1402 lines
58 KiB
C
1402 lines
58 KiB
C
|
|
/*
|
|
* This file is part of the micropython-ulab project,
|
|
*
|
|
* https://github.com/v923z/micropython-ulab
|
|
*
|
|
* The MIT License (MIT)
|
|
*
|
|
* Copyright (c) 2019-2021 Zoltán Vörös
|
|
* 2020 Scott Shawcroft for Adafruit Industries
|
|
* 2020 Taku Fukada
|
|
*/
|
|
|
|
#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 "../ulab.h"
|
|
#include "../ulab_tools.h"
|
|
#include "./carray/carray_tools.h"
|
|
#include "numerical.h"
|
|
|
|
enum NUMERICAL_FUNCTION_TYPE {
|
|
NUMERICAL_ALL,
|
|
NUMERICAL_ANY,
|
|
NUMERICAL_ARGMAX,
|
|
NUMERICAL_ARGMIN,
|
|
NUMERICAL_MAX,
|
|
NUMERICAL_MEAN,
|
|
NUMERICAL_MIN,
|
|
NUMERICAL_STD,
|
|
NUMERICAL_SUM,
|
|
};
|
|
|
|
//| """Numerical and Statistical functions
|
|
//|
|
|
//| Most of these functions take an "axis" argument, which indicates whether to
|
|
//| operate over the flattened array (None), or a particular axis (integer)."""
|
|
//|
|
|
//| from typing import Dict
|
|
//|
|
|
//| _ArrayLike = Union[ndarray, List[_float], Tuple[_float], range]
|
|
//|
|
|
//| _DType = int
|
|
//| """`ulab.numpy.int8`, `ulab.numpy.uint8`, `ulab.numpy.int16`, `ulab.numpy.uint16`, `ulab.numpy.float` or `ulab.numpy.bool`"""
|
|
//|
|
|
//| from builtins import float as _float
|
|
//| from builtins import bool as _bool
|
|
//|
|
|
//| int8: _DType
|
|
//| """Type code for signed integers in the range -128 .. 127 inclusive, like the 'b' typecode of `array.array`"""
|
|
//|
|
|
//| int16: _DType
|
|
//| """Type code for signed integers in the range -32768 .. 32767 inclusive, like the 'h' typecode of `array.array`"""
|
|
//|
|
|
//| float: _DType
|
|
//| """Type code for floating point values, like the 'f' typecode of `array.array`"""
|
|
//|
|
|
//| uint8: _DType
|
|
//| """Type code for unsigned integers in the range 0 .. 255 inclusive, like the 'H' typecode of `array.array`"""
|
|
//|
|
|
//| uint16: _DType
|
|
//| """Type code for unsigned integers in the range 0 .. 65535 inclusive, like the 'h' typecode of `array.array`"""
|
|
//|
|
|
//| bool: _DType
|
|
//| """Type code for boolean values"""
|
|
//|
|
|
|
|
static void numerical_reduce_axes(ndarray_obj_t *ndarray, int8_t axis, size_t *shape, int32_t *strides) {
|
|
// removes the values corresponding to a single axis from the shape and strides array
|
|
uint8_t index = ULAB_MAX_DIMS - ndarray->ndim + axis;
|
|
if((ndarray->ndim == 1) && (axis == 0)) {
|
|
index = 0;
|
|
shape[ULAB_MAX_DIMS - 1] = 1;
|
|
return;
|
|
}
|
|
for(uint8_t i = ULAB_MAX_DIMS - 1; i > 0; i--) {
|
|
if(i > index) {
|
|
shape[i] = ndarray->shape[i];
|
|
strides[i] = ndarray->strides[i];
|
|
} else {
|
|
shape[i] = ndarray->shape[i-1];
|
|
strides[i] = ndarray->strides[i-1];
|
|
}
|
|
}
|
|
}
|
|
|
|
#if ULAB_NUMPY_HAS_ALL | ULAB_NUMPY_HAS_ANY
|
|
static mp_obj_t numerical_all_any(mp_obj_t oin, mp_obj_t axis, uint8_t optype) {
|
|
bool anytype = optype == NUMERICAL_ALL ? 1 : 0;
|
|
if(mp_obj_is_type(oin, &ulab_ndarray_type)) {
|
|
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(oin);
|
|
uint8_t *array = (uint8_t *)ndarray->array;
|
|
if(ndarray->len == 0) { // return immediately with empty arrays
|
|
if(optype == NUMERICAL_ALL) {
|
|
return mp_const_true;
|
|
} else {
|
|
return mp_const_false;
|
|
}
|
|
}
|
|
// always get a float, so that we don't have to resolve the dtype later
|
|
mp_float_t (*func)(void *) = ndarray_get_float_function(ndarray->dtype);
|
|
ndarray_obj_t *results = NULL;
|
|
uint8_t *rarray = NULL;
|
|
shape_strides _shape_strides = tools_reduce_axes(ndarray, axis);
|
|
if(axis != mp_const_none) {
|
|
results = ndarray_new_dense_ndarray(_shape_strides.ndim, _shape_strides.shape, NDARRAY_BOOL);
|
|
rarray = results->array;
|
|
if(optype == NUMERICAL_ALL) {
|
|
memset(rarray, 1, results->len);
|
|
}
|
|
}
|
|
|
|
#if ULAB_MAX_DIMS > 3
|
|
size_t i = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
size_t j = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 1
|
|
size_t k = 0;
|
|
do {
|
|
#endif
|
|
size_t l = 0;
|
|
if(axis == mp_const_none) {
|
|
do {
|
|
#if ULAB_SUPPORTS_COMPLEX
|
|
if(ndarray->dtype == NDARRAY_COMPLEX) {
|
|
mp_float_t real = *((mp_float_t *)array);
|
|
mp_float_t imag = *((mp_float_t *)(array + sizeof(mp_float_t)));
|
|
if(((real != MICROPY_FLOAT_CONST(0.0)) | (imag != MICROPY_FLOAT_CONST(0.0))) & !anytype) {
|
|
// optype = NUMERICAL_ANY
|
|
return mp_const_true;
|
|
} else if(((real == MICROPY_FLOAT_CONST(0.0)) & (imag == MICROPY_FLOAT_CONST(0.0))) & anytype) {
|
|
// optype == NUMERICAL_ALL
|
|
return mp_const_false;
|
|
}
|
|
} else {
|
|
#endif
|
|
mp_float_t value = func(array);
|
|
if((value != MICROPY_FLOAT_CONST(0.0)) & !anytype) {
|
|
// optype = NUMERICAL_ANY
|
|
return mp_const_true;
|
|
} else if((value == MICROPY_FLOAT_CONST(0.0)) & anytype) {
|
|
// optype == NUMERICAL_ALL
|
|
return mp_const_false;
|
|
}
|
|
#if ULAB_SUPPORTS_COMPLEX
|
|
}
|
|
#endif
|
|
array += _shape_strides.strides[0];
|
|
l++;
|
|
} while(l < _shape_strides.shape[0]);
|
|
} else { // a scalar axis keyword was supplied
|
|
do {
|
|
#if ULAB_SUPPORTS_COMPLEX
|
|
if(ndarray->dtype == NDARRAY_COMPLEX) {
|
|
mp_float_t real = *((mp_float_t *)array);
|
|
mp_float_t imag = *((mp_float_t *)(array + sizeof(mp_float_t)));
|
|
if(((real != MICROPY_FLOAT_CONST(0.0)) | (imag != MICROPY_FLOAT_CONST(0.0))) & !anytype) {
|
|
// optype = NUMERICAL_ANY
|
|
*rarray = 1;
|
|
// since we are breaking out of the loop, move the pointer forward
|
|
array += _shape_strides.strides[0] * (_shape_strides.shape[0] - l);
|
|
break;
|
|
} else if(((real == MICROPY_FLOAT_CONST(0.0)) & (imag == MICROPY_FLOAT_CONST(0.0))) & anytype) {
|
|
// optype == NUMERICAL_ALL
|
|
*rarray = 0;
|
|
// since we are breaking out of the loop, move the pointer forward
|
|
