def __init__(self, r_v=3.1):
self._param_names = ['ebv']
self.param_names_latex = ['E(B-V)']
self._parameters = np.array([0.])
self._r_v = r_v
kknots = f99kknots(self._XKNOTS, r_v)
self._spline = splmake(self._XKNOTS, kknots, order=3)
def test_func():
from scipy import interpolate
import matplotlib.pyplot as plt
import matplotlib
matplotlib.interactive(False)
coef = np.array([[1, 1], [0, 1]]) # linear from 0 to 2
# coef = np.array([[1,1],[1,1],[0,2]]) # linear from 0 to 2
breaks = [0, 1, 2]
pp = PPform(coef, breaks, a=-100, b=100)
x = linspace(-1, 3, 20)
y = pp(x) # @UnusedVariable
x = linspace(0, 2 * pi + pi / 4, 20)
y = sin(x) + np.random.randn(x.size)
tck = interpolate.splrep(x, y, s=len(x)) # @UndefinedVariable
xnew = linspace(0, 2 * pi, 100)
ynew = interpolate.splev(xnew, tck, der=0) # @UndefinedVariable
tck0 = interpolate.splmake( # @UndefinedVariable
xnew,
ynew,
order=3,
kind='smoothest',
conds=None)
pp = interpolate.ppform.fromspline(*tck0) # @UndefinedVariable
plt.plot(x, y, "x", xnew, ynew, xnew, sin(xnew), x, y, "b", x, pp(x), 'g')
plt.legend(['Linear', 'Cubic Spline', 'True'])
plt.title('Cubic-spline interpolation')
plt.show()
t = np.arange(0, 1.1, .1)
x = np.sin(2 * np.pi * t)
y = np.cos(2 * np.pi * t)
_tck1, _u = interpolate.splprep([t, y], s=0) # @UndefinedVariable
tck2 = interpolate.splrep(t, y, s=len(t), task=0) # @UndefinedVariable
# interpolate.spl
tck = interpolate.splmake(t, y, order=3, kind='smoothest', conds=None)
self = interpolate.ppform.fromspline(*tck2) # @UndefinedVariable
plt.plot(t, self(t))
plt.show('hold')
pass
def __init__(self, a_v, r_v=3.1):
if not HAS_SCIPY:
raise ImportError("To use this function scipy needs to be installed")
from scipy.interpolate import splmake
self.a_v = a_v
self.r_v = r_v
kknots = cextinction.f99kknots(_f99_xknots, self.r_v)
self._spline = splmake(_f99_xknots, kknots, order=3)
def __init__(self, a_v, r_v=3.1):
if not HAS_SCIPY:
raise ImportError('To use this function scipy needs to be installed')
from scipy.interpolate import splmake
self.a_v = a_v
self.r_v = r_v
kknots = cextinction.f99kknots(_f99_xknots, self.r_v)
self._spline = splmake(_f99_xknots, kknots, order=3)
def build_and_write_spline_parameters(self,filename):
"""
Building the parameters for a spline is quite time consuming. It is best to
do this once, write to file, and be done with it.
This routine assumes self.build_scattering_kernel has already created the
appropriate arrays.
"""
