def interpolate(points, t):
"""points=(x,y), list of x's and y's. "t" ... list of x, for which we want
to evaluate the dependence "y". returns a list of "y" evaluated at the grid
"t".
Assumes t is sorted in ascending order
Assumes points x axis are sorted in ascending order
"""
xx, yy = points
x = t
t0 = numarray.compress(x <= min(xx), x)
# t1 = numarray.compress( (x>min(xx)) & (x<max(xx)), x )
t1 = numarray.compress((x > min(xx)) & (x < xx[-1]), x)
# t2 = numarray.compress(x>=max(xx), x)
t2 = numarray.compress(x >= xx[-1], x)
if (xx[1] - xx[0] == 0):
slope0 = sign(yy[1] - yy[0]) * 1e20
else:
slope0 = (yy[1] - yy[0]) / (xx[1] - xx[0])
slope2 = (yy[-1] - yy[-2]) / (xx[-1] - xx[-2])
indices = numarray.searchsorted(xx, t1)
x0 = xx[indices - 1]
x1 = xx[indices]
y0 = yy[indices - 1]
y1 = yy[indices]
slope = (y1 - y0) / (x1 - x0)
y1 = slope * (t1 - x0) + y0
y0 = slope0 * (t0 - xx[0]) + yy[0] # extrapolate
y2 = slope2 * (t2 - xx[-1]) + yy[-1] # extrapolate
y = numarray.concatenate((y0, y1, y2))
return y
def calculate_spectrogram(input_array, framesize, hopsize, window_function = numarray.linear_algebra.mlab.hanning, keep_bands_until = 0, axis = 0): """Calculate the spectrogram.""" do_fft = lambda arr: abs(numarray.fft.real_fft(arr, axis = axis))[:keep_bands_until] keep_bands_until = keep_bands_until or int(framesize / 2) input_shape = map(None, numarray.shape(input_array)) window = window_function(framesize) if len(input_shape) > 1: window = transpose(array([list(window)] * input_shape[1])) # print "input_shape:", shape(input_array) zeros_shape = input_shape # this allows for both 1 and 2 dim inputs zeros_shape[0] = framesize / 2 input_array_plus_zeros = numarray.concatenate((numarray.zeros(zeros_shape), input_array, numarray.zeros(zeros_shape))) fft_range = range(0, len(input_array) - framesize, hopsize) fft_array = numarray.zeros(([len(fft_range)] + map(None, numarray.shape(do_fft(window)))), # do_fft(window) is used here because it gives the right shape numarray.Float32) * 1.0 # this *1.0 is necessary! for result_counter, input_counter in enumerate(fft_range): frame = window * input_array_plus_zeros[input_counter : input_counter + framesize] fft_array[result_counter] = 10 * numarray.log10(0.1 + do_fft(frame)) return fft_array
def interpolate(points,t):
"""points=(x,y), list of x's and y's. "t" ... list of x, for which we want
to evaluate the dependence "y". returns a list of "y" evaluated at the grid
"t".
