diff --git a/slice-interpolation-for-zarpaint.ipynb b/slice-interpolation-for-zarpaint.ipynb index ca7b070..2bb99f6 100644 --- a/slice-interpolation-for-zarpaint.ipynb +++ b/slice-interpolation-for-zarpaint.ipynb @@ -36,7 +36,9 @@ "source": [ "import os\n", "import matplotlib.pyplot as plt\n", - "import numpy as np" + "import numpy as np\n", + "\n", + "%matplotlib inline" ] }, { @@ -54,40 +56,44 @@ "execution_count": 3, "id": "c88efc99", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/Users/genevieb/mambaforge/envs/zarpaint/lib/python3.9/site-packages/magicgui/widgets/_bases/value_widget.py\", line 57, in _on_value_change\n", - " self.changed.emit(value)\n", - " File \"psygnal/_signal.py\", line 682, in psygnal._signal.SignalInstance.emit\n", - " File \"psygnal/_signal.py\", line 724, in psygnal._signal.SignalInstance._run_emit_loop\n", - " File \"psygnal/_signal.py\", line 725, in psygnal._signal.SignalInstance._run_emit_loop\n", - " File \"psygnal/_signal.py\", line 745, in psygnal._signal.SignalInstance._run_emit_loop\n", - " File \"/Users/genevieb/mambaforge/envs/zarpaint/lib/python3.9/site-packages/toolz/functoolz.py\", line 306, in __call__\n", - " return self._partial(*args, **kwargs)\n", - " File \"/Users/genevieb/mambaforge/envs/zarpaint/lib/python3.9/site-packages/zarpaint/_zarpaint.py\", line 20, in _set_default_labels_path\n", - " source_image = source_image_event.value\n", - "AttributeError: 'Image' object has no attribute 'value'\n" - ] - } - ], + "outputs": [], "source": [ "viewer = napari.Viewer()\n", - "viewer.open_sample('napari', 'cells3d')\n", - "membrane_layer = viewer.layers[0]\n", - "nuclei_layer = viewer.layers[1]" + "# viewer.open_sample('napari', 'cells3d')\n", + "# membrane_layer = viewer.layers[0]\n", + "# nuclei_layer = viewer.layers[1]" ] }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 4, "id": "378903ae", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "from skimage import data\n", + "blobs = np.stack(\n", + " [\n", + " data.binary_blobs(\n", + " length=128, blob_size_fraction=0.05, n_dim=3, volume_fraction=f, seed=0\n", + " )\n", + " for f in np.linspace(0.05, 0.5, 10)\n", + " ],\n", + " axis=0,\n", + ")\n", + "viewer.add_image(blobs)\n", + "\n", "# # numpy label data\n", "# single_nuclei_label = tifffile.imread('single-nuclei-label.tif')\n", "# viewer.add_labels(single_nuclei_label)\n", @@ -110,14 +116,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "a34808c0", "metadata": {}, "outputs": [], "source": [ "# Make note of which image slices you labelled\n", - "slice_index_1 = 30\n", - "slice_index_2 = 38" + "slice_index_1 = 60\n", + "slice_index_2 = 70\n", + "interp_dim = 1" ] }, { @@ -130,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "119114bf", "metadata": {}, "outputs": [ @@ -146,9 +153,9 @@ " },\n", " 'driver': 'zarr',\n", " 'dtype': 'uint32',\n", - " 'kvstore': {'driver': 'file', 'path': 'labels.zarr/'},\n", + " 'kvstore': {'driver': 'file', 'path': 'example4d448pm/'},\n", " 'metadata': {\n", - " 'chunks': [1, 128, 128],\n", + " 'chunks': [1, 1, 128, 128],\n", " 'compressor': {\n", " 'blocksize': 0,\n", " 'clevel': 5,\n", @@ -161,12 +168,12 @@ " 'fill_value': 0,\n", " 'filters': None,\n", " 'order': 'C',\n", - " 'shape': [60, 256, 256],\n", + " 'shape': [10, 128, 128, 128],\n", " 'zarr_format': 2,\n", " },\n", " 'transform': {\n", - " 'input_exclusive_max': [[60], [256], [256]],\n", - " 'input_inclusive_min': [0, 0, 0],\n", + " 'input_exclusive_max': [[10], [128], [128], [128]],\n", + " 'input_inclusive_min': [0, 0, 0, 0],\n", " },\n", "})\n" ] @@ -181,7 +188,84 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 148, + "id": "c31ef2c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " ...\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]]\n", + "\n", + " [[0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " ...\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]]\n", + "\n", + " [[0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " ...\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]]\n", + "\n", + " ...\n", + "\n", + " [[0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " ...\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]]\n", + "\n", + " [[0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " ...\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]]\n", + "\n", + " [[0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " ...\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]\n", + " [0 0 0 ... 0 0 0]]]\n" + ] + } + ], + "source": [ + "print(np.take(label_layer.data, slice_index_1, axis=interp_dim))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c8950929", + "metadata": {}, + "outputs": [], + "source": [ + "image_1 = np.take(label_layer.data, slice_index_1, axis=interp_dim)\n", + "image_2 = np.take(label_layer.data, slice_index_2, axis=interp_dim)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "a6c7ab8d", "metadata": {}, "outputs": [ @@ -189,77 +273,156 @@ "name": "stdout", "output_type": "stream", "text": [ - "TensorStore({\n", - " 'context': {\n", - " 'cache_pool': {},\n", - " 'data_copy_concurrency': {},\n", - " 'file_io_concurrency': {},\n", - " },\n", - " 'driver': 'zarr',\n", - " 'dtype': 'uint32',\n", - " 'kvstore': {'driver': 'file', 'path': 'mylabels.zarr/'},\n", - " 'metadata': {\n", - " 'chunks': [1, 128, 128],\n", - " 'compressor': {\n", - " 'blocksize': 0,\n", - " 'clevel': 5,\n", - " 'cname': 'lz4',\n", - " 'id': 'blosc',\n", - " 'shuffle': 1,\n", - " },\n", - " 'dimension_separator': '.',\n", - " 'dtype': '" + "" ] }, - "execution_count": 6, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -271,14 +434,12 @@ } ], "source": [ - "image_1 = np.asarray(ts[slice_index_1])\n", - "image_2 = np.asarray(ts[slice_index_2])\n", "diff = image_1 - image_2\n", "\n", "fig, ax = plt.subplots(ncols=3, figsize=(10,3))\n", - "ax[0].imshow(image_1)\n", - "ax[1].imshow(image_2)\n", - "ax[2].imshow(diff)\n" + "ax[0].imshow(image_1[0]) # 4D original dataset\n", + "ax[1].imshow(image_2[0]) # 4D original dataset\n", + "ax[2].imshow(diff[0]) # 4D original dataset\n" ] }, { @@ -291,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "id": "a3657b13", "metadata": {}, "outputs": [], @@ -308,7 +469,7 @@ " label_layer.data[slices] = 0\n", " label_layer.refresh()\n", "\n", - " \n", + "\n", "def erase_all_slices(label_layer):\n", " for i in label_layer.data:\n", " i[...] = 0\n", @@ -325,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "83658b31", "metadata": {}, "outputs": [], @@ -356,8 +517,8 @@ "\n", "def xi_coords(shape, percent=0.5):\n", " slices = [slice(0, i) for i in shape]\n", - " xi = np.moveaxis(np.mgrid[slices], 0, -1).reshape(np.prod(shape), len(shape))\n", - " xi = xi = np.c_[np.full((np.prod(shape)), percent), xi]\n", + " xi = np.moveaxis(np.mgrid[slices], 0, -1).reshape(np.prod(shape), len(shape)).astype('float')\n", + " xi = np.insert(xi, interp_dim, percent, axis=1)\n", " return xi\n", "\n", "\n", @@ -383,11 +544,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 127, "id": "3be6323c", "metadata": {}, "outputs": [], "source": [ + "\n", + "\n", + "\n", + "\n", "# ## First draft, simply returns a numpy stack of the interpolated slices\n", "\n", "# def interpolate_between_slices(image_1, image_2, slice_index_1, slice_index_2, interp_dim=0, method='linear'):\n", @@ -419,19 +584,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 25, "id": "9b00f986", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done!\n", - "Please scroll through napari to see the interpolated label slices\n" - ] - } - ], + "outputs": [], "source": [ "## Second draft, writes directly into tensorstore zarr array\n", "\n", @@ -449,14 +605,453 @@ "\n", " for slice_number, percentage in slice_iterator(slice_index_1, slice_index_2):\n", " interpolated_img = interpolated_slice(percentage, points, values, interp_dim=interp_dim, method='linear')\n", - " label_layer.data[slice_number, interpolated_img] = label_id\n", + " indices = [slice(None) for _ in range(label_layer.data.ndim)]\n", + " indices[interp_dim] = slice_number\n", + " indices = tuple(indices)\n", + " label_layer.data[indices][interpolated_img] = label_id\n", " label_layer.refresh() # will update the current view\n", "\n", - "interpolate_between_slices(label_layer, image_1, image_2, slice_index_1, slice_index_2)\n", + "# interpolate_between_slices(label_layer, image_1, image_2, slice_index_1, slice_index_2, interp_dim=interp_dim)\n", + "# print(\"Done!