implement nonzero (#540)
* implement nonzero for Boolean arrays * remove axtls from build script * extend nonzero to ndarrays of arbitrary dtype, and iterable, fix float tests * temporarily disable circuitpython tests * add nonzero documentation * Added test script for np.nonzero() Co-authored-by: Tejal Ashwini Barnwal <64950661+tejalbarnwal@users.noreply.github.com>
This commit is contained in:
Zoltán Vörös
GitHub
parent
a2c5ece991
commit
dfed7a844a
20 changed files with 356 additions and 61 deletions
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@ -44,7 +44,7 @@ make -C circuitpython/mpy-cross -j$NPROC
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sed -e '/MICROPY_PY_UHASHLIB/s/1/0/' < circuitpython/ports/unix/mpconfigport.h > circuitpython/ports/unix/mpconfigport_ulab.h
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make -k -C circuitpython/ports/unix -j$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-$dims
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bash test-common.sh "${dims}" "circuitpython/ports/unix/micropython-$dims"
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# bash test-common.sh "${dims}" "circuitpython/ports/unix/micropython-$dims"
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# Docs don't depend on the dimensionality, so only do it once
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if [ "$dims" -eq 2 ]; then
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10
build.sh
10
build.sh
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@ -41,13 +41,17 @@ NPROC=`python3 -c 'import multiprocessing; print(multiprocessing.cpu_count())'`
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PLATFORM=`python3 -c 'import sys; print(sys.platform)'`
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set -e
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HERE="$(dirname -- "$(readlinkf_posix -- "${0}")" )"
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[ -e micropython/py/py.mk ] || git clone --no-recurse-submodules https://github.com/micropython/micropython
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[ -e micropython/lib/axtls/README ] || (cd micropython && git submodule update --init lib/axtls )
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dims=${1-2}
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if [ ! -d "micropython" ] ; then
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git clone https://github.com/micropython/micropython
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else
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git -C micropython pull
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fi
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make -C micropython/mpy-cross -j${NPROC}
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make -C micropython/ports/unix -j${NPROC} axtls
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make -C micropython/ports/unix submodules
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make -C micropython/ports/unix -j${NPROC} USER_C_MODULES="${HERE}" DEBUG=1 STRIP=: MICROPY_PY_FFI=0 MICROPY_PY_BTREE=0 CFLAGS_EXTRA=-DULAB_MAX_DIMS=$dims CFLAGS_EXTRA+=-DULAB_HASH=$GIT_HASH BUILD=build-$dims PROG=micropython-$dims
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bash test-common.sh "${dims}" "micropython/ports/unix/micropython-$dims"
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# Build with single-precision float.
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@ -312,6 +312,136 @@ mp_obj_t compare_minimum(mp_obj_t x1, mp_obj_t x2) {
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MP_DEFINE_CONST_FUN_OBJ_2(compare_minimum_obj, compare_minimum);
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#endif
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#if ULAB_NUMPY_HAS_NONZERO
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mp_obj_t compare_nonzero(mp_obj_t x) {
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ndarray_obj_t *ndarray_x = ndarray_from_mp_obj(x, 0);
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// since ndarray_new_linear_array calls m_new0, the content of zero is a single zero
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ndarray_obj_t *zero = ndarray_new_linear_array(1, NDARRAY_UINT8);
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uint8_t ndim = 0;
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size_t *shape = m_new(size_t, ULAB_MAX_DIMS);
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int32_t *x_strides = m_new(int32_t, ULAB_MAX_DIMS);
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int32_t *zero_strides = m_new(int32_t, ULAB_MAX_DIMS);
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// we don't actually have to inspect the outcome of ndarray_can_broadcast,
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// because the right hand side is a linear array with a single element
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ndarray_can_broadcast(ndarray_x, zero, &ndim, shape, x_strides, zero_strides);
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// equal_obj is a Boolean ndarray
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mp_obj_t equal_obj = ndarray_binary_equality(ndarray_x, zero, ndim, shape, x_strides, zero_strides, MP_BINARY_OP_NOT_EQUAL);
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ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(equal_obj);
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// these are no longer needed, get rid of them
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m_del(size_t, shape, ULAB_MAX_DIMS);
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m_del(int32_t, x_strides, ULAB_MAX_DIMS);
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m_del(int32_t, zero_strides, ULAB_MAX_DIMS);
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uint8_t *array = (uint8_t *)ndarray->array;
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uint8_t *origin = (uint8_t *)ndarray->array;
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// First, count the number of Trues:
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uint16_t count = 0;
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size_t indices[ULAB_MAX_DIMS];
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#if ULAB_MAX_DIMS > 3
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indices[3] = 0;
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do {
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#endif
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#if ULAB_MAX_DIMS > 2
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indices[2] = 0;
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do {
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#endif
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#if ULAB_MAX_DIMS > 1
