Here are the examples of the python api numpy.nditer taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
185 Examples
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_best_order_multi_index_1d():
# The multi-indices should be correct with any reordering
a = arange(4)
# 1D order
i = nditer(a, ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0,), (1,), (2,), (3,)])
# 1D reversed order
i = nditer(a[::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(3,), (2,), (1,), (0,)])
def test_iter_best_order_multi_index_2d():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_best_order_c_index_1d():
# The C index should be correct with any reordering
a = arange(4)
# 1D order
i = nditer(a, ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 1, 2, 3])
# 1D reversed order
i = nditer(a[::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [3, 2, 1, 0])
def test_iter_best_order_c_index_2d():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_best_order_f_index_1d():
# The Fortran index should be correct with any reordering
a = arange(4)
# 1D order
i = nditer(a, ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 1, 2, 3])
# 1D reversed order
i = nditer(a[::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [3, 2, 1, 0])
def test_iter_best_order_f_index_2d():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_slice():
a, b, c = np.arange(3), np.arange(3), np.arange(3.)
i = nditer([a, b, c], [], ['readwrite'])
i[0:2] = (3, 3)
assert_equal(a, [3, 1, 2])
assert_equal(b, [3, 1, 2])
assert_equal(c, [0, 1, 2])
i[1] = 12
assert_equal(i[0:2], [3, 12])
def test_iter_nbo_align_contig():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_allocate_output_simple():
# Check that the iterator will properly allocate outputs
# Simple case
a = arange(6)
i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')])
assert_equal(i.operands[1].shape, a.shape)
assert_equal(i.operands[1].dtype, np.dtype('f4'))
def test_iter_allocate_output_buffered_readwrite():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_allocate_output_buffered_readwrite():
# Allocated output with buffering + delay_bufalloc
a = arange(6)
i = nditer([a, None], ['buffered', 'delay_bufalloc'],
[['readonly'], ['allocate', 'readwrite']])
i.operands[1][:] = 1
i.reset()
for x in i:
x[1][...] += x[0][...]
assert_equal(i.operands[1], a+1)
def test_iter_allocate_output_itorder():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_allocate_output_opaxes():
# Specifying op_axes should work
a = arange(24, dtype='i4').reshape(2, 3, 4)
i = nditer([None, a], [], [['writeonly', 'allocate'], ['readonly']],
op_dtypes=[np.dtype('u4'), None],
op_axes=[[1, 2, 0], None])
assert_equal(i.operands[0].shape, (4, 2, 3))
assert_equal(i.operands[0].strides, (4, 48, 16))
assert_equal(i.operands[0].dtype, np.dtype('u4'))
def test_iter_allocate_output_types_promotion():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_allocate_output_types_byte_order():
# Verify the rules for byte order changes
# When there's just one input, the output type exactly matches
a = array([3], dtype='u4').newbyteorder()
i = nditer([a, None], [],
[['readonly'], ['writeonly', 'allocate']])
assert_equal(i.dtypes[0], i.dtypes[1])
# With two or more inputs, the output type is in native byte order
i = nditer([a, a, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
assert_(i.dtypes[0] != i.dtypes[2])
assert_equal(i.dtypes[0].newbyteorder('='), i.dtypes[2])
def test_iter_allocate_output_types_scalar():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_allocate_output_types_scalar():
# If the inputs are all scalars, the output should be a scalar
i = nditer([None, 1, 2.3, np.float32(12), np.complex128(3)], [],
[['writeonly', 'allocate']] + [['readonly']]*4)
assert_equal(i.operands[0].dtype, np.dtype('complex128'))
assert_equal(i.operands[0].ndim, 0)
def test_iter_allocate_output_subtype():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_remove_axis():
a = arange(24).reshape(2, 3, 4)
i = nditer(a, ['multi_index'])
i.remove_axis(1)
assert_equal([x for x in i], a[:, 0,:].ravel())
a = a[::-1,:,:]
i = nditer(a, ['multi_index'])
i.remove_axis(0)
assert_equal([x for x in i], a[0,:,:].ravel())
def test_iter_remove_multi_index_inner_loop():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_write_buffering():
# Test that buffering of writes is working
# F-order swapped array
a = np.arange(24).reshape(2, 3, 4).T.newbyteorder().byteswap()
i = nditer(a, ['buffered'],
[['readwrite', 'nbo', 'aligned']],
casting='equiv',
order='C',
buffersize=16)
x = 0
while not i.finished:
i[0] = x
x += 1
i.iternext()
assert_equal(a.ravel(order='C'), np.arange(24))
def test_iter_buffering_delayed_alloc():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_buffered_cast_simple():
