Locate, Download, and Read Argo Ocean Float Data
Project description
argopandas
The goal of argopandas is to provide seamless access to Argo NetCDF files using a pandas DataFrame
-based interface. It is a Python port of the argodata package for R. The package is under heavy development and we would love feedback on the interface or anything else about the package!
Installation
You can install the argopandas
package using pip
.
pip install git+https://github.com/ArgoCanada/argopandas.git
The package depends on pandas
, numpy
, netCDF4
, and pyarrow
, which install automatically if using pip
or you can install also your favourite Python package manager. The argopandas
package requires Python 3.6 or later.
Examples
The intended interface for most usage is contained in the argopandas
module. You can import this as argo
for pretty-looking syntax:
import argopandas as argo
# to make this work with GitHub
import pandas as pd
pd.set_option('display.notebook_repr_html', False)
The global indexes are available via argo.prof
, argo.meta
, argo.tech
, argo.traj
, argo.bio_prof
, argo.synthetic_prof
, and argo.bio_traj
.
argo.meta.head(5)
file profiler_type institution \
0 aoml/13857/13857_meta.nc 845 AO
1 aoml/13858/13858_meta.nc 845 AO
2 aoml/13859/13859_meta.nc 845 AO
3 aoml/15819/15819_meta.nc 845 AO
4 aoml/15820/15820_meta.nc 845 AO
date_update
0 2018-10-11 20:00:14+00:00
1 2018-10-11 20:00:15+00:00
2 2018-10-11 20:00:25+00:00
3 2018-10-11 20:00:16+00:00
4 2018-10-11 20:00:18+00:00
By defaut, downloads are lazily cached from the Ifremer https mirror. You can use argo.url_mirror()
or argo.file_mirror()
with argo.set_default_mirror()
to point argopandas
at your favourite copy of Argo.
To get Argo data from one or more NetCDF files, subset the indexes and use one of the table accessors to download, cache, and read variables aligned along common dimensions. The accessor you probably want is the .levels
accessor from the argo.prof
index:
argo.prof.head(5).levels[['PRES', 'TEMP']]
Downloading 5 files from 'https://data-argo.ifremer.fr/dac/aoml/13857/profiles'
Reading 5 files
PRES TEMP
file N_PROF N_LEVELS
aoml/13857/profiles/R13857_001.nc 0 0 11.900000 22.235001
1 17.000000 21.987000
2 22.100000 21.891001
3 27.200001 21.812000
4 32.299999 21.632000
... ... ...
aoml/13857/profiles/R13857_005.nc 0 102 976.500000 4.527000
103 986.700012 4.527000
104 996.799988 4.533000
105 1007.000000 4.487000
106 1017.200012 4.471000
[551 rows x 2 columns]
You can get data from every variable in an Argo NetCDF file using one of these accessors. The variables grouped in each are aligned along the same dimensions and are documented together in the Argo user's manual.
- All indexes have a
.info
accessor that contains length-one variables that aren't aligned along any dimensions argo.prof
:argo.prof.levels
,arog.prof.prof
,argo.prof.calib
,argo.prof.param
, andargo.prof.history
argo.traj
:argo.traj.cycle
,argo.traj.measurement
,argo.traj.param
, andargo.traj.history
argo.tech
:argo.tech.tech_param
argo.meta
:argo.meta.config_param
,argo.meta.missions
,argo.meta.trans_system
,argo.meta.positioning_system
,argo.meta.launch_config_param
,argo.meta.sensor
, andargo.meta.param
Once you have a data frame you do anything you'd do with a regular pd.DataFrame()
, like plot your data using the built-in plot method:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
for label, df in argo.prof.head(5).levels.groupby('file'):
df.plot(x='TEMP', y = 'PRES', ax=ax, label=label)
ax.invert_yaxis()
Reading 5 files
You can access all the index files for a particular float using argo.float()
, which lazily filters all the indexes for a particular float ID.
float_obj = argo.float(13857)
float_obj.meta.info
Downloading 'https://data-argo.ifremer.fr/ar_index_global_meta.txt.gz'
Downloading 'https://data-argo.ifremer.fr/dac/aoml/13857/13857_meta.nc'
Reading 1 file
DATA_TYPE FORMAT_VERSION HANDBOOK_VERSION \
file
aoml/13857/13857_meta.nc 0 Argo meta-data 3.1 1.2
DATE_CREATION DATE_UPDATE PLATFORM_NUMBER \
file
aoml/13857/13857_meta.nc 0 20181011200014 20181011200014 13857
PTT \
file
aoml/13857/13857_meta.nc 0 09335 ...
PLATFORM_FAMILY \
file
aoml/13857/13857_meta.nc 0 FLOAT ...
PLATFORM_TYPE \
file
aoml/13857/13857_meta.nc 0 PALACE
PLATFORM_MAKER \
file
aoml/13857/13857_meta.nc 0 WRC ...
... LAUNCH_QC START_DATE START_DATE_QC \
file ...
aoml/13857/13857_meta.nc 0 ... b'1' 19970719163000 b'1'
STARTUP_DATE STARTUP_DATE_QC \
file
aoml/13857/13857_meta.nc 0 19970719103000 b'1'
DEPLOYMENT_PLATFORM \
file
aoml/13857/13857_meta.nc 0 R/V Seward Johnson
DEPLOYMENT_CRUISE_ID \
file
aoml/13857/13857_meta.nc 0 97-03
DEPLOYMENT_REFERENCE_STATION_ID \
file
aoml/13857/13857_meta.nc 0 CTD 108 ...
END_MISSION_DATE END_MISSION_STATUS
file
aoml/13857/13857_meta.nc 0 NaN
[1 rows x 43 columns]
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