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lls_plots.py
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lls_plots.py
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import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
def plot_lls_metal_hist(column, met, min_col=17.0, max_col=19.5, nbins=30, xmin=-3, xmax=0):
column = column.flatten()
met = np.log10(10.0**(met.flatten()) / 0.0127)
select = (column > min_col) & (column < max_col)
print met.min(), met.max()
hist, edges = np.histogram(met[select], bins=nbins, range=[xmin,xmax])
print hist, edges
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
new_edges = np.zeros( edges.shape[0]-1 )
for index,min in enumerate(edges[:-1]):
max = edges[index+1]
new_edges[index] = (min + max)/2.0
print new_edges.shape,hist.shape
ax.plot(new_edges,hist)
ax.set_xlim([edges[0],edges[-1]])
fig.subplots_adjust(left=0.15, right=0.97, top=0.97, bottom=0.15)
ax.set_xlabel(r'Z/Z${}_\odot$')
fig.savefig('met_pdf.pdf')
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
met = met[select] # select only LLS's
met.sort() # sort their metallicity
cum_val = np.arange( met.shape[0] ) / (1.0* met.shape[0] )
cum_val = cum_val[::-1]
ax.plot(met,cum_val, label='Sim all LLS')
ax.set_xlim([edges[0],edges[-1]])
fig.subplots_adjust(left=0.15, right=0.97, top=0.97, bottom=0.15)
cdf1=np.array( [0.188,0.250,0.386,0.474,0.579,0.790]) # metalpoor
err1=np.array( [0.098,0.108,0.124,0.134,0.143,0.165] )
z1 =np.array( [-2.0,-2.2,-2.5,-2.7,-2.9,-3.45] )
cdf2=np.array( [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8] ) # ;blind
z2 =np.array( [-0.75,-1.55,-1.8,-2.0,-2.05,-2.1,-2.2,-2.6] )
err2=np.array( [0.095,0.126,0.145,0.155,0.158,0.155,0.145,0.126] )
ax.scatter(z1, cdf1, color='b', label='Metal Poor')
ax.scatter(z2, cdf2, color='r', label='Blind')
ax.legend()
ax.set_ylabel('CDF (>[M/H])')
ax.set_xlabel(r'[M/H]')
fig.savefig('met_cum_dist.pdf')
f = open('lss_metallicity_cdf.txt','w')
for index in np.arange(cum_val.shape[0]):
line = '{:.8} {:.8}\n'.format(met[index], cum_val[index])
f.write(line)
f.close()
def plot_column_vs_met(column, met, nbins=100, xmin=14, xmax=22, ymin=-6, ymax=1, weights=None):
my_x = column.flatten() # already in log(N_H)
my_y = np.log10(10.0**(met.flatten()) / 0.0127) # already in log(Z)
if weights==None:
weights=np.zeros( my_x.shape )+1.0
print my_x.shape
print my_y.shape
hist, xedges, yedges = np.histogram2d(my_x, my_y, bins=nbins, range=[[xmin,xmax],[ymin,ymax]], weights=weights)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1] ]
hist = np.log10(hist+1)
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
ax.imshow(hist.T,extent=extent,interpolation='nearest',origin='lower')
# ax.plot(np.log10([n_h_down, n_h_down]), [-100, 100],linewidth=3, color='k')
# ax.plot(np.log10([n_h_up, n_h_up]), [-100, 100],linewidth=3, color='k')
# ax.plot([-100,100], [-2.5, -2.5], linewidth=3, color='k')
ax.set_xlim([xedges[0],xedges[-1]])
ax.set_ylim([yedges[0],yedges[-1]])
ax.set(aspect=(1.0*(xmax-xmin))/(1.0*(ymax-ymin)))
fig.subplots_adjust(left=0.15, right=0.97, top=0.97, bottom=0.15)
ax.set_xlabel(r'N${}_{HI}$ [cm${}^{-2}$]')
ax.set_ylabel(r'Z/Z${}_\odot$')
# redshift_label=str(redshifts[snap])
# redshift_label='z='+redshift_label[:3]
# ax.text(0, -5.5, redshift_label, color='w' )
fig.savefig('col_vs_met.pdf')
