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[python/ulab] Updated ulab

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Laury
2021-12-17 21:36:19 +01:00
parent d5dbb8df6f
commit ed06b4e48b
43 changed files with 666 additions and 619 deletions
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243
python/port/mod/ulab/ndarray.c
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@@ -16,11 +16,11 @@
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <py/runtime.h>
#include <py/binary.h>
#include <py/obj.h>
#include <py/objtuple.h>
#include <py/objint.h>
#include "py/runtime.h"
#include "py/binary.h"
#include "py/obj.h"
#include "py/objtuple.h"
#include "py/objint.h"
#include "ulab_tools.h"
#include "ndarray.h"
@@ -45,195 +45,6 @@ mp_uint_t ndarray_print_edgeitems = NDARRAY_PRINT_EDGEITEMS;
//| possible. Numpy's documentation can be found at
//| https://docs.scipy.org/doc/numpy/index.html"""
//|
//| from typing import Dict
//|
//| _DType = int
//| """`ulab.int8`, `ulab.uint8`, `ulab.int16`, `ulab.uint16`, `ulab.float` or `ulab.bool`"""
//|
//| _float = float
//| """Type alias of the bulitin float"""
//|
//| _bool = bool
//| """Type alias of the bulitin bool"""
//|
//| _Index = Union[int, slice, ulab.ndarray, Tuple[Union[int, slice], ...]]
//|
//| class ndarray:
//| """1- and 2- dimensional ndarray"""
//|
//| def __init__(
//| self,
//| values: Union[ndarray, Iterable[Union[_float, _bool, Iterable[Any]]]],
//| *,
//| dtype: _DType = ulab.float
//| ) -> None:
//| """:param sequence values: Sequence giving the initial content of the ndarray.
//| :param ~ulab._DType dtype: The type of ndarray values, `ulab.int8`, `ulab.uint8`, `ulab.int16`, `ulab.uint16`, `ulab.float` or `ulab.bool`
//|
//| The ``values`` sequence can either be another ~ulab.ndarray, sequence of numbers
//| (in which case a 1-dimensional ndarray is created), or a sequence where each
//| subsequence has the same length (in which case a 2-dimensional ndarray is
//| created).
//|
//| Passing a `ulab.ndarray` and a different dtype can be used to convert an ndarray
//| from one dtype to another.
//|
//| In many cases, it is more convenient to create an ndarray from a function
//| like `zeros` or `linspace`.
//|
//| `ulab.ndarray` implements the buffer protocol, so it can be used in many
//| places an `array.array` can be used."""
//| ...
//|
//| shape: Tuple[int, ...]
//| """The size of the array, a tuple of length 1 or 2"""
//|
//| size: int
//| """The number of elements in the array"""
//|
//| itemsize: int
//| """The size of a single item in the array"""
//|
//| strides: Tuple[int, ...]
//| """Tuple of bytes to step in each dimension, a tuple of length 1 or 2"""
//|
//| def copy(self) -> ulab.ndarray:
//| """Return a copy of the array"""
//| ...
//|
//| def dtype(self) -> _DType:
//| """Returns the dtype of the array"""
//| ...
//|
//| def flatten(self, *, order: str = "C") -> ulab.ndarray:
//| """:param order: Whether to flatten by rows ('C') or columns ('F')
//|
//| Returns a new `ulab.ndarray` object which is always 1 dimensional.
//| If order is 'C' (the default", then the data is ordered in rows;
//| If it is 'F', then the data is ordered in columns. "C" and "F" refer
//| to the typical storage organization of the C and Fortran languages."""
//| ...
//|
//| def reshape(self, shape: Tuple[int, ...]) -> ulab.ndarray:
//| """Returns an ndarray containing the same data with a new shape."""
//| ...
//|
//| def sort(self, *, axis: Optional[int] = 1) -> None:
//| """:param axis: Whether to sort elements within rows (0), columns (1), or elements (None)"""
//| ...
//|
//| def tobytes(self) -> bytearray:
//| """Return the raw data bytes in the ndarray"""
//| ...
//|
//| def transpose(self) -> ulab.ndarray:
//| """Swap the rows and columns of a 2-dimensional ndarray"""
//| ...
//|
//| def __add__(self, other: Union[ndarray, _float]) -> ulab.ndarray:
//| """Adds corresponding elements of the two ndarrays, or adds a number to all
//| elements of the ndarray. If both arguments are ndarrays, their sizes must match."""
//| ...
//| def __radd__(self, other: _float) -> ulab.ndarray: ...