Assumes t is sorted in ascending order
Assumes points x axis are sorted in ascending order
"""
xx,yy=points
x=t
t0 = numarray.compress(x<=min(xx), x)
# t1 = numarray.compress( (x>min(xx)) & (x<max(xx)), x )
t1 = numarray.compress( (x>min(xx)) & (x<xx[-1]), x )
# t2 = numarray.compress(x>=max(xx), x)
t2 = numarray.compress(x>=xx[-1], x)
if (xx[1]-xx[0] == 0):
slope0 = sign(yy[1]-yy[0])*1e20
else:
slope0 = (yy[1]-yy[0])/(xx[1]-xx[0])
slope2 = (yy[-1]-yy[-2])/(xx[-1]-xx[-2])
indices = numarray.searchsorted(xx,t1)
x0 = xx[indices-1]
x1 = xx[indices]
y0 = yy[indices-1]
y1 = yy[indices]
slope = (y1-y0)/(x1-x0)
y1 = slope*(t1-x0)+y0
y0 = slope0*(t0-xx[0])+yy[0] # extrapolate
y2 = slope2*(t2-xx[-1])+yy[-1] # extrapolate
y = numarray.concatenate((y0,y1,y2))
return y
def create_households_for_estimation(agent_set, dbcon):
estimation_set = HouseholdSet(in_base=dbcon, in_storage_type="mysql", in_place="households_for_estimation")
agent_set.unload_nonderived_attributes()
agent_set.load_dataset(attributes="*")
estimation_set.load_dataset(agent_set.get_nonderived_attribute_names())
for attr in agent_set.get_attribute_names():
agent_set.set[attr].set_data(concatenate((estimation_set.set[attr].get_data(), agent_set.set[attr].get_data())))
agent_set.update_id_mapping()
agent_set.update_size()
return (agent_set, arange(estimation_set.size()))
def create_households_for_estimation(agent_set, dbcon):
estimation_set = HouseholdSet(in_base = dbcon, in_storage_type="mysql", \
in_place="households_for_estimation")
agent_set.unload_nonderived_attributes()
agent_set.load_dataset(attributes='*')
estimation_set.load_dataset(agent_set.get_nonderived_attribute_names())
for attr in agent_set.get_attribute_names():
agent_set.set[attr].set_data(concatenate((estimation_set.set[attr].get_data(), agent_set.set[attr].get_data())))
agent_set.update_id_mapping()
agent_set.update_size()
return (agent_set, arange(estimation_set.size()))
def setParameters(self, mu = None, sigma = None, wi = None, sigma_type = 'full', \
tied_sigma = False, isAdjustable = False):
#============================================================
# set the mean :
# self.mean[i] = the mean for dimension i
# self.mean.shape = (self.nvalues, q1,q2,...,qn)
# where qi is the size of discrete parent i
if mu == None:
# set all mu to zeros
mu = na.zeros(shape=([self.nvalues]+self.discrete_parents_shape), \
type='Float32')
try:
mu = na.array(shape=[self.nvalues]+self.discrete_parents_shape, \
type='Float32')
except:
raise 'Could not convert mu to numarray of shape : %s, discrete parents = %s' %(str(self.discrete_parents_shape),
str([dp.name for dp in self.discrete_parents]))
self.mean = mu
#============================================================
# set the covariance :
# self.sigma[i,j] = the covariance between dimension i and j
# self.sigma.shape = (nvalues,nvalues,q1,q2,...,qn)
# where qi is the size of discrete parent i
if sigma == None:
eye = na.identity(self.nvalues, type = 'Float32')[...,na.NewAxis]
if len(self.discrete_parents) > 0:
q = reduce(lambda a,b:a*b,self.discrete_parents_shape) # number of different configurations for the parents
sigma = na.concatenate([eye]*q, axis=2)
sigma = na.array(sigma,shape=[self.nvalues,self.nvalues]+self.discrete_parents_shape)
try:
sigma = na.array(sigma, shape=[self.nvalues,self.nvalues]+self.discrete_parents_shape, type='Float32')
except:
raise 'Not a valid covariance matrix'
self.sigma = sigma
#============================================================
# set the weights :
# self.weights[i,j] = the regression for dimension i and continuous parent j
# self.weights.shape = (nvalues,x1,x2,...,xn,q1,q2,...,qn)
# where xi is the size of continuous parent i)
# and qi is the size of discrete parent i
if wi == None:
wi = na.ones(shape=[self.nvalues]+self.parents_shape, type='Float32')
try:
wi = na.array(wi, shape=[self.nvalues]+self.parents_shape, type='Float32')
except:
raise 'Not a valid weight'
self.weights = wi
def __init__(self, parent, framesize = None, hopsize = 100, number_of_vectors_used = 15): if not framesize: lp = len(parent) #logfs = min(int(scipy.log2(lp / 32)), 10) log2 = lambda x: math.log(x) / math.log(2) logfs = min(int(log2(lp / 32)), 10) framesize = pow(2, logfs) print framesize if hopsize > framesize / 4: hopsize = framesize / 4 self.hopsize = hopsize self.framesize = framesize #if len(parent) < framesize: # self._delete_from_parents() # raise UnderflowError, "Thingy is too small...\nHere I pull the plug in order to avoid a segfaulty thingy." input_array = parent self.feature_vectors = None interesting_parts = input_array[:, :number_of_vectors_used] interesting_parts = numarray.transpose(interesting_parts) for row in interesting_parts: specspectrum = calculate_spectrogram(row, framesize = framesize, hopsize = hopsize) import cPickle z, lambdas, EOFs = svdeofs.svdeofs(specspectrum) print ".", s = z[:, :15] svd_fft_fft_vectors = numarray.transpose(s) #print "svd_eofs ok!" if self.feature_vectors is None: self.feature_vectors = numarray.transpose(svd_fft_fft_vectors) else: self.feature_vectors = numarray.concatenate((self.feature_vectors, numarray.transpose(svd_fft_fft_vectors)), 1) #print "svd_fft_fft_vectors shapes:", shape(svd_fft_fft_vectors), shape(self.feature_vectors) z, lambdas, EOFs = svdeofs.svdeofs(self.feature_vectors) self.feature_vectors = z[:, :15] self.arr = self.feature_vectors
def file2matrix(fileName):
"""
Given a file "fileName", this script converts the contents of the
file to a numarray matrix of size (m,) or (m,n).
