plot1.py

#  load and plot  stripe82calibStars_v2.6.dat

# " produce rms vs. magnitude plot using CRTS data (to show that their [Graham et al. 2011] fig. 10 is bogus - that variation # is not due to quasar variability but due to their errors)"


import numpy as np

import matplotlib.pyplot as plt

arr=np.load('my_array1.npy')

# u  mmu (mean magnitude)  vs  u  rms (root mean square) 

# plt.title('u-band rms vs. magnitude')
# plt.xlabel('Mean u magnitude')
# plt.ylabel('Root-mean-square scatter')
# plt.scatter(arr[1:1000,8], arr[1:1000,10])
# plt.xlim(15,)  # print only those dimmer than 15th mag 
# plt.savefig('u_band_mean_mag.png')
# plt.show()

# plot ugriz as separate plots
nrows=1006848
band_names=['u','g','r','i','z']
band_cols_mmu=[8,14,20,26,32] # columns with mmu : mean magnitude
band_cols_med=[7,13,19,25,31] # columns with median magnitude 

# col_med + 3 : rms
# col_mmu + 2 : rms  

i=0
for col in band_cols_mmu:
    # plt.xlim(15,)  # print only those dimmer than 15th mag 
    plt.clf()
    
    
    
    x = arr[0:nrows,col]
    y = arr[0:nrows,col+2]
    fig1 = plt.figure()
    plt.plot(x,y,'.r')
    plt.title(band_names[i]+'-band rms vs. mean magnitude')
    plt.xlabel('Mean '+band_names[i]+' magnitude')
    plt.ylabel('Root-mean-square scatter') 
    nbins =300
    H, xedges,yedges = np.histogram2d(x,y,bins=nbins)
    H = np.rot90(H)
    H = np.flipud(H)
    Hmasked = np.ma.masked_where(H==0,H)
    fig2 = plt.figure()
    plt.pcolormesh(xedges, yedges, Hmasked)
    plt.xlabel('Mean '+band_names[i]+' magnitude')
    plt.ylabel('Root-mean-square scatter') 
    cbar = plt.colorbar()
    cbar.ax.set_ylabel('Counts')
    fname='new_mean_'+str(nrows)+'_rows_'+band_names[i]+'.png'
    plt.savefig(fname)

    i=i+1
# something would have to be corrected to get rid of the horizontal bands, but 
# I'm not really sure what - I don't claim full understanding of the code above
# but I think this strange effect might have something to do with binning or the 
# way that masking here works   --> need to read more on that 
# 2dhistogram adapted from http://oceanpython.org/2013/02/25/2d-histogram/