Merge branch 'master' into spectrum

.github/workflows/build.yml

@@ -16,6 +16,7 @@ on:
 
 jobs:
   micropython:
+    continue-on-error: true
     strategy:
         matrix:
             os:
@@ -28,10 +29,10 @@ jobs:
       env:
         GITHUB_CONTEXT: ${{ toJson(github) }}
       run: echo "$GITHUB_CONTEXT"
-    - name: Set up Python 3.10
-      uses: actions/setup-python@v1
+    - name: Set up Python 3.12
+      uses: actions/setup-python@v5
       with:
-        python-version: "3.10"
+        python-version: "3.12"
 
     - name: Install requirements
       run: |
@@ -44,10 +45,10 @@ jobs:
         gcc --version
         python3 --version
     - name: Checkout ulab
-      uses: actions/checkout@v1
+      uses: actions/checkout@v4
 
     - name: Checkout micropython repo
-      uses: actions/checkout@v2
+      uses: actions/checkout@v4
       with:
         repository: micropython/micropython
         path: micropython
@@ -56,6 +57,7 @@ jobs:
       run: ./build.sh ${{ matrix.dims }}
 
   circuitpython:
+    continue-on-error: true
     strategy:
         matrix:
             os:
@@ -68,18 +70,18 @@ jobs:
       env:
         GITHUB_CONTEXT: ${{ toJson(github) }}
       run: echo "$GITHUB_CONTEXT"
-    - name: Set up Python 3.10
-      uses: actions/setup-python@v1
+    - name: Set up Python 3.12
+      uses: actions/setup-python@v5
       with:
-        python-version: "3.10"
+        python-version: "3.12"
 
     - name: Versions
       run: |
         gcc --version
         python3 --version
 
     - name: Checkout ulab
-      uses: actions/checkout@v1
+      uses: actions/checkout@v4
 
     - name: Install requirements
       run: |

README.md

@@ -1,6 +1,6 @@
 # ulab
 
-[![Documentation Status](https://readthedocs.org/projects/micropython-ulab-robert/badge/?version=latest)](https://micropython-ulab-robert.readthedocs.io/en/latest/?badge=latest)
+[![Documentation Status](https://readthedocs.org/projects/micropython-ulab/badge/?version=latest)](https://micropython-ulab.readthedocs.io/en/latest/index.html)
 
 `ulab` is a `numpy`-like array manipulation library for [micropython](http://micropython.org/) and [CircuitPython](https://circuitpython.org/).
 The module is written in C, defines compact containers (`ndarray`s) for numerical data of one to four
@@ -47,7 +47,7 @@ the `imag`, and `real` properties are automatically included.
 
 ## `numpy` and `scipy` functions
 
-In addition, `ulab` includes [universal functions](https://micropython-ulab.readthedocs.io/en/latest/numpy-universal.html), [many `numpy` functions](https://micropython-ulab.readthedocs.io/en/latest/numpy-functions.html), and functions from the [`numpy.fft`](https://micropython-ulab.readthedocs.io/en/latest/numpy-fft.html), [`numpy.linalg`](https://micropython-ulab.readthedocs.io/en/latest/numpy-linalg.html), [`scipy.linalg`](https://micropython-ulab.readthedocs.io/en/latest/scipy-linalg.html), [`scipy.optimize`](https://micropython-ulab.readthedocs.io/en/latest/scipy-optimize.html), [`scipy.signal`](https://micropython-ulab.readthedocs.io/en/latest/scipy-signal.html), and [`scipy.special`](https://micropython-ulab.readthedocs.io/en/latest/scipy-special.html) modules. A complete list of available routines can be found under [micropython-ulab](https://micropython-ulab.readthedocs.io/en/latest).
+In addition, `ulab` includes [universal functions](https://micropython-ulab.readthedocs.io/en/latest/numpy-universal.html), [many `numpy` functions](https://micropython-ulab.readthedocs.io/en/latest/numpy-functions.html), and functions from the [`numpy.fft`](https://micropython-ulab.readthedocs.io/en/latest/numpy-fft.html), [`numpy.linalg`](https://micropython-ulab.readthedocs.io/en/latest/numpy-linalg.html), [`numpy.random`](https://micropython-ulab.readthedocs.io/en/latest/numpy-random.html), [`scipy.linalg`](https://micropython-ulab.readthedocs.io/en/latest/scipy-linalg.html), [`scipy.optimize`](https://micropython-ulab.readthedocs.io/en/latest/scipy-optimize.html), [`scipy.signal`](https://micropython-ulab.readthedocs.io/en/latest/scipy-signal.html), and [`scipy.special`](https://micropython-ulab.readthedocs.io/en/latest/scipy-special.html) modules. A complete list of available routines can be found under [micropython-ulab](https://micropython-ulab.readthedocs.io/en/latest).
 
