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Merge branch 'release_merge' of https://github.com/like2000/PyHEADTAIL
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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‎.gitignore‎

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#*
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.project
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.pydevproject
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*.h5
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*.h5part
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.ipynb_checkpoints

‎__init__.py‎

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from ._version import __version__
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from general.element import Element, Printing
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from particles.slicing import clean_slices
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# print ' '
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# print ' ;Cfttttt11111111tttt1f0f. '
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# print ' ,GttttfG0GGG00000GGG0G0G0G0G0G0Ct1t1fG: '
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# print ' ,GtttLG000GGGGGGGGGGG00GGGGGGGGGGGGGG0GGG0GCtttLt '
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# print ' .CtttC0G00GGGGGGGGGGG00t1t1GGGGGGGGGGGGGGGGGG0GG0G00Gtttt '
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# print ' G1tf000GGGGGGGGGG000811tGG0GL0GGGGGGGGGGGGGGGGGGGGGGGGGGGGG11C '
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# print ' GtttGG0G00GGGGGGGGG08t1GGG0;;0G000GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGtt: '
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# print ' 01tGG00GGGGGGGGGGGG0tt000ft::::GGG00GGGGGGGGGGGGGGGGGGG0GGG0G0000000GftG '
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# print ' L1tLf. 8ttGGGGGGGGGGGGGG00GtGG8fft:::::;0GG@0GGGGGGGGGGGGG00G088888888888GttfLLLt1C '
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# print ' CtG0G0GG8888t.G1tGGGGGGGGGGGGGGG0fGG0ffff;:::::::0G08GGGGGGGGGGGG888888888888f '
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# print ' .tt0GGGGG0888Ctf0GGGGGGGGGGGG0G0000GGGG0Gt;:;:::;0GG88GGGGG8808888888888C '
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# print ' C1GGGGGG0G01t00GGGGGGGGGGGGG00000000GGGGGGGG;::;GGG80GGGGGGGG0Lt1t@@i '
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# print ' 1tGGGGG0811G00GGGGGGGGGGGGGGGG0880G0GGGGGG0G0;L0G08GGGGGGGGGGG0GGG1ttf '
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# print ' t1t0GG08t1G0GGGGGGGGGGGGG0GtLGGGG0GLtGGGGGGGGG8G08GGGGGGGGGGGGGGGGG00tttf '
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# print ' 0tCGG8tt00GGGGGGGGG0G0CtG0GG0GGG000GGt00GGGG000GGGGGGGGGGGGGGGGGGGGGG0G1tG '
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# print ' 0G0G81G0GGGGGGGGGG0Gf00GG8888@8@80GGG0CGGG0GGGGGGGGGGGGGGGGGGGGGGGGGG000ttL '
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# print ' C11GG080GGGGGGGG00t0GG08@111: .i1t80GG08GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGt1C '
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# print ' f0@tGGGGGGGGGGG00C0GG88i1 1GG0G0GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG0GGttC '
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# print ' G88ffG0GGGGGG0G0GGG0811. .0GG08GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG0Gt1L '
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# print ' C .C .0G1800GGGGGG0G00f0811 .0G00GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG0G11i '