array += _shape_strides.strides[0] * (_shape_strides.shape[0] - l);
|
|
break;
|
|
}
|
|
} else {
|
|
#endif
|
|
mp_float_t value = func(array);
|
|
if((value != MICROPY_FLOAT_CONST(0.0)) & !anytype) {
|
|
// optype == NUMERICAL_ANY
|
|
*rarray = 1;
|
|
// since we are breaking out of the loop, move the pointer forward
|
|
array += _shape_strides.strides[0] * (_shape_strides.shape[0] - l);
|
|
break;
|
|
} else if((value == MICROPY_FLOAT_CONST(0.0)) & anytype) {
|
|
// optype == NUMERICAL_ALL
|
|
*rarray = 0;
|
|
// since we are breaking out of the loop, move the pointer forward
|
|
array += _shape_strides.strides[0] * (_shape_strides.shape[0] - l);
|
|
break;
|
|
}
|
|
#if ULAB_SUPPORTS_COMPLEX
|
|
}
|
|
#endif
|
|
array += _shape_strides.strides[0];
|
|
l++;
|
|
} while(l < _shape_strides.shape[0]);
|
|
}
|
|
#if ULAB_MAX_DIMS > 1
|
|
rarray += _shape_strides.increment;
|
|
array -= _shape_strides.strides[0] * _shape_strides.shape[0];
|
|
array += _shape_strides.strides[ULAB_MAX_DIMS - 1];
|
|
k++;
|
|
} while(k < _shape_strides.shape[ULAB_MAX_DIMS - 1]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
array -= _shape_strides.strides[ULAB_MAX_DIMS - 1] * _shape_strides.shape[ULAB_MAX_DIMS - 1];
|
|
array += _shape_strides.strides[ULAB_MAX_DIMS - 2];
|
|
j++;
|
|
} while(j < _shape_strides.shape[ULAB_MAX_DIMS - 2]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 3
|
|
array -= _shape_strides.strides[ULAB_MAX_DIMS - 2] * _shape_strides.shape[ULAB_MAX_DIMS - 2];
|
|
array += _shape_strides.strides[ULAB_MAX_DIMS - 3];
|
|
i++;
|
|
} while(i < _shape_strides.shape[ULAB_MAX_DIMS - 3]);
|
|
#endif
|
|
if(axis == mp_const_none) {
|
|
// the innermost loop fell through, so return the result here
|
|
if(!anytype) {
|
|
return mp_const_false;
|
|
} else {
|
|
return mp_const_true;
|
|
}
|
|
}
|
|
return results;
|
|
} else if(mp_obj_is_int(oin) || mp_obj_is_float(oin)) {
|
|
return mp_obj_is_true(oin) ? mp_const_true : mp_const_false;
|
|
} else {
|
|
mp_obj_iter_buf_t iter_buf;
|
|
mp_obj_t item, iterable = mp_getiter(oin, &iter_buf);
|
|
while((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
if(!mp_obj_is_true(item) & !anytype) {
|
|
return mp_const_false;
|
|
} else if(mp_obj_is_true(item) & anytype) {
|
|
return mp_const_true;
|
|
}
|
|
}
|
|
}
|
|
return anytype ? mp_const_true : mp_const_false;
|
|
}
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_SUM | ULAB_NUMPY_HAS_MEAN | ULAB_NUMPY_HAS_STD
|
|
static mp_obj_t numerical_sum_mean_std_iterable(mp_obj_t oin, uint8_t optype, size_t ddof) {
|
|
mp_float_t value = MICROPY_FLOAT_CONST(0.0);
|
|
mp_float_t M = MICROPY_FLOAT_CONST(0.0);
|
|
mp_float_t m = MICROPY_FLOAT_CONST(0.0);
|
|
mp_float_t S = MICROPY_FLOAT_CONST(0.0);
|
|
mp_float_t s = MICROPY_FLOAT_CONST(0.0);
|
|
size_t count = 0;
|
|
mp_obj_iter_buf_t iter_buf;
|
|
mp_obj_t item, iterable = mp_getiter(oin, &iter_buf);
|
|
while((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
value = mp_obj_get_float(item);
|
|
m = M + (value - M) / (count + 1);
|
|
s = S + (value - M) * (value - m);
|
|
M = m;
|
|
S = s;
|
|
count++;
|
|
}
|
|
if(optype == NUMERICAL_SUM) {
|
|
return mp_obj_new_float(m * count);
|
|
} else if(optype == NUMERICAL_MEAN) {
|
|
return count > 0 ? mp_obj_new_float(m) : mp_obj_new_float(MICROPY_FLOAT_CONST(0.0));
|
|
} else { // this should be the case of the standard deviation
|
|
return count > ddof ? mp_obj_new_float(MICROPY_FLOAT_C_FUN(sqrt)(s / (count - ddof))) : mp_obj_new_float(MICROPY_FLOAT_CONST(0.0));
|
|
}
|
|
}
|
|
|
|
static mp_obj_t numerical_sum_mean_std_ndarray(ndarray_obj_t *ndarray, mp_obj_t axis, uint8_t optype, size_t ddof) {
|
|
COMPLEX_DTYPE_NOT_IMPLEMENTED(ndarray->dtype)
|
|
uint8_t *array = (uint8_t *)ndarray->array;
|
|
shape_strides _shape_strides = tools_reduce_axes(ndarray, axis);
|
|
|
|
if(axis == mp_const_none) {
|
|
// work with the flattened array
|
|
if((optype == NUMERICAL_STD) && (ddof > ndarray->len)) {
|
|
// if there are too many degrees of freedom, there is no point in calculating anything
|
|
return mp_obj_new_float(MICROPY_FLOAT_CONST(0.0));
|
|
}
|
|
mp_float_t (*func)(void *) = ndarray_get_float_function(ndarray->dtype);
|
|
mp_float_t M = MICROPY_FLOAT_CONST(0.0);
|
|
mp_float_t m = MICROPY_FLOAT_CONST(0.0);
|
|
mp_float_t S = MICROPY_FLOAT_CONST(0.0);
|
|
mp_float_t s = MICROPY_FLOAT_CONST(0.0);
|
|
size_t count = 0;
|
|
|
|
#if ULAB_MAX_DIMS > 3
|
|
size_t i = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
size_t j = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 1
|
|
size_t k = 0;
|
|
do {
|
|
#endif
|
|
size_t l = 0;
|
|
do {
|
|
count++;
|
|
mp_float_t value = func(array);
|
|
m = M + (value - M) / (mp_float_t)count;
|
|
if(optype == NUMERICAL_STD) {
|
|
s = S + (value - M) * (value - m);
|
|
S = s;
|
|
}
|
|
M = m;
|
|
array += _shape_strides.strides[ULAB_MAX_DIMS - 1];
|
|
l++;
|
|
} while(l < _shape_strides.shape[ULAB_MAX_DIMS - 1]);
|
|
#if ULAB_MAX_DIMS > 1
|
|
array -= _shape_strides.strides[ULAB_MAX_DIMS - 1] * _shape_strides.shape[ULAB_MAX_DIMS - 1];
|
|
array += _shape_strides.strides[ULAB_MAX_DIMS - 2];
|
|
k++;
|
|
} while(k < _shape_strides.shape[ULAB_MAX_DIMS - 2]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
array -= _shape_strides.strides[ULAB_MAX_DIMS - 2] * _shape_strides.shape[ULAB_MAX_DIMS - 2];
|
|
array += _shape_strides.strides[ULAB_MAX_DIMS - 3];
|
|
j++;
|
|
} while(j < _shape_strides.shape[ULAB_MAX_DIMS - 3]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 3
|
|
array -= _shape_strides.strides[ULAB_MAX_DIMS - 3] * _shape_strides.shape[ULAB_MAX_DIMS - 3];
|
|
array += _shape_strides.strides[ULAB_MAX_DIMS - 4];
|
|
i++;
|
|
} while(i < _shape_strides.shape[ULAB_MAX_DIMS - 4]);
|
|
#endif
|
|
if(optype == NUMERICAL_SUM) {
|
|
// numpy returns an integer for integer input types
|
|
