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def cmap_discretize(cmap, N):
    """Return a discrete colormap from the continuous colormap cmap.
    
        cmap: colormap instance, eg. cm.jet. 
        N: number of colors.
    """
    if type(cmap) == str:
        cmap = get_cmap(cmap)
    colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.)))
    colors_rgba = cmap(colors_i)

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#!/usr/bin/env python

import numpy as np
from astropy import units as u
from astropy import constants as const

@u.quantity_input # input validation decorator

def photon_ring_angular_size(mass: u.Msun, dist: u.pc):

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def calc_time_to_next_comm(start=None, debug_prints=False):
    '''
    Queries the Kadi event database for the next several (scheduled) comm passes.
    Calculates the duration between now (in UTC) and the next comm pass.
    If that duration is negative (i.e. that comm already happened), it searches
    the list of passes for the first positive time delta, i.e. the first comm pass in the future.
    Returns a string in reporting the time to the next comm pass.
    '''

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import os
import shutil
import glob

filelist = glob.glob('*.png')


for image in filelist:
    stringarray = image.split('_')
    namearray = stringarray[-1].split('---')[-1].split('.')[0].split('-')

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#!/usr/bin/env python3

'''
A script for my cutie by her boy
'''

import numpy as np
import pandas as pd

import xlrd

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import json

from matplotlib.colors import LinearSegmentedColormap
from matplotlib.cm import register_cmap

cdict_sunset = {'red': [[0.0, 0.10980392156862745, 0.10980392156862745], [0.09090909090909091, 0.14901960784313725, 0.14901960784313725], [0.18181818181818182, 0.17254901960784313, 0.17254901960784313], [0.2727272727272727, 0.22745098039215686, 0.22745098039215686], [0.36363636363636365, 0.2549019607843137, 0.2549019607843137], [0.45454545454545453, 0.2901960784313726, 0.2901960784313726], [0.5454545454545454, 0.38823529411764707, 0.38823529411764707], [0.6363636363636364, 0.5333333333333333, 0.5333333333333333], [0.7272727272727273, 0.7058823529411765, 0.7058823529411765], [0.8181818181818182, 0.8392156862745098, 0.8392156862745098], [0.9090909090909091, 0.9764705882352941, 0.9764705882352941], [1.0, 1.0, 1.0]], 'green': [[0.0, 0.09803921568627451, 0.09803921568627451], [0.09090909090909091, 0.1411764705882353, 0.1411764705882353], [0.18181818181818182, 0.16470588235294117, 0.16470588235294117], [0.272

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#!/usr/bin/env/python

import requests
from astropy.io import ascii

table = ascii.read('all_astro2020_whitepapers.csv')

numbers = table['Response ID']
people = table['Last Name:Principal Author']
urls = table['1:Upload File']

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#!/usr/bin/env python

import glob

import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits

from astropy.table import Table, vstack

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def make_stars(kintable, fovimage, redshift, name, snthresh=100, velocity_thresh=None, project_by_median=True, project_by_redshift=False, cropbox=None, zoom=False, zoombox=None, wcs=True, nancolor='white', colorbar_scalefactor=0.047, vel_vmin=-500, vel_vmax=500, disp_vmin=0, disp_vmax=300, save=True, file_save_directory="./"):
    
    table = fits.getdata(kintable)
    #hdr = fits.getheader(fovimage)
    
    x = table['x_cor']
    y = table['y_cor']
    
    # Get the 2D dimensions into which you'll paint this data
    fovdata = fits.getdata(fovimage)

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def fusemusealma(muse_moment_fits, alma_moment_fits):
    '''
    Open MUSE and ALMA HDUs, pick up their WCSs, 
    drop needless ALMA WCS axes, reproject 
    ALMA to MUSE, then output new HDUs.  
    '''
    museHDU = fits.open(get_pkg_data_filename(muse_moment_fits))
    almaHDU = fits.open(get_pkg_data_filename(alma_moment_fits))