This notebook shows the wavelength calibration of the greed detector. Link to Figure 15a.

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In [ ]:
import matplotlib.pyplot as plt
import numpy as np
from astropy.io import fits
from astropy.nddata import CCDData
import astropy.units as u
from glob import glob
import os
from astropy.visualization import ZScaleInterval, ImageNormalize, LogStretch, AsymmetricPercentileInterval
import h5py 
from PIL import Image
from datetime import datetime, timedelta
from ccdproc import ImageFileCollection
import pandas as pd
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib.ticker import AutoLocator, AutoMinorLocator, FixedLocator, FixedFormatter, LogLocator
import cmcrameri.cm as cmcm
from juanfit import SpectrumFitSingle, SpectrumFitRow
from specutils.utils.wcs_utils import vac_to_air, air_to_vac
from scipy import ndimage
from scipy.interpolate import interp1d
In [ ]:
with h5py.File("../../sav/Eclipse/Bias/master_bias_dc_green_1s_proto.h5", 'r') as hf:
    bias_dc_green_1s = hf['image'][:]
In [ ]:
green_path = "../../src/EclipseSpectra2017/MikesData/VaderEclipseDayGreen2017aug21/"
helium_im_collection = ImageFileCollection(green_path,glob_include="HeliumGreenpoint1s_2465*.fit")
                            # filenames=["HeliumGreenpoint1s_24605.fit","HeliumGreenpoint1s_24606.fit","HeliumGreenpoint1s_24607.fit",
                            # "HeliumGreenpoint1s_24608.fit","HeliumGreenpoint1s_24609.fit","HeliumGreenpoint1s_24610.fit",
                            # "HeliumGreenpoint1s_24611.fit","HeliumGreenpoint1s_24612.fit","HeliumGreenpoint1s_24613.fit",
                            # "HeliumGreenpoint1s_24614.fit"])
helium_im_df = helium_im_collection.summary.to_pandas()
In [ ]:
helium_im_df
Out[ ]:
file simple bitpix naxis naxis1 naxis2 bzero bscale datamin datamax ... ypixsz xbinning ybinning xorgsubf yorgsubf xpossubf ypossubf cblack cwhite swcreate
0 HeliumGreenpoint1s_24650.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 756 30056 Artemis Capture
1 HeliumGreenpoint1s_24651.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 756 30781 Artemis Capture
2 HeliumGreenpoint1s_24652.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 747 31067 Artemis Capture
3 HeliumGreenpoint1s_24653.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 759 30684 Artemis Capture
4 HeliumGreenpoint1s_24654.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 755 29932 Artemis Capture
5 HeliumGreenpoint1s_24655.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 757 30677 Artemis Capture
6 HeliumGreenpoint1s_24656.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 757 29019 Artemis Capture
7 HeliumGreenpoint1s_24657.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 752 29345 Artemis Capture
8 HeliumGreenpoint1s_24658.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 757 30203 Artemis Capture
9 HeliumGreenpoint1s_24659.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 756 30207 Artemis Capture

10 rows × 24 columns

In [ ]:
helium_image_cube = np.zeros((1040,1392,10))

for ii, row_ in helium_im_df.iterrows():
    helium_image_cube[:,:,ii] = CCDData.read(os.path.join(green_path,row_["file"]),unit="adu").data - bias_dc_green_1s

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198634 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198634 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198646 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198646 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198657 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198657 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198669 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198669 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198681 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198681 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198692 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198692 from DATE-OBS'.
In [ ]:
helium_im_aver = np.mean(helium_image_cube,axis=2)
norm = ImageNormalize(helium_im_aver, stretch=LogStretch())
fig, ax = plt.subplots(figsize=(10,8))
im = ax.pcolormesh(np.arange(1392),np.arange(1040),helium_im_aver,norm=norm,cmap=cmcm.lajolla, rasterized=True)
#plt.colorbar(im)
ax.axis("scaled")
bottom_pixel = 540
top_pixel = 550
ax.axhspan(bottom_pixel,top_pixel,color="red",alpha=0.5)

ax.tick_params(labelsize=16)
In [ ]:
with h5py.File("../../sav/Eclipse/Curvature/master_curvature_green.h5", 'r') as hf:
    xpos_map_coordinate = hf['xpos_map_coordinate'][:]
    xstart_pixel = hf['xpos_map_coordinate'].attrs['xstart_pixel']
    xend_pixel = hf['xpos_map_coordinate'].attrs['xend_pixel']