"""
# Open the file
open1 = open(fileName, 'r')
line = open1.readline()
# A single line from the file is read. This should be the number
# of columns. Spliting "line" results in a list, the length of
# which is the number of columns: cols
cols = len(line.split())
# x is the placeholder for the data.
# The placeholder matrix is made of concatenating
# every line that is read from the file.
# The type code is float.
x = numarray.zeros(cols, type='f')
while (line):
temp = []
list_elements = line.split()
for e in list_elements:
temp.append(float(e))
x = numarray.concatenate((x, numarray.array(temp)))
line = open1.readline()
open1.close()
# concatenate((x,x)) is along the dimension 1,
# each new addition is an increase in columns,
# the total length of x gives m*n. We determine
# the rows as
rows = len(x) / cols
# We have added an aritifical row, so if row == 2
# the file has tuple, hence the data consists of
# everything after our artificial set of columns
if rows == 2:
return x[cols:]
else:
# If there are m+1 rows, we resize the data to (m+1)xn
# and return everything other than the artificial first
# row of zeros
x = numarray.resize(x, (rows, cols))
return x[1:, :]
def scanSequence(mix, bg, seq,scoring='mix'):
"""
Scores all positions of a sequence with the given model and background.
@param mix: MixtureModel object
@param bg: background MixtureModel object
@param seq: sequence as list of nucleotides
@param scoring: flag to determine the scoring scheme used for the mixtures.
'compmax' means maximum density over the components, 'mix' means true mixture density
@return: list of position-wise log-odd scores
"""
# convert sequence to internal representation, alphabet of seq must be DNA
alph = mixture.Alphabet(['A','C','G','T'])
f = lambda x: alph.internal(x)
seq=map(f,seq)
dnr = mix.components[0].dist_nr
# init with dummy value at first position
s = numarray.array([[-1]+ seq[0:dnr-1]])
score = []
for i in range(dnr-1,len(seq),1):
# shift query sequence by one position
s[0] = numarray.concatenate( [s[0][1:],numarray.array([seq[i]])],0)
if scoring == 'compmax':
# score as maximum over components
c_m_l = numarray.zeros(mix.G,numarray.Float)
for i in range(mix.G):
c_m_l[i] = mix.components[i].pdf(s)[0]
m_l = c_m_l.max()
elif scoring == 'mix':
m_l = mix.pdf(s)[0]
bg_l = bg.pdf(s)[0]
score.append(m_l-bg_l)
return score
def __init__(self, v, mu = None, sigma = None, wi = None, \
sigma_type = 'full', tied_sigma = False, \
isAdjustable = True, ignoreFamily = False):
Distribution.__init__(self, v, isAdjustable=isAdjustable, \
ignoreFamily=ignoreFamily)
self.distribution_type = 'Gaussian'
# check that current node is continuous
if v.discrete:
raise 'Node must be continuous'
self.discrete_parents = [parent for parent in self.parents \
if parent.discrete]
self.continuous_parents = [parent for parent in self.parents \
if not parent.discrete]
self.discrete_parents_shape = [dp.nvalues for dp \
in self.discrete_parents]
self.parents_shape = [p.nvalues for p in self.parents]
if not self.parents_shape:
self.parents_shape = [0]