# Test that buffering can handle a simple cast
a = np.arange(10, dtype='f4')
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f8')],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f4'))
def test_iter_buffered_cast_byteswapped():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_buffering_string():
# Safe casting disallows shrinking strings
a = np.array(['abc', 'a', 'abcd'], dtype=np.bytes_)
assert_equal(a.dtype, np.dtype('S4'))
assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
op_dtypes='S2')
i = nditer(a, ['buffered'], ['readonly'], op_dtypes='S6')
assert_equal(i[0], b'abc')
assert_equal(i[0].dtype, np.dtype('S6'))
a = np.array(['abc', 'a', 'abcd'], dtype=np.unicode)
assert_equal(a.dtype, np.dtype('U4'))
assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
op_dtypes='U2')
i = nditer(a, ['buffered'], ['readonly'], op_dtypes='U6')
assert_equal(i[0], u'abc')
assert_equal(i[0].dtype, np.dtype('U6'))
def test_iter_buffering_growinner():
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_buffering_growinner():
# Test that the inner loop grows when no buffering is needed
a = np.arange(30)
i = nditer(a, ['buffered', 'growinner', 'external_loop'],
buffersize=5)
# Should end up with just one inner loop here
assert_equal(i[0].size, a.size)
@dec.slow
3
Source : test_nditer.py
with GNU General Public License v3.0
from adityaprakash-bobby
with GNU General Public License v3.0
from adityaprakash-bobby
def test_iter_no_broadcast():
# Test that the no_broadcast flag works
a = np.arange(24).reshape(2, 3, 4)
b = np.arange(6).reshape(2, 3, 1)
c = np.arange(12).reshape(3, 4)
nditer([a, b, c], [],
[['readonly', 'no_broadcast'],
['readonly'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly', 'no_broadcast'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly'], ['readonly', 'no_broadcast']])
class TestIterNested(object):
3
Source : test_nditer.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_iter_slice():
a, b, c = np.arange(3), np.arange(3), np.arange(3.)
i = nditer([a, b, c], [], ['readwrite'])
with i:
i[0:2] = (3, 3)
assert_equal(a, [3, 1, 2])
assert_equal(b, [3, 1, 2])
assert_equal(c, [0, 1, 2])
i[1] = 12
assert_equal(i[0:2], [3, 12])
def test_iter_assign_mapping():
3
Source : test_nditer.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_iter_allocate_output_buffered_readwrite():
# Allocated output with buffering + delay_bufalloc
a = arange(6)
i = nditer([a, None], ['buffered', 'delay_bufalloc'],
[['readonly'], ['allocate', 'readwrite']])
with i:
i.operands[1][:] = 1
i.reset()
for x in i:
x[1][...] += x[0][...]
assert_equal(i.operands[1], a+1)
def test_iter_allocate_output_itorder():
3
Source : test_nditer.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_iter_write_buffering():
# Test that buffering of writes is working
# F-order swapped array
a = np.arange(24).reshape(2, 3, 4).T.newbyteorder().byteswap()
i = nditer(a, ['buffered'],
[['readwrite', 'nbo', 'aligned']],
casting='equiv',
order='C',
buffersize=16)
x = 0
with i:
while not i.finished:
i[0] = x
x += 1
i.iternext()
assert_equal(a.ravel(order='C'), np.arange(24))
def test_iter_buffering_delayed_alloc():
3
Source : test_nditer.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_iter_buffered_cast_simple():
# Test that buffering can handle a simple cast
a = np.arange(10, dtype='f4')
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f8')],
buffersize=3)
with i:
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f4'))
def test_iter_buffered_cast_byteswapped():
3
Source : test_nditer.py
with MIT License
from alvarobartt
with MIT License
from alvarobartt
def test_iter_buffering_growinner():
# Test that the inner loop grows when no buffering is needed
a = np.arange(30)
i = nditer(a, ['buffered', 'growinner', 'external_loop'],
buffersize=5)
# Should end up with just one inner loop here
assert_equal(i[0].size, a.size)
@pytest.mark.slow
3
Source : utils.py
with Apache License 2.0
from criteo-research
with Apache License 2.0
from criteo-research
def compute_2i_regularization_id(prods, num_products):
"""Compute the ID for the regularization for the 2i approach"""
reg_ids = []
# Loop through batch and compute if the product ID is greater than the number of products
for x in np.nditer(prods):
if x >= num_products:
reg_ids.append(x)
elif x < num_products:
reg_ids.append(x + num_products) # Add number of products to create the 2i representation
return np.asarray(reg_ids)
def compute_treatment_or_control(prods, num_products):
3
Source : utils.py
with Apache License 2.0
from criteo-research
with Apache License 2.0
from criteo-research
def compute_treatment_or_control(prods, num_products):
"""Compute if product is in treatment or control"""
