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momenta!

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hiro98 2020-04-02 16:35:43 +02:00
parent a895b40672
commit 0872ebf553
3 changed files with 15 additions and 15 deletions
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16
prog/python/qqgg/analytical_xs.org
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@ -412,8 +412,8 @@ save_fig(fig, 'histo_cos_theta', 'xs', size=(4,3))
**** Observables **** Observables
Now we define some utilities to draw real 4-momentum samples. Now we define some utilities to draw real 4-momentum samples.
#+begin_src jupyter-python :exports both :tangle tangled/xs.py #+begin_src jupyter-python :exports both :tangle tangled/xs.py
def sample_momentums(sample_num, interval, charge, esp, seed=None): def sample_momenta(sample_num, interval, charge, esp, seed=None):
"""Samples `sample_num` unweighted photon 4-momentums from the """Samples `sample_num` unweighted photon 4-momenta from the
cross-section. cross-section.
:param sample_num: number of samples to take :param sample_num: number of samples to take
@ -423,7 +423,7 @@ Now we define some utilities to draw real 4-momentum samples.
:param seed: the seed for the rng, optional, default is system :param seed: the seed for the rng, optional, default is system
time time
:returns: an array of 4 photon momentums :returns: an array of 4 photon momenta
:rtype: np.ndarray :rtype: np.ndarray
""" """
@ -438,9 +438,9 @@ Now we define some utilities to draw real 4-momentum samples.
sinθ = np.sqrt(1-cosθ**2) sinθ = np.sqrt(1-cosθ**2)
return np.array([1, sinθ*np.cos(φ), sinθ*np.sin(φ), cosθ])*esp/2 return np.array([1, sinθ*np.cos(φ), sinθ*np.sin(φ), cosθ])*esp/2
momentums = np.array([make_momentum(esp, cosθ, φ) \ momenta = np.array([make_momentum(esp, cosθ, φ) \
for cosθ, φ in np.array([cosθ_sample, φ_sample]).T]) for cosθ, φ in np.array([cosθ_sample, φ_sample]).T])
return momentums return momenta
#+end_src #+end_src
#+RESULTS: #+RESULTS:
@ -455,7 +455,7 @@ analogous to transforming the distribution itself.
def p_t(p): def p_t(p):
"""Transverse momentum """Transverse momentum
:param p: array of 4-momentums :param p: array of 4-momenta
""" """
return np.linalg.norm(p[:,1:3], axis=1) return np.linalg.norm(p[:,1:3], axis=1)
@ -463,7 +463,7 @@ analogous to transforming the distribution itself.
def η(p): def η(p):
"""Pseudo rapidity. """Pseudo rapidity.
:param p: array of 4-momentums :param p: array of 4-momenta
""" """
return np.arccosh(np.linalg.norm(p[:,1:], axis=1)/p_t(p))*np.sign(p[:, 3]) return np.arccosh(np.linalg.norm(p[:,1:], axis=1)/p_t(p))*np.sign(p[:, 3])
@ -474,7 +474,7 @@ analogous to transforming the distribution itself.
Lets try it out. Lets try it out.
#+begin_src jupyter-python :exports both :results raw drawer #+begin_src jupyter-python :exports both :results raw drawer
momentum_sample = sample_momentums(2000, interval_cosθ, charge, esp) momentum_sample = sample_momenta(2000, interval_cosθ, charge, esp)
momentum_sample momentum_sample
#+end_src #+end_src

4
prog/python/qqgg/tangled/observables.py
View file

@ -4,7 +4,7 @@ import numpy as np
def p_t(p): def p_t(p):
"""Transverse momentum """Transverse momentum
:param p: array of 4-momentums :param p: array of 4-momenta
""" """
return np.linalg.norm(p[:,1:3], axis=1) return np.linalg.norm(p[:,1:3], axis=1)
@ -12,7 +12,7 @@ def p_t(p):
def η(p): def η(p):
"""Pseudo rapidity. """Pseudo rapidity.
:param p: array of 4-momentums :param p: array of 4-momenta
""" """
return np.arccosh(np.linalg.norm(p[:,1:], axis=1)/p_t(p))*np.sign(p[:, 3]) return np.arccosh(np.linalg.norm(p[:,1:], axis=1)/p_t(p))*np.sign(p[:, 3])

10
prog/python/qqgg/tangled/xs.py
View file

@ -117,8 +117,8 @@ def total_xs_eta(η, charge, esp):
return 2*np.pi*f*(F(η[0]) - F(η[1])) return 2*np.pi*f*(F(η[0]) - F(η[1]))
def sample_momentums(sample_num, interval, charge, esp, seed=None): def sample_momenta(sample_num, interval, charge, esp, seed=None):
"""Samples `sample_num` unweighted photon 4-momentums from the """Samples `sample_num` unweighted photon 4-momenta from the
cross-section. cross-section.
:param sample_num: number of samples to take :param sample_num: number of samples to take
@ -128,7 +128,7 @@ def sample_momentums(sample_num, interval, charge, esp, seed=None):
:param seed: the seed for the rng, optional, default is system :param seed: the seed for the rng, optional, default is system
time time
:returns: an array of 4 photon momentums :returns: an array of 4 photon momenta
:rtype: np.ndarray :rtype: np.ndarray
""" """
@ -143,6 +143,6 @@ def sample_momentums(sample_num, interval, charge, esp, seed=None):
sinθ = np.sqrt(1-cosθ**2) sinθ = np.sqrt(1-cosθ**2)
return np.array([1, sinθ*np.cos(φ), sinθ*np.sin(φ), cosθ])*esp/2 return np.array([1, sinθ*np.cos(φ), sinθ*np.sin(φ), cosθ])*esp/2
momentums = np.array([make_momentum(esp, cosθ, φ) \ momenta = np.array([make_momentum(esp, cosθ, φ) \
for cosθ, φ in np.array([cosθ_sample, φ_sample]).T]) for cosθ, φ in np.array([cosθ_sample, φ_sample]).T])
return momentums return momenta