density of pdr and dr

scripts/device_ratio_pdr.py

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+import scipy.stats
+import pandas as pd
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+curdir = os.getcwd()
+path_delim = '/'
+df = pd.read_excel(curdir + path_delim + "data/tests_28_10_2023_19_16.xlsx", sheet_name="data")
+
+df = df[["calculatedRatio", "deviceRatio", "led1Average", "led1Buffer", "led1Sample", "led2Average", "led2Buffer", "led2Sample"]]
+# print(df.to_numpy())
+
+# # Contingency table
+# observed = [
+#     [25, 15, 10],
+#     [10, 20, 15],
+#     [15, 10, 20]
+# ]
+
+observed = df
+# # Perform the chi-square test for independence
+chi2, p, dof, expected = scipy.stats.chi2_contingency(observed)
+
+print(f"Chi-square statistic: {chi2}")
+print(f"P-value: {p}")
+print(f"Degrees of freedom: {dof}")
+print("Expected frequencies:")
+print(expected)
+
+df['calculatedRatio'].plot.kde()
+df['deviceRatio'].plot.kde()
+# plt.legend(['calculatedRatio'])
+plt.legend(["calculatedRatio", "deviceRatio"], loc ="upper right") 
+plt.show()
+
+# Observed frequencies
+observed = np.array(df['led1Buffer'].to_numpy())
+
+# Expected frequencies
+expected = np.array(df['led2Buffer'].to_numpy())  # Assuming equal expected frequencies
+
+# Perform the chi-square goodness-of-fit test
+chi2, p = scipy.stats.chisquare(observed, f_exp=expected)
+
+print(f"Chi-square statistic: {chi2}")
+print(f"P-value: {p}")