id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
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25,900 | quantopian/zipline | zipline/pipeline/factors/basic.py | _ExponentialWeightedFactor.from_halflife | def from_halflife(cls, inputs, window_length, halflife, **kwargs):
"""
Convenience constructor for passing ``decay_rate`` in terms of half
life.
Forwards ``decay_rate`` as ``exp(log(.5) / halflife)``. This provides
the behavior equivalent to passing `halflife` to pandas.ewma.
... | python | def from_halflife(cls, inputs, window_length, halflife, **kwargs):
"""
Convenience constructor for passing ``decay_rate`` in terms of half
life.
Forwards ``decay_rate`` as ``exp(log(.5) / halflife)``. This provides
the behavior equivalent to passing `halflife` to pandas.ewma.
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Forwards ``decay_rate`` as ``exp(log(.5) / halflife)``. This provides
the behavior equivalent to passing `halflife` to pandas.ewma.
Examples
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.. code-block:: python
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25,901 | quantopian/zipline | zipline/pipeline/factors/basic.py | _ExponentialWeightedFactor.from_center_of_mass | def from_center_of_mass(cls,
inputs,
window_length,
center_of_mass,
**kwargs):
"""
Convenience constructor for passing `decay_rate` in terms of center of
mass.
Forwards `decay... | python | def from_center_of_mass(cls,
inputs,
window_length,
center_of_mass,
**kwargs):
"""
Convenience constructor for passing `decay_rate` in terms of center of
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Forwards `decay_rate` as `1 - (1 / 1 + center_of_mass)`. This provides
behavior equivalent to passing `center_of_mass` to pandas.ewma.
Examples
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.. code-block:: python
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25,902 | quantopian/zipline | zipline/utils/math_utils.py | tolerant_equals | def tolerant_equals(a, b, atol=10e-7, rtol=10e-7, equal_nan=False):
"""Check if a and b are equal with some tolerance.
Parameters
----------
a, b : float
The floats to check for equality.
atol : float, optional
The absolute tolerance.
rtol : float, optional
The relative ... | python | def tolerant_equals(a, b, atol=10e-7, rtol=10e-7, equal_nan=False):
"""Check if a and b are equal with some tolerance.
Parameters
----------
a, b : float
The floats to check for equality.
atol : float, optional
The absolute tolerance.
rtol : float, optional
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25,903 | quantopian/zipline | zipline/utils/math_utils.py | round_if_near_integer | def round_if_near_integer(a, epsilon=1e-4):
"""
Round a to the nearest integer if that integer is within an epsilon
of a.
"""
if abs(a - round(a)) <= epsilon:
return round(a)
else:
return a | python | def round_if_near_integer(a, epsilon=1e-4):
"""
Round a to the nearest integer if that integer is within an epsilon
of a.
"""
if abs(a - round(a)) <= epsilon:
return round(a)
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25,904 | quantopian/zipline | zipline/pipeline/factors/factor.py | binop_return_dtype | def binop_return_dtype(op, left, right):
"""
Compute the expected return dtype for the given binary operator.
Parameters
----------
op : str
Operator symbol, (e.g. '+', '-', ...).
left : numpy.dtype
Dtype of left hand side.
right : numpy.dtype
Dtype of right hand sid... | python | def binop_return_dtype(op, left, right):
"""
Compute the expected return dtype for the given binary operator.
Parameters
----------
op : str
Operator symbol, (e.g. '+', '-', ...).
left : numpy.dtype
Dtype of left hand side.
right : numpy.dtype
Dtype of right hand sid... | [
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25,905 | quantopian/zipline | zipline/pipeline/factors/factor.py | binary_operator | def binary_operator(op):
"""
Factory function for making binary operator methods on a Factor subclass.
Returns a function, "binary_operator" suitable for implementing functions
like __add__.
"""
# When combining a Factor with a NumericalExpression, we use this
# attrgetter instance to defer... | python | def binary_operator(op):
"""
Factory function for making binary operator methods on a Factor subclass.
Returns a function, "binary_operator" suitable for implementing functions
like __add__.
"""
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25,906 | quantopian/zipline | zipline/pipeline/factors/factor.py | reflected_binary_operator | def reflected_binary_operator(op):
"""
Factory function for making binary operator methods on a Factor.
Returns a function, "reflected_binary_operator" suitable for implementing
functions like __radd__.
"""
assert not is_comparison(op)
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@... | python | def reflected_binary_operator(op):
"""
Factory function for making binary operator methods on a Factor.
Returns a function, "reflected_binary_operator" suitable for implementing
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"""
assert not is_comparison(op)
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25,907 | quantopian/zipline | zipline/pipeline/factors/factor.py | unary_operator | def unary_operator(op):
"""
Factory function for making unary operator methods for Factors.
"""
# Only negate is currently supported.
valid_ops = {'-'}
if op not in valid_ops:
raise ValueError("Invalid unary operator %s." % op)
@with_doc("Unary Operator: '%s'" % op)
@with_name(u... | python | def unary_operator(op):
"""
Factory function for making unary operator methods for Factors.
"""
# Only negate is currently supported.
valid_ops = {'-'}
if op not in valid_ops:
raise ValueError("Invalid unary operator %s." % op)
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25,908 | quantopian/zipline | zipline/pipeline/factors/factor.py | function_application | def function_application(func):
"""
Factory function for producing function application methods for Factor
subclasses.
"""
if func not in NUMEXPR_MATH_FUNCS:
raise ValueError("Unsupported mathematical function '%s'" % func)
@with_doc(func)
@with_name(func)
def mathfunc(self):
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"""
Factory function for producing function application methods for Factor
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"""
if func not in NUMEXPR_MATH_FUNCS:
raise ValueError("Unsupported mathematical function '%s'" % func)
@with_doc(func)
@with_name(func)
def mathfunc(self):
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25,909 | quantopian/zipline | zipline/pipeline/factors/factor.py | winsorize | def winsorize(row, min_percentile, max_percentile):
"""
This implementation is based on scipy.stats.mstats.winsorize
"""
a = row.copy()
nan_count = isnan(row).sum()
nonnan_count = a.size - nan_count
# NOTE: argsort() sorts nans to the end of the array.
idx = a.argsort()
# Set value... | python | def winsorize(row, min_percentile, max_percentile):
"""
This implementation is based on scipy.stats.mstats.winsorize
"""
a = row.copy()
nan_count = isnan(row).sum()
nonnan_count = a.size - nan_count
# NOTE: argsort() sorts nans to the end of the array.
idx = a.argsort()
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25,910 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.demean | def demean(self, mask=NotSpecified, groupby=NotSpecified):
"""
Construct a Factor that computes ``self`` and subtracts the mean from
row of the result.
