This notebook shows the wavelength calibration of the red detector. Link to Figure 15b.

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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
In [ ]:
with h5py.File("../../sav/Eclipse/Bias/master_bias_dc_red_1s_proto.h5", 'r') as hf:
    bias_dc_red_1s = hf['image'][:]
In [ ]:
red_path = "../../src/EclipseSpectra2017/MikesData/VaderEclipseDayRed2017aug21/"
helium_im_collection = ImageFileCollection(red_path,
                            glob_include="HeliumrRedpoint01s_920*.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 HeliumrRedpoint01s_9200.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 719 14008 Artemis Capture
1 HeliumrRedpoint01s_9201.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 719 13359 Artemis Capture
2 HeliumrRedpoint01s_9202.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 723 17295 Artemis Capture
3 HeliumrRedpoint01s_9203.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 725 17460 Artemis Capture
4 HeliumrRedpoint01s_9204.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 724 15923 Artemis Capture
5 HeliumrRedpoint01s_9205.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 718 13700 Artemis Capture
6 HeliumrRedpoint01s_9206.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 718 15532 Artemis Capture
7 HeliumrRedpoint01s_9207.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 717 14252 Artemis Capture
8 HeliumrRedpoint01s_9208.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 718 13283 Artemis Capture
9 HeliumrRedpoint01s_9209.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 717 15560 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(red_path,row_["file"]),unit="adu").data - bias_dc_red_1s

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198356 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198356 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198368 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198368 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198380 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198380 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198391 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198391 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198403 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.198403 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))
ax.pcolormesh(np.arange(1392),np.arange(1040),helium_im_aver,norm=norm,cmap=cmcm.lajolla, rasterized=True)
ax.axis("scaled")
bottom_pixel = 485
top_pixel = 495
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_red.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_D3_slice_1 = slice(142 - xstart_pixel,162 - 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=[151],
                    int_max_init=[20000],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(502 - xstart_pixel,522 - 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=[512],
                    int_max_init=[35000],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(882 - xstart_pixel,902 - 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=[890],
                    int_max_init=[30000],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_D3_slice_4 = slice(1287 - xstart_pixel,1302 - xstart_pixel)
HeI_D3_model_4 = SpectrumFitSingle(data=helium_spectrum_example[HeI_D3_slice_4], wvl=ccdx_array[HeI_D3_slice_4],line_number=1,line_wvl_init=[1294],
                    int_max_init=[17000],fwhm_init=[3],same_width=False)
HeI_D3_model_4.run_lse(absolute_sigma=False)
HeI_D3_model_4.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_6678_slice_1 = slice(392 - xstart_pixel,407 - xstart_pixel)
HeI_6678_model_1 = SpectrumFitSingle(data=helium_spectrum_example[HeI_6678_slice_1], wvl=ccdx_array[HeI_6678_slice_1],line_number=1,line_wvl_init=[398],
                    int_max_init=[15000],fwhm_init=[1],same_width=False)
HeI_6678_model_1.run_lse(absolute_sigma=False)
HeI_6678_model_1.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_6678_slice_2 = slice(817 - xstart_pixel,832 - xstart_pixel)
HeI_6678_model_2 = SpectrumFitSingle(data=helium_spectrum_example[HeI_6678_slice_2], wvl=ccdx_array[HeI_6678_slice_2],line_number=1,line_wvl_init=[824],
                    int_max_init=[15000],fwhm_init=[3],same_width=False)
HeI_6678_model_2.run_lse(absolute_sigma=False)
HeI_6678_model_2.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_6678_slice_3 = slice(1272 - xstart_pixel,1287 - xstart_pixel)
HeI_6678_model_3 = SpectrumFitSingle(data=helium_spectrum_example[HeI_6678_slice_3], wvl=ccdx_array[HeI_6678_slice_3],line_number=1,line_wvl_init=[1279],
                    int_max_init=[12000],fwhm_init=[3],same_width=False)
HeI_6678_model_3.run_lse(absolute_sigma=True)
HeI_6678_model_3.plot(plot_fit=True)

/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.")
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
HeI_7066_slice_1 = slice(252 - xstart_pixel,272 - xstart_pixel)
HeI_7066_model_1 = SpectrumFitSingle(data=helium_spectrum_example[HeI_7066_slice_1], wvl=ccdx_array[HeI_7066_slice_1],line_number=1,line_wvl_init=[401],
                    int_max_init=[15000],fwhm_init=[1],same_width=False)
# HeI_7066_model_1.run_lse(absolute_sigma=False)
HeI_7066_model_1.plot(plot_fit=False)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
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, HeI_D3_model_4)])
                    
