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circuitpython-ulab/code/numerical.c

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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) 2019-2020 Zoltán Vörös
*/
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include "py/obj.h"
#include "py/objint.h"
#include "py/runtime.h"
#include "py/builtin.h"
#include "py/misc.h"
#include "compat.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_NONE } },
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
{ MP_QSTR_num, MP_ARG_INT, {.u_int = 50} },
{ MP_QSTR_endpoint, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_TRUE} },
{ MP_QSTR_retstep, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_FALSE} },
{ 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(translate("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);
}
}
void axis_sorter(ndarray_obj_t *ndarray, mp_obj_t axis, size_t *m, size_t *n, size_t *N,
size_t *increment, size_t *len, size_t *start_inc) {
if(axis == mp_const_none) { // flatten the array
*m = 1;
*n = 1;
*len = ndarray->array->len;
*N = 1;
*increment = 1;
*start_inc = ndarray->array->len;
} else if((mp_obj_get_int(axis) == 1)) { // along the horizontal axis
*m = ndarray->m;
*n = 1;
*len = ndarray->n;
*N = ndarray->m;
*increment = 1;
*start_inc = ndarray->n;
} else { // along vertical axis
*m = 1;
*n = ndarray->n;
*len = ndarray->m;
*N = ndarray->n;
*increment = ndarray->n;
*start_inc = 1;
}
}
mp_obj_t numerical_sum_mean_std_iterable(mp_obj_t oin, uint8_t optype, size_t ddof) {
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_SUM) {
return mp_obj_new_float(sum);
} else if(optype == NUMERICAL_MEAN) {
return mp_obj_new_float(sum/len);
} else { // this should be the case of the standard deviation
// TODO: note that we could get away with a single pass, if we used the Weldorf algorithm
// That should save a fair amount of time, because we would have to extract the values only once
iterable = mp_getiter(oin, &iter_buf);
sum /= len; // this is now the mean!
while ((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
value = mp_obj_get_float(item) - sum;
sq_sum += value * value;
}
return mp_obj_new_float(MICROPY_FLOAT_C_FUN(sqrt)(sq_sum/(len-ddof)));
}
}
STATIC mp_obj_t numerical_sum_mean_ndarray(ndarray_obj_t *ndarray, mp_obj_t axis, uint8_t optype) {
size_t m, n, increment, start, start_inc, N, len;
axis_sorter(ndarray, axis, &m, &n, &N, &increment, &len, &start_inc);
ndarray_obj_t *results = create_new_ndarray(m, n, NDARRAY_FLOAT);
mp_float_t sum, sq_sum;
mp_float_t *farray = (mp_float_t *)results->array->items;
for(size_t j=0; j < N; j++) { // result index
start = j * start_inc;
sum = sq_sum = 0.0;
if(ndarray->array->typecode == NDARRAY_UINT8) {
RUN_SUM(ndarray, uint8_t, optype, len, start, increment);
} else if(ndarray->array->typecode == NDARRAY_INT8) {
RUN_SUM(ndarray, int8_t, optype, len, start, increment);
} else if(ndarray->array->typecode == NDARRAY_UINT16) {
RUN_SUM(ndarray, uint16_t, optype, len, start, increment);
} else if(ndarray->array->typecode == NDARRAY_INT16) {
RUN_SUM(ndarray, int16_t, optype, len, start, increment);
} else { // this will be mp_float_t, no need to check
RUN_SUM(ndarray, mp_float_t, optype, len, start, increment);
}
if(optype == NUMERICAL_SUM) {
farray[j] = sum;
} else { // this is the case of the mean
farray[j] = sum / len;
}
}
if(results->array->len == 1) {
return mp_obj_new_float(farray[0]);
}
return MP_OBJ_FROM_PTR(results);
}
mp_obj_t numerical_std_ndarray(ndarray_obj_t *ndarray, mp_obj_t axis, size_t ddof) {
size_t m, n, increment, start, start_inc, N, len;
mp_float_t sum, sum_sq;
axis_sorter(ndarray, axis, &m, &n, &N, &increment, &len, &start_inc);
if(ddof > len) {
mp_raise_ValueError(translate("ddof must be smaller than length of data set"));
}
ndarray_obj_t *results = create_new_ndarray(m, n, NDARRAY_FLOAT);
mp_float_t *farray = (mp_float_t *)results->array->items;
for(size_t j=0; j < N; j++) { // result index
start = j * start_inc;
sum = 0.0;
sum_sq = 0.0;
if(ndarray->array->typecode == NDARRAY_UINT8) {
RUN_STD(ndarray, uint8_t, len, start, increment);
} else if(ndarray->array->typecode == NDARRAY_INT8) {
RUN_STD(ndarray, int8_t, len, start, increment);
} else if(ndarray->array->typecode == NDARRAY_UINT16) {
RUN_STD(ndarray, uint16_t, len, start, increment);
} else if(ndarray->array->typecode == NDARRAY_INT16) {
RUN_STD(ndarray, int16_t, len, start, increment);
