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[WIP] Fix ndarray constructor #305

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f4f757a
- Let ndarray constructor take shape rather than values as req arg. A…
CallumJHays Feb 3, 2021
4079013
Fix incorrect type stubs
CallumJHays Feb 3, 2021
8e68401
fix and move ndarrau(shape=...)
CallumJHays Feb 10, 2021
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28 changes: 27 additions & 1 deletion code/ndarray.c
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Original file line number Diff line number Diff line change
Expand Up @@ -935,7 +935,33 @@ mp_obj_t ndarray_make_new(const mp_obj_type_t *type, size_t n_args, size_t n_kw,
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(type, n_args, n_kw, args, &kw_args);
uint8_t dtype = ndarray_init_helper(n_args, args, &kw_args);
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Wouldn't it be simpler to move this whole block to make_new_core? That would solve the circuitpytthon problem.

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Sounds like a plan 🏂


mp_obj_t mp_shape = args[0];
mp_obj_t mp_ndim_maybe = mp_obj_len_maybe(mp_shape);
// single-number shapes "x" are interpreted the same as "(x,)"
uint8_t ndim;
size_t shape[ULAB_MAX_DIMS];

if (mp_ndim_maybe == MP_OBJ_NULL) {
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This will fail with ndarray(-12.3). You can make it safe by explicitly checking for the input type:

micropython-ulab/code/ulab_create.c

Line 29 in ab964b9

if(MP_OBJ_IS_INT(oshape)) {

ndim = 1;
shape[ULAB_MAX_DIMS - 1] = MP_OBJ_SMALL_INT_VALUE(mp_shape);
} else {
mp_int_t mp_int;
mp_obj_get_int_maybe(mp_ndim_maybe, &mp_int);
ndim = MP_OBJ_SMALL_INT_VALUE(mp_int);

mp_obj_iter_buf_t iter_buf;
mp_obj_t mp_shape_iter = mp_getiter(mp_shape, &iter_buf);

for (uint8_t i = ULAB_MAX_DIMS - ndim; i < ULAB_MAX_DIMS; i++) {
shape[i] = MP_OBJ_SMALL_INT_VALUE(mp_iternext(mp_shape_iter));
}
}

return MP_OBJ_FROM_PTR(
ndarray_new_dense_ndarray(ndim, shape, dtype)
);
}
#endif

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20 changes: 10 additions & 10 deletions code/numpy/approx/approx.c
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Expand Up @@ -28,17 +28,17 @@ const mp_obj_float_t approx_trapz_dx = {{&mp_type_float}, MICROPY_FLOAT_CONST(1.

#if ULAB_NUMPY_HAS_INTERP
//| def interp(
//| x: ulab.array,
//| xp: ulab.array,
//| fp: ulab.array,
//| x: ulab.ndarray,
//| xp: ulab.ndarray,
//| fp: ulab.ndarray,
//| *,
//| left: Optional[float] = None,
//| right: Optional[float] = None
//| ) -> ulab.array:
//| ) -> ulab.ndarray:
//| """
//| :param ulab.array x: The x-coordinates at which to evaluate the interpolated values.
//| :param ulab.array xp: The x-coordinates of the data points, must be increasing
//| :param ulab.array fp: The y-coordinates of the data points, same length as xp
//| :param ulab.ndarray x: The x-coordinates at which to evaluate the interpolated values.
//| :param ulab.ndarray xp: The x-coordinates of the data points, must be increasing
//| :param ulab.ndarray fp: The y-coordinates of the data points, same length as xp
//| :param left: Value to return for ``x < xp[0]``, default is ``fp[0]``.
//| :param right: Value to return for ``x > xp[-1]``, default is ``fp[-1]``.
//|
Expand Down Expand Up @@ -137,10 +137,10 @@ MP_DEFINE_CONST_FUN_OBJ_KW(approx_interp_obj, 2, approx_interp);
#endif

