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J535D165/recordlinkage | recordlinkage/api.py | Index.random | def random(self, *args, **kwargs):
"""Add a random index.
Shortcut of :class:`recordlinkage.index.Random`::
from recordlinkage.index import Random
indexer = recordlinkage.Index()
indexer.add(Random())
"""
indexer = Random()
self.add(indexer)
return self | python | def random(self, *args, **kwargs):
"""Add a random index.
Shortcut of :class:`recordlinkage.index.Random`::
from recordlinkage.index import Random
indexer = recordlinkage.Index()
indexer.add(Random())
"""
indexer = Random()
self.add(indexer)
return self | [
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Shortcut of :class:`recordlinkage.index.Random`::
from recordlinkage.index import Random
indexer = recordlinkage.Index()
indexer.add(Random()) | [
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J535D165/recordlinkage | recordlinkage/api.py | Compare.exact | def exact(self, *args, **kwargs):
"""Compare attributes of pairs exactly.
Shortcut of :class:`recordlinkage.compare.Exact`::
from recordlinkage.compare import Exact
indexer = recordlinkage.Compare()
indexer.add(Exact())
"""
compare = Exact(*args, **kwargs)
self.add(compare)
return self | python | def exact(self, *args, **kwargs):
"""Compare attributes of pairs exactly.
Shortcut of :class:`recordlinkage.compare.Exact`::
from recordlinkage.compare import Exact
indexer = recordlinkage.Compare()
indexer.add(Exact())
"""
compare = Exact(*args, **kwargs)
self.add(compare)
return self | [
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Shortcut of :class:`recordlinkage.compare.Exact`::
from recordlinkage.compare import Exact
indexer = recordlinkage.Compare()
indexer.add(Exact()) | [
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J535D165/recordlinkage | recordlinkage/api.py | Compare.string | def string(self, *args, **kwargs):
"""Compare attributes of pairs with string algorithm.
Shortcut of :class:`recordlinkage.compare.String`::
from recordlinkage.compare import String
indexer = recordlinkage.Compare()
indexer.add(String())
"""
compare = String(*args, **kwargs)
self.add(compare)
return self | python | def string(self, *args, **kwargs):
"""Compare attributes of pairs with string algorithm.
Shortcut of :class:`recordlinkage.compare.String`::
from recordlinkage.compare import String
indexer = recordlinkage.Compare()
indexer.add(String())
"""
compare = String(*args, **kwargs)
self.add(compare)
return self | [
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Shortcut of :class:`recordlinkage.compare.String`::
from recordlinkage.compare import String
indexer = recordlinkage.Compare()
indexer.add(String()) | [
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J535D165/recordlinkage | recordlinkage/api.py | Compare.numeric | def numeric(self, *args, **kwargs):
"""Compare attributes of pairs with numeric algorithm.
Shortcut of :class:`recordlinkage.compare.Numeric`::
from recordlinkage.compare import Numeric
indexer = recordlinkage.Compare()
indexer.add(Numeric())
"""
compare = Numeric(*args, **kwargs)
self.add(compare)
return self | python | def numeric(self, *args, **kwargs):
"""Compare attributes of pairs with numeric algorithm.
Shortcut of :class:`recordlinkage.compare.Numeric`::
from recordlinkage.compare import Numeric
indexer = recordlinkage.Compare()
indexer.add(Numeric())
"""
compare = Numeric(*args, **kwargs)
self.add(compare)
return self | [
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Shortcut of :class:`recordlinkage.compare.Numeric`::
from recordlinkage.compare import Numeric
indexer = recordlinkage.Compare()
indexer.add(Numeric()) | [
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J535D165/recordlinkage | recordlinkage/api.py | Compare.geo | def geo(self, *args, **kwargs):
"""Compare attributes of pairs with geo algorithm.
Shortcut of :class:`recordlinkage.compare.Geographic`::
from recordlinkage.compare import Geographic
indexer = recordlinkage.Compare()
indexer.add(Geographic())
"""
compare = Geographic(*args, **kwargs)
self.add(compare)
return self | python | def geo(self, *args, **kwargs):
"""Compare attributes of pairs with geo algorithm.
Shortcut of :class:`recordlinkage.compare.Geographic`::
from recordlinkage.compare import Geographic
indexer = recordlinkage.Compare()
indexer.add(Geographic())
"""
compare = Geographic(*args, **kwargs)
self.add(compare)
return self | [
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Shortcut of :class:`recordlinkage.compare.Geographic`::
from recordlinkage.compare import Geographic
indexer = recordlinkage.Compare()
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J535D165/recordlinkage | recordlinkage/api.py | Compare.date | def date(self, *args, **kwargs):
"""Compare attributes of pairs with date algorithm.
Shortcut of :class:`recordlinkage.compare.Date`::
from recordlinkage.compare import Date
indexer = recordlinkage.Compare()
indexer.add(Date())
"""
compare = Date(*args, **kwargs)
self.add(compare)
return self | python | def date(self, *args, **kwargs):
"""Compare attributes of pairs with date algorithm.
Shortcut of :class:`recordlinkage.compare.Date`::
from recordlinkage.compare import Date
indexer = recordlinkage.Compare()
indexer.add(Date())
"""
compare = Date(*args, **kwargs)
self.add(compare)
return self | [
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Shortcut of :class:`recordlinkage.compare.Date`::
from recordlinkage.compare import Date
indexer = recordlinkage.Compare()
indexer.add(Date()) | [
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J535D165/recordlinkage | recordlinkage/measures.py | reduction_ratio | def reduction_ratio(links_pred, *total):
"""Compute the reduction ratio.
The reduction ratio is 1 minus the ratio candidate matches and the maximum
number of pairs possible.
Parameters
----------
links_pred: int, pandas.MultiIndex
The number of candidate record pairs or the pandas.MultiIndex with
record pairs.
*total: pandas.DataFrame object(s)
The DataFrames are used to compute the full index size with the
full_index_size function.
Returns
-------
float
The reduction ratio.
"""
n_max = full_index_size(*total)
if isinstance(links_pred, pandas.MultiIndex):
links_pred = len(links_pred)
if links_pred > n_max:
raise ValueError("n has to be smaller of equal n_max")
return 1 - links_pred / n_max | python | def reduction_ratio(links_pred, *total):
"""Compute the reduction ratio.
The reduction ratio is 1 minus the ratio candidate matches and the maximum
number of pairs possible.
Parameters
----------
links_pred: int, pandas.MultiIndex
The number of candidate record pairs or the pandas.MultiIndex with
record pairs.
*total: pandas.DataFrame object(s)
The DataFrames are used to compute the full index size with the
full_index_size function.
Returns
-------
float
The reduction ratio.
"""
n_max = full_index_size(*total)
if isinstance(links_pred, pandas.MultiIndex):
links_pred = len(links_pred)
if links_pred > n_max:
raise ValueError("n has to be smaller of equal n_max")
return 1 - links_pred / n_max | [
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The number of candidate record pairs or the pandas.MultiIndex with
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J535D165/recordlinkage | recordlinkage/measures.py | full_index_size | def full_index_size(*args):
"""Compute the number of records in a full index.
Compute the number of records in a full index without building the index
itself. The result is the maximum number of record pairs possible. This
function is especially useful in measures like the `reduction_ratio`.
Deduplication: Given a DataFrame A with length N, the full index size is
N*(N-1)/2. Linking: Given a DataFrame A with length N and a DataFrame B
with length M, the full index size is N*M.
Parameters
----------
*args: int, pandas.MultiIndex, pandas.Series, pandas.DataFrame
A pandas object or a int representing the length of a dataset to link.
When there is one argument, it is assumed that the record linkage is
a deduplication process.
Examples
--------
Use integers:
>>> full_index_size(10) # deduplication: 45 pairs
>>> full_index_size(10, 10) # linking: 100 pairs
or pandas objects
>>> full_index_size(DF) # deduplication: len(DF)*(len(DF)-1)/2 pairs
>>> full_index_size(DF, DF) # linking: len(DF)*len(DF) pairs
"""
# check if a list or tuple is passed as argument
if len(args) == 1 and isinstance(args[0], (list, tuple)):
args = tuple(args[0])
if len(args) == 1:
n = get_length(args[0])
size = int(n * (n - 1) / 2)
else:
size = numpy.prod([get_length(arg) for arg in args])
return size | python | def full_index_size(*args):
"""Compute the number of records in a full index.
Compute the number of records in a full index without building the index
itself. The result is the maximum number of record pairs possible. This
function is especially useful in measures like the `reduction_ratio`.
Deduplication: Given a DataFrame A with length N, the full index size is
N*(N-1)/2. Linking: Given a DataFrame A with length N and a DataFrame B
with length M, the full index size is N*M.
Parameters
----------
*args: int, pandas.MultiIndex, pandas.Series, pandas.DataFrame
A pandas object or a int representing the length of a dataset to link.
When there is one argument, it is assumed that the record linkage is
a deduplication process.
Examples
--------
Use integers:
>>> full_index_size(10) # deduplication: 45 pairs
>>> full_index_size(10, 10) # linking: 100 pairs
or pandas objects
>>> full_index_size(DF) # deduplication: len(DF)*(len(DF)-1)/2 pairs
>>> full_index_size(DF, DF) # linking: len(DF)*len(DF) pairs
"""
# check if a list or tuple is passed as argument
if len(args) == 1 and isinstance(args[0], (list, tuple)):
args = tuple(args[0])
if len(args) == 1:
n = get_length(args[0])
size = int(n * (n - 1) / 2)
else:
size = numpy.prod([get_length(arg) for arg in args])
return size | [
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Deduplication: Given a DataFrame A with length N, the full index size is
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Parameters
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A pandas object or a int representing the length of a dataset to link.
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Examples
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>>> full_index_size(10) # deduplication: 45 pairs
>>> full_index_size(10, 10) # linking: 100 pairs
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>>> full_index_size(DF) # deduplication: len(DF)*(len(DF)-1)/2 pairs
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J535D165/recordlinkage | recordlinkage/measures.py | true_positives | def true_positives(links_true, links_pred):
"""Count the number of True Positives.
Returns the number of correctly predicted links, also called the number of
True Positives (TP).
Parameters
----------
links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
Returns
-------
int
The number of correctly predicted links.
"""
links_true = _get_multiindex(links_true)
links_pred = _get_multiindex(links_pred)
return len(links_true & links_pred) | python | def true_positives(links_true, links_pred):
"""Count the number of True Positives.
Returns the number of correctly predicted links, also called the number of
True Positives (TP).
Parameters
----------
links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
Returns
-------
int
The number of correctly predicted links.
"""
links_true = _get_multiindex(links_true)
links_pred = _get_multiindex(links_pred)
return len(links_true & links_pred) | [
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links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
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Returns
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J535D165/recordlinkage | recordlinkage/measures.py | true_negatives | def true_negatives(links_true, links_pred, total):
"""Count the number of True Negatives.
Returns the number of correctly predicted non-links, also called the
number of True Negatives (TN).
Parameters
----------
links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
total: int, pandas.MultiIndex
The count of all record pairs (both links and non-links). When the
argument is a pandas.MultiIndex, the length of the index is used.
Returns
-------
int
The number of correctly predicted non-links.
"""
links_true = _get_multiindex(links_true)
links_pred = _get_multiindex(links_pred)
if isinstance(total, pandas.MultiIndex):
total = len(total)
return int(total) - len(links_true | links_pred) | python | def true_negatives(links_true, links_pred, total):
"""Count the number of True Negatives.
Returns the number of correctly predicted non-links, also called the
number of True Negatives (TN).
Parameters
----------
links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
total: int, pandas.MultiIndex
The count of all record pairs (both links and non-links). When the
argument is a pandas.MultiIndex, the length of the index is used.