If ``mask`` is supplied, ignore values where ``mask`` returns False
when computing row means, and output NaN anywhere the mask ... | python | def demean(self, mask=NotSpecified, groupby=NotSpecified):
"""
Construct a Factor that computes ``self`` and subtracts the mean from
row of the result.
If ``mask`` is supplied, ignore values where ``mask`` returns False
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25,911 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.zscore | def zscore(self, mask=NotSpecified, groupby=NotSpecified):
"""
Construct a Factor that Z-Scores each day's results.
The Z-Score of a row is defined as::
(row - row.mean()) / row.stddev()
If ``mask`` is supplied, ignore values where ``mask`` returns False
when compu... | python | def zscore(self, mask=NotSpecified, groupby=NotSpecified):
"""
Construct a Factor that Z-Scores each day's results.
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(row - row.mean()) / row.stddev()
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25,912 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.rank | def rank(self,
method='ordinal',
ascending=True,
mask=NotSpecified,
groupby=NotSpecified):
"""
Construct a new Factor representing the sorted rank of each column
within each row.
Parameters
----------
method : str, {'or... | python | def rank(self,
method='ordinal',
ascending=True,
mask=NotSpecified,
groupby=NotSpecified):
"""
Construct a new Factor representing the sorted rank of each column
within each row.
Parameters
----------
method : str, {'or... | [
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25,913 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.pearsonr | def pearsonr(self, target, correlation_length, mask=NotSpecified):
"""
Construct a new Factor that computes rolling pearson correlation
coefficients between `target` and the columns of `self`.
This method can only be called on factors which are deemed safe for use
as inputs to o... | python | def pearsonr(self, target, correlation_length, mask=NotSpecified):
"""
Construct a new Factor that computes rolling pearson correlation
coefficients between `target` and the columns of `self`.
This method can only be called on factors which are deemed safe for use
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25,914 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.spearmanr | def spearmanr(self, target, correlation_length, mask=NotSpecified):
"""
Construct a new Factor that computes rolling spearman rank correlation
coefficients between `target` and the columns of `self`.
This method can only be called on factors which are deemed safe for use
as inpu... | python | def spearmanr(self, target, correlation_length, mask=NotSpecified):
"""
Construct a new Factor that computes rolling spearman rank correlation
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This method can only be called on factors which are deemed safe for use
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25,915 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.linear_regression | def linear_regression(self, target, regression_length, mask=NotSpecified):
"""
Construct a new Factor that performs an ordinary least-squares
regression predicting the columns of `self` from `target`.
This method can only be called on factors which are deemed safe for use
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"""
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25,916 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.winsorize | def winsorize(self,
min_percentile,
max_percentile,
mask=NotSpecified,
groupby=NotSpecified):
"""
Construct a new factor that winsorizes the result of this factor.
Winsorizing changes values ranked less than the minimum per... | python | def winsorize(self,
min_percentile,
max_percentile,
mask=NotSpecified,
groupby=NotSpecified):
"""
Construct a new factor that winsorizes the result of this factor.
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25,917 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.quantiles | def quantiles(self, bins, mask=NotSpecified):
"""
Construct a Classifier computing quantiles of the output of ``self``.
Every non-NaN data point the output is labelled with an integer value
from 0 to (bins - 1). NaNs are labelled with -1.
If ``mask`` is supplied, ignore data p... | python | def quantiles(self, bins, mask=NotSpecified):
"""
Construct a Classifier computing quantiles of the output of ``self``.
Every non-NaN data point the output is labelled with an integer value
from 0 to (bins - 1). NaNs are labelled with -1.
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25,918 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.top | def top(self, N, mask=NotSpecified, groupby=NotSpecified):
"""
Construct a Filter matching the top N asset values of self each day.
If ``groupby`` is supplied, returns a Filter matching the top N asset
values for each group.
Parameters
----------
N : int
... | python | def top(self, N, mask=NotSpecified, groupby=NotSpecified):
"""
Construct a Filter matching the top N asset values of self each day.
If ``groupby`` is supplied, returns a Filter matching the top N asset
values for each group.
Parameters
----------
N : int
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25,919 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.bottom | def bottom(self, N, mask=NotSpecified, groupby=NotSpecified):
"""
Construct a Filter matching the bottom N asset values of self each day.
If ``groupby`` is supplied, returns a Filter matching the bottom N
asset values for each group.
Parameters
----------
N : in... | python | def bottom(self, N, mask=NotSpecified, groupby=NotSpecified):
"""
Construct a Filter matching the bottom N asset values of self each day.
If ``groupby`` is supplied, returns a Filter matching the bottom N
asset values for each group.
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25,920 | quantopian/zipline | zipline/pipeline/factors/factor.py | Factor.percentile_between | def percentile_between(self,
min_percentile,
max_percentile,
mask=NotSpecified):
"""
Construct a new Filter representing entries from the output of this
Factor that fall within the percentile range defined by min_pe... | python | def percentile_between(self,
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mask=NotSpecified):
"""
Construct a new Filter representing entries from the output of this
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25,921 | quantopian/zipline | zipline/pipeline/factors/factor.py | Rank._validate | def _validate(self):
"""
Verify that the stored rank method is valid.
"""
if self._method not in _RANK_METHODS:
raise UnknownRankMethod(
method=self._method,
choices=set(_RANK_METHODS),
)
return super(Rank, self)._validate() | python | def _validate(self):
"""
Verify that the stored rank method is valid.
"""
if self._method not in _RANK_METHODS:
raise UnknownRankMethod(
method=self._method,
choices=set(_RANK_METHODS),
)
return super(Rank, self)._validate() | [
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25,922 | quantopian/zipline | zipline/pipeline/factors/factor.py | Rank._compute | def _compute(self, arrays, dates, assets, mask):
"""
For each row in the input, compute a like-shaped array of per-row
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"""
return masked_rankdata_2d(
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"""
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25,923 | quantopian/zipline | zipline/utils/pandas_utils.py | find_in_sorted_index | def find_in_sorted_index(dts, dt):
"""
Find the index of ``dt`` in ``dts``.
This function should be used instead of `dts.get_loc(dt)` if the index is
large enough that we don't want to initialize a hash table in ``dts``. In
particular, this should always be used on minutely trading calendars.
... | python | def find_in_sorted_index(dts, dt):
"""
Find the index of ``dt`` in ``dts``.
This function should be used instead of `dts.get_loc(dt)` if the index is
large enough that we don't want to initialize a hash table in ``dts``. In
particular, this should always be used on minutely trading calendars.
