def __init__(self, n, p, approximate=True, name='_binomial*'):
Nameable.__init__(self, name)
#Python implementation
use_normal = approximate and (n*p > 5) and n*(1-p) > 5
if use_normal:
loc = n*p
scale = np.sqrt(n*p*(1-p))
def sample_function(vectorisation_idx):
try:
N = len(vectorisation_idx)
except TypeError:
N = int(vectorisation_idx)
return np.random.normal(loc, scale, size=N)
else:
def sample_function(vectorisation_idx):
try:
N = len(vectorisation_idx)
except TypeError:
N = int(vectorisation_idx)
return np.random.binomial(n, p, size=N)
Function.__init__(self, pyfunc=lambda: sample_function(1),
arg_units=[], return_unit=1, stateless=False)
self.implementations.add_implementation('numpy', sample_function)
for target, func in BinomialFunction.implementations.iteritems():
code, dependencies = func(n=n, p=p, use_normal=use_normal,
name=self.name)
self.implementations.add_implementation(target, code,
dependencies=dependencies,
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: {'%s_values' % self.name: 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)
def __init__(self, values, dt, name=None):
if name is None:
name = '_timedarray*'
Nameable.__init__(self, name)
unit = get_unit(values)
values = np.asarray(values)
self.values = values
dt = float(dt)
self.dt = 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):
i = np.clip(np.int_(np.float_(t) / dt + 0.5), 0, len(values)-1)
return values[i] * unit
Function.__init__(self, pyfunc=timed_array_func)
# Implementation for numpy, without units
def unitless_timed_array_func(t):
i = np.clip(np.int_(np.float_(t) / dt + 0.5), 0, len(values)-1)
return values[i]
unitless_timed_array_func._arg_units = [second]
unitless_timed_array_func._return_unit = unit
# Implementation for C++
cpp_code = {'support_code': '''
inline double _timedarray_%NAME%(const double t, const double _dt, const int _num_values, const double* _values)
{
int i = (int)(t/_dt + 0.5); // rounds to nearest int for positive values
if(i<0)
i = 0;
if(i>=_num_values)
i = _num_values-1;
return _values[i];
}
'''.replace('%NAME%', self.name),
'hashdefine_code': '''
#define %NAME%(t) _timedarray_%NAME%(t, _%NAME%_dt, _%NAME%_num_values, _%NAME%_values)
'''.replace('%NAME%', self.name)}
namespace = {'_%s_dt' % self.name: self.dt,
'_%s_num_values' % self.name: len(self.values),
'_%s_values' % self.name: self.values}
add_implementations(self, codes={'cpp': cpp_code,
'numpy': unitless_timed_array_func},
namespaces={'cpp': namespace},
names={'cpp': self.name})
def __init__(self, n, p, approximate=True, name='_binomial*'):
Nameable.__init__(self, name)
#Python implementation
use_normal = approximate and (n * p > 5) and n * (1 - p) > 5
if use_normal:
loc = n * p
scale = np.sqrt(n * p * (1 - p))
def sample_function(vectorisation_idx):
try:
N = len(vectorisation_idx)
except TypeError:
N = int(vectorisation_idx)
return np.random.normal(loc, scale, size=N)
else:
def sample_function(vectorisation_idx):
try:
N = len(vectorisation_idx)
except TypeError:
N = int(vectorisation_idx)
return np.random.binomial(n, p, size=N)
Function.__init__(self,
pyfunc=lambda: sample_function(1),
arg_units=[],
return_unit=1,
stateless=False)
self.implementations.add_implementation('numpy', sample_function)
# Common pre-calculations for C++ and Cython
if use_normal:
loc = n * p
scale = np.sqrt(n * p * (1 - p))
else:
reverse = p > 0.5
if reverse:
P = 1.0 - p
else:
P = p
q = 1.0 - P
qn = np.exp(n * np.log(q))
bound = min(n, n * P + 10.0 * np.sqrt(n * P * q + 1))
# C++ implementation
# Inversion transform sampling
if use_normal:
loc = n * p
scale = np.sqrt(n * p * (1 - p))
cpp_code = '''
float %NAME%(const int vectorisation_idx)
{
return _randn(vectorisation_idx) * %SCALE% + %LOC%;
}
'''
cpp_code = replace(
cpp_code, {
'%SCALE%': '%.15f' % scale,
'%LOC%': '%.15f' % loc,
'%NAME%': self.name
})
dependencies = {'_randn': DEFAULT_FUNCTIONS['randn']}
else:
# The following code is an almost exact copy of numpy's
# rk_binomial_inversion function
# (numpy/random/mtrand/distributions.c)
cpp_code = '''
long %NAME%(const int vectorisation_idx)
{
long X = 0;
double px = %QN%;
double U = _rand(vectorisation_idx);
while (U > px)
{
X++;
if (X > %BOUND%)
{
X = 0;
px = %QN%;
