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janpipek/physt | physt/binnings.py | human_binning | def human_binning(data=None, bin_count: Optional[int] = None, *, range=None, **kwargs) -> FixedWidthBinning:
"""Construct fixed-width ninning schema with bins automatically optimized to human-friendly widths.
Typical widths are: 1.0, 25,0, 0.02, 500, 2.5e-7, ...
Parameters
----------
bin_count: Nu... | python | def human_binning(data=None, bin_count: Optional[int] = None, *, range=None, **kwargs) -> FixedWidthBinning:
"""Construct fixed-width ninning schema with bins automatically optimized to human-friendly widths.
Typical widths are: 1.0, 25,0, 0.02, 500, 2.5e-7, ...
Parameters
----------
bin_count: Nu... | [
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Typical widths are: 1.0, 25,0, 0.02, 500, 2.5e-7, ...
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bin_count: Number of bins
range: Optional[tuple]
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janpipek/physt | physt/binnings.py | quantile_binning | def quantile_binning(data=None, bins=10, *, qrange=(0.0, 1.0), **kwargs) -> StaticBinning:
"""Binning schema based on quantile ranges.
This binning finds equally spaced quantiles. This should lead to
all bins having roughly the same frequencies.
Note: weights are not (yet) take into account for calcul... | python | def quantile_binning(data=None, bins=10, *, qrange=(0.0, 1.0), **kwargs) -> StaticBinning:
"""Binning schema based on quantile ranges.
This binning finds equally spaced quantiles. This should lead to
all bins having roughly the same frequencies.
Note: weights are not (yet) take into account for calcul... | [
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This binning finds equally spaced quantiles. This should lead to
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Note: weights are not (yet) take into account for calculating
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bins: sequence or Optional[int]
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janpipek/physt | physt/binnings.py | static_binning | def static_binning(data=None, bins=None, **kwargs) -> StaticBinning:
"""Construct static binning with whatever bins."""
return StaticBinning(bins=make_bin_array(bins), **kwargs) | python | def static_binning(data=None, bins=None, **kwargs) -> StaticBinning:
"""Construct static binning with whatever bins."""
return StaticBinning(bins=make_bin_array(bins), **kwargs) | [
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janpipek/physt | physt/binnings.py | integer_binning | def integer_binning(data=None, **kwargs) -> StaticBinning:
"""Construct fixed-width binning schema with bins centered around integers.
Parameters
----------
range: Optional[Tuple[int]]
min (included) and max integer (excluded) bin
bin_width: Optional[int]
group "bin_width" integers ... | python | def integer_binning(data=None, **kwargs) -> StaticBinning:
"""Construct fixed-width binning schema with bins centered around integers.
Parameters
----------
range: Optional[Tuple[int]]
min (included) and max integer (excluded) bin
bin_width: Optional[int]
group "bin_width" integers ... | [
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janpipek/physt | physt/binnings.py | fixed_width_binning | def fixed_width_binning(data=None, bin_width: Union[float, int] = 1, *, range=None, includes_right_edge=False, **kwargs) -> FixedWidthBinning:
"""Construct fixed-width binning schema.
Parameters
----------
bin_width: float
range: Optional[tuple]
(min, max)
align: Optional[float]
... | python | def fixed_width_binning(data=None, bin_width: Union[float, int] = 1, *, range=None, includes_right_edge=False, **kwargs) -> FixedWidthBinning:
"""Construct fixed-width binning schema.
Parameters
----------
bin_width: float
range: Optional[tuple]
(min, max)
align: Optional[float]
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janpipek/physt | physt/binnings.py | exponential_binning | def exponential_binning(data=None, bin_count: Optional[int] = None, *, range=None, **kwargs) -> ExponentialBinning:
"""Construct exponential binning schema.
Parameters
----------
bin_count: Optional[int]
Number of bins
range: Optional[tuple]
(min, max)
See also
--------
... | python | def exponential_binning(data=None, bin_count: Optional[int] = None, *, range=None, **kwargs) -> ExponentialBinning:
"""Construct exponential binning schema.
Parameters
----------
bin_count: Optional[int]
Number of bins
range: Optional[tuple]
(min, max)
See also
--------
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janpipek/physt | physt/binnings.py | calculate_bins | def calculate_bins(array, _=None, *args, **kwargs) -> BinningBase:
"""Find optimal binning from arguments.
Parameters
----------
array: arraylike
Data from which the bins should be decided (sometimes used, sometimes not)
_: int or str or Callable or arraylike or Iterable or BinningBase
... | python | def calculate_bins(array, _=None, *args, **kwargs) -> BinningBase:
"""Find optimal binning from arguments.
Parameters
----------
array: arraylike
Data from which the bins should be decided (sometimes used, sometimes not)
_: int or str or Callable or arraylike or Iterable or BinningBase
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janpipek/physt | physt/binnings.py | ideal_bin_count | def ideal_bin_count(data, method: str = "default") -> int:
"""A theoretically ideal bin count.
Parameters
----------
data: array_likes
Data to work on. Most methods don't use this.
method: str
Name of the method to apply, available values:
- default (~sturges)
- ... | python | def ideal_bin_count(data, method: str = "default") -> int:
"""A theoretically ideal bin count.
