def run(self, script,data):
for k,v in self.stateData.items():
data[k] = v
data['stateData'] = self.stateData
interpreter = Interpreter(data)
interpreter.eval(script,0,True)
for k,v in interpreter.symtable.items():
if type(v) in [str,int,float,bool]:
#print 'saving {} in stateData'.format(k)
self.stateData[k] = v
interpreter = None
def evaluate_dice_list(self):
"""
Evaluates each dice in the dice list, constructs and evaluates final string
"""
if self.verbosity:
print(f"--> evaluate_dice_list")
self.eval_expression = ""
for i in range(len(self.dice_list)):
if isinstance(self.dice_list[i], Die):
if self.verbosity:
print(f"dice_list[{i}] is Die")
roll_result = self.dice_list[i].evaluate()
if self.verbosity:
print(f"roll_result: {roll_result}")
self.eval_expression += str(roll_result)
else:
if self.verbosity:
print(f"dice_list[{i}] is not Die")
self.eval_expression += self.dice_list[i]
# Evaluate the constructed string
aeval = Interpreter()
self.result = aeval.eval(self.eval_expression)
if self.verbosity:
print(f"result: {self.result}")
def test_eval():
aeval = Interpreter()
expr_str = "r[0]**4 * r[1]**2 +" * 10000 + '2*r[2]'
with DeepRecursionCtx():
expr = aeval.parse(expr_str)
aeval.symtable['r'] = [1, 2, 3]
res = aeval.eval(expr)
assert not math.isnan(res)
async def calc(self, ctx, *, expression: str):
""" Evaluates a math expression. """
terp = Interpreter()
result = terp.eval(expression)
if result != '' and result is not None:
await ctx.send(result)
else:
await ctx.send('Empty result.')
def eval(self, expr, lineno=0, show_errors=True):
res = OrigInterpreter.eval(self,
expr,
lineno=lineno,
show_errors=show_errors)
if len(self.error) > 0:
for err in self.error:
t, v, tb = err.exc_info
raise t, v, tb
return res
class Python(Plugin):
def on_attach(self, config):
self.env = Interpreter()
def on_message(self, message):
# also text starts with '>>>' will be interpreted as python
if message['text'].startswith('>>>'):
return self.py(message['text'][12:].strip())
@command
def py(self, *args):
"""evaluates python code
ex) !py 1+1
"""
# XXX: parser unwraps double quote of outside of argument
line = u' '.join(args)
print self.env.eval(line)
return unicode(self.env.eval(line))
class ModelReferenceSystem(Tree):
def __init__(self):
self.expressions = Expression()
self.interpreter = Interpreter()
super().__init__()
def create_node(self, name, parent=None):
if parent is not None:
id = "{id}_{name}".format(id=parent, name=name)
else:
id = name
print(name, id, parent)
mrs_node = MRS_Node(self, name, id)
return self.add_node(mrs_node, parent)
def eval_topo(self, topo):
for idx, elt in enumerate(topo):
ast_executable_extended = "{id}={expr}".format(id=elt,
expr=self.get_node(elt).execute_str)
print(idx, ast_executable_extended)
self.interpreter.eval(ast_executable_extended)
#print(self.interpreter.symtable)
return self.interpreter.symtable
def operator_expr(self, expr):
"""Evaluate an expression composed of single-mode operators.
See ``Mode.OPERATORS`` for the full list of supported operators.
Args:
expr (str): String representation of the operator expression
to evaluate.
Returns:
``qutip.Qobj``: Evaluated operator expression.
"""
symbols = {name: getattr(self, name) for name in self.OPERATORS}
symtable = make_symbol_table(use_numpy=True, **symbols)
aeval = Interpreter(symtable=symtable)
return aeval.eval(expr)
def safeEval(expr, scope): scope = dict(**scope) for name in scope: if getattr(scope[name], "wrapped", False) is True: scope[name] = scope[name](scope) interpreter = Interpreter( usersyms=scope, use_numpy=False, minimal=False ) res = interpreter.eval(expr, show_errors=False) if interpreter.error: raise interpreter.error return res
from py_expression_eval import Parser
parser = Parser()
valor = parser.parse('2 * 3').evaluate({})
print(valor)
from asteval import Interpreter
aeval = Interpreter()
txt = """nmax = 1e8
a = sqrt(arange(nmax))
"""
valor = aeval.eval(txt)
print(valor)
class Parameters(dict):
"""A dictionary of Parameter objects.
