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How to use test_diff method in avocado

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test_omni_hro.py

Source:test_omni_hro.py

1"""Unit tests for OMNI HRO special functions."""2import datetime as dt3import numpy as np4import pandas as pds5import pysat6from pysatNASA.instruments import omni_hro7class TestOMNICustom(object):8    """Unit tests for `pysat.instrument.methods.cdaweb`."""9    def setup(self):10        """Set up the unit test environment for each method."""11        # Load a test instrument12        self.testInst = pysat.Instrument('pysat', 'testing', tag='',13                                         num_samples=12, clean_level='clean')14        self.testInst.load(2009, 1)15        # Recast time in minutes rather than seconds16        self.testInst.data.index = \17            pds.Series([t + dt.timedelta(seconds=(60 - i))18                        + dt.timedelta(minutes=i)19                        for i, t in enumerate(self.testInst.data.index)])20        # Add IMF data21        self.testInst['BX_GSM'] = pds.Series([3.17384966, 5.98685138,22                                              1.78749668, 0.38628409,23                                              2.73080263, 1.58814078,24                                              5.24880448, 3.92347300,25                                              5.59494670, 0.93246592,26                                              5.23676319, 1.14214992],27                                             index=self.testInst.data.index)28        self.testInst['BY_GSM'] = pds.Series([3.93531272, 2.50331246,29                                              0.99765539, 1.07203600,30                                              5.43752734, 5.10629137,31                                              0.59588891, 2.19412638,32                                              0.15550858, 3.75433603,33                                              4.82323932, 3.61784563],34                                             index=self.testInst.data.index)35        self.testInst['BZ_GSM'] = pds.Series([3.94396168, 5.61163579,36                                              4.02930788, 5.47347958,37                                              5.69823962, 0.47219819,38                                              1.47760461, 3.47187188,39                                              4.12581021, 4.40641671,40                                              2.87780562, 0.58539121],41                                             index=self.testInst.data.index)42        self.testInst['flow_speed'] = \43            pds.Series([394.396168, 561.163579,44                        402.930788, 547.347958,45                        569.823962, 47.219819,46                        147.760461, 347.187188,47                        412.581021, 440.641671,48                        287.780562, 58.539121],49                       index=self.testInst.data.index)50        return51    def teardown(self):52        """Clean up the unit test environment after each method."""53        del self.testInst54        return55    def test_clock_angle(self):56        """Test results of calculate_clock_angle."""57        # Run the clock angle routine58        omni_hro.calculate_clock_angle(self.testInst)59        # Set test clock angle60        test_angle = np.array([44.93710732, 24.04132437, 13.90673288,61                               11.08167359, 43.65882745, 84.71666707,62                               21.96325222, 32.29174675, 2.15855047,63                               40.43151704, 59.17741091, 80.80882619])64        # Test the difference.  