    ypos_map_coordinate = hf['ypos_map_coordinate'][:]
    ystart_pixel = hf['ypos_map_coordinate'].attrs['ystart_pixel']
    yend_pixel = hf['ypos_map_coordinate'].attrs['yend_pixel']
In [ ]:
testx_slice_mapcoor = slice(xstart_pixel,xend_pixel)
testy_slice_mapcoor = slice(ystart_pixel,yend_pixel)
helium_trans_mapcoor = ndimage.map_coordinates(helium_im_aver[testy_slice_mapcoor, testx_slice_mapcoor],(ypos_map_coordinate, xpos_map_coordinate),
                                                order=1)
In [ ]:
fig, ax = plt.subplots(figsize=(16,8),constrained_layout=True)
im = ax.pcolormesh(np.arange(testx_slice_mapcoor.start,testx_slice_mapcoor.stop),np.arange(testy_slice_mapcoor.start,testy_slice_mapcoor.stop),
            helium_trans_mapcoor,norm=norm,cmap=cmcm.lajolla,rasterized=True)
ax.tick_params(labelsize=18)
ax.axhspan(bottom_pixel,top_pixel,color="red",alpha=0.5)
clb_ax = inset_axes(ax,width="5%",height= "100%",loc='lower left',
                bbox_to_anchor=(1.02, 0., 1, 1),
                 bbox_transform=ax.transAxes,
                 borderpad=0)
clb = plt.colorbar(im,pad = 0.05,orientation='vertical',ax=ax,cax=clb_ax)
clb_ax.tick_params(labelsize=16)
clb_ax.yaxis.get_offset_text().set_fontsize(16)
clb_ax.set_ylabel('',fontsize=16)
Out[ ]:
Text(0, 0.5, '')
In [ ]:
helium_spectrum_example = np.mean(helium_trans_mapcoor[bottom_pixel - ystart_pixel:top_pixel - ystart_pixel,:],axis=0)
fig, ax = plt.subplots(figsize=(18,6))
ccdx_array = np.arange(xstart_pixel, xend_pixel)
ax.step(ccdx_array, helium_spectrum_example,where="mid")
ax.tick_params(labelsize=16)
In [ ]:
HeI_5016_slice_1 = slice(275 - xstart_pixel ,295 - xstart_pixel)
HeI_5016_model_1 = SpectrumFitSingle(data=helium_spectrum_example[HeI_5016_slice_1], wvl=ccdx_array[HeI_5016_slice_1],line_number=1,line_wvl_init=[283],
                    int_max_init=[12000],fwhm_init=[1],same_width=False)
HeI_5016_model_1.run_lse(absolute_sigma=False)
HeI_5016_model_1.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_5016_slice_2 = slice(605 - xstart_pixel,620 - xstart_pixel)
HeI_5016_model_2 = SpectrumFitSingle(data=helium_spectrum_example[HeI_5016_slice_2], wvl=ccdx_array[HeI_5016_slice_2],line_number=1,line_wvl_init=[612],
                    int_max_init=[12000],fwhm_init=[2],same_width=False)
HeI_5016_model_2.run_lse(absolute_sigma=False)
HeI_5016_model_2.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_5016_slice_3 = slice(918 - xstart_pixel,935 - xstart_pixel)
HeI_5016_model_3 = SpectrumFitSingle(data=helium_spectrum_example[HeI_5016_slice_3], wvl=ccdx_array[HeI_5016_slice_3],line_number=1,line_wvl_init=[925],
                    int_max_init=[6000],fwhm_init=[2],same_width=False)
HeI_5016_model_3.run_lse(absolute_sigma=False)
HeI_5016_model_3.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_5016_slice_4 = slice(1220 - xstart_pixel,1235 - xstart_pixel)
HeI_5016_model_4 = SpectrumFitSingle(data=helium_spectrum_example[HeI_5016_slice_4], wvl=ccdx_array[HeI_5016_slice_4],line_number=1,line_wvl_init=[1227],
                    int_max_init=[2000],fwhm_init=[2],same_width=False)
HeI_5016_model_4.run_lse(absolute_sigma=False)
