From 48c53cc095b8fee41313f424c53bd0da22752a8f Mon Sep 17 00:00:00 2001 From: renaudjester Date: Mon, 16 Dec 2024 18:07:10 +0100 Subject: [PATCH] doc: update overview --- doc/usage/quickoverview.ipynb | 3099 ++++++++++++++++++++++++++++++--- doc/usage/shared-options.rst | 8 +- 2 files changed, 2815 insertions(+), 292 deletions(-) diff --git a/doc/usage/quickoverview.ipynb b/doc/usage/quickoverview.ipynb index 1ab0a145..3faf4c22 100644 --- a/doc/usage/quickoverview.ipynb +++ b/doc/usage/quickoverview.ipynb @@ -449,25 +449,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO - 2024-10-23T08:34:04Z - Dataset version was not specified, the latest one was selected: \"202211\"\n", - "INFO - 2024-10-23T08:34:04Z - Dataset part was not specified, the first one was selected: \"default\"\n", - "INFO - 2024-10-23T08:34:06Z - Service was not specified, the default one was selected: \"arco-time-series\"\n", - "INFO - 2024-10-23T08:34:07Z - Downloading using service arco-time-series...\n", - "INFO - 2024-10-23T08:34:09Z - Estimated size of the dataset file is 61.855 MB\n", - "Estimated size of the data that needs to be downloaded to obtain the result: 2814 MB\n", - "This is a very rough estimate that is generally higher than the actual size of the data that needs to be downloaded.\n", - "INFO - 2024-10-23T08:34:09Z - Writing to local storage. Please wait...\n" + "INFO - 2024-12-16T16:55:21Z - Selected dataset version: \"202411\"\n", + "INFO - 2024-12-16T16:55:21Z - Selected dataset part: \"default\"\n", + "INFO - 2024-12-16T16:55:24Z - Starting download. Please wait...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "75e8924847e84564b05b12724c9701a6", + "model_id": "09291c6edeb9405aa4f36d804004491e", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/2802 [00:00
<xarray.Dataset>\n",
-       "Dimensions:    (depth: 50, latitude: 1081, longitude: 865, time: 1303)\n",
+       "Dimensions:    (depth: 50, latitude: 1078, longitude: 871, time: 760)\n",
        "Coordinates:\n",
        "  * depth      (depth) float32 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
-       "  * latitude   (latitude) float32 26.0 26.03 26.06 26.08 ... 55.94 55.97 56.0\n",
-       "  * longitude  (longitude) float32 -19.0 -18.97 -18.94 ... 4.944 4.972 5.0\n",
-       "  * time       (time) datetime64[ns] 2021-04-03 2021-04-04 ... 2024-10-26\n",
+       "  * latitude   (latitude) float64 26.17 26.19 26.22 26.25 ... 56.03 56.06 56.08\n",
+       "  * longitude  (longitude) float64 -19.08 -19.06 -19.03 ... 5.029 5.057 5.085\n",
+       "  * time       (time) datetime64[ns] 2022-11-23 2022-11-24 ... 2024-12-21\n",
        "Data variables: (12/14)\n",
-       "    chl        (time, depth, latitude, longitude) float32 ...\n",
-       "    dissic     (time, depth, latitude, longitude) float32 ...\n",
-       "    fe         (time, depth, latitude, longitude) float32 ...\n",
-       "    nh4        (time, depth, latitude, longitude) float32 ...\n",
-       "    no3        (time, depth, latitude, longitude) float32 ...\n",
-       "    nppv       (time, depth, latitude, longitude) float32 ...\n",
+       "    chl        (time, depth, latitude, longitude) float32 dask.array<chunksize=(50, 1, 1078, 871), meta=np.ndarray>\n",
+       "    dissic     (time, depth, latitude, longitude) float32 dask.array<chunksize=(50, 1, 1078, 871), meta=np.ndarray>\n",
+       "    fe         (time, depth, latitude, longitude) float32 dask.array<chunksize=(50, 1, 1078, 871), meta=np.ndarray>\n",
+       "    nh4        (time, depth, latitude, longitude) float32 dask.array<chunksize=(50, 1, 1078, 871), meta=np.ndarray>\n",
+       "    no3        (time, depth, latitude, longitude) float32 dask.array<chunksize=(50, 1, 1078, 871), meta=np.ndarray>\n",
+       "    nppv       (time, depth, latitude, longitude) float32 dask.array<chunksize=(50, 1, 1078, 871), meta=np.ndarray>\n",
        "    ...         ...\n",
-       "    phyc       (time, depth, latitude, longitude) float32 ...\n",
-       "    po4        (time, depth, latitude, longitude) float32 ...\n",
-       "    si         (time, depth, latitude, longitude) float32 ...\n",
-       "    spco2      (time, latitude, longitude) float32 ...\n",
