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Fake cluster #16

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94 changes: 94 additions & 0 deletions create_non_clusters_isochrones.py
Original file line number Diff line number Diff line change
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'''
--------------------------------------------------------------------------------
Simple script to create non-clusters from isochrones.
Run as:
>> python create_non_clusters_isochrones.py <n>
where 'n' is the total number of desired non-clusters.
--------------------------------------------------------------------------------
'''

import numpy as np
import pandas as pd
from numpy import random
import sys, os
import matplotlib.pyplot as plt

# add dust extinction
def add_dust_extinction():
pass

def hess( col, mag ):
#histogram definition
xyrange = [[-1,2],[-5,4.0]] # data range
bins = [20,20] # number of bins
thresh = 3 #density threshold

# histogram the data
hh, locx, locy = np.histogram2d(col, mag, range=xyrange, bins=bins)
posx = np.digitize(col, locx)
posy = np.digitize(mag, locy)

#select points within the histogram
ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
hhsub = hh[posx[ind] - 1, posy[ind] - 1]
col1, mag1 = [], []
hh[hh < thresh] = np.nan

return hh, xyrange

def plotHessDiagram(non_cluster_df):
# xlim, ylim = [-0.5, 2], [4, -5]
g = non_cluster_df['G']
b_p = non_cluster_df['Bp']
r_p = non_cluster_df['Rp']

# plotting
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111)

hh, xyrange = hess( b_p - r_p, g )
im = ax.imshow( np.flipud(hh.T), cmap="viridis", extent= np.array(xyrange).flatten(),
interpolation='nearest', origin='upper', aspect="auto")
cb = plt.colorbar(im, ax=ax)

ax.set_xlabel('$B_{p} - R_{p}$')
ax.set_ylabel('G')
ax.invert_yaxis()
outfile = "non_cluster.png"
fig.savefig(outfile, dpi=300)
plt.show()

def make_non_cluster(id):
# no. of stars in the non-cluster
n = np.random.randint(30, high=500)

# load isochrone
isochrone = np.loadtxt("isochrone.dat", comments="#")

# shuffle and select n stars
temp_list = np.arange( isochrone.shape[0] )
np.random.shuffle( temp_list )
subsetIdx = temp_list[ :n ]
subsetIdx = np.array( subsetIdx )

non_cluster = np.column_stack((isochrone[subsetIdx, -3:], np.repeat(id, n)))

return non_cluster

if __name__=='__main__':
# no. of stars that you want in your cluster
n_non_cluster = int(sys.argv[1])

non_clusters = []
for id in range(n_non_cluster):
non_clusters.append( make_non_cluster(id) )

# col_names = ['G', 'Bp', 'Rp', 'cluster_id']
arr_non_clusters = np.concatenate([c for c in non_clusters])
np.save("data/non_clusters_isochrones.npy", arr_non_clusters)


# cols = ['Zini', 'MH', 'logAge', 'Mini', 'int_IMF', 'Mass', 'logL', 'logTe',\
# 'logg', 'label', 'McoreTP', 'C_O', 'period0', 'period1', 'pmode', 'Mloss',\
# 'tau1m', 'X', 'Y', 'Xc', 'Xn', 'Xo', 'Cexcess', 'Z', 'mbolmag',\
# 'Gmag', 'G_BPmag', 'G_RPmag']
1 change: 1 addition & 0 deletions data/isochrone.dat
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