if(ndarray->dtype == NDARRAY_FLOAT) {
|
|
return mp_obj_new_float(M * ndarray->len);
|
|
} else {
|
|
return mp_obj_new_int((int32_t)MICROPY_FLOAT_C_FUN(round)(M * ndarray->len));
|
|
}
|
|
} else if(optype == NUMERICAL_MEAN) {
|
|
return mp_obj_new_float(M);
|
|
} else { // this must be the case of the standard deviation
|
|
// we have already made certain that ddof < ndarray->len holds
|
|
return mp_obj_new_float(MICROPY_FLOAT_C_FUN(sqrt)(S / (ndarray->len - ddof)));
|
|
}
|
|
} else {
|
|
ndarray_obj_t *results = NULL;
|
|
uint8_t *rarray = NULL;
|
|
mp_float_t *farray = NULL;
|
|
if(optype == NUMERICAL_SUM) {
|
|
results = ndarray_new_dense_ndarray(_shape_strides.ndim, _shape_strides.shape, ndarray->dtype);
|
|
rarray = (uint8_t *)results->array;
|
|
// TODO: numpy promotes the output to the highest integer type
|
|
if(ndarray->dtype == NDARRAY_UINT8) {
|
|
RUN_SUM(uint8_t, array, results, rarray, _shape_strides);
|
|
} else if(ndarray->dtype == NDARRAY_INT8) {
|
|
RUN_SUM(int8_t, array, results, rarray, _shape_strides);
|
|
} else if(ndarray->dtype == NDARRAY_UINT16) {
|
|
RUN_SUM(uint16_t, array, results, rarray, _shape_strides);
|
|
} else if(ndarray->dtype == NDARRAY_INT16) {
|
|
RUN_SUM(int16_t, array, results, rarray, _shape_strides);
|
|
} else {
|
|
// for floats, the sum might be inaccurate with the naive summation
|
|
// call mean, and multiply with the number of samples
|
|
farray = (mp_float_t *)results->array;
|
|
RUN_MEAN_STD(mp_float_t, array, farray, _shape_strides, MICROPY_FLOAT_CONST(0.0), 0);
|
|
mp_float_t norm = (mp_float_t)_shape_strides.shape[0];
|
|
// re-wind the array here
|
|
farray = (mp_float_t *)results->array;
|
|
for(size_t i=0; i < results->len; i++) {
|
|
*farray++ *= norm;
|
|
}
|
|
}
|
|
} else {
|
|
bool isStd = optype == NUMERICAL_STD ? 1 : 0;
|
|
results = ndarray_new_dense_ndarray(_shape_strides.ndim, _shape_strides.shape, NDARRAY_FLOAT);
|
|
farray = (mp_float_t *)results->array;
|
|
// we can return the 0 array here, if the degrees of freedom is larger than the length of the axis
|
|
if((optype == NUMERICAL_STD) && (_shape_strides.shape[0] <= ddof)) {
|
|
return MP_OBJ_FROM_PTR(results);
|
|
}
|
|
mp_float_t div = optype == NUMERICAL_STD ? (mp_float_t)(_shape_strides.shape[0] - ddof) : MICROPY_FLOAT_CONST(0.0);
|
|
if(ndarray->dtype == NDARRAY_UINT8) {
|
|
RUN_MEAN_STD(uint8_t, array, farray, _shape_strides, div, isStd);
|
|
} else if(ndarray->dtype == NDARRAY_INT8) {
|
|
RUN_MEAN_STD(int8_t, array, farray, _shape_strides, div, isStd);
|
|
} else if(ndarray->dtype == NDARRAY_UINT16) {
|
|
RUN_MEAN_STD(uint16_t, array, farray, _shape_strides, div, isStd);
|
|
} else if(ndarray->dtype == NDARRAY_INT16) {
|
|
RUN_MEAN_STD(int16_t, array, farray, _shape_strides, div, isStd);
|
|
} else {
|
|
RUN_MEAN_STD(mp_float_t, array, farray, _shape_strides, div, isStd);
|
|
}
|
|
}
|
|
if(results->ndim == 0) { // return a scalar here
|
|
return mp_binary_get_val_array(results->dtype, results->array, 0);
|
|
}
|
|
return MP_OBJ_FROM_PTR(results);
|
|
}
|
|
return mp_const_none;
|
|
}
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_ARGMINMAX
|
|
static mp_obj_t numerical_argmin_argmax_iterable(mp_obj_t oin, uint8_t optype) {
|
|
if(MP_OBJ_SMALL_INT_VALUE(mp_obj_len_maybe(oin)) == 0) {
|
|
mp_raise_ValueError(translate("attempt to get argmin/argmax of an empty sequence"));
|
|
}
|
|
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 item;
|
|
uint8_t op = 0; // argmin, min
|
|
if((optype == NUMERICAL_ARGMAX) || (optype == NUMERICAL_MAX)) op = 1;
|
|
item = mp_iternext(iterable);
|
|
mp_obj_t best_obj = item;
|
|
mp_float_t value, best_value = mp_obj_get_float(item);
|
|
value = best_value;
|
|
while((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
idx++;
|
|
value = mp_obj_get_float(item);
|
|
if((op == 0) && (value < best_value)) {
|
|
best_obj = item;
|
|
best_idx = idx;
|
|
best_value = value;
|
|
} else if((op == 1) && (value > best_value)) {
|
|
best_obj = item;
|
|
best_idx = idx;
|
|
best_value = value;
|
|
}
|
|
}
|
|
if((optype == NUMERICAL_ARGMIN) || (optype == NUMERICAL_ARGMAX)) {
|
|
return MP_OBJ_NEW_SMALL_INT(best_idx);
|
|
} else {
|
|
return best_obj;
|
|
}
|
|
}
|
|
|
|
static mp_obj_t numerical_argmin_argmax_ndarray(ndarray_obj_t *ndarray, mp_obj_t axis, uint8_t optype) {
|
|
// TODO: treat the flattened array
|
|
if(ndarray->len == 0) {
|
|
mp_raise_ValueError(translate("attempt to get (arg)min/(arg)max of empty sequence"));
|
|
}
|
|
|
|
if(axis == mp_const_none) {
|
|
// work with the flattened array
|
|
mp_float_t (*func)(void *) = ndarray_get_float_function(ndarray->dtype);
|
|
uint8_t *array = (uint8_t *)ndarray->array;
|
|
mp_float_t best_value = func(array);
|
|
mp_float_t value;
|
|
size_t index = 0, best_index = 0;
|
|
|
|
#if ULAB_MAX_DIMS > 3
|
|
size_t i = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
size_t j = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 1
|
|
size_t k = 0;
|
|
do {
|
|
#endif
|
|
size_t l = 0;
|
|
do {
|
|
value = func(array);
|
|
if((optype == NUMERICAL_ARGMAX) || (optype == NUMERICAL_MAX)) {
|
|
if(best_value < value) {
|
|
best_value = value;
|
|
best_index = index;
|
|
}
|
|
} else {
|
|
if(best_value > value) {
|
|
best_value = value;
|
|
best_index = index;
|
|
}
|
|
}
|
|
array += ndarray->strides[ULAB_MAX_DIMS - 1];
|
|
l++;
|
|
index++;
|
|
} while(l < ndarray->shape[ULAB_MAX_DIMS - 1]);
|
|
#if ULAB_MAX_DIMS > 1
|
|
array -= ndarray->strides[ULAB_MAX_DIMS - 1] * ndarray->shape[ULAB_MAX_DIMS-1];
|
|
array += ndarray->strides[ULAB_MAX_DIMS - 2];
|
|
k++;
|
|
} while(k < ndarray->shape[ULAB_MAX_DIMS - 2]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
array -= ndarray->strides[ULAB_MAX_DIMS - 2] * ndarray->shape[ULAB_MAX_DIMS-2];
|
|
array += ndarray->strides[ULAB_MAX_DIMS - 3];
|
|
j++;
