#
# Copyright (C) 2010, 2016, 2017, 2018, 2019, 2020, 2021
# Smithsonian Astrophysical Observatory
#
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
"""Allow models to be defined and combined.
A single model is defined by the parameters of the model - represented
as sherpa.models.model.Parameter instances - and the function that
takes the parameter values along with an array of grid values. The
main classes are:
- Model which is the base class and defines most of the interfaces.
- ArithmeticConstantModel and ArithmeticFunctionModel for representing
a constant value or a function.
- ArithmeticModel is the main base class for deriving user models since
it supports combining models (e.g. by addition or multiplication) and
a cache to reduce evaluation time at the expense of memory use.
- RegriddableModel builds on ArithmeticModel to allow a model to be
evaluated on a different grid to that requested: most model classes
are derived from the 1D and 2D variants of RegriddableModel.
- CompositeModel which is used to represent a model expression, that
is combined models, such as `m1 * (m2 + m3)`
- UnaryOpModel for model expressions such as `- m1`.
- BinaryOpModel for model expressions such as `m1 + m2`.
- NestedModel for applying one model to another.
- SimulFitModel for fitting multiple models and datasets.
Model cache
-----------
The ArithmeticModel class and modelCacher1d decorator provide basic
support for caching one-dimensional model evaluations - that is, to
avoid re-calculating the model. The idea is to save the results of the
latest calls to a model and return the values from the cache,
hopefully saving time at the expense of using more memory. This is
most effective when the same model is used with multiple datasets
which all have the same grid.
The _use_caching attribute of the model is used to determine whether
the cache is used, but this setting can be over-ridden by the startup
method, which is automatically called by the fit and est_errors
methods of a sherpa.fit.Fit object.
The cache_clear and cache_status methods of ArithmeticModel and
CompositeModel allow you to clear the cache and display to the
standard output the cache status of each model component.
Example
-------
The following class implements a simple scale model which has a single
parameter (`scale`) which defaults to 1. It can be used for both
non-integrated and integrated datasets of any dimensionality (see
sherpa.models.basic.Scale1D and sherpa.models.basic.Scale2D)::
class ScaleND(ArithmeticModel):
'''A constant value per element.'''
def __init__(self, name='scalend'):
self.scale = Parameter(name, 'scale', 1)
self.integrate = False
pars = (self.scale, )
ArithmeticModel.__init__(self, name, pars)
def calc(self, *args, **kwargs):
return self.scale.val * np.ones(len(args[0]))
"""
import functools
import logging
import warnings
import numpy
from sherpa.models.regrid import EvaluationSpace1D, ModelDomainRegridder1D, EvaluationSpace2D, ModelDomainRegridder2D
from sherpa.utils import SherpaFloat, NoNewAttributesAfterInit
from sherpa.utils.err import ModelErr
from sherpa.utils import formatting
from .parameter import Parameter
# What routine do we use for the hash in modelCacher1d? As we do not
# need cryptographic security go for a "quick" algorithm, but md5 is
# not guaranteed to always be present. There has been no attempt to
# check the run times of these routines for the expected data sizes
# they will be used with.
#
try:
from hashlib import md5 as hashfunc
except ImportError:
from hashlib import sha256 as hashfunc
info = logging.getLogger(__name__).info
warning = logging.getLogger(__name__).warning
__all__ = ('Model', 'CompositeModel', 'SimulFitModel',
'ArithmeticConstantModel', 'ArithmeticModel', 'RegriddableModel1D', 'RegriddableModel2D',
'UnaryOpModel', 'BinaryOpModel', 'FilterModel', 'modelCacher1d',
'ArithmeticFunctionModel', 'NestedModel', 'MultigridSumModel')
def boolean_to_byte(boolean_value):
"""Convert a boolean to a byte value.
Parameters
----------
boolean_value : bool
The value to convert. If not a boolean then it is
treated as `False`.
Returns
-------
val : bytes
b'1' if `True` otherwise b'0'.
"""
bmap = {True: b'1', False: b'0'}
return bmap.get(boolean_value, b'0')
[docs]def modelCacher1d(func):
"""A decorater to cache 1D ArithmeticModel evalutions.
Apply to the `calc` method of a 1D model to allow the model
evaluation to be cached. The decision is based on the
`_use_caching` attribute of the cache along with the `integrate`
setting, the evaluation grid, and parameter values.