//|
//| def __sub__(self, other: Union[ndarray, _float]) -> ulab.ndarray:
//| """Subtracts corresponding elements of the two ndarrays, or subtracts a number from all
//| elements of the ndarray. If both arguments are ndarrays, their sizes must match."""
//| ...
//| def __rsub__(self, other: _float) -> ulab.ndarray: ...
//|
//| def __mul__(self, other: Union[ndarray, _float]) -> ulab.ndarray:
//| """Multiplies corresponding elements of the two ndarrays, or multiplies
//| all elements of the ndarray by a number. If both arguments are ndarrays,
//| their sizes must match."""
//| ...
//| def __rmul__(self, other: _float) -> ulab.ndarray: ...
//|
//| def __div__(self, other: Union[ndarray, _float]) -> ulab.ndarray:
//| """Multiplies corresponding elements of the two ndarrays, or divides
//| all elements of the ndarray by a number. If both arguments are ndarrays,
//| their sizes must match."""
//| ...
//| def __rdiv__(self, other: _float) -> ulab.ndarray: ...
//|
//| def __pow__(self, other: Union[ndarray, _float]) -> ulab.ndarray:
//| """Computes the power (x**y) of corresponding elements of the the two ndarrays,
//| or one number and one ndarray. If both arguments are ndarrays, their sizes
//| must match."""
//| ...
//| def __rpow__(self, other: _float) -> ulab.ndarray: ...
//|
//| def __inv__(self) -> ulab.ndarray:
//| ...
//| def __neg__(self) -> ulab.ndarray:
//| ...
//| def __pos__(self) -> ulab.ndarray:
//| ...
//| def __abs__(self) -> ulab.ndarray:
//| ...
//| def __len__(self) -> int:
//| ...
//| def __lt__(self, other: Union[ndarray, _float]) -> ulab.ndarray:
//| ...
//| def __le__(self, other: Union[ndarray, _float]) -> ulab.ndarray:
//| ...
//| def __gt__(self, other: Union[ndarray, _float]) -> ulab.ndarray:
//| ...
//| def __ge__(self, other: Union[ndarray, _float]) -> ulab.ndarray:
//| ...
//|
//| def __iter__(self) -> Union[Iterator[ndarray], Iterator[_float]]:
//| ...
//|
//| def __getitem__(self, index: _Index) -> Union[ndarray, _float]:
//| """Retrieve an element of the ndarray."""
//| ...
//|
//| def __setitem__(self, index: _Index, value: Union[ndarray, _float]) -> None:
//| """Set an element of the ndarray."""
//| ...
//|
//| _ArrayLike = Union[ndarray, List[_float], Tuple[_float], range]
//| """`ulab.ndarray`, ``List[float]``, ``Tuple[float]`` or `range`"""
//|
//| 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"""
//|
//| def get_printoptions() -> Dict[str, int]:
//| """Get printing options"""
//| ...
//|
//| def set_printoptions(threshold: Optional[int] = None, edgeitems: Optional[int] = None) -> None:
//| """Set printing options"""
//| ...
//|
//| def ndinfo(array: ulab.ndarray) -> None:
//| ...
//|
//| def array(
//| values: Union[ndarray, Iterable[Union[_float, _bool, Iterable[Any]]]],
//| *,
//| dtype: _DType = ulab.float
//| ) -> ulab.ndarray:
//| """alternate constructor function for `ulab.ndarray`. Mirrors numpy.array"""
//| ...
