def split_raw_record_name(line):
"""Splits the raw record name of the first line of a record from the rest of the record"""
m = re.match(r'(\s*\$[A-za-z]+)(.*)', line, flags=re.MULTILINE | re.DOTALL)
if m:
return m.group(1, 2)
else:
raise ModelSyntaxError(f'Bad record name in: {line}')
def validate(self):
in_problem = False
for record in self.records:
if in_problem and record.name == 'SIZES':
raise ModelSyntaxError('The SIZES record must come before the first PROBLEM record')
elif record.name == 'PROBLEM':
in_problem = True
if hasattr(record, 'validate'):
record.validate()
def get_pred_pk_record(self):
pred = self.control_stream.get_records('PRED')
if not pred:
pk = self.control_stream.get_records('PK')
if not pk:
raise ModelSyntaxError('Model has no $PK or $PRED')
return pk[0]
else:
return pred[0]
def estimation_steps(self):
try:
return self._estimation_steps
except AttributeError:
pass
steps = []
records = self.control_stream.get_records('ESTIMATION')
covrec = self.control_stream.get_records('COVARIANCE')
for record in records:
value = record.get_option('METHOD')
if value is None or value == '0' or value == 'ZERO':
name = 'fo'
elif value == '1' or value == 'CONDITIONAL' or value == 'COND':
name = 'foce'
else:
if value in list_supported_est():
name = value
else:
raise ModelSyntaxError(
f'Non-recognized estimation method in: {str(record.root)}'
)
if record.has_option('INTERACTION') or record.has_option('INTER'):
interaction = True
else:
interaction = False
if covrec:
cov = True
else:
cov = False
options_stored = ['METHOD', 'METH', 'INTERACTION', 'INTER']
options_left = [
option for option in record.all_options
if option.key not in options_stored
]
meth = EstimationMethod(name,
interaction=interaction,
cov=cov,
options=options_left)
steps.append(meth)
self._estimation_steps = steps
self._old_estimation_steps = copy.deepcopy(steps)
return steps
def _block_flags(self):
"""Get a tuple of all interesting flags for block"""
fix = bool(self.root.find('FIX'))
var = bool(self.root.find('VAR'))
sd = bool(self.root.find('SD'))
cov = bool(self.root.find('COV'))
corr = bool(self.root.find('CORR'))
cholesky = bool(self.root.find('CHOLESKY'))
for node in self.root.all('omega'):
if node.find('FIX'):
if fix:
raise ModelSyntaxError(
'Cannot specify option FIX more than once')
else:
fix = True
if node.find('VAR'):
if var or sd or cholesky:
raise ModelSyntaxError(
'Cannot specify either option VARIANCE, SD or '
'CHOLESKY more than once')
else:
var = True
if node.find('SD'):
if sd or var or cholesky:
raise ModelSyntaxError(
'Cannot specify either option VARIANCE, SD or '
'CHOLESKY more than once')
else:
sd = True
if node.find('COV'):
if cov or corr:
raise ModelSyntaxError(
'Cannot specify either option COVARIANCE or '
'CORRELATION more than once')
else:
cov = True
if node.find('CORR'):
if corr or cov:
raise ModelSyntaxError(
'Cannot specify either option COVARIANCE or '
'CORRELATION more than once')
else:
corr = True
if node.find('CHOLESKY'):
if cholesky or var or sd:
raise ModelSyntaxError(
'Cannot specify either option VARIANCE, SD or '
'CHOLESKY more than once')
else:
cholesky = True
return fix, sd, corr, cholesky
def _find_rates(model, ncomps):
pkrec = model.control_stream.get_records('PK')[0]
for stat in pkrec.statements:
if hasattr(stat, 'symbol'):
name = stat.symbol.name
m = re.match(r'^K(\d+)(T\d+)?$', name)
if m:
if m.group(2):
from_n = int(m.group(1))
to_n = int(m.group(2)[1:])
else:
n = m.group(1)
if len(n) == 2:
from_n = int(n[0])
to_n = int(n[1])
elif len(n) == 3:
f1 = int(n[0])
t1 = int(n[1:])
f2 = int(n[0:2])
t2 = int(n[2:])
q1 = f1 <= ncomps and t1 <= ncomps and t1 != 0
q2 = f2 <= ncomps and t2 <= ncomps
if q1 and q2:
raise ModelSyntaxError(
f'Rate parameter {n} is ambiguous. ' f'Use the KiTj notation.'
