeftString}\\\\\\\"\\\\' '\\n 'gsnRightString=\\\\'\\\\\\\"${gsnRightString}\\\\\\\"\\\\' gsnCenterString=\\\\'\\\\\\\"${gsnCenterString}\\\\\\\"\\\\' '\\n 'gsnStringFont=\\\\'\\\\\\\"${gsnStringFont}\\\\\\\"\\\\' gsnStringFontHeightF=${gsnStringFontHeightF} )', format=\\\"graph\\\") \\n # \\n cscript('lines' , '(ncl -Q '+ scriptpath +'lineplot.ncl infile=\\\\'\\\\\\\"${mmin}\\\\\\\"\\\\' '\\n 'plotname=\\\\'\\\\\\\"${out}\\\\\\\"\\\\' var=\\\\'\\\\\\\"${var}\\\\\\\"\\\\' title=\\\\'\\\\\\\"${title}\\\\\\\"\\\\' '\\n 'linp=${linp} labels=\\\\'\\\\\\\"${labels}\\\\\\\"\\\\' colors=\\\\'\\\\\\\"${colors}\\\\\\\"\\\\' thickness=${thickness}'\\n 'T_axis=\\\\'\\\\\\\"${T_axis}\\\\\\\"\\\\' fmt=\\\\'\\\\\\\"${fmt}\\\\\\\"\\\\' && '\\n 'convert ${out} -trim ${out}) ', format=\\\"png\\\")\\n #\\n cscript('curves' , '(ncl -Q '+ scriptpath +'curves.ncl infile=\\\\'\\\\\\\"${mmin}\\\\\\\"\\\\' '\\n 'plotname=\\\\'\\\\\\\"${out}\\\\\\\"\\\\' var=\\\\'\\\\\\\"${var}\\\\\\\"\\\\' title=\\\\'\\\\\\\"${title}\\\\\\\"\\\\' '\\n 'labels=\\\\'\\\\\\\"${labels}\\\\\\\"\\\\' colors=\\\\'\\\\\\\"${colors}\\\\\\\"\\\\' thickness=${thickness} && '\\n 'convert ${out} -trim ${out}) ', format=\\\"png\\\")\\n #\\n # cpdfcrop : pdfcrop by preserving metadata\\n #\\n cscript('cpdfcrop' , 'pdfcrop ${in} ${out} ', format=\\\"pdf\\\")\\n # \\n cscript('ncdump' , 'ncdump -h ${in} ', format=\\\"txt\\\")\\n #\\n if (os.system(\\\"type cdfmean >/dev/null 2>&1\\\")== 0 ) :\\n load_cdftools_operators()\\n else :\\n clogger.warning(\\\"No Cdftool available\\\")\",\n \"def _ProcessTemplate(self,topdir):\\n self.dicomdir = \\\"%s/anatomicals\\\" % self.topdir\\n self.rawdir = \\\"%s/raw\\\" % topdir\\n self.rawdirs = {}\\n tmplt = self._GetTemplate()\\n if self.opts.outdir is not None:\\n# Override template output directory.\\n tmplt['top_outdir'] = self.opts.outdir\\n self.tmplt = tmplt\\n if len(tmplt['top_outdir']) == 0:\\n tmplt['top_outdir'] = os.path.realpath(self.topdir)\\n raise RuntimeError('Template file must specify an output directory.')\\n tmplt['top_outdir'] = os.path.realpath(tmplt['top_outdir'])\\n if '/home' in tmplt['top_outdir'][:7]:\\n raise RuntimeError('Image data cannot be stored in the /home partition. Change the \\\"top_outdir\\\" entry in the template file: %s.' % (' '.join(self.templates)))\\n# tmplt['subject'] = 'orig'\\n self.procdir = os.path.abspath(\\\"%s/%s\\\" % \\\\\\n (tmplt['top_outdir'],tmplt['subject']))\\n target = os.path.abspath('%s/../..' % tmplt['top_outdir'])\\n if not ismounted(target):\\n raise RuntimeError('Could not access partition at %s' % target)\\n\\n self.anatdir = \\\"%s/anat\\\" % self.procdir\\n self.fmapdir = \\\"%s/%s\\\" % (self.procdir,tmplt['fmap']['outdir'])\\n self.dtidir = \\\"%s/%s\\\" % (self.procdir,tmplt['dti']['outdir'])\\n self.logdir = \\\"%s/%s\\\" % (self.procdir,tmplt['logdir'])\\n self.skip = tmplt.get('skip', DEFAULT_SKIP)\\n self.acq_tr = tmplt.get('acq_tr',None)\\n self.episetup_dir = \\\"%s/%s\\\" % (self.procdir,tmplt['first_epi'])\\n self.fsl_cmpblty = tmplt.get('fsl_compatibility',False)\\n self.epi_file_format = self.tmplt['epi_file_format']\\n self.censor_thresh = tmplt.get('censor_threshold', 2.)\\n self.censor_interleave = tmplt.get('censor_interleave', True)\\n# self.server_userid = self.tmplt.get('server_userid','default')\\n\\n# Overide flags for aligning EPIs and skull-stripping with command-\\n# line options.\\n if self.opts.align_fmaps:\\n self.align_fmaps = True\\n else:\\n self.align_fmaps = self.tmplt.get('epi_align', False)\\n\\n if self.opts.no_align_fmaps:\\n self.no_align_fmaps = True\\n else:\\n self.no_align_fmaps = self.tmplt.get('no_epi_align', False)\\n\\n if self.opts.skull_strip:\\n self.skull_strip = True\\n else:\\n self.skull_strip = self.tmplt.get('skull_strip', False)\\n\\n# Create log file now so it can be used immediately.\\n if not os.path.exists(self.logdir):\\n if self.verbose:\\n print 'mkdir %s' % self.logdir\\n if not self.opts.fake_opts:\\n self.MakeDir(self.logdir)\\n\\n self._ProcessTemplateEpiInfo()\",\n \"def edit_template(self, template):\\n macro = []\\n for line in template:\\n search_result = re.search(line_to_edit, line)\\n if search_result != None:\\n line = line[(search_result.start()+1):]\\n if line.find(\\\"/rat/procset\\\") >= 0:\\n parts = line.split(\\\" \\\")\\n new_line = parts[0] + \\\" \\\" + parts[1] + \\\" \\\\\\\"\\\"\\n new_line += self._path\\n new_line += \\\"\\\\\\\"\\\\n\\\"\\n line = new_line\\n if line.find(\\\"/generator/add\\\") >= 0:\\n parts = line.split(\\\" \\\")\\n new_line = parts[0] + \\\" \\\" + parts[1] + \\\" \\\"\\n parts2 = parts[2].split(\\\":\\\")\\n new_line += self._generator.get_generator() + \\\":\\\" + parts2[1]\\n new_line += \\\"\\\\n\\\"\\n line = new_line\\n if line.find(\\\"/generator/vtx/set\\\") >= 0:\\n parts = line.split(\\\" \\\")\\n new_line = parts[0] + \\\" \\\"\\n new_line += self._generator.get_type() + \\\" \\\"\\n new_line += self._generator.get_isotope().get_name() + \\\" \\\"\\n new_line += str(int(self._generator.get_level())) + \\\" \\\"\\n new_line += str(int(self._generator.get_mode())) + \\\" \\\"\\n new_line += str(self._generator.get_e_lo()) + \\\" \\\"\\n new_line += str(self._generator.get_e_hi())\\n new_line += \\\"\\\\n\\\"\\n line = new_line\\n try:\\n assert(re.match\\\\\\n (r\\\"^.*\\\\s[0-9]{1,2}?\\\\s[0-9]{1,2}?\\\\s\\\"\\n \\\"[0-9]+\\\\.[0-9]+\\\\s[0-9]+\\\\.[0-9]+$\\\", line)\\\\\\n != None),\\\\\\n \\\"/generator/vtx/set line has incorrect format\\\"\\n except AssertionError as detail:\\n print \\\"generate_spectrum.edit_template: error,\\\", detail\\n sys.exit(1)\\n if line.find(\\\"/rat/run/start\\\") >= 0:\\n parts = line.split(\\\" \\\")\\n new_line = parts[0] + \\\" \\\" + str(self._n_events)\\n new_line += \\\"\\\\n\\\"\\n line = new_line\\n macro.append(line)\\n return macro\",\n \"def IPTinput_gen(Nom,beta,ef,mutld):\\n\\tV,D =0.01,1\\n\\tf1 = open(\\\"Delta.inp\\\", 'w')\\n\\tf2 = open(\\\"G0.inp\\\", 'w')\\n\\tDelta=[]\\n\\tfor n in range(Nom):\\n\\t iom = (2.*n+1)*pi/beta\\n\\t Redelta=-V**2/(2.0*D)*log( ((D/2.-mutld)**2+iom**2)/((D/2.+mutld)**2+iom**2))\\n\\t Imdelta=-V**2/D*(arctan((D/2.-mutld)/iom)-arctan((-D/2.-mutld)/iom))\\n\\t print>>f1,iom,Redelta,Imdelta\\n\\t G0= 1./( 1.0j*iom-(ef-mutld)-(Redelta+1.0j*Imdelta) )\\n\\t print>>f2,iom,G0.real,G0.imag\\n\\tf1.close()\\n\\tf2.close()\",\n \"def calc_resources(self):\\n self.popula = self.energy = self.popula_used = self.energy_used = 0\\n self.cnt_public = self.cnt_shop = self.cnt_1 = self.cnt_2 = self.cnt_3 = self.cnt_4 = self.cnt_5 = self.cnt_office = 0\\n self.popula += self.extra_pop\\n for i in range(20):\\n b = self.b[i]\\n if b == 'T':\\n self.popula += self.f[i] * 2\\n self.energy_used += 1\\n elif b == 'O':\\n self.popula_used += 1\\n self.energy_used += 1\\n self.cnt_office += self.f[i]\\n elif b == 'U':\\n self.popula_used += 1\\n self.cnt_public += 1\\n elif b == 'S':\\n self.energy_used += 1\\n self.cnt_shop += 1\\n elif b == '1':\\n self.popula += 1\\n self.energy += 1\\n self.popula_used += 1\\n self.cnt_1 += 1\\n elif b == '2':\\n self.popula_used += 1\\n self.cnt_2 += 1\\n elif b == '3':\\n self.popula_used += 1\\n self.cnt_3 += 1\\n elif b == '4':\\n self.popula += 2\\n self.popula_used += 1\\n self.cnt_4 += 1\\n elif b == '5':\\n self.energy += 2\\n self.popula_used += 1\\n self.cnt_5 += 1\\n elif b == 'A':\\n self.energy += 2\\n self.popula_used += 1\\n elif b == 'F':\\n self.energy += 3\\n self.popula_used += 1\\n elif b == 'G':\\n self.popula += 1\\n if 'tvst' in args.exp:\\n self.popula += self.cnt_shop\\n if 'ward' in args.exp:\\n self.popula += 3\\n if 'elec' in args.exp:\\n self.energy += 3\\n if 'capi' in args.exp:\\n self.popula_used += 2\\n if 'fire' in args.exp:\\n self.popula_used += 1\\n if 'park' in args.exp:\\n self.popula_used += 1\",\n \"def isolate_relevant_information(self):\\n\\n def get_formula(oncat_formula):\\n \\\"\\\"\\\"will need to go from something like\\n \\\"${value/10e11}`\\\"\\n to something more pythonic\\n \\\"{value/10e11}\\\"\\\"\\\"\\n regular_expression = r'\\\\$(?P<formula>.+)\\\\`'\\n m = re.search(regular_expression, oncat_formula)\\n if m:\\n return m.group('formula')\\n else:\\n return \\\"\\\"\\n\\n template_information = {}\\n for _index, _element in enumerate(self._oncat_default_template):\\n _title = _element[\\\"name\\\"]\\n _path = _element[\\\"path\\\"]\\n if \\\"units\\\" in _element:\\n _units = _element[\\\"units\\\"]\\n else:\\n _units = \\\"\\\"\\n if \\\"transform\\\" in _element:\\n _formula = get_formula(_element[\\\"transform\\\"])\\n else:\\n _formula = \\\"\\\"\\n template_information[_index] = {'title': _title,\\n 'path': _path,\\n 'units': _units,\\n 'formula': _formula}\\n self.template_information = template_information\",\n \"def compute_partials(self, inputs, partials):\\n t = inputs['t']\\n\\n nn = self.options['num_nodes']\\n\\n omega = self.options['omega']\\n gha = self.options['gha0'] + omega * t\\n\\n partials['q_IE', 't'][:nn] = -np.sin(gha) * omega\\n partials['q_IE', 't'][nn:2*nn] = -np.cos(gha) * omega\",\n \"def apply_to(self, template):\\n pass\",\n \"def format_template(commit_info, nb):\\n\\n nb_path = os.path.abspath(nb).replace('ipynb', 'md')\\n with open(nb_path, 'r+') as file:\\n template = NbTemplate(file.read())\\n updated = template.substitute(commit_info)\\n file.seek(0)\\n file.write(updated)\\n file.truncate()\",\n \"def applyPhotoZ (self,arr):\\n print \\\"Applying Template SED PZs\\\"\\n\\n ztrue = arr['z']\\n\\n #select a template\\n templates = ['El_B2004a.sed']+['Sbc_B2004a.sed','Scd_B2004a.sed']\\n templates = templates +['Im_B2004a.sed','SB3_B2004a.sed','SB2_B2004a.sed','ssp_25Myr_z008.sed','ssp_5Myr_z008.sed']\\n\\n #read in f_mod files, interpolate, get values of f_mod_b\\n ngals = len(ztrue)\\n\\n f_mod_o = np.zeros((self.nb, ngals))\\n for z in range(ngals):\\n #currently templates are randomly chosen but probably should be an input with true z\\n templateno = np.random.choice(range(self.nt))\\n for b in range(self.nb):\\n spl = InterpolatedUnivariateSpline(self.z_grid, self.f_mod[:,templateno,b])\\n f_mod_o[b][z] = spl(ztrue[z])\\n\\n #select sigma_b - 10% for now\\n sigma = 0.1*f_mod_o\\n #select observed fluxes f_obs_b = f_mod_b + sigma_b*rando\\n f_obs = f_mod_o+ sigma * (np.random.normal(0.,1.,self.nb*ngals).reshape((self.nb,ngals)))\\n # I don't seem to be able to find a more efficient way\\n arrx=np.zeros(ngals,dtype=[('pz_f_obs',float,(self.nb,)),('pz_flux_sigma',float,(self.nb,))])\\n arrx['pz_f_obs']=f_obs.T\\n arrx['pz_flux_sigma']=sigma.T\\n arr = recfunctions.merge_arrays((arr,arrx),flatten=True,usemask=False)\\n return arr\",\n \"def buildInstruction(ops: List[ghidra.program.model.pcode.PcodeOp], fallthruoffset: int, paramshift: int, addrFactory: ghidra.program.model.address.AddressFactory) -> unicode:\\n ...\",\n \"def fillBackgroundTemplates(opt):\\n\\n totalBkg={}\\n templates=[]\\n\\n #import signal events\\n data=ROOT.TChain('data')\\n for f in [os.path.join(opt.input,x) for x in os.listdir(opt.input) if 'Data13TeV' in x]:\\n if 'MuonEG' in f : continue\\n data.AddFile(f)\\n\\n #define final preselection cuts\\n cuts='xangle==%d'%opt.xangle\\n if len(opt.presel) : cuts += ' && ' + opt.presel \\n if opt.csiacc:\\n csiCuts ='csi1>%f && csi1<%f && '%opt.csiacc[opt.xangle][0]\\n csiCuts+='csi2>%f && csi2<%f'%opt.csiacc[opt.xangle][1]\\n cuts=csiCuts if len(cuts)==0 else '{0} && {1}'.format(cuts,csiCuts)\\n\\n #loop over categories build templates\\n for icat in range(len(opt.categs)):\\n\\n #apply category cuts\\n categCut=opt.categs[icat]\\n categCut=cuts if len(categCut)==0 else '%s && %s'%(categCut,cuts)\\n catName='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\\n\\n print '\\\\t',catName,categCut\\n\\n #background modelling histos\\n histos=[]\\n data_obs=None\\n for name,pfix in [('bkg_'+catName,'mix'),('bkg_%s_bkgShape'%catName,'mixem')]:\\n\\n templCuts=categCut.replace('csi1',pfix+'csi1')\\n templCuts=templCuts.replace('csi2',pfix+'csi2')\\n data.Draw('{0}mmiss >> h({1},{2},{3})'.format(pfix,opt.nbins,opt.mMin,opt.mMax),templCuts,'goff')\\n h=data.GetHistogram()\\n histos.append(h.Clone(name))\\n histos[-1].SetDirectory(0)\\n\\n if len(histos)==1:\\n totalBkg[icat]=h.Integral()\\n if not opt.unblind :\\n data_obs=h.Clone('data_obs_'+catName)\\n data_obs.SetDirectory(0)\\n\\n h.Reset('ICE')\\n templates += defineProcessTemplates(histos)\\n\\n #observed data in this category if unblinding\\n if opt.unblind:\\n data.Draw('mmiss >> h({1},{2},{3})'.format(opt.nbins,opt.mMin,opt.mMax),categCut,'goff')\\n data_obs=data.GetHistogram().Clone('data_obs_'+catName)\\n data_obs.SetDirectory(0)\\n\\n templates.append(data_obs)\\n\\n print '\\\\t total background:',totalBkg\\n return totalBkg,templates\",\n \"def healpix_template(ncomp, nside, coordinate=None):\\n\\n temp = so_map()\\n\\n if ncomp == 3:\\n temp.data = np.zeros((3, 12 * nside ** 2))\\n else:\\n temp.data = np.zeros((12 * nside ** 2))\\n\\n temp.pixel = \\\"HEALPIX\\\"\\n temp.ncomp = ncomp\\n temp.nside = nside\\n temp.geometry = \\\"healpix geometry\\\"\\n temp.coordinate = coordinate\\n return temp\",\n \"def _get_template_data(vm_data: Dict[str, Any], span: Span) -> Optional[Dict[str, Any]]:\\n vm_id = vm_data['id']\\n Windows.logger.debug(f'Compiling template data for VM #{vm_id}')\\n data: Dict[str, Any] = {key: None for key in Windows.template_keys}\\n\\n data['vm_identifier'] = f'{vm_data[\\\"project\\\"][\\\"id\\\"]}_{vm_id}'\\n data['image_answer_file_name'] = vm_data['image']['answer_file_name']\\n\\n data['image_filename'] = vm_data['image']['filename']\\n # check if file exists at /mnt/images/HyperV/VHDXs/\\n path = '/mnt/images/HyperV/VHDXs/'\\n child_span = opentracing.tracer.start_span('vm_image_file_download', child_of=span)\\n if not Windows.check_image(data['image_filename'], path):\\n # download the file\\n downloaded, errors = Windows.download_image(data['image_filename'], path)\\n if not downloaded:\\n for error in errors:\\n Windows.logger.error(error)\\n vm_data['errors'].append(error)\\n return None\\n child_span.finish()\\n\\n # RAM is needed in MB for the builder but we take it in in GB (1024, not 1000)\\n data['ram'] = vm_data['ram'] * 1024\\n data['cpu'] = vm_data['cpu']\\n data['dns'] = vm_data['dns']\\n\\n # Generate encrypted passwords\\n data['admin_password'] = Windows._password_generator(size=12)\\n # Also save the password back to the VM data dict\\n vm_data['admin_password'] = data['admin_password']\\n\\n # Check for the primary storage\\n if not any(storage['primary'] for storage in vm_data['storages']):\\n error = 'No primary storage drive found. Expected one primary storage drive'\\n Windows.logger.error(error)\\n vm_data['errors'].append(error)\\n return None\\n\\n data['storages'] = vm_data['storages']\\n data['storage_type'] = vm_data['storage_type']\\n\\n # Get the Networking details\\n data['vlans'] = []\\n data['ip_addresses'] = []\\n data['default_ips'] = []\\n data['default_gateway'] = ''\\n data['default_netmask_int'] = ''\\n data['default_vlan'] = ''\\n\\n # The private IPs for the VM will be the one we need to pass to the template\\n vm_data['ip_addresses'].reverse()\\n ip_addresses = []\\n subnets = []\\n for ip in vm_data['ip_addresses']:\\n if IPAddress(ip['address']).is_private():\\n ip_addresses.append(ip)\\n subnets.append({\\n 'address_range': ip['subnet']['address_range'],\\n 'vlan': ip['subnet']['vlan'],\\n 'id': ip['subnet']['id'],\\n })\\n # Removing duplicates\\n subnets = [dict(tuple_item) for tuple_item in {tuple(subnet.items()) for subnet in subnets}]\\n # sorting nics (each subnet is one nic)\\n for subnet in subnets:\\n non_default_ips = []\\n gateway, netmask_int = subnet['address_range'].split('/')\\n vlan = str(subnet['vlan'])\\n data['vlans'].append(vlan)\\n\\n for ip_address in ip_addresses:\\n address = ip_address['address']\\n if ip_address['subnet']['id'] == subnet['id']:\\n # Pick the default ips if any\\n if vm_data['gateway_subnet'] is not None:\\n if subnet['id'] == vm_data['gateway_subnet']['id']:\\n data['default_ips'].append(address)\\n data['default_gateway'] = gateway\\n data['default_netmask_int'] = netmask_int\\n data['default_vlan'] = vlan\\n continue\\n # else store the non gateway subnet ips\\n non_default_ips.append(address)\\n\\n if len(non_default_ips) > 0:\\n data['ip_addresses'].append({\\n 'ips': non_default_ips,\\n 'gateway': gateway,\\n 'netmask_int': netmask_int,\\n 'vlan': vlan,\\n })\\n\\n # Add locale data to the VM\\n data['language'] = 'en_IE'\\n data['timezone'] = 'GMT Standard Time'\\n\\n # Get the host name of the server\\n host_name = None\\n for interface in vm_data['server_data']['interfaces']:\\n if interface['enabled'] is True and interface['ip_address'] is not None:\\n if IPAddress(str(interface['ip_address'])).version == 6:\\n host_name = interface['hostname']\\n break\\n if host_name is None:\\n error = f'Host name is not found for the server # {vm_data[\\\"server_id\\\"]}'\\n Windows.logger.error(error)\\n vm_data['errors'].append(error)\\n return None\\n\\n # Add the host information to the data\\n data['host_name'] = host_name\\n data['network_drive_url'] = settings.NETWORK_DRIVE_URL\\n data['vms_path'] = settings.HYPERV_VMS_PATH\\n\\n return data\",\n \"def New(*args, **kargs):\\n obj = itkIsoDataThresholdCalculatorHDSS.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def generate_latex_output(self, calc_spec_tex_file_handle, sim, ui=None,\\r\\n res_dir=None):\\r\\n average_pressure = np.mean(self.sun.press[self.sun.press > 0])\\r\\n numeric_output_data = [\\r\\n ('pressure, max.', to_preci(np.amax(self.sun.press), 4),\\r\\n UnitTex.pressure.value, 'unverified'),\\r\\n ('pressure, av.', to_preci(average_pressure, 4),\\r\\n UnitTex.pressure.value, 'unverified'),\\r\\n ('e_a,kin sun, max.', to_preci(np.amax(self.sun.e_akin), 4),\\r\\n UnitTex.eakin.value, 'unverified'),\\r\\n ('e_a,kin planet, max.', to_preci(np.amax(self.planet.e_akin), 4),\\r\\n UnitTex.eakin.value, 'unverified'),\\r\\n ('pv_rel, max.', to_preci(np.amax(self.pv), 4),\\r\\n UnitTex.pvrel.value, 'unverified'),\\r\\n ('contact area', to_preci(self.sun.get_area(self.sun.press), 4),\\r\\n UnitTex.area.value, 'unverified')]\\r\\n table_calc_summary = []\\r\\n for key, value, unit, status in sorted(numeric_output_data):\\r\\n table_calc_summary.append(\\r\\n (re.sub('_', '\\\\_', key), value, unit, status))\\r\\n\\r\\n latex_variables = {'table_calc_summary': table_calc_summary,\\r\\n 'contact_plot1': '{}{}contact1.png'.format(\\r\\n SubDir.tex_figs_rel_to_tex_file.value, '/'),\\r\\n 'pressure_plot1': '{}{}pressure1.png'.format(\\r\\n SubDir.tex_figs_rel_to_tex_file.value, '/'),\\r\\n 'energy_plot1': '{}{}energy1.png'.format(\\r\\n SubDir.tex_figs_rel_to_tex_file.value, '/')}\\r\\n template_calc_specific = get_calc_specific_latex_template(\\r\\n TexTempl.RingOnRing.value, sim)\\r\\n with open(calc_spec_tex_file_handle, 'w') as f:\\r\\n f.write(template_calc_specific.render(latex_variables))\",\n \"def no_icp(source, templates, home=None, use_2d=False):\\n if source.shape[0] == 0:\\n return create_config(0,0,0, fitness=0)\\n origorig = source.copy()\\n source, xinit, yinit, theta = init_transform(source)\\n\\n remove_out = False\\n if remove_out:\\n iq_range=0.5\\n pcnt = (1 - iq_range) / 2\\n xqlow, xqhigh = np.quantile(source[:,0], [pcnt, 1-pcnt])\\n yqlow, yqhigh = np.quantile(source[:,1], [pcnt, 1-pcnt])\\n xiqr, yiqr = xqhigh-xqlow, yqhigh-yqlow\\n no_out = (source[:,0]<xqhigh+1.5*xiqr) & (source[:,0]>xqlow-1.5*xiqr) &\\\\\\n (source[:,1]<yqhigh+1.5*yiqr) & (source[:,1]>yqlow-1.5*yiqr)\\n source = source[no_out,:]\\n\\n remove_low = False\\n if remove_low:\\n not_low = (source[:,-1]>0.15) & (source[:,-1]<1.4)\\n source = source[not_low,:]\\n\\n if source.shape[0] == 0:\\n return create_config(0,0,0,fitness=0)\\n\\n\\n # Recenter the data independently from point density\\n xmin, xmax = np.min(source[:,0]), np.max(source[:,0])\\n ymin, ymax = np.min(source[:,1]), np.max(source[:,1])\\n x_center, y_center = xmin+(xmax-xmin)/2, ymin+(ymax-ymin)/2\\n center2 = rotate(-theta, np.array([[x_center, y_center, 0]]), axis=\\\"z\\\")\\n xinit += center2[0][0]\\n yinit += center2[0][1]\\n source = source - [x_center, y_center, 0]\\n\\n angle_diffs = [-5.,-2.,-1.,0.,1.,2.,5.]\\n center_diffs = [-0.2,-0.15,-0.10,-0.07,-0.05,-0.01,0,0.01,0.05,0.07,0.10,0.15,0.2]\\n lengths = [2.,2.1]\\n widths = [1.]\\n trials = list(product(center_diffs, center_diffs, angle_diffs, lengths, widths))\\n\\n trialmap = np.array([np.array([xinit, yinit, np.degrees(theta)-90, 0.0, 0.0])+np.array(t) for t in trials])\\n results = multi_grid_iou(origorig[:, :2], trialmap, 0.05)\\n best = np.argmax(results)\\n cfg = create_config(*trialmap[best])\\n cfg[\\\"bed\\\"][\\\"fitness\\\"] = np.max(results)\\n return cfg\",\n \"def New(*args, **kargs):\\n obj = itkIsoDataThresholdCalculatorHFUS.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def calc_ST_operation(m_exhaust_GT_kgpers, T_exhaust_GT_K, T_sup_K, fuel_type):\\n\\n # calaulate High Pressure (HP) and Low Pressure (LP) mass flow of a double pressure steam turbine\\n temp_i_K = (0.9 * ((6 / 48.2) ** (0.4 / 1.4) - 1) + 1) * (T_exhaust_GT_K - ST_DELTA_T)\\n if fuel_type == 'NG':\\n Mexh = 103.7 * 44E-3 + 196.2 * 18E-3 + 761.4 * 28E-3 + 200.5 * (CC_AIRRATIO - 1) * 32E-3 \\\\\\n + 200.5 * (CC_AIRRATIO - 1) * 3.773 * 28E-3\\n ncp_exh = 103.7 * 44 * 0.846 + 196.2 * 18 * 1.8723 + 761.4 * 28 * 1.039 \\\\\\n + 200.5 * (CC_AIRRATIO - 1) * 32 * 0.918 + 200.5 * (CC_AIRRATIO - 1) * 3.773 * 28 * 1.039\\n cp_exh = ncp_exh / Mexh # J/kgK\\n else:\\n Mexh = 98.5 * 44E-3 + 116 * 18E-3 + 436.8 * 28E-3 + 115.5 * (CC_AIRRATIO - 1) * 32E-3 \\\\\\n + 115.5 * (CC_AIRRATIO - 1) * 3.773 * 28E-3\\n ncp_exh = 98.5 * 44 * 0.846 + 116 * 18 * 1.8723 + 436.8 * 28 * 1.039 \\\\\\n + 115.5 * (CC_AIRRATIO - 1) * 32 * 0.918 + 115.5 * (CC_AIRRATIO - 1) * 3.773 * 28 * 1.039\\n cp_exh = ncp_exh / Mexh # J/kgK\\n\\n a = np.array([[1653E3 + HEAT_CAPACITY_OF_WATER_JPERKGK * (T_exhaust_GT_K - ST_DELTA_T - 534.5), \\\\\\n HEAT_CAPACITY_OF_WATER_JPERKGK * (temp_i_K - 534.5)], \\\\\\n [HEAT_CAPACITY_OF_WATER_JPERKGK * (534.5 - 431.8), \\\\\\n 2085.8E3 + HEAT_CAPACITY_OF_WATER_JPERKGK * (534.5 - 431.8)]])\\n b = np.array([m_exhaust_GT_kgpers * cp_exh * (T_exhaust_GT_K - (534.5 + ST_DELTA_T)), \\\\\\n m_exhaust_GT_kgpers * cp_exh * (534.5 - 431.8)])\\n [mdotHP_kgpers, mdotLP_kgpers] = np.linalg.solve(a, b)\\n\\n # calculate thermal output\\n T_cond_0_K = T_sup_K + CC_DELTA_T_DH # condensation temperature constrained by the DH network temperature\\n pres0 = (0.0261 * (T_cond_0_K - 273) ** 2 - 2.1394 * (T_cond_0_K - 273) + 52.893) * 1E3\\n\\n delta_h_evap_Jperkg = (-2.4967 * (T_cond_0_K - 273) + 2507) * 1E3\\n q_output_ST_W = (mdotHP_kgpers + mdotLP_kgpers) * delta_h_evap_Jperkg # thermal output of ST\\n\\n # calculate electricity output\\n h_HP_Jperkg = (2.5081 * (T_exhaust_GT_K - ST_DELTA_T - 273) + 2122.7) * 1E3 # J/kg\\n h_LP_Jperkg = (2.3153 * (temp_i_K - 273) + 2314.7) * 1E3 # J/kg\\n h_cond_Jperkg = (1.6979 * (T_cond_0_K - 273) + 2506.6) * 1E3 # J/kg\\n\\n el_produced_ST_W = mdotHP_kgpers * (h_HP_Jperkg - h_LP_Jperkg) + \\\\\\n (mdotHP_kgpers + mdotLP_kgpers) * (h_LP_Jperkg - h_cond_Jperkg) # turbine electricity output\\n\\n el_input_compressor_W = SPEC_VOLUME_STEAM * (\\n mdotLP_kgpers * (6E5 - pres0) + (mdotHP_kgpers + mdotLP_kgpers) * (48.2E5 - 6E5)) # compressor electricity use\\n\\n el_output_ST_W = ST_GEN_ETA * (el_produced_ST_W - el_input_compressor_W) # gross electricity production of turbine\\n\\n return q_output_ST_W, el_output_ST_W\",\n \"def _model(self, t, theta, period, tmpid):\\n template = self.templates[tmpid]\\n phase = (t / period - theta[2]) % 1\\n return theta[0] + theta[1] * template(phase)\",\n \"def preparehspiceidvgGEO1v2(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,HFINparam,TFIN_TOPparam,TFIN_BASEparam,EOTparam,NBODYparam,NFINparam,PHIGparam,RSHSparam,RSHDparam):\\n #make an aux copy of hspice file to simulate\\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\\n #make an aux copy of modelcard file to simulate\\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\\n\\n #update path of model and modelcard\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\\\\\"modelcardaux.nmos\\\\\\\"')\\n\\n #bias update\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'HFINparam',HFINparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_TOPparam', TFIN_TOPparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_BASEparam',TFIN_BASEparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'EOTparam', EOTparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'PHIGparam', PHIGparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHSparam', RSHSparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHDparam', RSHDparam)\",\n \"def generate_template():\\n date = input(\\\"What day is the template for? dd/mm/yy: \\\")\\n day = input(\\\"What day of the week is this?: \\\")\\n if \\\"/\\\" not in date:\\n date_components = [date[0:2], date[2:4], date[4:]]\\n else:\\n date_components = date.split(\\\"/\\\")\\n file_name = \\\"{}{}{}.table\\\".format(date_components[0], date_components[1], date_components[2])\\n try:\\n template_file = open(\\\"templates/{}_template.table\\\".format(day), \\\"r\\\")\\n except IOError:\\n print(\\\"source template not found\\\")\\n else:\\n source_lines = []\\n output_file = open(file_name, \\\"w\\\")\\n for line in template_file:\\n source_lines.append(line)\\n output_file.write(line)\\n print(\\\"template generated successfully for: {} on date: {}\\\".format(day, date))\",\n \"def compute(self, inputs, outputs):\\n\\toutputs['TOW'] = inputs['W_battery'] + inputs['W_payload'] + inputs['W_empty']\",\n \"def _base_troposphere_template(self):\\n template = troposphere.Template()\\n template.add_parameter(\\n troposphere.Parameter(\\n \\\"Stage\\\",\\n Default=\\\"dev\\\",\\n Description=\\\"Name of the Stage\\\",\\n Type=\\\"String\\\",\\n )\\n )\\n\\n template.add_parameter(\\n troposphere.Parameter(\\n \\\"Region\\\",\\n Description=\\\"AWS Region\\\",\\n Type=\\\"String\\\",\\n )\\n )\\n return template\",\n \"def test7():\\n from wpylib.text_tools import str_snippet as snippet\\n from wpylib.params.params_flat import ActiveReadValue as Act\\n defaults = {\\n 'nbasis': 320,\\n 'npart': 37,\\n 'deltau': 0.025,\\n }\\n print \\\"test7()\\\"\\n p = params(defaults, nbasis=332)\\n p.input_template = Act(lambda P: snippet(\\\"\\\"\\\"\\\\\\n input = {\\n nbasis = %(nbasis)d\\n npart = %(npart)d\\n deltau = %(deltau)s\\n };\\n code = {\\n %(code_template)s\\n };\\n \\\"\\\"\\\") % P)\\n p.code_template = Act(lambda P: snippet(\\\"\\\"\\\"\\\\\\n for (i = 1; %(nbasis)d; ++i) {\\n print i, %(deltau)g\\n }\\n \\\"\\\"\\\") % P)\\n print \\\"\\\\nInput template 1:\\\"\\n print p.input_template\\n print \\\"\\\\n--- The case above was OK. ---\\\\n\\\"\\n\\n print \\\"\\\\n--- The case below was not OK. ---\\\\nThe nbasis and deltau args were not updated in two printouts below\\\"\\n Q = params(p)\\n Q.nbasis = 327\\n print \\\"\\\\nInput template 2 after updating nbasis:\\\"\\n print Q.input_template\\n\\n\\n print \\\"\\\\n--- The case below was OK, but it requires flattening of the search dicts. ---\\\\n\\\"\\n\\n R = params(p, _flatten_=True)\\n R.nbasis = 327\\n pprint(R)\\n print len(R._list_)\\n print \\\"\\\\nInput template 2 after flattening and updating nbasis:\\\"\\n print R.input_template\\n\\n print \\\"\\\\nHere is the generator script:\\\\n*%(input_template)s*\\\" % p._create_(_flatten_=True)._update_(dict(deltau=2.775))\",\n \"def nd_2_nz_compute(self):\\n tik_instance = self.set_tik_instance()\\n format_transfer_case = self.set_format_transfer_case()\\n if format_transfer_case == 0:\\n tik_instance = self.format_transfer_case_zero(tik_instance)\\n elif format_transfer_case == 1:\\n tik_instance = self.format_transfer_case_one(tik_instance)\\n elif format_transfer_case == 2:\\n tik_instance = self.format_transfer_case_two(tik_instance)\\n elif format_transfer_case == 3:\\n tik_instance = self.format_transfer_case_three(tik_instance)\\n elif format_transfer_case == 4:\\n tik_instance = self.format_transfer_case_four(tik_instance)\\n elif format_transfer_case == 5:\\n tik_instance = self.format_transfer_case_five(tik_instance)\\n elif format_transfer_case == 6:\\n tik_instance = self.format_transfer_case_six(tik_instance)\\n elif format_transfer_case == 7:\\n tik_instance = self.format_transfer_case_seven(tik_instance)\\n elif format_transfer_case == 8:\\n tik_instance = self.format_transfer_case_eight(tik_instance)\\n return tik_instance\",\n \"def compute_equivalent_widths(templates, coeffs, covar, max_R=5000, Ndraw=1000, seed=0):\\n \\n if False:\\n # Testing\\n templates = t1\\n chi2, coeffsx, coeffs_errx, covarx = mb.xfit_at_z(z=tfit['z'], templates=templates, fitter='nnls', fit_background=True, get_uncertainties=2)\\n covar = covarx[mb.N:,mb.N:]\\n coeffs = coeffsx[mb.N:]\\n \\n # Array versions of the templates\\n wave, flux_arr, is_line = array_templates(templates, max_R=max_R)\\n keys = np.array(list(templates.keys()))\\n \\n EWdict = OrderedDict()\\n for key in keys[is_line]:\\n EWdict[key] = (0., 0., 0.)\\n \\n # Only worry about templates with non-zero coefficients, which should\\n # be accounted for in the covariance array (with get_uncertainties=2)\\n clip = coeffs != 0\\n # No valid lines\\n if (is_line & clip).sum() == 0:\\n return EWdict\\n \\n # Random draws from the covariance matrix\\n covar_clip = covar[clip,:][:,clip]\\n if seed is not None:\\n np.random.seed(seed)\\n \\n draws = np.random.multivariate_normal(coeffs[clip], covar_clip, size=Ndraw)\\n \\n # Evaluate the continuum fits from the draws \\n continuum = np.dot(draws*(~is_line[clip]), flux_arr[clip,:])\\n \\n # Compute the emission line EWs\\n tidx = np.where(is_line[clip])[0]\\n for ix in tidx:\\n key = keys[clip][ix]\\n\\n # Line template\\n line = np.dot(draws[:,ix][:,None], flux_arr[clip,:][ix,:][None,:])\\n \\n # Where line template non-zero\\n mask = flux_arr[clip,:][ix,:] > 0\\n ew_i = np.trapz((line/continuum)[:,mask], wave[mask], axis=1)\\n \\n EWdict[key] = np.percentile(ew_i, [16., 50., 84.])\\n \\n return EWdict\",\n \"def generate_template_std(classname, outfile, doc='', MathJax=False, login=False):\\n code = '''\\\\\\n<!DOCTYPE html>\\n<html lang=\\\"en\\\">\\n <head>\\n <meta charset=\\\"utf-8\\\" />\\n <title>Flask %(classname)s app</title>\\n </head>\\n <body>\\n''' % vars()\\n if MathJax:\\n code += '''\\n<script type=\\\"text/x-mathjax-config\\\">\\nMathJax.Hub.Config({\\n TeX: {\\n equationNumbers: { autoNumber: \\\"AMS\\\" },\\n extensions: [\\\"AMSmath.js\\\", \\\"AMSsymbols.js\\\", \\\"autobold.js\\\"]\\n }\\n});\\n</script>\\n<script type=\\\"text/javascript\\\"\\n src=\\\"http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\\\">\\n</script>\\n<!-- Fix slow MathJax rendering in IE8 -->\\n<meta http-equiv=\\\"X-UA-Compatible\\\" content=\\\"IE=EmulateIE7\\\">\\n'''\\n if login:\\n code += '''\\n {% if is_anonymous %}\\n <p align=\\\"right\\\"><a href=\\\"/login\\\">Login</a>\\n / <a href=\\\"/reg\\\">Register</a></p>\\n {% else %}\\n <p align=\\\"right\\\">Logged in as {{user.username}}<br>\\n <a href=\\\"/old\\\">Previous simulations<a/><br>\\n <a href=\\\"/logout\\\">Logout</a></p>\\n {% endif %}\\n'''\\n code += '''\\\\\\n %(doc)s\\n\\n <!-- Input and Results are typeset as a two-column table -->\\n <table>\\n <tr>\\n <td valign=\\\"top\\\">\\n <h2>Input:</h2>\\n\\n <form method=post action=\\\"\\\" enctype=\\\"multipart/form-data\\\">\\n <table>\\n {%% for field in form %%}\\n <tr><td>{{ field.name }}</td>\\n <td>{{ field(size=20) }}</td>\\n <td>{%% if field.errors %%}\\n <ul class=errors>\\n {%% for error in field.errors %%}\\n <li>{{ error }}</li>\\n {%% endfor %%}</ul>\\n {%% endif %%}</td></tr>\\n {%% endfor %%}\\n </table>\\n <p><input type=\\\"submit\\\" value=\\\"Compute\\\">\\n </form></p>\\n </td>\\n\\n <td valign=\\\"top\\\">\\n {%% if result != None %%}\\n <h2>Results:</h2>\\n {{ result|safe }}\\n''' % vars()\\n\\n if login:\\n code +='''\\n {% if not is_anonymous %}\\n <h3>Comments:</h3>\\n <form method=post action=\\\"/add_comment\\\">\\n <textarea name=\\\"comments\\\" rows=\\\"4\\\" cols=\\\"40\\\"></textarea>\\n <p><input type=\\\"submit\\\" value=\\\"Add\\\">\\n </form>\\n {% endif %}\\n'''\\n code += '''\\n {% endif %}\\n </td>\\n </tr>\\n </table>\\n </body>\\n</html>'''\\n\\n if outfile is None:\\n return code\\n else:\\n f = open(outfile, 'w')\\n f.write(code)\\n f.close()\",\n \"def evaluate_template(template: str, prefix=\\\"$\\\", sufix=\\\"$\\\"):\\n return get_context().evaluate_template(template, prefix=prefix, sufix=sufix)\",\n \"def energy_balance_func_doc(self, label):\\n latex = (\\n r'0= T_\\\\mathrm{in} - T_{\\\\mathrm{out,}j}'\\n r'\\\\; \\\\forall j \\\\in \\\\text{outlets}'\\n )\\n return generate_latex_eq(self, latex, label)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61078596","0.5807358","0.565082","0.5624882","0.5605698","0.5569253","0.5561828","0.5541212","0.5455167","0.54004294","0.53838813","0.5322327","0.53214216","0.531339","0.52193123","0.5200469","0.5189568","0.5186714","0.5178152","0.5171504","0.51658136","0.51631576","0.5146836","0.51354545","0.5099525","0.50849986","0.50356984","0.5023937","0.49993128","0.4999067","0.49949577","0.49689344","0.4965527","0.4957409","0.49485102","0.49441352","0.49408203","0.49168226","0.4905662","0.48940986","0.4885481","0.48578134","0.48333678","0.48298496","0.48181713","0.48164365","0.48162717","0.4815641","0.4815193","0.48074237","0.4806898","0.47965994","0.47902453","0.47893068","0.4778728","0.47753555","0.47738168","0.47713912","0.4743925","0.47429708","0.4740643","0.47290102","0.4728255","0.4723564","0.47197777","0.47033125","0.47032753","0.47022468","0.4698571","0.4695536","0.46892464","0.46886024","0.46885562","0.4684295","0.46803746","0.46803516","0.46790278","0.4676041","0.4667567","0.46623716","0.46589327","0.4656808","0.46553662","0.4652082","0.46515158","0.4643948","0.46397734","0.463726","0.46367875","0.46351326","0.46330488","0.46250674","0.46215582","0.4619963","0.46186936","0.46173656","0.46170563","0.461579","0.46125576","0.4611721","0.46089074"],"string":"[\n \"0.61078596\",\n \"0.5807358\",\n \"0.565082\",\n \"0.5624882\",\n \"0.5605698\",\n \"0.5569253\",\n \"0.5561828\",\n \"0.5541212\",\n \"0.5455167\",\n \"0.54004294\",\n \"0.53838813\",\n \"0.5322327\",\n \"0.53214216\",\n \"0.531339\",\n \"0.52193123\",\n \"0.5200469\",\n \"0.5189568\",\n \"0.5186714\",\n \"0.5178152\",\n \"0.5171504\",\n \"0.51658136\",\n \"0.51631576\",\n \"0.5146836\",\n \"0.51354545\",\n \"0.5099525\",\n \"0.50849986\",\n \"0.50356984\",\n \"0.5023937\",\n \"0.49993128\",\n \"0.4999067\",\n \"0.49949577\",\n \"0.49689344\",\n \"0.4965527\",\n \"0.4957409\",\n \"0.49485102\",\n \"0.49441352\",\n \"0.49408203\",\n \"0.49168226\",\n \"0.4905662\",\n \"0.48940986\",\n \"0.4885481\",\n \"0.48578134\",\n \"0.48333678\",\n \"0.48298496\",\n \"0.48181713\",\n \"0.48164365\",\n \"0.48162717\",\n \"0.4815641\",\n \"0.4815193\",\n \"0.48074237\",\n \"0.4806898\",\n \"0.47965994\",\n \"0.47902453\",\n \"0.47893068\",\n \"0.4778728\",\n \"0.47753555\",\n \"0.47738168\",\n \"0.47713912\",\n \"0.4743925\",\n \"0.47429708\",\n \"0.4740643\",\n \"0.47290102\",\n \"0.4728255\",\n \"0.4723564\",\n \"0.47197777\",\n \"0.47033125\",\n \"0.47032753\",\n \"0.47022468\",\n \"0.4698571\",\n \"0.4695536\",\n \"0.46892464\",\n \"0.46886024\",\n \"0.46885562\",\n \"0.4684295\",\n \"0.46803746\",\n \"0.46803516\",\n \"0.46790278\",\n \"0.4676041\",\n \"0.4667567\",\n \"0.46623716\",\n \"0.46589327\",\n \"0.4656808\",\n \"0.46553662\",\n \"0.4652082\",\n \"0.46515158\",\n \"0.4643948\",\n \"0.46397734\",\n \"0.463726\",\n \"0.46367875\",\n \"0.46351326\",\n \"0.46330488\",\n \"0.46250674\",\n \"0.46215582\",\n \"0.4619963\",\n \"0.46186936\",\n \"0.46173656\",\n \"0.46170563\",\n \"0.461579\",\n \"0.46125576\",\n \"0.4611721\",\n \"0.46089074\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76499,"cells":{"query":{"kind":"string","value":"Set up private and public key into DiffieHellman object."},"document":{"kind":"string","value":"def setup_keys(self, dh_object, public_key, private_key):\n public_numbers = DHPublicNumbers(public_key, dh_object.parameter_numbers)\n private_numbers = DHPrivateNumbers(private_key, public_numbers)\n dh_object.private_key = private_numbers.private_key(default_backend())"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self):\n self._keypair = RSA.generate(2048)\n self.public_key = self._keypair.publickey().exportKey()","def setUp(self):\n\n self.private_key = self.get_new_key()\n self.public_key = self.private_key.public_key()\n\n self.pem_private_key = self.private_key.private_bytes(\n serialization.Encoding.PEM,\n serialization.PrivateFormat.PKCS8,\n serialization.NoEncryption(),\n )\n self.encrypted_pem_private_key = self.private_key.private_bytes(\n serialization.Encoding.PEM,\n serialization.PrivateFormat.PKCS8,\n serialization.BestAvailableEncryption(self.private_key_password),\n )\n\n self.pem_public_key = self.public_key.public_bytes(\n serialization.Encoding.PEM, serialization.PublicFormat.PKCS1\n )","def create_keys(self):\n crypto_tool = CryptoTools()\n # creating RSA keys for the signer user\n public_key, private_key = crypto_tool.create_key_with_entropy()\n self.priv_key = crypto_tool.get_pem_format(private_key).decode(\"utf-8\")\n self.pub_key = crypto_tool.get_pem_format(public_key).decode(\"utf-8\")","def __init__(self):\n publicKeyFileName = \"serverPublicKey\"\n privateKeyFileName = \"serverPrivateKey.pem\"\n try:\n f = open(privateKeyFileName, 'rb')\n self.keys = RSA.importKey(f.read())\n except:\n self.keys = RSA.generate(1024)\n self.publickey = self.keys.publickey()\n # export public and private keys\n privHandle = open(privateKeyFileName, 'wb')\n privHandle.write(self.keys.exportKey('PEM'))\n privHandle.close()\n \n pubHandle = open(publicKeyFileName, 'wb')\n pubHandle.write(self.keys.publickey().exportKey())\n pubHandle.close()\n self.publickey = self.keys.publickey()","def __init__(self):\n self.public_key = None\n self._private_key = None","def private_key(self):","def create_key ():","def diffiehellman_mitm_sim(prime, base):\n alice = {}\n\n #Alice generates their public key an sends to 'bob'\n alice['dh'] = DiffieHellman(prime, base, secret_key=secrets.randbelow(prime))\n alice_pub = alice['dh'].gen_public_key()\n\n (prime, base, key_for_bob) = yield (prime, base, alice_pub)\n\n \n\n #bob recieves 'alice's' public key, generates their own public key and\n #the shared key. Sends their public key ot 'alice'\n bob = {'dh':DiffieHellman(prime, base, secret_key=secrets.randbelow(prime))}\n bob_pup = bob['dh'].gen_public_key()\n bob['dh'].gen_shared_key(key_for_bob)\n\n key_for_alice = yield bob_pup\n\n ### Alice recieves Bob's public key, generates the shared key and encrypts\n ### message for bob\n\n alice['dh'].gen_shared_key(key_for_alice)\n \n alice['sha1'] = SHA1(bso.int_to_bytes(alice['dh'].shared_key))\n alice['cipher'] = AES_CBC(alice['sha1'].digest()[:16], secrets.token_bytes(16))\n alice_ciphertext = alice['cipher'].encrypt(b'Message to Bob')\n alice_ciphertext += alice['cipher'].IV\n\n ciphertext_for_bob = yield alice_ciphertext\n \n #Bob recieves the ciphertext, decrypts it and send a reply.\n\n bob['sha1'] = SHA1(bso.int_to_bytes(bob['dh'].shared_key))\n bob['cipher'] = AES_CBC(bob['sha1'].digest()[:16], secrets.token_bytes(16))\n bob_ciphertext = bob['cipher'].encrypt(b'Message to Alice')\n bob_ciphertext += bob['cipher'].IV\n\n ciphertext_for_alice = yield bob_ciphertext\n\n ### Finally alice decrypts bobs reply\n\n alice['cipher'].decrypt(ciphertext_for_alice[:-16], ciphertext_for_alice[-16:])","def setup():\n # Check if d has been printed in private.rsa\n # (if d is missing, n assumed missing)\n ifp = open(\"private.rsa\")\n num_lines = sum(1 for line in ifp)\n ifp.close()\n\n if num_lines == 2:\n ifp = open(\"private.rsa\")\n p, q = int(ifp.readline()), int(ifp.readline())\n\n ifp = open(\"public.rsa\")\n e = int(ifp.readline())\n\n # Append d to the private file\n ofp = open(\"private.rsa\", \"a\")\n from basicnumbertheory.cryptomath import findModInverse\n ofp.write(\"\\n\" + str(findModInverse(e, (p - 1) * (q - 1))))\n\n # Append n to the private file\n ofp = open(\"public.rsa\", \"a\")\n ofp.write(\"\\n\" + str(p * q))","def public_from_private(self, private_key):","def create_keypair(self):\n # NOTE: currently we rely on zmq for convenience, but we may use libnacl directly\n # if we want to isolate this module from zmq dependency.\n public_key, private_key = zmq.curve_keypair()\n return public_key, private_key","def public_key(self):","def mk_keyobj_from_private_key_der(self, derdat):\n self.private_key_obj = serialization.load_der_private_key(derdat, password=None, backend=default_backend())\n self._get_naive_private_key_bytes()\n self.public_key_obj = self.private_key_obj.public_key()\n self._get_naive_public_key_bytes()","def __init__(self, key, data):\n hmac_result = hmac.new(key, data, hashlib.sha512).digest()\n\n self.private_key = PrivateKey(hmac_result[0:32])\n self.chain_code = hmac_result[32:]","def __init__(self, type_encryption, directory_key_private, directory_key_public):\n\t\t# class variables\n\t\tself.type_encryption = type_encryption\n\t\tself.directory_key_private = directory_key_private\n\t\tself.directory_key_public = directory_key_public\n\n\t\t# check keys\n\t\tself._publicKey = ''\n\t\tself._privateKey = ''\n\t\tself.verify_path() # verify the are not corrupted","def private_key():\n return \"Toholampi summer festival 2017 has the most harcore rock bands\"","def __init__(self, gen_priv_key: bool = False, priv_key_path: str = None):\n self.priv_key = None\n self.pub_key = None\n\n # max size = (bytes(rsa) - 2 * bytes(hash) - 2),\n # currently hard-coded to 190 = 256 - 2 * 32 - 2\n self.max_encrypt_size = 190\n\n if gen_priv_key:\n self.priv_key = RSA.generate(2048)\n if priv_key_path is not None:\n path = pathlib.Path(priv_key_path)\n with open(path.as_posix(), 'w') as f:\n f.write(self.priv_key.export_key().decode('utf-8'))\n elif priv_key_path is not None:\n path = pathlib.Path(priv_key_path)\n if path.is_file():\n self.priv_key = RSA.importKey(open(path.as_posix()).read())\n else:\n raise Exception(\"Failed to open file {}\".format(path.as_posix))\n\n if self.priv_key is not None:\n self.pub_key = self.priv_key.publickey()\n\n # delegate encrypt/decrypt function\n self.cipher = PKCS1_OAEP.new(self.priv_key, hashAlgo=SHA256)\n self.decrypt = self.cipher.decrypt","def __init__(self, p=P_NIST, g=G_NIST):\n dh_a = self._random_int(p)\n dh_A = gmp.powmod(g, dh_a, p)\n\n self._dh_p = p\n self._dh_g = g\n self._secret_key = dh_a\n self._public_key = dh_A","def __init__(self, sk=None, n=None, h=None):\r\n if sk:\r\n self.n = sk.n\r\n self.h = sk.h\r\n elif n and h:\r\n self.n = n\r\n self.h = h\r\n else:\r\n raise Exception(\"Public Key construction failed: insufficient/wrong arguments\")\r\n\r\n self.signature_bound = Params[self.n][\"sig_bound\"]\r\n self.sig_bytelen = Params[self.n][\"sig_bytelen\"]","def __init__(self,\n master_secret=None,\n private_key=None,\n private_key_file=None,\n public_key=None,\n public_key_file=None,\n algorithm='HS256',\n expiration_delta=datetime.timedelta(hours=6),\n leeway=0,\n verify_expiration=True,\n issuer=None,\n auth_header_prefix='JWT',\n userid_claim='sub'\n ):\n self.master_secret = master_secret\n self.private_key = private_key\n self.private_key_file = private_key_file\n self.public_key = public_key\n self.public_key_file = public_key_file\n self.algorithm = algorithm\n self.expiration_delta = expiration_delta\n self.leeway = leeway\n self.verify_expiration = verify_expiration\n self.issuer = issuer\n self.auth_header_prefix = auth_header_prefix\n self.userid_claim = userid_claim","def diffie_hellman(self, pub: bytes) -> bytes:\n ...","def __init__(self, curve=None, private_key=None, public_key=None):\n self.curve = curve\n self.private_key = None\n self.public_key = None\n if private_key:\n self.load_private_key(private_key)\n if public_key:\n self.load_received_public_key(public_key)","def __init__(self, public_key):\n self._pk = ed25519.Ed25519PublicKey.from_public_bytes(public_key.bytes)","def make_final_key(prime, public, private):\n\n key = (public ** private) % prime\n return key","def create_handshake_key_pair(cls) -> Tuple[bytes, bytes]:\n ...","def __init__(self, private_key):\n self._sk = ed25519.Ed25519PrivateKey.from_private_bytes(private_key.bytes)","def generate_key(seed):\n private_key = sha256(seed)\n public_key = privtopub(private_key)\n return {\"private\": private_key, \"public\": public_key}","def __init__(self, private_key):\n if private_key:\n if isinstance(private_key, str): # base58 encoded string\n self.private_key = PrivateKey.from_b58check(private_key)\n else:\n self.private_key = private_key\n self.public_key = self.private_key.public_key\n else:\n self.private_key = None\n self.public_key = None","def __set_or_create_key_if_not_exist(self):\n\n # instantiate PKI class:\n pki = PKI(username=self.username, password=self.password)\n\n # load private key into object. key is ready to be used to sign already imported\n privkey = pki.load_priv_key()\n\n # if it is an empty list then no key created and saved on username so generate new key\n if not privkey:\n pki.generate_pub_priv_key()\n privkey = pki.load_priv_key()\n\n # set self.privkey to privkey\n self.privkey = privkey","def staticstatic(self, private_key, public_key):","def _init_key_settings(self):\n self.minKeySize = 1023\n self.maxKeySize = 8193\n self.rsaSigHashes = list(RSA_SIGNATURE_HASHES)\n self.rsaSchemes = list(RSA_SCHEMES)\n self.dsaSigHashes = list(DSA_SIGNATURE_HASHES)\n self.virtual_hosts = []\n # DH key settings\n self.eccCurves = list(CURVE_NAMES)\n self.dhParams = None\n self.dhGroups = list(ALL_DH_GROUP_NAMES)\n self.defaultCurve = \"secp256r1\"\n self.keyShares = [\"secp256r1\", \"x25519\"]\n self.padding_cb = None\n self.use_heartbeat_extension = True\n self.heartbeat_response_callback = None","def generate_keys(self):\n\n # TODO: Store keys encrypted\n rsa1 = RsaPrivateKey.Generate()\n self.sign_private = str(rsa1)\n self.sign_public = str(rsa1.public_key)\n\n rsa2 = RsaPrivateKey.Generate()\n self.crypt_private = str(rsa2)\n self.crypt_public = str(rsa2.public_key)","def __init__(self, pubkey, e=65537):\n if isinstance(pubkey, int):\n self.key = RSA.RsaKey(n=pubkey, e=e)\n\n else:\n if not isinstance(pubkey, str):\n raise ValueError('pubkey must be str or int.')\n\n if '----' in pubkey:\n try:\n self.key = RSA.import_key(pubkey)\n except Exception as e:\n print(e)\n else:\n if pubkey == pubkey.lower():\n pubkey = int(pubkey, 16)\n self.key = RSA.RsaKey(n=pubkey, e=e)\n else:\n pubkey = '-----BEGIN PUBLIC KEY-----\\n' + pubkey + '\\n-----END PUBLIC KEY-----'\n try:\n self.key = RSA.import_key(pubkey)\n except Exception as e:\n print(e)","def main():\n\n prime = 0xffffffffffffffffc90fdaa22168c234c4c6628b80dc1cd129024e088a67cc74020bbea63b139b22514a08798e3404ddef9519b3cd3a431b302b0a6df25f14374fe1356d6d51c245e485b576625e7ec6f44c42e9a637ed6b0bff5cb6f406b7edee386bfb5a899fa5ae9f24117c4b1fe649286651ece45b3dc2007cb8a163bf0598da48361c55d39a69163fa8fd24cf5f83655d23dca3ad961c62f356208552bb9ed529077096966d670c354e4abc9804f1746c08ca237327ffffffffffffffff\n base = 2\n \n connection = diffiehellman_mitm_sim(prime, base)\n\n # intercept alices public key\n prime, base , _ = next(connection)\n\n # send prime instead of alices public key to bob. Recieve Bobs public key, \n # which we forget as it is not needs. The shared kill will be 0.\n\n connection.send((prime, base, prime))\n\n #Send prime as bob's public key to alice. We have ensured that the shared\n #hared secret key is 0. Recieve Alice's ciphertext for bob\n ciphertext_a2b = connection.send(prime)\n\n # decrypt\n malcolm = AES_CBC(SHA1(bso.int_to_bytes(0)).digest()[:16], b'0'*16)\n messages = []\n messages.append(bso.remove_padding_pkcs7(malcolm.decrypt(ciphertext_a2b[:-16], ciphertext_a2b[-16:])))\n\n #Send the ciphertext to bob. Recieve his response\n ciphertext_b2a = connection.send(ciphertext_a2b)\n\n messages.append(bso.remove_padding_pkcs7(malcolm.decrypt(ciphertext_b2a[:-16], ciphertext_b2a[-16:])))\n\n assert messages[0] == b'Message to Bob'\n assert messages[1] == b'Message to Alice'\n\n \n return","def setup(self):\n self.build_serverkeyhash()\n self.build_agent_pubkey()\n self.load_registration_key()","def __init__(self, public_key=None):\n self.public_key = self.convert_public_key_to_ecdsa(public_key) if public_key else public_key","def link(self, secret=None, secondSecret=None):\r\n if hasattr(dposlib, \"core\"):\r\n if secret:\r\n keys = dposlib.core.crypto.getKeys(secret)\r\n self.senderPublicKey = keys[\"publicKey\"]\r\n self._privateKey = keys[\"privateKey\"]\r\n if secondSecret:\r\n keys = dposlib.core.crypto.getKeys(secondSecret)\r\n self._secondPrivateKey = keys[\"privateKey\"]","def _manually_initialize(self):\n self.mnemonic = Mnemonic(self.language)\n\n if not self.words:\n # Initialized but still locked\n return\n\n # Validate words first\n self.validate_words()\n\n assert isinstance(self.passphrase, bytes), 'Passphrase must be in bytes'\n\n # Master seed\n seed = self.mnemonic.to_seed(self.words, self.passphrase.decode('utf-8'))\n\n # Master node\n from pycoin.networks.registry import network_for_netcode\n _register_pycoin_networks()\n network = network_for_netcode('htr')\n key = network.keys.bip32_seed(seed)\n\n # Until account key should be hardened\n # Chain path = 44'/0'/0'/0\n # 44' (hardened) -> BIP44\n # 280' (hardened) -> Coin type (280 = hathor)\n # 0' (hardened) -> Account\n # 0 -> Chain\n self.chain_key = key.subkey_for_path('44H/280H/0H/0')\n\n for key in self.chain_key.children(self.initial_key_generation, 0, False):\n self._key_generated(key, key.child_index())","def __init__(self, globalKey, publicKey, resourceName, **rest):\n super(SshKey, self).__init__({\n \"globalKey\": globalKey,\n \"publicKey\": publicKey,\n \"resourceName\": resourceName,\n }, **rest)","def generate_key(self):\n\n self.key = Fernet.generate_key()\n self.cryptor = Fernet(self.key)","def test_create_and_import_encrypted_rsa(self):\n name = \"key_encrypted\"\n password = \"123456\"\n bits= 3072\n generate_and_write_rsa_keypair(name, bits, password)\n private_key = import_rsa_key_from_file(name, password)\n public_key = import_rsa_key_from_file(name + \".pub\")\n\n securesystemslib.formats.KEY_SCHEMA.check_match(private_key)\n self.assertTrue(private_key[\"keyval\"].get(\"private\"))\n self.assertTrue(\n securesystemslib.formats.PUBLIC_KEY_SCHEMA.matches(public_key))","def __init__(self, prev_hash: bytes, time: float, user_id: str, password: str):\r\n super().