Returns
-------
int
The number of correctly predicted non-links.
"""
links_true = _get_multiindex(links_true)
links_pred = _get_multiindex(links_pred)
if isinstance(total, pandas.MultiIndex):
total = len(total)
return int(total) - len(links_true | links_pred) | [
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J535D165/recordlinkage | recordlinkage/measures.py | false_positives | def false_positives(links_true, links_pred):
"""Count the number of False Positives.
Returns the number of incorrect predictions of true non-links. (true non-
links, but predicted as links). This value is known as the number of False
Positives (FP).
Parameters
----------
links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
Returns
-------
int
The number of false positives.
"""
links_true = _get_multiindex(links_true)
links_pred = _get_multiindex(links_pred)
return len(links_pred.difference(links_true)) | python | def false_positives(links_true, links_pred):
"""Count the number of False Positives.
Returns the number of incorrect predictions of true non-links. (true non-
links, but predicted as links). This value is known as the number of False
Positives (FP).
Parameters
----------
links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
Returns
-------
int
The number of false positives.
"""
links_true = _get_multiindex(links_true)
links_pred = _get_multiindex(links_pred)
return len(links_pred.difference(links_true)) | [
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The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
Returns
-------
int
The number of false positives. | [
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J535D165/recordlinkage | recordlinkage/measures.py | false_negatives | def false_negatives(links_true, links_pred):
"""Count the number of False Negatives.
Returns the number of incorrect predictions of true links. (true links,
but predicted as non-links). This value is known as the number of False
Negatives (FN).
Parameters
----------
links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
Returns
-------
int
The number of false negatives.
"""
links_true = _get_multiindex(links_true)
links_pred = _get_multiindex(links_pred)
return len(links_true.difference(links_pred)) | python | def false_negatives(links_true, links_pred):
"""Count the number of False Negatives.
Returns the number of incorrect predictions of true links. (true links,
but predicted as non-links). This value is known as the number of False
Negatives (FN).
Parameters
----------
links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
Returns
-------
int
The number of false negatives.
"""
links_true = _get_multiindex(links_true)
links_pred = _get_multiindex(links_pred)
return len(links_true.difference(links_pred)) | [
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J535D165/recordlinkage | recordlinkage/measures.py | confusion_matrix | def confusion_matrix(links_true, links_pred, total=None):
"""Compute the confusion matrix.
The confusion matrix is of the following form:
+----------------------+-----------------------+----------------------+
| | Predicted Positives | Predicted Negatives |
+======================+=======================+======================+
| **True Positives** | True Positives (TP) | False Negatives (FN) |
+----------------------+-----------------------+----------------------+
| **True Negatives** | False Positives (FP) | True Negatives (TN) |
+----------------------+-----------------------+----------------------+
The confusion matrix is an informative way to analyse a prediction. The
matrix can used to compute measures like precision and recall. The count
of true prositives is [0,0], false negatives is [0,1], true negatives
is [1,1] and false positives is [1,0].
Parameters
----------
links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
total: int, pandas.MultiIndex
The count of all record pairs (both links and non-links). When the
argument is a pandas.MultiIndex, the length of the index is used. If
the total is None, the number of True Negatives is not computed.
Default None.
Returns
-------
numpy.array
The confusion matrix with TP, TN, FN, FP values.
Note
----
The number of True Negatives is computed based on the total argument.
This argument is the number of record pairs of the entire matrix.
"""
links_true = _get_multiindex(links_true)
links_pred = _get_multiindex(links_pred)
tp = true_positives(links_true, links_pred)
fp = false_positives(links_true, links_pred)
fn = false_negatives(links_true, links_pred)
if total is None:
tn = numpy.nan
else:
tn = true_negatives(links_true, links_pred, total)
return numpy.array([[tp, fn], [fp, tn]]) | python | def confusion_matrix(links_true, links_pred, total=None):
"""Compute the confusion matrix.
The confusion matrix is of the following form:
+----------------------+-----------------------+----------------------+
| | Predicted Positives | Predicted Negatives |
+======================+=======================+======================+
| **True Positives** | True Positives (TP) | False Negatives (FN) |
+----------------------+-----------------------+----------------------+
| **True Negatives** | False Positives (FP) | True Negatives (TN) |
+----------------------+-----------------------+----------------------+
The confusion matrix is an informative way to analyse a prediction. The
matrix can used to compute measures like precision and recall. The count
of true prositives is [0,0], false negatives is [0,1], true negatives
is [1,1] and false positives is [1,0].
Parameters
----------
links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The true (or actual) links.
links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
total: int, pandas.MultiIndex
The count of all record pairs (both links and non-links). When the
argument is a pandas.MultiIndex, the length of the index is used. If
the total is None, the number of True Negatives is not computed.
Default None.
Returns
-------
numpy.array
The confusion matrix with TP, TN, FN, FP values.
Note
----
The number of True Negatives is computed based on the total argument.
This argument is the number of record pairs of the entire matrix.
"""
links_true = _get_multiindex(links_true)
links_pred = _get_multiindex(links_pred)
tp = true_positives(links_true, links_pred)
fp = false_positives(links_true, links_pred)
fn = false_negatives(links_true, links_pred)
if total is None:
tn = numpy.nan
else:
tn = true_negatives(links_true, links_pred, total)
return numpy.array([[tp, fn], [fp, tn]]) | [
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| **True Positives** | True Positives (TP) | False Negatives (FN) |
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| **True Negatives** | False Positives (FP) | True Negatives (TN) |
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links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
The predicted links.
total: int, pandas.MultiIndex
The count of all record pairs (both links and non-links). When the
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J535D165/recordlinkage | recordlinkage/contrib/compare/random/random.py | RandomContinuous.compute | def compute(self, pairs, x=None, x_link=None):
"""Return continuous random values for each record pair.
Parameters
----------
pairs : pandas.MultiIndex
A pandas MultiIndex with the record pairs to compare. The indices
in the MultiIndex are indices of the DataFrame(s) to link.
x : pandas.DataFrame
The DataFrame to link. If `x_link` is given, the comparing is a
linking problem. If `x_link` is not given, the problem is one of
deduplication.
x_link : pandas.DataFrame, optional
The second DataFrame.
Returns
-------
pandas.Series, pandas.DataFrame, numpy.ndarray
The result of comparing record pairs (the features). Can be
a tuple with multiple pandas.Series, pandas.DataFrame,
numpy.ndarray objects.
"""
df_empty = pd.DataFrame(index=pairs)
return self._compute(
tuple([df_empty]),
tuple([df_empty])
) | python | def compute(self, pairs, x=None, x_link=None):
"""Return continuous random values for each record pair.
Parameters
----------
pairs : pandas.MultiIndex
A pandas MultiIndex with the record pairs to compare. The indices
in the MultiIndex are indices of the DataFrame(s) to link.
x : pandas.DataFrame
The DataFrame to link. If `x_link` is given, the comparing is a
linking problem. If `x_link` is not given, the problem is one of
deduplication.
x_link : pandas.DataFrame, optional
The second DataFrame.
Returns
-------
pandas.Series, pandas.DataFrame, numpy.ndarray
The result of comparing record pairs (the features). Can be
a tuple with multiple pandas.Series, pandas.DataFrame,
numpy.ndarray objects.
"""
df_empty = pd.DataFrame(index=pairs)
return self._compute(
tuple([df_empty]),
tuple([df_empty])
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J535D165/recordlinkage | recordlinkage/base.py | _parallel_compare_helper | def _parallel_compare_helper(class_obj, pairs, x, x_link=None):
"""Internal function to overcome pickling problem in python2."""
return class_obj._compute(pairs, x, x_link) | python | def _parallel_compare_helper(class_obj, pairs, x, x_link=None):
"""Internal function to overcome pickling problem in python2."""
return class_obj._compute(pairs, x, x_link) | [
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J535D165/recordlinkage | recordlinkage/base.py | chunk_pandas | def chunk_pandas(frame_or_series, chunksize=None):
"""Chunk a frame into smaller, equal parts."""
if not isinstance(chunksize, int):
raise ValueError('argument chunksize needs to be integer type')
bins = np.arange(0, len(frame_or_series), step=chunksize)
for b in bins:
yield frame_or_series[b:b + chunksize] | python | def chunk_pandas(frame_or_series, chunksize=None):
"""Chunk a frame into smaller, equal parts."""
if not isinstance(chunksize, int):
raise ValueError('argument chunksize needs to be integer type')
bins = np.arange(0, len(frame_or_series), step=chunksize)
for b in bins:
yield frame_or_series[b:b + chunksize] | [
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J535D165/recordlinkage | recordlinkage/base.py | BaseIndex.add | def add(self, model):
"""Add a index method.
This method is used to add index algorithms. If multiple algorithms
are added, the union of the record pairs from the algorithm is taken.
Parameters
----------
model : list, class
A (list of) index algorithm(s) from
:mod:`recordlinkage.index`.
"""
if isinstance(model, list):
self.algorithms = self.algorithms + model
else:
self.algorithms.append(model) | python | def add(self, model):
"""Add a index method.
This method is used to add index algorithms. If multiple algorithms
are added, the union of the record pairs from the algorithm is taken.
Parameters
----------
model : list, class
A (list of) index algorithm(s) from
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"""
if isinstance(model, list):
self.algorithms = self.algorithms + model
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J535D165/recordlinkage | recordlinkage/base.py | BaseIndexAlgorithm._dedup_index | def _dedup_index(self, df_a):
"""Build an index for deduplicating a dataset.
Parameters
----------
df_a : (tuple of) pandas.Series
The data of the DataFrame to build the index with.
Returns
-------
pandas.MultiIndex
A pandas.MultiIndex with record pairs. Each record pair
contains the index values of two records. The records are
sampled from the lower triangular part of the matrix.
"""
pairs = self._link_index(df_a, df_a)
# Remove all pairs not in the lower triangular part of the matrix.
# This part can be inproved by not comparing the level values, but the
# level itself.
pairs = pairs[pairs.labels[0] > pairs.labels[1]]
return pairs | python | def _dedup_index(self, df_a):
"""Build an index for deduplicating a dataset.
Parameters
----------
df_a : (tuple of) pandas.Series
The data of the DataFrame to build the index with.
Returns
-------
pandas.MultiIndex
A pandas.MultiIndex with record pairs. Each record pair
contains the index values of two records. The records are
sampled from the lower triangular part of the matrix.
"""
pairs = self._link_index(df_a, df_a)
# Remove all pairs not in the lower triangular part of the matrix.
# This part can be inproved by not comparing the level values, but the
# level itself.
pairs = pairs[pairs.labels[0] > pairs.labels[1]]
return pairs | [
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J535D165/recordlinkage | recordlinkage/base.py | BaseCompareFeature._compute | def _compute(self, left_on, right_on):
"""Compare the data on the left and right.
:meth:`BaseCompareFeature._compute` and
:meth:`BaseCompareFeature.compute` differ on the accepted
arguments. `_compute` accepts indexed data while `compute`
accepts the record pairs and the DataFrame's.
Parameters
----------
left_on : (tuple of) pandas.Series
Data to compare with `right_on`
right_on : (tuple of) pandas.Series
Data to compare with `left_on`
Returns
-------
pandas.Series, pandas.DataFrame, numpy.ndarray
The result of comparing record pairs (the features). Can be
a tuple with multiple pandas.Series, pandas.DataFrame,
numpy.ndarray objects.
"""
result = self._compute_vectorized(*tuple(left_on + right_on))
return result | python | def _compute(self, left_on, right_on):
"""Compare the data on the left and right.
