def main(args):
user_input = read_input(args.input)
if user_input['path_to_obs'] == 'None':
raise IOError('It is not possible to continue a process without determining a static data set.')
m1 = imp.load_source(args.functions[:-3], args.functions)
user_input['simulation_func'] = getattr(m1, user_input['simulation_func'][0])
if isinstance(user_input['distance_func'][0], str):
user_input['distance_func'] = getattr(m1, user_input['distance_func'][0])
#check dimension of distance output
dtemp = user_input['distance_func'](user_input['dataset1'], user_input)
user_input['dist_dim'] = len(dtemp)
for l1 in range(user_input['npar']):
par = user_input['param_to_fit'][l1]
if isinstance(user_input['prior'][par]['func'], str):
user_input['prior'][par]['func'] = getattr(m1, user_input['prior_func'][l1])
#initiate ABC construct
sampler_ABC = ABC(params=user_input)
#define finished particle system index
sampler_ABC.T = int(args.PS)
#continue from previous run
sampler_ABC.ContinueStoppedRun(sampler_ABC.T, nruns=int(user_input['nruns'][0]))
def main(args):
user_input = read_input(args.input)
if args.functions != None:
m1 = imp.load_source(args.functions[:-3], args.functions)
if isinstance(user_input['distance_func'][0], str):
user_input['distance_func'] = getattr(
m1, user_input['distance_func'][0])
for l1 in range(user_input['npar']):
par = user_input['param_to_fit'][l1]
if isinstance(user_input['prior'][par]['func'], str):
user_input['prior'][par]['func'] = getattr(
m1, user_input['prior_func'][l1])
#initiate ABC construct
sampler_ABC = ABC(params=user_input)
#build first particle system
sys1 = sampler_ABC.BuildFirstPSystem()
#update particle system until convergence
sampler_ABC.fullABC(nruns=int(user_input['nruns'][0]))
def main( args ):
user_input = read_input(args.input)
m1 = imp.load_source(args.functions[:-3], args.functions)
user_input['simulation_func'] = getattr(m1, user_input['simulation_func'][0])
if isinstance(user_input['distance_func'], list):
user_input['distance_func'] = getattr(m1, user_input['distance_func'][0])
for element in user_input['param_to_fit']:
if isinstance(user_input['prior'][element]['func'], str):
user_input['prior'][element]['func'] = getattr(m1, user_input['prior'][element]['func'])
for pvar in user_input[element + '_prior_par_name'][:user_input[element + '_prior_par_name'].index('#')]:
indx = user_input[element + '_prior_par_name'].index(pvar)
user_input['prior'][element][pvar] = float(user_input[element + '_prior_par_val'][indx])
#check if observed data exist, simulate in case negative
if user_input['path_to_obs'] == 'None':
user_input['dataset1'] = user_input['simulation_func'](user_input['simulation_input'])
#save data to file
op1 = open('synthetic_data.dat', 'w')
for line in user_input['dataset1']:
for item in line:
op1.write(str(item) + ' ')
op1.write('\n')
op1.close()
dist_try = user_input['distance_func'](user_input['dataset1'], user_input)
if 'dist_dim' not in user_input.keys():
user_input['dist_dim'] = len(dist_try)
#initiate ABC construct
sampler_ABC = ABC(params=user_input)
#build first particle system
sys1 = sampler_ABC.BuildFirstPSystem()
#update particle system until convergence
sampler_ABC.fullABC(nruns=int(user_input['nruns'][0]))
#plot results
if len(user_input['param_to_fit'] ) == 1 :
plot_1p(sampler_ABC.T, 'results.pdf', user_input)
elif len(user_input['param_to_fit'] ) == 2 :
plot_2p(sampler_ABC.T, 'results.pdf', user_input)
elif len(user_input['param_to_fit'] ) == 3 :
plot_3p(sampler_ABC.T, 'results.pdf', user_input)
else:
raise ValueError('Only 1, 2, and 3 dimensional plots are implemented so far!')