__init__(prev_hash, time, user_id)\r\n\r\n # Derive a public key from the password. Add user_id to password as salt\r\n self.public_key: ec.EllipticCurvePublicKey = Block.password_to_key(password + user_id).public_key()","def init(x_in):\n global public_keys, secret_keys, x\n x = func.get_bits(x_in)\n\n public_keys, secret_keys = [], []\n\n elgamal.init_g_p_q()\n for i in range(3):\n create_keys(i)","def __init__(self, key_info):\n if (key_info.type != client_pb2.KeyInfo.ECDSA):\n raise error.UnsupportedAlgorithmError(\n \"Expected ECDSA key, but got key type %d\" % key_info.type)\n\n # Will raise a PemError on invalid encoding\n self.__der, _ = pem.from_pem(key_info.pem_key, self.__READ_MARKERS)\n try:\n self.__key = ecdsa.VerifyingKey.from_der(self.__der)\n except ecdsa.der.UnexpectedDER as e:\n raise error.EncodingError(e)","def load_key():","def recipient_public_key(self):","def generate(self, module):\n\n # If size is wrong, delete the key. A new key will be generated in the next step.\n if self.key_current_size != self.size and not self.ignore_size:\n self.remove()\n self.key_exists = False\n else:\n self.changed = False\n\n # If there is no key or user has set \"force\"\n if not self.key_exists or self.force:\n if self.type == \"RSA\":\n self.key = crypto_rsa.generate_private_key(public_exponent=65537, key_size=self.size, backend=crypto_default_backend())\n elif self.type == \"DSA\":\n self.key = crypto_dsa.generate_private_key(key_size=self.size, backend=crypto_default_backend())\n elif self.type == \"ECDSA\":\n if self.size == 256:\n self.curve = crypto_ec.SECP256R1()\n elif self.size == 384:\n self.curve = crypto_ec.SECP384R1()\n elif self.size == 521:\n self.curve = crypto_ec.SECP521R1()\n self.key = crypto_ec.generate_private_key(curve=self.curve, backend=crypto_default_backend())\n elif self.type == \"ED25519\":\n self.size = 128\n self.curve = \"EC25519\"\n else:\n raise HostkeyError(\"Unknown key type.\")\n\n if self.type != \"ED25519\":\n self.privkey = self.key.private_bytes(crypto_serialization.Encoding.PEM, crypto_serialization.PrivateFormat.PKCS8, crypto_serialization.NoEncryption())\n self.pubkey = self.key.public_key().public_bytes(crypto_serialization.Encoding.OpenSSH, crypto_serialization.PublicFormat.OpenSSH)\n\n try:\n privfile = os.open(self.fullpath, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, self.mode)\n os.write(privfile, self.privkey)\n os.close(privfile)\n pubfile = os.open(self.fullpath + \".pub\", os.O_WRONLY | os.O_CREAT | os.O_TRUNC, self.mode)\n os.write(pubfile, self.pubkey)\n os.close(pubfile)\n self.changed = True\n except IOError:\n self.remove()\n raise HostkeyError(get_exception())\n else:\n # use ssh-keygen to generate ED25519 Hostkeys\n # Keyfile must not exist, as there is no \"force-overwrite\" in ssh-keygen\n self.remove()\n retcode = subprocess.call([\"ssh-keygen\", \"-q\", \"-t\", \"ed25519\", \"-N\", '', \"-f\", self.fullpath])\n self.changed = True\n else:\n self.changed = False\n\n file_args = module.load_file_common_arguments(module.params)\n file_args['path'] = self.fullpath\n if module.set_fs_attributes_if_different(file_args, False):\n self.changed = True\n file_args['path'] = self.fullpath + \".pub\"\n file_args['mode'] = self.pubmode\n if module.set_fs_attributes_if_different(file_args, False):\n self.changed = True","def setup2():\n ht2 = HashTable()\n ht2.set('Apple', 'Dash')\n ht2.set('Flutter', 'Sparkle')\n ht2.set('Pinky', 'Pony')\n return ht2","def get_asymm_keys(parameters):\n\tprivate_key = parameters.generate_private_key()\n\treturn private_key,private_key.public_key()","def generate_keys() -> tuple:\n private_key = ecdsa.SigningKey.generate(curve=curve)\n public_key = private_key.get_verifying_key()\n\n private_key = encode_private_key(private_key)\n public_key = encode_public_key(public_key)\n\n return public_key, private_key","def __init__(self, rsa_key):\r\n if isinstance(rsa_key, tuple):\r\n self.keypair = Crypto.PublicKey.RSA.construct(rsa_key)\r\n else:\r\n self._InitFromString(rsa_key)","def do_new(argv):\n\n global PRIVATE_KEY\n\n if not PRIVATE_KEY:\n PRIVATE_KEY = wallet.get_private_key()\n else:\n get_new = yes_or_no(\"Private key already exist, do you want generate new one ?\")\n if get_new:\n PRIVATE_KEY = wallet.get_private_key()\n print(\"Private Key: '\" + PRIVATE_KEY + \"'\")\n cmpr_pub_key = wallet.get_compressed_public_key(PRIVATE_KEY, 1)\n addr = wallet.public_key_to_address(cmpr_pub_key, 0)\n open(\"data/address\", \"w\").write(addr)\n print(\"Public key was saved to 'data/cmpr_pub_key'\")","def __init__(self, name, key, hash_, vendor):\n self.name = name\n self.key = key\n self.hash = hash_\n self.vendor = vendor","def alt_stubbed_receiver() -> PublicKey:\n return PublicKey(\"J3dxNj7nDRRqRRXuEMynDG57DkZK4jYRuv3Garmb1i98\")","def __init__(self, username, password):\n self.username = username\n self.password = password\n self.privkey = None\n\n # sets self.privkey\n self.__set_or_create_key_if_not_exist()","def generate_ecdh_key_pair() -> tuple[X25519PrivateKey, bytes]:\n private_key = X25519PrivateKey.generate()\n public_key_raw = private_key.public_key().public_bytes(\n serialization.Encoding.Raw, serialization.PublicFormat.Raw\n )\n return private_key, public_key_raw","def do_minerkey(argv):\n\n global PRIVATE_KEY\n\n if not PRIVATE_KEY:\n print(\"Error: private key is missing. Use command 'new' to generate key\")\n else:\n PRIVATE_KEY = wallet.get_private_key()\n minerkey = wallet.private_key_to_wif(PRIVATE_KEY, 0, 0)\n file = open(\"data/minerkey\", \"w\")\n file.write(minerkey)\n print(\"Minerkey was created in WIF format and saved to 'data/minerkey'\")\n file.close()","def dh(self):\n self.cli.add_msg(\"Establishing Encryption Key...\")\n dh_message = self.connection.recv(DH_MSG_SIZE)\n # Unpack p, g, and server_key from the server's dh message\n p, g, server_key = DH.unpack(dh_message)\n # Generate a randomized private key\n private_key = DH.gen_private_key()\n # Send the server a public key which used the previously\n # Generated private key and both g and p\n public_key = DH.gen_public_key(g, private_key, p)\n self.connection.sendall(DH.package(public_key, LEN_PK))\n # Calculate shared key\n shared_key = DH.get_shared_key(server_key, private_key, p)\n # print(\"Shared Key: {}\".format(shared_key))\n self.cli.add_msg(\"Encryption Key: {}\".format(binascii.hexlify(shared_key).decode(\"utf-8\")))\n return shared_key","def mk_keyobj_from_private_key_pem(self, pemdat_string):\n if isinstance(pemdat_string, str):\n pemdat_string = pemdat_string.encode()\n self.private_key_obj = serialization.load_pem_private_key(pemdat_string, password=None, backend=default_backend())\n self._get_naive_private_key_bytes()\n self.public_key_obj = self.private_key_obj.public_key()\n self._get_naive_public_key_bytes()","def get_rsa_asymn_keys(public_exponent = 65537, key_size = 2048, bc = backend):\n\tprivate_key = asymmetric.rsa.generate_private_key(public_exponent = public_exponent, key_size = key_size, backend = bc)\n\treturn private_key,private_key.public_key()","def __init__(self, key):\n self.bs = 16\n self.key = hashlib.sha256(key.encode()).digest()","def gen_private_key():\n return DH.b2i(Random.new().read(DH_SIZE))","def __init__(self, public_key, private_key, token, token_secret, base_url='http://api.telldus.com'):\n self.public_key = public_key\n self.private_key = private_key\n self.token = token\n self.token_secret = token_secret\n\n self.base_url = base_url\n\n self.oauth = self.generate_temp_session()","def shared_key(private_key,public_key):\n\treturn private_key.exchange(public_key)","def __init__(self,key):\n self.block_size = 32\n self.key = hashlib.sha256(key).digest()","def generate_elgamal_auxiliary_key_pair() -> AuxiliaryKeyPair:\n elgamal_key_pair = elgamal_keypair_random()\n return AuxiliaryKeyPair(\n elgamal_key_pair.secret_key.to_hex(),\n elgamal_key_pair.public_key.to_hex(),\n )","def _InitFromString(self, text):\r\n # First, remove all whitespace:\r\n text = re.sub(_WHITESPACE_RE, '', text)\r\n\r\n # Parse out the period-separated components\r\n match = _KEY_RE.match(text)\r\n if not match:\r\n raise ValueError('Badly formatted key string: \"%s\"', text)\r\n\r\n private_exp = match.group('private_exp')\r\n if private_exp:\r\n private_exp = _B64ToNum(private_exp)\r\n else:\r\n private_exp = None\r\n self.keypair = Crypto.PublicKey.RSA.construct(\r\n (_B64ToNum(match.group('mod')),\r\n _B64ToNum(match.group('exp')),\r\n private_exp))","def __init__(self, identifier=None, seed=None, alias=None):\n\n # should be stored securely/privately\n self.seed = seed if seed else randombytes(32)\n\n # generates key pair based on seed\n self.sk = SigningKey(seed=self.seed)\n\n # helper for signing\n self.naclSigner = NaclSigner(self.sk)\n\n # this is the public key used to verify signatures (securely shared\n # before-hand with recipient)\n hex_verkey = hexlify(self.naclSigner.verraw)\n self.verkey = hexToFriendly(hex_verkey)\n\n self._identifier = identifier or self.verkey\n\n self._alias = alias","def __init__(self, generator=2, group=17, keyLength=540):\n min_keyLength = 180\n\n default_generator = 2\n valid_generators = [2, 3, 5, 7]\n\n # Sanity check fors generator and keyLength\n if(generator not in valid_generators):\n print(\"Error: Invalid generator. Using default.\")\n self.generator = default_generator\n else:\n self.generator = generator\n\n if(keyLength < min_keyLength):\n print(\"Error: keyLength is too small. Setting to minimum.\")\n self.keyLength = min_keyLength\n else:\n self.keyLength = keyLength\n\n self.prime = self.getPrime(group)\n\n self.privateKey = self.genPrivateKey(keyLength)\n self.publicKey = self.genPublicKey()","def exchange_key(connection, pub_key):\r\n\r\n if main.diffe_key_exchange is False:\r\n # Get the server's public key\r\n server_pub_key_bytes = connection.recv(1024)\r\n\r\n # Send public key\r\n connection.sendall(rsa.PublicKey.save_pkcs1(pub_key))\r\n\r\n else:\r\n # Rounds of bit-shifting and XOR\r\n rounds = 64\r\n\r\n while True:\r\n\r\n # Generate 4096-bit keys (RFC 3526 Group 16)\r\n client_diffe_key = pyDHE.new(16)\r\n shared_secret = client_diffe_key.negotiate(connection)\r\n\r\n # Encrypt\r\n encrypted = int(binascii.hexlify(rsa.PublicKey.save_pkcs1(pub_key)).decode(), 16)\r\n for x in range(0, rounds):\r\n encrypted = encrypted ^ (shared_secret ** rounds)\r\n encrypted = encrypted << rounds\r\n encrypted = int(str(encrypted)[::-1])\r\n\r\n # Decrypt\r\n decrypted = encrypted\r\n decrypted = int(str(decrypted)[::-1])\r\n for x in range(rounds, 0, -1):\r\n decrypted = decrypted >> rounds\r\n decrypted = decrypted ^ (shared_secret ** rounds)\r\n\r\n # Check if able to decrypt\r\n try:\r\n binascii.unhexlify(hex(decrypted)[2:]).decode()\r\n client_success = True\r\n\r\n # Generate new keys upon failure and try again\r\n except UnicodeDecodeError:\r\n client_success = False\r\n pass\r\n except binascii.Error:\r\n client_success = False\r\n pass\r\n\r\n # Notify client about encryption status\r\n server_success = connection.recv(1024)\r\n if client_success is False:\r\n connection.send(b'DHE')\r\n else:\r\n connection.send(b'CONTINUE')\r\n\r\n # Get encryption status from client\r\n if client_success is False or server_success == b'DHE':\r\n pass\r\n elif server_success == b'CONTINUE':\r\n break\r\n\r\n # Hold encrypted server key\r\n server_encrypted = b''\r\n\r\n # Receive encrypted key from the server\r\n while True:\r\n data = connection.recv(8192)\r\n if data == b'ENDED':\r\n break\r\n elif data[-5:] == b'ENDED':\r\n server_encrypted += data[:-5]\r\n break\r\n server_encrypted += data\r\n\r\n # Send the encrypted key to the server\r\n connection.sendall(bytes(hex(encrypted).encode()))\r\n connection.send(b'ENDED')\r\n\r\n # Decrypt the client's public key\r\n decrypted = int(server_encrypted, 16)\r\n decrypted = int(str(int(decrypted))[::-1])\r\n for x in range(rounds, 0, -1):\r\n decrypted = decrypted >> rounds\r\n decrypted = decrypted ^ (shared_secret ** rounds)\r\n\r\n server_pub_key_bytes = binascii.unhexlify(hex(decrypted)[2:]).decode()\r\n\r\n server_pub_key = rsa.PublicKey.load_pkcs1(server_pub_key_bytes)\r\n # Determine max message size\r\n max_message_size = common.byte_size(server_pub_key.n) - 11\r\n\r\n # Return crypto key information\r\n return server_pub_key, server_pub_key_bytes, max_message_size","def new_key_pair(self):\n from plonevotecryptolib.KeyPair import KeyPair # avoids circular imports\n return KeyPair(self)","def setup(pk_outfile=const.ABE_PK_FILE, msk_outfile=const.ABE_MSK_FILE, pairing_group_curve=const.PAIRING_GROUP_CURVE,\n debug=0):\n\n # Instantiate a bilinear pairing map with the given curve\n pairing_group = PairingGroup(pairing_group_curve)\n\n # CP-ABE\n cpabe = CPabe_BSW07(pairing_group)\n\n # Create public and master secret keys\n (pk, msk) = cpabe.setup()\n\n if debug: # ONLY USE FOR DEBUG\n print('CP-ABE PUBLIC KEY =', pk)\n print('CP-ABE MASTER SECRET KEY =', msk)\n\n # Save keys on given output files\n with open(pk_outfile, 'w') as fout:\n fout.write(objectToBytes(pk, pairing_group).hex())\n\n with open(msk_outfile, 'w') as fout:\n fout.write(objectToBytes(msk, pairing_group).hex())","def __init__(self, passphrase):\n self.__key_stretches = 256 * 1024\n self.__magic_number = 0x7A6A5A4A\n self.__file_version = 1\n self.__key = self._produce_key(passphrase)\n self.__iv = self._produce_iv(self.__key)\n return","def __init__(self,\r\n ephemeral_public_key=None,\r\n public_key_hash=None,\r\n transaction_id=None):\r\n\r\n # Initialize members of the class\r\n self.public_key_hash = public_key_hash\r\n self.ephemeral_public_key = ephemeral_public_key\r\n self.transaction_id = transaction_id","def mk_keyobj_from_private_key(self, privkey):\n bn = BACKEND_KP.private_key_obj._backend._ffi.NULL\n bn_ptr = BACKEND_KP.private_key_obj._backend._lib.BN_bin2bn(privkey, len(privkey), bn)\n secret_val = BACKEND_KP.private_key_obj._backend._bn_to_int(bn_ptr)\n\n if self.curvetype == KeyType.ECDSA_P256v1:\n self.private_key_obj = ec.derive_private_key(secret_val, ec.SECP256R1(), default_backend())\n elif self.curvetype == KeyType.ECDSA_SECP256k1:\n self.private_key_obj = ec.derive_private_key(secret_val, ec.SECP256K1(), default_backend())\n self._get_naive_private_key_bytes()\n self.public_key_obj = self.private_key_obj.public_key()\n self._get_naive_public_key_bytes()","def derive_keys(self, master_salt, master_secret):\n\n self.sender_key = self._kdf(master_salt, master_secret, self.sender_id, 'Key')\n self.recipient_key = self._kdf(master_salt, master_secret, self.recipient_id, 'Key')\n\n self.common_iv = self._kdf(master_salt, master_secret, b\"\", 'IV')","def test_set_public_key_setter(self) -> None:\n\n expected = self.pem_public_key.decode()\n\n encryptor = DataEncryption()\n encryptor.set_public_key(self.pem_public_key)\n\n # pylint: disable=protected-access\n actual = encryptor._loaded_public_key.public_bytes(\n serialization.Encoding.PEM, serialization.PublicFormat.PKCS1\n ).decode()\n\n self.assertEqual(expected, actual)","def generate_rsa_key_pair(self):\n\t\tprint \"Started rsa key generation\"\n\t\tkey = RSA.generate(self.key_size, randfunc=self.random_number_generator)\n\t\t\t\n\t\tpub_key = key.publickey().exportKey()\n\t\tprint pub_key\n\t\t\n\n\t\tpriv_key = key.exportKey()\n\t\tprint \"Private key\", priv_key \n\t\tprint \"Note: Normally, the private key should be protected. For the purposes of this demo, I'm printing it to terminal.\"","def generateKeys(bits=256):\n #print \"generating first prime number\"\n p = generatePrime(bits/2)\n #print \"generating second prime number\"\n q = generatePrime(bits/2)\n \n assert p != q\n #print p, \"\\n\", q\n assert gcd(p*q, (p-1)*(q-1)) == 1\n \n priv = PrivateKey(p, q)\n pub = PublicKey(p, q)\n \n priv.saveToFile()\n pub.saveToFile()\n \n return priv, pub","def generate(self):\n if self.curvetype == KeyType.ECDSA_P256v1:\n self.private_key_obj = ec.generate_private_key(ec.SECP256R1(), default_backend())\n elif self.curvetype == KeyType.ECDSA_SECP256k1:\n self.private_key_obj = ec.generate_private_key(ec.SECP256K1(), default_backend())\n self.public_key_obj = self.private_key_obj.public_key()\n self._get_naive_private_key_bytes()\n self._get_naive_public_key_bytes()","def __init__(self, key_bytes, public=True):\n self.G = _globalECG\n if public:\n self.sec = None\n self.pub = EcPt.from_binary(key_bytes, self.G)\n self.optim = None\n else:\n self.sec = Bn.from_binary(sha256(key_bytes).digest())\n self.pub = self.sec * self.G.generator()\n self.optim = do_ecdsa_setup(self.G, self.sec)","def test_set_private_key_setter(self) -> None:\n\n expected = self.pem_private_key.decode()\n\n encryptor = DataEncryption()\n encryptor.set_private_key(self.pem_private_key)\n\n # pylint: disable=protected-access\n actual = encryptor._loaded_private_key.private_bytes(\n serialization.Encoding.PEM,\n serialization.PrivateFormat.PKCS8,\n serialization.NoEncryption(),\n ).decode()\n\n self.assertEqual(expected, actual)","def __init__(__self__, *,\n primary_auth_key_hash: pulumi.Input[str],\n secondary_auth_key_hash: pulumi.Input[str]):\n pulumi.set(__self__, \"primary_auth_key_hash\", primary_auth_key_hash)\n pulumi.set(__self__, \"secondary_auth_key_hash\", secondary_auth_key_hash)","def create_rsa_key_pair() -> Tuple[str, str]:\n key = RSA.generate(RSA_KEY_STRENGTH)\n public_key = key.publickey().export_key().decode()\n private_key = key.export_key().decode()\n return public_key, private_key","def __init__( self, masterKey ):\n\n assert len(masterKey) == const.MASTER_KEY_SIZE\n\n checkKeys()\n\n # The random name is used to recognize previously issued tickets.\n #self.keyName = mycrypto.weak_random(NAME_LENGTH)\n\n # Initialization vector for AES-CBC.\n self.IV = mycrypto.strong_random(IV_LENGTH)\n\n # The server's actual (encrypted) protocol state.\n self.state = ProtocolState(masterKey)\n\n # AES and HMAC key to protect the ticket.\n self.symmTicketKey = AESKey\n self.hmacTicketKey = HMACKey","def __init__(self, pubkey=None):","def stubbed_receiver() -> PublicKey:\n return PublicKey(\"J3dxNj7nDRRqRRXuEMynDG57DkZK4jYRuv3Garmb1i99\")","def keygen(bits, private, public):\n\n rsa = Rsa()\n keys = rsa.generate_keys(bits)\n\n formatter = TomlKeyFormatter()\n\n private.write(formatter.to_string(keys.private_key))\n public.write(formatter.to_string(keys.public_key))","def add_local_ssh_key(self):\n return\n user_ssh_key = open('/home/<$user>/.ssh/id_rsa.pub').read()\n key = digitalocean.SSHKey(token=self.secret_token,\n name='machine-name',\n public_key=user_ssh_key)\n key.create()","def __init__(self, **options):\n\n super().__init__(**options)\n\n self._private_key = None\n self._public_key = None\n\n self._load_keys(**options)","def gen_key_pair():\n sk = gen_secret_key(BITCOIN.gen.n)\n pk = PublicKey.from_sk(sk)\n return sk, pk","def __init__(self, hass, key):\n\n\n self.yangli = None\n self.yinli = None\n self.wuxing = None\n self.chongsha = None\n self.baiji = None\n self.jishen = None\n self.yi = None\n self.xiongshen = None\n self.ji = None\n \n self.hass = hass\n\n self.url = \"http://v.juhe.cn/laohuangli/d\"\n self.key = key","def dh_get_key():\n G = EcGroup()\n priv_dec = G.order().random()\n pub_enc = priv_dec * G.generator()\n return (G, priv_dec, pub_enc)","def genKey(self, otherKey):\n self.sharedSecret = self.genSecret(self.privateKey, otherKey)\n #print(\"Shared secret:\")\n #print(self.sharedSecret)\n s = hashlib.sha256()\n s.update(bytes(str(self.sharedSecret).encode()))\n self.key = s.digest()","def generate_rsa_server_keys() -> Tuple[bytes, bytes]:\n from cryptography.hazmat.primitives import serialization as crypto_serialization\n from cryptography.hazmat.primitives.asymmetric import rsa\n from cryptography.hazmat.backends import default_backend as crypto_default_backend\n\n # Generate the key\n key = rsa.generate_private_key(\n backend=crypto_default_backend(),\n public_exponent=65537,\n key_size=2048\n )\n\n # Get the private key in the standard PEM/PKCS8 format for SSH private keys.\n private_key = key.private_bytes(\n crypto_serialization.Encoding.PEM,\n crypto_serialization.PrivateFormat.OpenSSH,\n crypto_serialization.NoEncryption())\n\n # Get the public key in the standard OpenSSH format.\n public_key = key.public_key().public_bytes(\n crypto_serialization.Encoding.OpenSSH,\n crypto_serialization.PublicFormat.OpenSSH\n )\n\n # Write the two keys.\n with open('host_rsa', 'wb') as f:\n f.write(private_key)\n\n with open('host_rsa.pub', 'wb') as f:\n f.write(public_key)\n\n # Return them.\n return private_key, public_key","def PublicKey(self) -> _n_9_t_1:","def PublicKey(self) -> _n_9_t_1:","def _init_keys(self):\n\n basic_constraints = crypto.X509Extension('basicConstraints'.encode('ascii'), True,\n 'CA:TRUE, pathlen:0'.encode('ascii'))\n serial = self._get_serial()\n pkey = self._create_pkey(self.commonname, serial)\n self._create_cert(pkey, self.commonname, serial, [basic_constraints], expire=30*365)","def _create_rsa_key_pair(self, length, public_exponent=65537):\n self.logger.info(\n \"Generating an RSA key pair with length: {0}, and \"\n \"public_exponent: {1}\".format(\n length, public_exponent\n )\n )\n try:\n private_key = rsa.generate_private_key(\n public_exponent=public_exponent,\n key_size=length,\n backend=default_backend())\n public_key = private_key.public_key()\n\n private_bytes = private_key.private_bytes(\n serialization.Encoding.DER,\n serialization.PrivateFormat.PKCS8,\n serialization.NoEncryption())\n public_bytes = public_key.public_bytes(\n serialization.Encoding.DER,\n serialization.PublicFormat.PKCS1)\n except Exception as e:\n self.logger.exception(e)\n raise exceptions.CryptographicFailure(\n \"An error occurred while generating the RSA key pair. \"\n \"See the server log for more information.\"\n )\n\n public_key = {\n 'value': public_bytes,\n 'format': enums.KeyFormatType.PKCS_1,\n 'public_exponent': public_exponent\n }\n private_key = {\n 'value': private_bytes,\n 'format': enums.KeyFormatType.PKCS_8,\n 'public_exponent': public_exponent\n }\n\n return public_key, private_key","def test_set_private_key(self) -> None:\n\n expected = self.pem_private_key.decode()\n\n encryptor = DataEncryption(private_key=self.pem_private_key)\n\n # pylint: disable=protected-access\n actual = encryptor._loaded_private_key.private_bytes(\n serialization.Encoding.PEM,\n serialization.PrivateFormat.PKCS8,\n serialization.NoEncryption(),\n ).decode()\n\n self.assertEqual(expected, actual)"],"string":"[\n \"def __init__(self):\\n self._keypair = RSA.generate(2048)\\n self.public_key = self._keypair.publickey().exportKey()\",\n \"def setUp(self):\\n\\n self.private_key = self.get_new_key()\\n self.public_key = self.private_key.public_key()\\n\\n self.pem_private_key = self.private_key.private_bytes(\\n serialization.Encoding.PEM,\\n serialization.PrivateFormat.PKCS8,\\n serialization.NoEncryption(),\\n )\\n self.encrypted_pem_private_key = self.private_key.private_bytes(\\n serialization.Encoding.PEM,\\n serialization.PrivateFormat.PKCS8,\\n serialization.BestAvailableEncryption(self.private_key_password),\\n )\\n\\n self.pem_public_key = self.public_key.public_bytes(\\n serialization.Encoding.PEM, serialization.PublicFormat.PKCS1\\n )\",\n \"def create_keys(self):\\n crypto_tool = CryptoTools()\\n # creating RSA keys for the signer user\\n public_key, private_key = crypto_tool.create_key_with_entropy()\\n self.priv_key = crypto_tool.get_pem_format(private_key).decode(\\\"utf-8\\\")\\n self.pub_key = crypto_tool.get_pem_format(public_key).decode(\\\"utf-8\\\")\",\n \"def __init__(self):\\n publicKeyFileName = \\\"serverPublicKey\\\"\\n privateKeyFileName = \\\"serverPrivateKey.pem\\\"\\n try:\\n f = open(privateKeyFileName, 'rb')\\n self.keys = RSA.importKey(f.read())\\n except:\\n self.keys = RSA.generate(1024)\\n self.publickey = self.keys.publickey()\\n # export public and private keys\\n privHandle = open(privateKeyFileName, 'wb')\\n privHandle.write(self.keys.exportKey('PEM'))\\n privHandle.close()\\n \\n pubHandle = open(publicKeyFileName, 'wb')\\n pubHandle.write(self.keys.publickey().exportKey())\\n pubHandle.close()\\n self.publickey = self.keys.publickey()\",\n \"def __init__(self):\\n self.public_key = None\\n self._private_key = None\",\n \"def private_key(self):\",\n \"def create_key ():\",\n \"def diffiehellman_mitm_sim(prime, base):\\n alice = {}\\n\\n #Alice generates their public key an sends to 'bob'\\n alice['dh'] = DiffieHellman(prime, base, secret_key=secrets.randbelow(prime))\\n alice_pub = alice['dh'].gen_public_key()\\n\\n (prime, base, key_for_bob) = yield (prime, base, alice_pub)\\n\\n \\n\\n #bob recieves 'alice's' public key, generates their own public key and\\n #the shared key. Sends their public key ot 'alice'\\n bob = {'dh':DiffieHellman(prime, base, secret_key=secrets.randbelow(prime))}\\n bob_pup = bob['dh'].gen_public_key()\\n bob['dh'].gen_shared_key(key_for_bob)\\n\\n key_for_alice = yield bob_pup\\n\\n ### Alice recieves Bob's public key, generates the shared key and encrypts\\n ### message for bob\\n\\n alice['dh'].gen_shared_key(key_for_alice)\\n \\n alice['sha1'] = SHA1(bso.int_to_bytes(alice['dh'].shared_key))\\n alice['cipher'] = AES_CBC(alice['sha1'].digest()[:16], secrets.token_bytes(16))\\n alice_ciphertext = alice['cipher'].encrypt(b'Message to Bob')\\n alice_ciphertext += alice['cipher'].IV\\n\\n ciphertext_for_bob = yield alice_ciphertext\\n \\n #Bob recieves the ciphertext, decrypts it and send a reply.