{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this notebook to obtain \"expected\" values for \n",
"```\n",
"test_geom_imshow_nan_values.py\n",
"```\n",
"test suite."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"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": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"LetsPlot.set_theme(flavor_solarized_light())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"arr = np.array([\n",
" [50., 150., 200.],\n",
" [200., 100., 50.]\n",
" ])\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalization: 0.00015306472778320312\n",
"Clipping: 7.295608520507812e-05\n",
"image_2d: 0.00016307830810546875\n",
"png.Writer: 0.00023698806762695312\n",
"base64: 0.00018596649169921875\n"
]
},
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + geom_imshow(arr)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# With NaN values\n",
"\n",
"arr_nan = np.array([\n",
" [50., np.nan, 200.],\n",
" [np.nan, 100., 50.]\n",
" ])\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LA add alpha: 5.1021575927734375e-05\n",
"Normalization: 0.0002892017364501953\n",
"Clipping: 5.698204040527344e-05\n",
"image_2d: 6.29425048828125e-05\n",
"png.Writer: 0.00018787384033203125\n",
"base64: 2.7179718017578125e-05\n"
]
},
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + geom_imshow(arr_nan)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NaN values + cmap"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalization: 0.0001461505889892578\n",
"Clipping: 0.00010395050048828125\n",
"image_2d: 3.528594970703125e-05\n",
"png.Writer: 0.00505375862121582\n",
"base64: 0.00016117095947265625\n"
]
},
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + geom_imshow(arr_nan, cmap=\"magma\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'mapping': {},\n",
" 'data_meta': {},\n",
" 'theme': {'flavor': 'solarized_light'},\n",
" 'kind': 'plot',\n",
" 'scales': [],\n",
" 'layers': [{'geom': 'image',\n",
" 'mapping': {},\n",
" 'data_meta': {},\n",
" 'href': 'data:image/png;base64,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',\n",
" 'xmin': -0.5,\n",
" 'ymin': -0.5,\n",
" 'xmax': 2.5,\n",
" 'ymax': 1.5}],\n",
" 'metainfo_list': []}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_.as_dict()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
}