Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/cgosmeyer/goes_visualizer

Visualization tool to display the performance of each "true location" chip in a single GOES-R assessment image.

https://github.com/cgosmeyer/goes_visualizer

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.0%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Visualization tool to display the performance of each "true location" chip in a single GOES-R assessment image.

Basic Info
  • Host: GitHub
  • Owner: cgosmeyer
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 3.02 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 4 years ago · Last pushed over 4 years ago
Metadata Files
Readme License

README.md

GOES Visualizer

About

GOES-R is a series of NASA/NOAA geostationary satellites that image Earth's weather, oceans, and environment.

visualize.py: Visualize

Visualization tool to display the performance of each "true location" chip in a single GOES-R assessment image.

  1. Extracts each chip from selected scene from the "measurement" file and geolocates them by matching to a chip from either of the chip database files "navchipdb.csv" or "otherchipdb.csv".
  2. Displays the selected GOES image.
  3. Optionally can overlay a land/water mask on the GOES image.
  4. Optionally plots the selected (closest matching) scene from a single "measurement" file as vector arrows on top of the GOES image.

mvisualize.py: Multi-Visualize

Plots vector arrows from a scene or an averaged range of (closest-matching) scenes from multiple "measurement" files.

Installation (for the scientist)

Create a new anaconda environment with Python 3.6.7. For example,

conda create -n yourenvname python=3.6.7 anaconda

Then activate the environment and do the following to install the required packages.

conda install cartopy conda install netCDF4 conda install xarray=0.15.0 conda install -c conda-forge metpy conda install -c conda-forge basemap conda install -c conda-forge pint=0.9

Finally install the visualizer package itself in your anaconda environment by the following command.

python setup.py

How to Use

See the documentation in visualizer.py and mvisualizer.py. The command line options can be found by

``` python visualize.py --h

python mvisualize.py --h ```

Examples

The geolocation errors for each ground control chip scanned at time 18:10 on day 200 of 2021.

First using a basemap:

python visualize.py --i OR_ABI-L1b-RadF-M6C07_G16_s20212000510231_e20212000519550_c20212000519591.nc --m G16_NAV_measurements_2021_200.P20210723140726.csv.gz --s '1810-07192021'

Visualizer with basemap

Second using the 18:00 day 200 GOES-16 image in band 07:

python visualize.py --i OR_ABI-L1b-RadF-M6C07_G16_s20212001810229_e20212001819548_c20212001819594.nc --m G16_NAV_measurements_2021_200.P20210723140726.csv.gz --s '1810-07192021' --vmax 0.0001 --overlay

Visualizer with GOES-16 image

Owner

  • Name: C. Gosmeyer
  • Login: cgosmeyer
  • Kind: user

Software developer and data analyst for remote sensing instruments.

GitHub Events

Total
Last Year

Dependencies

setup.py pypi