Generated by Cython 0.29.20
Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.
Raw output: cfunc.c
+001: # cython: language_level=3, boundscheck=True, wraparound=False
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+002: import numpy as np
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+003: import psutil # For Memory Profiling
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+004: import os
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005: cimport cython
006:
007: #from scipy.special import logsumexp
008: from libc.math cimport exp, log # For doing Exponentials and Logs
009:
+010: DTYPE = np.float # The float data type
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011:
+012: cdef inline double logsumexp(double[:] vec):
static CYTHON_INLINE double __pyx_f_8hapsburg_5cfunc_logsumexp(__Pyx_memviewslice __pyx_v_vec) {
Py_ssize_t __pyx_v_i;
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013: """Do the Log of the Sum of Exponentials."""
014: cdef Py_ssize_t i # The iterator Variable
+015: cdef double result = 0.0
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+016: cdef double largest = vec[0]
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017:
+018: for i in range(1, vec.shape[0]): # Find Maximum in vec
__pyx_t_3 = (__pyx_v_vec.shape[0]);
__pyx_t_4 = __pyx_t_3;
for (__pyx_t_5 = 1; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
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+019: if (vec[i] > largest):
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+020: largest = vec[i]
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+021: for i in range(vec.shape[0]):
__pyx_t_3 = (__pyx_v_vec.shape[0]);
__pyx_t_4 = __pyx_t_3;
for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
__pyx_v_i = __pyx_t_5;
+022: result += exp(vec[i] - largest)
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}
+023: return largest + log(result)
__pyx_r = (__pyx_v_largest + log(__pyx_v_result)); goto __pyx_L0;
024:
+025: cdef inline long argmax(double[:] vec):
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026: """Return Max and ArgMax"""
027: cdef Py_ssize_t i # The iterator Variable.
+028: cdef int m = 0 # Position of the Maximum.
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+029: cdef double v = vec[0]
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030:
+031: for k in range(1, vec.shape[0]): # Find Maximum
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for (__pyx_t_5 = 1; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
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+032: if (vec[k] > v):
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/* … */
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+033: m, v = k, vec[k]
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__pyx_v_m = __pyx_t_7;
__pyx_v_v = __pyx_t_8;
+034: return m # Return Argmax
__pyx_r = __pyx_v_m; goto __pyx_L0;
035:
036:
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038: """Print the current Memory Usage in mB"""
+039: process = psutil.Process(os.getpid())
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043:
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__Pyx_RaiseBufferIndexError(__pyx_t_20);
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__pyx_t_20 = 0;
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067:
+068: fwd[j, i] = e_prob0[j, i] + logsumexp(trans_ll_view)
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__pyx_t_21 = __pyx_v_i;
__pyx_t_15 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_15 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_e_prob0.shape[0])) __pyx_t_15 = 0;
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}
069:
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for (__pyx_t_14 = 0; __pyx_t_14 < __pyx_t_10; __pyx_t_14+=1) {
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__Pyx_RaiseBufferIndexError(__pyx_t_15);
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__pyx_t_15 = 0;
} else if (unlikely(__pyx_t_28 >= __pyx_v_trans_ll_view.shape[0])) __pyx_t_15 = 0;
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__Pyx_RaiseBufferIndexError(__pyx_t_15);
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}
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__pyx_t_26 = (__pyx_v_i - 1);
__pyx_t_12 = -1;
if (__pyx_t_27 < 0) {
__pyx_t_12 = 0;
} else if (unlikely(__pyx_t_27 >= __pyx_v_bwd.shape[0])) __pyx_t_12 = 0;
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__Pyx_RaiseBufferIndexError(__pyx_t_12);
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}
075:
076: # Get total log likelihood
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__pyx_t_10 = __pyx_t_9;
for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {
__pyx_v_k = __pyx_t_11;
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__Pyx_RaiseBufferIndexError(__pyx_t_12);
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__pyx_v_tot_ll = __pyx_f_8hapsburg_5cfunc_logsumexp(__pyx_v_trans_ll_view);
080:
+081: print(f"Total Log likelihood: {tot_ll: .3f}")
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082:
083: # Combine the forward and backward calculations
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090:
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093:
094:
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143:
+144: for k in range(1, n_states): # Calculate logsum of ROH states:
__pyx_t_14 = __pyx_v_n_states;
__pyx_t_18 = __pyx_t_14;
for (__pyx_t_19 = 1; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {
__pyx_v_k = __pyx_t_19;
+145: trans_ll_view[k-1] = fwd[k, i - 1]
__pyx_t_17 = __pyx_v_k;
__pyx_t_16 = (__pyx_v_i - 1);
__pyx_t_20 = -1;
if (__pyx_t_17 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_17 >= __pyx_v_fwd.shape[0])) __pyx_t_20 = 0;
if (__pyx_t_16 < 0) {
__pyx_t_20 = 1;
} else if (unlikely(__pyx_t_16 >= __pyx_v_fwd.shape[1])) __pyx_t_20 = 1;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 145, __pyx_L1_error)
}
__pyx_t_15 = (__pyx_v_k - 1);
__pyx_t_20 = -1;
if (__pyx_t_15 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_15 >= __pyx_v_trans_ll_view.shape[0])) __pyx_t_20 = 0;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 145, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_trans_ll_view.data + __pyx_t_15 * __pyx_v_trans_ll_view.strides[0]) )) = (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_17 * __pyx_v_fwd.strides[0]) ) + __pyx_t_16 * __pyx_v_fwd.strides[1]) )));
}
+146: f_l = logsumexp(trans_ll_view) # Logsum of ROH States
__pyx_v_f_l = __pyx_f_8hapsburg_5cfunc_logsumexp(__pyx_v_trans_ll_view);
147:
148: # Do the 0 State:
+149: two_v_view[0] = fwd[0, i - 1] + t0[i, 0, 0] # Staying in 0 State