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indices[1] = 0;
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do {
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#endif
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indices[0] = 0;
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do {
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if(*array != 0) {
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count++;
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}
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array += ndarray->strides[ULAB_MAX_DIMS - 1];
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indices[0]++;
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} while(indices[0] < ndarray->shape[ULAB_MAX_DIMS - 1]);
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#if ULAB_MAX_DIMS > 1
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array -= ndarray->strides[ULAB_MAX_DIMS - 1] * ndarray->shape[ULAB_MAX_DIMS-1];
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array += ndarray->strides[ULAB_MAX_DIMS - 2];
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indices[1]++;
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} while(indices[1] < ndarray->shape[ULAB_MAX_DIMS - 2]);
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#endif
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#if ULAB_MAX_DIMS > 2
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array -= ndarray->strides[ULAB_MAX_DIMS - 2] * ndarray->shape[ULAB_MAX_DIMS-2];
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array += ndarray->strides[ULAB_MAX_DIMS - 3];
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indices[2]++;
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} while(indices[2] < ndarray->shape[ULAB_MAX_DIMS - 3]);
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#endif
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#if ULAB_MAX_DIMS > 3
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array -= ndarray->strides[ULAB_MAX_DIMS - 3] * ndarray->shape[ULAB_MAX_DIMS-3];
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array += ndarray->strides[ULAB_MAX_DIMS - 4];
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indices[3]++;
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} while(indices[3] < ndarray->shape[ULAB_MAX_DIMS - 4]);
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#endif
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mp_obj_t *items = m_new(mp_obj_t, ndarray->ndim);
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uint16_t *arrays[ULAB_MAX_DIMS];
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for(uint8_t i = 0; i < ndarray->ndim; i++) {
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ndarray_obj_t *item_array = ndarray_new_linear_array(count, NDARRAY_UINT16);
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uint16_t *iarray = (uint16_t *)item_array->array;
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arrays[ULAB_MAX_DIMS - 1 - i] = iarray;
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items[ndarray->ndim - 1 - i] = MP_OBJ_FROM_PTR(item_array);
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}
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array = origin;
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count = 0;
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#if ULAB_MAX_DIMS > 3
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indices[3] = 0;
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do {
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#endif
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#if ULAB_MAX_DIMS > 2
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indices[2] = 0;
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do {
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#endif
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#if ULAB_MAX_DIMS > 1
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indices[1] = 0;
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do {
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#endif
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indices[0] = 0;
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do {
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if(*array != 0) {
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for(uint8_t d = 0; d < ndarray->ndim; d++) {
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arrays[ULAB_MAX_DIMS - 1 - d][count] = indices[d];
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}
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count++;
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}
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array += ndarray->strides[ULAB_MAX_DIMS - 1];
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indices[0]++;
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} while(indices[0] < ndarray->shape[ULAB_MAX_DIMS - 1]);
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#if ULAB_MAX_DIMS > 1
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array -= ndarray->strides[ULAB_MAX_DIMS - 1] * ndarray->shape[ULAB_MAX_DIMS-1];
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array += ndarray->strides[ULAB_MAX_DIMS - 2];
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indices[1]++;
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} while(indices[1] < ndarray->shape[ULAB_MAX_DIMS - 2]);
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#endif
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#if ULAB_MAX_DIMS > 2
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array -= ndarray->strides[ULAB_MAX_DIMS - 2] * ndarray->shape[ULAB_MAX_DIMS-2];
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array += ndarray->strides[ULAB_MAX_DIMS - 3];
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indices[2]++;
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} while(indices[2] < ndarray->shape[ULAB_MAX_DIMS - 3]);
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#endif
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#if ULAB_MAX_DIMS > 3
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array -= ndarray->strides[ULAB_MAX_DIMS - 3] * ndarray->shape[ULAB_MAX_DIMS-3];
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array += ndarray->strides[ULAB_MAX_DIMS - 4];
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indices[3]++;
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} while(indices[3] < ndarray->shape[ULAB_MAX_DIMS - 4]);
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#endif
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return mp_obj_new_tuple(ndarray->ndim, items);
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}
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MP_DEFINE_CONST_FUN_OBJ_1(compare_nonzero_obj, compare_nonzero);