Example
-------
Allow `MyModel` model evaluations to be cached::
def MyModel(ArithmeticModel):
...
@modelCacher1d
def calc(self, *args, **kwargs):
...
"""
@functools.wraps(func)
def cache_model(cls, pars, xlo, *args, **kwargs):
use_caching = cls._use_caching
cache = cls._cache
cache_ctr = cls._cache_ctr
queue = cls._queue
# Counts all accesses, even those that do not use the cache.
cache_ctr['check'] += 1
digest = ''
if use_caching:
# Up until Sherpa 4.12.2 we used the kwargs to define the
# integrate setting, with
# boolean_to_byte(kwargs.get('integrate', False)) but
# unfortunately this is used in code like
#
# @modelCacher1d
# def calc(..):
# kwargs['integrate'] = self.integrate
# return somefunc(... **kwargs)
#
# and the decorator is applied to calc, which is not
# called with a integrate kwarg, rather than the call to
# somefunc, which was sent an integrate setting.
#
try:
integrate = cls.integrate
except AttributeError:
# Rely on the integrate kwarg as there's no
# model setting.
#
integrate = kwargs.get('integrate', False)
data = [numpy.array(pars).tobytes(),
boolean_to_byte(integrate),
numpy.asarray(xlo).tobytes()]
if args:
data.append(numpy.asarray(args[0]).tobytes())
token = b''.join(data)
digest = hashfunc(token).digest()
if digest in cache:
cache_ctr['hits'] += 1
return cache[digest].copy()
vals = func(cls, pars, xlo, *args, **kwargs)
if use_caching:
# remove first item in queue and remove from cache
key = queue.pop(0)
cache.pop(key, None)
# append newest model values to queue
queue.append(digest)
cache[digest] = vals.copy()
cache_ctr['misses'] += 1
return vals
return cache_model
[docs]class Model(NoNewAttributesAfterInit):
"""The base class for Sherpa models.
A model contains zero or more parameters that control the
predictions of the model when given one or more coordinates.
These parameters may represent some variable that describes
the model, such as the temperature of a black body, or the
computation, such as what form of interpolation to use.
Parameters
----------
name : str
A label for the model instance.
pars : sequence of sherpa.parameter.Parameter objects
The parameters of the model.
Notes
-----
Parameters can be accessed via the ``pars`` attribute, but it is
expected that they will generally be accessed directly, as the
class provides case-insensitive access to the parameter names
as object attributes. That is, if the model contains parameters
called ``breakFreq`` and ``norm``, and the instance is stored in
the variable ``mdl``, then the following can be used to access
the parameters::
print(f"Break frequency = {mdl.breakfreq.val}")
mdl.norm = 1.2e-3
"""
ndim = None
"The dimensionality of the model, if defined, or None."
def __init__(self, name, pars=()):
self.name = name
self.type = self.__class__.__name__.lower()
self.pars = tuple(pars)
self.is_discrete = False
NoNewAttributesAfterInit.__init__(self)
def __repr__(self):
return "<%s model instance '%s'>" % (type(self).__name__, self.name)
def __str__(self):
s = self.name
sep5 = '-' * 5
sep4 = '-' * 4
sep3 = '-' * 3
hfmt = '\n %-12s %-6s %12s %12s %12s %10s'
s += hfmt % ('Param', 'Type', 'Value', 'Min', 'Max', 'Units')
s += hfmt % (sep5, sep4, sep5, sep3, sep3, sep5)
for p in self.pars:
if p.hidden:
continue
if p.link is not None:
tp = 'linked'
elif p.frozen:
tp = 'frozen'
else:
tp = 'thawed'
if tp == 'linked':
linkstr = 'expr: %s' % p.link.fullname
s += ('\n %-12s %-6s %12g %24s %10s' %
(p.fullname, tp, p.val, linkstr, p.units))
else:
s += ('\n %-12s %-6s %12g %12g %12g %10s' %
(p.fullname, tp, p.val, p.min, p.max, p.units))
return s
def _repr_html_(self):
"""Return a HTML (string) representation of the model
"""
return html_model(self)
# This allows all models to be used in iteration contexts, whether or
# not they're composite
def __iter__(self):
return iter([self])
def __getattr__(self, name):
"""Access to parameters is case insensitive."""