__pyx_t_16 = 0;
__pyx_t_17 = (__pyx_v_i - 1);
__pyx_t_14 = -1;
if (__pyx_t_16 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_16 >= __pyx_v_fwd.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_17 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_17 >= __pyx_v_fwd.shape[1])) __pyx_t_14 = 1;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 149, __pyx_L1_error)
}
__pyx_t_13 = __pyx_v_i;
__pyx_t_12 = 0;
__pyx_t_11 = 0;
__pyx_t_14 = -1;
if (__pyx_t_13 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_13 >= __pyx_v_t0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_12 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_12 >= __pyx_v_t0.shape[1])) __pyx_t_14 = 1;
if (__pyx_t_11 < 0) {
__pyx_t_14 = 2;
} else if (unlikely(__pyx_t_11 >= __pyx_v_t0.shape[2])) __pyx_t_14 = 2;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 149, __pyx_L1_error)
}
__pyx_t_21 = 0;
__pyx_t_14 = -1;
if (__pyx_t_21 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_21 >= __pyx_v_two_v_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 149, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_two_v_view.data + __pyx_t_21 * __pyx_v_two_v_view.strides[0]) )) = ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_16 * __pyx_v_fwd.strides[0]) ) + __pyx_t_17 * __pyx_v_fwd.strides[1]) ))) + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_13 * __pyx_v_t0.strides[0]) ) + __pyx_t_12 * __pyx_v_t0.strides[1]) ) + __pyx_t_11 * __pyx_v_t0.strides[2]) ))));
+150: two_v_view[1] = f_l + t0[i, 1, 0] # Going into 0 State
__pyx_t_11 = __pyx_v_i;
__pyx_t_12 = 1;
__pyx_t_13 = 0;
__pyx_t_14 = -1;
if (__pyx_t_11 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_11 >= __pyx_v_t0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_12 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_12 >= __pyx_v_t0.shape[1])) __pyx_t_14 = 1;
if (__pyx_t_13 < 0) {
__pyx_t_14 = 2;
} else if (unlikely(__pyx_t_13 >= __pyx_v_t0.shape[2])) __pyx_t_14 = 2;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 150, __pyx_L1_error)
}
__pyx_t_17 = 1;
__pyx_t_14 = -1;
if (__pyx_t_17 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_17 >= __pyx_v_two_v_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 150, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_two_v_view.data + __pyx_t_17 * __pyx_v_two_v_view.strides[0]) )) = (__pyx_v_f_l + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_11 * __pyx_v_t0.strides[0]) ) + __pyx_t_12 * __pyx_v_t0.strides[1]) ) + __pyx_t_13 * __pyx_v_t0.strides[2]) ))));
+151: fwd[0, i] = e_prob0[0, i] + logsumexp(two_v_view)
__pyx_t_13 = 0;
__pyx_t_12 = __pyx_v_i;
__pyx_t_14 = -1;
if (__pyx_t_13 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_13 >= __pyx_v_e_prob0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_12 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_12 >= __pyx_v_e_prob0.shape[1])) __pyx_t_14 = 1;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 151, __pyx_L1_error)
}
__pyx_t_11 = 0;
__pyx_t_16 = __pyx_v_i;
__pyx_t_14 = -1;
if (__pyx_t_11 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_11 >= __pyx_v_fwd.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_16 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_16 >= __pyx_v_fwd.shape[1])) __pyx_t_14 = 1;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 151, __pyx_L1_error)
}
*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_11 * __pyx_v_fwd.strides[0]) ) + __pyx_t_16 * __pyx_v_fwd.strides[1]) )) = ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_e_prob0.data + __pyx_t_13 * __pyx_v_e_prob0.strides[0]) ) + __pyx_t_12 * __pyx_v_e_prob0.strides[1]) ))) + __pyx_f_8hapsburg_5cfunc_logsumexp(__pyx_v_two_v_view));
152:
153: ### Do the other states
154: # Preprocessing:
+155: three_v_view[0] = fwd[0, i - 1] + t0[i, 0, 1] # Coming from 0 State
__pyx_t_12 = 0;
__pyx_t_13 = (__pyx_v_i - 1);
__pyx_t_14 = -1;
if (__pyx_t_12 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_12 >= __pyx_v_fwd.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_13 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_13 >= __pyx_v_fwd.shape[1])) __pyx_t_14 = 1;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 155, __pyx_L1_error)
}
__pyx_t_22 = __pyx_v_i;
__pyx_t_23 = 0;
__pyx_t_24 = 1;
__pyx_t_14 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_t0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_23 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_23 >= __pyx_v_t0.shape[1])) __pyx_t_14 = 1;
if (__pyx_t_24 < 0) {
__pyx_t_14 = 2;
} else if (unlikely(__pyx_t_24 >= __pyx_v_t0.shape[2])) __pyx_t_14 = 2;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 155, __pyx_L1_error)
}
__pyx_t_25 = 0;
__pyx_t_14 = -1;
if (__pyx_t_25 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_25 >= __pyx_v_three_v_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 155, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_25 * __pyx_v_three_v_view.strides[0]) )) = ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_12 * __pyx_v_fwd.strides[0]) ) + __pyx_t_13 * __pyx_v_fwd.strides[1]) ))) + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_22 * __pyx_v_t0.strides[0]) ) + __pyx_t_23 * __pyx_v_t0.strides[1]) ) + __pyx_t_24 * __pyx_v_t0.strides[2]) ))));
+156: three_v_view[1] = f_l + t0[i, 1, 2] # Coming from other ROH State
__pyx_t_24 = __pyx_v_i;
__pyx_t_23 = 1;
__pyx_t_22 = 2;
__pyx_t_14 = -1;
if (__pyx_t_24 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_24 >= __pyx_v_t0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_23 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_23 >= __pyx_v_t0.shape[1])) __pyx_t_14 = 1;
if (__pyx_t_22 < 0) {
__pyx_t_14 = 2;
} else if (unlikely(__pyx_t_22 >= __pyx_v_t0.shape[2])) __pyx_t_14 = 2;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 156, __pyx_L1_error)
}
__pyx_t_13 = 1;
__pyx_t_14 = -1;
if (__pyx_t_13 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_13 >= __pyx_v_three_v_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 156, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_13 * __pyx_v_three_v_view.strides[0]) )) = (__pyx_v_f_l + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_24 * __pyx_v_t0.strides[0]) ) + __pyx_t_23 * __pyx_v_t0.strides[1]) ) + __pyx_t_22 * __pyx_v_t0.strides[2]) ))));
157:
+158: for j in range(1, n_states): # Do the final run over all states
__pyx_t_14 = __pyx_v_n_states;
__pyx_t_18 = __pyx_t_14;
for (__pyx_t_19 = 1; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {
__pyx_v_j = __pyx_t_19;
+159: three_v_view[2] = fwd[j, i-1] + stay
__pyx_t_22 = __pyx_v_j;
__pyx_t_23 = (__pyx_v_i - 1);
__pyx_t_20 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_fwd.shape[0])) __pyx_t_20 = 0;
if (__pyx_t_23 < 0) {