U = _rand(vectorisation_idx);
} else
{
U -= px;
px = ((%N%-X+1) * %P% * px)/(X*%Q%);
}
}
return %RETURN_VALUE%;
}
'''
cpp_code = replace(
cpp_code, {
'%N%': '%d' % n,
'%P%': '%.15f' % P,
'%Q%': '%.15f' % q,
'%QN%': '%.15f' % qn,
'%BOUND%': '%.15f' % bound,
'%RETURN_VALUE%': '%d-X' % n if reverse else 'X',
'%NAME%': self.name
})
dependencies = {'_rand': DEFAULT_FUNCTIONS['rand']}
self.implementations.add_implementation('cpp',
{'support_code': cpp_code},
dependencies=dependencies,
name=self.name)
# Cython implementation
# Inversion transform sampling
if use_normal:
cython_code = '''
cdef float %NAME%(const int vectorisation_idx):
return _randn(vectorisation_idx) * %SCALE% + %LOC%
'''
cython_code = replace(
cython_code, {
'%SCALE%': '%.15f' % scale,
'%LOC%': '%.15f' % loc,
'%NAME%': self.name
})
dependencies = {'_randn': DEFAULT_FUNCTIONS['randn']}
else:
# The following code is an almost exact copy of numpy's
# rk_binomial_inversion function
# (numpy/random/mtrand/distributions.c)
cython_code = '''
cdef long %NAME%(const int vectorisation_idx):
cdef long X = 0
cdef double px = %QN%
cdef double U = _rand(vectorisation_idx)
while U > px:
X += 1
if X > %BOUND%:
X = 0
px = %QN%
U = _rand(vectorisation_idx)
else:
U -= px
px = ((%N%-X+1) * %P% * px)/(X*%Q%)
return %RETURN_VALUE%
'''
cython_code = replace(
cython_code, {
'%N%': '%d' % n,
'%P%': '%.15f' % p,
'%Q%': '%.15f' % q,
'%QN%': '%.15f' % qn,
'%BOUND%': '%.15f' % bound,
'%RETURN_VALUE%': '%d-X' % n if reverse else 'X',
'%NAME%': self.name
})
dependencies = {'_rand': DEFAULT_FUNCTIONS['rand']}
self.implementations.add_implementation('cython',
cython_code,
dependencies=dependencies,
name=self.name)
def __init__(self, values, dt, name=None):
if name is None:
name = '_timedarray*'
Nameable.__init__(self, name)
unit = get_unit(values)
values = np.asarray(values)
self.values = values
dt = float(dt)
self.dt = 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):
i = np.clip(np.int_(np.float_(t) / dt + 0.5), 0, len(values)-1)
return values[i] * unit
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)
epsilon = dt / K
n_values = len(values)
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)
def create_cpp_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_dt, dt)
cpp_code = {'support_code': '''
inline double _timedarray_%NAME%(const double t, const int _num_values, const double* _values)
{
const double epsilon = %DT% / %K%;
int i = (int)((t/epsilon + 0.5)/%K%); // rounds to nearest int for positive values
if(i<0)
i = 0;
if(i>=_num_values)
i = _num_values-1;
return _values[i];
}
'''.replace('%NAME%', self.name).replace('%DT%', '%.18f' % dt).replace('%K%', str(K)),
'hashdefine_code': '''
#define %NAME%(t) _timedarray_%NAME%(t, _%NAME%_num_values, _%NAME%_values)
'''.replace('%NAME%', self.name)}
return cpp_code
def create_cpp_namespace(owner):
return {'_%s_num_values' % self.name: len(self.values),
'_%s_values' % self.name: self.values}
self.implementations.add_dynamic_implementation('cpp',
create_cpp_implementation,
create_cpp_namespace,
name=self.name)
def _init_2d(self):
unit = self.unit
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 values[time_step, i] * unit
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)
def create_cpp_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_dt, dt)
support_code = '''
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];
}
'''
support_code = replace(support_code, {'%NAME%': self.name,
'%DT%': '%.18f' % dt,
'%K%': str(K),
'%COLS%': str(self.values.shape[1]),
'%ROWS%': str(self.values.shape[0])})
cpp_code = {'support_code': support_code}
return cpp_code
def create_cpp_namespace(owner):
return {'%s_values' % self.name: self.values.astype(np.double,
order='C',
copy=False).ravel()}
self.implementations.add_dynamic_implementation('cpp',
code=create_cpp_implementation,
namespace=create_cpp_namespace,
name=self.name)
def create_cython_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_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%': self.name,
'%DT%': '%.18f' % dt,
'%K%': str(K),
'%COLS%': str(self.values.shape[1]),
'%ROWS%': str(self.values.shape[0])})
return code
def create_cython_namespace(owner):