\\n\\n bob['sha1'] = SHA1(bso.int_to_bytes(bob['dh'].shared_key))\\n bob['cipher'] = AES_CBC(bob['sha1'].digest()[:16], secrets.token_bytes(16))\\n bob_ciphertext = bob['cipher'].encrypt(b'Message to Alice')\\n bob_ciphertext += bob['cipher'].IV\\n\\n ciphertext_for_alice = yield bob_ciphertext\\n\\n ### Finally alice decrypts bobs reply\\n\\n alice['cipher'].decrypt(ciphertext_for_alice[:-16], ciphertext_for_alice[-16:])\",\n \"def setup():\\n # Check if d has been printed in private.rsa\\n # (if d is missing, n assumed missing)\\n ifp = open(\\\"private.rsa\\\")\\n num_lines = sum(1 for line in ifp)\\n ifp.close()\\n\\n if num_lines == 2:\\n ifp = open(\\\"private.rsa\\\")\\n p, q = int(ifp.readline()), int(ifp.readline())\\n\\n ifp = open(\\\"public.rsa\\\")\\n e = int(ifp.readline())\\n\\n # Append d to the private file\\n ofp = open(\\\"private.rsa\\\", \\\"a\\\")\\n from basicnumbertheory.cryptomath import findModInverse\\n ofp.write(\\\"\\\\n\\\" + str(findModInverse(e, (p - 1) * (q - 1))))\\n\\n # Append n to the private file\\n ofp = open(\\\"public.rsa\\\", \\\"a\\\")\\n ofp.write(\\\"\\\\n\\\" + str(p * q))\",\n \"def public_from_private(self, private_key):\",\n \"def create_keypair(self):\\n # NOTE: currently we rely on zmq for convenience, but we may use libnacl directly\\n # if we want to isolate this module from zmq dependency.\\n public_key, private_key = zmq.curve_keypair()\\n return public_key, private_key\",\n \"def public_key(self):\",\n \"def mk_keyobj_from_private_key_der(self, derdat):\\n self.private_key_obj = serialization.load_der_private_key(derdat, password=None, backend=default_backend())\\n self._get_naive_private_key_bytes()\\n self.public_key_obj = self.private_key_obj.public_key()\\n self._get_naive_public_key_bytes()\",\n \"def __init__(self, key, data):\\n hmac_result = hmac.new(key, data, hashlib.sha512).digest()\\n\\n self.private_key = PrivateKey(hmac_result[0:32])\\n self.chain_code = hmac_result[32:]\",\n \"def __init__(self, type_encryption, directory_key_private, directory_key_public):\\n\\t\\t# class variables\\n\\t\\tself.type_encryption = type_encryption\\n\\t\\tself.directory_key_private = directory_key_private\\n\\t\\tself.directory_key_public = directory_key_public\\n\\n\\t\\t# check keys\\n\\t\\tself._publicKey = ''\\n\\t\\tself._privateKey = ''\\n\\t\\tself.verify_path() # verify the are not corrupted\",\n \"def private_key():\\n return \\\"Toholampi summer festival 2017 has the most harcore rock bands\\\"\",\n \"def __init__(self, gen_priv_key: bool = False, priv_key_path: str = None):\\n self.priv_key = None\\n self.pub_key = None\\n\\n # max size = (bytes(rsa) - 2 * bytes(hash) - 2),\\n # currently hard-coded to 190 = 256 - 2 * 32 - 2\\n self.max_encrypt_size = 190\\n\\n if gen_priv_key:\\n self.priv_key = RSA.generate(2048)\\n if priv_key_path is not None:\\n path = pathlib.Path(priv_key_path)\\n with open(path.as_posix(), 'w') as f:\\n f.write(self.priv_key.export_key().decode('utf-8'))\\n elif priv_key_path is not None:\\n path = pathlib.Path(priv_key_path)\\n if path.is_file():\\n self.priv_key = RSA.importKey(open(path.as_posix()).read())\\n else:\\n raise Exception(\\\"Failed to open file {}\\\".format(path.as_posix))\\n\\n if self.priv_key is not None:\\n self.pub_key = self.priv_key.publickey()\\n\\n # delegate encrypt/decrypt function\\n self.cipher = PKCS1_OAEP.new(self.priv_key, hashAlgo=SHA256)\\n self.decrypt = self.cipher.decrypt\",\n \"def __init__(self, p=P_NIST, g=G_NIST):\\n dh_a = self._random_int(p)\\n dh_A = gmp.powmod(g, dh_a, p)\\n\\n self._dh_p = p\\n self._dh_g = g\\n self._secret_key = dh_a\\n self._public_key = dh_A\",\n \"def __init__(self, sk=None, n=None, h=None):\\r\\n if sk:\\r\\n self.n = sk.n\\r\\n self.h = sk.h\\r\\n elif n and h:\\r\\n self.n = n\\r\\n self.h = h\\r\\n else:\\r\\n raise Exception(\\\"Public Key construction failed: insufficient/wrong arguments\\\")\\r\\n\\r\\n self.signature_bound = Params[self.n][\\\"sig_bound\\\"]\\r\\n self.sig_bytelen = Params[self.n][\\\"sig_bytelen\\\"]\",\n \"def __init__(self,\\n master_secret=None,\\n private_key=None,\\n private_key_file=None,\\n public_key=None,\\n public_key_file=None,\\n algorithm='HS256',\\n expiration_delta=datetime.timedelta(hours=6),\\n leeway=0,\\n verify_expiration=True,\\n issuer=None,\\n auth_header_prefix='JWT',\\n userid_claim='sub'\\n ):\\n self.master_secret = master_secret\\n self.private_key = private_key\\n self.private_key_file = private_key_file\\n self.public_key = public_key\\n self.public_key_file = public_key_file\\n self.algorithm = algorithm\\n self.expiration_delta = expiration_delta\\n self.leeway = leeway\\n self.verify_expiration = verify_expiration\\n self.issuer = issuer\\n self.auth_header_prefix = auth_header_prefix\\n self.userid_claim = userid_claim\",\n \"def diffie_hellman(self, pub: bytes) -> bytes:\\n ...\",\n \"def __init__(self, curve=None, private_key=None, public_key=None):\\n self.curve = curve\\n self.private_key = None\\n self.public_key = None\\n if private_key:\\n self.load_private_key(private_key)\\n if public_key:\\n self.load_received_public_key(public_key)\",\n \"def __init__(self, public_key):\\n self._pk = ed25519.Ed25519PublicKey.from_public_bytes(public_key.bytes)\",\n \"def make_final_key(prime, public, private):\\n\\n key = (public ** private) % prime\\n return key\",\n \"def create_handshake_key_pair(cls) -> Tuple[bytes, bytes]:\\n ...\",\n \"def __init__(self, private_key):\\n self._sk = ed25519.Ed25519PrivateKey.from_private_bytes(private_key.bytes)\",\n \"def generate_key(seed):\\n private_key = sha256(seed)\\n public_key = privtopub(private_key)\\n return {\\\"private\\\": private_key, \\\"public\\\": public_key}\",\n \"def __init__(self, private_key):\\n if private_key:\\n if isinstance(private_key, str): # base58 encoded string\\n self.private_key = PrivateKey.from_b58check(private_key)\\n else:\\n self.private_key = private_key\\n self.public_key = self.private_key.public_key\\n else:\\n self.private_key = None\\n self.public_key = None\",\n \"def __set_or_create_key_if_not_exist(self):\\n\\n # instantiate PKI class:\\n pki = PKI(username=self.username, password=self.password)\\n\\n # load private key into object. key is ready to be used to sign already imported\\n privkey = pki.load_priv_key()\\n\\n # if it is an empty list then no key created and saved on username so generate new key\\n if not privkey:\\n pki.generate_pub_priv_key()\\n privkey = pki.load_priv_key()\\n\\n # set self.privkey to privkey\\n self.privkey = privkey\",\n \"def staticstatic(self, private_key, public_key):\",\n \"def _init_key_settings(self):\\n self.minKeySize = 1023\\n self.maxKeySize = 8193\\n self.rsaSigHashes = list(RSA_SIGNATURE_HASHES)\\n self.rsaSchemes = list(RSA_SCHEMES)\\n self.dsaSigHashes = list(DSA_SIGNATURE_HASHES)\\n self.virtual_hosts = []\\n # DH key settings\\n self.eccCurves = list(CURVE_NAMES)\\n self.dhParams = None\\n self.dhGroups = list(ALL_DH_GROUP_NAMES)\\n self.defaultCurve = \\\"secp256r1\\\"\\n self.keyShares = [\\\"secp256r1\\\", \\\"x25519\\\"]\\n self.padding_cb = None\\n self.use_heartbeat_extension = True\\n self.heartbeat_response_callback = None\",\n \"def generate_keys(self):\\n\\n # TODO: Store keys encrypted\\n rsa1 = RsaPrivateKey.Generate()\\n self.sign_private = str(rsa1)\\n self.sign_public = str(rsa1.public_key)\\n\\n rsa2 = RsaPrivateKey.Generate()\\n self.crypt_private = str(rsa2)\\n self.crypt_public = str(rsa2.public_key)\",\n \"def __init__(self, pubkey, e=65537):\\n if isinstance(pubkey, int):\\n self.key = RSA.RsaKey(n=pubkey, e=e)\\n\\n else:\\n if not isinstance(pubkey, str):\\n raise ValueError('pubkey must be str or int.')\\n\\n if '----' in pubkey:\\n try:\\n self.key = RSA.import_key(pubkey)\\n except Exception as e:\\n print(e)\\n else:\\n if pubkey == pubkey.lower():\\n pubkey = int(pubkey, 16)\\n self.key = RSA.RsaKey(n=pubkey, e=e)\\n else:\\n pubkey = '-----BEGIN PUBLIC KEY-----\\\\n' + pubkey + '\\\\n-----END PUBLIC KEY-----'\\n try:\\n self.key = RSA.import_key(pubkey)\\n except Exception as e:\\n print(e)\",\n \"def main():\\n\\n prime = 0xffffffffffffffffc90fdaa22168c234c4c6628b80dc1cd129024e088a67cc74020bbea63b139b22514a08798e3404ddef9519b3cd3a431b302b0a6df25f14374fe1356d6d51c245e485b576625e7ec6f44c42e9a637ed6b0bff5cb6f406b7edee386bfb5a899fa5ae9f24117c4b1fe649286651ece45b3dc2007cb8a163bf0598da48361c55d39a69163fa8fd24cf5f83655d23dca3ad961c62f356208552bb9ed529077096966d670c354e4abc9804f1746c08ca237327ffffffffffffffff\\n base = 2\\n \\n connection = diffiehellman_mitm_sim(prime, base)\\n\\n # intercept alices public key\\n prime, base , _ = next(connection)\\n\\n # send prime instead of alices public key to bob. Recieve Bobs public key, \\n # which we forget as it is not needs. The shared kill will be 0.\\n\\n connection.send((prime, base, prime))\\n\\n #Send prime as bob's public key to alice. We have ensured that the shared\\n #hared secret key is 0. Recieve Alice's ciphertext for bob\\n ciphertext_a2b = connection.send(prime)\\n\\n # decrypt\\n malcolm = AES_CBC(SHA1(bso.int_to_bytes(0)).digest()[:16], b'0'*16)\\n messages = []\\n messages.append(bso.remove_padding_pkcs7(malcolm.decrypt(ciphertext_a2b[:-16], ciphertext_a2b[-16:])))\\n\\n #Send the ciphertext to bob. Recieve his response\\n ciphertext_b2a = connection.send(ciphertext_a2b)\\n\\n messages.append(bso.remove_padding_pkcs7(malcolm.decrypt(ciphertext_b2a[:-16], ciphertext_b2a[-16:])))\\n\\n assert messages[0] == b'Message to Bob'\\n assert messages[1] == b'Message to Alice'\\n\\n \\n return\",\n \"def setup(self):\\n self.build_serverkeyhash()\\n self.build_agent_pubkey()\\n self.load_registration_key()\",\n \"def __init__(self, public_key=None):\\n self.public_key = self.convert_public_key_to_ecdsa(public_key) if public_key else public_key\",\n \"def link(self, secret=None, secondSecret=None):\\r\\n if hasattr(dposlib, \\\"core\\\"):\\r\\n if secret:\\r\\n keys = dposlib.core.crypto.getKeys(secret)\\r\\n self.senderPublicKey = keys[\\\"publicKey\\\"]\\r\\n self._privateKey = keys[\\\"privateKey\\\"]\\r\\n if secondSecret:\\r\\n keys = dposlib.core.crypto.getKeys(secondSecret)\\r\\n self._secondPrivateKey = keys[\\\"privateKey\\\"]\",\n \"def _manually_initialize(self):\\n self.mnemonic = Mnemonic(self.language)\\n\\n if not self.words:\\n # Initialized but still locked\\n return\\n\\n # Validate words first\\n self.validate_words()\\n\\n assert isinstance(self.passphrase, bytes), 'Passphrase must be in bytes'\\n\\n # Master seed\\n seed = self.mnemonic.to_seed(self.words, self.passphrase.decode('utf-8'))\\n\\n # Master node\\n from pycoin.networks.registry import network_for_netcode\\n _register_pycoin_networks()\\n network = network_for_netcode('htr')\\n key = network.keys.bip32_seed(seed)\\n\\n # Until account key should be hardened\\n # Chain path = 44'/0'/0'/0\\n # 44' (hardened) -> BIP44\\n # 280' (hardened) -> Coin type (280 = hathor)\\n # 0' (hardened) -> Account\\n # 0 -> Chain\\n self.chain_key = key.subkey_for_path('44H/280H/0H/0')\\n\\n for key in self.chain_key.children(self.initial_key_generation, 0, False):\\n self._key_generated(key, key.child_index())\",\n \"def __init__(self, globalKey, publicKey, resourceName, **rest):\\n super(SshKey, self).__init__({\\n \\\"globalKey\\\": globalKey,\\n \\\"publicKey\\\": publicKey,\\n \\\"resourceName\\\": resourceName,\\n }, **rest)\",\n \"def generate_key(self):\\n\\n self.key = Fernet.generate_key()\\n self.cryptor = Fernet(self.key)\",\n \"def test_create_and_import_encrypted_rsa(self):\\n name = \\\"key_encrypted\\\"\\n password = \\\"123456\\\"\\n bits= 3072\\n generate_and_write_rsa_keypair(name, bits, password)\\n private_key = import_rsa_key_from_file(name, password)\\n public_key = import_rsa_key_from_file(name + \\\".pub\\\")\\n\\n securesystemslib.formats.KEY_SCHEMA.check_match(private_key)\\n self.assertTrue(private_key[\\\"keyval\\\"].get(\\\"private\\\"))\\n self.assertTrue(\\n securesystemslib.formats.PUBLIC_KEY_SCHEMA.matches(public_key))\",\n \"def __init__(self, prev_hash: bytes, time: float, user_id: str, password: str):\\r\\n super().__init__(prev_hash, time, user_id)\\r\\n\\r\\n # Derive a public key from the password. Add user_id to password as salt\\r\\n self.public_key: ec.EllipticCurvePublicKey = Block.password_to_key(password + user_id).public_key()\",\n \"def init(x_in):\\n global public_keys, secret_keys, x\\n x = func.get_bits(x_in)\\n\\n public_keys, secret_keys = [], []\\n\\n elgamal.init_g_p_q()\\n for i in range(3):\\n create_keys(i)\",\n \"def __init__(self, key_info):\\n if (key_info.type != client_pb2.KeyInfo.ECDSA):\\n raise error.UnsupportedAlgorithmError(\\n \\\"Expected ECDSA key, but got key type %d\\\" % key_info.type)\\n\\n # Will raise a PemError on invalid encoding\\n self.__der, _ = pem.from_pem(key_info.pem_key, self.__READ_MARKERS)\\n try:\\n self.__key = ecdsa.VerifyingKey.from_der(self.__der)\\n except ecdsa.der.UnexpectedDER as e:\\n raise error.EncodingError(e)\",\n \"def load_key():\",\n \"def recipient_public_key(self):\",\n \"def generate(self, module):\\n\\n # If size is wrong, delete the key. A new key will be generated in the next step.\\n if self.key_current_size != self.size and not self.ignore_size:\\n self.remove()\\n self.key_exists = False\\n else:\\n self.changed = False\\n\\n # If there is no key or user has set \\\"force\\\"\\n if not self.key_exists or self.force:\\n if self.type == \\\"RSA\\\":\\n self.key = crypto_rsa.generate_private_key(public_exponent=65537, key_size=self.size, backend=crypto_default_backend())\\n elif self.type == \\\"DSA\\\":\\n self.key = crypto_dsa.generate_private_key(key_size=self.size, backend=crypto_default_backend())\\n elif self.type == \\\"ECDSA\\\":\\n if self.size == 256:\\n self.curve = crypto_ec.SECP256R1()\\n elif self.size == 384:\\n self.curve = crypto_ec.SECP384R1()\\n elif self.size == 521:\\n self.curve = crypto_ec.SECP521R1()\\n self.key = crypto_ec.generate_private_key(curve=self.curve, backend=crypto_default_backend())\\n elif self.type == \\\"ED25519\\\":\\n self.size = 128\\n self.curve = \\\"EC25519\\\"\\n else:\\n raise HostkeyError(\\\"Unknown key type.\\\")\\n\\n if self.type != \\\"ED25519\\\":\\n self.privkey = self.key.private_bytes(crypto_serialization.Encoding.PEM, crypto_serialization.PrivateFormat.PKCS8, crypto_serialization.NoEncryption())\\n self.pubkey = self.key.public_key().public_bytes(crypto_serialization.Encoding.OpenSSH, crypto_serialization.PublicFormat.OpenSSH)\\n\\n try:\\n privfile = os.open(self.fullpath, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, self.mode)\\n os.write(privfile, self.privkey)\\n os.close(privfile)\\n pubfile = os.open(self.fullpath + \\\".pub\\\", os.O_WRONLY | os.O_CREAT | os.O_TRUNC, self.mode)\\n os.write(pubfile, self.pubkey)\\n os.close(pubfile)\\n self.changed = True\\n except IOError:\\n self.remove()\\n raise HostkeyError(get_exception())\\n else:\\n # use ssh-keygen to generate ED25519 Hostkeys\\n # Keyfile must not exist, as there is no \\\"force-overwrite\\\" in ssh-keygen\\n self.remove()\\n retcode = subprocess.call([\\\"ssh-keygen\\\", \\\"-q\\\", \\\"-t\\\", \\\"ed25519\\\", \\\"-N\\\", '', \\\"-f\\\", self.fullpath])\\n self.changed = True\\n else:\\n self.changed = False\\n\\n file_args = module.load_file_common_arguments(module.params)\\n file_args['path'] = self.fullpath\\n if module.set_fs_attributes_if_different(file_args, False):\\n self.changed = True\\n file_args['path'] = self.fullpath + \\\".pub\\\"\\n file_args['mode'] = self.pubmode\\n if module.set_fs_attributes_if_different(file_args, False):\\n self.changed = True\",\n \"def setup2():\\n ht2 = HashTable()\\n ht2.set('Apple', 'Dash')\\n ht2.set('Flutter', 'Sparkle')\\n ht2.set('Pinky', 'Pony')\\n return ht2\",\n \"def get_asymm_keys(parameters):\\n\\tprivate_key = parameters.generate_private_key()\\n\\treturn private_key,private_key.public_key()\",\n \"def generate_keys() -> tuple:\\n private_key = ecdsa.SigningKey.generate(curve=curve)\\n public_key = private_key.get_verifying_key()\\n\\n private_key = encode_private_key(private_key)\\n public_key = encode_public_key(public_key)\\n\\n return public_key, private_key\",\n \"def __init__(self, rsa_key):\\r\\n if isinstance(rsa_key, tuple):\\r\\n self.keypair = Crypto.PublicKey.RSA.construct(rsa_key)\\r\\n else:\\r\\n self._InitFromString(rsa_key)\",\n \"def do_new(argv):\\n\\n global PRIVATE_KEY\\n\\n if not PRIVATE_KEY:\\n PRIVATE_KEY = wallet.get_private_key()\\n else:\\n get_new = yes_or_no(\\\"Private key already exist, do you want generate new one ?\\\")\\n if get_new:\\n PRIVATE_KEY = wallet.get_private_key()\\n print(\\\"Private Key: '\\\" + PRIVATE_KEY + \\\"'\\\")\\n cmpr_pub_key = wallet.get_compressed_public_key(PRIVATE_KEY, 1)\\n addr = wallet.public_key_to_address(cmpr_pub_key, 0)\\n open(\\\"data/address\\\", \\\"w\\\").write(addr)\\n print(\\\"Public key was saved to 'data/cmpr_pub_key'\\\")\",\n \"def __init__(self, name, key, hash_, vendor):\\n self.name = name\\n self.key = key\\n self.hash = hash_\\n self.vendor = vendor\",\n \"def alt_stubbed_receiver() -> PublicKey:\\n return PublicKey(\\\"J3dxNj7nDRRqRRXuEMynDG57DkZK4jYRuv3Garmb1i98\\\")\",\n \"def __init__(self, username, password):\\n self.username = username\\n self.password = password\\n self.privkey = None\\n\\n # sets self.privkey\\n self.__set_or_create_key_if_not_exist()\",\n \"def generate_ecdh_key_pair() -> tuple[X25519PrivateKey, bytes]:\\n private_key = X25519PrivateKey.generate()\\n public_key_raw = private_key.public_key().public_bytes(\\n serialization.Encoding.Raw, serialization.PublicFormat.Raw\\n )\\n return private_key, public_key_raw\",\n \"def do_minerkey(argv):\\n\\n global PRIVATE_KEY\\n\\n if not PRIVATE_KEY:\\n print(\\\"Error: private key is missing. Use command 'new' to generate key\\\")\\n else:\\n PRIVATE_KEY = wallet.get_private_key()\\n minerkey = wallet.private_key_to_wif(PRIVATE_KEY, 0, 0)\\n file = open(\\\"data/minerkey\\\", \\\"w\\\")\\n file.write(minerkey)\\n print(\\\"Minerkey was created in WIF format and saved to 'data/minerkey'\\\")\\n file.close()\",\n \"def dh(self):\\n self.cli.add_msg(\\\"Establishing Encryption Key...\\\")\\n dh_message = self.connection.recv(DH_MSG_SIZE)\\n # Unpack p, g, and server_key from the server's dh message\\n p, g, server_key = DH.unpack(dh_message)\\n # Generate a randomized private key\\n private_key = DH.gen_private_key()\\n # Send the server a public key which used the previously\\n # Generated private key and both g and p\\n public_key = DH.gen_public_key(g, private_key, p)\\n self.connection.sendall(DH.package(public_key, LEN_PK))\\n # Calculate shared key\\n shared_key = DH.get_shared_key(server_key, private_key, p)\\n # print(\\\"Shared Key: {}\\\".format(shared_key))\\n self.cli.add_msg(\\\"Encryption Key: {}\\\".format(binascii.hexlify(shared_key).decode(\\\"utf-8\\\")))\\n return shared_key\",\n \"def mk_keyobj_from_private_key_pem(self, pemdat_string):\\n if isinstance(pemdat_string, str):\\n pemdat_string = pemdat_string.encode()\\n self.private_key_obj = serialization.load_pem_private_key(pemdat_string, password=None, backend=default_backend())\\n self._get_naive_private_key_bytes()\\n self.public_key_obj = self.private_key_obj.public_key()\\n self._get_naive_public_key_bytes()\",\n \"def get_rsa_asymn_keys(public_exponent = 65537, key_size = 2048, bc = backend):\\n\\tprivate_key = asymmetric.rsa.generate_private_key(public_exponent = public_exponent, key_size = key_size, backend = bc)\\n\\treturn private_key,private_key.public_key()\",\n \"def __init__(self, key):\\n self.bs = 16\\n self.key = hashlib.sha256(key.encode()).digest()\",\n \"def gen_private_key():\\n return DH.b2i(Random.new().read(DH_SIZE))\",\n \"def __init__(self, public_key, private_key, token, token_secret, base_url='http://api.telldus.com'):\\n self.public_key = public_key\\n self.private_key = private_key\\n self.token = token\\n self.token_secret = token_secret\\n\\n self.base_url = base_url\\n\\n self.oauth = self.generate_temp_session()\",\n \"def shared_key(private_key,public_key):\\n\\treturn private_key.exchange(public_key)\",\n \"def __init__(self,key):\\n self.block_size = 32\\n self.key = hashlib.sha256(key).digest()\",\n \"def generate_elgamal_auxiliary_key_pair() -> AuxiliaryKeyPair:\\n elgamal_key_pair = elgamal_keypair_random()\\n return AuxiliaryKeyPair(\\n elgamal_key_pair.secret_key.to_hex(),\\n elgamal_key_pair.public_key.to_hex(),\\n )\",\n \"def _InitFromString(self, text):\\r\\n # First, remove all whitespace:\\r\\n text = re.sub(_WHITESPACE_RE, '', text)\\r\\n\\r\\n # Parse out the period-separated components\\r\\n match = _KEY_RE.match(text)\\r\\n if not match:\\r\\n raise ValueError('Badly formatted key string: \\\"%s\\\"', text)\\r\\n\\r\\n private_exp = match.group('private_exp')\\r\\n if private_exp:\\r\\n private_exp = _B64ToNum(private_exp)\\r\\n else:\\r\\n private_exp = None\\r\\n self.keypair = Crypto.PublicKey.RSA.construct(\\r\\n (_B64ToNum(match.group('mod')),\\r\\n _B64ToNum(match.group('exp')),\\r\\n private_exp))\",\n \"def __init__(self, identifier=None, seed=None, alias=None):\\n\\n # should be stored securely/privately\\n self.seed = seed if seed else randombytes(32)\\n\\n # generates key pair based on seed\\n self.sk = SigningKey(seed=self.seed)\\n\\n # helper for signing\\n self.naclSigner = NaclSigner(self.sk)\\n\\n # this is the public key used to verify signatures (securely shared\\n # before-hand with recipient)\\n hex_verkey = hexlify(self.naclSigner.verraw)\\n self.verkey = hexToFriendly(hex_verkey)\\n\\n self._identifier = identifier or self.verkey\\n\\n self._alias = alias\",\n \"def __init__(self, generator=2, group=17, keyLength=540):\\n min_keyLength = 180\\n\\n default_generator = 2\\n valid_generators = [2, 3, 5, 7]\\n\\n # Sanity check fors generator and keyLength\\n if(generator not in valid_generators):\\n print(\\\"Error: Invalid generator. Using default.\\\")\\n self.generator = default_generator\\n else:\\n self.generator = generator\\n\\n if(keyLength < min_keyLength):\\n print(\\\"Error: keyLength is too small. Setting to minimum.\\\")\\n self.keyLength = min_keyLength\\n else:\\n self.keyLength = keyLength\\n\\n self.prime = self.getPrime(group)\\n\\n self.privateKey = self.genPrivateKey(keyLength)\\n self.publicKey = self.genPublicKey()\",\n \"def exchange_key(connection, pub_key):\\r\\n\\r\\n if main.diffe_key_exchange is False:\\r\\n # Get the server's public key\\r\\n server_pub_key_bytes = connection.recv(1024)\\r\\n\\r\\n # Send public key\\r\\n connection.sendall(rsa.PublicKey.save_pkcs1(pub_key))\\r\\n\\r\\n else:\\r\\n # Rounds of bit-shifting and XOR\\r\\n rounds = 64\\r\\n\\r\\n while True:\\r\\n\\r\\n # Generate 4096-bit keys (RFC 3526 Group 16)\\r\\n client_diffe_key = pyDHE.new(16)\\r\\n shared_secret = client_diffe_key.negotiate(connection)\\r\\n\\r\\n # Encrypt\\r\\n encrypted = int(binascii.hexlify(rsa.PublicKey.save_pkcs1(pub_key)).decode(), 16)\\r\\n for x in range(0, rounds):\\r\\n encrypted = encrypted ^ (shared_secret ** rounds)\\r\\n encrypted = encrypted << rounds\\r\\n encrypted = int(str(encrypted)[::-1])\\r\\n\\r\\n # Decrypt\\r\\n decrypted = encrypted\\r\\n decrypted = int(str(decrypted)[::-1])\\r\\n for x in range(rounds, 0, -1):\\r\\n decrypted = decrypted >> rounds\\r\\n decrypted = decrypted ^ (shared_secret ** rounds)\\r\\n\\r\\n # Check if able to decrypt\\r\\n try:\\r\\n binascii.unhexlify(hex(decrypted)[2:]).decode()\\r\\n client_success = True\\r\\n\\r\\n # Generate new keys upon failure and try again\\r\\n except UnicodeDecodeError:\\r\\n client_success = False\\r\\n pass\\r\\n except binascii.Error:\\r\\n client_success = False\\r\\n pass\\r\\n\\r\\n # Notify client about encryption status\\r\\n server_success = connection.recv(1024)\\r\\n if client_success is False:\\r\\n connection.send(b'DHE')\\r\\n else:\\r\\n connection.send(b'CONTINUE')\\r\\n\\r\\n # Get encryption status from client\\r\\n if client_success is False or server_success == b'DHE':\\r\\n pass\\r\\n elif server_success == b'CONTINUE':\\r\\n break\\r\\n\\r\\n # Hold encrypted server key\\r\\n server_encrypted = b''\\r\\n\\r\\n # Receive encrypted key from the server\\r\\n while True:\\r\\n data = connection.recv(8192)\\r\\n if data == b'ENDED':\\r\\n break\\r\\n elif data[-5:] == b'ENDED':\\r\\n server_encrypted += data[:-5]\\r\\n break\\r\\n server_encrypted += data\\r\\n\\r\\n # Send the encrypted key to the server\\r\\n connection.sendall(bytes(hex(encrypted).encode()))\\r\\n connection.send(b'ENDED')\\r\\n\\r\\n # Decrypt the client's public key\\r\\n decrypted = int(server_encrypted, 16)\\r\\n decrypted = int(str(int(decrypted))[::-1])\\r\\n for x in range(rounds, 0, -1):\\r\\n decrypted = decrypted >> rounds\\r\\n decrypted = decrypted ^ (shared_secret ** rounds)\\r\\n\\r\\n server_pub_key_bytes = binascii.unhexlify(hex(decrypted)[2:]).decode()\\r\\n\\r\\n server_pub_key = rsa.PublicKey.load_pkcs1(server_pub_key_bytes)\\r\\n # Determine max message size\\r\\n max_message_size = common.byte_size(server_pub_key.n) - 11\\r\\n\\r\\n # Return crypto key information\\r\\n return server_pub_key, server_pub_key_bytes, max_message_size\",\n \"def new_key_pair(self):\\n from plonevotecryptolib.KeyPair import KeyPair # avoids circular imports\\n return KeyPair(self)\",\n \"def setup(pk_outfile=const.ABE_PK_FILE, msk_outfile=const.ABE_MSK_FILE, pairing_group_curve=const.PAIRING_GROUP_CURVE,\\n debug=0):\\n\\n # Instantiate a bilinear pairing map with the given curve\\n pairing_group = PairingGroup(pairing_group_curve)\\n\\n # CP-ABE\\n cpabe = CPabe_BSW07(pairing_group)\\n\\n # Create public and master secret keys\\n (pk, msk) = cpabe.setup()\\n\\n if debug: # ONLY USE FOR DEBUG\\n print('CP-ABE PUBLIC KEY =', pk)\\n print('CP-ABE MASTER SECRET KEY =', msk)\\n\\n # Save keys on given output files\\n with open(pk_outfile, 'w') as fout:\\n fout.write(objectToBytes(pk, pairing_group).hex())\\n\\n with open(msk_outfile, 'w') as fout:\\n fout.write(objectToBytes(msk, pairing_group).hex())\",\n \"def __init__(self, passphrase):\\n self.