It should contain all Parameter objects that are required to specify
a fit model. All minimization and Model fitting routines in lmfit will
use exactly one Parameters object, typically given as the first
argument to the objective function.
All keys of a Parameters() instance must be strings and valid Python
symbol names, so that the name must match ``[a-z_][a-z0-9_]*`` and
cannot be a Python reserved word.
All values of a Parameters() instance must be Parameter objects.
A Parameters() instance includes an `asteval` Interpreter used for
evaluation of constrained Parameters.
Parameters() support copying and pickling, and have methods to convert
to and from serializations using json strings.
"""
def __init__(self, asteval=None, usersyms=None):
"""
Arguments
---------
asteval : :class:`asteval.Interpreter`, optional
Instance of the `asteval.Interpreter` to use for constraint
expressions. If None (default), a new interpreter will be
created. **Warning: deprecated**, use `usersyms` if possible!
usersyms : dict, optional
Dictionary of symbols to add to the
:class:`asteval.Interpreter` (default is None).
"""
super().__init__(self)
self._asteval = asteval
if asteval is None:
self._asteval = Interpreter()
else:
msg = ("The use of the 'asteval' argument for the Parameters class"
" was deprecated in lmfit v0.9.12 and will be removed in a "
"later release. Please use the 'usersyms' argument instead!")
warnings.warn(FutureWarning(msg))
self._asteval = asteval
_syms = {}
_syms.update(SCIPY_FUNCTIONS)
if usersyms is not None:
_syms.update(usersyms)
for key, val in _syms.items():
self._asteval.symtable[key] = val
def copy(self):
"""Parameters.copy() should always be a deepcopy."""
return self.__deepcopy__(None)
def update(self, other):
"""Update values and symbols with another Parameters object."""
if not isinstance(other, Parameters):
raise ValueError("'%s' is not a Parameters object" % other)
self.add_many(*other.values())
for sym in other._asteval.user_defined_symbols():
self._asteval.symtable[sym] = other._asteval.symtable[sym]
return self
def __copy__(self):
"""Parameters.copy() should always be a deepcopy."""
return self.__deepcopy__(None)
def __deepcopy__(self, memo):
"""Implementation of Parameters.deepcopy().
The method needs to make sure that `asteval` is available and that
all individual Parameter objects are copied.
"""
_pars = self.__class__(asteval=None)
# find the symbols that were added by users, not during construction
unique_symbols = {key: self._asteval.symtable[key]
for key in self._asteval.user_defined_symbols()}
_pars._asteval.symtable.update(unique_symbols)
# we're just about to add a lot of Parameter objects to the newly
parameter_list = []
for key, par in self.items():
if isinstance(par, Parameter):
param = Parameter(name=par.name,
value=par.value,
min=par.min,
max=par.max)
param.vary = par.vary
param.brute_step = par.brute_step
param.stderr = par.stderr
param.correl = par.correl
param.init_value = par.init_value
param.expr = par.expr
param.user_data = par.user_data
parameter_list.append(param)
_pars.add_many(*parameter_list)
return _pars
def __setitem__(self, key, par):
"""Set items of Parameters object."""
if key not in self:
if not valid_symbol_name(key):
raise KeyError("'%s' is not a valid Parameters name" % key)
if par is not None and not isinstance(par, Parameter):
raise ValueError("'%s' is not a Parameter" % par)
dict.__setitem__(self, key, par)
par.name = key
par._expr_eval = self._asteval
self._asteval.symtable[key] = par.value
def __add__(self, other):
"""Add Parameters objects."""
if not isinstance(other, Parameters):
raise ValueError("'%s' is not a Parameters object" % other)
out = deepcopy(self)
out.add_many(*other.values())
for sym in other._asteval.user_defined_symbols():
if sym not in out._asteval.symtable:
out._asteval.symtable[sym] = other._asteval.symtable[sym]
return out
def __iadd__(self, other):
"""Add/assign Parameters objects."""
self.update(other)
return self
def __array__(self):
"""Convert Parameters to array."""
return array([float(k) for k in self.values()])
def __reduce__(self):
"""Reduce Parameters instance such that it can be pickled."""
# make a list of all the parameters
params = [self[k] for k in self]
# find the symbols from _asteval.symtable, that need to be remembered.
sym_unique = self._asteval.user_defined_symbols()
unique_symbols = {key: deepcopy(self._asteval.symtable[key])
for key in sym_unique}
return self.__class__, (), {'unique_symbols': unique_symbols,
'params': params}
def __setstate__(self, state):
"""Unpickle a Parameters instance.
