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micropython-ulab/code/numerical.c
Zoltán Vörös 3379f0b897 fixed linking error with sin/sqrt
2019-11-01 14:42:08 +01:00

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/*
* This file is part of the micropython-ulab project,
*
* https://github.com/v923z/micropython-ulab
*
* The MIT License (MIT)
*
* Copyright (c) 2019 Zoltán Vörös
*/
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include "py/obj.h"
#include "py/runtime.h"
#include "py/builtin.h"
#include "py/misc.h"
#include "numerical.h"
enum NUMERICAL_FUNCTION_TYPE {
NUMERICAL_MIN,
NUMERICAL_MAX,
NUMERICAL_ARGMIN,
NUMERICAL_ARGMAX,
NUMERICAL_SUM,
NUMERICAL_MEAN,
NUMERICAL_STD,
};
mp_obj_t numerical_linspace(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj) } },
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj) } },
{ MP_QSTR_num, MP_ARG_INT, {.u_int = 50} },
{ MP_QSTR_endpoint, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_true_obj)} },
{ MP_QSTR_retstep, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_false_obj)} },
{ MP_QSTR_dtype, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = NDARRAY_FLOAT} },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(2, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
uint16_t len = args[2].u_int;
if(len < 2) {
mp_raise_ValueError("number of points must be at least 2");
}
mp_float_t value, step;
value = mp_obj_get_float(args[0].u_obj);
uint8_t typecode = args[5].u_int;
if(args[3].u_obj == mp_const_true) step = (mp_obj_get_float(args[1].u_obj)-value)/(len-1);
else step = (mp_obj_get_float(args[1].u_obj)-value)/len;
ndarray_obj_t *ndarray = create_new_ndarray(1, len, typecode);
if(typecode == NDARRAY_UINT8) {
uint8_t *array = (uint8_t *)ndarray->array->items;
for(size_t i=0; i < len; i++, value += step) array[i] = (uint8_t)value;
} else if(typecode == NDARRAY_INT8) {
int8_t *array = (int8_t *)ndarray->array->items;
for(size_t i=0; i < len; i++, value += step) array[i] = (int8_t)value;
} else if(typecode == NDARRAY_UINT16) {
uint16_t *array = (uint16_t *)ndarray->array->items;
for(size_t i=0; i < len; i++, value += step) array[i] = (uint16_t)value;
} else if(typecode == NDARRAY_INT16) {
int16_t *array = (int16_t *)ndarray->array->items;
for(size_t i=0; i < len; i++, value += step) array[i] = (int16_t)value;
} else {
mp_float_t *array = (mp_float_t *)ndarray->array->items;
for(size_t i=0; i < len; i++, value += step) array[i] = value;
}
if(args[4].u_obj == mp_const_false) {
return MP_OBJ_FROM_PTR(ndarray);
} else {
mp_obj_t tuple[2];
tuple[0] = ndarray;
tuple[1] = mp_obj_new_float(step);
return mp_obj_new_tuple(2, tuple);
}
}
mp_obj_t numerical_sum_mean_std_array(mp_obj_t oin, uint8_t optype) {
mp_float_t value, sum = 0.0, sq_sum = 0.0;
mp_obj_iter_buf_t iter_buf;
mp_obj_t item, iterable = mp_getiter(oin, &iter_buf);
mp_int_t len = mp_obj_get_int(mp_obj_len(oin));
while ((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
value = mp_obj_get_float(item);
sum += value;
if(optype == NUMERICAL_STD) {
sq_sum += value*value;
}
}
if(optype == NUMERICAL_SUM) {
return mp_obj_new_float(sum);
} else if(optype == NUMERICAL_MEAN) {
return mp_obj_new_float(sum/len);
} else {
sum /= len; // this is now the mean!
return mp_obj_new_float(MICROPY_FLOAT_C_FUN(sqrt)(sq_sum/len-sum*sum));
}
}
STATIC mp_float_t numerical_sum_mean_std_single_line(void *data, size_t start, size_t stop,
size_t stride, uint8_t typecode, uint8_t optype) {
mp_float_t sum = 0.0, sq_sum = 0.0, value;
size_t len = 0;
for(size_t i=start; i < stop; i+=stride, len++) {
value = ndarray_get_float_value(data, typecode, i);
sum += value;
if(optype == NUMERICAL_STD) {
sq_sum += value*value;
}
}
if(len == 0) {
mp_raise_ValueError("data length is 0!");
}
if(optype == NUMERICAL_SUM) {
return sum;
} else if(optype == NUMERICAL_MEAN) {
return sum/len;
} else {
sum /= len; // this is now the mean!
