diff --git a/docs/source/conf.py b/docs/source/conf.py
index e8d1947821e55030aa994a38329899a9a45faf11..5e52fe6260e272d19cc50c4081d9ce6c036cb413 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -54,6 +54,7 @@ nb_execution_allow_errors = True
nbsphinx_kernel_name = 'python3'
#nbsphinx_timeout = -1
nb_execution_timeout = -1
+execution_excludepatterns = ['*era5*.ipynb']
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
diff --git a/docs/source/intake.rst b/docs/source/intake.rst
index e2e31c34ec20cf802013b67c466da4b47e95b946..bf4fcbeafe756d133d8d225987a2045778959af3 100644
--- a/docs/source/intake.rst
+++ b/docs/source/intake.rst
@@ -8,6 +8,7 @@ This is a training series on the cataloging package *intake*.
tutorial_intake-1-introduction.ipynb
tutorial_intake-1-2-dkrz-catalogs.ipynb
+ tutorial_intake-1-3-dkrz-catalogs-era5.ipynb
tutorial_intake-2-subset-catalogs.ipynb
tutorial_intake-3-merge-catalogs.ipynb
tutorial_intake-4-preprocessing-derived-vars.ipynb
diff --git a/docs/source/tutorial_intake-1-3-dkrz-catalogs-era5.ipynb b/docs/source/tutorial_intake-1-3-dkrz-catalogs-era5.ipynb
new file mode 120000
index 0000000000000000000000000000000000000000..5ec3875e5c3c7203e130efa2a91948dee7dce8df
--- /dev/null
+++ b/docs/source/tutorial_intake-1-3-dkrz-catalogs-era5.ipynb
@@ -0,0 +1 @@
+../../notebooks/demo/tutorial_intake-1-3-dkrz-catalogs-era5.ipynb
\ No newline at end of file
diff --git a/notebooks/clear_and_prepare_for_ci.ipynb b/notebooks/clear_and_prepare_for_ci.ipynb
index 7e51a9b1a09001c3fcb8d3990e7f348c3e47c91a..a400ce755bd6b2acb8d72798f695fb3df6ceb05d 100644
--- a/notebooks/clear_and_prepare_for_ci.ipynb
+++ b/notebooks/clear_and_prepare_for_ci.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -11,11 +11,11 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
- "nbs = !find ./ -mindepth 2 -name \"*.ipynb\" ! -name \"*checkpoint.ipynb\" -type f "
+ "nbs = !find ./ -mindepth 2 -name \"*.ipynb\" ! -name \"*checkpoint.ipynb\" ! -name \"*era5*\" -type f "
]
},
{
@@ -84,9 +84,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3 (based on the module python3/2022.01)",
"language": "python",
- "name": "python3"
+ "name": "python3_2022_01"
},
"language_info": {
"codemirror_mode": {
@@ -98,7 +98,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.0"
+ "version": "3.9.9"
}
},
"nbformat": 4,
diff --git a/notebooks/demo/tutorial_intake-1-3-dkrz-catalogs-era5.ipynb b/notebooks/demo/tutorial_intake-1-3-dkrz-catalogs-era5.ipynb
index 5a6c5e01770b9922d619b21b4975a76d006de35f..a2bf3696a9cfe41d9470d12420dcd1662dadab68 100644
--- a/notebooks/demo/tutorial_intake-1-3-dkrz-catalogs-era5.ipynb
+++ b/notebooks/demo/tutorial_intake-1-3-dkrz-catalogs-era5.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Intake I part 3 - DKRZ Catalogs: ERA5 data\n",
"\n",
- "DKRZ intake catalogs cover different projects. This notebook describes ERA5 and the catalog for its data."
+ "DKRZ intake catalogs cover different projects. This notebook describes the data project ERA5 and the catalog for the ERA5 data."
