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pypa/pipenv | pipenv/vendor/jinja2/compiler.py | CodeGenerator.pop_assign_tracking | def pop_assign_tracking(self, frame):
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pypa/pipenv | pipenv/vendor/jinja2/compiler.py | CodeGenerator.visit_Block | def visit_Block(self, node, frame):
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pypa/pipenv | pipenv/vendor/jinja2/compiler.py | CodeGenerator.visit_Extends | def visit_Extends(self, node, frame):
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pypa/pipenv | pipenv/vendor/jinja2/compiler.py | CodeGenerator.visit_Include | def visit_Include(self, node, frame):
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pypa/pipenv | pipenv/vendor/jinja2/compiler.py | CodeGenerator.visit_Import | def visit_Import(self, node, frame):
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pypa/pipenv | pipenv/vendor/jinja2/compiler.py | CodeGenerator.visit_FromImport | def visit_FromImport(self, node, frame):
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pypa/pipenv | pipenv/vendor/backports/weakref.py | finalize.detach | def detach(self):
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pypa/pipenv | pipenv/vendor/backports/weakref.py | finalize.atexit | def atexit(self):
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pypa/pipenv | pipenv/patched/notpip/_vendor/html5lib/treebuilders/etree_lxml.py | tostring | def tostring(element):
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pypa/pipenv | pipenv/vendor/jinja2/visitor.py | NodeVisitor.get_visitor | def get_visitor(self, node):
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pypa/pipenv | pipenv/vendor/jinja2/visitor.py | NodeVisitor.visit | def visit(self, node, *args, **kwargs):
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pypa/pipenv | pipenv/vendor/jinja2/visitor.py | NodeTransformer.visit_list | def visit_list(self, node, *args, **kwargs):
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pypa/pipenv | pipenv/vendor/pep517/wrappers.py | default_subprocess_runner | def default_subprocess_runner(cmd, cwd=None, extra_environ=None):
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pypa/pipenv | pipenv/vendor/pep517/wrappers.py | Pep517HookCaller.build_wheel | def build_wheel(
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Returns the name of the newly created file.
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/bninception.py | bninception | def bninception(num_classes=1000, pretrained='imagenet'):
r"""BNInception model architecture from <https://arxiv.org/pdf/1502.03167.pdf>`_ paper.
"""
model = BNInception(num_classes=num_classes)
if pretrained is not None:
settings = pretrained_settings['bninception'][pretrained]
assert n... | python | def bninception(num_classes=1000, pretrained='imagenet'):
r"""BNInception model architecture from <https://arxiv.org/pdf/1502.03167.pdf>`_ paper.
"""
model = BNInception(num_classes=num_classes)
if pretrained is not None:
settings = pretrained_settings['bninception'][pretrained]
assert n... | [
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/fbresnet/resnet152_load.py | conv3x3 | def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=True) | python | def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=True) | [
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/fbresnet/resnet152_load.py | resnet18 | def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])... | python | def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/fbresnet/resnet152_load.py | resnet50 | def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])... | python | def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])... | [
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/nasnet_mobile.py | nasnetamobile | def nasnetamobile(num_classes=1000, pretrained='imagenet'):
r"""NASNetALarge model architecture from the
`"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper.
"""
if pretrained:
settings = pretrained_settings['nasnetamobile'][pretrained]
assert num_classes == settings['num_classes'], \
... | python | def nasnetamobile(num_classes=1000, pretrained='imagenet'):
r"""NASNetALarge model architecture from the
`"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper.