return MICROPY_FLOAT_C_FUN(sqrt)(sq_sum/len-sum*sum);
}
}
STATIC mp_obj_t numerical_sum_mean_std_matrix(mp_obj_t oin, mp_obj_t axis, uint8_t optype) {
ndarray_obj_t *in = MP_OBJ_TO_PTR(oin);
if((axis == mp_const_none) || (in->m == 1) || (in->n == 1)) {
// return the value for the flattened array
return mp_obj_new_float(numerical_sum_mean_std_single_line(in->array->items, 0,
in->array->len, 1, in->array->typecode, optype));
} else {
uint8_t _axis = mp_obj_get_int(axis);
size_t m = (_axis == 0) ? 1 : in->m;
size_t n = (_axis == 0) ? in->n : 1;
size_t len = in->array->len;
mp_float_t sms;
// TODO: pass in->array->typcode to create_new_ndarray
ndarray_obj_t *out = create_new_ndarray(m, n, NDARRAY_FLOAT);
// TODO: these two cases could probably be combined in a more elegant fashion...
if(_axis == 0) { // vertical
for(size_t i=0; i < n; i++) {
sms = numerical_sum_mean_std_single_line(in->array->items, i, len,
n, in->array->typecode, optype);
((float_t *)out->array->items)[i] = sms;
}
} else { // horizontal
for(size_t i=0; i < m; i++) {
sms = numerical_sum_mean_std_single_line(in->array->items, i*in->n,
(i+1)*in->n, 1, in->array->typecode, optype);
((float_t *)out->array->items)[i] = sms;
}
}
return MP_OBJ_FROM_PTR(out);
}
}
size_t numerical_argmin_argmax_array(ndarray_obj_t *in, size_t start,
size_t stop, size_t stride, uint8_t op) {
size_t best_idx = start;
if(in->array->typecode == NDARRAY_UINT8) {
ARG_MIN_LOOP(in, uint8_t, start, stop, stride, op);
} else if(in->array->typecode == NDARRAY_INT8) {
ARG_MIN_LOOP(in, int8_t, start, stop, stride, op);
} else if(in->array->typecode == NDARRAY_UINT16) {
ARG_MIN_LOOP(in, uint16_t, start, stop, stride, op);
} else if(in->array->typecode == NDARRAY_INT16) {
ARG_MIN_LOOP(in, uint16_t, start, stop, stride, op);
} else if(in->array->typecode == NDARRAY_FLOAT) {
ARG_MIN_LOOP(in, mp_float_t, start, stop, stride, op);
}
return best_idx;
}
void copy_value_into_ndarray(ndarray_obj_t *target, ndarray_obj_t *source, size_t target_idx, size_t source_idx) {
// since we are simply copying, it doesn't matter, whether the arrays are signed or unsigned,
// we can cast them in any way we like
// This could also be done with byte copies. I don't know, whether that would have any benefits
if((target->array->typecode == NDARRAY_UINT8) || (target->array->typecode == NDARRAY_INT8)) {
((uint8_t *)target->array->items)[target_idx] = ((uint8_t *)source->array->items)[source_idx];
} else if((target->array->typecode == NDARRAY_UINT16) || (target->array->typecode == NDARRAY_INT16)) {
((uint16_t *)target->array->items)[target_idx] = ((uint16_t *)source->array->items)[source_idx];
} else {
((float *)target->array->items)[target_idx] = ((float *)source->array->items)[source_idx];
}
}
STATIC mp_obj_t numerical_argmin_argmax(mp_obj_t oin, mp_obj_t axis, uint8_t optype) {
if(MP_OBJ_IS_TYPE(oin, &mp_type_tuple) || MP_OBJ_IS_TYPE(oin, &mp_type_list) ||
MP_OBJ_IS_TYPE(oin, &mp_type_range)) {
// This case will work for single iterables only
size_t idx = 0, best_idx = 0;
mp_obj_iter_buf_t iter_buf;
mp_obj_t iterable = mp_getiter(oin, &iter_buf);
mp_obj_t best_obj = MP_OBJ_NULL;
mp_obj_t item;
mp_uint_t op = MP_BINARY_OP_LESS;
if((optype == NUMERICAL_ARGMAX) || (optype == NUMERICAL_MAX)) op = MP_BINARY_OP_MORE;
while ((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
if ((best_obj == MP_OBJ_NULL) || (mp_binary_op(op, item, best_obj) == mp_const_true)) {
best_obj = item;
best_idx = idx;
}
idx++;
}
if((optype == NUMERICAL_ARGMIN) || (optype == NUMERICAL_ARGMAX)) {
return MP_OBJ_NEW_SMALL_INT(best_idx);
} else {
return best_obj;
}
} else if(mp_obj_is_type(oin, &ulab_ndarray_type)) {
ndarray_obj_t *in = MP_OBJ_TO_PTR(oin);
size_t best_idx;
if((axis == mp_const_none) || (in->m == 1) || (in->n == 1)) {
// return the value for the flattened array
best_idx = numerical_argmin_argmax_array(in, 0, in->array->len, 1, optype);
if((optype == NUMERICAL_ARGMIN) || (optype == NUMERICAL_ARGMAX)) {
return MP_OBJ_NEW_SMALL_INT(best_idx);
} else {
if(in->array->typecode == NDARRAY_FLOAT) {
return mp_obj_new_float(ndarray_get_float_value(in->array->items, in->array->typecode, best_idx));
} else {
return mp_binary_get_val_array(in->array->typecode, in->array->items, best_idx);
}
}
} else { // we have to work with a full matrix here
uint8_t _axis = mp_obj_get_int(axis);