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25,924 | quantopian/zipline | zipline/utils/pandas_utils.py | nearest_unequal_elements | def nearest_unequal_elements(dts, dt):
"""
Find values in ``dts`` closest but not equal to ``dt``.
Returns a pair of (last_before, first_after).
When ``dt`` is less than any element in ``dts``, ``last_before`` is None.
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``... | python | def nearest_unequal_elements(dts, dt):
"""
Find values in ``dts`` closest but not equal to ``dt``.
Returns a pair of (last_before, first_after).
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25,925 | quantopian/zipline | zipline/utils/pandas_utils.py | categorical_df_concat | def categorical_df_concat(df_list, inplace=False):
"""
Prepare list of pandas DataFrames to be used as input to pd.concat.
Ensure any columns of type 'category' have the same categories across each
dataframe.
Parameters
----------
df_list : list
List of dataframes with same columns.... | python | def categorical_df_concat(df_list, inplace=False):
"""
Prepare list of pandas DataFrames to be used as input to pd.concat.
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25,926 | quantopian/zipline | zipline/utils/pandas_utils.py | check_indexes_all_same | def check_indexes_all_same(indexes, message="Indexes are not equal."):
"""Check that a list of Index objects are all equal.
Parameters
----------
indexes : iterable[pd.Index]
Iterable of indexes to check.
Raises
------
ValueError
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"""... | python | def check_indexes_all_same(indexes, message="Indexes are not equal."):
"""Check that a list of Index objects are all equal.
Parameters
----------
indexes : iterable[pd.Index]
Iterable of indexes to check.
Raises
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25,927 | quantopian/zipline | zipline/pipeline/loaders/events.py | required_event_fields | def required_event_fields(next_value_columns, previous_value_columns):
"""
Compute the set of resource columns required to serve
``next_value_columns`` and ``previous_value_columns``.
"""
# These metadata columns are used to align event indexers.
return {
TS_FIELD_NAME,
SID_FIELD... | python | def required_event_fields(next_value_columns, previous_value_columns):
"""
Compute the set of resource columns required to serve
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# These metadata columns are used to align event indexers.
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25,928 | quantopian/zipline | zipline/pipeline/loaders/events.py | validate_column_specs | def validate_column_specs(events, next_value_columns, previous_value_columns):
"""
Verify that the columns of ``events`` can be used by an EventsLoader to
serve the BoundColumns described by ``next_value_columns`` and
``previous_value_columns``.
"""
required = required_event_fields(next_value_co... | python | def validate_column_specs(events, next_value_columns, previous_value_columns):
"""
Verify that the columns of ``events`` can be used by an EventsLoader to
serve the BoundColumns described by ``next_value_columns`` and
``previous_value_columns``.
"""
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25,929 | quantopian/zipline | zipline/pipeline/loaders/events.py | EventsLoader.split_next_and_previous_event_columns | def split_next_and_previous_event_columns(self, requested_columns):
"""
Split requested columns into columns that should load the next known
value and columns that should load the previous known value.
Parameters
----------
requested_columns : iterable[BoundColumn]
... | python | def split_next_and_previous_event_columns(self, requested_columns):
"""
Split requested columns into columns that should load the next known
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requested_columns : iterable[BoundColumn]
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25,930 | quantopian/zipline | zipline/lib/labelarray.py | compare_arrays | def compare_arrays(left, right):
"Eq check with a short-circuit for identical objects."
return (
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) | python | def compare_arrays(left, right):
"Eq check with a short-circuit for identical objects."
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25,931 | quantopian/zipline | zipline/lib/labelarray.py | LabelArray.from_codes_and_metadata | def from_codes_and_metadata(cls,
codes,
categories,
reverse_categories,
missing_value):
"""
Rehydrate a LabelArray from the codes and metadata.
Parameters
----... | python | def from_codes_and_metadata(cls,
codes,
categories,
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missing_value):
"""
Rehydrate a LabelArray from the codes and metadata.
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25,932 | quantopian/zipline | zipline/lib/labelarray.py | LabelArray.as_int_array | def as_int_array(self):
"""
Convert self into a regular ndarray of ints.
This is an O(1) operation. It does not copy the underlying data.
"""
return self.view(
type=ndarray,
dtype=unsigned_int_dtype_with_size_in_bytes(self.itemsize),
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"""
Convert self into a regular ndarray of ints.
This is an O(1) operation. It does not copy the underlying data.
"""
return self.view(
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25,933 | quantopian/zipline | zipline/lib/labelarray.py | LabelArray.as_categorical | def as_categorical(self):
"""
Coerce self into a pandas categorical.
This is only defined on 1D arrays, since that's all pandas supports.
"""
if len(self.shape) > 1:
raise ValueError("Can't convert a 2D array to a categorical.")
with ignore_pandas_nan_catego... | python | def as_categorical(self):
"""
Coerce self into a pandas categorical.
This is only defined on 1D arrays, since that's all pandas supports.
"""
if len(self.shape) > 1:
raise ValueError("Can't convert a 2D array to a categorical.")
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25,934 | quantopian/zipline | zipline/lib/labelarray.py | LabelArray.as_categorical_frame | def as_categorical_frame(self, index, columns, name=None):
"""
Coerce self into a pandas DataFrame of Categoricals.
"""
if len(self.shape) != 2:
raise ValueError(
"Can't convert a non-2D LabelArray into a DataFrame."
)
expected_shape = (le... | python | def as_categorical_frame(self, index, columns, name=None):
"""
Coerce self into a pandas DataFrame of Categoricals.
"""
if len(self.shape) != 2:
raise ValueError(
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25,935 | quantopian/zipline | zipline/lib/labelarray.py | LabelArray.set_scalar | def set_scalar(self, indexer, value):
"""
Set scalar value into the array.
Parameters
----------
indexer : any
The indexer to set the value at.
value : str
The value to assign at the given locations.
Raises
------
ValueErr... | python | def set_scalar(self, indexer, value):
"""
Set scalar value into the array.
Parameters
----------
indexer : any
The indexer to set the value at.
value : str
The value to assign at the given locations.
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25,936 | quantopian/zipline | zipline/lib/labelarray.py | LabelArray.empty_like | def empty_like(self, shape):
"""
Make an empty LabelArray with the same categories as ``self``, filled
with ``self.missing_value``.
"""
return type(self).from_codes_and_metadata(
codes=np.full(
shape,
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"""
Make an empty LabelArray with the same categories as ``self``, filled
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"""
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codes=np.full(
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25,937 | quantopian/zipline | zipline/lib/labelarray.py | LabelArray.map_predicate | def map_predicate(self, f):
"""
Map a function from str -> bool element-wise over ``self``.