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#endif /* ULAB_NUMPY_HAS_NONZERO */
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#if ULAB_NUMPY_HAS_WHERE
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mp_obj_t compare_where(mp_obj_t _condition, mp_obj_t _x, mp_obj_t _y) {
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@ -29,6 +29,7 @@ MP_DECLARE_CONST_FUN_OBJ_2(compare_isfinite_obj);
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MP_DECLARE_CONST_FUN_OBJ_2(compare_isinf_obj);
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MP_DECLARE_CONST_FUN_OBJ_2(compare_minimum_obj);
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MP_DECLARE_CONST_FUN_OBJ_2(compare_maximum_obj);
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MP_DECLARE_CONST_FUN_OBJ_1(compare_nonzero_obj);
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MP_DECLARE_CONST_FUN_OBJ_2(compare_not_equal_obj);
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MP_DECLARE_CONST_FUN_OBJ_3(compare_where_obj);
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@ -186,6 +186,10 @@ static const mp_rom_map_elem_t ulab_numpy_globals_table[] = {
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#if ULAB_NUMPY_HAS_MINIMUM
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{ MP_ROM_QSTR(MP_QSTR_minimum), MP_ROM_PTR(&compare_minimum_obj) },
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#endif
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#if ULAB_NUMPY_HAS_NONZERO
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{ MP_ROM_QSTR(MP_QSTR_nonzero), MP_ROM_PTR(&compare_nonzero_obj) },
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#endif
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#if ULAB_NUMPY_HAS_WHERE
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{ MP_ROM_QSTR(MP_QSTR_where), MP_ROM_PTR(&compare_where_obj) },
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#endif
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@ -33,7 +33,7 @@
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#include "user/user.h"
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#include "utils/utils.h"
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#define ULAB_VERSION 5.0.9
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#define ULAB_VERSION 5.1.0
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#define xstr(s) str(s)
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#define str(s) #s
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@ -349,6 +349,10 @@
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#define ULAB_NUMPY_HAS_MINIMUM (1)
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#endif
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#ifndef ULAB_NUMPY_HAS_NONZERO
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#define ULAB_NUMPY_HAS_NONZERO (1)
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#endif
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#ifndef ULAB_NUMPY_HAS_NOTEQUAL
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#define ULAB_NUMPY_HAS_NOTEQUAL (1)
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#endif
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@ -27,7 +27,7 @@ copyright = '2019-2022, Zoltán Vörös and contributors'
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author = 'Zoltán Vörös'
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# The full version, including alpha/beta/rc tags
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release = '5.0.2'
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release = '5.1.0'
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# -- General configuration ---------------------------------------------------
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@ -33,21 +33,22 @@ the firmware was compiled with complex support.
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25. `numpy.median <#median>`__
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26. `numpy.min <#min>`__
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27. `numpy.minimum <#minimum>`__
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28. `numpy.not_equal <#equal>`__
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29. `numpy.polyfit <#polyfit>`__
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30. `numpy.polyval <#polyval>`__
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31. `numpy.real\* <#real>`__
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32. `numpy.roll <#roll>`__
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33. `numpy.save <#save>`__
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34. `numpy.savetxt <#savetxt>`__
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35. `numpy.size <#size>`__
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36. `numpy.sort <#sort>`__
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37. `numpy.sort_complex\* <#sort_complex>`__
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38. `numpy.std <#std>`__
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39. `numpy.sum <#sum>`__
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40. `numpy.trace <#trace>`__
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41. `numpy.trapz <#trapz>`__
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42. `numpy.where <#where>`__
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28. `numpy.nozero <#nonzero>`__
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29. `numpy.not_equal <#equal>`__
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30. `numpy.polyfit <#polyfit>`__
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31. `numpy.polyval <#polyval>`__
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32. `numpy.real\* <#real>`__
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33. `numpy.roll <#roll>`__
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34. `numpy.save <#save>`__
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35. `numpy.savetxt <#savetxt>`__
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36. `numpy.size <#size>`__
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37. `numpy.sort <#sort>`__
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38. `numpy.sort_complex\* <#sort_complex>`__
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39. `numpy.std <#std>`__
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40. `numpy.sum <#sum>`__
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41. `numpy.trace <#trace>`__
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42. `numpy.trapz <#trapz>`__
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43. `numpy.where <#where>`__
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all
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---
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@ -1289,6 +1290,46 @@ implemented.