"""
if pretrained:
settings = pretrained_settings['nasnetamobile'][pretrained]
assert num_classes == settings['num_classes'], \
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/cafferesnet.py | cafferesnet101 | def cafferesnet101(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes)
if pretrained is not None:
settings = pretrained... | python | def cafferesnet101(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes)
if pretrained is not None:
settings = pretrained... | [
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/fbresnet.py | fbresnet152 | def fbresnet152(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = FBResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes)
if pretrained is not None:
settings = pretra... | python | def fbresnet152(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = FBResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes)
if pretrained is not None:
settings = pretra... | [
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/torchvision_models.py | alexnet | def alexnet(num_classes=1000, pretrained='imagenet'):
r"""AlexNet model architecture from the
`"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
"""
# https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py
model = models.alexnet(pretrained=False)
if pretrained ... | python | def alexnet(num_classes=1000, pretrained='imagenet'):
r"""AlexNet model architecture from the
`"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
"""
# https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py
model = models.alexnet(pretrained=False)
if pretrained ... | [
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/torchvision_models.py | densenet121 | def densenet121(num_classes=1000, pretrained='imagenet'):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = models.densenet121(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['densenet121'][... | python | def densenet121(num_classes=1000, pretrained='imagenet'):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = models.densenet121(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['densenet121'][... | [
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/torchvision_models.py | inceptionv3 | def inceptionv3(num_classes=1000, pretrained='imagenet'):
r"""Inception v3 model architecture from
`"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
"""
model = models.inception_v3(pretrained=False)
if pretrained is not None:
settings = pretrai... | python | def inceptionv3(num_classes=1000, pretrained='imagenet'):
r"""Inception v3 model architecture from
`"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
"""
model = models.inception_v3(pretrained=False)
if pretrained is not None:
settings = pretrai... | [
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/torchvision_models.py | resnet50 | def resnet50(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-50 model.
"""
model = models.resnet50(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['resnet50'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify... | python | def resnet50(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-50 model.
"""
model = models.resnet50(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['resnet50'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify... | [
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/torchvision_models.py | squeezenet1_0 | def squeezenet1_0(num_classes=1000, pretrained='imagenet'):
r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
accuracy with 50x fewer parameters and <0.5MB model size"
<https://arxiv.org/abs/1602.07360>`_ paper.
"""
model = models.squeezenet1_0(pretrained=False)
if pretraine... | python | def squeezenet1_0(num_classes=1000, pretrained='imagenet'):
r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
accuracy with 50x fewer parameters and <0.5MB model size"
<https://arxiv.org/abs/1602.07360>`_ paper.
"""
model = models.squeezenet1_0(pretrained=False)
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/torchvision_models.py | vgg11 | def vgg11(num_classes=1000, pretrained='imagenet'):
"""VGG 11-layer model (configuration "A")
"""
model = models.vgg11(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['vgg11'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify... | python | def vgg11(num_classes=1000, pretrained='imagenet'):
"""VGG 11-layer model (configuration "A")
"""
model = models.vgg11(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['vgg11'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify... | [
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Cadene/pretrained-models.pytorch | examples/imagenet_eval.py | adjust_learning_rate | def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = args.lr * (0.1 ** (epoch // 30))
for param_group in optimizer.param_groups:
param_group['lr'] = lr | python | def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = args.lr * (0.1 ** (epoch // 30))
for param_group in optimizer.param_groups:
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"""
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r"""NASNetALarge model architecture from the
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url : str
The URL to download.
destination : str, None
The destination of the file. If None is given the file is saved to a temporary directory.
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The URL to download.
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Cadene/pretrained-models.pytorch | pretrainedmodels/datasets/utils.py | AveragePrecisionMeter.value | def value(self):
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Return:
ap (FloatTensor): 1xK tensor, with avg precision for each class k
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Cadene/pretrained-models.pytorch | pretrainedmodels/models/polynet.py | polynet | def polynet(num_classes=1000, pretrained='imagenet'):
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https://arxiv.org/abs/1611.05725
"""
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"""PolyNet architecture from the paper
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https://arxiv.org/abs/1611.05725
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quantopian/zipline | zipline/utils/cache.py | CachedObject.unwrap | def unwrap(self, dt):
"""
Get the cached value.
Returns
-------
value : object
The cached value.
Raises
------
Expired
Raised when `dt` is greater than self.expires.
"""
expires = self._expires
if expires i... | python | def unwrap(self, dt):
"""
Get the cached value.
Returns
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value : object
The cached value.
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Raised when `dt` is greater than self.expires.
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quantopian/zipline | zipline/utils/cache.py | ExpiringCache.get | def get(self, key, dt):
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key : any
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dt : datetime
The time of the lookup.
Returns
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result : any
The value for ``key``.
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key : any
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dt : datetime
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quantopian/zipline | zipline/utils/cache.py | working_dir.ensure_dir | def ensure_dir(self, *path_parts):
"""Ensures a subdirectory of the working directory.
