Skip to main content

No project description provided

Project description

coloc_sat

PyPI Version Documentation Status

coloc_sat is a Python package for co-locating satellite data products. It allows you to co-locate data from different satellite sources based on provided paths and common variable names. This README provides an installation guide and instructions for usage. This package also allows co-location listings. Input satellites / missions that can be treated by this tool are the following : WindSat / SMOS / SMAP / SAR (L1/L2) / ERA5 / HY2. SAR satellites are RCM, RadarSat-2 and Sentinel1.

Installation

Make sure you have Python 3.9 or higher installed.

Using pip

pip install coloc-sat

Using conda

conda install -c conda-forge coloc_sat

Usage

Configuration

Before using coloc_sat, you need to configure the paths to your satellite data products and define common variable names. Follow the steps below:

  1. Create a directory named coloc_sat in your home directory.
  2. Inside the coloc_sat directory, create a file named localconfig.yml.

In localconfig.yml, fill in the paths to your satellite products following the schema below:

paths:
  SMOS:
    - '/path/to/SMOS/%Y/%(dayOfYear)/*%Y%m%d*.nc'
    - '/path2/to/SMOS//%Y/%(dayOfYear)/*%Y%m%d*.nc'
  HY2:
    - '/path/to/hy2/%Y/%(dayOfYear)/*%Y%m%d*.nc'
  ERA5:
    - '/path/to/era5/%Y/%m/era_5-copernicus__%Y%m%d.nc'
  RS2:
    L1:
      - '/path/to/rs2/L1/*/%Y/%(dayOfYear)/RS2*%Y%m%d*'
    L2:
      - '/path/to/rs2/L2/*/%Y/%(dayOfYear)/RS2_OK*/RS2_*%Y%m%d*/post_processing/nclight_L2M/rs2*owi*%Y%m%d*0003*_ll_gd.nc'
  S1:
    L1:
      - '/path/to/s1/L1/*/*/%Y/%(dayOfYear)/S1*%Y%m%d*SAFE'
    L2:
      - '/path/to/s1/L2/*/%Y/%(dayOfYear)/S1*%Y%m%d*/post_processing/nclight_L2M/s1*owi*%Y%m%d*000003*_ll_gd.nc'
      - '/path2/to/s1/L2/*/%Y/%(dayOfYear)/S1*%Y%m%d*/post_processing/nclight_L2M/s1*owi*%Y%m%d*0003*_ll_gd.nc'
  RCM:
    L1:
      - '/path/to/rcm/L1/*/%Y/%(dayOfYear)/RCM*%Y%m%d*'
    L2: []
  WS:
    - '/path/to/windsat/%Y/%(dayOfYear)/wsat_%Y%m%d*.gz'
  SMAP:
    - '/path/to/smap/%Y/%(dayOfYear)/RSS_smap_*.nc'
    - '/path2/to/smap/%Y/%(dayOfYear)/RSS_smap_*.nc'
common_var_names:
  wind_speed: wind_speed
  wind_direction: wind_direction_ecmwf
  wind_from_direction: wind_from_direction
  longitude: lon
  latitude: lat
  time: time

Replace the paths with the actual paths to your satellite data products. Use the placeholders %Y, %m, %d, and %(dayOfYear) to automatically parse dates from the paths.

Co-locating Data

Once you've configured the paths and common variable names, you can use coloc_sat to co-locate the data. Import the package and start co-locating your data based on your needs.

Now, import the package:

import coloc_sat

Then, define important variables for the co-location:

delta_time=60
destination_folder = '/tmp'
listing = True
product_generation = True
product1 = '/path/to/s1/l2/s1a-ew-owi-cm-20181009t142906-20181009t143110-000003-02A122_ll_gd.nc'

Example code for co-locating a satellite product with a mission:

ds_name = 'SMOS'
# Call the generation tool
generator = coloc_sat.GenerateColoc(product1_id=product1, ds_name=ds_namedelta_time=delta_time, product_generation=product_generation, 
                            listing=listing, destination_folder=destination_folder)
# save the results (listing and / or co-location products)
generator.save_results()

NOTE : It is also possible to use this co-location generator with the console. Here are examples.

a) This first example shows how to generate a coloc between 2 specified products:

Coloc_2_products --product1_id /path/to/rs2/L2/rs2--owi-cm-20141004t210600-20141004t210715-00003-BDBE0_ll_gd.nc --product2_id path/to/s1/L2/s1a-iw-owi-cm-20141004t211657-20141004t211829-000003-002FF5_ll_gd.nc --listing --product_generation

b) This second example shows how to generate all possible coloc between a product and a mission (all products from this mission):

Coloc_between_product_and_mission --product1_id /path/to/rs2/L2/rs2--owi-cm-20141004t210600-20141004t210715-00003-BDBE0_ll_gd.nc --mission_name S1 --listing --product_generation

Example of resulting listing of co-located products

Default parameters for the listing filename is 'listing_coloc_' + 'MISSION_NAME1' + '_' + 'MISSION_NAME2' + '_' + 'delta_time' + '.txt'

Example of product_name : 'listing_coloc_ERA5_SAR_60.txt'

Note : For RCM, RadarSat-2 and RCM, 'SAR' is used.

