Source code for brian2.core.variables

'''
Classes used to specify the type of a function, variable or common
sub-expression.
'''
import collections
import functools
import numbers

import sympy
import numpy as np

from brian2.utils.stringtools import get_identifiers, word_substitute
from brian2.units.fundamentalunits import (Quantity, Unit,
                                           fail_for_dimension_mismatch,
                                           have_same_dimensions)
from brian2.units.allunits import second
from brian2.utils.logger import get_logger

from .base import weakproxy_with_fallback, device_override
from .preferences import prefs

__all__ = ['Variable',
           'Constant',
           'AttributeVariable',
           'ArrayVariable',
           'DynamicArrayVariable',
           'Subexpression',
           'AuxiliaryVariable',
           'VariableView',
           'Variables',
           'LinkedVariable',
           'linked_var'
           ]


logger = get_logger(__name__)


[docs]def get_dtype(obj): ''' Helper function to return the `numpy.dtype` of an arbitrary object. Parameters ---------- obj : object Any object (but typically some kind of number or array). Returns ------- dtype : `numpy.dtype` The type of the given object. ''' if hasattr(obj, 'dtype'): return obj.dtype else: return np.obj2sctype(type(obj))
[docs]def get_dtype_str(val): ''' Returns canonical string representation of the dtype of a value or dtype Returns ------- dtype_str : str The numpy dtype name ''' if isinstance(val, np.dtype): return val.name if isinstance(val, type): return get_dtype_str(val()) is_bool = (val is True or val is False or val is np.True_ or val is np.False_) if is_bool: return 'bool' if hasattr(val, 'dtype'): return get_dtype_str(val.dtype) if isinstance(val, numbers.Number): return get_dtype_str(np.array(val).dtype) return 'unknown[%s, %s]' % (str(val), val.__class__.__name__)
[docs]def variables_by_owner(variables, owner): owner_name = getattr(owner, 'name', None) return dict([(varname, var) for varname, var in variables.iteritems() if getattr(var.owner, 'name', None) is owner_name])
[docs]class Variable(object): ''' An object providing information about model variables (including implicit variables such as ``t`` or ``xi``). This class should never be instantiated outside of testing code, use one of its subclasses instead. Parameters ---------- name : 'str' The name of the variable. Note that this refers to the *original* name in the owning group. The same variable may be known under other names in other groups (e.g. the variable ``v`` of a `NeuronGroup` is known as ``v_post`` in a `Synapse` connecting to the group). unit : `Unit` The unit of the variable. owner : `Nameable`, optional The object that "owns" this variable, e.g. the `NeuronGroup` or `Synapses` object that declares the variable in its model equations. Defaults to ``None`` (the value used for `Variable` objects without an owner, e.g. external `Constant`\ s). dtype : `dtype`, optional The dtype used for storing the variable. Defaults to the preference `core.default_scalar.dtype`. scalar : bool, optional Whether the variable is a scalar value (``True``) or vector-valued, e.g. defined for every neuron (``False``). Defaults to ``False``. constant: bool, optional Whether the value of this variable can change during a run. Defaults to ``False``. read_only : bool, optional Whether this is a read-only variable, i.e. a variable that is set internally and cannot be changed by the user (this is used for example for the variable ``N``, the number of neurons in a group). Defaults to ``False``. ''' def __init__(self, name, unit, owner=None, dtype=None, scalar=False, constant=False, read_only=False, dynamic=False): #: The variable's unit. self.unit = unit #: The variable's name. self.name = name #: The `Group` to which this variable belongs. self.owner = weakproxy_with_fallback(owner) if owner is not None else None #: The dtype used for storing the variable. self.dtype = dtype if dtype is None: self.dtype = prefs.core.default_float_dtype if self.is_boolean: if not have_same_dimensions(unit, 1): raise ValueError('Boolean variables can only be dimensionless') #: Whether the variable is a scalar self.scalar = scalar #: Whether the variable is constant during a run self.constant = constant #: Whether the variable is read-only self.read_only = read_only #: Whether the variable is dynamically sized (only for non-scalars) self.dynamic = dynamic @property def is_boolean(self): return np.issubdtype(np.bool, self.dtype) @property def dim(self): ''' The dimensions of this variable. ''' return self.unit.dim @property def dtype_str(self): ''' String representation of the numpy dtype ''' return get_dtype_str(self)
[docs] def get_value(self): ''' Return the value associated with the variable (without units). This is the way variables are accessed in generated code. ''' raise TypeError('Cannot get value for variable %s' % self)
[docs] def set_value(self, value): ''' Set the value associated with the variable. ''' raise TypeError('Cannot set value for variable %s' % self)
[docs] def get_value_with_unit(self): ''' Return the value associated with the variable (with units). ''' return Quantity(self.get_value(), self.unit.dimensions)
[docs] def get_addressable_value(self, name, group): ''' Get the value (without units) of this variable in a form that can be indexed in the context of a group. For example, if a postsynaptic variable ``x`` is accessed in a synapse ``S`` as ``S.x_post``, the synaptic indexing scheme can be used. Parameters ---------- name : str The name of the variable group : `Group` The group providing the context for the indexing. Note that this `group` is not necessarily the same as `Variable.owner`: a variable owned by a `NeuronGroup` can be indexed in a different way if accessed via a `Synapses` object. Returns ------- variable : object The variable in an indexable form (without units). ''' return self.get_value()
[docs] def get_addressable_value_with_unit(self, name, group): ''' Get the value (with units) of this variable in a form that can be indexed in the context of a group. For example, if a postsynaptic variable ``x`` is accessed in a synapse ``S`` as ``S.x_post``, the synaptic indexing scheme can be used. Parameters ---------- name : str The name of the variable group : `Group` The group providing the context for the indexing. Note that this `group` is not necessarily the same as `Variable.owner`: a variable owned by a `NeuronGroup` can be indexed in a different way if accessed via a `Synapses` object. Returns ------- variable : object The variable in an indexable form (with units). ''' return self.get_value_with_unit()
[docs] def get_len(self): ''' Get the length of the value associated with the variable or ``0`` for a scalar variable. ''' if self.scalar: return 0 else: return len(self.get_value())