\")\n", + "# print(\"Please scroll through napari to see the interpolated label slices\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "51b5ee0e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10, 128, 128)\n" + ] + } + ], + "source": [ + "percentage = 0.5\n", + "points, values = point_and_values(image_1, image_2, interp_dim=interp_dim)\n", + "interpolated_img = interpolated_slice(percentage, points, values, interp_dim=interp_dim, method='linear')\n", + "print(interpolated_img.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "4f92243e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(slice(None, None, None), 50, slice(None, None, None), slice(None, None, None))\n" + ] + } + ], + "source": [ + "slice_number = 50\n", + "indices = [slice(None) for _ in range(label_layer.data.ndim)]\n", + "indices[interp_dim] = slice_number\n", + "indices = tuple(indices)\n", + "print(indices)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3f61ef08", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5, 50, 64, 64)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viewer.dims.current_step" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "702dc496", + "metadata": {}, + "outputs": [], + "source": [ + "label_layer.data[indices] = 2\n", + "label_layer.refresh() # will update the current view\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "2618bfc0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label_layer.data[indices][interpolated_img].write(label_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "0744b5a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done!\n", + "Please scroll through napari to see the interpolated label slices\n" + ] + } + ], + "source": [ + "interp_dim = 1\n", + "interpolate_between_slices(label_layer, image_1, image_2, slice_index_1, slice_index_2, interp_dim=interp_dim)\n", "print(\"Done!\")\n", - "print(\"Please scroll through napari to see the interpolated label slices\")\n" + "print(\"Please scroll through napari to see the interpolated label slices\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "bcaf0137", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "label_id = 1\n", + "image_1 = image_1.astype(bool)\n", + "image_2 = image_2.astype(bool)\n", + "\n", + "points, values = point_and_values(image_1, image_2, interp_dim=interp_dim)\n", + "\n", + "\n", + "#for slice_number, percentage in slice_iterator(slice_index_1, slice_index_2):\n", + "# interpolated_img = interpolated_slice(percentage, points, values, interp_dim=interp_dim, method='linear')\n", + "# label_layer.data[slice_number, interpolated_img] = label_id\n", + "#label_layer.refresh() # will update the current view" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "a911c2b9", + "metadata": {}, + "outputs": [], + "source": [ + "percentage = 0.5\n", + "percent = percentage\n", + "\n", + "img_shape = list(values.shape)\n", + "del img_shape[interp_dim]\n", + "# Calculate the interpolated slice\n", + "\n", + "# xi = xi_coords(img_shape, percent=percent)\n", + "method='linear'\n", + "shape = img_shape\n", + "slices = [slice(0, i) for i in shape]\n", + "xi = np.moveaxis(np.mgrid[slices], 0, -1).reshape(np.prod(shape), len(shape)).astype('float')\n", + "xi = np.insert(xi, interp_dim, percent, axis=1)\n", + "\n", + "interpolated_img = interpn(points, values, xi, method=method)\n", + "interpolated_img = np.reshape(interpolated_img, img_shape) > 0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "ee19afac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 128, 128)" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interpolated_img.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "9857f388", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10, 1, 128, 128)\n" + ] + } + ], + "source": [ + "slice_number = 50\n", + "temp = np.expand_dims(interpolated_img, interp_dim)\n", + "print(temp.