:meth:`BaseCompareFeature._compute` and
:meth:`BaseCompareFeature.compute` differ on the accepted
arguments. `_compute` accepts indexed data while `compute`
accepts the record pairs and the DataFrame's.
Parameters
----------
left_on : (tuple of) pandas.Series
Data to compare with `right_on`
right_on : (tuple of) pandas.Series
Data to compare with `left_on`
Returns
-------
pandas.Series, pandas.DataFrame, numpy.ndarray
The result of comparing record pairs (the features). Can be
a tuple with multiple pandas.Series, pandas.DataFrame,
numpy.ndarray objects.
"""
result = self._compute_vectorized(*tuple(left_on + right_on))
return result | [
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Data to compare with `left_on`
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J535D165/recordlinkage | recordlinkage/base.py | BaseCompare.compare_vectorized | def compare_vectorized(self, comp_func, labels_left, labels_right,
*args, **kwargs):
"""Compute the similarity between values with a callable.
This method initialises the comparing of values with a custom
function/callable. The function/callable should accept
numpy.ndarray's.
Example
-------
>>> comp = recordlinkage.Compare()
>>> comp.compare_vectorized(custom_callable, 'first_name', 'name')
>>> comp.compare(PAIRS, DATAFRAME1, DATAFRAME2)
Parameters
----------
comp_func : function
A comparison function. This function can be a built-in function
or a user defined comparison function. The function should accept
numpy.ndarray's as first two arguments.
labels_left : label, pandas.Series, pandas.DataFrame
The labels, Series or DataFrame to compare.
labels_right : label, pandas.Series, pandas.DataFrame
The labels, Series or DataFrame to compare.
*args :
Additional arguments to pass to callable comp_func.
**kwargs :
Additional keyword arguments to pass to callable comp_func.
(keyword 'label' is reserved.)
label : (list of) label(s)
The name of the feature and the name of the column. IMPORTANT:
This argument is a keyword argument and can not be part of the
arguments of comp_func.
"""
label = kwargs.pop('label', None)
if isinstance(labels_left, tuple):
labels_left = list(labels_left)
if isinstance(labels_right, tuple):
labels_right = list(labels_right)
feature = BaseCompareFeature(
labels_left, labels_right, args, kwargs, label=label)
feature._f_compare_vectorized = comp_func
self.add(feature) | python | def compare_vectorized(self, comp_func, labels_left, labels_right,
*args, **kwargs):
"""Compute the similarity between values with a callable.
This method initialises the comparing of values with a custom
function/callable. The function/callable should accept
numpy.ndarray's.
Example
-------
>>> comp = recordlinkage.Compare()
>>> comp.compare_vectorized(custom_callable, 'first_name', 'name')
>>> comp.compare(PAIRS, DATAFRAME1, DATAFRAME2)
Parameters
----------
comp_func : function
A comparison function. This function can be a built-in function
or a user defined comparison function. The function should accept
numpy.ndarray's as first two arguments.
labels_left : label, pandas.Series, pandas.DataFrame
The labels, Series or DataFrame to compare.
labels_right : label, pandas.Series, pandas.DataFrame
The labels, Series or DataFrame to compare.
*args :
Additional arguments to pass to callable comp_func.
**kwargs :
Additional keyword arguments to pass to callable comp_func.
(keyword 'label' is reserved.)
label : (list of) label(s)
The name of the feature and the name of the column. IMPORTANT:
This argument is a keyword argument and can not be part of the
arguments of comp_func.
"""
label = kwargs.pop('label', None)
if isinstance(labels_left, tuple):
labels_left = list(labels_left)
if isinstance(labels_right, tuple):
labels_right = list(labels_right)
feature = BaseCompareFeature(
labels_left, labels_right, args, kwargs, label=label)
feature._f_compare_vectorized = comp_func
self.add(feature) | [
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This method initialises the comparing of values with a custom
function/callable. The function/callable should accept
numpy.ndarray's.
Example
-------
>>> comp = recordlinkage.Compare()
>>> comp.compare_vectorized(custom_callable, 'first_name', 'name')
>>> comp.compare(PAIRS, DATAFRAME1, DATAFRAME2)
Parameters
----------
comp_func : function
A comparison function. This function can be a built-in function
or a user defined comparison function. The function should accept
numpy.ndarray's as first two arguments.
labels_left : label, pandas.Series, pandas.DataFrame
The labels, Series or DataFrame to compare.
labels_right : label, pandas.Series, pandas.DataFrame
The labels, Series or DataFrame to compare.
*args :
Additional arguments to pass to callable comp_func.
**kwargs :
Additional keyword arguments to pass to callable comp_func.
(keyword 'label' is reserved.)
label : (list of) label(s)
The name of the feature and the name of the column. IMPORTANT:
This argument is a keyword argument and can not be part of the
arguments of comp_func. | [
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J535D165/recordlinkage | recordlinkage/base.py | BaseCompare._get_labels_left | def _get_labels_left(self, validate=None):
"""Get all labels of the left dataframe."""
labels = []
for compare_func in self.features:
labels = labels + listify(compare_func.labels_left)
# check requested labels (for better error messages)
if not is_label_dataframe(labels, validate):
error_msg = "label is not found in the dataframe"
raise KeyError(error_msg)
return unique(labels) | python | def _get_labels_left(self, validate=None):
"""Get all labels of the left dataframe."""
labels = []
for compare_func in self.features:
labels = labels + listify(compare_func.labels_left)
# check requested labels (for better error messages)
if not is_label_dataframe(labels, validate):
error_msg = "label is not found in the dataframe"
raise KeyError(error_msg)
return unique(labels) | [
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J535D165/recordlinkage | recordlinkage/base.py | BaseCompare._get_labels_right | def _get_labels_right(self, validate=None):
"""Get all labels of the right dataframe."""
labels = []
for compare_func in self.features:
labels = labels + listify(compare_func.labels_right)
# check requested labels (for better error messages)
if not is_label_dataframe(labels, validate):
error_msg = "label is not found in the dataframe"
raise KeyError(error_msg)
return unique(labels) | python | def _get_labels_right(self, validate=None):
"""Get all labels of the right dataframe."""
labels = []
for compare_func in self.features:
labels = labels + listify(compare_func.labels_right)
# check requested labels (for better error messages)
if not is_label_dataframe(labels, validate):
error_msg = "label is not found in the dataframe"
raise KeyError(error_msg)
return unique(labels) | [
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J535D165/recordlinkage | recordlinkage/base.py | BaseCompare._union | def _union(self, objs, index=None, column_i=0):
"""Make a union of the features.
The term 'union' is based on the terminology of scikit-learn.
"""
feat_conc = []
for feat, label in objs:
# result is tuple of results
if isinstance(feat, tuple):
if label is None:
label = [None] * len(feat)
partial_result = self._union(
zip(feat, label), column_i=column_i)
feat_conc.append(partial_result)
column_i = column_i + partial_result.shape[1]
# result is pandas.Series.
elif isinstance(feat, pandas.Series):
feat.reset_index(drop=True, inplace=True)
if label is None:
label = column_i
feat.rename(label, inplace=True)
feat_conc.append(feat)
column_i = column_i + 1
# result is pandas.DataFrame
elif isinstance(feat, pandas.DataFrame):
feat.reset_index(drop=True, inplace=True)
if label is None:
label = np.arange(column_i, column_i + feat.shape[1])
feat.columns = label
feat_conc.append(feat)
column_i = column_i + feat.shape[1]
# result is numpy 1d array
elif is_numpy_like(feat) and len(feat.shape) == 1:
if label is None:
label = column_i
f = pandas.Series(feat, name=label, copy=False)
feat_conc.append(f)
column_i = column_i + 1
# result is numpy 2d array
elif is_numpy_like(feat) and len(feat.shape) == 2:
if label is None:
label = np.arange(column_i, column_i + feat.shape[1])
feat_df = pandas.DataFrame(feat, columns=label, copy=False)
if label is None:
feat_df.columns = [None for _ in range(feat_df.shape[1])]
feat_conc.append(feat_df)
column_i = column_i + feat.shape[1]
# other results are not (yet) supported
else:
raise ValueError("expected numpy.ndarray or "
"pandas object to be returned, "
"got '{}'".format(feat.__class__.__name__))
result = pandas.concat(feat_conc, axis=1, copy=False)
if index is not None:
result.set_index(index, inplace=True)
return result | python | def _union(self, objs, index=None, column_i=0):
"""Make a union of the features.
The term 'union' is based on the terminology of scikit-learn.
"""
feat_conc = []
for feat, label in objs:
# result is tuple of results
if isinstance(feat, tuple):
if label is None:
label = [None] * len(feat)
partial_result = self._union(
zip(feat, label), column_i=column_i)
feat_conc.append(partial_result)
column_i = column_i + partial_result.shape[1]
# result is pandas.Series.
elif isinstance(feat, pandas.Series):
feat.reset_index(drop=True, inplace=True)
if label is None:
label = column_i
feat.rename(label, inplace=True)
feat_conc.append(feat)
column_i = column_i + 1
# result is pandas.DataFrame
elif isinstance(feat, pandas.DataFrame):
feat.reset_index(drop=True, inplace=True)
if label is None:
label = np.arange(column_i, column_i + feat.shape[1])
feat.columns = label
feat_conc.append(feat)
column_i = column_i + feat.shape[1]
# result is numpy 1d array
elif is_numpy_like(feat) and len(feat.shape) == 1:
if label is None:
label = column_i
f = pandas.Series(feat, name=label, copy=False)
feat_conc.append(f)
column_i = column_i + 1
# result is numpy 2d array
elif is_numpy_like(feat) and len(feat.shape) == 2:
if label is None:
label = np.arange(column_i, column_i + feat.shape[1])
feat_df = pandas.DataFrame(feat, columns=label, copy=False)
if label is None:
feat_df.columns = [None for _ in range(feat_df.shape[1])]
feat_conc.append(feat_df)
column_i = column_i + feat.shape[1]
# other results are not (yet) supported
else:
raise ValueError("expected numpy.ndarray or "
"pandas object to be returned, "
"got '{}'".format(feat.__class__.__name__))
result = pandas.concat(feat_conc, axis=1, copy=False)
if index is not None:
result.set_index(index, inplace=True)
return result | [
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J535D165/recordlinkage | recordlinkage/base.py | BaseClassifier.predict | def predict(self, comparison_vectors):
"""Predict the class of the record pairs.
Classify a set of record pairs based on their comparison vectors into
matches, non-matches and possible matches. The classifier has to be
trained to call this method.
Parameters
----------
comparison_vectors : pandas.DataFrame
Dataframe with comparison vectors.
return_type : str
Deprecated. Use recordlinkage.options instead. Use the option
`recordlinkage.set_option('classification.return_type', 'index')`
instead.
Returns
-------
pandas.Series
A pandas Series with the labels 1 (for the matches) and 0 (for the
non-matches).
"""
logging.info("Classification - predict matches and non-matches")
# make the predicition
prediction = self._predict(comparison_vectors.values)
self._post_predict(prediction)
# format and return the result
return self._return_result(prediction, comparison_vectors) | python | def predict(self, comparison_vectors):
"""Predict the class of the record pairs.
Classify a set of record pairs based on their comparison vectors into
matches, non-matches and possible matches. The classifier has to be
trained to call this method.
Parameters
----------
comparison_vectors : pandas.DataFrame
Dataframe with comparison vectors.
return_type : str
Deprecated. Use recordlinkage.options instead. Use the option
`recordlinkage.set_option('classification.return_type', 'index')`
instead.
Returns
-------
pandas.Series
A pandas Series with the labels 1 (for the matches) and 0 (for the
non-matches).