# generate the splines
# The function splmake has inteface:
#
# (xk, Ck, order) = splmake(xk, yk, order)
#
# There is no point in storing "xk" and "order" as part of the output tuple.
# Spleval will need this information in the form of a tuple, which we can re-build
# at evalation time.
print ' ==== Computing spline of SR ====='
list_xi, re_SR_spline, order = splmake(self.list_xi, N.real(self.SR), order = self.spline_order)
list_xi, im_SR_spline, order = splmake(self.list_xi, N.imag(self.SR), order = self.spline_order)
print ' ==== Computing spline of SI ====='
list_xi, re_SI_spline, order = splmake(self.list_xi, N.real(self.SI), order = self.spline_order)
list_xi, im_SI_spline, order = splmake(self.list_xi, N.imag(self.SI), order = self.spline_order)
# same length for all splines
spline_length = len(im_SI_spline)
re_TR_spline = N.zeros([2,len(self.list_hw_ext),spline_length ])
im_TR_spline = N.zeros([2,len(self.list_hw_ext),spline_length ])
re_TI_spline = N.zeros([2,len(self.list_hw_ext),spline_length ])
im_TI_spline = N.zeros([2,len(self.list_hw_ext),spline_length ])
for i_eta, eta in enumerate(self.list_eta):
for i_hw_ext, hw_ext in enumerate( self.list_hw_ext):
print ' ==== Computing spline for TR and TI, eta = %i, hw_ext = %4.3f meV ====='%(eta,1000*hw_ext)
list_xi, re_TR_spline[i_eta,i_hw_ext,:], order = splmake(self.list_xi, N.real(self.TR[i_eta,i_hw_ext,:]), order = self.spline_order)
list_xi, im_TR_spline[i_eta,i_hw_ext,:], order = splmake(self.list_xi, N.imag(self.TR[i_eta,i_hw_ext,:]), order = self.spline_order)
list_xi, re_TI_spline[i_eta,i_hw_ext,:], order = splmake(self.list_xi, N.real(self.TI[i_eta,i_hw_ext,:]), order = self.spline_order)
list_xi, im_TI_spline[i_eta,i_hw_ext,:], order = splmake(self.list_xi, N.imag(self.TI[i_eta,i_hw_ext,:]), order = self.spline_order)
write_splmake( re_SR_spline, im_SR_spline, re_SI_spline, im_SI_spline,
re_TR_spline, im_TR_spline, re_TI_spline, im_TI_spline,
filename, self.spline_order, self.list_xi, self.list_hw_ext,
self.mu, self.beta, self.kernel_Gamma_width, self.Green_Gamma_width)
def test_func():
from scipy import interpolate
import matplotlib.pyplot as plt
import matplotlib
matplotlib.interactive(False)
coef = np.array([[1, 1], [0, 1]]) # linear from 0 to 2
# coef = np.array([[1,1],[1,1],[0,2]]) # linear from 0 to 2
breaks = [0, 1, 2]
pp = PPform(coef, breaks, a=-100, b=100)
x = linspace(-1, 3, 20)
y = pp(x) # @UnusedVariable
x = linspace(0, 2 * pi + pi / 4, 20)
y = sin(x) + np.random.randn(x.size)
tck = interpolate.splrep(x, y, s=len(x)) # @UndefinedVariable
xnew = linspace(0, 2 * pi, 100)
ynew = interpolate.splev(xnew, tck, der=0) # @UndefinedVariable
tck0 = interpolate.splmake( # @UndefinedVariable
xnew, ynew, order=3, kind='smoothest', conds=None)
pp = interpolate.ppform.fromspline(*tck0) # @UndefinedVariable
plt.plot(x, y, "x", xnew, ynew, xnew, sin(xnew), x, y, "b", x, pp(x), 'g')
plt.legend(['Linear', 'Cubic Spline', 'True'])
plt.title('Cubic-spline interpolation')
plt.show()
t = np.arange(0, 1.1, .1)
x = np.sin(2 * np.pi * t)
y = np.cos(2 * np.pi * t)
_tck1, _u = interpolate.splprep([t, y], s=0) # @UndefinedVariable
tck2 = interpolate.splrep(t, y, s=len(t), task=0) # @UndefinedVariable
# interpolate.spl
tck = interpolate.splmake(t, y, order=3, kind='smoothest', conds=None)
self = interpolate.ppform.fromspline(*tck2) # @UndefinedVariable
plt.plot(t, self(t))
plt.show('hold')
pass
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 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
"""
f = ExtinctionF99(a_v, r_v)
return f(wave)
# fm07 knots for spline
_fm07_r_v = 3.1
_fm07_xknots = np.array(
[0.0, 0.25, 0.50, 0.75, 1.0, 1.0e4 / 5530.0, 1.0e4 / 4000.0, 1.0e4 / 3300.0, 1.0e4 / 2700.0, 1.0e4 / 2600.0]
)
_fm07_kknots = cextinction.fm07kknots(_fm07_xknots)
try:
from scipy.interpolate import splmake
_fm07_spline = splmake(_fm07_xknots, _fm07_kknots, order=3)
except ImportError:
pass
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.
.. [1] Fitzpatrick, E. L. 1999, PASP, 111, 63
.. [2] Fitzpatrick, E. L. & Massa, D. 1990, ApJS, 72, 163
"""
f = ExtinctionF99(a_v, r_v)
return f(wave)
# fm07 knots for spline
_fm07_r_v = 3.1
_fm07_xknots = np.array([0., 0.25, 0.50, 0.75, 1., 1.e4/5530., 1.e4/4000.,
1.e4/3300., 1.e4/2700., 1.e4/2600.])
_fm07_kknots = cextinction.fm07kknots(_fm07_xknots)
try:
from scipy.interpolate import splmake
_fm07_spline = splmake(_fm07_xknots, _fm07_kknots, order=3)
except ImportError:
pass
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.