dustapprox.tools package#

Convenient tools to generate models and plots.

Submodules#

dustapprox.tools.downloader module#

A simple tool to download files from URLs

dustapprox.tools.downloader.download_file(link: str, file_name: str, overwrite: bool = False) → str[source]#

Download a file on disk from url

Parameters

  • link (str) – url of the file

  • file_name (str) – path and filename of the download location

  • overwrite (bool) – set to re-download (default False)

Return type

Returns the filename of the data

dustapprox.tools.grid module#

Generate a grid of models with extinction from an atmosphere library

Example of script that produces a grid of dust attenuated stellar models from an atmosphere library.

This example can run in parallel on multiple processes or cores.

See also

compute_photometric_grid()

dustapprox.tools.grid.compute_photometric_grid(sources='models/Kurucz2003all/*.fl.dat.txt', n_jobs=1, verbose=0)[source]#

Run the computations of the photometric grid in parallel

Parameters

  • sources (str) – pattern of atmospehric models to process (using glob syntax)

  • n_jobs (int) – number of parallel processes to run (default: 1, -1 for as many as CPUs)

  • verbose (int) – verbosity level (default: 0)

Returns

Dataframe with the photometric values for each passband

Return type

pd.DataFrame

dustapprox.tools.parallel module#

Parallel processing with joblib and tqdm.

dustapprox.tools.parallel.tqdm_joblib(tqdm_object)[source]#

Context manager to patch joblib to report into tqdm progress bar given as argument

Solution adapted from Stackoverflow.

import time
from joblib import Parallel, delayed
def some_method(wait_time):
    time.sleep(wait_time)

with tqdm_joblib(tqdm(desc="My method", total=10)) as progress_bar:
    Parallel(n_jobs=2)(delayed(some_method)(0.2) for i in range(10))