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "c6c9ad3d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[[False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " ...,\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False]]],\n", + "\n", + "\n", + " [[[False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " ...,\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False]]],\n", + "\n", + "\n", + " [[[False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " ...,\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False]]],\n", + "\n", + "\n", + " ...,\n", + "\n", + "\n", + " [[[False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " ...,\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False]]],\n", + "\n", + "\n", + " [[[False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " ...,\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False]]],\n", + "\n", + "\n", + " [[[False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " ...,\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False],\n", + " [False, False, False, ..., False, False, False]]]])" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "b9f2a13e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 50, 54, 52],\n", + " [ 5, 50, 54, 53]], dtype=int64)" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indices = np.argwhere(interpolated_img)\n", + "indices = np.insert(indices, interp_dim, slice_number, axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "df3e8414", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.5, 0. , 0. , 0. ],\n", + " [ 0.5, 0. , 0. , 1. ],\n", + " [ 0.5, 0. , 0. , 2. ],\n", + " ...,\n", + " [ 0.5, 9. , 127. , 125. ],\n", + " [ 0.5, 9. , 127. , 126. ],\n", + " [ 0.5, 9. , 127. , 127. ]])" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xi" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d0c69ad", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef63b9c3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.interpolate import interpn\n", + "from scipy.ndimage import distance_transform_edt\n", + "\n", + "\n", + "def distance_transform(image):\n", + " \"\"\"Distance transform for a boolean image.\n", + " \n", + " Returns positive values inside the object,\n", + " and negative values outside.\n", + " \"\"\"\n", + " image = image.astype(bool)\n", + " edt = distance_transform_edt(image) - distance_transform_edt(~image)\n", + " return edt\n", + "\n", + "\n", + "def point_and_values(image_1, image_2, interp_dim=0):\n", + " edt_1 = distance_transform(image_1)\n", + " edt_2 = distance_transform(image_2)\n", + " values = np.stack([edt_1, edt_2], axis=interp_dim)\n", + " points = tuple([np.arange(i) for i in values.shape])\n", + " return points, values\n", + "\n", + "\n", + "def xi_coords(shape, percent=0.5):\n", + " slices = [slice(0, i) for i in shape]\n", + " xi = np.moveaxis(np.mgrid[slices], 0, -1).reshape(np.prod(shape), len(shape))\n", + " xi = xi = np.c_[np.full((np.prod(shape)), percent), xi]\n", + " return xi\n", + "\n", + "\n", + "def slice_iterator(slice_index_1, slice_index_2):\n", + " intermediate_slices = np.arange(slice_index_1 + 1, slice_index_2)\n", + " n_slices = slice_index_2 - slice_index_1 + 1 # inclusive\n", + " stepsize = 1 / n_slices\n", + " intermediate_percentages = np.arange(0 + stepsize, 1, stepsize)\n", + " return zip(intermediate_slices, intermediate_percentages)\n", + "\n", + "\n", + "def interpolated_slice(percent, points, values, interp_dim=0, method='linear'):\n", + " # Find the original image shape\n", + " img_shape = list(values.shape)\n", + " del img_shape[interp_dim]\n", + " # Calculate the interpolated slice\n", + " xi = xi_coords(img_shape, percent=percent)\n", + " interpolated_img = interpn(points, values, xi, method=method)\n", + " interpolated_img = np.reshape(interpolated_img, img_shape) > 0\n", + " return interpolated_img\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "864c8bda", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33f94119", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f23e2b74", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9c2522c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e8d31e7", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "449d7c17", @@ -583,8 +1178,11 @@ } ], "metadata": { + "interpreter": { + "hash": "e8f0b44cf16a59ebfa2ed0b00c97bf93787b28c44f2aafb01610def517855b78" + }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.9.12 ('napari')", "language": "python", "name": "python3" },