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rsignell-usgs/harmonie_kerchunk.ipynb

Created April 15, 2022 10:40
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harmonie_kerchunk.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "cde65ca7-664e-4f2b-96fe-fea335304b4f",
"metadata": {},
"source": [
"# Explore Kerchunking Harmonie data\n",
"Explore zipped test data from:\n",
"https://stackoverflow.com/questions/70952226/how-to-merge-different-shaped-netcdf4-files"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d7cb9a56-0b18-479c-b141-c0efd223913a",
"metadata": {},
"outputs": [],
"source": [
"import fsspec\n",
"import xarray as xr\n",
"from kerchunk.hdf import SingleHdf5ToZarr \n",
"from pathlib import Path\n",
"import ujson"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "04447c0d-5e30-4098-86f2-a15ab53b7110",
"metadata": {},
"outputs": [],
"source": [
"fs = fsspec.filesystem('file')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fc78c714-baf5-466f-8d4f-4231a9cb4828",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/shared/users/rsignell/notebooks/testing/data/test_data_stackoverflow/harmonie_knmi_2021_03_20_18_30_18.nc',\n",
" '/shared/users/rsignell/notebooks/testing/data/test_data_stackoverflow/harmonie_knmi_2021_03_20_18_30_19.nc',\n",
" '/shared/users/rsignell/notebooks/testing/data/test_data_stackoverflow/harmonie_knmi_2021_03_20_18_30_20.nc',\n",
" '/shared/users/rsignell/notebooks/testing/data/test_data_stackoverflow/harmonie_knmi_2021_03_20_18_30_21.nc',\n",
" '/shared/users/rsignell/notebooks/testing/data/test_data_stackoverflow/harmonie_knmi_2021_03_20_18_30_22.nc']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flist = fs.glob('data/test_data_stackoverflow/*.nc')\n",
"flist[:5]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4de70622-7302-4294-8195-e97ff48ad457",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/shared/users/rsignell/notebooks/testing/data/test_data_stackoverflow/harmonie_knmi_2021_03_20_18_30_18.nc'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flist[0]"
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},
{
"cell_type": "code",
"execution_count": 5,
"id": "c4458615-6ccc-4aef-95fd-777d0f0a21b4",
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"source": [
"for f in flist:\n",
" print(xr.open_dataset(flist[0]).x.values)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e695d569-fd36-4177-b1bd-394ad66d174b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"for f in flist:\n",
" print(xr.open_dataset(flist[0]).dt_calc.values)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "24277fcf-41ad-4dd3-8dbe-0a2798b4b6f7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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],
"source": [
"for f in flist:\n",
" print(xr.open_dataset(flist[0]).dt_fore[0].values)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c9dc3c3f-c232-44b3-adaf-ed180eb92311",
"metadata": {},
"outputs": [],
"source": [
"json_dir = 'jsons'"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "427aeefe-585f-4c28-a86a-576cc5049a27",
"metadata": {},
"outputs": [],
"source": [
"#generate a json from a netCDF\n",
"def gen_json(u):\n",
" #open the file (u) with desiganted parameters (so), call it infile\n",
" with fs.open(u, **so) as infile:\n",
" #inline_threshold: chunks smaller than 300 (I'm guessing mb?) are included in output\n",
" h5chunks = SingleHdf5ToZarr(infile, u, inline_threshold=300)\n",
" #Path().stem is from the pathlib library; returns the file name w/o the extension\n",
" fstem = Path(u).stem\n",
" #here we create the output file path\n",
" outf = f'{json_dir}/{fstem}.json'\n",
" #print(outf)\n",
" #wb: write binary file\n",
" with fs.open(outf, 'wb') as f:\n",
" #create json file and write it to the path specified above (f)\n",
" #dumps: dictionary to string\n",
" #translate: translate contents of HDF5 to Zarr\n",
" f.write(ujson.dumps(h5chunks.translate()).encode());"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "79fcb3a0-3fa7-48e3-9fe0-6ccc3bd516d9",
"metadata": {},
"outputs": [],
"source": [
"so = dict(mode='rb')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "bbd9033b-72b7-4611-9714-9842515312d4",
"metadata": {},
"outputs": [],
"source": [
"#loop through list of netCDFs and put them through our function for generating jsons\n",
"for f in flist:\n",
" gen_json(f)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "401f1a0b-2456-4e2f-8e73-4fe1fd1e2461",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/shared/users/rsignell/notebooks/testing/jsons/harmonie_knmi_2021_03_20_18_30_18.json',\n",