HeI_5016_model_4.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_D3_slice_1 = slice(353 - xstart_pixel,368 - xstart_pixel)
HeI_D3_model_1 = SpectrumFitSingle(data=helium_spectrum_example[HeI_D3_slice_1], wvl=ccdx_array[HeI_D3_slice_1],line_number=1,line_wvl_init=[359],
                    int_max_init=[8000],fwhm_init=[1],same_width=False)
HeI_D3_model_1.run_lse(absolute_sigma=False)
HeI_D3_model_1.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_D3_slice_2 = slice(730 - xstart_pixel,750 - xstart_pixel)
HeI_D3_model_2 = SpectrumFitSingle(data=helium_spectrum_example[HeI_D3_slice_2], wvl=ccdx_array[HeI_D3_slice_2],line_number=1,line_wvl_init=[738],
                    int_max_init=[4000],fwhm_init=[1],same_width=False)
HeI_D3_model_2.run_lse(absolute_sigma=False)
HeI_D3_model_2.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_D3_slice_3 = slice(1090 - xstart_pixel,1103 - xstart_pixel)
HeI_D3_model_3 = SpectrumFitSingle(data=helium_spectrum_example[HeI_D3_slice_3], wvl=ccdx_array[HeI_D3_slice_3],line_number=1,line_wvl_init=[1099],
                    int_max_init=[14000],fwhm_init=[2],same_width=False)
HeI_D3_model_3.run_lse(absolute_sigma=False)
HeI_D3_model_3.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_5016_pos = np.array([HeI_5016_model.line_wvl_fit[0] for HeI_5016_model in 
                    (HeI_5016_model_1, HeI_5016_model_2, HeI_5016_model_3, HeI_5016_model_4)])
HeI_D3_pos = np.array([HeI_D3_model.line_wvl_fit[0] for HeI_D3_model in 
                    (HeI_D3_model_1, HeI_D3_model_2, HeI_D3_model_3)])
In [ ]:
HeI_5016_pos[1:] - HeI_5016_pos[:-1]
Out[ ]:
array([328.44383383, 313.49414565, 301.67680836])
In [ ]:
HeI_D3_pos[1:] - HeI_D3_pos[:-1]
Out[ ]:
array([379.07832206, 360.38300741])
In [ ]:
hydrogen_im_collection = ImageFileCollection(green_path,
                            glob_include="HydrogenGreen1s_2471*.fit")
hydrogen_im_df = hydrogen_im_collection.summary.to_pandas()
In [ ]:
hydrogen_im_df
Out[ ]:
file simple bitpix naxis naxis1 naxis2 bzero bscale datamin datamax ... ypixsz xbinning ybinning xorgsubf yorgsubf xpossubf ypossubf cblack cwhite swcreate
0 HydrogenGreen1s_24710.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 760 7179 Artemis Capture
1 HydrogenGreen1s_24711.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 763 8029 Artemis Capture
2 HydrogenGreen1s_24712.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 759 8628 Artemis Capture
3 HydrogenGreen1s_24713.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 763 6774 Artemis Capture
4 HydrogenGreen1s_24714.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 763 6873 Artemis Capture
5 HydrogenGreen1s_24715.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 762 10427 Artemis Capture
6 HydrogenGreen1s_24716.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 763 10391 Artemis Capture
7 HydrogenGreen1s_24717.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 762 10278 Artemis Capture
8 HydrogenGreen1s_24718.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 762 10415 Artemis Capture
9 HydrogenGreen1s_24719.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 762 10398 Artemis Capture

