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@apatlpo
Created July 20, 2018 11:06
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plot fluidsim output with xarray
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from glob import glob\n",
"import xarray as xr\n",
"import matplotlib.pyplot as plt\n",
"from fluidsim import load_sim_for_plot"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/aponte/Sim_data/sw2l_test_128x128_S1000000x1000000_2018-07-20_12-18-14\n"
]
}
],
"source": [
"# get latest simulation\n",
"cwd = os.getcwd()\n",
"simdirs = [os.path.join(cwd,d) for d in os.listdir(cwd) if os.path.isdir(d) and 'sw2l' in d]\n",
"simdir = max(simdirs, key=os.path.getmtime)\n",
"print(simdir)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/aponte/Sim_data/sw2l_test_128x128_S1000000x1000000_2018-07-20_12-18-14/state_phys_t000.000.nc\n"
]
}
],
"source": [
"# sort nc files\n",
"filenames = sorted(glob(simdir+'/state_phys_*.nc'), key=lambda x: float(x.strip('.nc').split('phys_t')[1]) )\n",
"print(filenames[0])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<xarray.Dataset>\n",
"Dimensions: (time: 41, x: 128, y: 128)\n",
"Dimensions without coordinates: time, x, y\n",
"Data variables:\n",
" h0 (time, y, x) float64 dask.array<shape=(41, 128, 128), chunksize=(1, 128, 128)>\n",
" h1 (time, y, x) float64 dask.array<shape=(41, 128, 128), chunksize=(1, 128, 128)>\n",
" rot0 (time, y, x) float64 dask.array<shape=(41, 128, 128), chunksize=(1, 128, 128)>\n",
" rot1 (time, y, x) float64 dask.array<shape=(41, 128, 128), chunksize=(1, 128, 128)>\n",
" ux0 (time, y, x) float64 dask.array<shape=(41, 128, 128), chunksize=(1, 128, 128)>\n",
" ux1 (time, y, x) float64 dask.array<shape=(41, 128, 128), chunksize=(1, 128, 128)>\n",
" uy0 (time, y, x) float64 dask.array<shape=(41, 128, 128), chunksize=(1, 128, 128)>\n",
" uy1 (time, y, x) float64 dask.array<shape=(41, 128, 128), chunksize=(1, 128, 128)>\n",
"Attributes:\n",
" what: obj state_phys for solveq2d\n",
" name_type_variables: state_phys\n",
" time: 0\n",
" it: 0\n"
]
}
],
"source": [
"ds = xr.open_mfdataset(filenames, concat_dim='time', engine='h5netcdf', group='state_phys')\n",
"print(ds)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x118653ef0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds.h0.isel(time=0).plot()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x119518588>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds.h0.isel(x=50).plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"## notes\n",
"\n",
"- coordinate informations (time, x, y) are not in ds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
@ashwinvis
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With the latest commit, you can replace line [3] with

from fluidsim.base.output.phys_fields import SetOfPhysFieldFiles

phys = SetOfPhysFieldFiles()
filenames = phys.path_files

see towards the end of https://gist.github.com/ashwinvis/854db533578b28b041e9890def53ed0a

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