__pyx_t_20 = 1;
} else if (unlikely(__pyx_t_23 >= __pyx_v_fwd.shape[1])) __pyx_t_20 = 1;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 159, __pyx_L1_error)
}
__pyx_t_24 = 2;
__pyx_t_20 = -1;
if (__pyx_t_24 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_24 >= __pyx_v_three_v_view.shape[0])) __pyx_t_20 = 0;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 159, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_24 * __pyx_v_three_v_view.strides[0]) )) = ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_22 * __pyx_v_fwd.strides[0]) ) + __pyx_t_23 * __pyx_v_fwd.strides[1]) ))) + __pyx_v_stay);
+160: fwd[j, i] = e_prob0[j, i] + logsumexp(three_v_view)
__pyx_t_23 = __pyx_v_j;
__pyx_t_22 = __pyx_v_i;
__pyx_t_20 = -1;
if (__pyx_t_23 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_23 >= __pyx_v_e_prob0.shape[0])) __pyx_t_20 = 0;
if (__pyx_t_22 < 0) {
__pyx_t_20 = 1;
} else if (unlikely(__pyx_t_22 >= __pyx_v_e_prob0.shape[1])) __pyx_t_20 = 1;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 160, __pyx_L1_error)
}
__pyx_t_12 = __pyx_v_j;
__pyx_t_26 = __pyx_v_i;
__pyx_t_20 = -1;
if (__pyx_t_12 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_12 >= __pyx_v_fwd.shape[0])) __pyx_t_20 = 0;
if (__pyx_t_26 < 0) {
__pyx_t_20 = 1;
} else if (unlikely(__pyx_t_26 >= __pyx_v_fwd.shape[1])) __pyx_t_20 = 1;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 160, __pyx_L1_error)
}
*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_12 * __pyx_v_fwd.strides[0]) ) + __pyx_t_26 * __pyx_v_fwd.strides[1]) )) = ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_e_prob0.data + __pyx_t_23 * __pyx_v_e_prob0.strides[0]) ) + __pyx_t_22 * __pyx_v_e_prob0.strides[1]) ))) + __pyx_f_8hapsburg_5cfunc_logsumexp(__pyx_v_three_v_view));
}
}
161:
162: #############################
163: ### Do the Backward Algorithm
+164: for i in range(n_loci-1, 0, -1): # Run backward recursion
for (__pyx_t_10 = (__pyx_v_n_loci - 1); __pyx_t_10 > 0; __pyx_t_10-=1) {
__pyx_v_i = __pyx_t_10;
+165: stay = log(t[i, 1, 1] - t[i, 1, 2])
__pyx_t_22 = __pyx_v_i;
__pyx_t_23 = 1;
__pyx_t_27 = 1;
__pyx_t_8 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_t.shape[0])) __pyx_t_8 = 0;
if (__pyx_t_23 < 0) {
__pyx_t_8 = 1;
} else if (unlikely(__pyx_t_23 >= __pyx_v_t.shape[1])) __pyx_t_8 = 1;
if (__pyx_t_27 < 0) {
__pyx_t_8 = 2;
} else if (unlikely(__pyx_t_27 >= __pyx_v_t.shape[2])) __pyx_t_8 = 2;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 165, __pyx_L1_error)
}
__pyx_t_28 = __pyx_v_i;
__pyx_t_29 = 1;
__pyx_t_30 = 2;
__pyx_t_8 = -1;
if (__pyx_t_28 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_28 >= __pyx_v_t.shape[0])) __pyx_t_8 = 0;
if (__pyx_t_29 < 0) {
__pyx_t_8 = 1;
} else if (unlikely(__pyx_t_29 >= __pyx_v_t.shape[1])) __pyx_t_8 = 1;
if (__pyx_t_30 < 0) {
__pyx_t_8 = 2;
} else if (unlikely(__pyx_t_30 >= __pyx_v_t.shape[2])) __pyx_t_8 = 2;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 165, __pyx_L1_error)
}
__pyx_v_stay = log(((*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t.data + __pyx_t_22 * __pyx_v_t.strides[0]) ) + __pyx_t_23 * __pyx_v_t.strides[1]) ) + __pyx_t_27 * __pyx_v_t.strides[2]) ))) - (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t.data + __pyx_t_28 * __pyx_v_t.strides[0]) ) + __pyx_t_29 * __pyx_v_t.strides[1]) ) + __pyx_t_30 * __pyx_v_t.strides[2]) )))));
166:
+167: for k in range(1, n_states): # Calculate logsum of ROH states:
__pyx_t_8 = __pyx_v_n_states;
__pyx_t_9 = __pyx_t_8;
for (__pyx_t_19 = 1; __pyx_t_19 < __pyx_t_9; __pyx_t_19+=1) {
__pyx_v_k = __pyx_t_19;
+168: trans_ll_view[k-1] = bwd[k, i] + e_prob0[k, i]
__pyx_t_30 = __pyx_v_k;
__pyx_t_29 = __pyx_v_i;
__pyx_t_14 = -1;
if (__pyx_t_30 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_30 >= __pyx_v_bwd.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_29 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_29 >= __pyx_v_bwd.shape[1])) __pyx_t_14 = 1;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 168, __pyx_L1_error)
}
__pyx_t_28 = __pyx_v_k;
__pyx_t_27 = __pyx_v_i;
__pyx_t_14 = -1;
if (__pyx_t_28 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_28 >= __pyx_v_e_prob0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_27 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_27 >= __pyx_v_e_prob0.shape[1])) __pyx_t_14 = 1;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 168, __pyx_L1_error)
}
__pyx_t_23 = (__pyx_v_k - 1);
__pyx_t_14 = -1;
if (__pyx_t_23 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_23 >= __pyx_v_trans_ll_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
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}
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170:
171: # Do the 0 State:
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175:
176: ### Do the other states
177: # Preprocessing:
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180:
+181: for j in range(1, n_states): # Do the final run over all states
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184:
185: # Get total log likelihood
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189:
190: # Combine the forward and backward calculations
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204:
205:
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208: """Takes emission and transition probabilities, and calculates posteriors.
209: Uses speed-up specific for Genotype data (pooling same transition rates)
210: Low-Mem: Do no save the full FWD BWD and Posterior. Use temporary
211: Arrays for saving.
212: Input:
213: Emission probabilities [k x l] (log space) (log space)
214: Transition probabilities (infinitesimal) [k x k] (normal space)
215: Initialized fwd and bwd probabilities [k x l] (log space)
216: t: Transition Matrix: [l x 3 x 3] (normal space)
217: full: Boolean whether to return everything"""
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223:
224: # Initialize Posterior and Transition Probabilities
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if (__pyx_t_7 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_7 >= __pyx_v_two_v_view.shape[0])) __pyx_t_14 = 0;
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*((double *) ( /* dim=0 */ (__pyx_v_two_v_view.data + __pyx_t_7 * __pyx_v_two_v_view.strides[0]) )) = ((*((double *) ( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_17 * __pyx_v_fwd.strides[0]) ))) + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_15 * __pyx_v_t0.strides[0]) ) + __pyx_t_13 * __pyx_v_t0.strides[1]) ) + __pyx_t_12 * __pyx_v_t0.strides[2]) ))));
+270: two_v_view[1] = f_l + t0[i, 1, 0] # Going into 0 State
