fblanke/tvstud-download-digital
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Format

Browse Source
This commit is contained in:
Felix Blanke 2023-09-07 13:11:32 +02:00
parent 06c68c5167
commit 76d890980c
1 changed files with 30 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
regression.py
View File

@ -3,16 +3,14 @@ from pathlib import Path
import fire
import matplotlib
import matplotlib.pyplot as plt
import scipy
import numpy as np
import pandas as pd
from wsgi import create_fig, create_plot_df, plot
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():
@ -30,14 +28,13 @@ def create_dfs():
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")
plot(plot_df, landesbez_str=[None], max_shading_date="2023-10-02")
plt.gcf().set_size_inches(10, 5)
@ -62,13 +59,17 @@ def main():
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(
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')}")
print(
f"Ziel {target} erreicht am {pd.Timestamp(target_reached_date).strftime('%Y-%m-%d %X')}"
)
num_skipped_days = 2
@ -77,10 +78,25 @@ def main():
delta = 3500 - data[-1]
target_line = data[-1] + delta / (x[-1] - curr_time) * (x[data.index.argmax() + num_skipped_days:] - curr_time)
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.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.")