HeI_6678_pos = np.array([HeI_6678_model.line_wvl_fit[0] for HeI_6678_model in 
                    (HeI_6678_model_1, HeI_6678_model_2, HeI_6678_model_3)])
In [ ]:
HeI_D3_pos
Out[ ]:
array([ 151.42087797,  511.98042575,  890.53249223, 1293.07419638])
In [ ]:
HeI_D3_pos[1:] - HeI_D3_pos[:-1]
Out[ ]:
array([360.55954777, 378.55206649, 402.54170415])
In [ ]:
HeI_6678_pos
Out[ ]:
array([ 399.10069904,  824.0045151 , 1278.63884219])
In [ ]:
HeI_6678_pos[1:] - HeI_6678_pos[:-1]
Out[ ]:
array([424.90381606, 454.63432709])
In [ ]:
hydrogen_im_collection = ImageFileCollection(red_path,
                            glob_include="HydrogenRedpoint1s_947*.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 HydrogenRedpoint1s_9470.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 726 25799 Artemis Capture
1 HydrogenRedpoint1s_9471.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 730 24838 Artemis Capture
2 HydrogenRedpoint1s_9472.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 730 24237 Artemis Capture
3 HydrogenRedpoint1s_9473.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 732 25086 Artemis Capture
4 HydrogenRedpoint1s_9474.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 732 25004 Artemis Capture
5 HydrogenRedpoint1s_9475.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 726 24843 Artemis Capture
6 HydrogenRedpoint1s_9476.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 723 24729 Artemis Capture
7 HydrogenRedpoint1s_9477.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 724 24676 Artemis Capture
8 HydrogenRedpoint1s_9478.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 728 23696 Artemis Capture
9 HydrogenRedpoint1s_9479.fit True 16 2 1392 1040 32768.0 1.0 0.0 65535.0 ... 6.45 1 1 0 0 0 0 726 26298 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(red_path,row_["file"]),unit="adu").data - bias_dc_red_1s

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206852 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206852 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206863 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206863 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206875 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206875 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206887 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206887 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206898 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206898 from DATE-OBS'.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206910 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57987.206910 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=(16,10),constrained_layout=True)
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 [ ]:
fig, ax = plt.subplots(figsize=(18,6))
ax.step(ccdx_array,  hydrogen_trans_mapcoor[96,:],where="mid")
ax.tick_params(labelsize=16)
ax.set_xlim(850,900)
Out[ ]:
(850.0, 900.0)
In [ ]:
fig, ax = plt.subplots(figsize=(18,6))
ax.step(np.arange(1392),  hydrogen_im_aver[446,:],where="mid")
ax.tick_params(labelsize=16)
ax.set_xlim(850,900)
Out[ ]:
(850.0, 900.0)
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 [ ]:
Halpha_slice_1 = slice(47 - xstart_pixel,67 - xstart_pixel)
Halpha_model_1 = SpectrumFitSingle(data=hydrogen_spectrum_example[Halpha_slice_1], wvl=ccdx_array[Halpha_slice_1],line_number=1,line_wvl_init=[56],
                    int_max_init=[6000],fwhm_init=[1],same_width=False)
Halpha_model_1.run_lse(absolute_sigma=False)
Halpha_model_1.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
Halpha_slice_2 = slice(447 - xstart_pixel,467 - xstart_pixel)
Halpha_model_2 = SpectrumFitSingle(data=hydrogen_spectrum_example[Halpha_slice_2], wvl=ccdx_array[Halpha_slice_2],line_number=1,line_wvl_init=[455],
                    int_max_init=[17000],fwhm_init=[1],same_width=False)
Halpha_model_2.run_lse(absolute_sigma=False)
Halpha_model_2.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
Halpha_slice_3 = slice(867 - xstart_pixel,887 - xstart_pixel)
Halpha_model_3 = SpectrumFitSingle(data=hydrogen_spectrum_example[Halpha_slice_3], wvl=ccdx_array[Halpha_slice_3],line_number=1,line_wvl_init=[877],
                    int_max_init=[30000],fwhm_init=[1],same_width=False)
Halpha_model_3.run_lse(absolute_sigma=False)
Halpha_model_3.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
Halpha_slice_4 = slice(1317 - xstart_pixel,1337 - xstart_pixel)
Halpha_model_4 = SpectrumFitSingle(data=hydrogen_spectrum_example[Halpha_slice_4], wvl=ccdx_array[Halpha_slice_4],line_number=1,line_wvl_init=[1327],
                    int_max_init=[40000],fwhm_init=[1],same_width=False)
Halpha_model_4.run_lse(absolute_sigma=False)
Halpha_model_4.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
Halpha_pos = np.array([Halpha_model.line_wvl_fit[0] for Halpha_model in 
                    (Halpha_model_1, Halpha_model_2, Halpha_model_3, Halpha_model_4)])
In [ ]:
Halpha_pos[1:] - Halpha_pos[:-1]
Out[ ]:
array([399.02799639, 420.70144803, 450.59173664])
In [ ]:
scale_factor = 48
HeI_D3_start_order_guess = 55
HeI_6678_start_order_guess = 49
Halpha_start_order_guess = 50