# set defaults
# set all mu to zeros
self.mean = na.zeros(shape=([self.nvalues] + \
self.discrete_parents_shape), type='Float32')
# set sigma to ones along the diagonal
eye = na.identity(self.nvalues, type = 'Float32')[..., na.NewAxis]
if len(self.discrete_parents) > 0:
q = reduce(lambda a, b:a * b, self.discrete_parents_shape) # number of different configurations for the parents
sigma = na.concatenate([eye] * q, axis=2)
self.sigma = na.array(sigma, shape=[self.nvalues, self.nvalues] + \
self.discrete_parents_shape)
# set weights to
self.weights = na.ones(shape=[self.nvalues] + self.parents_shape, type='Float32')
# set the parameters : mean, sigma, weights
self.setParameters(mu=mu, sigma=sigma, wi=wi, sigma_type=sigma_type, \
tied_sigma=tied_sigma, isAdjustable=isAdjustable)
def _updateVertexPosition( self ):
# Draw Colormap Line set
self.vertexPositions = numarray.concatenate( ( self.vertexPositions[0:256],
self.vertexPositions[256:512]),
axis = 1 ).copy()
self.vertexPositions.setshape( 512, 3 )
scalar = self.vertexPositions[:,1]
sclrMinVal = scalar.max()
sclrMaxVal = scalar.min()
fct = (len(self.cMapColors)-1) / (sclrMaxVal - sclrMinVal)
sclrIdx = fct * (scalar - sclrMinVal) + 0.5
sclrIdx = numarray.clip( sclrIdx, 0, len(self.cMapColors)-1 )
colorIdx = self.cmapRGBA[sclrIdx]
colorIdx = colorIdx.tolist()
colorIdx.reverse()
self.vertexPropertyObj.vertex.setValues( 0, self.vertexPositions )
self.vertexPropertyObj.orderedRGBA.setValues( 0, colorIdx )
plot.hline(0)
residplot.setTitle("%s: %i MeV, %.1f deg\n ks prob=%.2e" %
(self.irfs, energy, inclination, ks_prob[1]))
return energy, inclination, ks_prob[1]
def _interpolate(self, x, y, xval):
if xval > x[-1]:
return 1
indx = bisect.bisect(x, xval) - 1
yval = ((xval - x[indx]) / (x[indx + 1] - x[indx]) *
(y[indx + 1] - y[indx]) + y[indx])
return yval
if __name__ == '__main__':
seps = num.concatenate(
(num.arange(12, 19), num.arange(19, 21, 0.3), num.arange(21, 25)))
energies = (30, 100, 1000, 1e4)
# energies = (1e3, 3e3, 1e4, 3e4)
incs = (0, 10, 30)
nt = plot.newNTuple(([], [], []), ('energy', 'inc', 'ks prob'))
plot.Scatter(nt, 'energy', 'ks prob')
plot.Scatter(nt, 'inc', 'ks prob')
def createPlots(irfs, seps, energies, inclinations):
my_tests = PsfTests(irfs, seps)
for energy in energies:
for inclination in inclinations:
my_tests.plotResults(energy, inclination, plot_residuals=True)
nt.addRow(my_tests.plot_rspgenIntegral(energy, inclination))
def sample(self,
varlist=None,
dt=None,
tlo=None,
thi=None,
eventdata=None,
precise=False,
useGlobalTime=True):
"""Uniformly sample the named trajectory over range indicated.
If optional 'eventdata' argument included, the event points are included
in the mesh (e.g. for plotting purposes).
The order of variable names in varlist is ignored.
precise=True causes the trajectory position to be evaluated precisely
at the t values specified, which will invoke slow interpolation.
precise=False (default) causes the nearest underlying mesh positions
to be used, if available (otherwise the behaviour is the same as
precise=True)
If dt is not given, the underlying time mesh is used, if available.
