regression.py

@@ -1,115 +0,0 @@
-import datetime
-from pathlib import Path
-
-import fire
-import matplotlib
-import matplotlib.dates as mdates
-import matplotlib.pyplot as plt
-import matplotlib.ticker as mtick
-import numpy as np
-import pandas as pd
-import scipy
-
-from wsgi import create_fig, create_plot_df, plot
-
-
-def create_dfs():
-    last_file = sorted(Path("data").iterdir())[-1]
-    key = last_file.name[:10]
-
-    with (Path("data") / f"{key}_data.ods").open("rb") as ff:
-        df = pd.read_excel(ff, sheet_name="digital", index_col=0).astype(
-            {"Digitale Befragung": "Int32"}
-        )
-    with (Path("data") / f"{key}_state_data.ods").open("rb") as ff:
-        df_state = pd.read_excel(ff, sheet_name="digital", index_col=0).astype(
-            {"Digitale Befragung": "Int32"}
-        )
-
-    plot_df = create_plot_df(None, None)
-
-    return df, df_state, plot_df
-
-
-def main():
-    df, df_state, plot_df = create_dfs()
-
-    plot(plot_df, landesbez_str=[None], max_shading_date="2023-10-02")
-
-    plt.gcf().set_size_inches(10, 5)
-
-    target_time = pd.Timestamp("2023-10-01")
-    xlim = plt.xlim()
-    plt.xlim(xlim[0], pd.Timestamp("2023-10-02"))
-
-    plt.ylim(0, 3500 * 1.025)
-
-    data = plot_df.dropna().sum(1)
-    data = data.iloc[3:]
-    casted_timepoints = data.index.to_numpy().astype(np.int64)
-    reg = scipy.stats.linregress(casted_timepoints, data)
-
-    print(f"Regression R^2: {reg.rvalue**2:.6f}")
-
-    date_range = pd.date_range(start="2023-08-21 10:00:00", end=target_time)
-    date_range = date_range.to_series(index=np.arange(len(date_range)))
-    date_range.loc[len(date_range)] = target_time
-
-    regression_curve = lambda x: reg.intercept + reg.slope * x.astype(np.int64)
-    vals = regression_curve(date_range.to_numpy())
-    print(f"Projizierte Teilnahme am {target_time}: {vals[-1]:.2f}")
-    now = pd.Timestamp.now()
-    print(
-        f"Projizierte Teilnahme jetzt: {regression_curve(pd.Series([now]).to_numpy()).item():.2f}"
-    )
-
-    print()
-
-    for target in [1500, 2500, 3500]:
-        target_reached_date = (target - reg.intercept) / reg.slope
-        print(
-            f"Ziel {target} erreicht am {pd.Timestamp(target_reached_date).strftime('%Y-%m-%d %X')}"
-        )
-
-    num_skipped_days = 2
-
-    x = date_range.to_numpy().astype(np.int64)
-    curr_time = x[data.index.argmax() + num_skipped_days]
-
-    delta = 3500 - data[-1]
-
-    target_line = data[-1] + delta / (x[-1] - curr_time) * (
-        x[data.index.argmax() + num_skipped_days :] - curr_time
-    )
-
-    plt.plot(
-        date_range,
-        vals,
-        label=f"Lineare Regression ($R^2={reg.rvalue**2:.3f}$)",
-        color="tab:blue",
-        zorder=1,
-    )
-    plt.plot(
-        date_range[data.index.argmax() + num_skipped_days :],
-        target_line,
-        label="Ziellinie",
-        color="tab:orange",
-        linestyle=":",
-        zorder=1,
-    )
-    # plt.gca().relim()      # make sure all the data fits
-    # plt.gca().autoscale()  # auto-scale
-    plt.xlabel("Zeit in Tagen ab dem 15.08.")
-    plt.axvline(x=target_time, color="tab:red", linestyle="--")
-    plt.legend()
-    plt.gca().xaxis.set_major_locator(matplotlib.ticker.NullLocator())
-    plt.gca().xaxis.set_major_locator(matplotlib.ticker.NullLocator())
-    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%d.%m."))
-
-    plt.gca().set_xticks([target_time])
-    plt.title("Projektion Teilnahme an Digitaler Beschäftigtenbefragung")
-    plt.savefig("plots/regression.png")
-
-
-if __name__ == "__main__":
-    fire.Fire(main)