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_D3_wvl_guess_new = 5875.621 * np.arange(HeI_D3_start_order_guess + order_new,
                             HeI_D3_start_order_guess + order_new + 4 )/scale_factor
    HeI_6678_wvl_guess_new = 6678.151 * np.arange(HeI_6678_start_order_guess + order_new,
                                HeI_6678_start_order_guess + order_new +3)/scale_factor
    Halpha_wvl_guess_new = 6562.79  * np.arange(Halpha_start_order_guess + order_new,
                             Halpha_start_order_guess + order_new + 4 )/scale_factor
    
    poly_fit_d3_param = np.polyfit(HeI_D3_pos, HeI_D3_wvl_guess_new, 2)
    poly_fit_d3_func = np.poly1d(poly_fit_d3_param)
    poly_fit_d3_curve = poly_fit_d3_func(ccdx_array)

    poly_fit_6678_param = np.polyfit(HeI_6678_pos, HeI_6678_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_halpha_param = np.polyfit(Halpha_pos, Halpha_wvl_guess_new, 2)
    poly_fit_halpha_func = np.poly1d(poly_fit_halpha_param)
    poly_fit_halpha_curve = poly_fit_halpha_func(ccdx_array)

    ln1, = ax.plot(ccdx_array, poly_fit_d3_curve,color="#CC543A",lw=2,
            label=r"He \textsc{i} $\rm D_3$")
    ax.text(ccdx_array[0], poly_fit_d3_curve[0],"{:2d}".format(HeI_D3_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} 6678")
    ax.text(ccdx_array[0], poly_fit_6678_curve[0],"{:2d}".format(HeI_6678_start_order_guess + order_new),
            va="center",ha="right",fontsize=18,color="#0089A7")

    ln3, = ax.plot(ccdx_array, poly_fit_halpha_curve,color="#90B44B",lw=2,
            label=r"H$\alpha$")
    ax.text(ccdx_array[-1], poly_fit_halpha_curve[-1],"{:2d}".format(Halpha_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 left',fontsize = 18,frameon=False)

ax.tick_params(labelsize=16)
In [ ]:
base_order = 50
HeI_D3_start_order_guess = 55
HeI_6678_start_order_guess = 49
Halpha_start_order_guess = 49

HeI_D3_wvl_guess = 5875.621 * np.arange(HeI_D3_start_order_guess, HeI_D3_start_order_guess + 4)/base_order
HeI_6678_wvl_guess = 6678.151 * np.arange(HeI_6678_start_order_guess, HeI_6678_start_order_guess + 3)/base_order
Halpha_wvl_guess = 6562.79 * np.arange(Halpha_start_order_guess, Halpha_start_order_guess + 4)/base_order

line_pos_all = np.concatenate([HeI_D3_pos, HeI_6678_pos,Halpha_pos])
line_wvl_guess_all = np.concatenate([HeI_D3_wvl_guess, HeI_6678_wvl_guess,Halpha_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)
red_abs_shift = 1.173
poly_fit_curve_shift = poly_fit_func(ccdx_array - red_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_D3_wvl_guess_new = 5875.621 * np.arange(HeI_D3_start_order_guess + order_new,
                             HeI_D3_start_order_guess + order_new + 4 )/base_order
    HeI_6678_wvl_guess_new = 6678.151 * np.arange(HeI_6678_start_order_guess + order_new,
                                HeI_6678_start_order_guess + order_new +3)/base_order
    Halpha_wvl_guess_new = 6562.79 * np.arange(Halpha_start_order_guess + order_new,
                                Halpha_start_order_guess + order_new +4)/base_order

