[2]:
from argopy import DataFetcher as ArgoDataFetcher
/home/docs/checkouts/readthedocs.org/user_builds/argopy/checkouts/v0.1.4/argopy/plotters.py:33: UserWarning: argopy requires cartopy installed for full map plotting functionality
warnings.warn("argopy requires cartopy installed for full map plotting functionality")
Manipulating data¶
Once you fetched data, argopy comes with a handy xarray.Dataset accessor namespace argo to perform specific manipulation of the data.
Transformation¶
Points vs profiles¶
Fetched data are returned as a 1D array collection of measurements:
[3]:
argo_loader = ArgoDataFetcher().region([-75,-55,30.,40.,0,100., '2011-01-01', '2011-01-15'])
ds_points = argo_loader.to_xarray()
print(ds_points)
<xarray.Dataset>
Dimensions: (N_POINTS: 524)
Coordinates:
LONGITUDE (N_POINTS) float64 -66.77 -66.77 -66.77 ... -64.59 -64.59
TIME (N_POINTS) datetime64[ns] 2011-01-02T11:14:06 ... 2011-01-14T01:05:21
* N_POINTS (N_POINTS) int64 0 1 2 3 4 5 6 ... 518 519 520 521 522 523
LATITUDE (N_POINTS) float64 37.28 37.28 37.28 ... 33.07 33.07 33.07
Data variables:
CYCLE_NUMBER (N_POINTS) int64 150 150 150 150 150 150 ... 13 13 13 13 13
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 4900803 4900803 ... 5903377 5903377
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 ... 95.97 97.97 99.97
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 36.67 36.67 36.67 ... 36.67 36.67 36.67
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 19.46 19.47 19.47 ... 19.2 19.2 19.2
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:
cdm_altitude_proxy: pres
cdm_data_type: TrajectoryProfile
cdm_profile_variables: cycle_number, data_type, format_version, handb...
cdm_trajectory_variables: platform_number, project_name, pi_name, platfo...
Conventions: Argo-3.1, CF-1.6, COARDS, ACDD-1.3
creator_email: support@argo.net
creator_name: Argo
creator_url: http://www.argo.net/
Easternmost_Easting: -56.578
featureType: TrajectoryProfile
geospatial_lat_max: 37.28
geospatial_lat_min: 30.082
geospatial_lat_units: degrees_north
geospatial_lon_max: -56.578
geospatial_lon_min: -73.343
geospatial_lon_units: degrees_east
history: 2020-06-24T18:00:48Z (local files)\n2020-06-24...
id: ArgoFloats
infoUrl: http://www.argo.net/
institution: Argo
keywords: adjusted, argo, array, assembly, best, centre,...
keywords_vocabulary: GCMD Science Keywords
license: The data may be used and redistributed for fre...
Northernmost_Northing: 37.28
references: http://www.argodatamgt.org/Documentation
source: Argo float
sourceUrl: (local files)
Southernmost_Northing: 30.082
standard_name_vocabulary: CF Standard Name Table v29
summary: Argo float vertical profiles from Coriolis Glo...
time_coverage_end: 2011-01-14T01:05:21Z
time_coverage_start: 2011-01-02T11:14:06Z
title: Argo Float Measurements
user_manual_version: 3.1
Westernmost_Easting: -73.343
DATA_ID: ARGO
DOI: http://doi.org/10.17882/42182
Fetched_from: http://www.ifremer.fr/erddap
Fetched_by: docs
Fetched_date: 2020/06/24
Fetched_constraints: phy_[x=-75.00/-55.00; y=30.00/40.00; z=0.0/100...
Fetched_uri: http://www.ifremer.fr/erddap/tabledap/ArgoFloa...