def plot_column_map(column, slice=0, nmin=14, nmax=22, savebase='HI_map_slice'):
this_slice=column[slice,:,:]
fig = plt.figure(figsize=(15,15))
ax = fig.add_subplot(1, 1, 1)
# first map for low density material
norm = mpl.colors.Normalize(vmin=10, vmax=nmin)
cmap = plt.cm.Blues
mapper = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
rgba = mapper.to_rgba(this_slice)
rgba[:,:,3] = 0.6
ax.imshow(rgba)
# now make LLS and up image with transparent background
norm = mpl.colors.Normalize(vmin=nmin, vmax=nmax)
cmap = plt.cm.spectral
mapper = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
rgba = mapper.to_rgba(this_slice)
low_pixel_index = this_slice < nmin
rgba[low_pixel_index,3] = 0.0
ax.imshow(rgba, vmin=nmin, vmax=nmax)
position=fig.add_axes([0.85,0.1,0.02,0.8]) ## the parameters are the specified position you set
# plt.imshow(this_slice)
mini_ax = fig.add_axes([0.000, 0.001, 0.000, 0.001])
dummy = np.zeros( (5,5) )
f = mini_ax.imshow(dummy, cmap='spectral', vmin=nmin, vmax=nmax)
mini_ax.axes.get_xaxis().set_visible(False)
mini_ax.axes.get_yaxis().set_visible(False)
cbar = plt.colorbar(f, cmap=cmap, cax=position) #, ticks=np.arange(5)+15)
# cbar.set_cmap('spring')
cbar.ax.tick_params(labelsize=25)
cbar.ax.set_ylabel(r'log(N${}_{HI}$ [cm${}^{-2}$])', fontsize=30)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
# position=fig.add_axes([0.85,0.1,0.02,0.8]) ## the parameters are the specified position you set
# position.set_ylabel(r'log(N${}_{HI}$)')
#a cbar = plt.colorbar(cax=position, cmap=cmap) #, ticks=np.arange(5)+15)
# cbar.ax.tick_params(labelsize=25)
# cbar.ax.set_ylabel(r'log(N${}_{HI}$ [cm${}^{-2}$])', fontsize=30)
left = 0.03
delta= 0.8
fig.subplots_adjust(left=left, right=left+delta, top=1.0 - (1.0-delta)/2.0, bottom=(1.0-delta)/2.0 )
fig.savefig(savebase+'_'+str(slice)+'.pdf', dpi=512)
def plot_metallicity_map(column, met, slice=0, ncut=16.5, savebase='met_map', min_met=-5, max_met=0):
this_met=np.log10( (10.0**met[slice,:,:])/0.013 )
this_col=column[slice,:,:]
fig = plt.figure(figsize=(15,15))
ax = fig.add_subplot(1, 1, 1)
met = np.log10( (10.0**met)/0.013 )
# first map for low density material
# norm = mpl.colors.Normalize(vmin=-5, vmax=0)
# cmap = plt.cm.spectral
# mapper = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
# rgba = mapper.to_rgba(this_met)
# rgba[:,:,3] = 0.3
# ax.imshow(rgba)
# now make LLS and up image with transparent background
norm = mpl.colors.Normalize(vmin=-5, vmax=0)
cmap = plt.cm.spectral
mapper = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
rgba = mapper.to_rgba(this_met)
low_pixel_index = this_col < ncut
rgba[low_pixel_index,3] = 0.0
ax.imshow(rgba, vmin=-5, vmax=0)
position=fig.add_axes([0.85,0.1,0.02,0.8]) ## the parameters are the specified position you set
mini_ax = fig.add_axes([0.000, 0.001, 0.000, 0.001])
dummy = np.zeros( (5,5) )
f = mini_ax.imshow(dummy, cmap='spectral', vmin=-5, vmax=0)
mini_ax.axes.get_xaxis().set_visible(False)
mini_ax.axes.get_yaxis().set_visible(False)
cbar = plt.colorbar(f, cmap=cmap, cax=position) #, ticks=np.arange(5)+15)
cbar.ax.tick_params(labelsize=25)
cbar.ax.set_ylabel(r'[M/H]', fontsize=30)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
left = 0.03
delta= 0.8
fig.subplots_adjust(left=left, right=left+delta, top=1.0 - (1.0-delta)/2.0, bottom=(1.0-delta)/2.0 )
fig.savefig(savebase+'_'+str(slice)+'.pdf', dpi=512)