    ln1 = ax.scatter(HeI_D3_pos, HeI_D3_wvl_guess_new,marker= "s",s=200,facecolor="#CC543A",
                alpha=0.9,edgecolor='white',linewidth=2,label=r"He \textsc{i} $\rm D_3$")
    ln2 = ax.scatter(HeI_6678_pos, HeI_6678_wvl_guess_new,marker= "D",s=200,facecolor="#0089A7",
                alpha=0.9,edgecolor='white',linewidth=2,label=r"He \textsc{i} 6678")
    ln3 = ax.scatter(Halpha_pos, Halpha_wvl_guess_new,marker= "X",s=200,facecolor="#90B44B",
                alpha=0.9,edgecolor='white',linewidth=2,label=r"H$\alpha$")
    if ii == order_variation*2:
        leg = [ln1,ln2,ln3]
        ax.legend(leg,[leg_.get_label() for leg_ in leg],loc = 'upper left',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 = "TotalitySequenceRed_7723.fit"
totality_frame = CCDData.read(os.path.join(red_path, totality_image_fname),unit="adu")

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57986.740764 from DATE-OBS'. [astropy.wcs.wcs]
WARNING:astropy:FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57986.740764 from DATE-OBS'.
In [ ]:
totality_image = (totality_frame.data - bias_dc_red_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(0,210),slice(260,610),slice(660,1020),slice(1080,1300)]
for fit_interval in fit_intervals:
    ax.axvspan(fit_interval.start + xstart_pixel,fit_interval.stop + xstart_pixel,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 0x18b25a350>]
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 [ ]:
FeX_6374_slice_1 = slice(257 - xstart_pixel,282 - xstart_pixel)
FeX_6374_model_1 = SpectrumFitSingle(data=totality_spectrum_no_background[FeX_6374_slice_1], wvl=ccdx_array[FeX_6374_slice_1],line_number=1,line_wvl_init=[269],
                    int_max_init=[700],fwhm_init=[1],same_width=False)
FeX_6374_model_1.run_lse(absolute_sigma=False)
FeX_6374_model_1.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
poly_fit_func(FeX_6374_model_1.line_wvl_fit[0])*base_order/6374.51
Out[ ]:
51.00170014611809
In [ ]:
vac_to_air(6376.2900*u.Angstrom)
Out[ ]:
$6374.4306 \; \mathrm{\mathring{A}}$
In [ ]:
FeX_6374_slice_2 = slice(652 - xstart_pixel,682 - xstart_pixel)
FeX_6374_model_2 = SpectrumFitSingle(data=totality_spectrum_no_background[FeX_6374_slice_2], wvl=ccdx_array[FeX_6374_slice_2],line_number=1,line_wvl_init=[667],
                    int_max_init=[1000],fwhm_init=[1],same_width=False)
FeX_6374_model_2.run_lse(absolute_sigma=False)
FeX_6374_model_2.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
poly_fit_func(FeX_6374_model_2.line_wvl_fit[0])*base_order/6374.51
Out[ ]:
52.00141905680704
In [ ]:
FeX_6374_slice_3 = slice(1072 - xstart_pixel,1107 - xstart_pixel)
FeX_6374_model_3 = SpectrumFitSingle(data=totality_spectrum_no_background[FeX_6374_slice_3], wvl=ccdx_array[FeX_6374_slice_3],line_number=1,line_wvl_init=[1089],
                    int_max_init=[8000],fwhm_init=[3],same_width=False)
FeX_6374_model_3.run_lse(absolute_sigma=False)
FeX_6374_model_3.plot(plot_fit=True)
Out[ ]:
<AxesSubplot:ylabel='Intensity'>
In [ ]:
poly_fit_func(FeX_6374_model_3.line_wvl_fit[0])*base_order/6374.51
Out[ ]:
52.9998944568259
In [ ]:
FeX_6374_pos = np.array([FeX_6374_model.line_wvl_fit[0] for FeX_6374_model in 
                    (FeX_6374_model_1, FeX_6374_model_2, FeX_6374_model_3)])

Figure 15b¶

back to top

In [ ]:
FeX_6374_start_order_guess = 51
FeX_6374_wvl_guess = 6374.51 * np.arange(FeX_6374_start_order_guess, FeX_6374_start_order_guess + 3)/base_order