def __len__(self): return self.get_len() def __repr__(self): description = ('<{classname}(unit={unit}, ' ' dtype={dtype}, scalar={scalar}, constant={constant},' ' read_only={read_only})>') return description.format(classname=self.__class__.__name__, unit=repr(self.unit), dtype=repr(self.dtype), scalar=repr(self.scalar), constant=repr(self.constant), read_only=repr(self.read_only)) # ------------------------------------------------------------------------------ # Concrete classes derived from `Variable` -- these are the only ones ever # instantiated. # ------------------------------------------------------------------------------
[docs]class Constant(Variable): ''' A scalar constant (e.g. the number of neurons ``N``). Information such as the dtype or whether this variable is a boolean are directly derived from the `value`. Most of the time `Variables.add_constant` should be used instead of instantiating this class directly. Parameters ---------- name : str The name of the variable unit : `Unit` The unit of the variable. Note that the variable itself (as referenced by value) should never have units attached. value: reference to the variable value The value of the constant. owner : `Nameable`, optional The object that "owns" this variable, for constants that belong to a specific group, e.g. the ``N`` constant for a `NeuronGroup`. External constants will have ``None`` (the default value). ''' def __init__(self, name, unit, value, owner=None): # Determine the type of the value is_bool = (value is True or value is False or value is np.True_ or value is np.False_) if is_bool: dtype = np.bool else: dtype = get_dtype(value) # Use standard Python types if possible for numpy scalars (generates # nicer code for C++ when using weave) if getattr(value, 'shape', None) == () and hasattr(value, 'dtype'): numpy_type = value.dtype if np.can_cast(numpy_type, np.int_): value = int(value) elif np.can_cast(numpy_type, np.float_): value = float(value) elif np.can_cast(numpy_type, np.complex_): value = complex(value) elif value is np.True_: value = True elif value is np.False_: value = False #: The constant's value self.value = value super(Constant, self).__init__(unit=unit, name=name, owner=owner, dtype=dtype, scalar=True, constant=True, read_only=True)
[docs] def get_value(self): return self.value
[docs]class AuxiliaryVariable(Variable): ''' Variable description for an auxiliary variable (most likely one that is added automatically to abstract code, e.g. ``_cond`` for a threshold condition), specifying its type and unit for code generation. Most of the time `Variables.add_auxiliary_variable` should be used instead of instantiating this class directly. Parameters ---------- name : str The name of the variable unit : `Unit` The unit of the variable. dtype : `dtype`, optional The dtype used for storing the variable. If none is given, defaults to `core.default_float_dtype`. scalar : bool, optional Whether the variable is a scalar value (``True``) or vector-valued, e.g. defined for every neuron (``False``). Defaults to ``False``. ''' def __init__(self, name, unit, dtype=None, scalar=False): super(AuxiliaryVariable, self).__init__(unit=unit, name=name, dtype=dtype, scalar=scalar)
[docs] def get_value(self): raise TypeError('Cannot get the value for an auxiliary variable.')
[docs]class AttributeVariable(Variable): ''' An object providing information about a value saved as an attribute of an object. Instead of saving a reference to the value itself, we save the name of the attribute. This way, we get the correct value if the attribute is overwritten with a new value (e.g. in the case of ``clock.t_``). Most of the time `Variables.add_attribute_variable` should be used instead of instantiating this class directly. The object value has to be accessible by doing ``getattr(obj, attribute)``. Variables of this type are considered read-only. Parameters ---------- name : str The name of the variable unit : `Unit` The unit of the variable obj : object The object storing the attribute. attribute : str The name of the attribute storing the variable's value. `attribute` has to be an attribute of `obj`. dtype : `dtype`, optional The dtype used for storing the variable. If none is given, defaults to `core.default_float_dtype`. owner : `Nameable`, optional The object that "owns" this variable, e.g. the `NeuronGroup` to which a ``dt`` value belongs (even if it is the attribute of a `Clock` object). Defaults to ``None``. constant : bool, optional Whether the attribute's value is constant during a run. Defaults to ``False``. scalar : bool, optional Whether the variable is a scalar value (``True``) or vector-valued, e.g. defined for every neuron (``False``). Defaults to ``True``. ''' def __init__(self, name, unit, obj, attribute, dtype=None, owner=None, constant=False, scalar=True): super(AttributeVariable, self).__init__(unit=unit, owner=owner, name=name, dtype=dtype, constant=constant, scalar=scalar, read_only=True) #: The object storing the `attribute` self.obj = obj #: The name of the attribute storing the variable's value self.attribute = attribute
[docs] def get_value(self): return getattr(self.obj, self.attribute)
def __repr__(self): description = ('{classname}(unit={unit}, obj=<{obj}>, ' 'attribute={attribute}, constant={constant})') return description.format(classname=self.__class__.__name__, unit=repr(self.unit), obj=self.obj, attribute=repr(self.attribute), constant=repr(self.constant))
[docs]class ArrayVariable(Variable): ''' An object providing information about a model variable stored in an array (for example, all state variables). Most of the time `Variables.add_array` should be used instead of instantiating this class directly. Parameters ---------- name : 'str' The name of the variable. Note that this refers to the *original* name in the owning group. The same variable may be known under other names in other groups (e.g. the variable ``v`` of a `NeuronGroup` is known as ``v_post`` in a `Synapse` connecting to the group). unit : `Unit` The unit of the variable owner : `Nameable` The object that "owns" this variable, e.g. the `NeuronGroup` or `Synapses` object that declares the variable in its model equations. size : int The size of the array device : `Device` The device responsible for the memory access. dtype : `dtype`, optional The dtype used for storing the variable. If none is given, defaults to `core.default_float_dtype`. constant : bool, optional Whether the variable's value is constant during a run. Defaults to ``False``. scalar : bool, optional Whether this array is a 1-element array that should be treated like a scalar (e.g. for a single delay value across synapses). Defaults to ``False``. read_only : bool, optional Whether this is a read-only variable, i.e. a variable that is set internally and cannot be changed by the user. Defaults to ``False``. unique : bool, optional Whether the values in this array are all unique. This information is only important for variables used as indices and does not have to reflect the actual contents of the array but only the possibility of non-uniqueness (e.g. synaptic indices are always unique but the corresponding pre- and post-synaptic indices are not). Defaults to ``False``. ''' def __init__(self, name, unit, owner, size, device, dtype=None, constant=False, scalar=False, read_only=False, dynamic=False, unique=False): super(ArrayVariable, self).__init__(unit=unit, name=name, owner=owner, dtype=dtype, scalar=scalar, constant=constant, read_only=read_only, dynamic=dynamic) #: Wether all values in this arrays are necessarily unique (only #: relevant for index variables). self.unique = unique #: The `Device` responsible for memory access. self.device = device #: The size of this variable. self.size = size if scalar and size != 1: raise ValueError(('Scalar variables need to have size 1, not ' 'size %d.') % size) #: Another variable, on which the write is conditioned (e.g. a variable #: denoting the absence of refractoriness) self.conditional_write = None