} else { // this will be mp_float_t, no need to check
RUN_STD(ndarray, mp_float_t, len, start, increment);
}
farray[j] = MICROPY_FLOAT_C_FUN(sqrt)(sum_sq/(len - ddof));
}
if(results->array->len == 1) {
return mp_obj_new_float(farray[0]);
}
return MP_OBJ_FROM_PTR(results);
}
mp_obj_t numerical_argmin_argmax_iterable(mp_obj_t oin, mp_obj_t axis, uint8_t optype) {
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;
}
}
mp_obj_t numerical_argmin_argmax_ndarray(ndarray_obj_t *ndarray, mp_obj_t axis, uint8_t optype) {
size_t m, n, increment, start, start_inc, N, len;
axis_sorter(ndarray, axis, &m, &n, &N, &increment, &len, &start_inc);
ndarray_obj_t *results;
if((optype == NUMERICAL_ARGMIN) || (optype == NUMERICAL_ARGMAX)) {
// we could save some RAM by taking NDARRAY_UINT8, if the dimensions
// are smaller than 256, but the code would become more involving
// (we would also need extra flash space)
results = create_new_ndarray(m, n, NDARRAY_UINT16);
} else {
results = create_new_ndarray(m, n, ndarray->array->typecode);
}
for(size_t j=0; j < N; j++) { // result index
start = j * start_inc;
if((ndarray->array->typecode == NDARRAY_UINT8) || (ndarray->array->typecode == NDARRAY_INT8)) {
if((optype == NUMERICAL_MAX) || (optype == NUMERICAL_MIN)) {
RUN_ARGMIN(ndarray, results, uint8_t, uint8_t, len, start, increment, optype, j);
} else {
RUN_ARGMIN(ndarray, results, uint8_t, uint16_t, len, start, increment, optype, j);
}
} else if((ndarray->array->typecode == NDARRAY_UINT16) || (ndarray->array->typecode == NDARRAY_INT16)) {
RUN_ARGMIN(ndarray, results, uint16_t, uint16_t, len, start, increment, optype, j);
} else {
if((optype == NUMERICAL_MAX) || (optype == NUMERICAL_MIN)) {
RUN_ARGMIN(ndarray, results, mp_float_t, mp_float_t, len, start, increment, optype, j);
} else {
RUN_ARGMIN(ndarray, results, mp_float_t, uint16_t, len, start, increment, optype, j);
}
}
}
return MP_OBJ_FROM_PTR(results);
}
STATIC mp_obj_t numerical_function(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args, uint8_t optype) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE} } ,
{ MP_QSTR_axis, MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(n_args, 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(translate("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(optype) {
case NUMERICAL_MIN:
case NUMERICAL_ARGMIN:
case NUMERICAL_MAX:
case NUMERICAL_ARGMAX:
return numerical_argmin_argmax_iterable(oin, axis, optype);
case NUMERICAL_SUM:
case NUMERICAL_MEAN:
return numerical_sum_mean_std_iterable(oin, optype, 0);
default: // we should never reach this point, but whatever
return mp_const_none;
}
} else if(MP_OBJ_IS_TYPE(oin, &ulab_ndarray_type)) {
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(oin);
switch(optype) {
case NUMERICAL_MIN:
case NUMERICAL_MAX:
case NUMERICAL_ARGMIN:
case NUMERICAL_ARGMAX:
return numerical_argmin_argmax_ndarray(ndarray, axis, optype);
case NUMERICAL_SUM:
case NUMERICAL_MEAN:
return numerical_sum_mean_ndarray(ndarray, axis, optype);
default:
mp_raise_NotImplementedError(translate("operation is not implemented on ndarrays"));
}
} else {
mp_raise_TypeError(translate("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) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } } ,
{ MP_QSTR_axis, MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
{ MP_QSTR_ddof, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = 0} },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(n_args, 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;
size_t ddof = args[2].u_int;
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(translate("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)) {
return numerical_sum_mean_std_iterable(oin, NUMERICAL_STD, ddof);
} else if(MP_OBJ_IS_TYPE(oin, &ulab_ndarray_type)) {
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(oin);
return numerical_std_ndarray(ndarray, axis, ddof);
} else {
mp_raise_TypeError(translate("input must be tuple, list, range, or ndarray"));
}
return mp_const_none;
}
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_NONE } },
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
};
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(translate("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_NONE } },
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
};
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(translate("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(translate("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_NONE } },
