def function(self):
method = self.chooseAlgorithmComboBox.currentText()
datakey = self.chooseDataComboBox.currentText()
# return method parameters and parameters that changed
methodParameters, _changed = self.getMethodParams(self.chooseAlgorithmComboBox.currentIndex())
datakey_new = datakey + '-Baseline Removed-' + method + str(_changed)
datakey_baseline = datakey + '-Baseline-' + method + str(_changed)
self.datakeys.append(datakey_new)
self.datakeys.append(datakey_baseline)
self.data[datakey_new] = spectral_data(self.data[datakey].df.copy(deep=True))
self.data[datakey_new].remove_baseline(method, segment=True, params=methodParameters)
self.data[datakey_baseline] = spectral_data(self.data[datakey_new].df_baseline)
def run(self):
method = self.chooseAlgorithmComboBox.currentText()
datakey = self.chooseDataComboBox.currentText()
xvars = [str(x.text()) for x in self.xVariableList.selectedItems()]
yvars = [('comp', str(y.text()))
for y in self.yVariableList.selectedItems()]
yrange = [
self.yMinDoubleSpinBox.value(),
self.yMaxDoubleSpinBox.value()
]
params, modelkey = self.getMethodParams(
self.chooseAlgorithmComboBox.currentIndex())
y = np.array(self.data[datakey].df[yvars])
match = np.squeeze((y > yrange[0]) & (y < yrange[1]))
data_for_cv = spectral_data(self.data[datakey].df.ix[match])
# Warning: Params passing through cv.cv(params) needs to be in lists
# Example: {'n_components': [4], 'scale': [False]}
cv_obj = cv.cv(params)
self.data[
datakey].df, self.cv_results, cvmodels, cvmodelkeys = cv_obj.do_cv(
data_for_cv.df,
xcols=xvars,
ycol=yvars,
yrange=yrange,
method=method)
for n, key in enumerate(cvmodelkeys):
self.modelkeys.append(key)
self.models[key] = cvmodels[n]
self.model_xvars[key] = xvars
self.model_yvars[key] = yvars
if method != 'GP':
coef = np.squeeze(cvmodels[n].model.coef_)
coef = pd.DataFrame(coef)
coef.index = pd.MultiIndex.from_tuples(
self.data[datakey].df[xvars].columns.values)
coef = coef.T
coef[('meta', 'Model')] = key
try:
coef[('meta', 'Intercept')] = cvmodels[n].model.intercept_
except:
pass
try:
self.data['Model Coefficients'] = spectral_data(
pd.concat([self.data['Model Coefficients'].df, coef]))
except:
self.data['Model Coefficients'] = spectral_data(coef)
self.datakeys.append('Model Coefficients')
self.datakeys.append('CV Results ' + modelkey)
self.data['CV Results ' + modelkey] = self.cv_results
def run(self):
# return method parameters and parameters that changed
method = self.chooseAlgorithmComboBox.currentText()
datakey = self.chooseDataComboBox.currentText()
# return method parameters and parameters that changed
methodParameters, _changed = self.getMethodParams(self.chooseAlgorithmComboBox.currentIndex())
datakey_new = datakey + '-Baseline Removed-' + method + str(_changed)
datakey_baseline = datakey + '-Baseline-' + method + str(_changed)
self.datakeys.append(datakey_new)
self.datakeys.append(datakey_baseline)
self.data[datakey_new] = spectral_data(self.data[datakey].df.copy(deep=True))
self.data[datakey_new].remove_baseline(method, segment=True, params=methodParameters)
self.data[datakey_baseline] = spectral_data(self.data[datakey_new].df_baseline)
#@@TODO make sure that this is the data that we want to propagate.
