1228 lines
51 KiB
C
1228 lines
51 KiB
C
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/*
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* This file is part of the micropython-ulab project,
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*
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* https://github.com/v923z/micropython-ulab
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*
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* The MIT License (MIT)
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*
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* Copyright (c) 2019-2020 Zoltán Vörös
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*/
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#include <unistd.h>
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include "py/runtime.h"
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#include "py/binary.h"
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#include "py/obj.h"
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#include "py/objtuple.h"
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#include "ndarray.h"
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mp_uint_t ndarray_print_threshold = NDARRAY_PRINT_THRESHOLD;
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mp_uint_t ndarray_print_edgeitems = NDARRAY_PRINT_EDGEITEMS;
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//| """Manipulate numeric data similar to numpy
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//| `ulab` is a numpy-like module for micropython, meant to simplify and
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//| speed up common mathematical operations on arrays. The primary goal was to
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//| implement a small subset of numpy that might be useful in the context of a
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//| microcontroller. This means low-level data processing of linear (array) and
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//| two-dimensional (matrix) data.
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//| `ulab` is adapted from micropython-ulab, and the original project's
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//| documentation can be found at
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//| https://micropython-ulab.readthedocs.io/en/latest/
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//| `ulab` is modeled after numpy, and aims to be a compatible subset where
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//| possible. Numpy's documentation can be found at
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//| https://docs.scipy.org/doc/numpy/index.html"""
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//|
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//| class array:
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//| """1- and 2- dimensional array"""
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//| def __init__(self, values, *, dtype=float):
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//| """:param sequence values: Sequence giving the initial content of the array.
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//| :param dtype: The type of array values, ``int8``, ``uint8``, ``int16``, ``uint16``, or ``float``
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//| The `values` sequence can either be another ~ulab.array, sequence of numbers
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//| (in which case a 1-dimensional array is created), or a sequence where each
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//| subsequence has the same length (in which case a 2-dimensional array is
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//| created).
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//| Passing a ~ulab.array and a different dtype can be used to convert an array
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//| from one dtype to another.
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//| In many cases, it is more convenient to create an array from a function
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//| like `zeros` or `linspace`.
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//| `ulab.array` implements the buffer protocol, so it can be used in many
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//| places an `array.array` can be used."""
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//| ...
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//|
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//| shape: tuple = ...
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//| """The size of the array, a tuple of length 1 or 2"""
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//|
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//| size: int = ...
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//| """The number of elements in the array"""
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//|
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//| itemsize: int = ...
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//| """The number of elements in the array"""
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//|
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//| def flatten(self, *, order='C'):
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//| """:param order: Whether to flatten by rows ('C') or columns ('F')
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//| Returns a new `ulab.array` object which is always 1 dimensional.
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//| If order is 'C' (the default", then the data is ordered in rows;
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//| If it is 'F', then the data is ordered in columns. "C" and "F" refer
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//| to the typical storage organization of the C and Fortran languages."""
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//| ...
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//|
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//| def sort(self, *, axis=1):
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//| """:param axis: Whether to sort elements within rows (0), columns (1), or elements (None)"""
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//| ...
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//|
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//| def transpose(self):
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//| """Swap the rows and columns of a 2-dimensional array"""
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//| ...
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//|
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//| def __add__(self):
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//| """Adds corresponding elements of the two arrays, or adds a number to all
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//| elements of the array. If both arguments are arrays, their sizes must match."""
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//| ...
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//|
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//| def __sub__(self):
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//| """Subtracts corresponding elements of the two arrays, or adds a number to all
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//| elements of the array. If both arguments are arrays, their sizes must match."""
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//| ...
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//|
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//| def __mul__(self):
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//| """Multiplies corresponding elements of the two arrays, or multiplies
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//| all elements of the array by a number. If both arguments are arrays,
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//| their sizes must match."""
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//| ...
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//|
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//| def __div__(self):
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//| """Multiplies corresponding elements of the two arrays, or divides
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//| all elements of the array by a number. If both arguments are arrays,
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//| their sizes must match."""
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//| ...
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//|
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//| def __pow__():
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//| """Computes the power (x**y) of corresponding elements of the the two arrays,
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//| or one number and one array. If both arguments are arrays, their sizes
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//| must match."""
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//| ...
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//|
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//| def __getitem__():
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//| """Retrieve an element of the array."""
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//| ...
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//|
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//| def __setitem__():
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//| """Set an element of the array."""
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//| ...
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//|
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//| int8 = ...
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//| """Type code for signed integers in the range -128 .. 127 inclusive, like the 'b' typecode of `array.array`"""
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//|
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//| int16 = ...
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//| """Type code for signed integers in the range -32768 .. 32767 inclusive, like the 'h' typecode of `array.array`"""
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//|
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//| float = ...
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//| """Type code for floating point values, like the 'f' typecode of `array.array`"""
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//|
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//| uint8 = ...
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//| """Type code for unsigned integers in the range 0 .. 255 inclusive, like the 'H' typecode of `array.array`"""
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//|
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//| uint16 = ...
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//| """Type code for unsigned integers in the range 0 .. 65535 inclusive, like the 'h' typecode of `array.array`"""
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//|
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// This function is copied verbatim from objarray.c
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STATIC mp_obj_array_t *array_new(char typecode, size_t n) {
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int typecode_size = mp_binary_get_size('@', typecode, NULL);
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mp_obj_array_t *o = m_new_obj(mp_obj_array_t);
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// this step could probably be skipped: we are never going to store a bytearray per se
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#if MICROPY_PY_BUILTINS_BYTEARRAY && MICROPY_PY_ARRAY
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o->base.type = (typecode == BYTEARRAY_TYPECODE) ? &mp_type_bytearray : &mp_type_array;
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#elif MICROPY_PY_BUILTINS_BYTEARRAY
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o->base.type = &mp_type_bytearray;
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#else
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o->base.type = &mp_type_array;
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#endif
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o->typecode = typecode;
