Organize pipeline notebook

environment.yml

@@ -1,4 +1,4 @@
-name: snazzy-test-env
+name: snazzy-env
 channels:
   - conda-forge
   - defaults

snazzy_processing/notebooks/snazzy-processing-pipeline.ipynb

@@ -8,7 +8,12 @@
     "\n",
     "Integrates all of the modules from the package, to go from raw data to csv output.\n",
     "More information about each module can be found in the other Jupyter Notebooks.\n",
-    "You should change the `experiment name` and the `img_path` at the fist cell."
+    "You should change the `experiment name` and the `img_path` at the fist cell.\n",
+    "\n",
+    "If the raw data is in nd2 format, it can be converted directly using the pipeline.\n",
+    "For other formats, the image must me converted to tif using another application, for example ImageJ.\n",
+    "\n",
+    "Adjust the image paths to where the nd2 file is, and where the tif file will be saved."
    ]
   },
   {
@@ -27,38 +32,14 @@
     "\n",
     "from snazzy_processing import pipeline, slice_img\n",
     "\n",
-    "experiment_name = '20240611_25C'\n",
+    "experiment_name = '20240611'\n",
     "root_dir = Path.cwd().parent\n",
     "project_dir = root_dir.joinpath('data', experiment_name)\n",
     "res_dir = root_dir.joinpath('results', experiment_name)\n",
     "res_dir.mkdir(parents=True, exist_ok=True)\n",
     "\n",
-    "# Provide an absolute path for the raw tif image\n",
-    "# If you are starting with another image format, ignore this line and convert the\n",
-    "# image in the next cell\n",
-    "img_path = ''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the raw data is in nd2 format, it must first be converted to a tif file, and that tif file should be used in the other cells of this jupyter notebook.\n",
-    "\n",
-    "Adjust the image paths to where the nd2 file is, and where the tif file will be saved."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_path = Path.home()\n",
-    "# Path where the raw image is located:\n",
-    "nd2_path = base_path.joinpath(\"Documents\", \"raw_data\", f\"{experiment_name}.nd2\")\n",
-    "# Path where the new tiff file will be saved:\n",
-    "img_path = base_path.joinpath(\"Documents\", \"raw_data\", f\"{experiment_name}.tif\")"
+    "# Provide an absolute path for the raw tif or nd2 image\n",
+    "img_path = Path.home().joinpath(\"Documents\", \"raw_data\", f\"{experiment_name}.nd2\")"
    ]
   },
   {
@@ -74,13 +55,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "first_frames = f\"first_frames.tif\"\n",
+    "first_frames = \"first_frames.tif\"\n",
     "first_frames_path = root_dir.joinpath(\"results\", experiment_name, first_frames)\n",
     "if first_frames_path.exists():\n",
     "    print(f\"{first_frames_path.stem} already exists.\\nPlease choose another path.\")\n",
     "else:\n",
-    "    file_path = nd2_path if nd2_path else img_path\n",
-    "    slice_img.save_first_frames_as_tiff(file_path, first_frames_path, 10)"
+    "    slice_img.save_first_frames_as_tiff(img_path, first_frames_path, 10)"
    ]
   },
   {
@@ -103,11 +83,11 @@
     "img = slice_img.get_first_image(first_frames_path)\n",
     "\n",
     "coords = slice_img.calculate_slice_coordinates(\n",
-    "    first_frames_path, n_cols=4, thres_adjust=-5\n",
+    "    first_frames_path, n_cols=2, thres_adjust=-10\n",
     ")\n",
     "\n",
     "# If necessary, manually change the bboxes size by changing w and h:\n",
-    "boundaries = slice_img.increase_bbox(coords, w=150, h=40, shape=img.shape)\n",
+    "boundaries = slice_img.increase_bbox(coords, w=190, h=40, shape=img.shape)\n",
     "\n",
     "rect_coords = [slice_img.boundary_to_rect_coords(b) for b in boundaries.values()]\n",
     "recs = [Rectangle((y, x), w, h) for (x, y, w, h) in rect_coords]\n",
@@ -159,7 +139,7 @@
     "clean_up_data = False\n",
     "# List of the ids of the embryos that should be processed\n",
     "# The ids are the bbox numbers from the previous cell output\n",
-    "embryos = [1, 2, 3, 4, 5]\n",
+    "embryos = []\n",
     "# Interval (number of frames) used to calculate VNC length\n",
     "vnc_length_interval = 10\n",
     "# Window (number of frames) to calculate VNC ROI (which is then used to calculate activity)\n",
@@ -170,9 +150,11 @@
     "embs_dest.mkdir(parents=True, exist_ok=True)\n",
     "\n",
     "# nd2 to tif\n",
-    "if nd2_path:\n",
+    "if img_path.suffix == \".nd2\":\n",
     "    print(\"Converting from nd2 to tif\")\n",
-    "    slice_img.save_as_tiff(nd2_path, img_path)\n",
+    "    output_path = img_path.parent.joinpath(f\"{img_path.stem}.tif\")\n",
+    "    slice_img.save_as_tiff(img_path, output_path)\n",
+    "    img_path = output_path\n",
     "\n",
     "# tif to individual movies\n",
     "print(\"Cropping individual movies\")\n",
@@ -212,7 +194,7 @@
     "# write params used\n",
     "output_path = root_dir.joinpath(\"results\", experiment_name, \"params.txt\")\n",
     "pipeline.log_params(\n",
-    "    path=output_path,\n",
+    "    output_path=output_path,\n",
     "    experiment_name=experiment_name,\n",
     "    embryos=embryos,\n",
     "    window=window,\n",

snazzy_processing/snazzy_processing/slice_img.py

@@ -60,7 +60,11 @@ def save_first_frames_as_tiff(file: Path, dest_path: Path, n: int):
         print(f"File '{dest.name}' already exists.")
         return
     if file.suffix == ".tif" or file.suffix == ".tiff":
-        initial_frames = imread(file, key=slice(0, n))
+        # tiffile will slice over pages, so we need to reshape
+        initial_frames = imread(file, key=slice(0, 2 * n))
+        _, y, x = initial_frames.shape
+        initial_frames = np.reshape(initial_frames, (n, 2, y, x))
+
         imwrite(dest_path, initial_frames)
     else:
         with ND2File(file) as f:
@@ -426,6 +430,7 @@ def get_first_image(img_path: Path):
 
     The channel 2 frames are averaged and equalized, for better visualization."""
     img = imread(img_path)
+    print(img.shape)
     first_frame = np.average(img[:, 1, :, :], axis=0)
     return equalize_hist(first_frame)