def main(args):
user_input = read_input(args.input)
if args.functions:
m1 = imp.load_source(args.functions[:-3], args.functions)
for l1 in range(user_input['npar']):
par = user_input['param_to_fit'][l1]
if isinstance(user_input['prior'][par]['func'], str):
user_input['prior'][par]['func'] = getattr(
m1, user_input['prior_func'][l1])
if isinstance(user_input['simulation_func'][0], str):
user_input['simulation_func'] = getattr(
m1, user_input['simulation_func'][0])
if isinstance(user_input['distance_func'][0], str):
user_input['distance_func'] = getattr(
m1, user_input['distance_func'][0])
#check if observed data exist, simulate in case negative
if user_input['path_to_obs'] == 'None':
user_input['dataset1'] = user_input['simulation_func'](
user_input['simulation_input'])
if 'dist_dim' not in user_input.keys():
user_input['dist_dim'] = len(user_input['distance_func'](
user_input['dataset1'], user_input))
#plot results
if len(user_input['param_to_fit']) == 1:
plot_1p(int(args.PS), 'results.pdf', user_input)
elif len(user_input['param_to_fit']) == 2:
plot_2p(int(args.PS), 'results.pdf', user_input)
elif len(user_input['param_to_fit']) == 3:
plot_3p(int(args.PS), 'results.pdf', user_input)
else:
raise ValueError(
'Only 1, 2 and 3 dimensional plots are implemented so far!')
def main(args):
user_input = read_input(args.input)
user_input['simulation_func'] = numcosmo_sim_cluster
if user_input['path_to_obs'] == 'None':
raise IOError(
'It is not possible to continue a process without determining a static data set.'
)
#initiate NumCosmo object necessary for simulation
Cosmo = ChooseParamsInput()
Cosmo.params = user_input['simulation_input']
Cosmo.params["OL"] = 1. - Cosmo.params['Om'] - Cosmo.params['Ob']
#assign input for simulation
user_input['simulation_params'] = Cosmo.params
if args.functions != None:
m1 = imp.load_source(args.functions[:-3], args.functions)
if isinstance(user_input['distance_func'][0], str):
user_input['distance_func'] = getattr(
m1, user_input['distance_func'][0])
dtemp = user_input['distance_func'](user_input['dataset1'],
user_input)
user_input['dist_dim'] = len(dtemp)
for par in user_input['param_to_fit']:
l1 = user_input['param_to_fit'].index(par)
if isinstance(user_input['prior'][par]['func'], str):
user_input['prior'][par]['func'] = getattr(
m1, user_input['prior_func'][l1])
#initiate ABC construct
sampler_ABC = ABC(params=user_input)
#define finished particle system index
sampler_ABC.T = int(args.PS)
#continue from previous run
sampler_ABC.ContinueStoppedRun(int(args.PS),
nruns=int(user_input['nruns'][0]))
def main( args ):
user_input = read_input( args.input )
if args.functions:
m1 = imp.load_source( args.functions[:-3], args.functions )
for l1 in range( user_input['npar'] ):
par = user_input['param_to_fit'][l1]
if isinstance( user_input['prior'][par]['func'], str):
user_input['prior'][par]['func'] = getattr(m1, user_input['prior_func'][l1])
if isinstance(user_input['simulation_func'][0], str):
user_input['simulation_func'] = getattr(m1, user_input['simulation_func'][0])
if isinstance(user_input['distance_func'][0], str):
user_input['distance_func'] = getattr(m1, user_input['distance_func'][0])
#check if observed data exist, simulate in case negative
if user_input['path_to_obs'] == 'None':
user_input['dataset1'] = user_input['simulation_func'](user_input['simulation_input'])
if 'dist_dim' not in user_input.keys():
user_input['dist_dim'] = len(user_input['distance_func'](user_input['dataset1'], user_input))
#plot results
if len( user_input['param_to_fit'] ) == 1 :
plot_1p( int( args.PS ), 'results.pdf', user_input)
elif len( user_input['param_to_fit'] ) == 2 :
plot_2p( int( args.PS ), 'results.pdf', user_input )
elif len( user_input['param_to_fit'] ) == 3 :
plot_3p( int( args.PS ), 'results.pdf', user_input )
else:
raise ValueError('Only 1, 2 and 3 dimensional plots are implemented so far!')
def main( args ):
user_input = read_input( args.input )
user_input['simulation_func'] = numcosmo_sim_cluster
if user_input['path_to_obs'] == 'None':
raise IOError('It is not possible to continue a process without determining a static data set.')
