Merge branch 'review-kayano'

analyze_lab_placement_survey.py

@@ -34,7 +34,8 @@ def extract_values_from_body(body):
         table_part = body[m.end():]
 
         count_rows = re.findall(
-            r"^\s*(-?\d+(?:\.\d+)?)\s+(\d+)\s*$", table_part, flags=re.MULTILINE
+            r"^\s*(-?\d+(?:\.\d+)?)\s+(\d+)\s*$", table_part,
+            flags=re.MULTILINE
         )
 
         values = []
@@ -378,7 +379,8 @@ def annotate_target_values(ax, target_values, color):
             rotation=90,
             color=color,
             fontsize=9,
-            bbox={"facecolor": "white", "edgecolor": "none", "alpha": 0.75, "pad": 1.2},
+            bbox={"facecolor": "white", "edgecolor": "none",
+                  "alpha": 0.75, "pad": 1.2},
         )
 
 
@@ -444,7 +446,8 @@ def plot_data(data, lower=10, upper=None, target_lab=10, bin_width=10):
 
     df = filter_plot_data(df, lower=lower, upper=upper)
     labs = sorted(df["研究室番号"].unique())
-    target_values = df.loc[df["研究室番号"] == target_lab, "値"].to_numpy(dtype=float)
+    target_values = df.loc[df["研究室番号"] ==
+                           target_lab, "値"].to_numpy(dtype=float)
 
     apply_plot_style()
     colors = plt.get_cmap("tab10").colors
@@ -459,7 +462,8 @@ def plot_data(data, lower=10, upper=None, target_lab=10, bin_width=10):
     )
 
     draw_lab_boxplot(axs[0], df, labs, colors[0], rng)
-    draw_gp_histogram(axs[1], df, target_values, bin_width, colors[0], colors[3])
+    draw_gp_histogram(axs[1], df, target_values,
+                      bin_width, colors[0], colors[3])
 
     for ax in axs:
         style_axis(ax)
@@ -467,6 +471,7 @@ def plot_data(data, lower=10, upper=None, target_lab=10, bin_width=10):
     plt.show()
     return fig, axs
 
+
 def main():
     parser = argparse.ArgumentParser(
         description="Google Forms のコピペ結果から研究室別GPを集計する"
@@ -491,7 +496,8 @@ def main():
         text = f.read()
 
     df = parse_google_form_results(text)
-    df_r = summarize_by_lab(df).sort_values(by="GP mean", ascending=False)
+    # df_r = summarize_by_lab(df).sort_values(by="GP mean", ascending=False)
+    df_r = summarize_by_lab(df)
 
     print_summary(df, df_r)