if "_par_index" == name:
if self.__dict__.get('_par_index') is None:
self.__dict__['_par_index'] = {}
return self.__dict__['_par_index']
lowered_name = name.lower()
def warn(oname, nname):
wmsg = 'Parameter name {} is deprecated'.format(oname) + \
' for model {}, '.format(type(self).__name__) + \
'use {} instead'.format(nname)
warnings.warn(wmsg, DeprecationWarning)
parameter = self._par_index.get(lowered_name)
if parameter is not None:
if lowered_name in parameter.aliases:
warn(lowered_name, parameter.name)
return parameter
NoNewAttributesAfterInit.__getattribute__(self, name)
def __setattr__(self, name, val):
par = getattr(self, name.lower(), None)
if (par is not None) and isinstance(par, Parameter):
# When setting an attribute that is a Parameter, set the parameter's
# value instead.
par.val = val
else:
NoNewAttributesAfterInit.__setattr__(self, name, val)
if isinstance(val, Parameter):
# Update parameter index
self._par_index[val.name.lower()] = val
if val.aliases:
# Update index of aliases, if necessary
for alias in val.aliases:
self._par_index[alias] = val
[docs] def startup(self, cache=False):
"""Called before a model may be evaluated multiple times.
Parameters
----------
cache : bool, optional
Should a cache be used when evaluating the models.
See Also
--------
teardown
"""
raise NotImplementedError
[docs] def calc(self, p, *args, **kwargs):
"""Evaluate the model on a grid.
Parameters
----------
p : sequence of numbers
The parameter values to use. The order matches the
``pars`` field.
*args
The model grid. The values can be scalar or arrays,
and the number depends on the dimensionality of the
model and whether it is being evaluated over an
integrated grid or at a point (or points).
"""
raise NotImplementedError
[docs] def teardown(self):
"""Called after a model may be evaluated multiple times.
See Also
--------
startup
"""
raise NotImplementedError
[docs] def guess(self, dep, *args, **kwargs):
"""Set an initial guess for the parameter values.
Attempt to set the parameter values, and ranges, for
the model to match the data values. This is intended
as a rough guess, so it is expected that the model
is only evaluated a small number of times, if at all.
"""
raise NotImplementedError
[docs] def get_center(self):
raise NotImplementedError
[docs] def set_center(self, *args, **kwargs):
raise NotImplementedError
def __call__(self, *args, **kwargs):
# A bit of trickery, to make model creation
# in IPython happen without raising errors, when
# model is made automatically callable
if (len(args) == 0 and len(kwargs) == 0):
return self
return self.calc([p.val for p in self.pars], *args, **kwargs)
def _get_thawed_pars(self):
return [p.val for p in self.pars if not p.frozen]
def _set_thawed_pars(self, vals):
tpars = [p for p in self.pars if not p.frozen]
ngot = len(vals)
nneed = len(tpars)
if ngot != nneed:
raise ModelErr('numthawed', nneed, ngot)
for p, v in zip(tpars, vals):
v = SherpaFloat(v)
if v < p.hard_min:
p.val = p.min
warning(('value of parameter %s is below minimum; ' +
'setting to minimum') % p.fullname)
elif v > p.hard_max:
p.val = p.max
warning(('value of parameter %s is above maximum; ' +
'setting to maximum') % p.fullname)
else:
p._val = v
thawedpars = property(_get_thawed_pars, _set_thawed_pars,
doc='Access to the thawed parameters of the model')
def _get_thawed_par_mins(self):
return [p.min for p in self.pars if not p.frozen]
def _set_thawed_pars_mins(self, vals):
tpars = [p for p in self.pars if not p.frozen]
ngot = len(vals)
nneed = len(tpars)
if ngot != nneed:
raise ModelErr('numthawed', nneed, ngot)
for p, v in zip(tpars, vals):
v = SherpaFloat(v)
if v < p.hard_min:
p.min = p.hard_min
warning(('value of parameter %s minimum is below ' +
'hard minimum; ' +
'setting to hard minimum') % p.fullname)
elif v > p.hard_max:
p.min = p.hard_max
warning(('value of parameter %s minimum is above ' +
'hard maximum; ' +
'setting to hard maximum') % p.fullname)
else:
p._min = v
thawedparmins = property(_get_thawed_par_mins, _set_thawed_pars_mins,
doc='Access to the minimum limits for the thawed parameters')
def _get_thawed_par_maxes(self):
return [p.max for p in self.pars if not p.frozen]
def _set_thawed_pars_maxes(self, vals):
tpars = [p for p in self.pars if not p.frozen]
ngot = len(vals)
nneed = len(tpars)
if ngot != nneed:
raise ModelErr('numthawed', nneed, ngot)
for p, v in zip(tpars, vals):
v = SherpaFloat(v)
if v < p.hard_min:
p.max = p.hard_min
warning(('value of parameter %s maximum is below ' +
'hard minimum; ' +
'setting to hard minimum') % p.fullname)
elif v > p.hard_max:
p.max = p.hard_max
warning(('value of parameter %s maximum is above ' +
'hard maximum; ' +
'setting to hard maximum') % p.fullname)
else:
p._max = v
thawedparmaxes = property(_get_thawed_par_maxes, _set_thawed_pars_maxes,
doc='Access to the maximum limits for the thawed parameters')
def _get_thawed_par_hardmins(self):
return [p.hard_min for p in self.pars if not p.frozen]
thawedparhardmins = property(_get_thawed_par_hardmins,
doc='The hard minimum values for the thawed parameters.')
