Removed the matplotlib legacy import, cleaned out legacy code

from __future__ import division

import itertools

from struct import unpack

import sys

import pandas as pd

import pvl

import numpy as np

from matplotlib.collections import LineCollection

from pylab import *

import argparse

import os

import math

import scipy.stats as ss

from autocnet.utils.utils import find_in_dict, getnearest

from autocnet.utils.utils import find_in_dict

from autocnet.spectral.spectra import Spectra

#Photometric Constants

global c1,c2,c3,l,cosi,cose,xl_fixed

c1 = -0.019

c2 = 0.000242

c3 = -.00000146

l = 1.0 + (c1*(30)) + (c2*(30**2)) + (c3*(30**3))

cosi = math.cos(math.radians(30))

cose = math.cos(math.radians(0))

xl_fixed= ((2*l*(cosi /(cosi + cose)))) + ((1-l)*cosi)

def openreferenceimage(dirpath, fname):

    from osgeo import gdal

    imagepath = os.path.join(dirpath, fname)

    imagepath = imagepath.split('.')[0]

    try:

        ds = gdal.Open(imagepath + 'P.jpg')

    except Exception:

        ds = gdal.Open(imagepath +'.jpg')

    bandcount = ds.RasterCount

    xsize = ds.RasterXSize

    ysize = ds.RasterYSize

    img = ds.GetRasterBand(1).ReadAsArray()

    return ds, img

#TODO: The spectra should inhert from a SpectraABC, monkey patch smoothing in.

class Spectral_Profiler(object):

                self.spectra[i] = Spectra(self.spectra[i])

        def get_band_numbers(self, *args):

            """

            This parses the wavelenth list,finds the mean wavelength closest to the

            provided wavelength, and returns the index of that value.  One (1) is added

            to the index to grab the correct band.

            Parameters

            ----------

            *args: A variable number of input wavelengths to map to bands

            Returns

            -------

            bands: A variable length list of bands.  These are in the same order they are

            provided in.  Beware that altering the order will cause unexpected results.

"""

            bands = []

            for wv in args:

                bands.append(getnearest(self.wavelengths, wv))

            return bands

        def cleanspectra(self, spectral_df, qa_threshold=2000):

            """

            Clean the spectra's dataframe using the qa array

class SpectralSeries(pd.Series):

    def __init__(self, **kwargs):

        super(SpectralSeries, self).__init__(*args, **kwargs)

            """

def parse_coefficients(coefficient_table):

    '''

            ylabel('Reflectance', fontsize=10)

            title('Continuum Removed Spectrum', fontsize=12)

    show()

"""