"""
if varlist is None:
varlist_sorted = self.varnames
else:
assert isinstance(varlist, list), 'varlist argument must be a list'
varlist_sorted = copy.copy(varlist)
varlist_sorted.sort()
tlo_base = self.indepdomain.get(0)
thi_base = self.indepdomain.get(1)
if tlo is None:
tlo = tlo_base
elif useGlobalTime:
tlo = tlo - self.globalt0
if tlo < tlo_base:
tlo = tlo_base
if tlo >= thi_base:
raise ValueError, "tlo too large"
elif tlo < tlo_base:
tlo = tlo_base
elif tlo >= thi_base:
raise ValueError, "tlo too large"
if thi is None:
thi = thi_base
elif useGlobalTime:
thi = thi - self.globalt0
if thi > thi_base:
thi = thi_base
if thi <= tlo_base:
raise ValueError, "thi too small"
elif thi > thi_base:
thi = thi_base
elif thi <= tlo_base:
raise ValueError, "thi too small"
assert tlo < thi, 't start point must be less than t endpoint'
if dt is not None and dt >= abs(thi - tlo):
if precise:
print "dt = %f for interval [%f,%f]" % (dt, tlo, thi)
raise ValueError('dt must be smaller than time interval')
else:
dt = (thi - tlo) / 10.
## do_events = eventdata is not None
if not precise or dt is None:
# attempt to use underlying mesh for each variable,
# if available
meshdict = self.underlyingMesh()
meshes_ok = False
if meshdict is not None:
if not any([meshdict[v] is None for v in varlist_sorted]):
# ensure all vars' meshes are identical!
firstvar_tmesh = meshdict[varlist_sorted[0]][0]
if not type(firstvar_tmesh) in [Array, NArray]:
firstvar_tmesh = array(firstvar_tmesh)
meshes_ok = True
# explicitly checking that the arrays are the same
# is very slow -- just catch a resulting error later
# try:
# for v in varlist_sorted[1:]:
# assert all(meshdict[v][0] == firstvar_tmesh)
# meshes_ok = True
# except AssertionError:
# meshes_ok = False
if meshes_ok:
loix_a = firstvar_tmesh >= tlo
loix_a = loix_a.tolist()
try:
loix = loix_a.index(1)
except ValueError:
loix = 0
hiix_a = firstvar_tmesh > thi
hiix_a = hiix_a.tolist()
try:
hiix = hiix_a.index(1)
except ValueError:
hiix = len(firstvar_tmesh)
tmesh = firstvar_tmesh[loix:hiix]
#do_events = False
if dt is None:
if not meshes_ok:
raise ValueError("Underlying mesh of trajectory is not"
" the same for all variables: dt must "
"be specified in this case")
else:
tmesh = concatenate([arange(tlo, thi, dt), [thi]])
loix = 0
hiix = len(tmesh)
if eventdata: #and do_events:
if isinstance(eventdata, dict):
assert type(eventdata.values()[0]) in [Array, NArray, list], \
"eventdata must be list of times or dictionary of lists of times"
evtlist = orderEventData(eventdata, nonames=True)
elif type(eventdata) in [Array, NArray, list]:
evtlist = eventdata
else:
raise TypeError, \
"eventdata must be list of times or dictionary of lists of times"
if evtlist != []:
tmesh, ins_ixs = insertInOrder(tmesh, evtlist, True)
else:
ins_ixs = []
if len(tmesh) > 0:
if dt is None:
# meshes_ok already checked
## coorddict = {}
## for v in varlist_sorted:
## coorddict[v] = meshdict[v][1]
coorddict = dict(
zip(varlist_sorted,
[m[1][loix:hiix] for m in sortedDictValues(meshdict)]))
if eventdata: #and do_events:
