This notebook compares line widths variations measured by 3PAMIS to a great number of previous observations in open- and closed-field structures. Link to Figure 10.
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import numpy as np
import pandas as pd
# import juanfit
import matplotlib
from matplotlib import rcParams
matplotlib.use('pgf')
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
from PyPDF2 import PdfFileMerger
rcParams['pgf.texsystem'] = 'pdflatex'
rcParams['pgf.preamble'] = r'\usepackage[T1]{fontenc} \usepackage[utf8x]{inputenc}' + \
r'\usepackage{hyperref} \hypersetup{hidelinks,' + \
r'colorlinks=true, urlcolor=blue, linkcolor=blue}' + \
r'\usepackage{amsmath} \usepackage{amssymb}'
rcParams['text.usetex'] = True
rcParams['font.family'] = 'serif'
rcParams['font.serif'] = 'CMU serif'
rcParams['axes.linewidth'] = 1.5
import os
import astropy.units as u
import astropy.constants as const
from cycler import cycler
from scipy.io import readsav
import h5py
import cmcrameri.cm as cmcm
# # default_cycler = (cycler(color=['#1F77B4','#AEC7E8','#FF7F0E','#FFBB78','#2CA02C','#98DF8A',
# '#D62728','#FF9896','#9467BD','#C5B0D5','#8C564B','#C49C94',
# '#E377C2','#F7B6D2','#7F7F7F','#C7C7C7','#BCBD22','#DBDB8D',
# '#17BECF','#9EDAE5',]))
plt.rcParams["axes.prop_cycle"] = plt.cycler("color", plt.cm.tab20.colors)
# plt.rcParams["axes.prop_cycle"] = plt.cycler("color", cmcm.batlow(np.linspace(0,1,20)))
c = const.c.cgs.value
k_B = const.k_B.cgs.value
amu = const.u.cgs.value
with h5py.File("../../sav/Eclipse/FitResults/FeXIV_FeX_cuts.h5","r") as hf:
FeXIV_ss_r = hf["df_FeXIV_ss_r"][:]
FeXIV_ss_veff = hf["df_FeXIV_ss_veff"][:]
FeXIV_ss_veff_err = hf["df_FeXIV_ss_veff_err"][:]
FeXIV_ar2_r = hf["df_FeXIV_ar2_r"][:]
FeXIV_ar2_veff = hf["df_FeXIV_ar2_veff"][:]
FeXIV_ar2_veff_err = hf["df_FeXIV_ar2_veff_err"][:]
FeX_ar2_r = hf["df_FeX_ar2_r"][:]
FeX_ar2_veff = hf["df_FeX_ar2_veff"][:]
FeX_ar2_veff_err = hf["df_FeX_ar2_veff_err"][:]
FeX_ns_r = hf["df_FeX_ns_r"][:]
FeX_ns_veff = hf["df_FeX_ns_veff"][:]
FeX_ns_veff_err = hf["df_FeX_ns_veff_err"][:]
FeX_ch_r = hf["df_FeX_ch_r"][:]
FeX_ch_veff = hf["df_FeX_ch_veff"][:]
FeX_ch_veff_err = hf["df_FeX_ch_veff_err"][:]
FeX_ss_r = hf["df_FeX_ss_r"][:]
FeX_ss_veff = hf["df_FeX_ss_veff"][:]
FeX_ss_veff_err = hf["df_FeX_ss_veff_err"][:]
df_Wilhelm2005_MgX_CDS = pd.read_csv("../../sav/All_digitize/Wilhelm2005_MgX_CDS_dlamb_pm.csv")
df_Wilhelm2005_MgX_CDS.columns = df_Wilhelm2005_MgX_CDS.columns.str.replace(' ', '')
Wilhelm2005_MgX_r = df_Wilhelm2005_MgX_CDS["x"]
Wilhelm2005_MgX_veff = df_Wilhelm2005_MgX_CDS["y"]*0.01/624.941*c/1e5
df_Wilhelm2005_MgX_SUMER = pd.read_csv("../../sav/All_digitize/Wilhelm2005_MgX_625_SUMER_v1e.csv")
df_Wilhelm2005_MgX_SUMER.columns = df_Wilhelm2005_MgX_SUMER.columns.str.replace(' ', '')
Wilhelm2005_MgX_SUMER_r = df_Wilhelm2005_MgX_SUMER["x"]
Wilhelm2005_MgX_SUMER_veff = df_Wilhelm2005_MgX_SUMER["y"]
df_Wilhelm2005_FeXII_SUMER = pd.read_csv("../../sav/All_digitize/Wilhelm2005_FeXII_1242_SUMER_v1e.csv")
df_Wilhelm2005_FeXII_SUMER.columns = df_Wilhelm2005_FeXII_SUMER.columns.str.replace(' ', '')
Wilhelm2005_FeXII_SUMER_r = df_Wilhelm2005_FeXII_SUMER["x"]
Wilhelm2005_FeXII_SUMER_veff = df_Wilhelm2005_FeXII_SUMER["y"]
df_Hassler1990_MgX_609 = pd.read_csv("../../sav/All_digitize/Hassler1990_MgX_609_vGauss.csv")
df_Hassler1990_MgX_609.columns = df_Hassler1990_MgX_609.columns.str.replace(' ', '')
Hassler1990_MgX_609_r = df_Hassler1990_MgX_609["x"]
Hassler1990_MgX_609_veff = df_Hassler1990_MgX_609["y"]/np.sqrt(4*np.log(2))
df_DelZanna2019_FeXIII_202 = pd.read_csv("../../sav/All_digitize/DelZanna2019_FeXIII_202_EIS_vnth_Te1dot4MK.csv")
df_DelZanna2019_FeXIII_202.columns = df_DelZanna2019_FeXIII_202.columns.str.replace(' ', '')
DelZanna2019_FeXIII_202_r = df_DelZanna2019_FeXIII_202["x"]
DelZanna2019_FeXIII_202_veff = df_DelZanna2019_FeXIII_202["y"]*1e5
DelZanna2019_FeXIII_202_veff = np.sqrt(DelZanna2019_FeXIII_202_veff**2 + 2*k_B*1.4e6/55.85/amu)/1e5
df_Gupta2017_FeX_184 = pd.read_csv("../../sav/All_digitize/Gupta2017_FeX_184_vnth_Mmabovelimb_AR1.csv")
df_Gupta2017_FeX_184.sort_values(by=['x'], inplace=True)
df_Gupta2017_FeX_184.columns = df_Gupta2017_FeX_184.columns.str.replace(' ', '')
Gupta2017_FeX_184_r = df_Gupta2017_FeX_184["x"]/696. + 1
Gupta2017_FeX_184_veff = np.sqrt((df_Gupta2017_FeX_184["y"]*1e5)**2 + 2*k_B*1e6/55.85/amu)/1e5