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

ln1 = ax.scatter(HeI_D3_pos, HeI_D3_wvl_guess/10.,marker= "s",s=250,facecolor="#CC543A",
            alpha=0.9,edgecolor='white',linewidth=2,label=r"\textbf{He \textsc{i}} $\boldsymbol{\rm D_3}$")
ln2 = ax.scatter(HeI_6678_pos, HeI_6678_wvl_guess/10.,marker= "D",s=250,facecolor="#0089A7",
            alpha=0.9,edgecolor='white',linewidth=2,label=r"\textbf{He \textsc{i} 667.8\,nm}")
ln3 = ax.scatter(Halpha_pos, Halpha_wvl_guess/10.,marker= "X",s=250,facecolor="#90B44B",
            alpha=0.9,edgecolor='white',linewidth=2,label=r"\textbf{H}$\boldsymbol{\alpha}$")
ln4 = ax.scatter(FeX_6374_pos, FeX_6374_wvl_guess/10.,marker= "H",s=250,facecolor="#FFB11B",
            alpha=0.9,edgecolor='white',linewidth=2,label=r"\textbf{Fe \textsc{x} 637.4\,nm}")

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

ax.plot(ccdx_array, poly_fit_curve/10.,lw=2,ls="--",color="#91989F",zorder=0)
ax.text(Halpha_pos[1], (Halpha_wvl_guess[1]+20)/10., r"\textbf{{{:2d}}}".format(Halpha_start_order_guess+1),color="#90B44B",
        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="#CC543A",
        fontsize=18,ha="center",va="center")
ax.text(HeI_6678_pos[1], (HeI_6678_wvl_guess[1]+20)/10., r"\textbf{{{:2d}}}".format(HeI_6678_start_order_guess+1),color="#0089A7",
        fontsize=18,ha="center",va="center")
ax.text(FeX_6374_pos[1], (FeX_6374_wvl_guess[1]+20)/10., r"\textbf{{{:2d}}}".format(FeX_6374_start_order_guess+1),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} $\boldsymbol{m\lambda/50}$ \textbf{[nm]}",fontsize=18)
ax.set_xlabel(r"\textbf{CCD-X [Pixel]}",fontsize=18)
ax.text(0.97,0.04,r"\textbf{Red Detector}",fontsize=18,transform=ax.transAxes,ha="right",va="bottom")
# plt.savefig(fname="../../figs/calib/wvl_calib_red_eclipse_2017.pdf",dpi=300)
plt.savefig(fname="../../figs/ms/wvl_calib_red.pdf",dpi=300,format="pdf",bbox_inches="tight")
In [ ]:
with h5py.File("../../sav/Eclipse/Wavelength/master_wavelength_curvature_red.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 [ ]:
fig, ax = plt.subplots(figsize=(8,6),constrained_layout=True)

ax.step(ccdx_array[HeI_D3_slice_2],np.average(helium_trans_mapcoor[:5,HeI_D3_slice_2],axis=0),where="mid")
ax.step(np.arange(HeI_D3_slice_2.start+xstart_pixel, HeI_D3_slice_2.stop+xstart_pixel), 
        np.average(helium_im_aver[350:355,HeI_D3_slice_2.start+xstart_pixel:HeI_D3_slice_2.stop+xstart_pixel],axis=0),where="mid")
Out[ ]:
[<matplotlib.lines.Line2D at 0x193c5ff10>]
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=[512],
                    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_red_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 [ ]:
HeI_6678_model_2_row_data = np.average(helium_trans_mapcoor[:,HeI_6678_slice_2].reshape(-1,5,HeI_6678_slice_2.stop - HeI_6678_slice_2.start),axis=1)
HeI_6678_model_2_row = SpectrumFitRow(data=HeI_6678_model_2_row_data, wvl=ccdx_array[HeI_6678_slice_2],line_number=1,line_wvl_init=[824],
                    int_max_init=[4000],fwhm_init=[1],same_width=False)
HeI_6678_model_2_row.run_lse(absolute_sigma=False)
In [ ]:
HeI_6678_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_6678_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_6678_model_2_row.plot_variation(var="int")

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='Total Intensity $I_0$'>
In [ ]:
with h5py.File("../../sav/Eclipse/InstWidth/HeI_6678_2_width_red_curcorr.h5", 'w') as hf:
    df_linewidth = hf.create_dataset("HeI_6678_fwhm",  data=HeI_6678_model_2_row.fwhm_fit)
    df_linewidth_err = hf.create_dataset("HeI_6678_fwhm_err",  data=HeI_6678_model_2_row.fwhm_err)
    df_linewidth.attrs["xstart_pixel"] = xstart_pixel
    df_linewidth.attrs["xend_pixel"] = xend_pixel