#if ULAB_NUMPY_HAS_TRAPZ
//| def trapz(y: ulab.array, x: Optional[ulab.array] = None, dx: float = 1.0) -> float:
//| def trapz(y: ulab.ndarray, x: Optional[ulab.ndarray] = None, dx: float = 1.0) -> float:
//| """
//| :param 1D ulab.array y: the values of the dependent variable
//| :param 1D ulab.array x: optional, the coordinates of the independent variable. Defaults to uniformly spaced values.
//| :param 1D ulab.ndarray y: the values of the dependent variable
//| :param 1D ulab.ndarray x: optional, the coordinates of the independent variable. Defaults to uniformly spaced values.
//| :param float dx: the spacing between sample points, if x=None
//|
//| Returns the integral of y(x) using the trapezoidal rule.
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12 changes: 6 additions & 6 deletions code/numpy/fft/fft.c
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Expand Up @@ -26,10 +26,10 @@
//|


//| def fft(r: ulab.array, c: Optional[ulab.array] = None) -> Tuple[ulab.array, ulab.array]:
//| def fft(r: ulab.ndarray, c: Optional[ulab.ndarray] = None) -> Tuple[ulab.ndarray, ulab.ndarray]:
//| """
//| :param ulab.array r: A 1-dimension array of values whose size is a power of 2
//| :param ulab.array c: An optional 1-dimension array of values whose size is a power of 2, giving the complex part of the value
//| :param ulab.ndarray r: A 1-dimension array of values whose size is a power of 2
//| :param ulab.ndarray c: An optional 1-dimension array of values whose size is a power of 2, giving the complex part of the value
//| :return tuple (r, c): The real and complex parts of the FFT
//|
//| Perform a Fast Fourier Transform from the time domain into the frequency domain
Expand All @@ -48,10 +48,10 @@ static mp_obj_t fft_fft(size_t n_args, const mp_obj_t *args) {

MP_DEFINE_CONST_FUN_OBJ_VAR_BETWEEN(fft_fft_obj, 1, 2, fft_fft);

//| def ifft(r: ulab.array, c: Optional[ulab.array] = None) -> Tuple[ulab.array, ulab.array]:
//| def ifft(r: ulab.ndarray, c: Optional[ulab.ndarray] = None) -> Tuple[ulab.ndarray, ulab.ndarray]:
//| """
//| :param ulab.array r: A 1-dimension array of values whose size is a power of 2
//| :param ulab.array c: An optional 1-dimension array of values whose size is a power of 2, giving the complex part of the value
//| :param ulab.ndarray r: A 1-dimension array of values whose size is a power of 2
//| :param ulab.ndarray c: An optional 1-dimension array of values whose size is a power of 2, giving the complex part of the value
//| :return tuple (r, c): The real and complex parts of the inverse FFT
//|
//| Perform an Inverse Fast Fourier Transform from the frequeny domain into the time domain"""
Expand Down
26 changes: 13 additions & 13 deletions code/numpy/linalg/linalg.c
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Expand Up @@ -44,10 +44,10 @@ static ndarray_obj_t *linalg_object_is_square(mp_obj_t obj) {
#endif

#if ULAB_MAX_DIMS > 1
//| def cholesky(A: ulab.array) -> ulab.array:
//| def cholesky(A: ulab.ndarray) -> ulab.ndarray:
//| """
//| :param ~ulab.array A: a positive definite, symmetric square matrix
//| :return ~ulab.array L: a square root matrix in the lower triangular form
//| :param ~ulab.ndarray A: a positive definite, symmetric square matrix
//| :return ~ulab.ndarray L: a square root matrix in the lower triangular form
//| :raises ValueError: If the input does not fulfill the necessary conditions
//|
//| The returned matrix satisfies the equation m=LL*"""
Expand Down Expand Up @@ -111,7 +111,7 @@ static mp_obj_t linalg_cholesky(mp_obj_t oin) {