__pyx_t_12 = __pyx_v_i;
__pyx_t_13 = 1;
__pyx_t_15 = 0;
__pyx_t_14 = -1;
if (__pyx_t_12 < 0) {
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if (__pyx_t_13 < 0) {
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} else if (unlikely(__pyx_t_13 >= __pyx_v_t0.shape[1])) __pyx_t_14 = 1;
if (__pyx_t_15 < 0) {
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if (unlikely(__pyx_t_14 != -1)) {
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}
*((double *) ( /* dim=0 */ (__pyx_v_two_v_view.data + __pyx_t_17 * __pyx_v_two_v_view.strides[0]) )) = (__pyx_v_f_l + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_12 * __pyx_v_t0.strides[0]) ) + __pyx_t_13 * __pyx_v_t0.strides[1]) ) + __pyx_t_15 * __pyx_v_t0.strides[2]) ))));
+271: tmp[0] = e_prob0[0, i] + logsumexp(two_v_view)
__pyx_t_15 = 0;
__pyx_t_13 = __pyx_v_i;
__pyx_t_14 = -1;
if (__pyx_t_15 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_15 >= __pyx_v_e_prob0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_13 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_13 >= __pyx_v_e_prob0.shape[1])) __pyx_t_14 = 1;
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__pyx_t_12 = 0;
__pyx_t_14 = -1;
if (__pyx_t_12 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_12 >= __pyx_v_tmp.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
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*((double *) ( /* dim=0 */ (__pyx_v_tmp.data + __pyx_t_12 * __pyx_v_tmp.strides[0]) )) = ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_e_prob0.data + __pyx_t_15 * __pyx_v_e_prob0.strides[0]) ) + __pyx_t_13 * __pyx_v_e_prob0.strides[1]) ))) + __pyx_f_8hapsburg_5cfunc_logsumexp(__pyx_v_two_v_view));
272:
273: ### Do the other states
274: # Preprocessing:
+275: three_v_view[0] = fwd[0] + t0[i, 0, 1] # Coming from 0 State
__pyx_t_13 = 0;
__pyx_t_14 = -1;
if (__pyx_t_13 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_13 >= __pyx_v_fwd.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
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}
__pyx_t_15 = __pyx_v_i;
__pyx_t_21 = 0;
__pyx_t_22 = 1;
__pyx_t_14 = -1;
if (__pyx_t_15 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_15 >= __pyx_v_t0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_21 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_21 >= __pyx_v_t0.shape[1])) __pyx_t_14 = 1;
if (__pyx_t_22 < 0) {
__pyx_t_14 = 2;
} else if (unlikely(__pyx_t_22 >= __pyx_v_t0.shape[2])) __pyx_t_14 = 2;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
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}
__pyx_t_23 = 0;
__pyx_t_14 = -1;
if (__pyx_t_23 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_23 >= __pyx_v_three_v_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
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}
*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_23 * __pyx_v_three_v_view.strides[0]) )) = ((*((double *) ( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_13 * __pyx_v_fwd.strides[0]) ))) + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_15 * __pyx_v_t0.strides[0]) ) + __pyx_t_21 * __pyx_v_t0.strides[1]) ) + __pyx_t_22 * __pyx_v_t0.strides[2]) ))));
+276: three_v_view[1] = f_l + t0[i, 1, 2] # Coming from other ROH State
__pyx_t_22 = __pyx_v_i;
__pyx_t_21 = 1;
__pyx_t_15 = 2;
__pyx_t_14 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_t0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_21 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_21 >= __pyx_v_t0.shape[1])) __pyx_t_14 = 1;
if (__pyx_t_15 < 0) {
__pyx_t_14 = 2;
} else if (unlikely(__pyx_t_15 >= __pyx_v_t0.shape[2])) __pyx_t_14 = 2;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 276, __pyx_L1_error)
}
__pyx_t_13 = 1;
__pyx_t_14 = -1;
if (__pyx_t_13 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_13 >= __pyx_v_three_v_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 276, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_13 * __pyx_v_three_v_view.strides[0]) )) = (__pyx_v_f_l + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_22 * __pyx_v_t0.strides[0]) ) + __pyx_t_21 * __pyx_v_t0.strides[1]) ) + __pyx_t_15 * __pyx_v_t0.strides[2]) ))));
277:
+278: for j in range(1, n_states): # Do the final run over all states
__pyx_t_14 = __pyx_v_n_states;
__pyx_t_18 = __pyx_t_14;
for (__pyx_t_19 = 1; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {
__pyx_v_j = __pyx_t_19;
+279: three_v_view[2] = fwd[j] + stay
__pyx_t_15 = __pyx_v_j;
__pyx_t_20 = -1;
if (__pyx_t_15 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_15 >= __pyx_v_fwd.shape[0])) __pyx_t_20 = 0;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 279, __pyx_L1_error)
}
__pyx_t_21 = 2;
__pyx_t_20 = -1;
if (__pyx_t_21 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_21 >= __pyx_v_three_v_view.shape[0])) __pyx_t_20 = 0;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 279, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_21 * __pyx_v_three_v_view.strides[0]) )) = ((*((double *) ( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_15 * __pyx_v_fwd.strides[0]) ))) + __pyx_v_stay);
+280: tmp[j] = e_prob0[j, i] + logsumexp(three_v_view)
__pyx_t_15 = __pyx_v_j;
__pyx_t_22 = __pyx_v_i;
__pyx_t_20 = -1;
if (__pyx_t_15 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_15 >= __pyx_v_e_prob0.shape[0])) __pyx_t_20 = 0;
if (__pyx_t_22 < 0) {
__pyx_t_20 = 1;
} else if (unlikely(__pyx_t_22 >= __pyx_v_e_prob0.shape[1])) __pyx_t_20 = 1;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 280, __pyx_L1_error)
}
__pyx_t_24 = __pyx_v_j;
__pyx_t_20 = -1;
if (__pyx_t_24 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_24 >= __pyx_v_tmp.shape[0])) __pyx_t_20 = 0;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 280, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_tmp.data + __pyx_t_24 * __pyx_v_tmp.strides[0]) )) = ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_e_prob0.data + __pyx_t_15 * __pyx_v_e_prob0.strides[0]) ) + __pyx_t_22 * __pyx_v_e_prob0.strides[1]) ))) + __pyx_f_8hapsburg_5cfunc_logsumexp(__pyx_v_three_v_view));
}
281:
282: ### Make tmp new fwd vec:
+283: for j in range(0, n_states):
__pyx_t_14 = __pyx_v_n_states;
__pyx_t_18 = __pyx_t_14;
for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {
__pyx_v_j = __pyx_t_19;
+284: fwd[j] = tmp[j]
__pyx_t_22 = __pyx_v_j;
__pyx_t_20 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_tmp.shape[0])) __pyx_t_20 = 0;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 284, __pyx_L1_error)
}
__pyx_t_15 = __pyx_v_j;
__pyx_t_20 = -1;
if (__pyx_t_15 < 0) {
__pyx_t_20 = 0;
} else if (unlikely(__pyx_t_15 >= __pyx_v_fwd.shape[0])) __pyx_t_20 = 0;
if (unlikely(__pyx_t_20 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_20);