Parameters
----------
state : dict
state['unique_symbols'] is a dictionary containing symbols
that need to be injected into `_asteval.symtable`.
state['params'] is a list of Parameter instances to be added.
"""
# first update the Interpreter symbol table. This needs to be done
# first because Parameter's early in the list may depend on later
# Parameter's. This leads to problems because add_many eventually leads
# to a Parameter value being retrieved with _getval, which, if the
# dependent value hasn't already been added to the symtable, leads to
# an Error. Another way of doing this would be to remove all the expr
# from the Parameter instances before they get added, then to restore
# them.
symtab = self._asteval.symtable
for key, val in state['unique_symbols'].items():
if key not in symtab:
symtab[key] = val
# then add all the parameters
self.add_many(*state['params'])
def __repr__(self):
"""__repr__ from OrderedDict."""
if not self:
return '%s()' % (self.__class__.__name__,)
return '%s(%r)' % (self.__class__.__name__, list(self.items()))
def eval(self, expr):
"""Evaluate a statement using the `asteval` Interpreter.
Parameters
----------
expr : str
An expression containing parameter names and other symbols
recognizable by the `asteval` Interpreter.
Returns
-------
float
The result of evaluating the expression.
"""
return self._asteval.eval(expr)
def update_constraints(self):
"""Update all constrained parameters.
This method ensures that dependencies are evaluated as needed.
"""
requires_update = {name for name, par in self.items() if par._expr is
not None}
updated_tracker = set(requires_update)
def _update_param(name):
"""Update a parameter value, including setting bounds.
For a constrained parameter (one with an `expr` defined), this
first updates (recursively) all parameters on which the
parameter depends (using the 'deps' field).
"""
par = self.__getitem__(name)
if par._expr_eval is None:
par._expr_eval = self._asteval
for dep in par._expr_deps:
if dep in updated_tracker:
_update_param(dep)
self._asteval.symtable[name] = par.value
updated_tracker.discard(name)
for name in requires_update:
_update_param(name)
def pretty_repr(self, oneline=False):
"""Return a pretty representation of a Parameters class.
Parameters
----------
oneline : bool, optional
If True prints a one-line parameters representation (default
is False).
Returns
-------
s: str
Parameters representation.
"""
if oneline:
return self.__repr__()
s = "Parameters({\n"
for key in self.keys():
s += " '%s': %s, \n" % (key, self[key])
s += " })\n"
return s
def pretty_print(self, oneline=False, colwidth=8, precision=4, fmt='g',
columns=['value', 'min', 'max', 'stderr', 'vary', 'expr',
'brute_step']):
"""Pretty-print of parameters data.
Parameters
----------
oneline : bool, optional
If True prints a one-line parameters representation (default
is False).
colwidth : int, optional
Column width for all columns specified in `columns` (default
is 8).
precision : int, optional
Number of digits to be printed after floating point (default
is 4).
fmt : {'g', 'e', 'f'}, optional
Single-character numeric formatter. Valid values are: `'g'`
floating point and exponential (default), `'e'` exponential,
or `'f'` floating point.
columns : :obj:`list` of :obj:`str`, optional
List of :class:`Parameter` attribute names to print (default
is to show all attributes).
"""
if oneline:
print(self.pretty_repr(oneline=oneline))
return
name_len = max(len(s) for s in self)
allcols = ['name'] + columns
title = '{:{name_len}} ' + len(columns) * ' {:>{n}}'
print(title.format(*allcols, name_len=name_len, n=colwidth).title())
numstyle = '{%s:>{n}.{p}{f}}' # format for numeric columns
otherstyles = dict(name='{name:<{name_len}} ', stderr='{stderr!s:>{n}}',
vary='{vary!s:>{n}}', expr='{expr!s:>{n}}',
brute_step='{brute_step!s:>{n}}')
line = ' '.join([otherstyles.get(k, numstyle % k) for k in allcols])
for name, values in sorted(self.items()):
pvalues = {k: getattr(values, k) for k in columns}
pvalues['name'] = name
# stderr is a special case: it is either numeric or None (i.e. str)
if 'stderr' in columns and pvalues['stderr'] is not None:
pvalues['stderr'] = (numstyle % '').format(
pvalues['stderr'], n=colwidth, p=precision, f=fmt)
elif 'brute_step' in columns and pvalues['brute_step'] is not None:
pvalues['brute_step'] = (numstyle % '').format(
pvalues['brute_step'], n=colwidth, p=precision, f=fmt)
print(line.format(name_len=name_len, n=colwidth, p=precision,
f=fmt, **pvalues))
def _repr_html_(self):
"""Return a HTML representation of parameters data."""
return params_html_table(self)
def add(self, name, value=None, vary=True, min=-inf, max=inf, expr=None,
brute_step=None):
"""Add a Parameter.
