Usage¶

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

[2]:

from argopy import DataFetcher as ArgoDataFetcher
ds = ArgoDataFetcher().region([-75, -45, 20, 30, 0, 100, '2011', '2012']).to_xarray()

/home/docs/checkouts/readthedocs.org/user_builds/argopy/checkouts/v0.1.3/argopy/plotters.py:31: UserWarning: argopy requires cartopy installed for full map plotting functionality
  warnings.warn("argopy requires cartopy installed for full map plotting functionality")

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:

[3]:

print(ds)

<xarray.Dataset>
Dimensions:          (N_POINTS: 29023)
Coordinates:
    TIME             (N_POINTS) datetime64[ns] 2011-01-01T11:49:19 ... 2011-12-31T20:31:01
    LATITUDE         (N_POINTS) float64 24.54 24.54 24.54 ... 22.58 22.58 22.58
    LONGITUDE        (N_POINTS) float64 -45.14 -45.14 -45.14 ... -54.11 -54.11
  * N_POINTS         (N_POINTS) int64 0 1 2 3 4 ... 29019 29020 29021 29022
Data variables:
    CYCLE_NUMBER     (N_POINTS) int64 23 23 23 23 23 23 23 ... 23 23 23 23 23 23
    DATA_MODE        (N_POINTS) <U1 'D' 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D' 'D'
    DIRECTION        (N_POINTS) <U1 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER  (N_POINTS) int64 1901463 1901463 ... 6901052 6901052
    POSITION_QC      (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    PRES             (N_POINTS) float64 5.0 10.0 15.0 20.0 ... 70.7 80.2 90.7
    PRES_QC          (N_POINTS) int64 1 1 1 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 ... 37.24 37.19 37.16
    PSAL_QC          (N_POINTS) int64 1 1 1 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 ... 25.54 24.78 24.14
    TEMP_QC          (N_POINTS) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1
    TIME_QC          (N_POINTS) int64 1 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:         http://www.ifremer.fr/erddap
    Fetched_by:           docs
    Fetched_date:         2020/05/15
    Fetched_constraints:  phy_[x=-75.00/-45.00; y=20.00/30.00; z=0.0/100.0; t...
    Fetched_url:          http://www.ifremer.fr/erddap/tabledap/ArgoFloats.cs...
    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 argopy.argo.point2profile():

[4]:

ds = ds.argo.point2profile()
print(ds)

<xarray.Dataset>
Dimensions:          (N_LEVELS: 55, N_PROF: 1419)
Coordinates:
    TIME             (N_PROF) datetime64[ns] 2011-01-01T11:49:19 ... 2011-12-31T20:31:01
    LATITUDE         (N_PROF) float64 24.54 25.04 21.48 ... 26.85 20.66 22.58
    LONGITUDE        (N_PROF) float64 -45.14 -51.58 -60.82 ... -53.52 -54.11
  * N_LEVELS         (N_LEVELS) int64 0 1 2 3 4 5 6 7 ... 48 49 50 51 52 53 54
  * N_PROF           (N_PROF) int64 42 664 298 5 527 ... 204 368 526 1272 1375
Data variables:
    CYCLE_NUMBER     (N_PROF) int64 23 10 135 23 119 160 ... 193 171 165 4 23
    DATA_MODE        (N_PROF) <U1 'D' 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D' 'D'
    DIRECTION        (N_PROF) <U1 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A' 'A'
    PLATFORM_NUMBER  (N_PROF) int64 1901463 4901211 4900818 ... 6900935 6901052
    POSITION_QC      (N_PROF) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1
    PRES             (N_PROF, N_LEVELS) float64 5.0 10.0 15.0 ... nan nan nan
    PRES_QC          (N_PROF) int64 1 1 1 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 37.45 ... nan nan
    PSAL_QC          (N_PROF) int64 1 1 1 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 24.09 ... nan nan
    TEMP_QC          (N_PROF) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1
    TIME_QC          (N_PROF) int64 1 1 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:         http://www.ifremer.fr/erddap
    Fetched_by:           docs
    Fetched_date:         2020/05/15
    Fetched_constraints:  phy_[x=-75.00/-45.00; y=20.00/30.00; z=0.0/100.0; t...
    Fetched_url:          http://www.ifremer.fr/erddap/tabledap/ArgoFloats.cs...
    history:              Variables filtered according to DATA_MODE; Variable...

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

[5]:

ds = ArgoDataFetcher().float(6902746).to_xarray()

or for a float profile using the cycle number:

[6]:

ds = ArgoDataFetcher().profile(6902755, 12).to_xarray()