df_Gupta2017_FeXIV_274 = pd.read_csv("../../sav/All_digitize/Gupta2017_FeXIV_274_vnth_Mmabovelimb_AR1.csv")
df_Gupta2017_FeXIV_274.sort_values(by=['x'], inplace=True)
df_Gupta2017_FeXIV_274.columns = df_Gupta2017_FeXIV_274.columns.str.replace(' ', '')
Gupta2017_FeXIV_274_r = df_Gupta2017_FeXIV_274["x"]/696. + 1
Gupta2017_FeXIV_274_veff = np.sqrt((df_Gupta2017_FeXIV_274["y"]*1e5)**2 + 2*k_B*2e6/55.85/amu)/1e5
df_Gupta2019_FeX_184 = pd.read_csv("../../sav/All_digitize/Gupta2019_FeX_184_veff.csv")
df_Gupta2019_FeX_184.sort_values(by=['x'], inplace=True)
df_Gupta2019_FeX_184.columns = df_Gupta2019_FeX_184.columns.str.replace(' ', '')
Gupta2019_FeX_184_r = df_Gupta2019_FeX_184["x"]
Gupta2019_FeX_184_veff = df_Gupta2019_FeX_184["y"]
df_Gupta2019_FeXII_193 = pd.read_csv("../../sav/All_digitize/Gupta2019_FeXII_193_veff.csv")
df_Gupta2019_FeXII_193.sort_values(by=['x'], inplace=True)
df_Gupta2019_FeXII_193.columns = df_Gupta2019_FeXII_193.columns.str.replace(' ', '')
Gupta2019_FeXII_193_r = df_Gupta2019_FeXII_193["x"]
Gupta2019_FeXII_193_veff = df_Gupta2019_FeXII_193["y"]
df_Hahn2014_FeXII_195 = pd.read_csv("../../sav/All_digitize/Hahn2014_FeXII_195_veff.csv")
df_Hahn2014_FeXII_195.sort_values(by=['x'], inplace=True)
df_Hahn2014_FeXII_195.columns = df_Hahn2014_FeXII_195.columns.str.replace(' ', '')
Hahn2014_FeXII_195_r = df_Hahn2014_FeXII_195["x"]
Hahn2014_FeXII_195_veff = df_Hahn2014_FeXII_195["y"]
idl_sav_Landi2003 = readsav("../../sav/Landi2003/vnth_north_withr.save",verbose=True)
Landi2003_r = idl_sav_Landi2003["rnorth"]
Landi2003_NeVIII_veff = np.sqrt((idl_sav_Landi2003["vne8"]*1e5)**2 + 2*k_B*10**idl_sav_Landi2003["temp1"]/20.18/amu)/1e5
Landi2003_SiXI_veff = np.sqrt((idl_sav_Landi2003["vsi11"]*1e5)**2 + 2*k_B*10**idl_sav_Landi2003["temp1"]/28.09/amu)/1e5
Landi2003_NaIX_veff = np.sqrt((idl_sav_Landi2003["vna9"]*1e5)**2 + 2*k_B*10**idl_sav_Landi2003["temp1"]/22.99/amu)/1e5
Landi2003_MgIX_veff = np.sqrt((idl_sav_Landi2003["vmg9"]*1e5)**2 + 2*k_B*10**idl_sav_Landi2003["temp1"]/24.31/amu)/1e5
-------------------------------------------------- Date: Tue Sep 12 14:17:03 2023 User: yjzhu Host: Yingjies-MacBook-Pro.local -------------------------------------------------- Format: 11 Architecture: x86_64 Operating System: darwin IDL Version: 8.2 -------------------------------------------------- Successfully read 25 records of which: - 1 are of type TIMESTAMP - 1 are of type VERSION - 22 are of type VARIABLE -------------------------------------------------- Available variables: - vo6 [<class 'numpy.ndarray'>] - vne8 [<class 'numpy.ndarray'>] - vna9 [<class 'numpy.ndarray'>] - vmg9 [<class 'numpy.ndarray'>] - vmg10 [<class 'numpy.ndarray'>] - vsi11 [<class 'numpy.ndarray'>] - vs10 [<class 'numpy.ndarray'>] - vfe11 [<class 'numpy.ndarray'>] - vfe12 [<class 'numpy.ndarray'>] - dvo6 [<class 'numpy.ndarray'>] - dvne8 [<class 'numpy.ndarray'>] - dvna9 [<class 'numpy.ndarray'>] - dvmg9 [<class 'numpy.ndarray'>] - dvmg10 [<class 'numpy.ndarray'>] - dvsi11 [<class 'numpy.ndarray'>] - dvs10 [<class 'numpy.ndarray'>] - dvfe11 [<class 'numpy.ndarray'>] - dvfe12 [<class 'numpy.ndarray'>] - rnorth [<class 'numpy.ndarray'>] - north [<class 'numpy.ndarray'>] - dnorth [<class 'numpy.ndarray'>] - temp1 [<class 'numpy.ndarray'>] --------------------------------------------------
df_Doschek2000 = pd.read_excel("../../sav/All_digitize/Doschek2000_vnth_Te1dot3MK.xlsx")
Doschek2000_r = df_Doschek2000["r"]
Doschek2000_OVI_veff = np.sqrt((df_Doschek2000["OVI_1032"]*1e5)**2 + 2*k_B*1.3e6/15.999/amu)/1e5
Doschek2000_NeVIII_veff = np.sqrt((df_Doschek2000["NeVIII_770"]*1e5)**2 + 2*k_B*1.3e6/20.18/amu)/1e5
Doschek2000_MgX_veff = np.sqrt((df_Doschek2000["MgX_609"]*1e5)**2 + 2*k_B*1.3e6/24.305/amu)/1e5
Doschek2000_SiXII_veff = np.sqrt((df_Doschek2000["SiXII_499"]*1e5)**2 + 2*k_B*1.3e6/28.09/amu)/1e5
Doschek2000_FeX_veff = np.sqrt((df_Doschek2000["FeX_1028"]*1e5)**2 + 2*k_B*1.3e6/55.85/amu)/1e5
Doschek2000_SiVIII_veff = np.sqrt((df_Doschek2000["SiVIII_1445"]*1e5)**2 + 2*k_B*1.3e6/28.09/amu)/1e5
df_Singh2006_FeXIV = pd.read_csv("../../sav/All_digitize/Singh2006_FeXIV5303_FWHMangstrom_Arcsecabovelimb.csv")
df_Singh2006_FeXIV.columns = df_Singh2006_FeXIV.columns.str.replace(' ', '')
df_Singh2006_FeXIV.sort_values(by=['x'], inplace=True)
Singh2006_FeXIV_r = df_Singh2006_FeXIV["x"]/960. + 1.