Parameters
----------
name : str or Parameter
If ``name`` refers to a Parameter object it will be added directly
to the Parameters instance, otherwise a new Parameter object with name
``string`` is created before adding it. In both cases, ``name`` must
match ``[a-z_][a-z0-9_]*`` and cannot be a Python reserved word.
value : float, optional
Numerical Parameter value, typically the *initial value*.
vary : bool, optional
Whether the Parameter is varied during a fit (default is True).
min : float, optional
Lower bound for value (default is ``-numpy.inf``, no lower
bound).
max : float, optional
Upper bound for value (default is ``numpy.inf``, no upper
bound).
expr : str, optional
Mathematical expression used to constrain the value during the
fit (default is None).
brute_step : float, optional
Step size for grid points in the `brute` method (default is
None).
Examples
--------
>>> params = Parameters()
>>> params.add('xvar', value=0.50, min=0, max=1)
>>> params.add('yvar', expr='1.0 - xvar')
which is equivalent to:
>>> params = Parameters()
>>> params['xvar'] = Parameter(name='xvar', value=0.50, min=0, max=1)
>>> params['yvar'] = Parameter(name='yvar', expr='1.0 - xvar')
"""
if isinstance(name, Parameter):
self.__setitem__(name.name, name)
else:
self.__setitem__(name, Parameter(value=value, name=name, vary=vary,
min=min, max=max, expr=expr,
brute_step=brute_step))
def add_many(self, *parlist):
"""Add many parameters, using a sequence of tuples.
Parameters
----------
*parlist : :obj:`sequence` of :obj:`tuple` or Parameter
A sequence of tuples, or a sequence of `Parameter` instances.
If it is a sequence of tuples, then each tuple must contain at
least a `name`. The order in each tuple must be
``(name, value, vary, min, max, expr, brute_step)``.
Examples
--------
>>> params = Parameters()
# add with tuples: (NAME VALUE VARY MIN MAX EXPR BRUTE_STEP)
>>> params.add_many(('amp', 10, True, None, None, None, None),
... ('cen', 4, True, 0.0, None, None, None),
... ('wid', 1, False, None, None, None, None),
... ('frac', 0.5))
# add a sequence of Parameters
>>> f = Parameter('par_f', 100)
>>> g = Parameter('par_g', 2.)
>>> params.add_many(f, g)
"""
__params = []
for par in parlist:
if not isinstance(par, Parameter):
par = Parameter(*par)
__params.append(par)
par._delay_asteval = True
self.__setitem__(par.name, par)
for para in __params:
para._delay_asteval = False
def valuesdict(self):
"""Return an ordered dictionary of parameter values.
Returns
-------
dict
A dictionary of :attr:`name`::attr:`value` pairs for each
Parameter.
"""
return {p.name: p.value for p in self.values()}
def dumps(self, **kws):
"""Represent Parameters as a JSON string.
Parameters
----------
**kws : optional
Keyword arguments that are passed to `json.dumps`.
Returns
-------
str
JSON string representation of Parameters.
See Also
--------
dump, loads, load, json.dumps
"""
params = [p.__getstate__() for p in self.values()]
sym_unique = self._asteval.user_defined_symbols()
unique_symbols = {key: encode4js(deepcopy(self._asteval.symtable[key]))
for key in sym_unique}
return json.dumps({'unique_symbols': unique_symbols,
'params': params}, **kws)
def loads(self, s, **kws):
"""Load Parameters from a JSON string.
Parameters
----------
**kws : optional
Keyword arguments that are passed to `json.loads`.
Returns
-------
Parameters
Updated Parameters from the JSON string.
Notes
-----
Current Parameters will be cleared before loading the data from
the JSON string.
See Also
--------
dump, dumps, load, json.loads
"""
self.clear()
tmp = json.loads(s, **kws)
unique_symbols = {key: decode4js(tmp['unique_symbols'][key]) for key
in tmp['unique_symbols']}
state = {'unique_symbols': unique_symbols, 'params': []}
for parstate in tmp['params']:
_par = Parameter(name='')
_par.__setstate__(parstate)
state['params'].append(_par)
self.__setstate__(state)
return self
def dump(self, fp, **kws):
"""Write JSON representation of Parameters to a file-like object.