)
if q1:
from_n = f1
to_n = t1
elif q2:
from_n = f2
to_n = t2
else:
# Too large to or from compartment index. What would NONMEM do?
# Could also be too large later
continue
elif len(n) == 4:
from_n = int(n[0:2])
to_n = int(n[2:])
if to_n == 0:
to_n = ncomps
yield from_n, to_n, symbol(name)
def compartmental_model(model, advan, trans, des=None):
if advan == 'ADVAN1':
cm = CompartmentalSystem()
central = cm.add_compartment('CENTRAL')
output = cm.add_compartment('OUTPUT')
cm.add_flow(central, output, _advan1and2_trans(trans))
dose = _dosing(model, 1)
central.dose = dose
central.lag_time = get_alag(model, 1)
ass = _f_link_assignment(model, central)
comp_map = {'CENTRAL': 1, 'OUTPUT': 2}
elif advan == 'ADVAN2':
cm = CompartmentalSystem()
depot = cm.add_compartment('DEPOT')
central = cm.add_compartment('CENTRAL')
output = cm.add_compartment('OUTPUT')
cm.add_flow(central, output, _advan1and2_trans(trans))
cm.add_flow(depot, central, symbol('KA'))
dose = _dosing(model, 1)
depot.dose = dose
depot.lag_time = get_alag(model, 1)
central.lag_time = get_alag(model, 2)
ass = _f_link_assignment(model, central)
comp_map = {'DEPOT': 1, 'CENTRAL': 2, 'OUTPUT': 3}
elif advan == 'ADVAN3':
cm = CompartmentalSystem()
central = cm.add_compartment('CENTRAL')
peripheral = cm.add_compartment('PERIPHERAL')
output = cm.add_compartment('OUTPUT')
k, k12, k21 = _advan3_trans(trans)
cm.add_flow(central, output, k)
cm.add_flow(central, peripheral, k12)
cm.add_flow(peripheral, central, k21)
dose = _dosing(model, 1)
central.dose = dose
central.lag_time = get_alag(model, 1)
peripheral.lag_time = get_alag(model, 2)
ass = _f_link_assignment(model, central)
comp_map = {'CENTRAL': 1, 'PERIPHERAL': 2, 'OUTPUT': 3}
elif advan == 'ADVAN4':
cm = CompartmentalSystem()
depot = cm.add_compartment('DEPOT')
central = cm.add_compartment('CENTRAL')
peripheral = cm.add_compartment('PERIPHERAL')
output = cm.add_compartment('OUTPUT')
k, k23, k32, ka = _advan4_trans(trans)
cm.add_flow(depot, central, ka)
cm.add_flow(central, output, k)
cm.add_flow(central, peripheral, k23)
cm.add_flow(peripheral, central, k32)
dose = _dosing(model, 1)
depot.dose = dose
depot.lag_time = get_alag(model, 1)
central.lag_time = get_alag(model, 2)
peripheral.lag_time = get_alag(model, 3)
ass = _f_link_assignment(model, central)
comp_map = {'DEPOT': 1, 'CENTRAL': 2, 'PERIPHERAL': 3, 'OUTPUT': 4}
elif advan == 'ADVAN5' or advan == 'ADVAN7':
cm = CompartmentalSystem()
modrec = model.control_stream.get_records('MODEL')[0]
defobs = None
defdose = None
central = None
depot = None
first_dose = None
compartments = []
for i, (name, opts) in enumerate(modrec.compartments()):
comp = cm.add_compartment(name)
if 'DEFOBSERVATION' in opts:
defobs = comp
if 'DEFDOSE' in opts:
defdose = comp
dose_no = i + 1
if name == 'CENTRAL':
central = comp
elif name == 'DEPOT':
depot = comp
depot_no = i + 1
if first_dose is None and 'NODOSE' not in opts:
first_dose = comp
first_dose_no = i + 1
compartments.append(comp)
output = cm.add_compartment('OUTPUT')
compartments.append(output)
comp_map = {comp.name: i + 1 for i, comp in enumerate(compartments)}
ncomp = i + 2
if not defobs:
if central:
defobs = central
else:
defobs = compartments[0]
if not defdose:
if depot:
defdose = depot
dose_no = depot_no
elif first_dose is not None:
defdose = first_dose
dose_no = first_dose_no
else:
raise ModelSyntaxError('Dosing compartment is unknown')
for from_n, to_n, rate in _find_rates(model, ncomp):
cm.add_flow(compartments[from_n - 1], compartments[to_n - 1], rate)
dose = _dosing(model, dose_no)
defdose.dose = dose