__key_stretches = 256 * 1024\\n self.__magic_number = 0x7A6A5A4A\\n self.__file_version = 1\\n self.__key = self._produce_key(passphrase)\\n self.__iv = self._produce_iv(self.__key)\\n return\",\n \"def __init__(self,\\r\\n ephemeral_public_key=None,\\r\\n public_key_hash=None,\\r\\n transaction_id=None):\\r\\n\\r\\n # Initialize members of the class\\r\\n self.public_key_hash = public_key_hash\\r\\n self.ephemeral_public_key = ephemeral_public_key\\r\\n self.transaction_id = transaction_id\",\n \"def mk_keyobj_from_private_key(self, privkey):\\n bn = BACKEND_KP.private_key_obj._backend._ffi.NULL\\n bn_ptr = BACKEND_KP.private_key_obj._backend._lib.BN_bin2bn(privkey, len(privkey), bn)\\n secret_val = BACKEND_KP.private_key_obj._backend._bn_to_int(bn_ptr)\\n\\n if self.curvetype == KeyType.ECDSA_P256v1:\\n self.private_key_obj = ec.derive_private_key(secret_val, ec.SECP256R1(), default_backend())\\n elif self.curvetype == KeyType.ECDSA_SECP256k1:\\n self.private_key_obj = ec.derive_private_key(secret_val, ec.SECP256K1(), default_backend())\\n self._get_naive_private_key_bytes()\\n self.public_key_obj = self.private_key_obj.public_key()\\n self._get_naive_public_key_bytes()\",\n \"def derive_keys(self, master_salt, master_secret):\\n\\n self.sender_key = self._kdf(master_salt, master_secret, self.sender_id, 'Key')\\n self.recipient_key = self._kdf(master_salt, master_secret, self.recipient_id, 'Key')\\n\\n self.common_iv = self._kdf(master_salt, master_secret, b\\\"\\\", 'IV')\",\n \"def test_set_public_key_setter(self) -> None:\\n\\n expected = self.pem_public_key.decode()\\n\\n encryptor = DataEncryption()\\n encryptor.set_public_key(self.pem_public_key)\\n\\n # pylint: disable=protected-access\\n actual = encryptor._loaded_public_key.public_bytes(\\n serialization.Encoding.PEM, serialization.PublicFormat.PKCS1\\n ).decode()\\n\\n self.assertEqual(expected, actual)\",\n \"def generate_rsa_key_pair(self):\\n\\t\\tprint \\\"Started rsa key generation\\\"\\n\\t\\tkey = RSA.generate(self.key_size, randfunc=self.random_number_generator)\\n\\t\\t\\t\\n\\t\\tpub_key = key.publickey().exportKey()\\n\\t\\tprint pub_key\\n\\t\\t\\n\\n\\t\\tpriv_key = key.exportKey()\\n\\t\\tprint \\\"Private key\\\", priv_key \\n\\t\\tprint \\\"Note: Normally, the private key should be protected. For the purposes of this demo, I'm printing it to terminal.\\\"\",\n \"def generateKeys(bits=256):\\n #print \\\"generating first prime number\\\"\\n p = generatePrime(bits/2)\\n #print \\\"generating second prime number\\\"\\n q = generatePrime(bits/2)\\n \\n assert p != q\\n #print p, \\\"\\\\n\\\", q\\n assert gcd(p*q, (p-1)*(q-1)) == 1\\n \\n priv = PrivateKey(p, q)\\n pub = PublicKey(p, q)\\n \\n priv.saveToFile()\\n pub.saveToFile()\\n \\n return priv, pub\",\n \"def generate(self):\\n if self.curvetype == KeyType.ECDSA_P256v1:\\n self.private_key_obj = ec.generate_private_key(ec.SECP256R1(), default_backend())\\n elif self.curvetype == KeyType.ECDSA_SECP256k1:\\n self.private_key_obj = ec.generate_private_key(ec.SECP256K1(), default_backend())\\n self.public_key_obj = self.private_key_obj.public_key()\\n self._get_naive_private_key_bytes()\\n self._get_naive_public_key_bytes()\",\n \"def __init__(self, key_bytes, public=True):\\n self.G = _globalECG\\n if public:\\n self.sec = None\\n self.pub = EcPt.from_binary(key_bytes, self.G)\\n self.optim = None\\n else:\\n self.sec = Bn.from_binary(sha256(key_bytes).digest())\\n self.pub = self.sec * self.G.generator()\\n self.optim = do_ecdsa_setup(self.G, self.sec)\",\n \"def test_set_private_key_setter(self) -> None:\\n\\n expected = self.pem_private_key.decode()\\n\\n encryptor = DataEncryption()\\n encryptor.set_private_key(self.pem_private_key)\\n\\n # pylint: disable=protected-access\\n actual = encryptor._loaded_private_key.private_bytes(\\n serialization.Encoding.PEM,\\n serialization.PrivateFormat.PKCS8,\\n serialization.NoEncryption(),\\n ).decode()\\n\\n self.assertEqual(expected, actual)\",\n \"def __init__(__self__, *,\\n primary_auth_key_hash: pulumi.Input[str],\\n secondary_auth_key_hash: pulumi.Input[str]):\\n pulumi.set(__self__, \\\"primary_auth_key_hash\\\", primary_auth_key_hash)\\n pulumi.set(__self__, \\\"secondary_auth_key_hash\\\", secondary_auth_key_hash)\",\n \"def create_rsa_key_pair() -> Tuple[str, str]:\\n key = RSA.generate(RSA_KEY_STRENGTH)\\n public_key = key.publickey().export_key().decode()\\n private_key = key.export_key().decode()\\n return public_key, private_key\",\n \"def __init__( self, masterKey ):\\n\\n assert len(masterKey) == const.MASTER_KEY_SIZE\\n\\n checkKeys()\\n\\n # The random name is used to recognize previously issued tickets.\\n #self.keyName = mycrypto.weak_random(NAME_LENGTH)\\n\\n # Initialization vector for AES-CBC.\\n self.IV = mycrypto.strong_random(IV_LENGTH)\\n\\n # The server's actual (encrypted) protocol state.\\n self.state = ProtocolState(masterKey)\\n\\n # AES and HMAC key to protect the ticket.\\n self.symmTicketKey = AESKey\\n self.hmacTicketKey = HMACKey\",\n \"def __init__(self, pubkey=None):\",\n \"def stubbed_receiver() -> PublicKey:\\n return PublicKey(\\\"J3dxNj7nDRRqRRXuEMynDG57DkZK4jYRuv3Garmb1i99\\\")\",\n \"def keygen(bits, private, public):\\n\\n rsa = Rsa()\\n keys = rsa.generate_keys(bits)\\n\\n formatter = TomlKeyFormatter()\\n\\n private.write(formatter.to_string(keys.private_key))\\n public.write(formatter.to_string(keys.public_key))\",\n \"def add_local_ssh_key(self):\\n return\\n user_ssh_key = open('/home/<$user>/.ssh/id_rsa.pub').read()\\n key = digitalocean.SSHKey(token=self.secret_token,\\n name='machine-name',\\n public_key=user_ssh_key)\\n key.create()\",\n \"def __init__(self, **options):\\n\\n super().__init__(**options)\\n\\n self._private_key = None\\n self._public_key = None\\n\\n self._load_keys(**options)\",\n \"def gen_key_pair():\\n sk = gen_secret_key(BITCOIN.gen.n)\\n pk = PublicKey.from_sk(sk)\\n return sk, pk\",\n \"def __init__(self, hass, key):\\n\\n\\n self.yangli = None\\n self.yinli = None\\n self.wuxing = None\\n self.chongsha = None\\n self.baiji = None\\n self.jishen = None\\n self.yi = None\\n self.xiongshen = None\\n self.ji = None\\n \\n self.hass = hass\\n\\n self.url = \\\"http://v.juhe.cn/laohuangli/d\\\"\\n self.key = key\",\n \"def dh_get_key():\\n G = EcGroup()\\n priv_dec = G.order().random()\\n pub_enc = priv_dec * G.generator()\\n return (G, priv_dec, pub_enc)\",\n \"def genKey(self, otherKey):\\n self.sharedSecret = self.genSecret(self.privateKey, otherKey)\\n #print(\\\"Shared secret:\\\")\\n #print(self.sharedSecret)\\n s = hashlib.sha256()\\n s.update(bytes(str(self.sharedSecret).encode()))\\n self.key = s.digest()\",\n \"def generate_rsa_server_keys() -> Tuple[bytes, bytes]:\\n from cryptography.hazmat.primitives import serialization as crypto_serialization\\n from cryptography.hazmat.primitives.asymmetric import rsa\\n from cryptography.hazmat.backends import default_backend as crypto_default_backend\\n\\n # Generate the key\\n key = rsa.generate_private_key(\\n backend=crypto_default_backend(),\\n public_exponent=65537,\\n key_size=2048\\n )\\n\\n # Get the private key in the standard PEM/PKCS8 format for SSH private keys.\\n private_key = key.private_bytes(\\n crypto_serialization.Encoding.PEM,\\n crypto_serialization.PrivateFormat.OpenSSH,\\n crypto_serialization.NoEncryption())\\n\\n # Get the public key in the standard OpenSSH format.\\n public_key = key.public_key().public_bytes(\\n crypto_serialization.Encoding.OpenSSH,\\n crypto_serialization.PublicFormat.OpenSSH\\n )\\n\\n # Write the two keys.\\n with open('host_rsa', 'wb') as f:\\n f.write(private_key)\\n\\n with open('host_rsa.pub', 'wb') as f:\\n f.write(public_key)\\n\\n # Return them.\\n return private_key, public_key\",\n \"def PublicKey(self) -> _n_9_t_1:\",\n \"def PublicKey(self) -> _n_9_t_1:\",\n \"def _init_keys(self):\\n\\n basic_constraints = crypto.X509Extension('basicConstraints'.encode('ascii'), True,\\n 'CA:TRUE, pathlen:0'.encode('ascii'))\\n serial = self._get_serial()\\n pkey = self._create_pkey(self.commonname, serial)\\n self._create_cert(pkey, self.commonname, serial, [basic_constraints], expire=30*365)\",\n \"def _create_rsa_key_pair(self, length, public_exponent=65537):\\n self.logger.info(\\n \\\"Generating an RSA key pair with length: {0}, and \\\"\\n \\\"public_exponent: {1}\\\".format(\\n length, public_exponent\\n )\\n )\\n try:\\n private_key = rsa.generate_private_key(\\n public_exponent=public_exponent,\\n key_size=length,\\n backend=default_backend())\\n public_key = private_key.public_key()\\n\\n private_bytes = private_key.private_bytes(\\n serialization.Encoding.DER,\\n serialization.PrivateFormat.PKCS8,\\n serialization.NoEncryption())\\n public_bytes = public_key.public_bytes(\\n serialization.Encoding.DER,\\n serialization.PublicFormat.PKCS1)\\n except Exception as e:\\n self.logger.exception(e)\\n raise exceptions.CryptographicFailure(\\n \\\"An error occurred while generating the RSA key pair. \\\"\\n \\\"See the server log for more information.\\\"\\n )\\n\\n public_key = {\\n 'value': public_bytes,\\n 'format': enums.KeyFormatType.PKCS_1,\\n 'public_exponent': public_exponent\\n }\\n private_key = {\\n 'value': private_bytes,\\n 'format': enums.KeyFormatType.PKCS_8,\\n 'public_exponent': public_exponent\\n }\\n\\n return public_key, private_key\",\n \"def test_set_private_key(self) -> None:\\n\\n expected = self.pem_private_key.decode()\\n\\n encryptor = DataEncryption(private_key=self.pem_private_key)\\n\\n # pylint: disable=protected-access\\n actual = encryptor._loaded_private_key.private_bytes(\\n serialization.Encoding.PEM,\\n serialization.PrivateFormat.PKCS8,\\n serialization.NoEncryption(),\\n ).decode()\\n\\n self.assertEqual(expected, actual)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6717525","0.6687376","0.6348768","0.6244429","0.61797005","0.6172647","0.604477","0.59947807","0.59289205","0.59186375","0.59086144","0.5873395","0.5848099","0.5782095","0.5766761","0.5744683","0.5743434","0.5717811","0.5714252","0.56985873","0.5693162","0.56766367","0.5660258","0.5656971","0.5653682","0.56531495","0.56308883","0.5625609","0.56027323","0.56006205","0.5593355","0.557804","0.556129","0.5555613","0.55451906","0.55359954","0.5531721","0.5529843","0.55291927","0.550028","0.54956114","0.5481369","0.548069","0.547796","0.546602","0.5464594","0.5451544","0.5447006","0.54399943","0.54287875","0.5422422","0.54137284","0.54061186","0.5377205","0.5372335","0.536367","0.5363103","0.5358999","0.5355966","0.53532237","0.53382987","0.5334462","0.53170687","0.53165865","0.53104293","0.5307164","0.5294441","0.5290272","0.52901113","0.5280867","0.52799314","0.5273046","0.52590144","0.5255404","0.5253771","0.52525276","0.5239966","0.5239731","0.52384233","0.5232333","0.5227801","0.52223885","0.522045","0.5218299","0.52174026","0.5210327","0.51974666","0.51972276","0.5190003","0.5189758","0.51893944","0.51870894","0.5186219","0.5180663","0.51784015","0.51751816","0.51751816","0.5175084","0.5172746","0.51652205"],"string":"[\n \"0.6717525\",\n \"0.6687376\",\n \"0.6348768\",\n \"0.6244429\",\n \"0.61797005\",\n \"0.6172647\",\n \"0.604477\",\n \"0.59947807\",\n \"0.59289205\",\n \"0.59186375\",\n \"0.59086144\",\n \"0.5873395\",\n \"0.5848099\",\n \"0.5782095\",\n \"0.5766761\",\n \"0.5744683\",\n \"0.5743434\",\n \"0.5717811\",\n \"0.5714252\",\n \"0.56985873\",\n \"0.5693162\",\n \"0.56766367\",\n \"0.5660258\",\n \"0.5656971\",\n \"0.5653682\",\n \"0.56531495\",\n \"0.56308883\",\n \"0.5625609\",\n \"0.56027323\",\n \"0.56006205\",\n \"0.5593355\",\n \"0.557804\",\n \"0.556129\",\n \"0.5555613\",\n \"0.55451906\",\n \"0.55359954\",\n \"0.5531721\",\n \"0.5529843\",\n \"0.55291927\",\n \"0.550028\",\n \"0.54956114\",\n \"0.5481369\",\n \"0.548069\",\n \"0.547796\",\n \"0.546602\",\n \"0.5464594\",\n \"0.5451544\",\n \"0.5447006\",\n \"0.54399943\",\n \"0.54287875\",\n \"0.5422422\",\n \"0.54137284\",\n \"0.54061186\",\n \"0.5377205\",\n \"0.5372335\",\n \"0.536367\",\n \"0.5363103\",\n \"0.5358999\",\n \"0.5355966\",\n \"0.53532237\",\n \"0.53382987\",\n \"0.5334462\",\n \"0.53170687\",\n \"0.53165865\",\n \"0.53104293\",\n \"0.5307164\",\n \"0.5294441\",\n \"0.5290272\",\n \"0.52901113\",\n \"0.5280867\",\n \"0.52799314\",\n \"0.5273046\",\n \"0.52590144\",\n \"0.5255404\",\n \"0.5253771\",\n \"0.52525276\",\n \"0.5239966\",\n \"0.5239731\",\n \"0.52384233\",\n \"0.5232333\",\n \"0.5227801\",\n \"0.52223885\",\n \"0.522045\",\n \"0.5218299\",\n \"0.52174026\",\n \"0.5210327\",\n \"0.51974666\",\n \"0.51972276\",\n \"0.5190003\",\n \"0.5189758\",\n \"0.51893944\",\n \"0.51870894\",\n \"0.5186219\",\n \"0.5180663\",\n \"0.51784015\",\n \"0.51751816\",\n \"0.51751816\",\n \"0.5175084\",\n \"0.5172746\",\n \"0.51652205\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7158014"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":764,"numItemsPerPage":100,"numTotalItems":95772,"offset":76400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjUyMjY2NywianRpIjoiNDNjYTBlMGItNDI4Mi00ODBmLWEwZGItMThmZmY3OTJkNGM3Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY1MjYyNjcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.NiSwSM9l8yqiOqrSgeD3uK1UXiLwaNfAemj7RIQsF2--9zutNmc5lYEF4bG6u5sHc4nSVK3qus-_hh7pOcAbCw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Add regex filter for list_buckets | def list_buckets(self, regex=".*", verbose=False):
r = re.compile(regex)
if verbose:
return [b for b in self.client.list_buckets()['Buckets'] if r.match(b['Name'])]
else:
return [b['Name'] for b in self.client.list_buckets()['Buckets'] if r.match(b['Name'])] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_buckets():\n pass",
"def lst_and_pattern (filer_lst, pattern):\r\n new_filter_lst=[]\r\n for word in filer_lst:\r\n if word_and_pattern(word,pattern):\r\n new_filter_lst.append(word)\r\n return new_filter_lst",
"def manipulate_bucketlist():\n pass",
"def filter(self,... | [
"0.586817",
"0.55524653",
"0.52830935",
"0.5272527",
"0.52650857",
"0.5254678",
"0.5222747",
"0.52066493",
"0.5205841",
"0.51563156",
"0.5121045",
"0.5078473",
"0.50659263",
"0.5062066",
"0.50272703",
"0.50206465",
"0.49811077",
"0.4968093",
"0.49588022",
"0.49551994",
"0.495... | 0.65801555 | 0 |
Deletes an empty S3 bucket. If the force boolean is set to True, the contents of the Bucket will first be deleted before deleting the bucket. | def rm_buckets(self, buckets, force=False):
if type(buckets) is list:
for bucket in buckets:
if force:
# Delete contents in buckets
self.s3.Bucket(bucket).objects.all().delete()
self.s3.Bucket(bucket).delete()
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_bucket(Bucket=None):\n pass",
"def empty_bucket(self):\n self.s3_handle = boto.connect_s3()\n EmrProcessing.bucket_name = self.generate_unique_name()\n EmrProcessing.bucket = \\\n self.s3_handle.create_bucket(EmrProcessing.bucket_name)\n EmrProcessing.bucket.d... | [
"0.7062464",
"0.65046525",
"0.6470507",
"0.64680624",
"0.64647424",
"0.6415332",
"0.6399446",
"0.6285246",
"0.6284969",
"0.62828344",
"0.621096",
"0.6130257",
"0.6106059",
"0.60756004",
"0.6058106",
"0.6045744",
"0.60262597",
"0.6025205",
"0.60139114",
"0.600774",
"0.60076284... | 0.6157551 | 11 |
Herlper function for sagemaker endpoint to get the model. | def model_fn(model_dir):
model = models.resnet50(pretrained=True)
_ = model.eval()
modules=list(model.children())[:-1]
model=nn.Sequential(*modules)
for p in model.parameters():
p.requires_grad = False
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
model ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_model(model=gin.REQUIRED):\n return model",
"def model(self) -> 'outputs.ModelDefinitionResponse':\n return pulumi.get(self, \"model\")",
"def get_model(self):\n # just return the first model, since all replicas are the same\n return self.call_async(0, '_async_get_model').gen()",
... | [
"0.7410248",
"0.71194136",
"0.70928663",
"0.6839625",
"0.6834955",
"0.6794878",
"0.6664329",
"0.66214645",
"0.6599524",
"0.6549601",
"0.65286404",
"0.6512882",
"0.6486325",
"0.64742947",
"0.64740545",
"0.6469362",
"0.64668727",
"0.6442762",
"0.643793",
"0.63912064",
"0.639053... | 0.0 | -1 |
Helper function for sagemaker endpoint to process in input before passing it to the model for inference. | def input_fn(request_body, request_content_type='application/json'):
if request_content_type =='application/json':
data = json.loads(request_body)
data = data['inputs']
im_bytes = base64.b64decode(data) # im_bytes is a binary image
im_file = BytesIO(im_bytes) # convert im... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_inputs(self, inputs):",
"def processInputs(self):",
"def handle(self, data, context):\n \n model_input = self.preprocess(data)\n model_out = self.inference(model_input)\n return self.postprocess(model_out)",
"def reconstruct_input_ext(self, model_in):",
"def _handleI... | [
"0.7226697",
"0.68308276",
"0.66645557",
"0.65536714",
"0.62912107",
"0.62821937",
"0.6260069",
"0.6256583",
"0.61012626",
"0.6086074",
"0.6085198",
"0.60781735",
"0.6076996",
"0.60497326",
"0.60426235",
"0.6018581",
"0.60154974",
"0.6012859",
"0.60122675",
"0.5994633",
"0.59... | 0.6316356 | 4 |
Helper function to predict on an image using the model | def predict_fn(input_object, model):
if torch.cuda.is_available():
input_object = input_object.cuda()
input_object = torch.unsqueeze(input_object, 0)
with torch.no_grad():
prediction = model(input_object)
return prediction | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict(model, img):\n\tx = image.img_to_array(img)\n\tx = np.expand_dims(x, axis=0)\n\tx = preprocess_input(x)\n\tpreds = model.predict(x)\n\treturn preds[0]",
"def predict(model, img, target_size=(229, 229)): #fixed size for InceptionV3 architecture\r\n if img.size != target_size:\r\n img = img.resize(... | [
"0.8867019",
"0.8529402",
"0.85072654",
"0.85072654",
"0.84197754",
"0.83164746",
"0.8135886",
"0.8070641",
"0.80379456",
"0.8025133",
"0.8017958",
"0.79701",
"0.79278815",
"0.7926884",
"0.78860533",
"0.78191215",
"0.77941465",
"0.7787423",
"0.77626723",
"0.7732514",
"0.76403... | 0.0 | -1 |
Helper function to process the predictions of the model before returning to the user. | def output_fn(predictions, content_type):
assert content_type == 'application/json'
res = predictions.cpu().numpy().tolist()
return json.dumps(res) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, predictor_model) -> None:\n self.save_result(self.evaluate(predictor_model))",
"def post_process_predictions(self, labels, scene):\n pass",
"def postprocess(self, prediction_dict, **params):\r\n pass",
"def predict(self, model, context, data):\n pass",
"def po... | [
"0.72092456",
"0.7191402",
"0.7156282",
"0.71427643",
"0.7134665",
"0.7034721",
"0.69430745",
"0.68148786",
"0.6766181",
"0.67454034",
"0.67367357",
"0.6718753",
"0.67157257",
"0.6698016",
"0.6688639",
"0.6684228",
"0.66806656",
"0.66806656",
"0.6632434",
"0.662545",
"0.66078... | 0.0 | -1 |
fits the list of models to the training data, thereby obtaining in each case an evaluation score after GridSearchCV crossvalidation | def fit(self, train_features, train_actuals):
for name in self.models.keys():
print('-'*shutil.get_terminal_size().columns)
print("evaluating {}".format(name).center(columns))
print('-'*shutil.get_terminal_size().columns)
estimator = self.models[name]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(train_data, train_target):\r\n for name in models.keys():\r\n est = models[name]\r\n est_params = params2[name]\r\n gscv = GridSearchCV(estimator=est, param_grid=est_params, cv=5)\r\n gscv.fit(train_data, train_target)\r\n print(\"best parameter... | [
"0.7954486",
"0.7731242",
"0.7684677",
"0.7534514",
"0.7332475",
"0.73269665",
"0.724954",
"0.7048197",
"0.7026531",
"0.6991581",
"0.698729",
"0.6975688",
"0.69701505",
"0.6937093",
"0.69133115",
"0.6913214",
"0.69001627",
"0.6897572",
"0.688162",
"0.68772936",
"0.6853344",
... | 0.77692807 | 1 |
prints a summary report, ranking the models in terms of highest evaluation score | def evaluation(self):
rows_list = []
for name in self.single_classifier_best.keys():
row = {}
row['algorithm'] = name
row[self.scoring_metric] = self.single_classifier_best[name].best_score_
rows_list.append(row)
scoring_df = pd.DataFrame... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def report(results, n_top=1):\n for i in range(1, n_top + 1):\n candidates = np.flatnonzero(results['rank_test_score'] == i)\n for candidate in candidates:\n print(f\"Model with rank: {i}\")\n print(f\"Mean validation score: {results['mean_test_score'][candidate]} (std: {resu... | [
"0.74202234",
"0.7092146",
"0.69710517",
"0.6924033",
"0.6867207",
"0.6762245",
"0.6656235",
"0.6645233",
"0.6630509",
"0.6622392",
"0.6621752",
"0.6604896",
"0.6507348",
"0.6497034",
"0.6488497",
"0.6474265",
"0.64617085",
"0.64440054",
"0.64366543",
"0.6434623",
"0.6421479"... | 0.6728507 | 6 |
To setup as many loggers as you want | def setup_logger(name, log_file, formatter, level=logging.INFO):
handler = logging.FileHandler(log_file, encoding='utf-8')
handler.setFormatter(formatter)
logger = logging.getLogger(name)
logger.setLevel(level)
logger.addHandler(handler)
return logger | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_logging():\n for name, logger in loggers.items():\n logger.setLevel(LOGGING_MAPPING.get(options.logging, logging.DEBUG))\n handler = logging.FileHandler(\n getattr(options, '{}_log_file_path'.format(name))\n )\n formatter = logging.Formatter(\n '%(asct... | [
"0.7959943",
"0.7823022",
"0.75609726",
"0.73259217",
"0.72952944",
"0.7275272",
"0.7097864",
"0.7073631",
"0.7028831",
"0.69955486",
"0.69838715",
"0.6981615",
"0.6956217",
"0.693831",
"0.69381726",
"0.6936294",
"0.6925124",
"0.6917455",
"0.69084036",
"0.69073987",
"0.689739... | 0.0 | -1 |
Attempts to load all .py files in cogs/ as cog extensions. Returns a dictionary which maps cog names to a boolean value (True = successfully loaded; False = not successfully loaded). | async def load_all_extensions(self, reload=False):
succeeded = {}
for extension in get_extensions():
try:
if reload or extension not in self.cogs_loaded:
self.load_extension(f'cogs.{extension}')
l.info(f"Loaded extension '{extension}'")... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_cogs(self):\n\n path = \"cogs/\" # Should always have a trailing slash\n import_path = path.replace(\"/\", \".\")\n extensions: list[str] = [\n import_path + file.replace(\".py\", \"\")\n for file in os.listdir(path)\n if os.path.isfile(f\"{path}{file... | [
"0.7493008",
"0.6975065",
"0.6765176",
"0.67549616",
"0.6704383",
"0.6446257",
"0.6245783",
"0.6138608",
"0.6025422",
"0.59247637",
"0.58972794",
"0.5765539",
"0.572911",
"0.5679842",
"0.5670242",
"0.5634018",
"0.5585175",
"0.55337536",
"0.54403317",
"0.54370046",
"0.54080385... | 0.71769613 | 1 |
This event triggers when the bot joins a guild. | async def on_guild_join(self, guild):
l.info(f"Joined {guild.name} with {guild.member_count} users!") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def on_guild_join(self, guild: discord.Guild) -> None:\n logger.info(f'Added to new guild: {guild.name} ({guild.id})')\n\n with self.get_session() as session:\n _guild: Guild = session.query(Guild).get(guild.id)\n if _guild is None:\n session.add(Guild(id=gu... | [
"0.7710114",
"0.7107106",
"0.6898389",
"0.68539816",
"0.6643167",
"0.66358715",
"0.65689564",
"0.64946175",
"0.6435609",
"0.64033157",
"0.6399424",
"0.6319097",
"0.6283885",
"0.6263786",
"0.6236404",
"0.6235235",
"0.6224685",
"0.6198134",
"0.6194945",
"0.61734843",
"0.6164683... | 0.7720383 | 0 |
This event triggers on every message received by the bot. Including ones that it sent itself. | async def on_message(self, message):
if message.author.bot:
return # Ignore all bots.