Parameters
----------
fp : file-like object
An open and `.write()`-supporting file-like object.
**kws : optional
Keyword arguments that are passed to `dumps`.
Returns
-------
int
Return value from `fp.write()`: the number of characters
written.
See Also
--------
dumps, load, json.dump
"""
return fp.write(self.dumps(**kws))
def load(self, fp, **kws):
"""Load JSON representation of Parameters from a file-like object.
Parameters
----------
fp : file-like object
An open and `.read()`-supporting file-like object.
**kws : optional
Keyword arguments that are passed to `loads`.
Returns
-------
Parameters
Updated Parameters loaded from `fp`.
See Also
--------
dump, loads, json.load
"""
return self.loads(fp.read(), **kws)
class ExpressionModel(Model):
idvar_missing = "No independent variable found in\n %s"
idvar_notfound = "Cannot find independent variables '%s' in\n %s"
no_prefix = "ExpressionModel does not support `prefix` argument"
def __init__(self,
expr,
independent_vars=None,
init_script=None,
nan_policy='raise',
**kws):
"""Generate a model from user-supplied expression.
Parameters
----------
expr : str
Mathematical expression for model.
independent_vars : list of str or None, optional
Variable names to use as independent variables.
init_script : str or None, optional
Initial script to run in asteval interpreter.
nan_policy : str, optional
How to handle NaN and missing values in data. Must be one of:
'raise' (default), 'propagate', or 'omit'. See Notes below.
missing : str, optional
Synonym for 'nan_policy' for backward compatibility.
**kws : optional
Keyword arguments to pass to :class:`Model`.
Notes
-----
1. each instance of ExpressionModel will create and using its own
version of an asteval interpreter.
2. prefix is **not supported** for ExpressionModel.
3. nan_policy sets what to do when a NaN or missing value is seen in
the data. Should be one of:
- 'raise' : Raise a ValueError (default)
- 'propagate' : do nothing
- 'omit' : (was 'drop') drop missing data
4. The `missing` argument is deprecated in lmfit 0.9.8 and will be
removed in a later version. Use `nan_policy` instead, as it is
consistent with the Minimizer class.
"""
# create ast evaluator, load custom functions
self.asteval = Interpreter()
for name in lineshapes.functions:
self.asteval.symtable[name] = getattr(lineshapes, name, None)
if init_script is not None:
self.asteval.eval(init_script)
# save expr as text, parse to ast, save for later use
self.expr = expr.strip()
self.astcode = self.asteval.parse(self.expr)
# find all symbol names found in expression
sym_names = get_ast_names(self.astcode)
if independent_vars is None and 'x' in sym_names:
independent_vars = ['x']
if independent_vars is None:
raise ValueError(self.idvar_missing % (self.expr))
# determine which named symbols are parameter names,
# try to find all independent variables
idvar_found = [False] * len(independent_vars)
param_names = []
for name in sym_names:
if name in independent_vars:
idvar_found[independent_vars.index(name)] = True
elif name not in param_names and name not in self.asteval.symtable:
param_names.append(name)
# make sure we have all independent parameters
if not all(idvar_found):
lost = []
for ix, found in enumerate(idvar_found):
if not found:
lost.append(independent_vars[ix])
lost = ', '.join(lost)
raise ValueError(self.idvar_notfound % (lost, self.expr))
kws['independent_vars'] = independent_vars
if 'prefix' in kws:
raise Warning(self.no_prefix)
def _eval(**kwargs):
for name, val in kwargs.items():
self.asteval.symtable[name] = val
return self.asteval.run(self.astcode)
kws["nan_policy"] = nan_policy
super(ExpressionModel, self).__init__(_eval, **kws)
# set param names here, and other things normally
# set in _parse_params(), which will be short-circuited.
self.independent_vars = independent_vars
self._func_allargs = independent_vars + param_names
self._param_names = param_names
self._func_haskeywords = True
self.def_vals = {}
def __repr__(self):
"""TODO: docstring in magic method."""
return "<lmfit.ExpressionModel('%s')>" % (self.expr)
def _parse_params(self):
"""Over-write ExpressionModel._parse_params with `pass`.
This prevents normal parsing of function for parameter names.