Singh2006_FeXIV_veff = df_Singh2006_FeXIV["y"]/5303.*c/1e5/np.sqrt(4*np.log(2))
df_Singh2006_FeX = pd.read_csv("../../sav/All_digitize/Singh2006_FeX6374_FWHMangstrom_Arcsecabovelimb.csv")
df_Singh2006_FeX.columns = df_Singh2006_FeX.columns.str.replace(' ', '')
df_Singh2006_FeX.sort_values(by=['x'], inplace=True)
Singh2006_FeX_r = df_Singh2006_FeX["x"]/960. + 1.
Singh2006_FeX_veff = df_Singh2006_FeX["y"]/6374.*c/1e5/np.sqrt(4*np.log(2))
df_Singh2011_FeXIV_equatorial = pd.read_csv("../../sav/All_digitize/Singh2011_FeXIV_5303_veffFWHM_equatorial.csv")
df_Singh2011_FeXIV_equatorial.columns = df_Singh2011_FeXIV_equatorial.columns.str.replace(' ', '')
df_Singh2011_FeXIV_equatorial.sort_values(by=['x'], inplace=True)
Singh2011_FeXIV_equatorial_r = df_Singh2011_FeXIV_equatorial["x"]
Singh2011_FeXIV_equatorial_veff = df_Singh2011_FeXIV_equatorial["y"]/np.sqrt(4*np.log(2))
df_Singh2011_FeX_equatorial = pd.read_csv("../../sav/All_digitize/Singh2011_FeX_6374_veffFWHM_equatorial.csv")
df_Singh2011_FeX_equatorial.columns = df_Singh2011_FeX_equatorial.columns.str.replace(' ', '')
df_Singh2011_FeX_equatorial.sort_values(by=['x'], inplace=True)
Singh2011_FeX_equatorial_r = df_Singh2011_FeX_equatorial["x"]
Singh2011_FeX_equatorial_veff = df_Singh2011_FeX_equatorial["y"]/np.sqrt(4*np.log(2))
df_Singh2011_FeXIV_pole = pd.read_csv("../../sav/All_digitize/Singh2011_FeXIV_5303_veffFWHM_pole.csv")
df_Singh2011_FeXIV_pole.columns = df_Singh2011_FeXIV_pole.columns.str.replace(' ', '')
df_Singh2011_FeXIV_pole.sort_values(by=['x'], inplace=True)
Singh2011_FeXIV_pole_r = df_Singh2011_FeXIV_pole["x"]
Singh2011_FeXIV_pole_veff = df_Singh2011_FeXIV_pole["y"]/np.sqrt(4*np.log(2))
df_Singh2011_FeX_pole = pd.read_csv("../../sav/All_digitize/Singh2011_FeX_6374_veffFWHM_pole.csv")
df_Singh2011_FeX_pole.columns = df_Singh2011_FeX_pole.columns.str.replace(' ', '')
df_Singh2011_FeX_pole.sort_values(by=['x'], inplace=True)
Singh2011_FeX_pole_r = df_Singh2011_FeX_pole["x"]
Singh2011_FeX_pole_veff = df_Singh2011_FeX_pole["y"]/np.sqrt(4*np.log(2))
df_Koutchmy2005_FeXIV = pd.read_csv("../../sav/All_digitize/Koutchmy2005_FeXIV_5303_FWHMangstrom.csv")
df_Koutchmy2005_FeXIV.columns = df_Koutchmy2005_FeXIV.columns.str.replace(' ', '')
df_Koutchmy2005_FeXIV.sort_values(by=['x'], inplace=True)
Koutchmy2005_FeXIV_r = df_Koutchmy2005_FeXIV["x"] + 1
Koutchmy2005_FeXIV_veff = df_Koutchmy2005_FeXIV["y"]/5303*c/1e5/np.sqrt(4*np.log(2))
df_Mierla2008_FeXIV = pd.read_csv("../../sav/All_digitize/Mierla2008_FeXIV_5303_LASCO_Teff_1996.csv")
df_Mierla2008_FeXIV.columns = df_Mierla2008_FeXIV.columns.str.replace(' ', '')
df_Mierla2008_FeXIV.sort_values(by=['x'], inplace=True)
Mierla2008_FeXIV_r = df_Mierla2008_FeXIV["x"]
Mierla2008_FeXIV_veff = np.sqrt(2*k_B*df_Mierla2008_FeXIV["y"]*1e6/55.85/amu)/1e5
df_Mierla2008_FeX = pd.read_csv("../../sav/All_digitize/Mierla2008_FeX_6374_LASCO_Teff_1996.csv")
df_Mierla2008_FeX.columns = df_Mierla2008_FeX.columns.str.replace(' ', '')
df_Mierla2008_FeX.sort_values(by=['x'], inplace=True)
Mierla2008_FeX_r = df_Mierla2008_FeX["x"]
Mierla2008_FeX_veff = np.sqrt(2*k_B*df_Mierla2008_FeX["y"]*1e6/55.85/amu)/1e5
df_Mierla2008_FeXIV_1998 = pd.read_csv("../../sav/All_digitize/Mierla2008_FeXIV_5303_LASCO_FHWM_nm_1998.csv")
df_Mierla2008_FeXIV_1998.columns = df_Mierla2008_FeXIV_1998.columns.str.replace(' ', '')
df_Mierla2008_FeXIV_1998.sort_values(by=['x'], inplace=True)
Mierla2008_FeXIV_1998_r = df_Mierla2008_FeXIV_1998["x"]
Mierla2008_FeXIV_1998_veff = df_Mierla2008_FeXIV_1998["y"]/530.3*c/1e5/np.sqrt(4*np.log(2))
df_Bazin2013_FeXIV = pd.read_csv("../../sav/All_digitize/Bazin2013_FeXIV_5303_FWHMangstrom_HeightMm.csv")
df_Bazin2013_FeXIV.columns = df_Bazin2013_FeXIV.columns.str.replace(' ', '')
df_Bazin2013_FeXIV.sort_values(by=['x'], inplace=True)
Bazin2013_FeXIV_r = df_Bazin2013_FeXIV["x"]/696. + 1
Bazin2013_FeXIV_veff = df_Bazin2013_FeXIV["y"]/5303*c/1e5/np.sqrt(4*np.log(2))
df_Contesse2004_FeXIV = pd.read_csv("../../sav/All_digitize/Contesse2004_FeXIV_5303_FWHMnm_Arcminabovelimb.csv")
df_Contesse2004_FeXIV.columns = df_Contesse2004_FeXIV.columns.str.replace(' ', '')
df_Contesse2004_FeXIV.sort_values(by=['x'], inplace=True)
