def mu(self, T, p):
"""Expecting T in Kelvin, p in bar"""
if self.gas:
kboltzmann_in_eVK = 8.6173324e-5
# interpolate given grid
try:
val = interp1d(T, self.T_grid, self.G_grid) + \
kboltzmann_in_eVK * T * log(p)
except Exception, e:
raise Exception('Could not find JANAF tables for %s.'
% self.name)
else:
return val
def mu(self, T, p):
"""Expecting T in Kelvin, p in bar"""
if self.gas:
kboltzmann_in_eVK = 8.6173324e-5
# interpolate given grid
try:
val = interp1d(T, self.T_grid, self.G_grid) + \
kboltzmann_in_eVK * T * log(p)
except Exception, e:
print('Did you install JANAF tables?')
raise
else:
return val
def adaptive_density_grid(grid,x,k=10,eps=0.01,fill=None):
'''
Follow the knn_1d_density estimation with interpolation of the
density on a grid
fill: if not given will fill with the mean rate
'''
centers,density = knn_1d_density(x,k,eps=eps)
if len(centers)!=len(density):
warn('something is wrong')
warn(len(centers),len(density))
N = min(len(centers),len(density))
centers = centers[:N]
density = density[:N]
if fill is None: fill=mean(density)
y = np.interp1d(centers,density,bounds_error=0,fill_value=fill)(grid)
return y
def timeseries_at(self, variable, t):
"""
Return the value of a time series at the given time.
:param variable: Variable name.
:param t: Time.
:returns: The interpolated value of the time series.
:raises: KeyError
"""
values = self.__timeseries_import_dict[variable]
t_idx = bisect.bisect_left(self.__timeseries_import_times, t)
if self.__timeseries_import_times[t_idx] == t:
return values[t_idx]
else:
return np.interp1d(t, self.__timeseries_import_times, values)
