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cosmic_dustbox
Commit 70a3bfa6 authored by Martin Glatzle's avatar Martin Glatzle
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Add WD log normal and power law sdists.

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Cosmic_dustbox/sdist.py deleted 100644 → 0
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###############################################################################
import numpy as _np
import math
###############################################################################
class SizeDist(object):
"""
Grain size distribution.
Computes 1/n_H * dn_gr/da [1/L], where n_H is the hydrogen nuclei number
density, n_gr(a) is the number density of grains <= a and a is a grain
size. Note that this is not a distribution in the mathematical sense. Since
it is commonly referred to as such in the community, though, and for lack
of a better term, this convention is maintained here. It might, however, be
changed in the future. See [1]_ and specifically Sec. 1 for an example.
Parameters
----------
sizeMin : scalar Quantity [L]
Low end size cutoff of the distribution.
sizeMax : scalar Quantity [L]
High end size cutoff of the distribution.
func : callable
Must take a single Quantity with array value of sizes and return
1/n_H * dn_gr/da [1/L]. It is called in the limits set by `sizeMin`
and `sizeMax` when an instance of this class is called.
Attributes
----------
sizeMin : scalar Quantity [L]
Directly taken from parameters.
sizeMax : scalar Quantity [L]
Directly taken from parameters.
func : callable
Directly taken from parameters.
References
----------
.. [1] Weingartner & Draine 2001ApJ...548..296W
Examples
--------
>>> import astropy.units as u
>>> def f(s): \
return 1.0/s.unit
>>> a = SizeDist(3.5*u.angstrom, 1*u.micron, f)
>>> a(_np.logspace(-11, -5, 10)*u.m)
<Quantity [0., 0., 0., 1., 1., 1., 1., 1., 0., 0.] 1 / m>
"""
def __init__(self, sizeMin, sizeMax, func):
"""
See class docstring.
"""
self.sizeMin = sizeMin
self.sizeMax = sizeMax
self.func = func
return
def __call__(self, sizes):
"""
Compute 1/n_H * dn_gr/da [1/L].
Returns func(sizes) in limits set by sizeMin and sizeMax and zero
elsewhere.
Parameters
----------
sizes : array-valued Quantity [L]
Grain sizes at which to evaluate func.
Returns
-------
r : array-valued Quantity [1/L]
"""
r = _np.zeros(len(sizes))/sizes.unit
ind = _np.where((sizes >= self.sizeMin) & (sizes <= self.sizeMax))
r[ind] = self.func(sizes[ind])
return r
def __add__(self, other):
"""
Commutative addition of size distributions.
Overloading of ``+`` operator. Can add an instance of ``SizeDist`` (or
subclass thereof), any callable or a scalar to ``self.func``. No check
on units is performed. Returns an instance of ``self.__class__``, the
``func`` attribute of which is defined to return the corresponding sum.
If ``other`` is an instance of ``SizeDist`` (or subclass), take maximum
of the two ``sizeMin`` attributes and minimum of the two ``sizeMax``
attributes.
Parameters
----------
other : SizeDist, callable or scalar-valued Quantity [1/L]
Is added to ``self.func``.
Returns
-------
: ``self.__class__``
Instance of own class with corresponding ``func`` attribute.
Examples
--------
>>> import astropy.units as u
>>> def f(s): \
return 1.0/s.unit
>>> a = SizeDist(1*u.angstrom, 1*u.micron, f)
>>> b = SizeDist(10*u.angstrom, 10*u.micron, f)
>>> c = a + b
>>> c(_np.logspace(-11, -4, 10)*u.m)
<Quantity [0., 0., 0., 2., 2., 2., 2., 0., 0., 0.] 1 / m>
"""
if issubclass(other.__class__, SizeDist):
# find new size limits
sizeMin = max(self.sizeMin, other.sizeMin)
sizeMax = min(self.sizeMax, other.sizeMax)
# new differential number density is sum
def func(sizes):
return self.func(sizes) + other.func(sizes)
return self.__class__(sizeMin, sizeMax, func)
elif callable(other):
def func(sizes):
return self.func(sizes) + other(sizes)
return self.__class__(self.sizeMin, self.sizeMax, func)
else:
def func(sizes):
return other + self.func(sizes)
return self.__class__(self.sizeMin, self.sizeMax, func)
# make addition commutative
__radd__ = __add__
def __mul__(self, other):
if callable(other):
def func(sizes):
return self.function(sizes) * other(sizes)
return self.__class__(self.sizeMin, self.sizeMax, func)
else:
def func(sizes):
return other * self.function(sizes)
return self.__class__(self.sizeMin, self.sizeMax, func)
# make multiplication commutative
__rmul__ = __mul__
class PowerLaw(SizeDist):
"""
Parameters
----------
sizeMin : scalar Quantity [L]
Low end size cutoff of the distribution.
sizeMax : scalar Quantity [L]
High end size cutoff of the distribution.
power : float
Log-slope of the size distribution.
