Removed io_ccs and associated test file.

plio/io/io_ccs.py

-# This code is used to read individual ChemCam CCS files
-# Header data is stored as attributes of the data frame
-# White space is stripped from the column names
-import os
-import time
-
-import numpy as np
-import pandas as pd
-import scipy.io as io
-
-from plio.utils.utils import lookup
-from plio.utils.utils import file_search
-
-
-def CCAM_CSV(input_data):
-    try:
-        df = pd.read_csv(input_data, header=14, engine='c')
-        cols = list(df.columns.values)
-        df.columns = [i.strip().replace('# ', '') for i in cols]  # strip whitespace from column names
-        df.set_index(['wave'], inplace=True)  # use wavelengths as indices
-        # read the file header and put information into the dataframe as new columns
-        metadata = pd.read_csv(input_data, sep='=', nrows=14, comment=',', engine='c', index_col=0, header=None)
-
-    except:  # handle files with an extra header row containing temperature
-        df = pd.read_csv(input_data, header=15, engine='c')
-        cols = list(df.columns.values)
-        df.columns = [i.strip().replace('# ', '') for i in cols]  # strip whitespace from column names
-        df.set_index(['wave'], inplace=True)  # use wavelengths as indices
-        # read the file header and put information into the dataframe as new columns
-        metadata = pd.read_csv(input_data, sep='=', nrows=15, comment=',', engine='c', index_col=0, header=None)
-
-    df.index = [['wvl'] * len(df.index),
-                df.index.values.round(4)]  # create multiindex so spectra can be easily extracted with a single key
-    df = df.T  # transpose so that each spectrum is a row
-
-    # remove extraneous stuff from the metadataindices
-    metadata.index = [i.strip().strip('# ').replace(' FLOAT', '').lower() for i in metadata.index.values]
-    metadata = metadata.T
-
-    # extract info from the file name
-    fname = os.path.basename(input_data)
-    metadata['sclock'] = fname[4:13]
-    metadata['seqid'] = fname[25:34].upper()
-    metadata['Pversion'] = fname[34:36]
-
-    # duplicate the metadata for each row in the df
-    metadata = metadata.append([metadata] * (len(df.index) - 1), ignore_index=True)
-    metadata.index = df.index  # make the indices match
-    metadata.columns = [['meta'] * len(metadata.columns), metadata.columns.values]  # make the columns into multiindex
-    df = pd.concat([metadata, df], axis=1)  # combine the spectra with the metadata
-    return df
-
-
-def CCAM_SAV(input_data, ave=True):
-    # read the IDL .SAV file
-
-    data = io.readsav(input_data, python_dict=True)
-
-    # put the spectra into data frames and combine them
-    df_UV = pd.DataFrame(data['uv'], index=data['defuv'])
-    df_VIS = pd.DataFrame(data['vis'], index=data['defvis'])
-    df_VNIR = pd.DataFrame(data['vnir'], index=data['defvnir'])
-    df_spect = pd.concat([df_UV, df_VIS, df_VNIR])
-    df_spect.columns = ['shot' + str(i + 1) for i in
-                        df_spect.columns]  # add 1 to the columns so they correspond to shot number
-
-    df_aUV = pd.DataFrame(data['auv'], index=data['defuv'], columns=['average'])
-    df_aVIS = pd.DataFrame(data['avis'], index=data['defvis'], columns=['average'])
-    df_aVNIR = pd.DataFrame(data['avnir'], index=data['defvnir'], columns=['average'])
-    df_ave = pd.concat([df_aUV, df_aVIS, df_aVNIR])
-
-    df_mUV = pd.DataFrame(data['muv'], index=data['defuv'], columns=['median'])
-    df_mVIS = pd.DataFrame(data['mvis'], index=data['defvis'], columns=['median'])
-    df_mVNIR = pd.DataFrame(data['mvnir'], index=data['defvnir'], columns=['median'])
-    df_med = pd.concat([df_mUV, df_mVIS, df_mVNIR])
-
-    df = pd.concat([df_spect, df_ave, df_med], axis=1)
-    # create multiindex to access wavelength values
-    # also, round the wavlength values to a more reasonable level of precision
-    df.index = [['wvl'] * len(df.index), df.index.values.round(4)]
-    # transpose so that spectra are rows rather than columns
-    df = df.T
-
-    # extract metadata from the file name and add it to the data frame
-    # use the multiindex label "meta" for all metadata
-
-    fname = os.path.basename(input_data)
-
-    # for some reason, some ChemCam files have the 'darkname' key, others call it 'darkspect'
-    # this try-except pair converts to 'darkname' when needed
-    try:
-        data['darkname']
-    except:
-        data['darkname'] = data['darkspec']
-
-    metadata = [fname,
-                fname[4:13],
-                fname[25:34].upper(),
-                fname[34:36],
-                data['continuumvismin'],