__PYX_ERR(0, 284, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_15 * __pyx_v_fwd.strides[0]) )) = (*((double *) ( /* dim=0 */ (__pyx_v_tmp.data + __pyx_t_22 * __pyx_v_tmp.strides[0]) )));
}
+285: post_view[i] = fwd[0] # Add to 0-State Posterior
__pyx_t_22 = 0;
__pyx_t_14 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_fwd.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 285, __pyx_L1_error)
}
__pyx_t_25 = __pyx_v_i;
__pyx_t_14 = -1;
if (__pyx_t_25 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_25 >= __pyx_v_post_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 285, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_post_view.data + __pyx_t_25 * __pyx_v_post_view.strides[0]) )) = (*((double *) ( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_22 * __pyx_v_fwd.strides[0]) )));
}
286:
287: ### Get total log likelihood
+288: for k in range(n_states): # Simply sum the two 1D arrays
__pyx_t_8 = __pyx_v_n_states;
__pyx_t_10 = __pyx_t_8;
for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {
__pyx_v_k = __pyx_t_11;
+289: trans_ll_view1[k] = fwd[k] + bwd[k]
__pyx_t_22 = __pyx_v_k;
__pyx_t_14 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_fwd.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 289, __pyx_L1_error)
}
__pyx_t_26 = __pyx_v_k;
__pyx_t_14 = -1;
if (__pyx_t_26 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_26 >= __pyx_v_bwd.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 289, __pyx_L1_error)
}
__pyx_t_27 = __pyx_v_k;
__pyx_t_14 = -1;
if (__pyx_t_27 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_27 >= __pyx_v_trans_ll_view1.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 289, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_trans_ll_view1.data + __pyx_t_27 * __pyx_v_trans_ll_view1.strides[0]) )) = ((*((double *) ( /* dim=0 */ (__pyx_v_fwd.data + __pyx_t_22 * __pyx_v_fwd.strides[0]) ))) + (*((double *) ( /* dim=0 */ (__pyx_v_bwd.data + __pyx_t_26 * __pyx_v_bwd.strides[0]) ))));
}
+290: tot_ll = logsumexp(trans_ll_view1)
__pyx_v_tot_ll = __pyx_f_8hapsburg_5cfunc_logsumexp(__pyx_v_trans_ll_view1);
291:
292: #############################
293: ### Do the Backward Algorithm
294: ## last0-State Posterior
+295: post_view[n_loci-1] = post_view[n_loci-1] + bwd[0] - tot_ll
__pyx_t_26 = (__pyx_v_n_loci - 1);
__pyx_t_8 = -1;
if (__pyx_t_26 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_26 >= __pyx_v_post_view.shape[0])) __pyx_t_8 = 0;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 295, __pyx_L1_error)
}
__pyx_t_22 = 0;
__pyx_t_8 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_bwd.shape[0])) __pyx_t_8 = 0;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 295, __pyx_L1_error)
}
__pyx_t_28 = (__pyx_v_n_loci - 1);
__pyx_t_8 = -1;
if (__pyx_t_28 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_28 >= __pyx_v_post_view.shape[0])) __pyx_t_8 = 0;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 295, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_post_view.data + __pyx_t_28 * __pyx_v_post_view.strides[0]) )) = (((*((double *) ( /* dim=0 */ (__pyx_v_post_view.data + __pyx_t_26 * __pyx_v_post_view.strides[0]) ))) + (*((double *) ( /* dim=0 */ (__pyx_v_bwd.data + __pyx_t_22 * __pyx_v_bwd.strides[0]) )))) - __pyx_v_tot_ll);
296:
+297: for i in range(n_loci-1, 0, -1): # Run backward recursion
for (__pyx_t_11 = (__pyx_v_n_loci - 1); __pyx_t_11 > 0; __pyx_t_11-=1) {
__pyx_v_i = __pyx_t_11;
+298: stay = log(t[i, 1, 1] - t[i, 1, 2])
__pyx_t_22 = __pyx_v_i;
__pyx_t_26 = 1;
__pyx_t_29 = 1;
__pyx_t_8 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_t.shape[0])) __pyx_t_8 = 0;
if (__pyx_t_26 < 0) {
__pyx_t_8 = 1;
} else if (unlikely(__pyx_t_26 >= __pyx_v_t.shape[1])) __pyx_t_8 = 1;
if (__pyx_t_29 < 0) {
__pyx_t_8 = 2;
} else if (unlikely(__pyx_t_29 >= __pyx_v_t.shape[2])) __pyx_t_8 = 2;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 298, __pyx_L1_error)
}
__pyx_t_30 = __pyx_v_i;
__pyx_t_31 = 1;
__pyx_t_32 = 2;
__pyx_t_8 = -1;
if (__pyx_t_30 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_30 >= __pyx_v_t.shape[0])) __pyx_t_8 = 0;
if (__pyx_t_31 < 0) {
__pyx_t_8 = 1;
} else if (unlikely(__pyx_t_31 >= __pyx_v_t.shape[1])) __pyx_t_8 = 1;
if (__pyx_t_32 < 0) {
__pyx_t_8 = 2;
} else if (unlikely(__pyx_t_32 >= __pyx_v_t.shape[2])) __pyx_t_8 = 2;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 298, __pyx_L1_error)
}
__pyx_v_stay = log(((*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t.data + __pyx_t_22 * __pyx_v_t.strides[0]) ) + __pyx_t_26 * __pyx_v_t.strides[1]) ) + __pyx_t_29 * __pyx_v_t.strides[2]) ))) - (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t.data + __pyx_t_30 * __pyx_v_t.strides[0]) ) + __pyx_t_31 * __pyx_v_t.strides[1]) ) + __pyx_t_32 * __pyx_v_t.strides[2]) )))));
299:
+300: for k in range(1, n_states): # Calculate logsum of ROH states:
__pyx_t_8 = __pyx_v_n_states;
__pyx_t_10 = __pyx_t_8;
for (__pyx_t_19 = 1; __pyx_t_19 < __pyx_t_10; __pyx_t_19+=1) {
__pyx_v_k = __pyx_t_19;
+301: trans_ll_view[k-1] = bwd[k] + e_prob0[k, i]
__pyx_t_32 = __pyx_v_k;
__pyx_t_14 = -1;
if (__pyx_t_32 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_32 >= __pyx_v_bwd.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
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__pyx_t_31 = __pyx_v_k;
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__pyx_t_14 = -1;
if (__pyx_t_31 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_31 >= __pyx_v_e_prob0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_30 < 0) {
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} else if (unlikely(__pyx_t_30 >= __pyx_v_e_prob0.shape[1])) __pyx_t_14 = 1;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
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}
__pyx_t_29 = (__pyx_v_k - 1);
__pyx_t_14 = -1;
if (__pyx_t_29 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_29 >= __pyx_v_trans_ll_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
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}
*((double *) ( /* dim=0 */ (__pyx_v_trans_ll_view.data + __pyx_t_29 * __pyx_v_trans_ll_view.strides[0]) )) = ((*((double *) ( /* dim=0 */ (__pyx_v_bwd.data + __pyx_t_32 * __pyx_v_bwd.strides[0]) ))) + (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_e_prob0.data + __pyx_t_31 * __pyx_v_e_prob0.strides[0]) ) + __pyx_t_30 * __pyx_v_e_prob0.strides[1]) ))));
}
+302: f_l = logsumexp(trans_ll_view) # Logsum of ROH States
__pyx_v_f_l = __pyx_f_8hapsburg_5cfunc_logsumexp(__pyx_v_trans_ll_view);
303:
304: # Do the 0 State:
+305: two_v_view[0] = bwd[0] + t0[i, 0, 0] + e_prob0[0, i] # Staying in 0 State
