def get_spline_TI(self, list_epsilon, eta, hw, sign_Gamma):
"""
Compute the interpolated value for TR, using splines, which we assume are already computed.
"""
# find the index of eta
i_eta = self.get_i_eta_index(eta)
# find the indices of closest frequencies in table
# as well as interpolation coefficients
i_hw_1, i_hw_2, alpha_1, alpha_2 = self.get_hw_indices_and_interpolation(hw)
# evaluate spline
u = N.real ( list_epsilon )
re_tuple_1 = (self.list_xi, self.re_TI_spline[i_eta,i_hw_1,:], self.spline_order)
im_tuple_1 = (self.list_xi, self.im_TI_spline[i_eta,i_hw_1,:], self.spline_order)
T_I_1 = sign_Gamma*complex(1.,0.)*spleval(re_tuple_1,u)+ complex(0.,1.)*spleval(im_tuple_1,u)
re_tuple_2 = (self.list_xi, self.re_TI_spline[i_eta,i_hw_2,:], self.spline_order)
im_tuple_2 = (self.list_xi, self.im_TI_spline[i_eta,i_hw_2,:], self.spline_order)
T_I_2 = sign_Gamma*complex(1.,0.)*spleval(re_tuple_2,u)+ complex(0.,1.)*spleval(im_tuple_2,u)
T_I = alpha_1*T_I_1+alpha_2*T_I_2
return T_I
def get_spline_SI(self, list_epsilon, sign_Gamma):
"""
Compute the interpolated value for SR, using splines, which we assume are already computed.
"""
# evaluate spline
u = N.real ( list_epsilon )
re_tuple = (self.list_xi, self.re_SI_spline, self.spline_order)
im_tuple = (self.list_xi, self.im_SI_spline, self.spline_order)
S_I = sign_Gamma*complex(1.,0.)*spleval(re_tuple,u)+ complex(0.,1.)*spleval(im_tuple,u)
return S_I
def __call__(self, wave):
if not HAS_SCIPY:
raise ImportError("To use this function scipy needs to be installed")
from scipy.interpolate import spleval
wave_shape = wave.shape
wave = _process_wave(wave)
_check_wave(wave, 909.091 * u.angstrom, 6.0 * u.micron)
res = np.empty_like(wave.__array__(), dtype=np.float64)
# Analytic function in the UV.
uvmask = wave < (2700.0 * u.angstrom)
if np.any(uvmask):
res[uvmask] = cextinction.f99uv(wave[uvmask].value, self.a_v, self.r_v)
# Spline in the Optical/IR
oirmask = ~uvmask
if np.any(oirmask):
k = spleval(self._spline, 1.0 / wave[oirmask].to("micron"))
res[oirmask] = self.a_v / self.r_v * (k + self.r_v)
return res.reshape(wave_shape)
def __call__(self, wave):
if not HAS_SCIPY:
raise ImportError('To use this function scipy needs to be installed')
from scipy.interpolate import spleval
wave_shape = wave.shape
wave = _process_wave(wave)
_check_wave(wave, 909.091* u.angstrom, 6. * u.micron)
res = np.empty_like(wave.__array__(), dtype=np.float64)
# Analytic function in the UV.
uvmask = wave < (2700. * u.angstrom)
if np.any(uvmask):
res[uvmask] = cextinction.f99uv(wave[uvmask].value, self.a_v, self.r_v)
# Spline in the Optical/IR
oirmask = ~uvmask
if np.any(oirmask):
k = spleval(self._spline, 1. / wave[oirmask].to('micron'))
res[oirmask] = self.a_v / self.r_v * (k + self.r_v)
return res.reshape(wave_shape)
def splines(x, t, k=3, deriv=0):
n = len(t) - 1
I = np.eye(n + k)
return np.vstack(spleval(
(t.copy(), I[i], k),
x,
deriv=deriv) for i in range(n + k))
def subsampleInterpolatedFibers(self, quantitiyOfPointsPerfiber):
if '_interpolate' not in dir():
raise NotImplementedError('scipy module could not be imported, this operation is not implemented')
self._quantitiyOfPointsPerfiber = quantitiyOfPointsPerfiber
self._subsampledFibers = []
s = _numpy.linspace(0,1,quantitiyOfPointsPerfiber)
i=0.
for f in self._fibers:
i+=1
tck,u = _interpolate.splprep( f.T )
self._subsampledFibers.append( _numpy.transpose(_interpolate.spleval(s,tck)))
self._subsampledFibers = _numpy.array( self._subsampledFibers )
self._interpolated = True
def propagate(self, wave, flux):
ext = np.empty(len(wave), dtype=np.float)
# Analytic function in the UV.
uvmask = wave < 2700.
if np.any(uvmask):
a_v = self._parameters[0] * self._r_v
ext[uvmask] = f99uv(wave[uvmask], a_v, self._r_v)
# Spline in the Optical/IR
oirmask = ~uvmask
if np.any(oirmask):
k = spleval(self._spline, 1.e4 / wave[oirmask])
ext[oirmask] = self._parameters[0] * (k + self._r_v)
trans = 10.**(-0.4 * ext)
return trans * flux
def subsampleInterpolatedFibers(self, quantitiyOfPointsPerfiber):
if '_interpolate' not in dir():
raise NotImplementedError(
'scipy module could not be imported, this operation is not implemented'
)
self._quantitiyOfPointsPerfiber = quantitiyOfPointsPerfiber
self._subsampledFibers = []
s = _numpy.linspace(0, 1, quantitiyOfPointsPerfiber)
i = 0.