Parameters
----------
data: array_likes
Data to work on. Most methods don't use this.
method: str
Name of the method to apply, available values:
- default (~sturges)
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janpipek/physt | physt/binnings.py | as_binning | def as_binning(obj, copy: bool = False) -> BinningBase:
"""Ensure that an object is a binning
Parameters
---------
obj : BinningBase or array_like
Can be a binning, numpy-like bins or full physt bins
copy : If true, ensure that the returned object is independent
"""
if isinstance(ob... | python | def as_binning(obj, copy: bool = False) -> BinningBase:
"""Ensure that an object is a binning
Parameters
---------
obj : BinningBase or array_like
Can be a binning, numpy-like bins or full physt bins
copy : If true, ensure that the returned object is independent
"""
if isinstance(ob... | [
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obj : BinningBase or array_like
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janpipek/physt | physt/binnings.py | BinningBase.to_dict | def to_dict(self) -> OrderedDict:
"""Dictionary representation of the binning schema.
This serves as template method, please implement _update_dict
"""
result = OrderedDict()
result["adaptive"] = self._adaptive
result["binning_type"] = type(self).__name__
self._u... | python | def to_dict(self) -> OrderedDict:
"""Dictionary representation of the binning schema.
This serves as template method, please implement _update_dict
"""
result = OrderedDict()
result["adaptive"] = self._adaptive
result["binning_type"] = type(self).__name__
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janpipek/physt | physt/binnings.py | BinningBase.is_regular | def is_regular(self, rtol: float = 1.e-5, atol: float = 1.e-8) -> bool:
"""Whether all bins have the same width.
Parameters
----------
rtol, atol : numpy tolerance parameters
"""
return np.allclose(np.diff(self.bins[1] - self.bins[0]), 0.0, rtol=rtol, atol=atol) | python | def is_regular(self, rtol: float = 1.e-5, atol: float = 1.e-8) -> bool:
"""Whether all bins have the same width.
Parameters
----------
rtol, atol : numpy tolerance parameters
"""
return np.allclose(np.diff(self.bins[1] - self.bins[0]), 0.0, rtol=rtol, atol=atol) | [
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janpipek/physt | physt/binnings.py | BinningBase.is_consecutive | def is_consecutive(self, rtol: float = 1.e-5, atol: float = 1.e-8) -> bool:
"""Whether all bins are in a growing order.
Parameters
----------
rtol, atol : numpy tolerance parameters
"""
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... | python | def is_consecutive(self, rtol: float = 1.e-5, atol: float = 1.e-8) -> bool:
"""Whether all bins are in a growing order.
Parameters
----------
rtol, atol : numpy tolerance parameters
"""
if self.inconsecutive_allowed:
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janpipek/physt | physt/binnings.py | BinningBase.adapt | def adapt(self, other: 'BinningBase'):
"""Adapt this binning so that it contains all bins of another binning.
Parameters
----------
other: BinningBase
"""
# TODO: in-place arg
if np.array_equal(self.bins, other.bins):
return None, None
elif no... | python | def adapt(self, other: 'BinningBase'):
"""Adapt this binning so that it contains all bins of another binning.
Parameters
----------
other: BinningBase
"""
# TODO: in-place arg
if np.array_equal(self.bins, other.bins):
return None, None
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janpipek/physt | physt/binnings.py | BinningBase.numpy_bins | def numpy_bins(self) -> np.ndarray:
"""Bins in the numpy format
This might not be available for inconsecutive binnings.
Returns
-------
edges: np.ndarray
shape=(bin_count+1,)
"""
if self._numpy_bins is None:
self._numpy_bins = to_numpy_bi... | python | def numpy_bins(self) -> np.ndarray:
"""Bins in the numpy format
This might not be available for inconsecutive binnings.
Returns
-------
edges: np.ndarray
shape=(bin_count+1,)
"""
if self._numpy_bins is None:
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Returns
-------
edges: np.ndarray
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janpipek/physt | physt/binnings.py | BinningBase.numpy_bins_with_mask | def numpy_bins_with_mask(self) -> Tuple[np.ndarray, np.ndarray]:
"""Bins in the numpy format, including the gaps in inconsecutive binnings.
Returns
-------
edges, mask: np.ndarray
See Also
--------
bin_utils.to_numpy_bins_with_mask
"""
bwm = to_n... | python | def numpy_bins_with_mask(self) -> Tuple[np.ndarray, np.ndarray]:
"""Bins in the numpy format, including the gaps in inconsecutive binnings.
Returns
-------
edges, mask: np.ndarray
See Also
--------
bin_utils.to_numpy_bins_with_mask
"""
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janpipek/physt | physt/binnings.py | StaticBinning.as_static | def as_static(self, copy: bool = True) -> 'StaticBinning':
"""Convert binning to a static form.
Returns
-------
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A new static binning with a copy of bins.
Parameters
----------
copy : if True, returns itself (already satisfying condition... | python | def as_static(self, copy: bool = True) -> 'StaticBinning':
"""Convert binning to a static form.
Returns
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janpipek/physt | physt/compat/dask.py | histogram1d | def histogram1d(data, bins=None, *args, **kwargs):
"""Facade function to create one-dimensional histogram using dask.
Parameters
----------
data: dask.DaskArray or array-like
See also
--------
physt.histogram
"""
import dask
if not hasattr(data, "dask"):
data = dask.arr... | python | def histogram1d(data, bins=None, *args, **kwargs):
"""Facade function to create one-dimensional histogram using dask.
Parameters
----------
data: dask.DaskArray or array-like
See also
--------
physt.histogram
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janpipek/physt | physt/compat/dask.py | histogram2d | def histogram2d(data1, data2, bins=None, *args, **kwargs):
"""Facade function to create 2D histogram using dask."""
# TODO: currently very unoptimized! for non-dasks
import dask
if "axis_names" not in kwargs:
if hasattr(data1, "name") and hasattr(data2, "name"):
kwargs["axis_names"] ... | python | def histogram2d(data1, data2, bins=None, *args, **kwargs):
"""Facade function to create 2D histogram using dask."""