10 rows × 24 columns

In [ ]:
hydrogen_image_cube = np.zeros((1040,1392,10))

for ii, row_ in hydrogen_im_df.iterrows():
    hydrogen_image_cube[:,:,ii] = CCDData.read(os.path.join(green_path,row_["file"]),unit="adu").data - bias_dc_green_1s

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204606 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204606 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204618 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204618 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204630 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204630 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204653 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204653 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204664 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204664 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204688 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204688 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204699 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204699 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204711 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204711 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204734 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204734 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204745 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.204745 from DATE-OBS'.
In [ ]:
hydrogen_im_aver = np.mean(hydrogen_image_cube,axis=2)
norm = ImageNormalize(hydrogen_im_aver, stretch=LogStretch())
fig, ax = plt.subplots(figsize=(10,8))
ax.pcolormesh(np.arange(1392),np.arange(1040),hydrogen_im_aver,norm=norm,cmap=cmcm.lajolla, rasterized=True,)
ax.axis("scaled")
ax.axhspan(bottom_pixel,top_pixel,color="red",alpha=0.5)

ax.tick_params(labelsize=16)
In [ ]:
hydrogen_trans_mapcoor = ndimage.map_coordinates(hydrogen_im_aver[testy_slice_mapcoor, testx_slice_mapcoor],(ypos_map_coordinate, xpos_map_coordinate),
                                                order=1)
In [ ]:
fig, ax = plt.subplots(figsize=(16,8),constrained_layout=True)
im = ax.pcolormesh(np.arange(testx_slice_mapcoor.start,testx_slice_mapcoor.stop),np.arange(testy_slice_mapcoor.start,testy_slice_mapcoor.stop),
            hydrogen_trans_mapcoor,norm=norm,cmap=cmcm.lajolla,rasterized=True)
ax.tick_params(labelsize=18)
ax.axhspan(bottom_pixel,top_pixel,color="red",alpha=0.5)
clb_ax = inset_axes(ax,width="5%",height= "100%",loc='lower left',
                bbox_to_anchor=(1.02, 0., 1, 1),
                 bbox_transform=ax.transAxes,
                 borderpad=0)
clb = plt.colorbar(im,pad = 0.05,orientation='vertical',ax=ax,cax=clb_ax)
clb_ax.tick_params(labelsize=16)
clb_ax.yaxis.get_offset_text().set_fontsize(16)
clb_ax.set_ylabel('',fontsize=16)
Out[ ]:
Text(0, 0.5, '')
In [ ]:
hydrogen_spectrum_example = np.mean(hydrogen_trans_mapcoor[bottom_pixel - ystart_pixel:top_pixel - ystart_pixel,:],axis=0)
fig, ax = plt.subplots(figsize=(18,6))
ax.step(ccdx_array, hydrogen_spectrum_example,where="mid")
ax.tick_params(labelsize=16)
In [ ]:
Hbeta_slice_1 = slice(317 - xstart_pixel,332 - xstart_pixel)
Hbeta_model_1 = SpectrumFitSingle(data=hydrogen_spectrum_example[Hbeta_slice_1], wvl=ccdx_array[Hbeta_slice_1],line_number=1,line_wvl_init=[325],
                    int_max_init=[10000],fwhm_init=[1],same_width=False)
Hbeta_model_1.run_lse(absolute_sigma=False)
Hbeta_model_1.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
Hbeta_slice_2 = slice(632 - xstart_pixel,662 - xstart_pixel)
Hbeta_model_2 = SpectrumFitSingle(data=hydrogen_spectrum_example[Hbeta_slice_2], wvl=ccdx_array[Hbeta_slice_2],line_number=1,line_wvl_init=[642],
                    int_max_init=[12000],fwhm_init=[1],same_width=False,mask=[[644,654]])
Hbeta_model_2.run_lse(absolute_sigma=False)
Hbeta_model_2.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
Hbeta_slice_3 = slice(937 - xstart_pixel,958 - xstart_pixel)
Hbeta_model_3 = SpectrumFitSingle(data=hydrogen_spectrum_example[Hbeta_slice_3], wvl=ccdx_array[Hbeta_slice_3],line_number=1,line_wvl_init=[944],
                    int_max_init=[8000],fwhm_init=[1],same_width=False)
Hbeta_model_3.run_lse(absolute_sigma=False)
Hbeta_model_3.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
Hbeta_slice_4 = slice(1227 - xstart_pixel,1247 - xstart_pixel)
Hbeta_model_4 = SpectrumFitSingle(data=hydrogen_spectrum_example[Hbeta_slice_4], wvl=ccdx_array[Hbeta_slice_4],line_number=1,line_wvl_init=[1237],
                    int_max_init=[5000],fwhm_init=[1],same_width=False)
Hbeta_model_4.run_lse(absolute_sigma=False)
Hbeta_model_4.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
Hbeta_pos = np.array([Hbeta_model.line_wvl_fit[0] for Hbeta_model in 
                    (Hbeta_model_1, Hbeta_model_2, Hbeta_model_3, Hbeta_model_4)])
In [ ]:
Hbeta_pos[1:] - Hbeta_pos[:-1]
Out[ ]:
array([316.57369247, 302.81643023, 291.92965481])
In [ ]:
scale_factor = 60
HeI_5016_start_order_guess = 67
HeI_D3_start_order_guess = 57
Hbeta_start_order_guess = 69

order_variation = 10
fig, ax = plt.subplots(figsize=(14,order_variation*2 + 2))

for ii, order_new in enumerate(np.arange(-order_variation, order_variation + 1)):
    HeI_5016_wvl_guess_new = 5015.6783  * np.arange(HeI_5016_start_order_guess + order_new,
                             HeI_5016_start_order_guess + order_new - 4,-1)/scale_factor
    HeI_D3_wvl_guess_new = 5875.621 * np.arange(HeI_D3_start_order_guess + order_new,
                                HeI_D3_start_order_guess + order_new - 3,-1)/scale_factor
    Hbeta_wvl_guess_new = 4861.35 * np.arange(Hbeta_start_order_guess + order_new,
                             Hbeta_start_order_guess + order_new - 4,-1)/scale_factor
    
    poly_fit_5016_param = np.polyfit(HeI_5016_pos, HeI_5016_wvl_guess_new, 2)
    poly_fit_5016_func = np.poly1d(poly_fit_5016_param)
    poly_fit_5016_curve = poly_fit_5016_func(ccdx_array)

    poly_fit_6678_param = np.polyfit(HeI_D3_pos, HeI_D3_wvl_guess_new, 2)
    poly_fit_6678_func = np.poly1d(poly_fit_6678_param)
    poly_fit_6678_curve = poly_fit_6678_func(ccdx_array)

    poly_fit_hbeta_param = np.polyfit(Hbeta_pos, Hbeta_wvl_guess_new, 2)
    poly_fit_hbeta_func = np.poly1d(poly_fit_hbeta_param)
    poly_fit_hbeta_curve = poly_fit_hbeta_func(ccdx_array)

    ln1, = ax.plot(ccdx_array, poly_fit_5016_curve,color="#CC543A",lw=2,
            label=r"He \textsc{i} 5016")
    ax.text(ccdx_array[0], poly_fit_5016_curve[0],"{:2d}".format(HeI_5016_start_order_guess + order_new),
            va="center",ha="right",fontsize=18,color="#CC543A")