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o->free = 0;
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o->len = n;
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o->items = m_new(byte, typecode_size * o->len);
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return o;
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}
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#ifdef OPENMV
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void mp_obj_slice_indices(mp_obj_t self_in, mp_int_t length, mp_bound_slice_t *result) {
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mp_obj_slice_t *self = MP_OBJ_TO_PTR(self_in);
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mp_int_t start, stop, step;
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if (self->step == mp_const_none) {
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step = 1;
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} else {
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step = mp_obj_get_int(self->step);
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if (step == 0) {
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mp_raise_ValueError(translate("slice step can't be zero"));
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}
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}
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if (step > 0) {
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// Positive step
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if (self->start == mp_const_none) {
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start = 0;
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} else {
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start = mp_obj_get_int(self->start);
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if (start < 0) {
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start += length;
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}
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start = MIN(length, MAX(start, 0));
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}
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if (self->stop == mp_const_none) {
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stop = length;
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} else {
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stop = mp_obj_get_int(self->stop);
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if (stop < 0) {
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stop += length;
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}
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stop = MIN(length, MAX(stop, 0));
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}
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} else {
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// Negative step
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if (self->start == mp_const_none) {
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start = length - 1;
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} else {
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start = mp_obj_get_int(self->start);
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if (start < 0) {
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start += length;
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}
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start = MIN(length - 1, MAX(start, -1));
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}
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if (self->stop == mp_const_none) {
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stop = -1;
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} else {
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stop = mp_obj_get_int(self->stop);
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if (stop < 0) {
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stop += length;
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}
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stop = MIN(length - 1, MAX(stop, -1));
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}
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}
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result->start = start;
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result->stop = stop;
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result->step = step;
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}
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#endif
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mp_float_t ndarray_get_float_value(void *data, uint8_t typecode, size_t index) {
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if(typecode == NDARRAY_UINT8) {
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return (mp_float_t)((uint8_t *)data)[index];
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} else if(typecode == NDARRAY_INT8) {
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return (mp_float_t)((int8_t *)data)[index];
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} else if(typecode == NDARRAY_UINT16) {
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return (mp_float_t)((uint16_t *)data)[index];
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} else if(typecode == NDARRAY_INT16) {
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return (mp_float_t)((int16_t *)data)[index];
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} else {
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return (mp_float_t)((mp_float_t *)data)[index];
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}
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}
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bool ndarray_object_is_nditerable(mp_obj_t o_in) {
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if(MP_OBJ_IS_TYPE(o_in, &ulab_ndarray_type) ||
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MP_OBJ_IS_TYPE(o_in, &mp_type_tuple) ||
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MP_OBJ_IS_TYPE(o_in, &mp_type_list) ||
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MP_OBJ_IS_TYPE(o_in, &mp_type_range)) {
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return true;
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}
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return false;
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}
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void fill_array_iterable(mp_float_t *array, mp_obj_t iterable) {
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mp_obj_iter_buf_t x_buf;
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mp_obj_t x_item, x_iterable = mp_getiter(iterable, &x_buf);
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size_t i=0;
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while ((x_item = mp_iternext(x_iterable)) != MP_OBJ_STOP_ITERATION) {
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array[i] = (mp_float_t)mp_obj_get_float(x_item);
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i++;
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}
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}
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mp_obj_t ndarray_set_printoptions(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
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static const mp_arg_t allowed_args[] = {
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{ MP_QSTR_threshold, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_none} },
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{ MP_QSTR_edgeitems, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = mp_const_none} },
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};
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mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
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mp_arg_parse_all(n_args, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
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if(args[0].u_rom_obj != mp_const_none) {
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ndarray_print_threshold = mp_obj_get_int(args[0].u_rom_obj);
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}
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if(args[1].u_rom_obj != mp_const_none) {
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ndarray_print_edgeitems = mp_obj_get_int(args[1].u_rom_obj);
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}
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return mp_const_none;
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}
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MP_DEFINE_CONST_FUN_OBJ_KW(ndarray_set_printoptions_obj, 0, ndarray_set_printoptions);
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mp_obj_t ndarray_get_printoptions(void) {
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mp_obj_t dict = mp_obj_new_dict(2);
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mp_obj_dict_store(MP_OBJ_FROM_PTR(dict), MP_OBJ_NEW_QSTR(MP_QSTR_threshold), mp_obj_new_int(ndarray_print_threshold));
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mp_obj_dict_store(MP_OBJ_FROM_PTR(dict), MP_OBJ_NEW_QSTR(MP_QSTR_edgeitems), mp_obj_new_int(ndarray_print_edgeitems));
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return dict;
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}
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MP_DEFINE_CONST_FUN_OBJ_0(ndarray_get_printoptions_obj, ndarray_get_printoptions);
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void ndarray_print_row(const mp_print_t *print, mp_obj_array_t *data, size_t n0, size_t n) {
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mp_print_str(print, "[");
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if((n <= ndarray_print_threshold) || (n <= 2*ndarray_print_edgeitems)) { // if the array is short, print everything
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mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0), PRINT_REPR);
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for(size_t i=1; i < n; i++) {
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mp_print_str(print, ", ");
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mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0+i), PRINT_REPR);
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}
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} else {
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mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0), PRINT_REPR);
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for(size_t i=1; i < ndarray_print_edgeitems; i++) {
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mp_print_str(print, ", ");
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mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0+i), PRINT_REPR);
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}
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mp_printf(print, ", ..., ");
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mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0+n-ndarray_print_edgeitems), PRINT_REPR);
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for(size_t i=1; i < ndarray_print_edgeitems; i++) {
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mp_print_str(print, ", ");
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mp_obj_print_helper(print, mp_binary_get_val_array(data->typecode, data->items, n0+n-ndarray_print_edgeitems+i), PRINT_REPR);
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}
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}
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mp_print_str(print, "]");
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}
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void ndarray_print(const mp_print_t *print, mp_obj_t self_in, mp_print_kind_t kind) {
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(void)kind;
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ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
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mp_print_str(print, "array(");
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if(self->array->len == 0) {
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mp_print_str(print, "[]");
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} else {
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if((self->m == 1) || (self->n == 1)) {
