def test_add_at(self):
ri = numpy.random.randint
for nd in range(1, 5):
m, n, rest = ri(3, 50), ri(51, 100), tuple(ri(3, 10, nd - 1))
source = ri(-100, 100, (n,) + rest)
map = ri(0, m, n)
expected, actual = numpy.zeros((2, m) + rest)
numpy.add.at(expected, map, source)
RefBase._add_at(actual, map, source)
assert numpy.allclose(expected, actual)
def test_linear_interpolation(self):
t_start = 0.0
t_end = 1.0
y_start = 4.0
y_end = 8.0
t_mid = 0.5
val = RefBase.linear_interp1d(t_start, t_end, y_start, y_end, t_mid)
assert val == 6.0
def evaluate(self, var):
"""
Generate a discrete representation of the equation for the space
represented by ``var``.
"""
_pattern = RefBase.evaluate(self.equation, global_dict=self.parameters)
_pattern /= max(_pattern)
_pattern *= self.parameters["a"]
return _pattern
def evaluate(self, var):
"""
Generate a discrete representation of the equation for the space
represented by ``var``.
.. note: numexpr doesn't support gamma function
"""
# get gamma functions
self.parameters["gamma_a_1"] = sp_gamma(self.parameters["a_1"])
self.parameters["gamma_a_2"] = sp_gamma(self.parameters["a_2"])
return RefBase.evaluate(self.equation, global_dict=self.parameters)
def evaluate(self, var):
"""
Generate a discrete representation of the equation for the space
represented by ``var``.
The argument ``var`` can represent a distance, or effective distance,
for each node in a simulation. Or a time, or in principle any arbitrary
`` space ``. ``var`` can be a single number, a numpy.ndarray or a
?scipy.sparse_matrix? TODO: think this last one is true, need to check
as we need it for LocalConnectivity...
"""
ns = {'var': var}
ns.update(self.parameters)
return RefBase.evaluate(self.equation, ns)
def evaluate(self, var):
"""
Generate a discrete representation of the equation for the space
represented by ``var``.
.. note: numexpr doesn't support factorial yet
"""
# compute the factorial
n = int(self.parameters["n"])
product = 1
for i in range(n - 1):
product *= i + 1
self.parameters["factorial"] = product
_pattern = RefBase.evaluate(self.equation, global_dict=self.parameters)
_pattern /= max(_pattern)
_pattern *= self.parameters["a"]
return _pattern
def evaluate(self, var):
"""
Generate a discrete representation of the equation for the space
represented by ``var``.
The argument ``var`` can represent a distance, or effective distance,
for each node in a simulation. Or a time, or in principle any arbitrary
`` space ``. ``var`` can be a single number, a numpy.ndarray or a
?scipy.sparse_matrix? TODO: think this last one is true, need to check
as we need it for LocalConnectivity...
"""
# rolling in the deep ...
onset = self.parameters["onset"]
off = var < onset
var = numpy.roll(var, off.sum() + 1)
var[..., off] = 0.0
_pattern = RefBase.evaluate(self.equation, global_dict=self.parameters)
_pattern[..., off] = 0.0
return _pattern