Smooth individually CSF and SC T2starw signal before taking the ratio

Fixes #74

data_processing.ipynb

@@ -330,7 +330,7 @@
     "# Go back to root data folder\n",
     "os.chdir(path_data)\n",
     "\n",
-    "def smooth_data(data, window_size=40):\n",
+    "def smooth_data(data, window_size=50):\n",
     "    \"\"\" Apply a simple moving average to smooth the data. \"\"\"\n",
     "    return uniform_filter1d(data, size=window_size, mode='nearest')\n",
     "\n",
@@ -371,14 +371,16 @@
     "        file_csv = os.path.join(path_results, f\"{subject}_acq-{shim_method}_T2starw_label-SC.csv\")\n",
     "        df = pd.read_csv(file_csv)\n",
     "        data_sc = df['WA()']\n",
+    "        data_sc_smoothed = smooth_data(data_sc)\n",
     "\n",
     "        # Get signal in CSF\n",
     "        file_csv = os.path.join(path_results, f\"{subject}_acq-{shim_method}_T2starw_label-CSF.csv\")\n",
     "        df = pd.read_csv(file_csv)\n",
     "        data_csf = df['WA()']\n",
+    "        data_csf_smoothed = smooth_data(data_csf)\n",
     "        \n",
     "        # Compute ratio\n",
-    "        data_sc_csf_ratio = data_csf / data_sc\n",
+    "        data_sc_csf_ratio = data_csf_smoothed / data_sc_smoothed\n",
     "\n",
     "        # Normalize the x-axis to a 0-1 scale for each subject\n",
     "        x_subject = np.linspace(0, 1, len(data_sc_csf_ratio))\n",
@@ -387,10 +389,7 @@
     "        interp_func = interp1d(x_subject, data_sc_csf_ratio, 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",
-    "\n",
-    "        method_data.append(smoothed_data)\n",
+    "        method_data.append(resampled_data)\n",
     "\n",
     "        # If there's data for this shim method, plot it\n",
     "        if method_data:\n",