85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
from typing import List
|
|
import argparse
|
|
|
|
import numpy as np
|
|
import scipy.stats as stats
|
|
import matplotlib.pyplot as plt
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("-g", "--graph", action="store_true", default=False, help="Plot graph")
|
|
parser.add_argument("-s", "--save", default="", help="Graph save location")
|
|
args = parser.parse_args()
|
|
graph = args.graph
|
|
save = args.save
|
|
|
|
|
|
def analyze(name: str, data: List[np.ndarray]):
|
|
#print(f"Checking if normally distributed for {name}")
|
|
#for i in range(len(data)):
|
|
# _, normal_p = stats.shapiro(data[i])
|
|
# if normal_p > 0.05:
|
|
# print(f"\tGroup {i}: normally distributed")
|
|
# else:
|
|
# print(f"\tGroup {i}: NOT normally distributed")
|
|
|
|
filtered_data = []
|
|
for index, item in enumerate(data):
|
|
if len(item) > 5:
|
|
filtered_data.append(item)
|
|
else:
|
|
print(f"Data group at index {index} removed due to insufficient size ({len(item)})")
|
|
|
|
F, p = stats.kruskal(*filtered_data)
|
|
print(f"F-stats for {name}: {F}")
|
|
print(f"p-value for {name}: {p}")
|
|
|
|
if round(p, 4) > 0.05:
|
|
print("statistically insignificant\n")
|
|
return F, p
|
|
|
|
print("statistically significant")
|
|
tukey_results = stats.tukey_hsd(*filtered_data)
|
|
print(tukey_results)
|
|
|
|
return F, p
|
|
|
|
|
|
def plot_violin(data, labels, Fs, ps, title):
|
|
if not graph:
|
|
return
|
|
|
|
grade_names = ["Priemer", "Matematika", "Slovenčina", "Angličtina"]
|
|
grade_name_labels = ["Priemer známok", "Známka z matematiky", "Známka zo slovenčiny", "Známka z angličtiny"]
|
|
|
|
fig, axs = plt.subplots(2, 2)
|
|
fig.suptitle(title)
|
|
fig.set_size_inches(12, 9)
|
|
|
|
for j in range(2):
|
|
for k in range(2):
|
|
index = j * 2 + k
|
|
step = 1 if index > 0 else 0.5
|
|
|
|
axs[j, k].violinplot(data[index], showmedians=True)
|
|
axs[j, k].set_title(grade_names[index])
|
|
axs[j, k].set_xlabel(title, fontweight="bold")
|
|
axs[j, k].set_ylabel(grade_name_labels[index], fontweight="bold")
|
|
axs[j, k].set_xticks(np.arange(1, len(labels) + 1), labels=labels)
|
|
axs[j, k].set_yticks(np.arange(1, 5.01, step))
|
|
|
|
F = round(Fs[index], 2)
|
|
p = round(ps[index], 4)
|
|
axs[j, k].text(0.01, 0.99, f"F-stat: {F:.2f}\np-val: {p:.4f}", ha="left", va="top", transform=axs[j, k].transAxes,
|
|
fontweight="bold")
|
|
|
|
medians = list([np.median(a) for a in data[index]])
|
|
for l in range(len(medians)):
|
|
median = round(medians[l], 2)
|
|
axs[j, k].text(l + 1.05, median + 0.05, f"{median}")
|
|
|
|
fig.tight_layout()
|
|
if save != "":
|
|
plt.savefig(save)
|
|
else:
|
|
plt.show()
|