    ln2, = ax.plot(ccdx_array, poly_fit_6678_curve,color="#0089A7",lw=2,
            label=r"He \textsc{i} $\rm D_3$")
    ax.text(ccdx_array[0], poly_fit_6678_curve[0],"{:2d}".format(HeI_D3_start_order_guess + order_new),
            va="center",ha="right",fontsize=18,color="#0089A7")

    ln3, = ax.plot(ccdx_array, poly_fit_hbeta_curve,color="#90B44B",lw=2,
            label=r"H$\beta$")
    ax.text(ccdx_array[-1], poly_fit_hbeta_curve[-1],"{:2d}".format(Hbeta_start_order_guess + order_new),
            va="center",ha="left",fontsize=18,color="#90B44B")

    if ii == order_variation*2:
        leg = [ln1,ln2,ln3]
        ax.legend(leg,[leg_.get_label() for leg_ in leg],loc = 'upper right',fontsize = 18,frameon=False)

ax.tick_params(labelsize=16)
In [ ]:
base_order = 60
HeI_5016_start_order_guess = 67
HeI_D3_start_order_guess = 57
Hbeta_start_order_guess = 69

HeI_5016_wvl_guess = 5015.6783 * np.arange(HeI_5016_start_order_guess, HeI_5016_start_order_guess - 4,-1)/base_order
HeI_D3_wvl_guess = 5875.621 * np.arange(HeI_D3_start_order_guess, HeI_D3_start_order_guess - 3,-1)/base_order
Hbeta_wvl_guess = 4861.35 * np.arange(Hbeta_start_order_guess, Hbeta_start_order_guess - 4,-1)/base_order

line_pos_all = np.concatenate([HeI_5016_pos, HeI_D3_pos,Hbeta_pos])
line_wvl_guess_all = np.concatenate([HeI_5016_wvl_guess, HeI_D3_wvl_guess,Hbeta_wvl_guess])

poly_fit_param = np.polyfit(line_pos_all, line_wvl_guess_all, 2)
poly_fit_func = np.poly1d(poly_fit_param)
poly_fit_curve = poly_fit_func(ccdx_array)
green_abs_shift = -5.557
poly_fit_curve_shift = poly_fit_func(ccdx_array - green_abs_shift)

order_variation = 2
fig, ax = plt.subplots(figsize=(14,order_variation*4 + 2))


for ii, order_new in enumerate(np.arange(-order_variation, order_variation + 1)):
    HeI_5016_wvl_guess_new = 5015.6783 * np.arange(HeI_5016_start_order_guess + order_new,
                             HeI_5016_start_order_guess + order_new - 4,-1)/base_order
    HeI_D3_wvl_guess_new = 5875.621 * np.arange(HeI_D3_start_order_guess + order_new,
                                HeI_D3_start_order_guess + order_new - 3,-1)/base_order
    Hbeta_wvl_guess_new = 4861.35 * np.arange(Hbeta_start_order_guess + order_new,
                                Hbeta_start_order_guess + order_new - 4,-1)/base_order

    ln1 = ax.scatter(HeI_5016_pos, HeI_5016_wvl_guess_new,marker= "s",s=200,facecolor="#CC543A",
                alpha=0.9,edgecolor='white',linewidth=2,label=r"He \textsc{i} 5016")
    ln2 = ax.scatter(HeI_D3_pos, HeI_D3_wvl_guess_new,marker= "D",s=200,facecolor="#0089A7",
                alpha=0.9,edgecolor='white',linewidth=2,label=r"He \textsc{i} $\rm D_3$")
    ln3 = ax.scatter(Hbeta_pos, Hbeta_wvl_guess_new,marker= "X",s=200,facecolor="#90B44B",
                alpha=0.9,edgecolor='white',linewidth=2,label=r"H$\beta$")
    if ii == order_variation*2:
        leg = [ln1,ln2,ln3]
        ax.legend(leg,[leg_.get_label() for leg_ in leg],loc = 'upper right',fontsize = 18,frameon=False)

    ax.plot(ccdx_array, poly_fit_curve*base_order/(base_order + order_new),lw=2,ls="--",color="#91989F",zorder=0)


# for plot_order in np.arange(base_order - 5, base_order + 5):
#     ax.scatter()

ax.tick_params(labelsize=16)
ax.set_xlabel("CCD-X [pixel]",fontsize=18)
ax.set_ylabel(r"Wavelength $\lambda$\,[\mbox{\AA}]",fontsize=18)
Out[ ]:
Text(0, 0.5, 'Wavelength $\\lambda$\\,[\\mbox{\\AA}]')
In [ ]:
totality_image_fname = "TotalitySequenceGreen_22717(2).fit"
totality_frame = CCDData.read(os.path.join(green_path, totality_image_fname),unit="adu")