for f in self._fibers:
i += 1
tck, u = _interpolate.splprep(f.T)
self._subsampledFibers.append(
_numpy.transpose(_interpolate.spleval(s, tck)))
self._subsampledFibers = _numpy.array(self._subsampledFibers)
self._interpolated = True
def upsample(data, factor):
"""
upsample array of data by a given factor using cubic splines
array.shape is assumed to be (num_events, num_values_per_event)
"""
# vpe is values per event
num_m, num_vpe = data.shape
# new values appear only between old values, hence the "-1"
up_num_vpe = (num_vpe - 1) * factor + 1
axis = arange(0, up_num_vpe, factor)
up_axis = arange(up_num_vpe)
# up_data = zeros((num_m, up_num_vpe))
# for i in range(num_m):
# up_data[i] = spline(axis, data[i], up_axis)
splines = splmake(axis, data.T)
up_data = spleval(splines, up_axis)
return up_data.T
def extinction_fm07(wave, a_v):
"""Fitzpatrick & Massa (2007) extinction model for R_V = 3.1.
The Fitzpatrick & Massa (2007) [1]_ model, which has a slightly
different functional form from that of Fitzpatrick (1999) [3]_
(`extinction_f99`). Fitzpatrick & Massa (2007) claim it is
preferable, although it is unclear if signficantly so (Gordon et
al. 2009 [2]_). Defined from 910 A to 6 microns.
.. note :: This model is not R_V dependent.
{0}
References
----------
.. [1] Fitzpatrick, E. L. & Massa, D. 2007, ApJ, 663, 320
.. [2] Gordon, K. D., Cartledge, S., & Clayton, G. C. 2009, ApJ, 705, 1320
.. [3] Fitzpatrick, E. L. 1999, PASP, 111, 63
"""
if not HAS_SCIPY:
raise ImportError('To use this function scipy needs to be installed')
from scipy.interpolate import spleval
wave_shape = wave.shape
wave = _process_wave(wave)
_check_wave(wave, 909.091 * u.angstrom, 6.0 * u.micron)
res = np.empty_like(wave.__array__(), dtype=np.float64)
# Simple analytic function in the UV
uvmask = wave < (2700. * u.angstrom)
if np.any(uvmask):
res[uvmask] = cextinction.fm07uv(wave[uvmask].value, a_v)
# Spline in the Optical/IR
oirmask = ~uvmask
if np.any(oirmask):
k = spleval(_fm07_spline, (1. / wave[oirmask].to('micron')).value)
res[oirmask] = a_v / _fm07_r_v * (k + _fm07_r_v)
return res.reshape(wave_shape)
def extinction_fm07(wave, a_v):
"""Fitzpatrick & Massa (2007) extinction model for R_V = 3.1.
The Fitzpatrick & Massa (2007) [1]_ model, which has a slightly
different functional form from that of Fitzpatrick (1999) [3]_
(`extinction_f99`). Fitzpatrick & Massa (2007) claim it is
preferable, although it is unclear if signficantly so (Gordon et
al. 2009 [2]_). Defined from 910 A to 6 microns.
.. note :: This model is not R_V dependent.
{0}
References
----------
.. [1] Fitpatrick, E. L. & Massa, D. 2007, ApJ, 663, 320
.. [2] Gordon, K. D., Cartledge, S., & Clayton, G. C. 2009, ApJ, 705, 1320
.. [3] Fitzpatrick, E. L. 1999, PASP, 111, 63
"""
if not HAS_SCIPY:
raise ImportError('To use this function scipy needs to be installed')
from scipy.interpolate import spleval
wave_shape = wave.shape
wave = _process_wave(wave)
_check_wave(wave, 909.091 * u.angstrom, 6.0 * u.micron)
res = np.empty_like(wave.__array__(), dtype=np.float64)
# Simple analytic function in the UV
uvmask = wave < (2700. * u.angstrom)
if np.any(uvmask):
res[uvmask] = cextinction.fm07uv(wave[uvmask].value, a_v) * wave.unit
# Spline in the Optical/IR
oirmask = ~uvmask
if np.any(oirmask):
k = spleval(_fm07_spline, (1. / wave[oirmask].to('micron')).value)
res[oirmask] = a_v / _fm07_r_v * (k + _fm07_r_v)
return res.reshape(wave_shape)
def upsample(data, factor):
"""
upsample array of data by a given factor using cubic splines
array.shape is assumed to be (num_events, num_values_per_event)
"""
# vpe is values per event
num_m, num_vpe = data.shape
# new values appear only between old values, hence the "-1"
up_num_vpe = (num_vpe - 1) * factor + 1
axis = arange(0, up_num_vpe, factor)
up_axis = arange(up_num_vpe)
# up_data = zeros((num_m, up_num_vpe))
# for i in xrange(num_m):
# up_data[i] = spline(axis, data[i], up_axis)
splines = splmake(axis, data.T)
up_data = spleval(splines, up_axis)
return up_data.T