"""
logging.info("Classification - predict matches and non-matches")
# make the predicition
prediction = self._predict(comparison_vectors.values)
self._post_predict(prediction)
# format and return the result
return self._return_result(prediction, comparison_vectors) | [
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Classify a set of record pairs based on their comparison vectors into
matches, non-matches and possible matches. The classifier has to be
trained to call this method.
Parameters
----------
comparison_vectors : pandas.DataFrame
Dataframe with comparison vectors.
return_type : str
Deprecated. Use recordlinkage.options instead. Use the option
`recordlinkage.set_option('classification.return_type', 'index')`
instead.
Returns
-------
pandas.Series
A pandas Series with the labels 1 (for the matches) and 0 (for the
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J535D165/recordlinkage | recordlinkage/base.py | BaseClassifier.prob | def prob(self, comparison_vectors, return_type=None):
"""Compute the probabilities for each record pair.
For each pair of records, estimate the probability of being a match.
Parameters
----------
comparison_vectors : pandas.DataFrame
The dataframe with comparison vectors.
return_type : str
Deprecated. (default 'series')
Returns
-------
pandas.Series or numpy.ndarray
The probability of being a match for each record pair.
"""
if return_type is not None:
warnings.warn("The argument 'return_type' is removed. "
"Default value is now 'series'.",
VisibleDeprecationWarning, stacklevel=2)
logging.info("Classification - compute probabilities")
prob_match = self._prob_match(comparison_vectors.values)
return pandas.Series(prob_match, index=comparison_vectors.index) | python | def prob(self, comparison_vectors, return_type=None):
"""Compute the probabilities for each record pair.
For each pair of records, estimate the probability of being a match.
Parameters
----------
comparison_vectors : pandas.DataFrame
The dataframe with comparison vectors.
return_type : str
Deprecated. (default 'series')
Returns
-------
pandas.Series or numpy.ndarray
The probability of being a match for each record pair.
"""
if return_type is not None:
warnings.warn("The argument 'return_type' is removed. "
"Default value is now 'series'.",
VisibleDeprecationWarning, stacklevel=2)
logging.info("Classification - compute probabilities")
prob_match = self._prob_match(comparison_vectors.values)
return pandas.Series(prob_match, index=comparison_vectors.index) | [
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comparison_vectors : pandas.DataFrame
The dataframe with comparison vectors.
return_type : str
Deprecated. (default 'series')
Returns
-------
pandas.Series or numpy.ndarray
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J535D165/recordlinkage | recordlinkage/base.py | BaseClassifier._return_result | def _return_result(self, result, comparison_vectors=None):
"""Return different formatted classification results.
"""
return_type = cf.get_option('classification.return_type')
if type(result) != np.ndarray:
raise ValueError("numpy.ndarray expected.")
# return the pandas.MultiIndex
if return_type == 'index':
return comparison_vectors.index[result.astype(bool)]
# return a pandas.Series
elif return_type == 'series':
return pandas.Series(
result,
index=comparison_vectors.index,
name='classification')
# return a numpy.ndarray
elif return_type == 'array':
return result
# return_type not known
else:
raise ValueError(
"return_type {} unknown. Choose 'index', 'series' or "
"'array'".format(return_type)) | python | def _return_result(self, result, comparison_vectors=None):
"""Return different formatted classification results.
"""
return_type = cf.get_option('classification.return_type')
if type(result) != np.ndarray:
raise ValueError("numpy.ndarray expected.")
# return the pandas.MultiIndex
if return_type == 'index':
return comparison_vectors.index[result.astype(bool)]
# return a pandas.Series
elif return_type == 'series':
return pandas.Series(
result,
index=comparison_vectors.index,
name='classification')
# return a numpy.ndarray
elif return_type == 'array':
return result
# return_type not known
else:
raise ValueError(
"return_type {} unknown. Choose 'index', 'series' or "
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J535D165/recordlinkage | recordlinkage/datasets/generate.py | binary_vectors | def binary_vectors(n, n_match, m=[0.9] * 8, u=[0.1] * 8,
random_state=None, return_links=False, dtype=np.int8):
"""Generate random binary comparison vectors.
This function is used to generate random comparison vectors. The
result of each comparison is a binary value (0 or 1).
Parameters
----------
n : int
The total number of comparison vectors.
n_match : int
The number of matching record pairs.
m : list, default [0.9] * 8, optional
A list of m probabilities of each partially identifying
variable. The m probability is the probability that an
identifier in matching record pairs agrees.
u : list, default [0.9] * 8, optional
A list of u probabilities of each partially identifying
variable. The u probability is the probability that an
identifier in non-matching record pairs agrees.
random_state : int or numpy.random.RandomState, optional
Seed for the random number generator with an integer or numpy
RandomState object.
return_links: bool
When True, the function returns also the true links.
dtype: numpy.dtype
The dtype of each column in the returned DataFrame.
Returns
-------
pandas.DataFrame
A dataframe with comparison vectors.
"""
if len(m) != len(u):
raise ValueError("the length of 'm' is not equal the length of 'u'")
if n_match >= n or n_match < 0:
raise ValueError("the number of matches is bounded by [0, n]")
# set the random seed
np.random.seed(random_state)
matches = []
nonmatches = []
sample_set = np.array([0, 1], dtype=dtype)
for i, _ in enumerate(m):
p_mi = [1 - m[i], m[i]]
p_ui = [1 - u[i], u[i]]
comp_mi = np.random.choice(sample_set, (n_match, 1), p=p_mi)
comp_ui = np.random.choice(sample_set, (n - n_match, 1), p=p_ui)
nonmatches.append(comp_ui)
matches.append(comp_mi)
match_block = np.concatenate(matches, axis=1)
nonmatch_block = np.concatenate(nonmatches, axis=1)
data_np = np.concatenate((match_block, nonmatch_block), axis=0)
index_np = np.random.randint(1001, 1001 + n * 2, (n, 2))
data_col_names = ['c_%s' % (i + 1) for i in range(len(m))]
data_mi = pd.MultiIndex.from_arrays([index_np[:, 0], index_np[:, 1]])
data_df = pd.DataFrame(data_np, index=data_mi, columns=data_col_names)
features = data_df.sample(frac=1, random_state=random_state)
if return_links:
links = data_mi[:n_match]
return features, links
else:
return features | python | def binary_vectors(n, n_match, m=[0.9] * 8, u=[0.1] * 8,
random_state=None, return_links=False, dtype=np.int8):
"""Generate random binary comparison vectors.
This function is used to generate random comparison vectors. The
result of each comparison is a binary value (0 or 1).
Parameters
----------
n : int
The total number of comparison vectors.
n_match : int
The number of matching record pairs.
m : list, default [0.9] * 8, optional
A list of m probabilities of each partially identifying
variable. The m probability is the probability that an
identifier in matching record pairs agrees.
u : list, default [0.9] * 8, optional
A list of u probabilities of each partially identifying
variable. The u probability is the probability that an
identifier in non-matching record pairs agrees.
random_state : int or numpy.random.RandomState, optional
Seed for the random number generator with an integer or numpy
RandomState object.
return_links: bool
When True, the function returns also the true links.
dtype: numpy.dtype
The dtype of each column in the returned DataFrame.
Returns
-------
pandas.DataFrame
A dataframe with comparison vectors.
"""
if len(m) != len(u):
raise ValueError("the length of 'm' is not equal the length of 'u'")
if n_match >= n or n_match < 0:
raise ValueError("the number of matches is bounded by [0, n]")
# set the random seed
np.random.seed(random_state)
matches = []
nonmatches = []
sample_set = np.array([0, 1], dtype=dtype)
for i, _ in enumerate(m):
p_mi = [1 - m[i], m[i]]
p_ui = [1 - u[i], u[i]]
comp_mi = np.random.choice(sample_set, (n_match, 1), p=p_mi)
comp_ui = np.random.choice(sample_set, (n - n_match, 1), p=p_ui)
nonmatches.append(comp_ui)
matches.append(comp_mi)
match_block = np.concatenate(matches, axis=1)
nonmatch_block = np.concatenate(nonmatches, axis=1)
data_np = np.concatenate((match_block, nonmatch_block), axis=0)
index_np = np.random.randint(1001, 1001 + n * 2, (n, 2))
data_col_names = ['c_%s' % (i + 1) for i in range(len(m))]
data_mi = pd.MultiIndex.from_arrays([index_np[:, 0], index_np[:, 1]])
data_df = pd.DataFrame(data_np, index=data_mi, columns=data_col_names)
features = data_df.sample(frac=1, random_state=random_state)
if return_links:
links = data_mi[:n_match]
return features, links
else:
return features | [
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The total number of comparison vectors.
n_match : int
The number of matching record pairs.
m : list, default [0.9] * 8, optional
A list of m probabilities of each partially identifying
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A list of u probabilities of each partially identifying
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Seed for the random number generator with an integer or numpy
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return_links: bool
When True, the function returns also the true links.
dtype: numpy.dtype
The dtype of each column in the returned DataFrame.
Returns
-------
pandas.DataFrame
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J535D165/recordlinkage | recordlinkage/classifiers.py | FellegiSunter._match_class_pos | def _match_class_pos(self):
"""Return the position of the match class."""
# TODO: add notfitted warnings
if self.kernel.classes_.shape[0] != 2:
raise ValueError("Number of classes is {}, expected 2.".format(
self.kernel.classes_.shape[0]))
# # get the position of match probabilities
# classes = list(self.kernel.classes_)
# return classes.index(1)
return 1 | python | def _match_class_pos(self):
"""Return the position of the match class."""
# TODO: add notfitted warnings
if self.kernel.classes_.shape[0] != 2:
raise ValueError("Number of classes is {}, expected 2.".format(
self.kernel.classes_.shape[0]))
# # get the position of match probabilities
# classes = list(self.kernel.classes_)
# return classes.index(1)
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J535D165/recordlinkage | recordlinkage/classifiers.py | FellegiSunter._nonmatch_class_pos | def _nonmatch_class_pos(self):
"""Return the position of the non-match class."""
# TODO: add notfitted warnings
if self.kernel.classes_.shape[0] != 2:
raise ValueError("Number of classes is {}, expected 2.".format(
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# # get the position of match probabilities
# classes = list(self.kernel.classes_)
# return classes.index(0)
return 0 | python | def _nonmatch_class_pos(self):
"""Return the position of the non-match class."""
# TODO: add notfitted warnings
if self.kernel.classes_.shape[0] != 2:
raise ValueError("Number of classes is {}, expected 2.".format(
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J535D165/recordlinkage | recordlinkage/classifiers.py | FellegiSunter.log_weights | def log_weights(self):
"""Log weights as described in the FS framework."""
m = self.kernel.feature_log_prob_[self._match_class_pos()]
u = self.kernel.feature_log_prob_[self._nonmatch_class_pos()]
return self._prob_inverse_transform(m - u) | python | def log_weights(self):
"""Log weights as described in the FS framework."""
m = self.kernel.feature_log_prob_[self._match_class_pos()]
u = self.kernel.feature_log_prob_[self._nonmatch_class_pos()]
return self._prob_inverse_transform(m - u) | [
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J535D165/recordlinkage | recordlinkage/classifiers.py | FellegiSunter.weights | def weights(self):
"""Weights as described in the FS framework."""
m = self.kernel.feature_log_prob_[self._match_class_pos()]
u = self.kernel.feature_log_prob_[self._nonmatch_class_pos()]
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m = self.kernel.feature_log_prob_[self._match_class_pos()]
u = self.kernel.feature_log_prob_[self._nonmatch_class_pos()]
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J535D165/recordlinkage | recordlinkage/classifiers.py | KMeansClassifier._initialise_classifier | def _initialise_classifier(self, comparison_vectors):
"""Set the centers of the clusters."""
