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Raw output: LIF_funcs_cython.c
+001: """
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002: Defines functions for leaky integrate and fire model using Cython
003: References:
004: https://blog.paperspace.com/faster-numpy-array-processing-ndarray-cython/
005: https://cython.readthedocs.io/en/latest/src/tutorial/cython_tutorial.html
006: https://cython.readthedocs.io/en/latest/src/tutorial/numpy.html
007: """
008:
+009: import numpy as np
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010: cimport numpy as np
011: cimport cython
+012: from scipy.stats import norm
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013:
+014: DTYPE = np.float
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015: ctypedef np.float_t DTYPE_t
016:
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018: '''
019: Given an array x, returns x normalized to 0 and 1
020: '''
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025: '''
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030:
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057: '''
058: Given an instance of Simulation class (sim),
059: and a neuron index (i),
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062:
063: template from Kakaria and de Bivort (2017)
064: '''
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081:
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083:
084: @cython.boundscheck(False) # turn off bounds-checking for entire function
085: @cython.wraparound(False) # turn off negative index wrapping for entire function
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087: int Nsteps, float dt,
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095: Run leaky integrate and fire model using Cython.
096:
097: Returns voltage table (V), current table (I),
098: and action potential counter (APMask), which are
099: tables of size (rows: # neurons, columns: # timepoints)
100:
101: Code modeled from Kakaria and de Bivort (2017)
102: '''
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117:
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119: cdef int t, i
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