__pyx_t_30 = 0;
__pyx_t_8 = -1;
if (__pyx_t_30 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_30 >= __pyx_v_bwd.shape[0])) __pyx_t_8 = 0;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
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}
__pyx_t_31 = __pyx_v_i;
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__pyx_t_26 = 0;
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if (__pyx_t_31 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_31 >= __pyx_v_t0.shape[0])) __pyx_t_8 = 0;
if (__pyx_t_32 < 0) {
__pyx_t_8 = 1;
} else if (unlikely(__pyx_t_32 >= __pyx_v_t0.shape[1])) __pyx_t_8 = 1;
if (__pyx_t_26 < 0) {
__pyx_t_8 = 2;
} else if (unlikely(__pyx_t_26 >= __pyx_v_t0.shape[2])) __pyx_t_8 = 2;
if (unlikely(__pyx_t_8 != -1)) {
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}
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if (__pyx_t_22 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_e_prob0.shape[0])) __pyx_t_8 = 0;
if (__pyx_t_33 < 0) {
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} else if (unlikely(__pyx_t_33 >= __pyx_v_e_prob0.shape[1])) __pyx_t_8 = 1;
if (unlikely(__pyx_t_8 != -1)) {
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}
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if (__pyx_t_34 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_34 >= __pyx_v_two_v_view.shape[0])) __pyx_t_8 = 0;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
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}
*((double *) ( /* dim=0 */ (__pyx_v_two_v_view.data + __pyx_t_34 * __pyx_v_two_v_view.strides[0]) )) = (((*((double *) ( /* dim=0 */ (__pyx_v_bwd.data + __pyx_t_30 * __pyx_v_bwd.strides[0]) ))) + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_31 * __pyx_v_t0.strides[0]) ) + __pyx_t_32 * __pyx_v_t0.strides[1]) ) + __pyx_t_26 * __pyx_v_t0.strides[2]) )))) + (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_e_prob0.data + __pyx_t_22 * __pyx_v_e_prob0.strides[0]) ) + __pyx_t_33 * __pyx_v_e_prob0.strides[1]) ))));
+306: two_v_view[1] = f_l + t0[i, 0, 1] # Going into 0 State
__pyx_t_33 = __pyx_v_i;
__pyx_t_22 = 0;
__pyx_t_26 = 1;
__pyx_t_8 = -1;
if (__pyx_t_33 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_33 >= __pyx_v_t0.shape[0])) __pyx_t_8 = 0;
if (__pyx_t_22 < 0) {
__pyx_t_8 = 1;
} else if (unlikely(__pyx_t_22 >= __pyx_v_t0.shape[1])) __pyx_t_8 = 1;
if (__pyx_t_26 < 0) {
__pyx_t_8 = 2;
} else if (unlikely(__pyx_t_26 >= __pyx_v_t0.shape[2])) __pyx_t_8 = 2;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 306, __pyx_L1_error)
}
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__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_32 >= __pyx_v_two_v_view.shape[0])) __pyx_t_8 = 0;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 306, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_two_v_view.data + __pyx_t_32 * __pyx_v_two_v_view.strides[0]) )) = (__pyx_v_f_l + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_33 * __pyx_v_t0.strides[0]) ) + __pyx_t_22 * __pyx_v_t0.strides[1]) ) + __pyx_t_26 * __pyx_v_t0.strides[2]) ))));
+307: tmp[0] = logsumexp(two_v_view)
__pyx_t_26 = 0;
__pyx_t_8 = -1;
if (__pyx_t_26 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_26 >= __pyx_v_tmp.shape[0])) __pyx_t_8 = 0;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 307, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_tmp.data + __pyx_t_26 * __pyx_v_tmp.strides[0]) )) = __pyx_f_8hapsburg_5cfunc_logsumexp(__pyx_v_two_v_view);
308:
309: ### Do the other states
310: # Preprocessing:
+311: three_v_view[0] = e_prob0[0, i] + bwd[0] + t0[i, 1, 0]
__pyx_t_22 = 0;
__pyx_t_33 = __pyx_v_i;
__pyx_t_8 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_e_prob0.shape[0])) __pyx_t_8 = 0;
if (__pyx_t_33 < 0) {
__pyx_t_8 = 1;
} else if (unlikely(__pyx_t_33 >= __pyx_v_e_prob0.shape[1])) __pyx_t_8 = 1;
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__Pyx_RaiseBufferIndexError(__pyx_t_8);
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}
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__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_31 >= __pyx_v_bwd.shape[0])) __pyx_t_8 = 0;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
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}
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__pyx_t_36 = 0;
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__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_30 >= __pyx_v_t0.shape[0])) __pyx_t_8 = 0;
if (__pyx_t_35 < 0) {
__pyx_t_8 = 1;
} else if (unlikely(__pyx_t_35 >= __pyx_v_t0.shape[1])) __pyx_t_8 = 1;
if (__pyx_t_36 < 0) {
__pyx_t_8 = 2;
} else if (unlikely(__pyx_t_36 >= __pyx_v_t0.shape[2])) __pyx_t_8 = 2;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
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}
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__pyx_t_8 = -1;
if (__pyx_t_37 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_37 >= __pyx_v_three_v_view.shape[0])) __pyx_t_8 = 0;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
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}
*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_37 * __pyx_v_three_v_view.strides[0]) )) = (((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_e_prob0.data + __pyx_t_22 * __pyx_v_e_prob0.strides[0]) ) + __pyx_t_33 * __pyx_v_e_prob0.strides[1]) ))) + (*((double *) ( /* dim=0 */ (__pyx_v_bwd.data + __pyx_t_31 * __pyx_v_bwd.strides[0]) )))) + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_30 * __pyx_v_t0.strides[0]) ) + __pyx_t_35 * __pyx_v_t0.strides[1]) ) + __pyx_t_36 * __pyx_v_t0.strides[2]) ))));
+312: three_v_view[1] = f_l + t0[i, 1, 2] # Coming from other ROH State
__pyx_t_36 = __pyx_v_i;
__pyx_t_35 = 1;
__pyx_t_30 = 2;
__pyx_t_8 = -1;
if (__pyx_t_36 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_36 >= __pyx_v_t0.shape[0])) __pyx_t_8 = 0;
if (__pyx_t_35 < 0) {
__pyx_t_8 = 1;
} else if (unlikely(__pyx_t_35 >= __pyx_v_t0.shape[1])) __pyx_t_8 = 1;
if (__pyx_t_30 < 0) {
__pyx_t_8 = 2;
} else if (unlikely(__pyx_t_30 >= __pyx_v_t0.shape[2])) __pyx_t_8 = 2;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 312, __pyx_L1_error)
}
__pyx_t_31 = 1;
__pyx_t_8 = -1;
if (__pyx_t_31 < 0) {
__pyx_t_8 = 0;
} else if (unlikely(__pyx_t_31 >= __pyx_v_three_v_view.shape[0])) __pyx_t_8 = 0;
if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
__PYX_ERR(0, 312, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_31 * __pyx_v_three_v_view.strides[0]) )) = (__pyx_v_f_l + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t0.data + __pyx_t_36 * __pyx_v_t0.strides[0]) ) + __pyx_t_35 * __pyx_v_t0.strides[1]) ) + __pyx_t_30 * __pyx_v_t0.strides[2]) ))));
313:
+314: for j in range(1, n_states): # Do the final run over all states