# Set the start point of the classifier.
self.kernel.init = numpy.array(
[[0.05] * len(list(comparison_vectors)),
[0.95] * len(list(comparison_vectors))]) | python | def _initialise_classifier(self, comparison_vectors):
"""Set the centers of the clusters."""
# Set the start point of the classifier.
self.kernel.init = numpy.array(
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J535D165/recordlinkage | recordlinkage/utils.py | is_label_dataframe | def is_label_dataframe(label, df):
"""check column label existance"""
setdiff = set(label) - set(df.columns.tolist())
if len(setdiff) == 0:
return True
else:
return False | python | def is_label_dataframe(label, df):
"""check column label existance"""
setdiff = set(label) - set(df.columns.tolist())
if len(setdiff) == 0:
return True
else:
return False | [
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J535D165/recordlinkage | recordlinkage/utils.py | listify | def listify(x, none_value=[]):
"""Make a list of the argument if it is not a list."""
if isinstance(x, list):
return x
elif isinstance(x, tuple):
return list(x)
elif x is None:
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return [x] | python | def listify(x, none_value=[]):
"""Make a list of the argument if it is not a list."""
if isinstance(x, list):
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J535D165/recordlinkage | recordlinkage/utils.py | multi_index_to_frame | def multi_index_to_frame(index):
"""
Replicates MultiIndex.to_frame, which was introduced in pandas 0.21,
for the sake of backwards compatibility.
"""
return pandas.DataFrame(index.tolist(), index=index, columns=index.names) | python | def multi_index_to_frame(index):
"""
Replicates MultiIndex.to_frame, which was introduced in pandas 0.21,
for the sake of backwards compatibility.
"""
return pandas.DataFrame(index.tolist(), index=index, columns=index.names) | [
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J535D165/recordlinkage | recordlinkage/utils.py | index_split | def index_split(index, chunks):
"""Function to split pandas.Index and pandas.MultiIndex objects.
Split :class:`pandas.Index` and :class:`pandas.MultiIndex` objects
into chunks. This function is based on :func:`numpy.array_split`.
Parameters
----------
index : pandas.Index, pandas.MultiIndex
A pandas.Index or pandas.MultiIndex to split into chunks.
chunks : int
The number of parts to split the index into.
Returns
-------
list
A list with chunked pandas.Index or pandas.MultiIndex objects.
"""
Ntotal = index.shape[0]
Nsections = int(chunks)
if Nsections <= 0:
raise ValueError('number sections must be larger than 0.')
Neach_section, extras = divmod(Ntotal, Nsections)
section_sizes = ([0] + extras * [Neach_section + 1] +
(Nsections - extras) * [Neach_section])
div_points = numpy.array(section_sizes).cumsum()
sub_ind = []
for i in range(Nsections):
st = div_points[i]
end = div_points[i + 1]
sub_ind.append(index[st:end])
return sub_ind | python | def index_split(index, chunks):
"""Function to split pandas.Index and pandas.MultiIndex objects.
Split :class:`pandas.Index` and :class:`pandas.MultiIndex` objects
into chunks. This function is based on :func:`numpy.array_split`.
Parameters
----------
index : pandas.Index, pandas.MultiIndex
A pandas.Index or pandas.MultiIndex to split into chunks.
chunks : int
The number of parts to split the index into.
Returns
-------
list
A list with chunked pandas.Index or pandas.MultiIndex objects.
"""
Ntotal = index.shape[0]
Nsections = int(chunks)
if Nsections <= 0:
raise ValueError('number sections must be larger than 0.')
Neach_section, extras = divmod(Ntotal, Nsections)
section_sizes = ([0] + extras * [Neach_section + 1] +
(Nsections - extras) * [Neach_section])
div_points = numpy.array(section_sizes).cumsum()
sub_ind = []
for i in range(Nsections):
st = div_points[i]
end = div_points[i + 1]
sub_ind.append(index[st:end])
return sub_ind | [
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J535D165/recordlinkage | recordlinkage/utils.py | frame_indexing | def frame_indexing(frame, multi_index, level_i, indexing_type='label'):
"""Index dataframe based on one level of MultiIndex.
Arguments
---------
frame : pandas.DataFrame
The datafrme to select records from.
multi_index : pandas.MultiIndex
A pandas multiindex were one fo the levels is used to sample the
dataframe with.
level_i : int, str
The level of the multiIndex to index on.
indexing_type : str
The type of indexing. The value can be 'label' or 'position'.
Default 'label'.
"""
if indexing_type == "label":
data = frame.loc[multi_index.get_level_values(level_i)]
data.index = multi_index
elif indexing_type == "position":
data = frame.iloc[multi_index.get_level_values(level_i)]
data.index = multi_index
else:
raise ValueError("indexing_type needs to be 'label' or 'position'")
return data | python | def frame_indexing(frame, multi_index, level_i, indexing_type='label'):
"""Index dataframe based on one level of MultiIndex.
Arguments
---------
frame : pandas.DataFrame
The datafrme to select records from.
multi_index : pandas.MultiIndex
A pandas multiindex were one fo the levels is used to sample the
dataframe with.
level_i : int, str
The level of the multiIndex to index on.
indexing_type : str
The type of indexing. The value can be 'label' or 'position'.
Default 'label'.
"""
if indexing_type == "label":
data = frame.loc[multi_index.get_level_values(level_i)]
data.index = multi_index
elif indexing_type == "position":
data = frame.iloc[multi_index.get_level_values(level_i)]
data.index = multi_index
else:
raise ValueError("indexing_type needs to be 'label' or 'position'")
return data | [
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J535D165/recordlinkage | recordlinkage/utils.py | fillna | def fillna(series_or_arr, missing_value=0.0):
"""Fill missing values in pandas objects and numpy arrays.
Arguments
---------
series_or_arr : pandas.Series, numpy.ndarray
The numpy array or pandas series for which the missing values
need to be replaced.
missing_value : float, int, str
The value to replace the missing value with. Default 0.0.
Returns
-------
pandas.Series, numpy.ndarray
The numpy array or pandas series with the missing values
filled.
"""
if pandas.notnull(missing_value):
if isinstance(series_or_arr, (numpy.ndarray)):
series_or_arr[numpy.isnan(series_or_arr)] = missing_value
else:
series_or_arr.fillna(missing_value, inplace=True)
return series_or_arr | python | def fillna(series_or_arr, missing_value=0.0):
"""Fill missing values in pandas objects and numpy arrays.
Arguments
---------
series_or_arr : pandas.Series, numpy.ndarray
The numpy array or pandas series for which the missing values
need to be replaced.
missing_value : float, int, str
The value to replace the missing value with. Default 0.0.
Returns
-------
pandas.Series, numpy.ndarray
The numpy array or pandas series with the missing values
filled.
"""
if pandas.notnull(missing_value):
if isinstance(series_or_arr, (numpy.ndarray)):
series_or_arr[numpy.isnan(series_or_arr)] = missing_value
else:
series_or_arr.fillna(missing_value, inplace=True)
return series_or_arr | [
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chrisdev/django-pandas | django_pandas/utils.py | get_related_model | def get_related_model(field):
"""Gets the related model from a related field"""
model = None
if hasattr(field, 'related_model') and field.related_model: # pragma: no cover
model = field.related_model
# Django<1.8 doesn't have the related_model API, so we need to use rel,
# which was removed in Django 2.0
elif hasattr(field, 'rel') and field.rel: # pragma: no cover
model = field.rel.to
return model | python | def get_related_model(field):
"""Gets the related model from a related field"""
model = None
if hasattr(field, 'related_model') and field.related_model: # pragma: no cover
model = field.related_model
# Django<1.8 doesn't have the related_model API, so we need to use rel,
# which was removed in Django 2.0
elif hasattr(field, 'rel') and field.rel: # pragma: no cover
model = field.rel.to
return model | [
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chrisdev/django-pandas | django_pandas/managers.py | DataFrameQuerySet.to_timeseries | def to_timeseries(self, fieldnames=(), verbose=True,
index=None, storage='wide',
values=None, pivot_columns=None, freq=None,
coerce_float=True, rs_kwargs=None):
"""
A convenience method for creating a time series DataFrame i.e the
DataFrame index will be an instance of DateTime or PeriodIndex
Parameters
----------
fieldnames: The model field names(columns) to utilise in creating
the DataFrame. You can span a relationships in the usual
Django ORM way by using the foreign key field name
separated by double underscores and refer to a field
in a related model.
index: specify the field to use for the index. If the index
field is not in fieldnames it will be appended. This
is mandatory for timeseries.
storage: Specify if the queryset uses the
``wide`` format
date | col1| col2| col3|
-----------|------|-----|-----|
2001-01-01-| 100.5| 23.3| 2.2|
2001-02-01-| 106.3| 17.0| 4.6|
2001-03-01-| 111.7| 11.1| 0.7|
or the `long` format.
date |values| names|
-----------|------|------|
2001-01-01-| 100.5| col1|
2001-02-01-| 106.3| col1|
2001-03-01-| 111.7| col1|
2001-01-01-| 23.3| col2|
2001-02-01-| 17.0| col2|
2001-01-01-| 23.3| col2|
2001-02-01-| 2.2| col3|
2001-03-01-| 4.6| col3|
2001-03-01-| 0.7| col3|
pivot_columns: Required once the you specify `long` format
storage. This could either be a list or string
identifying the field name or combination of field.
If the pivot_column is a single column then the
unique values in this column become a new columns in
the DataFrame If the pivot column is a list the values
in these columns are concatenated (using the '-'
as a separator) and these values are used for the new
timeseries columns
values: Also required if you utilize the `long` storage the
values column name is use for populating new frame values
freq: The offset string or object representing a target conversion
rs_kwargs: A dictonary of keyword arguments based on the
``pandas.DataFrame.resample`` method
verbose: If this is ``True`` then populate the DataFrame with the
human readable versions of any foreign key fields else use
the primary keys values else use the actual values set
in the model.
coerce_float: Attempt to convert values to non-string, non-numeric
objects (like decimal.Decimal) to floating point.
"""
assert index is not None, 'You must supply an index field'
assert storage in ('wide', 'long'), 'storage must be wide or long'
if rs_kwargs is None:
rs_kwargs = {}
if storage == 'wide':
df = self.to_dataframe(fieldnames, verbose=verbose, index=index,
coerce_float=coerce_float, datetime_index=True)
else:
df = self.to_dataframe(fieldnames, verbose=verbose,
coerce_float=coerce_float, datetime_index=True)
assert values is not None, 'You must specify a values field'
assert pivot_columns is not None, 'You must specify pivot_columns'
if isinstance(pivot_columns, (tuple, list)):
df['combined_keys'] = ''
for c in pivot_columns:
df['combined_keys'] += df[c].str.upper() + '.'
df['combined_keys'] += values.lower()
df = df.pivot(index=index,
columns='combined_keys',
values=values)
else:
df = df.pivot(index=index,
columns=pivot_columns,
values=values)
if freq is not None:
df = df.resample(freq, **rs_kwargs)
return df | python | def to_timeseries(self, fieldnames=(), verbose=True,
index=None, storage='wide',
values=None, pivot_columns=None, freq=None,
coerce_float=True, rs_kwargs=None):
"""
A convenience method for creating a time series DataFrame i.e the
DataFrame index will be an instance of DateTime or PeriodIndex
Parameters
----------
fieldnames: The model field names(columns) to utilise in creating
the DataFrame. You can span a relationships in the usual
Django ORM way by using the foreign key field name
separated by double underscores and refer to a field
in a related model.
index: specify the field to use for the index. If the index
field is not in fieldnames it will be appended. This
is mandatory for timeseries.
storage: Specify if the queryset uses the
``wide`` format
date | col1| col2| col3|
-----------|------|-----|-----|
2001-01-01-| 100.5| 23.3| 2.2|
2001-02-01-| 106.3| 17.0| 4.6|
2001-03-01-| 111.7| 11.1| 0.7|
or the `long` format.
date |values| names|
-----------|------|------|
2001-01-01-| 100.5| col1|
2001-02-01-| 106.3| col1|
2001-03-01-| 111.7| col1|
2001-01-01-| 23.3| col2|
2001-02-01-| 17.0| col2|
2001-01-01-| 23.3| col2|
2001-02-01-| 2.2| col3|
2001-03-01-| 4.6| col3|
2001-03-01-| 0.7| col3|
pivot_columns: Required once the you specify `long` format
storage. This could either be a list or string
identifying the field name or combination of field.