# Return the control product places and treatment places as 1's in a binary matrix.
ids = []
for x in np.nditer(prods):
# Greater than the number of products
if x >= num_products:
ids.append(0)
elif x < num_products:
ids.append(1)
# create the binary mask and return
return np.asarray(ids), np.logical_not(np.asarray(ids)).astype(int)
def compute_bootstraps_2i(sess, model, test_user_batch, test_product_batch, test_label_batch, test_logits, ap_mse_loss, ap_log_loss):
3
Source : test_nditer.py
with Apache License 2.0
from dashanji
with Apache License 2.0
from dashanji
def test_iter_buffering_string():
# Safe casting disallows shrinking strings
a = np.array(['abc', 'a', 'abcd'], dtype=np.bytes_)
assert_equal(a.dtype, np.dtype('S4'))
assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
op_dtypes='S2')
i = nditer(a, ['buffered'], ['readonly'], op_dtypes='S6')
assert_equal(i[0], b'abc')
assert_equal(i[0].dtype, np.dtype('S6'))
a = np.array(['abc', 'a', 'abcd'], dtype=np.unicode_)
assert_equal(a.dtype, np.dtype('U4'))
assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
op_dtypes='U2')
i = nditer(a, ['buffered'], ['readonly'], op_dtypes='U6')
assert_equal(i[0], u'abc')
assert_equal(i[0].dtype, np.dtype('U6'))
def test_iter_buffering_growinner():
3
Source : test_nditer.py
with Apache License 2.0
from dashanji
with Apache License 2.0
from dashanji
def test_iter_no_broadcast():
# Test that the no_broadcast flag works
a = np.arange(24).reshape(2, 3, 4)
b = np.arange(6).reshape(2, 3, 1)
c = np.arange(12).reshape(3, 4)
nditer([a, b, c], [],
[['readonly', 'no_broadcast'],
['readonly'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly', 'no_broadcast'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly'], ['readonly', 'no_broadcast']])
class TestIterNested:
3
Source : metrics.py
with BSD 3-Clause "New" or "Revised" License
from deep500
with BSD 3-Clause "New" or "Revised" License
from deep500
def end(self, outputs: Dict[str, np.ndarray]):
out = outputs[self._label]
for outclass in np.nditer(out):
self.hist[outclass] += 1
def measure(self, *unused) -> List[int]:
3
Source : selection.py
with BSD 3-Clause "New" or "Revised" License
from FeatureLabs
with BSD 3-Clause "New" or "Revised" License
from FeatureLabs
def _edge_maker(adj, thresh):
'''Make all edges at a given threshold. Prerequisite
to make the associated graph.
'''
it = np.nditer(adj, flags=['multi_index'])
edges = []
for val in it:
if val < = thresh:
edges.append(it.multi_index)
return edges
def find_connected_components(vertices, edges):
3
Source : varlib.py
with MIT License
from GeoStat-Framework
with MIT License
from GeoStat-Framework
def __iter__(self):
"""Iterate over Observations."""
if self.state == "transient":
self.__it = np.nditer(self.time, flags=["multi_index"])
else:
self.__itfinished = False
return self
def __next__(self):
3
Source : formats.py
with BSD 3-Clause "New" or "Revised" License
from holzschu
with BSD 3-Clause "New" or "Revised" License
from holzschu
def value(self):
iterator = np.nditer([self.jd1, self.jd2, None],
flags=['refs_ok', 'zerosize_ok'],
op_dtypes=[None, None, object])
for jd1, jd2, out in iterator:
jd1_, jd2_ = day_frac(jd1, jd2)
out[...] = datetime.timedelta(days=jd1_,
microseconds=jd2_*86400*1e6)
return self.mask_if_needed(iterator.operands[-1])
def _validate_jd_for_storage(jd):
3
Source : magnetic_time.py
with MIT License
from igp-gravity
with MIT License
from igp-gravity
def reference(cls, times, lats_ngp, lons_ngp):
times = asarray(times)
lats_ngp = asarray(lats_ngp)
lons_ngp = asarray(lons_ngp)
results = empty(times.shape)
iterator = nditer(
[times, lats_ngp, lons_ngp, results],
op_flags=[
['readonly'], ['readonly'], ['readonly'], ['writeonly'],
],
)
for time, lat_ngp, lon_ngp, result in iterator:
result[...] = cls.ref_mjd2000_to_magnetic_universal_time(
time, lat_ngp, lon_ngp
)
return results
@staticmethod
3
Source : dpnpimpl.py
with Apache License 2.0
from IntelPython
with Apache License 2.0
from IntelPython
def _check_finite_matrix(a):
for v in np.nditer(a):
if not np.isfinite(v.item()):
raise np.linalg.LinAlgError("Array must not contain infs or NaNs.")
