{ "cells": [ { "cell_type": "markdown", "id": "705600ae-ee96-4326-8712-bad50960f870", "metadata": {}, "source": [ "# Read parquet kerchunk catalog" ] }, { "cell_type": "code", "execution_count": 1, "id": "e364d80a-2e58-4d20-908e-ee05fd51cf11", "metadata": {}, "outputs": [], "source": [ "import fsspec\n", "import xarray as xr" ] }, { "cell_type": "code", "execution_count": 2, "id": "c5e000f8-d3d8-4fa3-8513-5774732f6c23", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 286 ms, sys: 30.5 ms, total: 316 ms\n", "Wall time: 421 ms\n" ] } ], "source": [ "%%time\n", "# references are on an OSN pod (no credentials needed)\n", "url = \"s3://gfts-reference-data/CMEMS_v6r1_NWS_PHY_NRT_NL_01hav_AN_2D_combined.parq/\"\n", "\n", "target_opts = {\"anon\": False}\n", "\n", "# netcdf files are on the AWS public dataset program (no credentials needed)\n", "remote_opts = {\"anon\": False}\n", "\n", "fs = fsspec.filesystem(\n", " \"reference\",\n", " fo=url,\n", " remote_protocol=\"s3\",\n", " remote_options=remote_opts,\n", " target_options=target_opts,\n", ")\n", "m = fs.get_mapper(\"\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "4744b47f-20fd-475e-b5a5-5c11298ee32d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.1 s, sys: 191 ms, total: 2.29 s\n", "Wall time: 2.63 s\n" ] } ], "source": [ "%%time\n", "ds = xr.open_dataset(\n", " m, engine=\"zarr\", chunks={}, backend_kwargs={\"consolidated\": False}\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "id": "1ee76201-9860-4e05-93b8-f8140558cd60", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 521GB\n",
       "Dimensions:    (latitude: 551, longitude: 936, time: 18048)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28\n",
       "  * longitude  (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n",
       "  * time       (time) datetime64[ns] 144kB 2022-04-02T00:30:00 ... 2024-04-22...\n",
       "Data variables:\n",
       "    mlotst     (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>\n",
       "    thetao     (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>\n",
       "    ubar       (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>\n",
       "    uo         (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>\n",
       "    vbar       (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>\n",
       "    vo         (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>\n",
       "    zos        (time, latitude, longitude) float64 74GB dask.array<chunksize=(1, 551, 936), meta=np.ndarray>\n",
       "Attributes: (12/13)\n",
       "    Conventions:     CF-1.8\n",
       "    comment:         \n",
       "    contact:         https://marine.copernicus.eu/contact\n",
       "    domain_name:     NWS36\n",
       "    field_date:      20220402\n",
       "    field_type:      mean\n",
       "    ...              ...\n",
       "    forecast_type:   analysis\n",
       "    institution:     Nologin (Spain)\n",
       "    licence:         https://marine.copernicus.eu/user-corner/service-commitm...\n",
       "    references:      http://marine.copernicus.eu/\n",
       "    source:          NEMO3.6\n",
       "    title:           Ocean surface hourly mean fields for the North West Shel...
" ], "text/plain": [ " Size: 521GB\n", "Dimensions: (latitude: 551, longitude: 936, time: 18048)\n", "Coordinates:\n", " * latitude (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28\n", " * longitude (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n", " * time (time) datetime64[ns] 144kB 2022-04-02T00:30:00 ... 2024-04-22...\n", "Data variables:\n", " mlotst (time, latitude, longitude) float64 74GB dask.array\n", " thetao (time, latitude, longitude) float64 74GB dask.array\n", " ubar (time, latitude, longitude) float64 74GB dask.array\n", " uo (time, latitude, longitude) float64 74GB dask.array\n", " vbar (time, latitude, longitude) float64 74GB dask.array\n", " vo (time, latitude, longitude) float64 74GB dask.array\n", " zos (time, latitude, longitude) float64 74GB dask.array\n", "Attributes: (12/13)\n", " Conventions: CF-1.8\n", " comment: \n", " contact: https://marine.copernicus.eu/contact\n", " domain_name: NWS36\n", " field_date: 20220402\n", " field_type: mean\n", " ... ...\n", " forecast_type: analysis\n", " institution: Nologin (Spain)\n", " licence: https://marine.copernicus.eu/user-corner/service-commitm...\n", " references: http://marine.copernicus.eu/\n", " source: NEMO3.6\n", " title: Ocean surface hourly mean fields for the North West Shel..." ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "code", "execution_count": 5, "id": "0742258e-1bdd-47f4-879e-ea95ffafc5e3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'thetao' (time: 18048, latitude: 551, longitude: 936)> Size: 74GB\n",