Parameters
----------
path_parts : iterable[str]
The parts of the path after the working directory.
"""
path = self.getpath(*path_parts)
ensure_directory(path)
... | python | def ensure_dir(self, *path_parts):
"""Ensures a subdirectory of the working directory.
Parameters
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path_parts : iterable[str]
The parts of the path after the working directory.
"""
path = self.getpath(*path_parts)
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quantopian/zipline | zipline/data/in_memory_daily_bars.py | verify_frames_aligned | def verify_frames_aligned(frames, calendar):
"""
Verify that DataFrames in ``frames`` have the same indexing scheme and are
aligned to ``calendar``.
Parameters
----------
frames : list[pd.DataFrame]
calendar : trading_calendars.TradingCalendar
Raises
------
ValueError
I... | python | def verify_frames_aligned(frames, calendar):
"""
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frames : list[pd.DataFrame]
calendar : trading_calendars.TradingCalendar
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quantopian/zipline | zipline/data/in_memory_daily_bars.py | InMemoryDailyBarReader.get_value | def get_value(self, sid, dt, field):
"""
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----------
sid : int
The asset identifier.
day : datetime64-like
Midnight of the day for which data is requested.
field : string
The price field. e.g. ('open', 'high', 'low', 'close', ... | python | def get_value(self, sid, dt, field):
"""
Parameters
----------
sid : int
The asset identifier.
day : datetime64-like
Midnight of the day for which data is requested.
field : string
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quantopian/zipline | zipline/data/in_memory_daily_bars.py | InMemoryDailyBarReader.get_last_traded_dt | def get_last_traded_dt(self, asset, dt):
"""
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----------
asset : zipline.asset.Asset
The asset identifier.
dt : datetime64-like
Midnight of the day for which data is requested.
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dt : datetime64-like
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asset : zipline.asset.Asset
The asset identifier.
dt : datetime64-like
Midnight of the day for which data is requested.
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quantopian/zipline | zipline/utils/functional.py | same | def same(*values):
"""
Check if all values in a sequence are equal.
Returns True on empty sequences.
Examples
--------
>>> same(1, 1, 1, 1)
True
>>> same(1, 2, 1)
False
>>> same()
True
"""
if not values:
return True
first, rest = values[0], values[1:]
... | python | def same(*values):
"""
Check if all values in a sequence are equal.
Returns True on empty sequences.
Examples
--------
>>> same(1, 1, 1, 1)
True
>>> same(1, 2, 1)
False
>>> same()
True
"""
if not values:
return True
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A sequence of dicts all sharing the same keys.
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-------
zipped : dict
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"""
Parameters
----------
*dicts : iterable[dict]
A sequence of dicts all sharing the same keys.
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-------
zipped : dict
A dict whose keys are the union of all keys in *dicts, and whose values
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----------
it : iterable[tuple]
An iterable of tuples. ``unzip`` should map ensure that these are
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elem_len : int or None
The expected element length. I... | python | def _gen_unzip(it, elem_len):
"""Helper for unzip which checks the lengths of each element in it.
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it : iterable[tuple]
An iterable of tuples. ``unzip`` should map ensure that these are
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"""Unzip a length n sequence of length m sequences into m seperate length
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Parameters
----------
seq : iterable[iterable]
The sequence to unzip.
elem_len : int, optional
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seq : iterable[iterable]
The sequence to unzip.
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quantopian/zipline | zipline/utils/functional.py | getattrs | def getattrs(value, attrs, default=_no_default):
"""
Perform a chained application of ``getattr`` on ``value`` with the values
in ``attrs``.
If ``default`` is supplied, return it if any of the attribute lookups fail.
Parameters
----------
value : object
Root of the lookup chain.
... | python | def getattrs(value, attrs, default=_no_default):
"""
Perform a chained application of ``getattr`` on ``value`` with the values
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If ``default`` is supplied, return it if any of the attribute lookups fail.
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value : object
Root of the lookup chain.
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quantopian/zipline | zipline/utils/functional.py | set_attribute | def set_attribute(name, value):
"""
Decorator factory for setting attributes on a function.
Doesn't change the behavior of the wrapped function.
Examples
--------
>>> @set_attribute('__name__', 'foo')
... def bar():
... return 3
...