|
|
} while(j < ndarray->shape[ULAB_MAX_DIMS - 3]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 3
|
|
array -= ndarray->strides[ULAB_MAX_DIMS - 3] * ndarray->shape[ULAB_MAX_DIMS-3];
|
|
array += ndarray->strides[ULAB_MAX_DIMS - 4];
|
|
i++;
|
|
} while(i < ndarray->shape[ULAB_MAX_DIMS - 4]);
|
|
#endif
|
|
|
|
if((optype == NUMERICAL_ARGMIN) || (optype == NUMERICAL_ARGMAX)) {
|
|
return mp_obj_new_int(best_index);
|
|
} else {
|
|
if(ndarray->dtype == NDARRAY_FLOAT) {
|
|
return mp_obj_new_float(best_value);
|
|
} else {
|
|
return MP_OBJ_NEW_SMALL_INT((int32_t)best_value);
|
|
}
|
|
}
|
|
} else {
|
|
int8_t ax = tools_get_axis(axis, ndarray->ndim);
|
|
|
|
uint8_t *array = (uint8_t *)ndarray->array;
|
|
size_t *shape = m_new(size_t, ULAB_MAX_DIMS);
|
|
memset(shape, 0, sizeof(size_t)*ULAB_MAX_DIMS);
|
|
int32_t *strides = m_new(int32_t, ULAB_MAX_DIMS);
|
|
memset(strides, 0, sizeof(uint32_t)*ULAB_MAX_DIMS);
|
|
numerical_reduce_axes(ndarray, ax, shape, strides);
|
|
uint8_t index = ULAB_MAX_DIMS - ndarray->ndim + ax;
|
|
|
|
ndarray_obj_t *results = NULL;
|
|
|
|
if((optype == NUMERICAL_ARGMIN) || (optype == NUMERICAL_ARGMAX)) {
|
|
results = ndarray_new_dense_ndarray(MAX(1, ndarray->ndim-1), shape, NDARRAY_INT16);
|
|
} else {
|
|
results = ndarray_new_dense_ndarray(MAX(1, ndarray->ndim-1), shape, ndarray->dtype);
|
|
}
|
|
|
|
uint8_t *rarray = (uint8_t *)results->array;
|
|
|
|
if(ndarray->dtype == NDARRAY_UINT8) {
|
|
RUN_ARGMIN(ndarray, uint8_t, array, results, rarray, shape, strides, index, optype);
|
|
} else if(ndarray->dtype == NDARRAY_INT8) {
|
|
RUN_ARGMIN(ndarray, int8_t, array, results, rarray, shape, strides, index, optype);
|
|
} else if(ndarray->dtype == NDARRAY_UINT16) {
|
|
RUN_ARGMIN(ndarray, uint16_t, array, results, rarray, shape, strides, index, optype);
|
|
} else if(ndarray->dtype == NDARRAY_INT16) {
|
|
RUN_ARGMIN(ndarray, int16_t, array, results, rarray, shape, strides, index, optype);
|
|
} else {
|
|
RUN_ARGMIN(ndarray, mp_float_t, array, results, rarray, shape, strides, index, optype);
|
|
}
|
|
if(results->len == 1) {
|
|
return mp_binary_get_val_array(results->dtype, results->array, 0);
|
|
}
|
|
return MP_OBJ_FROM_PTR(results);
|
|
}
|
|
return mp_const_none;
|
|
}
|
|
#endif
|
|
|
|
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_const_none} } ,
|
|
{ MP_QSTR_axis, MP_ARG_OBJ, { .u_rom_obj = mp_const_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_is_int(axis))) {
|
|
mp_raise_TypeError(translate("axis must be None, or an integer"));
|
|
}
|
|
|
|
if((optype == NUMERICAL_ALL) || (optype == NUMERICAL_ANY)) {
|
|
return numerical_all_any(oin, axis, 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)) {
|
|
switch(optype) {
|
|
case NUMERICAL_MIN:
|
|
case NUMERICAL_ARGMIN:
|
|
case NUMERICAL_MAX:
|
|
case NUMERICAL_ARGMAX:
|
|
return numerical_argmin_argmax_iterable(oin, 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:
|
|
COMPLEX_DTYPE_NOT_IMPLEMENTED(ndarray->dtype)
|
|
return numerical_argmin_argmax_ndarray(ndarray, axis, optype);
|
|
case NUMERICAL_SUM:
|
|
case NUMERICAL_MEAN:
|
|
COMPLEX_DTYPE_NOT_IMPLEMENTED(ndarray->dtype)
|
|
return numerical_sum_mean_std_ndarray(ndarray, axis, optype, 0);
|
|
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;
|
|
}
|
|
|
|
#if ULAB_NUMPY_HAS_SORT | NDARRAY_HAS_SORT
|
|
static 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;
|
|
if(inplace == 1) {
|
|
ndarray = MP_OBJ_TO_PTR(oin);
|
|
} else {
|
|
ndarray = ndarray_copy_view(MP_OBJ_TO_PTR(oin));
|
|
}
|
|
COMPLEX_DTYPE_NOT_IMPLEMENTED(ndarray->dtype)
|
|
|
|
int8_t ax = 0;
|
|
if(axis == mp_const_none) {
|
|
// flatten the array
|
|
#if ULAB_MAX_DIMS > 1
|
|
for(uint8_t i=0; i < ULAB_MAX_DIMS - 1; i++) {
|
|
ndarray->shape[i] = 0;
|
|
ndarray->strides[i] = 0;
|
|
}
|
|
ndarray->shape[ULAB_MAX_DIMS - 1] = ndarray->len;
|
|
ndarray->strides[ULAB_MAX_DIMS - 1] = ndarray->itemsize;
|
|
ndarray->ndim = 1;
|
|
#endif
|
|
} else {
|
|
ax = tools_get_axis(axis, ndarray->ndim);
|
|
}
|
|
|
|
size_t *shape = m_new(size_t, ULAB_MAX_DIMS);
|
|
memset(shape, 0, sizeof(size_t)*ULAB_MAX_DIMS);
|
|
int32_t *strides = m_new(int32_t, ULAB_MAX_DIMS);
|
|
memset(strides, 0, sizeof(uint32_t)*ULAB_MAX_DIMS);
|
|
numerical_reduce_axes(ndarray, ax, shape, strides);
|
|
ax = ULAB_MAX_DIMS - ndarray->ndim + ax;
|
|
// we work with the typed array, so re-scale the stride
|
|
int32_t increment = ndarray->strides[ax] / ndarray->itemsize;
|
|
|
|
uint8_t *array = (uint8_t *)ndarray->array;
|
|
if((ndarray->dtype == NDARRAY_UINT8) || (ndarray->dtype == NDARRAY_INT8)) {
|
|
HEAPSORT(ndarray, uint8_t, array, shape, strides, ax, increment, ndarray->shape[ax]);
|
|
} else if((ndarray->dtype == NDARRAY_INT16) || (ndarray->dtype == NDARRAY_INT16)) {
|
|
HEAPSORT(ndarray, uint16_t, array, shape, strides, ax, increment, ndarray->shape[ax]);
|
|
} else {
|
|
HEAPSORT(ndarray, mp_float_t, array, shape, strides, ax, increment, ndarray->shape[ax]);
|
|
}
|
|
if(inplace == 1) {
|
|
return mp_const_none;
|
|
} else {
|
|
return MP_OBJ_FROM_PTR(ndarray);
|
|
}
|
|
}
|
|
#endif /* ULAB_NUMERICAL_HAS_SORT | NDARRAY_HAS_SORT */
|
|
|
|
#if ULAB_NUMPY_HAS_ALL
|
|
mp_obj_t numerical_all(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_ALL);
|
|
}
|
|
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_all_obj, 1, numerical_all);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_ANY
|
|
mp_obj_t numerical_any(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_ANY);
|
|
}
|
|
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_any_obj, 1, numerical_any);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_ARGMINMAX
|
|
//| def argmax(array: _ArrayLike, *, axis: Optional[int] = None) -> int:
|
|
//| """Return the index of the maximum element of the 1D array"""
|
|
//| ...