``f`` will be applied exactly once to each non-missing unique value in
``self``. Missing values will always return False.
"""
# Functions passed to this are of type str -> bool. ... | python | def map_predicate(self, f):
"""
Map a function from str -> bool element-wise over ``self``.
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``self``. Missing values will always return False.
"""
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25,938 | quantopian/zipline | zipline/lib/labelarray.py | LabelArray.map | def map(self, f):
"""
Map a function from str -> str element-wise over ``self``.
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25,939 | quantopian/zipline | zipline/finance/execution.py | asymmetric_round_price | def asymmetric_round_price(price, prefer_round_down, tick_size, diff=0.95):
"""
Asymmetric rounding function for adjusting prices to the specified number
of places in a way that "improves" the price. For limit prices, this means
preferring to round down on buys and preferring to round up on sells.
F... | python | def asymmetric_round_price(price, prefer_round_down, tick_size, diff=0.95):
"""
Asymmetric rounding function for adjusting prices to the specified number
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25,940 | quantopian/zipline | zipline/data/bundles/csvdir.py | csvdir_bundle | def csvdir_bundle(environ,
asset_db_writer,
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end_session,
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25,941 | quantopian/zipline | zipline/pipeline/api_utils.py | restrict_to_dtype | def restrict_to_dtype(dtype, message_template):
"""
A factory for decorators that restrict Term methods to only be callable on
Terms with a specific dtype.
This is conceptually similar to
zipline.utils.input_validation.expect_dtypes, but provides more flexibility
for providing error messages th... | python | def restrict_to_dtype(dtype, message_template):
"""
A factory for decorators that restrict Term methods to only be callable on
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25,942 | quantopian/zipline | zipline/sources/benchmark_source.py | BenchmarkSource.daily_returns | def daily_returns(self, start, end=None):
"""Returns the daily returns for the given period.
Parameters
----------
start : datetime
The inclusive starting session label.
end : datetime, optional
The inclusive ending session label. If not provided, treat
... | python | def daily_returns(self, start, end=None):
"""Returns the daily returns for the given period.
Parameters
----------
start : datetime
The inclusive starting session label.
end : datetime, optional
The inclusive ending session label. If not provided, treat
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end : datetime, optional
The inclusive ending session label. If not provided, treat
``start`` as a scalar key.
Return... | [
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25,943 | quantopian/zipline | zipline/sources/benchmark_source.py | BenchmarkSource._initialize_precalculated_series | def _initialize_precalculated_series(self,
asset,
trading_calendar,
trading_days,
data_portal):
"""
Internal method that pre-calculates the ... | python | def _initialize_precalculated_series(self,
asset,
trading_calendar,
trading_days,
data_portal):
"""
Internal method that pre-calculates the ... | [
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Parameters
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asset: Asset to use
trading_calendar: TradingCalendar
trading_days: pd.DateTimeIndex
data_portal: DataPortal
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25,944 | quantopian/zipline | zipline/utils/run_algo.py | load_extensions | def load_extensions(default, extensions, strict, environ, reload=False):
"""Load all of the given extensions. This should be called by run_algo
or the cli.
Parameters
----------
default : bool
Load the default exension (~/.zipline/extension.py)?
extension : iterable[str]
The pat... | python | def load_extensions(default, extensions, strict, environ, reload=False):
"""Load all of the given extensions. This should be called by run_algo
or the cli.
Parameters
----------
default : bool
Load the default exension (~/.zipline/extension.py)?
extension : iterable[str]
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Load the default exension (~/.zipline/extension.py)?
extension : iterable[str]
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25,945 | quantopian/zipline | zipline/utils/run_algo.py | run_algorithm | def run_algorithm(start,
end,
initialize,
capital_base,
handle_data=None,
before_trading_start=None,
analyze=None,
data_frequency='daily',
bundle='quantopian-quandl',
... | python | def run_algorithm(start,
end,
initialize,
capital_base,
handle_data=None,
before_trading_start=None,
analyze=None,
data_frequency='daily',
bundle='quantopian-quandl',
... | [
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end : datetime
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25,946 | quantopian/zipline | zipline/data/data_portal.py | DataPortal.handle_extra_source | def handle_extra_source(self, source_df, sim_params):
"""
Extra sources always have a sid column.
We expand the given data (by forward filling) to the full range of
the simulation dates, so that lookup is fast during simulation.
"""
if source_df is None:
retu... | python | def handle_extra_source(self, source_df, sim_params):
"""
Extra sources always have a sid column.
We expand the given data (by forward filling) to the full range of
the simulation dates, so that lookup is fast during simulation.
"""
if source_df is None:
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25,947 | quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_last_traded_dt | def get_last_traded_dt(self, asset, dt, data_frequency):
"""
Given an asset and dt, returns the last traded dt from the viewpoint
of the given dt.
If there is a trade on the dt, the answer is dt provided.
"""
return self._get_pricing_reader(data_frequency).get_last_trade... | python | def get_last_traded_dt(self, asset, dt, data_frequency):
"""
Given an asset and dt, returns the last traded dt from the viewpoint
of the given dt.
If there is a trade on the dt, the answer is dt provided.
"""
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25,948 | quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_adjustments | def get_adjustments(self, assets, field, dt, perspective_dt):
"""
Returns a list of adjustments between the dt and perspective_dt for the
given field and list of assets
Parameters
----------
assets : list of type Asset, or Asset
The asset, or assets whose adj... | python | def get_adjustments(self, assets, field, dt, perspective_dt):
"""
Returns a list of adjustments between the dt and perspective_dt for the
given field and list of assets
Parameters
----------
assets : list of type Asset, or Asset
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25,949 | quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_adjusted_value | def get_adjusted_value(self, asset, field, dt,
perspective_dt,
data_frequency,
spot_value=None):
"""
Returns a scalar value representing the value
of the desired asset's field at the given dt with adjustments applie... | python | def get_adjusted_value(self, asset, field, dt,
perspective_dt,
data_frequency,
spot_value=None):
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Returns a scalar value representing the value
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25,950 | quantopian/zipline | zipline/data/data_portal.py | DataPortal._get_history_daily_window | def _get_history_daily_window(self,
assets,
end_dt,
bar_count,
field_to_use,
data_frequency):
"""
Internal method that returns a dataf... | python | def _get_history_daily_window(self,
assets,
end_dt,
bar_count,
field_to_use,
data_frequency):
"""
Internal method that returns a dataf... | [
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25,951 | quantopian/zipline | zipline/data/data_portal.py | DataPortal._get_history_minute_window | def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
try:
... | python | def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
try:
... | [
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25,952 | quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_history_window | def get_history_window(self,
assets,
end_dt,
bar_count,
frequency,
field,
data_frequency,
ffill=True):
"""
Public A... | python | def get_history_window(self,
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end_dt,
bar_count,
frequency,
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data_frequency,
ffill=True):
"""
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assets : list of zipline.data.Asset objects
The assets whose data is desired.
bar_count: int
The number of bars desired.
