oco_vistool.py

The purpose of this script is to pull different RGB images (depending on the user's choice from Encoding.csv) from Worldview using the NASA GIBS API and overlay various OCO-2/3 data fields for case study analysis in support of OCO-2/3 cloud and aerosol screening validation.

It can be used as a command line tool to plot any data in the OCO-2/3 CO2 or SIF Lite file, filtered by warn level, quality flag, and/or footprint number where applicable.
It can also be called as a function from within another Python program for expanded use with other datasets.

GETTING STARTED

1. Clone this code
   git clone http://github.com/hcronk/oco2_modis_vistool

   We suggest obtaining the package via a git clone rather than downloading the tarball because the tool is still under active development. A clone will enable you to quickly update your copy to the current stable version using a git pull command on your end, rather than downloading and comparing files.

2. Make sure you have the necessary system and Python requirements.

   Option 1: Conda Environment

   Miniconda Installation Instructions:

   • Linux: https://www.cira.colostate.edu/wiki/miniconda-python-distribution-on-linux/
   • Mac: https://www.cira.colostate.edu/wiki/miniconda-python-distribution-on-mac/
   • Windows: https://www.cira.colostate.edu/wiki/miniconda-python-distribution-on-windows/

   From the cloned directory, run conda env create -f environment.yml to create the conda environment. To activate the environment, use conda activate oco_vistool and to deactivate, use conda deactivate. More information about working with conda environments can be found at https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

   Option 2: Docker Container

   The Dockerfile included in the repository can be used to create an appropriate Docker image and container environment.

   Building the image

   Build the image using the command docker build -t oco_vistool:1.0 . Although the tag (-t) is optional, it will assign a name and version to the resulting image which will make it easily locatable and also provide a mechanism to have multiple versions available for testing and using upgrades. Note the . assumes you are working in the directory that contains the Dockerfile. To provide an alternate path, trade the period out for -f /path/to/the/Dockerfile

   Creating a container from the image

   Although the default Docker behavior will create a container that runs as root, it is best practice to declare your own user when you create the container to avoid messy security issues. For compatability with the host machine, you can use your user and preferred group ids in the container, or if compatability is not an issue, you may simply create a new user:group identification for the container. To get your user and group id on the host (provided that you are working in a linux-like environment), use id -u [user name] and id -g [group name].

   To create and run a container from the image, use docker run -it --name oco_vistool -u uid:gid -v /path/to/data/on/host:/path/to/data/in/the/container oco_vistool:1.0, adding -v flags before the image name as necessary to provide any data mounts needed for the code to be able to access the necessary input data. Again, the --name is optional but makes the container easily locatable.

   Entering the container

   The -it flag in the docker build command will start an interactive session within the container, where you will be dropped in the /ECO1280_int_scripts directory where the conversion scripts are located. If the container is stopped (you can check by running docker ps), start it by running docker start oco_vistool and then enter it using docker exec -it oco_vistool /bin/bash.

3. Test run from the command line
   python oco_vistool.py

4. Look for the output PNG and HDF5 in the code directory

5. Update the JSON configuration file with information from your scene of interest. Options include:

   Required:

   date (string, format:"YYYY-MM-DD"):
   The date of your case study. This is used to get the correct RGB.

   sensor (string): The sensor name that is desired to be used for the background image. Supported sensors at the moment are: "Worldview", "GOES16_ABI_C", "GOES16_ABI_F", "GOES17_ABI_C", "GOES17_ABI_F", "Himawari-08".

   Required if sensor is "Worldview":

   layer (integer, format: integer between 0 and maximum code number in Encoding.csv): The code of the background layer to be used by the vistool. Each layer is given its code (encoded) in the Encoding.csv file in the code directory. The file contains the relation: layer name (by the NASA Worldview standards) - code (generated unique number). So, when adding new layers to the CSV, use the above standards.

   Required if sensor is GOES or Himawari:

   files_loc (string, format: "local" or "aws"): Abstract location of the background files for plotting. Either "aws" if one wants to download those files from the AWS S3 bucket, Or "local" if wants to use some local pre-downloaded files.

   data_home (string): If files_loc is "aws", data_home is a directory where the files will be downloaded. If files_loc is "local", data_home is the path to the already allocated background files.

   Only one of the following parts is required:

   geo_upper_left (list, format:[lat, lon]):
   The latitude and longitude coordinates of the upper left hand corner of your area of interest.

   geo_lower_right (list, format:[lat, lon]):
   The latitude and longitude coordinates of the lower right hand corner of your area of interest.

   OR

   target_id (string): ID of the SAM target which will be parsed from the targets.csv file (its lat/lon box will be derived).

   OR

   target_name (string): Name of the SAM target which will be parsed from the targets.csv file (its lat/lon box will be derived).