# TODO: currently very unoptimized! for non-dasks
import dask
if "axis_names" not in kwargs:
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janpipek/physt | physt/util.py | all_subclasses | def all_subclasses(cls: type) -> Tuple[type, ...]:
"""All subclasses of a class.
From: http://stackoverflow.com/a/17246726/2692780
"""
subclasses = []
for subclass in cls.__subclasses__():
subclasses.append(subclass)
subclasses.extend(all_subclasses(subclass))
return tuple(subcl... | python | def all_subclasses(cls: type) -> Tuple[type, ...]:
"""All subclasses of a class.
From: http://stackoverflow.com/a/17246726/2692780
"""
subclasses = []
for subclass in cls.__subclasses__():
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janpipek/physt | physt/util.py | find_subclass | def find_subclass(base: type, name: str) -> type:
"""Find a named subclass of a base class.
Uses only the class name without namespace.
"""
class_candidates = [klass
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"""Find a named subclass of a base class.
Uses only the class name without namespace.
"""
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janpipek/physt | physt/histogram_collection.py | HistogramCollection.add | def add(self, histogram: Histogram1D):
"""Add a histogram to the collection."""
if self.binning and not self.binning == histogram.binning:
raise ValueError("Cannot add histogram with different binning.")
self.histograms.append(histogram) | python | def add(self, histogram: Histogram1D):
"""Add a histogram to the collection."""
if self.binning and not self.binning == histogram.binning:
raise ValueError("Cannot add histogram with different binning.")
self.histograms.append(histogram) | [
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janpipek/physt | physt/histogram_collection.py | HistogramCollection.normalize_bins | def normalize_bins(self, inplace: bool = False) -> "HistogramCollection":
"""Normalize each bin in the collection so that the sum is 1.0 for each bin.
Note: If a bin is zero in all collections, the result will be inf.
"""
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janpipek/physt | physt/histogram_collection.py | HistogramCollection.multi_h1 | def multi_h1(cls, a_dict: Dict[str, Any], bins=None, **kwargs) -> "HistogramCollection":
"""Create a collection from multiple datasets."""
from physt.binnings import calculate_bins
mega_values = np.concatenate(list(a_dict.values()))
binning = calculate_bins(mega_values, bins, **kwargs)
... | python | def multi_h1(cls, a_dict: Dict[str, Any], bins=None, **kwargs) -> "HistogramCollection":
"""Create a collection from multiple datasets."""
from physt.binnings import calculate_bins
mega_values = np.concatenate(list(a_dict.values()))
binning = calculate_bins(mega_values, bins, **kwargs)
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janpipek/physt | physt/histogram_collection.py | HistogramCollection.to_json | def to_json(self, path: Optional[str] = None, **kwargs) -> str:
"""Convert to JSON representation.
Parameters
----------
path: Where to write the JSON.
Returns
-------
The JSON representation.
"""
from .io import save_json
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"""Convert to JSON representation.
Parameters
----------
path: Where to write the JSON.
Returns
-------
The JSON representation.
"""
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janpipek/physt | physt/histogram_base.py | HistogramBase._get_axis | def _get_axis(self, name_or_index: AxisIdentifier) -> int:
"""Get a zero-based index of an axis and check its existence."""
# TODO: Add unit test
if isinstance(name_or_index, int):
if name_or_index < 0 or name_or_index >= self.ndim:
raise ValueError("No such axis, mus... | python | def _get_axis(self, name_or_index: AxisIdentifier) -> int:
"""Get a zero-based index of an axis and check its existence."""
# TODO: Add unit test
if isinstance(name_or_index, int):
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janpipek/physt | physt/histogram_base.py | HistogramBase.shape | def shape(self) -> Tuple[int, ...]:
"""Shape of histogram's data.
Returns
-------
One-element tuple with the number of bins along each axis.
"""
return tuple(bins.bin_count for bins in self._binnings) | python | def shape(self) -> Tuple[int, ...]:
"""Shape of histogram's data.
Returns
-------
One-element tuple with the number of bins along each axis.
"""
return tuple(bins.bin_count for bins in self._binnings) | [
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janpipek/physt | physt/histogram_base.py | HistogramBase.set_dtype | def set_dtype(self, value, check: bool = True):
"""Change data type of the bin contents.
Allowed conversions:
- from integral to float types
- between the same category of type (float/integer)
- from float types to integer if weights are trivial
Parameters
-----... | python | def set_dtype(self, value, check: bool = True):
"""Change data type of the bin contents.
Allowed conversions:
- from integral to float types
- between the same category of type (float/integer)
- from float types to integer if weights are trivial
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janpipek/physt | physt/histogram_base.py | HistogramBase._coerce_dtype | def _coerce_dtype(self, other_dtype):
"""Possibly change the bin content type to allow correct operations with other operand.
Parameters
----------
other_dtype : np.dtype or type
"""
if self._dtype is None:
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... | python | def _coerce_dtype(self, other_dtype):
"""Possibly change the bin content type to allow correct operations with other operand.
Parameters
----------
other_dtype : np.dtype or type
"""
if self._dtype is None:
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janpipek/physt | physt/histogram_base.py | HistogramBase.normalize | def normalize(self, inplace: bool = False, percent: bool = False) -> "HistogramBase":
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Parameters
----------
inplace: If True, updates itself. If False (default), returns copy
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janpipek/physt | physt/histogram_base.py | HistogramBase._change_binning | def _change_binning(self, new_binning, bin_map: Iterable[Tuple[int, int]], axis: int = 0):
"""Set new binnning and update the bin contents according to a map.
