From e35e88e6ac8538b8f1b1215d4af2af97dfcdeaf8 Mon Sep 17 00:00:00 2001
From: jcohenadad <jcohen@polymtl.ca>
Date: Tue, 9 Jan 2024 23:15:47 -0500
Subject: [PATCH] Fixed output statistics

---
 data_processing.ipynb | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/data_processing.ipynb b/data_processing.ipynb
index 6ee1def..5bb6feb 100644
--- a/data_processing.ipynb
+++ b/data_processing.ipynb
@@ -417,7 +417,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "95ab4e6d",
+   "id": "f8b90a09",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -439,26 +439,33 @@
     "            all_data.extend(wa_data)\n",
     "\n",
     "        if all_data:\n",
-    "            A = np.mean(all_data)\n",
-    "            SD = np.std(all_data)\n",
-    "            data_summary.append([subject, shim_mode, A, SD])\n",
+    "            mean_data = np.mean(all_data)\n",
+    "            sd_data = np.std(all_data)\n",
+    "            data_summary.append([subject, shim_mode, mean_data, sd_data])\n",
     "\n",
     "# Convert to DataFrame and save to CSV\n",
     "df_summary = pd.DataFrame(data_summary, columns=['Subject', 'Shim_Mode', 'Average', 'Standard_Deviation'])\n",
     "df_summary.to_csv(os.path.join(path_results, 'subject_shim_mode_summary.csv'), index=False)\n",
     "\n",
-    "# Step 3: Compute statistics across subjects\n",
-    "df_grouped = df_summary.groupby('Shim_Mode').agg({'Average': 'mean', 'Standard_Deviation': 'mean'}).reset_index()\n",
-    "df_grouped.to_csv(os.path.join(path_results, 'average_across_subjects.csv'), index=False)\n",
+    "# Compute statistics across subjects\n",
+    "grouped_means = df_summary.groupby('Shim_Mode').agg({'Average': ['mean', 'std'], 'Standard_Deviation': ['mean', 'std']})\n",
     "\n",
-    "# Step 4: ANOVA and Posthoc Tests\n",
+    "# Format mean ± standard deviation\n",
+    "grouped_means['Average_formatted'] = grouped_means['Average']['mean'].map(\"{:.2f}\".format) + \" ± \" + grouped_means['Average']['std'].map(\"{:.2f}\".format)\n",
+    "grouped_means['Standard_Deviation_formatted'] = grouped_means['Standard_Deviation']['mean'].map(\"{:.2f}\".format) + \" ± \" + grouped_means['Standard_Deviation']['std'].map(\"{:.2f}\".format)\n",
+    "\n",
+    "# Drop multi-level index and only keep formatted columns\n",
+    "grouped_means = grouped_means.drop(columns=['Average', 'Standard_Deviation'])\n",
+    "grouped_means.columns = ['Average', 'Standard_Deviation']  # Rename columns for clarity\n",
+    "grouped_means.reset_index().to_csv(os.path.join(path_results, 'average_across_subjects.csv'), index=False)\n",
+    "\n",
+    "# Perform ANOVA and Posthoc Tests\n",
     "anova_result = f_oneway(*[group[\"Average\"].values for name, group in df_summary.groupby(\"Shim_Mode\")])\n",
     "print(\"ANOVA Result:\", anova_result)\n",
     "\n",
-    "# Perform posthoc test if ANOVA is significant\n",
     "if anova_result.pvalue < 0.05:\n",
     "    posthoc_result = pairwise_tukeyhsd(df_summary['Average'], df_summary['Shim_Mode'])\n",
-    "    print(\"Posthoc Tukey HSD Result:\\n\", posthoc_result)"
+    "    print(\"Posthoc Tukey HSD Result:\\n\", posthoc_result)\n"
    ]
   }
  ],