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ndarray_print_row(print, self->array, 0, self->array->len);
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} else {
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// TODO: add vertical ellipses
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mp_print_str(print, "[");
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ndarray_print_row(print, self->array, 0, self->n);
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for(size_t i=1; i < self->m; i++) {
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mp_print_str(print, ",\n\t ");
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ndarray_print_row(print, self->array, i*self->n, self->n);
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}
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mp_print_str(print, "]");
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}
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}
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if(self->array->typecode == NDARRAY_UINT8) {
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mp_print_str(print, ", dtype=uint8)");
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} else if(self->array->typecode == NDARRAY_INT8) {
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mp_print_str(print, ", dtype=int8)");
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} else if(self->array->typecode == NDARRAY_UINT16) {
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mp_print_str(print, ", dtype=uint16)");
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} else if(self->array->typecode == NDARRAY_INT16) {
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mp_print_str(print, ", dtype=int16)");
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} else if(self->array->typecode == NDARRAY_FLOAT) {
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mp_print_str(print, ", dtype=float)");
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}
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}
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void ndarray_assign_elements(mp_obj_array_t *data, mp_obj_t iterable, uint8_t typecode, size_t *idx) {
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// assigns a single row in the matrix
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mp_obj_t item;
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while ((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
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mp_binary_set_val_array(typecode, data->items, (*idx)++, item);
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}
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}
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ndarray_obj_t *create_new_ndarray(size_t m, size_t n, uint8_t typecode) {
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// Creates the base ndarray with shape (m, n), and initialises the values to straight 0s
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ndarray_obj_t *ndarray = m_new_obj(ndarray_obj_t);
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ndarray->base.type = &ulab_ndarray_type;
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ndarray->m = m;
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ndarray->n = n;
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mp_obj_array_t *array = array_new(typecode, m*n);
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ndarray->bytes = m * n * mp_binary_get_size('@', typecode, NULL);
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// this should set all elements to 0, irrespective of the of the typecode (all bits are zero)
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// we could, perhaps, leave this step out, and initialise the array only, when needed
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memset(array->items, 0, ndarray->bytes);
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ndarray->array = array;
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return ndarray;
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}
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mp_obj_t ndarray_copy(mp_obj_t self_in) {
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// returns a verbatim (shape and typecode) copy of self_in
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ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
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ndarray_obj_t *out = create_new_ndarray(self->m, self->n, self->array->typecode);
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memcpy(out->array->items, self->array->items, self->bytes);
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return MP_OBJ_FROM_PTR(out);
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}
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STATIC uint8_t ndarray_init_helper(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
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static const mp_arg_t allowed_args[] = {
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{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = mp_const_none } },
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{ MP_QSTR_dtype, MP_ARG_KW_ONLY | MP_ARG_INT, {.u_int = NDARRAY_FLOAT } },
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};
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mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
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mp_arg_parse_all(n_args, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
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uint8_t dtype = args[1].u_int;
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return dtype;
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}
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STATIC mp_obj_t ndarray_make_new_core(size_t n_args, const mp_obj_t *args, mp_map_t *kw_args) {
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uint8_t dtype = ndarray_init_helper(n_args, args, kw_args);
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if(MP_OBJ_IS_TYPE(args[0], &ulab_ndarray_type)) {
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ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(args[0]);
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if(dtype == ndarray->array->typecode) {
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return ndarray_copy(args[0]);
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}
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ndarray_obj_t *ndarray_new = create_new_ndarray(ndarray->m, ndarray->n, dtype);
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mp_obj_t item;
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if((ndarray->array->typecode == NDARRAY_FLOAT) &&(dtype != NDARRAY_FLOAT)) {
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for(size_t i=0; i < ndarray->array->len; i++) {
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mp_float_t f = ndarray_get_float_value(ndarray->array->items, ndarray->array->typecode, i);
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item = mp_obj_new_int((int32_t)MICROPY_FLOAT_C_FUN(floor)(f));
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mp_binary_set_val_array(dtype, ndarray_new->array->items, i, item);
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}
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} else {
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for(size_t i=0; i < ndarray->array->len; i++) {
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item = mp_binary_get_val_array(ndarray->array->typecode, ndarray->array->items, i);
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mp_binary_set_val_array(dtype, ndarray_new->array->items, i, item);
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}
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}
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return MP_OBJ_FROM_PTR(ndarray_new);
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}
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size_t len1, len2 = 0, i = 0;
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mp_obj_t len_in = mp_obj_len_maybe(args[0]);
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if (len_in == MP_OBJ_NULL) {
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mp_raise_ValueError(translate("first argument must be an iterable"));
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} else {
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// len1 is either the number of rows (for matrices), or the number of elements (row vectors)
|
|
len1 = MP_OBJ_SMALL_INT_VALUE(len_in);
|
|
}
|
|
|
|
// We have to figure out, whether the first element of the iterable is an iterable itself
|
|
// Perhaps, there is a more elegant way of handling this
|
|
mp_obj_iter_buf_t iter_buf1;
|
|
mp_obj_t item1, iterable1 = mp_getiter(args[0], &iter_buf1);
|
|
while ((item1 = mp_iternext(iterable1)) != MP_OBJ_STOP_ITERATION) {
|
|
len_in = mp_obj_len_maybe(item1);
|
|
if(len_in != MP_OBJ_NULL) { // indeed, this seems to be an iterable
|
|
// Next, we have to check, whether all elements in the outer loop have the same length
|
|
if(i > 0) {
|
|
if(len2 != (size_t)MP_OBJ_SMALL_INT_VALUE(len_in)) {
|
|
mp_raise_ValueError(translate("iterables are not of the same length"));
|
|
}
|
|
}
|
|
len2 = MP_OBJ_SMALL_INT_VALUE(len_in);
|
|
i++;
|
|
}
|
|
}
|
|
// By this time, it should be established, what the shape is, so we can now create the array
|
|
ndarray_obj_t *self = create_new_ndarray((len2 == 0) ? 1 : len1, (len2 == 0) ? len1 : len2, dtype);
|
|
iterable1 = mp_getiter(args[0], &iter_buf1);
|
|
i = 0;
|
|
if(len2 == 0) { // the first argument is a single iterable
|
|
ndarray_assign_elements(self->array, iterable1, dtype, &i);
|
|
} else {
|
|
mp_obj_iter_buf_t iter_buf2;
|
|
mp_obj_t iterable2;
|
|
|
|
while ((item1 = mp_iternext(iterable1)) != MP_OBJ_STOP_ITERATION) {
|
|
iterable2 = mp_getiter(item1, &iter_buf2);
|
|
ndarray_assign_elements(self->array, iterable2, dtype, &i);
|
|
}
|
|
}
|
|
return MP_OBJ_FROM_PTR(self);
|
|
}
|
|
|
|
#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(n_args, 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);
|
|
mp_map_t kw_args;
|
|
mp_map_init_fixed_table(&kw_args, n_kw, args + n_args);
|
|
return ndarray_make_new_core(n_args, args, &kw_args);
|
|
}
|
|
#endif
|
|
|
|
static size_t slice_length(mp_bound_slice_t slice) {
|
|
ssize_t len, correction = 1;
|
|
if(slice.step > 0) correction = -1;
|
|
len = (ssize_t)(slice.stop - slice.start + (slice.step + correction)) / slice.step;
|
|
if(len < 0) return 0;
|
|
return (size_t)len;
|
|
}
|
|
|
|
static size_t true_length(mp_obj_t bool_list) {
|
|
// returns the number of Trues in a Boolean list
|
|
// I wonder, wouldn't this be faster, if we looped through bool_list->items instead?
|
|
mp_obj_iter_buf_t iter_buf;
|
|
mp_obj_t item, iterable = mp_getiter(bool_list, &iter_buf);
|
|
size_t trues = 0;
|
|
while((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
if(!mp_obj_is_bool(item)) {
|
|
// numpy seems to be a little bit inconsistent in when an index is considered
|
|
// to be True/False. Bail out immediately, if the items are not True/False
|
|
mp_raise_TypeError(translate("wrong index type"));
|
|
}
|
|
if(mp_obj_is_true(item)) {
|
|
trues++;
|
|
}
|
|
}
|
|
return trues;
|
|
}
|
|
|
|
static mp_bound_slice_t generate_slice(mp_int_t n, mp_obj_t index) {
|
|
// micropython seems to have difficulties with negative steps
|
|
mp_bound_slice_t slice;
|
|
if(MP_OBJ_IS_TYPE(index, &mp_type_slice)) {
|
|
mp_obj_slice_indices(index, n, &slice);
|
|
} else if(MP_OBJ_IS_INT(index)) {
|
|
mp_int_t _index = mp_obj_get_int(index);
|
|
if(_index < 0) {
|
|
_index += n;
|
|
}
|
|
if((_index >= n) || (_index < 0)) {
|
|
mp_raise_msg(&mp_type_IndexError, translate("index is out of bounds"));
|
|
}
|
|
slice.start = _index;
|
|
slice.stop = _index + 1;
|
|
slice.step = 1;
|
|
} else {
|
|
mp_raise_msg(&mp_type_IndexError, translate("indices must be integers, slices, or Boolean lists"));
|
|
}
|
|
return slice;
|
|
}
|
|
|
|
static mp_bound_slice_t simple_slice(int16_t start, int16_t stop, int16_t step) {
|
|
mp_bound_slice_t slice;
|
|
slice.start = start;
|
|
slice.stop = stop;
|
|
slice.step = step;
|
|
return slice;
|
|
}
|
|
|
|
static void insert_binary_value(ndarray_obj_t *ndarray, size_t nd_index, ndarray_obj_t *values, size_t value_index) {
|
|
// there is probably a more elegant implementation...
