Remove Task 1.py

BolonkinNM/Task 1/Task 1.py

@@ -1,82 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-
-col_names = ['T(C)', 'Td(C)', 'HR%', 'ff(kmh)', 'Gust(kmh)', 'P_0(HPa)', 'P_sea(HPa)']
-
-data = pd.read_csv(
-    "data_meteo.txt",
-    sep=r'\s+',
-    skiprows=1,
-    usecols=[2, 3, 4, 5, 6, 7, 8],
-    names=col_names,
-    engine='python'
-)
-
-print("загружено записей (строк):", len(data))
-
-data = data.apply(pd.to_numeric, errors='coerce')
-n_before = len(data)
-data = data.dropna(subset=col_names)
-n_after = len(data)
-
-print(f"после приведения к числам и dropna: {n_after} строк (удалено {n_before - n_after})")
-
-def correlation(vec1, vec2, center=False):
-    v1 = np.asarray(vec1, dtype=float)
-    v2 = np.asarray(vec2, dtype=float)
-
-    if center:
-        v1 = v1 - np.mean(v1)
-        v2 = v2 - np.mean(v2)
-
-    dot_product = np.sum(v1 * v2)
-    norm1 = np.sqrt(np.sum(v1 ** 2))
-    norm2 = np.sqrt(np.sum(v2 ** 2))
-
-    if norm1 == 0 or norm2 == 0:
-        return np.nan
-
-    return dot_product / (norm1 * norm2)
-
-n = len(col_names)
-
-raw_matrix = np.zeros((n, n))
-center_matrix = np.zeros((n, n))
-
-for i in range(n):
-    for j in range(n):
-        r_raw = correlation(data.iloc[:, i], data.iloc[:, j], center=False)
-        r_center = correlation(data.iloc[:, i], data.iloc[:, j], center=True)
-
-        raw_matrix[i, j] = np.round(r_raw, 3) if not np.isnan(r_raw) else np.nan
-        center_matrix[i, j] = np.round(r_center, 3) if not np.isnan(r_center) else np.nan
-
-df_raw = pd.DataFrame(raw_matrix, index=col_names, columns=col_names)
-df_center = pd.DataFrame(center_matrix, index=col_names, columns=col_names)
-
-print("\nкорреляция (raw):")
-print(df_raw.to_string())
-
-print("\nкорреляция (centered):")
-print(df_center.to_string())
-
-print("\nразница (centered - raw):")
-print((df_center - df_raw).round(3).to_string())
-
-plt.figure(figsize=(14, 8))
-
-for col in col_names:
-    centered_values = data[col] - data[col].mean()
-    plt.plot(centered_values.values, label=col, marker='.', linewidth=1, markersize=4)
-
-plt.axhline(0, linestyle='--')
-plt.title("центрированные метеоданные")
-plt.xlabel("номер измерения")
-plt.ylabel("отклонение от среднего")
-plt.legend()
-plt.grid()
-plt.tight_layout()
-
-plt.savefig("meteo_analysis.png", dpi=150)
-plt.show()