>>> bar()
3
>>> bar.__name__
... | python | def set_attribute(name, value):
"""
Decorator factory for setting attributes on a function.
Doesn't change the behavior of the wrapped function.
Examples
--------
>>> @set_attribute('__name__', 'foo')
... def bar():
... return 3
...
>>> bar()
3
>>> bar.__name__
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>>> @set_attribute('__name__', 'foo')
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quantopian/zipline | zipline/utils/functional.py | foldr | def foldr(f, seq, default=_no_default):
"""Fold a function over a sequence with right associativity.
Parameters
----------
f : callable[any, any]
The function to reduce the sequence with.
The first argument will be the element of the sequence; the second
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"""Fold a function over a sequence with right associativity.
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quantopian/zipline | zipline/utils/functional.py | invert | def invert(d):
"""
Invert a dictionary into a dictionary of sets.
>>> invert({'a': 1, 'b': 2, 'c': 1}) # doctest: +SKIP
{1: {'a', 'c'}, 2: {'b'}}
"""
out = {}
for k, v in iteritems(d):
try:
out[v].add(k)
except KeyError:
out[v] = {k}
return out | python | def invert(d):
"""
Invert a dictionary into a dictionary of sets.
>>> invert({'a': 1, 'b': 2, 'c': 1}) # doctest: +SKIP
{1: {'a', 'c'}, 2: {'b'}}
"""
out = {}
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quantopian/zipline | zipline/examples/olmar.py | simplex_projection | def simplex_projection(v, b=1):
r"""Projection vectors to the simplex domain
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Implementation Time: 2011 June 17 by Bin@libin AT pmail.ntu.edu.sg
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r"""Projection vectors to the simplex domain
Implemented according to the paper: Efficient projections onto the
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Implementation Time: 2011 June 17 by Bin@libin AT pmail.ntu.edu.sg
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quantopian/zipline | zipline/examples/__init__.py | run_example | def run_example(example_name, environ):
"""
Run an example module from zipline.examples.
"""
mod = EXAMPLE_MODULES[example_name]
register_calendar("YAHOO", get_calendar("NYSE"), force=True)
return run_algorithm(
initialize=getattr(mod, 'initialize', None),
handle_data=getattr(m... | python | def run_example(example_name, environ):
"""
Run an example module from zipline.examples.
"""
mod = EXAMPLE_MODULES[example_name]
register_calendar("YAHOO", get_calendar("NYSE"), force=True)
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quantopian/zipline | zipline/pipeline/factors/statistical.py | vectorized_beta | def vectorized_beta(dependents, independent, allowed_missing, out=None):
"""
Compute slopes of linear regressions between columns of ``dependents`` and
``independent``.
Parameters
----------
dependents : np.array[N, M]
Array with columns of data to be regressed against ``independent``.
... | python | def vectorized_beta(dependents, independent, allowed_missing, out=None):
"""
Compute slopes of linear regressions between columns of ``dependents`` and
``independent``.
Parameters
----------
dependents : np.array[N, M]
Array with columns of data to be regressed against ``independent``.
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quantopian/zipline | zipline/data/treasuries_can.py | _format_url | def _format_url(instrument_type,
instrument_ids,
start_date,
end_date,
earliest_allowed_date):
"""
Format a URL for loading data from Bank of Canada.
"""
return (
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instrument_ids,
start_date,
end_date,
earliest_allowed_date):
"""
Format a URL for loading data from Bank of Canada.
"""
return (
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quantopian/zipline | zipline/data/treasuries_can.py | load_frame | def load_frame(url, skiprows):
"""
Load a DataFrame of data from a Bank of Canada site.
"""
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url,
skiprows=skiprows,
skipinitialspace=True,
na_values=["Bank holiday", "Not available"],
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"""
Load a DataFrame of data from a Bank of Canada site.
"""
return pd.read_csv(
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skiprows=skiprows,
skipinitialspace=True,
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quantopian/zipline | zipline/data/treasuries_can.py | check_known_inconsistencies | def check_known_inconsistencies(bill_data, bond_data):
"""
There are a couple quirks in the data provided by Bank of Canada.
Check that no new quirks have been introduced in the latest download.
