Fix an error causing xtick label wrong

dabest/plotter.py

@@ -805,32 +805,32 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
     ticks_with_counts = []
     ticks_loc = rawdata_axes.get_xticks()
     rawdata_axes.xaxis.set_major_locator(matplotlib.ticker.FixedLocator(ticks_loc))
-    for xticklab in rawdata_axes.xaxis.get_ticklabels():
-        t = xticklab.get_text()
-        # Extract the text after the last newline, if present
-        te = t[t.rfind("\n") + len("\n"):] if t.rfind("\n") != -1 else t
-
+    def lookup_value(text, counts):
         try:
-            # Try to access 'counts' directly with 'te'.
-            N = str(counts.loc[te])
+            return str(counts.loc[text])
         except KeyError:
-            # If direct access fails, attempt a numeric interpretation.
             try:
-                # Attempt to convert 'te' to numeric (float or int, as appropriate)
-                numeric_key = pd.to_numeric(te, errors='coerce')
-                # 'pd.to_numeric()' will convert strings to float or int, as appropriate,
-                # and will return NaN if conversion fails. It preserves integers.
-                if pd.notnull(numeric_key):  # Check if conversion was successful
-                    N = str(counts.loc[numeric_key])
+                numeric_key = pd.to_numeric(text, errors='coerce')
+                if pd.notnull(numeric_key):
+                    return str(counts.loc[numeric_key])
                 else:
-                    raise ValueError  # Raise an error to trigger the except block
+                    raise ValueError
             except (ValueError, KeyError):
-                # Handle cases where 'te' cannot be converted or the converted key doesn't exist
-                print(f"Key '{te}' not found in counts.")
-                N = "N/A"
+                print(f"Key '{text}' not found in counts.")
+                return "N/A"
+    for xticklab in rawdata_axes.xaxis.get_ticklabels():
+        t = xticklab.get_text()
+        # Extract the text after the last newline, if present
+        if t.rfind("\n") != -1:
+            te = t[t.rfind("\n") + len("\n"):]
+            value = lookup_value(te, counts)
+            te = t
+        else:
+            te = t
+            value = lookup_value(te, counts)
 
         # Append the modified tick label with the count to the list
-        ticks_with_counts.append(f"{te}\nN = {N}")
+        ticks_with_counts.append(f"{te}\nN = {value}")
 
 
     if plot_kwargs["fontsize_rawxlabel"] is not None:

nbs/API/plotter.ipynb

@@ -864,32 +864,32 @@
     "    ticks_with_counts = []\n",
     "    ticks_loc = rawdata_axes.get_xticks()\n",
     "    rawdata_axes.xaxis.set_major_locator(matplotlib.ticker.FixedLocator(ticks_loc))\n",
-    "    for xticklab in rawdata_axes.xaxis.get_ticklabels():\n",
-    "        t = xticklab.get_text()\n",
-    "        # Extract the text after the last newline, if present\n",
-    "        te = t[t.rfind(\"\\n\") + len(\"\\n\"):] if t.rfind(\"\\n\") != -1 else t\n",
-    "\n",
+    "    def lookup_value(text, counts):\n",
     "        try:\n",
-    "            # Try to access 'counts' directly with 'te'.\n",
-    "            N = str(counts.loc[te])\n",
+    "            return str(counts.loc[text])\n",
     "        except KeyError:\n",
-    "            # If direct access fails, attempt a numeric interpretation.\n",
     "            try:\n",
-    "                # Attempt to convert 'te' to numeric (float or int, as appropriate)\n",
-    "                numeric_key = pd.to_numeric(te, errors='coerce')\n",
-    "                # 'pd.to_numeric()' will convert strings to float or int, as appropriate,\n",
-    "                # and will return NaN if conversion fails. It preserves integers.\n",
-    "                if pd.notnull(numeric_key):  # Check if conversion was successful\n",
-    "                    N = str(counts.loc[numeric_key])\n",
+    "                numeric_key = pd.to_numeric(text, errors='coerce')\n",
+    "                if pd.notnull(numeric_key):\n",
+    "                    return str(counts.loc[numeric_key])\n",
     "                else:\n",
-    "                    raise ValueError  # Raise an error to trigger the except block\n",
+    "                    raise ValueError\n",
     "            except (ValueError, KeyError):\n",
-    "                # Handle cases where 'te' cannot be converted or the converted key doesn't exist\n",
-    "                print(f\"Key '{te}' not found in counts.\")\n",
-    "                N = \"N/A\"\n",
+    "                print(f\"Key '{text}' not found in counts.\")\n",
+    "                return \"N/A\"\n",
+    "    for xticklab in rawdata_axes.xaxis.get_ticklabels():\n",
+    "        t = xticklab.get_text()\n",
+    "        # Extract the text after the last newline, if present\n",
+    "        if t.rfind(\"\\n\") != -1:\n",
+    "            te = t[t.rfind(\"\\n\") + len(\"\\n\"):]\n",
+    "            value = lookup_value(te, counts)\n",
+    "            te = t\n",
+    "        else:\n",
+    "            te = t\n",
+    "            value = lookup_value(te, counts)\n",
     "\n",
     "        # Append the modified tick label with the count to the list\n",
-    "        ticks_with_counts.append(f\"{te}\\nN = {N}\")\n",
+    "        ticks_with_counts.append(f\"{te}\\nN = {value}\")\n",
     "\n",
     "\n",
     "    if plot_kwargs[\"fontsize_rawxlabel\"] is not None:\n",

