Skip to main content

ModImpNet (MODIS Import to netCDF): Import Snow cover NDSI and Land Surface Temperature (LST) from MODIS and produces a netCDF file from configuration files

Project description

ModImpNet

ModImpNet is a package that allows for automatic import of Snow cover NDSI and Land Surface Temperature (LST) from MODIS and produces a netCDF file from configuration files. The user only needs to provide a .toml and a .csv configuration files. Furthermore, credentials to AppEARS API (https://appeears.earthdatacloud.nasa.gov/api/) need to be specified in a .netrc file.

Thanks to this tool, the user will get snow cover and land surface temperature time series for any point location into a standardized netCDF format.

Installation

Use the package manager pip to install ModImpNet (find the PyPi page here: https://pypi.org/project/ModImpNet/).

pip install ModImpNet

Install all the required packages (dependencies) from the requirements.txt file.

pip install -r requirements.txt

Place requirements.txt in the directory where you plan to run the command. If the file is in a different directory, specify its path, for example, path/to/requirements.txt.

Usage

The package is better used directly on the command line with the built-in CLI

ModImpNet -f /<path>/<folder_name>/par/MODIS_config.toml

It is recommended to stick to the folder structure adopted in the folder ModImpNet/examples/MODIS_test_Martha. Simply create a folder for each application, and within it, create a par/ folder. This will hold two configuration files. Create the following directory structure::

/<folder_name>
    /par
        MODIS_config.toml
        MODIS_stations.csv

The two files are:

  1. MODIS_config.toml: downloads and scales reanalysis data to produce meteorological time series at any point location:
[name]
task_name = <task_name>

[directories]
# this is the home directory that will contain par/ and download/
home_dir        = '<path>/<folder_name>'
# this is the download destination directory
dest_dir        = '<path>/<folder_name>/download'
# directory where the .netrc credential file is stored
credential_dir  = '<path_to_credential_directory>'
# path to the csv configuration file 
config_csv_path = '<path>/<folder_name>/par/MODIS_stations.csv'

[config]
# in the format YYYY/MM/DD to match with GlobSim
startDate  = '<YYYY/MM/DD>'
endDate    = '<YYYY/MM/DD>'

[download]
# how long shall we wait (in seconds) between submitting a task and giving up, suggested 1day=86400s
max_wait   = 86400
# time between two status checks (in seconds), suggested 30s
time_sleep = 30
  1. MODIS_stations.csv: Land surface model of the mass and energy balance of the hydrological cycle which simulates ground thermal properties:
id,latitude,longitude
Site_name_1,lat_1,lon_1
...
Site_name_n,lat_n,lon_n

Once the package is run, a new download/ foilder is automatically created, and is populated with two csv files containing the snow cover and land surface temperature data, together with a text log file. Finally, when the conversion is done too, the final netCDF file is also created. We get the following directory structure::

/<folder_name>
    /par
        MODIS_config.toml
        MODIS_stations.csv
    /download
        <task_name>_MOD10A1_061_results.csv
        <task_name>_MOD11A1_061_results.csv
        MODIS_log_<datetime>.txt

Examples

The user can find some inspiration on how to use ModImpNet by looking at the examples provided.

License

GNU GPLv3

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

modimpnet-0.0.7.tar.gz (48.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

modimpnet-0.0.7-py3-none-any.whl (37.7 kB view details)

Uploaded Python 3

File details

Details for the file modimpnet-0.0.7.tar.gz.

File metadata

  • Download URL: modimpnet-0.0.7.tar.gz
  • Upload date:
  • Size: 48.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.0

File hashes

Hashes for modimpnet-0.0.7.tar.gz
Algorithm Hash digest
SHA256 87d7b1cc8bb3d04a8d98554deefc681deaca2238bd686fed8c8f55b9eca975e8
MD5 aec1751658415dbe1139d0516ec92521
BLAKE2b-256 90650e732741e7ca25149dae3656933c5e3a4baa2f0a868b27a936aebb9d1dd9

See more details on using hashes here.

File details

Details for the file modimpnet-0.0.7-py3-none-any.whl.

File metadata

  • Download URL: modimpnet-0.0.7-py3-none-any.whl
  • Upload date:
  • Size: 37.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.0

File hashes

Hashes for modimpnet-0.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 47f6393b01b760a93ea93882e77ec76e87d9a208e08becf01621b4f41918a7b6
MD5 a72b250f0aa86d8bf9c69a08dfde6799
BLAKE2b-256 dd1e722ca253c564ef6603c5c7427df5652d5998a745e4188e740474095d2e2d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page