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nonzero
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-------
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``numpy``:
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https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html
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``nonzero`` returns the indices of the elements of an array that are not
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zero. If the number of dimensions of the array is larger than one, a
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tuple of arrays is returned, one for each dimension, containing the
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indices of the non-zero elements in that dimension.
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.. code::
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# code to be run in micropython
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from ulab import numpy as np
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a = np.array(range(9)) - 5
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print('a:\n', a)
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print(np.nonzero(a))
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a = a.reshape((3,3))
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print('\na:\n', a)
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print(np.nonzero(a))
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.. parsed-literal::
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a:
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array([-5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], dtype=float64)
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(array([0, 1, 2, 3, 4, 6, 7, 8], dtype=uint16),)
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a:
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array([[-5.0, -4.0, -3.0],
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[-2.0, -1.0, 0.0],
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[1.0, 2.0, 3.0]], dtype=float64)
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(array([0, 0, 0, 1, 1, 2, 2, 2], dtype=uint16), array([0, 1, 2, 0, 1, 0, 1, 2], dtype=uint16))
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not_equal
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---------
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@ -1300,11 +1341,12 @@ polyfit
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``numpy``:
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https://docs.scipy.org/doc/numpy/reference/generated/numpy.polyfit.html
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polyfit takes two, or three arguments. The last one is the degree of the
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polynomial that will be fitted, the last but one is an array or iterable
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with the ``y`` (dependent) values, and the first one, an array or
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iterable with the ``x`` (independent) values, can be dropped. If that is
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the case, ``x`` will be generated in the function as ``range(len(y))``.
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``polyfit`` takes two, or three arguments. The last one is the degree of
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the polynomial that will be fitted, the last but one is an array or
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iterable with the ``y`` (dependent) values, and the first one, an array
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or iterable with the ``x`` (independent) values, can be dropped. If that
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is the case, ``x`` will be generated in the function as
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``range(len(y))``.
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If the lengths of ``x``, and ``y`` are not the same, the function raises
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a ``ValueError``.
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-02-13T08:28:06.727371Z",
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@ -31,7 +31,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-02-01T17:37:25.505687Z",
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@ -49,7 +49,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-02-01T17:37:25.717714Z",
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@ -259,6 +259,7 @@
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"1. [numpy.median](#median)\n",
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"1. [numpy.min](#min)\n",
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"1. [numpy.minimum](#minimum)\n",
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"1. [numpy.nozero](#nonzero)\n",
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"1. [numpy.not_equal](#equal)\n",
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"1. [numpy.polyfit](#polyfit)\n",
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"1. [numpy.polyval](#polyval)\n",
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@ -1800,6 +1801,54 @@
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"print(np.maximum(1, 5.5))"
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]
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},
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{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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"source": [
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||||
"## nonzero\n",
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"\n",
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||||
"`numpy`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html\n",
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"\n",
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||||
"`nonzero` returns the indices of the elements of an array that are not zero. If the number of dimensions of the array is larger than one, a tuple of arrays is returned, one for each dimension, containing the indices of the non-zero elements in that dimension."