await self.process_commands(message) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def callback_botmessage(self, message):\n pass",
"def callback_botmessage(self, message):\n pass",
"def callback_botmessage(self, message):\n pass",
"async def on_message(self, message: \"steam.Message\") -> None:",
"async def on_message(self, message: discord.Message) -> None:\n\n ... | [
"0.73473126",
"0.73473126",
"0.73473126",
"0.70146674",
"0.69713724",
"0.6857231",
"0.6850092",
"0.6839847",
"0.6813499",
"0.6741119",
"0.6662499",
"0.65737396",
"0.65624034",
"0.6557369",
"0.6556639",
"0.654079",
"0.6522952",
"0.65155643",
"0.64774394",
"0.6470487",
"0.64679... | 0.7366499 | 0 |
Retrieve the SMILES string describing the drug in the parsed HTML document. We get the SMILES string by locating the HTML id "smiles" and then moving to the next html element. This process is slightly complicated by the fact that the SMILES string is confused for an email due to the presence of @ characters, and so we ... | def get_smiles(parsed_drug_doc):
# Source: https://stackoverflow.com/questions/36911296/scraping-of-protected-email
def decode_email(e):
de = ""
k = int(e[:2], 16)
for i in range(2, len(e)-1, 2):
de += chr(int(e[i:i+2], 16)^k)
return de
email_protected_map = {... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_smiles(molecule_etree):\n SMILES_XPATH = 'hunterdb:Structure/hunterdb:CanonicalSmiles/child::text()'\n return molecule_etree.xpath(SMILES_XPATH, namespaces=HUNTER_DB_NAMESPACE_DICT)[0]",
"def to_smiles(rdm):\n smi = _rd_chem.MolToSmiles(rdm)\n return smi",
"def get_smiles(self, canonica... | [
"0.6535551",
"0.57447743",
"0.56670135",
"0.5550063",
"0.54159063",
"0.5406357",
"0.5403899",
"0.54001135",
"0.5337975",
"0.5326419",
"0.5228338",
"0.5192157",
"0.51822424",
"0.5178734",
"0.51761544",
"0.5174183",
"0.51671416",
"0.51321954",
"0.5125165",
"0.50927156",
"0.5081... | 0.79599553 | 0 |
Retrieve the targets gene names and actions. For the drugs with targets, we can find the section using the id "targets". Those targets may have a gene name listed or not. If the target has a gene name listed, it may have zero or many actions associated. | def get_gene_action_pairs(parsed_drug_doc):
gene_action_pairs = []
for target in parsed_drug_doc.select('#targets .card-body'):
# We may have zero or one gene names.
gene_name_section = target.find(id='gene-name')
if gene_name_section:
gene_name = gene_name_section.next_sibl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_targets(self):\n\t\treturn self.prDoc['inputs']['data'][0]['targets']",
"def targets(self) -> Optional[Union[np.ndarray, Dict[str, np.ndarray]]]:\n return self._targets",
"def targets(self):\n\n\t\tstatus, targets = self.execute(self.mission, 'target_list', self.kingdom)\n\n\t\t# Nothing specifi... | [
"0.7006874",
"0.6568634",
"0.6560847",
"0.6494938",
"0.64691794",
"0.64381784",
"0.6420706",
"0.6389015",
"0.63432276",
"0.63212293",
"0.62894803",
"0.6287679",
"0.6269645",
"0.61702716",
"0.6157602",
"0.6147551",
"0.61349255",
"0.6131919",
"0.6126978",
"0.61217403",
"0.61176... | 0.60427153 | 22 |
Retrieves the alternative identifiers for other drug info sources. Finds the section using the id "externallinks". | def get_external_links(parsed_drug_doc):
external_link_info = list(parsed_drug_doc.find(id='external-links').next_sibling.dl.children)
external_links = {}
for i in range(0, len(external_link_info), 2):
source = external_link_info[i].text
value = external_link_info[i+1].text
# Ignori... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_possible_ids(self):\n ids = []\n\n dest_data = requests.get(\"https://api.wdpro.disney.go.com/facility-service/destinations/{}\".format(self.__anc_dest_id), headers=getHeaders()).json()\n data = requests.get(dest_data['links']['entertainmentVenues']['href'], headers=getHeaders()).json(... | [
"0.49276513",
"0.4869413",
"0.47767136",
"0.4770535",
"0.46834496",
"0.4679981",
"0.46615496",
"0.4660064",
"0.4631776",
"0.46157798",
"0.4613686",
"0.4605003",
"0.46038538",
"0.45956227",
"0.4586876",
"0.45623347",
"0.4550497",
"0.45440876",
"0.45408326",
"0.4525997",
"0.449... | 0.63020116 | 0 |
Retrieves a set of information for a given Drugbank drug identifier. | def get_info_for_identifier(identifier):
page = requests.get(f"https://www.drugbank.ca/drugs/{identifier}")
parsed_drug_doc = BeautifulSoup(page.text, 'html.parser')
smiles = get_smiles(parsed_drug_doc)
gene_action_pairs = get_gene_action_pairs(parsed_drug_doc)
external_links = get_external_links(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def details(self, identifier):\n return self.client.request_with_method(Methods.GET % (self.name, identifier,))",
"def _get_drug_entry(self,cid):\n result = None\n search_str = \"drugbank:drug/drugbank:drugbank-id/.[@primary='true']/..[drugbank:drugbank-id='%s']\" % (cid) \n result =... | [
"0.61482173",
"0.60460675",
"0.5905877",
"0.5839129",
"0.57571423",
"0.5755548",
"0.559288",
"0.5558541",
"0.55240154",
"0.54870063",
"0.5484556",
"0.5463918",
"0.5458747",
"0.5449955",
"0.5438414",
"0.5438065",
"0.542378",
"0.5415125",
"0.5409009",
"0.5404084",
"0.5365181",
... | 0.7483154 | 0 |
Returns a psycopg2 connection and cursor for performing SQL operations. | def get_postgres_conn_and_cursor(user, password, host):
conn = psycopg2.connect(user=user, password=password, host=host, connect_timeout=10)
cursor = conn.cursor()
return conn, cursor | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect():\n conn = psycopg2.connect(\"dbname=tournament\")\n cursor = conn.cursor()\n return conn, cursor",
"def connect_to_db(cls):\n conn = psycopg2.connect(os.environ['DATABASE_URL'])\n conn.autocommit = True\n cursor = conn.cursor()\n\n return cursor",
"def databas... | [
"0.7233092",
"0.7218752",
"0.71600825",
"0.71189475",
"0.70791066",
"0.70302516",
"0.7020216",
"0.6912193",
"0.6889989",
"0.68199986",
"0.67865926",
"0.67730397",
"0.67703015",
"0.6745654",
"0.66291225",
"0.6611288",
"0.6598095",
"0.65797776",
"0.6574622",
"0.657343",
"0.6567... | 0.6713615 | 14 |
Retrieves the info from Drugbank for the provided identifiers, prepares insert statements for the info and then performs them. | def transact_drug_info(identifiers, user, password, host):
drug_info = []
for identifier in identifiers:
drug_info.append(get_info_for_identifier(identifier))
def xform_for_insert(t):
return str(t).replace('"', "'").replace("None", "NULL")
drugs_txs = []
gene_action_pairs_txs = []... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_into_db(self, database):\n\n # insert person\n keys = \"\"\n values = \"\"\n for key, value in self.person.items():\n # location\n if key == \"location\":\n # ensure location is in table\n database.select(f\"\"\"DO $do$ BEGI... | [
"0.5740032",
"0.544129",
"0.54230183",
"0.53802603",
"0.5290045",
"0.52740854",
"0.524837",
"0.5180443",
"0.5175086",
"0.5168618",
"0.5155777",
"0.51552916",
"0.5112063",
"0.51112425",
"0.5108119",
"0.5073048",
"0.5065976",
"0.50578666",
"0.50480336",
"0.50229603",
"0.5017675... | 0.6826996 | 0 |
determine if madlib function is substituting input values correctly | def test_madlib_substitution():
actual = madlib(input_values)
expected = output_text
assert actual == expected | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_SMEL_args():\n testing_function('sme', bilinear=False)",
"def needs_correction(self, var):\n global matmul_registry\n if var in matmul_registry:\n return True\n else:\n return False",
"def test_value_not_masked(self):\n out = mask_args_value(\"quantum fluctuations\", \"qua... | [
"0.5692555",
"0.5676673",
"0.5592143",
"0.5510311",
"0.5429809",
"0.5424574",
"0.5415874",
"0.5388544",
"0.5388544",
"0.5327953",
"0.53016853",
"0.53009725",
"0.5289353",
"0.5279792",
"0.52717566",
"0.5237211",
"0.52188444",
"0.5212372",
"0.52014846",
"0.5173144",
"0.51723444... | 0.64386356 | 0 |
check that the correct output is written to the output file | def test_madlib_file_write():
madlib(input_values)
file_text = ''
with open('assets/updated_madlib_text', 'r') as file:
for line in file:
file_text += line
assert file_text == output_text | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_outfile():\n\n out_file = random_filename()\n if os.path.isfile(out_file):\n os.remove(out_file)\n\n try:\n cmd = f'{prg} --cdhit {cdhit} --proteins {proteins} -o {out_file}'\n rv, out = getstatusoutput(cmd)\n assert rv == 0\n\n assert out == ('Wrote 309 of 220,... | [
"0.7176397",
"0.704145",
"0.69386715",
"0.67337406",
"0.66338044",
"0.6622791",
"0.6578286",
"0.6571789",
"0.65504324",
"0.6517622",
"0.651574",
"0.6431474",
"0.64171875",
"0.6410496",
"0.6388531",
"0.6375434",
"0.6352387",
"0.6347393",
"0.62924546",
"0.6279441",
"0.62728095"... | 0.5768482 | 92 |
A supplemental reason explaining why the error occurred. | def reason(self) -> Optional[str]:
return self._reason | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def error_reason(self):\n return self._error_reason",
"def error(self, e):\n return \"{}: {} ({})\".format(e.__class__.__name__, e.__doc__, e.message)",
"def getReason():",
"def error_message(self):\n return u'Something wrong with {}, ' \\\n u'try switch to another series p... | [
"0.7387442",
"0.7348241",
"0.73455167",
"0.72017676",
"0.69890606",
"0.6819124",
"0.66734445",
"0.66404766",
"0.65591633",
"0.65591633",
"0.65591633",
"0.6522138",
"0.65061563",
"0.6491381",
"0.6451334",
"0.64434433",
"0.6396516",
"0.638023",
"0.63696414",
"0.63674456",
"0.63... | 0.6052552 | 70 |
The date and time when the error occurred. | def created_at(self) -> datetime.datetime:
return self._created_at | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def last_error_time(self) -> str:\n return pulumi.get(self, \"last_error_time\")",
"def creationTime(self):\n \n if not self.logMessage is None :\n return self.logMessage[\"date\"]",
"def get_error(self):\n return self.exc_info",
"def get_system_date_and_time(self):\n ... | [
"0.77884203",
"0.68652815",
"0.6218321",
"0.61531395",
"0.6124649",
"0.6088202",
"0.60838395",
"0.6066886",
"0.606426",
"0.6055204",
"0.60251206",
"0.6005831",
"0.6005831",
"0.6005831",
"0.5967726",
"0.596764",
"0.596764",
"0.596764",
"0.596764",
"0.596764",
"0.596764",
"0.... | 0.0 | -1 |
The assembly in which the error occurred. | def assembly(self) -> servo.Assembly:
return self._assembly | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assembly(self):\n return self._assembly",
"def AssemblyName(self) -> str:",
"def DependentAssembly(self) -> str:",
"def AssemblyFullName(self) -> str:",
"def cloud_assembly_artifact(self) -> aws_cdk.aws_codepipeline.Artifact:\n return self._values.get(\"cloud_assembly_artifact\")",
"def... | [
"0.7182016",
"0.68485934",
"0.6469226",
"0.6340841",
"0.56903553",
"0.56903553",
"0.56903553",
"0.56903553",
"0.56903553",
"0.56903553",
"0.5680746",
"0.5550747",
"0.5528744",
"0.5395854",
"0.5395854",
"0.5395854",
"0.5395854",
"0.5395854",
"0.5395854",
"0.5271151",
"0.524506... | 0.59339756 | 4 |
The servo that was active when the error occurred. | def servo(self) -> Optional[servo.Servo]:
return self._servo | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def servo_active(self, *args, **kwargs) -> Any:\n pass",
"def status(self):\n return self._bp.get_motor_status(self._port)",
"def servo_on(self):\n self.logger.info('Setting servo ON')\n self.electronics.move_servo(1)\n self.config['servo']['status'] = 1",
"def controller_s... | [
"0.6637851",
"0.59305996",
"0.57332313",
"0.5726711",
"0.5699285",
"0.56845474",
"0.5684502",
"0.56725603",
"0.56126374",
"0.55574274",
"0.55151606",
"0.54930377",
"0.5473341",
"0.54207635",
"0.5410516",
"0.53283364",
"0.5266262",
"0.5254195",
"0.52259874",
"0.52212095",
"0.5... | 0.6539491 | 1 |
The connector that was active when the error occurred. | def connector(self) -> Optional[servo.Connector]:
return self._connector | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_connect(self):\n\t\treturn self.connect",
"def connector(self):\n if '_connector' not in self.__dict__:\n from meerschaum.connectors.parse import parse_instance_keys\n conn = parse_instance_keys(self.connector_keys)\n if conn:\n self._connector = con... | [
"0.6550373",
"0.65121484",
"0.63851035",
"0.6338511",
"0.6338511",
"0.6338511",
"0.6338511",
"0.61361617",
"0.601682",
"0.6010793",
"0.601007",
"0.5977922",
"0.59642935",
"0.59213513",
"0.59213513",
"0.5902373",
"0.58938384",
"0.58938384",
"0.58938384",
"0.58899015",
"0.58890... | 0.60405296 | 8 |
The event that was executing when the error occurred. | def event(self) -> Optional[servo.Event]:
return self._event | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def error_received(self, exc):\n print('Error received:', exc)",
"def error(self):\n return self._decorator_wrapper(EventName.error)",
"def get_error(self):\n return self.e",
"def on_error(self, event: ThreadResult):\n if self._on_error is not None:\n self._on_error(eve... | [
"0.6877498",
"0.6843506",
"0.6646025",
"0.662441",
"0.6434842",
"0.63798445",
"0.63421637",
"0.63362867",
"0.63362867",
"0.63362867",
"0.62905455",
"0.6284273",
"0.6255658",
"0.62415266",
"0.6234767",
"0.619402",
"0.61851525",
"0.617187",
"0.6160047",
"0.61394197",
"0.6126857... | 0.0 | -1 |
Convert string with ',' string float to float | def stof(fstr):
return float(fstr.replace(',', '.')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_float(s):\n return float(s.replace(',', '.'))",
"def __parse_float(str):\n return float(str.strip().replace(',','.'))",
"def string_to_float(value):\n # if no periods (.) then assume commas are decimal separators\n if '.' not in value:\n value = value.replace(',', '.')\n # if de... | [
"0.87650466",
"0.8430263",
"0.82144356",
"0.7461303",
"0.7387697",
"0.7356403",
"0.72832",
"0.72675294",
"0.7224392",
"0.7190248",
"0.7182437",
"0.71822655",
"0.715909",
"0.706006",
"0.70235217",
"0.69940567",
"0.69926935",
"0.6990524",
"0.6957815",
"0.68797594",
"0.68766946"... | 0.80367935 | 3 |
Try to find all relevant dart tools. | def configure(cnf):
try:
cnf.find_program('dart', var='DART')
except:
Logs.warn("Couldn't find dart executable. It isn't necessary, but why don't you have it...?")
cnf.find_program('dart2js', var='DART2JS')
try:
cnf.find_program('pub', var='PUB')
except:
Logs.warn("Co... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_tools_exist(WARNINGS):\n tools_list = []\n Warning_out = WARNINGS + \"Tool executable warning: \"\n try:\n flash.Flash(args.flash)\n tools_list.append(\"flash\")\n except ValueError:\n Warning_out = Warning_out + \"Flash not in path\"\n try:\n error_correction.E... | [
"0.6521446",
"0.64147955",
"0.62229186",
"0.5955167",
"0.5926633",
"0.58311886",
"0.5808472",
"0.57827175",
"0.57409465",
"0.57075655",
"0.57007235",
"0.5684141",
"0.565486",
"0.5642937",
"0.56153864",
"0.5611737",
"0.56057674",
"0.5543945",
"0.5537143",
"0.5501987",
"0.54407... | 0.0 | -1 |
Create the output folder for the whole dart project output, sift through all source files and make them available as nodes for later processing. | def process_dart(self):
self.dartfiles = set()
self.jsfiles = set()
self.htmlfiles = set()
self.cssfiles = set()
self.otherfiles = set()
for src in self.source:
if isinstance(src,str):
node = self.path.find_node(src)
else:
node = src
if node.suffix... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_all_files():\n for (name, fn) in lang_module.targets.items():\n path = of_g.options.install_dir + '/' + name\n os.system(\"mkdir -p %s\" % os.path.dirname(path))\n with open(path, \"w\") as outfile:\n fn(outfile, os.path.basename(name))\n print(\"Wrote content... | [
"0.71295595",
"0.6861794",
"0.6717942",
"0.6612976",
"0.6493361",
"0.63685995",
"0.63087445",
"0.63026834",
"0.63007575",
"0.6255641",
"0.6225875",
"0.6217281",
"0.6183958",
"0.6169037",
"0.6132974",
"0.6101475",
"0.609835",
"0.6097953",
"0.6091003",
"0.6087792",
"0.608724",
... | 0.68588585 | 2 |
Copy over all source files (html, css, dart, js, etc.) to the target directory. | def apply_dart(self):
shutil.copyfile(self.env['DART_JS_BOOTSTRAP'], self.outdir.make_node('dart.js').abspath())
for filetype in ['dartfiles','jsfiles','htmlfiles','cssfiles','otherfiles']:
files = getattr(self, filetype)
for f in files:
if f.is_bld():
outf = self.out... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy_source():\n shutil.copytree(\"src\", os.path.join(BUILD_DIR, \"src\"))\n for file in os.listdir(\".\"):\n if os.path.isfile(file):\n shutil.copyfile(file, os.path.join(BUILD_DIR, file))",
"def copy_files(self):\n files = ['LICENSE.md', 'CONTRIBUTING.md']\n ... | [
"0.7654222",
"0.6983259",
"0.6957278",
"0.6688869",
"0.6667071",
"0.6636383",
"0.648613",
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"0.61103636",
"0.6088972",
"0.60825247",
"0.6061819",
"0.5998159",
... | 0.6076376 | 19 |
Sets the scope for visualization | def set_scope(self, scope):
self.vis.set_scope(scope) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scope(self, scope):\n self._scope = scope",
"def scope(self, scope):\n\n self._scope = scope",
"def set_scope(self, value):\n self._set_one_attribute(self.AttributeNames.SCOPE, value)\n return self",
"def scope(self, name):\r\n raise NotImplementedError",
"def scope_name(... | [
"0.71557474",
"0.6992773",
"0.64821863",
"0.6437079",
"0.6400237",
"0.62426734",
"0.62259716",
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"0.58180714",
"0.57963246",
"0.57376325",
"0.566702",
"0.5613966",
"0.56080... | 0.8688092 | 0 |
Draws a map in the background | def draw_map(self):
self.vis.draw_map() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _draw_map(screen):\n my_map = HexMap(80, 80, _hex_size=10)\n my_map.generate_with_random_walk(150, iterations=25)\n for tile in my_map:\n # print(tile)\n color = COLORS[tile.type]\n\n tile_color = _modify_color(color)\n pygame.draw.polygon(screen, tile_color, tile.corners)\... | [
"0.7438594",
"0.71875733",
"0.71379495",
"0.71011156",
"0.7013649",
"0.68864465",
"0.68812746",
"0.6869771",
"0.6788096",
"0.6728911",
"0.67023903",
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"0.65258896",
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"0.6514239",
"0.6453493",
"0.64481187",
"0.64455163",
"0.6381603",
"0.6366488",
"0.635... | 0.7902193 | 0 |
Draws the routes of the ships | def draw_routes(self):
self.vis.draw_routes() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def visualize_routes(self):\n visualize_tsp.plotTSP([self.best_solution], self.coords)",
"def show_positioned_routes(self, routes):\n print(55*\"-\")\n print(\" Positioned Routes:\")\n print(55*\"-\")\n for elem in routes:\n print(str(elem))",
"def dump(self):\n ... | [
"0.68161446",
"0.63527113",
"0.6300826",
"0.6296435",
"0.6266359",
"0.60612214",
"0.601034",
"0.6004469",
"0.600043",
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"0.5910172",
"0.58955467",
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"0.5875609",
"0.58717746",
"0.58306396",
"0.58220094",
"0.58145154",
"0.5808135",
"0.579943... | 0.7103001 | 0 |
Draws the predictions of the ships | def draw_predictions(self):
self.vis.draw_predictions() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw(self, frame):\n for prediction in self.predictions:\n prediction.draw(frame)",
"def plot_predictions(self):\n\n plt.title(\"Targets vs. Predictions\")\n plt.plot(self.T, label=\"Targets\")\n plt.plot(self.Y, label=\"Predictions\")\n plt.xlabel(\"Sample numbe... | [
"0.626214",
"0.6031829",
"0.59917396",
"0.59532815",
"0.5945221",
"0.59262586",
"0.591139",
"0.5855403",
"0.58285314",
"0.5797049",
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"0.5700845",
"0.56753343",
"0.56701136",
"0.56557083",
"0.565118",
"0.5646596",
"0.5598298"... | 0.6511887 | 0 |
Starts the PyQt5Application maximized | def show_dialog(self):
self.showMaximized()
sys.exit(self.app.exec_()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def showMaximized(self):\n self.usualSize = self.size()\n self.setWindowState(Qt.WindowMaximized)\n self.move(0, 0)\n self.setFixedSize(QSize(self.screenSize.width(), self.screenSize.height()))\n self.maximized = True\n QWidget.showMaximized(self)",
"def maximize_app( ap... | [
"0.7441274",
"0.7221831",
"0.6910993",
"0.6865379",
"0.6588485",
"0.65783817",
"0.64921206",
"0.6491779",
"0.6343574",
"0.6277894",
"0.6226824",
"0.6132522",
"0.6104767",
"0.61045426",
"0.6081701",
"0.60019153",
"0.5911355",
"0.5893951",
"0.5888113",
"0.5679614",
"0.5611324",... | 0.6986234 | 2 |
Zooms to calculated or to user scope | def __button_zoom_clicked(self):
self.zoom_to_calc_scope = not self.zoom_to_calc_scope
self.vis.change_zoom(self.zoom_to_calc_scope)
if self.zoom_to_calc_scope:
self.button_zoom.setText("Zoom to user scope")
else:
self.button_zoom.setText("Zoom to calculated scope... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zoom_to(self):\n # Will seek user feedback. QGIS will\n # Pan to first layer loaded",
"def zoom(self, amount):\n pass",
"def apply_zoom(self):\n self.maparea.setTransform(self.zoom_levels[self.cur_zoom][1])\n self.scene.draw_visible_area()",
"def __zoom(self):\n ... | [
"0.70761585",
"0.69927603",
"0.68108034",
"0.65833044",
"0.65501726",
"0.65094495",
"0.65086645",
"0.64987355",
"0.6489018",
"0.64820343",
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"0.62361455",
"0.6220067",
"0.62044436",
"0.618074",
"0.61599994",
"0.6158776",
"0.6... | 0.7127061 | 0 |
Changes the color of route lines and route patches | def __button_routes_line_color_clicked(self):
color = QColorDialog.getColor()
if color.isValid():
self.vis.change_route_line_color(color.name()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def color_way(graph, way):\n ox.plot_graph_route(graph, way)",
"def __button_routes_marker_color_clicked(self):\n color = QColorDialog.getColor()\n if color.isValid():\n self.vis.change_route_marker_color(color.name())",
"def _set_color(self, r):\n c = COLORS[self.color]\n ... | [
"0.6682972",
"0.6390528",
"0.6147725",
"0.61077327",
"0.60268617",
"0.5987641",
"0.58574235",
"0.58475643",
"0.5789604",
"0.5737006",
"0.5722419",
"0.5721481",
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"0.5715089",
"0.5713998",
"0.57114846",
"0.5698987",
"0.5686852",
"0.56804526",
"0.56774586",
"0.56742... | 0.67418104 | 0 |
Changes the color of route markers | def __button_routes_marker_color_clicked(self):
color = QColorDialog.getColor()
if color.isValid():
self.vis.change_route_marker_color(color.name()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_markers_color(self, markers_color):\n self.markers_color = markers_color\n self.update_markers(self.markers)",
"def set_markers_color(self, markers_color):\n self._set_markers_color(markers_color, key=\"model\")",
"def _set_markers_color(self, markers_color, key):\n self.mar... | [
"0.7044057",
"0.7002465",
"0.677413",
"0.6731408",
"0.65059507",
"0.61693764",
"0.60807544",
"0.59966314",
"0.59300774",
"0.5908406",
"0.58413565",
"0.58325946",
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"0.57990813",
"0.5794199",
"0.5764045",
"0.57388026",
"0.5721148",
"0.5715555",
"0.5715183",
"0.5710... | 0.7659686 | 0 |
Changes the color of prediction lines and prediction patches | def __button_prediction_line_color_clicked(self):
color = QColorDialog.getColor()
if color.isValid():
self.vis.change_prediction_line_color(color.name()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_color(self):\n self.plot(update_traces=False, update_waveforms=True)",
"def changeColor(self):\n self.layer.new_colormap()",
"def __button_prediction_marker_color_clicked(self):\n color = QColorDialog.getColor()\n if color.isValid():\n self.vis.change_predictio... | [
"0.62123764",
"0.61652744",
"0.60644865",
"0.5744471",
"0.5587503",
"0.5579864",
"0.54944956",
"0.5475637",
"0.54724866",
"0.54577696",
"0.54441255",
"0.54431957",
"0.5403518",
"0.53859484",
"0.5351867",
"0.53359157",
"0.53151727",
"0.530714",
"0.52710307",
"0.5262313",
"0.52... | 0.62771994 | 0 |
Changes the color of prediction markers | def __button_prediction_marker_color_clicked(self):
color = QColorDialog.getColor()
if color.isValid():
self.vis.change_prediction_marker_color(color.name()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_markers_color(self, markers_color):\n self._set_markers_color(markers_color, key=\"model\")",
"def set_markers_color(self, markers_color):\n self.markers_color = markers_color\n self.update_markers(self.markers)",
"def _set_markers_color(self, markers_color, key):\n self.mar... | [
"0.7043545",
"0.6805905",
"0.65569264",
"0.6435788",
"0.6404955",
"0.63046265",
"0.62484235",
"0.59784675",
"0.59488225",
"0.5729172",
"0.5657184",
"0.5614262",
"0.5593371",
"0.55903167",
"0.55818754",
"0.555754",
"0.55339634",
"0.5522944",
"0.55088085",
"0.5441992",
"0.54185... | 0.7464549 | 0 |
Changes the line width and patch size | def __button_line_width_clicked(self):
val, okPressed = QInputDialog.getDouble(self, "Set line width","Value:", self.line_thickness, 0, 100, 4)
if okPressed:
self.vis.change_line_width(val)
self.line_thickness = val | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linewidth(self, size: float):\n for line in self.ax.lines:\n line.set_linewidth(size)\n self.canvas.draw()",
"def set_line_thickness(thickness):\r\n global _current_line_thickness\r\n _current_line_thickness = thickness",
"def setLineWidth(w=1):\n dislin.linwid(w)",
"def... | [
"0.7279491",
"0.7156996",
"0.7128549",
"0.7085972",
"0.6764721",
"0.65739036",
"0.65367657",
"0.6462017",
"0.6433259",
"0.6371664",
"0.63704294",
"0.63312036",
"0.6316157",
"0.6217226",
"0.61715096",
"0.6160285",
"0.61116457",
"0.6103658",
"0.6094712",
"0.6082826",
"0.6024881... | 0.6979117 | 4 |
Changes the marker size | def __button_marker_size_clicked(self):
val, okPressed = QInputDialog.getDouble(self, "Set marker size","Value:", self.marker_size, 0, 100, 4)
if okPressed:
self.vis.change_marker_size(val)
self.marker_size = val | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_markers_size(self, markers_size):\n self.markers_size = markers_size\n self.update_markers(self.markers)",
"def _set_markers_size(self, markers_size, key):\n self.markers_size[key] = markers_size\n self._update_markers(self.markers, key)",
"def changeSize(self, value):\n ... | [
"0.7957097",
"0.79468244",
"0.7109392",
"0.6834395",
"0.6767795",
"0.66597164",
"0.6571444",
"0.6549198",
"0.65221936",
"0.6460304",
"0.6456009",
"0.64492106",
"0.6431153",
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"0.641807",
"0.641807",
"0.63952523",
"0.63742155",
"0.6358185",
"0.6348079",
"0.6309966"... | 0.8122282 | 0 |
Shows only nth marker | def __button_mark_every_clicked(self):
val, okPressed = QInputDialog.getInt(self, "Set every n-th marker","Value:", self.mark_every, 1, 1000)
if okPressed:
self.vis.change_mark_every(val)
self.mark_every = val | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_markers(num_markers):\n markers = ['^','o','P','X','*', 'd','<', '>', ',','|', '1','2','3','4','s','p','*','h','+']\n if(num_markers>18):\n sys.exit(\"cannot create more than 18 markers, refactor your code; force exiting\")\n\n return markers[0:num_markers]",
"def alert(n):\n f... | [
"0.61066335",
"0.58480793",
"0.58365047",
"0.57498217",
"0.5747987",
"0.5717928",
"0.5526804",
"0.54910606",
"0.54452956",
"0.5429945",
"0.54014564",
"0.53442556",
"0.53102845",
"0.52922887",
"0.52334106",
"0.5230203",
"0.5225732",
"0.52170223",
"0.5191547",
"0.51543796",
"0.... | 0.58969164 | 1 |
Changes the anomaly threshold and redraw the anomaly annotations, if necessary | def __slider_anomaly_threshold_value_changed(self):
val = self.slider_anomaly_threshold.value() / 10000
self.label_anomaly_threshold.setText("Anomaly threshold: %.4f" % round(val, 5))
self.vis.change_anomaly_thresh(val)
self.vis.show_anomalies(self.checkbox_anomalies.isChecked())
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def anomaly():\n\n #Load anomaly dataset\n anomaly_data = LoadDataset(\"dataset/kaggle_anomalies/\",0)\n anomaly_data, anomaly_label, val, val_label = anomaly_data.load_data()\n for i in range (len(anomaly_label)):\n anomaly_label[i] = anomaly_label[i] + 5\n\n #Concatinate test and anomaly\n ... | [
"0.63055974",
"0.59972256",
"0.5510011",
"0.549272",
"0.5430622",
"0.54203653",
"0.5420277",
"0.53936905",
"0.5356829",
"0.53355956",
"0.52786005",
"0.52670705",
"0.5250499",
"0.5238121",
"0.52195436",
"0.52043974",
"0.5202424",
"0.51902163",
"0.5183443",
"0.51810884",
"0.517... | 0.7162049 | 0 |
takes the parsed color and returns it in matplotlib format | def parse_color(color):
return (color[0], color[1], color[2]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def color_val_matplotlib(color):\n color = mmcv.color_val(color)\n color = [color / 255 for color in color[::-1]]\n return tuple(color)",
"def getColor(self, _color):\n c = _color.split()\n \n for n in range(len(c)):\n c[n] = float(c[n])\n \n return c",
"d... | [
"0.6960643",
"0.68371934",
"0.6820142",
"0.66417915",
"0.66331017",
"0.6586802",
"0.657952",
"0.6547312",
"0.6425396",
"0.6418397",
"0.63776857",
"0.63485354",
"0.63365304",
"0.6317147",
"0.6297241",
"0.6231265",
"0.6167969",
"0.6115033",
"0.60773015",
"0.60769904",
"0.603920... | 0.68683803 | 1 |
returns an argument parse, which parses the arugments, which are needed / optional for this component. | def arg_parser():
parser = argparse.ArgumentParser(add_help=False)
group = parser.add_argument_group("visualization")
group.add_argument("--routes_line_color", nargs=3, action="store", type=float, default=[0.6, 0.2, 0.8],
help="the color of the routes line", metavar=("r", "g", "b"))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def arg_parse():\n p = ap.ArgumentParser()\n p.add_argument()\n return p.parse_args()",
"def _parse_args():\n parser = argparse.ArgumentParser(description=\"\")\n #parser.add_argument(\"args\", metavar=\"N\", type=str, nargs=\"*\", help=\"Positional arguments.\")\n #parser.add_argument(\"\", de... | [
"0.7382645",
"0.73573893",
"0.7320075",
"0.7235577",
"0.71779126",
"0.7156848",
"0.7137164",
"0.7116912",
"0.7058954",
"0.7051867",
"0.7033093",
"0.70195925",
"0.7019295",
"0.7008903",
"0.69974977",
"0.6989396",
"0.69782114",
"0.69701517",
"0.69482166",
"0.6924753",
"0.690785... | 0.0 | -1 |
Converts the object into a dictionary used for serializing | def to_dict(self):
result = {'Id': self.get_client_line_id(), 'AgencyId': self.id, 'Number': self.get_line_number(), \
'Name': self.name.upper(), 'Dir': self.direction, 'Stops': [s.to_dict() for s in self.stops], \
'Map': [l.to_dict() for l in self.route], 'Night': self.is_ni... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self) -> dict:",
"def to_dict(self) -> Dict:\n return {'object_id': self.object_id, 'data_id': self.data_id}",
"def dict(self):\n return objToDict(self)",
"def to_obj(self):\n return dict()",
"def convert_to_dict(self):\n # Populate the dictionary with object meta d... | [
"0.7948245",
"0.7880991",
"0.7832231",
"0.7826991",
"0.77938557",
"0.77315027",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
"0.77305317",
... | 0.0 | -1 |
Converts the object into a dictionary used for serializing | def to_dict(self):
result = {'Id': self.id, 'Na': self.name, \
'Sc': self.schedule.to_dict(), 'Lc': self.location.to_dict()}
if len(self.connections)>0:
result['Co'] = self.connections_to_string()
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self) -> dict:",
"def to_dict(self) -> Dict:\n return {'object_id': self.object_id, 'data_id': self.data_id}",
"def dict(self):\n return objToDict(self)",
"def to_obj(self):\n return dict()",
"def convert_to_dict(self):\n # Populate the dictionary with object meta d... | [
"0.79485124",
"0.7880778",
"0.7831247",
"0.782649",
"0.7794245",
"0.77308095",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
"0.7730069",
... | 0.0 | -1 |
Receives a matrix of coordinates and transforms it into a list of Locations | def coordinates_to_locations(coordinates):
np_coords = np.array(coordinates)
longs, lats = transform(Proj(init=EPSG_IN), Proj(init=EPSG_OUT), np_coords[:, 0], np_coords[:, 1])
length = len(lats)
result = []
for i in range(length):
loc = Location(lats[i], longs[i])
raw_location = extr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matrix2list(vertex_matrix): \n flat_array = vertex_matrix.tolist() \n xy_list = [] \n for i in range(0, len(flat_array)): \n xy_list.append( flat_array[i][0] ) \n xy_list.append( flat_array[i][1] ) \n return xy_list",
"def coords_to_positions(self, coords):\n return [self.t... | [
"0.63898116",
"0.63502413",
"0.62159",
"0.6157633",
"0.61507696",
"0.6145018",
"0.6046968",
"0.6008495",
"0.59859973",
"0.59842753",
"0.5973625",
"0.5972133",
"0.5960086",
"0.59585255",
"0.593453",
"0.5930561",
"0.5909759",
"0.59049857",
"0.5903449",
"0.5894739",
"0.5884177",... | 0.6808166 | 0 |
Given ('503846.58851256', '4791736.67290404') returns (503846, 4791736) | def extract_raw_simple_coordinates (raw_location):
pattern = re.compile('(\d+).(\d+)?')