[docs] def set_conditional_write(self, var): if not var.is_boolean: raise TypeError(('A variable can only be conditionally writeable ' 'depending on a boolean variable, %s is not ' 'boolean.') % var.name) self.conditional_write = var
[docs] def get_value(self): return self.device.get_value(self)
[docs] def set_value(self, value): self.device.fill_with_array(self, value)
[docs] def get_len(self): return self.size
[docs] def get_addressable_value(self, name, group): return VariableView(name=name, variable=self, group=group, unit=None)
[docs] def get_addressable_value_with_unit(self, name, group): return VariableView(name=name, variable=self, group=group, unit=self.unit)
[docs]class DynamicArrayVariable(ArrayVariable): ''' An object providing information about a model variable stored in a dynamic array (used in `Synapses`). Most of the time `Variables.add_dynamic_array` should be used instead of instantiating this class directly. Parameters ---------- name : 'str' The name of the variable. Note that this refers to the *original* name in the owning group. The same variable may be known under other names in other groups (e.g. the variable ``v`` of a `NeuronGroup` is known as ``v_post`` in a `Synapse` connecting to the group). unit : `Unit` The unit of the variable owner : `Nameable` The object that "owns" this variable, e.g. the `NeuronGroup` or `Synapses` object that declares the variable in its model equations. size : int or tuple of int The (initial) size of the variable. device : `Device` The device responsible for the memory access. dtype : `dtype`, optional The dtype used for storing the variable. If none is given, defaults to `core.default_float_dtype`. constant : bool, optional Whether the variable's value is constant during a run. Defaults to ``False``. constant_size : bool, optional Whether the size of the variable is constant during a run. Defaults to ``True``. scalar : bool, optional Whether this array is a 1-element array that should be treated like a scalar (e.g. for a single delay value across synapses). Defaults to ``False``. read_only : bool, optional Whether this is a read-only variable, i.e. a variable that is set internally and cannot be changed by the user. Defaults to ``False``. unique : bool, optional Whether the values in this array are all unique. This information is only important for variables used as indices and does not have to reflect the actual contents of the array but only the possibility of non-uniqueness (e.g. synaptic indices are always unique but the corresponding pre- and post-synaptic indices are not). Defaults to ``False``. ''' def __init__(self, name, unit, owner, size, device, dtype=None, constant=False, constant_size=True, resize_along_first=False, scalar=False, read_only=False, unique=False): if isinstance(size, int): dimensions = 1 else: dimensions = len(size) #: The number of dimensions self.dimensions = dimensions if constant and not constant_size: raise ValueError('A variable cannot be constant and change in size') #: Whether the size of the variable is constant during a run. self.constant_size = constant_size #: Whether this array will be only resized along the first dimension self.resize_along_first = resize_along_first super(DynamicArrayVariable, self).__init__(unit=unit, owner=owner, name=name, size=size, device=device, constant=constant, dtype=dtype, scalar=scalar, dynamic=True, read_only=read_only, unique=unique)
[docs] def resize(self, new_size): ''' Resize the dynamic array. Calls `self.device.resize` to do the actual resizing. Parameters ---------- new_size : int or tuple of int The new size. ''' if self.resize_along_first: self.device.resize_along_first(self, new_size) else: self.device.resize(self, new_size) self.size = new_size
[docs]class Subexpression(Variable): ''' An object providing information about a named subexpression in a model. Most of the time `Variables.add_subexpression` should be used instead of instantiating this class directly. Parameters ---------- name : str The name of the subexpression. unit : `Unit` The unit of the subexpression. owner : `Group` The group to which the expression refers. expr : str The subexpression itself. device : `Device` The device responsible for the memory access. dtype : `dtype`, optional The dtype used for the expression. Defaults to `core.default_float_dtype`. scalar: bool, optional Whether this is an expression only referring to scalar variables. Defaults to ``False`` ''' def __init__(self, name, unit, owner, expr, device, dtype=None, scalar=False): super(Subexpression, self).__init__(unit=unit, owner=owner, name=name, dtype=dtype, scalar=scalar, constant=False, read_only=True) #: The `Device` responsible for memory access self.device = device #: The expression defining the subexpression self.expr = expr.strip() if scalar: from brian2.parsing.sympytools import str_to_sympy # We check here if the corresponding sympy expression contains a # reference to _vectorisation_idx which indicates that an implicitly # vectorized function (e.g. rand() ) has been used. We do not allow # this since it would lead to incorrect results when substituted into # vector equations sympy_expr = str_to_sympy(self.expr) if sympy.Symbol('_vectorisation_idx') in sympy_expr.atoms(): raise SyntaxError(('The scalar subexpression %s refers to an ' 'implicitly vectorized function -- this is ' 'not allowed since it leads to different ' 'interpretations of this subexpression ' 'depending on whether it is used in a ' 'scalar or vector context.') % name) #: The identifiers used in the expression self.identifiers = get_identifiers(expr)
[docs] def get_addressable_value(self, name, group): return VariableView(name=name, variable=self, group=group, unit=None)
[docs] def get_addressable_value_with_unit(self, name, group): return VariableView(name=name, variable=self, group=group, unit=self.unit)
def __contains__(self, var): return var in self.identifiers def __repr__(self): description = ('<{classname}(name={name}, unit={unit}, dtype={dtype}, ' 'expr={expr}, owner=<{owner}>)>') return description.format(classname=self.__class__.__name__, name=repr(self.name), unit=repr(self.unit), dtype=repr(self.dtype), expr=repr(self.expr), owner=self.owner.name) # ------------------------------------------------------------------------------ # Classes providing views on variables and storing variables information # ------------------------------------------------------------------------------
