"""
Implementation of `TimedArray`.
"""
import numpy as np
from brian2.core.clocks import defaultclock
from brian2.core.functions import Function
from brian2.units.allunits import second
from brian2.units.fundamentalunits import check_units, get_dimensions, Quantity, \
get_unit
from brian2.core.names import Nameable
from brian2.utils.caching import CacheKey
from brian2.utils.logger import get_logger
from brian2.utils.stringtools import replace
__all__ = ['TimedArray']
logger = get_logger(__name__)
def _find_K(group_dt, dt):
dt_ratio = dt / group_dt
if dt_ratio > 1 and np.floor(dt_ratio) != dt_ratio:
logger.warn(('Group uses a dt of %s while TimedArray uses dt '
'of %s (ratio: 1/%s) → time grids not aligned') %
(group_dt*second, dt*second, dt_ratio), once=True)
# Find an upsampling factor that should avoid rounding issues even
# for multistep methods
K = max(int(2**np.ceil(np.log2(8/group_dt*dt))), 1)
return K
def _generate_cpp_code_1d(values, dt, name):
def cpp_impl(owner):
K = _find_K(owner.clock.dt_, dt)
code = """
static inline double %NAME%(const double t)
{
const double epsilon = %DT% / %K%;
int i = (int)((t/epsilon + 0.5)/%K%);
if(i < 0)
i = 0;
if(i >= %NUM_VALUES%)
i = %NUM_VALUES%-1;
return _namespace%NAME%_values[i];
}
""".replace('%NAME%', name).replace('%DT%', f'{dt:.18f}').replace(
'%K%', str(K)).replace('%NUM_VALUES%', str(len(values)))
return code
return cpp_impl
def _generate_cpp_code_2d(values, dt, name):
def cpp_impl(owner):
K = _find_K(owner.clock.dt_, dt)
support_code = """
static inline double %NAME%(const double t, const int i)
{
const double epsilon = %DT% / %K%;
if (i < 0 || i >= %COLS%)
return NAN;
int timestep = (int)((t/epsilon + 0.5)/%K%);
if(timestep < 0)
timestep = 0;
else if(timestep >= %ROWS%)
timestep = %ROWS%-1;
return _namespace%NAME%_values[timestep*%COLS% + i];
}
"""
code = replace(support_code, {'%NAME%': name,
'%DT%': f'{dt:.18f}',
'%K%': str(K),
'%COLS%': str(values.shape[1]),
'%ROWS%': str(values.shape[0])})
return code
return cpp_impl
def _generate_cython_code_1d(values, dt, name):
def cython_impl(owner):
K = _find_K(owner.clock.dt_, dt)
code = """
cdef double %NAME%(const double t):
global _namespace%NAME%_values
cdef double epsilon = %DT% / %K%
cdef int i = (int)((t/epsilon + 0.5)/%K%)
if i < 0:
i = 0
if i >= %NUM_VALUES%:
i = %NUM_VALUES% - 1
return _namespace%NAME%_values[i]
""".replace('%NAME%', name).replace('%DT%', f'{dt:.18f}').replace(
'%K%', str(K)).replace('%NUM_VALUES%', str(len(values)))
return code
return cython_impl
def _generate_cython_code_2d(values, dt, name):
def cython_impl(owner):
K = _find_K(owner.clock.dt_, dt)
code = """
cdef double %NAME%(const double t, const int i):
global _namespace%NAME%_values
cdef double epsilon = %DT% / %K%
if i < 0 or i >= %COLS%:
return _numpy.nan
cdef int timestep = (int)((t/epsilon + 0.5)/%K%)
if timestep < 0:
timestep = 0
elif timestep >= %ROWS%:
timestep = %ROWS%-1
return _namespace%NAME%_values[timestep*%COLS% + i]
"""
code = replace(code, {'%NAME%': name,
'%DT%': f'{dt:.18f}',
'%K%': str(K),
'%COLS%': str(values.shape[1]),
'%ROWS%': str(values.shape[0])})
return code
return cython_impl
[docs]class TimedArray(Function, Nameable, CacheKey):
"""
TimedArray(values, dt, name=None)
A function of time built from an array of values. The returned object can
be used as a function, including in model equations etc. The resulting
function has to be called as `funcion_name(t)` if the provided value array
is one-dimensional and as `function_name(t, i)` if it is two-dimensional.
Parameters
----------
values : ndarray or `Quantity`
An array of values providing the values at various points in time. This
array can either be one- or two-dimensional. If it is two-dimensional
it's first dimension should be the time.
dt : `Quantity`
The time distance between values in the `values` array.
name : str, optional
A unique name for this object, see `Nameable` for details. Defaults
to ``'_timedarray*'``.
Notes
-----
For time values corresponding to elements outside of the range of `values`
provided, the first respectively last element is returned.
Examples
--------
>>> from brian2 import *
>>> ta = TimedArray([1, 2, 3, 4] * mV, dt=0.1*ms)
>>> print(ta(0.3*ms))
4. mV
>>> G = NeuronGroup(1, 'v = ta(t) : volt')
>>> mon = StateMonitor(G, 'v', record=True)
>>> net = Network(G, mon)
>>> net.run(1*ms) # doctest: +ELLIPSIS
...