test.py

@@ -0,0 +1,88 @@
+import numpy as np
+from scipy.stats import norm
+import pandas as pd
+import matplotlib as mpl
+import os
+from pathlib import Path
+
+import matplotlib.ticker as Ticker
+import matplotlib.pyplot as plt
+
+from dabest._api import load
+
+import dabest
+
+columns = [1, 2.0]
+columns_str = ["1", "2.0"]
+# create a test database
+N = 100
+df = pd.DataFrame(np.vstack([np.random.normal(loc=i, size=(N,)) for i in range(len(columns))]).T, columns=columns_str)
+females = np.repeat("Female", N / 2).tolist()
+males = np.repeat("Male", N / 2).tolist()
+df['gender'] = females + males
+
+# Add an `id` column for paired data plotting.
+df['ID'] = pd.Series(range(1, N + 1))
+
+
+db = dabest.load(data=df, idx=columns_str, paired="baseline", id_col="ID")
+print(db.mean_diff)
+db.mean_diff.plot();
+
+# def create_demo_dataset(seed=9999, N=20):
+#     import numpy as np
+#     import pandas as pd
+#     from scipy.stats import norm  # Used in generation of populations.
+
+#     np.random.seed(9999)  # Fix the seed so the results are replicable.
+#     # pop_size = 10000 # Size of each population.
+
+#     # Create samples
+#     c1 = norm.rvs(loc=3, scale=0.4, size=N)
+#     c2 = norm.rvs(loc=3.5, scale=0.75, size=N)
+#     c3 = norm.rvs(loc=3.25, scale=0.4, size=N)
+
+#     t1 = norm.rvs(loc=3.5, scale=0.5, size=N)
+#     t2 = norm.rvs(loc=2.5, scale=0.6, size=N)
+#     t3 = norm.rvs(loc=3, scale=0.75, size=N)
+#     t4 = norm.rvs(loc=3.5, scale=0.75, size=N)
+#     t5 = norm.rvs(loc=3.25, scale=0.4, size=N)
+#     t6 = norm.rvs(loc=3.25, scale=0.4, size=N)
+
+#     # Add a `gender` column for coloring the data.
+#     females = np.repeat("Female", N / 2).tolist()
+#     males = np.repeat("Male", N / 2).tolist()
+#     gender = females + males
+
+#     # Add an `id` column for paired data plotting.
+#     id_col = pd.Series(range(1, N + 1))
+
+#     # Combine samples and gender into a DataFrame.
+#     df = pd.DataFrame(
+#         {
+#             "Control 1": c1,
+#             "Test 1": t1,
+#             "Control 2": c2,
+#             "Test 2": t2,
+#             "Control 3": c3,
+#             "Test 3": t3,
+#             "Test 4": t4,
+#             "Test 5": t5,
+#             "Test 6": t6,
+#             "Gender": gender,
+#             "ID": id_col,
+#         }
+#     )
+
+#     return df
+
+
+# df = create_demo_dataset()
+
+# two_groups_unpaired = load(df, idx=("Control 1", "Test 1"))
+
+# two_groups_paired = load(
+#     df, idx=("Control 1", "Test 1"), paired="baseline", id_col="ID"
+# )
+
+# two_groups_unpaired.mean_diff.plot()