for i, comp in enumerate(compartments):
if i == len(compartments) - 1:
break
comp.lag_time = get_alag(model, i)
ass = _f_link_assignment(model, defobs)
elif advan == 'ADVAN10':
cm = CompartmentalSystem()
central = cm.add_compartment('CENTRAL')
output = cm.add_compartment('OUTPUT')
vm = symbol('VM')
km = symbol('KM')
dose = _dosing(model, 1)
central.dose = dose
t = symbol('t')
cm.add_flow(central, output, vm / (km + sympy.Function(central.amount.name)(t)))
central.lag_time = get_alag(model, 1)
ass = _f_link_assignment(model, central)
comp_map = {'CENTRAL': 1, 'OUTPUT': 2}
elif advan == 'ADVAN11':
cm = CompartmentalSystem()
central = cm.add_compartment('CENTRAL')
per1 = cm.add_compartment('PERIPHERAL1')
per2 = cm.add_compartment('PERIPHERAL2')
output = cm.add_compartment('OUTPUT')
k, k12, k21, k13, k31 = _advan11_trans(trans)
cm.add_flow(central, output, k)
cm.add_flow(central, per1, k12)
cm.add_flow(per1, central, k21)
cm.add_flow(central, per2, k13)
cm.add_flow(per2, central, k31)
dose = _dosing(model, 1)
central.dose = dose
central.lag_time = get_alag(model, 1)
per1.lag_time = get_alag(model, 2)
per2.lag_time = get_alag(model, 3)
ass = _f_link_assignment(model, central)
comp_map = {'CENTRAL': 1, 'PERIPHERAL1': 2, 'PERIPHERAL2': 3, 'OUTPUT': 4}
elif advan == 'ADVAN12':
cm = CompartmentalSystem()
depot = cm.add_compartment('DEPOT')
central = cm.add_compartment('CENTRAL')
per1 = cm.add_compartment('PERIPHERAL1')
per2 = cm.add_compartment('PERIPHERAL2')
output = cm.add_compartment('OUTPUT')
k, k23, k32, k24, k42, ka = _advan12_trans(trans)
cm.add_flow(depot, central, ka)
cm.add_flow(central, output, k)
cm.add_flow(central, per1, k23)
cm.add_flow(per1, central, k32)
cm.add_flow(central, per2, k24)
cm.add_flow(per2, central, k42)
dose = _dosing(model, 1)
depot.dose = dose
depot.lag_time = get_alag(model, 1)
central.lag_time = get_alag(model, 2)
per1.lag_time = get_alag(model, 3)
per2.lag_time = get_alag(model, 4)
ass = _f_link_assignment(model, central)
comp_map = {'DEPOT': 1, 'CENTRAL': 2, 'PERIPHERAL1': 3, 'PERIPHERAL2': 4, 'OUTPUT': 5}
elif des and all(str(s.symbol).startswith('DADT') for s in des.statements):
rec_model = model.control_stream.get_records('MODEL')[0]
subs_dict, comp_names = dict(), dict()
comps = [c for c, _ in rec_model.compartments()]
t = symbol('t')
for i, c in enumerate(comps, 1):
a = Function(f'A_{c}')
subs_dict[f'DADT({i})'] = Derivative(a(t))
subs_dict[f'A({i})'] = a(t)
comp_names[f'A({i})'] = a
sset = des.statements
sset.subs(subs_dict)
a_out = Function('A_OUTPUT')
dose = _dosing(model, 1)
ics = {v(0): sympy.Integer(0) for v in comp_names.values()}
ics[a_out(0)] = sympy.Integer(0)
ics[comp_names['A(1)'](0)] = dose.amount
dadt_dose = sset.find_assignment(str(subs_dict['DADT(1)']))
if len(comps) > 1:
dadt_rest = [Eq(s.symbol, s.expression) for s in sset if s != dadt_dose]
lhs_sum = dadt_dose.expression
for eq in dadt_rest:
lhs_sum += eq.rhs
dadt_out = Eq(Derivative(a_out(t)), -lhs_sum)
dadt_rest.append(sympy.simplify(dadt_out))
else:
dadt_rest = [Eq(Derivative(a_out(t)), dadt_dose.expression * -1)]
if isinstance(dose, Infusion):
dadt_dose.expression += Piecewise(
(dose.amount / dose.duration, dose.duration > t), (0, True)
)
ics[comp_names['A(1)'](0)] = sympy.Integer(0)
eqs = [Eq(dadt_dose.symbol, dadt_dose.expression)] + dadt_rest
ode = ExplicitODESystem(eqs, ics)
ass = _f_link_assignment(model, symbol('A_CENTRAL'))
return ode, ass
else:
return None
model._compartment_map = comp_map
return cm, ass
def validate(self):
""" Syntax validation of this data record
Assumes only on $DATA exists in this $PROBLEM.
