Installation

Install from PyPI:

pip install qa4sm-api

Or from source:

git clone https://github.com/awst-austria/qa4sm-api.git
cd qa4sm-api
pip install -e .

Verify installation

Run the following commands in your console to verify that the page was installed.

# Test installation
qa4sm --version

# Check available commands
qa4sm --help

Quick Start

Here are some example commands on using the API in a python application. See the full documentation at https://awst-austria.github.io/qa4sm-api/ for more examples.

>>> from qa4sm_api.client_api import Connection, ValidationConfiguration
>>> conn = Connection()
Hi, <username>! You're successfully logged in at https://qa4sm.eu/api/!

>>> conn.datasets()[['short_name']]
                        short_name
id
1                     C3S_combined
2                          SMAP_L3
3                            ASCAT
4                             ISMN
5                            GLDAS
6              ESA_CCI_SM_combined
7                          SMOS_IC
...                         ...

>>> conn.versions("C3S_combined")
     short_name pretty_name  ... time_range_end geographical_range
1   C3S_V201706     v201706  ...     2022-10-10               None
10  C3S_V201812     v201812  ...     2018-12-31               None
28  C3S_V201912     v201912  ...     2019-12-31               None
31  C3S_V202012     v202012  ...     2020-12-31               None
45  C3S_V202212     v202212  ...     2025-07-20               None
58  C3S_V202312     v202312  ...     2025-07-20               None
70  C3S_V202505     v202505  ...     2024-12-31               None

>>> config = conn.download_configuration("9aeb663b-e24e-4541-8331-6ec3e0318d1f")
>>> print(config.data)
{'name_tag': 'Test Case  QA4SM_VA_metrics - Test scaling_no_scaling_DEFAULT', 'interval_from': '1978-11-01', 'interval_to': '2024-12-31', 'temporal_matching': 12, 'anomalies_method': 'none', 'anomalies_from': None, 'anomalies_to': None, 'min_lat': 34.0, 'min_lon': -11.2, 'max_lat': 71.6, 'max_lon': 48.3, 'scaling_method': 'none', 'metrics': [{'id': 'tcol', 'value': False}, {'id': 'bootstrap_tcol_cis', 'value': False}, {'id': 'stability_metrics', 'value': False}], 'intra_annual_metrics': {'intra_annual_metrics': False, 'intra_annual_type': '', 'intra_annual_overlap': None}, 'dataset_configs': [{'dataset_id': 1, 'version_id': 70, 'variable_id': 1, 'is_spatial_reference': False, 'is_temporal_reference': True, 'is_scaling_reference': False, 'basic_filters': [1], 'parametrised_filters': []}, {'dataset_id': 4, 'version_id': 69, 'variable_id': 4, 'is_spatial_reference': True, 'is_temporal_reference': False, 'is_scaling_reference': False, 'basic_filters': [1, 2], 'parametrised_filters': [{'id': 18, 'parameters': 'AMMA-CATCH,DAHRA,TAHMO,SD_DEM,CHINA,CTP_SMTMN,HiWATER_EHWSN,HSC_SEOLMACHEON,IIT_KANPUR,KHOREZM,MAQU,MONGOLIA,MySMNet,RUSWET-AGRO,RUSWET-GRASS,RUSWET-VALDAI,SKKU,SW-WHU,KIHS_CMC,KIHS_SMC,VDS,NAQU,NGARI,SMN-SDR,SONTE-China,WIT-Network,AACES,OZNET,SASMAS,BIEBRZA_S-1,CALABRIA,CAMPANIA,FMI,FR_Aqui,GROW,GTK,HOBE,HYDROL-NET_PERUGIA,IMA_CAN1,METEROBS,MOL-RAO,ORACLE,REMEDHUS,RSMN,SMOSMANIA,SWEX_POLAND,TERENO,UDC_SMOS,UMBRIA,UMSUOL,VAS,WEGENERNET,WSMN,HOAL,IPE,COSMOS-UK,LABFLUX,NVE,Ru_CFR,STEMS,TWENTE,XMS-CAT,ARM,AWDN,BNZ-LTER,FLUXNET-AMERIFLUX,ICN,IOWA,PBO_H2O,RISMA,SCAN,SNOTEL,SOILSCAPE,USCRN,USDA-ARS,TxSON,LAB-net,PTSMN'}, {'id': 24, 'parameters': '0.00,0.10'}]}], 'settings_changes': {'filters': [], 'anomalies': False, 'scaling': False, 'variables': [], 'versions': []}}

>>> r = conn.start_validation(config)
>>> print(r.status)
'SCHEDULED'