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57986.740718 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57986.740718 from DATE-OBS'.
In [ ]:
totality_image = (totality_frame.data - bias_dc_green_1s)//totality_frame.header["EXPTIME"]
norm_totality = ImageNormalize(totality_image, stretch=LogStretch())
fig, ax = plt.subplots(figsize=(10,8))
ax.pcolormesh(np.arange(1392),np.arange(1040),totality_image,norm=norm_totality,cmap=cmcm.lajolla, rasterized=True)
ax.axis("scaled")
ax.axhspan(bottom_pixel,top_pixel,color="red",alpha=0.5)
ax.tick_params(labelsize=16)
In [ ]:
totality_trans_mapcoor = ndimage.map_coordinates(totality_image[testy_slice_mapcoor, testx_slice_mapcoor],(ypos_map_coordinate, xpos_map_coordinate),
                                                order=1)
In [ ]:
fig, ax = plt.subplots(figsize=(16,8),constrained_layout=True)
im = ax.pcolormesh(np.arange(testx_slice_mapcoor.start,testx_slice_mapcoor.stop),np.arange(testy_slice_mapcoor.start,testy_slice_mapcoor.stop),
            totality_trans_mapcoor,norm=norm_totality,cmap=cmcm.lajolla,rasterized=True)
ax.tick_params(labelsize=18)
ax.axhspan(bottom_pixel,top_pixel,color="red",alpha=0.5)
clb_ax = inset_axes(ax,width="5%",height= "100%",loc='lower left',
                bbox_to_anchor=(1.02, 0., 1, 1),
                 bbox_transform=ax.transAxes,
                 borderpad=0)
clb = plt.colorbar(im,pad = 0.05,orientation='vertical',ax=ax,cax=clb_ax)
clb_ax.tick_params(labelsize=16)
clb_ax.yaxis.get_offset_text().set_fontsize(16)
clb_ax.set_ylabel('',fontsize=16)
Out[ ]:
Text(0, 0.5, '')
In [ ]:
totality_spectrum_example = np.mean(totality_trans_mapcoor[bottom_pixel - ystart_pixel:top_pixel - ystart_pixel,:],axis=0)
fig, ax = plt.subplots(figsize=(18,6))
ax.step(ccdx_array, totality_spectrum_example,where="mid")
ax.tick_params(labelsize=16)

fit_intervals = [slice(60,370),slice(430,720),slice(780,1050),slice(1100,1340)]
for fit_interval in fit_intervals:
    ax.axvspan(fit_interval.start,fit_interval.stop,color="yellow",alpha=0.3)

background_poly_order = 7
wvl_array_to_fit = np.concatenate([ccdx_array[fit_interval] for fit_interval in fit_intervals])
spectra_to_fit = np.concatenate([totality_spectrum_example[fit_interval] for fit_interval in fit_intervals])
background_poly_params = np.polyfit(wvl_array_to_fit,spectra_to_fit,background_poly_order)
background_poly_func = np.poly1d(background_poly_params)
background_poly_curve = background_poly_func(ccdx_array)

ax.plot(ccdx_array, background_poly_curve, label="{:.0f}th".format(background_poly_order))
Out[ ]:
[<matplotlib.lines.Line2D at 0x1741f15d0>]
In [ ]:
totality_spectrum_no_background = totality_spectrum_example - background_poly_curve
fig, ax = plt.subplots(figsize=(18,6),constrained_layout=True)
ax.step(ccdx_array, totality_spectrum_no_background, where="mid")
ax.tick_params(labelsize=18)
ax.grid("on")
In [ ]:
# FeXIV_5303_slice_1 = slice(30,60)
# FeXIV_5303_model_1 = SpectrumFitSingle(data=totality_spectrum_no_background[FeXIV_5303_slice_1], wvl=ccdx_array[FeXIV_5303_slice_1],line_number=1,line_wvl_init=[44],
#                     int_max_init=[300],fwhm_init=[1],same_width=False)
# FeXIV_5303_model_1.run_lse(absolute_sigma=False)
# FeXIV_5303_model_1.plot(plot_fit=True)
In [ ]:
FeXIV_5303_slice_2 = slice(395 - xstart_pixel,425 - xstart_pixel)
FeXIV_5303_model_2 = SpectrumFitSingle(data=totality_spectrum_no_background[FeXIV_5303_slice_2], wvl=ccdx_array[FeXIV_5303_slice_2],line_number=1,line_wvl_init=[407],
                    int_max_init=[5000],fwhm_init=[1],same_width=False)
FeXIV_5303_model_2.run_lse(absolute_sigma=False)
FeXIV_5303_model_2.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
FeXIV_5303_slice_3 = slice(735 - xstart_pixel,765 - xstart_pixel)
FeXIV_5303_model_3 = SpectrumFitSingle(data=totality_spectrum_no_background[FeXIV_5303_slice_3], wvl=ccdx_array[FeXIV_5303_slice_3],line_number=1,line_wvl_init=[749],
                    int_max_init=[3500],fwhm_init=[1],same_width=False)
FeXIV_5303_model_3.run_lse(absolute_sigma=False)
FeXIV_5303_model_3.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
FeXIV_5303_slice_4 = slice(1060 - xstart_pixel,1090 - xstart_pixel)
FeXIV_5303_model_4 = SpectrumFitSingle(data=totality_spectrum_no_background[FeXIV_5303_slice_4], wvl=ccdx_array[FeXIV_5303_slice_4],line_number=1,line_wvl_init=[1075],
                    int_max_init=[2000],fwhm_init=[1],same_width=False)
FeXIV_5303_model_4.run_lse(absolute_sigma=False)
FeXIV_5303_model_4.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
# FeXIV_5303_slice_5 = slice(1375,1392)
# FeXIV_5303_model_5 = SpectrumFitSingle(data=totality_spectrum_example[FeXIV_5303_slice_5], wvl=ccdx_array[FeXIV_5303_slice_5],line_number=1,line_wvl_init=[1383],
#                     int_max_init=[3000],fwhm_init=[1],same_width=False)
# FeXIV_5303_model_5.run_lse(absolute_sigma=False)
# FeXIV_5303_model_5.plot(plot_fit=True)
In [ ]:
for FeXIV_5303_model_ in (FeXIV_5303_model_2,FeXIV_5303_model_3,FeXIV_5303_model_4):
    print(poly_fit_func(FeXIV_5303_model_.line_wvl_fit[0])*base_order/5302.86)