return {'%s_values' % self.name: self.values.astype(np.double,
order='C',
copy=False).ravel()}
self.implementations.add_dynamic_implementation('cython',
code=create_cython_implementation,
namespace=create_cython_namespace,
name=self.name)
def _init_1d(self):
unit = self.unit
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 values[i] * unit
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)
def create_cpp_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_dt, dt)
support_code = '''
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%', self.name).replace('%DT%', '%.18f' % dt).replace('%K%', str(K)).replace('%NUM_VALUES%', str(len(self.values)))
cpp_code = {'support_code': support_code}
return cpp_code
def create_cpp_namespace(owner):
return {'%s_values' % self.name: self.values}
self.implementations.add_dynamic_implementation('cpp',
code=create_cpp_implementation,
namespace=create_cpp_namespace,
name=self.name)
def create_cython_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_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%', self.name).replace('%DT%', '%.18f' % dt).replace('%K%', str(K)).replace('%NUM_VALUES%', str(len(self.values)))
return code
def create_cython_namespace(owner):
return {'%s_values' % self.name: self.values}
self.implementations.add_dynamic_implementation('cython',
code=create_cython_implementation,
namespace=create_cython_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)
def create_cpp_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_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];
}
'''
support_code = replace(
support_code, {
'%NAME%': self.name,
'%DT%': '%.18f' % dt,
'%K%': str(K),
'%COLS%': str(self.values.shape[1]),
'%ROWS%': str(self.values.shape[0])
})
cpp_code = {'support_code': support_code}
return cpp_code
def create_cpp_namespace(owner):
return {
'%s_values' % self.name:
self.values.astype(np.double, order='C', copy=False).ravel()
}
self.implementations.add_dynamic_implementation(
'cpp',
code=create_cpp_implementation,
namespace=create_cpp_namespace,
name=self.name)
def create_cython_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_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%': self.name,
'%DT%': '%.18f' % dt,
'%K%': str(K),
'%COLS%': str(self.values.shape[1]),
'%ROWS%': str(self.values.shape[0])
})
return code
def create_cython_namespace(owner):
return {
'%s_values' % self.name:
self.values.astype(np.double, order='C', copy=False).ravel()
}
self.implementations.add_dynamic_implementation(
'cython',
code=create_cython_implementation,
namespace=create_cython_namespace,
name=self.name)
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)
def create_cpp_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_dt, dt)
support_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%',
self.name).replace('%DT%', '%.18f' % dt).replace(
'%K%', str(K)).replace('%NUM_VALUES%',
str(len(self.values)))
cpp_code = {'support_code': support_code}
return cpp_code
def create_cpp_namespace(owner):
return {'%s_values' % self.name: self.values}
self.implementations.add_dynamic_implementation(
'cpp',
code=create_cpp_implementation,
namespace=create_cpp_namespace,
name=self.name)
def create_cython_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_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%',
self.name).replace('%DT%', '%.18f' % dt).replace(
'%K%', str(K)).replace('%NUM_VALUES%',
str(len(self.values)))
return code
def create_cython_namespace(owner):
return {'%s_values' % self.name: self.values}
self.implementations.add_dynamic_implementation(
'cython',
code=create_cython_implementation,
namespace=create_cython_namespace,
name=self.name)
def __init__(self, values, dt, name=None):
if name is None:
name = '_timedarray*'
Nameable.__init__(self, name)
unit = get_unit(values)
values = np.asarray(values)
self.values = values
dt = float(dt)
self.dt = 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):
i = np.clip(np.int_(np.float_(t) / dt + 0.5), 0, len(values) - 1)
return values[i] * unit
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)
epsilon = dt / K
n_values = len(values)
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)
def create_cpp_implementation(owner):