x_result = pattern.match(str(raw_location[0]))
y_result = pattern.match(str(raw_location[1]))
return int(x_result[1]), int(y_result[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_tuple(self, string):\n a = re.search('\\((\\d+\\.\\d+), (\\d+\\.\\d+)\\)', string)\n if not a:\n return None\n else:\n return (float(a.group(1)), float(a.group(2)))",
"def key_to_coordinates(key):\n stripkey = key.strip(\"(\").strip(\")\").split(\", \")\n ... | [
"0.60521793",
"0.6008657",
"0.59300464",
"0.57546574",
"0.573588",
"0.5650845",
"0.56116116",
"0.55786246",
"0.55596447",
"0.55537593",
"0.5545964",
"0.5532772",
"0.54754543",
"0.5463573",
"0.5441758",
"0.54177845",
"0.5398144",
"0.5393655",
"0.5357906",
"0.53475595",
"0.5346... | 0.5193754 | 32 |
Initialises a new ReadingLoader instance to read and process data from files generated by the RED reading task. Arguments data_dir String. Path to the directory that contains data files that need to be loaded. Keyword Arguments output_path String. Path to the file in which processed data needs to be stored, or None to ... | def __init__(self, data_dir, output_path=None, task_name="ReadingTest", \
answer_file=None):
# Define the default answer_file.
if answer_file is None:
answer_file = os.path.join( \
os.path.dirname(os.path.abspath(__file__)), \
"reading_ans... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, data_dir, output_path=None, task_name=\"Q1_Questions\"):\n \n # Remember the task name.\n self._task_name = task_name\n # Load all data.\n self.load_from_directory(data_dir, task_name)\n self.process_raw_data()\n if not (output_path is None):\n ... | [
"0.68810457",
"0.6347282",
"0.63151276",
"0.60448104",
"0.59106815",
"0.5840321",
"0.57810795",
"0.5776484",
"0.5745916",
"0.5745365",
"0.569666",
"0.5693174",
"0.56790227",
"0.5678662",
"0.5673701",
"0.56613",
"0.5648839",
"0.5613989",
"0.5597921",
"0.55950487",
"0.5570015",... | 0.6832593 | 1 |
Loads data from a single file. This function overwrites the parent's load_from_file function to allow for the checking of answers. Arguments file_path String. Path to the file that needs to be loaded. Keyword arguments delimiter String. Delimiter for the data file. Default = "," missing List. List of values that code f... | def load_from_file(self, file_path, delimiter=",", missing=None, \
auto_typing=True, string_vars=None):
# Load the data from a file.
raw = read_behaviour(file_path, delimiter=",", missing=None, \
auto_typing=True, string_vars=["Sentence", "Response"])
# ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_from_file(self, file_path, delimiter=\",\", missing=None, \\\n auto_typing=True, string_vars=None):\n \n # Load the data from a file.\n raw = read_behaviour(file_path, delimiter=\",\", missing=None, \\\n auto_typing=True, string_vars=[\"Response\"])\n \n ... | [
"0.73351383",
"0.5946088",
"0.5934415",
"0.58590823",
"0.5856287",
"0.5807811",
"0.57449085",
"0.5613772",
"0.5591136",
"0.55231375",
"0.5500807",
"0.5500807",
"0.54131764",
"0.5364186",
"0.5352303",
"0.5316944",
"0.5308802",
"0.53043306",
"0.52103776",
"0.5204313",
"0.517564... | 0.6975702 | 1 |
Computes the variables that need to be computed from this task, and stores them in the self.data dict. This has one key for every variable of interest, and each of these keys points to a NumPy array with shape (N,) where N is the number of participants. The processed data comes from the self.raw dict, so make sure that... | def process_raw_data(self):
# Define some variables of interest.
vor = ["n_sentences", "n_correct", "p_correct", "median_RT", \
"mean_RT", "stdev_RT", "scaled_stdev_RT"]
# Get all participant names, or return straight away if no data was
# loaded yet.
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_raw_data(self):\n \n # Get all participant names, or return straight away if no data was\n # loaded yet.\n if hasattr(self, \"raw\"):\n participants = self.raw.keys()\n participants.sort()\n else:\n self.data = None\n return... | [
"0.735433",
"0.6486896",
"0.62451255",
"0.61603683",
"0.6160291",
"0.605454",
"0.58846754",
"0.587648",
"0.587125",
"0.58672607",
"0.5865694",
"0.5862777",
"0.5860213",
"0.5857704",
"0.5793308",
"0.57828593",
"0.5727415",
"0.5701312",
"0.56965584",
"0.5690695",
"0.5669493",
... | 0.77125794 | 0 |
Extracts the path from the remote URL | def repo_full_name_from_remote(remote_url):
# Check whether we have a https or ssh url
if remote_url.startswith("https"):
path = urllib.parse.urlparse(remote_url)
path = path.path
# Remove the intial '/'
path = path[1:]
# Remove extension
path = os.path.splitext(p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetPathFromUrl(url):\n return __ParseUrl(url)[2]",
"def _remote_path(self):\n return self._remote_dir",
"def _parse_source(self, response):\n return response.url",
"def process_url(url):\n parsed = urlparse(url)\n if parsed.scheme:\n return parsed.netloc, parsed.path\n else... | [
"0.71459997",
"0.6760801",
"0.6729793",
"0.6680723",
"0.6593853",
"0.65536875",
"0.655141",
"0.64805835",
"0.64625865",
"0.6397612",
"0.6395385",
"0.63676685",
"0.6367012",
"0.63624704",
"0.62920296",
"0.6290658",
"0.62889403",
"0.62657434",
"0.6243308",
"0.6237244",
"0.62331... | 0.64504325 | 9 |
Make a list of all modules installed in this repository Returns a tuple of two lists, one for local modules and one for nfcore modules. The local modules are represented as direct filepaths to the module '.nf' file. Nfcore module are returned as file paths to the module directories. In case the module contains several ... | def get_installed_modules(dir, repo_type="modules"):
# initialize lists
local_modules = []
nfcore_modules = []
local_modules_dir = None
nfcore_modules_dir = os.path.join(dir, "modules", "nf-core")
# Get local modules
if repo_type == "pipeline":
local_modules_dir = os.path.join(dir, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_modules(self) -> Optional[List[str]]:\n module_list: List[str] = []\n for forge_module in self._forge_modules:\n module_list.append(forge_module.name)\n for git_module in self._git_modules:\n module_list.append(git_module.name)\n return module_list",
"de... | [
"0.68389815",
"0.6744586",
"0.6596824",
"0.65812105",
"0.6420615",
"0.6393576",
"0.6108857",
"0.6055035",
"0.60373896",
"0.6012906",
"0.5990414",
"0.5955053",
"0.5934572",
"0.59338784",
"0.5891398",
"0.5880061",
"0.5828444",
"0.5819975",
"0.5815788",
"0.5812122",
"0.58095336"... | 0.7559931 | 0 |
Flags a comment. Confirmation on GET, action on POST. | def flag(request, comment_id):
try:
comment = comments.get_object(pk=comment_id, site__id__exact=SITE_ID)
except comments.CommentDoesNotExist:
raise Http404
if request.POST:
userflags.flag(comment, request.user)
return HttpResponseRedirect('%sdone/' % request.path)
return... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flag_comment(request, submission_id, comment_id):\n # If not flagged, flag and decrement author's karma.\n # If flagged, undo flag and increment author's karma.\n # If upvoted, undo upvote and decrement author's karma.\n # If downvoted, undo upvote and increment author's karma.\n # No need to d... | [
"0.686533",
"0.67600733",
"0.6324674",
"0.6263772",
"0.6260797",
"0.61571467",
"0.6092327",
"0.6090107",
"0.6030129",
"0.60185254",
"0.59647995",
"0.596228",
"0.5958102",
"0.59516764",
"0.5940332",
"0.5812686",
"0.5787282",
"0.5782282",
"0.5781615",
"0.5746764",
"0.5725129",
... | 0.74663377 | 0 |
Deletes a comment. Confirmation on GET, action on POST. | def delete(request, comment_id):
try:
comment = comments.get_object(pk=comment_id, site__id__exact=SITE_ID)
except comments.CommentDoesNotExist:
raise Http404
if not comments.user_is_moderator(request.user):
raise Http404
if request.POST:
# If the comment has already been... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_comment(request, comment_id):\n raise NotImplementedError",
"def delete_comment(self, id, **args): \n args.update(id=id)\n return self.fetch(\"/comment/delete\", post_args=args)",
"def delete_comment(request, course_id, comment_id):\r\n comment = cc.Comment.find(comment_id)\r\n ... | [
"0.84090346",
"0.7925234",
"0.78713894",
"0.778777",
"0.77292603",
"0.7653056",
"0.7631221",
"0.75838006",
"0.747952",
"0.74310815",
"0.741415",
"0.73892677",
"0.72891563",
"0.7151106",
"0.7113485",
"0.70712",
"0.7050842",
"0.7023403",
"0.69454706",
"0.6891005",
"0.6881565",
... | 0.75836974 | 8 |
Set up test variables. | def setUpClass(cls):
user = 'postgres'
password = 'password'
host = '127.0.0.1'
port = '5432'
database = 'postgres'
conn_params = [user, password, host, port, database]
ddl = "DROP SCHEMA IF EXISTS test CASCADE"
pg_conn = PostgreSQL(*conn_params)
p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n test_env_setup()",
"def _set_up():\n repl._setUp = self.setUp",
"def setUp(self):\n\n # setup init variables\n self.init_vars = {\n 'suppress_logfile': True,\n 'verbosity': 0,\n 'mothur_seed': 54321,\n }\n\n #... | [
"0.7683675",
"0.7621648",
"0.75302994",
"0.73465705",
"0.73079515",
"0.73079515",
"0.72646254",
"0.7220895",
"0.7213839",
"0.7211513",
"0.72097087",
"0.7171401",
"0.7158472",
"0.7151176",
"0.714493",
"0.7141824",
"0.7123144",
"0.711939",
"0.7099598",
"0.7091641",
"0.7083642",... | 0.0 | -1 |
Check connection with Postgresql database. | def test_init(self):
self.assertEqual(str(PostgreSQL(*self.conn_params).engine),
"Engine(postgresql://test:***@127.0.0.1:"
"5432/postgres)") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check():\n conn = None\n try:\n # read connection parameters\n params = config()\n \n # connect to the PostgreSQL server\n print('Connecting to the PostgreSQL database...')\n conn = psycopg2.connect(**params)\n \n # create a cursor\n cur = conn.cursor... | [
"0.8388398",
"0.77379507",
"0.7704035",
"0.7534378",
"0.7430744",
"0.7258017",
"0.7247929",
"0.72414553",
"0.72246337",
"0.7213338",
"0.71397334",
"0.70725214",
"0.7063897",
"0.69838053",
"0.69833976",
"0.6895949",
"0.68938076",
"0.6883648",
"0.68471646",
"0.6838498",
"0.6829... | 0.0 | -1 |
Check execute method launching arbitrary sql queries. | def test_execute(self):
pg_conn = PostgreSQL(*self.conn_params)
sql = f'''CREATE TABLE table1 (id integer, column1 varchar(100),
column2 float)'''
pg_conn.execute(sql)
sql = "INSERT INTO table1 (id, column1, column2) " \
"VALUES (1, 'Varchar text (100 char... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute_query(self, *args, **kwargs):",
"def run(self, sql, *args):\n return self.database.execute(sql, args)",
"def _execute(self, db):\n raise NotImplementedError",
"def execute(self):\n if self.sql is None:\n self.sql = self.construct_query()\n # Only SQL strings... | [
"0.7710323",
"0.7611448",
"0.760573",
"0.75472224",
"0.7468983",
"0.73805314",
"0.73273337",
"0.73039377",
"0.7269179",
"0.7221843",
"0.71986544",
"0.7172537",
"0.71460295",
"0.7133281",
"0.71269566",
"0.71252114",
"0.7111323",
"0.7058849",
"0.7052643",
"0.7040104",
"0.701126... | 0.0 | -1 |
Check if multiple SQL statements are correctly executed. | def test_execute_multiple(self):
pg_conn = PostgreSQL(*self.conn_params)
sql = f"""CREATE TABLE table1 (id integer, column1 varchar(100),
column2 float);
INSERT INTO table1 (id, column1, column2)
VALUES (1, 'Varchar; text; (100 char)',
123456789.01... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_multiple_sql_statements(statements, fetch=True, cur=None, conn=None, commit=True):\n\n try:\n if conn is None:\n logger.error(\"Connection cannot be None.\")\n raise ValueError(\"Connection cannot be None.\")\n\n if cur is None:\n cur = conn.cursor()\n\n ... | [
"0.6614053",
"0.6523383",
"0.64463997",
"0.6432209",
"0.6429317",
"0.63709897",
"0.636691",
"0.6331006",
"0.6307495",
"0.62796277",
"0.6218031",
"0.6213825",
"0.61856246",
"0.61269885",
"0.6086156",
"0.60775757",
"0.6075473",
"0.6038134",
"0.6027315",
"0.60235554",
"0.6019292... | 0.64520967 | 2 |
Saves a few button presses by opening a database and returning the output as a dict | def newConn():
g = sql.connect('user.db')
e = g.cursor()
return {
"close": g.close,
"commit": g.commit,
"cursor": e
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_press_save(self):\n\n if self.dbChk.isChecked():\n self.processed_fields['db'] = self.dbPath.text()\n self.dbForm = DBFormWindow(self.processed_fields, self)\n self.dbForm.show()\n\n if self.savePathChk.isChecked():\n if self.savePath.text():\n ... | [
"0.6564143",
"0.6236265",
"0.6148969",
"0.61240685",
"0.611488",
"0.60713863",
"0.5928489",
"0.5880718",
"0.5758679",
"0.56460655",
"0.56113094",
"0.561073",
"0.5597487",
"0.5554949",
"0.55506355",
"0.5547778",
"0.5542317",
"0.55413216",
"0.553563",
"0.5512199",
"0.5506875",
... | 0.0 | -1 |
Success function to verify the script was successful | def success(a):
print(f'\n\n{a} successfully completed its mission\n\n')
return f'{a} successfully completed its mission' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def success(self):\n self.succeeded = True",
"def successful(self) -> bool:\n pass",
"def print_success():\n print \"Success!\\n\"\n return True",
"def test_success(self):\n result = self.test_client.success\n\n assert result == 1",
"def execute_success(self, *args, **kwar... | [
"0.75061536",
"0.73862875",
"0.726931",
"0.704134",
"0.7001747",
"0.68722284",
"0.67791456",
"0.6714529",
"0.6711764",
"0.6644389",
"0.6643627",
"0.6592407",
"0.6574083",
"0.6555387",
"0.65435743",
"0.6534694",
"0.65240014",
"0.6515918",
"0.6512348",
"0.6486815",
"0.6478787",... | 0.0 | -1 |
Generates a fresh new iV set and inserts it into the database | def ivgen(pokeId):
h = other.newConn()
close = h['close']
commit = h['commit']
c = h['cursor']
c.execute("""
INSERT INTO pokeiv
(ID,
HPiv,
Atkiv,
Defiv,
SpAtkiv,
SpDefiv,
Speediv)
VALUES(?,?,?,?,?,?,?)
""", (
pokeId,
random.r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new_set(*, ctx: context.ContextLevel, **kwargs) -> irast.Set:\n ir_set = irast.Set(**kwargs)\n ctx.all_sets.append(ir_set)\n return ir_set",
"def prep(self):\n sq1 = 'create table TCVR ( ID, T, C, V, R , primary key ( ID ) ) ;'\n sq2 = 'create table IDX ( ID , A , primary key(A) ) ; '\... | [
"0.6114309",
"0.5982679",
"0.58786047",
"0.5813862",
"0.57588804",
"0.57500505",
"0.5716826",
"0.5708162",
"0.57074517",
"0.5674362",
"0.5631915",
"0.5614042",
"0.5603859",
"0.55749154",
"0.5509987",
"0.54872024",
"0.5473928",
"0.54687345",
"0.54646945",
"0.5453792",
"0.54522... | 0.5858126 | 3 |
Load the pretrained model weights | def load_model_weights(self):
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_weights(self):\n if isinstance(self.pretrained, str):\n logger = get_root_logger()\n logger.info(f'load model from: {self.pretrained}')\n load_checkpoint(self, self.pretrained, strict=False, logger=logger)\n elif self.pretrained is None:\n pass\n ... | [
"0.81840134",
"0.80532205",
"0.7929311",
"0.7929236",
"0.78598017",
"0.7832963",
"0.78319764",
"0.777211",
"0.7706451",
"0.76879597",
"0.7657897",
"0.76478493",
"0.76099664",
"0.760729",
"0.7571413",
"0.7569362",
"0.7523776",
"0.75005925",
"0.75003874",
"0.7451586",
"0.741773... | 0.8207032 | 0 |
Set models to eval phase | def set_models_eval(self):
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_eval(self):\n for m in self.models.values():\n m.eval()",
"def set_eval(self):\n self.model.eval()",
"def _set_eval(self):\n\n if self.model.__dict__['training']:\n self.model.eval()",
"def eval(self, logger=None):\n self.model.eval()\n self.mo... | [
"0.8009308",
"0.77162427",
"0.7589191",
"0.7418761",
"0.7214322",
"0.7055874",
"0.66177076",
"0.6542605",
"0.63525766",
"0.63517594",
"0.63286924",
"0.6325084",
"0.6280866",
"0.6221048",
"0.62003845",
"0.61510843",
"0.61048037",
"0.61048037",
"0.6102818",
"0.6097981",
"0.6087... | 0.7940041 | 1 |
Get predictions from the model | def get_predictions(self, seq, seq_mask, seq_lens, batch, xyzhe, simulator_next_action):
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict(self): \n return self.model.predict(self.test_x)",
"def predictions(self, model):\n return get_predictions_from_df(\n model=model, df=self.prediction_df,\n fixed_effects=self.fixed_effects,\n random_effect=self.random_effect,\n spline=s... | [
"0.8116896",
"0.8030417",
"0.8019336",
"0.7744747",
"0.7712386",
"0.7704468",
"0.77032965",
"0.76695955",
"0.76569694",
"0.7620984",
"0.76174486",
"0.7608288",
"0.7607634",
"0.75987077",
"0.75403714",
"0.7524296",
"0.75212175",
"0.75183904",
"0.749199",
"0.7470694",
"0.745553... | 0.0 | -1 |
Divides the deck in the argument into different most suitable groups. | def build_groups(self, computer_deck):
computer_deck.sort()
for card in computer_deck:
self.add_card_to_grps(card)
self.grps = sorted(self.grps, key = lambda x: -len(x))
logger.info(f"In computer.py/build_groups: computer: {self}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def comp10001go_score_group(cards):\n \n # Put int a dictionary for each card which is scored based on its value\n # For example, J is 11, Q is 12 and K is 13, Ace is 20\n \n values = {'2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9, \n '0': 10, 'J': 11, 'Q': 12, 'K': 13, ... | [
"0.5854269",
"0.58211976",
"0.57251054",
"0.5717913",
"0.5682591",
"0.56796926",
"0.5642106",
"0.5632018",
"0.5632018",
"0.5628769",
"0.5623917",
"0.5568811",
"0.5563554",
"0.5456946",
"0.5428732",
"0.5418285",
"0.54075015",
"0.5389676",
"0.5361663",
"0.5338872",
"0.53152716"... | 0.6242387 | 0 |
Recieves a card from a `Dealer` object and returns 1 card back to `Dealer`. | def get_card(self, card):
self.add_card_to_grps(card)
self.grps = sorted(self.grps, key = lambda x: -len(x))
# check if # of cards forming sets is more than 5; if yes, then break the set to allow computer to form runs
num_set_cards = 0
pos = -1
for i in range(len(self.grps)):
if len(self.grps[i]) > 1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deal_card(self):\n return self._deal(1)[0]",
"def get_card (self, card):\n\t\treturn self._card",
"def deal_card():\n cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\n card = random.choice(cards)\n return card",
"def get_card(self):\n return self.card",
"def deal(self):\n c... | [
"0.7169827",
"0.706437",
"0.6729612",
"0.67081213",
"0.66025114",
"0.65416914",
"0.65415263",
"0.653731",
"0.65286857",
"0.64949375",
"0.6493163",
"0.6435554",
"0.63877463",
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"0.6334788",
"0.6334788",
"0.6331747",
"0.63296753",
"0.62849206",
"0.62837",
"0.62802374... | 0.5650065 | 68 |
Adds the card in the argument to most suitable group in self.grps | def add_card_to_grps(self, card):
if card.val == self.card_joker.val or card.val == 0:
self.jokers.append(card)
else:
new_grp = True
for grp in self.grps[::-1]:
if len(grp) >= 4:
continue
is_a_set = True
val = grp[0].val
for grp_card in grp:
if grp_card.val != val:
is_a_s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addCard(self,card:Card):\r\n self.cards.append(card)",
"def add_card(self, card):\n self.get_cards().append(card)",
"def add_card(self, card):\r\n self.hand.append(card)",
"def add(self, card):\n if card != None:\n self.cards.append(card)",
"def add_card(self, car... | [
"0.6217423",
"0.61076695",
"0.6085147",
"0.6068018",
"0.60551304",
"0.60551304",
"0.60551304",
"0.60551304",
"0.60133755",
"0.6000306",
"0.59344244",
"0.59153795",
"0.5910493",
"0.5905643",
"0.59050494",
"0.5889543",
"0.58820134",
"0.5878261",
"0.5874322",
"0.5856528",
"0.583... | 0.7281589 | 0 |
Checks if the computer can win with the current lives and sets. | def did_computer_win(self):
num_jokers = len(self.jokers)
# check if pure run exists; try forming pure run from groups in range [1, len(self.grps)] so we can form an impure run from the largest group
# in case we don't find pure groups then check for pure group at self.grps[0]
is_pure_run = False
pure_run_p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_for_win(self):\n slots_available = any(\n [slot.available for slot in self.board.iter_slots() if not slot.mine]\n )\n if not slots_available:\n self.status = GameStatusEnum.won\n self.end_time = datetime.utcnow()",
"def check_win(self):\n re... | [
"0.7037453",
"0.6626272",
"0.65832794",
"0.65387475",
"0.6430472",
"0.6387966",
"0.63278025",
"0.6289832",
"0.6274004",
"0.6270504",
"0.6228436",
"0.62095755",
"0.6208901",
"0.6195936",
"0.61956155",
"0.6182761",
"0.6178062",
"0.6167011",
"0.61247855",
"0.6034586",
"0.59895",... | 0.609196 | 19 |
Makes a decision to choose a card from main deck or the discard pile. Returns True if computer chooses a card from discard_pile. | def make_move(self, dealer, discard_pile_card):
sleep(2)
choose_from_discard = True
# if discard pile is empty, choose from main deck
if discard_pile_card is None:
choose_from_discard = False
else:
# make a list of all the cards that I can use to make sets and lives
cards_needed = []
cards_n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def player_discard(self, inpt):\n \n if inpt.isdigit() == False:\n return 0\n if int(inpt) > len(self.player_hand):\n print(\"\\nNumber of card entered is greater than number of cards\")\n print(\"Please try again \\n\")\n return 0\n if self.p... | [
"0.69175714",
"0.66825265",
"0.65739834",
"0.6558178",
"0.6550377",
"0.65189797",
"0.62760115",
"0.6226463",
"0.60765135",
"0.606275",
"0.5966651",
"0.5925703",
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"0.5850044",
"0.5840374",
"0.5808426",
"0.57955235",
"0.5780476",
"0.5769852",
"0.57402384",
"0.571717... | 0.73254395 | 0 |
String overloading to display the current state of computer. | def __str__(self):
string = "{Jokers: "
for card in self.jokers:
string += str(card)+", "
string += "}"
for i in range(len(self.grps)):
string += ", {group "+str(i+1)+": "
for card in self.grps[i]:
string += str(card)+", "
string += "}"
return string | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def state(self) -> str:",
"def display_state(self):\r\n\r\n print('\\n')\r\n print('>>CURRENT STATE')\r\n ct = 0\r\n for i in self.state:\r\n for j in i:\r\n if j == -1:\r\n val = 'X'\r\n else:\r\n val = st... | [
"0.7629006",
"0.7334032",
"0.72432625",
"0.72137904",
"0.6987641",
"0.6929009",
"0.68520045",
"0.6841385",
"0.6839816",
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"0.66683453",
"0.6668119",
"0.66540915",
"0.6580619",
"0.65587723",
"0.6485935",
"0.6461961... | 0.0 | -1 |
Initializes the model, creates the required layers. | def __init__(self, name, config):
super(RelationalNetwork, self).__init__(name, RelationalNetwork, config)
