{ "cells": [ { "cell_type": "markdown", "id": "dca63629", "metadata": {}, "source": [ "# stackview\n", "Interactive image stack viewing in jupyter notebooks based on \n", "[ipycanvas](https://ipycanvas.readthedocs.io/) and \n", "[ipywidgets](https://ipywidgets.readthedocs.io/en/latest/)." ] }, { "cell_type": "markdown", "id": "79a8abbc", "metadata": {}, "source": [ "## Usage\n", "You can use `stackview` from within jupyter notebooks as shown below.\n", "\n", "Starting point is a 3D image dataset provided as numpy array. " ] }, { "cell_type": "code", "execution_count": 1, "id": "774acd58-1c4a-4673-bd81-50d9ff53f55b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.7.1'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import stackview\n", "stackview.__version__" ] }, { "cell_type": "code", "execution_count": 2, "id": "49a9caad-0ce0-4a91-ad44-2cd06c3ad111", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from skimage.io import imread\n", "from skimage.filters import gaussian" ] }, { "cell_type": "code", "execution_count": 3, "id": "21067204", "metadata": {}, "outputs": [], "source": [ "image = imread('https://github.com/haesleinhuepf/stackview/blob/main/docs/data/Haase_MRT_tfl3d1.tif?raw=true', plugin='tifffile')" ] }, { "cell_type": "markdown", "id": "42963cad", "metadata": {}, "source": [ "## Slice\n", "You can then view it slice-by-slice:" ] }, { "cell_type": "code", "execution_count": 4, "id": "35c9eccc-63a4-4e55-aaf6-3d67787b3474", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3, 160, 160)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image[:3].shape" ] }, { "cell_type": "code", "execution_count": 5, "id": "4cf3f9d3", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fc4ae16efa734e919f03d701dd12e632", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=160, width=160),)),)), IntSlider(value=1, desc…" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.slice(image[:3], continuous_update=True)" ] }, { "cell_type": "markdown", "id": "39843d28-d31e-46a7-bc33-5cf3d8cabd6e", "metadata": {}, "source": [ "# Insight\n", "You can also get a static view using a maximum-intensity projection with additional information." ] }, { "cell_type": "code", "execution_count": 6, "id": "be919845-f8fc-4dd6-8b0b-6e729c9d0fc2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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image" ] }, { "cell_type": "code", "execution_count": 11, "id": "ab1dc868", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1e7ac7ce712d4fc19916ca7969d30f9b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=160, width=160),)),)), IntSlider(value=60, des…" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.curtain(image, modified_image, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "dbd725c9-5867-4e47-8b7c-b7006fd2982b", "metadata": {}, "source": [ "One can also use the curtain to visualize semantic segmentation results as label images." ] }, { "cell_type": "code", "execution_count": 12, "id": "e421b46f-f738-4048-9a77-09dc54070715", "metadata": {}, "outputs": [], "source": [ "labels = (image > 5000)*1 + (image > 15000)*1 + (image > 30000)*1" ] }, { "cell_type": "code", "execution_count": 13, "id": "24f8a418-b261-4a6e-97de-025046326a13", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2ea5bae8c28e47cf81529ed7fcd6b409", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=160, width=160),)),)), IntSlider(value=60, des…" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.curtain(image, labels, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "ffda520d-0451-499e-a5e7-08b6a1220d1e", "metadata": {}, "source": [ "The curtain can also be partially transparent using the alpha value." ] }, { "cell_type": "code", "execution_count": 14, "id": "91a89968-9f2c-48bb-abfa-d6e123dcf51d", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d9d58b90cf804af9b29865a8f57f14ea", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=160, width=160),)),)), IntSlider(value=60, des…" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.curtain(image, labels, continuous_update=True, alpha=0.3)" ] }, { "cell_type": "markdown", "id": "187cf539", "metadata": {}, "source": [ "The curtain also works with 2D data" ] }, { "cell_type": "code", "execution_count": 15, "id": "2f21ee1d", "metadata": {}, "outputs": [], "source": [ "slice_image = imread('https://github.com/haesleinhuepf/stackview/blob/main/docs/data/blobs.tif?raw=true', plugin='tifffile')" ] }, { "cell_type": "code", "execution_count": 