If you prefer to work with a 2D array collection of vertical profiles, simply transform the dataset with argopy.xarray.ArgoAccessor.point2profile():
[4]:
ds_profiles = ds_points.argo.point2profile()
print(ds_profiles)
<xarray.Dataset>
Dimensions: (N_LEVELS: 50, N_PROF: 18)
Coordinates:
TIME (N_PROF) datetime64[ns] 2011-01-02T11:14:06 ... 2011-01-14T01:05:21
LONGITUDE (N_PROF) float64 -66.77 -71.17 -64.93 ... -72.75 -64.59
* N_LEVELS (N_LEVELS) int64 0 1 2 3 4 5 6 7 ... 43 44 45 46 47 48 49
LATITUDE (N_PROF) float64 37.28 33.98 32.88 ... 37.03 34.39 33.07
* N_PROF (N_PROF) int64 7 13 15 0 6 2 9 4 11 5 1 12 10 17 3 8 14 16
Data variables:
CYCLE_NUMBER (N_PROF) int64 150 3 11 100 180 280 ... 17 62 148 151 4 13
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 4900803 4901218 5903377 ... 4901218 5903377
POSITION_QC (N_PROF) int64 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 ... 99.97 nan
PRES_QC (N_PROF) int64 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
PSAL (N_PROF, N_LEVELS) float64 36.67 36.67 36.67 ... 36.67 nan
PSAL_QC (N_PROF) int64 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
TEMP (N_PROF, N_LEVELS) float64 19.46 19.47 19.47 ... 19.2 nan
TEMP_QC (N_PROF) int64 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
Attributes:
cdm_altitude_proxy: pres
cdm_data_type: TrajectoryProfile
cdm_profile_variables: cycle_number, data_type, format_version, handb...
cdm_trajectory_variables: platform_number, project_name, pi_name, platfo...
Conventions: Argo-3.1, CF-1.6, COARDS, ACDD-1.3
creator_email: support@argo.net
creator_name: Argo
creator_url: http://www.argo.net/
Easternmost_Easting: -56.578
featureType: TrajectoryProfile
geospatial_lat_max: 37.28
geospatial_lat_min: 30.082
geospatial_lat_units: degrees_north
geospatial_lon_max: -56.578
geospatial_lon_min: -73.343
geospatial_lon_units: degrees_east
history: 2020-06-24T18:00:48Z (local files)\n2020-06-24...
id: ArgoFloats
infoUrl: http://www.argo.net/
institution: Argo
keywords: adjusted, argo, array, assembly, best, centre,...
keywords_vocabulary: GCMD Science Keywords
license: The data may be used and redistributed for fre...
Northernmost_Northing: 37.28
references: http://www.argodatamgt.org/Documentation
source: Argo float
sourceUrl: (local files)
Southernmost_Northing: 30.082
standard_name_vocabulary: CF Standard Name Table v29
summary: Argo float vertical profiles from Coriolis Glo...
time_coverage_end: 2011-01-14T01:05:21Z
time_coverage_start: 2011-01-02T11:14:06Z
title: Argo Float Measurements
user_manual_version: 3.1
Westernmost_Easting: -73.343
DATA_ID: ARGO
DOI: http://doi.org/10.17882/42182
Fetched_from: http://www.ifremer.fr/erddap
Fetched_by: docs
Fetched_date: 2020/06/24
Fetched_constraints: phy_[x=-75.00/-55.00; y=30.00/40.00; z=0.0/100...
Fetched_uri: http://www.ifremer.fr/erddap/tabledap/ArgoFloa...
You can simply reverse this transformation with the argopy.argo.profile2point():
[5]:
ds = ds_profiles.argo.profile2point()
print(ds)
<xarray.Dataset>
Dimensions: (N_POINTS: 524)
Coordinates:
* N_POINTS (N_POINTS) int64 0 1 2 3 4 5 6 ... 518 519 520 521 522 523
TIME (N_POINTS) datetime64[ns] 2011-01-02T11:14:06 ... 2011-01-14T01:05:21
LONGITUDE (N_POINTS) float64 -66.77 -66.77 -66.77 ... -64.59 -64.59
LATITUDE (N_POINTS) float64 37.28 37.28 37.28 ... 33.07 33.07 33.07
Data variables:
CYCLE_NUMBER (N_POINTS) int64 150 150 150 150 150 150 ... 13 13 13 13 13
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 4900803 4900803 ... 5903377 5903377
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 ... 95.97 97.97 99.97
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 36.67 36.67 36.67 ... 36.67 36.67 36.67
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 19.46 19.47 19.47 ... 19.2 19.2 19.2
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:
cdm_altitude_proxy: pres
cdm_data_type: TrajectoryProfile
cdm_profile_variables: cycle_number, data_type, format_version, handb...
cdm_trajectory_variables: platform_number, project_name, pi_name, platfo...
