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authorAaron LI <aly@aaronly.me>2017-08-01 09:45:43 +0800
committerAaron LI <aly@aaronly.me>2017-08-01 09:45:43 +0800
commit77b5fb23539ee8d3c244335d17dd6b1baeda8950 (patch)
treec6b6a9c201d1eea61d0e12a322456db426e15af8 /fg21sim
parentcf0732585dac122c4a45649b240efadedf782181 (diff)
downloadfg21sim-77b5fb23539ee8d3c244335d17dd6b1baeda8950.tar.bz2
clusters/emission.py: Fix bug in "F()"
Signed-off-by: Aaron LI <aly@aaronly.me>
Diffstat (limited to 'fg21sim')
-rw-r--r--fg21sim/extragalactic/clusters/emission.py27
1 files changed, 14 insertions, 13 deletions
diff --git a/fg21sim/extragalactic/clusters/emission.py b/fg21sim/extragalactic/clusters/emission.py
index 99f8a22..9d668f7 100644
--- a/fg21sim/extragalactic/clusters/emission.py
+++ b/fg21sim/extragalactic/clusters/emission.py
@@ -48,6 +48,14 @@ class SynchrotronEmission:
The assumed uniform magnetic field within the cluster ICM.
Unit: [uG]
"""
+ # The lower and upper cuts for the kernel function ``F(x)``.
+ Fxmin = 1e-5
+ Fxmax = 20.0
+ # Number of samples within [Fxmin, Fxmax] to do interpolation
+ # for the kernel function.
+ # NOTE: The kernel function is quiet smooth and slow-varying.
+ Fsamples = 128
+
def __init__(self, gamma, n_e, B):
self.gamma = np.asarray(gamma)
self.n_e = np.asarray(n_e)
@@ -120,24 +128,17 @@ class SynchrotronEmission:
Calculated kernel function values.
"""
if not hasattr(self, "_F_interp"):
- # Interpolate the kernel function and cache the results
- #
- # The lower and upper cuts
- xmin = 1e-5
- xmax = 20.0
- # Number of samples within [xmin, xmax]
- # NOTE: this kernel function is quiet smooth and slow-varying.
- nsamples = 128
- # Make an interpolation
- xx = np.logspace(np.log10(xmin), np.log10(xmax), num=nsamples)
+ # Make an interpolation and cache
+ xx = np.logspace(np.log10(self.Fxmin), np.log10(self.Fxmax),
+ num=self.Fsamples)
Fxx = [xp * integrate.quad(lambda t: scipy.special.kv(5/3, t),
a=xp, b=np.inf)[0]
for xp in xx]
self._F_interp = interpolate.interp1d(xx, Fxx, kind="quadratic")
- x = np.array(x) # Make a copy!
- x[x < xmin] = xmin
- x[x > xmax] = xmax
+ x = np.array(x) # Make a copy as it will be modified below!
+ x[x < self.Fxmin] = self.Fxmin
+ x[x > self.Fxmax] = self.Fxmax
y = self._F_interp(x)
return y