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author | Mario Mulansky <mario.mulansky@gmx.net> | 2018-09-20 10:49:42 -0700 |
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committer | GitHub <noreply@github.com> | 2018-09-20 10:49:42 -0700 |
commit | 34bd30415dd93a2425ce566627e24ee9483ada3e (patch) | |
tree | dcfa9164d46e3cf501a1e8dcf4970f350063561a /pyspike/cython/directionality_python_backend.py | |
parent | 44d23620d2faa78ca74437fbd3f1b95da722a853 (diff) |
Spike Order support (#39)0.6.0
* reorganized directionality module
* further refactoring of directionality
* completed python directionality backend
* added SPIKE-Sync based filtering
new function filter_by_spike_sync removes spikes that have a multi-variate
Spike Sync value below some threshold
not yet fully tested, python backend missing.
* spike sync filtering, cython sim ann
Added function for filtering out events based on a threshold for the spike
sync values. Usefull for focusing on synchronous events during directionality
analysis.
Also added cython version of simulated annealing for performance.
* added coincidence single profile to python backend
missing function in python backend added, identified and fixed a bug in the
implementation as well
* updated test case to new spike sync behavior
* python3 fixes
* another python3 fix
* reorganized directionality module
* further refactoring of directionality
* completed python directionality backend
* added SPIKE-Sync based filtering
new function filter_by_spike_sync removes spikes that have a multi-variate
Spike Sync value below some threshold
not yet fully tested, python backend missing.
* spike sync filtering, cython sim ann
Added function for filtering out events based on a threshold for the spike
sync values. Usefull for focusing on synchronous events during directionality
analysis.
Also added cython version of simulated annealing for performance.
* added coincidence single profile to python backend
missing function in python backend added, identified and fixed a bug in the
implementation as well
* updated test case to new spike sync behavior
* python3 fixes
* another python3 fix
* Fix absolute imports in directionality measures
* remove commented code
* Add directionality to docs, bump version
* Clean up directionality module, add doxy.
* Remove debug print from tests
* Fix bug in calling Python backend
* Fix incorrect integrals in PieceWiseConstFunc (#36)
* Add (some currently failing) tests for PieceWiseConstFunc.integral
* Fix implementation of PieceWiseConstFunc.integral
Just by adding a special condition for when we are only taking an
integral "between" two edges of a PieceWiseConstFunc
All tests now pass.
Fixes #33.
* Add PieceWiseConstFunc.integral tests for ValueError
* Add testing bounds of integral
* Raise ValueError in function implementation
* Fix incorrect integrals in PieceWiseLinFunc (#38)
Integrals of piece-wise linear functions were incorrect if the
requested interval lies completely between two support points.
This has been fixed, and a unit test exercising this behavior
was added.
Fixes #38
* Add Spike Order example and Tutorial section
Adds an example computing spike order profile and the optimal
spike train order. Also adds a section on spike train order to the
tutorial.