# insert var values at events (SLOW!)
# can only insert to lists, so have to convert coorddict arrays
# to lists first.
for tpos in xrange(len(ins_ixs)):
tix = ins_ixs[tpos]
t = evtlist[tpos]
x = self(t, varlist_sorted)
for v in varlist_sorted:
try:
coorddict[v].insert(tix, x[v])
except AttributeError:
coorddict[v] = coorddict[v].tolist()
coorddict[v].insert(tix, x[v])
if useGlobalTime and self.globalt0 != 0:
tmesh = tmesh + self.globalt0
return Pointset({
'coorddict': coorddict,
'coordtype': Float,
'coordnames': varlist_sorted,
'indepvararray': tmesh,
'indepvarname': self.indepvarname,
'indepvartype': Float,
'norm': self._normord,
'name': self.name + "_sample"
})
if not precise:
# get closest mesh indices corresponding to tmesh
# times for first var
try:
try:
closest = findClosestArray(firstvar_tmesh, tmesh,
dt / 2)
find_ixs = True
except UnboundLocalError:
# meshes_ok==False and firstvar_tmesh never created
# so don't need to find indexes of closest meshpoints,
# just use original mesh
find_ixs = False
coorddict = {}
if find_ixs:
closest_unique = makeSeqUnique(closest, True, True)
if useGlobalTime and self.globalt0 != 0:
tmesh = firstvar_tmesh[
closest_unique] + self.globalt0
else:
tmesh = firstvar_tmesh[closest_unique]
for v in varlist_sorted:
dat = self.variables[v].output.datapoints[1]
try:
coorddict[v] = dat[closest_unique]
except TypeError:
dat = array(dat)
coorddict[v] = dat[closest_unique]
else:
for v in varlist_sorted:
coorddict[v] = self.variables[v].output.datapoints[
1]
return Pointset({
'coorddict': coorddict,
'coordtype': Float,
'coordnames': varlist_sorted,
'indepvararray': tmesh,
'indepvarname': self.indepvarname,
'indepvartype': Float,
'norm': self._normord,
'name': self.name + "_sample"
})
except (AttributeError, IndexError):
# meshes of variables did not match up, so
# continue to 'else' case, as if precise=True
pass
# else mesh not available for some variables so we have
# a mixed-source trajectory, and simple solution is to
# treat as if precise==True
# Cases: either precise == True or could not extract
# underlying meshes for some variables, or the meshes were not
# identical.
pset = Pointset({
'coordarray':
self(tmesh, varlist_sorted, useGlobalTime=False).toarray(),
'coordnames':
varlist_sorted,
'indepvararray':
array(tmesh),
'indepvarname':
self.indepvarname,
'name':
self.name + "_sample"
})
# if varlist was not sorted, call to self could potentially return
# an array in a different order to that of varlist.
if useGlobalTime and self.globalt0 != 0:
pset.indepvararray += self.globalt0
pset.makeIxMaps()
return pset
else:
return None
def sample(self, varlist=None, dt=None, tlo=None, thi=None,
eventdata=None, precise=False, useGlobalTime=True):
"""Uniformly sample the named trajectory over range indicated.
If optional 'eventdata' argument included, the event points are included
in the mesh (e.g. for plotting purposes).
The order of variable names in varlist is ignored.
precise=True causes the trajectory position to be evaluated precisely
at the t values specified, which will invoke slow interpolation.
precise=False (default) causes the nearest underlying mesh positions
to be used, if available (otherwise the behaviour is the same as
precise=True)
If dt is not given, the underlying time mesh is used, if available.
"""
if varlist is None:
varlist_sorted = self.varnames
else:
assert isinstance(varlist, list), 'varlist argument must be a list'
varlist_sorted = copy.copy(varlist)
varlist_sorted.sort()
tlo_base = self.indepdomain.get(0)
thi_base = self.indepdomain.get(1)
if tlo is None:
tlo = tlo_base
elif useGlobalTime:
tlo = tlo - self.globalt0
if tlo < tlo_base:
tlo = tlo_base
if tlo >= thi_base:
raise ValueError, "tlo too large"
elif tlo < tlo_base:
tlo = tlo_base
elif tlo >= thi_base:
raise ValueError, "tlo too large"
if thi is None:
thi = thi_base
elif useGlobalTime:
thi = thi - self.globalt0
if thi > thi_base:
thi = thi_base
if thi <= tlo_base:
raise ValueError, "thi too small"
elif thi > thi_base:
thi = thi_base
elif thi <= tlo_base:
raise ValueError, "thi too small"
assert tlo < thi, 't start point must be less than t endpoint'
if dt is not None and dt >= abs(thi-tlo):
if precise:
print "dt = %f for interval [%f,%f]"%(dt,tlo,thi)
raise ValueError('dt must be smaller than time interval')
else:
dt = (thi-tlo)/10.