def _get_thawed_par_hardmaxes(self):
return [p.hard_max for p in self.pars if not p.frozen]
thawedparhardmaxes = property(_get_thawed_par_hardmaxes,
doc='The hard maximum values for the thawed parameters.')
[docs] def reset(self):
"""Reset the parameter values."""
for p in self.pars:
p.reset()
[docs]class CompositeModel(Model):
"""Represent a model with composite parts.
Parameters
----------
name : str
The name for the collection of models.
parts : sequence of Model objects
The models.
Attributes
----------
parts : sequence of Model
"""
def __init__(self, name, parts):
self.parts = tuple(parts)
allpars = []
model_with_dim = None
for part in self.parts:
ndim = part.ndim
if ndim is not None:
if self.ndim is None:
self.ndim = ndim
model_with_dim = part
elif self.ndim != ndim:
raise ModelErr('Models do not match: ' +
'{}D ({}) and '.format(self.ndim, model_with_dim.name) +
'{}D ({})'.format(ndim, part.name))
for p in part.pars:
if p in allpars:
# If we already have a reference to this parameter, store
# a hidden, linked proxy instead
pnew = Parameter(p.modelname, p.name, 0.0, hidden=True)
pnew.link = p
p = pnew
allpars.append(p)
Model.__init__(self, name, allpars)
for part in self.parts:
try:
self.is_discrete = self.is_discrete or part.is_discrete
except:
warning("Could not determine whether the model is discrete.\n" +
"This probably means that you have restored a session saved with a previous version of Sherpa.\n" +
"Falling back to assuming that the model is continuous.\n")
self.is_discrete = False
def __iter__(self):
return iter(self._get_parts())
def _get_parts(self):
parts = []
for p in self.parts:
# A CompositeModel should not hold a reference to itself
assert (p is not self), (("'%s' object holds a reference to " +
"itself") % type(self).__name__)
parts.append(p)
if isinstance(p, CompositeModel):
parts.extend(p._get_parts())
# FIXME: do we want to remove duplicate components from parts?
return parts
[docs] def startup(self, cache=False):
pass
[docs] def teardown(self):
pass
[docs] def cache_clear(self):
"""Clear the cache for each component."""
for p in self.parts:
try:
p.cache_clear()
except AttributeError:
pass
[docs] def cache_status(self):
"""Display the cache status of each component.
Information on the cache - the number of "hits", "misses", and
"requests" - is displayed at the INFO logging level.
Example
-------
>>> mdl.cache_status()
xsphabs.gal size: 5 hits: 715 misses: 158 check= 873
powlaw1d.pl size: 5 hits: 633 misses: 240 check= 873
"""
for p in self.parts:
try:
p.cache_status()
except AttributeError:
pass
[docs]class SimulFitModel(CompositeModel):
"""Store multiple models.
This class is for use with sherpa.data.DataSimulFit.
Parameters
----------
name : str
The name for the collection of models.
parts : sequence of Model objects
The models.
Attributes
----------
parts : sequence of Model
See Also
--------
sherpa.data.DataSimulFit
Examples
--------
>>> m1 = Polynom1D('m1')
>>> m2 = Gauss1D('g1')
>>> mall = SimulFitModel('comp', (m1, m1 + m2))
If dall is a DataSimulFit object then the model components
can be evaluated for the composite object using:
>>> ymdl = dall.eval_model_to_fit(mall)
"""
def __iter__(self):
return iter(self.parts)
[docs] def startup(self, cache=False):
for part in self:
part.startup(cache)
CompositeModel.startup(self, cache)
[docs] def teardown(self):
for part in self:
part.teardown()
CompositeModel.teardown(self)
# TODO: what benefit does this provide versus just using the number?
# I guess it does simplify any attempt to parse the components of
# a model expression.
#
[docs]class ArithmeticConstantModel(Model):
"""Represent a constant value, or values.