self.setCurrentData(datakey_new)
def do_get_data(self, filename, keyname):
try:
print('Loading data file: ' + str(filename))
self.data[keyname] = spectral_data(pd.read_csv(filename, header=[0, 1]))
self.datakeys.append(keyname)
pass
except Exception as e:
print('Problem reading data: {}'.format(e))
def run(self):
method = self.chooseAlgorithmComboBox.currentText()
datakey = self.chooseDataComboBox.currentText()
xvars = [str(x.text()) for x in self.xVariableList.selectedItems()]
yvars = [('comp', str(y.text()))
for y in self.yVariableList.selectedItems()]
yrange = [
self.yMinDoubleSpinBox.value(),
self.yMaxDoubleSpinBox.value()
]
params, modelkey = self.getMethodParams(
self.chooseAlgorithmComboBox.currentIndex())
modelkey = "{} - {} - ({}, {}) {}".format(method, yvars[0][-1],
yrange[0], yrange[1],
modelkey)
self.modelkeys.append(modelkey)
print(params, modelkey)
self.models[modelkey] = regression.regression([method], [yrange],
[params])
x = self.data[datakey].df[xvars]
y = self.data[datakey].df[yvars]
x = np.array(x)
y = np.array(y)
ymask = np.squeeze((y > yrange[0]) & (y < yrange[1]))
y = y[ymask]
x = x[ymask, :]
self.models[modelkey].fit(x, y)
self.model_xvars[modelkey] = xvars
self.model_yvars[modelkey] = yvars
coef = np.squeeze(self.models[modelkey].model.coef_)
coef = pd.DataFrame(coef)
coef.index = pd.MultiIndex.from_tuples(
self.data[datakey].df[xvars].columns.values)
coef = coef.T
coef[('meta', 'Model')] = modelkey
try:
self.data['Model Coefficients'] = spectral_data(
pd.concat([self.data['Model Coefficients'].df, coef]))
except:
self.data['Model Coefficients'] = spectral_data(coef)
self.datakeys.append('Model Coefficients')
self.current_model = modelkey
self.current_data = datakey
def function(self):
params = self.getGuiParams()
filename = params['fileNameLineEdit']
keyname = params['dataSetNameLineEdit']
print('Loading data file: ' + str(filename))
if keyname in self.datakeys:
print("That data set name is already in use. Try something else.")
else:
self.data[keyname] = spectral_data(
pd.read_csv(filename, header=[0, 1], verbose=True))
self.datakeys.append(keyname)
def run(self):
datakey = self.chooseDataComboBox.currentText()
colname = self.splitOnUniqueValuesOfComboBox.currentText()
vars_level0 = self.data[datakey].df.columns.get_level_values(0)
vars_level1 = self.data[datakey].df.columns.get_level_values(1)
vars_level1 = list(vars_level1[vars_level0 != 'wvl'])
vars_level0 = list(vars_level0[vars_level0 != 'wvl'])
colname = (vars_level0[vars_level1.index(colname)], colname)
coldata = np.array([str(i) for i in self.data[datakey].df[colname]])
unique_values = np.unique(coldata)
for i in unique_values:
new_datakey = datakey + ' - ' + str(i)
self.datakeys.append(new_datakey)
self.data[new_datakey] = spectral_data(
self.data[datakey].df.ix[coldata == i])
def jsc_batch(directory, LUT_files, searchstring='*.txt', to_csv=None):
# Read in the lookup tables to expand filename metadata
refdata = read_refdata(LUT_files)
# get the list of files that match the search string in the given directory
filelist = file_search(directory, searchstring)
spectIDs = [] # create an empty list to hold the spectrometer IDs
libsIDs = []
timestamps = []
locs = []
for file in filelist:
filesplit = os.path.basename(file).split('_')
spectIDs.append(
filesplit[6]) # get the spectrometer IDs for each file in the list
libsIDs.append(filesplit[0])
timestamps.append(filesplit[-1].split('.')[0])
locs.append(filesplit[1])
spectIDs_unique = np.unique(spectIDs) # get the unique spectrometer IDs
libsIDs_unique = np.unique(libsIDs)
dfs = [
] # create an empty list to hold the data frames for each spectrometer
# loop through each LIBS ID
alldata = []
for ID in libsIDs_unique:
print('Working on : ' + str(ID))
sublist = filelist[np.in1d(libsIDs, ID)]
locs = []
for file in sublist:
locs.append(os.path.basename(file).split('_')[1])
locs_unique = np.unique(locs)
# loop through each location for that libs ID
for loc in locs_unique:
print(loc)
sub_sublist = sublist[np.in1d(
locs, loc)] # get the files for that LIBSID and location