Conventions: Argo-3.1, CF-1.6, COARDS, ACDD-1.3
creator_email: support@argo.net
creator_name: Argo
creator_url: http://www.argo.net/
Easternmost_Easting: -56.578
featureType: TrajectoryProfile
geospatial_lat_max: 37.28
geospatial_lat_min: 30.082
geospatial_lat_units: degrees_north
geospatial_lon_max: -56.578
geospatial_lon_min: -73.343
geospatial_lon_units: degrees_east
history: 2020-06-24T18:00:48Z (local files)\n2020-06-24...
id: ArgoFloats
infoUrl: http://www.argo.net/
institution: Argo
keywords: adjusted, argo, array, assembly, best, centre,...
keywords_vocabulary: GCMD Science Keywords
license: The data may be used and redistributed for fre...
Northernmost_Northing: 37.28
references: http://www.argodatamgt.org/Documentation
source: Argo float
sourceUrl: (local files)
Southernmost_Northing: 30.082
standard_name_vocabulary: CF Standard Name Table v29
summary: Argo float vertical profiles from Coriolis Glo...
time_coverage_end: 2011-01-14T01:05:21Z
time_coverage_start: 2011-01-02T11:14:06Z
title: Argo Float Measurements
user_manual_version: 3.1
Westernmost_Easting: -73.343
DATA_ID: ARGO
DOI: http://doi.org/10.17882/42182
Fetched_from: http://www.ifremer.fr/erddap
Fetched_by: docs
Fetched_date: 2020/06/24
Fetched_constraints: phy_[x=-75.00/-55.00; y=30.00/40.00; z=0.0/100...
Fetched_uri: http://www.ifremer.fr/erddap/tabledap/ArgoFloa...
Interpolation to standard levels¶
Once your dataset is a collection of vertical profiles, you can interpolate variables on standard pressure levels using argopy.xarray.ArgoAccessor.interp_std_levels() with your levels as input :
[6]:
ds_interp = ds_profiles.argo.interp_std_levels([0,10,20,30,40,50])
print(ds_interp)
<xarray.Dataset>
Dimensions: (N_PROF: 18, PRES_INTERPOLATED: 6)
Coordinates:
TIME (N_PROF) datetime64[ns] 2011-01-02T11:14:06 ... 2011-01-14T01:05:21
LONGITUDE (N_PROF) float64 -66.77 -71.17 -64.93 ... -72.75 -64.59
* PRES_INTERPOLATED (PRES_INTERPOLATED) int64 0 10 20 30 40 50
LATITUDE (N_PROF) float64 37.28 33.98 32.88 ... 37.03 34.39 33.07
* N_PROF (N_PROF) int64 7 13 15 0 6 2 9 4 ... 1 12 10 17 3 8 14 16
Data variables:
CYCLE_NUMBER (N_PROF) float64 150.0 3.0 11.0 100.0 ... 151.0 4.0 13.0
DATA_MODE (N_PROF) object 'D' 'D' 'D' 'D' 'D' ... 'D' 'D' 'D' 'D'
DIRECTION (N_PROF) object 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
PLATFORM_NUMBER (N_PROF) float64 4.901e+06 4.901e+06 ... 5.903e+06
PRES (N_PROF, PRES_INTERPOLATED) float64 5.0 10.0 ... 50.0
PSAL (N_PROF, PRES_INTERPOLATED) float64 36.67 36.67 ... 36.68
TEMP (N_PROF, PRES_INTERPOLATED) float64 19.46 19.47 ... 19.24
Attributes:
cdm_altitude_proxy: pres
cdm_data_type: TrajectoryProfile
cdm_profile_variables: cycle_number, data_type, format_version, handb...
cdm_trajectory_variables: platform_number, project_name, pi_name, platfo...
Conventions: Argo-3.1, CF-1.6, COARDS, ACDD-1.3
creator_email: support@argo.net
creator_name: Argo
creator_url: http://www.argo.net/
Easternmost_Easting: -56.578
featureType: TrajectoryProfile
geospatial_lat_max: 37.28
geospatial_lat_min: 30.082
geospatial_lat_units: degrees_north
geospatial_lon_max: -56.578
geospatial_lon_min: -73.343
geospatial_lon_units: degrees_east
history: 2020-06-24T18:00:48Z (local files)\n2020-06-24...
id: ArgoFloats
infoUrl: http://www.argo.net/
institution: Argo
keywords: adjusted, argo, array, assembly, best, centre,...
keywords_vocabulary: GCMD Science Keywords
license: The data may be used and redistributed for fre...
