@@ -42,9 +42,9 @@
     "\n",
     "\"\"\"\n",
     "\n",
-    "analysis_folder = r\"S:\\ASAP_Imaging_Data\\Main_Survey\\20241105_oligomers_in_cells\\caudate\\neurons\\PD_analysis\"\n",
+    "analysis_folder = \"/scratch/sycamore-asap/ASAP_Imaging_Data/Main_Survey/20241105_oligomers_in_cells/caudate/neurons/PD_analysis\"\n",
     "analysis_file = os.path.abspath(\n",
-    "    r\"S:\\ASAP_Imaging_Data\\Main_Survey\\20241105_oligomers_in_cells\\caudate\\neurons\\PD_analysis\\spot_analysis_0_abovephotonthreshold.csv\"\n",
+    "    os.path.join(analysis_folder, \"20250304\", \"spot_analysis_0_abovephotonthreshold.csv\")\n",
     ")  # example file, replace as you wish. Always input as the absolute path of a raw string\n",
     "threshold = 0.0\n",
     "protein_string = \"C1\"\n",
@@ -54,7 +54,10 @@
     "analysis_data = pl.read_csv(analysis_file)\n",
     "z_project_first = [True, True]\n",
     "\n",
-    "cell_punctum_analysis_file = r\"S:\\ASAP_Imaging_Data\\Main_Survey\\20241105_oligomers_in_cells\\caudate\\neurons\\PD_analysis\\single_cell_coincidence_mincellsize_2000_photonthreshold_0p0_photons_combined_threshold_percentile_0_k_4_outliersremoved_abovethreshold.csv\"\n",
+    "cell_punctum_analysis_file = os.path.join(\n",
+    "    analysis_folder,\n",
+    "    \"single_cell_coincidence_mincellsize_2000_photonthreshold_0p0_photons_combined_threshold_percentile_0_k_4_outliersremoved_abovethreshold.csv\",\n",
+    ")\n",
     "cell_punctum_analysis = pl.read_csv(cell_punctum_analysis_file)\n",
     "cell_punctum_analysis = cell_punctum_analysis.filter(pl.col(\"puncta_cell_likelihood\") != np.NAN)"
    ]
@@ -977,30 +980,44 @@
     "A_F = AnalysisFunctions.Analysis_Functions()\n",
     "import os\n",
     "\n",
+    "def _to_linux_path(windows_path):\n",
+    "    \"\"\"Converts a stored Windows-style S:\\\\... path (baked into older analysis\n",
+    "    CSVs from when this data was analysed on Windows) to the equivalent Linux\n",
+    "    path used everywhere now.\"\"\"\n",
+    "    posix_path = windows_path.replace(\"\\\\\", \"/\")\n",
+    "    if posix_path.startswith(\"S:/\"):\n",
+    "        posix_path = \"/scratch/sycamore-asap\" + posix_path[2:]\n",
+    "    return posix_path\n",
+    "\n",
+    "\n",
     "for i in np.arange(len(n_cells_per_image)):\n",
     "    cell_file = np.argsort(n_cells_per_image)[i]\n",
-    "    file = image_filenames[cell_file]  # we take a file we're interested in\n",
+    "    file = image_filenames[cell_file]  # we take a file we're interested in (as stored in the CSV -- keep as-is for filtering below)\n",
+    "    file_linux = _to_linux_path(file)  # the same file, as an actual path on this machine\n",
     "    # now find the corresponding cell mask\n",
     "    mask_file = os.path.join(\n",
     "        analysis_folder,\n",
     "        str(\n",
-    "            os.path.split(file)[-1].split(imtype)[0].split(protein_string)[0]\n",
+    "            os.path.split(file_linux)[-1].split(imtype)[0].split(protein_string)[0]\n",
     "            + cell_string\n",
     "            + \"_cellMask.tiff\"\n",
     "        ),\n",
     "    )\n",
     "    \n",
     "    # and the corresponding raw cell data\n",
-    "    raw_cell_file = str(file.split(imtype)[0].split(protein_string)[0]+cell_string+imtype)\n",
+    "    raw_cell_file = str(file_linux.split(imtype)[0].split(protein_string)[0]+cell_string+imtype)\n",