"""
if len(self.root.all('ignchar')) > 1:
raise ModelSyntaxError('More than one IGNORE=c')
def compartmental_model(model, advan, trans):
if advan == 'ADVAN1':
cm = CompartmentalSystem()
central = cm.add_compartment('CENTRAL')
output = cm.add_compartment('OUTPUT')
cm.add_flow(central, output, _advan1and2_trans(trans))
dose = _dosing(model, 1)
central.dose = dose
central.lag_time = get_alag(model, 1)
ass = _f_link_assignment(model, central)
comp_map = {'CENTRAL': 1, 'OUTPUT': 2}
elif advan == 'ADVAN2':
cm = CompartmentalSystem()
depot = cm.add_compartment('DEPOT')
central = cm.add_compartment('CENTRAL')
output = cm.add_compartment('OUTPUT')
cm.add_flow(central, output, _advan1and2_trans(trans))
cm.add_flow(depot, central, symbol('KA'))
dose = _dosing(model, 1)
depot.dose = dose
depot.lag_time = get_alag(model, 1)
central.lag_time = get_alag(model, 2)
ass = _f_link_assignment(model, central)
comp_map = {'DEPOT': 1, 'CENTRAL': 2, 'OUTPUT': 3}
elif advan == 'ADVAN3':
cm = CompartmentalSystem()
central = cm.add_compartment('CENTRAL')
peripheral = cm.add_compartment('PERIPHERAL')
output = cm.add_compartment('OUTPUT')
k, k12, k21 = _advan3_trans(trans)
cm.add_flow(central, output, k)
cm.add_flow(central, peripheral, k12)
cm.add_flow(peripheral, central, k21)
dose = _dosing(model, 1)
central.dose = dose
central.lag_time = get_alag(model, 1)
peripheral.lag_time = get_alag(model, 2)
ass = _f_link_assignment(model, central)
comp_map = {'CENTRAL': 1, 'PERIPHERAL': 2, 'OUTPUT': 3}
elif advan == 'ADVAN4':
cm = CompartmentalSystem()
depot = cm.add_compartment('DEPOT')
central = cm.add_compartment('CENTRAL')
peripheral = cm.add_compartment('PERIPHERAL')
output = cm.add_compartment('OUTPUT')
k, k23, k32, ka = _advan4_trans(trans)
cm.add_flow(depot, central, ka)
cm.add_flow(central, output, k)
cm.add_flow(central, peripheral, k23)
cm.add_flow(peripheral, central, k32)
dose = _dosing(model, 1)
depot.dose = dose
depot.lag_time = get_alag(model, 1)
central.lag_time = get_alag(model, 2)
peripheral.lag_time = get_alag(model, 3)
ass = _f_link_assignment(model, central)
comp_map = {'DEPOT': 1, 'CENTRAL': 2, 'PERIPHERAL': 3, 'OUTPUT': 4}
elif advan == 'ADVAN5' or advan == 'ADVAN7':
cm = CompartmentalSystem()
modrec = model.control_stream.get_records('MODEL')[0]
defobs = None
defdose = None
central = None
depot = None
first_dose = None
compartments = []
for i, (name, opts) in enumerate(modrec.compartments()):
comp = cm.add_compartment(name)
if 'DEFOBSERVATION' in opts:
defobs = comp
if 'DEFDOSE' in opts:
defdose = comp
dose_no = i + 1
if name == 'CENTRAL':
central = comp
elif name == 'DEPOT':
depot = comp
depot_no = i + 1
if first_dose is None and 'NODOSE' not in opts:
first_dose = comp
first_dose_no = i + 1
compartments.append(comp)
output = cm.add_compartment('OUTPUT')
compartments.append(output)
comp_map = {comp.name: i + 1 for i, comp in enumerate(compartments)}
ncomp = i + 2
if not defobs:
if central:
defobs = central
else:
defobs = compartments[0]
if not defdose:
if depot:
defdose = depot
dose_no = depot_no
elif first_dose is not None:
defdose = first_dose
dose_no = first_dose_no
else:
raise ModelSyntaxError('Dosing compartment is unknown')
for from_n, to_n, rate in _find_rates(model, ncomp):
cm.add_flow(compartments[from_n - 1], compartments[to_n - 1], rate)
dose = _dosing(model, dose_no)
defdose.dose = dose
for i, comp in enumerate(compartments):