Northernmost_Northing: 37.28
references: http://www.argodatamgt.org/Documentation
source: Argo float
sourceUrl: (local files)
Southernmost_Northing: 30.082
standard_name_vocabulary: CF Standard Name Table v29
summary: Argo float vertical profiles from Coriolis Glo...
time_coverage_end: 2011-01-14T01:05:21Z
time_coverage_start: 2011-01-02T11:14:06Z
title: Argo Float Measurements
user_manual_version: 3.1
Westernmost_Easting: -73.343
DATA_ID: ARGO
DOI: http://doi.org/10.17882/42182
Fetched_from: http://www.ifremer.fr/erddap
Fetched_by: docs
Fetched_date: 2020/06/24
Fetched_constraints: phy_[x=-75.00/-55.00; y=30.00/40.00; z=0.0/100...
Fetched_uri: http://www.ifremer.fr/erddap/tabledap/ArgoFloa...
Filters¶
If you fetched data with the expert mode, you may want to use filters to help you curate the data.
[To be added]
Data models¶
By default argopy works with xarray.DataSet and comes with the xarray.Dataset accessor namespace argo.
For your own analysis, you may prefer to work with a Pandas dataframe.
[7]:
df = ArgoDataFetcher().profile(6902746, 34).to_dataframe()
df
[7]:
| CYCLE_NUMBER | DATA_MODE | DIRECTION | PLATFORM_NUMBER | POSITION_QC | PRES | PRES_QC | PSAL | PSAL_QC | TEMP | TEMP_QC | TIME_QC | LONGITUDE | TIME | LATITUDE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N_POINTS | |||||||||||||||
| 0 | 34 | D | A | 6902746 | 1 | 3.0 | 1 | 36.262001 | 1 | 27.212000 | 1 | 1 | -58.119 | 2017-12-20 06:58:00 | 18.983 |
| 1 | 34 | D | A | 6902746 | 1 | 4.0 | 1 | 36.262001 | 1 | 27.212000 | 1 | 1 | -58.119 | 2017-12-20 06:58:00 | 18.983 |
| 2 | 34 | D | A | 6902746 | 1 | 5.0 | 1 | 36.263000 | 1 | 27.212999 | 1 | 1 | -58.119 | 2017-12-20 06:58:00 | 18.983 |
| 3 | 34 | D | A | 6902746 | 1 | 6.0 | 1 | 36.262001 | 1 | 27.212000 | 1 | 1 | -58.119 | 2017-12-20 06:58:00 | 18.983 |
| 4 | 34 | D | A | 6902746 | 1 | 7.0 | 1 | 36.262001 | 1 | 27.214001 | 1 | 1 | -58.119 | 2017-12-20 06:58:00 | 18.983 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 104 | 34 | D | A | 6902746 | 1 | 1913.0 | 1 | 34.976002 | 1 | 3.710000 | 1 | 1 | -58.119 | 2017-12-20 06:58:00 | 18.983 |
| 105 | 34 | D | A | 6902746 | 1 | 1938.0 | 1 | 34.980999 | 1 | 3.718000 | 1 | 1 | -58.119 | 2017-12-20 06:58:00 | 18.983 |
| 106 | 34 | D | A | 6902746 | 1 | 1964.0 | 1 | 34.984001 | 1 | 3.698000 | 1 | 1 | -58.119 | 2017-12-20 06:58:00 | 18.983 |
| 107 | 34 | D | A | 6902746 | 1 | 1988.0 | 1 | 34.983002 | 1 | 3.668000 | 1 | 1 | -58.119 | 2017-12-20 06:58:00 | 18.983 |
| 108 | 34 | D | A | 6902746 | 1 | 2007.0 | 1 | 34.983002 | 1 | 3.644000 | 1 | 1 | -58.119 | 2017-12-20 06:58:00 | 18.983 |
109 rows × 15 columns
but keep in mind that this is merely a short cut for the xarray.Dataset.to_dataframe() method.
Data saving¶
Once you have your Argo data as xarray.Dataset, simply use the awesome possibilities of xarray like xarray.Dataset.to_netcdf() or xarray.Dataset.to_zarr().