Contesse2004_FeXIV_r = df_Contesse2004_FeXIV["x"]/16. + 1
Contesse2004_FeXIV_veff = df_Contesse2004_FeXIV["y"]/530.3*c/1e5/np.sqrt(4*np.log(2))
df_Banerjee1998_SiVIII = pd.read_csv("../../sav/All_digitize/Banerjee1998_SiVIII_1445_vnth_Te1MK_Arcsecabovelimb.csv")
df_Banerjee1998_SiVIII.columns = df_Banerjee1998_SiVIII.columns.str.replace(' ', '')
df_Banerjee1998_SiVIII.sort_values(by=['x'], inplace=True)
Banerjee1998_SiVIII_r = df_Banerjee1998_SiVIII["x"]/960. + 1
Banerjee1998_SiVIII_veff = np.sqrt((df_Banerjee1998_SiVIII["y"]*1e5)**2 + 2*k_B*1e6/28.09/amu)/1e5
df_Moran2003_MgX = pd.read_csv("../../sav/All_digitize/Moran2003_MgX_SUMER_v1e.csv")
df_Moran2003_MgX.columns = df_Moran2003_MgX.columns.str.replace(' ', '')
df_Moran2003_MgX.sort_values(by=['x'], inplace=True)
Moran2003_MgX_r = df_Moran2003_MgX["x"]
Moran2003_MgX_veff = df_Moran2003_MgX["y"]
df_Moran2003_NaIX = pd.read_csv("../../sav/All_digitize/Moran2003_NaIX_SUMER_v1e.csv")
df_Moran2003_NaIX.columns = df_Moran2003_NaIX.columns.str.replace(' ', '')
df_Moran2003_NaIX.sort_values(by=['x'], inplace=True)
Moran2003_NaIX_r = df_Moran2003_NaIX["x"]
Moran2003_NaIX_veff = df_Moran2003_NaIX["y"]
df_Moran_SiVIII = pd.read_csv("../../sav/All_digitize/Moran2003_SiVIII_SUMER_v1e.csv")
df_Moran_SiVIII.columns = df_Moran_SiVIII.columns.str.replace(' ', '')
df_Moran_SiVIII.sort_values(by=['x'], inplace=True)
Moran_SiVIII_r = df_Moran_SiVIII["x"]
Moran_SiVIII_veff = df_Moran_SiVIII["y"]
df_Hahn2012_FeX = pd.read_csv("../../sav/All_digitize/Hahn2012_FeX_184_veff.csv")
df_Hahn2012_FeX.columns = df_Hahn2012_FeX.columns.str.replace(' ', '')
df_Hahn2012_FeX.sort_values(by=['x'], inplace=True)
Hahn2012_FeX_r = df_Hahn2012_FeX["x"]
Hahn2012_FeX_veff = df_Hahn2012_FeX["y"]
df_Dolla2008_FeX = pd.read_csv("../../sav/All_digitize/Dolla2008_FeX_1028_HWat1e_angstrom_arcsecabove.csv")
df_Dolla2008_FeX.columns = df_Dolla2008_FeX.columns.str.replace(' ', '')
df_Dolla2008_FeX.sort_values(by=['x'], inplace=True)
Dolla2008_FeX_r = df_Dolla2008_FeX["x"]/960. + 1
Dolla2008_FeX_veff = (df_Dolla2008_FeX["y"]+0.03)/1028.*c/1e5*np.sqrt(2)
df_Dolla2008_MgX = pd.read_csv("../../sav/All_digitize/Dolla2008_MgX_625_HWat1e_angstrom_arcsecabove.csv")
df_Dolla2008_MgX.columns = df_Dolla2008_MgX.columns.str.replace(' ', '')
df_Dolla2008_MgX.sort_values(by=['x'], inplace=True)
Dolla2008_MgX_r = df_Dolla2008_MgX["x"]/960. + 1
Dolla2008_MgX_veff = df_Dolla2008_MgX["y"]/2/625.*c/1e5*np.sqrt(2)
df_Dolla2008_MgIX = pd.read_csv("../../sav/All_digitize/Dolla2008_MgIX_706_Teff_arcsecabove.csv")
df_Dolla2008_MgIX.columns = df_Dolla2008_MgIX.columns.str.replace(' ', '')
df_Dolla2008_MgIX.sort_values(by=['x'], inplace=True)
Dolla2008_MgIX_r = df_Dolla2008_MgIX["x"]/960. + 1
Dolla2008_MgIX_veff = np.sqrt(2*k_B*df_Dolla2008_MgIX["y"]*1e6/24.305/amu)/1e5
df_Hassler1994_MgX = pd.read_csv("../../sav/All_digitize/Hassler1994_FeX_6374_dlamb1e_angstrom.csv")
df_Hassler1994_MgX.columns = df_Hassler1994_MgX.columns.str.replace(' ', '')
df_Hassler1994_MgX.sort_values(by=['x'], inplace=True)
Hassler1994_MgX_r = df_Hassler1994_MgX["x"]
Hassler1994_MgX_veff = df_Hassler1994_MgX["y"]/6374.*c/1e5
df_Wilhelm1998_MgIX = pd.read_csv("../../sav/All_digitize/Wilhelm1998_MgIX_706_veff.csv")
df_Wilhelm1998_MgIX.columns = df_Wilhelm1998_MgIX.columns.str.replace(' ', '')
df_Wilhelm1998_MgIX.sort_values(by=['x'], inplace=True)
Wilhelm1998_MgIX_r = df_Wilhelm1998_MgIX["x"]
Wilhelm1998_MgIX_veff = df_Wilhelm1998_MgIX["y"]
df_Wilhelm1998_SiVIII = pd.read_csv("../../sav/All_digitize/Wilhelm1998_SiVIII_1445_veff.csv")
df_Wilhelm1998_SiVIII.columns = df_Wilhelm1998_SiVIII.columns.str.replace(' ', '')
df_Wilhelm1998_SiVIII.sort_values(by=['x'], inplace=True)
Wilhelm1998_SiVIII_r = df_Wilhelm1998_SiVIII["x"]
Wilhelm1998_SiVIII_veff = df_Wilhelm1998_SiVIII["y"]
df_Kohl1999_MgX = pd.read_csv("../../sav/All_digitize/Kohl1999_MgX_625_v1e_UVCS.csv")
df_Kohl1999_MgX.columns = df_Kohl1999_MgX.columns.str.replace(' ', '')
df_Kohl1999_MgX.sort_values(by=['x'], inplace=True)
Kohl1999_MgX_r = df_Kohl1999_MgX["x"]
Kohl1999_MgX_veff = df_Kohl1999_MgX["y"]