#initiate NumCosmo object necessary for simulation
Cosmo=ChooseParamsInput()
Cosmo.params = user_input['simulation_input']
Cosmo.params["OL"] = 1.- Cosmo.params['Om']-Cosmo.params['Ob']
#assign input for simulation
user_input['simulation_params'] = Cosmo.params
if args.functions != None:
m1 = imp.load_source( args.functions[:-3], args.functions )
if isinstance(user_input['distance_func'][0], str):
user_input['distance_func'] = getattr(m1, user_input['distance_func'][0])
dtemp = user_input['distance_func'](user_input['dataset1'], user_input)
user_input['dist_dim'] = len(dtemp)
for par in user_input['param_to_fit']:
l1 = user_input['param_to_fit'].index(par)
if isinstance( user_input['prior'][par]['func'], str):
user_input['prior'][par]['func'] = getattr(m1, user_input['prior_func'][l1])
#initiate ABC construct
sampler_ABC = ABC(params = user_input)
#define finished particle system index
sampler_ABC.T = int(args.PS)
#continue from previous run
sampler_ABC.ContinueStoppedRun(int(args.PS), nruns=int(user_input['nruns'][0]))
def main( args ):
user_input = read_input(args.input)
if args.functions != None:
m1 = imp.load_source( args.functions[:-3], args.functions )
if isinstance(user_input['distance_func'][0], str):
user_input['distance_func'] = getattr(m1, user_input['distance_func'][0])
for l1 in range(user_input['npar']):
par = user_input['param_to_fit'][l1]
if isinstance(user_input['prior'][par]['func'], str):
user_input['prior'][par]['func'] = getattr(m1, user_input['prior_func'][l1])
#initiate ABC construct
sampler_ABC = ABC(params=user_input)
#build first particle system
sys1 = sampler_ABC.BuildFirstPSystem()
#update particle system until convergence
sampler_ABC.fullABC(nruns=int(user_input['nruns'][0]))
def main(args):
params = read_input( args.input )
if args.functions != None:
m1 = imp.load_source( args.functions[:-3], args.functions )
if isinstance(params['distance_func'], list):
params['distance_func'] = getattr(m1, params['distance_func'][0])
if isinstance(params['simulation_func'], list):
params['simulation_func'] = getattr(m1, params['simulation_func'][0])
for par in params['param_to_fit']:
if isinstance(params['prior'][par]['func'], str):
params['prior'][par]['func'] = getattr(m1, params['prior_func'][params['param_to_fit'].index(par)])
if not args.output:
output_file = raw_input('Enter root for output files (no extension): ')
else:
output_file = args.output
if 'dataset1' not in params:
params['dataset1'] = params['simulation_func'](params['simulation_input'])
if 'cov' in params['simulation_input']:
try:
fname_cov = args.covariance
params['simulation_input']['cov'] = np.loadtxt(fname_cov)
params['cov'] = np.loadtxt(fname_cov)
except IOError:
print 'Provide name of file containing covariance matrix!'
#test distance between identical cataloges
distance_equal = np.atleast_1d(params['distance_func'](params['dataset1'], params))
if distance_equal.any() != 0.0:
raise ValueError('Distance for 2 identical cataloges = ' + str(distance_equal))
else:
print 'Distance between identical cataloges = ' + str(distance_equal)
#test a single distance calculation
new_sim_input = DrawAllParams(params['prior'])
for j in xrange(params['npar']):
params['simulation_input'][params['param_to_fit'][j]] = new_sim_input[j]
data_simul = params['simulation_func'](params['simulation_input'])
try:
distance_single = params['distance_func'](data_simul, params)
print 'New parameter value = ' + str(new_sim_input)
print 'Distance between observed and simulated data = ' + str(distance_single)
except ValueError:
print 'Error in calculating single distance with parameters:'
for item in params['param_to_fit']:
print item + '=' + str(params['simulation_input'][item])
if str(distance_single) is 'nan':
print 'NaN found!'