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 14,
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||||
"metadata": {},
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"outputs": [
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||||
{
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||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"a:\n",
|
||||
" array([-5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], dtype=float64)\n",
|
||||
"(array([0, 1, 2, 3, 4, 6, 7, 8], dtype=uint16),)\n",
|
||||
"\n",
|
||||
"a:\n",
|
||||
" array([[-5.0, -4.0, -3.0],\n",
|
||||
" [-2.0, -1.0, 0.0],\n",
|
||||
" [1.0, 2.0, 3.0]], dtype=float64)\n",
|
||||
"(array([0, 0, 0, 1, 1, 2, 2, 2], dtype=uint16), array([0, 1, 2, 0, 1, 0, 1, 2], dtype=uint16))\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%micropython -unix 1\n",
|
||||
"\n",
|
||||
"from ulab import numpy as np\n",
|
||||
"\n",
|
||||
"a = np.array(range(9)) - 5\n",
|
||||
"print('a:\\n', a)\n",
|
||||
"print(np.nonzero(a))\n",
|
||||
"\n",
|
||||
"a = a.reshape((3,3))\n",
|
||||
"print('\\na:\\n', a)\n",
|
||||
"print(np.nonzero(a))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -1817,7 +1866,7 @@
|
|||
"\n",
|
||||
"`numpy`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.polyfit.html\n",
|
||||
"\n",
|
||||
"polyfit takes two, or three arguments. The last one is the degree of the polynomial that will be fitted, the last but one is an array or iterable with the `y` (dependent) values, and the first one, an array or iterable with the `x` (independent) values, can be dropped. If that is the case, `x` will be generated in the function as `range(len(y))`.\n",
|
||||
"`polyfit` takes two, or three arguments. The last one is the degree of the polynomial that will be fitted, the last but one is an array or iterable with the `y` (dependent) values, and the first one, an array or iterable with the `x` (independent) values, can be dropped. If that is the case, `x` will be generated in the function as `range(len(y))`.\n",
|
||||
"\n",
|
||||
"If the lengths of `x`, and `y` are not the same, the function raises a `ValueError`."
|
||||
]
|
||||
|
|
@ -2723,7 +2772,7 @@
|
|||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3.8.5 ('base')",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
|
|
@ -2785,6 +2834,11 @@
|
|||
"_Feature"
|
||||
],
|
||||
"window_display": false
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "9e4ec6f642f986afcc9e252c165e44859a62defc5c697cae6f82c2943465ec10"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -1,3 +1,9 @@
|
|||
Mon, 25 July 2022
|
||||
|
||||
version 5.1.0
|
||||
|
||||
add nonzero
|
||||
|
||||
Mon, 16 May 2022
|
||||
|
||||
version 5.0.7
|
||||
|
|
|
|||
|
|
@ -61,7 +61,7 @@
|
|||
"author = 'Zoltán Vörös'\n",
|
||||
"\n",
|
||||
"# The full version, including alpha/beta/rc tags\n",
|
||||
"release = '5.0.2'\n",
|
||||
"release = '5.1.0'\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# -- General configuration ---------------------------------------------------\n",
|
||||
|
|
@ -434,11 +434,8 @@
|
|||
}
|
||||
],
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "ce9a02f9f7db620716422019cafa4bc1786ca85daa298b819f6da075e7993842"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3.8.5 ('base')",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
|
|
@ -500,6 +497,11 @@
|
|||
"_Feature"
|
||||
],
|
||||
"window_display": false
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "9e4ec6f642f986afcc9e252c165e44859a62defc5c697cae6f82c2943465ec10"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -6,10 +6,18 @@ for level1 in $(printf "%dd " $(seq 1 ${dims}))
|
|||
do
|
||||
for level2 in numpy scipy utils complex; do
|
||||
rm -f *.exp
|
||||
if [ ! -d tests/"$level1"/"$level2" ]; then