" '/shared/users/rsignell/notebooks/testing/jsons/harmonie_knmi_2021_03_20_18_30_19.json',\n",
" '/shared/users/rsignell/notebooks/testing/jsons/harmonie_knmi_2021_03_20_18_30_20.json',\n",
" '/shared/users/rsignell/notebooks/testing/jsons/harmonie_knmi_2021_03_20_18_30_21.json',\n",
" '/shared/users/rsignell/notebooks/testing/jsons/harmonie_knmi_2021_03_20_18_30_22.json']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#create and view a sorted list of all jsons created in the previous step\n",
"json_list = fs.glob(f'{json_dir}/*.json')\n",
"json_list[:5]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8d577f59-3a84-4d88-bbaf-ac392a183822",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (dt_fore: 49, x: 31, y: 54)\n",
"Coordinates:\n",
" dt_calc datetime64[ns] ...\n",
" * dt_fore (dt_fore) float64 1.616e+18 ... 1.616e+18\n",
" lat (x, y) float64 dask.array&lt;chunksize=(31, 54), meta=np.ndarray&gt;\n",
" lon (x, y) float64 dask.array&lt;chunksize=(31, 54), meta=np.ndarray&gt;\n",
"Dimensions without coordinates: x, y\n",
"Data variables: (12/27)\n",
" air_temperature_2m (dt_fore, x, y) float16 dask.array&lt;chunksize=(13, 16, 27), meta=np.ndarray&gt;\n",
" air_temperature_500 (dt_fore, x, y) float16 dask.array&lt;chunksize=(13, 16, 27), meta=np.ndarray&gt;\n",
" air_temperature_850 (dt_fore, x, y) float16 dask.array&lt;chunksize=(13, 16, 27), meta=np.ndarray&gt;\n",
" dewpoint_2m (dt_fore, x, y) float16 dask.array&lt;chunksize=(13, 16, 27), meta=np.ndarray&gt;\n",
" geopotential_height_500 (dt_fore, x, y) float64 dask.array&lt;chunksize=(13, 8, 14), meta=np.ndarray&gt;\n",
" geopotential_height_850 (dt_fore, x, y) float64 dask.array&lt;chunksize=(13, 8, 14), meta=np.ndarray&gt;\n",
" ... ...\n",
" wind_direction_50 (dt_fore, x, y) float64 dask.array&lt;chunksize=(13, 8, 14), meta=np.ndarray&gt;\n",
" wind_speed_10 (dt_fore, x, y) float64 dask.array&lt;chunksize=(13, 8, 14), meta=np.ndarray&gt;\n",
" wind_speed_100 (dt_fore, x, y) float64 dask.array&lt;chunksize=(13, 8, 14), meta=np.ndarray&gt;\n",
" wind_speed_200 (dt_fore, x, y) float64 dask.array&lt;chunksize=(13, 8, 14), meta=np.ndarray&gt;\n",
" wind_speed_300 (dt_fore, x, y) float64 dask.array&lt;chunksize=(13, 8, 14), meta=np.ndarray&gt;\n",
" wind_speed_50 (dt_fore, x, y) float64 dask.array&lt;chunksize=(13, 8, 14), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-749cbfd6-4c7a-48d0-a99a-ea26408f15f2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-749cbfd6-4c7a-48d0-a99a-ea26408f15f2' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>dt_fore</span>: 49</li><li><span>x</span>: 31</li><li><span>y</span>: 54</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-90c9c617-f73a-4b11-865b-2e5143f72e87' class='xr-section-summary-in' type='checkbox' checked><label for='section-90c9c617-f73a-4b11-865b-2e5143f72e87' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dt_calc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4ec9af14-0f66-421d-8a97-3b9787dce697' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ec9af14-0f66-421d-8a97-3b9787dce697' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddac5279-58f6-4a58-a373-1cc58c616939' class='xr-var-data-in' type='checkbox'><label for='data-ddac5279-58f6-4a58-a373-1cc58c616939' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2021-03-20T18:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dt_fore</span></div><div class='xr-var-dims'>(dt_fore)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.616e+18 1.616e+18 ... 1.616e+18</div><input id='attrs-c9a94a79-c347-4632-ab8a-154b9c9a2a90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9a94a79-c347-4632-ab8a-154b9c9a2a90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-305677bd-5945-4d4a-b313-1fed31b1caeb' class='xr-var-data-in' type='checkbox'><label for='data-305677bd-5945-4d4a-b313-1fed31b1caeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.616263e+18, 1.616267e+18, 1.616270e+18, 1.616274e+18, 1.616278e+18,\n",
" 1.616281e+18, 1.616285e+18, 1.616288e+18, 1.616292e+18, 1.616296e+18,\n",
" 1.616299e+18, 1.616303e+18, 1.616306e+18, 1.616310e+18, 1.616314e+18,\n",
" 1.616317e+18, 1.616321e+18, 1.616324e+18, 1.616328e+18, 1.616332e+18,\n",
" 1.616335e+18, 1.616339e+18, 1.616342e+18, 1.616346e+18, 1.616350e+18,\n",
" 1.616353e+18, 1.616357e+18, 1.616360e+18, 1.616364e+18, 1.616368e+18,\n",
" 1.616371e+18, 1.616375e+18, 1.616378e+18, 1.616382e+18, 1.616386e+18,\n",
" 1.616389e+18, 1.616393e+18, 1.616396e+18, 1.616400e+18, 1.616404e+18,\n",