63.01759122090472
62.01778828094451
61.01877577241366
In [ ]:
FeXIV_5303_pos = np.array([FeXIV_5303_model.line_wvl_fit[0] for FeXIV_5303_model in 
                    (FeXIV_5303_model_2, FeXIV_5303_model_3,FeXIV_5303_model_4)])

Figure 15a¶

back to top

In [ ]:
FeXIV_5303_start_order_guess = 63
FeXIV_5303_wvl_guess = 5302.86 * np.arange(FeXIV_5303_start_order_guess, FeXIV_5303_start_order_guess - 3, -1)/base_order

fig, ax = plt.subplots(figsize=(8,7),constrained_layout=True)

ln1 = ax.scatter(HeI_5016_pos, HeI_5016_wvl_guess/10.,marker= "s",s=250,facecolor="#CC543A",
            alpha=0.9,edgecolor='white',linewidth=2,label=r"\textbf{He \textsc{i} 501.6\,nm}")
ln2 = ax.scatter(HeI_D3_pos, HeI_D3_wvl_guess/10.,marker= "D",s=250,facecolor="#0089A7",
            alpha=0.9,edgecolor='white',linewidth=2,label=r"\textbf{He \textsc{i} $\boldsymbol{\rm D_3}$}")
ln3 = ax.scatter(Hbeta_pos, Hbeta_wvl_guess/10.,marker= "X",s=250,facecolor="#90B44B",
            alpha=0.9,edgecolor='white',linewidth=2,label=r"\textbf{H}$\boldsymbol{\beta}$")
ln4 = ax.scatter(FeXIV_5303_pos, FeXIV_5303_wvl_guess/10.,marker= "H",s=250,facecolor="#FFB11B",
            alpha=0.9,edgecolor='white',linewidth=2,label=r"\textbf{Fe \textsc{xiv} 530.3\,nm}")

leg = [ln1,ln2,ln3,ln4]
ax.legend(leg,[leg_.get_label() for leg_ in leg],loc = 'upper right',fontsize = 18,frameon=False)

ax.plot(ccdx_array, poly_fit_curve/10.,lw=2,ls="--",color="#91989F",zorder=0)
ax.text(Hbeta_pos[1], (Hbeta_wvl_guess[1]+20)/10., r"\textbf{{{:2d}}}".format(Hbeta_start_order_guess-1),color="#90B44B",
        fontsize=18,ha="center",va="center")
ax.text(HeI_5016_pos[0], (HeI_5016_wvl_guess[0]+20)/10., r"\textbf{{{:2d}}}".format(HeI_5016_start_order_guess),color="#CC543A",
        fontsize=18,ha="center",va="center")
ax.text(HeI_D3_pos[1], (HeI_D3_wvl_guess[1]+20)/10., r"\textbf{{{:2d}}}".format(HeI_D3_start_order_guess-1),color="#0089A7",
        fontsize=18,ha="center",va="center")
ax.text(FeXIV_5303_pos[0], (FeXIV_5303_wvl_guess[0]+20)/10., r"\textbf{{{:2d}}}".format(FeXIV_5303_start_order_guess),color="#FFB11B",
        fontsize=18,ha="center",va="center")