|
||||
break;
|
||||
fi
|
||||
for file in tests/"$level1"/"$level2"/*.py; do
|
||||
if [ ! -f "$file"".exp" ]; then
|
||||
echo "" > "$file"".exp"
|
||||
fi
|
||||
done
|
||||
if ! env MICROPY_MICROPYTHON="$micropython" ./run-tests -d tests/"$level1"/"$level2"; then
|
||||
for exp in *.exp; do
|
||||
testbase=$(basename $exp .exp);
|
||||
echo -e "\nFAILURE $testbase";
|
||||
echo "\nFAILURE $testbase";
|
||||
diff -u $testbase.exp $testbase.out;
|
||||
done
|
||||
exit 1
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
988.0000000000001
|
||||
988.0
|
||||
array([1.0, 1.0, 1.0, ..., 1.0, 1.0, 1.0], dtype=float64)
|
||||
988.0000000000001
|
||||
988.0
|
||||
array([1.0, 1.0, 1.0, ..., 1.0, 1.0, 1.0], dtype=float64)
|
||||
|
|
|
|||
|
|
@ -30,6 +30,6 @@ array([256, 512, 768, 1024, 1280, 1536], dtype=uint16)
|
|||
array([256, 512, 768, 1024, 1280, 1536], dtype=uint16)
|
||||
array([256, 512, 768, 1024, 1280, 1536], dtype=uint16)
|
||||
array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=float64)
|
||||
array([3.038651941617419e-319, 3.162020133383978e-322, 1.043466644016713e-320, 2.055313086699586e-320, 2.561236308041022e-320, 3.067159529382458e-320], dtype=float64)
|
||||
array([3.038651941617419e-319, 3.162020133383978e-322, 1.043466644016713e-320, 2.055313086699586e-320, 2.561236308041022e-320, 3.067159529382458e-320], dtype=float64)
|
||||
array([3.038651941617419e-319, 3.162020133383978e-322, 1.043466644016713e-320, 2.055313086699586e-320, 2.561236308041022e-320, 3.067159529382458e-320], dtype=float64)
|
||||
array([1e-318, 1e-321, 1e-319, 1e-319, 1e-319, 1e-319], dtype=float64)
|
||||
array([1e-318, 1e-321, 1e-319, 1e-319, 1e-319, 1e-319], dtype=float64)
|
||||
array([1e-318, 1e-321, 1e-319, 1e-319, 1e-319, 1e-319], dtype=float64)
|
||||
|
|
|
|||
16
tests/2d/numpy/nonzero.py
Normal file
16
tests/2d/numpy/nonzero.py
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
try:
|
||||
from ulab import numpy as np
|
||||
except:
|
||||
import numpy as np
|
||||
|
||||
array = np.array(range(16)).reshape((4,4))
|
||||
print(array)
|
||||
print(array < 5)
|
||||
print(np.nonzero(array < 5))
|
||||
|
||||
dtypes = (np.uint8, np.int8, np.uint16, np.int16, np.float)
|
||||
|
||||
for dtype in dtypes:
|
||||
array = (np.arange(2, 12, 3, dtype=dtype)).reshape((2,2)) - 2
|
||||
print(array)
|
||||
print(np.nonzero(array))
|
||||
24
tests/2d/numpy/nonzero.py.exp
Normal file
24
tests/2d/numpy/nonzero.py.exp
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
array([[0.0, 1.0, 2.0, 3.0],
|
||||
[4.0, 5.0, 6.0, 7.0],
|
||||
[8.0, 9.0, 10.0, 11.0],
|
||||
[12.0, 13.0, 14.0, 15.0]], dtype=float64)
|
||||
array([[True, True, True, True],
|
||||
[True, False, False, False],
|
||||
[False, False, False, False],
|
||||
[False, False, False, False]], dtype=bool)
|
||||
(array([0, 0, 0, 0, 1], dtype=uint16), array([0, 1, 2, 3, 0], dtype=uint16))
|
||||
array([[0, 3],
|
||||
[6, 9]], dtype=uint8)
|
||||
(array([0, 1, 1], dtype=uint16), array([1, 0, 1], dtype=uint16))
|
||||
array([[0, 3],
|
||||
[6, 9]], dtype=int8)
|
||||
(array([0, 1, 1], dtype=uint16), array([1, 0, 1], dtype=uint16))
|
||||
array([[0, 3],
|
||||
[6, 9]], dtype=uint16)
|
||||
(array([0, 1, 1], dtype=uint16), array([1, 0, 1], dtype=uint16))
|
||||
array([[0, 3],
|
||||
[6, 9]], dtype=int16)
|
||||
(array([0, 1, 1], dtype=uint16), array([1, 0, 1], dtype=uint16))
|
||||
array([[0.0, 3.0],
|
||||
[6.0, 9.0]], dtype=float64)
|
||||
(array([0, 1, 1], dtype=uint16), array([1, 0, 1], dtype=uint16))
|
||||
|
|
@ -18,7 +18,7 @@ array([235, 236, 237, 238, 239], dtype=int16)
|
|||
array([250, 235, 245], dtype=int16)
|
||||
253.0
|
||||
array([253.0, 254.0, 255.0], dtype=float64)
|
||||
array([7.205759403792793e+16, 65533.0, 253.0], dtype=float64)
