argopandas 0.0.3.9000

Locate, Download, and Read Argo Ocean Float Data

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, and argo.prof.history
• argo.traj: argo.traj.cycle, argo.traj.measurement, argo.traj.param, and argo.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, and argo.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]