|
|
//|
|
|
|
|
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_DEFINE_CONST_FUN_OBJ_KW(numerical_argmax_obj, 1, numerical_argmax);
|
|
|
|
//| def argmin(array: _ArrayLike, *, axis: Optional[int] = None) -> int:
|
|
//| """Return the index of the minimum element of the 1D array"""
|
|
//| ...
|
|
//|
|
|
|
|
static 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_DEFINE_CONST_FUN_OBJ_KW(numerical_argmin_obj, 1, numerical_argmin);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_ARGSORT
|
|
//| def argsort(array: ulab.numpy.ndarray, *, axis: int = -1) -> ulab.numpy.ndarray:
|
|
//| """Returns an array which gives indices into the input array from least to greatest."""
|
|
//| ...
|
|
//|
|
|
|
|
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_const_none } },
|
|
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, { .u_rom_obj = mp_const_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);
|
|
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);
|
|
COMPLEX_DTYPE_NOT_IMPLEMENTED(ndarray->dtype)
|
|
if(args[1].u_obj == mp_const_none) {
|
|
// bail out, though dense arrays could still be sorted
|
|
mp_raise_NotImplementedError(translate("argsort is not implemented for flattened arrays"));
|
|
}
|
|
// Since we are returning an NDARRAY_UINT16 array, bail out,
|
|
// if the axis is longer than what we can hold
|
|
for(uint8_t i=0; i < ULAB_MAX_DIMS; i++) {
|
|
if(ndarray->shape[i] > 65535) {
|
|
mp_raise_ValueError(translate("axis too long"));
|
|
}
|
|
}
|
|
int8_t ax = tools_get_axis(args[1].u_obj, ndarray->ndim);
|
|
|
|
size_t *shape = m_new(size_t, ULAB_MAX_DIMS);
|
|
memset(shape, 0, sizeof(size_t)*ULAB_MAX_DIMS);
|
|
int32_t *strides = m_new(int32_t, ULAB_MAX_DIMS);
|
|
memset(strides, 0, sizeof(uint32_t)*ULAB_MAX_DIMS);
|
|
numerical_reduce_axes(ndarray, ax, shape, strides);
|
|
|
|
// We could return an NDARRAY_UINT8 array, if all lengths are shorter than 256
|
|
ndarray_obj_t *indices = ndarray_new_ndarray(ndarray->ndim, ndarray->shape, NULL, NDARRAY_UINT16);
|
|
int32_t *istrides = m_new(int32_t, ULAB_MAX_DIMS);
|
|
memset(istrides, 0, sizeof(uint32_t)*ULAB_MAX_DIMS);
|
|
numerical_reduce_axes(indices, ax, shape, istrides);
|
|
for(uint8_t i=0; i < ULAB_MAX_DIMS; i++) {
|
|
istrides[i] /= sizeof(uint16_t);
|
|
}
|
|
|
|
ax = ULAB_MAX_DIMS - ndarray->ndim + ax;
|
|
// we work with the typed array, so re-scale the stride
|
|
int32_t increment = ndarray->strides[ax] / ndarray->itemsize;
|
|
uint16_t iincrement = indices->strides[ax] / sizeof(uint16_t);
|
|
|
|
uint8_t *array = (uint8_t *)ndarray->array;
|
|
uint16_t *iarray = (uint16_t *)indices->array;
|
|
|
|
// fill in the index values
|
|
#if ULAB_MAX_DIMS > 3
|
|
size_t j = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
size_t k = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 1
|
|
size_t l = 0;
|
|
do {
|
|
#endif
|
|
uint16_t m = 0;
|
|
do {
|
|
*iarray = m++;
|
|
iarray += iincrement;
|
|
} while(m < indices->shape[ax]);
|
|
#if ULAB_MAX_DIMS > 1
|
|
iarray -= iincrement * indices->shape[ax];
|
|
iarray += istrides[ULAB_MAX_DIMS - 1];
|
|
l++;
|
|
} while(l < shape[ULAB_MAX_DIMS - 1]);
|
|
iarray -= istrides[ULAB_MAX_DIMS - 1] * shape[ULAB_MAX_DIMS - 1];
|
|
iarray += istrides[ULAB_MAX_DIMS - 2];
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
k++;
|
|
} while(k < shape[ULAB_MAX_DIMS - 2]);
|
|
iarray -= istrides[ULAB_MAX_DIMS - 2] * shape[ULAB_MAX_DIMS - 2];
|
|
iarray += istrides[ULAB_MAX_DIMS - 3];
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 3
|
|
j++;
|
|
} while(j < shape[ULAB_MAX_DIMS - 3]);
|
|
#endif
|
|
// reset the array
|
|
iarray = indices->array;
|
|
|
|
if((ndarray->dtype == NDARRAY_UINT8) || (ndarray->dtype == NDARRAY_INT8)) {
|
|
HEAP_ARGSORT(ndarray, uint8_t, array, shape, strides, ax, increment, ndarray->shape[ax], iarray, istrides, iincrement);
|
|
} else if((ndarray->dtype == NDARRAY_UINT16) || (ndarray->dtype == NDARRAY_INT16)) {
|
|
HEAP_ARGSORT(ndarray, uint16_t, array, shape, strides, ax, increment, ndarray->shape[ax], iarray, istrides, iincrement);
|
|
} else {
|
|
HEAP_ARGSORT(ndarray, mp_float_t, array, shape, strides, ax, increment, ndarray->shape[ax], iarray, istrides, iincrement);
|
|
}
|
|
return MP_OBJ_FROM_PTR(indices);
|
|
}
|
|
|
|
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argsort_obj, 1, numerical_argsort);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_CROSS
|
|
//| def cross(a: ulab.numpy.ndarray, b: ulab.numpy.ndarray) -> ulab.numpy.ndarray:
|
|
//| """Return the cross product of two vectors of length 3"""
|
|
//| ...