df_Hara1999_FeX = pd.read_csv("../../sav/All_digitize/Hara1999_FeX_6374_F2_FWHM_angstrom_y_arcsec.csv")
df_Hara1999_FeX.columns = df_Hara1999_FeX.columns.str.replace(' ', '')
df_Hara1999_FeX.sort_values(by=['x'], inplace=True)
df_Hara1999_FeX_xy = pd.read_csv("../../sav/All_digitize/Hara1999_FeX_6374_F2_xy_arcsec.csv")
df_Hara1999_FeX_xy.columns = df_Hara1999_FeX_xy.columns.str.replace(' ', '')
df_Hara1999_FeX_xy.sort_values(by=['y'], inplace=True)
df_Hara1999_FeX_xy["r"] = np.sqrt(df_Hara1999_FeX_xy["x"]**2 + df_Hara1999_FeX_xy["y"]**2)
df_Hara1999_FeX_xy["fwhm"] = df_Hara1999_FeX["y"]
Hara1999_FeX_r = df_Hara1999_FeX_xy["r"]/960. + 1
Hara1999_FeX_veff = df_Hara1999_FeX_xy["fwhm"]/6374.*c/1e5/np.sqrt(4*np.log(2))
df_Hara1999_FeXIV = pd.read_csv("../../sav/All_digitize/Hara1999_FeXIV_5303_F4_FWHM_angstrom_y_arcsec.csv")
df_Hara1999_FeXIV.columns = df_Hara1999_FeXIV.columns.str.replace(' ', '')
df_Hara1999_FeXIV.sort_values(by=['x'], inplace=True)
df_Hara1999_FeXIV_xy = pd.read_csv("../../sav/All_digitize/Hara1999_FeXIV_5303_F4_xy_arcsec.csv")
df_Hara1999_FeXIV_xy.columns = df_Hara1999_FeXIV_xy.columns.str.replace(' ', '')
df_Hara1999_FeXIV_xy.sort_values(by=['y'], inplace=True)
df_Hara1999_FeXIV_xy["r"] = np.sqrt(df_Hara1999_FeXIV_xy["x"]**2 + df_Hara1999_FeXIV_xy["y"]**2)
df_Hara1999_FeXIV_xy["fwhm"] = df_Hara1999_FeXIV["y"]
Hara1999_FeXIV_r = df_Hara1999_FeXIV_xy["r"]/960. + 1
Hara1999_FeXIV_veff = df_Hara1999_FeXIV_xy["fwhm"]/5303.*c/1e5/np.sqrt(4*np.log(2))
The preview of Figure 10 is not available in the notebook because the following code generates a PDF file contains NASA/ADS bibliography links.
merger = PdfFileMerger()
savefig_fname = "../../figs/ms/comparison.pdf"
linewidth = 2
# fig, (ax1,ax2,ax3) = plt.subplots(3,1,figsize=(10,15),constrained_layout=True,gridspec_kw={'top':0.9})
fig = plt.figure(figsize=(11,15),constrained_layout=True)
gs = fig.add_gridspec(3, 1, height_ratios=[1, 1, 0.9],hspace=0.2)
gs0 = gs[0].subgridspec(1, 2, width_ratios=[1, 1],wspace=0.05)
gs1 = gs[1].subgridspec(1, 2, width_ratios=[1, 1],wspace=0.05)
gs2 = gs[2].subgridspec(1, 6,wspace=0.05,width_ratios=[1, 1, 1, 1, 1.07, 1.07])
ax1 = fig.add_subplot(gs0[0])
ax2 = fig.add_subplot(gs0[1])
ax3 = fig.add_subplot(gs1[0])
ax4 = fig.add_subplot(gs1[1])
ax5 = fig.add_subplot(gs2[:4])
# ax6 = fig.add_subplot(gs2[1])
# fig, ((ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2,figsize=(11,15),constrained_layout=True,gridspec_kw={'top':0.9,'hspace':0.2,'wspace':0})
# ax6.set_visible(False)
ax1.plot(Wilhelm2005_MgX_SUMER_r, Wilhelm2005_MgX_SUMER_veff,lw = linewidth,alpha=0.8,
label=r'Wilhelm et al. \href{https://ui.adsabs.harvard.edu/abs/2005A%26A...435..733W/abstract}{(2005)} Mg \textsc{x}*' + "\n" + \
r'62.5\,nm SUMER 2003')
# ax1.plot(Wilhelm2005_FeXII_SUMER_r, Wilhelm2005_FeXII_SUMER_veff, lw = linewidth,alpha=0.8,
# label=r'Wilhelm et al. \href{https://ui.adsabs.harvard.edu/abs/2005A%26A...435..733W/abstract}{(2005)} Fe \textsc{xii}')
ax1.plot(Hassler1990_MgX_609_r, Hassler1990_MgX_609_veff, lw = linewidth,alpha=0.8,
label=r'Hassler et al. \href{https://ui.adsabs.harvard.edu/abs/1990ApJ...348L..77H/abstract}{(1990)} Mg \textsc{x}*' + "\n" + \
r'60.9\,nm Rocket 1988')
ax2.plot(DelZanna2019_FeXIII_202_r, DelZanna2019_FeXIII_202_veff,lw = linewidth,alpha=0.8,
label=r'Del Zanna et al. \href{https://ui.adsabs.harvard.edu/abs/2019A%26A...631A.163D/abstract}{(2019)} Fe \textsc{xiii}' + "\n" + \
r'20.2\,nm EIS 2007')
# ax1.plot(Gupta2019_FeXII_193_r, Gupta2019_FeXII_193_veff,lw = linewidth,alpha=0.8,
# label=r'Gupta et al. \href{https://ui.adsabs.harvard.edu/abs/2019A%26A...627A..62G/abstract}{(2019)} Fe \textsc{xii}')
# ax1.plot(Hahn2014_FeXII_195_r, Hahn2014_FeXII_195_veff,lw = linewidth,alpha=0.8,
# label=r'Hahn et al. \href{https://ui.adsabs.harvard.edu/abs/2014ApJ...795..111H/abstract}{(2014)} Fe \textsc{xii}')
# ax1.plot(Landi2003_r, Landi2003_NeVIII_veff,lw = linewidth,alpha=0.8,
# label=r'Landi et al. \href{https://ui.adsabs.harvard.edu/abs/2003ApJ...592..607L/abstract}{(2003)} Ne \textsc{viii}')
# ax1.plot(Landi2003_r, Landi2003_SiXI_veff,lw = linewidth,alpha=0.8,
# label=r'Landi et al. \href{https://ui.adsabs.harvard.edu/abs/2003ApJ...592..607L/abstract}{(2003)} Si \textsc{xi}')
ax1.plot(Landi2003_r, Landi2003_MgIX_veff,lw = linewidth,alpha=0.8,
