from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import ctypes
import c2raytools as c2t
# the data we want to analyse, a 3D array of floats
uv = c2t.read_cbin('/home/gorgel/Documents/C2ray/dtbox_smth7.96.cbin')
# the size of the data we want to analyse, a 1D array of three integers (same as the -x -y -z options)
usizev = np.array([504, 504, 504])
# integer: number of bins to use (same as -b option)
numbins = int(10)
#float: lowest value of threshold (same as -l option)
low = float(0)
# float: highest values of threshold (same as -h option)
high = float(10)
output_array = np.zeros((3, numbins + 1, 5), dtype=np.float32)
# vsumv - the output, a 3D array of floats. The size is (3,numbins+1,5)
def mink(input_array, array_size, nr_bins, low, high, output_array):
lib_file = '/home/gorgel/Dropbox/simon/plugg/masterarbete/minkowski_files/MINKOWSKI2_PY/testpython/Minkowski_python/minkowski_python.so'
lib = ctypes.cdll.LoadLibrary(lib_file)
func = lib.minkowski
return func(ctypes.c_void_p(input_array.ctypes.data), ctypes.c_void_p(array_size.ctypes.data),\
out_beam_nufig = './figs/lc_b3nu44_244Mpc_f2_0_250.eps'
out_all_beam_nufig = './figs/lc_b3nu44_244Mpc_all.eps'
z_filename = '../1244Mpc_f2_8.2S_250_pyt/lc_pic_boxes/out0_z.dat'
c2t.set_sim_constants(boxsize_cMpc = 244.)
# Read in z file, set smoothing, and open output files
z_arr = np.loadtxt(z_filename)
nu = c2t.cosmology.z_to_nu(z_arr)[::-1]
dnu = 0.44
beam = 3.
mesh = 250
# Read in lightcones
cbin1 = c2t.read_cbin(cbin1_file)
cbin2 = c2t.read_cbin(cbin2_file)
cbin3 = c2t.read_cbin(cbin3_file)
cbin4 = c2t.read_cbin(cbin4_file)
cb_ticks=[0,10,20,30,40,50,60,70]
xtick_zlabels = np.arange(int(np.floor(z_arr.min())),int(np.ceil(z_arr.max())),2)
xtick_zlocs = []
j = 0
for i in range(1,len(z_arr)):
if j >= len(xtick_zlabels): break
if (abs(xtick_zlabels[j] - z_arr[i-1]) > abs(xtick_zlabels[j] - z_arr[i])): continue
else:
xtick_zlocs.append(i-1)
j += 1
beam = 3
dnu = 0.44
c2t.set_sim_constants(boxsize_cMpc = 244.)
for i in range(1,len(z_arr)):
output_filename = output_path+'%.3f.dat'%(z_arr[i])
out1_filename = out1_path+'%.3f.dat'%(z_arr[i])
out2_filename = out2_path+'%.3f.dat'%(z_arr[i])
out3_filename = out3_path+'%.3f.dat'%(z_arr[i])
print 'z = %.3f'% (z_arr[i])
dT_file = ''.join(glob.glob('./dT_boxes/dT_%.3f.cbin'%(z_arr[i])))
print 'dT file = %s' % dT_file
dT_box = c2t.read_cbin(dT_file, bits=64, order='F')
dT_rsd_file = ''.join(glob.glob('./dT_pv_boxes/dT_pv_%.3f.cbin'%(z_arr[i])))
print 'dT_pv file = %s' % dT_rsd_file
dT_rsd_box = c2t.read_cbin(dT_rsd_file, bits=64, order='F')
dTraw_mean = dT_box.mean()
dTraw_rsd_mean = dT_rsd_box.mean()
dTraw_var = np.mean(pow(dT_box-dTraw_mean,2))
dTraw_rsd_var = np.mean(pow(dT_rsd_box-dTraw_rsd_mean,2))
print 'Calculating coeval dT PDFs...'
dTraw_hist, dTraw_bin_edges = np.histogram(dT_box,bins=100,density=True)
dTraw_rsd_hist, dTraw_rsd_bin_edges = np.histogram(dT_rsd_box,bins=100,density=True)
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import ctypes
import c2raytools as c2t
# the data we want to analyse, a 3D array of floats
uv = c2t.read_cbin('/home/gorgel/Documents/C2ray/dtbox_smth7.96.cbin')
# the size of the data we want to analyse, a 1D array of three integers (same as the -x -y -z options)
usizev = np.array([504,504,504])
# integer: number of bins to use (same as -b option)
numbins = int(10)
#float: lowest value of threshold (same as -l option)
low = float(0)
# float: highest values of threshold (same as -h option)
high = float(10)
output_array = np.zeros((3,numbins+1,5), dtype=np.float32)
# vsumv - the output, a 3D array of floats. The size is (3,numbins+1,5)
def mink(input_array, array_size, nr_bins, low, high, output_array):
lib_file = '/home/gorgel/Dropbox/simon/plugg/masterarbete/minkowski_files/MINKOWSKI2_PY/testpython/Minkowski_python/minkowski_python.so'
lib = ctypes.cdll.LoadLibrary(lib_file)
func = lib.minkowski
return func(ctypes.c_void_p(input_array.ctypes.data), ctypes.c_void_p(array_size.ctypes.data),\
ctypes.c_int(nr_bins), ctypes.c_float(low) , ctypes.c_float(high) ,ctypes.c_void_p(output_array.ctypes.data))