|
|
//|
|
|
|
|
static mp_obj_t numerical_cross(mp_obj_t _a, mp_obj_t _b) {
|
|
if (!mp_obj_is_type(_a, &ulab_ndarray_type) || !mp_obj_is_type(_b, &ulab_ndarray_type)) {
|
|
mp_raise_TypeError(translate("arguments must be ndarrays"));
|
|
}
|
|
ndarray_obj_t *a = MP_OBJ_TO_PTR(_a);
|
|
ndarray_obj_t *b = MP_OBJ_TO_PTR(_b);
|
|
COMPLEX_DTYPE_NOT_IMPLEMENTED(a->dtype)
|
|
COMPLEX_DTYPE_NOT_IMPLEMENTED(b->dtype)
|
|
if((a->ndim != 1) || (b->ndim != 1) || (a->len != b->len) || (a->len != 3)) {
|
|
mp_raise_ValueError(translate("cross is defined for 1D arrays of length 3"));
|
|
}
|
|
|
|
mp_float_t *results = m_new(mp_float_t, 3);
|
|
results[0] = ndarray_get_float_index(a->array, a->dtype, 1) * ndarray_get_float_index(b->array, b->dtype, 2);
|
|
results[0] -= ndarray_get_float_index(a->array, a->dtype, 2) * ndarray_get_float_index(b->array, b->dtype, 1);
|
|
results[1] = -ndarray_get_float_index(a->array, a->dtype, 0) * ndarray_get_float_index(b->array, b->dtype, 2);
|
|
results[1] += ndarray_get_float_index(a->array, a->dtype, 2) * ndarray_get_float_index(b->array, b->dtype, 0);
|
|
results[2] = ndarray_get_float_index(a->array, a->dtype, 0) * ndarray_get_float_index(b->array, b->dtype, 1);
|
|
results[2] -= ndarray_get_float_index(a->array, a->dtype, 1) * ndarray_get_float_index(b->array, b->dtype, 0);
|
|
|
|
/* The upcasting happens here with the rules
|
|
|
|
- if one of the operarands is a float, the result is always float
|
|
- operation on identical types preserves type
|
|
|
|
uint8 + int8 => int16
|
|
uint8 + int16 => int16
|
|
uint8 + uint16 => uint16
|
|
int8 + int16 => int16
|
|
int8 + uint16 => uint16
|
|
uint16 + int16 => float
|
|
|
|
*/
|
|
|
|
uint8_t dtype = NDARRAY_FLOAT;
|
|
if(a->dtype == b->dtype) {
|
|
dtype = a->dtype;
|
|
} else if(((a->dtype == NDARRAY_UINT8) && (b->dtype == NDARRAY_INT8)) || ((a->dtype == NDARRAY_INT8) && (b->dtype == NDARRAY_UINT8))) {
|
|
dtype = NDARRAY_INT16;
|
|
} else if(((a->dtype == NDARRAY_UINT8) && (b->dtype == NDARRAY_INT16)) || ((a->dtype == NDARRAY_INT16) && (b->dtype == NDARRAY_UINT8))) {
|
|
dtype = NDARRAY_INT16;
|
|
} else if(((a->dtype == NDARRAY_UINT8) && (b->dtype == NDARRAY_UINT16)) || ((a->dtype == NDARRAY_UINT16) && (b->dtype == NDARRAY_UINT8))) {
|
|
dtype = NDARRAY_UINT16;
|
|
} else if(((a->dtype == NDARRAY_INT8) && (b->dtype == NDARRAY_INT16)) || ((a->dtype == NDARRAY_INT16) && (b->dtype == NDARRAY_INT8))) {
|
|
dtype = NDARRAY_INT16;
|
|
} else if(((a->dtype == NDARRAY_INT8) && (b->dtype == NDARRAY_UINT16)) || ((a->dtype == NDARRAY_UINT16) && (b->dtype == NDARRAY_INT8))) {
|
|
dtype = NDARRAY_UINT16;
|
|
}
|
|
|
|
ndarray_obj_t *ndarray = ndarray_new_linear_array(3, dtype);
|
|
if(dtype == NDARRAY_UINT8) {
|
|
uint8_t *array = (uint8_t *)ndarray->array;
|
|
for(uint8_t i=0; i < 3; i++) array[i] = (uint8_t)results[i];
|
|
} else if(dtype == NDARRAY_INT8) {
|
|
int8_t *array = (int8_t *)ndarray->array;
|
|
for(uint8_t i=0; i < 3; i++) array[i] = (int8_t)results[i];
|
|
} else if(dtype == NDARRAY_UINT16) {
|
|
uint16_t *array = (uint16_t *)ndarray->array;
|
|
for(uint8_t i=0; i < 3; i++) array[i] = (uint16_t)results[i];
|
|
} else if(dtype == NDARRAY_INT16) {
|
|
int16_t *array = (int16_t *)ndarray->array;
|
|
for(uint8_t i=0; i < 3; i++) array[i] = (int16_t)results[i];
|
|
} else {
|
|
mp_float_t *array = (mp_float_t *)ndarray->array;
|
|
for(uint8_t i=0; i < 3; i++) array[i] = results[i];
|
|
}
|
|
m_del(mp_float_t, results, 3);
|
|
return MP_OBJ_FROM_PTR(ndarray);
|
|
}
|
|
|
|
MP_DEFINE_CONST_FUN_OBJ_2(numerical_cross_obj, numerical_cross);
|
|
|
|
#endif /* ULAB_NUMERICAL_HAS_CROSS */
|
|
|
|
#if ULAB_NUMPY_HAS_DIFF
|
|
//| def diff(array: ulab.numpy.ndarray, *, n: int = 1, axis: int = -1) -> ulab.numpy.ndarray:
|
|
//| """Return the numerical derivative of successive elements of the array, as
|
|
//| an array. axis=None is not supported."""
|
|
//| ...
|
|
//|
|
|
|
|
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_const_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(n_args, 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 *ndarray = MP_OBJ_TO_PTR(args[0].u_obj);
|
|
COMPLEX_DTYPE_NOT_IMPLEMENTED(ndarray->dtype)
|
|
int8_t ax = args[2].u_int;
|
|
if(ax < 0) ax += ndarray->ndim;
|
|
|
|
if((ax < 0) || (ax > ndarray->ndim - 1)) {
|
|
mp_raise_ValueError(translate("index out of range"));
|
|
}
|
|
|
|
if((args[1].u_int < 0) || (args[1].u_int > 9)) {
|
|
mp_raise_ValueError(translate("differentiation order out of range"));
|
|
}
|
|
uint8_t N = (uint8_t)args[1].u_int;
|
|
uint8_t index = ULAB_MAX_DIMS - ndarray->ndim + ax;
|
|
if(N > ndarray->shape[index]) {
|
|
mp_raise_ValueError(translate("differentiation order out of range"));
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
size_t *shape = m_new(size_t, ULAB_MAX_DIMS);
|
|
memset(shape, 0, sizeof(size_t)*ULAB_MAX_DIMS);
|
|
for(uint8_t i=0; i < ULAB_MAX_DIMS; i++) {
|
|
shape[i] = ndarray->shape[i];
|
|
if(i == index) {
|
|
shape[i] -= N;
|
|
}
|
|
}
|
|
uint8_t *array = (uint8_t *)ndarray->array;
|
|
ndarray_obj_t *results = ndarray_new_dense_ndarray(ndarray->ndim, shape, ndarray->dtype);
|
|
uint8_t *rarray = (uint8_t *)results->array;
|
|
|
|
memset(shape, 0, sizeof(size_t)*ULAB_MAX_DIMS);
|
|
int32_t *strides = m_new(int32_t, ULAB_MAX_DIMS);
|
|
memset(strides, 0, sizeof(int32_t)*ULAB_MAX_DIMS);
|
|
numerical_reduce_axes(ndarray, ax, shape, strides);
|
|
|
|
if(ndarray->dtype == NDARRAY_UINT8) {
|
|
RUN_DIFF(ndarray, uint8_t, array, results, rarray, shape, strides, index, stencil, N);
|
|
} else if(ndarray->dtype == NDARRAY_INT8) {
|
|
RUN_DIFF(ndarray, int8_t, array, results, rarray, shape, strides, index, stencil, N);
|
|
} else if(ndarray->dtype == NDARRAY_UINT16) {
|
|
RUN_DIFF(ndarray, uint16_t, array, results, rarray, shape, strides, index, stencil, N);
|
|
} else if(ndarray->dtype == NDARRAY_INT16) {
|
|
RUN_DIFF(ndarray, int16_t, array, results, rarray, shape, strides, index, stencil, N);
|
|
} else {
|
|
RUN_DIFF(ndarray, mp_float_t, array, results, rarray, shape, strides, index, stencil, N);
|
|
}
|
|
m_del(int8_t, stencil, N+1);
|
|
m_del(size_t, shape, ULAB_MAX_DIMS);
|
|
m_del(int32_t, strides, ULAB_MAX_DIMS);
|
|
return MP_OBJ_FROM_PTR(results);
|
|
}
|
|
|
|
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_diff_obj, 1, numerical_diff);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_FLIP
|
|
//| def flip(array: ulab.numpy.ndarray, *, axis: Optional[int] = None) -> ulab.numpy.ndarray:
|
|
//| """Returns a new array that reverses the order of the elements along the
|
|
//| given axis, or along all axes if axis is 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_const_none } },
|
|
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_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);
|
|
|
|
if(!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type)) {
|
|
mp_raise_TypeError(translate("flip argument must be an ndarray"));
|
|
}
|
|
|
|
ndarray_obj_t *results = NULL;
|
|
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(args[0].u_obj);
|
|
if(args[1].u_obj == mp_const_none) { // flip the flattened array
|
|
results = ndarray_new_linear_array(ndarray->len, ndarray->dtype);
|
|
ndarray_copy_array(ndarray, results, 0);
|
|
uint8_t *rarray = (uint8_t *)results->array;
|
|
rarray += (results->len - 1) * results->itemsize;
|
|
results->array = rarray;
|
|
results->strides[ULAB_MAX_DIMS - 1] = -results->strides[ULAB_MAX_DIMS - 1];
|
|
} else if(mp_obj_is_int(args[1].u_obj)){
|
|
int8_t ax = tools_get_axis(args[1].u_obj, ndarray->ndim);
|
|
|
|
ax = ULAB_MAX_DIMS - ndarray->ndim + ax;
|
|
int32_t offset = (ndarray->shape[ax] - 1) * ndarray->strides[ax];
|
|
results = ndarray_new_view(ndarray, ndarray->ndim, ndarray->shape, ndarray->strides, offset);
|
|
results->strides[ax] = -results->strides[ax];
|
|
} else {
|
|
mp_raise_TypeError(translate("wrong axis index"));
|
|
}
|
|
return results;
|
|
}
|
|
|
|
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_flip_obj, 1, numerical_flip);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_MINMAX
|
|
//| def max(array: _ArrayLike, *, axis: Optional[int] = None) -> _float:
|
|
//| """Return the maximum element of the 1D array"""
|
|
//| ...