if i == len(compartments) - 1:
break
comp.lag_time = get_alag(model, i)
ass = _f_link_assignment(model, defobs)
elif advan == 'ADVAN10':
cm = CompartmentalSystem()
central = cm.add_compartment('CENTRAL')
output = cm.add_compartment('OUTPUT')
vm = symbol('VM')
km = symbol('KM')
dose = _dosing(model, 1)
central.dose = dose
t = symbol('t')
cm.add_flow(central, output, vm / (km + sympy.Function(central.amount.name)(t)))
central.lag_time = get_alag(model, 1)
ass = _f_link_assignment(model, central)
comp_map = {'CENTRAL': 1, 'OUTPUT': 2}
elif advan == 'ADVAN11':
cm = CompartmentalSystem()
central = cm.add_compartment('CENTRAL')
per1 = cm.add_compartment('PERIPHERAL1')
per2 = cm.add_compartment('PERIPHERAL2')
output = cm.add_compartment('OUTPUT')
k, k12, k21, k13, k31 = _advan11_trans(trans)
cm.add_flow(central, output, k)
cm.add_flow(central, per1, k12)
cm.add_flow(per1, central, k21)
cm.add_flow(central, per2, k13)
cm.add_flow(per2, central, k31)
dose = _dosing(model, 1)
central.dose = dose
central.lag_time = get_alag(model, 1)
per1.lag_time = get_alag(model, 2)
per2.lag_time = get_alag(model, 3)
ass = _f_link_assignment(model, central)
comp_map = {'CENTRAL': 1, 'PERIPHERAL1': 2, 'PERIPHERAL2': 3, 'OUTPUT': 4}
elif advan == 'ADVAN12':
cm = CompartmentalSystem()
depot = cm.add_compartment('DEPOT')
central = cm.add_compartment('CENTRAL')
per1 = cm.add_compartment('PERIPHERAL1')
per2 = cm.add_compartment('PERIPHERAL2')
output = cm.add_compartment('OUTPUT')
k, k23, k32, k24, k42, ka = _advan12_trans(trans)
cm.add_flow(depot, central, ka)
cm.add_flow(central, output, k)
cm.add_flow(central, per1, k23)
cm.add_flow(per1, central, k32)
cm.add_flow(central, per2, k24)
cm.add_flow(per2, central, k42)
dose = _dosing(model, 1)
depot.dose = dose
depot.lag_time = get_alag(model, 1)
central.lag_time = get_alag(model, 2)
per1.lag_time = get_alag(model, 3)
per2.lag_time = get_alag(model, 4)
ass = _f_link_assignment(model, central)
comp_map = {'DEPOT': 1, 'CENTRAL': 2, 'PERIPHERAL1': 3, 'PERIPHERAL2': 4, 'OUTPUT': 5}
else:
return None
model._compartment_map = comp_map
return cm, ass
def parameters(self, start_omega, previous_size):
"""Get a ParameterSet for this omega record
"""
row = start_omega
block = self.root.find('block')
bare_block = self.root.find('bare_block')
same = bool(self.root.find('same'))
parameters = ParameterSet()
if not (block or bare_block):
for node in self.root.all('diag_item'):
init = node.init.NUMERIC
fixed = bool(node.find('FIX'))
sd = bool(node.find('SD'))
var = bool(node.find('VAR'))
n = node.n.INT if node.find('n') else 1
if sd and var:
raise ModelSyntaxError(f'Initial estimate for {self.name.upper} cannot be both'
f' on SD and VAR scale\n{self.root}')
if init == 0 and not fixed:
raise ModelSyntaxError(f'If initial estimate for {self.name.upper} is 0 it'
f' must be set to FIX')
if sd:
init = init ** 2
for _ in range(n):
name = f'{self.name}({row},{row})'
param = Parameter(name, init, lower=0, fix=fixed)
parameters.add(param)
row += 1
size = 1
next_omega = row
else:
inits = []
if bare_block:
size = previous_size
else:
size = self.root.block.size.INT
fix, sd, corr, cholesky = self._block_flags()
for node in self.root.all('omega'):
init = node.init.NUMERIC
n = node.n.INT if node.find('n') else 1
inits += [init] * n
if not same:
if size != pharmpy.math.triangular_root(len(inits)):
raise ModelSyntaxError('Wrong number of inits in BLOCK')
