{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `extent` parameter in `geom_imshow()` \n",
"\n",
"A list of four numbers: `[left, right, bottom, top]` that\n",
"defines the image bounding box\n",
"in terms of 'data coordinates'."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
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"shell.execute_reply": "2024-04-17T07:28:29.672374Z"
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"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"from lets_plot import *\n",
"\n",
"LetsPlot.setup_html()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The default `extent` is derived from the image's array dimentions: `[-0.5, ncol-0.5, -0.5, nrow-0.5]`.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# An 2x2 pix image by default has extent [-0.5, 1.5, -0.5, 1.5]\n",
"arr = np.array([\n",
" [[150, 0, 0], [0, 150, 0]],\n",
" [[0, 0, 150], [150, 150, 0]]\n",
" ])\n",
"\n",
"ggplot() + geom_imshow(arr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## When does image `extent` need to be set explicitly\n",
"\n",
"Let's assume that our image doesn't exist in vacuum but is related to some data, \n",
"and the image' bounding box in the 'data coordinates' is: \n",
"\n",
"- `(x, y) = (0, -1)`\n",
"- `(width, height) = (8, 3)`.\n",
"\n",
"The image `extent` in this case would be: `[0, 8, -1, 3]`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:28:29.718438Z",
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"shell.execute_reply": "2024-04-17T07:28:29.720583Z"
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"outputs": [],
"source": [
"# Generate demo data.\n",
"np.random.seed(42)\n",
"\n",
"cov0=[[1, -.8], \n",
" [-.8, 1]] \n",
"cov1=[[ 10, .1],\n",
" [.1, .1]]\n",
"\n",
"x0, y0 = np.random.multivariate_normal(mean=[4,0], cov=cov0, size=400).T\n",
"x1, y1 = np.random.multivariate_normal(mean=[4,1], cov=cov1, size=400).T\n",
"\n",
"data = dict(\n",
" x = np.concatenate((x0,x1)),\n",
" y = np.concatenate((y0,y1))\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:28:29.721904Z",
"iopub.status.busy": "2024-04-17T07:28:29.721765Z",
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"shell.execute_reply": "2024-04-17T07:28:29.829429Z"
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(data, aes('x', 'y')) + geom_density2d() + ggsize(620, 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's show the data and the image on the same plot."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:28:29.831178Z",
"iopub.status.busy": "2024-04-17T07:28:29.830907Z",
"iopub.status.idle": "2024-04-17T07:28:29.939678Z",
"shell.execute_reply": "2024-04-17T07:28:29.939368Z"
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(ggplot(data, aes('x', 'y')) + \n",
" geom_imshow(arr, extent=[0, 8, -1, 3]) + \n",
" geom_density2d(color=\"yellow\") + \n",
" ggsize(620, 300) + \n",
" flavor_high_contrast_dark())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \n",
"## Flipping and transposing image using `extent`"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:28:29.940903Z",
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"shell.execute_reply": "2024-04-17T07:28:30.654848Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(225, 225, 3)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load image file.\n",
"from PIL import Image\n",
"import requests\n",
"from io import BytesIO\n",
"\n",
"response = requests.get('https://github.com/JetBrains/lets-plot-docs/raw/master/source/examples/cookbook/images/fisher_boat.png')\n",
"\n",
"image = Image.open(BytesIO(response.content))\n",
"boat_arr = np.asarray(image)\n",
"boat_arr.shape\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:28:30.656230Z",
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + geom_imshow(image_data=boat_arr)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:28:30.670691Z",
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dim = 100\n",
"\n",
"ext_base = [0, dim, 0, dim]\n",
"ext_flip_x = [dim, 0, 0, dim]\n",
"ext_flip_y = [0, dim, dim, 0]\n",
"ext_transpose = [dim, 0, dim, 0]\n",
"\n",
"label_data = dict(\n",
" x = [50, 150, 150, 50],\n",
" y = [200, 200, 0, 0],\n",
" label = [\"original\", \"flip columns\", \"flip rows\", \"transpose\"]\n",
")\n",
"\n",
"(ggplot() + \n",
" geom_imshow(\n",
" boat_arr, \n",
" extent=[v + dim if i in (2, 3) else v for i, v in enumerate(ext_base)]\n",
" ) + \n",
" geom_imshow(\n",
" boat_arr, \n",
" extent=[v + dim for v in ext_flip_x]\n",
" ) + \n",
" geom_imshow(\n",
" boat_arr, \n",
" extent=[v + dim if i in (0, 1) else v for i, v in enumerate(ext_flip_y)]\n",
" ) + \n",
" geom_imshow(\n",
" boat_arr, \n",
" extent=ext_transpose\n",
" ) + \n",
" geom_hline(yintercept=100, color=\"yellow\", tooltips=\"none\") + \n",
" geom_vline(xintercept=100, color=\"yellow\", tooltips=\"none\") + \n",
" geom_label(aes(\"x\", \"y\", label=\"label\"), data=label_data) +\n",
" ggsize(700, 700)\n",
")"
]
}
],
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