"""
inconsistent_dates = bill_data.index.sym_diff(bond_data.index)
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... | python | def check_known_inconsistencies(bill_data, bond_data):
"""
There are a couple quirks in the data provided by Bank of Canada.
Check that no new quirks have been introduced in the latest download.
"""
inconsistent_dates = bill_data.index.sym_diff(bond_data.index)
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quantopian/zipline | zipline/data/treasuries_can.py | earliest_possible_date | def earliest_possible_date():
"""
The earliest date for which we can load data from this module.
"""
today = pd.Timestamp('now', tz='UTC').normalize()
# Bank of Canada only has the last 10 years of data at any given time.
return today.replace(year=today.year - 10) | python | def earliest_possible_date():
"""
The earliest date for which we can load data from this module.
"""
today = pd.Timestamp('now', tz='UTC').normalize()
# Bank of Canada only has the last 10 years of data at any given time.
return today.replace(year=today.year - 10) | [
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quantopian/zipline | zipline/finance/slippage.py | fill_price_worse_than_limit_price | def fill_price_worse_than_limit_price(fill_price, order):
"""
Checks whether the fill price is worse than the order's limit price.
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----------
fill_price: float
The price to check.
order: zipline.finance.order.Order
The order whose limit price to check.
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... | python | def fill_price_worse_than_limit_price(fill_price, order):
"""
Checks whether the fill price is worse than the order's limit price.
Parameters
----------
fill_price: float
The price to check.
order: zipline.finance.order.Order
The order whose limit price to check.
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"""
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Parameters
----------
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Internal utility method to return the trailing mean volume over the
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quantopian/zipline | zipline/pipeline/term.py | validate_dtype | def validate_dtype(termname, dtype, missing_value):
"""
Validate a `dtype` and `missing_value` passed to Term.__new__.
Ensures that we know how to represent ``dtype``, and that missing_value
is specified for types without default missing values.
Returns
-------
validated_dtype, validated_m... | python | def validate_dtype(termname, dtype, missing_value):
"""
Validate a `dtype` and `missing_value` passed to Term.__new__.
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quantopian/zipline | zipline/pipeline/term.py | _assert_valid_categorical_missing_value | def _assert_valid_categorical_missing_value(value):
"""
Check that value is a valid categorical missing_value.
Raises a TypeError if the value is cannot be used as the missing_value for
a categorical_dtype Term.
"""
label_types = LabelArray.SUPPORTED_SCALAR_TYPES
if not isinstance(value, la... | python | def _assert_valid_categorical_missing_value(value):
"""
Check that value is a valid categorical missing_value.
Raises a TypeError if the value is cannot be used as the missing_value for
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quantopian/zipline | zipline/pipeline/term.py | Term._pop_params | def _pop_params(cls, kwargs):
"""
Pop entries from the `kwargs` passed to cls.__new__ based on the values
in `cls.params`.
Parameters
----------
kwargs : dict
The kwargs passed to cls.__new__.
Returns
-------
params : list[(str, objec... | python | def _pop_params(cls, kwargs):
"""
Pop entries from the `kwargs` passed to cls.__new__ based on the values
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----------
kwargs : dict
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quantopian/zipline | zipline/pipeline/term.py | Term._static_identity | def _static_identity(cls,
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dtype,
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ndim,
params):
"""
Return the identity of the Term that would be constructed from the... | python | def _static_identity(cls,
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params):
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quantopian/zipline | zipline/pipeline/term.py | Term._init | def _init(self, domain, dtype, missing_value, window_safe, ndim, params):
"""
Parameters
----------
domain : zipline.pipeline.domain.Domain
The domain of this term.
dtype : np.dtype
Dtype of this term's output.
missing_value : object
Mi... | python | def _init(self, domain, dtype, missing_value, window_safe, ndim, params):
"""
Parameters
----------
domain : zipline.pipeline.domain.Domain
The domain of this term.
dtype : np.dtype
Dtype of this term's output.
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quantopian/zipline | zipline/pipeline/term.py | ComputableTerm.dependencies | def dependencies(self):
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The number of extra rows needed for each of our inputs to compute this
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"""
extra_input_rows = max(0, self.window_length - 1)
out = {}
for term in self.inputs:
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out[self.mask] = 0
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"""
The number of extra rows needed for each of our inputs to compute this
term.