if not cholesky:
A = pharmpy.math.flattened_to_symmetric(inits)
if corr:
for i in range(size):
for j in range(size):
if i != j:
if sd:
A[i, j] = A[i, i] * A[j, j] * A[i, j]
else:
A[i, j] = math.sqrt(A[i, i]) * math.sqrt(A[j, j]) * A[i, j]
if sd:
np.fill_diagonal(A, A.diagonal()**2)
else:
L = np.zeros((size, size))
inds = np.tril_indices_from(L)
L[inds] = inits
A = L @ L.T
for i in range(size):
for j in range(0, i + 1):
name = f'{self.name}({i + start_omega},{j + start_omega})'
init = A[i, j]
lower = None if i != j else 0
param = Parameter(name, init, lower=lower, fix=fix)
parameters.add(param)
next_omega = start_omega + size
return parameters, next_omega, size
def parameters(self, start_omega, previous_size, seen_labels=None):
"""Get a Parameters for this omega record"""
row = start_omega
block = self.root.find('block')
bare_block = self.root.find('bare_block')
same = bool(self.root.find('same'))
parameters = Parameters()
coords = []
try:
self.comment_map
except AttributeError:
self.comment_map = dict()
if seen_labels is None:
seen_labels = set()
if not (block or bare_block):
for node in self.root.all('diag_item'):
init = node.init.NUMERIC
fixed = bool(node.find('FIX'))
sd = bool(node.find('SD'))
var = bool(node.find('VAR'))
n = node.n.INT if node.find('n') else 1
if sd and var:
raise ModelSyntaxError(
f'Initial estimate for {self.name.upper()} cannot be both'
f' on SD and VAR scale\n{self.root}'
)
if init == 0 and not fixed:
raise ModelSyntaxError(
f'If initial estimate for {self.name.upper()} is 0 it'
f' must be set to FIX'
)
if sd:
init = init ** 2
for _ in range(n):
name = self._find_label(node, seen_labels)
comment = self._get_name(node)
if not name:
name = f'{self.name}({row},{row})'
if comment:
self.comment_map[name] = comment
seen_labels.add(name)
coords.append((row, row))
param = Parameter(name, init, lower=0, fix=fixed)
parameters.append(param)
row += 1
size = 1
next_omega = row
else:
inits = []
if bare_block:
size = previous_size
else:
size = self.root.block.size.INT
fix, sd, corr, cholesky = self._block_flags()
labels = []
for node in self.root.all('omega'):
init = node.init.NUMERIC
n = node.n.INT if node.find('n') else 1
inits += [init] * n
name = self._find_label(node, seen_labels)
if name is not None:
seen_labels.add(name)
labels.append(name)
if n > 1:
labels.extend([None] * (n - 1))
if not same:
if size != pharmpy.math.triangular_root(len(inits)):
raise ModelSyntaxError('Wrong number of inits in BLOCK')
if not cholesky:
A = pharmpy.math.flattened_to_symmetric(inits)
if corr:
for i in range(size):
for j in range(size):
if i != j:
if sd:
A[i, j] = A[i, i] * A[j, j] * A[i, j]
else:
A[i, j] = math.sqrt(A[i, i]) * math.sqrt(A[j, j]) * A[i, j]
if sd:
np.fill_diagonal(A, A.diagonal() ** 2)
else:
L = np.zeros((size, size))
inds = np.tril_indices_from(L)
L[inds] = inits
A = L @ L.T
label_index = 0
for i in range(size):
for j in range(0, i + 1):
name = labels[label_index]
if name is None:
name = f'{self.name}({i + start_omega},{j + start_omega})'
coords.append((i + start_omega, j + start_omega))
init = A[i, j]
lower = None if i != j else 0
param = Parameter(name, init, lower=lower, fix=fix)
parameters.append(param)
label_index += 1
next_omega = start_omega + size
try:
self.name_map
except AttributeError:
self.name_map = {name: c for i, (name, c) in enumerate(zip(parameters.names, coords))}
return parameters, next_omega, size