-       "    zeu        (time, latitude, longitude) float32 ...\n",
-       "    zooc       (time, depth, latitude, longitude) float32 ...\n",
+       "    phyc       (time, depth, latitude, longitude) float32 dask.array<chunksize=(50, 1, 1078, 871), meta=np.ndarray>\n",
+       "    po4        (time, depth, latitude, longitude) float32 dask.array<chunksize=(50, 1, 1078, 871), meta=np.ndarray>\n",
+       "    si         (time, depth, latitude, longitude) float32 dask.array<chunksize=(50, 1, 1078, 871), meta=np.ndarray>\n",
+       "    spco2      (time, latitude, longitude) float32 dask.array<chunksize=(50, 1078, 871), meta=np.ndarray>\n",
+       "    zeu        (time, latitude, longitude) float32 dask.array<chunksize=(50, 1078, 871), meta=np.ndarray>\n",
+       "    zooc       (time, depth, latitude, longitude) float32 dask.array<chunksize=(50, 1, 1078, 871), meta=np.ndarray>\n",
        "Attributes:\n",
-       "    source:       NEMO3.6-PISCES3.6\n",
-       "    institution:  Nologin (Spain)\n",
        "    title:        Biogeochemical 3D daily mean fields for the Iberia-Biscay-I...\n",
-       "    contact:      mailto: servicedesk.cmems@mercator-ocean.eu\n",
-       "    Conventions:  CF-1.0\n",
-       "    references:   http://marine.copernicus.eu/
  • title :
    Biogeochemical 3D daily mean fields for the Iberia-Biscay-Ireland (IBI) region
    comment :
    references :
    http://marine.copernicus.eu/
    institution :
    NOW Systems (Spain)
    contact :
    https://marine.copernicus.eu/contact
    source :
    NEMO3.6-PISCES3.6
    Conventions :
    CF-1.8
    • " ], "text/plain": [ "\n", - "Dimensions: (depth: 50, latitude: 1081, longitude: 865, time: 1303)\n", + "Dimensions: (depth: 50, latitude: 1078, longitude: 871, time: 760)\n", "Coordinates:\n", " * depth (depth) float32 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n", - " * latitude (latitude) float32 26.0 26.03 26.06 26.08 ... 55.94 55.97 56.0\n", - " * longitude (longitude) float32 -19.0 -18.97 -18.94 ... 4.944 4.972 5.0\n", - " * time (time) datetime64[ns] 2021-04-03 2021-04-04 ... 2024-10-26\n", + " * latitude (latitude) float64 26.17 26.19 26.22 26.25 ... 56.03 56.06 56.08\n", + " * longitude (longitude) float64 -19.08 -19.06 -19.03 ... 5.029 5.057 5.085\n", + " * time (time) datetime64[ns] 2022-11-23 2022-11-24 ... 2024-12-21\n", "Data variables: (12/14)\n", - " chl (time, depth, latitude, longitude) float32 ...\n", - " dissic (time, depth, latitude, longitude) float32 ...\n", - " fe (time, depth, latitude, longitude) float32 ...\n", - " nh4 (time, depth, latitude, longitude) float32 ...\n", - " no3 (time, depth, latitude, longitude) float32 ...\n", - " nppv (time, depth, latitude, longitude) float32 ...\n", + " chl (time, depth, latitude, longitude) float32 dask.array\n", + " dissic (time, depth, latitude, longitude) float32 dask.array\n", + " fe (time, depth, latitude, longitude) float32 dask.array\n", + " nh4 (time, depth, latitude, longitude) float32 dask.array\n", + " no3 (time, depth, latitude, longitude) float32 dask.array\n", + " nppv (time, depth, latitude, longitude) float32 dask.array\n", " ... ...\n", - " phyc (time, depth, latitude, longitude) float32 ...\n", - " po4 (time, depth, latitude, longitude) float32 ...\n", - " si (time, depth, latitude, longitude) float32 ...\n", - " spco2 (time, latitude, longitude) float32 ...\n", - " zeu (time, latitude, longitude) float32 ...\n", - " zooc (time, depth, latitude, longitude) float32 ...\n", + " phyc (time, depth, latitude, longitude) float32 dask.array\n", + " po4 (time, depth, latitude, longitude) float32 dask.array\n", + " si (time, depth, latitude, longitude) float32 dask.array\n", + " spco2 (time, latitude, longitude) float32 dask.array\n", + " zeu (time, latitude, longitude) float32 dask.array\n", + " zooc (time, depth, latitude, longitude) float32 dask.array\n", "Attributes:\n", - " source: NEMO3.6-PISCES3.6\n", - " institution: Nologin (Spain)\n", " title: Biogeochemical 3D daily mean fields for the Iberia-Biscay-I...