#generate grid for distance behaviour inspection
ngrid = int(raw_input('Enter number of draws in parameter grid: '))
param_grid = [DrawAllParams(params['prior']) for j1 in xrange(ngrid)]
#open output data file
op = open(output_file + '.dat', 'w')
for par in params['param_to_fit']:
op.write(par + ' ')
for i1 in xrange(len(distance_equal)):
op.write('dist' + str(i1 + 1) + ' ')
op.write('\n')
grid = []
for pars in param_grid:
if params['screen']:
print 'Particle index: ' + str(len(grid)+1)
grid_element = list(pars)
for j1 in xrange(params['npar']):
params['simulation_input'][params['param_to_fit'][j1]] = pars[j1]
data_simul_grid = params['simulation_func'](params['simulation_input'])
distance_grid = np.atleast_1d(params['distance_func'](data_simul_grid, params))
if sum(distance_grid) < 9**9:
for item in grid_element:
op.write(str(item) + ' ')
for elem in distance_grid:
grid_element.append(elem)
op.write(str(elem) + ' ')
op.write('\n')
grid.append(grid_element)
op.close()
grid = np.array(grid)
#plot distance behaviour
plt.figure()
plt.subplots_adjust(top = 0.95, right=0.985, left=0.075, bottom=0.075,
wspace=0.25, hspace=0.25)
n=0
for par_indx in xrange(params['npar']):
p1 = params['param_to_fit'][par_indx]
for dist_indx in xrange(len(distance_single)):
n = n + 1
ylim = np.std( grid[:,params['npar'] + dist_indx])
plt.subplot(params['npar'], len(distance_single), n)
plt.scatter(grid[:,par_indx], grid[:,params['npar'] + dist_indx])
plt.xlabel(params['param_to_fit'][par_indx], fontsize=14)
plt.ylabel('distance' + str(dist_indx + 1), fontsize=14)
#plt.xticks(range(int(params['prior'][p1]['min']) - 1,
# int(params['prior'][p1]['max']) + 1, int(params['prior'][p1]['max'] - params['prior'][p1]['min'] +2)/5), fontsize=8)
plt.yticks(fontsize = 8)
plt.ylim(-0.25*ylim, ylim)
plt.savefig(output_file + '.pdf')
plt.close()
print '\n Figure containing distance results is stored in ' + output_file + '.pdf'
10 - 16 December 2107, Passo del Tonale - Italy """ from cosmoabc.priors import flat_prior from cosmoabc.ABC_sampler import ABC from cosmoabc.ABC_functions import read_input from cosmoabc.plots import plot_2p import numpy as np from my_functions import my_distance, my_simulation, my_prior #user input file filename = 'user.input' #read user input Parameters = read_input(filename) # update dictionary of user input parameters # with customized functions Parameters['distance_func'] = my_distance Parameters['simulation_func'] = my_simulation Parameters['prior']['mean']['func'] = my_prior # in case you want to generate a pseudo-observed data set Parameters['dataset1'] = my_simulation(Parameters['simulation_input']) #calculate distance between 2 catalogues dtemp = my_distance(Parameters['dataset1'], Parameters) #determine dimension of distance output Parameters['dist_dim'] = len(dtemp)
def main(args):
user_input = read_input(args.input)
m1 = imp.load_source(args.functions[:-3], args.functions)
user_input['simulation_func'] = getattr(m1,
user_input['simulation_func'][0])
if isinstance(user_input['distance_func'], list):
user_input['distance_func'] = getattr(m1,
user_input['distance_func'][0])
for element in user_input['param_to_fit']:
if isinstance(user_input['prior'][element]['func'], str):
user_input['prior'][element]['func'] = getattr(
m1, user_input['prior'][element]['func'])
for pvar in user_input[element + '_prior_par_name'][:user_input[
element + '_prior_par_name'].index('#')]:
indx = user_input[element + '_prior_par_name'].index(pvar)
user_input['prior'][element][pvar] = float(
user_input[element + '_prior_par_val'][indx])
#check if observed data exist, simulate in case negative
if user_input['path_to_obs'] == 'None':
user_input['dataset1'] = user_input['simulation_func'](
user_input['simulation_input'])
#save data to file
op1 = open('synthetic_data.dat', 'w')
for line in user_input['dataset1']:
for item in line:
op1.write(str(item) + ' ')
op1.write('\n')
op1.close()
dist_try = user_input['distance_func'](user_input['dataset1'], user_input)
if 'dist_dim' not in user_input.keys():
user_input['dist_dim'] = len(dist_try)
#initiate ABC construct
sampler_ABC = ABC(params=user_input)
#build first particle system
sys1 = sampler_ABC.BuildFirstPSystem()
#update particle system until convergence
sampler_ABC.fullABC(nruns=int(user_input['nruns'][0]))
#plot results
if len(user_input['param_to_fit']) == 1:
plot_1p(sampler_ABC.T, 'results.pdf', user_input)
elif len(user_input['param_to_fit']) == 2:
plot_2p(sampler_ABC.T, 'results.pdf', user_input)
elif len(user_input['param_to_fit']) == 3:
plot_3p(sampler_ABC.T, 'results.pdf', user_input)
else:
raise ValueError(
'Only 1, 2, and 3 dimensional plots are implemented so far!')