       "dask.array<open_dataset-thetao, shape=(18048, 551, 936), dtype=float64, chunksize=(1, 551, 936), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28\n",
       "  * longitude  (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n",
       "  * time       (time) datetime64[ns] 144kB 2022-04-02T00:30:00 ... 2024-04-22...\n",
       "Attributes: (12/14)\n",
       "    easting:        longitude\n",
       "    latitude_max:   61.2819f\n",
       "    latitude_min:   46.0036f\n",
       "    long_name:      Temperature\n",
       "    longitude_max:  9.977f\n",
       "    longitude_min:  -15.996f\n",
       "    ...             ...\n",
       "    unit_long:      degrees_C\n",
       "    units:          degrees_C\n",
       "    valid_max:      22000\n",
       "    valid_min:      -12000\n",
       "    z_max:          0.494025f\n",
       "    z_min:          0.494025f
" ], "text/plain": [ " Size: 74GB\n", "dask.array\n", "Coordinates:\n", " * latitude (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28\n", " * longitude (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n", " * time (time) datetime64[ns] 144kB 2022-04-02T00:30:00 ... 2024-04-22...\n", "Attributes: (12/14)\n", " easting: longitude\n", " latitude_max: 61.2819f\n", " latitude_min: 46.0036f\n", " long_name: Temperature\n", " longitude_max: 9.977f\n", " longitude_min: -15.996f\n", " ... ...\n", " unit_long: degrees_C\n", " units: degrees_C\n", " valid_max: 22000\n", " valid_min: -12000\n", " z_max: 0.494025f\n", " z_min: 0.494025f" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds[\"thetao\"]" ] }, { "cell_type": "code", "execution_count": 6, "id": "a1abf473-21de-465f-b133-a9196d58df8a", "metadata": {}, "outputs": [], "source": [ "da = ds[\"thetao\"].sel(time=\"2022-04-02 00:00\", method=\"nearest\").load()" ] }, { 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       "    latitude_min:   46.0036f\n",
       "    long_name:      Temperature\n",
       "    longitude_max:  9.977f\n",
       "    longitude_min:  -15.996f\n",
       "    ...             ...\n",
       "    unit_long:      degrees_C\n",
       "    units:          degrees_C\n",
       "    valid_max:      22000\n",
       "    valid_min:      -12000\n",
       "    z_max:          0.494025f\n",
       "    z_min:          0.494025f
" ], "text/plain": [ " Size: 4MB\n", "array([[13.31200016, 13.32400016, 13.33900016, ..., nan,\n", " nan, nan],\n", " [13.32600016, 13.34100016, 13.34700016, ..., nan,\n", " nan, nan],\n", " [13.34300016, 13.35300016, 13.34600016, ..., nan,\n", " nan, nan],\n", " ...,\n", " [ 8.45599993, 8.49499993, 8.53999993, ..., nan,\n", " nan, nan],\n", " [ 8.44899993, 8.47899993, 8.53999993, ..., nan,\n", " nan, nan],\n", " [ 8.45999993, 8.47099993, 8.53399993, ..., nan,\n", " nan, nan]])\n", "Coordinates:\n", " * latitude (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28\n", " * longitude (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n", " time datetime64[ns] 8B 2022-04-02T00:30:00\n", "Attributes: (12/14)\n", " easting: longitude\n", " latitude_max: 61.2819f\n", " latitude_min: 46.0036f\n", " long_name: Temperature\n", " longitude_max: 9.977f\n", " longitude_min: -15.996f\n", " ... ...\n", " unit_long: degrees_C\n", " units: degrees_C\n", " valid_max: 22000\n", " valid_min: -12000\n", " z_max: 0.494025f\n", " z_min: 0.494025f" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da" ] }, { "cell_type": "code", "execution_count": 9, "id": "964d4928-8499-4609-9c91-e6be13ac0403", "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
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\n", "
\n", "" ], "text/plain": [ ":DynamicMap []\n", " :Image [longitude,latitude] (thetao)" ] }, "execution_count": 9, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p1004" } }, "output_type": "execute_result" } ], "source": [ "da.hvplot.quadmesh(x=\"longitude\", y=\"latitude\", rasterize=True, data_aspect=1)" ] }, { "cell_type": "markdown", "id": "8712890f-a134-4188-a540-07f944c44b6c", "metadata": {}, "source": [ "## Read 3D data" ] }, { "cell_type": "code", "execution_count": 10, "id": "f31a8bb9-ec32-44d6-9020-fbfee8d466fe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 20.6 ms, sys: 1.34 ms, total: 21.9 ms\n", "Wall time: 67.7 ms\n" ] } ], "source": [ "%%time\n", "# references are on an OSN pod (no credentials needed)\n", "url = \"s3://gfts-reference-data/CMEMS_v6r1_NWS_PHY_NRT_NL_3D_combined.parq/\"\n", "\n", "target_opts = {\"anon\": False}\n", "\n", "# netcdf files are on the AWS public dataset program (no credentials needed)\n", "remote_opts = {\"anon\": False}\n", "\n", "fs3D = fsspec.filesystem(\n", " \"reference\",\n", " fo=url,\n", " remote_protocol=\"s3\",\n", " remote_options=remote_opts,\n", " target_options=target_opts,\n", ")\n", "m3D = fs3D.get_mapper(\"\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "23352f78-e44b-4429-a734-910f47f1cce1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 102 ms, sys: 620 µs, total: 102 ms\n", "Wall time: 413 ms\n" ] } ], "source": [ "%%time\n", "ds3D = xr.open_dataset(\n", " m3D, engine=\"zarr\", chunks={}, backend_kwargs={\"consolidated\": False}\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "id": "7b74b53a-97c0-4e7c-9437-5f14f9e542c8", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 6TB\n",
       "Dimensions:    (depth: 50, latitude: 551, longitude: 936, time: 7104)\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
       "  * latitude   (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28\n",
       "  * longitude  (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n",
       "  * time       (time) datetime64[ns] 57kB 2023-07-02T00:30:00 ... 2024-04-22T...\n",
       "Data variables:\n",
       "    so         (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>\n",
       "    thetao     (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>\n",
       "    uo         (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>\n",
       "    vo         (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(1, 1, 551, 936), meta=np.ndarray>\n",
       "Attributes: (12/13)\n",
       "    Conventions:     CF-1.8\n",
       "    comment:         \n",
       "    contact:         https://marine.copernicus.eu/contact\n",
       "    domain_name:     NWS36\n",
       "    field_date:      20230702\n",
       "    field_type:      mean\n",
       "    ...              ...\n",
       "    forecast_type:   hindcast\n",
       "    institution:     Nologin (Spain)\n",
       "    licence:         https://marine.copernicus.eu/user-corner/service-commitm...\n",
       "    references:      http://marine.copernicus.eu/\n",
       "    source:          NEMO3.6\n",
       "    title:           Ocean 3D hourly mean fields for the North West Shelf (NW...
" ], "text/plain": [ " Size: 6TB\n", "Dimensions: (depth: 50, latitude: 551, longitude: 936, time: 7104)\n", "Coordinates:\n", " * depth (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n", " * latitude (latitude) float32 2kB 46.0 46.03 46.06 ... 61.23 61.25 61.28\n", " * longitude (longitude) float32 4kB -16.0 -15.97 -15.94 ... 9.921 9.949 9.977\n", " * time (time) datetime64[ns] 57kB 2023-07-02T00:30:00 ... 2024-04-22T...\n", "Data variables:\n", " so (time, depth, latitude, longitude) float64 1TB dask.array\n", " thetao (time, depth, latitude, longitude) float64 1TB dask.array\n", " uo (time, depth, latitude, longitude) float64 1TB dask.array\n", " vo (time, depth, latitude, longitude) float64 1TB dask.array\n", "Attributes: (12/13)\n", " Conventions: CF-1.8\n", " comment: \n", " contact: https://marine.copernicus.eu/contact\n", " domain_name: NWS36\n", " field_date: 20230702\n", " field_type: mean\n", " ... ...\n", " forecast_type: hindcast\n", " institution: Nologin (Spain)\n", " licence: https://marine.copernicus.eu/user-corner/service-commitm...\n", " references: http://marine.copernicus.eu/\n", " source: NEMO3.6\n", " title: Ocean 3D hourly mean fields for the North West Shelf (NW..." ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds3D" ] }, { "cell_type": "code", "execution_count": 13, "id": "8892d239-0447-45fe-84a8-080b7a92a305", "metadata": {}, "outputs": [], "source": [ "da3D = (\n", " ds3D[\"thetao\"].isel(depth=0).sel(time=\"2022-04-02 00:00\", method=\"nearest\").load()\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "id": "501ac389-3bf7-4836-9f3a-df84943e7ed3", "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
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\n", "" ], "text/plain": [ ":DynamicMap []\n", " :Image [longitude,latitude] (thetao)" ] }, "execution_count": 14, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p1076" } }, "output_type": "execute_result" } ], "source": [ "da3D.hvplot.quadmesh(x=\"longitude\", y=\"latitude\", rasterize=True, data_aspect=1)" ] }, { "cell_type": "code", "execution_count": null, "id": "f0b66ba3-8a9b-463a-bab7-5f52f1230538", "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": "3.11.8" } }, "nbformat": 4, "nbformat_minor": 5 }