\n", - " contact: mailto: servicedesk.cmems@mercator-ocean.eu\n", - " Conventions: CF-1.0\n", - " references: http://marine.copernicus.eu/" + " comment: \n", + " references: http://marine.copernicus.eu/\n", + " institution: NOW Systems (Spain)\n", + " contact: https://marine.copernicus.eu/contact\n", + " source: NEMO3.6-PISCES3.6\n", + " Conventions: CF-1.8" ] }, - "execution_count": 14, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -1097,16 +3355,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO - 2024-10-18T16:08:46Z - Dataset version was not specified, the latest one was selected: \"202211\"\n", - "INFO - 2024-10-18T16:08:46Z - Dataset part was not specified, the first one was selected: \"default\"\n", - "INFO - 2024-10-18T16:08:47Z - Service was not specified, the default one was selected: \"arco-time-series\"\n" + "INFO - 2024-12-16T16:57:24Z - Selected dataset version: \"202411\"\n", + "INFO - 2024-12-16T16:57:24Z - Selected dataset part: \"default\"\n" ] }, { @@ -1143,7 +3400,6 @@ "}\n", "\n", "html[theme=dark],\n", - "html[data-theme=dark],\n", "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", @@ -1476,23 +3732,24 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.Dataset> Size: 13MB\n",
-       "Dimensions:    (depth: 50, latitude: 37, longitude: 73, time: 6)\n",
+       "
    <xarray.Dataset>\n",
+       "Dimensions:    (depth: 50, latitude: 36, longitude: 72, time: 6)\n",
        "Coordinates:\n",
-       "  * depth      (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
-       "  * latitude   (latitude) float32 148B 43.0 43.03 43.06 ... 43.94 43.97 44.0\n",
-       "  * longitude  (longitude) float32 292B -5.0 -4.972 -4.944 ... -3.028 -3.0\n",
-       "  * time       (time) datetime64[ns] 48B 2024-10-17 2024-10-18 ... 2024-10-22\n",
+       "  * depth      (depth) float32 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n",
+       "  * latitude   (latitude) float64 43.03 43.05 43.08 43.11 ... 43.94 43.97 44.0\n",
+       "  * longitude  (longitude) float64 -4.999 -4.971 -4.944 ... -3.082 -3.055 -3.027\n",
+       "  * time       (time) datetime64[ns] 2024-12-15 2024-12-16 ... 2024-12-20\n",
        "Data variables:\n",
-       "    chl        (time, depth, latitude, longitude) float64 6MB ...\n",
-       "    o2         (time, depth, latitude, longitude) float64 6MB ...\n",
+       "    chl        (time, depth, latitude, longitude) float32 dask.array<chunksize=(6, 2, 36, 5), meta=np.ndarray>\n",
+       "    o2         (time, depth, latitude, longitude) float32 dask.array<chunksize=(6, 2, 36, 5), meta=np.ndarray>\n",
        "Attributes:\n",
-       "    institution:  Nologin (Spain)\n",
-       "    contact:      mailto: servicedesk.cmems@mercator-ocean.eu\n",
-       "    Conventions:  CF-1.0\n",
        "    title:        Biogeochemical 3D daily mean fields for the Iberia-Biscay-I...\n",
+       "    comment:      \n",
        "    references:   http://marine.copernicus.eu/\n",
-       "    source:       NEMO3.6-PISCES3.6
  • latitude
    (latitude)
    float64
    43.03 43.05 43.08 ... 43.97 44.0
    axis :
    Y
    unit_long :
    Degrees North
    long_name :
    Latitude
    units :
    degrees_north
    standard_name :
    latitude
    array([43.026986, 43.054764, 43.082543, 43.110322, 43.1381  , 43.165879,\n",
+       "       43.193657, 43.221436, 43.249215, 43.276993, 43.304772, 43.332551,\n",
+       "       43.360329, 43.388108, 43.415886, 43.443665, 43.471444, 43.499222,\n",
+       "       43.527001, 43.55478 , 43.582558, 43.610337, 43.638116, 43.665894,\n",
+       "       43.693673, 43.721451, 43.74923 , 43.777009, 43.804787, 43.832566,\n",
+       "       43.860345, 43.888123, 43.915902, 43.94368 , 43.971459, 43.999238])
  • longitude
    (longitude)
    float64
    -4.999 -4.971 ... -3.055 -3.027
    axis :
    X
    unit_long :
    Degrees East
    long_name :
    Longitude
    units :
    degrees_east
    standard_name :
    longitude
    array([-4.999076, -4.971297, -4.943518, -4.91574 , -4.887961, -4.860183,\n",
+       "       -4.832404, -4.804625, -4.776847, -4.749068, -4.721289, -4.693511,\n",
+       "       -4.665732, -4.637954, -4.610175, -4.582396, -4.554618, -4.526839,\n",
+       "       -4.49906 , -4.471282, -4.443503, -4.415724, -4.387946, -4.360167,\n",