C : scalar Quantity [L]**(-1-power)
Normalization of the size distribution.
"""
def __init__(self, sizeMin, sizeMax, power, C):
def f(a):
return C*a**power
super(self).__init__(sizeMin, sizeMax, f)
return
class Log_Normal(SizeDist):
def __init__(self,sizeMin, sizeMax, rho,sgma,bc,b,a0):
def f(a):
m = rho * 4 * a**3 / 3
def B_val(i):
nominator = (3 * _np.exp(-4.5* sgma**2) * bc[i] * m)
denominator = ((2*_np.pi**2)**1.5 * rho * a0[i]**3 \
* sgma * (1 + math.erf((3 * sgma/_np.sqrt(2)) + \
(math.log(a0[i]/3.5 *_u.angstrom) / (sgma * _np.sqrt(2)) ))))
if denominator != 0:
B += nominator / denominator
return B
sd = []
if sizeMin > a:
n = 0
for i in range(0,2):
_B = B_val(i)
n += _B * math.exp(-0.5*(log(val/a0[i]) \
/sgma))
sd.append(n)
else:
sd.append(0)
return sd
super(self).__init__(sizeMin, sizeMax, f)
return
class WD01_dst(SizeDist):
def __init__(self, sizeMin, sizeMax, a_t, beta, a_c, alpha, C):
def f(a):
def F_func(beta,size):
if beta[indx] >= 0:
F = 1 + (beta * size) / a_t
else:
F = 1 / (1 - (beta * size) / a_t)
return F
def exp_func(size):
if sizeMin < size < a_t:
expf = 1
else:
expf = math.exp(-((size - a_t)/a_c)**3)
return expf
dst = []
if a > sizeMin:
n = math.pow(a / a_t, alpha)/a \
* F_func(beta,a) * exp_func(a)
dst.append(n)
else:
dst.append(0)
return dst
super(self).__init__(sizeMin, sizeMax, f)
return
###############################################################################
if __name__ == "__main__":
import doctest
doctest.testmod()
###############################################################################
\ No newline at end of file
cosmic_dustbox/sdist.py
View file @ 70a3bfa6
###############################################################################
import astropy.units as _u
import numpy as _np
###############################################################################
......@@ -17,22 +18,22 @@ class SizeDist(object):
Parameters
----------
sizeMin : scalar Quantity [L]
Low end size cutoff of the distribution.
Low end size cutoff of the distribution.
sizeMax : scalar Quantity [L]
High end size cutoff of the distribution.
High end size cutoff of the distribution.
func : callable
Must take a single Quantity with array value of sizes and return
1/n_H * dn_gr/da [1/L]. It is called in the limits set by `sizeMin`
and `sizeMax` when an instance of this class is called.
Must take a single Quantity with array value of sizes and return
1/n_H * dn_gr/da [1/L]. It is called in the limits set by `sizeMin`
and `sizeMax` when an instance of this class is called.
Attributes
----------
sizeMin : scalar Quantity [L]
Directly taken from parameters.
Directly taken from parameters.
sizeMax : scalar Quantity [L]
Directly taken from parameters.
Directly taken from parameters.
func : callable
Directly taken from parameters.
Directly taken from parameters.
References
----------
......@@ -41,12 +42,11 @@ class SizeDist(object):
Examples
--------
>>> import astropy.units as u
>>> def f(s): \
return 1.0/s.unit
>>> a = SizeDist(3.5*u.angstrom, 1*u.micron, f)
>>> a(_np.logspace(-11, -5, 10)*u.m)
<Quantity [ 0., 0., 0., 1., 1., 1., 1., 1., 0., 0.] 1 / m>
<Quantity [0., 0., 0., 1., 1., 1., 1., 1., 0., 0.] 1 / m>
"""
def __init__(self, sizeMin, sizeMax, func):
"""
......@@ -67,7 +67,7 @@ class SizeDist(object):
Parameters
----------
sizes : array-valued Quantity [L]
Grain sizes at which to evaluate func.
Grain sizes at which to evaluate func.