def main(args):
params = read_input(args.input)
if args.functions != None:
m1 = imp.load_source(args.functions[:-3], args.functions)
if isinstance(params['distance_func'], list):
params['distance_func'] = getattr(m1, params['distance_func'][0])
if isinstance(params['simulation_func'], list):
params['simulation_func'] = getattr(m1,
params['simulation_func'][0])
for par in params['param_to_fit']:
if isinstance(params['prior'][par]['func'], str):
params['prior'][par]['func'] = getattr(
m1,
params['prior_func'][params['param_to_fit'].index(par)])
if not args.output:
output_file = raw_input(
'Enter root for output files (no extension): ')
else:
output_file = args.output
if 'dataset1' not in params:
params['dataset1'] = params['simulation_func'](
params['simulation_input'])
if 'cov' in params['simulation_input']:
try:
fname_cov = args.covariance
params['simulation_input']['cov'] = np.loadtxt(fname_cov)
params['cov'] = np.loadtxt(fname_cov)
except IOError:
print 'Provide name of file containing covariance matrix!'
#test distance between identical cataloges
distance_equal = np.atleast_1d(params['distance_func'](params['dataset1'],
params))
if distance_equal.any() != 0.0:
raise ValueError('Distance for 2 identical cataloges = ' +
str(distance_equal))
else:
print 'Distance between identical cataloges = ' + str(distance_equal)
#test a single distance calculation
new_sim_input = DrawAllParams(params['prior'])
for j in xrange(params['npar']):
params['simulation_input'][params['param_to_fit']
[j]] = new_sim_input[j]
data_simul = params['simulation_func'](params['simulation_input'])
try:
distance_single = params['distance_func'](data_simul, params)
print 'New parameter value = ' + str(new_sim_input)
print 'Distance between observed and simulated data = ' + str(
distance_single)
except ValueError:
print 'Error in calculating single distance with parameters:'
for item in params['param_to_fit']:
print item + '=' + str(params['simulation_input'][item])
if str(distance_single) is 'nan':
print 'NaN found!'
#generate grid for distance behaviour inspection
ngrid = int(raw_input('Enter number of draws in parameter grid: '))
param_grid = [DrawAllParams(params['prior']) for j1 in xrange(ngrid)]
#open output data file
op = open(output_file + '.dat', 'w')
for par in params['param_to_fit']:
op.write(par + ' ')
for i1 in xrange(len(distance_equal)):
op.write('dist' + str(i1 + 1) + ' ')
op.write('\n')
grid = []
for pars in param_grid:
if params['screen']:
print 'Particle index: ' + str(len(grid) + 1)
grid_element = list(pars)
for j1 in xrange(params['npar']):
params['simulation_input'][params['param_to_fit'][j1]] = pars[j1]
data_simul_grid = params['simulation_func'](params['simulation_input'])
distance_grid = np.atleast_1d(params['distance_func'](data_simul_grid,
params))
if sum(distance_grid) < 9**9:
for item in grid_element:
op.write(str(item) + ' ')
for elem in distance_grid:
grid_element.append(elem)
op.write(str(elem) + ' ')
op.write('\n')
grid.append(grid_element)
op.close()
grid = np.array(grid)
#plot distance behaviour
plt.figure()
plt.subplots_adjust(top=0.95,
right=0.985,
left=0.075,
bottom=0.075,
wspace=0.25,
hspace=0.25)
n = 0
for par_indx in xrange(params['npar']):
p1 = params['param_to_fit'][par_indx]
for dist_indx in xrange(len(distance_single)):
n = n + 1
ylim = np.std(grid[:, params['npar'] + dist_indx])
plt.subplot(params['npar'], len(distance_single), n)
plt.scatter(grid[:, par_indx], grid[:, params['npar'] + dist_indx])
plt.xlabel(params['param_to_fit'][par_indx], fontsize=14)
plt.ylabel('distance' + str(dist_indx + 1), fontsize=14)
#plt.xticks(range(int(params['prior'][p1]['min']) - 1,
# int(params['prior'][p1]['max']) + 1, int(params['prior'][p1]['max'] - params['prior'][p1]['min'] +2)/5), fontsize=8)
plt.yticks(fontsize=8)
plt.ylim(-0.25 * ylim, ylim)
plt.savefig(output_file + '.pdf')
plt.close()
print '\n Figure containing distance results is stored in ' + output_file + '.pdf'