+       "       -4.332389, -4.30461 , -4.276831, -4.249053, -4.221274, -4.193495,\n",
+       "       -4.165717, -4.137938, -4.11016 , -4.082381, -4.054602, -4.026824,\n",
+       "       -3.999045, -3.971266, -3.943488, -3.915709, -3.88793 , -3.860152,\n",
+       "       -3.832373, -3.804595, -3.776816, -3.749037, -3.721259, -3.69348 ,\n",
+       "       -3.665701, -3.637923, -3.610144, -3.582366, -3.554587, -3.526808,\n",
+       "       -3.49903 , -3.471251, -3.443472, -3.415694, -3.387915, -3.360137,\n",
+       "       -3.332358, -3.304579, -3.276801, -3.249022, -3.221243, -3.193465,\n",
+       "       -3.165686, -3.137907, -3.110129, -3.08235 , -3.054572, -3.026793])
  • time
    (time)
    datetime64[ns]
    2024-12-15 ... 2024-12-20
    axis :
    T
    unit_long :
    Hours Since 1950-01-01
    long_name :
    Time
    standard_name :
    time
    array(['2024-12-15T00:00:00.000000000', '2024-12-16T00:00:00.000000000',\n",
+       "       '2024-12-17T00:00:00.000000000', '2024-12-18T00:00:00.000000000',\n",
+       "       '2024-12-19T00:00:00.000000000', '2024-12-20T00:00:00.000000000'],\n",
+       "      dtype='datetime64[ns]')
    • chl
      (time, depth, latitude, longitude)
      float32
      dask.array<chunksize=(6, 2, 36, 5), meta=np.ndarray>
      unit_long :
      milligrams of chlorophyll per cubic meter
      long_name :
      Mass Concentration of Chlorophyll in Sea Water
      valid_min :
      0
      units :
      mg.m-3
      standard_name :
      mass_concentration_of_chlorophyll_a_in_sea_water
      valid_max :
      20000
      \n", + " \n", + " \n", + " \n", + " \n", + "
      \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      Array Chunk
      Bytes 2.97 MiB 113.06 kiB
      Shape (6, 50, 36, 72) (6, 2, 36, 67)
      Dask graph 50 chunks in 6 graph layers
      Data type float32 numpy.ndarray
      \n", + "       		\n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " 6\n", + " 1\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " 72\n", + " 36\n", + " 50\n", + "\n", + "
    • o2
      (time, depth, latitude, longitude)
      float32
      dask.array<chunksize=(6, 2, 36, 5), meta=np.ndarray>
      unit_long :
      millimoles of Oxygen per cubic meter
      long_name :
      Mole Concentration of Dissolved Oxygen in Sea Water
      valid_min :
      0
      units :
      mmol.m-3
      standard_name :
      mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
      valid_max :
      3200
      \n", + " \n", + " \n", + " \n", + " \n", + "
      \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      Array Chunk
      Bytes 2.97 MiB 113.06 kiB
      Shape (6, 50, 36, 72) (6, 2, 36, 67)
      Dask graph 50 chunks in 6 graph layers
      Data type float32 numpy.ndarray
      \n", + "       		\n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " 6\n", + " 1\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " 72\n", + " 36\n", + " 50\n", + "\n", + "
    • depth
      PandasIndex
      PandasIndex(Index([0.4940253794193268, 1.5413753986358643, 2.6456685066223145,\n",
+       "       3.8194947242736816,  5.078223705291748,  6.440614223480225,\n",
+       "         7.92956018447876,  9.572997093200684,  11.40500259399414,\n",
+       "       13.467138290405273, 15.810072898864746, 18.495559692382812,\n",
+       "        21.59881591796875, 25.211408615112305,  29.44472885131836,\n",
+       "        34.43415451049805, 40.344051361083984, 47.373687744140625,\n",
+       "        55.76428985595703,   65.8072738647461,  77.85385131835938,\n",
+       "         92.3260726928711, 109.72927856445312, 130.66598510742188,\n",
+       "       155.85072326660156,  186.1255645751953,  222.4751739501953,\n",
+       "        266.0402526855469,    318.12744140625,  380.2130126953125,\n",
+       "         453.937744140625,  541.0889282226562,  643.5668334960938,\n",
+       "        763.3330688476562,  902.3392944335938,  1062.439697265625,\n",
+       "       1245.2911376953125,    1452.2509765625,  1684.284423828125,\n",
+       "       1941.8934326171875,  2225.077880859375,  2533.336181640625,\n",
+       "         2865.70263671875,       3220.8203125,  3597.031982421875,\n",