 ## `ulab` utilities
 
@@ -112,7 +112,6 @@ of the user manual.
 1. `MaixPy` https://github.com/sipeed/MaixPy
 1. `OpenMV` https://github.com/openmv/openmv
 1. `pimoroni-pico` https://github.com/pimoroni/pimoroni-pico
-3. `pycom` https://pycom.io/
 
 ## Compiling
 

build-cp.sh

@@ -41,8 +41,7 @@ HERE="$(dirname -- "$(readlinkf_posix -- "${0}")" )"
 rm -rf circuitpython/extmod/ulab; ln -s "$HERE" circuitpython/extmod/ulab
 dims=${1-2}
 make -C circuitpython/mpy-cross -j$NPROC
-sed -e '/MICROPY_PY_UHASHLIB/s/1/0/' < circuitpython/ports/unix/mpconfigport.h > circuitpython/ports/unix/mpconfigport_ulab.h
-make -k -C circuitpython/ports/unix -j$NPROC DEBUG=1 MICROPY_PY_FFI=0 MICROPY_PY_BTREE=0 MICROPY_SSL_AXTLS=0 MICROPY_PY_USSL=0 CFLAGS_EXTRA="-DMP_CONFIGFILE=\"<mpconfigport_ulab.h>\" -Wno-tautological-constant-out-of-range-compare -Wno-unknown-pragmas -DULAB_MAX_DIMS=$dims" BUILD=build-$dims PROG=micropython-$dims
+make -k -C circuitpython/ports/unix -j$NPROC DEBUG=1 MICROPY_PY_FFI=0 MICROPY_PY_BTREE=0 MICROPY_SSL_AXTLS=0 MICROPY_PY_USSL=0 CFLAGS_EXTRA="-Wno-tautological-constant-out-of-range-compare -Wno-unknown-pragmas -DULAB_MAX_DIMS=$dims" BUILD=build-$dims PROG=micropython-$dims
 
 # bash test-common.sh "${dims}" "circuitpython/ports/unix/micropython-$dims"
 

code/ndarray.c

@@ -6,7 +6,7 @@
  *
  * The MIT License (MIT)
  *
- * Copyright (c) 2019-2022 Zoltán Vörös
+ * Copyright (c) 2019-2024 Zoltán Vörös
  *               2020 Jeff Epler for Adafruit Industries
  *               2020 Taku Fukada
 */
@@ -509,8 +509,9 @@ static size_t multiply_size(size_t a, size_t b) {
     return result;
 }
 
-ndarray_obj_t *ndarray_new_ndarray(uint8_t ndim, size_t *shape, int32_t *strides, uint8_t dtype) {
+ndarray_obj_t *ndarray_new_ndarray(uint8_t ndim, size_t *shape, int32_t *strides, uint8_t dtype, uint8_t *buffer) {
     // Creates the base ndarray with shape, and initialises the values to straight 0s
+    // optionally, values can be supplied via the last argument
     ndarray_obj_t *ndarray = m_new_obj(ndarray_obj_t);
     ndarray->base.type = &ulab_ndarray_type;
     ndarray->dtype = dtype == NDARRAY_BOOL ? NDARRAY_UINT8 : dtype;
@@ -536,9 +537,13 @@ ndarray_obj_t *ndarray_new_ndarray(uint8_t ndim, size_t *shape, int32_t *strides
 