__pyx_t_8 = __pyx_v_n_states;
__pyx_t_10 = __pyx_t_8;
for (__pyx_t_19 = 1; __pyx_t_19 < __pyx_t_10; __pyx_t_19+=1) {
__pyx_v_j = __pyx_t_19;
+315: three_v_view[2] = e_prob0[j, i] + bwd[j] + stay
__pyx_t_30 = __pyx_v_j;
__pyx_t_35 = __pyx_v_i;
__pyx_t_14 = -1;
if (__pyx_t_30 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_30 >= __pyx_v_e_prob0.shape[0])) __pyx_t_14 = 0;
if (__pyx_t_35 < 0) {
__pyx_t_14 = 1;
} else if (unlikely(__pyx_t_35 >= __pyx_v_e_prob0.shape[1])) __pyx_t_14 = 1;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 315, __pyx_L1_error)
}
__pyx_t_36 = __pyx_v_j;
__pyx_t_14 = -1;
if (__pyx_t_36 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_36 >= __pyx_v_bwd.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 315, __pyx_L1_error)
}
__pyx_t_33 = 2;
__pyx_t_14 = -1;
if (__pyx_t_33 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_33 >= __pyx_v_three_v_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 315, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_33 * __pyx_v_three_v_view.strides[0]) )) = (((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_e_prob0.data + __pyx_t_30 * __pyx_v_e_prob0.strides[0]) ) + __pyx_t_35 * __pyx_v_e_prob0.strides[1]) ))) + (*((double *) ( /* dim=0 */ (__pyx_v_bwd.data + __pyx_t_36 * __pyx_v_bwd.strides[0]) )))) + __pyx_v_stay);
+316: tmp[j] = logsumexp(three_v_view) # Fill in the backward Probability
__pyx_t_36 = __pyx_v_j;
__pyx_t_14 = -1;
if (__pyx_t_36 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_36 >= __pyx_v_tmp.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 316, __pyx_L1_error)
}
*((double *) ( /* dim=0 */ (__pyx_v_tmp.data + __pyx_t_36 * __pyx_v_tmp.strides[0]) )) = __pyx_f_8hapsburg_5cfunc_logsumexp(__pyx_v_three_v_view);
}
317:
318: ### Make tmp new bwd vec:
+319: for j in range(0, n_states):
__pyx_t_8 = __pyx_v_n_states;
__pyx_t_10 = __pyx_t_8;
for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_10; __pyx_t_19+=1) {
__pyx_v_j = __pyx_t_19;
+320: bwd[j] = tmp[j]
__pyx_t_35 = __pyx_v_j;
__pyx_t_14 = -1;
if (__pyx_t_35 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_35 >= __pyx_v_tmp.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
__PYX_ERR(0, 320, __pyx_L1_error)
}
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__pyx_t_14 = -1;
if (__pyx_t_30 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_30 >= __pyx_v_bwd.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_14);
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*((double *) ( /* dim=0 */ (__pyx_v_bwd.data + __pyx_t_30 * __pyx_v_bwd.strides[0]) )) = (*((double *) ( /* dim=0 */ (__pyx_v_tmp.data + __pyx_t_35 * __pyx_v_tmp.strides[0]) )));
}
321:
322: ### Finalize the 0 Posterior
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__pyx_t_35 = (__pyx_v_i - 1);
__pyx_t_8 = -1;
if (__pyx_t_35 < 0) {
__pyx_t_8 = 0;
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__Pyx_RaiseBufferIndexError(__pyx_t_8);
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__pyx_t_8 = 0;
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if (unlikely(__pyx_t_8 != -1)) {
__Pyx_RaiseBufferIndexError(__pyx_t_8);
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__pyx_t_8 = 0;
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*((double *) ( /* dim=0 */ (__pyx_v_post_view.data + __pyx_t_38 * __pyx_v_post_view.strides[0]) )) = (((*((double *) ( /* dim=0 */ (__pyx_v_post_view.data + __pyx_t_35 * __pyx_v_post_view.strides[0]) ))) + (*((double *) ( /* dim=0 */ (__pyx_v_bwd.data + __pyx_t_22 * __pyx_v_bwd.strides[0]) )))) - __pyx_v_tot_ll);
}
324:
+325: print(f"Total Log likelihood: {tot_ll: .3f}")
__pyx_t_2 = PyFloat_FromDouble(__pyx_v_tot_ll); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 325, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_1 = __Pyx_PyObject_Format(__pyx_t_2, __pyx_kp_u_3f); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 325, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyUnicode_Concat(__pyx_kp_u_Total_Log_likelihood, __pyx_t_1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 325, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_CallOneArg(__pyx_builtin_print, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 325, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+326: print_memory_usage() ## For MEMORY_BENCH
__Pyx_GetModuleGlobalName(__pyx_t_2, __pyx_n_s_print_memory_usage); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 326, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_2))) { __pyx_t_3 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_3)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_3); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_1 = (__pyx_t_3) ? __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_t_3) : __Pyx_PyObject_CallNoArg(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 326, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
327:
+328: if full==False:
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378:
379: # Do the States from collapsed states
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*((long *) ( /* dim=0 */ (__pyx_v_three_vi_view.data + __pyx_t_18 * __pyx_v_three_vi_view.strides[0]) )) = (__pyx_v_m + 1);
382:
+383: two_v_view[1] = v + t_mat0[i, 1, 0]
__pyx_t_20 = __pyx_v_i;
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*((double *) ( /* dim=0 */ (__pyx_v_two_v_view.data + __pyx_t_23 * __pyx_v_two_v_view.strides[0]) )) = (__pyx_v_v + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t_mat0.data + __pyx_t_20 * __pyx_v_t_mat0.strides[0]) ) + __pyx_t_21 * __pyx_v_t_mat0.strides[1]) ) + __pyx_t_22 * __pyx_v_t_mat0.strides[2]) ))));
+384: three_v_view[1] = v + t_mat0[i, 1, 2] # Move in from other ROH
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__pyx_t_21 = 1;
__pyx_t_20 = 2;
__pyx_t_14 = -1;
if (__pyx_t_22 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_22 >= __pyx_v_t_mat0.shape[0])) __pyx_t_14 = 0;
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*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_24 * __pyx_v_three_v_view.strides[0]) )) = (__pyx_v_v + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t_mat0.data + __pyx_t_22 * __pyx_v_t_mat0.strides[0]) ) + __pyx_t_21 * __pyx_v_t_mat0.strides[1]) ) + __pyx_t_20 * __pyx_v_t_mat0.strides[2]) ))));
385:
386: ### Do the zero State
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__pyx_t_20 = 0;
__pyx_t_21 = (__pyx_v_i - 1);
__pyx_t_14 = -1;
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if (__pyx_t_21 < 0) {
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} else if (unlikely(__pyx_t_21 >= __pyx_v_mp.shape[1])) __pyx_t_14 = 1;
