diff --git a/regression.py b/regression.py
index b5fd14c..16a56ae 100644
--- a/regression.py
+++ b/regression.py
@@ -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.")