def calc_wetzel09_disruption(param_fname):
clusters, central = get_clusters(param_fname, core_host_mass=True)
fit_mi, fit_rd = get_fit_param(param_fname)
stepz = dtk.StepZ(sim_name='AlphaQ')
z = stepz.get_z(401)
print(z)
core_t_dyn = wetzel09_disruption_time(0.25, clusters.core_m,
clusters.core_host_mass, z)
print(core_t_dyn)
plt.figure()
lg_min = np.min(np.log10(core_t_dyn))
lg_max = np.max(np.log10(core_t_dyn))
print(lg_min, lg_max)
xbins = np.logspace(lg_min, lg_max, 10)
print(xbins)
xbins_cen = dtk.log_bins_avg(xbins)
h, _ = np.histogram(core_t_dyn, bins=xbins)
plt.loglog(
xbins_cen,
h,
)
plt.figure()
plt.hist2d(np.log10(clusters.core_m),
np.log10(clusters.core_host_mass),
bins=100,
cmap='Blues',
norm=clr.LogNorm())
plt.figure()
h, xbins = np.histogram(np.log10(clusters.core_host_mass /
clusters.core_m),
bins=100)
xbins_cen = dtk.bins_avg(xbins)
plt.plot(xbins_cen, h, label='All Cores')
plt.xlabel('log10(Mhost/Msat)')
plt.ylabel('Counts')
if fit_mi < 1e3:
fit_mi = 10**fit_mi
slct = clusters.core_m > fit_mi
h, xbins = np.histogram(np.log10(clusters.core_host_mass[slct] /
clusters.core_m[slct]),
bins=100)
xbins_cen = dtk.bins_avg(xbins)
plt.plot(xbins_cen, h, label='>M_infall')
slct = (clusters.core_m > fit_mi) & (clusters.core_r < fit_rd)
h, xbins = np.histogram(np.log10(clusters.core_host_mass[slct] /
clusters.core_m[slct]),
bins=100)
xbins_cen = dtk.bins_avg(xbins)
plt.plot(xbins_cen, h, label='Fit Cores')
plt.legend(loc='best')
#!/usr/bin/env python2.7
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as clr
import h5py
import dtk
import sys
from astropy.cosmology import WMAP7 as cosmo
import time
from scipy.interpolate import interp1d
h = 0.702
stepz = dtk.StepZ(200, 0, 500)
zs = np.linspace(0, 1.5, 1000)
z_to_dl = interp1d(zs, cosmo.luminosity_distance(zs))
step2nan_str_z = {}
param = dtk.Param(sys.argv[1])
output = param.get_string('output')
output_mod = output.replace('.hdf5', '_mod.hdf5')
hfile = h5py.File(output_mod, 'a')
redshift = hfile['galaxyProperties/redshiftHubble'].value
dl = z_to_dl(redshift)
print "done getting the luminosity distance..."
adjust = -2.5 * np.log10(1 + redshift) + 5 * np.log10(dl) + 25.0
fof_cat = Catalog(fof_loc)
sod_cat = Catalog(sod_loc)
core_cat = Catalog(core_loc)
zmr_sdss = np.load(zmr_loc)
zmr_sdss = npzfile_to_dic(zmr_sdss)
print zmr_sdss.keys()
step1 = param.get_int("step")
steps = param.get_int_list("steps")
z_in = param.get_float("z_in")
z_out = param.get_float("z_out")
num_steps = param.get_int("num_steps")
stepz = dtk.StepZ(z_in, z_out, num_steps)
n2merger = N2Merger(n2lib_loc)
fof_cat.add_steps(steps)
sod_cat.add_steps(steps)
core_cat.add_steps(steps)
fof_cat.add_data_name("fof_halo_tag")
fof_cat.add_data_name("fof_halo_mass")
fof_cat.add_data_name("fof_halo_center_x")
fof_cat.add_data_name("fof_halo_center_y")
fof_cat.add_data_name("fof_halo_center_z")
sod_cat.add_data_name("fof_halo_tag")
sod_cat.add_data_name("sod_halo_mass")
plt.figure()
plt.plot(cat['sod_halo_radius'], R200c_col/1000.0/0.7*a, '.', alpha=0.3)
plt.yscale('log')
plt.xscale('log')
plt.title('radius')
plot_1to1()
plt.figure()
plt.plot(cat['sod_halo_radius'], (R200c_col/1000.0/0.7*a)/cat['sod_halo_radius'], '.', alpha=0.3)
plt.xscale('log')
plt.show()
if __name__ == "__main__":
cosmology.setCosmology('WMAP7')
param = dtk.Param(sys.argv[1])
steps = param.get_int_list("steps")
sod_location = param.get_string("sod_location")
sod_output = param.get_string("sod_output")
input_h = param.get_float("input_h")
output_h = param.get_float("output_h")
write_catalog = param.get_bool("write_catalog")
h_scaling = input_h/output_h
stepz = dtk.StepZ(sim_name = 'AlphaQ')
print(colossus.__file__)
for step in steps:
redshift = stepz.get_z(step)
convert_halo_m200c_to_m200m(sod_location, sod_output, step,
redshift, h_scaling,
write_catalog=write_catalog)
check_halo_conversion(sod_location, step, redshift)
