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trough

Python Package using Conda codecov

GitHub GitHub last commit Lines of code

Example

Example

Features

  • Download Madrigal TEC, OMNI and DMSP SSUSI data
  • Process datasets into more convenient xarray data structures and save as NetCDF
  • Automatically label main ionospheric trough

Usage

  1. Clone Repo
  2. create conda environment: conda env create -f environment.yml -n trough python=3.9
  3. conda activate trough
  4. pip install apexpy
  5. if you get a numpy error when you try to import apexpy: pip install --upgrade nump
  6. install trough with pip install -e .
  7. copy config.json.example --> config.json and change any options you want
  8. run with python -m trough config.json
  9. wait for it to finish (can take several days if you are running 5+ years)
  10. add import trough in your code and access the data using trough.get_data

Config

Main Options

Config Option Definition
base_dir base directory of trough downloads and processing, directories for downloading and processing will be created from here
madrigal_user_name name supplied to MadrigalWeb
madrigal_user_email email supplied to MadrigalWeb
madrigal_user_affil affiliation supplied to MadrigalWeb
nasa_spdf_download_method "http" or "ftp" (default)
lat_res latitude resolution of processed TEC maps (degrees Apex magnetic latitude)
lon_res longitude resolution of processed TEC maps (degrees Apex magnetic longitude)
time_res_unit time resolution units (passed to np.timedelta64)
time_res_n time resolution in units specified above (passed to np.timedelta64)
script_name which script to run, available scripts are in trough/scripts.py
start_date start date of interval (YYYYMMDD, YYYYMMDD_hh, YYYYMMDD_hhmm, or YYYYMMDD_hhmmss)
end_date end date of interval, see "start_date" for format
keep_download whether or not to keep the downloaded files (not recommended)
trough_id_params trough labeling algorithm parameters, see below

Trough Labeling Options

Config Option Definition
bg_est_shape background estimation filter size in pixels [time, latitude, longitude]
model_weight_max maximum value of L2 regularization before multiplication by coefficient l2_weight
rbf_bw RBF bandwidth, number of pixels to half weight
tv_hw total variation horizontal weight
tv_vw total variation vertical weight
l2_weight L2 regularization coefficient
tv_weight TV regularization coefficient
perimeter_th minimum perimeter for a connected component in a label image
area_th minimum area for a connected component in a label image
threshold score threshold below which a pixel is not labeled as MIT
closing_rad radius for disk structuring element passed to skimage.morphology.binary_closing