## do_events = eventdata is not None
if not precise or dt is None:
# attempt to use underlying mesh for each variable,
# if available
meshdict = self.underlyingMesh()
meshes_ok = False
if meshdict is not None:
if not any([meshdict[v] is None for v in varlist_sorted]):
# ensure all vars' meshes are identical!
firstvar_tmesh = meshdict[varlist_sorted[0]][0]
if not type(firstvar_tmesh) in [Array, NArray]:
firstvar_tmesh = array(firstvar_tmesh)
meshes_ok = True
# explicitly checking that the arrays are the same
# is very slow -- just catch a resulting error later
# try:
# for v in varlist_sorted[1:]:
# assert all(meshdict[v][0] == firstvar_tmesh)
# meshes_ok = True
# except AssertionError:
# meshes_ok = False
if meshes_ok:
loix_a = firstvar_tmesh>=tlo
loix_a = loix_a.tolist()
try:
loix = loix_a.index(1)
except ValueError:
loix = 0
hiix_a = firstvar_tmesh>thi
hiix_a = hiix_a.tolist()
try:
hiix = hiix_a.index(1)
except ValueError:
hiix = len(firstvar_tmesh)
tmesh = firstvar_tmesh[loix:hiix]
#do_events = False
if dt is None:
if not meshes_ok:
raise ValueError("Underlying mesh of trajectory is not"
" the same for all variables: dt must "
"be specified in this case")
else:
tmesh = concatenate([arange(tlo, thi, dt),[thi]])
loix = 0
hiix = len(tmesh)
if eventdata: #and do_events:
if isinstance(eventdata, dict):
assert type(eventdata.values()[0]) in [Array, NArray, list], \
"eventdata must be list of times or dictionary of lists of times"
evtlist = orderEventData(eventdata, nonames=True)
elif type(eventdata) in [Array, NArray, list]:
evtlist = eventdata
else:
raise TypeError, \
"eventdata must be list of times or dictionary of lists of times"
if evtlist != []:
tmesh, ins_ixs = insertInOrder(tmesh, evtlist, True)
else:
ins_ixs = []
if len(tmesh) > 0:
if dt is None:
# meshes_ok already checked
## coorddict = {}
## for v in varlist_sorted:
## coorddict[v] = meshdict[v][1]
coorddict = dict(zip(varlist_sorted,
[m[1][loix:hiix] for m in sortedDictValues(meshdict)]))
if eventdata: #and do_events:
# insert var values at events (SLOW!)
# can only insert to lists, so have to convert coorddict arrays
# to lists first.
for tpos in xrange(len(ins_ixs)):
tix = ins_ixs[tpos]
t = evtlist[tpos]
x = self(t, varlist_sorted)
for v in varlist_sorted:
try:
coorddict[v].insert(tix, x[v])
except AttributeError:
coorddict[v] = coorddict[v].tolist()
coorddict[v].insert(tix, x[v])
if useGlobalTime and self.globalt0 != 0:
tmesh = tmesh + self.globalt0
return Pointset({'coorddict': coorddict,
'coordtype': Float,
'coordnames': varlist_sorted,
'indepvararray': tmesh,
'indepvarname': self.indepvarname,
'indepvartype': Float,
'norm': self._normord,
'name': self.name + "_sample"})
if not precise:
# get closest mesh indices corresponding to tmesh
# times for first var
try:
try:
closest = findClosestArray(firstvar_tmesh,tmesh,dt/2)
find_ixs = True
except UnboundLocalError:
# meshes_ok==False and firstvar_tmesh never created
# so don't need to find indexes of closest meshpoints,
# just use original mesh
find_ixs = False
coorddict = {}
if find_ixs:
closest_unique = makeSeqUnique(closest, True, True)
if useGlobalTime and self.globalt0 != 0:
tmesh = firstvar_tmesh[closest_unique] + self.globalt0
else:
tmesh = firstvar_tmesh[closest_unique]
for v in varlist_sorted:
dat = self.variables[v].output.datapoints[1]
try:
coorddict[v] = dat[closest_unique]
except TypeError:
dat = array(dat)
coorddict[v] = dat[closest_unique]
else:
for v in varlist_sorted:
coorddict[v] = self.variables[v].output.datapoints[1]
return Pointset({'coorddict': coorddict,
'coordtype': Float,
'coordnames': varlist_sorted,
'indepvararray': tmesh,
'indepvarname': self.indepvarname,
'indepvartype': Float,
'norm': self._normord,
'name': self.name + "_sample"})
except (AttributeError, IndexError):
