def bin_data(var1,
var2,
ax,
normalize,
redshift,
filename,
nbins=10,
sim='MR'):
data_path = get_data_dir()
df = fetch_lgalaxies(redshift=redshift,
data_path=data_path,
simulation=sim)
df = make_selection(df, redshift=redshift)
x = df[var1]
y = df[var2]
x = x.as_matrix()
y = y.as_matrix()
median, bin_centres, per_50, per_16, per_84, per_25, per_75 = fit_median(
x, y, nbins)
np.savetxt(
'./binned_data/' + filename + '_z' + str(redshift) + '.txt',
np.c_[bin_centres, median, per_50, per_16, per_84, per_25, per_75])
if redshift == 0:
hb = ax[redshift].hexbin(x,
y,
gridsize=150,
bins='log',
mincnt=5,
cmap='gist_gray')
min = hb.norm.vmin
max = hb.norm.vmax
normalize = matplotlib.colors.Normalize(vmin=min, vmax=max)
#print (normalize)
else:
ax[redshift].hexbin(x,
y,
gridsize=150,
bins='log',
mincnt=5,
cmap='gist_gray') #,norm=normalize)
import pickle
fout = open('./pkl_hists/' + filename + '_z' + str(redshift) + '.pkl',
'wb')
cloudpickle.dump(ax[redshift], fout)
return bin_centres, median, per_50, per_16, per_84, per_25, per_75, normalize
def bin_data2(var1, var2, redshift, filename, nbins=10, sim='MR'):
df = fetch_lgalaxies(redshift=redshift,
data_path='../prepare_output/',
simulation=sim)
#df = fetch_lgalaxies(redshift=redshift,simulation='MR')
df = make_selection(df, redshift=redshift)
x = df[var1]
y = df[var2]
x = x.as_matrix()
y = y.as_matrix()
median, bin_centres, per_50, per_16, per_84, per_25, per_75 = fit_median(
x, y, nbins)
return bin_centres, median, per_50, per_16, per_84, per_25, per_75
def bin_highz_data(var1, var2, redshift, filename, nbins=10):
df = fetch_lgalaxies(redshift=redshift,
data_path='../prepare_output/',
simulation='MR')
#df = fetch_lgalaxies(redshift=redshift,simulation='MR')
df = make_selection(df, redshift=redshift)
x = df[var1]
y = df[var2]
x = x.as_matrix()
y = y.as_matrix()
median, bin_centres, per_50, per_16, per_84, per_25, per_75 = fit_median(
x, y, nbins)
np.savetxt(
'./binned_data/' + filename + '_z' + str(redshift) + '.txt',
np.c_[bin_centres, median, per_50, per_16, per_84, per_25, per_75])
return bin_centres, median, per_50, per_16, per_84, per_25, per_75
sharey=False,
figsize=(9, 9))
ax = axs.reshape(-1)
fig.subplots_adjust(hspace=0.3)
fig.subplots_adjust(wspace=0.3)
dfs = []
DR = []
DMdensity = []
z = []
for loop in range(9, 14):
data_path = get_data_dir()
df = fetch_lgalaxies(redshift=loop, data_path=data_path, simulation='MR')
df = make_selection(df, redshift=loop)
new_df = pd.DataFrame()
new_df['SM'] = df['SM']
new_df['DM'] = df['DM']
new_df['OX_Z'] = df['OX_Z']
new_df['SFR'] = df['Sfr']
new_df['DR_AGB'] = df['DustRate_AGB']
new_df['DR_SNII'] = df['DustRate_SNII']
new_df['DR_SNIA'] = df['DustRate_SNIA']
new_df['DR_GROW'] = df['DustRate_GROW']
new_df['DR_DEST'] = df['DustRate_DEST']
dfs.append(new_df)
def plot_SM_DM(redshift_low, redshift_high, filename):
#fig, axs = plt.subplots(nrows=3, ncols=3, sharex=True, sharey=True, figsize=(9,9))
#ax = axs.reshape(-1)
plt.figure(figsize=(9, 9))
redshift = 0
normalize = 0
df = fetch_lgalaxies(redshift=redshift,
data_path='../prepare_output/',
simulation='MR')
df = make_selection(df, redshift=redshift)
x = df[df.SM > 9].SM
y = df[df.SM > 9].DM
df2 = fetch_lgalaxies(redshift=redshift,
data_path='../prepare_output/',
simulation='MRII')
df2 = make_selection(df2, redshift=redshift)
x2 = df2['SM']
y2 = df2['DM']
bin_centres, median, per_50, per_16, per_84, per_25, per_75 = bin_data2(
'SM', 'DM', redshift, filename, nbins=30, sim='MR')
bin_centres2, median2, per_502, per_162, per_842, per_252, per_752 = bin_data2(
'SM', 'DM', redshift, filename, nbins=30, sim='MRII')
hb = plt.hexbin(x2,
y2,
gridsize=150,
bins='log',
mincnt=1,
cmap='gist_gray')
min = hb.norm.vmin
max = hb.norm.vmax
normalize = matplotlib.colors.Normalize(vmin=min, vmax=max)
#ax[redshift].hexbin(x,y,gridsize=150,bins='log',mincnt=5,cmap='gist_gray')#,norm=normalize)
print(min, max)
#normalize = matplotlib.colors.Normalize(vmin=0, vmax=4)
plt.hexbin(x,
y,
gridsize=150,
bins='log',
mincnt=1,
cmap='gist_gray',
norm=normalize)
#plt.hexbin(x2,y2,gridsize=150,bins='log',mincnt=1,cmap='gist_gray')#,norm=normalize)
plt.xlim([6, 11.97])
plt.ylim([0, 9.98])
#fig.text(8.2,0.3,"z = "+str(loop), fontsize = 16)
#plot_params(ax[loop],loop,'SM','DM')
#plot_observations(ax[loop],loop,"SM_DM")
plt.plot(bin_centres,
per_50,
c='k',
zorder=10,
linewidth=2,
label='L-Galaxies')
plt.plot(bin_centres, per_16, 'k--', zorder=10, linewidth=2)
plt.plot(bin_centres, per_84, 'k--', zorder=10, linewidth=2)
plt.plot(bin_centres2,
per_502,
c='r',
zorder=10,
linewidth=2,
label='L-Galaxies')
plt.plot(bin_centres2, per_162, 'r--', zorder=10, linewidth=2)
plt.plot(bin_centres2, per_842, 'r--', zorder=10, linewidth=2)
#[i.set_linewidth(2.1) for i in ax[loop].spines.values()]
pylab.savefig('./figs/' + filename + '.png', bbox_inches=0)