{
"cells": [
{
"cell_type": "markdown",
"id": "2b82f2ad-bf15-4e12-a454-eeec138bd68c",
"metadata": {},
"source": [
"# ... slice a dataset based on a condition?\n",
"Good practice is to have dimension attached to the datasets. The dimension scales can be used to decide what to slice exactly. The following example first generates a dataset and the slices it depending on the value in the first dimension, in this case the time:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9b881f18-fbae-4e52-883a-6424f1247138",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
" \n",
" \n",
" /(4) \n",
" \n",
"\n",
" __h5rdmtoolbox_version__ : 1.0.0 \n",
" \n",
" \n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import h5rdmtoolbox as h5tbx\n",
"import numpy as np\n",
"\n",
"h5tbx.use(None)\n",
"\n",
"with h5tbx.File() as h5:\n",
" h5.create_dataset('time', data=range(0, 100), make_scale=True)\n",
" h5.create_dataset('x', data=range(0, 100), make_scale=True)\n",
" h5.create_dataset('y', data=range(0, 200), make_scale=True)\n",
" h5.create_dataset('data', np.random.rand(100, 200, 100), attach_scale=('time', 'y', 'x'))\n",
" data = h5.data[:]\n",
" h5.dump()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d72de154-ea4b-4396-a163-740e2ac1e0f8",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with h5tbx.File(h5.hdf_filename) as h5:\n",
" h5.data[h5.data.time > 5.4, :, :].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ebf1201-ff5b-46ab-8f8e-dcd3413f6d5d",
"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",
"pygments_lexer": "ipython3",
"version": "3.8.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}