16, "id": "bd7d1317", "metadata": {}, "outputs": [], "source": [ "from skimage.filters import threshold_otsu\n", "binary = (slice_image > threshold_otsu(slice_image))" ] }, { "cell_type": "code", "execution_count": 17, "id": "652721ec-6dfd-4518-9554-a3335ef27c6a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((254, 256), (254, 256))" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "binary.shape, slice_image.shape" ] }, { "cell_type": "code", "execution_count": 18, "id": "6b5dd31a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "26d38f5137ec4939880674425e76a09f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=254, width=256),)),)), IntSlider(value=127, de…" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.curtain(slice_image, binary, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "c15807a9-2de5-45e0-93bd-e0e952da0fce", "metadata": {}, "source": [ "Also label images are supported. Images are shown as labels in case their pixel type is (unsigned) integer 32-bit or 32-bit." ] }, { "cell_type": "code", "execution_count": 19, "id": "f39bacd7-6ca3-4c28-bb4f-25d2251b0c9a", "metadata": {}, "outputs": [], "source": [ "from skimage.measure import label\n", "labels = label(binary)" ] }, { "cell_type": "code", "execution_count": 20, "id": "1ad031f5-ab80-473c-95c5-12bd08256d56", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "86625c3dbe1d45f2aaa940a2e0e9fd7f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=254, width=256),)),)), IntSlider(value=127, de…" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.curtain(slice_image, labels, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "4767b2f9", "metadata": {}, "source": [ "## Side-by-side\n", "A side-by-side view of two stacks is also available. It might be useful for colocalization visualization or showing subsequent time points of a timelapse." ] }, { "cell_type": "code", "execution_count": 21, "id": "7bf91429", "metadata": {}, "outputs": [], "source": [ "image_stack = imread('https://github.com/haesleinhuepf/stackview/blob/main/docs/data/CalibZAPWfixed_000154_max.tif?raw=true', plugin='tifffile').swapaxes(1,2)" ] }, { "cell_type": "code", "execution_count": 22, "id": "5ad9ebf9", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "062b2418e4a0497586daf391e4134d3c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(HBox(children=(VBox(children=(ImageWidget(height=235, width=389),)),)), HBox(chi…" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.side_by_side(image_stack[1:], image_stack[:-1], continuous_update=True, display_width=300)" ] }, { "cell_type": "code", "execution_count": 23, "id": "97a16fec-21a0-4aa6-8dee-660ce345238e", "metadata": {}, "outputs": [], "source": [ "labels_stack = np.asarray([label(image > 100) for image in image_stack])" ] }, { "cell_type": "code", "execution_count": 24, "id": "b948942f-b1c1-474f-a8cb-cf3f104b65bf", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1e04993c618b436aa02fd7d0bbd9845c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(HBox(children=(VBox(children=(ImageWidget(height=389, width=235),)),)), HBox(chi…" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.side_by_side(image_stack, labels_stack, continuous_update=True, display_width=300)" ] }, { "cell_type": "markdown", "id": "3267a413-90df-461f-ac3d-531bd4f1d6e3", "metadata": {}, "source": [ "## Switch\n", "If there are many images you would like to inspect, you can pass them as dictionary or list to `switch()`." ] }, { "cell_type": "code", "execution_count": 25, "id": "6a3c528a-164a-47f8-8f89-bb06961d035c", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7376b18c7b49467586ad8476c5833aaf", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=254, width=256),)),)), HBox(children=(Button(d…" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.switch({\n", " \"image\":slice_image,\n", " \"binary\":binary,\n", " \"labels\":labels\n", "})" ] }, { "cell_type": "code", "execution_count": 26, "id": "c2300e4a-37cf-4c71-a59d-8b1e03bb6104", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "aa6c3cf773f04b0688cef3d358165255", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=389, width=235),)),)), HBox(children=(Button(d…" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.switch([image_stack[1:], image_stack[:-1]])" ] }, { "cell_type": "code", "execution_count": 27, "id": "5eefda44-4c39-435f-aef7-1cc173c7d5ff", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5ad4862e429943a89c50e4b3aa0603f3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=254, width=256),)),)), HBox(children=(Button(d…" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.switch([\n", " slice_image,\n", " binary,\n", " labels\n", "])" ] }, { "cell_type": "code", "execution_count": 