group_dt = owner.clock.dt_
K = _find_K(group_dt, dt)
cpp_code = {
'support_code':
'''
inline double _timedarray_%NAME%(const double t, const int _num_values, const double* _values)
{
const double epsilon = %DT% / %K%;
int i = (int)((t/epsilon + 0.5)/%K%); // rounds to nearest int for positive values
if(i<0)
i = 0;
if(i>=_num_values)
i = _num_values-1;
return _values[i];
}
'''.replace('%NAME%', self.name).replace('%DT%',
'%.18f' % dt).replace(
'%K%', str(K)),
'hashdefine_code':
'''
#define %NAME%(t) _timedarray_%NAME%(t, _%NAME%_num_values, _%NAME%_values)
'''.replace('%NAME%', self.name)
}
return cpp_code
def create_cpp_namespace(owner):
return {
'_%s_num_values' % self.name: len(self.values),
'_%s_values' % self.name: self.values
}
self.implementations.add_dynamic_implementation(
'cpp',
create_cpp_implementation,
create_cpp_namespace,
name=self.name)
def __init__(self, n, p, approximate=True, name='_binomial*'):
Nameable.__init__(self, name)
#Python implementation
use_normal = approximate and (n*p > 5) and n*(1-p) > 5
if use_normal:
loc = n*p
scale = np.sqrt(n*p*(1-p))
def sample_function(vectorisation_idx):
try:
N = len(vectorisation_idx)
except TypeError:
N = int(vectorisation_idx)
return np.random.normal(loc, scale, size=N)
else:
def sample_function(vectorisation_idx):
try:
N = len(vectorisation_idx)
except TypeError:
N = int(vectorisation_idx)
return np.random.binomial(n, p, size=N)
Function.__init__(self, pyfunc=lambda: sample_function(1),
arg_units=[], return_unit=1, stateless=False)
self.implementations.add_implementation('numpy', sample_function)
# Common pre-calculations for C++ and Cython
if use_normal:
loc = n*p
scale = np.sqrt(n*p*(1-p))
else:
reverse = p > 0.5
if reverse:
P = 1.0 - p
else:
P = p
q = 1.0 - P
qn = np.exp(n * np.log(q))
bound = min(n, n*P + 10.0*np.sqrt(n*P*q + 1))
# C++ implementation
# Inversion transform sampling
if use_normal:
loc = n*p
scale = np.sqrt(n*p*(1-p))
cpp_code = '''
float %NAME%(const int vectorisation_idx)
{
return _randn(vectorisation_idx) * %SCALE% + %LOC%;
}
'''
cpp_code = replace(cpp_code, {'%SCALE%': '%.15f' % scale,
'%LOC%': '%.15f' % loc,
'%NAME%': self.name})
dependencies = {'_randn': DEFAULT_FUNCTIONS['randn']}
else:
# The following code is an almost exact copy of numpy's
# rk_binomial_inversion function
# (numpy/random/mtrand/distributions.c)
cpp_code = '''
long %NAME%(const int vectorisation_idx)
{
long X = 0;
double px = %QN%;
double U = _rand(vectorisation_idx);
while (U > px)
{
X++;
if (X > %BOUND%)
{
X = 0;
px = %QN%;
U = _rand(vectorisation_idx);
} else
{
U -= px;
px = ((%N%-X+1) * %P% * px)/(X*%Q%);
}
}
return %RETURN_VALUE%;
}
'''
cpp_code = replace(cpp_code, {'%N%': '%d' % n,
'%P%': '%.15f' % P,
'%Q%': '%.15f' % q,
'%QN%': '%.15f' % qn,
'%BOUND%': '%.15f' % bound,
'%RETURN_VALUE%': '%d-X' % n if reverse else 'X',
'%NAME%': self.name})
dependencies = {'_rand': DEFAULT_FUNCTIONS['rand']}
self.implementations.add_implementation('cpp', {'support_code': cpp_code},
dependencies=dependencies,
name=self.name)
# Cython implementation
# Inversion transform sampling
if use_normal:
cython_code = '''
cdef float %NAME%(const int vectorisation_idx):
return _randn(vectorisation_idx) * %SCALE% + %LOC%
'''
cython_code = replace(cython_code, {'%SCALE%': '%.15f' % scale,
'%LOC%': '%.15f' % loc,
'%NAME%': self.name})
dependencies = {'_randn': DEFAULT_FUNCTIONS['randn']}
else:
# The following code is an almost exact copy of numpy's
# rk_binomial_inversion function
# (numpy/random/mtrand/distributions.c)
cython_code = '''
cdef long %NAME%(const int vectorisation_idx):
cdef long X = 0
cdef double px = %QN%
cdef double U = _rand(vectorisation_idx)
while U > px:
X += 1
if X > %BOUND%:
X = 0
px = %QN%
U = _rand(vectorisation_idx)
else:
U -= px
px = ((%N%-X+1) * %P% * px)/(X*%Q%)
return %RETURN_VALUE%
'''
cython_code = replace(cython_code, {'%N%': '%d' % n,
'%P%': '%.15f' % p,
'%Q%': '%.15f' % q,
'%QN%': '%.15f' % qn,
'%BOUND%': '%.15f' % bound,
'%RETURN_VALUE%': '%d-X' % n if reverse else 'X',
'%NAME%': self.name})
dependencies = {'_rand': DEFAULT_FUNCTIONS['rand']}
self.implementations.add_implementation('cython', cython_code,
dependencies=dependencies,
name=self.name)
