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 data fetcher 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>
Dimensions: (N_POINTS: 9422)
Coordinates:
* N_POINTS (N_POINTS) int64 0 1 2 3 4 ... 9418 9419 9420 9421
LATITUDE (N_POINTS) float64 24.54 24.54 24.54 ... 24.96 24.96
LONGITUDE (N_POINTS) float64 -45.14 -45.14 ... -50.4 -50.4
TIME (N_POINTS) datetime64[ns] 2011-01-01T11:49:19 ... ...
Data variables: (12/13)
CONFIG_MISSION_NUMBER (N_POINTS) int32 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
CYCLE_NUMBER (N_POINTS) int32 23 23 23 23 23 23 ... 38 38 38 38 38
DATA_MODE (N_POINTS) <U1 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D'
DIRECTION (N_POINTS) <U1 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
PLATFORM_NUMBER (N_POINTS) int32 1901463 1901463 ... 1901463 1901463
POSITION_QC (N_POINTS) int32 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
... ...
PRES_QC (N_POINTS) int32 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
PSAL (N_POINTS) float64 37.45 37.45 37.45 ... 36.97 36.99
PSAL_QC (N_POINTS) int32 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
TEMP (N_POINTS) float64 24.08 24.08 24.09 ... 21.28 21.19
TEMP_QC (N_POINTS) int32 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
TIME_QC (N_POINTS) int32 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: 2023/03/28
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>
Dimensions: (N_PROF: 469, N_LEVELS: 55)
Coordinates:
* N_PROF (N_PROF) int64 15 286 125 0 223 ... 461 467 433 30
* N_LEVELS (N_LEVELS) int64 0 1 2 3 4 5 6 ... 49 50 51 52 53 54
LATITUDE (N_PROF) float64 24.54 25.04 21.48 ... 26.67 24.96
LONGITUDE (N_PROF) float64 -45.14 -51.58 ... -66.83 -50.4
TIME (N_PROF) datetime64[ns] 2011-01-01T11:49:19 ... 20...
Data variables: (12/13)
CONFIG_MISSION_NUMBER (N_PROF) int32 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 2 2 2 1
CYCLE_NUMBER (N_PROF) int32 23 10 135 23 119 160 ... 1 5 2 10 38
DATA_MODE (N_PROF) <U1 'D' 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D'
DIRECTION (N_PROF) <U1 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
PLATFORM_NUMBER (N_PROF) int32 1901463 4901211 ... 6900778 1901463
POSITION_QC (N_PROF) int32 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
... ...
PRES_QC (N_PROF) int32 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
PSAL (N_PROF, N_LEVELS) float64 37.45 37.45 ... nan nan
PSAL_QC (N_PROF) int32 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
TEMP (N_PROF, N_LEVELS) float64 24.08 24.08 ... nan nan
TEMP_QC (N_PROF) int32 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
TIME_QC (N_PROF) int32 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: 2023/03/28
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()