Hertzsprung-Russell Diagram
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
np.seterr(divide='ignore', invalid='ignore')
#source = "http://balbuceosastropy.blogspot.com/2014/03/construction-of-hertzsprung-russell.html"
filename = 'I_239_selection.tsv'
df = pd.read_table(filename, skiprows=44, sep=';', header=None, index_col=0, names=['HIP', 'Vmag', 'Plx', 'B-V', 'SpType'], skipfooter=1, engine='python')
print(df.head())
print(df.tail())
print(df.describe())
df_clean = df.applymap(lambda x: np.NaN if isinstance(x, str) and x.isspace() else x)
df_clean= df_clean.dropna()
print(df_clean.describe())
print(df_clean.shape)
df_clean['Vmag'] = df_clean['Vmag'].astype(np.float)
df_clean['Plx'] = df_clean['Plx'].astype(np.float)
df_clean['B-V'] = df_clean['B-V'].astype(np.float)
df_clean['M_V'] = (df_clean['Vmag'] + (5 * np.log10(df_clean['Plx']/100.)))
print(df_clean.head())
f = lambda s: (len(s) >= 2) and (s[0].isalpha()) and (s[1].isdigit())
i = df_clean['SpType'].apply(f)
df_clean = df_clean[i]
f = lambda s: s[0:2]
df_clean['SpType2'] = df_clean['SpType'].apply(f)
print(df_clean.shape)
print(df_clean.head())
f = lambda s: s[0]
classes = df_clean['SpType'].map(f)
print(classes.value_counts())
f = lambda s: s[0] in 'OBAFGKM'
df_clean = df_clean[df_clean['SpType'].map(f)]
f = lambda s: s[0]
classes = df_clean['SpType'].map(f)
print(classes.value_counts())
order = {'O':'0', 'B':'1', 'A':'2', 'F':'3', 'G':'4', 'K':'5', 'M':'6'}
f = lambda s: order[s[0]] + s[1]
df_clean['SpType2'] = df_clean['SpType2'].apply(f)
print(df_clean.head())
fig, ax = plt.subplots(figsize=(8,10))
ax.set_xlim(0, 70)
ax.set_ylim(15, -10)
ax.grid()
ax.set_title('H-R Diagram')
ax.title.set_fontsize(20)
ax.set_xlabel('Spectral Class')
ax.xaxis.label.set_fontsize(20)
ax.set_ylabel('Absolute Magnitude')
ax.yaxis.label.set_fontsize(20)
ax.scatter(df_clean['SpType2'].astype(np.int), df_clean['M_V'], s=50, edgecolors='none', alpha=0.015, c='k')
ax.set_xticks(range(5, 75, 10))
ax.set_xticklabels(['O', 'B', 'A', 'F', 'G', 'K', 'M'])
ax.tick_params(axis='both', labelsize=14)
plt.show()
f = lambda s: 'VII' in s
b = df_clean['SpType'].map(f)
print('Class VII: white dwarfs, that are %d stars' %sum(b))
f = lambda s: ('VI' in s) and ('VII' not in s)
b = df_clean['SpType'].map(f)
print('Class VI: sub-dwarfs, that are %d stars' %sum(b))
f = lambda s: ('V' in s) and ('VI' not in s) and ('IV' not in s)
b = df_clean['SpType'].map(f)
print('Class V: main-sequence, that are %d stars' %sum(b))
f = lambda s: 'IV' in s
b = df_clean['SpType'].map(f)
print('Class IV: subgiants, that are %d stars' %sum(b))
f = lambda s: 'III' in s
b = df_clean['SpType'].map(f)
print('Class III: giants, that are %d stars' %sum(b))
f = lambda s: ('II' in s) and ('III' not in s) and ('VII' not in s)
b = df_clean['SpType'].map(f)
print('Class II: bright giants, that are %d stars' %sum(b))
f = lambda s: ('I' in s) and ('II' not in s) and ('V' not in s)
b = df_clean['SpType'].map(f)
print('Class I: supergiants, that are %d stars' %sum(b))
f = lambda s: ('I' not in s) and ('V' not in s)
b = df_clean['SpType'].map(f)
print(sum(b))
def plot_lum_class(b, c, label):
x = df_clean['B-V'][b]
y = df_clean['M_V'][b]
ax.scatter(x, y, c = c, s=6, edgecolors='none', label = label)
fig, ax = plt.subplots(figsize=(8,10))
rect = ax.patch
rect.set_facecolor('0.6')
ax.set_xlim(-0.5, 2.5)
ax.set_ylim(15, -15)
ax.grid()
ax.set_title('H-R Diagram')
ax.title.set_fontsize(20)
ax.set_xlabel('Colour Index B-V')
ax.xaxis.label.set_fontsize(20)
ax.set_ylabel('Absolute Magnitude')
ax.yaxis.label.set_fontsize(20)
f = lambda s: 'VII' in s
b = df_clean['SpType'].map(f)
plot_lum_class(b, 'white', 'VII: white dwarfs')
f = lambda s: ('VI' in s) and ('VII' not in s)
b = df_clean['SpType'].map(f)
plot_lum_class(b, 'blue', 'VI: subdwarfs')
f = lambda s: ('V' in s) and ('VI' not in s) and ('IV' not in s)
b = df_clean['SpType'].map(f)
plot_lum_class(b, 'black', 'V: main sequence')
f = lambda s: 'IV' in s
b = df_clean['SpType'].map(f)
plot_lum_class(b, 'grey', 'IV: subgiants')
f = lambda s: 'III' in s
b = df_clean['SpType'].map(f)
plot_lum_class(b, 'green', 'III: giants')
f = lambda s: ('II' in s) and ('III' not in s) and ('VII' not in s)
b = df_clean['SpType'].map(f)
plot_lum_class(b, 'orange', 'II: bright giants')
f = lambda s: ('I' in s) and ('II' not in s) and ('V' not in s)
b = df_clean['SpType'].map(f)
plot_lum_class(b, 'yellow', 'I: supergiants')
ax.tick_params(axis='both', labelsize=14)
legend = ax.legend(scatterpoints=1, markerscale = 6, shadow=True)
frame = legend.get_frame()
frame.set_facecolor('0.90')
plt.show()