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

42
regression.py
View File

@ -3,16 +3,14 @@ from pathlib import Path
import fire import fire
import matplotlib 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.dates as mdates
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import matplotlib.ticker as mtick 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(): def create_dfs():
@ -30,7 +28,6 @@ def create_dfs():
plot_df = create_plot_df(None, None) plot_df = create_plot_df(None, None)
return df, df_state, plot_df return df, df_state, plot_df
@ -62,13 +59,17 @@ def main():
vals = regression_curve(date_range.to_numpy()) vals = regression_curve(date_range.to_numpy())
print(f"Projizierte Teilnahme am {target_time}: {vals[-1]:.2f}") print(f"Projizierte Teilnahme am {target_time}: {vals[-1]:.2f}")
now = pd.Timestamp.now() 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() print()
for target in [1500, 2500, 3500]: for target in [1500, 2500, 3500]:
target_reached_date = (target - reg.intercept) / reg.slope 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 num_skipped_days = 2
@ -77,10 +78,25 @@ def main():
delta = 3500 - data[-1] 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(
plt.plot(date_range[data.index.argmax() + num_skipped_days:], target_line, label="Ziellinie", color="tab:orange", linestyle=":", zorder=1) 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().relim() # make sure all the data fits
# plt.gca().autoscale() # auto-scale # plt.gca().autoscale() # auto-scale
plt.xlabel("Zeit in Tagen ab dem 15.08.") plt.xlabel("Zeit in Tagen ab dem 15.08.")