label=r'Landi et al. \href{https://ui.adsabs.harvard.edu/abs/2003ApJ...592..607L/abstract}{(2003)} Mg \textsc{ix}' + "\n" + \
r'70.6\,nm SUMER 1999')
ax1.plot(Landi2003_r, Landi2003_NaIX_veff,lw = linewidth,alpha=0.8,
label=r'Landi et al. \href{https://ui.adsabs.harvard.edu/abs/2003ApJ...592..607L/abstract}{(2003)} Na \textsc{ix}*' + "\n" + \
r'68.1\,nm SUMER 1999')
# ax1.plot(Doschek2000_r, Doschek2000_OVI_veff,lw = linewidth,alpha=0.8,
# label=r'Doschek et al. \href{https://ui.adsabs.harvard.edu/abs/2000ApJ...529..599D/abstract}{(2000)} O \textsc{vi}')
# ax1.plot(Doschek2000_r, Doschek2000_NeVIII_veff,lw = linewidth,alpha=0.8,
# label=r'Doschek et al. \href{https://ui.adsabs.harvard.edu/abs/2000ApJ...529..599D/abstract}{(2000)} Ne \textsc{viii}')
ax2.plot(Doschek2000_r, Doschek2000_SiXII_veff,lw = linewidth,alpha=0.8,
label=r'Doschek et al. \href{https://ui.adsabs.harvard.edu/abs/2000ApJ...529..599D/abstract}{(2000)} Si \textsc{xii}*' + "\n" + \
r'49.9\,nm SUMER 1996')
ax1.plot(Doschek2000_r, Doschek2000_FeX_veff,lw = linewidth,alpha=0.8,
label=r'Doschek et al. \href{https://ui.adsabs.harvard.edu/abs/2000ApJ...529..599D/abstract}{(2000)} Fe \textsc{x}' + "\n" + \
r'102.8\,nm SUMER 1996')
ax1.plot(Singh2011_FeX_equatorial_r, Singh2011_FeX_equatorial_veff,lw = linewidth,alpha=0.8,
label=r'Singh et al. \href{https://ui.adsabs.harvard.edu/abs/2011SoPh..270..213S/abstract}{(2011)} Fe \textsc{x}' + "\n" + \
r'637.4\,nm Eclipse 2009')
ax2.plot(Singh2011_FeXIV_equatorial_r, Singh2011_FeXIV_equatorial_veff,lw = linewidth,alpha=0.8,
label=r'Singh et al. \href{https://ui.adsabs.harvard.edu/abs/2011SoPh..270..213S/abstract}{(2011)} Fe \textsc{xiv}' + "\n" + \
r'530.3\,nm Eclipse 2009')
ax2.plot(Koutchmy2005_FeXIV_r, Koutchmy2005_FeXIV_veff,lw = linewidth,alpha=0.8,
label=r'Koutchmy et al. \href{https://ui.adsabs.harvard.edu/abs/2005ESASP.600E..26K/abstract}{(2005)} Fe \textsc{xiv}' + "\n" + \
r'530.3\,nm Eclipse 2001')
ax2.plot(Mierla2008_FeXIV_r, Mierla2008_FeXIV_veff,lw = linewidth,alpha=0.8,
label=r'Mierla et al. \href{https://ui.adsabs.harvard.edu/abs/2008A%26A...480..509M/abstract}{(2008)} Fe \textsc{xiv}' + "\n" + \
r'530.3\,nm LASCO/C1 1996')
ax1.plot(Mierla2008_FeX_r, Mierla2008_FeX_veff,lw = linewidth,alpha=0.8,
label=r'Mierla et al. \href{https://ui.adsabs.harvard.edu/abs/2008A%26A...480..509M/abstract}{(2008)} Fe \textsc{x}' + "\n" + \
r'637.4\,nm LASCO/C1 1996')
ax2.plot(Bazin2013_FeXIV_r, Bazin2013_FeXIV_veff,lw = linewidth,alpha=0.8,
label=r'Bazin et al. \href{https://ui.adsabs.harvard.edu/abs/2013PhDT.......577B/abstract}{(2013)} Fe \textsc{xiv}' + "\n" + \
r'530.3\,nm Eclipse 2012')
ax2.plot(Contesse2004_FeXIV_r, Contesse2004_FeXIV_veff,lw = linewidth,alpha=0.8,
label=r'Contesse et al. \href{https://ui.adsabs.harvard.edu/abs/2004AnGeo..22.3055C/abstract}{(2004)} Fe \textsc{xiv}' + "\n" + \
r'530.3\,nm Coronagraph 2002')
ax1.errorbar(FeX_ss_r,
FeX_ss_veff, FeX_ss_veff_err, color="#F596AA",ls="none",marker="X",
markersize=12,capsize=5,lw=2.5,label=r"Fe \textsc{x} QS 637.4\,nm" + "\n" + "3PAMIS 2017 Eclipse",zorder=15,markeredgecolor='white',
markeredgewidth=2.5,alpha=0.8,capthick=2.5, path_effects=[path_effects.SimpleLineShadow(offset=(1,-1)),
path_effects.Normal()])
ax2.errorbar(FeXIV_ss_r,
FeXIV_ss_veff, FeXIV_ss_veff_err, color="#F596AA",ls="none",marker="X",
markersize=12,capsize=5,lw=2.5,label=r"Fe \textsc{xiv} SS 530.3\,nm" + "\n" + "3PAMIS 2017 Eclipse",zorder=15,markeredgecolor='white',
markeredgewidth=2.5,alpha=0.8,capthick=2.5, path_effects=[path_effects.SimpleLineShadow(offset=(1,-1)),
path_effects.Normal()])
leg1 = ax1.legend(frameon=False,fontsize=12,
bbox_to_anchor=(-0.05,1.02,1.1,0.25),mode="expand",ncol=2,handletextpad=0.5,
handlelength=0.8,columnspacing=0.5,bbox_transform=ax1.transAxes)
leg2 = ax2.legend(frameon=False,fontsize=12,
bbox_to_anchor=(-0.05,1.02,1.1,0.25),mode="expand",ncol=2,handletextpad=0.5,
handlelength=0.8,columnspacing=0.5,bbox_transform=ax2.transAxes)
ax1.set_ylabel(r"$\boldsymbol{v_{\rm eff}}$ \textbf{[km\,s$^{-1}$]}",fontsize=12)
ax1.set_xlim(right=1.6)
ax1.set_ylim(10,70)
ax1.text(0.03,0.95,r"\textbf{(a) QS/Streamer Fe \textsc{x}-like}",transform=ax1.transAxes,fontsize=12,va="top",ha="left")
ax2.set_xlim(right=1.6)
ax2.set_ylim(10,70)
ax2.text(0.03,0.95,r"\textbf{(b) QS/Streamer Fe \textsc{xiv}-like}",transform=ax2.transAxes,fontsize=12,va="top",ha="left")
ax3.plot(Gupta2019_FeX_184_r, Gupta2019_FeX_184_veff,lw = linewidth,alpha=0.8,
label=r'Gupta et al. \href{https://ui.adsabs.harvard.edu/abs/2019A%26A...627A..62G/abstract}{(2019)} Fe \textsc{x}' + "\n" + \
r'18.45\,nm EIS 2007')
ax3.plot(Gupta2017_FeX_184_r, Gupta2017_FeX_184_veff,lw = linewidth,alpha=0.8,
label=r'Gupta et al. \href{https://ui.adsabs.harvard.edu/abs/2017ApJ...836....4G/abstract}{(2017)} Fe \textsc{x}' + "\n" + \
r'18.45\,nm EIS 2007')
3
ax3.plot(Singh2006_FeX_r, Singh2006_FeX_veff,lw = linewidth,alpha=0.8,
label=r'Singh et al. \href{https://ui.adsabs.harvard.edu/abs/2006ApJ...639..475S/abstract}{(2006)} Fe \textsc{x}' + "\n" + \
r'530.3\,nm Coronagraph 2003')
# ax3.plot(Hara1999_FeX_r, Hara1999_FeX_veff,lw = linewidth,alpha=0.8,
# label=r'Hara et al. \href{https://ui.adsabs.harvard.edu/abs/1999ApJ...513..969H/abstract}{(1999)} Fe \textsc{x}' + "\n" + \
# r'637.4\,nm Coronagraph 1993')
ax3.errorbar(FeX_ar2_r,
FeX_ar2_veff,FeX_ar2_veff_err, color="#F596AA",ls="none",marker="D",
markersize=8,capsize=5,lw=2.5,label=r"Fe \textsc{x} 637.4\,nm AR2" + "\n" + "3PAMIS 2017 Eclipse",zorder=15,markeredgecolor='white',
markeredgewidth=2.5,alpha=0.8,capthick=2.5, path_effects=[path_effects.SimpleLineShadow(offset=(1,-1)),
path_effects.Normal()])
ax3.set_ylabel(r"$\boldsymbol{v_{\rm eff}}$ \textbf{[km\,s$^{-1}$]}",fontsize=12)
ax3.text(0.03,0.95,r"\textbf{(c) AR Fe \textsc{x}-like}",transform=ax3.transAxes,fontsize=12,va="top",ha="left")
ax3.set_xlim(right=1.6)
ax3.set_ylim(18,45)
leg3 = ax3.legend(frameon=False,fontsize=12,
bbox_to_anchor=(-0.05,1.0,1.1,0.3),mode="expand",ncol=2,handletextpad=0.5,
handlelength=0.8,columnspacing=0.5,bbox_transform=ax3.transAxes)
ax4.plot(Singh2006_FeXIV_r, Singh2006_FeXIV_veff,lw = linewidth,alpha=0.8,
label=r'Singh et al. \href{https://ui.adsabs.harvard.edu/abs/2006ApJ...639..475S/abstract}{(2006)} Fe \textsc{xiv}' + "\n" + \
r'530.3\,nm Coronagraph 2003')
ax4.plot(Mierla2008_FeXIV_1998_r, Mierla2008_FeXIV_1998_veff, lw = linewidth,alpha=0.8,
label=r'Mierla et al. \href{https://ui.adsabs.harvard.edu/abs/2008A%26A...480..509M/abstract}{(2008)} Fe \textsc{xiv}' + "\n" + \
r'530.3\,nm LASCO/C1 1998')
ax4.plot(Gupta2017_FeXIV_274_r, Gupta2017_FeXIV_274_veff,lw = linewidth,alpha=0.8,
label=r'Gupta et al. \href{https://ui.adsabs.harvard.edu/abs/2017ApJ...836....4G/abstract}{(2017)} Fe \textsc{xiv}' + "\n" + \
r'27.4\,nm EIS 2007')
# ax4.plot(Hara1999_FeXIV_r, Hara1999_FeXIV_veff,lw = linewidth,alpha=0.8,
# label=r'Hara et al. \href{https://ui.adsabs.harvard.edu/abs/1999ApJ...513..969H/abstract}{(1999)} Fe \textsc{xiv}' + "\n" + \
# r'530.3\,nm Coronagraph 1993')
ax4.errorbar(FeXIV_ar2_r,
FeXIV_ar2_veff, FeXIV_ar2_veff_err, color="#F596AA",ls="none",marker="D",
markersize=8,capsize=5,lw=2.5,label=r"Fe \textsc{xiv} 530.3\, nm AR2" + "\n" + "3PAMIS 2017 Eclipse",zorder=15,markeredgecolor='white',
markeredgewidth=2.5,alpha=0.8,capthick=2.5, path_effects=[path_effects.SimpleLineShadow(offset=(1,-1)),
path_effects.Normal()])
ax4.text(0.95,0.95,r"\textbf{(d)AR Fe \textsc{xiv}-like}",transform=ax4.transAxes,fontsize=12,va="top",ha="right")
leg4 = ax4.legend(frameon=False,fontsize=12,
bbox_to_anchor=(-0.05,1.0,1.1,0.3),mode="expand",ncol=2,handletextpad=0.5,
handlelength=0.8,columnspacing=0.5,bbox_transform=ax4.transAxes)
ax4.set_xlim(right=1.6)
ax4.set_ylim(18,45)
ax5.plot(Banerjee1998_SiVIII_r,Banerjee1998_SiVIII_veff,lw = linewidth,alpha=0.8,
label=r'Banerjee et al. \href{https://ui.adsabs.harvard.edu/abs/1998A%26A...339..208B/abstract}{(1998)} Si \textsc{viii} ' + \
r'144.5\,nm SUMER 1996')
ax5.plot(Moran2003_MgX_r, Moran2003_MgX_veff,lw = linewidth,alpha=0.8,
label=r'Moran et al. \href{https://ui.adsabs.harvard.edu/abs/2003ApJ...598..657M/abstract}{(2003)} Mg \textsc{x}* ' + \