]
},
{
@@ -119,7 +119,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -137,7 +137,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -156,9 +156,28 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['dkrz_cmip5_archive',\n",
+ " 'dkrz_cmip5_disk',\n",
+ " 'dkrz_cmip6_cloud',\n",
+ " 'dkrz_cmip6_disk',\n",
+ " 'dkrz_cordex_disk',\n",
+ " 'dkrz_dyamond-winter_disk',\n",
+ " 'dkrz_era5_disk',\n",
+ " 'dkrz_nextgems_disk',\n",
+ " 'dkrz_palmod2_disk']"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"list(dkrz_catalog)"
]
@@ -172,9 +191,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/sw/spack-levante/mambaforge-4.11.0-0-Linux-x86_64-sobz6z/lib/python3.9/site-packages/intake_esm/utils.py:96: DtypeWarning: Columns (13,14) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+ " return pd.read_csv(catalog_path, **csv_kwargs), catalog_path\n"
+ ]
+ }
+ ],
"source": [
"col=dkrz_catalog.dkrz_era5_disk"
]
@@ -188,9 +216,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\"This is an ESM collection for ERA5 data accessible on the DKRZ's disk storage system in /work/bk1099/data/\""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"col.description"
]
@@ -204,9 +243,94 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<p><strong>dkrz_era5_disk catalog with 16 dataset(s) from 571576 asset(s)</strong>:</p> <div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>unique</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>project</th>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>era_id</th>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>dataType</th>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>level_type</th>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>frequency</th>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>stepType</th>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>table_id</th>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>code</th>\n",
+ " <td>111</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>validation_date</th>\n",
+ " <td>16326</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>initialization_date</th>\n",
+ " <td>561</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>uri</th>\n",
+ " <td>571576</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>format</th>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"col"
]
@@ -261,9 +385,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'default': ['step', 'long_name', 'short_name', 'path', 'units'],\n",
+ " 'type': 'list[str]'}"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"dkrz_catalog.metadata[\"parameters\"][\"additional_era5_columns\"]"
]
@@ -277,9 +413,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/sw/spack-levante/mambaforge-4.11.0-0-Linux-x86_64-sobz6z/lib/python3.9/site-packages/intake_esm/utils.py:96: DtypeWarning: Columns (13,14) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+ " return pd.read_csv(catalog_path, **csv_kwargs), catalog_path\n"
+ ]
+ }
+ ],
"source": [
"cols=dkrz_catalog._entries[\"dkrz_era5_disk\"]._open_args[\"csv_kwargs\"][\"usecols\"]+dkrz_catalog.metadata[\"parameters\"][\"additional_era5_columns\"][\"default\"]\n",
"col=dkrz_catalog.dkrz_era5_disk(csv_kwargs=dict(usecols=cols))"
@@ -303,9 +448,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'dataType': {'count': 2, 'values': ['fc', 'an']}}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"col.unique(\"dataType\")"
]
@@ -321,11 +477,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'frequency': {'count': 3, 'values': ['hourly', 'monthly', 'daily']}}"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"col.unique(\"frequency\")"
]
@@ -339,9 +506,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'level_type': {'count': 3,\n",
+ " 'values': ['pressure_level', 'surface', 'model_level']}}"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"col.unique(\"level_type\")"
]
@@ -356,9 +535,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'stepType': {'count': 6,\n",
+ " 'values': ['accum', 'avgid', 'max', 'instant', 'avgad', 'avgua']}}"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"col.unique(\"stepType\")"
]
@@ -385,9 +576,28 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('an', 'model_level', 'hourly'),\n",
+ " ('an', 'pressure_level', 'daily'),\n",
+ " ('an', 'pressure_level', 'hourly'),\n",
+ " ('an', 'surface', 'daily'),\n",
+ " ('an', 'surface', 'hourly'),\n",
+ " ('an', 'surface', 'monthly'),\n",
+ " ('fc', 'surface', 'daily'),\n",
+ " ('fc', 'surface', 'hourly'),\n",
+ " ('fc', 'surface', 'monthly')]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"list(col.df.groupby([\"dataType\", \"level_type\", \"frequency\"]).groups.keys())"
]
@@ -403,7 +613,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -421,9 +631,36 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'long_name': {'count': 16,\n",
+ " 'values': ['Vorticity (relative)',\n",
+ " 'V component of wind',\n",
+ " 'Ozone mass mixing ratio',\n",