|
|
mp_obj_t tmp = mp_binary_get_val_array(values->array->typecode, values->array->items, value_index);
|
|
if((values->array->typecode == NDARRAY_FLOAT) && (ndarray->array->typecode != NDARRAY_FLOAT)) {
|
|
// workaround: rounding seems not to work in the arm compiler
|
|
int32_t x = (int32_t)MICROPY_FLOAT_C_FUN(floor)(mp_obj_get_float(tmp)+0.5);
|
|
tmp = mp_obj_new_int(x);
|
|
}
|
|
mp_binary_set_val_array(ndarray->array->typecode, ndarray->array->items, nd_index, tmp);
|
|
}
|
|
|
|
static mp_obj_t insert_slice_list(ndarray_obj_t *ndarray, size_t m, size_t n,
|
|
mp_bound_slice_t row, mp_bound_slice_t column,
|
|
mp_obj_t row_list, mp_obj_t column_list,
|
|
ndarray_obj_t *values) {
|
|
if((m != values->m) && (n != values->n)) {
|
|
if(values->array->len != 1) { // not a single item
|
|
mp_raise_ValueError(translate("could not broadast input array from shape"));
|
|
}
|
|
}
|
|
size_t cindex, rindex;
|
|
// M, and N are used to manipulate how the source index is incremented in the loop
|
|
uint8_t M = 1, N = 1;
|
|
if(values->m == 1) {
|
|
M = 0;
|
|
}
|
|
if(values->n == 1) {
|
|
N = 0;
|
|
}
|
|
|
|
if(row_list == mp_const_none) { // rows are indexed by a slice
|
|
rindex = row.start;
|
|
if(column_list == mp_const_none) { // columns are indexed by a slice
|
|
for(size_t i=0; i < m; i++) {
|
|
cindex = column.start;
|
|
for(size_t j=0; j < n; j++) {
|
|
insert_binary_value(ndarray, rindex*ndarray->n+cindex, values, i*M*n+j*N);
|
|
cindex += column.step;
|
|
}
|
|
rindex += row.step;
|
|
}
|
|
} else { // columns are indexed by a Boolean list
|
|
mp_obj_iter_buf_t column_iter_buf;
|
|
mp_obj_t column_item, column_iterable;
|
|
for(size_t i=0; i < m; i++) {
|
|
column_iterable = mp_getiter(column_list, &column_iter_buf);
|
|
size_t j = 0;
|
|
cindex = 0;
|
|
while((column_item = mp_iternext(column_iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
if(mp_obj_is_true(column_item)) {
|
|
insert_binary_value(ndarray, rindex*ndarray->n+cindex, values, i*M*n+j*N);
|
|
j++;
|
|
}
|
|
cindex++;
|
|
}
|
|
rindex += row.step;
|
|
}
|
|
}
|
|
} else { // rows are indexed by a Boolean list
|
|
mp_obj_iter_buf_t row_iter_buf;
|
|
mp_obj_t row_item, row_iterable;
|
|
row_iterable = mp_getiter(row_list, &row_iter_buf);
|
|
size_t i = 0;
|
|
rindex = 0;
|
|
if(column_list == mp_const_none) { // columns are indexed by a slice
|
|
while((row_item = mp_iternext(row_iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
if(mp_obj_is_true(row_item)) {
|
|
cindex = column.start;
|
|
for(size_t j=0; j < n; j++) {
|
|
insert_binary_value(ndarray, rindex*ndarray->n+cindex, values, i*M*n+j*N);
|
|
cindex += column.step;
|
|
}
|
|
i++;
|
|
}
|
|
rindex++;
|
|
}
|
|
} else { // columns are indexed by a list
|
|
mp_obj_iter_buf_t column_iter_buf;
|
|
mp_obj_t column_item, column_iterable;
|
|
size_t j = 0;
|
|
cindex = 0;
|
|
while((row_item = mp_iternext(row_iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
if(mp_obj_is_true(row_item)) {
|
|
column_iterable = mp_getiter(column_list, &column_iter_buf);
|
|
while((column_item = mp_iternext(column_iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
if(mp_obj_is_true(column_item)) {
|
|
insert_binary_value(ndarray, rindex*ndarray->n+cindex, values, i*M*n+j*N);
|
|
j++;
|
|
}
|
|
cindex++;
|
|
}
|
|
i++;
|
|
}
|
|
rindex++;
|
|
}
|
|
}
|
|
}
|
|
return mp_const_none;
|
|
}
|
|
|
|
static mp_obj_t iterate_slice_list(ndarray_obj_t *ndarray, size_t m, size_t n,
|
|
mp_bound_slice_t row, mp_bound_slice_t column,
|
|
mp_obj_t row_list, mp_obj_t column_list,
|
|
ndarray_obj_t *values) {
|
|
if(values != NULL) {
|
|
return insert_slice_list(ndarray, m, n, row, column, row_list, column_list, values);
|
|
}
|
|
uint8_t _sizeof = mp_binary_get_size('@', ndarray->array->typecode, NULL);
|
|
ndarray_obj_t *out = create_new_ndarray(m, n, ndarray->array->typecode);
|
|
uint8_t *target = (uint8_t *)out->array->items;
|
|
uint8_t *source = (uint8_t *)ndarray->array->items;
|
|
size_t cindex, rindex;
|
|
if(row_list == mp_const_none) { // rows are indexed by a slice
|
|
rindex = row.start;
|
|
if(column_list == mp_const_none) { // columns are indexed by a slice
|
|
for(size_t i=0; i < m; i++) {
|
|
cindex = column.start;
|
|
for(size_t j=0; j < n; j++) {
|
|
memcpy(target+(i*n+j)*_sizeof, source+(rindex*ndarray->n+cindex)*_sizeof, _sizeof);
|
|
cindex += column.step;
|
|
}
|
|
rindex += row.step;
|
|
}
|
|
} else { // columns are indexed by a Boolean list
|
|
// TODO: the list must be exactly as long as the axis
|
|
mp_obj_iter_buf_t column_iter_buf;
|
|
mp_obj_t column_item, column_iterable;
|
|
for(size_t i=0; i < m; i++) {
|
|
column_iterable = mp_getiter(column_list, &column_iter_buf);
|
|
size_t j = 0;
|
|
cindex = 0;
|
|
while((column_item = mp_iternext(column_iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
if(mp_obj_is_true(column_item)) {
|
|
memcpy(target+(i*n+j)*_sizeof, source+(rindex*ndarray->n+cindex)*_sizeof, _sizeof);
|
|
j++;
|
|
}
|
|
cindex++;
|
|
}
|
|
rindex += row.step;
|
|
}
|
|
}
|
|
} else { // rows are indexed by a Boolean list
|
|
mp_obj_iter_buf_t row_iter_buf;
|
|
mp_obj_t row_item, row_iterable;
|
|
row_iterable = mp_getiter(row_list, &row_iter_buf);
|
|
size_t i = 0;
|
|
rindex = 0;
|
|
if(column_list == mp_const_none) { // columns are indexed by a slice
|
|
while((row_item = mp_iternext(row_iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
if(mp_obj_is_true(row_item)) {
|
|
cindex = column.start;
|
|
for(size_t j=0; j < n; j++) {
|
|
memcpy(target+(i*n+j)*_sizeof, source+(rindex*ndarray->n+cindex)*_sizeof, _sizeof);
|
|
cindex += column.step;
|
|
}
|
|
i++;
|
|
}
|
|
rindex++;
|
|
}
|
|
} else { // columns are indexed by a list
|
|
mp_obj_iter_buf_t column_iter_buf;
|
|
mp_obj_t column_item, column_iterable;
|
|
size_t j = 0;
|
|
cindex = 0;
|
|
while((row_item = mp_iternext(row_iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