"""
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out = {}
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quantopian/zipline | zipline/pipeline/term.py | ComputableTerm.to_workspace_value | def to_workspace_value(self, result, assets):
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the data into a format that can be used in a workspace to continue
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Parameters
----------
result : pd.Series
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quantopian/zipline | zipline/finance/position.py | Position.earn_stock_dividend | def earn_stock_dividend(self, stock_dividend):
"""
Register the number of shares we held at this dividend's ex date so
that we can pay out the correct amount on the dividend's pay date.
"""
return {
'payment_asset': stock_dividend.payment_asset,
'share_cou... | python | def earn_stock_dividend(self, stock_dividend):
"""
Register the number of shares we held at this dividend's ex date so
that we can pay out the correct amount on the dividend's pay date.
"""
return {
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quantopian/zipline | zipline/finance/position.py | Position.handle_split | def handle_split(self, asset, ratio):
"""
Update the position by the split ratio, and return the resulting
fractional share that will be converted into cash.
Returns the unused cash.
"""
if self.asset != asset:
raise Exception("updating split with the wrong a... | python | def handle_split(self, asset, ratio):
"""
Update the position by the split ratio, and return the resulting
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"""
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quantopian/zipline | zipline/finance/position.py | Position.adjust_commission_cost_basis | def adjust_commission_cost_basis(self, asset, cost):
"""
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to share a cost basis, even if they were executed in different
transactions with different commission costs, different prices, etc.
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"""
A note about cost-basis in zipline: all positions are considered
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quantopian/zipline | zipline/finance/position.py | Position.to_dict | def to_dict(self):
"""
Creates a dictionary representing the state of this position.
Returns a dict object of the form:
"""
return {
'sid': self.asset,
'amount': self.amount,
'cost_basis': self.cost_basis,
'last_sale_price': self.la... | python | def to_dict(self):
"""
Creates a dictionary representing the state of this position.
Returns a dict object of the form:
"""
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quantopian/zipline | zipline/utils/deprecate.py | deprecated | def deprecated(msg=None, stacklevel=2):
"""
Used to mark a function as deprecated.
Parameters
----------
msg : str
The message to display in the deprecation warning.
stacklevel : int
How far up the stack the warning needs to go, before
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"""
Used to mark a function as deprecated.
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The message to display in the deprecation warning.
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quantopian/zipline | zipline/data/history_loader.py | HistoryCompatibleUSEquityAdjustmentReader.load_pricing_adjustments | def load_pricing_adjustments(self, columns, dts, assets):
"""
Returns
-------
adjustments : list[dict[int -> Adjustment]]
A list, where each element corresponds to the `columns`, of
mappings from index to adjustment objects to apply at that index.
"""
... | python | def load_pricing_adjustments(self, columns, dts, assets):
"""
Returns
-------
adjustments : list[dict[int -> Adjustment]]
A list, where each element corresponds to the `columns`, of
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quantopian/zipline | zipline/data/history_loader.py | HistoryCompatibleUSEquityAdjustmentReader._get_adjustments_in_range | def _get_adjustments_in_range(self, asset, dts, field):
"""
Get the Float64Multiply objects to pass to an AdjustedArrayWindow.
For the use of AdjustedArrayWindow in the loader, which looks back
from current simulation time back to a window of data the dictionary is
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quantopian/zipline | zipline/data/history_loader.py | SlidingWindow.get | def get(self, end_ix):
"""
Returns
-------
out : A np.ndarray of the equity pricing up to end_ix after adjustments
and rounding have been applied.
"""
if self.most_recent_ix == end_ix:
return self.current
target = end_ix - self.cal_start... | python | def get(self, end_ix):
"""
Returns
-------
out : A np.ndarray of the equity pricing up to end_ix after adjustments
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"""
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quantopian/zipline | zipline/data/history_loader.py | HistoryLoader._ensure_sliding_windows | def _ensure_sliding_windows(self, assets, dts, field,
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"""
Ensure that there is a Float64Multiply window for each asset that can
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quantopian/zipline | zipline/data/history_loader.py | HistoryLoader.history | def history(self, assets, dts, field, is_perspective_after):
"""
A window of pricing data with adjustments applied assuming that the
end of the window is the day before the current simulation time.