Fills frequencies and errors with 0.
It's the caller's responsibility to provide correct binning and map.
Parameters
--... | python | def _change_binning(self, new_binning, bin_map: Iterable[Tuple[int, int]], axis: int = 0):
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Fills frequencies and errors with 0.
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janpipek/physt | physt/histogram_base.py | HistogramBase._reshape_data | def _reshape_data(self, new_size, bin_map, axis=0):
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Fills frequencies and errors with 0.
Parameters
----------
new_size: int
bin_map: Iterable[(old, new)] or int or None
If None, we can keep the data unchanged.
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"""Reshape data to match new binning schema.
Fills frequencies and errors with 0.
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new_size: int
bin_map: Iterable[(old, new)] or int or None
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janpipek/physt | physt/histogram_base.py | HistogramBase._apply_bin_map | def _apply_bin_map(self, old_frequencies, new_frequencies, old_errors2,
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"""Fill new data arrays using a map.
Parameters
----------
old_frequencies : np.ndarray
Source of frequencies data
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old_frequencies : np.ndarray
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janpipek/physt | physt/histogram_base.py | HistogramBase.has_same_bins | def has_same_bins(self, other: "HistogramBase") -> bool:
"""Whether two histograms share the same binning."""
if self.shape != other.shape:
return False
elif self.ndim == 1:
return np.allclose(self.bins, other.bins)
elif self.ndim > 1:
for i in range(s... | python | def has_same_bins(self, other: "HistogramBase") -> bool:
"""Whether two histograms share the same binning."""
if self.shape != other.shape:
return False
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return np.allclose(self.bins, other.bins)
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janpipek/physt | physt/histogram_base.py | HistogramBase.copy | def copy(self, include_frequencies: bool = True) -> "HistogramBase":
"""Copy the histogram.
Parameters
----------
include_frequencies : If false, all frequencies are set to zero.
"""
if include_frequencies:
frequencies = np.copy(self.frequencies)
... | python | def copy(self, include_frequencies: bool = True) -> "HistogramBase":
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Parameters
----------
include_frequencies : If false, all frequencies are set to zero.
"""
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janpipek/physt | physt/histogram_base.py | HistogramBase.to_dict | def to_dict(self) -> OrderedDict:
"""Dictionary with all data in the histogram.
This is used for export into various formats (e.g. JSON)
If a descendant class needs to update the dictionary in some way
(put some more information), override the _update_dict method.
"""
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"""Dictionary with all data in the histogram.
This is used for export into various formats (e.g. JSON)
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janpipek/physt | physt/histogram_base.py | HistogramBase._merge_meta_data | def _merge_meta_data(cls, first: "HistogramBase", second: "HistogramBase") -> dict:
"""Merge meta data of two histograms leaving only the equal values.
(Used in addition and subtraction)
"""
keys = set(first._meta_data.keys())
keys = keys.union(set(second._meta_data.keys()))
... | python | def _merge_meta_data(cls, first: "HistogramBase", second: "HistogramBase") -> dict:
"""Merge meta data of two histograms leaving only the equal values.
(Used in addition and subtraction)
"""
keys = set(first._meta_data.keys())
keys = keys.union(set(second._meta_data.keys()))
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janpipek/physt | physt/histogram1d.py | Histogram1D.mean | def mean(self) -> Optional[float]:
"""Statistical mean of all values entered into histogram.
This number is precise, because we keep the necessary data
separate from bin contents.
"""
if self._stats: # TODO: should be true always?
if self.total > 0:
... | python | def mean(self) -> Optional[float]:
"""Statistical mean of all values entered into histogram.
This number is precise, because we keep the necessary data
separate from bin contents.
"""
if self._stats: # TODO: should be true always?
if self.total > 0:
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janpipek/physt | physt/histogram1d.py | Histogram1D.std | def std(self) -> Optional[float]: #, ddof=0):
"""Standard deviation of all values entered into histogram.
This number is precise, because we keep the necessary data
separate from bin contents.
Returns
-------
float
"""
# TODO: Add DOF
if self._s... | python | def std(self) -> Optional[float]: #, ddof=0):
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This number is precise, because we keep the necessary data
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janpipek/physt | physt/histogram1d.py | Histogram1D.variance | def variance(self) -> Optional[float]: #, ddof: int = 0) -> float:
"""Statistical variance of all values entered into histogram.
This number is precise, because we keep the necessary data
separate from bin contents.
Returns
-------
float
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# TODO: Add... | python | def variance(self) -> Optional[float]: #, ddof: int = 0) -> float:
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This number is precise, because we keep the necessary data
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janpipek/physt | physt/histogram1d.py | Histogram1D.find_bin | def find_bin(self, value):
"""Index of bin corresponding to a value.
Parameters
----------
value: float
Value to be searched for.
Returns
-------
int
index of bin to which value belongs
(-1=underflow, N=overflow, None=not foun... | python | def find_bin(self, value):
"""Index of bin corresponding to a value.
Parameters
----------
value: float
Value to be searched for.
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int
index of bin to which value belongs
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janpipek/physt | physt/histogram1d.py | Histogram1D.fill | def fill(self, value, weight=1):
"""Update histogram with a new value.
Parameters
----------
value: float
Value to be added.
weight: float, optional
Weight assigned to the value.
Returns
-------
int
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Parameters
----------
value: float
Value to be added.
weight: float, optional
Weight assigned to the value.
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janpipek/physt | physt/histogram1d.py | Histogram1D.fill_n | def fill_n(self, values, weights=None, dropna: bool = True):
"""Update histograms with a set of values.