if(mp_obj_is_true(row_item)) {
|
|
column_iterable = mp_getiter(column_list, &column_iter_buf);
|
|
while((column_item = mp_iternext(column_iterable)) != MP_OBJ_STOP_ITERATION) {
|
|
if(mp_obj_is_true(column_item)) {
|
|
memcpy(target+(i*n+j)*_sizeof, source+(rindex*ndarray->n+cindex)*_sizeof, _sizeof);
|
|
j++;
|
|
}
|
|
cindex++;
|
|
}
|
|
i++;
|
|
}
|
|
rindex++;
|
|
}
|
|
}
|
|
}
|
|
return MP_OBJ_FROM_PTR(out);
|
|
}
|
|
|
|
static mp_obj_t ndarray_get_slice(ndarray_obj_t *ndarray, mp_obj_t index, ndarray_obj_t *values) {
|
|
mp_bound_slice_t row_slice = simple_slice(0, 0, 1), column_slice = simple_slice(0, 0, 1);
|
|
|
|
size_t m = 0, n = 0;
|
|
if(MP_OBJ_IS_INT(index) && (ndarray->m == 1) && (values == NULL)) {
|
|
// we have a row vector, and don't want to assign
|
|
column_slice = generate_slice(ndarray->n, index);
|
|
if(slice_length(column_slice) == 1) { // we were asked for a single item
|
|
// subscribe returns an mp_obj_t, if and only, if the index is an integer, and we have a row vector
|
|
return mp_binary_get_val_array(ndarray->array->typecode, ndarray->array->items, column_slice.start);
|
|
}
|
|
}
|
|
|
|
if(MP_OBJ_IS_INT(index) || MP_OBJ_IS_TYPE(index, &mp_type_slice)) {
|
|
if(ndarray->m == 1) { // we have a row vector
|
|
column_slice = generate_slice(ndarray->n, index);
|
|
row_slice = simple_slice(0, 1, 1);
|
|
} else { // we have a matrix
|
|
row_slice = generate_slice(ndarray->m, index);
|
|
column_slice = simple_slice(0, ndarray->n, 1); // take all columns
|
|
}
|
|
m = slice_length(row_slice);
|
|
n = slice_length(column_slice);
|
|
return iterate_slice_list(ndarray, m, n, row_slice, column_slice, mp_const_none, mp_const_none, values);
|
|
} else if(MP_OBJ_IS_TYPE(index, &mp_type_list)) {
|
|
n = true_length(index);
|
|
if(ndarray->m == 1) { // we have a flat array
|
|
// we might have to separate the n == 1 case
|
|
row_slice = simple_slice(0, 1, 1);
|
|
return iterate_slice_list(ndarray, 1, n, row_slice, column_slice, mp_const_none, index, values);
|
|
} else { // we have a matrix
|
|
return iterate_slice_list(ndarray, 1, n, row_slice, column_slice, mp_const_none, index, values);
|
|
}
|
|
}
|
|
else { // we certainly have a tuple, so let us deal with it
|
|
mp_obj_tuple_t *tuple = MP_OBJ_TO_PTR(index);
|
|
if(tuple->len != 2) {
|
|
mp_raise_msg(&mp_type_IndexError, translate("too many indices"));
|
|
}
|
|
if(!(MP_OBJ_IS_TYPE(tuple->items[0], &mp_type_list) ||
|
|
MP_OBJ_IS_TYPE(tuple->items[0], &mp_type_slice) ||
|
|
MP_OBJ_IS_INT(tuple->items[0])) ||
|
|
!(MP_OBJ_IS_TYPE(tuple->items[1], &mp_type_list) ||
|
|
MP_OBJ_IS_TYPE(tuple->items[1], &mp_type_slice) ||
|
|
MP_OBJ_IS_INT(tuple->items[1]))) {
|
|
mp_raise_msg(&mp_type_IndexError, translate("indices must be integers, slices, or Boolean lists"));
|
|
}
|
|
if(MP_OBJ_IS_TYPE(tuple->items[0], &mp_type_list)) { // rows are indexed by Boolean list
|
|
m = true_length(tuple->items[0]);
|
|
if(MP_OBJ_IS_TYPE(tuple->items[1], &mp_type_list)) {
|
|
n = true_length(tuple->items[1]);
|
|
return iterate_slice_list(ndarray, m, n, row_slice, column_slice,
|
|
tuple->items[0], tuple->items[1], values);
|
|
} else { // the column is indexed by an integer, or a slice
|
|
column_slice = generate_slice(ndarray->n, tuple->items[1]);
|
|
n = slice_length(column_slice);
|
|
return iterate_slice_list(ndarray, m, n, row_slice, column_slice,
|
|
tuple->items[0], mp_const_none, values);
|
|
}
|
|
|
|
} else { // rows are indexed by a slice, or an integer
|
|
row_slice = generate_slice(ndarray->m, tuple->items[0]);
|
|
m = slice_length(row_slice);
|
|
if(MP_OBJ_IS_TYPE(tuple->items[1], &mp_type_list)) { // columns are indexed by a Boolean list
|
|
n = true_length(tuple->items[1]);
|
|
return iterate_slice_list(ndarray, m, n, row_slice, column_slice,
|
|
mp_const_none, tuple->items[1], values);
|
|
} else { // columns are indexed by an integer, or a slice
|
|
column_slice = generate_slice(ndarray->n, tuple->items[1]);
|
|
n = slice_length(column_slice);
|
|
return iterate_slice_list(ndarray, m, n, row_slice, column_slice,
|
|
mp_const_none, mp_const_none, values);
|
|
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
mp_obj_t ndarray_subscr(mp_obj_t self_in, mp_obj_t index, mp_obj_t value) {
|
|
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
|
|
|
if (value == MP_OBJ_SENTINEL) { // return value(s)
|
|
return ndarray_get_slice(self, index, NULL);
|
|
} else { // assignment to slices; the value must be an ndarray, or a scalar
|
|
if(!MP_OBJ_IS_TYPE(value, &ulab_ndarray_type) &&
|
|
!MP_OBJ_IS_INT(value) && !mp_obj_is_float(value)) {
|
|
mp_raise_ValueError(translate("right hand side must be an ndarray, or a scalar"));
|
|
} else {
|
|
ndarray_obj_t *values = NULL;
|
|
if(MP_OBJ_IS_INT(value)) {
|
|
values = create_new_ndarray(1, 1, self->array->typecode);
|
|
mp_binary_set_val_array(values->array->typecode, values->array->items, 0, value);
|
|
} else if(mp_obj_is_float(value)) {
|
|
values = create_new_ndarray(1, 1, NDARRAY_FLOAT);
|
|
mp_binary_set_val_array(NDARRAY_FLOAT, values->array->items, 0, value);
|
|
} else {
|
|
values = MP_OBJ_TO_PTR(value);
|
|
}
|
|
return ndarray_get_slice(self, index, values);
|
|
}
|
|
}
|
|
return mp_const_none;
|
|
}
|
|
|
|
// itarray iterator
|
|
|
|
mp_obj_t ndarray_getiter(mp_obj_t o_in, mp_obj_iter_buf_t *iter_buf) {
|
|
return mp_obj_new_ndarray_iterator(o_in, 0, iter_buf);
|
|
}
|
|
|
|
typedef struct _mp_obj_ndarray_it_t {
|
|
mp_obj_base_t base;
|
|
mp_fun_1_t iternext;
|
|
mp_obj_t ndarray;
|
|
size_t cur;
|
|
} mp_obj_ndarray_it_t;
|
|
|
|
static mp_obj_t ndarray_iternext(mp_obj_t self_in) {
|
|
mp_obj_ndarray_it_t *self = MP_OBJ_TO_PTR(self_in);
|
|
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(self->ndarray);
|
|
// TODO: in numpy, ndarrays are iterated with respect to the first axis.