[docs]class LinkedVariable(object): ''' A simple helper class to make linking variables explicit. Users should use `linked_var` instead. Parameters ---------- group : `Group` The group through which the `variable` is accessed (not necessarily the same as ``variable.owner``. name : str The name of `variable` in `group` (not necessarily the same as ``variable.name``). variable : `Variable` The variable that should be linked. index : str or `ndarray`, optional An indexing array (or the name of a state variable), providing a mapping from the entries in the link source to the link target. ''' def __init__(self, group, name, variable, index=None): self.group = group self.name = name self.variable = variable self.index = index
[docs]def linked_var(group_or_variable, name=None, index=None): ''' Represents a link target for setting a linked variable. Parameters ---------- group_or_variable : `NeuronGroup` or `VariableView` Either a reference to the target `NeuronGroup` (e.g. ``G``) or a direct reference to a `VariableView` object (e.g. ``G.v``). In case only the group is specified, `name` has to be specified as well. name : str, optional The name of the target variable, necessary if `group_or_variable` is a `NeuronGroup`. index : str or `ndarray`, optional An indexing array (or the name of a state variable), providing a mapping from the entries in the link source to the link target. Examples -------- >>> from brian2 import * >>> G1 = NeuronGroup(10, 'dv/dt = -v / (10*ms) : volt') >>> G2 = NeuronGroup(10, 'v : volt (linked)') >>> G2.v = linked_var(G1, 'v') >>> G2.v = linked_var(G1.v) # equivalent ''' if isinstance(group_or_variable, VariableView): if name is not None: raise ValueError(('Cannot give a variable and a variable name at ' 'the same time.')) return LinkedVariable(group_or_variable.group, group_or_variable.name, group_or_variable.variable, index=index) elif name is None: raise ValueError('Need to provide a variable name') else: return LinkedVariable(group_or_variable, name, group_or_variable.variables[name], index=index)
[docs]class VariableView(object): ''' A view on a variable that allows to treat it as an numpy array while allowing special indexing (e.g. with strings) in the context of a `Group`. Parameters ---------- name : str The name of the variable (not necessarily the same as ``variable.name``). variable : `Variable` The variable description. group : `Group` The group through which the variable is accessed (not necessarily the same as `variable.owner`). unit : `Unit`, optional The unit to be used for the variable, should be `None` when a variable is accessed without units (e.g. when accessing ``G.var_``). ''' def __init__(self, name, variable, group, unit=None): self.name = name self.variable = variable self.index_var_name = group.variables.indices[name] if self.index_var_name in ('_idx', '0'): self.index_var = self.index_var_name else: self.index_var = group.variables[self.index_var_name] if isinstance(variable, Subexpression): # For subexpressions, we *always* have to go via codegen to get # their value -- since we cannot do this without the group, we # hold a strong reference self.group = group else: # For state variable arrays, we can do most access without the full # group, using the indexing reference below. We therefore only keep # a weak reference to the group. self.group = weakproxy_with_fallback(group) self.group_name = group.name # We keep a strong reference to the `Indexing` object so that basic # indexing is still possible, even if the group no longer exists self.indexing = self.group._indices self.unit = unit @property def dim(self): ''' The dimensions of this variable. ''' return self.unit.dim
[docs] def get_item(self, item, level=0, namespace=None): ''' Get the value of this variable. Called by `__getitem__`. Parameters ---------- item : slice, `ndarray` or string The index for the setting operation level : int, optional How much farther to go up in the stack to find the implicit namespace (if used, see `run_namespace`). namespace : dict-like, optional An additional namespace that is used for variable lookup (if not defined, the implicit namespace of local variables is used). ''' if isinstance(item, basestring): # Check whether the group still exists to give a more meaningful # error message if it does not try: self.group.name except ReferenceError: raise ReferenceError(('Cannot use string expressions, the ' 'group "%s", providing the context for ' 'the expression, no longer exists. ' 'Consider holding an explicit reference ' 'to it to keep it ' 'alive.') % self.group_name) values = self.get_with_expression(item, level=level+1, run_namespace=namespace) else: if isinstance(self.variable, Subexpression): values = self.get_subexpression_with_index_array(item, level=level+1, run_namespace=namespace) else: values = self.get_with_index_array(item) if self.unit is None: return values else: return Quantity(values, self.unit.dimensions)
def __getitem__(self, item): return self.get_item(item, level=1)
[docs] def set_item(self, item, value, level=0, namespace=None): ''' Set this variable. This function is called by `__setitem__` but there is also a situation where it should be called directly: if the context for string-based expressions is higher up in the stack, this function allows to set the `level` argument accordingly. Parameters ---------- item : slice, `ndarray` or string The index for the setting operation value : `Quantity`, `ndarray` or number The value for the setting operation level : int, optional How much farther to go up in the stack to find the implicit namespace (if used, see `run_namespace`). namespace : dict-like, optional An additional namespace that is used for variable lookup (if not defined, the implicit namespace of local variables is used). ''' variable = self.variable if variable.read_only: raise TypeError('Variable %s is read-only.' % self.name) # The second part is equivalent to item == slice(None) but formulating # it this way prevents a FutureWarning if one of the elements is a # numpy array if isinstance(item, slice) and (item.start is None and item.stop is None and item.step is None): item = 'True' check_units = self.unit is not None # Both index and values are strings, use a single code object do deal # with this situation if isinstance(value, basestring) and isinstance(item, basestring): self.set_with_expression_conditional(item, value, check_units=check_units, level=level+1, run_namespace=namespace) elif isinstance(item, basestring): try: float(value) # only checks for the exception try: # length-1 arrays are also convertible to float, but we # don't want the repr used later to be something like # array([...]). value = value[0] except (IndexError, TypeError): # was scalar already apparently pass except (TypeError, ValueError): if item != 'True': raise TypeError('When setting a variable based on a string ' 'index, the value has to be a string or a ' 'scalar.') if item == 'True': # We do not want to go through code generation for runtime self.set_with_index_array(slice(None), value, check_units=check_units) else: self.set_with_expression_conditional(item, repr(value), check_units=check_units, level=level+1, run_namespace=namespace) elif isinstance(value, basestring): self.set_with_expression(item, value, check_units=check_units, level=level+1, run_namespace=namespace) else: # No string expressions involved self.set_with_index_array(item, value, check_units=check_units)