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# print ' L 1 Lt 11GGGGGGG000ft81: .8; 0G0GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG0GG001t0 '
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# print ' L: .C 1 f t 8CG00GGGG0G181, 0G00 00G0GG0GGGGGGGGGGGGGGGGGGGGGGGG0GGGGGGGGGGGGtt8 '
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# print ' f. . .;iC .. C .tGLG0GGGGt01. 0, Gi 0GGGGGGGGGGGGGGGGGGG0GGGGGGGGGGGGGGGGGGGGGGGGtt8 '
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# print ' CL0t .G1111 : G 1 08t8L0000t11: 00@8L t0GGGGGGGGGGGGGGGGGGG80GGGGGGGGGGGGGGGGGGGGGGGG11L '
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# print ' 1 C1 i f , :,0C10G0Ltf: :C8@1 :00GGGGGGGGGGGGGGGGGGGGG8888880000GGGGGGGGGGGGGGCtt. '
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# print ' ,1G:C111. .1 i i i 0@80ttt10i G111 fGGG0GGGGGGGGGGGGGGGGGGGGG8888888888GGG0GGGGGGGGGGtt0 '
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# print ' ; tL1 01 ; Gf C 00. LG1 f f;;:::;;:8GGGGGGGGGGGGGGG0GGGt888888888880GGGGGGGG00Gtt. '
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# print ' i1i C1 i111 . C8@G i0,G;::;C1 .t;:;:;::::::0GGGG00GGGGGGGGGGG0G11 .G8@8888880GGGGGGGGGL1G '
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# print ' L111tt1111. 1; 1111. . @888@@888:;:;;:;;;0. :t::::;: :::::8GG88GGGGG0GGGGGGGGGC1, 8888@8800GGGGGG1t '
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# print ' 8 C1 f L11i ;. 1. ;.:;:::::::::::::::::::::;; ;;:::10G880GGGGGGGGGGGGGG0GL1 ,88888G0GGG0f1. '
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# print ' G .i, .1 G ,1110f L.::::::::::::::::::::;. i.L;;:;LG88888G0GGGGGGGGGGGGGG0tf ,88880GGGGt; '
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# print ' t111111; :11 f11L G .1.::::::::::::;CC;:;:;:::;;C88888888880GGGGGGGGGGGGG0G1G 0@8000G1t '
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# print ' L1Cf011. ;11 .11G t1.L ,L,.::::::;:::::;:::C@888888888888888G000GGGGGGGGGGt1 f880GtL '
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# print ' .G111111111. .1t. 111.. .:LG8888@88088@0tfL@G t88888888880GGGGGGGGG0GCti CC0tL '
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# print ' ;L111111110. .1C i :..::;ft888888888ffffffftt8. 08888880GGGGGGGGGGGtG tti '
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# print ' G11i,,,,. 0 :L..;:::::;ffC88888888fffi::::::..C; C88888GGG0GGGG0GtC f. '
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# print ' .Gf11111. :Cff.C ,tftL.,;:::::::::;f888888888881:;:::::::..i. 888800GGGGGG01L '
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# print ' i1111111111ffft;:.....::ft..:::::::::::::0G000GGGG8888880f;;;::::.. 88880GGGGGGtf '
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# print ' .. iffff;:::::::;ttC.::::::::::::::0GGGG0GC888888800t G::::;.G @88GGGGGGtf '
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# print ' . G::::::;1: L.;:;:::::::::::GG0G0Gf888888 Gi10::::;:. .;@8GGGGGtC '
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# print ' ,.:::::::::::::;0GG1 L888 1L fG:;:::::. 880GGG1C '
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# print ' .,:::::::::::;80i...... 1G tf;:::.; 880001. '
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# print ' :.:::::::::::t. . GftftC,G G0Gtt '
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# print ' :.:;::::::;i , Ct11G LC1i '
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# print ' 1:::::::1001 C . tf '
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# print ' '
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print ('PyHEADTAIL v' + __version__ + '\n\n')