+ " 'U component of wind',\n",
+ " 'Relative humidity',\n",
+ " 'Specific humidity',\n",
+ " 'Fraction of cloud cover',\n",
+ " 'Specific rain water content',\n",
+ " 'Specific cloud ice water content',\n",
+ " 'Potential vorticity',\n",
+ " 'Specific cloud liquid water content',\n",
+ " 'Temperature',\n",
+ " 'Divergence',\n",
+ " 'Vertical velocity',\n",
+ " 'Geopotential',\n",
+ " 'Specific snow water content']}}"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"cat.unique(\"long_name\")"
]
@@ -437,7 +674,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -454,9 +691,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'short_name': {'count': 1, 'values': ['t']}}"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"temp_hourly_pl.unique(\"short_name\")"
]
@@ -483,11 +731,94 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
+ "\t'table_id.stepType.level_type.frequency'\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "<style>\n",
+ " /* Turns off some styling */\n",
+ " progress {\n",
+ " /* gets rid of default border in Firefox and Opera. */\n",
+ " border: none;\n",
+ " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+ " background-size: auto;\n",
+ " }\n",
+ " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+ " background: #F44336;\n",
+ " }\n",
+ "</style>\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " <progress value='1' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " 100.00% [1/1 00:00<00:00]\n",
+ " </div>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'128.0.instant.pressure_level.hourly': <xarray.Dataset>\n",
+ " Dimensions: (time: 24, isobaricInhPa: 37, values: 542080)\n",
+ " Coordinates:\n",
+ " number int64 ...\n",
+ " * time (time) datetime64[ns] 1980-01-31 ... 1980-01-31T23:00:00\n",
+ " step timedelta64[ns] ...\n",
+ " * isobaricInhPa (isobaricInhPa) int64 1000 975 950 925 900 875 ... 7 5 3 2 1\n",
+ " latitude (values) float64 dask.array<chunksize=(542080,), meta=np.ndarray>\n",
+ " longitude (values) float64 dask.array<chunksize=(542080,), meta=np.ndarray>\n",
+ " valid_time (time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>\n",
+ " Dimensions without coordinates: values\n",
+ " Data variables:\n",
+ " t (time, isobaricInhPa, values) float32 dask.array<chunksize=(1, 37, 542080), meta=np.ndarray>\n",
+ " Attributes:\n",
+ " GRIB_edition: 1\n",
+ " GRIB_centre: ecmf\n",
+ " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n",
+ " GRIB_subCentre: 0\n",
+ " Conventions: CF-1.7\n",
+ " institution: European Centre for Medium-Range Weather Forecasts\n",
+ " history: 2022-06-21T18:04:52 GRIB to CDM+CF via cfgrib-0....\n",
+ " intake_esm_varname: 130\n",
+ " intake_esm_dataset_key: 128.0.instant.pressure_level.hourly}"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"temp_hourly_pl_xr_dict=temp_hourly_pl.to_dataset_dict(cdf_kwargs={\"engine\":\"cfgrib\",\n",
" \"chunks\":{\"time\":1}\n",
@@ -505,9 +836,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'aggregations': [{'attribute_name': 'code', 'type': 'union'}],\n",
+ " 'variable_column_name': 'code',\n",
+ " 'groupby_attrs': ['table_id', 'stepType', 'level_type', 'frequency']}"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"temp_hourly_pl.esmcol_data[\"aggregation_control\"]"
]
@@ -521,7 +865,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@@ -546,7 +890,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -563,9 +907,565 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+ "<defs>\n",
+ "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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+ " display: none;\n",
+ "}\n",
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+ " display: inline-block;\n",
+ " padding: 0;\n",
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+ " content: '(';\n",
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+ "}\n",
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+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\n",
+ "}\n",
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+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
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+ ".xr-var-list > li:nth-child(odd) > div,\n",
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+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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+ " padding-right: 10px;\n",
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+ "\n",
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+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
+ " width: auto;\n",
+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
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+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
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+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2 {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
+ "Dimensions: (values: 542080)\n",
+ "Coordinates:\n",
+ " number int64 ...\n",
