How to use the colorcet.palette_n function in colorcet

(cmap.startswith('cet_') or      # duplicates list below
                            cmap.startswith('Vega') or      # deprecated in matplotlib=2.1
                            cmap.startswith('spectral') )]) # deprecated in matplotlib=2.1
        except:
            pass
    if 'bokeh' in provider:
        try:
            from bokeh import palettes
            cmaps += info('bokeh', palettes.all_palettes)
            cmaps += info('bokeh', [p+'_r' for p in palettes.all_palettes])
        except:
            pass
    if 'colorcet' in provider:
        try:
            from colorcet import palette_n, glasbey_hv
            cet_maps = palette_n.copy()
            cet_maps['glasbey_hv'] = glasbey_hv # Add special hv-specific map
            cmaps += info('colorcet', cet_maps) 
            cmaps += info('colorcet', [p+'_r' for p in cet_maps])
        except:
            pass
    return sorted(unique_iterator(cmaps))

"""

import os
from yaml import safe_load
import requests
import datetime
import intake
import colorcet as cc
import numpy as np


here = os.path.abspath(os.path.dirname(__file__))
cache_path = os.path.join(here, '..', 'doc', '_static', 'cache')
cat = intake.open_catalog('https://raw.githubusercontent.com/ContinuumIO/anaconda-package-data/master/catalog/anaconda_package_data.yaml')

colors = cc.palette_n.rainbow[-20:80:-1]
top_of_colormap = 1e6
step = len(colors) /np.log10(top_of_colormap)

today = datetime.date.today()
first = today.replace(day=1)
last_month = first - datetime.timedelta(days=1)
try:
    monthly = cat.anaconda_package_data_by_month(year=last_month.year, month=last_month.month,
                                                 columns=['pkg_name', 'counts']).to_dask()
except:
    # if the last month isn't available, get the month before
    month_before = last_month.replace(day=1) - datetime.timedelta(days=1)
    monthly = cat.anaconda_package_data_by_month(year=month_before.year, month=month_before.month,
                                                columns=['pkg_name', 'counts']).to_dask()
per_package_downloads = monthly.groupby('pkg_name').sum().compute()