# Get key mappings.
self.key_feature_maps = self.stream_keys["feature_maps"]
self.key_question_encodings = self.stream_keys["question_encodings"]
self.key_outputs = self.st... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _init_layers(self):\n self._init_predictor()\n if self.use_edge_fusion:\n self._init_edge_module()",
"def initialisation(self):\n self.create_variables()\n self.create_placeholders()\n self.build_model()\n self.reset_lr(None, True)\n self.build_loss... | [
"0.75353396",
"0.7385353",
"0.7351519",
"0.7341105",
"0.73163855",
"0.72941434",
"0.7234489",
"0.72314245",
"0.7228721",
"0.7154608",
"0.7117338",
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"0.68781185",
"0.68505406",
"0.684789",
"0.68447316",
"0.68215775",
"0.67649424",
"0.673... | 0.0 | -1 |
Function returns a dictionary with definitions of input data that are required by the component. | def input_data_definitions(self):
return {
self.key_feature_maps: DataDefinition([-1, self.feature_maps_depth, self.feature_maps_height, self.feature_maps_width], [torch.Tensor], "Batch of feature maps [BATCH_SIZE x FEAT_DEPTH x FEAT_HEIGHT x FEAT_WIDTH]"),
self.key_question_encodings: D... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_inputs(self):\n return {}",
"def get_required_params():\n return {}",
"def input_data_definitions(self):\n return {\n self.key_inputs: DataDefinition(\n [-1]*(self.num_inputs_dims-1) + [self.input_size],\n [list]*(self.num_inputs_dims-1) ... | [
"0.67774713",
"0.67546266",
"0.6709142",
"0.65007764",
"0.6477465",
"0.6333413",
"0.6286046",
"0.6230515",
"0.6222384",
"0.62112576",
"0.6200324",
"0.61986625",
"0.61236846",
"0.60840017",
"0.60487604",
"0.602839",
"0.6024194",
"0.6021029",
"0.6011841",
"0.6009006",
"0.600900... | 0.68205595 | 0 |
Function returns a dictionary with definitions of output data produced the component. | def output_data_definitions(self):
return {
self.key_outputs: DataDefinition([-1, self.output_size], [torch.Tensor], "Batch of outputs [BATCH_SIZE x OUTPUT_SIZE]")
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def output_data_definitions(self):\n return {}",
"def _get_output_dictionary(self):\n\n return_dictionary = {}\n\n for output_path in self.provided_outputs:\n return_dictionary[output_path.full_path] = self.get_value(output_path)\n\n return return_dictionary",
"def output... | [
"0.79142004",
"0.7396703",
"0.72935414",
"0.714347",
"0.68933153",
"0.67715305",
"0.6712582",
"0.6655782",
"0.6595801",
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"0.6554832",
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"0.65083754",
"0.64225125",
"0.6382813",
"0.6379986",
"0.63664275",
"0.6361891",
"0.635461... | 0.7505002 | 1 |
Main forward pass of the model. | def forward(self, data_streams):
# Unpack DataStreams.
feat_m = data_streams[self.key_feature_maps]
enc_q = data_streams[self.key_question_encodings]
# List [FEAT_WIDTH x FEAT_HEIGHT] of tensors [BATCH SIZE x (2 * FEAT_DEPTH + QUESTION_SIZE)]
relational_inputs = []
# Ite... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward(self):\n pass",
"def forward(self):\n pass",
"def forward(self)->None:",
"def forward(self, inputs):\r\n #print (len(inputs))\r\n out = self.fc1(inputs)\r\n out = self.fc2(out)\r\n self.out = out\r\n return out\r\n #raise NotImplementedError... | [
"0.7822591",
"0.7822591",
"0.765289",
"0.7580732",
"0.75480294",
"0.7357169",
"0.73557156",
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"0.7254156",
"0.7254156",
"0.72511846",
"0.72418314",
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"0.72078264",
"0.72078264",
"0.7190793",
"0.71742815",
"0.7124694",
"0.7100066",
"0.7100066... | 0.0 | -1 |
Initializer for an Anchors layer. Args | def __init__(self, size, stride, ratios=None, scales=None, *args, **kwargs):
self.size = size
self.stride = stride
self.ratios = ratios
self.scales = scales
if ratios is None:
self.ratios = np.array([0.5, 1, 2], keras.backend.floatx()),
elif isinstance(rat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, size, stride, ratios=None, scales=None, *args, **kwargs):\n super(Anchors, self).__init__()\n # strides and sizes align with FPN feature outputs (p2-pn)\n self.size = size\n self.stride = stride\n # ratios and scales applied to all feature levels from FPN outpu... | [
"0.6520514",
"0.64596355",
"0.63083297",
"0.6220048",
"0.6083319",
"0.6031503",
"0.5834639",
"0.5821119",
"0.58128357",
"0.579758",
"0.5726126",
"0.57205045",
"0.5690522",
"0.56749034",
"0.56544065",
"0.56527907",
"0.5648447",
"0.56436676",
"0.5642388",
"0.56291455",
"0.56160... | 0.61980695 | 4 |
Get package version (without import the package, which may or may not work) | def get_version():
version_dict = {}
exec(open("src/chimera/version.py").read(), version_dict)
return version_dict['version'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_version():\n return __version__",
"def get_version():\n return __version__",
"def get_version():\n return __version__",
"def _get_package_version():\n file = join(get_root(), 'VERSION')\n\n if exists(file):\n with open(file) as file:\n return file.read()\n\n return... | [
"0.8257111",
"0.8257111",
"0.8257111",
"0.8252309",
"0.8199102",
"0.81818837",
"0.8052985",
"0.8032427",
"0.80265176",
"0.7993858",
"0.791491",
"0.7886565",
"0.78741765",
"0.7870378",
"0.78658634",
"0.7855305",
"0.7855305",
"0.7855305",
"0.7855305",
"0.7855305",
"0.7855305",
... | 0.75629634 | 47 |
Encodes a tree to a single string. | def serialize(self, root):
def dfs(root):
if not root:
res.append('None')
return
res.append(str(root.val))
dfs(root.left)
dfs(root.right)
res = []
dfs(root)
return ','.join(res) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tree_to_string(tree):\n if type(tree) == Tree:\n return sum(list(map(tree_to_string, tree.children)), [])\n else:\n return [str(tree)]",
"def serialize(node, tree=\"\"):\n \n \n if (not node): #Base case\n tree += \"# \"\n return tree\n tree += (str(node.val) + \" ... | [
"0.72254354",
"0.7174656",
"0.70803744",
"0.706792",
"0.7032909",
"0.7030312",
"0.7020698",
"0.69704413",
"0.6961067",
"0.69334626",
"0.692108",
"0.6879218",
"0.6797347",
"0.6790119",
"0.6790119",
"0.6789466",
"0.67704153",
"0.6743968",
"0.6743425",
"0.6740732",
"0.67361337",... | 0.6240413 | 76 |
Decodes your encoded data to tree. | def deserialize(self, data):
def recursiveDeserialize(stringList):
if stringList[0] == 'None':
stringList.pop(0)
return None
root = TreeNode(stringList[0])
stringList.pop(0)
root.left = recursiveDeserialize(str... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deserialize(self, data):\n # if data:\n # root = TreeNode(data.pop(0))\n # # root.val = data.pop\n if not data:\n return None\n data = data.split(' ')\n data = iter(data)\n\n def resucsbuild():\n try:\n val = next(dat... | [
"0.75939554",
"0.7463827",
"0.7094851",
"0.7064511",
"0.705998",
"0.7053173",
"0.7051886",
"0.7046027",
"0.70459527",
"0.7039219",
"0.69926393",
"0.6987736",
"0.69776005",
"0.6933219",
"0.6915955",
"0.69115806",
"0.68922126",
"0.68773776",
"0.68404627",
"0.68312407",
"0.68175... | 0.6754275 | 25 |
CREATING SIMPLE HASH FOR STRING SEGMENT | def hash(self, text):
hashval = 0
for i in xrange(0, len(text)):
hashval += ord(text[i])**i
return hashval | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hashing_info(string):#KEY HASHING FUNCTION\n nodeInfo = string.encode('utf-8')\n\n #md5 -> 2^7 = 128 bits\n hash_object = hashlib.md5()\n hash_object.update(nodeInfo)\n\n tmp = hash_object.hexdigest()\n tmp = int(tmp,16)\n\n result = tmp >> (128-16)\n return result",
"def hash(self) -... | [
"0.60646164",
"0.6036968",
"0.60261166",
"0.59612304",
"0.58949095",
"0.5887943",
"0.5800185",
"0.5748991",
"0.57090807",
"0.57031983",
"0.57023823",
"0.56927955",
"0.5658673",
"0.5615038",
"0.56005543",
"0.5593664",
"0.5588162",
"0.55861115",
"0.55836374",
"0.555482",
"0.554... | 0.0 | -1 |
COMPARES HASHES AND IF MATCHES COMPARES STRINGS | def comparison(self):
for i in xrange(len(self.string)-len(self.substring)+1):
if self.hash(self.string[i:i+len(self.substring)]) == self.hash(self.substring):
if self.string[i:i+len(self.substring)] == self.substring:
return i
return -1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compare_str(person, STRs):\n for key in STRs.keys():\n if (STRs[key] != (int(person[key]))):\n return False\n return True",
"def test_diff_inputs_diff_hash(self):\n # same strings, different salts\n self.assertNotEqual(\n hash_str(\"mystring\", salt=\"mysalt1\... | [
"0.6531683",
"0.6423325",
"0.6307448",
"0.6179165",
"0.61229",
"0.6099527",
"0.60961074",
"0.59638083",
"0.5948264",
"0.5800117",
"0.5774916",
"0.5744077",
"0.57222134",
"0.5716174",
"0.57060426",
"0.5671249",
"0.5648766",
"0.5632812",
"0.56064326",
"0.5602532",
"0.56011814",... | 0.565158 | 16 |
GETTING INPUT FROM USER | def get_input(self):
print(" String --> ", end='')
self.string = str(raw_input())
print(" Substring --> ", end='')
self.substring = str(raw_input())
return self.string, self.substring | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_input(user_input):\n return input(user_input)",
"def get_user_text_input(self):\n\t\tuser_input = raw_input('You: ')\n\t\treturn user_input",
"def get_user_input(self, msg):\n resp = input(msg)\n return resp",
"def getInput(self):\n self.userInput = self.entry.get()",
"def r... | [
"0.805252",
"0.7764863",
"0.7683693",
"0.7553891",
"0.74886185",
"0.7484124",
"0.7484124",
"0.7364146",
"0.7330842",
"0.7302058",
"0.72245455",
"0.72006255",
"0.7149695",
"0.70963407",
"0.7026797",
"0.7021885",
"0.70192075",
"0.7012941",
"0.7007525",
"0.69753206",
"0.6921373"... | 0.63883954 | 83 |
SEARCHING STRING FOR SUBSTRING | def search(self):
if self.substring in [None, ""]:
print("Invalid Value For Substring")
elif self.string in [None, ""]:
print("Invalid Value For String")
elif len(self.substring) > len(self.string):
print("Length of Substring Less Than String")
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_sub_string_index(self, sub):\n try:\n return self.__dna.index(sub)\n except ValueError:\n raise ValueError",
"def kmp_search(full_str, sub_str):\n n, m = len(full_str), len(sub_str)\n result = []\n pi = get_partial_match(sub_str)\n begin, matched = 0, 0\n ... | [
"0.68273073",
"0.67984945",
"0.65509725",
"0.65256196",
"0.64502376",
"0.6448377",
"0.6401995",
"0.633604",
"0.6309308",
"0.6277728",
"0.6256694",
"0.6214137",
"0.61992896",
"0.61225504",
"0.6088924",
"0.60244846",
"0.59980816",
"0.59627664",
"0.5954518",
"0.59534174",
"0.592... | 0.6877445 | 0 |
Arguments as input to search | def main():
try:
string = sys.argv[1]
substring = sys.argv[2]
except IndexError:
string = None
substring = None
try:
sys.argv[3]
except IndexError:
pass
else:
print(" More than expected Number of Arguments")
string = None
su... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search(self, *args, **kwargs):",
"def search(self, *args, **kwargs): # real signature unknown\n pass",
"def search():\n pass",
"def _processArgsToLogic_search(args, stdout, stderr) :\n\n if args.forceDownload :\n args.download = True\n # Initiliaze action flags\n args.actionFlag... | [
"0.82235056",
"0.7940697",
"0.7333606",
"0.71696407",
"0.69658285",
"0.68828195",
"0.6845856",
"0.6817347",
"0.6728852",
"0.667633",
"0.6644063",
"0.6619193",
"0.6605867",
"0.6574141",
"0.656597",
"0.65625703",
"0.65579206",
"0.65326357",
"0.636116",
"0.6324944",
"0.6296517",... | 0.0 | -1 |
Return ratio of cpu_dt / gpu_dt, which must be nonnegative numbers. If both arguments are zero, return NaN. If only gpu_dt is zero, return Inf. | def advantage(cpu_dt, gpu_dt):
assert gpu_dt >= 0 and cpu_dt >= 0
if gpu_dt == 0 and cpu_dt == 0:
return numpy.nan
elif gpu_dt == 0:
return numpy.inf
else:
return cpu_dt / gpu_dt | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def advantage(cpu_dt, gpu_dt):\r\n assert gpu_dt >= 0 and cpu_dt >= 0\r\n if gpu_dt == 0 and cpu_dt == 0:\r\n return numpy.nan\r\n elif gpu_dt == 0:\r\n return numpy.inf\r\n else:\r\n return cpu_dt / gpu_dt",
"def _get_cpu_percent(self):\n cpu_delta = None\n total_d... | [
"0.8289752",
"0.57311285",
"0.56998044",
"0.5637458",
"0.56284297",
"0.56284297",
"0.56284297",
"0.5575685",
"0.5518271",
"0.5502139",
"0.54596716",
"0.5448909",
"0.54423946",
"0.53975004",
"0.5327086",
"0.5315263",
"0.5311589",
"0.5273094",
"0.52623266",
"0.5258038",
"0.5258... | 0.8366045 | 0 |
The fct k_elemwise_unary_rowmajor_copy(used by cuda.copy()) in cuda_ndarray.cu is not well compiled with nvcc 3.0 and 3.1 beta. We found a workaround, so it sould work correctly. Without the workaround, this test fail. | def test_nvcc_bug():
shape = (5, 4)
aa = theano._asarray(numpy.random.rand(*shape), dtype='float32')
a = aa[::, ::-1]
b = cuda_ndarray.CudaNdarray(aa)[::, ::-1]
c = copy.copy(b)
d = copy.deepcopy(b)
assert numpy.allclose(a, numpy.asarray(b))
assert numpy.allclose(a, numpy.asarray(c))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_nvcc_bug():\r\n shape = (5, 4)\r\n aa = theano._asarray(numpy.random.rand(*shape), dtype='float32')\r\n a = aa[::, ::-1]\r\n\r\n b = cuda_ndarray.CudaNdarray(aa)[::, ::-1]\r\n c = copy.copy(b)\r\n d = copy.deepcopy(b)\r\n\r\n assert numpy.allclose(a, numpy.asarray(b))\r\n assert nu... | [
"0.72793794",
"0.6675478",
"0.6635744",
"0.6531311",
"0.641663",
"0.6413818",
"0.63276094",
"0.6325242",
"0.63044846",
"0.6269978",
"0.6264248",
"0.6245012",
"0.6241492",
"0.61380696",
"0.6119953",
"0.6025284",
"0.5999566",
"0.59449136",
"0.58952457",
"0.5869716",
"0.58495384... | 0.72629005 | 1 |
Now we don't automatically add dimensions to broadcast | def test_setitem_rightvalue_ndarray_fails():
a = numpy.arange(3 * 4 * 5)
a.resize((3, 4, 5))
a = theano._asarray(a, dtype='float32')
_a = cuda_ndarray.CudaNdarray(a)
b = theano._asarray([7, 8, 9, 10], dtype='float32')
_b = cuda_ndarray.CudaNdarray(b)
b5 = theano._asarray([7, 8, 9, 10, 11], ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_broadcast_dims():\r\n test((1, 2, 3))\r\n test((2, 1, 3))\r\n test((2, 3, 1))\r\n test2((1, 2, 3))\r\n test2((2, 1, 3))\r\n test2((2, 3, 1))",
"def broadcast() -> BroadcastDistribute:\n return _broadcast",
"def test_unbroadcast_addbroadcast(self):\r\n\r\n x = matrix()\r\n ... | [
"0.7132095",
"0.65440637",
"0.6465861",
"0.64307284",
"0.6218499",
"0.60986507",
"0.60443056",
"0.60276175",
"0.60101485",
"0.59697133",
"0.59408414",
"0.5924509",
"0.59179515",
"0.5914455",
"0.5913395",
"0.58950126",
"0.5822593",
"0.5822593",
"0.5752906",
"0.57098573",
"0.57... | 0.0 | -1 |
Runs the main loop of GA. | def mainGA(NAME, target_output, target_image):
global toolbox
print("Target image: {0} Target output: {1}".format(target_image, target_output))
sys.stdout.flush()
model = load_model(NAME)
fit = Fitness(NAME, model, target_image, target_output)
#Genetic operators
toolbox.register... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n GLib.MainLoop().run()",
"def run():\n main()",
"async def _main(self):\n while True:\n time.sleep(1)",
"def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAge... | [
"0.71880895",
"0.697151",
"0.68249995",
"0.6757761",
"0.6749307",
"0.67318606",
"0.67318606",
"0.6720229",
"0.670469",
"0.66957825",
"0.66361976",
"0.6631058",
"0.662005",
"0.65912545",
"0.65658116",
"0.65643406",
"0.6554115",
"0.6528591",
"0.6504754",
"0.6492748",
"0.6475534... | 0.0 | -1 |
This AWS Lambda function is invoked manually or by some other service or APIs. Then this function puts an event to an Amazon EventBridge. | def lambda_handler(event, context):
client = boto3.client('events')
event_to_put = {
"source": "aws-lambda-function"
}
event_to_put.update(**event)
try:
response = client.put_events(
Entries=[
{
'Source': 'learn.eventbridge',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_event(event: dict):\n\n eventbridge.put_events(Entries=[event])",
"def event(event, context):\n# Sample event:\n #\n # _event = { \"Records\":[\n # {\n # \"eventVersion\":\"2.1\",\n # \"eventSource\":\"aws:s3\",\n # \"awsRegion\":\"us-east-1\",\n ... | [
"0.68858373",
"0.6696065",
"0.66646093",
"0.66366494",
"0.6604961",
"0.6559077",
"0.6559077",
"0.6540618",
"0.6514244",
"0.65035176",
"0.64954466",
"0.64871466",
"0.64862555",
"0.6477732",
"0.6477732",
"0.6477732",
"0.6477732",
"0.6477732",
"0.6477732",
"0.6477732",
"0.647773... | 0.7800985 | 0 |
Correlate each feature in X, with y (some set of dummmy coded labels). | def correlateX(X, y, corr="spearman"):
X = np.array(X)
y = np.array(y)
## Force... just in case
checkX(X)
if corr == "pearson":
corrf = pearsonr
elif corr == "spearman":
corrf = spearmanr
else:
raise ValueError("stat was not valid.")
corrs = []... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _c_correlation(cls, X, y):\n su = np.zeros(X.shape[1])\n for i in np.arange(X.shape[1]):\n su[i] = cls._symmetrical_uncertainty(X[:, i], y)\n return su",
"def categorical_correlation(\n feature, target, dataframe, groupfunc, x_label=None, y_label=None\n):\n # Group the d... | [
"0.6572823",
"0.6219852",
"0.59368616",
"0.59086794",
"0.5891101",
"0.5826224",
"0.58046436",
"0.57727814",
"0.57616276",
"0.5715039",
"0.57040733",
"0.567008",
"0.5661006",
"0.56605536",
"0.5588689",
"0.5544566",
"0.5544468",
"0.543654",
"0.54303247",
"0.5425722",
"0.540244"... | 0.6124404 | 2 |
Create a desgin matrix from y, an array of integers. | def _create_dm(y, window):
pad = np.zeros(window, dtype=np.int)
y = np.concatenate([y, pad])
unique_y = np.unique(y)[np.unique(y) != 0]
dm = np.zeros((y.shape[0], window * unique_y.shape[0]), dtype=np.int)
for t in unique_y:
idx_h_a = np.arange(unique_y.shape[0])[unique_y == t] * window
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _to_matrix(self, y):\n if hasattr(y, \"shape\"):\n if len(y.shape) == 1:\n if isinstance(y, (pd.Series, pd.DataFrame)):\n y = y.to_numpy()\n y = y.reshape([-1, 1])\n else:\n y = np.array(y).reshape([-1, 1])\n\n return y... | [
"0.6806305",
"0.63377404",
"0.62698895",
"0.61298615",
"0.6084778",
"0.60796237",
"0.60313547",
"0.5960779",
"0.5950431",
"0.5856467",
"0.58421",
"0.58394974",
"0.5760737",
"0.5756875",
"0.575276",
"0.5740138",
"0.5682571",
"0.5678343",
"0.5664396",
"0.565078",
"0.5645922",
... | 0.55213827 | 27 |
Average trials for each feature in X, using Burock's (2000) method. | def fir(X, y, trial_index, window, tr):
# Norm then pad.
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X.astype(np.float))
X = np.vstack([X, np.ones((window, X.shape[1]), dtype=np.float)])
# Save the org y names
ynames = sorted(np.unique(y))
ynames = unique_sorted_wi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def score_samples(self, X):\n ...",
"def calculate_average_precision(class_name='', current_neuron_index=current_neuron_index, acts=acts,\n no_files_in_label=no_files_in_label, verbose=verbose, minx='',Q_stop=''):\n #\n current_neuron = acts.get_activations_for_neuron(... | [
"0.6152321",
"0.60114884",
"0.6004951",
"0.59376615",
"0.5898992",
"0.5895975",
"0.5889211",
"0.5870478",
"0.58671063",
"0.58182806",
"0.58060247",
"0.57809657",
"0.57648313",
"0.57583666",
"0.5748872",
"0.574189",
"0.57366794",
"0.5733893",
"0.572314",
"0.57068515",
"0.56871... | 0.0 | -1 |
Average trials for each feature in X | def eva(X, y, trial_index, window, tr):
evas = []
eva_names = []
scaler = MinMaxScaler(feature_range=(0, 1))
for j in range(X.shape[1]):
Xtrials = []
xj = X[:,j][:,np.newaxis] ## Need 2D
# Each feature into trials, rescale too
Xtrial, feature_names = by_trial(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def class_average(X):\n\t# compute average row vector\n\tmean_vector = np.mean(X, axis = 0)\n\treturn(mean_vector)",
"def calculate_mean_average_precision(class_name='', current_neuron_index=current_neuron_index, acts=acts, verbose=verbose, minx=0.000000001):\n #\n current_neuron = acts.get_activations_for... | [
"0.64128476",
"0.6214273",
"0.61815697",
"0.6136997",
"0.60902673",
"0.6084845",
"0.595498",
"0.5950753",
"0.5950473",
"0.5936966",
"0.59339607",
"0.593313",
"0.5920305",
"0.5912948",
"0.59116477",
"0.5895597",
"0.58873343",
"0.58857715",
"0.5872557",
"0.5864992",
"0.5842531"... | 0.5755531 | 33 |
Calculate band according to Stavenga et al (1993). | def stavenga1993_band_calculation(
x: np.ndarray, a: Union[float, np.ndarray], b: Union[float, np.ndarray]
) -> np.ndarray:
return np.exp(-a * x**2 * (1 + b * x + 3 / 8 * (b * x) ** 2)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_band(value, bands):\n for band in bands:\n if band > value:\n return band",
"def bands(self) -> int:\n ...",
"def calbands( band = 0, tmo = 30 ) :\n optimizeThresholds(band,tmo)\n flattenPhases(band,tmo)\n calibrateSpectra(band=band,tmo=tmo)",
"def DrawBands... | [
"0.6755474",
"0.6549916",
"0.6544884",
"0.62848055",
"0.6246133",
"0.62358135",
"0.62323934",
"0.61631835",
"0.61161864",
"0.6114344",
"0.608871",
"0.60167605",
"0.60042655",
"0.5974235",
"0.5936892",
"0.5935502",
"0.5911872",
"0.5887876",
"0.585313",
"0.58207893",
"0.5817405... | 0.71052694 | 0 |
Create Gaussian filter template normalized to the max. | def gaussian_template(
wavelengths: np.ndarray,
mean: Union[float, np.ndarray],
std: Union[float, np.ndarray] = 30.0,
) -> np.ndarray:
y = norm.pdf(wavelengths, mean, std)
return y / np.max(y, axis=-1, keepdims=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_gaussian_filter(size, sigma):\n h = size[0] #height of the template\n w = size[1] #width of the template \n if h % 2 == 0: h += 1 #add 1 if dimensions are even\n if w % 2 == 0: w += 1\n x = math.floor(h/2)\n y = math.floor(w/2) \n sum = 0\n #create ou... | [
"0.7409861",
"0.68018997",
"0.67854315",
"0.65037984",
"0.6479504",
"0.63791597",
"0.6300146",
"0.62202877",
"0.6173434",
"0.6151084",
"0.61396915",
"0.61290395",
"0.61198384",
"0.60909826",
"0.6076701",
"0.6069273",
"0.6066046",
"0.6016382",
"0.59799224",
"0.5979191",
"0.597... | 0.6193196 | 8 |
Calculate opsin template according to Stavenga et al (1993). | def stavenga1993_template(
wavelengths: np.ndarray,
alpha_max: Union[float, np.ndarray],
a_alpha: Union[float, np.ndarray] = 380.0,
b_alpha: Union[float, np.ndarray] = 6.09,
beta_max: Union[float, np.ndarray] = 350.0,
A_beta: Union[float, np.ndarray] = 0.29,
a_beta: Union[float, np.ndarray] ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def produce_13TeV_template(tag_name=\"HKHI\"):\n num_rebin = 1\n file_name = \"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\"+tag_name+\"_Lin.root\"\n print \"Input: \", file_name\n fin = ROOT.TFile.Open(file_name, \"read\")\n h_nom = fin.Get(\"bkg_total_gg_full\").Clone(\"bkg_nominal_old... | [
"0.63073283",
"0.56289643",
"0.5594479",
"0.5521098",
"0.54792696",
"0.54278696",
"0.53754073",
"0.5372962",
"0.5349426",
"0.53242576",
"0.52706194",
"0.52683187",
"0.5263925",
"0.52538013",
"0.52045524",
"0.516755",
"0.5156881",
"0.51513654",
"0.5134924",
"0.5134404",
"0.513... | 0.0 | -1 |
Calculate Opsin template according to Govardovskii et al (2000). | def govardovskii2000_template(
wavelengths: np.ndarray,
alpha_max: Union[float, np.ndarray],
A_alpha: Union[float, np.ndarray] = 69.7,
a_alpha1: Union[float, np.ndarray] = 0.8795,
a_alpha2: Union[float, np.ndarray] = 0.0459,
a_alpha3: Union[float, np.ndarray] = 300.0,
a_alpha4: Union[float, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def produce_13TeV_template(tag_name=\"HKHI\"):\n num_rebin = 1\n file_name = \"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\"+tag_name+\"_Lin.root\"\n print \"Input: \", file_name\n fin = ROOT.TFile.Open(file_name, \"read\")\n h_nom = fin.Get(\"bkg_total_gg_full\").Clone(\"bkg_nominal_old... | [
"0.61078596",
"0.5807358",
"0.565082",
"0.5624882",
"0.5605698",
"0.5569253",
"0.5561828",
"0.5541212",
"0.5455167",
"0.54004294",
"0.53838813",
"0.5322327",
"0.53214216",
"0.531339",
"0.52193123",
"0.5200469",
"0.5189568",
"0.5186714",
"0.5178152",
"0.5171504",
"0.51658136",... | 0.0 | -1 |
Set up private and public key into DiffieHellman object. | def setup_keys(self, dh_object, public_key, private_key):
public_numbers = DHPublicNumbers(public_key, dh_object.parameter_numbers)
private_numbers = DHPrivateNumbers(private_key, public_numbers)
dh_object.private_key = private_numbers.private_key(default_backend()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self._keypair = RSA.generate(2048)\n self.public_key = self._keypair.publickey().exportKey()",
"def setUp(self):\n\n self.private_key = self.get_new_key()\n self.public_key = self.private_key.public_key()\n\n self.pem_private_key = self.private_key.private... | [
"0.6717525",
"0.6687376",
"0.6348768",
"0.6244429",
"0.61797005",
"0.6172647",
"0.604477",
"0.59947807",
"0.59289205",
"0.59186375",
"0.59086144",
"0.5873395",
"0.5848099",
"0.5782095",
"0.5766761",
"0.5744683",
"0.5743434",
"0.5717811",
"0.5714252",
"0.56985873",
"0.5693162"... | 0.7158014 | 0 |
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