"""
pass
def eval(self, expr, lineno=0, show_errors=False):
return OrigInterpreter.eval(self,
expr,
lineno=lineno,
show_errors=show_errors)
class Parameters(OrderedDict):
"""An ordered dictionary of all the Parameter objects required to
specify a fit model. All minimization and Model fitting routines in
lmfit will use exactly one Parameters object, typically given as the
first argument to the objective function.
All keys of a Parameters() instance must be strings and valid Python
symbol names, so that the name must match ``[a-z_][a-z0-9_]*`` and
cannot be a Python reserved word.
All values of a Parameters() instance must be Parameter objects.
A Parameters() instance includes an asteval interpreter used for
evaluation of constrained Parameters.
Parameters() support copying and pickling, and have methods to convert
to and from serializations using json strings.
"""
def __init__(self, asteval=None, usersyms=None, *args, **kwds):
"""
Arguments
---------
asteval : :class:`asteval.Interpreter`, optional
Instance of the asteval Interpreter to use for constraint
expressions. If None, a new interpreter will be created.
Warning: *deprecated, use usersyms if possible*
usersyms : dictionary of symbols to add to the
:class:`asteval.Interpreter`.
*args : optional
Arguments.
**kwds : optional
Keyword arguments.
"""
super(Parameters, self).__init__(self)
self._asteval = asteval
if self._asteval is None:
self._asteval = Interpreter()
_syms = {}
_syms.update(SCIPY_FUNCTIONS)
if usersyms is not None:
_syms.update(usersyms)
for key, val in _syms.items():
self._asteval.symtable[key] = val
self.update(*args, **kwds)
def copy(self):
"""Parameters.copy() should always be a deepcopy."""
return self.__deepcopy__(None)
def __copy__(self):
"""Parameters.copy() should always be a deepcopy."""
return self.__deepcopy__(None)
def __deepcopy__(self, memo):
"""Implementation of Parameters.deepcopy().
The method needs to make sure that asteval is available and that all
individual Parameter objects are copied.
"""
_pars = Parameters(asteval=None)
# find the symbols that were added by users, not during construction
unique_symbols = {key: self._asteval.symtable[key]
for key in self._asteval.user_defined_symbols()}
_pars._asteval.symtable.update(unique_symbols)
# we're just about to add a lot of Parameter objects to the newly
parameter_list = []
for key, par in self.items():
if isinstance(par, Parameter):
param = Parameter(name=par.name,
value=par.value,
min=par.min,
max=par.max)
param.vary = par.vary
param.brute_step = par.brute_step
param.stderr = par.stderr
param.correl = par.correl
param.init_value = par.init_value
param.expr = par.expr
param.user_data = par.user_data
parameter_list.append(param)
_pars.add_many(*parameter_list)
return _pars
def __setitem__(self, key, par):
"""TODO: add magic method docstring."""
if key not in self:
if not valid_symbol_name(key):
raise KeyError("'%s' is not a valid Parameters name" % key)
if par is not None and not isinstance(par, Parameter):
raise ValueError("'%s' is not a Parameter" % par)
OrderedDict.__setitem__(self, key, par)
par.name = key
par._expr_eval = self._asteval
self._asteval.symtable[key] = par.value
def __add__(self, other):
"""Add Parameters objects."""
if not isinstance(other, Parameters):
raise ValueError("'%s' is not a Parameters object" % other)
out = deepcopy(self)
params = other.values()
out.add_many(*params)
return out
def __iadd__(self, other):
"""Add/assign Parameters objects."""
if not isinstance(other, Parameters):
raise ValueError("'%s' is not a Parameters object" % other)
params = other.values()
self.add_many(*params)
return self
def __array__(self):
"""Convert Parameters to array."""
return array([float(k) for k in self.values()])
def __reduce__(self):
"""Reduce Parameters instance such that it can be pickled."""
# make a list of all the parameters
params = [self[k] for k in self]
# find the symbols from _asteval.symtable, that need to be remembered.
sym_unique = self._asteval.user_defined_symbols()
unique_symbols = {key: deepcopy(self._asteval.symtable[key])
for key in sym_unique}
return self.__class__, (), {'unique_symbols': unique_symbols,
'params': params}
def __setstate__(self, state):
"""Unpickle a Parameters instance.