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25,953 | quantopian/zipline | zipline/data/data_portal.py | DataPortal._get_minute_window_data | def _get_minute_window_data(self, assets, field, minutes_for_window):
"""
Internal method that gets a window of adjusted minute data for an asset
and specified date range. Used to support the history API method for
minute bars.
Missing bars are filled with NaN.
Paramet... | python | def _get_minute_window_data(self, assets, field, minutes_for_window):
"""
Internal method that gets a window of adjusted minute data for an asset
and specified date range. Used to support the history API method for
minute bars.
Missing bars are filled with NaN.
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25,954 | quantopian/zipline | zipline/data/data_portal.py | DataPortal._get_daily_window_data | def _get_daily_window_data(self,
assets,
field,
days_in_window,
extra_slot=True):
"""
Internal method that gets a window of adjusted daily data for a sid
and specified date... | python | def _get_daily_window_data(self,
assets,
field,
days_in_window,
extra_slot=True):
"""
Internal method that gets a window of adjusted daily data for a sid
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25,955 | quantopian/zipline | zipline/data/data_portal.py | DataPortal._get_adjustment_list | def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
A... | python | def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
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25,956 | quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_splits | def get_splits(self, assets, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
assets : container
Assets for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
... | python | def get_splits(self, assets, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
assets : container
Assets for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
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25,957 | quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_stock_dividends | def get_stock_dividends(self, sid, trading_days):
"""
Returns all the stock dividends for a specific sid that occur
in the given trading range.
Parameters
----------
sid: int
The asset whose stock dividends should be returned.
trading_days: pd.Dateti... | python | def get_stock_dividends(self, sid, trading_days):
"""
Returns all the stock dividends for a specific sid that occur
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Parameters
----------
sid: int
The asset whose stock dividends should be returned.
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25,958 | quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_fetcher_assets | def get_fetcher_assets(self, dt):
"""
Returns a list of assets for the current date, as defined by the
fetcher data.
Returns
-------
list: a list of Asset objects.
"""
# return a list of assets for the current date, as defined by the
# fetcher sou... | python | def get_fetcher_assets(self, dt):
"""
Returns a list of assets for the current date, as defined by the
fetcher data.
Returns
-------
list: a list of Asset objects.
"""
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25,959 | quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_current_future_chain | def get_current_future_chain(self, continuous_future, dt):
"""
Retrieves the future chain for the contract at the given `dt` according
the `continuous_future` specification.
Returns
-------
future_chain : list[Future]
A list of active futures, where the firs... | python | def get_current_future_chain(self, continuous_future, dt):
"""
Retrieves the future chain for the contract at the given `dt` according
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Returns
-------
future_chain : list[Future]
A list of active futures, where the firs... | [
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25,960 | quantopian/zipline | zipline/utils/numpy_utils.py | coerce_to_dtype | def coerce_to_dtype(dtype, value):
"""
Make a value with the specified numpy dtype.
Only datetime64[ns] and datetime64[D] are supported for datetime dtypes.
"""
name = dtype.name
if name.startswith('datetime64'):
if name == 'datetime64[D]':
return make_datetime64D(value)
... | python | def coerce_to_dtype(dtype, value):
"""
Make a value with the specified numpy dtype.
Only datetime64[ns] and datetime64[D] are supported for datetime dtypes.
"""
name = dtype.name
if name.startswith('datetime64'):
if name == 'datetime64[D]':
return make_datetime64D(value)
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25,961 | quantopian/zipline | zipline/utils/numpy_utils.py | repeat_first_axis | def repeat_first_axis(array, count):
"""
Restride `array` to repeat `count` times along the first axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape... | python | def repeat_first_axis(array, count):
"""
Restride `array` to repeat `count` times along the first axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
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-------
result : array
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25,962 | quantopian/zipline | zipline/utils/numpy_utils.py | repeat_last_axis | def repeat_last_axis(array, count):
"""
Restride `array` to repeat `count` times along the last axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape a... | python | def repeat_last_axis(array, count):
"""
Restride `array` to repeat `count` times along the last axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
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25,963 | quantopian/zipline | zipline/utils/numpy_utils.py | isnat | def isnat(obj):
"""
Check if a value is np.NaT.
"""
if obj.dtype.kind not in ('m', 'M'):
raise ValueError("%s is not a numpy datetime or timedelta")
return obj.view(int64_dtype) == iNaT | python | def isnat(obj):
"""
Check if a value is np.NaT.
"""
if obj.dtype.kind not in ('m', 'M'):
raise ValueError("%s is not a numpy datetime or timedelta")
return obj.view(int64_dtype) == iNaT | [
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25,964 | quantopian/zipline | zipline/utils/numpy_utils.py | is_missing | def is_missing(data, missing_value):
"""
Generic is_missing function that handles NaN and NaT.
"""
if is_float(data) and isnan(missing_value):
return isnan(data)
elif is_datetime(data) and isnat(missing_value):
return isnat(data)
return (data == missing_value) | python | def is_missing(data, missing_value):
"""
Generic is_missing function that handles NaN and NaT.
"""
if is_float(data) and isnan(missing_value):
return isnan(data)
elif is_datetime(data) and isnat(missing_value):
return isnat(data)
return (data == missing_value) | [
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25,965 | quantopian/zipline | zipline/utils/numpy_utils.py | busday_count_mask_NaT | def busday_count_mask_NaT(begindates, enddates, out=None):
"""
Simple of numpy.busday_count that returns `float` arrays rather than int
arrays, and handles `NaT`s by returning `NaN`s where the inputs were `NaT`.
Doesn't support custom weekdays or calendars, but probably should in the
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S... | python | def busday_count_mask_NaT(begindates, enddates, out=None):
"""
Simple of numpy.busday_count that returns `float` arrays rather than int
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25,966 | quantopian/zipline | zipline/utils/numpy_utils.py | changed_locations | def changed_locations(a, include_first):
"""
Compute indices of values in ``a`` that differ from the previous value.
Parameters
----------
a : np.ndarray
The array on which to indices of change.
include_first : bool
Whether or not to consider the first index of the array as "cha... | python | def changed_locations(a, include_first):
"""
Compute indices of values in ``a`` that differ from the previous value.