28, "id": "d44f586a-7a0d-44e9-aa15-60d3238a0c8d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(512, 672, 3)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hela_cells = imread(\"data/hela-cells.tif\")\n", "hela_cells.shape" ] }, { "cell_type": "code", "execution_count": 29, "id": "238118b8-7f3e-4580-87cb-cad4728f87c6", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bf5315c60c074f7593905662b27d7bd6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=512, width=672),)),)), HBox(children=(ToggleBu…" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.switch(\n", " {\"lysosomes\": hela_cells[:,:,0],\n", " \"mitochondria\":hela_cells[:,:,1],\n", " \"nuclei\": hela_cells[:,:,2]\n", " },\n", " colormap=[\"pure_magenta\", \"pure_green\", \"pure_blue\"],\n", " toggleable=True\n", ")" ] }, { "cell_type": "markdown", "id": "a7a83822-8dd7-44dd-a289-7232e823995f", "metadata": {}, "source": [ "## Crop\n", "The `crop` widget allows to crop the image in Z,Y and X." ] }, { "cell_type": "code", "execution_count": 30, "id": "078a817f-aded-4ca9-b754-e4e7e32407b3", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6add961b4289462093f328ef9b966e1a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "_Cropper(children=(IntRangeSlider(value=(0, 100), description='Z'), IntRangeSlider(value=(0, 389), description…" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "crop_widget = stackview.crop(image_stack)\n", "crop_widget" ] }, { "cell_type": "markdown", "id": "f652e17b-d628-4623-8bbd-bb392c82454a", "metadata": {}, "source": [ "You can retrieve the range the user entered like this:" ] }, { "cell_type": "code", "execution_count": 31, "id": "088cfe7b-b09e-4bfa-a6ad-58aee2cb4697", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(slice(0, 100, 1), slice(0, 389, 1), slice(0, 235, 1))" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "crop_widget.range" ] }, { "cell_type": "markdown", "id": "b1fd55fd-7866-4603-9f97-908100f097fb", "metadata": {}, "source": [ "You can also get the cropped image directly." ] }, { "cell_type": "code", "execution_count": 32, "id": "5184f33f-f153-46f3-b799-e4e4945c02f0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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" ], "text/plain": [ "StackViewNDArray([[[255, 255, 255, ..., 30, 27, 20],\n", " [255, 255, 255, ..., 61, 53, 10],\n", " [255, 255, 255, ..., 70, 43, 22],\n", " ...,\n", " [ 39, 38, 62, ..., 33, 50, 49],\n", " [ 70, 78, 64, ..., 21, 40, 24],\n", " [ 55, 54, 68, ..., 40, 43, 33]],\n", "\n", " [[255, 255, 255, ..., 60, 33, 57],\n", " [255, 255, 255, ..., 96, 71, 52],\n", " [255, 255, 255, ..., 144, 105, 67],\n", " ...,\n", " [ 35, 60, 54, ..., 27, 40, 36],\n", " [ 40, 38, 47, ..., 59, 25, 44],\n", " [ 53, 21, 62, ..., 52, 24, 30]],\n", "\n", " [[255, 255, 255, ..., 180, 119, 122],\n", " [238, 230, 242, ..., 213, 153, 103],\n", " [208, 229, 223, ..., 222, 212, 137],\n", " ...,\n", " [ 43, 50, 53, ..., 32, 27, 33],\n", " [ 42, 63, 48, ..., 17, 29, 45],\n", " [ 40, 68, 41, ..., 22, 39, 39]],\n", "\n", " ...,\n", "\n", " [[168, 191, 203, ..., 176, 182, 150],\n", " [163, 188, 189, ..., 243, 212, 187],\n", " [142, 156, 165, ..., 231, 235, 248],\n", " ...,\n", " [155, 165, 164, ..., 77, 88, 99],\n", " [141, 140, 133, ..., 76, 94, 102],\n", " [136, 120, 111, ..., 73, 75, 106]],\n", "\n", " [[212, 194, 229, ..., 178, 181, 176],\n", " [194, 206, 188, ..., 217, 224, 213],\n", " [157, 173, 177, ..., 220, 243, 255],\n", " ...,\n", " [121, 133, 142, ..., 56, 84, 101],\n", " [121, 122, 122, ..., 75, 87, 105],\n", " [128, 117, 121, ..., 77, 97, 75]],\n", "\n", " [[168, 195, 230, ..., 155, 157, 157],\n", " [144, 180, 212, ..., 195, 191, 184],\n", " [141, 171, 168, ..., 200, 218, 248],\n", " ...,\n", " [132, 138, 142, ..., 48, 72, 82],\n", " [136, 128, 119, ..., 60, 68, 91],\n", " [155, 125, 115, ..., 76, 66, 82]]], dtype=uint8)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "crop_widget.crop()" ] }, { "cell_type": "markdown", "id": "18ea8070", "metadata": {}, "source": [ "## Interact\n", "You can also use `interact` to explore parameters of some supported functions that process images, e.g. from [scikit-image](https://scikit-image.org/):" ] }, { "cell_type": "code", "execution_count": 