# meshes of variables did not match up, so
# continue to 'else' case, as if precise=True
pass
# else mesh not available for some variables so we have
# a mixed-source trajectory, and simple solution is to
# treat as if precise==True
# Cases: either precise == True or could not extract
# underlying meshes for some variables, or the meshes were not
# identical.
pset = Pointset({'coordarray': self(tmesh, varlist_sorted,
useGlobalTime=False).toarray(),
'coordnames': varlist_sorted,
'indepvararray': array(tmesh),
'indepvarname': self.indepvarname,
'name': self.name + "_sample"})
# if varlist was not sorted, call to self could potentially return
# an array in a different order to that of varlist.
if useGlobalTime and self.globalt0 != 0:
pset.indepvararray += self.globalt0
pset.makeIxMaps()
return pset
else:
return None
def getDistribution(self):
self.distributionList = []
node = self.xbn.childNodes
if self.version == "1.0":
bnmodel = node[0].childNodes
statdyndist = bnmodel[1].childNodes
distribution = statdyndist[8].childNodes
else:
bnmodel = node[1].childNodes
statdyndist = bnmodel[1].childNodes
distribution = statdyndist[9].childNodes
for dist in distribution:
d = Distribution()
d.condelem = []
d.dpiIndex = []
d.dpiData = []
if dist.nodeType == Node.ELEMENT_NODE:
attrs = dist.attributes
for attrName in attrs.keys():
attrNode = attrs.get(attrName)
attrValue = attrNode.nodeValue
if attrName == "TYPE":
d.type = attrValue
for distInfos in dist.childNodes:
if distInfos.nodeType == Node.ELEMENT_NODE:
if distInfos.nodeName == "CONDSET":
for elem in distInfos.childNodes:
if elem.nodeType == Node.ELEMENT_NODE:
attrsb = elem.attributes
d.condelem.append(attrsb.get(attrsb.keys()[0]).nodeValue)
elif distInfos.nodeName == "PRIVATE":
if distInfos.nodeType == Node.ELEMENT_NODE:
attrsb = distInfos.attributes
d.name = attrsb.get(attrsb.keys()[0]).nodeValue
elif distInfos.nodeName == "DPIS":
for dpi in distInfos.childNodes:
if dpi.nodeName == "DPI":
d.dpiData.append(dpi.childNodes[0].nodeValue)
attrs = dpi.attributes
for attrName in attrs.keys():
attrNode = attrs.get(attrName)
attrValue = attrNode.nodeValue
if attrName == "INDEXES":
d.dpiIndex.append(attrValue)
if dist.nodeType == Node.ELEMENT_NODE:
self.distributionList.append(d)
for d in self.distributionList:
dist = self.G.v[d.name].distribution # the distribution class into the BNet
#---TODO: what about gaussians ???
dist.distribution_type = 'Multinomial'
if d.type == 'ci':
# conditionally independant values are defined
# fill the matrix with the conditionally independant term
new = array([float(da) for da in d.dpiData[0].split()],type='Float32') # transform a string into a numarray
for pa in dist.family[1:]:
new = new[...,NewAxis]
n_states = pa.nvalues # number of states for each parent
new = concatenate([new]*n_states, axis=-1)
# replace all values in the distribution with the ci values
dist[:]=new
if len(d.dpiIndex):
# when multiple elements (nodes with parents)
for data,index in zip(d.dpiData,d.dpiIndex):
# data, index are strings containing the data and index
ii = tuple([int(i) for i in index.split()]) # transform the string into a tuple of integers
# create a dictionnary with the name of the dimension and the value it takes
dictin = {} # e.g. dictin = {'Alternator':1,'FanBelt':0}
for pa,iii in zip(d.condelem,ii):
dictin[pa] = iii
dd = array([float(da) for da in data.split()],type='Float32') # transform a string into a numarray
dist[dictin] = dd
else:
# for nodes with no parents
# simply insert the data into the matrix
dd = array([float(da) for da in d.dpiData[0].split()],type='Float32')
dist[:] = dd
return self.distributionList