|
|
//|
|
|
|
|
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_DEFINE_CONST_FUN_OBJ_KW(numerical_max_obj, 1, numerical_max);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_MEAN
|
|
//| def mean(array: _ArrayLike, *, axis: Optional[int] = None) -> _float:
|
|
//| """Return the mean element of the 1D array, as a number if axis is None, otherwise as an array."""
|
|
//| ...
|
|
//|
|
|
|
|
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_DEFINE_CONST_FUN_OBJ_KW(numerical_mean_obj, 1, numerical_mean);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_MEDIAN
|
|
//| def median(array: ulab.numpy.ndarray, *, axis: int = -1) -> ulab.numpy.ndarray:
|
|
//| """Find the median value in an array along the given axis, or along all axes if axis is None."""
|
|
//| ...
|
|
//|
|
|
|
|
mp_obj_t numerical_median(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_const_none } },
|
|
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, { .u_rom_obj = mp_const_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);
|
|
if(!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type)) {
|
|
mp_raise_TypeError(translate("median argument must be an ndarray"));
|
|
}
|
|
|
|
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(args[0].u_obj);
|
|
if(ndarray->len == 0) {
|
|
return mp_obj_new_float(MICROPY_FLOAT_C_FUN(nan)(""));
|
|
}
|
|
|
|
ndarray = numerical_sort_helper(args[0].u_obj, args[1].u_obj, 0);
|
|
|
|
if((args[1].u_obj == mp_const_none) || (ndarray->ndim == 1)) {
|
|
// at this point, the array holding the sorted values should be flat
|
|
uint8_t *array = (uint8_t *)ndarray->array;
|
|
size_t len = ndarray->len;
|
|
array += (len >> 1) * ndarray->itemsize;
|
|
mp_float_t median = ndarray_get_float_value(array, ndarray->dtype);
|
|
if(!(len & 0x01)) { // len is an even number
|
|
array -= ndarray->itemsize;
|
|
median += ndarray_get_float_value(array, ndarray->dtype);
|
|
median *= MICROPY_FLOAT_CONST(0.5);
|
|
}
|
|
return mp_obj_new_float(median);
|
|
} else {
|
|
int8_t ax = tools_get_axis(args[1].u_obj, ndarray->ndim);
|
|
|
|
size_t *shape = m_new(size_t, ULAB_MAX_DIMS);
|
|
memset(shape, 0, sizeof(size_t)*ULAB_MAX_DIMS);
|
|
int32_t *strides = m_new(int32_t, ULAB_MAX_DIMS);
|
|
memset(strides, 0, sizeof(uint32_t)*ULAB_MAX_DIMS);
|
|
numerical_reduce_axes(ndarray, ax, shape, strides);
|
|
ax = ULAB_MAX_DIMS - ndarray->ndim + ax;
|
|
ndarray_obj_t *results = ndarray_new_dense_ndarray(ndarray->ndim-1, shape, NDARRAY_FLOAT);
|
|
mp_float_t *rarray = (mp_float_t *)results->array;
|
|
|
|
uint8_t *array = (uint8_t *)ndarray->array;
|
|
|
|
size_t len = ndarray->shape[ax];
|
|
|
|
#if ULAB_MAX_DIMS > 3
|
|
size_t i = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
size_t j = 0;
|
|
do {
|
|
#endif
|
|
size_t k = 0;
|
|
do {
|
|
array += ndarray->strides[ax] * (len >> 1);
|
|
mp_float_t median = ndarray_get_float_value(array, ndarray->dtype);
|
|
if(!(len & 0x01)) { // len is an even number
|
|
array -= ndarray->strides[ax];
|
|
median += ndarray_get_float_value(array, ndarray->dtype);
|
|
median *= MICROPY_FLOAT_CONST(0.5);
|
|
array += ndarray->strides[ax];
|
|
}
|
|
array -= ndarray->strides[ax] * (len >> 1);
|
|
array += strides[ULAB_MAX_DIMS - 1];
|
|
*rarray = median;
|
|
rarray++;
|
|
k++;
|
|
} while(k < shape[ULAB_MAX_DIMS - 1]);
|
|
#if ULAB_MAX_DIMS > 2
|
|
array -= strides[ULAB_MAX_DIMS - 1] * shape[ULAB_MAX_DIMS - 1];
|
|
array += strides[ULAB_MAX_DIMS - 2];
|
|
j++;
|
|
} while(j < shape[ULAB_MAX_DIMS - 2]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 3
|
|
array -= strides[ULAB_MAX_DIMS - 2] * shape[ULAB_MAX_DIMS-2];
|
|
array += strides[ULAB_MAX_DIMS - 3];
|
|
i++;
|
|
} while(i < shape[ULAB_MAX_DIMS - 3]);
|
|
#endif
|
|
|
|
return MP_OBJ_FROM_PTR(results);
|
|
}
|
|
return mp_const_none;
|
|
}
|
|
|
|
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_median_obj, 1, numerical_median);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_MINMAX
|
|
//| def min(array: _ArrayLike, *, axis: Optional[int] = None) -> _float:
|
|
//| """Return the minimum element of the 1D array"""
|
|
//| ...
|
|
//|
|
|
|
|
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_DEFINE_CONST_FUN_OBJ_KW(numerical_min_obj, 1, numerical_min);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_ROLL
|
|
//| def roll(array: ulab.numpy.ndarray, distance: int, *, axis: Optional[int] = None) -> None:
|
|
//| """Shift the content of a vector by the positions given as the second
|
|
//| argument. If the ``axis`` keyword is supplied, the shift is applied to
|
|
//| the given axis. The array is modified in place."""
|
|
//| ...
