{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The `alpha` parameter in `geom_imshow()`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:34:25.493451Z",
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{
"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": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:34:25.818925Z",
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"shell.execute_reply": "2024-04-17T07:34:26.178182Z"
}
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"outputs": [
{
"data": {
"text/plain": [
"(225, 225, 3)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load an image\n",
"\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",
"im_arr = np.asarray(image)\n",
"im_arr.shape\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:34:26.179777Z",
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = ggplot() + geom_point(x=112, y=112, color=\"dark-blue\", size=100, shape=15) + ggsize(450, 450)\n",
"p + ggtitle(\"The background blue square\") + ggsize(400, 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1. `alpha` parameter and color image"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:34:26.211822Z",
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"shell.execute_reply": "2024-04-17T07:34:26.238018Z"
}
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pair1 = GGBunch()\n",
"pair1.add_plot(p + geom_imshow(im_arr) + ggtitle(\"no alpha (opaque image)\"), 0, 0)\n",
"pair1.add_plot(p + geom_imshow(im_arr, alpha=0.7) + ggtitle(\"alpha=0.7\"), 500, 0)\n",
"pair1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2. `alpha` parameter and grayscale image"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:34:26.239777Z",
"iopub.status.busy": "2024-04-17T07:34:26.239662Z",
"iopub.status.idle": "2024-04-17T07:34:26.241529Z",
"shell.execute_reply": "2024-04-17T07:34:26.241354Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(225, 225)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"im_arr_gs = im_arr[:,:,0]\n",
"im_arr_gs.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:34:26.242715Z",
"iopub.status.busy": "2024-04-17T07:34:26.242576Z",
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"shell.execute_reply": "2024-04-17T07:34:26.256500Z"
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pair2 = GGBunch()\n",
"pair2.add_plot(p + geom_imshow(im_arr_gs) + ggtitle(\"no alpha (opaque image)\"), 0, 0)\n",
"pair2.add_plot(p + geom_imshow(im_arr_gs, alpha=0.7) + ggtitle(\"alpha=0.7\"), 500, 0)\n",
"pair2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3. `alpha` parameter and pseudo-color image"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:34:26.257904Z",
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"shell.execute_reply": "2024-04-17T07:34:26.272510Z"
}
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pair3 = GGBunch()\n",
"pair3.add_plot(p + geom_imshow(im_arr_gs, \"magma\") + ggtitle(\"no alpha (opaque image)\"), 0, 0)\n",
"pair3.add_plot(p + geom_imshow(im_arr_gs, \"magma\", alpha=0.7) + ggtitle(\"alpha=0.7\"), 500, 0)\n",
"pair3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 4. `alpha` parameter and grayscale image with transparent pixels (`NaN` values)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:34:26.273818Z",
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"shell.execute_reply": "2024-04-17T07:34:26.275026Z"
}
},
"outputs": [],
"source": [
"im_arr_gs_nan=im_arr_gs.copy()\n",
"im_arr_gs_nan=im_arr_gs_nan.astype(np.float64)\n",
"im_arr_gs_nan[im_arr_gs_nan < 80] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:34:26.276177Z",
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"shell.execute_reply": "2024-04-17T07:34:26.290321Z"
}
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pair4 = GGBunch()\n",
"pair4.add_plot(p + geom_imshow(im_arr_gs_nan, \"magma\", norm=False) + ggtitle(\"no alpha (opaque image)\"), 0, 0)\n",
"pair4.add_plot(p + geom_imshow(im_arr_gs_nan, \"magma\", norm=False, alpha=0.7) + ggtitle(\"alpha=0.7\"), 500, 0)\n",
"pair4"
]
}
],
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"display_name": "Python 3 (ipykernel)",
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