Returns
-------
......@@ -78,6 +78,150 @@ class SizeDist(object):
r[ind] = self.func(sizes[ind])
return r
def __add__(self, other):
"""
Commutative addition of size distributions.
Overloading of ``+`` operator. Can add an instance of ``SizeDist`` (or
subclass thereof), any callable or a scalar to ``self.func``. No check
on units is performed. Returns an instance of ``self.__class__``, the
``func`` attribute of which is defined to return the corresponding sum.
If ``other`` is an instance of ``SizeDist`` (or subclass), take maximum
of the two ``sizeMin`` attributes and minimum of the two ``sizeMax``
attributes.
Parameters
----------
other : SizeDist, callable or scalar-valued Quantity [1/L]
Is added to ``self.func``.
Returns
-------
: ``self.__class__``
Instance of own class with corresponding ``func`` attribute.
Examples
--------
>>> import astropy.units as u
>>> def f(s): \
return 1.0/s.unit
>>> a = SizeDist(1*u.angstrom, 1*u.micron, f)
>>> b = SizeDist(10*u.angstrom, 10*u.micron, f)
>>> c = a + b
>>> c(_np.logspace(-11, -4, 10)*u.m)
<Quantity [0., 0., 0., 2., 2., 2., 2., 0., 0., 0.] 1 / m>
"""
if issubclass(other.__class__, SizeDist):
# find new size limits
sizeMin = max(self.sizeMin, other.sizeMin)
sizeMax = min(self.sizeMax, other.sizeMax)
# new differential number density is sum
def func(sizes):
return self.func(sizes) + other.func(sizes)
return self.__class__(sizeMin, sizeMax, func)
elif callable(other):
def func(sizes):
return self.func(sizes) + other(sizes)
return self.__class__(self.sizeMin, self.sizeMax, func)
else:
def func(sizes):
return other + self.func(sizes)
return self.__class__(self.sizeMin, self.sizeMax, func)
# make addition commutative
__radd__ = __add__
def __mul__(self, other):
if callable(other):
def func(sizes):
return self.function(sizes) * other(sizes)
return self.__class__(self.sizeMin, self.sizeMax, func)
else:
def func(sizes):
return other * self.function(sizes)
return self.__class__(self.sizeMin, self.sizeMax, func)
# make multiplication commutative
__rmul__ = __mul__
class PowerLaw(SizeDist):
"""
Parameters
----------
sizeMin : scalar Quantity [L]
Low end size cutoff of the distribution.
sizeMax : scalar Quantity [L]
High end size cutoff of the distribution.
power : float
Log-slope of the size distribution.
C : scalar Quantity [L]**(-1-power)
Normalization of the size distribution.
"""
def __init__(self, sizeMin, sizeMax, power, C):
def f(a):
return C*a**power
super(self).__init__(sizeMin, sizeMax, f)
return
class Log_Normal(SizeDist):
def __init__(self, sizeMin, sizeMax, rho, sgma, bc, b, a0):
# mass of carbon atom
m_C = 12.0107*_u.u
def B_val(i):
nominator = (3 * _np.exp(-4.5 * sgma**2) * bc[i] * m_C)
denominator = (
(2*_np.pi**2)**1.5 * rho * a0[i]**3 * sgma *
(1 + _np.erf((3 * sgma/_np.sqrt(2)) +
(_np.log((a0[i]/3.5/_u.angstrom).decompose().value) / (sgma * _np.sqrt(2))))))
# FIXME: what do we do if the denominator is zero
if denominator != 0:
B = (nominator/denominator).decompose().value
return B
_B = [B_val(i) for i in range(2)]
def f(a):
return sum([_B(i)/a * _np.exp(-0.5*(
_np.log((a/a0[i]).decompose().value)/sgma)**2)
for i in range(2)])
super(self).__init__(sizeMin, sizeMax, f)
return
class WD01_dst(SizeDist):
def __init__(self, sizeMin, sizeMax, a_t, beta, a_c, alpha, C):
if beta >= 0:
def F(a):
return 1 + (beta * a) / a_t
else:
def F(a):
return 1 / (1 - (beta * a) / a_t)
def exp_func(a):
r = _np.ones_like(a.value)
ind = _np.where(a > a_t)
r[ind] = _np.exp(-(((a[ind] - a_t)/a_c).decompose().value)**3)
return r
def f(a):
return C*(a / a_t)**alpha/a \
* F(a) * exp_func(a)
super(self).__init__(sizeMin, sizeMax, f)
return
###############################################################################
if __name__ == "__main__":
......
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