Parameters
----------
assets : iterable of Assets
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quantopian/zipline | zipline/sources/requests_csv.py | PandasCSV.parse_date_str_series | def parse_date_str_series(format_str, tz, date_str_series, data_frequency,
trading_day):
"""
Efficient parsing for a 1d Pandas/numpy object containing string
representations of dates.
Note: pd.to_datetime is significantly faster when no format string is
... | python | def parse_date_str_series(format_str, tz, date_str_series, data_frequency,
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"""
Efficient parsing for a 1d Pandas/numpy object containing string
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quantopian/zipline | zipline/sources/requests_csv.py | PandasCSV._lookup_unconflicted_symbol | def _lookup_unconflicted_symbol(self, symbol):
"""
Attempt to find a unique asset whose symbol is the given string.
If multiple assets have held the given symbol, return a 0.
If no asset has held the given symbol, return a NaN.
"""
try:
uppered = symbol.upp... | python | def _lookup_unconflicted_symbol(self, symbol):
"""
Attempt to find a unique asset whose symbol is the given string.
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If no asset has held the given symbol, return a NaN.
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quantopian/zipline | zipline/gens/tradesimulation.py | AlgorithmSimulator.transform | def transform(self):
"""
Main generator work loop.
"""
algo = self.algo
metrics_tracker = algo.metrics_tracker
emission_rate = metrics_tracker.emission_rate
def every_bar(dt_to_use, current_data=self.current_data,
handle_data=algo.event_mana... | python | def transform(self):
"""
Main generator work loop.
"""
algo = self.algo
metrics_tracker = algo.metrics_tracker
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quantopian/zipline | zipline/gens/tradesimulation.py | AlgorithmSimulator._cleanup_expired_assets | def _cleanup_expired_assets(self, dt, position_assets):
"""
Clear out any assets that have expired before starting a new sim day.
Performs two functions:
1. Finds all assets for which we have open orders and clears any
orders whose assets are on or after their auto_close_dat... | python | def _cleanup_expired_assets(self, dt, position_assets):
"""
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quantopian/zipline | zipline/gens/tradesimulation.py | AlgorithmSimulator._get_daily_message | def _get_daily_message(self, dt, algo, metrics_tracker):
"""
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"""
perf_message = metrics_tracker.handle_market_close(
dt,
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"""
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quantopian/zipline | zipline/gens/tradesimulation.py | AlgorithmSimulator._get_minute_message | def _get_minute_message(self, dt, algo, metrics_tracker):
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"""
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quantopian/zipline | zipline/data/adjustments.py | SQLiteAdjustmentReader.load_adjustments | def load_adjustments(self,
dates,
assets,
should_include_splits,
should_include_mergers,
should_include_dividends,
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adjustment_type):
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quantopian/zipline | zipline/data/adjustments.py | SQLiteAdjustmentReader.unpack_db_to_component_dfs | def unpack_db_to_component_dfs(self, convert_dates=False):
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convert_dates : bool, optional
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quantopian/zipline | zipline/data/adjustments.py | SQLiteAdjustmentReader._df_dtypes | def _df_dtypes(self, table_name, convert_dates):
"""Get dtypes to use when unpacking sqlite tables as dataframes.
"""
out = self._raw_table_dtypes[table_name]
if convert_dates:
out = out.copy()
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"""Get dtypes to use when unpacking sqlite tables as dataframes.
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quantopian/zipline | zipline/data/adjustments.py | SQLiteAdjustmentWriter.calc_dividend_ratios | def calc_dividend_ratios(self, dividends):
"""
Calculate the ratios to apply to equities when looking back at pricing
history so that the price is smoothed over the ex_date, when the market
adjusts to the change in equity value due to upcoming dividend.
Returns
-------
... | python | def calc_dividend_ratios(self, dividends):
"""
Calculate the ratios to apply to equities when looking back at pricing
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adjusts to the change in equity value due to upcoming dividend.
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quantopian/zipline | zipline/data/adjustments.py | SQLiteAdjustmentWriter.write_dividend_data | def write_dividend_data(self, dividends, stock_dividends=None):
"""
Write both dividend payouts and the derived price adjustment ratios.