+       "         3992.48388671875,   4405.22412109375,   4833.29052734375,\n",
+       "          5274.7841796875,   5727.91650390625],\n",
+       "      dtype='float32', name='depth'))
    • latitude
      PandasIndex
      PandasIndex(Index([       43.02698567,         43.0547643,        43.08254293,\n",
+       "       43.110321559999996, 43.138100189999996, 43.165878819999996,\n",
+       "       43.193657449999996,        43.22143608,        43.24921471,\n",
+       "              43.27699334,        43.30477197,         43.3325506,\n",
+       "              43.36032923,        43.38810786,        43.41588649,\n",
+       "              43.44366512,        43.47144375,        43.49922238,\n",
+       "              43.52700101,        43.55477964,        43.58255827,\n",
+       "               43.6103369,        43.63811553,        43.66589416,\n",
+       "              43.69367279,        43.72145142,        43.74923005,\n",
+       "              43.77700868,        43.80478731,        43.83256594,\n",
+       "              43.86034457, 43.888123199999995, 43.915901829999996,\n",
+       "       43.943680459999996, 43.971459089999996, 43.999237719999996],\n",
+       "      dtype='float64', name='latitude'))
    • longitude
      PandasIndex
      PandasIndex(Index([ -4.999075690000001,         -4.97129706,  -4.943518430000001,\n",
+       "        -4.915739800000001, -4.8879611700000005,         -4.86018254,\n",
+       "        -4.832403910000001,  -4.804625280000001,         -4.77684665,\n",
+       "        -4.749068020000001,  -4.721289390000001,  -4.693510760000001,\n",
+       "               -4.66573213,  -4.637953500000001,  -4.610174870000001,\n",
+       "       -4.5823962400000005,  -4.554617610000001,  -4.526838980000001,\n",
+       "        -4.499060350000001,         -4.47128172,  -4.443503090000001,\n",
+       "        -4.415724460000001, -4.3879458300000005,  -4.360167200000001,\n",
+       "        -4.332388570000001,  -4.304609940000001,         -4.27683131,\n",
+       "        -4.249052680000001,  -4.221274050000001, -4.1934954200000005,\n",
+       "               -4.16571679,  -4.137938160000001,  -4.110159530000001,\n",
+       "                -4.0823809,  -4.054602270000001,  -4.026823640000001,\n",
+       "       -3.9990450100000006, -3.9712663800000008, -3.9434877500000005,\n",
+       "       -3.9157091200000007,  -3.887930490000001, -3.8601518600000007,\n",
+       "        -3.832373230000001, -3.8045946000000006,  -3.776815970000001,\n",
+       "       -3.7490373400000006, -3.7212587100000007, -3.6934800800000005,\n",
+       "       -3.6657014500000007,  -3.637922820000001, -3.6101441900000006,\n",
+       "        -3.582365560000001, -3.5545869300000006,  -3.526808300000001,\n",
+       "       -3.4990296700000005, -3.4712510400000007,  -3.443472410000001,\n",
+       "       -3.4156937800000007,  -3.387915150000001, -3.3601365200000006,\n",
+       "        -3.332357890000001, -3.3045792600000006, -3.2768006300000008,\n",
+       "        -3.249022000000001, -3.2212433700000007,  -3.193464740000001,\n",
+       "       -3.1656861100000007,  -3.137907480000001, -3.1101288500000006,\n",
+       "        -3.082350220000001, -3.0545715900000006, -3.0267929600000008],\n",
+       "      dtype='float64', name='longitude'))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2024-12-15', '2024-12-16', '2024-12-17', '2024-12-18',\n",
+       "               '2024-12-19', '2024-12-20'],\n",
+       "              dtype='datetime64[ns]', name='time', freq=None))
  • title :
    Biogeochemical 3D daily mean fields for the Iberia-Biscay-Ireland (IBI) region
    comment :
    references :
    http://marine.copernicus.eu/
    institution :
    NOW Systems (Spain)
    contact :
    https://marine.copernicus.eu/contact
    source :
    NEMO3.6-PISCES3.6
    Conventions :
    CF-1.8