   Optional:

   region (string, default: none):
   The region of your case study. This is used to build the default output filename in the command line execution.

   cmap (string, default: "jet"):
   The desired colormap or color for the overlay data.
   Built-in colormap options can be found here: http://matplotlib.org/examples/color/colormaps_reference.html
   Built-in single color options can be found here: http://matplotlib.org/examples/color/named_colors.html

   oco2_overlay_info (dict, default: none):
   The information for the OCO-2 variable to overlay on the Background image.
   {
   file (string):
   This is required for a variable overlay.
   The full path to the file that contains the variable to overlay.
   variable (string, format:"Preprocessors/dp_adp"):
   This is required for a variable overlay.
   The full path to the variable within the file specified.
   band_number (string or integer, format: "1" or 1):
   This is optional for variables that are stored per-band in the same array.
   variable_plot_lims (list, format:[min, max], default: [integer floor of variable's minimum value, integer ceiling of variable's maximum value]):
   The maximum and minimum values used for plotting and color assignment. Any data values beyond the min and max will show up on the plot as the minimum or maximum color, respectively.
   lat_name (string, format:"latitude"):
   This is required for a variable overlay for command line execution.
   The name of the latitude array associated with the variable. Used for subsetting and plotting.
   lon_name (string, format:"longitude"):
   This is required for a variable overlay for command line execution.
   The name of the longitude array associated with the variable. Used for subsetting and plotting.
   orbit (integer, format:11016):
   This is optional for a variable overlay.
   The OCO-2 orbit number. Used for subsetting.
   lite_QF (string, format:"good", default: "all"):
   The quality flag applied to the CO2 Lite File variable. Used for filtering.
   Valid options: "good", "bad", "all"
   lite_warn_lims (list, format:[0,15], default: [0,20]):
   The range of acceptable warn levels applied to the CO2 Lite File variable. Used for filtering.
   Valid options: integers from 0 to 20
   footprint (1 or 2 element list, format: [2:4] or [1], default: [1:8]):
   The footprint number applied to the CO2 or SIF Lite File variable. Used for filtering.
   transparency (integer between 0 and 1, format: 0, default: 1):
   The level of transparency applied to the OCO-2 data where 1 is opaque and 0 is transparent.
   }

   ground_site (list, format:[lat lon], default: none):
   The latitude and longitude coordinates of a ground site of interest. It will show up as a white star on the output plot.

   city_labels (string, default: none):
   This is optional to display city names on the output plot in the color specified in this string. They are on a 10m scale, and work best if you are displaying a small area.
   Python built-in named colors are available here: http://matplotlib.org/examples/color/named_colors.html

   out_plot_dir (string, default: the directory that you are running the code in):
   The full output directory path for the output plot.

   out_plot_name (string, default example: MODIS_Aqua_CorrectedReflectance_TrueColor_xco2_Amazon_20150701_B7302b_QF_good_WL_0to20.png):
   The name of the output plot.

   out_data_dir (string, default: the directory that you are running the code in):
   The full output directory path for the output data file.

   out_data_name (string, default example: xco2_Amazon_20150701_B7302b_QF_good_WL_0to20.h5):
   The name of the output data file.

   out_background_name (string, default example: MODIS_Aqua_CorrectedReflectance_TrueColor_Amazon_20150701.png):
   The name of the output background file, created if no overlay data is present or make_background_image is enabled.

Other Information

GIBS developer documentation: https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers

WMTS Capabilities File and Its Parser

There is a script in the code directory called "parse_wmts.py" which parses the wmts.xml capabilities file. The reason of this Python parser is to get the relation, layer name - layer specifications XML url, for all NASA Worldview layers (as of June 2021) into a separate CSV file. This relation CSV file is used for plotting and styling the output image. The parser used a copy of the wmts capabilities file which was downloaded into the code directory from: https://gibs.earthdata.nasa.gov/wmts/epsg4326/best/wmts.cgi?SERVICE=WMTS&REQUEST=GetCapabilities

AWS functionality

When either GOES or Himawari is chosen as a desired sensor in config.json, there will appear a required "files_loc" key. Two options for this key are: "aws" and "local". Starting with November 2021, if a user wants to use NOAA AWS S3 buckets as a source of background imagery ("files_loc": "aws"), one doesn't need to provide any personal AWS keys to the program. Downloading data from NOAA S3 buckets is handled automatically and anonymously by the vistool.

Encoding.csv

File with encodings for the Worldview layers, supported by the vistool. Each layer should be provided as its code in the configuration file.

Targets.csv

File with all accepted SAM targets. It has targets' names, ID's and centre coordinates which are used in the script for the lat/lon box construction.

Minimum command line call:
python oco_vistool.py

Minimum function call:

from oco_vistool import do_overlay_plot

do_overlay_plot([lat_of_upper_lefthand_corner, lon_of_upper_lefthand_corner],
                [lat_of_lower_righthand_corner, lon_of_lower_righthand_corner], 
                date, lat_data, lon_data, variable_data)

A Note about Questions, Suggestions, and Issues

For the benefit of the entire user community, it is requested that you submit questions, suggestions, and issues via the GitHub issue tracker so that everyone is able to see the work that is in progress. https://github.com/hcronk/oco2_modis_vistool/issues

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Developed and maintained by:
Heather Cronk
[email protected]