If the pivot_column is a single column then the
unique values in this column become a new columns in
the DataFrame If the pivot column is a list the values
in these columns are concatenated (using the '-'
as a separator) and these values are used for the new
timeseries columns
values: Also required if you utilize the `long` storage the
values column name is use for populating new frame values
freq: The offset string or object representing a target conversion
rs_kwargs: A dictonary of keyword arguments based on the
``pandas.DataFrame.resample`` method
verbose: If this is ``True`` then populate the DataFrame with the
human readable versions of any foreign key fields else use
the primary keys values else use the actual values set
in the model.
coerce_float: Attempt to convert values to non-string, non-numeric
objects (like decimal.Decimal) to floating point.
"""
assert index is not None, 'You must supply an index field'
assert storage in ('wide', 'long'), 'storage must be wide or long'
if rs_kwargs is None:
rs_kwargs = {}
if storage == 'wide':
df = self.to_dataframe(fieldnames, verbose=verbose, index=index,
coerce_float=coerce_float, datetime_index=True)
else:
df = self.to_dataframe(fieldnames, verbose=verbose,
coerce_float=coerce_float, datetime_index=True)
assert values is not None, 'You must specify a values field'
assert pivot_columns is not None, 'You must specify pivot_columns'
if isinstance(pivot_columns, (tuple, list)):
df['combined_keys'] = ''
for c in pivot_columns:
df['combined_keys'] += df[c].str.upper() + '.'
df['combined_keys'] += values.lower()
df = df.pivot(index=index,
columns='combined_keys',
values=values)
else:
df = df.pivot(index=index,
columns=pivot_columns,
values=values)
if freq is not None:
df = df.resample(freq, **rs_kwargs)
return df | [
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-----------|------|-----|-----|
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2001-02-01-| 106.3| 17.0| 4.6|
2001-03-01-| 111.7| 11.1| 0.7|
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chrisdev/django-pandas | django_pandas/managers.py | DataFrameQuerySet.to_dataframe | def to_dataframe(self, fieldnames=(), verbose=True, index=None,
coerce_float=False, datetime_index=False):
"""
Returns a DataFrame from the queryset
Paramaters
-----------
fieldnames: The model field names(columns) to utilise in creating
the DataFrame. You can span a relationships in the usual
Django ORM way by using the foreign key field name
separated by double underscores and refer to a field
in a related model.
index: specify the field to use for the index. If the index
field is not in fieldnames it will be appended. This
is mandatory for timeseries.
verbose: If this is ``True`` then populate the DataFrame with the
human readable versions for foreign key fields else
use the actual values set in the model
coerce_float: Attempt to convert values to non-string, non-numeric
objects (like decimal.Decimal) to floating point.
datetime_index: specify whether index should be converted to a
DateTimeIndex.
"""
return read_frame(self, fieldnames=fieldnames, verbose=verbose,
index_col=index, coerce_float=coerce_float,
datetime_index=datetime_index) | python | def to_dataframe(self, fieldnames=(), verbose=True, index=None,
coerce_float=False, datetime_index=False):
"""
Returns a DataFrame from the queryset
Paramaters
-----------
fieldnames: The model field names(columns) to utilise in creating
the DataFrame. You can span a relationships in the usual
Django ORM way by using the foreign key field name
separated by double underscores and refer to a field
in a related model.
index: specify the field to use for the index. If the index
field is not in fieldnames it will be appended. This
is mandatory for timeseries.
verbose: If this is ``True`` then populate the DataFrame with the
human readable versions for foreign key fields else
use the actual values set in the model
coerce_float: Attempt to convert values to non-string, non-numeric
objects (like decimal.Decimal) to floating point.
datetime_index: specify whether index should be converted to a
DateTimeIndex.
"""
return read_frame(self, fieldnames=fieldnames, verbose=verbose,
index_col=index, coerce_float=coerce_float,
datetime_index=datetime_index) | [
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chrisdev/django-pandas | django_pandas/io.py | read_frame | def read_frame(qs, fieldnames=(), index_col=None, coerce_float=False,
verbose=True, datetime_index=False):
"""
Returns a dataframe from a QuerySet
Optionally specify the field names/columns to utilize and
a field as the index
Parameters
----------
qs: The Django QuerySet.
fieldnames: The model field names to use in creating the frame.
You can span a relationship in the usual Django way
by using double underscores to specify a related field
in another model
You can span a relationship in the usual Django way
by using double underscores to specify a related field
in another model
index_col: specify the field to use for the index. If the index
field is not in the field list it will be appended
coerce_float : boolean, default False
Attempt to convert values to non-string, non-numeric data (like
decimal.Decimal) to floating point, useful for SQL result sets
verbose: boolean If this is ``True`` then populate the DataFrame with the
human readable versions of any foreign key fields else use
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The human readable version of the foreign key field is
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datetime_index: specify whether index should be converted to a
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"""
if fieldnames:
fieldnames = pd.unique(fieldnames)
if index_col is not None and index_col not in fieldnames:
# Add it to the field names if not already there
fieldnames = tuple(fieldnames) + (index_col,)
fields = to_fields(qs, fieldnames)
elif is_values_queryset(qs):
if django.VERSION < (1, 9): # pragma: no cover
annotation_field_names = list(qs.query.annotation_select)
if annotation_field_names is None:
annotation_field_names = []
extra_field_names = qs.extra_names
if extra_field_names is None:
extra_field_names = []
select_field_names = qs.field_names
else: # pragma: no cover
annotation_field_names = list(qs.query.annotation_select)
extra_field_names = list(qs.query.extra_select)
select_field_names = list(qs.query.values_select)
fieldnames = select_field_names + annotation_field_names + \
extra_field_names
fields = [None if '__' in f else qs.model._meta.get_field(f)
for f in select_field_names] + \
[None] * (len(annotation_field_names) + len(extra_field_names))
uniq_fields = set()
fieldnames, fields = zip(
*(f for f in zip(fieldnames, fields)
if f[0] not in uniq_fields and not uniq_fields.add(f[0])))
else:
fields = qs.model._meta.fields
fieldnames = [f.name for f in fields]
fieldnames += list(qs.query.annotation_select.keys())
if is_values_queryset(qs):
recs = list(qs)
else:
recs = list(qs.values_list(*fieldnames))
df = pd.DataFrame.from_records(recs, columns=fieldnames,
coerce_float=coerce_float)
if verbose:
update_with_verbose(df, fieldnames, fields)
if index_col is not None:
df.set_index(index_col, inplace=True)
if datetime_index:
df.index = pd.to_datetime(df.index, errors="ignore")
return df | python | def read_frame(qs, fieldnames=(), index_col=None, coerce_float=False,
verbose=True, datetime_index=False):
"""
Returns a dataframe from a QuerySet
Optionally specify the field names/columns to utilize and
a field as the index
Parameters
----------
qs: The Django QuerySet.
fieldnames: The model field names to use in creating the frame.
You can span a relationship in the usual Django way
by using double underscores to specify a related field
in another model
You can span a relationship in the usual Django way
by using double underscores to specify a related field
in another model
index_col: specify the field to use for the index. If the index
field is not in the field list it will be appended
coerce_float : boolean, default False
Attempt to convert values to non-string, non-numeric data (like
decimal.Decimal) to floating point, useful for SQL result sets
verbose: boolean If this is ``True`` then populate the DataFrame with the
human readable versions of any foreign key fields else use
the primary keys values.
The human readable version of the foreign key field is
defined in the ``__unicode__`` or ``__str__``
methods of the related class definition
datetime_index: specify whether index should be converted to a
DateTimeIndex.
"""
if fieldnames:
fieldnames = pd.unique(fieldnames)
if index_col is not None and index_col not in fieldnames:
# Add it to the field names if not already there
fieldnames = tuple(fieldnames) + (index_col,)
fields = to_fields(qs, fieldnames)
elif is_values_queryset(qs):
if django.VERSION < (1, 9): # pragma: no cover
annotation_field_names = list(qs.query.annotation_select)
if annotation_field_names is None:
annotation_field_names = []
extra_field_names = qs.extra_names
if extra_field_names is None:
extra_field_names = []
select_field_names = qs.field_names
else: # pragma: no cover
annotation_field_names = list(qs.query.annotation_select)
extra_field_names = list(qs.query.extra_select)
select_field_names = list(qs.query.values_select)
fieldnames = select_field_names + annotation_field_names + \
extra_field_names
fields = [None if '__' in f else qs.model._meta.get_field(f)
for f in select_field_names] + \
[None] * (len(annotation_field_names) + len(extra_field_names))
uniq_fields = set()
fieldnames, fields = zip(
*(f for f in zip(fieldnames, fields)
if f[0] not in uniq_fields and not uniq_fields.add(f[0])))
else:
fields = qs.model._meta.fields
fieldnames = [f.name for f in fields]
fieldnames += list(qs.query.annotation_select.keys())
if is_values_queryset(qs):
recs = list(qs)
else:
recs = list(qs.values_list(*fieldnames))
df = pd.DataFrame.from_records(recs, columns=fieldnames,
coerce_float=coerce_float)
if verbose:
update_with_verbose(df, fieldnames, fields)
if index_col is not None:
df.set_index(index_col, inplace=True)
if datetime_index:
df.index = pd.to_datetime(df.index, errors="ignore")
return df | [
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joowani/binarytree | binarytree/__init__.py | _is_balanced | def _is_balanced(root):
"""Return the height if the binary tree is balanced, -1 otherwise.
:param root: Root node of the binary tree.
:type root: binarytree.Node | None
:return: Height if the binary tree is balanced, -1 otherwise.
:rtype: int
"""
if root is None:
return 0
left = _is_balanced(root.left)
if left < 0:
return -1
right = _is_balanced(root.right)
if right < 0:
return -1
return -1 if abs(left - right) > 1 else max(left, right) + 1 | python | def _is_balanced(root):
"""Return the height if the binary tree is balanced, -1 otherwise.
:param root: Root node of the binary tree.
:type root: binarytree.Node | None
:return: Height if the binary tree is balanced, -1 otherwise.
:rtype: int
"""
if root is None:
return 0
left = _is_balanced(root.left)
if left < 0:
return -1
right = _is_balanced(root.right)
if right < 0:
return -1
return -1 if abs(left - right) > 1 else max(left, right) + 1 | [
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joowani/binarytree | binarytree/__init__.py | _build_bst_from_sorted_values | def _build_bst_from_sorted_values(sorted_values):
"""Recursively build a perfect BST from odd number of sorted values.
:param sorted_values: Odd number of sorted values.
:type sorted_values: [int | float]
:return: Root node of the BST.
:rtype: binarytree.Node
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root.left = _build_bst_from_sorted_values(sorted_values[:mid_index])
root.right = _build_bst_from_sorted_values(sorted_values[mid_index + 1:])
return root | python | def _build_bst_from_sorted_values(sorted_values):
"""Recursively build a perfect BST from odd number of sorted values.
:param sorted_values: Odd number of sorted values.
:type sorted_values: [int | float]
:return: Root node of the BST.