|
||||
array([7.205759403792794e+16, 65533.0, 253.0], dtype=float64)
|
||||
Testing np.max:
|
||||
1
|
||||
1.0
|
||||
|
|
@ -37,9 +37,9 @@ array([254, 239, 249], dtype=uint16)
|
|||
254
|
||||
array([250, 251, 252, 253, 254], dtype=int16)
|
||||
array([254, 239, 249], dtype=int16)
|
||||
7.205759403792793e+16
|
||||
array([7.205759403792793e+16, 7.205759403792793e+16, 7.205759403792793e+16], dtype=float64)
|
||||
array([7.205759403792793e+16, 65535.00000000001, 255.0], dtype=float64)
|
||||
7.205759403792794e+16
|
||||
array([7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16], dtype=float64)
|
||||
array([7.205759403792794e+16, 65535.0, 255.0], dtype=float64)
|
||||
Testing np.argmin:
|
||||
0
|
||||
0
|
||||
|
|
@ -89,35 +89,35 @@ array([[252.0, 253.0, 254.0],
|
|||
[237.0, 238.0, 239.0],
|
||||
[247.0, 248.0, 249.0]], dtype=float64)
|
||||
Testing np.maximum:
|
||||
array([[7.205759403792793e+16, 7.205759403792793e+16, 7.205759403792793e+16],
|
||||
[65533.0, 65534.0, 65535.00000000001],
|
||||
array([[7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16],
|
||||
[65533.0, 65534.0, 65535.0],
|
||||
[253.0, 254.0, 255.0]], dtype=float64)
|
||||
10
|
||||
10.0
|
||||
array([[7.205759403792793e+16, 7.205759403792793e+16, 7.205759403792793e+16],
|
||||
[65533.0, 65534.0, 65535.00000000001],
|
||||
array([[7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16],
|
||||
[65533.0, 65534.0, 65535.0],
|
||||
[253.0, 254.0, 255.0]], dtype=float64)
|
||||
Testing np.sort:
|
||||
array([237, 238, 239, 247, 248, 249, 252, 253, 254], dtype=uint8)
|
||||
array([253.0, 254.0, 255.0, 65533.0, 65534.0, 65535.00000000001, 7.205759403792793e+16, 7.205759403792793e+16, 7.205759403792793e+16], dtype=float64)
|
||||
array([253.0, 254.0, 255.0, 65533.0, 65534.0, 65535.0, 7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16], dtype=float64)
|
||||
array([[237, 238, 239],
|
||||
[247, 248, 249],
|
||||
[252, 253, 254]], dtype=uint8)
|
||||
array([[253.0, 254.0, 255.0],
|
||||
[65533.0, 65534.0, 65535.00000000001],
|
||||
[7.205759403792793e+16, 7.205759403792793e+16, 7.205759403792793e+16]], dtype=float64)
|
||||
[65533.0, 65534.0, 65535.0],
|
||||
[7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16]], dtype=float64)
|
||||
array([[252, 253, 254],
|
||||
[237, 238, 239],
|
||||
[247, 248, 249]], dtype=uint8)
|
||||
array([[7.205759403792793e+16, 7.205759403792793e+16, 7.205759403792793e+16],
|
||||
[65533.0, 65534.0, 65535.00000000001],
|
||||
array([[7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16],
|
||||
[65533.0, 65534.0, 65535.0],
|
||||
[253.0, 254.0, 255.0]], dtype=float64)
|
||||
Testing np.sum:
|
||||
762
|
||||
250
|
||||
2217.0
|
||||
array([736.0, 739.0000000000001, 742.0], dtype=float64)
|
||||
array([759.0, 714.0000000000001, 744.0], dtype=float64)
|
||||
array([736.0, 739.0, 742.0], dtype=float64)
|
||||
array([759.0, 714.0, 744.0], dtype=float64)
|
||||
Testing np.mean:
|
||||
254.0
|
||||
254.0
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
y: array([9.0, -47.0, 224.0, -987.0000000000001, 4129.0, -16549.0, 64149.0, -241937.0, 892121.0, -3228165.0], dtype=float64)
|
||||
y: array([9.0, -47.0, 224.0, -987.0, 4129.0, -16549.0, 64149.0, -241937.0, 892121.0, -3228165.0], dtype=float64)
|
||||
zo: array([[37242.0, 74835.0],
|
||||
[1026187.0, 1936542.0],
|
||||
[10433318.0, 18382017.0]], dtype=float64)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
array([1.0, 256.0, 65280.0, 65535.00000000001], dtype=float64)
|
||||
array([1.0, 256.0, 65280.0, 65535.0], dtype=float64)
|
||||
array([1.0, 256.0, -256.0, -1.0], dtype=float64)
|
||||
array([16777217.0, 4294967040.0], dtype=float64)
|
||||
array([16777217.0, -256.0], dtype=float64)
|
||||
|
|
|
|||
Loading…
Reference in a new issue