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Merge branch 'release_merge' of https://github.com/like2000/PyHEADTAIL
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Conflicts:
	cobra_functions/__init__.py
	cobra_functions/stats.pyx
	impedances/__init__.py
	monitors/__init__.py
	solvers/__init__.py
	trackers/__init__.py
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@aoeftiger
aoeftiger committed Nov 4, 2014
2 parents 5be1610 + 759a38e commit 93a0d8a
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3 changes: 3 additions & 0 deletions .gitignore
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Expand Up @@ -10,3 +10,6 @@ scripts/
#*
.project
.pydevproject
*.h5
*.h5part
.ipynb_checkpoints
50 changes: 50 additions & 0 deletions __init__.py
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from ._version import __version__
from general.element import Element, Printing
from particles.slicing import clean_slices
# print ' '
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print ('PyHEADTAIL v' + __version__ + '\n\n')
2 changes: 2 additions & 0 deletions _version.py
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__version__ = "1.0.1"

1 change: 1 addition & 0 deletions cobra_functions/__init__.py
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from .. import __version__
229 changes: 229 additions & 0 deletions cobra_functions/stats.pyx
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"""
@author Kevin Li, Michael Schenk
@date 31. January 2014
@brief Collection of cython functions to calculate statistics
of bunch and slice_set data.
@copyright CERN
"""
import numpy as np
cimport numpy as np
cimport libc.math as cmath

cimport cython.boundscheck
cimport cython.cdivision


@cython.boundscheck(False)
cpdef double mean(double[::1] u):
""" Cython function to calculate the mean value of dataset u. """
cdef double mean_u = 0
cdef unsigned int n = u.shape[0]

cdef unsigned int i
for i in xrange(n):
mean_u += u[i]
if n:
mean_u /= n

return mean_u

@cython.boundscheck(False)
cpdef double std(double[::1] u):
""" Cython function to calculate the standard deviation of
dataset u. """
cdef double mean_u = mean(u)
cdef double std_u = 0
cdef double du = 0

cdef unsigned int n = u.shape[0]
cdef unsigned int i
for i in xrange(n):
du = u[i] - mean_u
std_u += du * du
if n:
std_u /= n

return cmath.sqrt(std_u)

@cython.boundscheck(False)
cpdef double emittance(double[::1] u, double[::1] up):
""" Cython function to calculate the emittance of datasets
u and up, i.e. a coordinate-momentum pair. To calculate the
emittance, one needs the mean values of quantities u and
up. """
cdef double mean_u = mean(u)
cdef double mean_up = mean(up)

cdef double u2 = 0
cdef double up2 = 0
cdef double uup = 0
cdef double du = 0
cdef double dup = 0

cdef unsigned int n = u.shape[0]
cdef unsigned int i
for i in xrange(n):
du = u[i] - mean_u
dup = up[i] - mean_up

u2 += du * du
up2 += dup * dup
uup += du * dup
if n:
u2 /= n
up2 /= n
uup /= n

return cmath.sqrt(u2*up2 - uup*uup)


'''
Cython statistics functions for an instance of a SliceSet class.
'''

@cython.boundscheck(False)
@cython.cdivision(True)
cpdef count_macroparticles_per_slice(int[::1] slice_index_of_particle,
int[::1] particles_within_cuts,
int[::1] n_macroparticles):
""" Cython function to count the number of macroparticles in
each slice. """
cdef unsigned int n_particles_within_cuts = particles_within_cuts.shape[0]
cdef unsigned int s_idx, i

for i in xrange(n_particles_within_cuts):
s_idx = slice_index_of_particle[particles_within_cuts[i]]
n_macroparticles[s_idx] += 1


@cython.boundscheck(False)
@cython.cdivision(True)
cpdef sort_particle_indices_by_slice(int[::1] slice_index_of_particle,
int[::1] particles_within_cuts,
int[::1] slice_positions,
int[::1] particle_indices_by_slice):
""" Iterate once through all the particles within the slicing
region and assign their position in the bunch.z array to the
respective slice they are in.
This is to provide a method to the user that allows to see
which particles are in a specific slice (see
particle_indices_of_slice in SliceSet class). """
cdef unsigned int n_part_in_cuts = particles_within_cuts.shape[0]
cdef unsigned int n_slices = slice_positions.shape[0] - 1

cdef unsigned int[::1] pos_ctr = np.zeros(n_slices, dtype=np.uint32)
cdef unsigned int i, p_idx, s_idx
cdef unsigned int pos

for i in xrange(n_part_in_cuts):
p_idx = particles_within_cuts[i]
s_idx = slice_index_of_particle[p_idx]

pos = slice_positions[s_idx] + pos_ctr[s_idx]
particle_indices_by_slice[pos] = p_idx
pos_ctr[s_idx] += 1