Parameters
----------
a : np.ndarray
The array on which to indices of change.
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25,967 | quantopian/zipline | zipline/utils/date_utils.py | compute_date_range_chunks | def compute_date_range_chunks(sessions, start_date, end_date, chunksize):
"""Compute the start and end dates to run a pipeline for.
Parameters
----------
sessions : DatetimeIndex
The available dates.
start_date : pd.Timestamp
The first date in the pipeline.
end_date : pd.Timesta... | python | def compute_date_range_chunks(sessions, start_date, end_date, chunksize):
"""Compute the start and end dates to run a pipeline for.
Parameters
----------
sessions : DatetimeIndex
The available dates.
start_date : pd.Timestamp
The first date in the pipeline.
end_date : pd.Timesta... | [
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25,968 | quantopian/zipline | zipline/pipeline/engine.py | SimplePipelineEngine.run_pipeline | def run_pipeline(self, pipeline, start_date, end_date):
"""
Compute a pipeline.
Parameters
----------
pipeline : zipline.pipeline.Pipeline
The pipeline to run.
start_date : pd.Timestamp
Start date of the computed matrix.
end_date : pd.Time... | python | def run_pipeline(self, pipeline, start_date, end_date):
"""
Compute a pipeline.
Parameters
----------
pipeline : zipline.pipeline.Pipeline
The pipeline to run.
start_date : pd.Timestamp
Start date of the computed matrix.
end_date : pd.Time... | [
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25,969 | quantopian/zipline | zipline/pipeline/engine.py | SimplePipelineEngine.resolve_domain | def resolve_domain(self, pipeline):
"""Resolve a concrete domain for ``pipeline``.
"""
domain = pipeline.domain(default=self._default_domain)
if domain is GENERIC:
raise ValueError(
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"""Resolve a concrete domain for ``pipeline``.
"""
domain = pipeline.domain(default=self._default_domain)
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25,970 | quantopian/zipline | zipline/utils/api_support.py | require_initialized | def require_initialized(exception):
"""
Decorator for API methods that should only be called after
TradingAlgorithm.initialize. `exception` will be raised if the method is
called before initialize has completed.
Examples
--------
@require_initialized(SomeException("Don't do that!"))
de... | python | def require_initialized(exception):
"""
Decorator for API methods that should only be called after
TradingAlgorithm.initialize. `exception` will be raised if the method is
called before initialize has completed.
Examples
--------
@require_initialized(SomeException("Don't do that!"))
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25,971 | quantopian/zipline | zipline/utils/api_support.py | disallowed_in_before_trading_start | def disallowed_in_before_trading_start(exception):
"""
Decorator for API methods that cannot be called from within
TradingAlgorithm.before_trading_start. `exception` will be raised if the
method is called inside `before_trading_start`.
Examples
--------
@disallowed_in_before_trading_start(... | python | def disallowed_in_before_trading_start(exception):
"""
Decorator for API methods that cannot be called from within
TradingAlgorithm.before_trading_start. `exception` will be raised if the
method is called inside `before_trading_start`.
Examples
--------
@disallowed_in_before_trading_start(... | [
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Examples
--------
@disallowed_in_before_trading_start(SomeException("Don't do that!"))
def method(self):
... | [
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25,972 | quantopian/zipline | zipline/lib/normalize.py | naive_grouped_rowwise_apply | def naive_grouped_rowwise_apply(data,
group_labels,
func,
func_args=(),
out=None):
"""
Simple implementation of grouped row-wise function application.
Parameters
----------
... | python | def naive_grouped_rowwise_apply(data,
group_labels,
func,
func_args=(),
out=None):
"""
Simple implementation of grouped row-wise function application.
Parameters
----------
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Input array over which to apply a grouped function.
group_labels : ndarray[ndim=2, dtype=int64]
Labels to use to bucket inputs from array.
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25,973 | quantopian/zipline | zipline/assets/synthetic.py | make_rotating_equity_info | def make_rotating_equity_info(num_assets,
first_start,
frequency,
periods_between_starts,
asset_lifetime,
exchange='TEST'):
"""
Create a DataFrame representing li... | python | def make_rotating_equity_info(num_assets,
first_start,
frequency,
periods_between_starts,
asset_lifetime,
exchange='TEST'):
"""
Create a DataFrame representing li... | [
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Parameters
----------
num_assets : int
How many assets to create.
first_start : pd.Timestamp
The start date for the first asset.
frequency : str or pd.tseries.offsets.Offset... | [
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25,974 | quantopian/zipline | zipline/assets/synthetic.py | make_simple_equity_info | def make_simple_equity_info(sids,
start_date,
end_date,
symbols=None,
names=None,
exchange='TEST'):
"""
Create a DataFrame representing assets that exist for the full durat... | python | def make_simple_equity_info(sids,
start_date,
end_date,
symbols=None,
names=None,
exchange='TEST'):
"""
Create a DataFrame representing assets that exist for the full durat... | [
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sids : array-like of int
start_date : pd.Timestamp, optional
end_date : pd.Timestamp, optional
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25,975 | quantopian/zipline | zipline/assets/synthetic.py | make_simple_multi_country_equity_info | def make_simple_multi_country_equity_info(countries_to_sids,
countries_to_exchanges,
start_date,
end_date):
"""Create a DataFrame representing assets that exist for the full duration
bet... | python | def make_simple_multi_country_equity_info(countries_to_sids,
countries_to_exchanges,
start_date,
end_date):
"""Create a DataFrame representing assets that exist for the full duration
bet... | [
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25,976 | quantopian/zipline | zipline/assets/synthetic.py | make_jagged_equity_info | def make_jagged_equity_info(num_assets,
start_date,
first_end,
frequency,
periods_between_ends,
auto_close_delta):
"""
Create a DataFrame representing assets that all begin... | python | def make_jagged_equity_info(num_assets,
start_date,
first_end,
frequency,
periods_between_ends,
auto_close_delta):
"""
Create a DataFrame representing assets that all begin... | [
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num_assets : int
How many assets to create.
start_date : pd.Timestamp
The start date for all the assets.
first_end : pd.Timestamp
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25,977 | quantopian/zipline | zipline/assets/synthetic.py | make_future_info | def make_future_info(first_sid,
root_symbols,
years,
notice_date_func,
expiration_date_func,
start_date_func,
month_codes=None,
multiplier=500):
"""
Create a DataFra... | python | def make_future_info(first_sid,
root_symbols,
years,
notice_date_func,
expiration_date_func,
start_date_func,
month_codes=None,
multiplier=500):
"""
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first_sid : int
The first sid to use for assigning sids to the created contracts.... | [
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25,978 | quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.startswith | def startswith(self, prefix):
"""
Construct a Filter matching values starting with ``prefix``.