def __setitem__(self, item, value): self.set_item(item, value, level=1) @device_override('variableview_set_with_expression') def set_with_expression(self, item, code, check_units=True, level=0, run_namespace=None): ''' Sets a variable using a string expression. Is called by `VariableView.set_item` for statements such as ``S.var[:, :] = 'exp(-abs(i-j)/space_constant)*nS'`` Parameters ---------- item : `ndarray` The indices for the variable (in the context of this `group`). code : str The code that should be executed to set the variable values. Can contain references to indices, such as `i` or `j` check_units : bool, optional Whether to check the units of the expression. level : int, optional How much farther to go up in the stack to find the implicit namespace (if used, see `run_namespace`). run_namespace : dict-like, optional An additional namespace that is used for variable lookup (if not defined, the implicit namespace of local variables is used). ''' # Some fairly complicated code to raise a warning in ambiguous # situations, when indexing with a group. For example, in: # group.v[subgroup] = 'i' # the index 'i' is the index of 'group' ("absolute index") and not of # subgroup ("relative index") if hasattr(item, 'variables') or (isinstance(item, tuple) and any(hasattr(one_item, 'variables') for one_item in item)): # Determine the variables that are used in the expression from brian2.codegen.translation import get_identifiers_recursively identifiers = get_identifiers_recursively([code], self.group.variables) variables = self.group.resolve_all(identifiers, [], run_namespace=run_namespace, level=level+2) if not isinstance(item, tuple): index_groups = [item] else: index_groups = item for varname, var in variables.iteritems(): for index_group in index_groups: if not hasattr(index_group, 'variables'): continue if varname in index_group.variables or var.name in index_group.variables: indexed_var = index_group.variables.get(varname, index_group.variables.get(var.name)) if not indexed_var is var: logger.warn(('The string expression used for setting ' '{varname} refers to {referred_var} which ' 'might be ambiguous. It will be ' 'interpreted as referring to ' '{referred_var} in {group}, not as ' 'a variable of a group used for ' 'indexing.').format(varname=self.name, referred_var=varname, group=self.group.name, index_group=index_group.name), 'ambiguous_string_expression') break # no need to warn more than once for a variable indices = self.indexing(item) abstract_code = self.name + ' = ' + code variables = Variables(None) variables.add_array('_group_idx', unit=Unit(1), size=len(indices), dtype=np.int32) variables['_group_idx'].set_value(indices) # TODO: Have an additional argument to avoid going through the index # array for situations where iterate_all could be used from brian2.codegen.codeobject import create_runner_codeobj from brian2.devices.device import get_default_codeobject_class codeobj = create_runner_codeobj(self.group, abstract_code, 'group_variable_set', additional_variables=variables, check_units=check_units, level=level+2, run_namespace=run_namespace, codeobj_class=get_default_codeobject_class('codegen.string_expression_target')) codeobj() @device_override('variableview_set_with_expression_conditional') def set_with_expression_conditional(self, cond, code, check_units=True, level=0, run_namespace=None): ''' Sets a variable using a string expression and string condition. Is called by `VariableView.set_item` for statements such as ``S.var['i!=j'] = 'exp(-abs(i-j)/space_constant)*nS'`` Parameters ---------- cond : str The string condition for which the variables should be set. code : str The code that should be executed to set the variable values. check_units : bool, optional Whether to check the units of the expression. level : int, optional How much farther to go up in the stack to find the implicit namespace (if used, see `run_namespace`). run_namespace : dict-like, optional An additional namespace that is used for variable lookup (if not defined, the implicit namespace of local variables is used). ''' variable = self.variable if variable.scalar and cond != 'True': raise IndexError(('Cannot conditionally set the scalar variable ' '%s.') % self.name) abstract_code_cond = '_cond = '+cond abstract_code = self.name + ' = ' + code variables = Variables(None) variables.add_auxiliary_variable('_cond', unit=Unit(1), dtype=np.bool) from brian2.codegen.codeobject import (create_runner_codeobj, check_code_units) check_code_units(abstract_code_cond, self.group, additional_variables=variables, level=level+2, run_namespace=run_namespace) # TODO: Have an additional argument to avoid going through the index # array for situations where iterate_all could be used from brian2.devices.device import get_default_codeobject_class codeobj = create_runner_codeobj(self.group, {'condition': abstract_code_cond, 'statement': abstract_code}, 'group_variable_set_conditional', additional_variables=variables, check_units=check_units, level=level+2, run_namespace=run_namespace, codeobj_class=get_default_codeobject_class('codegen.string_expression_target')) codeobj() @device_override('variableview_get_with_expression') def get_with_expression(self, code, level=0, run_namespace=None): ''' Gets a variable using a string expression. Is called by `VariableView.get_item` for statements such as ``print G.v['g_syn > 0']``. Parameters ---------- code : str An expression that states a condition for elements that should be selected. Can contain references to indices, such as ``i`` or ``j`` and to state variables. For example: ``'i>3 and v>0*mV'``. level : int, optional How much farther to go up in the stack to find the implicit namespace (if used, see `run_namespace`). run_namespace : dict-like, optional An additional namespace that is used for variable lookup (if not defined, the implicit namespace of local variables is used). ''' variable = self.variable if variable.scalar: raise IndexError(('Cannot access the variable %s with a ' 'string expression, it is a scalar ' 'variable.') % self.name) # Add the recorded variable under a known name to the variables # dictionary. Important to deal correctly with # the type of the variable in C++ variables = Variables(None) variables.add_auxiliary_variable('_variable', unit=variable.unit, dtype=variable.dtype, scalar=variable.scalar) variables.add_auxiliary_variable('_cond', unit=Unit(1), dtype=np.bool) abstract_code = '_variable = ' + self.name + '\n' abstract_code += '_cond = ' + code from brian2.codegen.codeobject import (create_runner_codeobj, check_code_units) check_code_units(abstract_code, self.group, additional_variables=variables, level=level+2, run_namespace=run_namespace) from