def main(): input_data = open('input') basic_fuel_mass = calculate_mass_fuel(prepare_data(input_data), CalculationMethods.SIMPLE) total_fuel_mass = calculate_mass_fuel(prepare_data(input_data), CalculationMethods.TOTAL) print(f"{basic_fuel_mass=}") print(f"{total_fuel_mass=}")
def main(): geo_data, field_data = prepare_data(sys.argv[1:], chosen_times = ['0.0', '12.0']) conv = calc_convergence(geo_data, field_data, '12.0', solution) plot_data = [] norm = 'L2' field = 'psi' stat_file = open('moving_vort_conv.txt', 'w') save_conv(geo_data, conv, 'Moving vortices', norm, field, stat_file) for opt in conv.keys(): nys, errs = zip(*sorted(conv[opt][norm][field].items())) plot_data.append((nys, errs, opt)) ord_data = [] ord2 = lambda n : 2e-0 * (n / 24.) ** (-2) ny2 = np.array([300, 900]) nyt = 450 ord_data.append((ny2, ord2(ny2), nyt, ord2(nyt+10), '2nd order', -94 - 180 / pi * np.arctan(-2))) ord3 = lambda n : 1e-1 * (n / 24.) ** (-3) ny3 = np.array([300, 900]) nyt = 480 ord_data.append((ny3, ord3(ny3), nyt, ord3(nyt+20), '3rd order', -113 - 180 / pi * np.arctan(-3))) conv_plot(plot_data, ord_data, fname = 'moving_vort_conv.pdf') panel_plot(geo_data, field_data, opt = 'nug|abs|div_2nd|div_3rd', time = '12.0', ny = 192, fname = 'moving_vort_panel.pdf')
def main(): geo_data, field_data = prepare_data(sys.argv[1:]) conv = calc_convergence(geo_data, field_data, '1.0', solution) plot_data = [] norm = 'L2' field = 'psi' stat_file = open('manufactured_3d_conv.txt', 'w') save_conv(geo_data, conv, 'Manufactured solution in 3D', norm, field, stat_file) for opt in conv.keys(): nys, errs = zip(*sorted(conv[opt][norm][field].items())) plot_data.append((nys, errs, opt)) ord_data = [] ord2 = lambda n: 3e-2 * (n / float(9))**(-2) ny2 = np.array([40, 140]) nyt = 70 ord_data.append((ny2, ord2(ny2), nyt, ord2(nyt + 4), '2nd order', -92 - 180 / np.pi * np.arctan(-2))) ord3 = lambda n: 16e-3 * (n / float(9))**(-3) ny3 = np.array([40, 140]) nyt = 70 ord_data.append((ny3, ord3(ny3), nyt, ord3(nyt + 4), '3rd order', -110 - 180 / np.pi * np.arctan(-3))) conv_plot(plot_data, ord_data, fname='manufactured_3d_conv.pdf')
def main(): geo_data, field_data = prepare_data(sys.argv[1:]) conv = calc_convergence(geo_data, field_data, '1.0', solution) plot_data = [] norm = 'L2' field = 'psi' stat_file = open('manufactured_3d_conv.txt', 'w') save_conv(geo_data, conv, 'Manufactured solution in 3D', norm, field, stat_file) for opt in conv.keys(): nys, errs = zip(*sorted(conv[opt][norm][field].items())) plot_data.append((nys, errs, opt)) ord_data = [] ord2 = lambda n : 3e-2 * (n / float(9)) ** (-2) ny2 = np.array([40, 140]) nyt = 70 ord_data.append((ny2, ord2(ny2), nyt, ord2(nyt+4), '2nd order', -92 - 180 / np.pi * np.arctan(-2))) ord3 = lambda n : 16e-3 * (n / float(9)) ** (-3) ny3 = np.array([40, 140]) nyt = 70 ord_data.append((ny3, ord3(ny3), nyt, ord3(nyt+4), '3rd order', -110 - 180 / np.pi * np.arctan(-3))) conv_plot(plot_data, ord_data, fname = 'manufactured_3d_conv.pdf')
def main(): geo_data, field_data = prepare_data(sys.argv[1:]) conv = calc_convergence(geo_data, field_data, '5.0', solution) nys = [120, 240] mixing_diags = calc_mixing_diags(geo_data, field_data, nys) filament_diags = calc_filament_diags(geo_data, field_data, nys) for ny in nys: for opt in conv.keys(): write_stats(conv, mixing_diags, filament_diags, opt, ny)