@cython.boundscheck(False)
@cython.cdivision(True)
cpdef mean_per_slice(int[::1] slice_index_of_particle,
int[::1] particles_within_cuts,
int[::1] n_macroparticles,
double[::1] u, double[::1] mean_u):
""" Iterate once through all the particles within the
slicing region and calculate simultaneously the mean
value of quantity u for each slice separately. """
cdef unsigned int n_part_in_cuts = particles_within_cuts.shape[0]
cdef unsigned int n_slices = mean_u.shape[0]
cdef unsigned int p_idx, s_idx, i

for i in xrange(n_part_in_cuts):
p_idx = particles_within_cuts[i]
s_idx = slice_index_of_particle[p_idx]
mean_u[s_idx] += u[p_idx]

for i in xrange(n_slices):
if n_macroparticles[i]:
mean_u[i] /= n_macroparticles[i]


@cython.boundscheck(False)
@cython.cdivision(True)
cpdef std_per_slice(int[::1] slice_index_of_particle,
int[::1] particles_within_cuts,
int[::1] n_macroparticles,
double[::1] u, double[::1] std_u):
""" Iterate once through all the particles within the
slicing region and calculate simultaneously the
standard deviation of quantity u for each slice
separately. """
cdef unsigned int n_part_in_cuts = particles_within_cuts.shape[0]
cdef unsigned int n_slices = std_u.shape[0]
cdef unsigned int p_idx, s_idx, i
cdef double du

cdef double[::1] mean_u = np.zeros(n_slices, dtype=np.double)
mean_per_slice(slice_index_of_particle, particles_within_cuts,
n_macroparticles, u, mean_u)

for i in xrange(n_part_in_cuts):
p_idx = particles_within_cuts[i]
s_idx = slice_index_of_particle[p_idx]

du = u[p_idx] - mean_u[s_idx]
std_u[s_idx] += du * du

for i in xrange(n_slices):
if n_macroparticles[i]:
std_u[i] /= n_macroparticles[i]

std_u[i] = cmath.sqrt(std_u[i])


@cython.boundscheck(False)
@cython.cdivision(True)
cpdef emittance_per_slice(int[::1] slice_index_of_particle,
int[::1] particles_within_cuts,
int[::1] n_macroparticles,
double[::1] u, double[::1] up, double[::1] epsn_u):
""" Iterate once through all the particles within the
slicing region and calculate simultaneously the emittance
of quantities u and up, i.e. a coordinate-momentum pair,
for each slice separately. To calculate the emittance per
slice, one needs the mean values of quantities u and up
for each slice. """
cdef unsigned int n_part_in_cuts = particles_within_cuts.shape[0]
cdef unsigned int n_slices = epsn_u.shape[0]
cdef unsigned int p_idx, s_idx, i

# Determine mean values of u and up for each slice.
cdef double[::1] mean_u = np.zeros(n_slices, dtype=np.double)
cdef double[::1] mean_up = np.zeros(n_slices, dtype=np.double)
mean_per_slice(slice_index_of_particle, particles_within_cuts,
n_macroparticles, u, mean_u)
mean_per_slice(slice_index_of_particle, particles_within_cuts,
n_macroparticles, up, mean_up)

cdef double du, dup
cdef double[::1] u2 = np.zeros(n_slices, dtype=np.double)
cdef double[::1] up2 = np.zeros(n_slices, dtype=np.double)
cdef double[::1] uup = np.zeros(n_slices, dtype=np.double)

for i in xrange(n_part_in_cuts):
p_idx = particles_within_cuts[i]
s_idx = slice_index_of_particle[p_idx]

du = u[p_idx] - mean_u[s_idx]
dup = up[p_idx] - mean_up[s_idx]

u2[s_idx] += du * du
up2[s_idx] += dup * dup
uup[s_idx] += du * dup

for i in xrange(n_slices):
if n_macroparticles[i]:
u2[i] /= n_macroparticles[i]
up2[i] /= n_macroparticles[i]
uup[i] /= n_macroparticles[i]

epsn_u[i] = cmath.sqrt(u2[i]*up2[i] - uup[i]*uup[i])
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