Parameters
----------
val : number or sequence
The number, or numbers, to store.
name : str or None, optional
The display name. If not set the value is used when the value
is a scalar, otherwise it indicates an array of elements.
Attributes
----------
val : number
"""
def __init__(self, val, name=None):
val = SherpaFloat(val)
if name is None:
if numpy.isscalar(val):
name = str(val)
else:
name = '{}[{}]'.format(val.dtype.name,
','.join([str(s) for s in val.shape]))
self.name = name
self.val = val
# val has to be a scalar or 1D array, even if used with a 2D
# model, due to the way model evaluation works, so as we
# can't easily define the dimensionality of this model, we
# remove any dimensionality checking for this class.
#
self.ndim = None
Model.__init__(self, self.name)
def _get_val(self):
return self._val
def _set_val(self, val):
val = SherpaFloat(val)
if val.ndim > 1:
raise ModelErr('The constant must be a scalar or 1D, not 2D')
self._val = val
val = property(_get_val, _set_val,
doc='The constant value (scalar or 1D).')
[docs] def startup(self, cache=False):
pass
[docs] def calc(self, p, *args, **kwargs):
return self.val
[docs] def teardown(self):
pass
def _make_unop(op, opstr):
def func(self):
return UnaryOpModel(self, op, opstr)
return func
def _make_binop(op, opstr):
def func(self, rhs):
return BinaryOpModel(self, rhs, op, opstr)
def rfunc(self, lhs):
return BinaryOpModel(lhs, self, op, opstr)
return (func, rfunc)
[docs]class ArithmeticModel(Model):
"""Support combining model expressions and caching results."""
cache = 5
"""The maximum size of the cache."""
def __init__(self, name, pars=()):
self.integrate = True
# Model caching ability
self.cache = 5 # repeat the class definition
self._use_caching = True # FIXME: reduce number of variables?
self.cache_clear()
Model.__init__(self, name, pars)
[docs] def cache_clear(self):
"""Clear the cache."""
# It is not obvious what to set the queue length to
self._queue = ['']
self._cache = {}
self._cache_ctr = {'hits': 0, 'misses': 0, 'check': 0}
[docs] def cache_status(self):
"""Display the cache status.
Information on the cache - the number of "hits", "misses", and
"requests" - is displayed at the INFO logging level.
Example
-------
>>> pl.cache_status()
powlaw1d.pl size: 5 hits: 633 misses: 240 check= 873
"""
c = self._cache_ctr
info(f" {self.name:25s} size: {len(self._queue):4d} " +
f"hits: {c['hits']:5d} misses: {c['misses']:5d} " +
f"check: {c['check']:5d}")
# Unary operations
__neg__ = _make_unop(numpy.negative, '-')
__abs__ = _make_unop(numpy.absolute, 'abs')
# Binary operations
__add__, __radd__ = _make_binop(numpy.add, '+')
__sub__, __rsub__ = _make_binop(numpy.subtract, '-')
__mul__, __rmul__ = _make_binop(numpy.multiply, '*')
__div__, __rdiv__ = _make_binop(numpy.divide, '/')
__floordiv__, __rfloordiv__ = _make_binop(numpy.floor_divide, '//')
__truediv__, __rtruediv__ = _make_binop(numpy.true_divide, '/')
__mod__, __rmod__ = _make_binop(numpy.remainder, '%')
__pow__, __rpow__ = _make_binop(numpy.power, '**')
def __setstate__(self, state):
self.__dict__.update(state)
if '_use_caching' not in state:
self.__dict__['_use_caching'] = True
if '_queue' not in state:
self.__dict__['_queue'] = ['']
if '_cache' not in state:
self.__dict__['_cache'] = {}
self.__dict__['_cache_ctr'] = {'hits': 0, 'misses': 0, 'check': 0}
if 'cache' not in state:
self.__dict__['cache'] = 5
def __getitem__(self, filter):
return FilterModel(self, filter)
[docs] def startup(self, cache=False):
self.cache_clear()
self._use_caching = cache
if int(self.cache) > 0:
self._queue = [''] * int(self.cache)
frozen = numpy.array([par.frozen for par in self.pars], dtype=bool)
if len(frozen) > 0 and frozen.all():
self._use_caching = cache
[docs] def teardown(self):
self._use_caching = False
[docs] def apply(self, outer, *otherargs, **otherkwargs):
return NestedModel(outer, self, *otherargs, **otherkwargs)
class RegriddableModel(ArithmeticModel):
def regrid(self, *args, **kwargs):
raise NotImplementedError
[docs]class RegriddableModel1D(RegriddableModel):
"""Allow 1D models to be regridded."""