@register_jitable
3
Source : calibrate_mupots_intrinsics.py
with MIT License
from isarandi
with MIT License
from isarandi
def main():
if 'DATA_ROOT' not in os.environ:
print('Set the DATA_ROOT environment variable to the parent dir of the mupots directory.')
sys.exit(1)
intrinsics_per_sequence = {}
for i_seq in range(1, 21):
anno_path = f'{os.environ["DATA_ROOT"]}/mupots/TS{i_seq}/annot.mat'
anno = scipy.io.loadmat(anno_path, struct_as_record=False, squeeze_me=True)['annotations']
points2d = np.concatenate([x.annot2.T for x in np.nditer(anno) if x.isValidFrame])
points3d = np.concatenate([x.annot3.T for x in np.nditer(anno) if x.isValidFrame])
intrinsics_per_sequence[f'TS{i_seq}'] = estimate_intrinsic_matrix(points2d, points3d)
with open(f'{os.environ["DATA_ROOT"]}/mupots/camera_intrinsics.json', 'w') as file:
return json.dump(intrinsics_per_sequence, file)
def estimate_intrinsic_matrix(points2d, points3d):
3
Source : mscore.py
with GNU General Public License v3.0
from isjerryxiao
with GNU General Public License v3.0
from isjerryxiao
def __do_i_win(self):
unopened = 0
mines_opened = 0
for x in np.nditer(self.map):
if x < = 8:
unopened += 1
elif x in (19, DEAD):
mines_opened += 1
if mines_opened == self.mines:
return True
elif unopened == 0:
return True
else:
return False
def __open(self, row, col, automatic=False):
3
Source : box_finder.py
with MIT License
from j-towns
with MIT License
from j-towns
def box_finder_generic(arr, look_for, switch_to):
warn("Falling back on slow box finder for array with ndim > 6")
it = np.nditer(arr, flags=['multi_index'])
for k in it:
if k == look_for:
starts = it.multi_index
arr[starts] = switch_to
sizes = arr.ndim * [1]
for d in range(arr.ndim):
box_iter = test_boxes(starts, sizes, d)
test_box = next(box_iter)
while (starts[d] + sizes[d] < arr.shape[d]
and np.all(arr[test_box] == look_for)):
arr[test_box] = switch_to
sizes[d] = sizes[d] + 1
test_box = next(box_iter)
yield starts, sizes
def box_finder0(arr, look_for, switch_to):
3
Source : test_nditer.py
with MIT License
from ktraunmueller
with MIT License
from ktraunmueller
def test_iter_allocate_output_opaxes():
# Specifing op_axes should work
a = arange(24, dtype='i4').reshape(2, 3, 4)
i = nditer([None, a], [], [['writeonly', 'allocate'], ['readonly']],
op_dtypes=[np.dtype('u4'), None],
op_axes=[[1, 2, 0], None]);
assert_equal(i.operands[0].shape, (4, 2, 3))
assert_equal(i.operands[0].strides, (4, 48, 16))
assert_equal(i.operands[0].dtype, np.dtype('u4'))
def test_iter_allocate_output_types_promotion():
3
Source : test_nditer.py
with MIT License
from ktraunmueller
with MIT License
from ktraunmueller
def test_iter_allocate_output_types_byte_order():
# Verify the rules for byte order changes
# When there's just one input, the output type exactly matches
a = array([3], dtype='u4').newbyteorder()
i = nditer([a, None], [],
[['readonly'], ['writeonly', 'allocate']])
assert_equal(i.dtypes[0], i.dtypes[1]);
# With two or more inputs, the output type is in native byte order
i = nditer([a, a, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
assert_(i.dtypes[0] != i.dtypes[2]);
assert_equal(i.dtypes[0].newbyteorder('='), i.dtypes[2])
def test_iter_allocate_output_types_scalar():
3
Source : test_nditer.py
with MIT License
from ktraunmueller
with MIT License
from ktraunmueller
def test_iter_buffering_string():
# Safe casting disallows shrinking strings
a = np.array(['abc', 'a', 'abcd'], dtype=np.bytes_)
assert_equal(a.dtype, np.dtype('S4'));
assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
op_dtypes='S2')
i = nditer(a, ['buffered'], ['readonly'], op_dtypes='S6')