Parameters
----------
prefix : str
String prefix against which to compare values produced by ``self``.
Returns
-------
matches : Filter
... | python | def startswith(self, prefix):
"""
Construct a Filter matching values starting with ``prefix``.
Parameters
----------
prefix : str
String prefix against which to compare values produced by ``self``.
Returns
-------
matches : Filter
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25,979 | quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.endswith | def endswith(self, suffix):
"""
Construct a Filter matching values ending with ``suffix``.
Parameters
----------
suffix : str
String suffix against which to compare values produced by ``self``.
Returns
-------
matches : Filter
Fil... | python | def endswith(self, suffix):
"""
Construct a Filter matching values ending with ``suffix``.
Parameters
----------
suffix : str
String suffix against which to compare values produced by ``self``.
Returns
-------
matches : Filter
Fil... | [
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25,980 | quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.has_substring | def has_substring(self, substring):
"""
Construct a Filter matching values containing ``substring``.
Parameters
----------
substring : str
Sub-string against which to compare values produced by ``self``.
Returns
-------
matches : Filter
... | python | def has_substring(self, substring):
"""
Construct a Filter matching values containing ``substring``.
Parameters
----------
substring : str
Sub-string against which to compare values produced by ``self``.
Returns
-------
matches : Filter
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25,981 | quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.matches | def matches(self, pattern):
"""
Construct a Filter that checks regex matches against ``pattern``.
Parameters
----------
pattern : str
Regex pattern against which to compare values produced by ``self``.
Returns
-------
matches : Filter
... | python | def matches(self, pattern):
"""
Construct a Filter that checks regex matches against ``pattern``.
Parameters
----------
pattern : str
Regex pattern against which to compare values produced by ``self``.
Returns
-------
matches : Filter
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25,982 | quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.element_of | def element_of(self, choices):
"""
Construct a Filter indicating whether values are in ``choices``.
Parameters
----------
choices : iterable[str or int]
An iterable of choices.
Returns
-------
matches : Filter
Filter returning Tru... | python | def element_of(self, choices):
"""
Construct a Filter indicating whether values are in ``choices``.
Parameters
----------
choices : iterable[str or int]
An iterable of choices.
Returns
-------
matches : Filter
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25,983 | quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.to_workspace_value | def to_workspace_value(self, result, assets):
"""
Called with the result of a pipeline. This needs to return an object
which can be put into the workspace to continue doing computations.
This is the inverse of :func:`~zipline.pipeline.term.Term.postprocess`.
"""
if self.... | python | def to_workspace_value(self, result, assets):
"""
Called with the result of a pipeline. This needs to return an object
which can be put into the workspace to continue doing computations.
This is the inverse of :func:`~zipline.pipeline.term.Term.postprocess`.
"""
if self.... | [
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25,984 | quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier._to_integral | def _to_integral(self, output_array):
"""
Convert an array produced by this classifier into an array of integer
labels and a missing value label.
"""
if self.dtype == int64_dtype:
group_labels = output_array
null_label = self.missing_value
elif sel... | python | def _to_integral(self, output_array):
"""
Convert an array produced by this classifier into an array of integer
labels and a missing value label.
"""
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group_labels = output_array
null_label = self.missing_value
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25,985 | quantopian/zipline | zipline/pipeline/classifiers/classifier.py | CustomClassifier._allocate_output | def _allocate_output(self, windows, shape):
"""
Override the default array allocation to produce a LabelArray when we
have a string-like dtype.
"""
if self.dtype == int64_dtype:
return super(CustomClassifier, self)._allocate_output(
windows,
... | python | def _allocate_output(self, windows, shape):
"""
Override the default array allocation to produce a LabelArray when we
have a string-like dtype.
"""
if self.dtype == int64_dtype:
return super(CustomClassifier, self)._allocate_output(
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] | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/pipeline/classifiers/classifier.py#L517-L531 |
25,986 | quantopian/zipline | zipline/utils/input_validation.py | verify_indices_all_unique | def verify_indices_all_unique(obj):
"""
Check that all axes of a pandas object are unique.
Parameters
----------
obj : pd.Series / pd.DataFrame / pd.Panel
The object to validate.
Returns
-------
obj : pd.Series / pd.DataFrame / pd.Panel
The validated object, unchanged.
... | python | def verify_indices_all_unique(obj):
"""
Check that all axes of a pandas object are unique.
Parameters
----------
obj : pd.Series / pd.DataFrame / pd.Panel
The object to validate.
Returns
-------
obj : pd.Series / pd.DataFrame / pd.Panel
The validated object, unchanged.
... | [
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25,987 | quantopian/zipline | zipline/utils/input_validation.py | optionally | def optionally(preprocessor):
"""Modify a preprocessor to explicitly allow `None`.
Parameters
----------
preprocessor : callable[callable, str, any -> any]
A preprocessor to delegate to when `arg is not None`.
Returns
-------
optional_preprocessor : callable[callable, str, any -> a... | python | def optionally(preprocessor):
"""Modify a preprocessor to explicitly allow `None`.
Parameters
----------
preprocessor : callable[callable, str, any -> any]
A preprocessor to delegate to when `arg is not None`.
Returns
-------
optional_preprocessor : callable[callable, str, any -> a... | [
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25,988 | quantopian/zipline | zipline/utils/input_validation.py | ensure_dtype | def ensure_dtype(func, argname, arg):
"""
Argument preprocessor that converts the input into a numpy dtype.
Examples
--------
>>> import numpy as np
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(dtype=ensure_dtype)
... def foo(dtype):
... return dtype
.... | python | def ensure_dtype(func, argname, arg):
"""
Argument preprocessor that converts the input into a numpy dtype.
Examples
--------
>>> import numpy as np
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(dtype=ensure_dtype)
... def foo(dtype):
... return dtype
.... | [
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25,989 | quantopian/zipline | zipline/utils/input_validation.py | ensure_timezone | def ensure_timezone(func, argname, arg):
"""Argument preprocessor that converts the input into a tzinfo object.
Examples
--------
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(tz=ensure_timezone)
... def foo(tz):
... return tz
>>> foo('utc')
<UTC>
"""
... | python | def ensure_timezone(func, argname, arg):
"""Argument preprocessor that converts the input into a tzinfo object.
Examples
--------
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(tz=ensure_timezone)
... def foo(tz):
... return tz
>>> foo('utc')
<UTC>
"""
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25,990 | quantopian/zipline | zipline/utils/input_validation.py | ensure_timestamp | def ensure_timestamp(func, argname, arg):
"""Argument preprocessor that converts the input into a pandas Timestamp
object.