data = JSC(sub_sublist, refdata)
alldata.append(data)
pass
combined = pd.concat(alldata)
if to_csv is not None:
print('Writing combined data to: ' + to_csv)
combined.to_csv(to_csv)
return spectral_data(combined)
def jsc_batch(directory, LUT_files, searchstring='*.txt', to_csv=None):
# Read in the lookup tables to expand filename metadata
refdata = read_refdata(LUT_files)
# get the list of files that match the search string in the given directory
filelist = file_search(directory, searchstring)
spectIDs = [] # create an empty list to hold the spectrometer IDs
libsIDs = []
timestamps = []
locs = []
for file in filelist:
filesplit = os.path.basename(file).split('_')
spectIDs.append(filesplit[6]) # get the spectrometer IDs for each file in the list
libsIDs.append(filesplit[0])
timestamps.append(filesplit[-1].split('.')[0])
locs.append(filesplit[1])
spectIDs_unique = np.unique(spectIDs) # get the unique spectrometer IDs
libsIDs_unique = np.unique(libsIDs)
dfs = [] # create an empty list to hold the data frames for each spectrometer
# loop through each LIBS ID
alldata = []
for ID in libsIDs_unique:
print('Working on : ' + str(ID))
sublist = filelist[np.in1d(libsIDs, ID)]
locs = []
for file in sublist:
locs.append(os.path.basename(file).split('_')[1])
locs_unique = np.unique(locs)
# loop through each location for that libs ID
for loc in locs_unique:
print(loc)
sub_sublist = sublist[np.in1d(locs, loc)] # get the files for that LIBSID and location
data = JSC(sub_sublist, refdata)
alldata.append(data)
pass
combined = pd.concat(alldata)
if to_csv is not None:
print('Writing combined data to: ' + to_csv)
combined.to_csv(to_csv)
return spectral_data(combined)
def run(self):
match_vectors = []
logic_list = []
datakey = self.chooseData.currentText()
for i in self.operations:
values_tmp = i.GetValues()
if i.hidden == None:
match_vectors.append(self.evaluate_operation(datakey, values_tmp))
logic_list.append(values_tmp['logic'])
else:
if i.hidden.isChecked() == False:
match_vectors.append(self.evaluate_operation(datakey, values_tmp))
logic_list.append(values_tmp['logic'])
match_combined = np.all(match_vectors, axis=0)
print(self.data[datakey].df.shape)
self.data[datakey] = spectral_data(self.data[datakey].df.ix[~match_combined])
print(self.data[datakey].df.shape)
pass
filelist = glob.glob(r"E:\ChemCam\Calibration Data\LANL_testbed\Caltargets\*calib.sav")
filelist2 = glob.glob(r"E:\ChemCam\Calibration Data\LANL_testbed\Caltargets\test.sav")
data2 = readsav(filelist2[0])
data = readsav(filelist[0])
muv = data['calibspecmuv']
muv_orig = muv
x = data['defuv'] # numpy.arange(len(muv))
muv = numpy.array([muv, muv])
muv = pd.DataFrame(muv)
colnames = []
for i, j in enumerate(x):
colnames.append(('wvl', x[i]))
muv.columns = pd.MultiIndex.from_tuples(colnames)
muv = spectral_data(muv)
muv2 = spectral_data(muv)
muv.remove_baseline(method='ccam', params={'int_flag_': 2, 'lvmin_': 6, 'lv_': 10})
# muv2.remove_baseline(method='wavelet',params=) # this was causing setup.py to crash, it has been commented out
# muv_denoise,muv_noise=ccam_denoise.ccam_denoise(muv,sig=3,niter=4)
# plot.figure()
# plot.plot(muv_noise)
# muv_nocont,cont=baseline_code.ccam_remove_continuum.ccam_remove_continuum(x,muv,10,lvmin=6,int_flag=2)
plot.figure(figsize=[11, 8])
plot.plot(x, muv.df['wvl'].iloc[0], label='Continuum Removed', linewidth=0.5)
plot.plot(x, muv.df_baseline['wvl'].iloc[0], label='Continuum', linewidth=0.5)
plot.plot(x, muv_orig, label='Original', linewidth=0.5)
plot.plot(x, data2['muv_cont'], label='IDL Continuum', linestyle='--', linewidth=0.5)
plot.legend()
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 = []
if progressbar:
from PyQt5 import QtCore # only rely on PyQt5 if a progressbar object has been passed
progressbar.setWindowTitle('ChemCam data progress')
progressbar.setRange(0, filelist.size)
progressbar.show()
filecount = 0
for i, file in enumerate(filelist):
filecount = filecount + 1
print(file)
if is_sav:
tmp = CCAM_SAV(file, ave=ave)
else:
tmp = CCAM_CSV(file, ave=ave)
if i == 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!")