assert_equal(i[0], asbytes('abc'))
assert_equal(i[0].dtype, np.dtype('S6'))
a = np.array(['abc', 'a', 'abcd'], dtype=np.unicode)
assert_equal(a.dtype, np.dtype('U4'));
assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
op_dtypes='U2')
i = nditer(a, ['buffered'], ['readonly'], op_dtypes='U6')
assert_equal(i[0], sixu('abc'))
assert_equal(i[0].dtype, np.dtype('U6'))
def test_iter_buffering_growinner():
3
Source : test_nditer.py
with MIT License
from ktraunmueller
with MIT License
from ktraunmueller
def test_iter_no_broadcast():
# Test that the no_broadcast flag works
a = np.arange(24).reshape(2, 3, 4)
b = np.arange(6).reshape(2, 3, 1)
c = np.arange(12).reshape(3, 4)
i = nditer([a, b, c], [],
[['readonly', 'no_broadcast'], ['readonly'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly', 'no_broadcast'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly'], ['readonly', 'no_broadcast']])
def test_iter_nested_iters_basic():
3
Source : data_utils.py
with Apache License 2.0
from ludwig-ai
with Apache License 2.0
from ludwig-ai
def save_array(data_fp, array):
with open_file(data_fp, "w") as output_file:
for x in np.nditer(array):
output_file.write(str(x) + "\n")
# TODO(shreya): Confirm types of args
def load_pretrained_embeddings(embeddings_path: str, vocab: List[str]) -> np.ndarray:
3
Source : benchmark_data.py
with Apache License 2.0
from nasa
with Apache License 2.0
from nasa
def write_stacks(test_agraph_list):
filename = '../bingocpp/app/test-agraph-stacks.csv'
with open(filename, mode='w+') as stack_file:
stack_file_writer = csv.writer(stack_file, delimiter=',')
for agraph in test_agraph_list:
stack = []
for row in agraph._command_array:
for i in np.nditer(row):
stack.append(i)
stack_file_writer.writerow(stack)
stack_file.close()
def write_constants(test_agraph_list):
3
Source : gerador.py
with MIT License
from NatanaelAntonioli
with MIT License
from NatanaelAntonioli
def get_max_min(tipo, espaco, teto_ou_piso):
if tipo == 'min':
retornar = 99999999
for x in np.nditer(espaco):
if teto_ou_piso < x < retornar:
retornar = x
else:
retornar = 0
for x in np.nditer(espaco):
if retornar < x < teto_ou_piso:
retornar = x
return retornar
def get_distancia(lat1, lon1, lat2, lon2):
3
Source : interference.py
with GNU General Public License v3.0
from NJUOCR
with GNU General Public License v3.0
from NJUOCR
def interfere(self, img):
# todo 增加噪点
# white_noise
w_rate = self.rate
w_range = (self.max_val, self.max_val)
# np.nditer: numpy array自带的迭代器 参考网址:https://www.jianshu.com/p/f2bd63766204
# 按顺序遍历会出现噪点扎堆的请看
for x in np.nditer(img, op_flags=['readwrite']):
if rd.random() < w_rate:
x[...] = rd.randint(*w_range)
return img, None
class RandomResize(Interference):
3
Source : test_nditer.py
with Apache License 2.0
from pierreant
with Apache License 2.0
from pierreant
def test_iter_no_broadcast():
# Test that the no_broadcast flag works
a = np.arange(24).reshape(2, 3, 4)
b = np.arange(6).reshape(2, 3, 1)
c = np.arange(12).reshape(3, 4)
nditer([a, b, c], [],
[['readonly', 'no_broadcast'],
['readonly'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly', 'no_broadcast'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly'], ['readonly', 'no_broadcast']])
def test_iter_nested_iters_basic():
3
Source : c3objs.py
with Apache License 2.0
from q-optimize
with Apache License 2.0
from q-optimize
def __str__(self):
val = self.numpy()
ret = ""
for entry in np.nditer(val):
if self.unit != "undefined":
ret += num3str(entry) + self.unit + " "
else:
ret += num3str(entry, use_prefix=False) + " "
return ret
def subtract(self, val):
3
Source : pca_utils.py
