Fake cluster

create_non_clusters_isochrones.py

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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']

data/isochrone.dat

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+