|
|
size_t iter_end = 0;
|
|
if(ndarray->m == 1) {
|
|
iter_end = ndarray->array->len;
|
|
} else {
|
|
iter_end = ndarray->m;
|
|
}
|
|
if(self->cur < iter_end) {
|
|
if(ndarray->n == ndarray->array->len) { // we have a linear array
|
|
// read the current value
|
|
mp_obj_t value;
|
|
value = mp_binary_get_val_array(ndarray->array->typecode, ndarray->array->items, self->cur);
|
|
self->cur++;
|
|
return value;
|
|
} else { // we have a matrix, return the number of rows
|
|
ndarray_obj_t *value = create_new_ndarray(1, ndarray->n, ndarray->array->typecode);
|
|
// copy the memory content here
|
|
uint8_t *tmp = (uint8_t *)ndarray->array->items;
|
|
size_t strip_size = ndarray->n * mp_binary_get_size('@', ndarray->array->typecode, NULL);
|
|
memcpy(value->array->items, &tmp[self->cur*strip_size], strip_size);
|
|
self->cur++;
|
|
return value;
|
|
}
|
|
} else {
|
|
return MP_OBJ_STOP_ITERATION;
|
|
}
|
|
}
|
|
|
|
mp_obj_t mp_obj_new_ndarray_iterator(mp_obj_t ndarray, size_t cur, 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 = cur;
|
|
return MP_OBJ_FROM_PTR(o);
|
|
}
|
|
|
|
mp_obj_t ndarray_shape(mp_obj_t self_in) {
|
|
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
|
mp_obj_t tuple[2] = {
|
|
mp_obj_new_int(self->m),
|
|
mp_obj_new_int(self->n)
|
|
};
|
|
return mp_obj_new_tuple(2, tuple);
|
|
}
|
|
|
|
mp_obj_t ndarray_size(mp_obj_t self_in) {
|
|
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
|
return mp_obj_new_int(self->array->len);
|
|
}
|
|
|
|
mp_obj_t ndarray_itemsize(mp_obj_t self_in) {
|
|
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
|
return MP_OBJ_NEW_SMALL_INT(mp_binary_get_size('@', self->array->typecode, NULL));
|
|
}
|
|
|
|
mp_obj_t ndarray_flatten(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
|
|
static const mp_arg_t allowed_args[] = {
|
|
{ MP_QSTR_order, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_QSTR(MP_QSTR_C)} },
|
|
};
|
|
|
|
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
|
|
mp_arg_parse_all(n_args - 1, pos_args + 1, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
|
|
mp_obj_t self_copy = ndarray_copy(pos_args[0]);
|
|
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(self_copy);
|
|
|
|
GET_STR_DATA_LEN(args[0].u_obj, order, len);
|
|
if((len != 1) || ((memcmp(order, "C", 1) != 0) && (memcmp(order, "F", 1) != 0))) {
|
|
mp_raise_ValueError(translate("flattening order must be either 'C', or 'F'"));
|
|
}
|
|
|
|
// if order == 'C', we simply have to set m, and n, there is nothing else to do
|
|
if(memcmp(order, "F", 1) == 0) {
|
|
ndarray_obj_t *self = MP_OBJ_TO_PTR(pos_args[0]);
|
|
uint8_t _sizeof = mp_binary_get_size('@', self->array->typecode, NULL);
|
|
// get the data of self_in: we won't need a temporary buffer for the transposition
|
|
uint8_t *self_array = (uint8_t *)self->array->items;
|
|
uint8_t *array = (uint8_t *)ndarray->array->items;
|
|
size_t i=0;
|
|
for(size_t n=0; n < self->n; n++) {
|
|
for(size_t m=0; m < self->m; m++) {
|
|
memcpy(array+_sizeof*i, self_array+_sizeof*(m*self->n + n), _sizeof);
|
|
i++;
|
|
}
|
|
}
|
|
}
|
|
ndarray->n = ndarray->array->len;
|
|
ndarray->m = 1;
|
|
return self_copy;
|
|
}
|
|
|
|
// Binary operations
|
|
ndarray_obj_t *ndarray_from_mp_obj(mp_obj_t obj) {
|
|
// creates an ndarray from an micropython int or float
|
|
// if the input is an ndarray, it is returned
|
|
ndarray_obj_t *ndarray;
|
|
if(MP_OBJ_IS_INT(obj)) {
|
|
int32_t ivalue = mp_obj_get_int(obj);
|
|
if((ivalue > 0) && (ivalue < 256)) {
|
|
CREATE_SINGLE_ITEM(ndarray, uint8_t, NDARRAY_UINT8, ivalue);
|
|
} else if((ivalue > 255) && (ivalue < 65535)) {
|
|
CREATE_SINGLE_ITEM(ndarray, uint16_t, NDARRAY_UINT16, ivalue);
|
|
} else if((ivalue < 0) && (ivalue > -128)) {
|
|
CREATE_SINGLE_ITEM(ndarray, int8_t, NDARRAY_INT8, ivalue);
|
|
} else if((ivalue < -127) && (ivalue > -32767)) {
|
|
CREATE_SINGLE_ITEM(ndarray, int16_t, NDARRAY_INT16, ivalue);
|
|
} else { // the integer value clearly does not fit the ulab types, so move on to float
|
|
CREATE_SINGLE_ITEM(ndarray, mp_float_t, NDARRAY_FLOAT, ivalue);
|
|
}
|
|
} else if(mp_obj_is_float(obj)) {
|
|
mp_float_t fvalue = mp_obj_get_float(obj);
|
|
CREATE_SINGLE_ITEM(ndarray, mp_float_t, NDARRAY_FLOAT, fvalue);
|
|
} else if(MP_OBJ_IS_TYPE(obj, &ulab_ndarray_type)){
|
|
ndarray = MP_OBJ_TO_PTR(obj);
|
|
} else {
|
|
mp_raise_TypeError(translate("wrong operand type"));
|
|
}
|
|
return ndarray;
|
|
}
|
|
|
|
mp_obj_t ndarray_binary_op(mp_binary_op_t _op, mp_obj_t lhs, mp_obj_t rhs) {
|
|
// if the ndarray stands on the right hand side of the expression, simply swap the operands
|
|
ndarray_obj_t *ol, *or;
|
|
mp_binary_op_t op = _op;
|
|
if((op == MP_BINARY_OP_REVERSE_ADD) || (op == MP_BINARY_OP_REVERSE_MULTIPLY) ||
|
|
(op == MP_BINARY_OP_REVERSE_POWER) || (op == MP_BINARY_OP_REVERSE_SUBTRACT) ||
|
|
(op == MP_BINARY_OP_REVERSE_TRUE_DIVIDE)) {
|
|
ol = ndarray_from_mp_obj(rhs);
|
|
or = ndarray_from_mp_obj(lhs);
|
|
} else {
|
|
ol = ndarray_from_mp_obj(lhs);
|
|
or = ndarray_from_mp_obj(rhs);
|
|
}
|
|
if(op == MP_BINARY_OP_REVERSE_ADD) {
|
|
op = MP_BINARY_OP_ADD;
|
|
} else if(op == MP_BINARY_OP_REVERSE_MULTIPLY) {
|
|
op = MP_BINARY_OP_MULTIPLY;
|
|
} else if(op == MP_BINARY_OP_REVERSE_POWER) {
|
|
op = MP_BINARY_OP_POWER;
|
|
} else if(op == MP_BINARY_OP_REVERSE_SUBTRACT) {
|
|
op = MP_BINARY_OP_SUBTRACT;
|
|
} else if(op == MP_BINARY_OP_REVERSE_TRUE_DIVIDE) {
|
|
op = MP_BINARY_OP_TRUE_DIVIDE;