brian2.devices.device import get_default_codeobject_class codeobj = create_runner_codeobj(self.group, abstract_code, 'group_variable_get_conditional', additional_variables=variables, level=level+2, run_namespace=run_namespace, codeobj_class=get_default_codeobject_class('codegen.string_expression_target') ) return codeobj() @device_override('variableview_get_with_index_array') def get_with_index_array(self, item): variable = self.variable if variable.scalar: if not (isinstance(item, slice) and item == slice(None)): raise IndexError(('Illegal index for variable %s, it is a ' 'scalar variable.') % self.name) indices = np.array(0) else: indices = self.indexing(item, self.index_var) if variable.scalar: return variable.get_value()[0] else: return variable.get_value()[indices] @device_override('variableview_get_subexpression_with_index_array') def get_subexpression_with_index_array(self, item, level=0, run_namespace=None): variable = self.variable if variable.scalar: if not (isinstance(item, slice) and item == slice(None)): raise IndexError(('Illegal index for variable %s, it is a ' 'scalar variable.') % self.name) indices = np.array(0) else: indices = self.indexing(item, self.index_var) # For "normal" variables, we can directly access the underlying data # and use the usual slicing syntax. For subexpressions, however, we # have to evaluate code for the given indices variables = Variables(None, default_index='_group_index') variables.add_auxiliary_variable('_variable', unit=variable.unit, dtype=variable.dtype, scalar=variable.scalar) if indices.shape == (): single_index = True indices = np.array([indices]) else: single_index = False variables.add_array('_group_idx', unit=Unit(1), size=len(indices), dtype=np.int32) variables['_group_idx'].set_value(indices) # Force the use of this variable as a replacement for the original # index variable using_orig_index = [varname for varname, index in self.group.variables.indices.iteritems() if index == self.index_var_name] for varname in using_orig_index: variables.indices[varname] = '_idx' abstract_code = '_variable = ' + self.name + '\n' from brian2.codegen.codeobject import create_runner_codeobj from brian2.devices.device import get_default_codeobject_class codeobj = create_runner_codeobj(self.group, abstract_code, 'group_variable_get', # Setting the user code to an empty # string suppresses warnings if the # subexpression refers to variable # names that are also present in the # local namespace user_code='', needed_variables=['_group_idx'], additional_variables=variables, level=level+2, run_namespace=run_namespace, codeobj_class=get_default_codeobject_class('codegen.string_expression_target') ) result = codeobj() if single_index and not variable.scalar: return result[0] else: return result @device_override('variableview_set_with_index_array') def set_with_index_array(self, item, value, check_units): variable = self.variable if check_units: fail_for_dimension_mismatch(variable.unit, value, 'Incorrect unit for setting variable %s' % self.name) if variable.scalar: if not (isinstance(item, slice) and item == slice(None)): raise IndexError(('Illegal index for variable %s, it is a ' 'scalar variable.') % self.name) variable.get_value()[0] = value else: indices = self.indexing(item, self.index_var) q = Quantity(value, copy=False) if len(q.shape): if not len(q.shape) == 1 or len(q) != 1 and len(q) != len(indices): raise ValueError(('Provided values do not match the size ' 'of the indices, ' '%d != %d.') % (len(q), len(indices))) variable.get_value()[indices] = value # Allow some basic calculations directly on the ArrayView object def __array__(self, dtype=None): try: # This will fail for subexpressions that refer to external # parameters value = self[:] except ValueError: raise ValueError(('Cannot get the values for variable {var}. If it ' 'is a subexpression referring to external ' 'variables, use "group.{var}[:]" instead of ' '"group.{var}"'.format(var=self.variable.name))) return np.asanyarray(self[:], dtype=dtype) def __len__(self): return len(self.get_item(slice(None), level=1)) def __neg__(self): return -self.get_item(slice(None), level=1) def __pos__(self): return self.get_item(slice(None), level=1) def __add__(self, other): return self.get_item(slice(None), level=1) + other def __radd__(self, other): return other + self.get_item(slice(None), level=1) def __sub__(self, other): return self.get_item(slice(None), level=1) - other def __rsub__(self, other): return other - self.get_item(slice(None), level=1) def __mul__(self, other): return self.get_item(slice(None), level=1) * other def __rmul__(self, other): return other * self.get_item(slice(None), level=1) def __div__(self, other): return self.get_item(slice(None), level=1) / other def __truediv__(self, other): return self.get_item(slice(None), level=1) / other def __floordiv__(self, other): return self.get_item(slice(None), level=1) // other def __rdiv__(self, other): return other / self.get_item(slice(None), level=1) def __rtruediv__(self, other): return other / self.get_item(slice(None), level=1) def __rfloordiv__(self, other): return other // self.get_item(slice(None), level=1) def __iadd__(self, other): if isinstance(other, basestring): raise TypeError(('In-place modification with strings not ' 'supported. Use group.var = "var + expression" ' 'instead of group.var += "expression".')) elif isinstance(self.variable, Subexpression): raise TypeError('Cannot assign to a subexpression.') else: rhs = self[:] + other self[:] = rhs return self def __isub__(self, other): if isinstance(other, basestring): raise TypeError(('In-place modification with strings not ' 'supported. Use group.var = "var - expression" ' 'instead of group.var -= "expression".')) elif isinstance(self.variable, Subexpression): raise TypeError('Cannot assign to a subexpression.') else: rhs = self[:] - other self[:] = rhs return self def __imul__(self, other): if isinstance(other, basestring): raise TypeError(('In-place modification with strings not ' 'supported. Use group.var = "var * expression" ' 'instead of group.var *= "expression".')) elif isinstance(self.variable, Subexpression): raise TypeError('Cannot assign to a subexpression.') else: rhs = self[:] * other self[:] = rhs return self def __idiv__(self, other): if isinstance(other, basestring): raise TypeError(('In-place modification with strings not ' 'supported. Use group.var = "var / expression" ' 'instead of group.var /= "expression".')) elif isinstance(self.variable, Subexpression): raise TypeError('Cannot assign to a subexpression.') else: rhs = self[:] / other self[:] = rhs return self # Also allow logical comparisons def __eq__(self, other): return self.get_item(slice(None), level=1) == other def __ne__(self, other): return self.get_item(slice(None), level=1) != other def __lt__(self, other): return self.get_item(slice(None), level=1) < other def __le__(self, other): return self.get_item(slice(None), level=1) <= other def __gt__(self, other): return self.get_item(slice(None), level=1) > other def __ge__(self, other): return self.get_item(slice(None), level=1) >= other def __repr__(self): varname = self.name if self.unit is None: varname += '_' try: # This will fail for subexpressions that refer to external # parameters values = repr(self[:]) except ValueError: values = ('[Subexpression refers to external parameters. Use ' '"group.{var}[:]"]').format(var=self.variable.name) return '<%s.%s: %s>' % (self.group_name, varname, values)