ndim = 1
"A one-dimensional model."
[docs] def regrid(self, *args, **kwargs):
"""
The class RegriddableModel1D allows the user to evaluate in the
requested space then interpolate onto the data space. An optional
argument 'interp' enables the user to change the interpolation method.
Examples
--------
>>> import numpy as np
>>> from sherpa.models.basic import Box1D
>>> mybox = Box1D()
>>> request_space = np.arange(1, 10, 0.1)
>>> regrid_model = mybox.regrid(request_space, interp=linear_interp)
"""
valid_keys = ('interp',)
for key in kwargs.keys():
if key not in valid_keys:
raise TypeError("unknown keyword argument: '%s'" % key)
eval_space = EvaluationSpace1D(*args)
regridder = ModelDomainRegridder1D(eval_space, **kwargs)
regridder._make_and_validate_grid(args)
return regridder.apply_to(self)
[docs]class RegriddableModel2D(RegriddableModel):
"""Allow 2D models to be regridded."""
ndim = 2
"A two-dimensional model."
[docs] def regrid(self, *args, **kwargs):
eval_space = EvaluationSpace2D(*args)
regridder = ModelDomainRegridder2D(eval_space)
return regridder.apply_to(self)
[docs]class UnaryOpModel(CompositeModel, ArithmeticModel):
"""Apply an operator to a model expression.
Parameters
----------
arg : Model instance
The expression.
op : function reference
The ufunc to apply to the model values.
opstr : str
The symbol used to represent the operator.
Attributes
----------
arg : Model instance
The model.
op : function reference
The ufunc to apply to the model values.
opstr : str
The symbol used to represent the operator.
See Also
--------
BinaryOpModel
Examples
--------
>>> m1 = Gauss1d()
>>> m2 = UnaryOpModel(m1, numpy.negative, '-')
"""
[docs] @staticmethod
def wrapobj(obj):
return _wrapobj(obj, ArithmeticConstantModel)
def __init__(self, arg, op, opstr):
self.arg = self.wrapobj(arg)
self.op = op
self.opstr = opstr
CompositeModel.__init__(self, ('%s(%s)' % (opstr, self.arg.name)),
(self.arg,))
[docs] def calc(self, p, *args, **kwargs):
return self.op(self.arg.calc(p, *args, **kwargs))
[docs]class BinaryOpModel(CompositeModel, RegriddableModel):
"""Combine two model expressions.
Parameters
----------
lhs : Model instance
The left-hand sides of the expression.
rhs : Model instance
The right-hand sides of the expression.
op : function reference
The ufunc which combines two array values.
opstr : str
The symbol used to represent the operator.
Attributes
----------
lhs : Model instance
The left-hand sides of the expression.
rhs : Model instance
The right-hand sides of the expression.
op : function reference
The ufunc which combines two array values.
opstr : str
The symbol used to represent the operator.