{ 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(translate("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(translate("axis must be -1, 0, or 1"));
}
if((args[1].u_int < 0) || (args[1].u_int > 9)) {
mp_raise_ValueError(translate("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);
}
mp_obj_t numerical_sort_helper(mp_obj_t oin, mp_obj_t axis, uint8_t inplace) {
if(!mp_obj_is_type(oin, &ulab_ndarray_type)) {
mp_raise_TypeError(translate("sort argument must be an ndarray"));
}
ndarray_obj_t *ndarray;
mp_obj_t out;
if(inplace == 1) {
ndarray = MP_OBJ_TO_PTR(oin);
} else {
out = ndarray_copy(oin);
ndarray = MP_OBJ_TO_PTR(out);
}
size_t increment, start_inc, end, N;
if(axis == mp_const_none) { // flatten the array
ndarray->m = 1;
ndarray->n = ndarray->array->len;
increment = 1;
start_inc = ndarray->n;
end = ndarray->n;
N = ndarray->n;
} else if((mp_obj_get_int(axis) == -1) ||
(mp_obj_get_int(axis) == 1)) { // sort along the horizontal axis
increment = 1;
start_inc = ndarray->n;
end = ndarray->array->len;
N = ndarray->n;
} else if(mp_obj_get_int(axis) == 0) { // sort along vertical axis
increment = ndarray->n;
start_inc = 1;
end = ndarray->m;
N = ndarray->m;
} else {
mp_raise_ValueError(translate("axis must be -1, 0, None, or 1"));
}
size_t q, k, p, c;
for(size_t start=0; start < end; start+=start_inc) {
q = N;
k = (q >> 1);
if((ndarray->array->typecode == NDARRAY_UINT8) || (ndarray->array->typecode == NDARRAY_INT8)) {
HEAPSORT(uint8_t, ndarray);
} else if((ndarray->array->typecode == NDARRAY_INT16) || (ndarray->array->typecode == NDARRAY_INT16)) {
HEAPSORT(uint16_t, ndarray);
} else {
HEAPSORT(mp_float_t, ndarray);
}
}
if(inplace == 1) {
return mp_const_none;
} else {
return out;
}
}
// numpy function
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_NONE } },
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.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);
return numerical_sort_helper(args[0].u_obj, args[1].u_obj, 0);
}
// method of an ndarray
mp_obj_t numerical_sort_inplace(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_NONE } },
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.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);
return numerical_sort_helper(args[0].u_obj, args[1].u_obj, 1);
}
mp_obj_t numerical_argsort(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_NONE } },
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.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(translate("argsort argument must be an ndarray"));
}
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(args[0].u_obj);
size_t increment, start_inc, end, N, m, n;
if(args[1].u_obj == mp_const_none) { // flatten the array
m = 1;
n = ndarray->array->len;
ndarray->m = m;
ndarray->n = n;
increment = 1;
start_inc = ndarray->n;
end = ndarray->n;
N = n;
} else if((mp_obj_get_int(args[1].u_obj) == -1) ||
(mp_obj_get_int(args[1].u_obj) == 1)) { // sort along the horizontal axis
m = ndarray->m;
n = ndarray->n;
increment = 1;
start_inc = n;
end = ndarray->array->len;
N = n;
} else if(mp_obj_get_int(args[1].u_obj) == 0) { // sort along vertical axis
m = ndarray->m;
n = ndarray->n;
increment = n;
start_inc = 1;
end = m;
N = m;
} else {
mp_raise_ValueError(translate("axis must be -1, 0, None, or 1"));
}
// at the expense of flash, we could save RAM by creating
// an NDARRAY_UINT16 ndarray only, if needed, otherwise, NDARRAY_UINT8
ndarray_obj_t *indices = create_new_ndarray(m, n, NDARRAY_UINT16);
uint16_t *index_array = (uint16_t *)indices->array->items;
// initialise the index array
// if array is flat: 0 to indices->n
// if sorting vertically, identical indices are arranged row-wise
// if sorting horizontally, identical indices are arranged colunn-wise
for(uint16_t start=0; start < end; start+=start_inc) {
for(uint16_t s=0; s < N; s++) {
index_array[start+s*increment] = s;
}
}
size_t q, k, p, c;
for(size_t start=0; start < end; start+=start_inc) {
q = N;
k = (q >> 1);
if((ndarray->array->typecode == NDARRAY_UINT8) || (ndarray->array->typecode == NDARRAY_INT8)) {
HEAP_ARGSORT(uint8_t, ndarray, index_array);
} else if((ndarray->array->typecode == NDARRAY_INT16) || (ndarray->array->typecode == NDARRAY_INT16)) {
HEAP_ARGSORT(uint16_t, ndarray, index_array);
} else {
HEAP_ARGSORT(mp_float_t, ndarray, index_array);
}
}
return MP_OBJ_FROM_PTR(indices);
}
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