How to use test_signals method in pytest-django

Best Python code snippet using pytest-django_python

generate_test_signals.py

Source:generate_test_signals.py

1import pandas as pd2import numpy as np3from sklearn.model_selection import train_test_split4from feature_extraction import mean_absolute_value5from feature_extraction import variance6from feature_extraction import standard_error7from feature_extraction import root_mean_square8from feature_extraction import slope_sign_change9from feature_extraction import waveform_length10import matplotlib.pyplot as plt11import matplotlib.patches as mpatches12# Extract signal files13"""14from zipfile import ZipFile15file_name1 = "dataframe_no_signal_compressed.zip"16file_name2 = 'labelled_signal_data_epochs_compressed.zip'17with ZipFile(file_name1, 'r') as zip:18    zip.printdir()19    print('Extracting files from dataframe_no_signal_compressed.zip...')20    zip.extractall()21    print('Done')22with ZipFile(file_name2, 'r') as zip:23    zip.printdir()24    print('Extracting files from labelled_signal_data_epochs_compressed.zip...')25    zip.extractall()26    print('Done')27"""28# Load signal files29labelled_signals = np.loadtxt('./labelled_signal_data_epochs.txt')30df = pd.read_csv('./dataframe_no_signal.csv')31# Assemble into dataframe32df['signal'] = labelled_signals.tolist()33df['signal'] = df['signal'].apply(np.array)34df['signal'] = df['signal'].apply(lambda x: 1000*x) # Convert from V to mV35# X data will be the unique IDs, they will allow indexing of the signals properly36# y data will be class labels37cls_1_ids = df.loc[df['label']==1, 'id'].unique()38cls_2_ids = df.loc[df['label']==2, 'id'].unique()39cls_3_ids = df.loc[df['label']==3, 'id'].unique()40# ids and labels is order [1, 1, 1, 1, 1, ..., 1, 2, 2, 2, 2, 2, ..., 2]41cls_ids_12 = np.append(cls_1_ids, cls_2_ids)42cls_ids_23 = np.append(cls_2_ids, cls_3_ids)43cls_labels_12 = np.append(np.ones((cls_1_ids.size,)), np.ones((cls_2_ids.size,)) * 2)44cls_labels_23 = np.append(np.ones((cls_2_ids.size,)) * 2, np.ones((cls_3_ids.size,)) * 3)45# Split data46X_train_12, X_test_12, y_train, y_test = train_test_split(cls_ids_12, cls_labels_12, test_size=0.25, random_state=0)47X_train_23, X_test_23, y_train, y_test = train_test_split(cls_ids_23, cls_labels_23, test_size=0.25, random_state=0)48def get_train_signals_2a(cls, channel):49    """50    :param cls: class label (integer 1 or 2)51    :param channel: channel number (integer between 1 and 8)52    :return: N x 256 array of signals of selected class and channel53    """54    cls_signals = df.loc[(df['id'].isin(X_train_12)) & (df['label'] == cls), 'signal'].values.tolist()55    cls_signals = np.array(cls_signals)56    cls_signals = np.reshape(cls_signals, (cls_signals.shape[0] // 8, 8, 256))57    return cls_signals[:, channel-1, :]58def get_train_signals_2b(cls, channel):59    """60    :param cls: class label (integer 2 or 3)61    :param channel: channel number (integer between 1 and 8)62    :return: N x 256 array of signals of selected class and channel63    """64    cls_signals = df.loc[(df['id'].isin(X_train_23)) & (df['label'] == cls), 'signal'].values.tolist()65    cls_signals = np.array(cls_signals)66    cls_signals = np.reshape(cls_signals, (cls_signals.shape[0] // 8, 8, 256))67    return cls_signals[:, channel-1, :]68def test_classifier_2a(classifier_fun):69    """70    Tests accuracy of manual classifier function on test data for class 1 and 271    :param classifier_fun: function that returns class based on input 8x256 nd-array of a single signal epoch72    :return: Accuracy score73    """74    test_signals = df.loc[(df['id'].isin(X_test_12)), 'signal'].values.tolist()75    test_signals = np.array(test_signals)76    test_signals = np.reshape(test_signals, (test_signals.shape[0] // 8, 8, 256))77    # Get labels from df. Since it's repeated 8 times for each channel, only pull every 8th one78    ys = df.loc[(df['id'].isin(X_test_12)), 'label'].values79    ys = ys[::8]80    classifier_output = [classifier_fun(signal) for signal in test_signals]81    classifier_output = np.array(classifier_output)82    num_correct = np.count_nonzero(classifier_output == ys)83    accuracy = num_correct / ys.size84    print('{} correct out of {} in test dataset for 2a'.format(num_correct, ys.size))85    print('Classifier accuracy on test data: {:.3f}'.format(accuracy))86    return accuracy87def test_classifier_2b(classifier_fun):88    """89    Tests accuracy of manual classifier function on test data for class 2 and 390    :param classifier_fun: function that returns class based on input 8x256 nd-array of a single signal epoch91    :return: Accuracy score92    """93    test_signals = df.loc[(df['id'].isin(X_test_23)), 'signal'].values.tolist()94    test_signals = np.array(test_signals)95    test_signals = np.reshape(test_signals, (test_signals.shape[0] // 8, 8, 256))96    # Get labels from df. Since it's repeated 8 times for each channel, only pull every 8th one97    ys = df.loc[(df['id'].isin(X_test_23)), 'label'].values98    ys = ys[::8]99    classifier_output = [classifier_fun(signal) for signal in test_signals]100    classifier_output = np.array(classifier_output)101    num_correct = np.count_nonzero(classifier_output == ys)102    accuracy = num_correct / ys.size103    print('{} correct out of {} in test dataset for 2b'.format(num_correct, ys.size))104    print('Classifier accuracy on test data: {:.3f}'.format(accuracy))105    return accuracy106#class 1 and 2107def classifier_se_var_2a(x):108    """109    Classifies EMG segment, x, as either class 1 or 2110    :param x: 8 by 256 nd-array. Dimension 0 are the 8 channels. Dimension 1 is the signal for each channel111    :return: class label112    """113    feature_1 = standard_error(x[0, :])114    feature_2 = variance(x[0, :])115    if (feature_1 < 0.00175) and (feature_2 < 0.005):116        return 1117    else: return 2118#class 2 and 3119def classifier_mav_ssc_2b(x):120    feature_1 = mean_absolute_value(x[0, :])121    feature_2 = slope_sign_change(x[0, :])122    if (feature_1 < 0.08):123        return 2124    else: return 3125# get train signals for class 1,2 channel 1126"""127cls1_ch1 = get_train_signals_2a(1, 1)128cls2_ch1 = get_train_signals_2a(2, 1)129cls1_ch1.shape130cls1_ch1_se = np.apply_along_axis(standard_error, 1, cls1_ch1)131cls2_ch1_se = np.apply_along_axis(standard_error, 1, cls2_ch1)132cls1_ch1_var = np.apply_along_axis(variance, 1, cls1_ch1)133cls2_ch1_var = np.apply_along_axis(variance, 1, cls2_ch1)134cls1_ch1_se.shape135cls1_ch1_var.shape136"""137#get train signals for class 2,3 channel 4138cls2_ch3 = get_train_signals_2b(2, 3)139cls3_ch3 = get_train_signals_2b(3, 3)140# cls2_ch2 = get_train_signals_2b(2, 2)141# cls3_ch1 = get_train_signals_2b(3, 1)142cls2_ch3.shape143cls2_ch3_mav = np.apply_along_axis(mean_absolute_value, 1, cls2_ch3)144cls3_ch3_mav = np.apply_along_axis(mean_absolute_value, 1, cls3_ch3)145cls2_ch3_ssc= np.apply_along_axis(slope_sign_change, 1, cls2_ch3)146cls3_ch3_ssc = np.apply_along_axis(slope_sign_change, 1, cls3_ch3)147cls2_ch3_mav.shape148cls3_ch3_ssc.shape149#plot class 1,2 channel 1150"""151plt.figure(figsize=(12,8))152plt.scatter(cls1_ch1_se, cls1_ch1_var, c='green', label="Resting Features", s=4)153plt.scatter(cls2_ch1_se, cls2_ch1_var, c='red', label="Fist Features", s=4)154ax = plt.gca()155ax.add_patch(mpatches.Rectangle((0, 0), 0.00175, 0.005, fill = False, color = 'purple'))156plt.legend(loc='best')157plt.xlabel('Standard Error (CH1)')158plt.ylabel('Variance (CH1)')159plt.title("Feature plot for Class 1 (Resting) and Class 2 (Fist)")160plt.show()161"""162#plot class 2,3 channel 4163plt.figure(figsize=(12,8))164plt.scatter(cls2_ch3_mav, cls2_ch3_ssc, c='green', label="Fist Feature", s=4)165plt.scatter(cls3_ch3_mav, cls3_ch3_ssc, c='red', label="Wrist Flexion Feature", s=4)166ax = plt.gca()167# ax.add_patch(mpatches.Rectangle((0, 0), 0.00175, 0.005, fill = False, color = 'purple'))168plt.vlines(0.08, 22, 55)169plt.legend(loc='best')170plt.xlabel('Mean Absolute Value (CH3)')171plt.ylabel('Slope Sign Change (CH3)')172plt.title("Feature plot for Class 2 (Fist) and Class 3 (Wrist Flexion)")173plt.show()174#call test classifier functions175# test_classifier_2a(classifier_se_var_2a)...

