{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using parameters `norm, vmain, vmax` and `cmap` in `geom_imshow()`\n",
"\n",
"You can use parameters `norm, vmain, vmax` and `cmap` when rendering grayscale images.\n",
"\n",
"A grayscale image is specified by Numpy's 2D array where each element's value \n",
"represents the luminance of corresponding pixel in the image."
]
},
{
"cell_type": "code",
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"metadata": {
"execution": {
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"shell.execute_reply": "2024-04-26T11:53:17.209606Z"
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"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"from lets_plot import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-26T11:53:17.209606Z",
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{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-26T11:53:17.241009Z",
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"source": [
"# Generate 2D Numpy arrays. \n",
"# First two arrays both contain float numbers but have different range of values.\n",
"# The 3rd array contains int-s.\n",
"arr_f0 = np.linspace(.3, .7, 30).reshape(1, -1)\n",
"arr_f1 = np.linspace(30., 170., 30).reshape(1, -1)\n",
"arr_i = np.linspace(30, 170, 30, dtype=int).reshape(1, -1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
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"shell.execute_reply": "2024-04-26T11:53:17.272694Z"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"arr_f0: shape (1, 30), data range [0.3-0.7] float64\n",
"arr_f1: shape (1, 30), data range [30.0-170.0] float64\n",
"arr_i : shape (1, 30), data range [30-170] int32\n"
]
}
],
"source": [
"print(\"arr_f0: shape {}, data range [{}-{}] {}\".format(arr_f0.shape, arr_f0.min(), arr_f0.max(), arr_f0.dtype))\n",
"print(\"arr_f1: shape {}, data range [{}-{}] {}\".format(arr_f1.shape, arr_f1.min(), arr_f1.max(), arr_f1.dtype))\n",
"print(\"arr_i : shape {}, data range [{}-{}] {}\".format(arr_i.shape, arr_i.min(), arr_i.max(), arr_i.dtype))\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
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"shell.execute_reply": "2024-04-26T11:53:17.383826Z"
}
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"outputs": [],
"source": [
"# Setup suitable plot options for the demo.\n",
"p = (ggplot() + ggsize(450, 60) + geom_rect(xmin=-0.5, ymin=-0.5, xmax=29.5, ymax=0.5, color=\"black\", alpha=0) + \n",
"coord_cartesian() + theme_void())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Image normalization\n",
"\n",
"By default, `geom_imshow()` applies a linear scaling to transform data values to range [0-255]."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-26T11:53:17.385130Z",
"iopub.status.busy": "2024-04-26T11:53:17.385130Z",
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"shell.execute_reply": "2024-04-26T11:53:17.431085Z"
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"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" \n",
" "
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"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"(p + geom_imshow(arr_f0)).show()\n",
"(p + geom_imshow(arr_f1)).show()\n",
"(p + geom_imshow(arr_i)).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## \n",
"\n",
"You can disable image normalization using the `norm` parameter."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
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{
"data": {
"text/html": [
" \n",
" "
]
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"output_type": "display_data"
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" \n",
" "
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" \n",
" "
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},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"(p + geom_imshow(arr_f0, norm=False)).show()\n",
"(p + geom_imshow(arr_f1, norm=False)).show()\n",
"(p + geom_imshow(arr_i, norm=False)).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Define normalization range using `vmin, vmax` parameters"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-26T11:53:17.464601Z",
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{
"data": {
"text/html": [
" \n",
" "
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"metadata": {},
"output_type": "display_data"
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"data": {
"text/html": [
" \n",
" "
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"text/html": [
" \n",
" "
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},
"metadata": {},
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],
"source": [
"# Set upper limit less than the data max value.\n",
"(p + geom_imshow(arr_f0, vmax=.5)).show()\n",
"(p + geom_imshow(arr_f1, vmax=100)).show()\n",
"(p + geom_imshow(arr_i, vmax=100)).show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-26T11:53:17.494580Z",
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{
"data": {
"text/html": [
" \n",
" "
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" \n",
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"source": [
"# Set limits wider than the data entire range.\n",
"(p + geom_imshow(arr_f0, vmin=.4, vmax=.6)).show()\n",
"(p + geom_imshow(arr_f1, vmin=70, vmax=120)).show()\n",
"(p + geom_imshow(arr_i, vmin=50, vmax=150)).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Colormaps"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-26T11:53:17.526092Z",
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" \n",
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" \n",
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"metadata": {},
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],
"source": [
"(p + geom_imshow(arr_f0, cmap=\"magma\")).show()\n",
"(p + geom_imshow(arr_f1, cmap=\"viridis\")).show()\n",
"(p + geom_imshow(arr_i, cmap=\"plasma\")).show()"
]
}
],
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