🎨 center swarmplot labels

RasmussenLab · Nov 27, 2023 · db2469a · db2469a
1 parent e62f80b
commit db2469a
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Showing 2 changed files with 44 additions and 26 deletions.
diff --git a/project/10_4_ald_compare_single_pg.ipynb b/project/10_4_ald_compare_single_pg.ipynb
@@ -625,27 +625,36 @@
     "    tmp_dot.remove()\n",
     "\n",
     "    feat_observed = data[idx].dropna()\n",
-    "    label_template = '{method}\\n(N={n:,d}, q={q:.3f})'\n",
-    "    key = label_template.format(method='observed',\n",
-    "                                n=len(feat_observed),\n",
-    "                                q=float(qvalues.loc[idx, ('None', 'qvalue')])\n",
-    "                                )\n",
+    "\n",
+    "    def get_centered_label(method, n, q):\n",
+    "        model_str = f'{method}'\n",
+    "        stats_str = f'(N={n:,d}, q={q:.3f})'\n",
+    "        if len(model_str) > len(stats_str):\n",
+    "            stats_str = f\"{stats_str:<{len(model_str)}}\"\n",
+    "        else:\n",
+    "            model_str = f\"{model_str:<{len(stats_str)}}\"\n",
+    "        return f'{model_str}\\n{stats_str}'\n",
+    "\n",
+    "    key = get_centered_label(method='observed',\n",
+    "                             n=len(feat_observed),\n",
+    "                             q=float(qvalues.loc[idx, ('None', 'qvalue')])\n",
+    "                             )\n",
     "    to_plot = {key: feat_observed}\n",
     "    for method in model_keys:\n",
     "        try:\n",
     "            pred = pred_real_na.loc[pd.IndexSlice[:,\n",
     "                                                  idx], method].dropna().droplevel(-1)\n",
     "            if len(pred) == 0:\n",
     "                # in case no values was imputed -> qvalue is as based on measured\n",
-    "                key = label_template.format(method=method,\n",
-    "                                            n=len(pred),\n",
-    "                                            q=float(qvalues.loc[idx, ('None', 'qvalue')]\n",
-    "                                                    ))\n",
+    "                key = get_centered_label(method=method,\n",
+    "                                         n=len(pred),\n",
+    "                                         q=float(qvalues.loc[idx, ('None', 'qvalue')]\n",
+    "                                                 ))\n",
     "            elif qvalues.loc[idx, (method, 'qvalue')].notna().all():\n",
-    "                key = label_template.format(method=method,\n",
-    "                                            n=len(pred),\n",
-    "                                            q=float(qvalues.loc[idx, (method, 'qvalue')]\n",
-    "                                                    ))\n",
+    "                key = get_centered_label(method=method,\n",
+    "                                         n=len(pred),\n",
+    "                                         q=float(qvalues.loc[idx, (method, 'qvalue')]\n",
+    "                                                 ))\n",
     "            elif qvalues.loc[idx, (method, 'qvalue')].isna().all():\n",
     "                logger.info(f\"NA qvalues for {idx}: {method}\")\n",
     "                continue\n",

diff --git a/project/10_4_ald_compare_single_pg.py b/project/10_4_ald_compare_single_pg.py
@@ -332,27 +332,36 @@
     tmp_dot.remove()
 
     feat_observed = data[idx].dropna()
-    label_template = '{method}\n(N={n:,d}, q={q:.3f})'
-    key = label_template.format(method='observed',
-                                n=len(feat_observed),
-                                q=float(qvalues.loc[idx, ('None', 'qvalue')])
-                                )
+
+    def get_centered_label(method, n, q):
+        model_str = f'{method}'
+        stats_str = f'(N={n:,d}, q={q:.3f})'
+        if len(model_str) > len(stats_str):
+            stats_str = f"{stats_str:<{len(model_str)}}"
+        else:
+            model_str = f"{model_str:<{len(stats_str)}}"
+        return f'{model_str}\n{stats_str}'
+
+    key = get_centered_label(method='observed',
+                             n=len(feat_observed),
+                             q=float(qvalues.loc[idx, ('None', 'qvalue')])
+                             )
     to_plot = {key: feat_observed}
     for method in model_keys:
         try:
             pred = pred_real_na.loc[pd.IndexSlice[:,
                                                   idx], method].dropna().droplevel(-1)
             if len(pred) == 0:
                 # in case no values was imputed -> qvalue is as based on measured
-                key = label_template.format(method=method,
-                                            n=len(pred),
-                                            q=float(qvalues.loc[idx, ('None', 'qvalue')]
-                                                    ))
+                key = get_centered_label(method=method,
+                                         n=len(pred),
+                                         q=float(qvalues.loc[idx, ('None', 'qvalue')]
+                                                 ))
             elif qvalues.loc[idx, (method, 'qvalue')].notna().all():
-                key = label_template.format(method=method,
-                                            n=len(pred),
-                                            q=float(qvalues.loc[idx, (method, 'qvalue')]
-                                                    ))
+                key = get_centered_label(method=method,
+                                         n=len(pred),
+                                         q=float(qvalues.loc[idx, (method, 'qvalue')]
+                                                 ))
             elif qvalues.loc[idx, (method, 'qvalue')].isna().all():
                 logger.info(f"NA qvalues for {idx}: {method}")
                 continue