See Also
--------
UnaryOpModel
Examples
--------
>>> m1 = Gauss1d()
>>> m2 = Polynom1D()
>>> m = BinaryOpModel(m1, m2, numpy.add, '+')
"""
[docs] @staticmethod
def wrapobj(obj):
return _wrapobj(obj, ArithmeticConstantModel)
def __init__(self, lhs, rhs, op, opstr):
self.lhs = self.wrapobj(lhs)
self.rhs = self.wrapobj(rhs)
self.op = op
self.opstr = opstr
CompositeModel.__init__(self,
('(%s %s %s)' %
(self.lhs.name, opstr, self.rhs.name)),
(self.lhs, self.rhs))
[docs] def regrid(self, *args, **kwargs):
for part in self.parts:
# ArithmeticConstantModel does not support regrid by design
if not hasattr(part, 'regrid'):
continue
# The full model expression must be used
return part.__class__.regrid(self, *args, **kwargs)
raise ModelErr('Neither component supports regrid method')
[docs] def startup(self, cache=False):
self.lhs.startup(cache)
self.rhs.startup(cache)
CompositeModel.startup(self, cache)
[docs] def teardown(self):
self.lhs.teardown()
self.rhs.teardown()
CompositeModel.teardown(self)
[docs] def calc(self, p, *args, **kwargs):
nlhs = len(self.lhs.pars)
lhs = self.lhs.calc(p[:nlhs], *args, **kwargs)
rhs = self.rhs.calc(p[nlhs:], *args, **kwargs)
try:
val = self.op(lhs, rhs)
except ValueError:
raise ValueError("shape mismatch between '%s: %i' and '%s: %i'" %
(type(self.lhs).__name__, len(lhs),
type(self.rhs).__name__, len(rhs)))
return val
# TODO: do we actually make use of this functionality anywhere?
# We only have 1 test that checks this class, and it is an existence
# test (check that it works), not that it is used anywhere.
#
[docs]class FilterModel(CompositeModel, ArithmeticModel):
def __init__(self, model, filter):
self.model = model
self.filter = filter
if isinstance(filter, tuple):
filter_str = ','.join([self._make_filter_str(f) for f in filter])
else:
filter_str = self._make_filter_str(filter)
CompositeModel.__init__(self,
('(%s)[%s]' % (self.model.name, filter_str)),
(self.model,))
@staticmethod
def _make_filter_str(filter):
if not isinstance(filter, slice):
if filter is Ellipsis:
return '...'
return str(filter)
s = ''
if filter.start is not None:
s += str(filter.start)
s += ':'
if filter.stop is not None:
s += str(filter.stop)
if filter.step is not None:
s += ':%s' % filter.step
return s
[docs] def calc(self, p, *args, **kwargs):
return self.model.calc(p, *args, **kwargs)[self.filter]
[docs]class ArithmeticFunctionModel(Model):
"""Represent a callable function.
Parameters
----------
func : function reference
A callable function. It is called with the grid arguments and
any keyword arguments sent to calc(), but not the model
parameter values.
Attributes
----------
func : function reference
"""
def __init__(self, func):
if isinstance(func, Model):
raise ModelErr('badinstance', type(self).__name__)
if not callable(func):
raise ModelErr('noncall', type(self).__name__, type(func).__name__)
self.func = func
Model.__init__(self, func.__name__)
[docs] def calc(self, p, *args, **kwargs):
return self.func(*args, **kwargs)
[docs] def startup(self, cache=False):
pass
[docs] def teardown(self):
pass
[docs]class NestedModel(CompositeModel, ArithmeticModel):
"""Apply a model to the results of a model.
Parameters
----------
outer : Model instance
The model to apply second.
inner : Model instance
The model to apply first.
*otherargs
Arguments that are to be applied to the outer model.
**otherkwargs
Keyword arguments that are to be applied to the outer model.
Attributes
----------
outer : Model instance
The outer model.
inner : Model instance
The inner model.
otherargs
Arguments that are to be applied to the outer model.
otherkwargs
Keyword arguments that are to be applied to the outer model.
"""
[docs] @staticmethod
def wrapobj(obj):
return _wrapobj(obj, ArithmeticFunctionModel)
def __init__(self, outer, inner, *otherargs, **otherkwargs):
self.outer = self.wrapobj(outer)
self.inner = self.wrapobj(inner)
self.otherargs = otherargs
self.otherkwargs = otherkwargs
CompositeModel.__init__(self,
('%s(%s)' %
(self.outer.name, self.inner.name)),
(self.outer, self.inner))
[docs] def startup(self, cache=False):
self.inner.startup(cache)
self.outer.startup(cache)
CompositeModel.startup(self, cache)
[docs] def teardown(self):
self.inner.teardown()
self.outer.teardown()
CompositeModel.teardown(self)
[docs] def calc(self, p, *args, **kwargs):
nouter = len(self.outer.pars)
return self.outer.calc(p[:nouter],
self.inner.calc(p[nouter:], *args, **kwargs),
*self.otherargs, **self.otherkwargs)