" 1.616407e+18, 1.616411e+18, 1.616414e+18, 1.616418e+18, 1.616422e+18,\n",
" 1.616425e+18, 1.616429e+18, 1.616432e+18, 1.616436e+18])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(31, 54), meta=np.ndarray&gt;</div><input id='attrs-ea0fb1e5-0498-4834-85bd-561a50f430a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea0fb1e5-0498-4834-85bd-561a50f430a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf9dc326-c795-443c-bc8f-b22a916afc94' class='xr-var-data-in' type='checkbox'><label for='data-cf9dc326-c795-443c-bc8f-b22a916afc94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 13.08 kiB </td> <td> 13.08 kiB </td></tr>\n",
" <tr><th> Shape </th><td> (31, 54) </td> <td> (31, 54) </td></tr>\n",
" <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(31, 54), meta=np.ndarray&gt;</div><input id='attrs-593670fc-29f8-4372-b68a-85029092d820' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-593670fc-29f8-4372-b68a-85029092d820' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e8f94b7-9368-4200-b32c-02819872d8e9' class='xr-var-data-in' type='checkbox'><label for='data-2e8f94b7-9368-4200-b32c-02819872d8e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
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" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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" <tbody>\n",
" <tr><th> Bytes </th><td> 13.08 kiB </td> <td> 13.08 kiB </td></tr>\n",
" <tr><th> Shape </th><td> (31, 54) </td> <td> (31, 54) </td></tr>\n",
" <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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"</table>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-50580616-912f-481f-8f98-837f9031f887' class='xr-section-summary-in' type='checkbox' ><label for='section-50580616-912f-481f-8f98-837f9031f887' class='xr-section-summary' >Data variables: <span>(27)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air_temperature_2m</span></div><div class='xr-var-dims'>(dt_fore, x, y)</div><div class='xr-var-dtype'>float16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 16, 27), meta=np.ndarray&gt;</div><input id='attrs-d6f3c909-416e-4841-9afa-68c5641f036f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6f3c909-416e-4841-9afa-68c5641f036f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0912a136-3b85-4733-88ac-ac876c6b026c' class='xr-var-data-in' type='checkbox'><label for='data-0912a136-3b85-4733-88ac-ac876c6b026c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (dt_fore: 49, x: 31, y: 54)\n",
"Coordinates:\n",
" dt_calc datetime64[ns] ...\n",
" * dt_fore (dt_fore) float64 1.616e+18 ... 1.616e+18\n",
" lat (x, y) float64 dask.array<chunksize=(31, 54), meta=np.ndarray>\n",
" lon (x, y) float64 dask.array<chunksize=(31, 54), meta=np.ndarray>\n",
"Dimensions without coordinates: x, y\n",
"Data variables: (12/27)\n",
" air_temperature_2m (dt_fore, x, y) float16 dask.array<chunksize=(13, 16, 27), meta=np.ndarray>\n",
" air_temperature_500 (dt_fore, x, y) float16 dask.array<chunksize=(13, 16, 27), meta=np.ndarray>\n",
" air_temperature_850 (dt_fore, x, y) float16 dask.array<chunksize=(13, 16, 27), meta=np.ndarray>\n",
" dewpoint_2m (dt_fore, x, y) float16 dask.array<chunksize=(13, 16, 27), meta=np.ndarray>\n",
" geopotential_height_500 (dt_fore, x, y) float64 dask.array<chunksize=(13, 8, 14), meta=np.ndarray>\n",
" geopotential_height_850 (dt_fore, x, y) float64 dask.array<chunksize=(13, 8, 14), meta=np.ndarray>\n",
" ... ...\n",
" wind_direction_50 (dt_fore, x, y) float64 dask.array<chunksize=(13, 8, 14), meta=np.ndarray>\n",
" wind_speed_10 (dt_fore, x, y) float64 dask.array<chunksize=(13, 8, 14), meta=np.ndarray>\n",
" wind_speed_100 (dt_fore, x, y) float64 dask.array<chunksize=(13, 8, 14), meta=np.ndarray>\n",
" wind_speed_200 (dt_fore, x, y) float64 dask.array<chunksize=(13, 8, 14), meta=np.ndarray>\n",
" wind_speed_300 (dt_fore, x, y) float64 dask.array<chunksize=(13, 8, 14), meta=np.ndarray>\n",
" wind_speed_50 (dt_fore, x, y) float64 dask.array<chunksize=(13, 8, 14), meta=np.ndarray>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rpath = json_list[0]\n",
"fs3 = fsspec.filesystem(\"reference\", fo=rpath)\n",
"\n",
"m = fs3.get_mapper(\"\")\n",
"ds = xr.open_dataset(m, engine=\"zarr\",chunks={}, backend_kwargs={'consolidated':False})\n",
"\n",
"ds"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "default-pangeo",
"language": "python",
"name": "conda-env-default-pangeo-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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