Parameters
----------
values: array_like
weights: Optional[array_like]
drop_na: Optional[bool]
If true (default), all nan's are skipped.
"""
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"""Update histograms with a set of values.
Parameters
----------
values: array_like
weights: Optional[array_like]
drop_na: Optional[bool]
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janpipek/physt | physt/histogram1d.py | Histogram1D.to_xarray | def to_xarray(self) -> "xarray.Dataset":
"""Convert to xarray.Dataset"""
import xarray as xr
data_vars = {
"frequencies": xr.DataArray(self.frequencies, dims="bin"),
"errors2": xr.DataArray(self.errors2, dims="bin"),
"bins": xr.DataArray(self.bins, dims=("bin"... | python | def to_xarray(self) -> "xarray.Dataset":
"""Convert to xarray.Dataset"""
import xarray as xr
data_vars = {
"frequencies": xr.DataArray(self.frequencies, dims="bin"),
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janpipek/physt | physt/histogram1d.py | Histogram1D.from_xarray | def from_xarray(cls, arr: "xarray.Dataset") -> "Histogram1D":
"""Convert form xarray.Dataset
Parameters
----------
arr: The data in xarray representation
"""
kwargs = {'frequencies': arr["frequencies"],
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'errors2... | python | def from_xarray(cls, arr: "xarray.Dataset") -> "Histogram1D":
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Parameters
----------
arr: The data in xarray representation
"""
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janpipek/physt | physt/plotting/__init__.py | set_default_backend | def set_default_backend(name: str):
"""Choose a default backend."""
global _default_backend
if name == "bokeh":
raise RuntimeError("Support for bokeh has been discontinued. At some point, we may return to support holoviews.")
if not name in backends:
raise RuntimeError("Backend {0} is no... | python | def set_default_backend(name: str):
"""Choose a default backend."""
global _default_backend
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janpipek/physt | physt/plotting/__init__.py | _get_backend | def _get_backend(name: str = None):
"""Get a plotting backend.
Tries to get it using the name - or the default one.
"""
if not backends:
raise RuntimeError("No plotting backend available. Please, install matplotlib (preferred) or bokeh (limited).")
if not name:
name = _default_backe... | python | def _get_backend(name: str = None):
"""Get a plotting backend.
Tries to get it using the name - or the default one.
"""
if not backends:
raise RuntimeError("No plotting backend available. Please, install matplotlib (preferred) or bokeh (limited).")
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janpipek/physt | physt/plotting/__init__.py | plot | def plot(histogram: HistogramBase, kind: Optional[str] = None, backend: Optional[str] = None, **kwargs):
"""Universal plotting function.
All keyword arguments are passed to the plotting methods.
Parameters
----------
kind: Type of the plot (like "scatter", "line", ...), similar to pandas
"""
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"""Universal plotting function.
All keyword arguments are passed to the plotting methods.
Parameters
----------
kind: Type of the plot (like "scatter", "line", ...), similar to pandas
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janpipek/physt | physt/plotting/vega.py | enable_inline_view | def enable_inline_view(f):
"""Decorator to enable in-line viewing in Python and saving to external file.
It adds several parameters to each decorated plotted function:
Parameters
----------
write_to: str (optional)
Path to write vega JSON/HTML to.
write_format: "auto" | "json" | "html"... | python | def enable_inline_view(f):
"""Decorator to enable in-line viewing in Python and saving to external file.
It adds several parameters to each decorated plotted function:
Parameters
----------
write_to: str (optional)
Path to write vega JSON/HTML to.
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janpipek/physt | physt/plotting/vega.py | write_vega | def write_vega(vega_data, *, title: Optional[str], write_to: str, write_format: str = "auto", indent: int = 2):
"""Write vega dictionary to an external file.
Parameters
----------
vega_data : Valid vega data as dictionary
write_to: Path to write vega JSON/HTML to.
write_format: "auto" | "json" ... | python | def write_vega(vega_data, *, title: Optional[str], write_to: str, write_format: str = "auto", indent: int = 2):
"""Write vega dictionary to an external file.
Parameters
----------
vega_data : Valid vega data as dictionary
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janpipek/physt | physt/plotting/vega.py | display_vega | def display_vega(vega_data: dict, display: bool = True) -> Union['Vega', dict]:
"""Optionally display vega dictionary.
Parameters
----------
vega_data : Valid vega data as dictionary
display: Whether to try in-line display in IPython
"""
if VEGA_IPYTHON_PLUGIN_ENABLED and display:
... | python | def display_vega(vega_data: dict, display: bool = True) -> Union['Vega', dict]:
"""Optionally display vega dictionary.
Parameters
----------
vega_data : Valid vega data as dictionary
display: Whether to try in-line display in IPython
"""
if VEGA_IPYTHON_PLUGIN_ENABLED and display:
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janpipek/physt | physt/plotting/vega.py | bar | def bar(h1: Histogram1D, **kwargs) -> dict:
"""Bar plot of 1D histogram.
Parameters
----------
lw : float
Width of the line between bars
alpha : float
Opacity of the bars
hover_alpha: float
Opacity of the bars when hover on
"""
# TODO: Enable collections
# TO... | python | def bar(h1: Histogram1D, **kwargs) -> dict:
"""Bar plot of 1D histogram.
Parameters
----------
lw : float
Width of the line between bars
alpha : float
Opacity of the bars
hover_alpha: float
Opacity of the bars when hover on
"""
# TODO: Enable collections
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janpipek/physt | physt/plotting/vega.py | scatter | def scatter(h1: Histogram1D, **kwargs) -> dict:
"""Scatter plot of 1D histogram values.