:rtype: binarytree.Node
"""
if len(sorted_values) == 0:
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root = Node(sorted_values[mid_index])
root.left = _build_bst_from_sorted_values(sorted_values[:mid_index])
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joowani/binarytree | binarytree/__init__.py | _generate_random_leaf_count | def _generate_random_leaf_count(height):
"""Return a random leaf count for building binary trees.
:param height: Height of the binary tree.
:type height: int
:return: Random leaf count.
:rtype: int
"""
max_leaf_count = 2 ** height
half_leaf_count = max_leaf_count // 2
# A very naive way of mimicking normal distribution
roll_1 = random.randint(0, half_leaf_count)
roll_2 = random.randint(0, max_leaf_count - half_leaf_count)
return roll_1 + roll_2 or half_leaf_count | python | def _generate_random_leaf_count(height):
"""Return a random leaf count for building binary trees.
:param height: Height of the binary tree.
:type height: int
:return: Random leaf count.
:rtype: int
"""
max_leaf_count = 2 ** height
half_leaf_count = max_leaf_count // 2
# A very naive way of mimicking normal distribution
roll_1 = random.randint(0, half_leaf_count)
roll_2 = random.randint(0, max_leaf_count - half_leaf_count)
return roll_1 + roll_2 or half_leaf_count | [
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joowani/binarytree | binarytree/__init__.py | _generate_random_node_values | def _generate_random_node_values(height):
"""Return random node values for building binary trees.
:param height: Height of the binary tree.
:type height: int
:return: Randomly generated node values.
:rtype: [int]
"""
max_node_count = 2 ** (height + 1) - 1
node_values = list(range(max_node_count))
random.shuffle(node_values)
return node_values | python | def _generate_random_node_values(height):
"""Return random node values for building binary trees.
:param height: Height of the binary tree.
:type height: int
:return: Randomly generated node values.
:rtype: [int]
"""
max_node_count = 2 ** (height + 1) - 1
node_values = list(range(max_node_count))
random.shuffle(node_values)
return node_values | [
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joowani/binarytree | binarytree/__init__.py | _build_tree_string | def _build_tree_string(root, curr_index, index=False, delimiter='-'):
"""Recursively walk down the binary tree and build a pretty-print string.
In each recursive call, a "box" of characters visually representing the
current (sub)tree is constructed line by line. Each line is padded with
whitespaces to ensure all lines in the box have the same length. Then the
box, its width, and start-end positions of its root node value repr string
(required for drawing branches) are sent up to the parent call. The parent
call then combines its left and right sub-boxes to build a larger box etc.
:param root: Root node of the binary tree.
:type root: binarytree.Node | None
:param curr_index: Level-order_ index of the current node (root node is 0).
:type curr_index: int
:param index: If set to True, include the level-order_ node indexes using
the following format: ``{index}{delimiter}{value}`` (default: False).
:type index: bool
:param delimiter: Delimiter character between the node index and the node
value (default: '-').
:type delimiter:
:return: Box of characters visually representing the current subtree, width
of the box, and start-end positions of the repr string of the new root
node value.
:rtype: ([str], int, int, int)
.. _Level-order:
https://en.wikipedia.org/wiki/Tree_traversal#Breadth-first_search
"""
if root is None:
return [], 0, 0, 0
line1 = []
line2 = []
if index:
node_repr = '{}{}{}'.format(curr_index, delimiter, root.value)
else:
node_repr = str(root.value)
new_root_width = gap_size = len(node_repr)
# Get the left and right sub-boxes, their widths, and root repr positions
l_box, l_box_width, l_root_start, l_root_end = \
_build_tree_string(root.left, 2 * curr_index + 1, index, delimiter)
r_box, r_box_width, r_root_start, r_root_end = \
_build_tree_string(root.right, 2 * curr_index + 2, index, delimiter)
# Draw the branch connecting the current root node to the left sub-box
# Pad the line with whitespaces where necessary
if l_box_width > 0:
l_root = (l_root_start + l_root_end) // 2 + 1
line1.append(' ' * (l_root + 1))
line1.append('_' * (l_box_width - l_root))
line2.append(' ' * l_root + '/')
line2.append(' ' * (l_box_width - l_root))
new_root_start = l_box_width + 1
gap_size += 1
else:
new_root_start = 0
# Draw the representation of the current root node
line1.append(node_repr)
line2.append(' ' * new_root_width)
# Draw the branch connecting the current root node to the right sub-box
# Pad the line with whitespaces where necessary
if r_box_width > 0:
r_root = (r_root_start + r_root_end) // 2
line1.append('_' * r_root)
line1.append(' ' * (r_box_width - r_root + 1))
line2.append(' ' * r_root + '\\')
line2.append(' ' * (r_box_width - r_root))
gap_size += 1
new_root_end = new_root_start + new_root_width - 1
# Combine the left and right sub-boxes with the branches drawn above
gap = ' ' * gap_size
new_box = [''.join(line1), ''.join(line2)]
for i in range(max(len(l_box), len(r_box))):
l_line = l_box[i] if i < len(l_box) else ' ' * l_box_width
r_line = r_box[i] if i < len(r_box) else ' ' * r_box_width
new_box.append(l_line + gap + r_line)
# Return the new box, its width and its root repr positions
return new_box, len(new_box[0]), new_root_start, new_root_end | python | def _build_tree_string(root, curr_index, index=False, delimiter='-'):
"""Recursively walk down the binary tree and build a pretty-print string.
In each recursive call, a "box" of characters visually representing the
current (sub)tree is constructed line by line. Each line is padded with
whitespaces to ensure all lines in the box have the same length. Then the
box, its width, and start-end positions of its root node value repr string
(required for drawing branches) are sent up to the parent call. The parent
call then combines its left and right sub-boxes to build a larger box etc.
:param root: Root node of the binary tree.
:type root: binarytree.Node | None
:param curr_index: Level-order_ index of the current node (root node is 0).
:type curr_index: int
:param index: If set to True, include the level-order_ node indexes using
the following format: ``{index}{delimiter}{value}`` (default: False).
:type index: bool
:param delimiter: Delimiter character between the node index and the node
value (default: '-').
:type delimiter:
:return: Box of characters visually representing the current subtree, width
of the box, and start-end positions of the repr string of the new root
node value.
:rtype: ([str], int, int, int)
.. _Level-order:
https://en.wikipedia.org/wiki/Tree_traversal#Breadth-first_search
"""
if root is None:
return [], 0, 0, 0
line1 = []
line2 = []
if index:
node_repr = '{}{}{}'.format(curr_index, delimiter, root.value)
else:
node_repr = str(root.value)
new_root_width = gap_size = len(node_repr)
# Get the left and right sub-boxes, their widths, and root repr positions
l_box, l_box_width, l_root_start, l_root_end = \
_build_tree_string(root.left, 2 * curr_index + 1, index, delimiter)
r_box, r_box_width, r_root_start, r_root_end = \
_build_tree_string(root.right, 2 * curr_index + 2, index, delimiter)
# Draw the branch connecting the current root node to the left sub-box
# Pad the line with whitespaces where necessary
if l_box_width > 0:
l_root = (l_root_start + l_root_end) // 2 + 1
line1.append(' ' * (l_root + 1))
line1.append('_' * (l_box_width - l_root))
line2.append(' ' * l_root + '/')
line2.append(' ' * (l_box_width - l_root))
new_root_start = l_box_width + 1
gap_size += 1
else:
new_root_start = 0
# Draw the representation of the current root node
line1.append(node_repr)
line2.append(' ' * new_root_width)
# Draw the branch connecting the current root node to the right sub-box
# Pad the line with whitespaces where necessary
if r_box_width > 0:
r_root = (r_root_start + r_root_end) // 2
line1.append('_' * r_root)
line1.append(' ' * (r_box_width - r_root + 1))
line2.append(' ' * r_root + '\\')
line2.append(' ' * (r_box_width - r_root))
gap_size += 1
new_root_end = new_root_start + new_root_width - 1
# Combine the left and right sub-boxes with the branches drawn above
gap = ' ' * gap_size
new_box = [''.join(line1), ''.join(line2)]
for i in range(max(len(l_box), len(r_box))):
l_line = l_box[i] if i < len(l_box) else ' ' * l_box_width
r_line = r_box[i] if i < len(r_box) else ' ' * r_box_width
new_box.append(l_line + gap + r_line)
# Return the new box, its width and its root repr positions
return new_box, len(new_box[0]), new_root_start, new_root_end | [
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whitespaces to ensure all lines in the box have the same length. Then the
box, its width, and start-end positions of its root node value repr string
(required for drawing branches) are sent up to the parent call. The parent
call then combines its left and right sub-boxes to build a larger box etc.
:param root: Root node of the binary tree.
:type root: binarytree.Node | None
:param curr_index: Level-order_ index of the current node (root node is 0).
:type curr_index: int
:param index: If set to True, include the level-order_ node indexes using
the following format: ``{index}{delimiter}{value}`` (default: False).
:type index: bool
:param delimiter: Delimiter character between the node index and the node
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:type delimiter:
:return: Box of characters visually representing the current subtree, width
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node value.
:rtype: ([str], int, int, int)
.. _Level-order:
https://en.wikipedia.org/wiki/Tree_traversal#Breadth-first_search | [
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joowani/binarytree | binarytree/__init__.py | build | def build(values):
"""Build a tree from `list representation`_ and return its root node.
.. _list representation:
https://en.wikipedia.org/wiki/Binary_tree#Arrays
:param values: List representation of the binary tree, which is a list of
node values in breadth-first order starting from the root (current
node). If a node is at index i, its left child is always at 2i + 1,
right child at 2i + 2, and parent at floor((i - 1) / 2). None indicates
absence of a node at that index. See example below for an illustration.
:type values: [int | float | None]
:return: Root node of the binary tree.
:rtype: binarytree.Node
:raise binarytree.exceptions.NodeNotFoundError: If the list representation
is malformed (e.g. a parent node is missing).
**Example**:
.. doctest::
>>> from binarytree import build
>>>
>>> root = build([1, 2, 3, None, 4])
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
\\
4
<BLANKLINE>
.. doctest::
>>> from binarytree import build
>>>
>>> root = build([None, 2, 3]) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
NodeNotFoundError: parent node missing at index 0
"""
nodes = [None if v is None else Node(v) for v in values]
for index in range(1, len(nodes)):
node = nodes[index]
if node is not None:
parent_index = (index - 1) // 2
parent = nodes[parent_index]
if parent is None:
raise NodeNotFoundError(
'parent node missing at index {}'.format(parent_index))
setattr(parent, 'left' if index % 2 else 'right', node)
return nodes[0] if nodes else None | python | def build(values):
"""Build a tree from `list representation`_ and return its root node.
.. _list representation:
https://en.wikipedia.org/wiki/Binary_tree#Arrays
:param values: List representation of the binary tree, which is a list of
node values in breadth-first order starting from the root (current
node). If a node is at index i, its left child is always at 2i + 1,
right child at 2i + 2, and parent at floor((i - 1) / 2). None indicates
absence of a node at that index. See example below for an illustration.
:type values: [int | float | None]
:return: Root node of the binary tree.
:rtype: binarytree.Node
:raise binarytree.exceptions.NodeNotFoundError: If the list representation
is malformed (e.g. a parent node is missing).
