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)
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)
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)
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 setUp(self):
self.mu = 2.5
self.sigma = 1.0
self.n = 1000
self.params = {}
self.params['simulation_func'] = ysim
self.params['simulation_input'] = {'mu': self.mu, 'sigma':self.sigma, 'n':self.n}
self.params['dataset1'] = self.params['simulation_func']( self.params['simulation_input'] )
self.params['param_to_fit']=['mu', 'sigma']
self.params['screen'] = 0
self.params['Mini'] = 200
self.params['M'] = 100
self.params['delta'] =0.1
self.params['qthreshold'] = 0.75
self.params['file_root'] = os.getcwd() + '/test_PS'
self.params['distance_func'] = distance_quantiles
self.params['quantile_nodes'] = 20
self.params['split_output'] = [1]
self.params['prior'] = {}
self.params['prior']['sequence'] = self.params['param_to_fit']
self.params['prior']['mu'] = {}
self.params['prior']['mu']['func'] = flat_prior
self.params['prior']['mu']['pmin'] = 1.0
self.params['prior']['mu']['pmax'] = 4.0
self.params['prior']['mu']['min'] = 1.0
self.params['prior']['mu']['max'] = 4.0
self.params['prior']['sigma'] = {}
self.params['prior']['sigma']['func'] = flat_prior
self.params['prior']['sigma']['pmin'] = 0.001
self.params['prior']['sigma']['pmax'] = 3.0
self.params['prior']['sigma']['min'] = 0.001
self.params['prior']['sigma']['max'] = 3.0
#initiate ABC sampler
self.sampler_ABC = ABC( self.params )
self.W = [1.0/self.params['M'] for i in xrange( self.params['M'] )]
self.params = summ_quantiles(self.params['dataset1'], self.params)
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]))
#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) #initiate ABC sampler sampler_ABC = ABC(params=Parameters) #build first particle system sys1 = sampler_ABC.BuildFirstPSystem() #update particle system until convergence sampler_ABC.fullABC() #plot results plot_2p(sampler_ABC.T, 'results.pdf', Parameters)
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!')
class TestABC(unittest.TestCase):
def setUp(self):
self.mu = 2.5
self.sigma = 1.0
self.n = 1000
self.params = {}
self.params['simulation_func'] = ysim
self.params['simulation_input'] = {'mu': self.mu, 'sigma':self.sigma, 'n':self.n}
self.params['dataset1'] = self.params['simulation_func']( self.params['simulation_input'] )
self.params['param_to_fit']=['mu', 'sigma']
self.params['screen'] = 0
self.params['Mini'] = 200
self.params['M'] = 100
self.params['delta'] =0.1
self.params['qthreshold'] = 0.75
self.params['file_root'] = os.getcwd() + '/test_PS'
self.params['distance_func'] = distance_quantiles
self.params['quantile_nodes'] = 20
self.params['split_output'] = [1]
self.params['prior'] = {}
self.params['prior']['sequence'] = self.params['param_to_fit']
self.params['prior']['mu'] = {}
self.params['prior']['mu']['func'] = flat_prior
self.params['prior']['mu']['pmin'] = 1.0
self.params['prior']['mu']['pmax'] = 4.0
self.params['prior']['mu']['min'] = 1.0
self.params['prior']['mu']['max'] = 4.0
self.params['prior']['sigma'] = {}
self.params['prior']['sigma']['func'] = flat_prior
self.params['prior']['sigma']['pmin'] = 0.001
self.params['prior']['sigma']['pmax'] = 3.0
self.params['prior']['sigma']['min'] = 0.001
self.params['prior']['sigma']['max'] = 3.0
#initiate ABC sampler
self.sampler_ABC = ABC( self.params )
self.W = [1.0/self.params['M'] for i in xrange( self.params['M'] )]
self.params = summ_quantiles(self.params['dataset1'], self.params)
def test_DrawAllParams( self ):
#draw parameters
r1 = DrawAllParams(self.params['prior'])
res = []
for i1 in xrange(len(r1)):
par = self.params['param_to_fit'][i1]
if r1[i1] >= self.params['prior'][par]['min'] and r1[i1] <= self.params['prior'][par]['max']:
res.append(True)
else:
res.append(False)
self.assertEqual([True for item in r1], res)
def test_SetDistanceFromSimulation(self):
#set distance
r2 = SetDistanceFromSimulation(self.params)
self.assertTrue(r2 >= 0)
def test_plot(self):
self.params['ncores'] = get_cores()
self.sampler_ABC.fullABC(build_first_system=True)
if len(self.params['param_to_fit']) == 1:
plot_1p(self.sampler_ABC.T, 'results.pdf', self.params)
elif len(self.params['param_to_fit']) == 2:
plot_2p(self.sampler_ABC.T, 'results.pdf', self.params)
elif len(self.params['param_to_fit']) == 3:
plot_3p(self.sampler_ABC.T, 'results.pdf', self.params)
elif len(self.params['param_to_fit']) == 4:
plot_4p(self.sampler_ABC.T, 'results.pdf', self.params)