lm.fit_dict.zerolev_mean, flux[lm.fit_dict.idx0:lm.fit_dict.idx1], facecolor=dz.colorVector['green'], step='mid', alpha=0.5) dz.Axis.fill_between(wave[lm.fit_dict.idx2:lm.fit_dict.idx3], lm.fit_dict.zerolev_mean, flux[lm.fit_dict.idx2:lm.fit_dict.idx3], facecolor=dz.colorVector['dark blue'], step='mid', alpha=0.5) dz.Axis.fill_between(wave[lm.fit_dict.idx4:lm.fit_dict.idx5], lm.fit_dict.zerolev_mean, flux[lm.fit_dict.idx4:lm.fit_dict.idx5], facecolor=dz.colorVector['green'], step='mid', alpha=0.5) dz.Axis.plot(lm.fit_dict.x_resample, lm.fit_dict.y_resample, linestyle='--') dz.Axis.plot(lm.fit_dict.x_resample, lm.fit_dict.zerolev_resample, linestyle='--') dz.Axis.scatter(lm.Current_TheoLoc, lm.fit_dict.zerolev_mean) dz.FigWording(r'Wavelength $(\AA)$', 'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$', r'Object {} emission line: {}'.format(objName, line), loc='upper right') dz.display_fig() print 'DOne'
#Analyze file address CodeName, FileName, FileFolder = dz.Analyze_Address(FilesList[i]) #Import fits file fits_file = pf.open(FilesList[i]) Wave = fits_file[1].data['WAVELENGTH'] Int = fits_file[1].data['FLUX'] fits_file.close() dz.data_plot(Wave, Int, label=FileName.replace('_', '')) dz.FigWording(r'Wavelength $(\AA)$', 'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$', 'Calspec library') dz.Axis.set_yscale('log') dz.Axis.set_xlim(7000, 10000) dz.display_fig() # /home/vital/Dropbox/Astrophysics/Telescope Time/Standard_Stars/Calspec_Spectra/bd_17d4708_stisnic_006.fits # file_address = '/home/vital/Dropbox/Astrophysics/Telescope Time/Standard_Stars/Calspec_Spectra/bd_17d4708_stisnic_006.fits' # # fits_file = pf.open(file_address) # print fits_file[1].data['WAVELENGTH'] # print fits_file[1].data['FLUX'] # # fits_file.close() # #Recover objects list # Table_Address = '/home/vital/Dropbox/Astrophysics/Telescope Time/Standard_Stars/Calspec_list.csv' # Candiates_frame = pd.read_csv(Table_Address, delimiter = '; ', header = 0, index_col = 0)
#Plot the results dzp.FigConf() dzp.data_plot(dz.sspFit_dict['obs_wave'], dz.sspFit_dict['obs_flux'], label='obs_flux') dzp.data_plot(dz.sspFit_dict['obs_wave'], dz.sspFit_dict['zero_mask'], label='my mask') # dzp.data_plot(dz.fit_conf['obs_wave'], mis_cosas[1], label='Hector mask') # dzp.data_plot(dz.fit_conf['obs_wave'], mis_cosas[2], label='Hector fit') dzp.data_plot(dz.sspFit_dict['obs_wave'], fit_products['flux_sspFit'], label='my fit') dzp.FigWording('Wave', 'Flux', 'Input spectra') dzp.display_fig() # #Plot input spectra and regions # dzp.FigConf() # dzp.data_plot(dz.fit_conf['obs_wave'], dz.fit_conf['obs_flux'], label='obs_flux') # dzp.data_plot(dz.fit_conf['obs_wave'], dz.fit_conf['obs_flux_mask'], label='obs_flux_mask') # # dzp.data_plot(dz.fit_conf['obs_wave'], dz.fit_conf['Av_mask'], label='Av mask') # # dzp.data_plot(dz.fit_conf['obs_wave'], dz.fit_conf['obs_flux_err'], label='obs_flux_err') # # dz.data_plot(wave_unc, flux_unc_input, label='flux_unc_input') # # dz.data_plot(wave_unc, e_flux_unc, label='e_flux_unc') # # dz.data_plot(wave_unc, color_unc, label='color_unc') # # dz.data_plot(wave_unc, masked, label='masked') # # dz.data_plot(wave_unc, masked2, label='masked2') # # dz.data_plot(wave_unc, masked_Av, label='masked_Av') # # dz.data_plot(wave_unc, flux_masked, label='flux_masked') # # dz.data_plot(wave_unc, flux_masked2, label='flux_masked2')