33, "id": "41b75598", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "24e25a73e32043468aef60729ca823df", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(interactive(children=(IntSlider(value=0, description='footprint'), Output()), _dom_classes=('wi…" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage.filters.rank import maximum\n", "stackview.interact(maximum, slice_image)" ] }, { "cell_type": "code", "execution_count": 34, "id": "ba9bfc67", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cf68ab39879643b494953bc281aab267", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(interactive(children=(FloatSlider(value=1.0, description='sigma', max=25.0, step=1.0), Output()…" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage.filters import gaussian\n", "stackview.interact(gaussian, slice_image)" ] }, { "cell_type": "markdown", "id": "47751ce0", "metadata": {}, "source": [ "This might be interesting for custom functions executing image processing workflows." ] }, { "cell_type": "code", "execution_count": 35, "id": "11f6e31e", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7ab47a331537414081f50b82050e468d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(interactive(children=(FloatSlider(value=1.0, description='sigma', max=25.0, step=1.0), Checkbox…" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage.filters import gaussian, threshold_otsu, sobel\n", "def my_custom_code(image, sigma:float = 1, show_labels: bool = True):\n", " sigma = abs(sigma)\n", " blurred_image = gaussian(image, sigma=sigma)\n", " binary_image = blurred_image > threshold_otsu(blurred_image)\n", " edge_image = sobel(binary_image)\n", " \n", " if show_labels:\n", " return label(binary_image)\n", " else:\n", " return edge_image * 255 + image \n", "\n", "stackview.interact(my_custom_code, slice_image, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "51ef6a3f", "metadata": {}, "source": [ "If you want to configure the range of a slider explicitly, you need to hand over the [ipywidgets slider](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html#FloatSlider) as default value:" ] }, { "cell_type": "code", "execution_count": 36, "id": "8abc8e06", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b0582b593fc6497b86b355da60c14068", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(interactive(children=(FloatSlider(value=15.0, description='sigma'), Output()), _dom_classes=('w…" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage.filters import gaussian\n", "from ipywidgets import FloatSlider\n", "stackview.interact(gaussian, slice_image, sigma=FloatSlider(min=0, max=100, value=15), continuous_update=True)" ] }, { "cell_type": "markdown", "id": "a4e4c0b2-04e1-48bf-a1a9-70480fbece99", "metadata": {}, "source": [ "## Assist\n", "The `stackview.assist()` function can guide you through all imported (and supported) image processing functions." ] }, { "cell_type": "code", "execution_count": 37, "id": "f2cfc7b6-c868-4e40-8e60-2f9cbde70b50", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5003392910694a9f9abbbf9f61a7f2f7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(Text(value='', description='Search', placeholder='Type here to search'), Dropdown(description='…" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.assist(context=globals(), continuous_update=True)" ] }, { "cell_type": "markdown", "id": "077b7106", "metadata": {}, "source": [ "## Some more tests with silly images" ] }, { "cell_type": "code", "execution_count": 38, "id": "9cd2f2e5", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c66894c3fe3e4a27aab58595edf6f9bf", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=100, width=100),)),)), IntSlider(value=50, des…" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "silly_image = np.zeros((100, 100))\n", "silly_image[:,50:] = 1\n", "silly_image[50:] = silly_image[50:] + 2\n", "\n", "stackview.picker(silly_image.astype(np.uint32))" ] }, { "cell_type": "code", "execution_count": 39, "id": "c1c350b5", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9488288bd07c455b8498a216c6b8038d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=60, width=60),)),)), IntSlider(value=1, descri…" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "silly_image = np.zeros((3, 3))\n", "silly_image[:,1:] = 1\n", "silly_image[1:] = silly_image[1:] + 2\n", "\n", "stackview.picker(silly_image.astype(np.uint32), zoom_factor=20)" ] }, { "cell_type": "code", "execution_count": null, "id": "d8a7a29d-0fe9-445b-99f7-41974cf61ea9", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", 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