size_t m = (_axis == 0) ? 1 : in->m;
size_t n = (_axis == 0) ? in->n : 1;
size_t len = in->array->len;
ndarray_obj_t *ndarray = NULL;
if((optype == NUMERICAL_MAX) || (optype == NUMERICAL_MIN)) {
ndarray = create_new_ndarray(m, n, in->array->typecode);
} else { // argmin/argmax
// TODO: one might get away with uint8_t, if both m, and n < 255
ndarray = create_new_ndarray(m, n, NDARRAY_UINT16);
}
// TODO: these two cases could probably be combined in a more elegant fashion...
if(_axis == 0) { // vertical
for(size_t i=0; i < n; i++) {
best_idx = numerical_argmin_argmax_array(in, i, len, n, optype);
if((optype == NUMERICAL_MIN) || (optype == NUMERICAL_MAX)) {
copy_value_into_ndarray(ndarray, in, i, best_idx);
} else {
((uint16_t *)ndarray->array->items)[i] = (uint16_t)(best_idx / n);
}
}
} else { // horizontal
for(size_t i=0; i < m; i++) {
best_idx = numerical_argmin_argmax_array(in, i*in->n, (i+1)*in->n, 1, optype);
if((optype == NUMERICAL_MIN) || (optype == NUMERICAL_MAX)) {
copy_value_into_ndarray(ndarray, in, i, best_idx);
} else {
((uint16_t *)ndarray->array->items)[i] = (uint16_t)(best_idx - i*in->n);
}
}
}
return MP_OBJ_FROM_PTR(ndarray);
}
return mp_const_none;
}
mp_raise_TypeError("input type is not supported");
}
STATIC mp_obj_t numerical_function(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args, uint8_t type) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj)} } ,
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj)} },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(1, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
mp_obj_t oin = args[0].u_obj;
mp_obj_t axis = args[1].u_obj;
if((axis != mp_const_none) && (mp_obj_get_int(axis) != 0) && (mp_obj_get_int(axis) != 1)) {
// this seems to pass with False, and True...
mp_raise_ValueError("axis must be None, 0, or 1");
}
if(MP_OBJ_IS_TYPE(oin, &mp_type_tuple) || MP_OBJ_IS_TYPE(oin, &mp_type_list) ||
MP_OBJ_IS_TYPE(oin, &mp_type_range)) {
switch(type) {
case NUMERICAL_MIN:
case NUMERICAL_ARGMIN:
case NUMERICAL_MAX:
case NUMERICAL_ARGMAX:
return numerical_argmin_argmax(oin, axis, type);
case NUMERICAL_SUM:
case NUMERICAL_MEAN:
case NUMERICAL_STD:
return numerical_sum_mean_std_array(oin, type);
default: // we should never reach this point, but whatever
return mp_const_none;
}
} else if(MP_OBJ_IS_TYPE(oin, &ulab_ndarray_type)) {
switch(type) {
case NUMERICAL_MIN:
case NUMERICAL_MAX:
case NUMERICAL_ARGMIN:
case NUMERICAL_ARGMAX:
return numerical_argmin_argmax(oin, axis, type);
case NUMERICAL_SUM:
case NUMERICAL_MEAN:
case NUMERICAL_STD:
return numerical_sum_mean_std_matrix(oin, axis, type);
default:
mp_raise_NotImplementedError("operation is not implemented on ndarrays");
}
} else {
mp_raise_TypeError("input must be tuple, list, range, or ndarray");
}
return mp_const_none;
}
mp_obj_t numerical_min(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_MIN);
}
mp_obj_t numerical_max(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_MAX);
}
mp_obj_t numerical_argmin(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_ARGMIN);
}
mp_obj_t numerical_argmax(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_ARGMAX);
}
mp_obj_t numerical_sum(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_SUM);
}
mp_obj_t numerical_mean(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_MEAN);
}
mp_obj_t numerical_std(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
return numerical_function(n_args, pos_args, kw_args, NUMERICAL_STD);
}
mp_obj_t numerical_roll(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj) } },
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj) } },
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj)} },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(2, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
mp_obj_t oin = args[0].u_obj;
int16_t shift = mp_obj_get_int(args[1].u_obj);
if((args[2].u_obj != mp_const_none) &&
(mp_obj_get_int(args[2].u_obj) != 0) &&
(mp_obj_get_int(args[2].u_obj) != 1)) {
mp_raise_ValueError("axis must be None, 0, or 1");
}
ndarray_obj_t *in = MP_OBJ_TO_PTR(oin);
uint8_t _sizeof = mp_binary_get_size('@', in->array->typecode, NULL);
size_t len;
int16_t _shift;
uint8_t *array = (uint8_t *)in->array->items;
// TODO: transpose the matrix, if axis == 0. Though, that is hard on the RAM...