Parameters
----------
state : dict
state['unique_symbols'] is a dictionary containing symbols that
need to be injected into _asteval.symtable
state['params'] is a list of Parameter instances to be added
"""
# first update the Interpreter symbol table. This needs to be done
# first because Parameter's early in the list may depend on later
# Parameter's. This leads to problems because add_many eventually leads
# to a Parameter value being retrieved with _getval, which, if the
# dependent value hasn't already been added to the symtable, leads to
# an Error. Another way of doing this would be to remove all the expr
# from the Parameter instances before they get added, then to restore
# them.
self._asteval.symtable.update(state['unique_symbols'])
# then add all the parameters
self.add_many(*state['params'])
def eval(self, expr):
"""Evaluate a statement using the asteval Interpreter.
Parameters
----------
expr : string
An expression containing parameter names and other symbols
recognizable by the asteval Interpreter.
Returns
-------
The result of the expression.
"""
return self._asteval.eval(expr)
def update_constraints(self):
"""Update all constrained parameters, checking that dependencies are
evaluated as needed."""
requires_update = {name for name, par in self.items() if par._expr is
not None}
updated_tracker = set(requires_update)
def _update_param(name):
"""Update a parameter value, including setting bounds.
For a constrained parameter (one with an `expr` defined),
this first updates (recursively) all parameters on which the
parameter depends (using the 'deps' field).
"""
par = self.__getitem__(name)
if par._expr_eval is None:
par._expr_eval = self._asteval
for dep in par._expr_deps:
if dep in updated_tracker:
_update_param(dep)
self._asteval.symtable[name] = par.value
updated_tracker.discard(name)
for name in requires_update:
_update_param(name)
def pretty_repr(self, oneline=False):
"""Return a pretty representation of a Parameters class.
Parameters
----------
oneline : bool, optional
If True prints a one-line parameters representation (default is
False).
Returns
-------
s: str
Parameters representation.
"""
if oneline:
return super(Parameters, self).__repr__()
s = "Parameters({\n"
for key in self.keys():
s += " '%s': %s, \n" % (key, self[key])
s += " })\n"
return s
def pretty_print(self, oneline=False, colwidth=8, precision=4, fmt='g',
columns=['value', 'min', 'max', 'stderr', 'vary', 'expr',
'brute_step']):
"""Pretty-print of parameters data.
Parameters
----------
oneline : bool, optional
If True prints a one-line parameters representation (default is
False).
colwidth : int, optional
Column width for all columns specified in :attr:`columns`.
precision : int, optional
Number of digits to be printed after floating point.
fmt : {'g', 'e', 'f'}, optional
Single-character numeric formatter. Valid values are: 'f' floating
point, 'g' floating point and exponential, or 'e' exponential.
columns : :obj:`list` of :obj:`str`, optional
List of :class:`Parameter` attribute names to print.
"""
if oneline:
print(self.pretty_repr(oneline=oneline))
return
name_len = max(len(s) for s in self)
allcols = ['name'] + columns
title = '{:{name_len}} ' + len(columns) * ' {:>{n}}'
print(title.format(*allcols, name_len=name_len, n=colwidth).title())
numstyle = '{%s:>{n}.{p}{f}}' # format for numeric columns
otherstyles = dict(name='{name:<{name_len}} ', stderr='{stderr!s:>{n}}',
vary='{vary!s:>{n}}', expr='{expr!s:>{n}}',
brute_step='{brute_step!s:>{n}}')
line = ' '.join([otherstyles.get(k, numstyle % k) for k in allcols])
for name, values in sorted(self.items()):
pvalues = {k: getattr(values, k) for k in columns}
pvalues['name'] = name
# stderr is a special case: it is either numeric or None (i.e. str)
if 'stderr' in columns and pvalues['stderr'] is not None:
pvalues['stderr'] = (numstyle % '').format(
pvalues['stderr'], n=colwidth, p=precision, f=fmt)
elif 'brute_step' in columns and pvalues['brute_step'] is not None:
pvalues['brute_step'] = (numstyle % '').format(
pvalues['brute_step'], n=colwidth, p=precision, f=fmt)
print(line.format(name_len=name_len, n=colwidth, p=precision,
f=fmt, **pvalues))
def _repr_html_(self):
"""Returns a HTML representation of parameters data."""
return params_html_table(self)
def add(self, name, value=None, vary=True, min=-inf, max=inf, expr=None,
brute_step=None):
"""Add a Parameter.
