def prepare_data(gp):
if gp.getnewdata:
gf.get_pos_and_COM(gp)
gf.bin_data(gp)
if gp.getSigdata:
# if Sig convergence finished already
gf.read_Sigdata(gp)
gf.get_binned_data(gp)
if not gp.restart:
gp.files.populate_output_dir(gp)
gf.get_rhohalfs(gp)
#!/usr/bin/env ipython3
import numpy as np
import numpy.random as npr
from scipy.optimize import curve_fit
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as plt
import pdb
import gi_params
gp = gi_params.Params()
import gravimage
gravimage.prepare_data(gp)
import gi_file as gf
gf.get_binned_data(gp)
import import_path as ip
ip.insert_sys_path('/home/psteger/sci/darcoda/gravimage/programs/sphere/')
import gi_analytic as ga
import gi_physics as phys
import gi_class_cube as gcc
def traf(x):
return np.arctan(x)/np.pi+0.5
# \fn traf(x)
# transform [-10,10] interval into [0,1]
def invtraf(y):
return 2000*(y-0.5)
def analytic_rho(x):
import import_path as ip
ip.insert_sys_path(basedir + 'programs/')
ip.insert_sys_path(basedir + 'programs/sphere')
import gi_params as ngip
ngp = ngip.Params(tt)
print(ngp)
print('ngp.rinfty = ', ngp.rinfty)
import select_run as sr
ngp.pops = sr.get_pops(basedir)
print('working with ', ngp.pops, ' populations')
prepare_output_folder(basedir)
# check whether we need to read in ngp.dat, or whether we are plotting from inside gravimage main program
if len(ngp.dat.Sig) == 0:
import gi_file as glf
ngp.dat = glf.get_binned_data(ngp)
read_scale(ngp) # store half-light radii in gp.Xscale
import gi_helper as gh
Radii, Binmin, Binmax, Sigdat1, Sigerr1 = gh.readcol5(ngp.files.Sigfiles[0])
# [Xscale0], [Munit/Xscale0^2]
# verified that indeed the stored files in the run directory are used
ngp.xipol = Radii * ngp.Xscale[0] # [pc]
maxR = max(Radii) # [pc]
minR = min(Radii) # [pc]
Radii = np.hstack(
[minR / 8, minR / 4, minR / 2, Radii, 2 * maxR, 4 * maxR, 8 * maxR])
ngp.xepol = Radii * ngp.Xscale[0] # [pc]
pc = pcload_single_entries(basedir, ngp)
with open(basedir + 'pc', 'wb') as fn:
#!/usr/bin/env ipython3
import numpy as np
import numpy.random as npr
from scipy.optimize import curve_fit
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as plt
import pdb
import gi_params
gp = gi_params.Params()
import gi_file as gf
gf.get_binned_data(gp)
import import_path as ip
ip.insert_sys_path('/home/psteger/sci/darcoda/gravimage/programs/sphere/')
import gravimage
gravimage.prepare_data(gp)
import gi_analytic as ga
import gi_physics as phys
import gi_class_cube as gcc
gp.debug = True
def traf(x):
return np.arctan(x)/np.pi+0.5
# \fn traf(x)
# transform [-10,10] interval into [0,1]
import import_path as ip
ip.insert_sys_path(basedir+'programs/')
ip.insert_sys_path(basedir+'programs/sphere')
import gi_params as ngip
ngp = ngip.Params(tt)
print(ngp)
print('ngp.rinfty = ',ngp.rinfty)
import select_run as sr
ngp.pops = sr.get_pops(basedir)
print('working with ', ngp.pops, ' populations')
prepare_output_folder(basedir)
# check whether we need to read in ngp.dat, or whether we are plotting from inside gravimage main program
if len(ngp.dat.Sig) == 0:
import gi_file as glf
ngp.dat = glf.get_binned_data(ngp)
read_scale(ngp) # store half-light radii in gp.Xscale
import gi_helper as gh
Radii, Binmin, Binmax, Sigdat1, Sigerr1 = gh.readcol5(ngp.files.Sigfiles[0])
# [Xscale0], [Munit/Xscale0^2]
# verified that indeed the stored files in the run directory are used
ngp.xipol = Radii * ngp.Xscale[0] # [pc]
maxR = max(Radii) # [pc]
minR = min(Radii) # [pc]
Radii = np.hstack([minR/8, minR/4, minR/2, Radii, 2*maxR, 4*maxR, 8*maxR])
ngp.xepol = Radii * ngp.Xscale[0] # [pc]
pc = pcload_single_entries(basedir, ngp)
with open(basedir+'pc', 'wb') as fn:
pickle.dump(pc, fn)