MP_DEFINE_CONST_FUN_OBJ_1(linalg_cholesky_obj, linalg_cholesky);

//| def det(m: ulab.array) -> float:
//| def det(m: ulab.ndarray) -> float:
//| """
//| :param: m, a square matrix
//| :return float: The determinant of the matrix
Expand Down Expand Up @@ -182,10 +182,10 @@ MP_DEFINE_CONST_FUN_OBJ_1(linalg_det_obj, linalg_det);

#endif

//| def dot(m1: ulab.array, m2: ulab.array) -> Union[ulab.array, float]:
//| def dot(m1: ulab.ndarray, m2: ulab.ndarray) -> Union[ulab.ndarray, float]:
//| """
//| :param ~ulab.array m1: a matrix, or a vector
//| :param ~ulab.array m2: a matrix, or a vector
//| :param ~ulab.ndarray m1: a matrix, or a vector
//| :param ~ulab.ndarray m2: a matrix, or a vector
//|
//| Computes the product of two matrices, or two vectors. In the letter case, the inner product is returned."""
//| ...
Expand Down Expand Up @@ -247,7 +247,7 @@ static mp_obj_t linalg_dot(mp_obj_t _m1, mp_obj_t _m2) {
MP_DEFINE_CONST_FUN_OBJ_2(linalg_dot_obj, linalg_dot);

#if ULAB_MAX_DIMS > 1
//| def eig(m: ulab.array) -> Tuple[ulab.array, ulab.array]:
//| def eig(m: ulab.ndarray) -> Tuple[ulab.ndarray, ulab.ndarray]:
//| """
//| :param m: a square matrix
//| :return tuple (eigenvectors, eigenvalues):
Expand Down Expand Up @@ -308,9 +308,9 @@ static mp_obj_t linalg_eig(mp_obj_t oin) {

MP_DEFINE_CONST_FUN_OBJ_1(linalg_eig_obj, linalg_eig);

//| def inv(m: ulab.array) -> ulab.array:
//| def inv(m: ulab.ndarray) -> ulab.ndarray:
//| """
//| :param ~ulab.array m: a square matrix
//| :param ~ulab.ndarray m: a square matrix
//| :return: The inverse of the matrix, if it exists
//| :raises ValueError: if the matrix is not invertible
//|
Expand Down Expand Up @@ -346,9 +346,9 @@ static mp_obj_t linalg_inv(mp_obj_t o_in) {
MP_DEFINE_CONST_FUN_OBJ_1(linalg_inv_obj, linalg_inv);
#endif

//| def norm(x: ulab.array) -> float:
//| def norm(x: ulab.ndarray) -> float:
//| """
//| :param ~ulab.array x: a vector or a matrix
//| :param ~ulab.ndarray x: a vector or a matrix
//|
//| Computes the 2-norm of a vector or a matrix, i.e., ``sqrt(sum(x*x))``, however, without the RAM overhead."""
//| ...
Expand Down Expand Up @@ -388,7 +388,7 @@ MP_DEFINE_CONST_FUN_OBJ_1(linalg_norm_obj, linalg_norm);
#if ULAB_MAX_DIMS > 1
#if ULAB_LINALG_HAS_TRACE

//| def trace(m: ulab.array) -> float:
//| def trace(m: ulab.ndarray) -> float:
//| """
//| :param m: a square matrix
//|
Expand Down
16 changes: 8 additions & 8 deletions code/numpy/numerical/numerical.c
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Expand Up @@ -518,7 +518,7 @@ MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argmin_obj, 1, numerical_argmin);
#endif

#if ULAB_NUMPY_HAS_ARGSORT
//| def argsort(array: ulab.array, *, axis: int = -1) -> ulab.array:
//| def argsort(array: ulab.ndarray, *, axis: int = -1) -> ulab.ndarray:
//| """Returns an array which gives indices into the input array from least to greatest."""
//| ...
//|
Expand Down Expand Up @@ -627,7 +627,7 @@ MP_DEFINE_CONST_FUN_OBJ_KW(numerical_argsort_obj, 1, numerical_argsort);
#endif