ax.tick_params(labelsize=16,direction="in",which="both",right=True,top=True)
ax.tick_params(length=8,which="major")
ax.tick_params(length=6,which="minor")
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.set_ylabel(r"\textbf{Reduced Wavelength} $\boldsymbol{m\lambda/60}$ \textbf{[nm]}",fontsize=18)
ax.set_xlabel(r"\textbf{CCD-X [Pixel]}",fontsize=18)
ax.text(0.03,0.04,r"\textbf{Green Detector}",fontsize=18,transform=ax.transAxes,ha="left",va="bottom")
# plt.savefig(fname="../../figs/calib/wvl_calib_green_eclipse_2017.pdf",dpi=300,)
plt.savefig(fname="../../figs/ms/wvl_calib_green.pdf",dpi=300,format="pdf",bbox_inches="tight")
In [ ]:
with h5py.File("../../sav/Eclipse/Wavelength/master_wavelength_curvature_green.h5", 'w') as hf:
    df_wavelength = hf.create_dataset("wavelength_times_order",  data=poly_fit_curve * base_order)
    df_wavelength_abs = hf.create_dataset("wavelength_times_order_shift",  data=poly_fit_curve_shift * base_order)
    df_wavelength.attrs["xstart_pixel"] = xstart_pixel
    df_wavelength.attrs["xend_pixel"] = xend_pixel
In [ ]:
helium_trans_mapcoor.shape
Out[ ]:
(350, 1310)
In [ ]:
HeI_5016_model_2_row_data = np.average(helium_trans_mapcoor[:,HeI_5016_slice_2].reshape(-1,5,HeI_5016_slice_2.stop - HeI_5016_slice_2.start),axis=1)
HeI_5016_model_2_row = SpectrumFitRow(data=HeI_5016_model_2_row_data, wvl=ccdx_array[HeI_5016_slice_2],line_number=1,line_wvl_init=[612],
                    int_max_init=[5000],fwhm_init=[2],same_width=False)
HeI_5016_model_2_row.run_lse()

/Users/yjzhu/Desktop/Solar/MyPy/juanfit.py:221: UserWarning: No input errors, absolute_sigma=False will be used in the Chi2 fitting.
  warn("No input errors, absolute_sigma=False will be used in the Chi2 fitting.")
In [ ]:
HeI_5016_model_2_row.plot_variation()

WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='FWHM $\\Delta \\lambda$'>
In [ ]:
HeI_5016_model_2_row.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>, <AxesSubplot:>,
        <AxesSubplot:>]], dtype=object)
In [ ]:
HeI_D3_model_2_row_data = np.average(helium_trans_mapcoor[:,HeI_D3_slice_2].reshape(-1,5,HeI_D3_slice_2.stop - HeI_D3_slice_2.start),axis=1)
HeI_D3_model_2_row = SpectrumFitRow(data=HeI_D3_model_2_row_data, wvl=ccdx_array[HeI_D3_slice_2],line_number=1,line_wvl_init=[738],
                    int_max_init=[4000],fwhm_init=[1],same_width=False)
HeI_D3_model_2_row.run_lse(absolute_sigma=False)
In [ ]:
HeI_D3_model_2_row.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>, <AxesSubplot:>,
        <AxesSubplot:>]], dtype=object)
In [ ]:
HeI_D3_model_2_row.plot_variation()

WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='FWHM $\\Delta \\lambda$'>
In [ ]:
with h5py.File("../../sav/Eclipse/InstWidth/HeI_D3_2_width_green_curcorr.h5", 'w') as hf:
    df_linewidth = hf.create_dataset("HeI_D3_fwhm",  data=HeI_D3_model_2_row.fwhm_fit)
    df_linewidth_err = hf.create_dataset("HeI_D3_fwhm_err",  data=HeI_D3_model_2_row.fwhm_err)
    df_linewidth.attrs["xstart_pixel"] = xstart_pixel
    df_linewidth.attrs["xend_pixel"] = xend_pixel
In [ ]:
Hbeta_model_2_row_data = np.average(hydrogen_trans_mapcoor[:,Hbeta_slice_2].reshape(-1,5,Hbeta_slice_2.stop - Hbeta_slice_2.start),axis=1)
Hbeta_model_2_row = SpectrumFitRow(data=Hbeta_model_2_row_data, wvl=ccdx_array[Hbeta_slice_2],line_number=1,line_wvl_init=[642],
                    int_max_init=[12000],fwhm_init=[1],same_width=False,mask=[[644,654]])
Hbeta_model_2_row.run_lse()
In [ ]:
Hbeta_model_2_row.plot_variation()

WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='FWHM $\\Delta \\lambda$'>
In [ ]:
Hbeta_model_2_row.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>, <AxesSubplot:>,
        <AxesSubplot:>]], dtype=object)