if(shift < 0) {
_shift = -shift;
} else {
_shift = shift;
}
if((args[2].u_obj == mp_const_none) || (mp_obj_get_int(args[2].u_obj) == 1)) { // shift horizontally
uint16_t M;
if(args[2].u_obj == mp_const_none) {
len = in->array->len;
M = 1;
} else {
len = in->n;
M = in->m;
}
_shift = _shift % len;
if(shift < 0) _shift = len - _shift;
// TODO: if(shift > len/2), we should move in the opposite direction. That would save RAM
_shift *= _sizeof;
uint8_t *tmp = m_new(uint8_t, _shift);
for(size_t m=0; m < M; m++) {
memmove(tmp, &array[m*len*_sizeof], _shift);
memmove(&array[m*len*_sizeof], &array[m*len*_sizeof+_shift], len*_sizeof-_shift);
memmove(&array[(m+1)*len*_sizeof-_shift], tmp, _shift);
}
m_del(uint8_t, tmp, _shift);
return mp_const_none;
} else {
len = in->m;
// temporary buffer
uint8_t *_data = m_new(uint8_t, _sizeof*len);
_shift = _shift % len;
if(shift < 0) _shift = len - _shift;
_shift *= _sizeof;
uint8_t *tmp = m_new(uint8_t, _shift);
for(size_t n=0; n < in->n; n++) {
for(size_t m=0; m < len; m++) {
// this loop should fill up the temporary buffer
memmove(&_data[m*_sizeof], &array[(m*in->n+n)*_sizeof], _sizeof);
}
// now, the actual shift
memmove(tmp, _data, _shift);
memmove(_data, &_data[_shift], len*_sizeof-_shift);
memmove(&_data[len*_sizeof-_shift], tmp, _shift);
for(size_t m=0; m < len; m++) {
// this loop should dump the content of the temporary buffer into data
memmove(&array[(m*in->n+n)*_sizeof], &_data[m*_sizeof], _sizeof);
}
}
m_del(uint8_t, tmp, _shift);
m_del(uint8_t, _data, _sizeof*len);
return mp_const_none;
}
}
mp_obj_t numerical_flip(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj) } },
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj)} },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(1, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
if(!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type)) {
mp_raise_TypeError("flip argument must be an ndarray");
}
if((args[1].u_obj != mp_const_none) &&
(mp_obj_get_int(args[1].u_obj) != 0) &&
(mp_obj_get_int(args[1].u_obj) != 1)) {
mp_raise_ValueError("axis must be None, 0, or 1");
}
ndarray_obj_t *in = MP_OBJ_TO_PTR(args[0].u_obj);
mp_obj_t oout = ndarray_copy(args[0].u_obj);
ndarray_obj_t *out = MP_OBJ_TO_PTR(oout);
uint8_t _sizeof = mp_binary_get_size('@', in->array->typecode, NULL);
uint8_t *array_in = (uint8_t *)in->array->items;
uint8_t *array_out = (uint8_t *)out->array->items;
size_t len;
if((args[1].u_obj == mp_const_none) || (mp_obj_get_int(args[1].u_obj) == 1)) { // flip horizontally
uint16_t M = in->m;
len = in->n;
if(args[1].u_obj == mp_const_none) { // flip flattened array
len = in->array->len;
M = 1;
}
for(size_t m=0; m < M; m++) {
for(size_t n=0; n < len; n++) {
memcpy(array_out+_sizeof*(m*len+n), array_in+_sizeof*((m+1)*len-n-1), _sizeof);
}
}
} else { // flip vertically
for(size_t m=0; m < in->m; m++) {
for(size_t n=0; n < in->n; n++) {
memcpy(array_out+_sizeof*(m*in->n+n), array_in+_sizeof*((in->m-m-1)*in->n+n), _sizeof);
}
}
}
return out;
}
mp_obj_t numerical_diff(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj) } },
{ MP_QSTR_n, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = 1 } },
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = -1 } },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(1, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