There may be a 2 pi integer ambiguity65        test_diff = abs(test_angle - self.testInst['clock_angle'])66        assert np.all(test_diff < 1.0e-8)67        return68    def test_yz_plane_mag(self):69        """Test the Byz plane magnitude calculation."""70        # Run the clock angle routine71        omni_hro.calculate_clock_angle(self.testInst)72        # Calculate plane magnitude73        test_mag = np.array([5.57149172, 6.14467489, 4.15098040, 5.57747612,74                             7.87633407, 5.12807787, 1.59323538, 4.10707742,75                             4.12873986, 5.78891590, 5.61652942, 3.66489971])76        # Test the difference77        test_diff = abs(test_mag - self.testInst['BYZ_GSM'])78        assert np.all(test_diff < 1.0e-8)79        return80    def test_yz_plane_cv(self):81        """Test the IMF steadiness CV calculation."""82        # Run the clock angle and steadiness routines83        omni_hro.calculate_clock_angle(self.testInst)84        omni_hro.calculate_imf_steadiness(self.testInst, steady_window=5,85                                          min_window_frac=0.8)86        # Ensure the BYZ coefficient of variation is calculated correctly87        byz_cv = np.array([np.nan, 0.158620, 0.229267, 0.239404, 0.469371,88                           0.470944, 0.495892, 0.384522, 0.396275, 0.208209,89                           0.221267, np.nan])90        # Test the difference91        test_diff = abs(byz_cv - self.testInst['BYZ_CV'])92        assert test_diff[np.isnan(test_diff)].shape[0] == 293        assert np.all(test_diff[~np.isnan(test_diff)] < 1.0e-6)94        assert np.all(np.isnan(self.testInst['BYZ_CV']) == np.isnan(byz_cv))95        return96    def test_clock_angle_std(self):97        """Test the IMF steadiness standard deviation calculation."""98        # Run the clock angle and steadiness routines99        omni_hro.calculate_clock_angle(self.testInst)100        omni_hro.calculate_imf_steadiness(self.testInst, steady_window=5,101                                          min_window_frac=0.8)102        # Ensure the BYZ coefficient of variation is calculated correctly103        ca_std = np.array([np.nan, 13.317200, 14.429278, 27.278579,104                           27.468469, 25.500730, 27.673033, 27.512069,105                           19.043833, 26.616713, 29.250390, np.nan])106        # Test the difference107        test_diff = abs(ca_std - self.testInst['clock_angle_std'])108        assert test_diff[np.isnan(test_diff)].shape[0] == 2109        assert np.all(test_diff[~np.isnan(test_diff)] < 1.0e-6)110        assert np.all(np.isnan(self.testInst['clock_angle_std'])111                      == np.isnan(ca_std))112        return113    def test_dayside_recon(self):114        """Test the IMF steadiness standard deviation calculation."""115        # Run the clock angle and steadiness routines116        omni_hro.calculate_clock_angle(self.testInst)117        omni_hro.calculate_dayside_reconnection(self.testInst)118        # Ensure the BYZ coefficient of variation is calculated correctly119        rcon = np.array([698.297487, 80.233896, 3.033586, 2.216075,120                         1425.310083, 486.460306, 2.350339, 103.843722,121                         0.000720, 534.586320, 1464.596772, 388.974792])122        # Test the difference123        test_diff = abs(rcon - self.testInst['recon_day'])124        assert test_diff.shape[0] == 12125        assert np.all(test_diff < 1.0e-6)...