with BSD 3-Clause "New" or "Revised" License
from RoboticsClubIITJ
with BSD 3-Clause "New" or "Revised" License
from RoboticsClubIITJ
def gammaln(a):
b = []
for i in np.nditer(a):
b.append(gamma(i))
b = np.array(b).reshape(a.shape)
b = np.log(np.absolute(b))
return b
def assess_dimension(spectrum, rank, n_samples):
3
Source : framecode.py
with BSD 3-Clause "New" or "Revised" License
from spcl
with BSD 3-Clause "New" or "Revised" License
from spcl
def generate_constants(self, sdfg: SDFG, callsite_stream: CodeIOStream):
# Write constants
for cstname, (csttype, cstval) in sdfg.constants_prop.items():
if isinstance(csttype, data.Array):
const_str = "constexpr " + csttype.dtype.ctype + \
" " + cstname + "[" + str(cstval.size) + "] = {"
it = np.nditer(cstval, order='C')
for i in range(cstval.size - 1):
const_str += str(it[0]) + ", "
it.iternext()
const_str += str(it[0]) + "};\n"
callsite_stream.write(const_str, sdfg)
else:
callsite_stream.write("constexpr %s %s = %s;\n" % (csttype.dtype.ctype, cstname, sym2cpp(cstval)), sdfg)
def generate_fileheader(self, sdfg: SDFG, global_stream: CodeIOStream, backend: str = 'frame'):
3
Source : ReplaceDenormals.py
with MIT License
from UltronAI
with MIT License
from UltronAI
def ReplaceDenormals(net):
for name, param in net.named_parameters():
np_arr = param.data.numpy()
for x in np.nditer(np_arr, op_flags=['readwrite']):
if abs(x) < 1e-30:
x[...] = 1e-30
param.data = torch.from_numpy(np_arr)
3
Source : minisom.py
with Apache License 2.0
from victorca25
with Apache License 2.0
from victorca25
def _activate(self, x):
"""Updates matrix activation_map, in this matrix
the element i,j is the response of the neuron i,j to x."""
s = subtract(x, self._weights) # x - w
it = nditer(self._activation_map, flags=['multi_index'])
while not it.finished:
# || x - w ||
self._activation_map[it.multi_index] = fast_norm(s[it.multi_index])
it.iternext()
def activate(self, x):
3
Source : minisom.py
with Apache License 2.0
from victorca25
with Apache License 2.0
from victorca25
def random_weights_init(self, data):
"""Initializes the weights of the SOM
picking random samples from data."""
self._check_input_len(data)
it = nditer(self._activation_map, flags=['multi_index'])
while not it.finished:
rand_i = self._random_generator.randint(len(data))
self._weights[it.multi_index] = data[rand_i]
norm = fast_norm(self._weights[it.multi_index])
self._weights[it.multi_index] = self._weights[it.multi_index]
it.iternext()
def pca_weights_init(self, data):
3
Source : minisom.py
with Apache License 2.0
from victorca25
with Apache License 2.0
from victorca25
def distance_map(self):
"""Returns the distance map of the weights.
Each cell is the normalised sum of the distances between
a neuron and its neighbours."""
um = zeros((self._weights.shape[0], self._weights.shape[1]))
it = nditer(um, flags=['multi_index'])
while not it.finished:
for ii in range(it.multi_index[0]-1, it.multi_index[0]+2):
for jj in range(it.multi_index[1]-1, it.multi_index[1]+2):
if (ii >= 0 and ii < self._weights.shape[0] and
jj >= 0 and jj < self._weights.shape[1]):
w_1 = self._weights[ii, jj, :]
w_2 = self._weights[it.multi_index]
um[it.multi_index] += fast_norm(w_1-w_2)
it.iternext()
um = um/um.max()
return um
def activation_response(self, data):
3
Source : _property_array.py
with MIT License
from yoelcortes
with MIT License
from yoelcortes
def __setitem__(self, key, value):
items = self.base[key]
if isa(items, ndarray):
for i, v in np.nditer((items, value), flags=('refs_ok', 'zerosize_ok')):
i.item().value = v
else:
items.value = value
def __add__(self, other):
See More Examples