     "    cell_mask = IO.read_tiff(mask_file)\n",
     "    cell_image = IO.read_tiff_tophotons(raw_cell_file)\n",
-    "    puncta_image = IO.read_tiff_tophotons(file)\n",
+    "    puncta_image = IO.read_tiff_tophotons(file_linux)\n",
     "    puncta_values = analysis_data.filter(pl.col('image_filename') == file)\n",
-    "    oned_cell_mask, pil, centroids, areas = A_F.threshold_cell_areas(\n",
+    "    # threshold_cell_areas() no longer exists -- threshold_cell_areas_3d is now\n",
+    "    # the default cell-size filter (operates on the full 3D mask; note the real\n",
+    "    # return order is cell_mask, pil, areas, centroids -- areas before centroids).\n",
+    "    cell_mask_new, pil, areas, centroids = A_F.threshold_cell_areas_3d(\n",
     "        cell_mask,\n",
     "        lower_cell_size_threshold=lower_cell_size_threshold,\n",
-    "        z_project=[True, True],\n",
     "    )\n",
+    "    oned_cell_mask = cell_mask_new.sum(axis=0).clip(0, 1)\n",
     "    oned_puncta_image = np.sum(puncta_image, axis=-1)\n",
     "    oned_cell_image = np.sum(cell_image, axis=-1)\n",
     "    print(i)\n",
@@ -1016,28 +1033,32 @@
    "outputs": [],
    "source": [
     "    cell_file = np.argsort(n_cells_per_image)[14]\n",
-    "    file = image_filenames[cell_file]  # we take a file we're interested in\n",
+    "    file = image_filenames[cell_file]  # we take a file we're interested in (as stored in the CSV -- keep as-is for filtering below)\n",
+    "    file_linux = _to_linux_path(file)  # the same file, as an actual path on this machine\n",
     "    # now find the corresponding cell mask\n",
     "    mask_file = os.path.join(\n",
     "        analysis_folder,\n",
     "        str(\n",
-    "            os.path.split(file)[-1].split(imtype)[0].split(protein_string)[0]\n",
+    "            os.path.split(file_linux)[-1].split(imtype)[0].split(protein_string)[0]\n",
     "            + cell_string\n",
     "            + \"_cellMask.tiff\"\n",
     "        ),\n",
     "    )\n",
     "    \n",
     "    # and the corresponding raw cell data\n",
-    "    raw_cell_file = str(file.split(imtype)[0].split(protein_string)[0]+cell_string+imtype)\n",
+    "    raw_cell_file = str(file_linux.split(imtype)[0].split(protein_string)[0]+cell_string+imtype)\n",
     "    cell_mask = IO.read_tiff(mask_file)\n",
     "    cell_image = IO.read_tiff_tophotons(raw_cell_file)\n",
-    "    puncta_image = IO.read_tiff_tophotons(file)\n",
+    "    puncta_image = IO.read_tiff_tophotons(file_linux)\n",
     "    puncta_values = analysis_data.filter(pl.col('image_filename') == file)\n",
-    "    oned_cell_mask, pil, centroids, areas = A_F.threshold_cell_areas(\n",
+    "    # threshold_cell_areas() no longer exists -- threshold_cell_areas_3d is now\n",
+    "    # the default cell-size filter (operates on the full 3D mask; note the real\n",
+    "    # return order is cell_mask, pil, areas, centroids -- areas before centroids).\n",
+    "    cell_mask_new, pil, areas, centroids = A_F.threshold_cell_areas_3d(\n",
     "        cell_mask,\n",
     "        lower_cell_size_threshold=lower_cell_size_threshold,\n",
-    "        z_project=[True, True],\n",
     "    )\n",
+    "    oned_cell_mask = cell_mask_new.sum(axis=0).clip(0, 1)\n",
     "    oned_puncta_image = np.sum(puncta_image, axis=-1)\n",
     "    oned_cell_image = np.sum(cell_image, axis=-1)"
    ]