Content:

/path/to/era5/era_5-copernicus__20181009.nc:path/to/S1/L2/s1a-ew-owi-cm-20181009t142906-20181009t143110-000003-02A122_ll_gd.nc

Example of resulting xarray co-location product

Default parameters for the co-location product filename is 'sat_coloc_' + 'product_name1' + '__' + 'product_name2' + '.nc'

Example of product name: 'sat_coloc_s1a-ew-owi-cm-20181009t142906-20181009t143110-000003-02A122_ll_gd__era_5-copernicus__20181009.nc'

<xarray.Dataset>
    Dimensions:                            (lat: 14, lon: 9)
    Coordinates:
      * lon                                (lon) float32 -131.0 -130.5 ... -127.0
      * lat                                (lat) float32 13.5 14.0 ... 19.5 20.0
        time                               datetime64[ns] ...
        spatial_ref                        int64 ...
    Data variables: (12/52)
        wind_streaks_orientation_stddev_1  (lat, lon) float32 ...
        elevation_angle_1                  (lat, lon) float32 ...
        heading_angle_1                    (lat, lon) float32 ...
        nesz_cross_corrected_1             (lat, lon) float32 ...
        nrcs_co_1                          (lat, lon) float32 ...
        mask_flag_1                        (lat, lon) float32 ...
        ...                                 ...
        mwd_2                              (lat, lon) float32 ...
        tcw_2                              (lat, lon) float64 ...
        mwp_2                              (lat, lon) float32 ...
        tp_2                               (lat, lon) float64 ...
        mdww_2                             (lat, lon) float32 ...
        mpww_2                             (lat, lon) float32 ...
    Attributes: (12/28)
        Conventions_1:           CF-1.6
        title_1:                 SAR ocean surface wind field
        institution_1:           IFREMER/CLS
        reference_1:             Mouche Alexis, Chapron Bertrand, Knaff John, Zha...
        measurementDate_1:       2018-10-09T14:30:08Z
        sourceProduct_1:         s1a-ew-owi-cm-20181009t142906-20181009t143110-00...
        ...                      ...
        footprint_2:             POLYGON ((-131 13.5, -131 20, -127 20, -127 13.5...
        counted_points:          0
        vmax_m_s:                nan
        Bias:                    0
        Standard deviation:      0
        scatter_index:           nan

Important notes

This library is a Work-in-progress, so that some acquisition type combinations aren't treated yet:

truncated_swath swath daily_regular_grid model
truncated_swath listing=True, listing=True, listing=True, listing=True,
product_generation=True product_generation=False product_generation=True product_generation=True
swath listing=True, listing=False, listing=False, listing=True,
product_generation=False product_generation=False product_generation=False product_generation=False
daily_regular_grid listing=True, listing=False, listing=False, listing=True,
product_generation=True product_generation=False product_generation=False product_generation=False
model listing=True, listing=True, listing=True, listing=True,
product_generation=True product_generation=False product_generation=False product_generation=False

Acknowledgements

Special thanks to REMSS for their Windsat reader.


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

coloc_sat-1.0.3.tar.gz (69.3 kB view details)

Uploaded Source

Built Distribution

coloc_sat-1.0.3-py3-none-any.whl (78.8 kB view details)

Uploaded Python 3

File details

Details for the file coloc_sat-1.0.3.tar.gz.

File metadata

  • Download URL: coloc_sat-1.0.3.tar.gz
  • Upload date:
  • Size: 69.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.8

File hashes

Hashes for coloc_sat-1.0.3.tar.gz
Algorithm Hash digest
SHA256 7c5137a871947d3831c09bb7279d59ae5380f9bd211d94c11dee3bd7205c8591
MD5 96f48e1abc742cb3968f43ee89541307
BLAKE2b-256 5647f113100410b202203240b077786ac35049091a45f4f51b045ca86758575b

See more details on using hashes here.

File details

Details for the file coloc_sat-1.0.3-py3-none-any.whl.

File metadata

  • Download URL: coloc_sat-1.0.3-py3-none-any.whl
  • Upload date:
  • Size: 78.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.8

File hashes

Hashes for coloc_sat-1.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 8ddcf6a88c0eedff62c977ce4eb13f3dfc5af8e8f27da893ab75cdf1a52f48d5
MD5 0ff316f93bb68d8d5f90014688aea992
BLAKE2b-256 5d8434ff35808ae002f316c21082d8929a39369b2b3e863a268ed9f1bc87054a

See more details on using hashes here.

Supported by

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