if progressbar:
progressbar.setValue(filecount)
QtCore.QCoreApplication.processEvents()
pass
combined.loc[:, ('meta', 'sclock')] = pd.to_numeric(combined.loc[:, ('meta', 'sclock')])
if lookupfile is not None:
combined = lookup(combined, lookupfile=lookupfile.replace('[','').replace(']','').replace("'",'').replace(' ','').split(','))
if to_csv is not None:
combined.to_csv(to_csv)
return spectral_data(combined)
from libpysat.fileio import io_ccam_pds import libpysat.spectral.spectral_data as sd import pandas as pd import numpy as np path = r"C:\Users\rbanderson\Desktop\test_data\pdl" db = r"C:\Users\rbanderson\Documents\Projects\LIBS PDART\Sample_Data\full_db_mars_corrected_dopedTiO2_pandas_format.csv" data = spectral_data(io_ccam_pds.ccam_batch(path, '*CCS*.SAV', ave=True)) #data.peak_area() db = sd.spectral_data(pd.read_csv(db, header=[0, 1])) db.peak_area() pass
# Load one set of data that will be used to create the model
import pandas as pd
from libpysat.spectral.spectral_data import spectral_data
data1 = "G:/.csvfile/full_db_mars_corrected_dopedTiO2_pandas_format.csv"
data1 = spectral_data(pd.read_csv(data1, header=[0, 1], verbose=True))
# Load a second set of data that will serve as the "unknown" (even though in this test case I'm using some known data I had on hand)
data2 = "G:/.csvfile/lab_data_averages_pandas_format.csv"
data2 = spectral_data(pd.read_csv(data2, header=[0, 1], verbose=True))
# Interpolate the "unknown" data to the same wavelengths as the "known" data
data1.interp(data2.df['wvl'].columns)
# Mask out unwanted portions the first data set (example mask files are now included in the "inputs" directory)
maskfile = "G:/.csvfile/mask_minors_noise.csv"
data1.mask(maskfile, maskvar='wvl')
# Apply the same mask to the second data set
maskfile = "G:/.csvfile/mask_minors_noise.csv"
data2.mask(maskfile, maskvar='wvl')
# Normalize the spectra in the first data set so that the sum of each spectrum (from 0-1000 nm) is 1
data1.norm([(0, 1000)], 'wvl')
# Same normalization on the second data set
data2.norm([(0, 1000)], 'wvl')
# Get rid of rows in the first data set that don't have compositions for SiO2 (if you don't do this, it causes problems later on...) For JSC data
# Divide the first data set into 5 folds with similar distributions of SiO2 compositions. Set fold 3 to be used as the test set, use the remaining folds as a training set.
colname = ('comp', 'SiO2')
nfolds = 3
testfold = 2
data1.stratified_folds(nfolds=nfolds, sortby=colname)
data1_train = data1.rows_match(('meta', 'Folds'), [testfold], invert=True)
# Load one set of data that will be used to create the model
import pandas as pd
from libpysat.spectral.spectral_data import spectral_data
data1 = "G:/.csvfile/full_db_mars_corrected_dopedTiO2_pandas_format.csv"
data1 = spectral_data(pd.read_csv(data1, header=[0, 1], verbose=True))
# Load a second set of data that will serve as the "unknown" (even though in this test case I'm using some known data I had on hand)
data2 = "G:/.csvfile/lab_data_averages_pandas_format.csv"
data2 = spectral_data(pd.read_csv(data2, header=[0, 1], verbose=True))
# Interpolate the "unknown" data to the same wavelengths as the "known" data
data1.interp(data2.df['wvl'].columns)
# Mask out unwanted portions the first data set (example mask files are now included in the "inputs" directory)
maskfile = "G:/.csvfile/mask_minors_noise.csv"
data1.mask(maskfile, maskvar='wvl')
# Apply the same mask to the second data set
maskfile = "G:/.csvfile/mask_minors_noise.csv"
data2.mask(maskfile, maskvar='wvl')
# Normalize the spectra in the first data set so that the sum of each spectrum (from 0-1000 nm) is 1
data1.norm([(0, 1000)], 'wvl')
# Same normalization on the second data set
data2.norm([(0, 1000)], 'wvl')
# Get rid of rows in the first data set that don't have compositions for SiO2 (if you don't do this, it causes problems later on...) For JSC data
# Divide the first data set into 5 folds with similar distributions of SiO2 compositions. Set fold 3 to be used as the test set, use the remaining folds as a training set.