if(!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type)) {
mp_raise_TypeError("diff argument must be an ndarray");
}
ndarray_obj_t *in = MP_OBJ_TO_PTR(args[0].u_obj);
size_t increment, N, M;
if((args[2].u_int == -1) || (args[2].u_int == 1)) { // differentiate along the horizontal axis
increment = 1;
} else if(args[2].u_int == 0) { // differtiate along vertical axis
increment = in->n;
} else {
mp_raise_ValueError("axis must be -1, 0, or 1");
}
if((args[1].u_int < 0) || (args[1].u_int > 9)) {
mp_raise_ValueError("n must be between 0, and 9");
}
uint8_t n = args[1].u_int;
int8_t *stencil = m_new(int8_t, n+1);
stencil[0] = 1;
for(uint8_t i=1; i < n+1; i++) {
stencil[i] = -stencil[i-1]*(n-i+1)/i;
}
ndarray_obj_t *out;
if(increment == 1) { // differentiate along the horizontal axis
if(n >= in->n) {
out = create_new_ndarray(in->m, 0, in->array->typecode);
m_del(uint8_t, stencil, n);
return MP_OBJ_FROM_PTR(out);
}
N = in->n - n;
M = in->m;
} else { // differentiate along vertical axis
if(n >= in->m) {
out = create_new_ndarray(0, in->n, in->array->typecode);
m_del(uint8_t, stencil, n);
return MP_OBJ_FROM_PTR(out);
}
M = in->m - n;
N = in->n;
}
out = create_new_ndarray(M, N, in->array->typecode);
if(in->array->typecode == NDARRAY_UINT8) {
CALCULATE_DIFF(in, out, uint8_t, M, N, in->n, increment);
} else if(in->array->typecode == NDARRAY_INT8) {
CALCULATE_DIFF(in, out, int8_t, M, N, in->n, increment);
} else if(in->array->typecode == NDARRAY_UINT16) {
CALCULATE_DIFF(in, out, uint16_t, M, N, in->n, increment);
} else if(in->array->typecode == NDARRAY_INT16) {
CALCULATE_DIFF(in, out, int16_t, M, N, in->n, increment);
} else {
CALCULATE_DIFF(in, out, mp_float_t, M, N, in->n, increment);
}
m_del(int8_t, stencil, n);
return MP_OBJ_FROM_PTR(out);
}
/*
void heapsort(ndarray_obj_t *ndarray, size_t n, size_t increment) {
uint8_t type;
if((ndarray->array->typecode == NDARRAY_UINT8) || (ndarray->array->typecode == NDARRAY_INT8)) {
type = 0;
}
if((ndarray->array->typecode == NDARRAY_UINT16) || (ndarray->array->typecode == NDARRAY_INT16)) {
type = 1;
} else {
type = 2;
}
size_t i, j, k, l;
l = (n >> 1) + 1;
k = n;
uint8_t ui8_tmp, *ui8_array = NULL;
uint16_t ui16_tmp, *ui16_array = NULL;
mp_float_t float_tmp, *float_array = NULL;
for(;;) {
if (l > 1) {
if((ndarray->array->typecode == NDARRAY_UINT8) || (ndarray->array->typecode == NDARRAY_INT8)) {
ui8_tmp = ui8_array[--l];
}
} else {
tmp = array[k];
array[k] = array[1];
if (--k == 1) {
array[1] = tmp;
break;
}
}
i = l;
j = 2 * l;
while(j <= k) {
if(j < k && array[j] < array[j+1]) {
j++;
}
}
if(tmp < array[j]) {
array[i] = array[j];
i = j;
j <<= 1;
} else break;
}
array[i] = tmp;
}
mp_obj_t numerical_sort(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_PTR(&mp_const_none_obj) } },
{ MP_QSTR_n, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = 1 } },
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = -1 } },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(1, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
if(!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type)) {
mp_raise_TypeError("sort argument must be an ndarray");
}
return mp_const_none;
}*/
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