+ " step timedelta64[ns] ...\n",
+ " isobaricInhPa int64 500\n",
+ " latitude (values) float64 dask.array<chunksize=(542080,), meta=np.ndarray>\n",
+ " longitude (values) float64 dask.array<chunksize=(542080,), meta=np.ndarray>\n",
+ "Dimensions without coordinates: values\n",
+ "Data variables:\n",
+ " t (values) float32 dask.array<chunksize=(542080,), meta=np.ndarray></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-45196a02-ecb4-44c4-9c40-1b6c6b7851b3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45196a02-ecb4-44c4-9c40-1b6c6b7851b3' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>values</span>: 542080</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a755f5ef-278b-4156-a384-f73506250c0a' class='xr-section-summary-in' type='checkbox' checked><label for='section-a755f5ef-278b-4156-a384-f73506250c0a' class='xr-section-summary' >Coordinates: <span>(5)</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>number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-88121fbe-ac38-4eef-9d35-36ff450781be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88121fbe-ac38-4eef-9d35-36ff450781be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e23a4e9-c810-4268-9e28-ceb75eb2d8e2' class='xr-var-data-in' type='checkbox'><label for='data-8e23a4e9-c810-4268-9e28-ceb75eb2d8e2' 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'><dt><span>long_name :</span></dt><dd>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3180d41-e6a2-4b9b-8789-f8e7a72cbb73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3180d41-e6a2-4b9b-8789-f8e7a72cbb73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e04c94b-102e-422d-a1c5-8dcf9be5bc30' class='xr-var-data-in' type='checkbox'><label for='data-3e04c94b-102e-422d-a1c5-8dcf9be5bc30' 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'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype='timedelta64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isobaricInhPa</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>500</div><input id='attrs-9867bd8e-5bb7-4642-8be0-115010263c7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9867bd8e-5bb7-4642-8be0-115010263c7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adbd2ce5-ac65-4666-9414-fb8d28048991' class='xr-var-data-in' type='checkbox'><label for='data-adbd2ce5-ac65-4666-9414-fb8d28048991' 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'><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array(500)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(values)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(542080,), meta=np.ndarray></div><input id='attrs-ce8f1587-7677-49eb-92e4-19df9c87afe1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce8f1587-7677-49eb-92e4-19df9c87afe1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d450374d-2da5-4e41-b8c5-aa2d1ffb5064' class='xr-var-data-in' type='checkbox'><label for='data-d450374d-2da5-4e41-b8c5-aa2d1ffb5064' 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'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table>\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 4.14 MiB </td>\n",
+ " <td> 4.14 MiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (542080,) </td>\n",
+ " <td> (542080,) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Count </th>\n",
+ " <td> 2 Tasks </td>\n",
+ " <td> 1 Chunks </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Type </th>\n",
+ " <td> float64 </td>\n",
+ " <td> numpy.ndarray </td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ " </table>\n",
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+ "</svg>\n",
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"source": [
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]
@@ -580,9 +1480,396 @@
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+ ".xr-icon-file-text2 {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
+ "Dimensions: (values: 542080)\n",
+ "Coordinates:\n",
+ " number int64 0\n",
+ " step timedelta64[ns] 00:00:00\n",
+ " isobaricInhPa int64 500\n",
+ " latitude (values) float64 89.78 89.78 89.78 ... -89.78 -89.78 -89.78\n",
+ " longitude (values) float64 0.0 20.0 40.0 60.0 ... 300.0 320.0 340.0\n",
+ "Dimensions without coordinates: values\n",
+ "Data variables:\n",