>>> print(mon[0].v)
[ 1. 2. 3. 4. 4. 4. 4. 4. 4. 4.] mV
>>> ta2d = TimedArray([[1, 2], [3, 4], [5, 6]]*mV, dt=0.1*ms)
>>> G = NeuronGroup(4, 'v = ta2d(t, i%2) : volt')
>>> mon = StateMonitor(G, 'v', record=True)
>>> net = Network(G, mon)
>>> net.run(0.2*ms) # doctest: +ELLIPSIS
...
>>> print(mon.v[:])
[[ 1. 3.]
[ 2. 4.]
[ 1. 3.]
[ 2. 4.]] mV
"""
_cache_irrelevant_attributes = {'_id', 'values', 'pyfunc',
'implementations'}
#: Container for implementing functions for different targets
#: This container can be extended by other codegeneration targets/devices
#: The key has to be the name of the target, the value is a tuple of
#: functions, the first for a 1d array, the second for a 2d array.
#: The functions have to take three parameters: (values, dt, name), i.e. the
#: array values, their physical dimensions, the dt of the TimedArray, and
#: the name of the TimedArray. The functions have to return *a function*
#: that takes the `owner` argument (out of which they can get the context's
#: dt as `owner.clock.dt_`) and returns the code.
implementations = {
'cpp': (_generate_cpp_code_1d, _generate_cpp_code_2d),
'cython': (_generate_cython_code_1d, _generate_cython_code_2d)
}
@check_units(dt=second)
def __init__(self, values, dt, name=None):
if name is None:
name = '_timedarray*'
Nameable.__init__(self, name)
dimensions = get_dimensions(values)
self.dim = dimensions
values = np.asarray(values, dtype=np.float64)
self.values = values
dt = float(dt)
self.dt = dt
if values.ndim == 1:
self._init_1d()
elif values.ndim == 2:
self._init_2d()
else:
raise NotImplementedError("Only 1d and 2d arrays are supported "
"for TimedArray")
def _init_1d(self):
dimensions = self.dim
unit = get_unit(dimensions)
values = self.values
dt = self.dt
# Python implementation (with units), used when calling the TimedArray
# directly, outside of a simulation
@check_units(t=second, result=unit)
def timed_array_func(t):
# We round according to the current defaultclock.dt
K = _find_K(float(defaultclock.dt), dt)
epsilon = dt / K
i = np.clip(np.int_(np.round(np.asarray(t/epsilon)) / K),
0, len(values)-1)
return Quantity(values[i], dim=dimensions)
Function.__init__(self, pyfunc=timed_array_func)
# we use dynamic implementations because we want to do upsampling
# in a way that avoids rounding problems with the group's dt
def create_numpy_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_dt, dt)
n_values = len(values)
epsilon = dt / K
def unitless_timed_array_func(t):
timestep = np.clip(np.int_(np.round(t/epsilon) / K),
0, n_values-1)
return values[timestep]
unitless_timed_array_func._arg_units = [second]
unitless_timed_array_func._return_unit = unit
return unitless_timed_array_func
self.implementations.add_dynamic_implementation('numpy',
create_numpy_implementation)
namespace = lambda owner: {f'{self.name}_values': self.values}
for target, (func_1d, _) in TimedArray.implementations.items():
self.implementations.add_dynamic_implementation(target,
func_1d(self.values,
self.dt,
self.name),
namespace=namespace,
name=self.name)
def _init_2d(self):
dimensions = self.dim
unit = get_unit(dimensions)
values = self.values
dt = self.dt
# Python implementation (with units), used when calling the TimedArray
# directly, outside of a simulation
@check_units(i=1, t=second, result=unit)
def timed_array_func(t, i):
# We round according to the current defaultclock.dt
K = _find_K(float(defaultclock.dt), dt)
epsilon = dt / K
time_step = np.clip(np.int_(np.round(np.asarray(t/epsilon)) / K),
0, len(values)-1)
return Quantity(values[time_step, i], dim=dimensions)
Function.__init__(self, pyfunc=timed_array_func)
# we use dynamic implementations because we want to do upsampling
# in a way that avoids rounding problems with the group's dt
def create_numpy_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_dt, dt)
n_values = len(values)
epsilon = dt / K
def unitless_timed_array_func(t, i):
timestep = np.clip(np.int_(np.round(t/epsilon) / K),
0, n_values-1)
return values[timestep, i]
unitless_timed_array_func._arg_units = [second]
unitless_timed_array_func._return_unit = unit
return unitless_timed_array_func
self.implementations.add_dynamic_implementation('numpy',
create_numpy_implementation)
values_flat = self.values.astype(np.double,
order='C',
copy=False).ravel()
namespace = lambda owner: {f'{self.name}_values': values_flat}
for target, (_, func_2d) in TimedArray.implementations.items():
self.implementations.add_dynamic_implementation(target,
func_2d(self.values,
self.dt,
self.name),
namespace=namespace,
name=self.name)
[docs] def is_locally_constant(self, dt):
if dt > self.dt:
return False
dt_ratio = self.dt / float(dt)
if np.floor(dt_ratio) != dt_ratio:
logger.info(("dt of the TimedArray is not an integer multiple of "
"the group's dt, the TimedArray's return value can "
"therefore not be considered constant over one "
"timestep, making linear integration impossible."),
once=True)
return False
return True