"""
# First write the dividend payouts.
self._write_dividends(dividends)
self._write_stock_dividends(stock_dividends)
# Secon... | python | def write_dividend_data(self, dividends, stock_dividends=None):
"""
Write both dividend payouts and the derived price adjustment ratios.
"""
# First write the dividend payouts.
self._write_dividends(dividends)
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quantopian/zipline | zipline/data/adjustments.py | SQLiteAdjustmentWriter.write | def write(self,
splits=None,
mergers=None,
dividends=None,
stock_dividends=None):
"""
Writes data to a SQLite file to be read by SQLiteAdjustmentReader.
Parameters
----------
splits : pandas.DataFrame, optional
... | python | def write(self,
splits=None,
mergers=None,
dividends=None,
stock_dividends=None):
"""
Writes data to a SQLite file to be read by SQLiteAdjustmentReader.
Parameters
----------
splits : pandas.DataFrame, optional
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quantopian/zipline | zipline/pipeline/mixins.py | CustomTermMixin.compute | def compute(self, today, assets, out, *arrays):
"""
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"""
raise NotImplementedError(
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"""
Override this method with a function that writes a value into `out`.
"""
raise NotImplementedError(
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quantopian/zipline | zipline/pipeline/mixins.py | CustomTermMixin._allocate_output | def _allocate_output(self, windows, shape):
"""
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quantopian/zipline | zipline/pipeline/mixins.py | CustomTermMixin._compute | def _compute(self, windows, dates, assets, mask):
"""
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output array.
"""
format_inputs = self._format_inputs
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"""
Call the user's `compute` function on each window with a pre-built
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"""
format_inputs = self._format_inputs
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quantopian/zipline | zipline/pipeline/mixins.py | AliasedMixin.make_aliased_type | def make_aliased_type(cls, other_base):
"""
Factory for making Aliased{Filter,Factor,Classifier}.
"""
docstring = dedent(
"""
A {t} that names another {t}.
Parameters
----------
term : {t}
{{name}}
"""
... | python | def make_aliased_type(cls, other_base):
"""
Factory for making Aliased{Filter,Factor,Classifier}.
"""
docstring = dedent(
"""
A {t} that names another {t}.
Parameters
----------
term : {t}
{{name}}
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quantopian/zipline | zipline/pipeline/mixins.py | DownsampledMixin.compute_extra_rows | def compute_extra_rows(self,
all_dates,
start_date,
end_date,
min_extra_rows):
"""
Ensure that min_extra_rows pushes us back to a computation date.
Parameters
----------
a... | python | def compute_extra_rows(self,
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start_date,
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quantopian/zipline | zipline/pipeline/mixins.py | DownsampledMixin._compute | def _compute(self, inputs, dates, assets, mask):
"""
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"""
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assert to... | python | def _compute(self, inputs, dates, assets, mask):
"""
Compute by delegating to self._wrapped_term._compute on sample dates.
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quantopian/zipline | zipline/pipeline/mixins.py | DownsampledMixin.make_downsampled_type | def make_downsampled_type(cls, other_base):
"""
Factory for making Downsampled{Filter,Factor,Classifier}.
"""
docstring = dedent(
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A {t} that defers to another {t} at lower-than-daily frequency.
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----------
term : ... | python | def make_downsampled_type(cls, other_base):
"""
Factory for making Downsampled{Filter,Factor,Classifier}.
"""
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A {t} that defers to another {t} at lower-than-daily frequency.
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quantopian/zipline | zipline/utils/preprocess.py | preprocess | def preprocess(*_unused, **processors):
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----------
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quantopian/zipline | zipline/utils/preprocess.py | call | def call(f):
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] | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/utils/preprocess.py#L115-L139 | train |
quantopian/zipline | zipline/utils/preprocess.py | _build_preprocessed_function | def _build_preprocessed_function(func,
processors,
args_defaults,
varargs,
varkw):
"""
Build a preprocessed function with the same signature as `func`.
Uses `exec` internally ... | python | def _build_preprocessed_function(func,
processors,
args_defaults,
varargs,
varkw):
"""
Build a preprocessed function with the same signature as `func`.
Uses `exec` internally ... | [
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] | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/utils/preprocess.py#L142-L247 | train |
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