     // if the length is 0, still allocate a single item, so that contractions can be handled
     size_t len = multiply_size(ndarray->itemsize, MAX(1, ndarray->len));
-    uint8_t *array = m_new0(byte, len);
-    // this should set all elements to 0, irrespective of the of the dtype (all bits are zero)
-    // we could, perhaps, leave this step out, and initialise the array only, when needed
+    uint8_t *array;
+    array = buffer;
+    if(array == NULL) {
+        // this should set all elements to 0, irrespective of the of the dtype (all bits are zero)
+        // we could, perhaps, leave this step out, and initialise the array only, when needed
+        array = m_new0(byte, len);
+    }
     ndarray->array = array;
     ndarray->origin = array;
     return ndarray;
@@ -547,24 +552,20 @@ ndarray_obj_t *ndarray_new_ndarray(uint8_t ndim, size_t *shape, int32_t *strides
 ndarray_obj_t *ndarray_new_dense_ndarray(uint8_t ndim, size_t *shape, uint8_t dtype) {
     // creates a dense array, i.e., one, where the strides are derived directly from the shapes
     // the function should work in the general n-dimensional case
-    int32_t *strides = m_new(int32_t, ULAB_MAX_DIMS);
-    strides[ULAB_MAX_DIMS-1] = (int32_t)ulab_binary_get_size(dtype);
-    for(size_t i=ULAB_MAX_DIMS; i > 1; i--) {
-        strides[i-2] = strides[i-1] * MAX(1, shape[i-1]);
-    }
-    return ndarray_new_ndarray(ndim, shape, strides, dtype);
+    // int32_t *strides = m_new(int32_t, ULAB_MAX_DIMS);
+    // strides[ULAB_MAX_DIMS - 1] = (int32_t)ulab_binary_get_size(dtype);
+    // for(size_t i = ULAB_MAX_DIMS; i > 1; i--) {
+    //     strides[i-2] = strides[i-1] * MAX(1, shape[i-1]);
+    // }
+    return ndarray_new_ndarray(ndim, shape, NULL, dtype, NULL);
 }
 
 ndarray_obj_t *ndarray_new_ndarray_from_tuple(mp_obj_tuple_t *_shape, uint8_t dtype) {
     // creates a dense array from a tuple
     // the function should work in the general n-dimensional case
-    size_t *shape = m_new(size_t, ULAB_MAX_DIMS);
-    for(size_t i = 0; i < ULAB_MAX_DIMS; i++) {
-        if(i >= _shape->len) {
-            shape[ULAB_MAX_DIMS - 1 - i] = 0;
-        } else {
-            shape[ULAB_MAX_DIMS - 1 - i] = mp_obj_get_int(_shape->items[i]);
-        }
+    size_t *shape = m_new0(size_t, ULAB_MAX_DIMS);
+    for(size_t i = 0; i < _shape->len; i++) {
+        shape[ULAB_MAX_DIMS - 1 - i] = mp_obj_get_int(_shape->items[_shape->len - 1 - i]);
     }
     return ndarray_new_dense_ndarray(_shape->len, shape, dtype);
 }
@@ -654,7 +655,7 @@ ndarray_obj_t *ndarray_copy_view(ndarray_obj_t *source) {
     if(source->boolean) {
         dtype = NDARRAY_BOOL;
     }
-    ndarray_obj_t *ndarray = ndarray_new_ndarray(source->ndim, source->shape, strides, dtype);
+    ndarray_obj_t *ndarray = ndarray_new_ndarray(source->ndim, source->shape, strides, dtype, NULL);
     ndarray_copy_array(source, ndarray, 0);
     return ndarray;
 }
@@ -921,7 +922,7 @@ ndarray_obj_t *ndarray_from_iterable(mp_obj_t obj, uint8_t dtype) {
     return ndarray;
 }
 
-STATIC uint8_t ndarray_init_helper(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
+static uint8_t ndarray_init_helper(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
     static const mp_arg_t allowed_args[] = {
         { MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
         { MP_QSTR_dtype, MP_ARG_KW_ONLY | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_INT(NDARRAY_FLOAT) } },
@@ -944,7 +945,7 @@ STATIC uint8_t ndarray_init_helper(size_t n_args, const mp_obj_t *pos_args, mp_m
     return _dtype;
 }
 
-STATIC mp_obj_t ndarray_make_new_core(const mp_obj_type_t *type, size_t n_args, size_t n_kw, const mp_obj_t *args, mp_map_t *kw_args) {
+static mp_obj_t ndarray_make_new_core(const mp_obj_type_t *type, size_t n_args, size_t n_kw, const mp_obj_t *args, mp_map_t *kw_args) {
     uint8_t dtype = ndarray_init_helper(n_args, args, kw_args);
 
     if(mp_obj_is_type(args[0], &ulab_ndarray_type)) {
@@ -1888,7 +1889,7 @@ mp_obj_t ndarray_unary_op(mp_unary_op_t op, mp_obj_t self_in) {
             #if ULAB_SUPPORTS_COMPLEX
             if(self->dtype == NDARRAY_COMPLEX) {
                 int32_t *strides = strides_from_shape(self->shape, NDARRAY_FLOAT);
-                ndarray_obj_t *target = ndarray_new_ndarray(self->ndim, self->shape, strides, NDARRAY_FLOAT);
+                ndarray_obj_t *target = ndarray_new_ndarray(self->ndim, self->shape, strides, NDARRAY_FLOAT, NULL);
                 ndarray = MP_OBJ_TO_PTR(carray_abs(self, target));
             } else {
             #endif
@@ -2021,7 +2022,7 @@ mp_obj_t ndarray_reshape_core(mp_obj_t oin, mp_obj_t _shape, bool inplace) {
         mp_obj_t *items = m_new(mp_obj_t, 1);
         items[0] = _shape;
         shape = mp_obj_new_tuple(1, items);
-    } else {
+    } else { // at this point it's certain that _shape is a tuple
         shape = MP_OBJ_TO_PTR(_shape);
     }
 