r'60.9\,nm SUMER 1997')
ax5.plot(Moran2003_NaIX_r, Moran2003_NaIX_veff,lw = linewidth,alpha=0.8,
label=r'Moran et al. \href{https://ui.adsabs.harvard.edu/abs/2003ApJ...598..657M/abstract}{(2003)} Na \textsc{ix}* ' + \
r'68.1\,nm SUMER 1997')
ax5.plot(Moran_SiVIII_r, Moran_SiVIII_veff,lw = linewidth,alpha=0.8,
label=r'Moran et al. \href{https://ui.adsabs.harvard.edu/abs/2003ApJ...598..657M/abstract}{(2003)} Si \textsc{viii} ' + \
r'144.5\,nm SUMER 1997')
ax5.plot(Dolla2008_MgX_r, Dolla2008_MgX_veff,lw = linewidth,alpha=0.8,
label=r'Dolla et al. \href{https://ui.adsabs.harvard.edu/abs/2008A%26A...483..271D/abstract}{(2008)} Mg \textsc{x}* ' + \
r'62.5\,nm SUMER 2002')
ax5.plot(Dolla2008_FeX_r, Dolla2008_FeX_veff,lw = linewidth,alpha=0.8,
label=r'Dolla et al. \href{https://ui.adsabs.harvard.edu/abs/2008A%26A...483..271D/abstract}{(2008)} Fe \textsc{x} ' + \
r'102.8\,nm SUMER 2002')
# ax5.plot(Dolla2008_MgIX_r, Dolla2008_MgIX_veff,lw = linewidth,alpha=0.8,
# label=r'Dolla et al. \href{https://ui.adsabs.harvard.edu/abs/2008A%26A...483..271D/abstract}{(2008)} Mg \textsc{ix} ' + \
# r'70.6\,nm SUMER 2002')
ax5.plot(Hahn2012_FeX_r, Hahn2012_FeX_veff,lw = linewidth,alpha=0.8,
label=r'Hahn et al. \href{https://ui.adsabs.harvard.edu/abs/2012ApJ...753...36H/abstract}{(2012)} Fe \textsc{x} ' + \
r'18.4\,nm EIS 2009')
ax5.plot(Hassler1994_MgX_r, Hassler1994_MgX_veff,lw = linewidth,alpha=0.8,
label=r'Hassler et al. \href{https://ui.adsabs.harvard.edu/abs/1994SSRv...70..373H/abstract}{(1994)} Fe \textsc{x} ' + \
r'637.4\,nm Coronagraph 1992')
ax5.plot(Wilhelm1998_MgIX_r, Wilhelm1998_MgIX_veff,lw = linewidth,alpha=0.8,
label=r'Wilhelm et al. \href{https://ui.adsabs.harvard.edu/abs/1998ApJ...500.1023W/abstract}{(1998)} Mg \textsc{ix} ' + \
r'70.6\,nm SUMER 1996')
ax5.plot(Wilhelm1998_SiVIII_r, Wilhelm1998_SiVIII_veff,lw = linewidth,alpha=0.8,
label=r'Wilhelm et al. \href{https://ui.adsabs.harvard.edu/abs/1998ApJ...500.1023W/abstract}{(1998)} Si \textsc{viii} ' + \
r'144.5\,nm SUMER 1997')
ax5.plot(Singh2011_FeX_pole_r, Singh2011_FeX_pole_veff,lw = linewidth,alpha=0.8,
label=r'Singh et al. \href{https://ui.adsabs.harvard.edu/abs/2011SoPh..270..213S/abstract}{(2011)} Fe \textsc{x} ' + \
r'637.4\,nm Eclipse 2009')
# ax5.plot(Kohl1999_MgX_r, Kohl1999_MgX_veff,lw = linewidth,alpha=0.8,
# label=r'Kohl et al. \href{https://ui.adsabs.harvard.edu/abs/1999ApJ...510L..59K/abstract}{(1999)} Mg \textsc{x} ' + \
# r'62.5\,nm UVCS 1996')
ax5.errorbar(FeX_ch_r,
FeX_ch_veff, FeX_ch_veff_err, color="#F596AA",ls="none",marker="D",
markersize=8,capsize=5,lw=2.5,label=r"Fe \textsc{x} 637.4\,nm CH " + "3PAMIS 2017 Eclipse",zorder=15,markeredgecolor='white',
markeredgewidth=2.5,alpha=0.8,capthick=2.5, path_effects=[path_effects.SimpleLineShadow(offset=(1,-1)),
path_effects.Normal()])
leg5 = ax5.legend(frameon=False,fontsize=12,
bbox_to_anchor=(1.1,0.0,1,1),mode="expand",ncol=1,handletextpad=0.5,
handlelength=0.8,columnspacing=0.5,bbox_transform=ax5.transAxes,
borderaxespad=1.5)
for leg_ in [leg1,leg2,leg3,leg4,leg5]:
for legobj in leg_.legend_handles:
legobj.set_linewidth(4.0)
ax5.set_ylabel(r"$\boldsymbol{v_{\mathrm{eff}}}$ \textbf{[km\,s$^{-1}$]}",fontsize=12,labelpad=5)
ax5.set_xlabel(r"\textbf{Heliocentric Distance} $\boldsymbol{[R_{\odot}]}$",fontsize=12)
ax5.text(0.03,0.95,r"\textbf{(e) CH Fe \textsc{x}-like}",transform=ax5.transAxes,fontsize=12,va="top",ha="left")
ax5.set_xlim(left=1.0,right=1.4)
ax5.set_ylim(bottom=31,top=80)
for ax_ in (ax1,ax2,ax3,ax4,ax5):
ax_.tick_params(labelsize=12,direction="in",right=True,top=True,length=6,width=1.2)
ax_.minorticks_on()
ax_.tick_params(which="minor",direction="in",right=True,top=True,length=4,width=1.2)
plt.savefig(fname=savefig_fname, dpi=300, bbox_inches='tight')
merger.append(savefig_fname)
os.remove(savefig_fname)
merger.write(savefig_fname)
merger.close()
/var/folders/9p/kj06pc4s4m30vcklbzw2hhgw0000gn/T/ipykernel_5423/2234252988.py:268: UserWarning: constrained_layout not applied because axes sizes collapsed to zero. Try making figure larger or axes decorations smaller. plt.savefig(fname=savefig_fname, dpi=300, bbox_inches='tight')