Parameters
----------
name : str
Name of parameter. Must match ``[a-z_][a-z0-9_]*`` and cannot be
a Python reserved word.
value : float, optional
Numerical Parameter value, typically the *initial value*.
vary : bool, optional
Whether the Parameter is varied during a fit (default is True).
min : float, optional
Lower bound for value (default is `-numpy.inf`, no lower bound).
max : float, optional
Upper bound for value (default is `numpy.inf`, no upper bound).
expr : str, optional
Mathematical expression used to constrain the value during the fit.
brute_step : float, optional
Step size for grid points in the `brute` method.
Examples
--------
>>> params = Parameters()
>>> params.add('xvar', value=0.50, min=0, max=1)
>>> params.add('yvar', expr='1.0 - xvar')
which is equivalent to:
>>> params = Parameters()
>>> params['xvar'] = Parameter(name='xvar', value=0.50, min=0, max=1)
>>> params['yvar'] = Parameter(name='yvar', expr='1.0 - xvar')
"""
if isinstance(name, Parameter):
self.__setitem__(name.name, name)
else:
self.__setitem__(name, Parameter(value=value, name=name, vary=vary,
min=min, max=max, expr=expr,
brute_step=brute_step))
def add_many(self, *parlist):
"""Add many parameters, using a sequence of tuples.
Parameters
----------
parlist : :obj:`sequence` of :obj:`tuple` or :class:`Parameter`
A sequence of tuples, or a sequence of `Parameter` instances. If
it is a sequence of tuples, then each tuple must contain at least
the name. The order in each tuple must be `(name, value, vary,
min, max, expr, brute_step)`.
Examples
--------
>>> params = Parameters()
# add with tuples: (NAME VALUE VARY MIN MAX EXPR BRUTE_STEP)
>>> params.add_many(('amp', 10, True, None, None, None, None),
... ('cen', 4, True, 0.0, None, None, None),
... ('wid', 1, False, None, None, None, None),
... ('frac', 0.5))
# add a sequence of Parameters
>>> f = Parameter('par_f', 100)
>>> g = Parameter('par_g', 2.)
>>> params.add_many(f, g)
"""
for para in parlist:
if isinstance(para, Parameter):
self.__setitem__(para.name, para)
else:
param = Parameter(*para)
self.__setitem__(param.name, param)
def valuesdict(self):
"""Return an ordered dictionary of parameter values.
Returns
-------
OrderedDict
An ordered dictionary of :attr:`name`::attr:`value` pairs for each
Parameter.
"""
return OrderedDict((p.name, p.value) for p in self.values())
def dumps(self, **kws):
"""Represent Parameters as a JSON string.
Parameters
----------
**kws : optional
Keyword arguments that are passed to `json.dumps()`.
Returns
-------
str
JSON string representation of Parameters.
See Also
--------
dump(), loads(), load(), json.dumps()
"""
params = [p.__getstate__() for p in self.values()]
sym_unique = self._asteval.user_defined_symbols()
unique_symbols = {key: encode4js(deepcopy(self._asteval.symtable[key]))
for key in sym_unique}
return json.dumps({'unique_symbols': unique_symbols,
'params': params}, **kws)
def loads(self, s, **kws):
"""Load Parameters from a JSON string.
Parameters
----------
**kws : optional
Keyword arguments that are passed to `json.loads()`.
Returns
-------
:class:`Parameters`
Updated Parameters from the JSON string.
Notes
-----
Current Parameters will be cleared before loading the data from the
JSON string.
See Also
--------
dump(), dumps(), load(), json.loads()
"""
self.clear()
tmp = decode4js(json.loads(s, **kws))
state = {'unique_symbols': tmp['unique_symbols'],
'params': []}
for parstate in tmp['params']:
_par = Parameter()
_par.__setstate__(parstate)
state['params'].append(_par)
self.__setstate__(state)
return self
def dump(self, fp, **kws):
"""Write JSON representation of Parameters to a file-like object.
Parameters
----------
fp : file-like object
An open and ``.write()``-supporting file-like object.
**kws : optional
Keyword arguments that are passed to `dumps()`.
Returns
-------
None or int
Return value from `fp.write()`. None for Python 2.7 and the
number of characters written in Python 3.
See Also
--------
dump(), load(), json.dump()
"""
return fp.write(self.dumps(**kws))
def load(self, fp, **kws):
"""Load JSON representation of Parameters from a file-like object.
Parameters
----------
fp : file-like object
An open and ``.read()``-supporting file-like object.
**kws : optional
Keyword arguments that are passed to `loads()`.
Returns
-------
:class:`Parameters`
Updated Parameters loaded from `fp`.
See Also
--------
dump(), loads(), json.load()
"""
return self.loads(fp.read(), **kws)