    • " ], "text/plain": [ - " Size: 13MB\n", - "Dimensions: (depth: 50, latitude: 37, longitude: 73, time: 6)\n", + "\n", + "Dimensions: (depth: 50, latitude: 36, longitude: 72, time: 6)\n", "Coordinates:\n", - " * depth (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n", - " * latitude (latitude) float32 148B 43.0 43.03 43.06 ... 43.94 43.97 44.0\n", - " * longitude (longitude) float32 292B -5.0 -4.972 -4.944 ... -3.028 -3.0\n", - " * time (time) datetime64[ns] 48B 2024-10-17 2024-10-18 ... 2024-10-22\n", + " * depth (depth) float32 0.494 1.541 2.646 ... 5.275e+03 5.728e+03\n", + " * latitude (latitude) float64 43.03 43.05 43.08 43.11 ... 43.94 43.97 44.0\n", + " * longitude (longitude) float64 -4.999 -4.971 -4.944 ... -3.082 -3.055 -3.027\n", + " * time (time) datetime64[ns] 2024-12-15 2024-12-16 ... 2024-12-20\n", "Data variables:\n", - " chl (time, depth, latitude, longitude) float64 6MB ...\n", - " o2 (time, depth, latitude, longitude) float64 6MB ...\n", + " chl (time, depth, latitude, longitude) float32 dask.array\n", + " o2 (time, depth, latitude, longitude) float32 dask.array\n", "Attributes:\n", - " institution: Nologin (Spain)\n", - " contact: mailto: servicedesk.cmems@mercator-ocean.eu\n", - " Conventions: CF-1.0\n", " title: Biogeochemical 3D daily mean fields for the Iberia-Biscay-I...\n", + " comment: \n", " references: http://marine.copernicus.eu/\n", - " source: NEMO3.6-PISCES3.6" + " institution: NOW Systems (Spain)\n", + " contact: https://marine.copernicus.eu/contact\n", + " source: NEMO3.6-PISCES3.6\n", + " Conventions: CF-1.8" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1623,19 +4165,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(['2024-10-17T00:00:00.000000000', '2024-10-18T00:00:00.000000000',\n", - " '2024-10-19T00:00:00.000000000', '2024-10-20T00:00:00.000000000',\n", - " '2024-10-21T00:00:00.000000000', '2024-10-22T00:00:00.000000000'],\n", + "array(['2024-12-15T00:00:00.000000000', '2024-12-16T00:00:00.000000000',\n", + " '2024-12-17T00:00:00.000000000', '2024-12-18T00:00:00.000000000',\n", + " '2024-12-19T00:00:00.000000000', '2024-12-20T00:00:00.000000000'],\n", " dtype='datetime64[ns]')" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1655,12 +4197,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -1671,7 +4213,6 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "import xarray\n", "\n", "response_bay[\"chl\"].isel(depth=10).plot(col=\"time\", col_wrap=3)\n", "plt.suptitle(\"Temporal evolution at sea surface\", fontsize=20, y=1.2)\n", @@ -1693,78 +4234,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO - 2024-10-18T14:48:09Z - Dataset version was not specified, the latest one was selected: \"202211\"\n", - "INFO - 2024-10-18T14:48:09Z - Dataset part was not specified, the first one was selected: \"default\"\n", - "INFO - 2024-10-18T14:48:09Z - Service was not specified, the default one was selected: \"original-files\"\n", - "INFO - 2024-10-18T14:48:09Z - Downloading using service original-files...\n", - "INFO - 2024-10-18T14:48:10Z - Listing files on remote server...\n", - "1it [00:00, 1.09it/s]\n", - "INFO - 2024-10-18T14:48:11Z - You requested the download of the following files:\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221001_20221001_R20221017_AN04.nc - 82.48 MB - 2023-11-12T14:01:32.377000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221002_20221002_R20221017_AN05.nc - 82.77 MB - 2023-11-12T14:01:38.666000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221003_20221003_R20221017_AN06.nc - 82.81 MB - 2023-11-12T14:01:37.923000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221004_20221004_R20221017_AN07.nc - 82.64 MB - 2023-11-12T14:01:40.222000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221005_20221005_R20221024_AN01.nc - 82.31 MB - 2023-11-12T14:01:38.078000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221006_20221006_R20221024_AN02.nc - 82.45 MB - 2023-11-12T14:01:48.759000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221007_20221007_R20221024_AN03.nc - 82.41 MB - 2023-11-12T14:01:43.316000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221008_20221008_R20221024_AN04.nc - 82.48 MB - 2023-11-12T14:01:46.301000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221009_20221009_R20221024_AN05.nc - 82.24 MB - 2023-11-12T14:01:47.611000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221010_20221010_R20221024_AN06.nc - 82.04 MB - 