‎_version.py‎

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__version__ = "1.0.1"
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‎cobra_functions/__init__.py‎

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from .. import __version__

‎cobra_functions/stats.pyx‎

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"""
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@author Kevin Li, Michael Schenk
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@date 31. January 2014
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@brief Collection of cython functions to calculate statistics
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of bunch and slice_set data.
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@copyright CERN
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"""
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import numpy as np
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cimport numpy as np
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cimport libc.math as cmath
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cimport cython.boundscheck
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cimport cython.cdivision
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@cython.boundscheck(False)
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cpdef double mean(double[::1] u):
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""" Cython function to calculate the mean value of dataset u. """
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cdef double mean_u = 0
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cdef unsigned int n = u.shape[0]
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cdef unsigned int i
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for i in xrange(n):
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mean_u += u[i]
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if n:
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mean_u /= n
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return mean_u
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@cython.boundscheck(False)
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cpdef double std(double[::1] u):
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""" Cython function to calculate the standard deviation of
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dataset u. """
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cdef double mean_u = mean(u)
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cdef double std_u = 0
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cdef double du = 0
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cdef unsigned int n = u.shape[0]
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cdef unsigned int i
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for i in xrange(n):
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du = u[i] - mean_u
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std_u += du * du
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if n:
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std_u /= n
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return cmath.sqrt(std_u)
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@cython.boundscheck(False)
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cpdef double emittance(double[::1] u, double[::1] up):
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""" Cython function to calculate the emittance of datasets
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u and up, i.e. a coordinate-momentum pair. To calculate the
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emittance, one needs the mean values of quantities u and
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up. """
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cdef double mean_u = mean(u)
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cdef double mean_up = mean(up)
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cdef double u2 = 0
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cdef double up2 = 0
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cdef double uup = 0
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cdef double du = 0
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cdef double dup = 0
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cdef unsigned int n = u.shape[0]
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cdef unsigned int i
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for i in xrange(n):
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du = u[i] - mean_u
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dup = up[i] - mean_up
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u2 += du * du
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up2 += dup * dup
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uup += du * dup
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if n:
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u2 /= n
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up2 /= n
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uup /= n
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return cmath.sqrt(u2*up2 - uup*uup)
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'''
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Cython statistics functions for an instance of a SliceSet class.
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'''
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@cython.boundscheck(False)
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@cython.cdivision(True)
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cpdef count_macroparticles_per_slice(int[::1] slice_index_of_particle,
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int[::1] particles_within_cuts,
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int[::1] n_macroparticles):
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""" Cython function to count the number of macroparticles in
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each slice. """
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cdef unsigned int n_particles_within_cuts = particles_within_cuts.shape[0]
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cdef unsigned int s_idx, i
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for i in xrange(n_particles_within_cuts):
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s_idx = slice_index_of_particle[particles_within_cuts[i]]
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n_macroparticles[s_idx] += 1
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@cython.boundscheck(False)
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@cython.cdivision(True)
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cpdef sort_particle_indices_by_slice(int[::1] slice_index_of_particle,
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int[::1] particles_within_cuts,
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int[::1] slice_positions,
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int[::1] particle_indices_by_slice):
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""" Iterate once through all the particles within the slicing
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region and assign their position in the bunch.z array to the
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respective slice they are in.
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This is to provide a method to the user that allows to see
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which particles are in a specific slice (see
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particle_indices_of_slice in SliceSet class). """
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cdef unsigned int n_part_in_cuts = particles_within_cuts.shape[0]
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cdef unsigned int n_slices = slice_positions.shape[0] - 1
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cdef unsigned int[::1] pos_ctr = np.zeros(n_slices, dtype=np.uint32)
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cdef unsigned int i, p_idx, s_idx
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cdef unsigned int pos
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for i in xrange(n_part_in_cuts):