def main(): geo_data, field_data = prepare_data(sys.argv[1:], chosen_times=['0.0', '2.5', '5.0']) conv = calc_convergence(geo_data, field_data, '5.0', solution) plot_data = [] norm = 'L2' field = 'gh' for opt in conv.keys(): nys, errs = zip(*sorted(conv[opt][norm]['gh'].items())) plot_data.append((nys, errs, opt)) stat_file = open('reversing_deform_conv.txt', 'w') save_conv(geo_data, conv, 'Reversing deformational flow', norm, field, stat_file) stat_file.close() ord_data = [] ord2 = lambda n: 1.5e-1 * (n / 120.)**(-2) ny2 = np.array([400, 1000]) nyt = 580 ord_data.append((ny2, ord2(ny2), nyt, ord2(nyt + 10), '2nd order', -95 - 180 / np.pi * np.arctan(-2))) ord3 = lambda n: 1e-1 * (n / 120.)**(-3) ny3 = np.array([400, 1000]) nyt = 640 ord_data.append((ny3, ord3(ny3), nyt, ord3(nyt + 20), '3rd order', -115 - 180 / np.pi * np.arctan(-3))) conv_plot(plot_data, ord_data, fname='reversing_deform_conv.pdf') panel_plot(field_data[120]['nug|iga|div_2nd|div_3rd|fct']['2.5'], fname='reversing_deform_panel.pdf') mixing_ny = 240 mixing_diags = calc_mixing_diags(geo_data, field_data, nys=[mixing_ny]) stat_file = open('reversing_deform_mixing.txt', 'w') save_mixing_diags(mixing_diags, mixing_ny, stat_file) stat_file.close() plot_mixing(field_data, mixing_diags, mixing_ny, fname='reversing_deform_mixing.pdf')
def main(): geo_data, field_data = prepare_data(sys.argv[1:], chosen_times = ['0.0', '2.5', '5.0']) conv = calc_convergence(geo_data, field_data, '5.0', solution) plot_data = [] norm = 'L2' field = 'gh' for opt in conv.keys(): nys, errs = zip(*sorted(conv[opt][norm]['gh'].items())) plot_data.append((nys, errs, opt)) stat_file = open('reversing_deform_conv.txt', 'w') save_conv(geo_data, conv, 'Reversing deformational flow', norm, field, stat_file) stat_file.close() ord_data = [] ord2 = lambda n : 1.5e-1 * (n / 120.) ** (-2) ny2 = np.array([400, 1000]) nyt = 580 ord_data.append((ny2, ord2(ny2), nyt, ord2(nyt+10), '2nd order', -95 - 180 / np.pi * np.arctan(-2))) ord3 = lambda n : 1e-1 * (n / 120.) ** (-3) ny3 = np.array([400, 1000]) nyt = 640 ord_data.append((ny3, ord3(ny3), nyt, ord3(nyt+20), '3rd order', -115 - 180 / np.pi * np.arctan(-3))) conv_plot(plot_data, ord_data, fname = 'reversing_deform_conv.pdf') panel_plot(field_data[120]['nug|iga|div_2nd|div_3rd|fct']['2.5'], fname = 'reversing_deform_panel.pdf') mixing_ny = 240 mixing_diags = calc_mixing_diags(geo_data, field_data, nys = [mixing_ny]) stat_file = open('reversing_deform_mixing.txt', 'w') save_mixing_diags(mixing_diags, mixing_ny, stat_file) stat_file.close() plot_mixing(field_data, mixing_diags, mixing_ny, fname = 'reversing_deform_mixing.pdf')
def main(): geo_data, field_data = prepare_data(sys.argv[1:], chosen_times=['0.0', '12.0']) conv = calc_convergence(geo_data, field_data, '12.0', solution) plot_data = [] norm = 'L2' field = 'psi' stat_file = open('moving_vort_conv.txt', 'w') save_conv(geo_data, conv, 'Moving vortices', norm, field, stat_file) for opt in conv.keys(): nys, errs = zip(*sorted(conv[opt][norm][field].items())) plot_data.append((nys, errs, opt)) ord_data = [] ord2 = lambda n: 2e-0 * (n / 24.)**(-2) ny2 = np.array([300, 900]) nyt = 450 ord_data.append((ny2, ord2(ny2), nyt, ord2(nyt + 10), '2nd order', -94 - 180 / pi * np.arctan(-2))) ord3 = lambda n: 1e-1 * (n / 24.)**(-3) ny3 = np.array([300, 900]) nyt = 480 ord_data.append((ny3, ord3(ny3), nyt, ord3(nyt + 20), '3rd order', -113 - 180 / pi * np.arctan(-3))) conv_plot(plot_data, ord_data, fname='moving_vort_conv.pdf') panel_plot(geo_data, field_data, opt='nug|abs|div_2nd|div_3rd', time='12.0', ny=192, fname='moving_vort_panel.pdf')
def main(): """""" hp.download_files(cf.URLS, cf.TMP_DL_DIR) hp.decompress_files(cf.TMP_DL_DIR) numbers = hp.prepare_data(cf.TMP_DL_DIR) hp.load_db(numbers, cf.DB_PATH, cf.DB_NAME)
