<jupyter_start><jupyter_text>Plot of $N_{\mathrm{lines}}$ as a function of $T_{\mathrm{eff}}$ and instrumentFigure 1 in the paper.The goal of this figure will be number of identified lines versus Teff, for two spectrograph bandwidths-- HPF and IGRINS.<jupyter_code>import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format='retina'

import seaborn as sns
sns.set_context('paper', font_scale=2)
import pandas as pd
sns.set_context('paper', font_scale=1.5)
! ls -1 ../data/tables/performance/
fns = ['HPF_N_lines_prom0p005.csv','HPF_N_lines_prom0p01.csv','HPF_N_lines_prom0p02.csv',
       'IGRINS_N_lines_prom0p005.csv','IGRINS_N_lines_prom0p01.csv','IGRINS_N_lines_prom0p02.csv']
proms = [0.005, 0.01, 0.02, 0.005, 0.01, 0.02]
df = pd.DataFrame()
for fn, prom in zip(fns, proms):
        df_i = pd.read_csv('../data/tables/performance/'+fn)
        df_i['prom'] = prom
        df_i['inst'] = fn[0:3]
        df = df.append(df_i)
color_dict = {'HPF':'#d35400', 'IGR':'#2980b9'}
marker_dict = {0.005:'+', 0.01:'s', 0.02:'o'}
linestyle_dict = {'HPF':'none', 'IGR':'solid'}
instrument_name = {'HPF':'HPF', 'IGR':'IGRINS'}
fig = plt.figure(figsize=(5, 5))

for (inst, prom), group in df.groupby(['inst', 'prom']):
    plt.plot(group.Teff, group.N_lines, marker_dict[prom], 
             color=color_dict[inst], 
             linestyle=linestyle_dict[inst],
             label="{} Prom: {:0.3f}".format(instrument_name[inst], prom))
    
plt.xlabel('$T_{\mathrm{eff}}\;$ (K)')
plt.xlim(6800, 3000)
plt.ylabel('$N_{\mathrm{lines}}$')
plt.ylim(0, 25_000)
plt.legend(fontsize=10, ncol=2);
plt.savefig('../paper/paper1/figures/N_lines_vs_Teff_prom.png', dpi=300, bbox_inches='tight')
df.groupby(['inst', 'prom']).N_lines.mean().to_frame()/6700
1.3*1.2<jupyter_output><empty_output>
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