|
|
}
|
|
// One of the operands is a scalar
|
|
// TODO: conform to numpy with the upcasting
|
|
// TODO: implement in-place operators
|
|
// these are partial broadcasting rules: either the two arrays
|
|
// are of the same shape, or one of them is of length 1
|
|
if(((ol->m != or->m) || (ol->n != or->n))) {
|
|
if((ol->array->len != 1) && (or->array->len != 1)) {
|
|
if(op == MP_BINARY_OP_EQUAL) {
|
|
return mp_const_false;
|
|
} else if(op == MP_BINARY_OP_NOT_EQUAL) {
|
|
return mp_const_true;
|
|
}
|
|
mp_raise_ValueError(translate("operands could not be broadcast together"));
|
|
}
|
|
}
|
|
uint8_t linc = ol->array->len == 1 ? 0 : 1;
|
|
uint8_t rinc = or->array->len == 1 ? 0 : 1;
|
|
// do the partial broadcasting here
|
|
size_t m = MAX(ol->m, or->m);
|
|
size_t n = MAX(ol->n, or->n);
|
|
size_t len = MAX(ol->array->len, or->array->len);
|
|
if((ol->array->len == 0) || (or->array->len == 0)) {
|
|
len = 0;
|
|
}
|
|
switch(op) {
|
|
case MP_BINARY_OP_EQUAL:
|
|
case MP_BINARY_OP_NOT_EQUAL:
|
|
case MP_BINARY_OP_LESS:
|
|
case MP_BINARY_OP_LESS_EQUAL:
|
|
case MP_BINARY_OP_MORE:
|
|
case MP_BINARY_OP_MORE_EQUAL:
|
|
case MP_BINARY_OP_ADD:
|
|
case MP_BINARY_OP_SUBTRACT:
|
|
case MP_BINARY_OP_TRUE_DIVIDE:
|
|
case MP_BINARY_OP_MULTIPLY:
|
|
case MP_BINARY_OP_POWER:
|
|
// TODO: I believe, this part can be made significantly smaller (compiled size)
|
|
// by doing only the typecasting in the large ifs, and moving the loops outside
|
|
// These are the upcasting rules
|
|
// float always becomes 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
|
|
// The parameters of RUN_BINARY_LOOP are
|
|
// typecode of result, type_out, type_left, type_right, lhs operand, rhs operand, operator
|
|
if(ol->array->typecode == NDARRAY_UINT8) {
|
|
if(or->array->typecode == NDARRAY_UINT8) {
|
|
RUN_BINARY_LOOP(NDARRAY_UINT8, uint8_t, uint8_t, uint8_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_INT8) {
|
|
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, uint8_t, int8_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_UINT16) {
|
|
RUN_BINARY_LOOP(NDARRAY_UINT16, uint16_t, uint8_t, uint16_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_INT16) {
|
|
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, uint8_t, int16_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_FLOAT) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, uint8_t, mp_float_t, ol, or, op, m, n, len, linc, rinc);
|
|
}
|
|
} else if(ol->array->typecode == NDARRAY_INT8) {
|
|
if(or->array->typecode == NDARRAY_UINT8) {
|
|
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int8_t, uint8_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_INT8) {
|
|
RUN_BINARY_LOOP(NDARRAY_INT8, int8_t, int8_t, int8_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_UINT16) {
|
|
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int8_t, uint16_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_INT16) {
|
|
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int8_t, int16_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_FLOAT) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, int8_t, mp_float_t, ol, or, op, m, n, len, linc, rinc);
|
|
}
|
|
} else if(ol->array->typecode == NDARRAY_UINT16) {
|
|
if(or->array->typecode == NDARRAY_UINT8) {
|
|
RUN_BINARY_LOOP(NDARRAY_UINT16, uint16_t, uint16_t, uint8_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_INT8) {
|
|
RUN_BINARY_LOOP(NDARRAY_UINT16, uint16_t, uint16_t, int8_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_UINT16) {
|
|
RUN_BINARY_LOOP(NDARRAY_UINT16, uint16_t, uint16_t, uint16_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_INT16) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, uint16_t, int16_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_FLOAT) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, uint16_t, mp_float_t, ol, or, op, m, n, len, linc, rinc);
|
|
}
|
|
} else if(ol->array->typecode == NDARRAY_INT16) {
|
|
if(or->array->typecode == NDARRAY_UINT8) {
|
|
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int16_t, uint8_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_INT8) {
|
|
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int16_t, int8_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_UINT16) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, int16_t, uint16_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_INT16) {
|
|
RUN_BINARY_LOOP(NDARRAY_INT16, int16_t, int16_t, int16_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_FLOAT) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, uint16_t, mp_float_t, ol, or, op, m, n, len, linc, rinc);
|
|
}
|
|
} else if(ol->array->typecode == NDARRAY_FLOAT) {
|
|
if(or->array->typecode == NDARRAY_UINT8) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, mp_float_t, uint8_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_INT8) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, mp_float_t, int8_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_UINT16) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, mp_float_t, uint16_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_INT16) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, mp_float_t, int16_t, ol, or, op, m, n, len, linc, rinc);
|
|
} else if(or->array->typecode == NDARRAY_FLOAT) {
|
|
RUN_BINARY_LOOP(NDARRAY_FLOAT, mp_float_t, mp_float_t, mp_float_t, ol, or, op, m, n, len, linc, rinc);
|
|
}
|
|
}
|
|