Diffstat (limited to 'pyspike/cython/directionality_python_backend.py')
-rw-r--r-- | pyspike/cython/directionality_python_backend.py | 144 |
1 files changed, 144 insertions, 0 deletions
diff --git a/pyspike/cython/directionality_python_backend.py b/pyspike/cython/directionality_python_backend.py new file mode 100644 index 0000000..c1d820b --- /dev/null +++ b/pyspike/cython/directionality_python_backend.py @@ -0,0 +1,144 @@ +""" directionality_python_backend.py + +Collection of python functions that can be used instead of the cython +implementation. + +Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License + +""" + +import numpy as np + + +############################################################ +# spike_train_order_python +############################################################ +def spike_directionality_profile_python(spikes1, spikes2, t_start, t_end, + max_tau): + + def get_tau(spikes1, spikes2, i, j, max_tau): + m = t_end - t_start # use interval as initial tau + if i < len(spikes1)-1 and i > -1: + m = min(m, spikes1[i+1]-spikes1[i]) + if j < len(spikes2)-1 and j > -1: + m = min(m, spikes2[j+1]-spikes2[j]) + if i > 0: + m = min(m, spikes1[i]-spikes1[i-1]) + if j > 0: + m = min(m, spikes2[j]-spikes2[j-1]) + m *= 0.5 + if max_tau > 0.0: + m = min(m, max_tau) + return m + + N1 = len(spikes1) + N2 = len(spikes2) + i = -1 + j = -1 + d1 = np.zeros(N1) # directionality values + d2 = np.zeros(N2) # directionality values + while i + j < N1 + N2 - 2: + if (i < N1-1) and (j == N2-1 or spikes1[i+1] < spikes2[j+1]): + i += 1 + tau = get_tau(spikes1, spikes2, i, j, max_tau) + if j > -1 and spikes1[i]-spikes2[j] < tau: + # coincidence between the current spike and the previous spike + # spike in first spike train occurs after second + d1[i] = -1 + d2[j] = +1 + elif (j < N2-1) and (i == N1-1 or spikes1[i+1] > spikes2[j+1]): + j += 1 + tau = get_tau(spikes1, spikes2, i, j, max_tau) + if i > -1 and spikes2[j]-spikes1[i] < tau: + # coincidence between the current spike and the previous spike + # spike in second spike train occurs after first + d1[i] = +1 + d2[j] = -1 + else: # spikes1[i+1] = spikes2[j+1] + # advance in both spike trains + j += 1 + i += 1 + d1[i] = 0 + d2[j] = 0 + + return d1, d2 + + +############################################################ +# spike_train_order_python +############################################################ +def spike_train_order_profile_python(spikes1, spikes2, t_start, t_end, + max_tau): + + def get_tau(spikes1, spikes2, i, j, max_tau): + m = t_end - t_start # use interval as initial tau + if i < len(spikes1)-1 and i > -1: + m = min(m, spikes1[i+1]-spikes1[i]) + if j < len(spikes2)-1 and j > -1: + m = min(m, spikes2[j+1]-spikes2[j]) + if i > 0: + m = min(m, spikes1[i]-spikes1[i-1]) + if j > 0: + m = min(m, spikes2[j]-spikes2[j-1]) + m *= 0.5 + if max_tau > 0.0: + m = min(m, max_tau) + return m + + N1 = len(spikes1) + N2 = len(spikes2) + i = -1 + j = -1 + n = 0 + st = np.zeros(N1 + N2 + 2) # spike times + a = np.zeros(N1 + N2 + 2) # coincidences + mp = np.ones(N1 + N2 + 2) # multiplicity + while i + j < N1 + N2 - 2: + if (i < N1-1) and (j == N2-1 or spikes1[i+1] < spikes2[j+1]): + i += 1 + n += 1 + tau = get_tau(spikes1, spikes2, i, j, max_tau) + st[n] = spikes1[i] + if j > -1 and spikes1[i]-spikes2[j] < tau: + # coincidence between the current spike and the previous spike + # both get marked with 1 + a[n] = -1 + a[n-1] = -1 + elif (j < N2-1) and (i == N1-1 or spikes1[i+1] > spikes2[j+1]): + j += 1 + n += 1 + tau = get_tau(spikes1, spikes2, i, j, max_tau) + st[n] = spikes2[j] + if i > -1 and spikes2[j]-spikes1[i] < tau: + # coincidence between the current spike and the previous spike + # both get marked with 1 + a[n] = 1 + a[n-1] = 1 + else: # spikes1[i+1] = spikes2[j+1] + # advance in both spike trains + j += 1 + i += 1 + n += 1 + # add only one event with zero asymmetry value and multiplicity 2 + st[n] = spikes1[i] + a[n] = 0 + mp[n] = 2 + + st = st[:n+2] + a = a[:n+2] + mp = mp[:n+2] + + st[0] = t_start + st[len(st)-1] = t_end + if N1 + N2 > 0: + a[0] = a[1] + a[len(a)-1] = a[len(a)-2] + mp[0] = mp[1] + mp[len(mp)-1] = mp[len(mp)-2] + else: + a[0] = 1 + a[1] = 1 + + return st, a, mp |