@@ -2072,11 +2073,7 @@ mp_obj_t ndarray_reshape_core(mp_obj_t oin, mp_obj_t _shape, bool inplace) {
         if(inplace) {
             mp_raise_ValueError(MP_ERROR_TEXT("cannot assign new shape"));
         }
-        if(mp_obj_is_type(_shape, &mp_type_tuple)) {
-            ndarray = ndarray_new_ndarray_from_tuple(shape, source->dtype);
-        } else {
-            ndarray = ndarray_new_linear_array(source->len, source->dtype);
-        }
+        ndarray = ndarray_new_dense_ndarray(shape->len, new_shape, source->dtype);
         ndarray_copy_array(source, ndarray, 0);
     }
     return MP_OBJ_FROM_PTR(ndarray);

code/ndarray.h

@@ -188,7 +188,7 @@ int32_t *ndarray_contract_strides(ndarray_obj_t *, uint8_t );
 ndarray_obj_t *ndarray_from_iterable(mp_obj_t , uint8_t );
 ndarray_obj_t *ndarray_new_dense_ndarray(uint8_t , size_t *, uint8_t );
 ndarray_obj_t *ndarray_new_ndarray_from_tuple(mp_obj_tuple_t *, uint8_t );
-ndarray_obj_t *ndarray_new_ndarray(uint8_t , size_t *, int32_t *, uint8_t );
+ndarray_obj_t *ndarray_new_ndarray(uint8_t , size_t *, int32_t *, uint8_t , uint8_t *);
 ndarray_obj_t *ndarray_new_linear_array(size_t , uint8_t );
 ndarray_obj_t *ndarray_new_view(ndarray_obj_t *, uint8_t , size_t *, int32_t *, int32_t );
 bool ndarray_is_dense(ndarray_obj_t *);

code/numpy/approx.c

@@ -47,7 +47,7 @@ ULAB_DEFINE_FLOAT_CONST(approx_trapz_dx, MICROPY_FLOAT_CONST(1.0), 0x3f800000UL,
 //|     ...
 //|
 
-STATIC mp_obj_t approx_interp(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
+static mp_obj_t approx_interp(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
     static const mp_arg_t allowed_args[] = {
         { MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
         { MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
@@ -151,7 +151,7 @@ MP_DEFINE_CONST_FUN_OBJ_KW(approx_interp_obj, 2, approx_interp);
 //|     ...
 //|
 
-STATIC mp_obj_t approx_trapz(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
+static mp_obj_t approx_trapz(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
     static const mp_arg_t allowed_args[] = {
         { MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },
         { MP_QSTR_x, MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE } },

code/numpy/carray/carray.c

@@ -25,9 +25,11 @@
 
 #if ULAB_SUPPORTS_COMPLEX
 
+//| import builtins
+//|
 //| import ulab.numpy
 
-//| def real(val):
+//| def real(val: ulab.numpy.ndarray) -> ulab.numpy.ndarray:
 //|     """
 //|     Return the real part of the complex argument, which can be
 //|     either an ndarray, or a scalar."""
@@ -54,7 +56,7 @@ mp_obj_t carray_real(mp_obj_t _source) {
 
 MP_DEFINE_CONST_FUN_OBJ_1(carray_real_obj, carray_real);
 
-//| def imag(val):
+//| def imag(val: ulab.numpy.ndarray) -> ulab.numpy.ndarray:
 //|     """
 //|     Return the imaginary part of the complex argument, which can be
 //|     either an ndarray, or a scalar."""
@@ -82,7 +84,9 @@ MP_DEFINE_CONST_FUN_OBJ_1(carray_imag_obj, carray_imag);
 
 #if ULAB_NUMPY_HAS_CONJUGATE
 
-//| def conjugate(val):
+//| def conjugate(
+//|     val: builtins.complex | ulab.numpy.ndarray
+//| ) -> builtins.complex | ulab.numpy.ndarray:
 //|     """
 //|     Return the conjugate of the complex argument, which can be
 //|     either an ndarray, or a scalar."""