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):
nn = ga.rho_gaia(x, gp)[0] # 0 for DM, 1 for stars
return nn
# import os
# os.nice(1)
# optionally start with -i and -c switches, to batch start gaia and walk runs
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i", "--investigation", dest="investigation", default="", help="investigation to run: gaia, walk, hern, triax, discmock")
parser.add_option("-c", "--case", dest="case", default='-1', help="case: 1, 2, ..")
parser.add_option("-t", "--timestamp", dest="timestamp", default='-1', help="timestamp: 201501221224")
(options, args) = parser.parse_args()
print('gravimage.py '+options.investigation+' '+str(options.case)+' '+str(options.timestamp))
if options.timestamp != '-1':
import gi_base as gb
basepath = gb.get_basepath()
import import_path as ip
ip.insert_sys_path(basepath+"DT"+options.investigation+"/"+options.case+"/"+options.timestamp+"/programs/")
import gi_params
warnings.simplefilter('ignore') # set to 'error' when debugging
gp = gi_params.Params(options.timestamp, options.investigation, int(options.case))
if options.timestamp != '-1':
ip.remove_third()
#import os
#os.system('cd '+basepath+'DT'+options.investigation+'/'+options.case+'/'+options.timestamp)
gp.restart = True
gp.chi2_Sig_converged = 0
import gi_file as gf
def show(filepath):
subprocess.call(('xdg-open', filepath))
return
## \fn show(filepath) open the output (pdf) file for the user @param
#!/usr/bin/env ipython3 ## # @file # check deprojection and projection of nu # (c) 2015 Pascal Steger, [email protected] import pdb import numpy as np from pylab import * ion() import import_path as ip ip.insert_sys_path('/home/psteger/sci/darcoda/gravimage/programs/reducedata/') import gi_params as gp import gi_project as gip def run(gp): Rscale = []; Dens0Rscale = []; Dens0pc = []; Totmass_Tracers = [] rscale = []; dens0Rscale = []; dens0pc = []; totmass_tracers = [] for pop in range(3): A = np.loadtxt(gp.files.get_scale_file(pop), unpack=False, skiprows=1) Rscale.append(A[0]) Dens0Rscale.append(A[1]) Dens0pc.append(A[2]) Totmass_Tracers.append(A[3])
#!/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]
gh.progressbar((1.0 * current) / numofmodels)
MODEL = pickle.load(fi)
pc.add(MODEL)
except EOFError:
pass
print("")
return pc
## \fn pcload_single_entries(bn, gp)
# load all data into [chi^2, profiles] pairs, and add to a profile collection
# @param bn string
# @param gp global parameters
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
(options, args) = parser.parse_args()
# gh.LOG(1, 'plot_profiles '+str(options.investigate)+' '+str(options.case)+' '+str(options.latest))
if len(options.timestamps) == 0:
import select_run as sr
timestamp, basename = sr.run(options.investigate, options.case, options.latest)
options.timestamps=[timestamp]
else:
import gi_base as gb
basepath = gb.get_basepath()
basename = os.path.abspath(basepath+'DT'+options.investigate+'/'+str(options.case))+'/'
# use last timestamp for sample gp
import import_path as ip
tt = options.timestamps[-1]
ip.insert_sys_path(basename+tt+'/programs/')
ip.insert_sys_path(basename+tt+'/programs/sphere')
import gi_params as gip
gp = gip.Params(tt)
gp.pops = sr.get_pops(basename+tt+'/')
# find minimum number of iterations for all runs
minlinelen = 1e99
#import gi_collection as gc
for tt in options.timestamps:
linelen = 0
with open(basename+tt+'/pc2.save', 'rb') as fi:
dum = pickle.load(fi) # dummy variable
try:
while True:
linelen += 1
#!/usr/bin/env ipython3 ## # @file # check deprojection and projection of nu # (c) 2015 Pascal Steger, [email protected] import pdb import numpy as np from pylab import * ion() import import_path as ip ip.insert_sys_path('/home/psteger/sci/darcoda/gravimage/programs/reducedata/') import gi_params as gp import gi_project as gip def run(gp): Rscale = [] Dens0Rscale = [] Dens0pc = [] Totmass_Tracers = [] rscale = [] dens0Rscale = [] dens0pc = [] totmass_tracers = [] for pop in range(3):
current += 1
if current%100 == 0:
gh.progressbar((1.0*current)/numofmodels)
MODEL = pickle.load(fi)
pc.add(MODEL)
except EOFError:
pass
print("")
return pc
## \fn pcload_single_entries(bn, gp)
# load all data into [chi^2, profiles] pairs, and add to a profile collection
# @param bn string
# @param gp global parameters
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