split_pairs_XZ.py

Source:split_pairs_XZ.py

1#!/usr/bin/env python2import sys3import shutil4import matplotlib.pyplot as plt5import matplotlib.image as mpimg6def format_filename(name, number):7    num_zeros = "0"*(4 - len(number))8    filepath = "../lfw-deepfunneled/"+name+"/"+name+"_"+num_zeros+number+".jpg"9    return filepath10def split(train_file, test_file, train_same, train_diff, vali_same, vali_diff, test_same, test_diff):11    file_handler = open(train_file, "r", encoding="utf-8");12    for row in file_handler:13        temp=row.replace("\n", "");14        string= temp.split("\t"); # split it by whitespace15        converted = [x for x in string]16        if len(converted) ==3:17            train_same.append(converted);18        if len(converted) ==4:19            train_diff.append(converted);20    file_handler.close()21    22        23    file_handler = open( test_file, "r", encoding="utf-8")24    # We must first put the data in a list	25    for row in file_handler:26        temp=row.replace("\n", "");27        string= temp.split("\t") # split it by whitespace28        converted = [x for x in string]29        if len(converted) ==3:30            test_same.append(converted);31        if len(converted) ==4:32            test_diff.append(converted);33    file_handler.close()34    35    for i in range(len(test_same)//2):36        vali_same.append( test_same.pop() );37    38    for i in range( len(test_diff)//2 ):39        vali_diff.append( test_diff.pop() );40    41    print(len(train_same), len(vali_same), len(test_same) );42	43def get_image_plot(train_same, vali_same, test_diff):44	plt.figure();45	plt.subplot(3, 2, 1)46	img=mpimg.imread(format_filename(train_same[0][0], train_same[0][1] )  )47	imgplot = plt.imshow(img)48	plt.title(train_same[0][0]);49	plt.xticks([])50	plt.yticks([])51	52	plt.subplot(3, 2, 2)53	img=mpimg.imread(format_filename(train_same[0][0], train_same[0][2] )  )54	imgplot = plt.imshow(img)55	plt.title(train_same[0][0]);56	plt.xticks([])57	plt.yticks([])58	59	plt.subplot(3, 2, 3)60	img=mpimg.imread(format_filename(vali_same[0][0], vali_same[0][1] )  )61	imgplot = plt.imshow(img)62	plt.title(vali_same[0][0]);63	plt.xticks([])64	plt.yticks([])65	66	plt.subplot(3, 2, 4)67	img=mpimg.imread(format_filename(vali_same[0][0], vali_same[0][2] )  )68	imgplot = plt.imshow(img)69	plt.title(vali_same[0][0]);70	plt.xticks([])71	plt.yticks([])72	73	plt.subplot(3, 2, 5)74	img=mpimg.imread(format_filename(test_diff[0][0], test_diff[0][1] )  )75	imgplot = plt.imshow(img)76	plt.title(test_diff[0][0]);77	plt.xticks([])78	plt.yticks([])79	80	plt.subplot(3, 2, 6)81	img=mpimg.imread(format_filename(test_diff[0][2], test_diff[0][3] )  )82	imgplot = plt.imshow(img)83	plt.title(test_diff[0][2]);84	plt.xticks([])85	plt.yticks([])86	87	plt.tight_layout()88	plt.show()89	90def main():91	train_file="../pairsDevTrain.txt";92	test_file= "../pairsDevTest.txt";93	train_same=[];94	train_diff=[];95	vali_same=[];96	vali_diff=[];97	test_same= [];98	test_diff= [];99	100	split(train_file, test_file, train_same, train_diff, vali_same, vali_diff, test_same, test_diff);101	get_image_plot(train_same, vali_same, test_diff);102	103if __name__ == '__main__':104	main();105	106	...

lstm_single_output_forecasts.py

Source:lstm_single_output_forecasts.py

1from math import sqrt2import numpy as np3from sklearn.metrics import mean_squared_error4from base.base_forecasts import BaseForecasts5class LSTMSingleOutputForecasts(BaseForecasts):6    def __init__(self, model, data_loader, config):7        super(LSTMSingleOutputForecasts, self).__init__(data_loader, config)8        self.model = model9        #       tuple -> self.X_test = self.test_data[0], self.y_test = self.test_data[1]10        self.forecasts_rescaled = []11    def forecast(self, test_raw, test_data, i):12        self.forecasts_scaled = self.model.predict(13            test_data[0],14            batch_size=self.config.trainer.batch_size,15            verbose=self.config.trainer.verbose_training16        )17        test_diff = self.data_loader.data_transformer.reverse_transform(self.forecasts_scaled, test_data[0], i)18        test_raw = test_raw[self.config.data_loader.window_size:]19        # test_calculated = self.calculate_predicted(test_raw, test_diff)20        # test_raw = test_raw[1:]21        test_calculated = test_diff22        rmse = self.evaluate(test_raw, test_calculated)23        return test_raw, test_calculated, rmse24    def calculate_predicted(self, test_raw, test_diff):25        test_calculated = []26        for i in range(0, len(test_diff)):27            forecast = test_raw[i] + test_diff[i]28            test_calculated.append(forecast)29        test_calculated = np.array(test_calculated)30        return test_calculated31    def evaluate(self, test_raw, test_calculated):32        forecasts = test_calculated.flatten()33        rmse = sqrt(mean_squared_error(test_raw, forecasts))34        return rmse35    def forecast_without_model(self, test_raw, i):...

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