Best Python code snippet using pandera_python
test_ttests.py
Source: test_ttests.py
...129 passed = 0130 n_iter = 500131 for _ in range(n_iter):132 data1 = rng.normal(-5, 2, 100)133 ttest = one_sample_ttest(data1, -5)134 if ttest['P_value'] < .05:135 passed +=1136 self.assertAlmostEqual(passed / n_iter, .05, delta=.01) 137 def test_onesample_left_tailed(self):138 """Testing onesample left tailed."""139 rng = np.random.default_rng(9876138761251)140 passed = 0141 n_iter = 500142 for _ in range(n_iter):143 data1 = rng.normal(15, 1, 100)144 ttest = one_sample_ttest(data1, 15, 'left')145 if ttest['P_value'] < .05:146 passed +=1147 self.assertAlmostEqual(passed / n_iter, .05, delta=.01) 148 def test_one_sample_right_tailed(self):149 """Testing onesample right tailed."""150 rng = np.random.default_rng(615419864354)151 passed = 0152 n_iter = 500153 for _ in range(n_iter):154 data1 = rng.normal(12.2, 1, 100)155 ttest = one_sample_ttest(data1, 12.2, 'right')156 if ttest['P_value'] < .05:157 passed +=1158 self.assertAlmostEqual(passed / n_iter, .05, delta=.01)159class TestMiscTest(unittest.TestCase):160 """Test Fixture for random ttests."""161 def test_fail_tailed_option(self):162 """Testing bad tailed option."""163 with self.assertRaises(ValueError):164 _p_value_and_confidence_intervals(2.3, 100, 'greater')165 def test_confidence_intervals(self):166 """Testing the confidence interval test."""167 # Taken from a T-Test table168 # Two Tailed169 p, ci = _p_value_and_confidence_intervals(2.228, 10, 'two')...
StatsPythonRaw.py
Source: StatsPythonRaw.py
...34 except Exception as inst:35 report_exception(inst)363738def one_sample_ttest(df: pd.DataFrame) -> None:39 """ Perform a one-sample t-test """40 try:41 weight: list = df.iloc[:, 0].tolist()42 results = Stats.OneSampleTTest(25, weight)43 print_results(results, "One-sample t-test.")44 except Exception as inst:45 report_exception(inst)464748def two_sample_ttest(df_x1: pd.DataFrame, df_x2: pd.DataFrame) -> None:49 """ Run more statistical tests """50 try:51 x1: list = df_x1.iloc[:, 0].tolist()52 x2: list = df_x2.iloc[:, 0].tolist()5354 results: dict = Stats.TwoSampleTTest(x1, x2)55 print_results(results, "Two-sample t-test.")56 except Exception as inst:57 report_exception(inst)585960def plot_data(df_x1: pd.DataFrame, df_x2: pd.DataFrame) -> None:61 """ Plot the data """62 x1: list = df_x1.iloc[:, 0].tolist()63 x2: list = df_x2.iloc[:, 0].tolist()64 data: list = [x1, x2]65 66 green_diamond = dict(markerfacecolor='g', marker='D')67 fig1, ax = plt.subplots()68 ax.set_title('US versus Japan Petrol Consumption (mpg)')69 ax.set_xticklabels(['US', 'Japan'])70 ax.boxplot(data, flierprops=green_diamond)71 plt.show()727374if __name__ == "__main__":75 """ Define standard data sets used elsewhere """76 xs: list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]77 ys: list = [1, 3, 2, 5, 7, 8, 8, 9, 10, 12]7879 run_descriptive_statistics(xs)80 run_linear_regression(xs, ys)8182 ttest_summary_data()8384 filename: str = "../Data/weight.txt"8586 # Read in data frame87 df: pd.DataFrame = pd.read_csv(filename, header=None)8889 one_sample_ttest(df)9091 us_df: pd.DataFrame = pd.read_csv("../Data/us-mpg.txt", header=None)92 jp_df: pd.DataFrame = pd.read_csv("../Data/jp-mpg.txt", header=None)9394 plot_data(us_df, jp_df)95
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compare-2maps-within-subjects.py
Source: compare-2maps-within-subjects.py
...5from nilearn.image import math_img, mean_img, threshold_img6from nistats.second_level_model import SecondLevelModel7from nistats.thresholding import map_threshold8from nilearn.plotting import plot_glass_brain9def one_sample_ttest(filenames, name):10 design_matrix = pd.DataFrame([1] * len(filenames), columns=['intercept'])11 second_level_model = SecondLevelModel().fit(filenames, design_matrix=design_matrix)12 z_map = second_level_model.compute_contrast(output_type='z_score')13 nib.save(zmap, name + '.nii')14 thmap, threshold1 = map_threshold(z_map, level=.001, height_control='fpr', cluster_threshold=10)15 display = plot_glass_brain(thmap, display_mode='lzry', threshold=0, colorbar=True)16 display.savefig(name + '.png')17 display.close()18def compute_diffmaps(scans_A, scans_B):19 assert len(scans_A) == len(scans_B)20 list_fnames = []21 for sub, a, b in enumerate(zip(scans_A, scans_B)):22 diff = math_img('a - b', img1=a, img=b)23 fname = f'diff_{sub:03}'24 nib.save(fname, diff)25 list_fnames.append(fname)26 return fnames27def compare_2conds_within_subjects(scans_A, scans_B, name):28 files = compute_diffmaps(scans_A, scans_B)...
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