Examples
--------
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(ts=ensure_timestamp)
... def foo(ts):
... return ts
>>> foo('2014-01-0... | python | def ensure_timestamp(func, argname, arg):
"""Argument preprocessor that converts the input into a pandas Timestamp
object.
Examples
--------
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(ts=ensure_timestamp)
... def foo(ts):
... return ts
>>> foo('2014-01-0... | [
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25,991 | quantopian/zipline | zipline/utils/input_validation.py | expect_dtypes | def expect_dtypes(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs have expected numpy dtypes.
Examples
--------
>>> from numpy import dtype, arange, int8, float64
>>> @expect_dtypes(x=dtype(int8))
... def foo(x, y):
... return x, y
...
>... | python | def expect_dtypes(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs have expected numpy dtypes.
Examples
--------
>>> from numpy import dtype, arange, int8, float64
>>> @expect_dtypes(x=dtype(int8))
... def foo(x, y):
... return x, y
...
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25,992 | quantopian/zipline | zipline/utils/input_validation.py | expect_kinds | def expect_kinds(**named):
"""
Preprocessing decorator that verifies inputs have expected dtype kinds.
Examples
--------
>>> from numpy import int64, int32, float32
>>> @expect_kinds(x='i')
... def foo(x):
... return x
...
>>> foo(int64(2))
2
>>> foo(int32(2))
2
... | python | def expect_kinds(**named):
"""
Preprocessing decorator that verifies inputs have expected dtype kinds.
Examples
--------
>>> from numpy import int64, int32, float32
>>> @expect_kinds(x='i')
... def foo(x):
... return x
...
>>> foo(int64(2))
2
>>> foo(int32(2))
2
... | [
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25,993 | quantopian/zipline | zipline/utils/input_validation.py | expect_types | def expect_types(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs have expected types.
Examples
--------
>>> @expect_types(x=int, y=str)
... def foo(x, y):
... return x, y
...
>>> foo(2, '3')
(2, '3')
>>> foo(2.0, '3') # doctest: +NO... | python | def expect_types(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs have expected types.
Examples
--------
>>> @expect_types(x=int, y=str)
... def foo(x, y):
... return x, y
...
>>> foo(2, '3')
(2, '3')
>>> foo(2.0, '3') # doctest: +NO... | [
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25,994 | quantopian/zipline | zipline/utils/input_validation.py | make_check | def make_check(exc_type, template, pred, actual, funcname):
"""
Factory for making preprocessing functions that check a predicate on the
input value.
Parameters
----------
exc_type : Exception
The exception type to raise if the predicate fails.
template : str
A template stri... | python | def make_check(exc_type, template, pred, actual, funcname):
"""
Factory for making preprocessing functions that check a predicate on the
input value.
Parameters
----------
exc_type : Exception
The exception type to raise if the predicate fails.
template : str
A template stri... | [
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A template string to use to create error messages.
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25,995 | quantopian/zipline | zipline/utils/input_validation.py | expect_element | def expect_element(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs are elements of some
expected collection.
Examples
--------
>>> @expect_element(x=('a', 'b'))
... def foo(x):
... return x.upper()
...
>>> foo('a')
'A'
>>> foo('b... | python | def expect_element(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs are elements of some
expected collection.
Examples
--------
>>> @expect_element(x=('a', 'b'))
... def foo(x):
... return x.upper()
...
>>> foo('a')
'A'
>>> foo('b... | [
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25,996 | quantopian/zipline | zipline/utils/input_validation.py | expect_bounded | def expect_bounded(__funcname=_qualified_name, **named):
"""
Preprocessing decorator verifying that inputs fall INCLUSIVELY between
bounds.
Bounds should be passed as a pair of ``(min_value, max_value)``.
``None`` may be passed as ``min_value`` or ``max_value`` to signify that
the input is onl... | python | def expect_bounded(__funcname=_qualified_name, **named):
"""
Preprocessing decorator verifying that inputs fall INCLUSIVELY between
bounds.
Bounds should be passed as a pair of ``(min_value, max_value)``.
``None`` may be passed as ``min_value`` or ``max_value`` to signify that
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25,997 | quantopian/zipline | zipline/utils/input_validation.py | expect_dimensions | def expect_dimensions(__funcname=_qualified_name, **dimensions):
"""
Preprocessing decorator that verifies inputs are numpy arrays with a
specific dimensionality.
Examples
--------
>>> from numpy import array
>>> @expect_dimensions(x=1, y=2)
... def foo(x, y):
... return x[0] + y... | python | def expect_dimensions(__funcname=_qualified_name, **dimensions):
"""
Preprocessing decorator that verifies inputs are numpy arrays with a
specific dimensionality.
Examples
--------
>>> from numpy import array
>>> @expect_dimensions(x=1, y=2)
... def foo(x, y):
... return x[0] + y... | [
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25,998 | quantopian/zipline | zipline/utils/input_validation.py | coerce | def coerce(from_, to, **to_kwargs):
"""
A preprocessing decorator that coerces inputs of a given type by passing
them to a callable.
Parameters
----------
from : type or tuple or types
Inputs types on which to call ``to``.
to : function
Coercion function to call on inputs.
... | python | def coerce(from_, to, **to_kwargs):
"""
A preprocessing decorator that coerces inputs of a given type by passing
them to a callable.
Parameters
----------
from : type or tuple or types
Inputs types on which to call ``to``.
to : function
Coercion function to call on inputs.
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25,999 | quantopian/zipline | zipline/utils/input_validation.py | coerce_types | def coerce_types(**kwargs):
"""
Preprocessing decorator that applies type coercions.
Parameters
----------
**kwargs : dict[str -> (type, callable)]
Keyword arguments mapping function parameter names to pairs of
(from_type, to_type).
Examples
--------
>>> @coerce_types... | python | def coerce_types(**kwargs):
"""
Preprocessing decorator that applies type coercions.
Parameters
----------
**kwargs : dict[str -> (type, callable)]
Keyword arguments mapping function parameter names to pairs of
(from_type, to_type).
Examples
--------
>>> @coerce_types... | [
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] | Preprocessing decorator that applies type coercions.
Parameters
----------
**kwargs : dict[str -> (type, callable)]
Keyword arguments mapping function parameter names to pairs of
(from_type, to_type).
Examples
--------
>>> @coerce_types(x=(float, int), y=(int, str))
... d... | [
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"applies",
"type",
"coercions",
"."
] | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/utils/input_validation.py#L804-L826 |
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