def __init__(self,
params,
valarray=None,
response=False,
iteration_cb=None):
PSweep.__init__(self, iteration_cb)
self.response = response
self.params = params
self._start_at = 0
if valarray is None:
ok = 0
try:
# are all the value arrays the same length?
ok = len(set([len(p.values) for p in params]))
except:
pass
if ok != 1:
raise ValueError(
'Valarray needs to be set or all parameters need to set their values.'
)
self.num = len(params[0].values)
else:
if type(valarray) == list:
valarray = np.array(valarray)
if len(valarray.shape) == 1:
valarray = valarray.reshape(1, -1)
if not isinstance(valarray,
np.ndarray) or valarray.shape[0] != len(params):
raise ValueError(
'Valarray needs to be a 2D array with rows = number of params.'
)
self.num = valarray.shape[1]
for i, p in enumerate(self.params):
p.values = valarray[i]
def __init__(self,
params,
num,
ds=False,
response=True,
iteration_cb=None):
PSweep.__init__(self, iteration_cb)
self.params = params
num = int(num)
self.num = num
self.ds = ds
self.response = response
self._start_at = 0
if self.response:
# To generate a complete response surface, use Uniform distributions
# with the same range as the original distributions.
for p in self.params:
if ds:
p.values = UniformPDF(*p.pdf.range).ds(num)
else:
p.values = UniformPDF(*p.pdf.range).lhs(num)
else:
for p in self.params:
if ds:
p.values = p.pdf.ds(num)
else:
p.values = p.pdf.lhs(num)
def __init__(self, params, valarray=None, response=False, iteration_cb=None):
PSweep.__init__(self, iteration_cb)
self.response = response
self.params = params
self._start_at = 0
if valarray is None:
ok = 0
try:
# are all the value arrays the same length?
ok = len(set([len(p.values) for p in params]))
except:
pass
if ok != 1:
raise ValueError('Valarray needs to be set or all parameters need to set their values.')
self.num = len(params[0].values)
else:
if type(valarray) == list:
valarray = np.array(valarray)
if len(valarray.shape) == 1:
valarray = valarray.reshape(1,-1)
if not isinstance(valarray, np.ndarray) or valarray.shape[0] != len(params):
raise ValueError('Valarray needs to be a 2D array with rows = number of params.')
self.num = valarray.shape[1]
for i, p in enumerate(self.params):
p.values = valarray[i]
def __init__(self, params, numtrajectories,levels,gridjump, response=False, iteration_cb=None):
if response:
print('Morris method does not support creating response surfaces')
raise ValueError
#compare the state of the seeds to the ones set in the control script
a=pickle(random.getstate())
b=pickle(np.random.get_state())
f=open('random.getstate.2.txt','w')
f.write(a)
f.close()
f=open('np.random.get_state.2.txt','w')
f.write(b)
f.close()
#output of puq run is 1 elementary effect for each parameter per trajectory.
#Therefore, each parameter will have *numtrajectories* elementary effects.
PSweep.__init__(self, iteration_cb)
self.params = params
num = int(numtrajectories)
self.num = num*(len(params)+1) #total number of model runs for morris
self.response = response
self._start_at = 0
self.levels=levels
self.gridjump=gridjump
self._hf=None
self._salib_paramFile='==SALib_morris_params==.txt'
self._salib_realizationsFile='==SALib_morris_realizations==.txt'
self._salib_realizationsFile_verify='==0SALib_morris_realizations==.txt'
self._salib_analysisFile='==SALib_morris_outputs==.txt'
#generate morris samples N(D+1) x D numpy array. Rows are realizations, columns are params
#Each column is independent in the range [0,1]
#TODO: allow for correlation
self._samples=SAs.morris_oat.sample(N=num,D=len(params),num_levels=levels,grid_jump=gridjump)
#puq will evaluate the output by picking a sample from each parameter in the
#order specified in p.values
i=0
f=open(self._salib_paramFile,'w')
for p in self.params:
#map each column of _samples to a parameter, using the inverse cdf to transform it
#into the appropriate distribution.
p.values = p.pdf.ppf(self._samples[:,i])
i+=1
f.write('{}\t{}\t{}\n'.format(p.name,p.pdf.range[0],p.pdf.range[1]))
f.close()
#save the samples, as constructed by SALib for verification later.
# --removed. save the file directly instead since it was verified
# that puq evaluates the output in the order specified in p.values for each param.
#np.savetxt(self._salib_realizationsFile_verify,self._samples)
np.savetxt(self._salib_realizationsFile,self._samples)
def __init__(self, params, num, response=True, iteration_cb=None):
PSweep.__init__(self, iteration_cb)
self.params = params
num = int(num)
self.num = num
self.response = response
self._start_at = 0
if self.response:
# To generate a complete response surface, use Uniform distributions
# with the same range as the original distributions.
for p in self.params:
p.values = UniformPDF(*p.pdf.range).random(num)
else:
for p in self.params:
p.values = p.pdf.random(num)
def __init__(self, params, level, iteration_cb=None):
PSweep.__init__(self, iteration_cb)
self.params = params
self.level = int(level)
self._calculate_params()
def __init__(self, params, level, iteration_cb=None):
PSweep.__init__(self, iteration_cb)
self.params = params
self.level = int(level)
self._calculate_params()
def __init__(self,
params,
numtrajectories,
levels,
gridjump,
response=False,
iteration_cb=None):
if response:
print('Morris method does not support creating response surfaces')
raise ValueError
#compare the state of the seeds to the ones set in the control script
a = pickle(random.getstate())
b = pickle(np.random.get_state())
f = open('random.getstate.2.txt', 'w')
f.write(a)
f.close()
f = open('np.random.get_state.2.txt', 'w')
f.write(b)
f.close()
#output of puq run is 1 elementary effect for each parameter per trajectory.
#Therefore, each parameter will have *numtrajectories* elementary effects.
PSweep.__init__(self, iteration_cb)
self.params = params
num = int(numtrajectories)
self.num = num * (len(params) + 1
) #total number of model runs for morris
self.response = response
self._start_at = 0
self.levels = levels
self.gridjump = gridjump
self._hf = None
self._salib_paramFile = '==SALib_morris_params==.txt'
self._salib_realizationsFile = '==SALib_morris_realizations==.txt'
self._salib_realizationsFile_verify = '==0SALib_morris_realizations==.txt'
self._salib_analysisFile = '==SALib_morris_outputs==.txt'
#generate morris samples N(D+1) x D numpy array. Rows are realizations, columns are params
#Each column is independent in the range [0,1]
#TODO: allow for correlation
self._samples = SAs.morris_oat.sample(N=num,
D=len(params),
num_levels=levels,
grid_jump=gridjump)
#puq will evaluate the output by picking a sample from each parameter in the
#order specified in p.values
i = 0
f = open(self._salib_paramFile, 'w')
for p in self.params:
#map each column of _samples to a parameter, using the inverse cdf to transform it
#into the appropriate distribution.
p.values = p.pdf.ppf(self._samples[:, i])
i += 1
f.write('{}\t{}\t{}\n'.format(p.name, p.pdf.range[0],
p.pdf.range[1]))
f.close()
#save the samples, as constructed by SALib for verification later.
# --removed. save the file directly instead since it was verified
# that puq evaluates the output in the order specified in p.values for each param.
#np.savetxt(self._salib_realizationsFile_verify,self._samples)
np.savetxt(self._salib_realizationsFile, self._samples)