#if ULAB_NUMPY_HAS_CROSS
//| def cross(a: ulab.array, b: ulab.array) -> ulab.array:
//| def cross(a: ulab.ndarray, b: ulab.ndarray) -> ulab.ndarray:
//| """Return the cross product of two vectors of length 3"""
//| ...
//|
Expand Down Expand Up @@ -705,7 +705,7 @@ MP_DEFINE_CONST_FUN_OBJ_2(numerical_cross_obj, numerical_cross);
#endif /* ULAB_NUMERICAL_HAS_CROSS */

#if ULAB_NUMPY_HAS_DIFF
//| def diff(array: ulab.array, *, n: int = 1, axis: int = -1) -> ulab.array:
//| def diff(array: ulab.ndarray, *, n: int = 1, axis: int = -1) -> ulab.ndarray:
//| """Return the numerical derivative of successive elements of the array, as
//| an array. axis=None is not supported."""
//| ...
Expand Down Expand Up @@ -786,7 +786,7 @@ MP_DEFINE_CONST_FUN_OBJ_KW(numerical_diff_obj, 1, numerical_diff);
#endif

#if ULAB_NUMPY_HAS_FLIP
//| def flip(array: ulab.array, *, axis: Optional[int] = None) -> ulab.array:
//| def flip(array: ulab.ndarray, *, axis: Optional[int] = None) -> ulab.ndarray:
//| """Returns a new array that reverses the order of the elements along the
//| given axis, or along all axes if axis is None."""
//| ...
Expand Down Expand Up @@ -860,7 +860,7 @@ MP_DEFINE_CONST_FUN_OBJ_KW(numerical_mean_obj, 1, numerical_mean);
#endif

#if ULAB_NUMPY_HAS_MEDIAN
//| def median(array: ulab.array, *, axis: int = -1) -> ulab.array:
//| def median(array: ulab.ndarray, *, axis: int = -1) -> ulab.ndarray:
//| """Find the median value in an array along the given axis, or along all axes if axis is None."""
//| ...
//|
Expand Down Expand Up @@ -968,7 +968,7 @@ MP_DEFINE_CONST_FUN_OBJ_KW(numerical_min_obj, 1, numerical_min);
#endif

#if ULAB_NUMPY_HAS_ROLL
//| def roll(array: ulab.array, distance: int, *, axis: Optional[int] = None) -> None:
//| def roll(array: ulab.ndarray, distance: int, *, axis: Optional[int] = None) -> None:
//| """Shift the content of a vector by the positions given as the second
//| argument. If the ``axis`` keyword is supplied, the shift is applied to
//| the given axis. The array is modified in place."""
Expand Down Expand Up @@ -1133,7 +1133,7 @@ MP_DEFINE_CONST_FUN_OBJ_KW(numerical_roll_obj, 2, numerical_roll);
#endif

#if ULAB_NUMPY_HAS_SORT
//| def sort(array: ulab.array, *, axis: int = -1) -> ulab.array:
//| def sort(array: ulab.ndarray, *, axis: int = -1) -> ulab.ndarray:
//| """Sort the array along the given axis, or along all axes if axis is None.
//| The array is modified in place."""
//| ...
Expand Down Expand Up @@ -1209,7 +1209,7 @@ MP_DEFINE_CONST_FUN_OBJ_KW(numerical_std_obj, 1, numerical_std);
#endif

#if ULAB_NUMPY_HAS_SUM
//| def sum(array: _ArrayLike, *, axis: Optional[int] = None) -> Union[float, int, ulab.array]:
//| def sum(array: _ArrayLike, *, axis: Optional[int] = None) -> Union[float, int, ulab.ndarray]:
//| """Return the sum of the array, as a number if axis is None, otherwise as an array."""
//| ...
//|
Expand Down
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