Points are horizontally placed in bin centers.
Parameters
----------
shape : str
"""
shape = kwargs.pop("shape", DEFAULT_SCATTER_SHAPE)
# size = kwargs.pop("size", DEFAULT_SCATTER_SIZE)
mark_te... | python | def scatter(h1: Histogram1D, **kwargs) -> dict:
"""Scatter plot of 1D histogram values.
Points are horizontally placed in bin centers.
Parameters
----------
shape : str
"""
shape = kwargs.pop("shape", DEFAULT_SCATTER_SHAPE)
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janpipek/physt | physt/plotting/vega.py | line | def line(h1: Histogram1D, **kwargs) -> dict:
"""Line plot of 1D histogram values.
Points are horizontally placed in bin centers.
Parameters
----------
h1 : physt.histogram1d.Histogram1D
Dimensionality of histogram for which it is applicable
"""
lw = kwargs.pop("lw", DEFAULT_STROKE... | python | def line(h1: Histogram1D, **kwargs) -> dict:
"""Line plot of 1D histogram values.
Points are horizontally placed in bin centers.
Parameters
----------
h1 : physt.histogram1d.Histogram1D
Dimensionality of histogram for which it is applicable
"""
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janpipek/physt | physt/plotting/vega.py | _create_figure | def _create_figure(kwargs: Mapping[str, Any]) -> dict:
"""Create basic dictionary object with figure properties."""
return {
"$schema": "https://vega.github.io/schema/vega/v3.json",
"width": kwargs.pop("width", DEFAULT_WIDTH),
"height": kwargs.pop("height", DEFAULT_HEIGHT),
"padd... | python | def _create_figure(kwargs: Mapping[str, Any]) -> dict:
"""Create basic dictionary object with figure properties."""
return {
"$schema": "https://vega.github.io/schema/vega/v3.json",
"width": kwargs.pop("width", DEFAULT_WIDTH),
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janpipek/physt | physt/plotting/vega.py | _create_scales | def _create_scales(hist: HistogramBase, vega: dict, kwargs: dict):
"""Find proper scales for axes."""
if hist.ndim == 1:
bins0 = hist.bins.astype(float)
else:
bins0 = hist.bins[0].astype(float)
xlim = kwargs.pop("xlim", "auto")
ylim = kwargs.pop("ylim", "auto")
if xlim is "auto... | python | def _create_scales(hist: HistogramBase, vega: dict, kwargs: dict):
"""Find proper scales for axes."""
if hist.ndim == 1:
bins0 = hist.bins.astype(float)
else:
bins0 = hist.bins[0].astype(float)
xlim = kwargs.pop("xlim", "auto")
ylim = kwargs.pop("ylim", "auto")
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janpipek/physt | physt/plotting/vega.py | _create_axes | def _create_axes(hist: HistogramBase, vega: dict, kwargs: dict):
"""Create axes in the figure."""
xlabel = kwargs.pop("xlabel", hist.axis_names[0])
ylabel = kwargs.pop("ylabel", hist.axis_names[1] if len(hist.axis_names) >= 2 else None)
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"""Create axes in the figure."""
xlabel = kwargs.pop("xlabel", hist.axis_names[0])
ylabel = kwargs.pop("ylabel", hist.axis_names[1] if len(hist.axis_names) >= 2 else None)
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janpipek/physt | physt/plotting/vega.py | _create_tooltips | def _create_tooltips(hist: Histogram1D, vega: dict, kwargs: dict):
"""In one-dimensional plots, show values above the value on hover."""
if kwargs.pop("tooltips", False):
vega["signals"] = vega.get("signals", [])
vega["signals"].append({
"name": "tooltip",
"value": {},
... | python | def _create_tooltips(hist: Histogram1D, vega: dict, kwargs: dict):
"""In one-dimensional plots, show values above the value on hover."""
if kwargs.pop("tooltips", False):
vega["signals"] = vega.get("signals", [])
vega["signals"].append({
"name": "tooltip",
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janpipek/physt | physt/plotting/vega.py | _add_title | def _add_title(hist: HistogramBase, vega: dict, kwargs: dict):
"""Display plot title if available."""
title = kwargs.pop("title", hist.title)
if title:
vega["title"] = {
"text": title
} | python | def _add_title(hist: HistogramBase, vega: dict, kwargs: dict):
"""Display plot title if available."""
title = kwargs.pop("title", hist.title)
if title:
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janpipek/physt | physt/special.py | _prepare_data | def _prepare_data(data, transformed, klass, *args, **kwargs):
"""Transform data for binning.
Returns
-------
np.ndarray
"""
# TODO: Maybe include in the class itself?
data = np.asarray(data)
if not transformed:
data = klass.transform(data)
dropna = kwargs.get("dropna", Fals... | python | def _prepare_data(data, transformed, klass, *args, **kwargs):
"""Transform data for binning.
Returns
-------
np.ndarray
"""
# TODO: Maybe include in the class itself?
data = np.asarray(data)
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data = klass.transform(data)
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janpipek/physt | physt/special.py | polar_histogram | def polar_histogram(xdata, ydata, radial_bins="numpy", phi_bins=16,
transformed=False, *args, **kwargs):
"""Facade construction function for the PolarHistogram.
Parameters
----------
transformed : bool
phi_range : Optional[tuple]
range
"""
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transformed=False, *args, **kwargs):
"""Facade construction function for the PolarHistogram.
Parameters
----------
transformed : bool
phi_range : Optional[tuple]
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janpipek/physt | physt/special.py | spherical_histogram | def spherical_histogram(data=None, radial_bins="numpy", theta_bins=16, phi_bins=16, transformed=False, *args, **kwargs):
"""Facade construction function for the SphericalHistogram.