+ " t (values) float32 236.8 236.7 236.6 ... 237.5 237.4 237.4</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-6b272c3b-26fd-4c91-9bb1-e6de4c9614a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b272c3b-26fd-4c91-9bb1-e6de4c9614a6' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>values</span>: 542080</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f296c94d-14e7-4faa-a378-71f875f91200' class='xr-section-summary-in' type='checkbox' checked><label for='section-f296c94d-14e7-4faa-a378-71f875f91200' class='xr-section-summary' >Coordinates: <span>(5)</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>number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-f6518f7f-3daa-4112-bfe2-7dc9e5668455' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6518f7f-3daa-4112-bfe2-7dc9e5668455' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a249ab0d-86d9-40e0-b3eb-5b800fccf790' class='xr-var-data-in' type='checkbox'><label for='data-a249ab0d-86d9-40e0-b3eb-5b800fccf790' 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'><dt><span>long_name :</span></dt><dd>ensemble member numerical id</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00</div><input id='attrs-b8d4b223-9039-4bb3-b898-23aeb0ae0b14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8d4b223-9039-4bb3-b898-23aeb0ae0b14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-813bdf72-27f5-4cf4-8738-0e29ae09a7e1' class='xr-var-data-in' type='checkbox'><label for='data-813bdf72-27f5-4cf4-8738-0e29ae09a7e1' 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'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype='timedelta64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isobaricInhPa</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>500</div><input id='attrs-756ed783-7f3d-4ae6-9b2c-25177bc4da19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-756ed783-7f3d-4ae6-9b2c-25177bc4da19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afc7e388-2778-44a8-a3b2-3633bb0b7063' class='xr-var-data-in' type='checkbox'><label for='data-afc7e388-2778-44a8-a3b2-3633bb0b7063' 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'><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array(500)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(values)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.78 89.78 89.78 ... -89.78 -89.78</div><input id='attrs-853b3928-e32a-4696-b844-11f5eadd372c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-853b3928-e32a-4696-b844-11f5eadd372c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4b6fe7c-8a4f-4fce-b131-c741d9592835' class='xr-var-data-in' type='checkbox'><label for='data-e4b6fe7c-8a4f-4fce-b131-c741d9592835' 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'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 89.78487691, 89.78487691, 89.78487691, ..., -89.78487691,\n",
+ " -89.78487691, -89.78487691])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(values)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 20.0 40.0 ... 300.0 320.0 340.0</div><input id='attrs-85bb01cf-4265-4237-bc52-49beeb43d1d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85bb01cf-4265-4237-bc52-49beeb43d1d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85bd2345-1320-4e06-8604-e29b921df95c' class='xr-var-data-in' type='checkbox'><label for='data-85bd2345-1320-4e06-8604-e29b921df95c' 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'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0., 20., 40., ..., 300., 320., 340.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b9f0e49-9d65-44b5-9b83-bcf3aae58c6a' class='xr-section-summary-in' type='checkbox' checked><label for='section-5b9f0e49-9d65-44b5-9b83-bcf3aae58c6a' class='xr-section-summary' >Data variables: <span>(1)</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>t</span></div><div class='xr-var-dims'>(values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>236.8 236.7 236.6 ... 237.4 237.4</div><input id='attrs-b8fad260-9c69-41ab-8662-e1f3d275060b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8fad260-9c69-41ab-8662-e1f3d275060b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caeb7fc1-f6bd-42e2-a813-b15c22e89c98' class='xr-var-data-in' type='checkbox'><label for='data-caeb7fc1-f6bd-42e2-a813-b15c22e89c98' 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([236.83197, 236.72528, 236.60594, ..., 237.45265, 237.42348,\n",
+ " 237.4007 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d00a07e-ac38-454a-afcd-c9dc0fafd26d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1d00a07e-ac38-454a-afcd-c9dc0fafd26d' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (values: 542080)\n",
+ "Coordinates:\n",
+ " number int64 0\n",
+ " step timedelta64[ns] 00:00:00\n",
+ " isobaricInhPa int64 500\n",
+ " latitude (values) float64 89.78 89.78 89.78 ... -89.78 -89.78 -89.78\n",
+ " longitude (values) float64 0.0 20.0 40.0 60.0 ... 300.0 320.0 340.0\n",
+ "Dimensions without coordinates: values\n",
+ "Data variables:\n",
+ " t (values) float32 236.8 236.7 236.6 ... 237.5 237.4 237.4"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"t500mean.compute()"
]
@@ -596,9 +1883,32 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7fff53d7a610>]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"t500mean.t.plot()"
]
@@ -606,9 +1916,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "taucenv",
+ "display_name": "Python 3 (based on the module python3/2022.01)",
"language": "python",
- "name": "taucenv"
+ "name": "python3_2022_01"
},
"language_info": {
"codemirror_mode": {
@@ -620,7 +1930,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.4"
+ "version": "3.9.9"
},
"nbsphinx": {
"execute": "never"