[docs]class Variables(collections.Mapping): ''' A container class for storing `Variable` objects. Instances of this class are used as the `Group.variables` attribute and can be accessed as (read-only) dictionaries. Parameters ---------- owner : `Nameable` The object (typically a `Group`) "owning" the variables. default_index : str, optional The index to use for the variables (only relevant for `ArrayVariable` and `DynamicArrayVariable`). Defaults to ``'_idx'``. ''' def __init__(self, owner, default_index='_idx'): #: A reference to the `Group` owning these variables self.owner = weakproxy_with_fallback(owner) # The index that is used for arrays if no index is given explicitly self.default_index = default_index # We do the import here to avoid a circular dependency. from brian2.devices.device import get_device self.device = get_device() self._variables = {} #: A dictionary given the index name for every array name self.indices = collections.defaultdict(functools.partial(str, default_index)) # Note that by using functools.partial (instead of e.g. a lambda # function) above, this object remains pickable. def __getitem__(self, item): return self._variables[item] def __len__(self): return len(self._variables) def __iter__(self): return iter(self._variables) def _add_variable(self, name, var, index=None): if name in self._variables: raise KeyError(('The name "%s" is already present in the variables' ' dictionary.') % name) #TODO: do some check for the name, part of it has to be device-specific self._variables[name] = var if isinstance(var, ArrayVariable): # Tell the device to actually create the array (or note it down for # later code generation in standalone). self.device.add_array(var) if index is not None: self.indices[name] = index
[docs] def add_array(self, name, unit, size, values=None, dtype=None, constant=False, read_only=False, scalar=False, unique=False, index=None): ''' Add an array (initialized with zeros). Parameters ---------- name : str The name of the variable. unit : `Unit` The unit of the variable size : int The size of the array. values : `ndarray`, optional The values to initalize the array with. If not specified, the array is initialized to zero. dtype : `dtype`, optional The dtype used for storing the variable. If none is given, defaults to `core.default_float_dtype`. constant : bool, optional Whether the variable's value is constant during a run. Defaults to ``False``. scalar : bool, optional Whether this is a scalar variable. Defaults to ``False``, if set to ``True``, also implies that `size` equals 1. read_only : bool, optional Whether this is a read-only variable, i.e. a variable that is set internally and cannot be changed by the user. Defaults to ``False``. index : str, optional The index to use for this variable. Defaults to `Variables.default_index`. unique : bool, optional See `ArrayVariable`. Defaults to ``False``. ''' var = ArrayVariable(name=name, unit=unit, owner=self.owner, device=self.device, size=size, dtype=dtype, constant=constant, scalar=scalar, read_only=read_only, unique=unique) self._add_variable(name, var, index) if values is None: self.device.init_with_zeros(var) else: if len(values) != size: raise ValueError(('Size of the provided values does not match ' 'size: %d != %d') % (len(values), size)) self.device.init_with_array(var, values)
[docs] def add_dynamic_array(self, name, unit, size, values=None, dtype=None, constant=False, constant_size=True, resize_along_first=False, read_only=False, unique=False, scalar=False, index=None): ''' Add a dynamic array. Parameters ---------- name : str The name of the variable. unit : `Unit` The unit of the variable size : int or tuple of int The (initital) size of the array. values : `ndarray`, optional The values to initalize the array with. If not specified, the array is initialized to zero. dtype : `dtype`, optional The dtype used for storing the variable. If none is given, defaults to `core.default_float_dtype`. constant : bool, optional Whether the variable's value is constant during a run. Defaults to ``False``. constant_size : bool, optional Whether the size of the variable is constant during a run. Defaults to ``True``. scalar : bool, optional Whether this is a scalar variable. Defaults to ``False``, if set to ``True``, also implies that `size` equals 1. read_only : bool, optional Whether this is a read-only variable, i.e. a variable that is set internally and cannot be changed by the user. Defaults to ``False``. index : str, optional The index to use for this variable. Defaults to `Variables.default_index`. unique : bool, optional See `DynamicArrayVariable`. Defaults to ``False``. ''' var = DynamicArrayVariable(name=name, unit=unit, owner=self.owner, device=self.device, size=size, dtype=dtype, constant=constant, constant_size=constant_size, resize_along_first=resize_along_first, scalar=scalar, read_only=read_only, unique=unique) self._add_variable(name, var, index) if values is None and np.prod(size) > 0: self.device.init_with_zeros(var) elif values is not None: if len(values) != size: raise ValueError(('Size of the provided values does not match ' 'size: %d != %d') % (len(values), size)) if np.prod(size) > 0: self.device.init_with_array(var, values)
[docs] def add_arange(self, name, size, start=0, dtype=np.int32, constant=True, read_only=True, unique=True, index=None): ''' Add an array, initialized with a range of integers. Parameters ---------- name : str The name of the variable. size : int The size of the array. start : int The start value of the range. dtype : `dtype`, optional The dtype used for storing the variable. If none is given, defaults to `np.int32`. constant : bool, optional Whether the variable's value is constant during a run. Defaults to ``True``. read_only : bool, optional Whether this is a read-only variable, i.e. a variable that is set internally and cannot be changed by the user. Defaults to ``True``. index : str, optional The index to use for this variable. Defaults to `Variables.default_index`. unique : bool, optional See `ArrayVariable`. Defaults to ``True`` here. ''' self.add_array(name=name, unit=Unit(1), size=size, dtype=dtype, constant=constant, read_only=read_only, unique=unique, index=index) self.device.init_with_arange(self._variables[name], start)
