Usage

Usage#

To get access to Argo data, all you need is 2 lines of codes:

In [1]: from argopy import DataFetcher as ArgoDataFetcher

In [2]: ds = ArgoDataFetcher().region([-75, -45, 20, 30, 0, 100, '2011-01', '2011-06']).to_xarray()

In this example, we used a DataFetcher to get data for a given space/time region. We retrieved all Argo data measurements from 75W to 45W, 20N to 30N, 0db to 100db and from January to May 2011 (the max date is exclusive). Data are returned as a collection of measurements in a xarray.Dataset:

In [3]: ds
Out[3]: 
<xarray.Dataset> Size: 1MB
Dimensions:          (N_POINTS: 9422)
Coordinates:
  * N_POINTS         (N_POINTS) int64 75kB 0 1 2 3 4 ... 9418 9419 9420 9421
    LATITUDE         (N_POINTS) float64 75kB 24.54 24.54 24.54 ... 24.96 24.96
    LONGITUDE        (N_POINTS) float64 75kB -45.14 -45.14 ... -50.4 -50.4
    TIME             (N_POINTS) datetime64[ns] 75kB 2011-01-01T11:49:19 ... 2...
Data variables: (12/15)
    CYCLE_NUMBER     (N_POINTS) int64 75kB 23 23 23 23 23 23 ... 38 38 38 38 38
    DATA_MODE        (N_POINTS) <U1 38kB 'D' 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D'
    DIRECTION        (N_POINTS) <U1 38kB 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER  (N_POINTS) int64 75kB 1901463 1901463 ... 1901463 1901463
    POSITION_QC      (N_POINTS) int64 75kB 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    PRES             (N_POINTS) float32 38kB 5.0 10.0 15.0 ... 90.0 95.0 100.0
    ...               ...
    PSAL_ERROR       (N_POINTS) float32 38kB 0.01 0.01 0.01 ... 0.01017 0.01016
    PSAL_QC          (N_POINTS) int64 75kB 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    TEMP             (N_POINTS) float32 38kB 24.08 24.08 24.09 ... 21.28 21.19
    TEMP_ERROR       (N_POINTS) float32 38kB 0.002 0.002 0.002 ... 0.002 0.002
    TEMP_QC          (N_POINTS) int64 75kB 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    TIME_QC          (N_POINTS) int64 75kB 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         https://erddap.ifremer.fr/erddap
    Fetched_by:           docs
    Fetched_date:         2024/09/23
    Fetched_constraints:  [x=-75.00/-45.00; y=20.00/30.00; z=0.0/100.0; t=201...
    Fetched_uri:          ['https://erddap.ifremer.fr/erddap/tabledap/ArgoFlo...
    history:              Variables filtered according to DATA_MODE; Variable...

Fetched data are returned as a 1D array collection of measurements. If you prefer to work with a 2D array collection of vertical profiles, simply transform the dataset with the xarray.Dataset accessor method Dataset.argo.point2profile():

In [4]: ds = ds.argo.point2profile()

In [5]: ds
Out[5]: 
<xarray.Dataset> Size: 462kB
Dimensions:          (N_PROF: 469, N_LEVELS: 55)
Coordinates:
  * N_PROF           (N_PROF) int64 4kB 15 286 125 0 223 ... 371 461 467 433 30
  * N_LEVELS         (N_LEVELS) int64 440B 0 1 2 3 4 5 6 ... 49 50 51 52 53 54
    LATITUDE         (N_PROF) float64 4kB 24.54 25.04 21.48 ... 26.67 24.96
    LONGITUDE        (N_PROF) float64 4kB -45.14 -51.58 -60.82 ... -66.83 -50.4
    TIME             (N_PROF) datetime64[ns] 4kB 2011-01-01T11:49:19 ... 2011...
Data variables: (12/15)
    CYCLE_NUMBER     (N_PROF) int64 4kB 23 10 135 23 119 160 ... 163 1 5 2 10 38
    DATA_MODE        (N_PROF) <U1 2kB 'D' 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D'
    DIRECTION        (N_PROF) <U1 2kB 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER  (N_PROF) int64 4kB 1901463 4901211 ... 6900778 1901463
    POSITION_QC      (N_PROF) int64 4kB 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    PRES             (N_PROF, N_LEVELS) float32 103kB 5.0 10.0 15.0 ... nan nan
    ...               ...
    PSAL_ERROR       (N_PROF, N_LEVELS) float32 103kB 0.01 0.01 0.01 ... nan nan
    PSAL_QC          (N_PROF) int64 4kB 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    TEMP             (N_PROF, N_LEVELS) float32 103kB 24.08 24.08 ... nan nan
    TEMP_ERROR       (N_PROF) float32 2kB 0.002 0.002 0.002 ... 0.0025 0.002
    TEMP_QC          (N_PROF) int64 4kB 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    TIME_QC          (N_PROF) int64 4kB 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
Attributes:
    DATA_ID:              ARGO
    DOI:                  http://doi.org/10.17882/42182
    Fetched_from:         https://erddap.ifremer.fr/erddap
    Fetched_by:           docs
    Fetched_date:         2024/09/23
    Fetched_constraints:  [x=-75.00/-45.00; y=20.00/30.00; z=0.0/100.0; t=201...
    Fetched_uri:          ['https://erddap.ifremer.fr/erddap/tabledap/ArgoFlo...
    history:              Variables filtered according to DATA_MODE; Variable...

You can also fetch data for a specific float using its WMO number:

In [6]: ds = ArgoDataFetcher().float(6902746).to_xarray()

or for a float profile using the cycle number:

In [7]: ds = ArgoDataFetcher().profile(6902755, 12).to_xarray()