2023-11-12T14:01:49.426000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221011_20221011_R20221024_AN07.nc - 82.18 MB - 2023-11-12T14:01:53.948000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221012_20221012_R20221031_AN01.nc - 82.30 MB - 2023-11-12T14:01:59.761000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221013_20221013_R20221031_AN02.nc - 82.36 MB - 2023-11-12T14:01:59.417000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221014_20221014_R20221031_AN03.nc - 82.37 MB - 2023-11-12T14:01:58.859000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221015_20221015_R20221031_AN04.nc - 82.11 MB - 2023-11-12T14:02:01.737000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221016_20221016_R20221031_AN05.nc - 82.03 MB - 2023-11-12T14:02:11.336000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221017_20221017_R20221031_AN06.nc - 81.82 MB - 2023-11-12T14:02:12.479000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221018_20221018_R20221031_AN07.nc - 81.89 MB - 2023-11-12T14:02:07.042000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221019_20221019_R20221107_AN01.nc - 81.66 MB - 2023-11-12T14:02:09.998000Z\n", - "s3://mdl-native-10/native/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221020_20221020_R20221107_AN02.nc - 81.50 MB - 2023-11-12T14:02:13.928000Z\n", - "Printed 20 out of 757 files\n", - "\n", - "Total size of the download: 65.60 GB\n", - "\n", - "\n", - "Do you want to proceed with download? [Y/n]:" + "INFO - 2024-12-16T17:00:12Z - Selected dataset version: \"202411\"\n", + "INFO - 2024-12-16T17:00:12Z - Selected dataset part: \"default\"\n", + "INFO - 2024-12-16T17:00:12Z - Listing files on remote server...\n", + "1it [00:00, 2.30it/s]\n" ] } ], "source": [ "# Download all the files from a dataset\n", - "copernicusmarine.get(dataset_id=\"cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m\") " + "response_with_all_the_files = copernicusmarine.get(dataset_id=\"cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m\", dry_run=True) " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO - 2024-10-18T15:19:27Z - Dataset version was not specified, the latest one was selected: \"202211\"\n", - "INFO - 2024-10-18T15:19:27Z - Dataset part was not specified, the first one was selected: \"default\"\n", - "INFO - 2024-10-18T15:19:27Z - Service was not specified, the default one was selected: \"original-files\"\n", - "INFO - 2024-10-18T15:19:27Z - Downloading using service original-files...\n", - "INFO - 2024-10-18T15:19:27Z - Listing files on remote server...\n", - "1it [00:00, 1.90it/s]\n", - "Downloading files: 100%|██████████| 2/2 [00:05<00:00, 2.75s/it]" + "INFO - 2024-12-16T17:01:44Z - Selected dataset version: \"202411\"\n", + "INFO - 2024-12-16T17:01:44Z - Selected dataset part: \"default\"\n", + "INFO - 2024-12-16T17:01:44Z - Listing files on remote server...\n", + "1it [00:00, 2.68it/s]\n", + "Downloading files: 100%|██████████| 2/2 [00:07<00:00, 3.50s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "data/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221001_20221001_R20221017_AN04.nc\n", - "data/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202211/2022/10/CMEMS_v7r1_IBI_BIO_NRT_NL_01dav_20221003_20221003_R20221017_AN06.nc\n" + "data/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411/2024/12/CMEMS_v8r1_IBI_BIO_NRT_NL_01dav_20241220_20241220_R20241212_FC09.nc\n", + "data/IBI_ANALYSISFORECAST_BGC_005_004/cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411/2024/12/CMEMS_v8r1_IBI_BIO_NRT_NL_01dav_20241221_20241221_R20241212_FC10.nc\n" ] }, { @@ -1778,8 +4288,8 @@ "source": [ "# You can combine the filter and regex argument (it will be as an \"OR\" condition)\n", "response = copernicusmarine.get(dataset_id=\"cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m\", \n", - " filter=\"*01dav_20221001_20221001_R20221017_AN04*\", \n", - " regex=\"01dav_20221003_20221003_R20221017_AN06\", \n", + " filter=\"*20241221_20241221_R20241212_FC10*\", \n", + " regex=\"20241220_20241220_R20241212_FC09\", \n", " output_directory=\"data\", # we can specify the output directory\n", " overwrite=True, # if files already exist, they will be overwritten\n", " )\n", @@ -1797,35 +4307,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO - 2024-10-18T15:20:33Z - You forced selection of dataset version \"202211\"\n", - "INFO - 2024-10-18T15:20:33Z - Dataset part was not specified, the first one was selected: \"default\"\n", - "INFO - 2024-10-18T15:20:33Z - Service was not specified, the default one was selected: \"original-files\"\n", - "INFO - 2024-10-18T15:20:33Z - Downloading using service original-files...\n", - "INFO - 2024-10-18T15:20:33Z - Listing files on remote server...\n", - "1it [00:00, 1.91it/s]\n", - "INFO - 2024-10-18T15:20:33Z - No data to download\n" + "INFO - 2024-12-16T17:06:33Z - Selected dataset version: \"202411\"\n", + "INFO - 2024-12-16T17:06:33Z - Selected dataset part: \"default\"\n", + "INFO - 2024-12-16T17:06:33Z - Listing files on remote server...\n", + "1it [00:00, 2.64it/s]\n", + "INFO - 2024-12-16T17:06:34Z - No data to download\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found CMEMS_v8r1_IBI_BIO_NRT_NL_01dav_20241220_20241220_R20241212_FC09.nc on server and it was IGNORED\n", + "Found CMEMS_v8r1_IBI_BIO_NRT_NL_01dav_20241221_20241221_R20241212_FC10.nc on server and it was IGNORED\n" ] } ], "source": [ - "response = copernicusmarine.get(\n", + "response_sync = copernicusmarine.get(\n", " dataset_id=\"cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m\", \n", - " filter=\"*01dav_20221001_20221001_R20221017_AN04*\", \n", - " regex=\"01dav_20221003_20221003_R20221017_AN06\", \n", - " dataset_version=\"202211\", \n", + " filter=\"*20241221_20241221_R20241212_FC10*\", \n", + " regex=\"20241220_20241220_R20241212_FC09\", \n", + " dataset_version=\"202411\", \n", " output_directory=\"data\",\n", " sync=True,\n", " sync_delete=True, # delete the files that are not in the server\n", " max_concurrent_requests=0, # not in parallel\n", " # can be useful to be sure to not overload the process \n", - ")" + ")\n", + "\n", + "for file_metadata in response_sync.files:\n", + " print(f\"Found {file_metadata.filename} on server and it was {file_metadata.file_status}\")" ] }, { diff --git a/doc/usage/shared-options.rst b/doc/usage/shared-options.rst index 2062dd04..93771eda 100644 --- a/doc/usage/shared-options.rst +++ b/doc/usage/shared-options.rst @@ -6,12 +6,13 @@ Both ``subset`` and ``get`` commands provide these options. Some options are ava Option ``--overwrite`` and ``--skip-existing`` ************************************************ -By default, if the files already exist at the destination, new files will be created with a unique index (eg 'filename_(1).nc') if the file already exists. +By default, if the files already exist at the destination, new files will be created with a unique index (eg 'filename_(1).nc'). When ``--overwrite`` is specified, existing files will be overwritten. When ``--skip-existing`` is specified, the download of files that already exist at the output destination will be skipped. +The ``status`` and ``message`` in the response can indicate if the toolbox has overwritten or skipped a file. See in :ref:`Response types documentation ` the ``status`` and ``message`` fields for more information about request statuses. .. note:: @@ -20,7 +21,8 @@ See in :ref:`Response types documentation ` the ``status`` and ` Option ``--create-template`` ********************************* -This option creates a file in your current directory containing the request parameters. If specified, no other action will be performed. The file created will depend on the command used: +This option creates a file in your current directory containing the request parameters. +If specified, no other action will be performed. The file created will depend on the command used: - ``subset`` @@ -120,6 +122,8 @@ Option ``--credentials-file`` You can use the ``--credentials-file`` option to specify a credentials file. The file can be either ``.copernicusmarine-credentials``, ``motuclient-python.ini``, ``.netrc``, or ``_netrc``. +When using the option ``--check-credentials-valid`` with the ``login`` command, the ``--credentials-file`` option can be used the same way as with the ``subset`` and ``get`` commands. + .. _dataset version: Option ``--dataset-version``