xycoords='axes fraction',
color='red')
#write the MAD in the plot
"""
look into residplot - appears residual not propotrional to error (see SGD plot)
"""
print i
i += 1
test_index = sp.argmax(abs(error))
print i
i += 1
spectrum = Spectrum('/data2/cpb405/DR1_3/' +
test.filename.tolist()[test_index]) ###filename###
spectrum.plotFlux(ax=ax[1][1],
Tpred=final[test_index],
Teff=y_test[test_index])
print i
i += 1
ax[1][1].set_xlabel('Wavelength \ Angstroms')
ax[1][1].set_ylabel('Flux')
ax[1][1].set_title('Spectra and model blackbody curve\nfor greatest outlier')
ax[1][1].legend()
plt.tight_layout()
plt.show()
sns.residplot(temp, final[-1], lowess = True, ax = ax[1][0], line_kws={'color': 'red'})
ax[1][0].set_title('Residuals of Prediction')
ax[1][0].set_xlabel('Actual Temperature \ K')
ax[1][0].set_ylabel('Prediction Residual \ K')
#plot the residuals of the predicted temperatures
ax[1][0].annotate('MAD = {0:.2f}'.format(MAD[-1]), xy = (0.05, 0.90), xycoords = 'axes fraction', color = 'red')
#write the MAD in the plot
"""
look into residplot - appears residual not propotrional to error (see SGD plot)
"""
index = sp.argmax(abs(error))
df_index = df.loc[df.designation==designation[index]].index[0]
spectrum = Spectrum('/data2/mrs493/DR1/' + df.get_value(df_index,'filename'))
spectrum.plotFlux(ax = ax[1][1], Tpred = final[-1][index], Teff = temp[index])
ax[1][1].set_xlabel('Wavelength \ Angstroms')
ax[1][1].set_ylabel('Flux')
ax[1][1].set_title('Spectra and model blackbody curve\nfor greatest outlier')
ax[1][1].legend()
plt.tight_layout()
plt.show()
#spectrum.plotFlux(Tpred = final[-1][index], Teff = temp[index])
#plt.show()
'''
bp = sns.barplot([i[1] for i in imp][:hyp['max_features']],
[i[0] for i in imp][:hyp['max_features']],
ax=ax[1][0])
ax[1][0].set_xlabel('Features')
ax[1][0].set_ylabel('Importance')
ax[1][0].set_title('Feature Importance')
for tick in ax[1][0].get_xticklabels():
tick.set_rotation(90)
'''
for x in range(len(imp)):
bp.text(x,imp[x][0], '{:.2f}'.format(imp[x][0]), color='black', ha='center')
'''
test_index = sp.argmax(abs(error))
spectrum = Spectrum('/data2/mrs493/DR1_3/' +
test.filename.tolist()[test_index]) ###filename###
ax[1][1].set_xlabel('Wavelength \ Angstroms')
ax[1][1].set_ylabel('Flux')
ax[1][1].set_title('Spectra of Greatest Outlier')
if parameter == 'teff':
spectrum.plotFlux(ax=ax[1][1],
Tpred=final[test_index],
Teff=y_test[test_index],
label='Outlier',
log=False)
ax[1][1].legend()
else:
spectrum.plotFlux(ax=ax[1][1], label='Outlier', log=False)
plt.tight_layout()
ax1[1][0].set_title('Residuals of Prediction')
ax1[1][0].set_xlabel('Actual Temperature \ K')
ax1[1][0].set_ylabel('Prediction Residual \ K')
#plot the residuals of the predicted temperatures
ax1[1][0].annotate('MAD = {0:.2f}'.format(MADc),
xy=(0.05, 0.90),
xycoords='axes fraction',
color='red')
#write the MAD in the plot
"""
look into residplot - appears residual not propotrional to error (see SGD plot)
"""
test_index = sp.argmax(abs(errorc))
spectrum = Spectrum('/data2/mrs493/DR1/' + test.filename.tolist()[test_index])
spectrum.plotFlux(ax=ax1[1][1],
Tpred=finalc[test_index],
Teff=y_test[test_index])
ax1[1][1].set_xlabel('Wavelength \ Angstroms')
ax1[1][1].set_ylabel('Flux')
ax1[1][1].set_title('Spectra and model blackbody curve\nfor greatest outlier')
ax1[1][1].legend()
plt.tight_layout()
plt.show()
ft = ['BV', 'BR', 'BI', 'VR', 'VI', 'RI']
imp = clfc.feature_importances_
from fits import Spectrum
import glob
for fitsName in glob.glob('/data2/mrs493/DR1/*.fits')[:20]:
spectra = Spectrum(fitsName)
spectra.plotFlux()
plt.xlim([5750, 5850])
plt.show()
#!/usr/bin/env python3
import pandas as pd
import scipy as sp
import glob
from astropy.io import fits
from astropy.convolution import convolve, Box1DKernel
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
from fits import Spectrum
dataExample = Spectrum('/data2/cpb405/DR1/spec-55862-B6212_sp06-003.fits')
dataWavelength = dataExample.wavelength
width = 10
SDSS = pd.DataFrame(columns=[
'totalCounts', 'B', 'V', 'R', 'I', 'BV', 'BR', 'BI', 'VR', 'VI', 'RI',
'Ha', 'Hb', 'Hg'
])
letters = {
"B": [3980, 4920],
"V": [5070, 5950],
"R": [5890, 7270],
"I": [7310, 8810]
}
ax[1][0].set_title('Residuals of Prediction')
ax[1][0].set_xlabel('Actual Temperature \ K')
ax[1][0].set_ylabel('Prediction Residual \ K')
#plot the residuals of the predicted temperatures
ax[1][0].annotate('MAD = {0:.2f}'.format(MAD[-1]),
xy=(0.05, 0.90),
xycoords='axes fraction',
color='red')
#write the MAD in the plot
"""
look into residplot - appears residual not propotrional to error (see SGD plot)
"""
test_index = sp.argmax(abs(error))
spectrum = Spectrum('/data2/mrs493/DR1/' +
test.filename.tolist()[test_index])
spectrum.plotFlux(ax=ax[1][1],
Tpred=final[-1][test_index],
Teff=y_test[test_index])
ax[1][1].set_xlabel('Wavelength \ Angstroms')
ax[1][1].set_ylabel('Flux')
ax[1][1].set_title(
'Spectra and model blackbody curve\nfor greatest outlier')
ax[1][1].legend()
plt.tight_layout()
plt.show()
#spectrum.plotFlux(Tpred = final[-1][index], Teff = temp[index])
from fits import Spectrum
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
spectra = pd.read_csv('Files/error.csv')
for spectrum in spectra['file'].tolist():
try:
spec = Spectrum(spectrum)
fig, ax = plt.subplots()
spec.plotFlux(ax=ax)
ax.set_xlabel('Wavelength \ Angstrom')
ax.set_ylabel('Flux')
ax.set_title(spectrum[:-5])
plt.show()
except:
print('error opening ' + spectrum)