from sklearn.ensemble import GradientBoostingClassifier from sklearn.model_selection import cross_val_score from helpers import plot_feature_importance, plot_partial_dependence, prepare_data from collections import Counter import matplotlib.pyplot as plt path = r'data\dataset_telecom_01.csv' X, y, df, y_df = prepare_data(path) clf = GradientBoostingClassifier(n_estimators=200, learning_rate=0.1, subsample=0.5, max_depth=2, random_state=0) scores = cross_val_score(clf, X, y, cv=5) print(scores) # check if model has learned any pattern clf = GradientBoostingClassifier(n_estimators=200, learning_rate=0.1, subsample=0.5, max_depth=2, random_state=0).fit(X, y) plot_feature_importance(clf, df.columns, top_most_important=7) # show top features which had influence on churn plot_partial_dependence(clf, X, df.columns, ['Factor_03']) # Dependence of feature and churn plot_partial_dependence(clf, X, df.columns, ['MonthlyCharges']) # Dependence of feature and churn for col in df.columns: plot_partial_dependence(clf, X, df.columns, ['C_03_Electronic check'] + [col]) # Dependence of feature and churn
# If a pre-trained ResNet is required, load the weights. # This must be done AFTER the variables are initialized with sess.run(tf.global_variables_initializer()) if init_fn is not None: init_fn(sess) # Load a previous checkpoint if desired model_checkpoint_name = "checkpoints_1/latest_model_" + args.model + "_" + args.dataset + ".ckpt" if args.continue_training or not args.mode == "train": print('Loaded latest model checkpoint') saver.restore(sess, model_checkpoint_name) avg_scores_per_epoch = [] # Load the data print("Loading the data ...") train_input_names,train_output_names, val_input_names, val_output_names, test_input_names, test_output_names = helpers.prepare_data(args.dataset) ##-------------------------------------------------------------------------------------------------## if args.mode == "train": print("\n***** Begin training *****") print("Dataset -->", args.dataset) print("Model -->", args.model) print("Crop Height -->", args.crop_height) print("Crop Width -->", args.crop_width) print("Num Epochs -->", args.num_epochs) print("Batch Size -->", args.batch_size) print("Num Classes -->", num_classes) print("Class Balancing -->", args.class_balancing) print("Learning Rate -->", args.learn_rate) print("")
from helpers import prepare_data, graph_overfitting import numpy as np from sklearn.ensemble import GradientBoostingRegressor from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error import matplotlib.pyplot as plt path = r'data\dataset_telecom_01.csv' X, y, df, _ = prepare_data(path) months = df.pop("Factor_03") df['MonthlyCharges'] = np.log(df['MonthlyCharges'] + 1) X = df.values y_months = months.values ### Predicting after how many months a client will churn ### X_train, X_test, y_train, y_test = train_test_split(X, y_months, test_size=0.4, random_state=0) params = { 'n_estimators': 500, 'max_depth': 2, 'learning_rate': 0.01, 'subsample': 0.5, 'loss': 'ls' } reg_model = GradientBoostingRegressor(**params) reg_model.fit(X_train, y_train)
] # Replace values in banking_crisis with boolean values crises_df = crises_df.replace( {"banking_crisis": { "crisis": 1, "no_crisis": 0 }}) crises_df = crises_df[crises_df["year"] > 1957] # Gather all boolean crises from after 1957 return crises_df[crises_cols + ["cc3"]], crises_df["cc3"].unique() # Gather our dataframe and the countries we have minus the portuguese colonies crises_df, ccs = prepare_data() ccs = [cc for cc in ccs if get_colonist(cc) != "PRT"] # Plot the bar plots that vizualise the data the mwu-test is performed over plot_ranks(crises_df, ccs) # Perform the mann whitney u test for each tail for alternative in ["less", "greater"]: for crisis in crises_df: # Skip over country code column if crisis == "cc3": continue fra_sample, gbr_sample = [], [] # Gather each countries data for cc in ccs: