'''
Module providing `NumpyCodeObject`.
'''
import sys
from collections import Iterable
import numpy as np
from brian2.core.base import brian_object_exception
from brian2.core.preferences import prefs, BrianPreference
from brian2.core.variables import (DynamicArrayVariable, ArrayVariable,
AuxiliaryVariable, Subexpression)
from brian2.core.functions import Function
from ...codeobject import CodeObject, constant_or_scalar
from ...templates import Templater
from ...generators.numpy_generator import NumpyCodeGenerator
from ...targets import codegen_targets
__all__ = ['NumpyCodeObject']
# Preferences
prefs.register_preferences(
'codegen.runtime.numpy',
'Numpy runtime codegen preferences',
discard_units = BrianPreference(
default=False,
docs='''
Whether to change the namespace of user-specifed functions to remove
units.
'''
)
)
[docs]class LazyArange(Iterable):
'''
A class that can be used as a `~numpy.arange` replacement (with an implied
step size of 1) but does not actually create an array of values until
necessary. It is somewhat similar to the ``range()`` function in Python 3,
but does not use a generator. It is tailored to a special use case, the
``_vectorisation_idx`` variable in numpy templates, and not meant for
general use. The ``_vectorisation_idx`` is used for stateless function
calls such as ``rand()`` and for the numpy codegen target determines the
number of values produced by such a call. This will often be the number of
neurons or synapses, and this class avoids creating a new array of that size
at every code object call when all that is needed is the *length* of the
array.
Examples
--------
>>> from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange
>>> ar = LazyArange(10)
>>> len(ar)
10
>>> len(ar[:5])
5
>>> type(ar[:5])
<class 'brian2.codegen.runtime.numpy_rt.numpy_rt.LazyArange'>
>>> ar[5]
5
>>> for value in ar[3:7]:
... print(value)
...
3
4
5
6
>>> len(ar[np.array([1, 2, 3])])
3
'''
def __init__(self, stop, start=0, indices=None):
self.start = start
self.stop = stop
self.indices = indices
def __len__(self):
if self.indices is None:
return self.stop - self.start
else:
return len(self.indices)
def __getitem__(self, item):
if isinstance(item, slice):
if self.indices is None:
start, stop, step = item.start, item.stop, item.step
if step not in [None, 1]:
raise NotImplementedError('Step should be 1')
if start is None:
start = 0
if stop is None:
stop = len(self)
return LazyArange(start=self.start+start,
stop=min([self.start+stop, self.stop]))
else:
raise NotImplementedError('Cannot slice LazyArange with indices')
elif isinstance(item, np.ndarray):
if item.dtype == np.dtype(bool):
item = np.nonzero(item)[0] # convert boolean array into integers
if len(item) == 0:
return np.array([], dtype=np.int32)
if np.min(item) < 0 or np.max(item) > len(self):
raise IndexError('Indexing array contains out-of-bounds values')
return LazyArange(start=self.start, stop=self.stop, indices=item)
elif isinstance(item, int):
if self.indices is None:
index = self.start + item
if index >= self.stop:
raise IndexError(index)
return index
else:
return self.indices[item]
else:
raise TypeError('Can only index with integer, numpy array, or slice.')
def __iter__(self):
if self.indices is None:
return iter(np.arange(self.start, self.stop))
else:
return iter(self.indices)
# Allow conversion to a proper array with np.array(...)
def __array__(self, dtype=None):
if self.indices is None:
return np.arange(self.start, self.stop)
else:
return self.indices + self.start
# Allow basic arithmetics (used when shifting stuff for subgroups)
def __add__(self, other):
if isinstance(other, int):
return LazyArange(start=self.start + other, stop=self.stop + other)
else:
return NotImplemented
def __radd__(self, other):
return self.__add__(other)
def __sub__(self, other):
if isinstance(other, int):
return LazyArange(start=self.start - other, stop=self.stop - other)
else:
return NotImplemented
[docs]class NumpyCodeObject(CodeObject):
'''
Execute code using Numpy
Default for Brian because it works on all platforms.
'''
templater = Templater('brian2.codegen.runtime.numpy_rt', '.py_',
env_globals={'constant_or_scalar': constant_or_scalar})
generator_class = NumpyCodeGenerator
class_name = 'numpy'
def __init__(self, owner, code, variables, variable_indices,
template_name, template_source, name='numpy_code_object*'):
from brian2.devices.device import get_device
self.device = get_device()
self.namespace = {'_owner': owner,
# TODO: This should maybe go somewhere else
'logical_not': np.logical_not}
CodeObject.__init__(self, owner, code, variables, variable_indices,
template_name, template_source, name=name)
self.variables_to_namespace()
[docs] @classmethod
def is_available(cls):
# no test necessary for numpy
return True
[docs] def variables_to_namespace(self):
# Variables can refer to values that are either constant (e.g. dt)
# or change every timestep (e.g. t). We add the values of the
# constant variables here and add the names of non-constant variables
# to a list
# A list containing tuples of name and a function giving the value
self.nonconstant_values = []
for name, var in self.variables.iteritems():
if isinstance(var, (AuxiliaryVariable, Subexpression)):
continue
try:
if not hasattr(var, 'get_value'):
raise TypeError()
value = var.get_value()
except TypeError:
# Either a dummy Variable without a value or a Function object
if isinstance(var, Function):
impl = var.implementations[self.__class__].get_code(self.owner)
self.namespace[name] = impl
else:
self.namespace[name] = var
continue
if isinstance(var, ArrayVariable):
self.namespace[self.generator_class.get_array_name(var)] = value
if var.scalar and var.constant:
self.namespace[name] = value[0]
else:
self.namespace[name] = value
if isinstance(var, DynamicArrayVariable):
dyn_array_name = self.generator_class.get_array_name(var,
access_data=False)
self.namespace[dyn_array_name] = self.device.get_value(var,
access_data=False)
# Also provide the Variable object itself in the namespace (can be
# necessary for resize operations, for example)
self.namespace['_var_'+name] = var
# There is one type of objects that we have to inject into the
# namespace with their current value at each time step: dynamic
# arrays that change in size during runs (i.e. not synapses but
# e.g. the structures used in monitors)
if (isinstance(var, DynamicArrayVariable) and
var.needs_reference_update):
self.nonconstant_values.append((self.generator_class.get_array_name(var,
self.variables),
var.get_value))
[docs] def update_namespace(self):
# update the values of the non-constant values in the namespace
for name, func in self.nonconstant_values:
self.namespace[name] = func()
[docs] def compile(self):
super(NumpyCodeObject, self).compile()
self.compiled_code = compile(self.code, '(string)', 'exec')
[docs] def run(self):
try:
exec self.compiled_code in self.namespace
except Exception as exc:
message = ('An exception occured during the execution of code '
'object {}.\n').format(self.name)
lines = self.code.split('\n')
message += 'The error was raised in the following line:\n'
_, _, tb = sys.exc_info()
tb = tb.tb_next # Line in the code object's code
message += lines[tb.tb_lineno - 1] + '\n'
raise brian_object_exception(message, self.owner, exc)
# output variables should land in the variable name _return_values
if '_return_values' in self.namespace:
return self.namespace['_return_values']
codegen_targets.add(NumpyCodeObject)