# TODO: can we remove this as it is now unused?
#
[docs]class MultigridSumModel(CompositeModel, ArithmeticModel):
def __init__(self, models):
self.models = tuple(models)
name = '%s(%s)' % (type(self).__name__,
','.join([m.name for m in models]))
CompositeModel.__init__(self, name, self.models)
[docs] def calc(self, p, arglist):
vals = []
for model, args in zip(self.models, arglist):
# FIXME: we're not using p here (and therefore assuming that the
# parameter values have already been updated to match the contents
# of p)
vals.append(model(*args))
return sum(vals)
class RegridWrappedModel(CompositeModel, ArithmeticModel):
def __init__(self, model, wrapper):
self.model = self.wrapobj(model)
self.wrapper = wrapper
if hasattr(model, 'integrate'):
self.wrapper.integrate = model.integrate
CompositeModel.__init__(self,
"{}({})".format(self.wrapper.name,
self.model.name),
(self.model, ))
def calc(self, p, *args, **kwargs):
return self.wrapper.calc(p, self.model.calc, *args, **kwargs)
def get_center(self):
return self.model.get_center()
def set_center(self, *args, **kwargs):
return self.model.set_center(*args, **kwargs)
def guess(self, dep, *args, **kwargs):
return self.model.guess(dep, *args, **kwargs)
@property
def grid(self):
return self.wrapper.grid
@grid.setter
def grid(self, value):
self.wrapper.grid = value
@property
def evaluation_space(self):
return self.wrapper.evaluation_space
@staticmethod
def wrapobj(obj):
# TODO: choice of ArithmeticConstandModel or
# ArithmeticFunctionModel?
return _wrapobj(obj, ArithmeticFunctionModel)
def _wrapobj(obj, wrapper):
"""Wrap an object with the wrapper if needed.
Parameters
----------
obj
The input object.
wrapper
The wrapper class which is applied to obj if needed.
Returns
-------
newobj
This is either obj or wrapper(obj).
"""
# This has been placed outside the classes as
# the full list of classes are needed to be accessible
# when called.
#
if isinstance(obj, (ArithmeticModel, ArithmeticConstantModel, ArithmeticFunctionModel)):
return obj
return wrapper(obj)
# Notebook representation
#
def modelcomponents_to_list(model):
if hasattr(model, 'parts'):
modellist = []
for p in model.parts:
modellist.extend(modelcomponents_to_list(p))
return modellist
else:
return [model]
def html_model(mdl):
"""Construct the HTML to display the model."""
# Note that as this is a specialized table we do not use
# formatting.html_table but create everything directly.
#
complist = []
nrows = []
for comp in modelcomponents_to_list(mdl):
this_comp_nrows = 0
for par in comp.pars:
if par.hidden:
continue
else:
this_comp_nrows +=1
if this_comp_nrows > 0:
complist.append(comp)
nrows.append(this_comp_nrows)
out = '<table class="model">'
out += '<caption>Expression: {}</caption>'.format(formatting.clean_bracket(mdl.name))
out += '<thead><tr>'
cols = ['Component', 'Parameter', 'Thawed', 'Value',
'Min', 'Max', 'Units']
for col in cols:
out += '<th>{}</th>'.format(col)
out += '</tr></thead><tbody>'
for mcount, (n, comp) in enumerate(zip(nrows, complist)):
for i, par in enumerate(comp.pars):
style = '' if ((i > 0) or (mcount == 0)) else ' class="block"'
if par.hidden:
continue
def addtd(val):
"Use the parameter to convert to HTML"
return '<td>{}</td>'.format(par._val_to_html(val))
out += '<tr{}>'.format(style)
if i ==0 :
cls = "model-" + ("even" if mcount % 2 == 1 else "odd")
out += '<th class="{}" scope="rowgroup" '.format(cls)
out += 'rowspan={}>{}</th>'.format(n, comp.name)
out += '<td>{}</td>'.format(par.name)
if par.link is not None:
out += "<td>linked</td>"
else:
out += '<td><input disabled type="checkbox"'
if not par.frozen:
out += ' checked'
out += '></input></td>'
out += addtd(par.val)
if par.link is not None:
# 8592 is single left arrow
# 8656 is double left arrow
#
out += '<td colspan=2>⇐ {}</td>'.format(formatting.clean_bracket(par.link.fullname))
else:
out += addtd(par.min)
out += addtd(par.max)
out += '<td>{}</td>'.format(par._units_to_html())
out += '</tr>'
out += '</tbody></table>'
ls = ['<details open><summary>Model</summary>' + out + '</details>']
return formatting.html_from_sections(mdl, ls)