-                data['continuumvnirmin'],
-                data['continuumuvmin'],
-                data['continuumvnirend'],
-                data['distt'],
-                data['darkname'],
-                data['nshots'],
-                data['dnoiseiter'],
-                data['dnoisesig'],
-                data['matchedfilter']]
-    metadata = np.tile(metadata, (len(df.index), 1))
-    metadata_cols = list(zip(['meta'] * len(df.index), ['file',
-                                                        'sclock',
-                                                        'seqid',
-                                                        'Pversion',
-                                                        'continuumvismin',
-                                                        'continuumvnirmin',
-                                                        'continuumuvmin',
-                                                        'continuumvnirend',
-                                                        'distt',
-                                                        'dark',
-                                                        'nshots',
-                                                        'dnoiseiter',
-                                                        'dnoisesig',
-                                                        'matchedfilter']))
-    metadata = pd.DataFrame(metadata, columns=pd.MultiIndex.from_tuples(metadata_cols), index=df.index)
-
-    df = pd.concat([metadata, df], axis=1)
-    if ave == True:
-        df = df.loc['average']
-        df = df.to_frame().T
-    else:
-        pass
-
-    return df
-
-
-def ccam_batch(directory, searchstring='*.csv', to_csv=None, lookupfile=None, ave=True, progressbar=None):
-    # Determine if the file is a .csv or .SAV
-    if '.sav' in searchstring.lower():
-        is_sav = True
-    else:
-        is_sav = False
-    filelist = file_search(directory, searchstring)
-    basenames = np.zeros_like(filelist)
-    sclocks = np.zeros_like(filelist)
-    P_version = np.zeros_like(filelist, dtype='int')
-
-    # Extract the sclock and version for each file and ensure that only one
-    # file per sclock is being read, and that it is the one with the highest version number
-    for i, name in enumerate(filelist):
-        basenames[i] = os.path.basename(name)
-        sclocks[i] = basenames[i][4:13]  # extract the sclock
-        P_version[i] = basenames[i][-5:-4]  # extract the version
-
-    sclocks_unique = np.unique(sclocks)  # find unique sclocks
-    filelist_new = np.array([], dtype='str')
-    for i in sclocks_unique:
-        match = (sclocks == i)  # find all instances with matching sclocks
-        maxP = P_version[match] == max(P_version[match])  # find the highest version among these files
-        filelist_new = np.append(filelist_new, filelist[match][maxP])  # keep only the file with thei highest version
-
-    filelist = filelist_new
-    # Should add a progress bar for importing large numbers of files
-    dt = []
-
-    for i in filelist:
-        print(i)
-        try:
-            if is_sav:
-                t = time.time()
-                tmp = CCAM_SAV(i, ave=ave)
-                dt.append(time.time() - t)
-            else:
-                t = time.time()
-                tmp = CCAM_CSV(i)
-
-                dt.append(time.time() - t)
-            if i == filelist[0]:
-                combined = tmp
-
-            else:
-                # This ensures that rounding errors are not causing mismatches in columns
-                cols1 = list(combined['wvl'].columns)
-                cols2 = list(tmp['wvl'].columns)
-                if set(cols1) == set(cols2):
-                    combined = pd.concat([combined, tmp])
-                else:
-                    print("Wavelengths don't match!")
-        except:
-            pass
-
-    combined.loc[:, ('meta', 'sclock')] = pd.to_numeric(combined.loc[:, ('meta', 'sclock')])
-
-    if lookupfile is not None:
-        combined = lookup(combined, lookupfile=lookupfile)
-    if to_csv is not None:
-        combined.to_csv(to_csv)
-    return combined

plio/io/tests/test_io_ccs.py

-import os
-import sys
-import unittest
-
-sys.path.insert(0, os.path.abspath('..'))
-
-from plio.examples import get_path
-from plio.io import io_ccs
-
-class Test_CSS_IO(unittest.TestCase):
-
-    def setUp(self):
-        self.examplefile = get_path('CL5_398645626CCS_F0030004CCAM02013P3.csv')
-
-    def test_14_item_header_csv(self):
-        io_ccs.CCAM_CSV(self.examplefile)
-
-if __name__ == '__main__':
-    unittest.main()