Python numpy.core.numeric.newaxis() Examples
The following are 22
code examples of numpy.core.numeric.newaxis().
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Example #1
| Source File: utils.py From ProxImaL with MIT License | 6 votes |
|
def _stack(arrays, axis=0):
arrays = [np.asanyarray(arr) for arr in arrays]
if not arrays:
raise ValueError('need at least one array to stack')
shapes = set(arr.shape for arr in arrays)
if len(shapes) != 1:
raise ValueError('all input arrays must have the same shape')
result_ndim = arrays[0].ndim + 1
if not -result_ndim <= axis < result_ndim:
msg = 'axis {0} out of bounds [-{1}, {1})'.format(axis, result_ndim)
raise np.IndexError(msg)
if axis < 0:
axis += result_ndim
sl = (slice(None),) * axis + (numeric.newaxis,)
expanded_arrays = [arr[sl] for arr in arrays]
return numeric.concatenate(expanded_arrays, axis=axis)
Example #2
| Source File: utils.py From ProxImaL with MIT License | 5 votes |
|
def estimate_std(z, method='daub_reflect'):
# Estimates noise standard deviation assuming additive gaussian noise
# Check method
if (method not in NoiseEstMethod.values()) and (method in NoiseEstMethod.keys()):
method = NoiseEstMethod[method]
else:
raise Exception("Invalid noise estimation method.")
# Check shape
if len(z.shape) == 2:
z = z[..., np.newaxis]
elif len(z.shape) != 3:
raise Exception("Supports only up to 3D images.")
# Run on multichannel image
channels = z.shape[2]
dev = np.zeros(channels)
# Iterate over channels
for ch in range(channels):
# Daubechies denoising method
if method == NoiseEstMethod['daub_reflect'] or method == NoiseEstMethod['daub_replicate']:
daub6kern = np.array([0.03522629188571, 0.08544127388203, -0.13501102001025,
-0.45987750211849, 0.80689150931109, -0.33267055295008],
dtype=np.float32, order='F')
if method == NoiseEstMethod['daub_reflect']:
wav_det = cv2.sepFilter2D(z, -1, daub6kern, daub6kern,
borderType=cv2.BORDER_REFLECT_101)
else:
wav_det = cv2.sepFilter2D(z, -1, daub6kern, daub6kern,
borderType=cv2.BORDER_REPLICATE)
dev[ch] = np.median(np.absolute(wav_det)) / 0.6745
# Return standard deviation
return dev
Example #3
| Source File: index_tricks.py From keras-lambda with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #4
| Source File: index_tricks.py From recruit with Apache License 2.0 | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #5
| Source File: index_tricks.py From twitter-stock-recommendation with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #6
| Source File: index_tricks.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #7
| Source File: index_tricks.py From Carnets with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #8
| Source File: index_tricks.py From coffeegrindsize with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #9
| Source File: index_tricks.py From elasticintel with GNU General Public License v3.0 | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #10
| Source File: index_tricks.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #11
| Source File: index_tricks.py From ImageFusion with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #12
| Source File: index_tricks.py From mxnet-lambda with Apache License 2.0 | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #13
| Source File: index_tricks.py From pySINDy with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #14
| Source File: index_tricks.py From Fluid-Designer with GNU General Public License v3.0 | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #15
| Source File: index_tricks.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #16
| Source File: index_tricks.py From GraphicDesignPatternByPython with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #17
| Source File: index_tricks.py From Mastering-Elasticsearch-7.0 with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #18
| Source File: index_tricks.py From Computable with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None: start=0
if step is None: step=1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(int(math.ceil((key[k].stop - start)/(step*1.0))))
if isinstance(step, float) or \
isinstance(start, float) or \
isinstance(key[k].stop, float):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None: start=0
if step is None: step=1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None: start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop+step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #19
| Source File: index_tricks.py From vnpy_crypto with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #20
| Source File: index_tricks.py From auto-alt-text-lambda-api with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #21
| Source File: index_tricks.py From lambda-packs with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
Example #22
| Source File: index_tricks.py From lambda-packs with MIT License | 4 votes |
|
def __getitem__(self, key):
try:
size = []
typ = int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size.append(int(abs(step)))
typ = float
else:
size.append(
int(math.ceil((key[k].stop - start)/(step*1.0))))
if (isinstance(step, float) or
isinstance(start, float) or
isinstance(key[k].stop, float)):
typ = float
if self.sparse:
nn = [_nx.arange(_x, dtype=_t)
for _x, _t in zip(size, (typ,)*len(size))]
else:
nn = _nx.indices(size, typ)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
step = int(abs(step))
if step != 1:
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.newaxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None:
start = 0
if isinstance(step, complex):
step = abs(step)
length = int(step)
if step != 1:
step = (key.stop-start)/float(step-1)
stop = key.stop + step
return _nx.arange(0, length, 1, float)*step + start
else:
return _nx.arange(start, stop, step)
