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__all__ = ['gdrive_downloader']

import requests
import re
import os
import warnings


_codefinder = re.compile(r'confirm=(.+)&id=')
_namefinder = re.compile(r'filename="(.+)";')

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Nearest Neighbor Methods

Nearest Neighbor methods used for 2-dimensional interpolation. These methods include two methods of neighbor selection and two methods of weighting. To access any of the functionality, use the Nearest Neighbor Averaging (NNA) class.

Methodology

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__all__ = ['URSSession', 'asdc_downloader']


class URSSession:
    """
    Class to store URS credentials:

    * for use with requests
    * for use with netrc and dodsrc
    """

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__all__ = ['open_modis']
__version__ = '0.1.0'


def parse_hdfeos_metadata(string):
    """
    Borrowed from ARSET training materials
    """
    out = dict()
    lines = [i.replace('\t','') for i in string.split('\n')]

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__all__ = ['usstdatmdf', 'usstdatmds', 'M0', 'R', 'Rs', 'g', '__version__']
__version__ = '0.1.0'

__doc__ = """
This GIST implements the US Standard Atmosphere for Temperature, Pressure,
and Density. The constants and equations are implemented according to the U.S.
Standard Atmosphere (1976) and are only implemented through 84km. The results
can be presented as a Pandas DataFrame or an xarray Dataset.

Requirements

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import matplotlib.pyplot as plt
import matplotlib.colors as mc
import numpy as np
pmedges = np.asarray([0.0, 12.1, 35.5, 55.5, 150.5, 250.5, 500.5])
o8edges = np.asarray([0.0, 55, 71, 86, 106, 201, 500.])
o1edges = np.asarray([0.0, 60, 125,165,205, 405, 604.])

aqicolors = ['#00e400', '#ffff00', '#ff7e00', '#ff0000', '#8f3f97', '#7e0023']
colors = ['grey'] + aqicolors + ['k']