"""
dropna = kwargs.pop("dropna", True)
data = _prepare_data(data, transformed=transformed, klass=SphericalHistogram, dro... | python | def spherical_histogram(data=None, radial_bins="numpy", theta_bins=16, phi_bins=16, transformed=False, *args, **kwargs):
"""Facade construction function for the SphericalHistogram.
"""
dropna = kwargs.pop("dropna", True)
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janpipek/physt | physt/special.py | cylindrical_histogram | def cylindrical_histogram(data=None, rho_bins="numpy", phi_bins=16, z_bins="numpy", transformed=False, *args, **kwargs):
"""Facade construction function for the CylindricalHistogram.
"""
dropna = kwargs.pop("dropna", True)
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"""Facade construction function for the CylindricalHistogram.
"""
dropna = kwargs.pop("dropna", True)
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janpipek/physt | physt/special.py | TransformedHistogramMixin.projection | def projection(self, *axes, **kwargs):
"""Projection to lower-dimensional histogram.
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class attribute to suggest class for the projection. If the
arguments don't match any of the map keys, HistogramND is used. ... | python | def projection(self, *axes, **kwargs):
"""Projection to lower-dimensional histogram.
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janpipek/physt | physt/plotting/plotly.py | enable_collection | def enable_collection(f):
"""Call the wrapped function with a HistogramCollection as argument."""
@wraps(f)
def new_f(h: AbstractHistogram1D, **kwargs):
from physt.histogram_collection import HistogramCollection
if isinstance(h, HistogramCollection):
return f(h, **kwargs)
... | python | def enable_collection(f):
"""Call the wrapped function with a HistogramCollection as argument."""
@wraps(f)
def new_f(h: AbstractHistogram1D, **kwargs):
from physt.histogram_collection import HistogramCollection
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janpipek/physt | physt/plotting/plotly.py | bar | def bar(h: Histogram2D, *,
barmode: str = DEFAULT_BARMODE,
alpha: float = DEFAULT_ALPHA,
**kwargs):
"""Bar plot.
Parameters
----------
alpha: Opacity (0.0 - 1.0)
barmode : "overlay" | "group" | "stack"
"""
get_data_kwargs = pop_many(kwargs, "density", "cumulative", "... | python | def bar(h: Histogram2D, *,
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"""Bar plot.
Parameters
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alpha: Opacity (0.0 - 1.0)
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janpipek/physt | physt/plotting/folium.py | _bins_to_json | def _bins_to_json(h2):
"""Create GeoJSON representation of histogram bins
Parameters
----------
h2: physt.histogram_nd.Histogram2D
A histogram of coordinates (in degrees)
Returns
-------
geo_json : dict
"""
south = h2.get_bin_left_edges(0)
north = h2.get_bin_right_edges... | python | def _bins_to_json(h2):
"""Create GeoJSON representation of histogram bins
Parameters
----------
h2: physt.histogram_nd.Histogram2D
A histogram of coordinates (in degrees)
Returns
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geo_json : dict
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janpipek/physt | physt/plotting/folium.py | geo_map | def geo_map(h2, map=None, tiles='stamenterrain', cmap="wk", alpha=0.5, lw=1, fit_bounds=None, layer_name=None):
"""Show rectangular grid over a map.
Parameters
----------
h2: physt.histogram_nd.Histogram2D
A histogram of coordinates (in degrees: latitude, longitude)
map : folium.folium.Map
... | python | def geo_map(h2, map=None, tiles='stamenterrain', cmap="wk", alpha=0.5, lw=1, fit_bounds=None, layer_name=None):
"""Show rectangular grid over a map.
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janpipek/physt | physt/compat/geant4.py | load_csv | def load_csv(path):
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Parameters
----------
path: str
Path to the CSV file
Returns
-------
physt.histogram1d.Histogram1D or physt.histogram_nd.Histogram2D
"""
meta = []
data = []
with codecs.open... | python | def load_csv(path):
"""Loads a histogram as output from Geant4 analysis tools in CSV format.
Parameters
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path: str
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janpipek/physt | physt/compat/geant4.py | _get | def _get(pseudodict, key, single=True):
"""Helper method for getting values from "multi-dict"s"""
matches = [item[1] for item in pseudodict if item[0] == key]
if single:
return matches[0]
else:
return matches | python | def _get(pseudodict, key, single=True):
"""Helper method for getting values from "multi-dict"s"""
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openid/python-openid | openid/yadis/xrires.py | ProxyResolver.queryURL | def queryURL(self, xri, service_type=None):
"""Build a URL to query the proxy resolver.
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@type xri: unicode
@param service_type: The service type to resolve, if you desire
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@type serv... | python | def queryURL(self, xri, service_type=None):
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openid/python-openid | openid/yadis/accept.py | generateAcceptHeader | def generateAcceptHeader(*elements):
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openid/python-openid | openid/yadis/accept.py | parseAcceptHeader | def parseAcceptHeader(value):
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openid/python-openid | openid/message.py | Message.fromPostArgs | def fromPostArgs(cls, args):
"""Construct a Message containing a set of POST arguments.
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openid_args = {}
for key, value in args.items():
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"""Construct a Message containing a set of POST arguments.