|
|
//|
|
|
|
|
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_const_none } },
|
|
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
|
|
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_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);
|
|
|
|
if(!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type)) {
|
|
mp_raise_TypeError(translate("roll argument must be an ndarray"));
|
|
}
|
|
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(args[0].u_obj);
|
|
uint8_t *array = ndarray->array;
|
|
ndarray_obj_t *results = ndarray_new_dense_ndarray(ndarray->ndim, ndarray->shape, ndarray->dtype);
|
|
|
|
int32_t shift = mp_obj_get_int(args[1].u_obj);
|
|
int32_t _shift = shift < 0 ? -shift : shift;
|
|
|
|
size_t counter;
|
|
uint8_t *rarray = (uint8_t *)results->array;
|
|
|
|
if(args[2].u_obj == mp_const_none) { // roll the flattened array
|
|
_shift = _shift % results->len;
|
|
if(shift > 0) { // shift to the right
|
|
rarray += _shift * results->itemsize;
|
|
counter = results->len - _shift;
|
|
} else { // shift to the left
|
|
rarray += (results->len - _shift) * results->itemsize;
|
|
counter = _shift;
|
|
}
|
|
#if ULAB_MAX_DIMS > 3
|
|
size_t i = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
size_t j = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 1
|
|
size_t k = 0;
|
|
do {
|
|
#endif
|
|
size_t l = 0;
|
|
do {
|
|
memcpy(rarray, array, ndarray->itemsize);
|
|
rarray += results->itemsize;
|
|
array += ndarray->strides[ULAB_MAX_DIMS - 1];
|
|
l++;
|
|
if(--counter == 0) {
|
|
rarray = results->array;
|
|
}
|
|
} while(l < ndarray->shape[ULAB_MAX_DIMS - 1]);
|
|
#if ULAB_MAX_DIMS > 1
|
|
array -= ndarray->strides[ULAB_MAX_DIMS - 1] * ndarray->shape[ULAB_MAX_DIMS-1];
|
|
array += ndarray->strides[ULAB_MAX_DIMS - 2];
|
|
k++;
|
|
} while(k < ndarray->shape[ULAB_MAX_DIMS - 2]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
array -= ndarray->strides[ULAB_MAX_DIMS - 2] * ndarray->shape[ULAB_MAX_DIMS-2];
|
|
array += ndarray->strides[ULAB_MAX_DIMS - 3];
|
|
j++;
|
|
} while(j < ndarray->shape[ULAB_MAX_DIMS - 3]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 3
|
|
array -= ndarray->strides[ULAB_MAX_DIMS - 3] * ndarray->shape[ULAB_MAX_DIMS-3];
|
|
array += ndarray->strides[ULAB_MAX_DIMS - 4];
|
|
i++;
|
|
} while(i < ndarray->shape[ULAB_MAX_DIMS - 4]);
|
|
#endif
|
|
} else if(mp_obj_is_int(args[2].u_obj)){
|
|
int8_t ax = tools_get_axis(args[2].u_obj, ndarray->ndim);
|
|
|
|
size_t *shape = m_new(size_t, ULAB_MAX_DIMS);
|
|
memset(shape, 0, sizeof(size_t)*ULAB_MAX_DIMS);
|
|
int32_t *strides = m_new(int32_t, ULAB_MAX_DIMS);
|
|
memset(strides, 0, sizeof(int32_t)*ULAB_MAX_DIMS);
|
|
numerical_reduce_axes(ndarray, ax, shape, strides);
|
|
|
|
size_t *rshape = m_new(size_t, ULAB_MAX_DIMS);
|
|
memset(rshape, 0, sizeof(size_t)*ULAB_MAX_DIMS);
|
|
int32_t *rstrides = m_new(int32_t, ULAB_MAX_DIMS);
|
|
memset(rstrides, 0, sizeof(int32_t)*ULAB_MAX_DIMS);
|
|
numerical_reduce_axes(results, ax, rshape, rstrides);
|
|
|
|
ax = ULAB_MAX_DIMS - ndarray->ndim + ax;
|
|
uint8_t *_rarray;
|
|
_shift = _shift % results->shape[ax];
|
|
|
|
#if ULAB_MAX_DIMS > 3
|
|
size_t i = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
size_t j = 0;
|
|
do {
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 1
|
|
size_t k = 0;
|
|
do {
|
|
#endif
|
|
size_t l = 0;
|
|
_rarray = rarray;
|
|
if(shift < 0) {
|
|
rarray += (results->shape[ax] - _shift) * results->strides[ax];
|
|
counter = _shift;
|
|
} else {
|
|
rarray += _shift * results->strides[ax];
|
|
counter = results->shape[ax] - _shift;
|
|
}
|
|
do {
|
|
memcpy(rarray, array, ndarray->itemsize);
|
|
array += ndarray->strides[ax];
|
|
rarray += results->strides[ax];
|
|
if(--counter == 0) {
|
|
rarray = _rarray;
|
|
}
|
|
l++;
|
|
} while(l < ndarray->shape[ax]);
|
|
#if ULAB_MAX_DIMS > 1
|
|
rarray = _rarray;
|
|
rarray += rstrides[ULAB_MAX_DIMS - 1];
|
|
array -= ndarray->strides[ax] * ndarray->shape[ax];
|
|
array += strides[ULAB_MAX_DIMS - 1];
|
|
k++;
|
|
} while(k < shape[ULAB_MAX_DIMS - 1]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 2
|
|
rarray -= rstrides[ULAB_MAX_DIMS - 1] * rshape[ULAB_MAX_DIMS-1];
|
|
rarray += rstrides[ULAB_MAX_DIMS - 2];
|
|
array -= strides[ULAB_MAX_DIMS - 1] * shape[ULAB_MAX_DIMS-1];
|
|
array += strides[ULAB_MAX_DIMS - 2];
|
|
j++;
|
|
} while(j < shape[ULAB_MAX_DIMS - 2]);
|
|
#endif
|
|
#if ULAB_MAX_DIMS > 3
|
|
rarray -= rstrides[ULAB_MAX_DIMS - 2] * rshape[ULAB_MAX_DIMS-2];
|
|
rarray += rstrides[ULAB_MAX_DIMS - 3];
|
|
array -= strides[ULAB_MAX_DIMS - 2] * shape[ULAB_MAX_DIMS-2];
|
|
array += strides[ULAB_MAX_DIMS - 3];
|
|
i++;
|
|
} while(i < shape[ULAB_MAX_DIMS - 3]);
|
|
#endif
|
|
} else {
|
|
mp_raise_TypeError(translate("wrong axis index"));
|
|
}
|
|
return results;
|
|
}
|
|
|
|
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_roll_obj, 2, numerical_roll);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_SORT
|
|
//| def sort(array: ulab.numpy.ndarray, *, axis: int = -1) -> ulab.numpy.ndarray:
|
|
//| """Sort the array along the given axis, or along all axes if axis is None.
|
|
//| The array is modified in place."""
|
|
//| ...
|
|
//|
|
|
|
|
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_const_none } },
|
|
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, { .u_rom_obj = mp_const_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);
|
|
|
|
return numerical_sort_helper(args[0].u_obj, args[1].u_obj, 0);
|
|
}
|
|
|
|
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_sort_obj, 1, numerical_sort);
|
|
#endif
|
|
|
|
#if NDARRAY_HAS_SORT
|
|
// method of an ndarray
|
|
static 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_const_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(n_args, 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_DEFINE_CONST_FUN_OBJ_KW(numerical_sort_inplace_obj, 1, numerical_sort_inplace);
|
|
#endif /* NDARRAY_HAS_SORT */
|
|
|
|
#if ULAB_NUMPY_HAS_STD
|
|
//| def std(array: _ArrayLike, *, axis: Optional[int] = None, ddof: int = 0) -> _float:
|
|
//| """Return the standard deviation of the array, as a number if axis is None, otherwise as an array."""
|
|
//| ...
|
|
//|
|
|
|
|
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_const_none } } ,
|
|
{ MP_QSTR_axis, MP_ARG_OBJ, {.u_rom_obj = mp_const_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, or an integer"));
|
|
}
|
|
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_sum_mean_std_ndarray(ndarray, axis, NUMERICAL_STD, ddof);
|
|
} else {
|
|
mp_raise_TypeError(translate("input must be tuple, list, range, or ndarray"));
|
|
}
|
|
return mp_const_none;
|
|
}
|
|
|
|
MP_DEFINE_CONST_FUN_OBJ_KW(numerical_std_obj, 1, numerical_std);
|
|
#endif
|
|
|
|
#if ULAB_NUMPY_HAS_SUM
|
|
//| def sum(array: _ArrayLike, *, axis: Optional[int] = None) -> Union[_float, int, ulab.numpy.ndarray]:
|
|
//| """Return the sum of the array, as a number if axis is None, otherwise as an array."""
|
|
//| ...
|
|
//|
|
|
|
|
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_DEFINE_CONST_FUN_OBJ_KW(numerical_sum_obj, 1, numerical_sum);
|
|
#endif
|