[docs] def add_attribute_variable(self, name, unit, obj, attribute, dtype=None, constant=False, scalar=True): ''' Add a variable stored as an attribute of an object. Parameters ---------- name : str The name of the variable unit : `Unit` The unit of the variable obj : object The object storing the attribute. attribute : str The name of the attribute storing the variable's value. `attribute` has to be an attribute of `obj`. dtype : `dtype`, optional The dtype used for storing the variable. If none is given, uses the type of ``obj.attribute`` (which have to exist). constant : bool, optional Whether the attribute's value is constant during a run. Defaults to ``False``. scalar : bool, optional Whether the variable is a scalar value (``True``) or vector-valued, e.g. defined for every neuron (``False``). Defaults to ``True``. ''' if dtype is None: value = getattr(obj, attribute, None) if value is None: raise ValueError(('Cannot determine dtype for attribute "%s" ' 'of object "%r"') % (attribute, obj)) dtype = get_dtype(value) var = AttributeVariable(name=name, unit=unit, owner=self.owner, obj=obj, attribute=attribute, dtype=dtype, constant=constant, scalar=scalar) self._add_variable(name, var)
[docs] def add_constant(self, name, unit, value): ''' Add a scalar constant (e.g. the number of neurons `N`). Parameters ---------- name : str The name of the variable unit : `Unit` The unit of the variable. Note that the variable itself (as referenced by value) should never have units attached. value: reference to the variable value The value of the constant. ''' var = Constant(name=name, unit=unit, owner=self.owner, value=value) self._add_variable(name, var)
[docs] def add_subexpression(self, name, unit, expr, dtype=None, scalar=False, index=None): ''' Add a named subexpression. Parameters ---------- name : str The name of the subexpression. unit : `Unit` The unit of the subexpression. expr : str The subexpression itself. dtype : `dtype`, optional The dtype used for the expression. Defaults to `core.default_float_dtype`. scalar : bool, optional Whether this is an expression only referring to scalar variables. Defaults to ``False`` index : str, optional The index to use for this variable. Defaults to `Variables.default_index`. ''' var = Subexpression(name=name, unit=unit, expr=expr, owner=self.owner, dtype=dtype, device=self.device, scalar=scalar) self._add_variable(name, var, index=index)
[docs] def add_auxiliary_variable(self, name, unit, dtype=None, scalar=False): ''' Add an auxiliary variable (most likely one that is added automatically to abstract code, e.g. ``_cond`` for a threshold condition), specifying its type and unit for code generation. Parameters ---------- name : str The name of the variable unit : `Unit` The unit of the variable. dtype : `dtype`, optional The dtype used for storing the variable. If none is given, defaults to `core.default_float_dtype`. scalar : bool, optional Whether the variable is a scalar value (``True``) or vector-valued, e.g. defined for every neuron (``False``). Defaults to ``False``. ''' var = AuxiliaryVariable(name=name, unit=unit, dtype=dtype, scalar=scalar) self._add_variable(name, var)
[docs] def add_referred_subexpression(self, name, group, subexpr, index): identifiers = subexpr.identifiers substitutions = {} for identifier in identifiers: if not identifier in subexpr.owner.variables: # external variable --> nothing to do continue subexpr_var = subexpr.owner.variables[identifier] if hasattr(subexpr_var, 'owner'): new_name = '_%s_%s_%s' % (name, subexpr.owner.name, identifier) else: new_name = '_%s_%s' % (name, identifier) substitutions[identifier] = new_name subexpr_var_index = group.variables.indices[identifier] if subexpr_var_index == group.variables.default_index: subexpr_var_index = index elif subexpr_var_index == '0': pass # nothing to do for a shared variable elif index != self.default_index: index_var = self._variables.get(index, None) if isinstance(index_var, DynamicArrayVariable): raise TypeError(('Cannot link to subexpression %s: it refers ' 'to the variable %s which is indexed with the ' 'dynamic index %s.') % (name, identifier, subexpr_var_index)) else: self.add_reference(subexpr_var_index, group) self.indices[new_name] = subexpr_var_index if isinstance(subexpr_var, Subexpression): self.add_referred_subexpression(new_name, group, subexpr_var, subexpr_var_index) else: self.add_reference(new_name, group, identifier, subexpr_var_index) new_expr = word_substitute(subexpr.expr, substitutions) new_subexpr = Subexpression(name, subexpr.unit, self.owner, new_expr, device=subexpr.device, dtype=subexpr.dtype, scalar=subexpr.scalar) self._variables[name] = new_subexpr
[docs] def add_reference(self, name, group, varname=None, index=None): ''' Add a reference to a variable defined somewhere else (possibly under a different name). This is for example used in `Subgroup` and `Synapses` to refer to variables in the respective `NeuronGroup`. Parameters ---------- name : str The name of the variable (in this group, possibly a different name from `var.name`). group : `Group` The group from which `var` is referenced varname : str, optional The variable to refer to. If not given, defaults to `name`. index : str, optional The index that should be used for this variable (defaults to `Variables.default_index`). ''' if index is None: index = self.default_index if varname is None: varname = name if self.owner is not None and index in self.owner.variables: if (not self.owner.variables[index].read_only and group.variables.indices[varname] != group.variables.default_index): raise TypeError(('Cannot link variable %s to %s in group %s -- ' 'need to precalculate direct indices but ' 'index %s can change') % (name, varname, group.name, index)) # We don't overwrite existing names with references if not name in self._variables: var = group.variables[varname] if isinstance(var, Subexpression): self.add_referred_subexpression(name, group, var, index) else: self._variables[name] = var self.indices[name] = index
[docs] def add_references(self, group, varnames, index=None): ''' Add all `Variable` objects from a name to `Variable` mapping with the same name as in the original mapping. Parameters ---------- group : `Group` The group from which the `variables` are referenced varnames : iterable of str The variables that should be referred to in the current group index : str, optional The index to use for all the variables (defaults to `Variables.default_index`) ''' for name in varnames: self.add_reference(name, group, name, index)
[docs] def create_clock_variables(self, clock, prefix=''): ''' Convenience function to add the ``t`` and ``dt`` attributes of a `clock`. Parameters ---------- clock : `Clock` The clock that should be used for ``t`` and ``dt``. Note that the actual attributes referred to are ``t_`` and ``dt_``, i.e. the unitless values. prefix : str, optional A prefix for the variable names. Used for example in monitors to not confuse the dynamic array of recorded times with the current time in the recorded group. ''' for name, is_constant in [('t', False), ('dt', True)]: if prefix+name in self._variables: var = self._variables[prefix+name] if not isinstance(var, AttributeVariable): raise AssertionError(('%s is present in the variables ' 'dictionary but of ' 'type %s') % (prefix+name, type(var))) var.obj = clock # replace the clock object else: self.add_attribute_variable(prefix+name, unit=second, obj=clock, attribute=name+'_', dtype=np.float64, constant=is_constant)