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self = cls()
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@type success_response: openid.consumer.consumer.SuccessResponse
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openid/python-openid | openid/extensions/sreg.py | SRegResponse.get | def get(self, field_name, default=None):
"""Like dict.get, except that it checks that the field name is
defined by the simple registration specification"""
checkFieldName(field_name)
return self.data.get(field_name, default) | python | def get(self, field_name, default=None):
"""Like dict.get, except that it checks that the field name is
defined by the simple registration specification"""
checkFieldName(field_name)
return self.data.get(field_name, default) | [
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openid/python-openid | openid/server/trustroot.py | returnToMatches | def returnToMatches(allowed_return_to_urls, return_to):
"""Is the return_to URL under one of the supplied allowed
return_to URLs?
@since: 2.1.0
"""
for allowed_return_to in allowed_return_to_urls:
# A return_to pattern works the same as a realm, except that
# it's not allowed to us... | python | def returnToMatches(allowed_return_to_urls, return_to):
"""Is the return_to URL under one of the supplied allowed
return_to URLs?
@since: 2.1.0
"""
for allowed_return_to in allowed_return_to_urls:
# A return_to pattern works the same as a realm, except that
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openid/python-openid | openid/server/trustroot.py | getAllowedReturnURLs | def getAllowedReturnURLs(relying_party_url):
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@since: 2.1.0
"""
(rp_url_after_redirects, return_to_urls) = services.getServiceEndpoints(
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if rp_url_after_redirects != relying_party_u... | python | def getAllowedReturnURLs(relying_party_url):
"""Given a relying party discovery URL return a list of return_to URLs.
@since: 2.1.0
"""
(rp_url_after_redirects, return_to_urls) = services.getServiceEndpoints(
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openid/python-openid | openid/server/trustroot.py | verifyReturnTo | def verifyReturnTo(realm_str, return_to, _vrfy=getAllowedReturnURLs):
"""Verify that a return_to URL is valid for the given realm.
This function builds a discovery URL, performs Yadis discovery on
it, makes sure that the URL does not redirect, parses out the
return_to URLs, and finally checks to see if... | python | def verifyReturnTo(realm_str, return_to, _vrfy=getAllowedReturnURLs):
"""Verify that a return_to URL is valid for the given realm.
This function builds a discovery URL, performs Yadis discovery on
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openid/python-openid | openid/server/trustroot.py | TrustRoot.validateURL | def validateURL(self, url):
"""
Validates a URL against this trust root.
@param url: The URL to check
@type url: C{str}
@return: Whether the given URL is within this trust root.
@rtype: C{bool}
"""
url_parts = _parseURL(url)
if url_parts is ... | python | def validateURL(self, url):
"""
Validates a URL against this trust root.
@param url: The URL to check
@type url: C{str}
@return: Whether the given URL is within this trust root.
@rtype: C{bool}
"""
url_parts = _parseURL(url)
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openid/python-openid | openid/server/trustroot.py | TrustRoot.checkSanity | def checkSanity(cls, trust_root_string):
"""str -> bool
is this a sane trust root?
"""
trust_root = cls.parse(trust_root_string)
if trust_root is None:
return False
else:
return trust_root.isSane() | python | def checkSanity(cls, trust_root_string):
"""str -> bool
is this a sane trust root?
"""
trust_root = cls.parse(trust_root_string)
if trust_root is None:
return False
else:
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openid/python-openid | openid/server/trustroot.py | TrustRoot.checkURL | def checkURL(cls, trust_root, url):
"""quick func for validating a url against a trust root. See the
TrustRoot class if you need more control."""
tr = cls.parse(trust_root)
return tr is not None and tr.validateURL(url) | python | def checkURL(cls, trust_root, url):
"""quick func for validating a url against a trust root. See the
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tr = cls.parse(trust_root)
return tr is not None and tr.validateURL(url) | [
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openid/python-openid | openid/server/trustroot.py | TrustRoot.buildDiscoveryURL | def buildDiscoveryURL(self):
"""Return a discovery URL for this realm.
This function does not check to make sure that the realm is
valid. Its behaviour on invalid inputs is undefined.
@rtype: str
@returns: The URL upon which relying party discovery should be run
in... | python | def buildDiscoveryURL(self):
"""Return a discovery URL for this realm.
This function does not check to make sure that the realm is
valid. Its behaviour on invalid inputs is undefined.
@rtype: str
@returns: The URL upon which relying party discovery should be run
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openid/python-openid | openid/yadis/manager.py | YadisServiceManager.next | def next(self):
"""Return the next service
self.current() will continue to return that service until the
next call to this method."""
try:
self._current = self.services.pop(0)
except IndexError:
raise StopIteration
else:
return self._c... | python | def next(self):
"""Return the next service
self.current() will continue to return that service until the
next call to this method."""
try:
self._current = self.services.pop(0)
except IndexError:
raise StopIteration
else:
return self._c... | [
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openid/python-openid | openid/yadis/manager.py | Discovery.getNextService | def getNextService(self, discover):
"""Return the next authentication service for the pair of
user_input and session. This function handles fallback.
@param discover: a callable that takes a URL and returns a
list of services
@type discover: str -> [service]
@re... | python | def getNextService(self, discover):
"""Return the next authentication service for the pair of
user_input and session. This function handles fallback.
@param discover: a callable that takes a URL and returns a
list of services
@type discover: str -> [service]
@re... | [
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openid/python-openid | openid/yadis/manager.py | Discovery.cleanup | def cleanup(self, force=False):
"""Clean up Yadis-related services in the session and return
the most-recently-attempted service from the manager, if one
exists.
@param force: True if the manager should be deleted regardless
of whether it's a manager for self.url.
@retu... | python | def cleanup(self, force=False):
"""Clean up Yadis-related services in the session and return
the most-recently-attempted service from the manager, if one
exists.
@param force: True if the manager should be deleted regardless
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