From 619f2754e3adb4d7d93c1921e34aac15d9c18707 Mon Sep 17 00:00:00 2001
From: jcohenadad <jcohen@polymtl.ca>
Date: Tue, 23 Jan 2024 22:38:44 -0500
Subject: [PATCH] Output CSV for B1+ values under derivatives/results/

---
 data_processing.ipynb | 47 ++++++++++++++++++++++---------------------
 1 file changed, 24 insertions(+), 23 deletions(-)

diff --git a/data_processing.ipynb b/data_processing.ipynb
index d3428ca..6093aab 100644
--- a/data_processing.ipynb
+++ b/data_processing.ipynb
@@ -430,7 +430,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cf0074dc",
+   "id": "f2a6d765",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -604,7 +604,8 @@
     "for subject in subjects:\n",
     "    os.chdir(os.path.join(path_data, subject, \"fmap\"))\n",
     "    for shim_mode in shim_modes:\n",
-    "        !sct_extract_metric -i {subject}_acq-{shim_mode}_TB1map.nii.gz -f {subject}_acq-anat{shim_mode}_TB1TFL_seg.nii.gz -method wa -vert 3:9 -vertfile {subject}_acq-anat{shim_mode}_TB1TFL_seg_labeled.nii.gz -perslice 1 -o TB1map_{shim_mode}.csv"
+    "        fname_result_b1plus = os.path.join(path_results, f\"{subject}_TB1map_{shim_mode}.csv\")\n",
+    "        !sct_extract_metric -i {subject}_acq-{shim_mode}_TB1map.nii.gz -f {subject}_acq-anat{shim_mode}_TB1TFL_seg.nii.gz -method wa -vert 3:9 -vertfile {subject}_acq-anat{shim_mode}_TB1TFL_seg_labeled.nii.gz -perslice 1 -o {fname_result_b1plus}"
    ]
   },
   {
@@ -661,32 +662,32 @@
     "        # Initialize list to collect data for this shim method\n",
     "        method_data = []\n",
     "\n",
-    "        for file_path in glob.glob(f\"TB1map_*{shim_mode}*.csv\"):\n",
-    "            df = pd.read_csv(file_path)\n",
-    "            wa_data = df['WA()']\n",
+    "        file_csv = os.path.join(path_results, f\"{subject}_TB1map_{shim_mode}.csv\")\n",
+    "        df = pd.read_csv(file_csv)\n",
+    "        wa_data = df['WA()']\n",
     "\n",
-    "            # Normalize the x-axis to a 0-1 scale for each subject\n",
-    "            x_subject = np.linspace(0, 1, len(wa_data))\n",
+    "        # Normalize the x-axis to a 0-1 scale for each subject\n",
+    "        x_subject = np.linspace(0, 1, len(wa_data))\n",
     "\n",
-    "            # Interpolate to the fixed grid\n",
-    "            interp_func = interp1d(x_subject, wa_data, kind='linear', bounds_error=False, fill_value='extrapolate')\n",
-    "            resampled_data = interp_func(x_grid)\n",
+    "        # Interpolate to the fixed grid\n",
+    "        interp_func = interp1d(x_subject, wa_data, kind='linear', bounds_error=False, fill_value='extrapolate')\n",
+    "        resampled_data = interp_func(x_grid)\n",
     "\n",
-    "            # Apply smoothing\n",
-    "            smoothed_data = smooth_data(resampled_data)\n",
+    "        # Apply smoothing\n",
+    "        smoothed_data = smooth_data(resampled_data)\n",
     "\n",
-    "            method_data.append(smoothed_data)\n",
+    "        method_data.append(smoothed_data)\n",
     "\n",
-    "            # If there's data for this shim method, plot it\n",
-    "            if method_data:\n",
-    "                # Plotting each file's data separately\n",
-    "                for resampled_data in method_data:\n",
-    "                    ax.plot(x_grid, resampled_data, label=f\"{shim_mode}\")\n",
+    "        # If there's data for this shim method, plot it\n",
+    "        if method_data:\n",
+    "            # Plotting each file's data separately\n",
+    "            for resampled_data in method_data:\n",
+    "                ax.plot(x_grid, resampled_data, label=f\"{shim_mode}\")\n",
     "\n",
-    "                # Compute stats on the non-resampled data (to avoid interpolation errors)\n",
-    "                mean_data = np.mean(wa_data)\n",
-    "                sd_data = np.std(wa_data)\n",
-    "                data_stats.append([subject, shim_mode, mean_data, sd_data])\n",
+    "            # Compute stats on the non-resampled data (to avoid interpolation errors)\n",
+    "            mean_data = np.mean(wa_data)\n",
+    "            sd_data = np.std(wa_data)\n",
+    "            data_stats.append([subject, shim_mode, mean_data, sd_data])\n",
     "\n",
     "    # Set custom x-ticks\n",
     "    ax.set_xticks(custom_xticks)\n",
@@ -710,7 +711,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "90cdb2f6",
+   "id": "f062f23d",
    "metadata": {},
    "outputs": [],
    "source": [