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__pyx_t_25 = 0;
__pyx_t_26 = 0;
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if (__pyx_t_26 < 0) {
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} else if (unlikely(__pyx_t_26 >= __pyx_v_t_mat0.shape[2])) __pyx_t_14 = 2;
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*((double *) ( /* dim=0 */ (__pyx_v_two_v_view.data + __pyx_t_27 * __pyx_v_two_v_view.strides[0]) )) = ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_mp.data + __pyx_t_20 * __pyx_v_mp.strides[0]) ) + __pyx_t_21 * __pyx_v_mp.strides[1]) ))) + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t_mat0.data + __pyx_t_22 * __pyx_v_t_mat0.strides[0]) ) + __pyx_t_25 * __pyx_v_t_mat0.strides[1]) ) + __pyx_t_26 * __pyx_v_t_mat0.strides[2]) ))));
388:
+389: m = argmax(two_v_view) ### Do a Maximum
__pyx_v_m = __pyx_f_8hapsburg_5cfunc_argmax(__pyx_v_two_v_view);
+390: v = two_v_view[m]
__pyx_t_26 = __pyx_v_m;
__pyx_t_14 = -1;
if (__pyx_t_26 < 0) {
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__pyx_v_v = (*((double *) ( /* dim=0 */ (__pyx_v_two_v_view.data + __pyx_t_26 * __pyx_v_two_v_view.strides[0]) )));
+391: mp[0, i] = v + e_prob0[0, i] ### Set Max. Probability
__pyx_t_26 = 0;
__pyx_t_25 = __pyx_v_i;
__pyx_t_14 = -1;
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*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_mp.data + __pyx_t_22 * __pyx_v_mp.strides[0]) ) + __pyx_t_21 * __pyx_v_mp.strides[1]) )) = (__pyx_v_v + (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_e_prob0.data + __pyx_t_26 * __pyx_v_e_prob0.strides[0]) ) + __pyx_t_25 * __pyx_v_e_prob0.strides[1]) ))));
+392: pt[0, i] = two_vi_view[m] ### Set Pointer for Backtrace
__pyx_t_25 = __pyx_v_m;
__pyx_t_14 = -1;
if (__pyx_t_25 < 0) {
__pyx_t_14 = 0;
} else if (unlikely(__pyx_t_25 >= __pyx_v_two_vi_view.shape[0])) __pyx_t_14 = 0;
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*((long *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_pt.data + __pyx_t_26 * __pyx_v_pt.strides[0]) ) + __pyx_t_20 * __pyx_v_pt.strides[1]) )) = (*((long *) ( /* dim=0 */ (__pyx_v_two_vi_view.data + __pyx_t_25 * __pyx_v_two_vi_view.strides[0]) )));
393:
394: ### Do the other States
+395: three_v_view[0] = mp[0, i - 1] + t_mat0[i, 0, 1] # Move from 0 State
__pyx_t_25 = 0;
__pyx_t_28 = (__pyx_v_i - 1);
__pyx_t_14 = -1;
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if (__pyx_t_28 < 0) {
__pyx_t_14 = 1;
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__pyx_t_29 = __pyx_v_i;
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__pyx_t_31 = 1;
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__pyx_t_32 = 0;
__pyx_t_14 = -1;
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} else if (unlikely(__pyx_t_32 >= __pyx_v_three_v_view.shape[0])) __pyx_t_14 = 0;
if (unlikely(__pyx_t_14 != -1)) {
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*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_32 * __pyx_v_three_v_view.strides[0]) )) = ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_mp.data + __pyx_t_25 * __pyx_v_mp.strides[0]) ) + __pyx_t_28 * __pyx_v_mp.strides[1]) ))) + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t_mat0.data + __pyx_t_29 * __pyx_v_t_mat0.strides[0]) ) + __pyx_t_30 * __pyx_v_t_mat0.strides[1]) ) + __pyx_t_31 * __pyx_v_t_mat0.strides[2]) ))));
396:
+397: for k in range(1, n_states): # Find Maximum
__pyx_t_14 = __pyx_v_n_states;
__pyx_t_33 = __pyx_t_14;
for (__pyx_t_34 = 1; __pyx_t_34 < __pyx_t_33; __pyx_t_34+=1) {
__pyx_v_k = __pyx_t_34;
+398: three_v_view[2] = mp[k, i - 1] + t_mat0[i, 1, 1] # The Stay State
__pyx_t_31 = __pyx_v_k;
__pyx_t_30 = (__pyx_v_i - 1);
__pyx_t_35 = -1;
if (__pyx_t_31 < 0) {
__pyx_t_35 = 0;
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__pyx_t_29 = __pyx_v_i;
__pyx_t_28 = 1;
__pyx_t_25 = 1;
__pyx_t_35 = -1;
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__pyx_t_35 = -1;
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if (unlikely(__pyx_t_35 != -1)) {
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*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_36 * __pyx_v_three_v_view.strides[0]) )) = ((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_mp.data + __pyx_t_31 * __pyx_v_mp.strides[0]) ) + __pyx_t_30 * __pyx_v_mp.strides[1]) ))) + (*((double *) ( /* dim=2 */ (( /* dim=1 */ (( /* dim=0 */ (__pyx_v_t_mat0.data + __pyx_t_29 * __pyx_v_t_mat0.strides[0]) ) + __pyx_t_28 * __pyx_v_t_mat0.strides[1]) ) + __pyx_t_25 * __pyx_v_t_mat0.strides[2]) ))));
+399: three_vi_view[2] = k
__pyx_t_25 = 2;
__pyx_t_35 = -1;
if (__pyx_t_25 < 0) {
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*((long *) ( /* dim=0 */ (__pyx_v_three_vi_view.data + __pyx_t_25 * __pyx_v_three_vi_view.strides[0]) )) = __pyx_v_k;
400:
+401: m = argmax(three_v_view) ### Do a Maximum
__pyx_v_m = __pyx_f_8hapsburg_5cfunc_argmax(__pyx_v_three_v_view);
+402: v = three_v_view[m]
__pyx_t_28 = __pyx_v_m;
__pyx_t_35 = -1;
if (__pyx_t_28 < 0) {
__pyx_t_35 = 0;
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__pyx_v_v = (*((double *) ( /* dim=0 */ (__pyx_v_three_v_view.data + __pyx_t_28 * __pyx_v_three_v_view.strides[0]) )));
403:
+404: mp[k, i] = v + e_prob0[k, i] ### Set Max. Probability
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__pyx_t_29 = __pyx_v_i;
__pyx_t_35 = -1;
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*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_mp.data + __pyx_t_30 * __pyx_v_mp.strides[0]) ) + __pyx_t_31 * __pyx_v_mp.strides[1]) )) = (__pyx_v_v + (*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_e_prob0.data + __pyx_t_28 * __pyx_v_e_prob0.strides[0]) ) + __pyx_t_29 * __pyx_v_e_prob0.strides[1]) ))));
+405: pt[k, i] = three_vi_view[m] ### Set Pointer for Backtrace
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__pyx_t_35 = -1;
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__pyx_t_35 = -1;
if (__pyx_t_28 < 0) {
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}
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406:
407: ### Do the trace back
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409:
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412:
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for (__pyx_t_12 = (__pyx_v_n_loci - 1); __pyx_t_12 > 0; __pyx_t_12-=1) {
__pyx_v_i = __pyx_t_12;
414: # Always th pointer to the previous path
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__pyx_t_10 = -1;
if (__pyx_t_39 < 0) {
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__Pyx_RaiseBufferIndexError(__pyx_t_10);
__PYX_ERR(0, 415, __pyx_L1_error)
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416:
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