test_pemethod.py

Source:test_pemethod.py

...26    Ao = [1, -1.2, 0.36]27    Bo = [0, 0.5, 0.1]28    return [Ao, Bo]29@pytest.fixture30def test_signals(test_polynomials):31    # True test system parameters32    Ao = test_polynomials[0]33    Bo = test_polynomials[1]34    # Replicates the following experiment:35    # y(t) = Go(q)*u(t) + Ho(q)*e(t),36    # where u(t) is the system input and e(t) white noise37    N = 100                 # Number of samples38    u = -1 + 2*rand(N, 1)   # Defines input signal39    e = 0.01*randn(N, 1)    # Emulates gaussian white noise with std = 0.0140    # Calculates the y ARX: G(q) = B(q)/A(q) and H(q) = 1/A(q)41    y = lfilter(Bo, Ao, u, axis=0) #+ lfilter([1], Ao, e, axis=0)42    return [u, y, Ao, Bo]43# Defines sets of arguments for tests that request the respective fixtures44# Here, the values of na to nk are varied...

base.py

Source:base.py

1from test_utils.crawler import signals as test_signals2class Plugin(object):3    """4    This is a class to represent a plugin to the Crawler.5    Subclass it and define a start or stop function to be called on requests.6    Define a print_report function if your plugin outputs at the end of the run.7    """8    global_data = {}9    def __init__(self):10        #This should be refactored to call each of the subclasses.11        #Having them use the signal function signature is hacky..12        if hasattr(self, 'pre_request'):13            test_signals.pre_request.connect(self.pre_request)14        if hasattr(self, 'post_request'):15            test_signals.post_request.connect(self.post_request)16        if hasattr(self, 'start_run'):17            test_signals.start_run.connect(self.start_run)18        if hasattr(self, 'finish_run'):19            test_signals.finish_run.connect(self.finish_run)20        if hasattr(self, 'urls_parsed'):21            test_signals.urls_parsed.connect(self.urls_parsed)22        self.data = self.global_data[self.__class__.__name__] = {}23    """24    #These functions enable instance['test'] to save to instance.data25    def __setitem__(self, key, val):26        self.global_data[self.__class__.__name__][key] = val27    def __getitem__(self, key):28        return self.global_data[self.__class__.__name__][key]...

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