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p_idx = particles_within_cuts[i]
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s_idx = slice_index_of_particle[p_idx]
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pos = slice_positions[s_idx] + pos_ctr[s_idx]
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particle_indices_by_slice[pos] = p_idx
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pos_ctr[s_idx] += 1
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@cython.boundscheck(False)
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@cython.cdivision(True)
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cpdef mean_per_slice(int[::1] slice_index_of_particle,
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int[::1] particles_within_cuts,
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int[::1] n_macroparticles,
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double[::1] u, double[::1] mean_u):
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""" Iterate once through all the particles within the
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slicing region and calculate simultaneously the mean
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value of quantity u for each slice separately. """
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cdef unsigned int n_part_in_cuts = particles_within_cuts.shape[0]
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cdef unsigned int n_slices = mean_u.shape[0]
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cdef unsigned int p_idx, s_idx, i
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for i in xrange(n_part_in_cuts):
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p_idx = particles_within_cuts[i]
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s_idx = slice_index_of_particle[p_idx]
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mean_u[s_idx] += u[p_idx]
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for i in xrange(n_slices):
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if n_macroparticles[i]:
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mean_u[i] /= n_macroparticles[i]
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@cython.boundscheck(False)
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@cython.cdivision(True)
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cpdef std_per_slice(int[::1] slice_index_of_particle,
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int[::1] particles_within_cuts,
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int[::1] n_macroparticles,
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double[::1] u, double[::1] std_u):
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""" Iterate once through all the particles within the
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slicing region and calculate simultaneously the
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standard deviation of quantity u for each slice
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separately. """
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cdef unsigned int n_part_in_cuts = particles_within_cuts.shape[0]
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cdef unsigned int n_slices = std_u.shape[0]
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cdef unsigned int p_idx, s_idx, i
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cdef double du
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cdef double[::1] mean_u = np.zeros(n_slices, dtype=np.double)
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mean_per_slice(slice_index_of_particle, particles_within_cuts,
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n_macroparticles, u, mean_u)
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for i in xrange(n_part_in_cuts):
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p_idx = particles_within_cuts[i]
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s_idx = slice_index_of_particle[p_idx]
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du = u[p_idx] - mean_u[s_idx]
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std_u[s_idx] += du * du
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for i in xrange(n_slices):
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if n_macroparticles[i]:
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std_u[i] /= n_macroparticles[i]
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std_u[i] = cmath.sqrt(std_u[i])
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@cython.boundscheck(False)
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@cython.cdivision(True)
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cpdef emittance_per_slice(int[::1] slice_index_of_particle,
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int[::1] particles_within_cuts,
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int[::1] n_macroparticles,
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double[::1] u, double[::1] up, double[::1] epsn_u):
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""" Iterate once through all the particles within the
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slicing region and calculate simultaneously the emittance
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of quantities u and up, i.e. a coordinate-momentum pair,
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for each slice separately. To calculate the emittance per
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slice, one needs the mean values of quantities u and up
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for each slice. """
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cdef unsigned int n_part_in_cuts = particles_within_cuts.shape[0]
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cdef unsigned int n_slices = epsn_u.shape[0]
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cdef unsigned int p_idx, s_idx, i
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# Determine mean values of u and up for each slice.
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cdef double[::1] mean_u = np.zeros(n_slices, dtype=np.double)
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cdef double[::1] mean_up = np.zeros(n_slices, dtype=np.double)
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mean_per_slice(slice_index_of_particle, particles_within_cuts,
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n_macroparticles, u, mean_u)
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mean_per_slice(slice_index_of_particle, particles_within_cuts,
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n_macroparticles, up, mean_up)
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cdef double du, dup
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cdef double[::1] u2 = np.zeros(n_slices, dtype=np.double)
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cdef double[::1] up2 = np.zeros(n_slices, dtype=np.double)
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cdef double[::1] uup = np.zeros(n_slices, dtype=np.double)
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for i in xrange(n_part_in_cuts):
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p_idx = particles_within_cuts[i]
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s_idx = slice_index_of_particle[p_idx]
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du = u[p_idx] - mean_u[s_idx]
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dup = up[p_idx] - mean_up[s_idx]
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u2[s_idx] += du * du
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up2[s_idx] += dup * dup
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uup[s_idx] += du * dup
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for i in xrange(n_slices):
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if n_macroparticles[i]:
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u2[i] /= n_macroparticles[i]
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up2[i] /= n_macroparticles[i]
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uup[i] /= n_macroparticles[i]
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epsn_u[i] = cmath.sqrt(u2[i]*up2[i] - uup[i]*uup[i])

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