#ifdef CIRCUITPY
void ndarray_set_value(char typecode, void *p, size_t index, mp_obj_t val_in) {
@@ -257,7 +68,7 @@ void ndarray_set_value(char typecode, void *p, size_t index, mp_obj_t val_in) {
}
#endif
#if defined(MICROPY_VERSION_MAJOR) && MICROPY_VERSION_MAJOR == 1 && MICROPY_VERSION_MINOR == 12
#if defined(MICROPY_VERSION_MAJOR) && MICROPY_VERSION_MAJOR == 1 && MICROPY_VERSION_MINOR == 11
void mp_obj_slice_indices(mp_obj_t self_in, mp_int_t length, mp_bound_slice_t *result) {
mp_obj_slice_t *self = MP_OBJ_TO_PTR(self_in);
@@ -581,11 +392,13 @@ static void ndarray_print_row(const mp_print_t *print, ndarray_obj_t * ndarray,
mp_print_str(print, "]");
}
#if ULAB_MAX_DIMS > 1
static void ndarray_print_bracket(const mp_print_t *print, const size_t condition, const size_t shape, const char *string) {
if(condition < shape) {
mp_print_str(print, string);
}
}
#endif
void ndarray_print(const mp_print_t *print, mp_obj_t self_in, mp_print_kind_t kind) {
(void)kind;
@@ -596,9 +409,13 @@ void ndarray_print(const mp_print_t *print, mp_obj_t self_in, mp_print_kind_t ki
mp_print_str(print, "[]");
if(self->ndim > 1) {
mp_print_str(print, ", shape=(");
#if ULAB_MAX_DIMS > 1
for(uint8_t ndim = self->ndim; ndim > 1; ndim--) {
mp_printf(MP_PYTHON_PRINTER, "%d,", self->shape[ULAB_MAX_DIMS - ndim]);
}
#else
mp_printf(MP_PYTHON_PRINTER, "%d,", self->shape[0]);
#endif
mp_printf(MP_PYTHON_PRINTER, "%d)", self->shape[ULAB_MAX_DIMS - 1]);
}
} else {
@@ -1110,18 +927,6 @@ mp_obj_t ndarray_array_constructor(size_t n_args, const mp_obj_t *pos_args, mp_m
}
MP_DEFINE_CONST_FUN_OBJ_KW(ndarray_array_constructor_obj, 1, ndarray_array_constructor);
#ifdef CIRCUITPY
mp_obj_t ndarray_make_new(const mp_obj_type_t *type, size_t n_args, const mp_obj_t *args, mp_map_t *kw_args) {
(void) type;
mp_arg_check_num(n_args, kw_args, 1, 2, true);
size_t n_kw = 0;
if (kw_args != 0) {
n_kw = kw_args->used;
}
mp_map_init_fixed_table(kw_args, n_kw, args + n_args);
return ndarray_make_new_core(type, n_args, n_kw, args, kw_args);
}
#else
mp_obj_t ndarray_make_new(const mp_obj_type_t *type, size_t n_args, size_t n_kw, const mp_obj_t *args) {
(void) type;
mp_arg_check_num(n_args, n_kw, 1, 2, true);
@@ -1129,7 +934,6 @@ mp_obj_t ndarray_make_new(const mp_obj_type_t *type, size_t n_args, size_t n_kw,
mp_map_init_fixed_table(&kw_args, n_kw, args + n_args);
return ndarray_make_new_core(type, n_args, n_kw, args, &kw_args);
}
#endif
// broadcasting is used at a number of places, always include
bool ndarray_can_broadcast(ndarray_obj_t *lhs, ndarray_obj_t *rhs, uint8_t *ndim, size_t *shape, int32_t *lstrides, int32_t *rstrides) {
@@ -1555,12 +1359,12 @@ mp_obj_t ndarray_iternext(mp_obj_t self_in) {
mp_obj_t ndarray_new_ndarray_iterator(mp_obj_t ndarray, mp_obj_iter_buf_t *iter_buf) {
assert(sizeof(mp_obj_ndarray_it_t) <= sizeof(mp_obj_iter_buf_t));
mp_obj_ndarray_it_t *o = (mp_obj_ndarray_it_t*)iter_buf;
o->base.type = &mp_type_polymorph_iter;
o->iternext = ndarray_iternext;
o->ndarray = ndarray;
o->cur = 0;
return MP_OBJ_FROM_PTR(o);
mp_obj_ndarray_it_t *iter = (mp_obj_ndarray_it_t *)iter_buf;
iter->base.type = &mp_type_polymorph_iter;
iter->iternext = ndarray_iternext;
iter->ndarray = ndarray;
iter->cur = 0;
return MP_OBJ_FROM_PTR(iter);
}
#endif /* NDARRAY_IS_ITERABLE */
@@ -1675,12 +1479,13 @@ mp_obj_t ndarray_itemsize(mp_obj_t self_in) {
#if NDARRAY_HAS_SHAPE
mp_obj_t ndarray_shape(mp_obj_t self_in) {
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
mp_obj_t *items = m_new(mp_obj_t, self->ndim);
for(uint8_t i=0; i < self->ndim; i++) {
items[self->ndim - i - 1] = mp_obj_new_int(self->shape[ULAB_MAX_DIMS - i - 1]);
uint8_t nitems = MAX(1, self->ndim);
mp_obj_t *items = m_new(mp_obj_t, nitems);
for(uint8_t i = 0; i < nitems; i++) {
items[nitems - i - 1] = mp_obj_new_int(self->shape[ULAB_MAX_DIMS - i - 1]);
}
mp_obj_t tuple = mp_obj_new_tuple(self->ndim, items);
m_del(mp_obj_t, items, self->ndim);
mp_obj_t tuple = mp_obj_new_tuple(nitems, items);
m_del(mp_obj_t, items, nitems);
return tuple;
}
#endif