// this instruction should never be reached, but we have to make the compiler happy
|
|
return MP_OBJ_NULL;
|
|
break;
|
|
default:
|
|
return MP_OBJ_NULL; // op not supported
|
|
break;
|
|
}
|
|
return MP_OBJ_NULL;
|
|
}
|
|
|
|
mp_obj_t ndarray_unary_op(mp_unary_op_t op, mp_obj_t self_in) {
|
|
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
|
ndarray_obj_t *ndarray = NULL;
|
|
switch (op) {
|
|
case MP_UNARY_OP_LEN:
|
|
if(self->m > 1) {
|
|
return mp_obj_new_int(self->m);
|
|
} else {
|
|
return mp_obj_new_int(self->n);
|
|
}
|
|
break;
|
|
|
|
case MP_UNARY_OP_INVERT:
|
|
if(self->array->typecode == NDARRAY_FLOAT) {
|
|
mp_raise_ValueError(translate("operation is not supported for given type"));
|
|
}
|
|
// we can invert the content byte by byte, there is no need to distinguish
|
|
// between different typecodes
|
|
ndarray = MP_OBJ_TO_PTR(ndarray_copy(self_in));
|
|
{
|
|
uint8_t *array = (uint8_t *)ndarray->array->items;
|
|
for(size_t i=0; i < self->bytes; i++) array[i] = ~array[i];
|
|
return MP_OBJ_FROM_PTR(ndarray);
|
|
}
|
|
break;
|
|
|
|
case MP_UNARY_OP_NEGATIVE:
|
|
ndarray = MP_OBJ_TO_PTR(ndarray_copy(self_in));
|
|
if(self->array->typecode == NDARRAY_UINT8) {
|
|
uint8_t *array = (uint8_t *)ndarray->array->items;
|
|
for(size_t i=0; i < self->array->len; i++) array[i] = -array[i];
|
|
} else if(self->array->typecode == NDARRAY_INT8) {
|
|
int8_t *array = (int8_t *)ndarray->array->items;
|
|
for(size_t i=0; i < self->array->len; i++) array[i] = -array[i];
|
|
} else if(self->array->typecode == NDARRAY_UINT16) {
|
|
uint16_t *array = (uint16_t *)ndarray->array->items;
|
|
for(size_t i=0; i < self->array->len; i++) array[i] = -array[i];
|
|
} else if(self->array->typecode == NDARRAY_INT16) {
|
|
int16_t *array = (int16_t *)ndarray->array->items;
|
|
for(size_t i=0; i < self->array->len; i++) array[i] = -array[i];
|
|
} else {
|
|
mp_float_t *array = (mp_float_t *)ndarray->array->items;
|
|
for(size_t i=0; i < self->array->len; i++) array[i] = -array[i];
|
|
}
|
|
return MP_OBJ_FROM_PTR(ndarray);
|
|
break;
|
|
|
|
case MP_UNARY_OP_POSITIVE:
|
|
return ndarray_copy(self_in);
|
|
|
|
case MP_UNARY_OP_ABS:
|
|
if((self->array->typecode == NDARRAY_UINT8) || (self->array->typecode == NDARRAY_UINT16)) {
|
|
return ndarray_copy(self_in);
|
|
}
|
|
ndarray = MP_OBJ_TO_PTR(ndarray_copy(self_in));
|
|
if(self->array->typecode == NDARRAY_INT8) {
|
|
int8_t *array = (int8_t *)ndarray->array->items;
|
|
for(size_t i=0; i < self->array->len; i++) {
|
|
if(array[i] < 0) array[i] = -array[i];
|
|
}
|
|
} else if(self->array->typecode == NDARRAY_INT16) {
|
|
int16_t *array = (int16_t *)ndarray->array->items;
|
|
for(size_t i=0; i < self->array->len; i++) {
|
|
if(array[i] < 0) array[i] = -array[i];
|
|
}
|
|
} else {
|
|
mp_float_t *array = (mp_float_t *)ndarray->array->items;
|
|
for(size_t i=0; i < self->array->len; i++) {
|
|
if(array[i] < 0) array[i] = -array[i];
|
|
}
|
|
}
|
|
return MP_OBJ_FROM_PTR(ndarray);
|
|
break;
|
|
default: return MP_OBJ_NULL; // operator not supported
|
|
}
|
|
}
|
|
|
|
mp_obj_t ndarray_transpose(mp_obj_t self_in) {
|
|
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
|
|
|
// In the old matrix, the coordinate (m, n) is m*self->n + n
|
|
// We have to assign this to the coordinate (n, m) in the new
|
|
// matrix, i.e., to n*self->m + m (since the new matrix has self->m columns)
|
|
|
|
ndarray_obj_t *ndarray = create_new_ndarray(self->n, self->m, self->array->typecode);
|
|
// one-dimensional arrays can be transposed by simply swapping the dimensions
|
|
if((self->m == 1) || (self->n == 1)) {
|
|
memcpy(ndarray->array->items, self->array->items, self->bytes);
|
|
} else {
|
|
// the size of a single item in the array
|
|
uint8_t itemsize = mp_binary_get_size('@', self->array->typecode, NULL);
|
|
uint8_t *sarray = (uint8_t *)self->array->items;
|
|
uint8_t *narray = (uint8_t *)ndarray->array->items;
|
|
for(size_t m=0; m < self->m; m++) {
|
|
for(size_t n=0; n < self->n; n++) {
|
|
memcpy(narray+itemsize*(n*self->m + m), sarray, itemsize);
|
|
sarray += itemsize;
|
|
}
|
|
}
|
|
}
|
|
return MP_OBJ_FROM_PTR(ndarray);
|
|
}
|
|
|
|
MP_DEFINE_CONST_FUN_OBJ_1(ndarray_transpose_obj, ndarray_transpose);
|
|
|
|
mp_obj_t ndarray_reshape(mp_obj_t self_in, mp_obj_t shape) {
|
|
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
|
if(!MP_OBJ_IS_TYPE(shape, &mp_type_tuple) || (MP_OBJ_SMALL_INT_VALUE(mp_obj_len_maybe(shape)) != 2)) {
|
|
mp_raise_ValueError(translate("shape must be a 2-tuple"));
|
|
}
|
|
|
|
mp_obj_iter_buf_t iter_buf;
|
|
mp_obj_t item, iterable = mp_getiter(shape, &iter_buf);
|
|
uint16_t m, n;
|
|
item = mp_iternext(iterable);
|
|
m = mp_obj_get_int(item);
|
|
item = mp_iternext(iterable);
|
|
n = mp_obj_get_int(item);
|
|
if(m*n != self->m*self->n) {
|
|
// TODO: the proper error message would be "cannot reshape array of size %d into shape (%d, %d)"
|
|
mp_raise_ValueError(translate("cannot reshape array (incompatible input/output shape)"));
|
|
}
|
|
self->m = m;
|
|
self->n = n;
|
|
return MP_OBJ_FROM_PTR(self);
|
|
}
|
|
|
|
MP_DEFINE_CONST_FUN_OBJ_2(ndarray_reshape_obj, ndarray_reshape);
|
|
|
|
mp_int_t ndarray_get_buffer(mp_obj_t self_in, mp_buffer_info_t *bufinfo, mp_uint_t flags) {
|
|
ndarray_obj_t *self = MP_OBJ_TO_PTR(self_in);
|
|
// buffer_p.get_buffer() returns zero for success, while mp_get_buffer returns true for success
|
|
return !mp_get_buffer(self->array, bufinfo, flags);
|
|
}
|