colname = ('comp', 'SiO2')
nfolds = 3
testfold = 2
data1.stratified_folds(nfolds=nfolds, sortby=colname)
data1_train = data1.rows_match(('meta', 'Folds'), [testfold], invert=True)
data1_test = data1.rows_match(('meta', 'Folds'), [testfold])
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 = []
if progressbar:
progressbar.setWindowTitle('ChemCam data progress')
progressbar.setRange(0, filelist.size)
progressbar.show()
filecount = 0
for i in filelist:
filecount = filecount + 1
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
if progressbar:
progressbar.setValue(filecount)
QtCore.QCoreApplication.processEvents()
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 spectral_data(combined)
from libpysat.fileio import io_ccam_pds import libpysat.spectral.spectral_data as sd import pandas as pd import numpy as np path=r"C:\Users\rbanderson\Desktop\test_data\pdl" db=r"C:\Users\rbanderson\Documents\Projects\LIBS PDART\Sample_Data\full_db_mars_corrected_dopedTiO2_pandas_format.csv" data=io_ccam_pds.ccam_batch(path,'*CCS*.SAV',ave=True) #data.peak_area() db=sd.spectral_data(pd.read_csv(db, header=[0, 1])) db.peak_area() pass
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 07 12:29:20 2014
@author: rbanderson
"""
import matplotlib.pyplot as plot
import pandas as pd
from libpysat.spectral.spectral_data import spectral_data
# import mlpy.wavelet
filename = r"C:\Users\rbanderson\Documents\Projects\LIBS PDART\Sample_Data\JSC_data_combined_20170307.csv"
data = spectral_data(pd.read_csv(filename, header=[0, 1]))
data = data.df.ix[0:5]
colcheck = data_spect.columns.values < 350
data_spect = data_spect.iloc[:, colcheck]
data = spectral_data(data_spect)
data.remove_baseline(method='ccam',
params={
'int_flag_': 2,
'lvmin_': 6,
'lv_': 10
})
x = data.df.columns.values
plot.figure(figsize=[11, 8])
plot.plot(x, data_orig, label='Original', linewidth=0.5)
plot.plot(x, data.df.iloc[0], label='Continuum Removed', linewidth=0.5)
plot.plot(x, data.df_baseline.iloc[0], label='Continuum', linewidth=0.5)
r"E:\ChemCam\Calibration Data\LANL_testbed\Caltargets\*calib.sav")
filelist2 = glob.glob(
r"E:\ChemCam\Calibration Data\LANL_testbed\Caltargets\test.sav")
data2 = readsav(filelist2[0])
data = readsav(filelist[0])
muv = data['calibspecmuv']
muv_orig = muv
x = data['defuv'] # numpy.arange(len(muv))
muv = numpy.array([muv, muv])
muv = pd.DataFrame(muv)
colnames = []
for i, j in enumerate(x):
colnames.append(('wvl', x[i]))
muv.columns = pd.MultiIndex.from_tuples(colnames)
muv = spectral_data(muv)
muv2 = spectral_data(muv)
muv.remove_baseline(method='ccam',
params={
'int_flag_': 2,
'lvmin_': 6,
'lv_': 10
})
# muv2.remove_baseline(method='wavelet',params=) # this was causing setup.py to crash, it has been commented out
# muv_denoise,muv_noise=ccam_denoise.ccam_denoise(muv,sig=3,niter=4)
# plot.figure()
# plot.plot(muv_noise)
# muv_nocont,cont=baseline_code.ccam_remove_continuum.ccam_remove_continuum(x,muv,10,lvmin=6,int_flag=2)
plot.figure(figsize=[11, 8])
plot.plot(x, muv.df['wvl'].iloc[0], label='Continuum Removed', linewidth=0.5)
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 07 12:29:20 2014
@author: rbanderson
"""
import matplotlib.pyplot as plot
import pandas as pd
from libpysat.spectral.spectral_data import spectral_data
# import mlpy.wavelet
filename = r"C:\Users\rbanderson\Documents\Projects\LIBS PDART\Sample_Data\JSC_data_combined_20170307.csv"
data = spectral_data(pd.read_csv(filename, header=[0, 1]))
data = data.df.ix[0:5]
colcheck = data_spect.columns.values < 350
data_spect = data_spect.iloc[:, colcheck]
data = spectral_data(data_spect)
data.remove_baseline(method='ccam', params={'int_flag_': 2, 'lvmin_': 6, 'lv_': 10})
x = data.df.columns.values
plot.figure(figsize=[11, 8])
plot.plot(x, data_orig, label='Original', linewidth=0.5)
plot.plot(x, data.df.iloc[0], label='Continuum Removed', linewidth=0.5)
plot.plot(x, data.df_baseline.iloc[0], label='Continuum', linewidth=0.5)
plot.legend()
plot.savefig('cont_test.png', dpi=1000)
plot.show()
# plot.figure(figsize=[11,8])