**Example**:
.. doctest::
>>> from binarytree import build
>>>
>>> root = build([1, 2, 3, None, 4])
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
\\
4
<BLANKLINE>
.. doctest::
>>> from binarytree import build
>>>
>>> root = build([None, 2, 3]) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
NodeNotFoundError: parent node missing at index 0
"""
nodes = [None if v is None else Node(v) for v in values]
for index in range(1, len(nodes)):
node = nodes[index]
if node is not None:
parent_index = (index - 1) // 2
parent = nodes[parent_index]
if parent is None:
raise NodeNotFoundError(
'parent node missing at index {}'.format(parent_index))
setattr(parent, 'left' if index % 2 else 'right', node)
return nodes[0] if nodes else None | [
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:param values: List representation of the binary tree, which is a list of
node values in breadth-first order starting from the root (current
node). If a node is at index i, its left child is always at 2i + 1,
right child at 2i + 2, and parent at floor((i - 1) / 2). None indicates
absence of a node at that index. See example below for an illustration.
:type values: [int | float | None]
:return: Root node of the binary tree.
:rtype: binarytree.Node
:raise binarytree.exceptions.NodeNotFoundError: If the list representation
is malformed (e.g. a parent node is missing).
**Example**:
.. doctest::
>>> from binarytree import build
>>>
>>> root = build([1, 2, 3, None, 4])
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
\\
4
<BLANKLINE>
.. doctest::
>>> from binarytree import build
>>>
>>> root = build([None, 2, 3]) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
NodeNotFoundError: parent node missing at index 0 | [
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joowani/binarytree | binarytree/__init__.py | tree | def tree(height=3, is_perfect=False):
"""Generate a random binary tree and return its root node.
:param height: Height of the tree (default: 3, range: 0 - 9 inclusive).
:type height: int
:param is_perfect: If set to True (default: False), a perfect binary tree
with all levels filled is returned. If set to False, a perfect binary
tree may still be generated by chance.
:type is_perfect: bool
:return: Root node of the binary tree.
:rtype: binarytree.Node
:raise binarytree.exceptions.TreeHeightError: If height is invalid.
**Example**:
.. doctest::
>>> from binarytree import tree
>>>
>>> root = tree()
>>>
>>> root.height
3
.. doctest::
>>> from binarytree import tree
>>>
>>> root = tree(height=5, is_perfect=True)
>>>
>>> root.height
5
>>> root.is_perfect
True
.. doctest::
>>> from binarytree import tree
>>>
>>> root = tree(height=20) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
TreeHeightError: height must be an int between 0 - 9
"""
_validate_tree_height(height)
values = _generate_random_node_values(height)
if is_perfect:
return build(values)
leaf_count = _generate_random_leaf_count(height)
root = Node(values.pop(0))
leaves = set()
for value in values:
node = root
depth = 0
inserted = False
while depth < height and not inserted:
attr = random.choice(('left', 'right'))
if getattr(node, attr) is None:
setattr(node, attr, Node(value))
inserted = True
node = getattr(node, attr)
depth += 1
if inserted and depth == height:
leaves.add(node)
if len(leaves) == leaf_count:
break
return root | python | def tree(height=3, is_perfect=False):
"""Generate a random binary tree and return its root node.
:param height: Height of the tree (default: 3, range: 0 - 9 inclusive).
:type height: int
:param is_perfect: If set to True (default: False), a perfect binary tree
with all levels filled is returned. If set to False, a perfect binary
tree may still be generated by chance.
:type is_perfect: bool
:return: Root node of the binary tree.
:rtype: binarytree.Node
:raise binarytree.exceptions.TreeHeightError: If height is invalid.
**Example**:
.. doctest::
>>> from binarytree import tree
>>>
>>> root = tree()
>>>
>>> root.height
3
.. doctest::
>>> from binarytree import tree
>>>
>>> root = tree(height=5, is_perfect=True)
>>>
>>> root.height
5
>>> root.is_perfect
True
.. doctest::
>>> from binarytree import tree
>>>
>>> root = tree(height=20) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
TreeHeightError: height must be an int between 0 - 9
"""
_validate_tree_height(height)
values = _generate_random_node_values(height)
if is_perfect:
return build(values)
leaf_count = _generate_random_leaf_count(height)
root = Node(values.pop(0))
leaves = set()
for value in values:
node = root
depth = 0
inserted = False
while depth < height and not inserted:
attr = random.choice(('left', 'right'))
if getattr(node, attr) is None:
setattr(node, attr, Node(value))
inserted = True
node = getattr(node, attr)
depth += 1
if inserted and depth == height:
leaves.add(node)
if len(leaves) == leaf_count:
break
return root | [
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:param is_perfect: If set to True (default: False), a perfect binary tree
with all levels filled is returned. If set to False, a perfect binary
tree may still be generated by chance.
:type is_perfect: bool
:return: Root node of the binary tree.
:rtype: binarytree.Node
:raise binarytree.exceptions.TreeHeightError: If height is invalid.
**Example**:
.. doctest::
>>> from binarytree import tree
>>>
>>> root = tree()
>>>
>>> root.height
3
.. doctest::
>>> from binarytree import tree
>>>
>>> root = tree(height=5, is_perfect=True)
>>>
>>> root.height
5
>>> root.is_perfect
True
.. doctest::
>>> from binarytree import tree
>>>
>>> root = tree(height=20) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
TreeHeightError: height must be an int between 0 - 9 | [
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joowani/binarytree | binarytree/__init__.py | heap | def heap(height=3, is_max=True, is_perfect=False):
"""Generate a random heap and return its root node.
:param height: Height of the heap (default: 3, range: 0 - 9 inclusive).
:type height: int
:param is_max: If set to True (default: True), generate a max heap. If set
to False, generate a min heap. A binary tree with only the root node is
considered both a min and max heap.
:type is_max: bool
:param is_perfect: If set to True (default: False), a perfect heap with all
levels filled is returned. If set to False, a perfect heap may still be
generated by chance.
:type is_perfect: bool
:return: Root node of the heap.
:rtype: binarytree.Node
:raise binarytree.exceptions.TreeHeightError: If height is invalid.
**Example**:
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap()
>>>
>>> root.height
3
>>> root.is_max_heap
True
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap(4, is_max=False)
>>>
>>> root.height
4
>>> root.is_min_heap
True
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap(5, is_max=False, is_perfect=True)
>>>
>>> root.height
5
>>> root.is_min_heap
True
>>> root.is_perfect
True
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap(-1) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
TreeHeightError: height must be an int between 0 - 9
"""
_validate_tree_height(height)
values = _generate_random_node_values(height)
if not is_perfect:
# Randomly cut some of the leaf nodes away
random_cut = random.randint(2 ** height, len(values))
values = values[:random_cut]
if is_max:
negated = [-v for v in values]
heapq.heapify(negated)
return build([-v for v in negated])
else:
heapq.heapify(values)
return build(values) | python | def heap(height=3, is_max=True, is_perfect=False):
"""Generate a random heap and return its root node.
:param height: Height of the heap (default: 3, range: 0 - 9 inclusive).
:type height: int
:param is_max: If set to True (default: True), generate a max heap. If set
to False, generate a min heap. A binary tree with only the root node is
considered both a min and max heap.
:type is_max: bool
:param is_perfect: If set to True (default: False), a perfect heap with all
levels filled is returned. If set to False, a perfect heap may still be
generated by chance.
:type is_perfect: bool
:return: Root node of the heap.
:rtype: binarytree.Node
:raise binarytree.exceptions.TreeHeightError: If height is invalid.
**Example**:
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap()
>>>
>>> root.height
3
>>> root.is_max_heap
True
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap(4, is_max=False)
>>>
>>> root.height
4
>>> root.is_min_heap
True
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap(5, is_max=False, is_perfect=True)
>>>
>>> root.height
5
>>> root.is_min_heap
True
>>> root.is_perfect
True
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap(-1) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
TreeHeightError: height must be an int between 0 - 9
"""
_validate_tree_height(height)
values = _generate_random_node_values(height)
if not is_perfect:
# Randomly cut some of the leaf nodes away
random_cut = random.randint(2 ** height, len(values))
values = values[:random_cut]
if is_max:
negated = [-v for v in values]
heapq.heapify(negated)
return build([-v for v in negated])
else:
heapq.heapify(values)
return build(values) | [
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:param height: Height of the heap (default: 3, range: 0 - 9 inclusive).
:type height: int
:param is_max: If set to True (default: True), generate a max heap. If set
to False, generate a min heap. A binary tree with only the root node is
considered both a min and max heap.
:type is_max: bool
:param is_perfect: If set to True (default: False), a perfect heap with all
levels filled is returned. If set to False, a perfect heap may still be
generated by chance.
:type is_perfect: bool
:return: Root node of the heap.
:rtype: binarytree.Node
:raise binarytree.exceptions.TreeHeightError: If height is invalid.
**Example**:
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap()
>>>
>>> root.height
3
>>> root.is_max_heap
True
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap(4, is_max=False)
>>>
>>> root.height
4
>>> root.is_min_heap
True
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap(5, is_max=False, is_perfect=True)
>>>
>>> root.height
5
>>> root.is_min_heap
True
>>> root.is_perfect
True
.. doctest::
>>> from binarytree import heap
>>>
>>> root = heap(-1) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
TreeHeightError: height must be an int between 0 - 9 | [
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joowani/binarytree | binarytree/__init__.py | Node.pprint | def pprint(self, index=False, delimiter='-'):
"""Pretty-print the binary tree.
:param index: If set to True (default: False), display level-order_
indexes using the format: ``{index}{delimiter}{value}``.
:type index: bool
:param delimiter: Delimiter character between the node index and
the node value (default: '-').
:type delimiter: str | unicode
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1) # index: 0, value: 1
>>> root.left = Node(2) # index: 1, value: 2
>>> root.right = Node(3) # index: 2, value: 3
>>> root.left.right = Node(4) # index: 4, value: 4
>>>
>>> root.pprint()
<BLANKLINE>
__1
/ \\
2 3
\\
4
<BLANKLINE>
>>> root.pprint(index=True) # Format: {index}-{value}
<BLANKLINE>
_____0-1_
/ \\
1-2_ 2-3
\\
4-4
<BLANKLINE>
.. note::
If you do not need level-order_ indexes in the output string, use
:func:`binarytree.Node.__str__` instead.
.. _level-order:
https://en.wikipedia.org/wiki/Tree_traversal#Breadth-first_search
"""
lines = _build_tree_string(self, 0, index, delimiter)[0]
print('\n' + '\n'.join((line.rstrip() for line in lines))) | python | def pprint(self, index=False, delimiter='-'):
"""Pretty-print the binary tree.
:param index: If set to True (default: False), display level-order_
indexes using the format: ``{index}{delimiter}{value}``.
:type index: bool
:param delimiter: Delimiter character between the node index and
the node value (default: '-').
:type delimiter: str | unicode
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1) # index: 0, value: 1
>>> root.left = Node(2) # index: 1, value: 2
>>> root.right = Node(3) # index: 2, value: 3
>>> root.left.right = Node(4) # index: 4, value: 4
>>>
>>> root.pprint()
<BLANKLINE>
__1
/ \\
2 3
\\
4
<BLANKLINE>
>>> root.pprint(index=True) # Format: {index}-{value}
<BLANKLINE>
_____0-1_
/ \\
1-2_ 2-3
\\
4-4
<BLANKLINE>
.. note::
If you do not need level-order_ indexes in the output string, use
:func:`binarytree.Node.__str__` instead.
.. _level-order:
https://en.wikipedia.org/wiki/Tree_traversal#Breadth-first_search
"""
lines = _build_tree_string(self, 0, index, delimiter)[0]
print('\n' + '\n'.join((line.rstrip() for line in lines))) | [
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:type index: bool
:param delimiter: Delimiter character between the node index and
the node value (default: '-').
:type delimiter: str | unicode
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1) # index: 0, value: 1
>>> root.left = Node(2) # index: 1, value: 2
>>> root.right = Node(3) # index: 2, value: 3
>>> root.left.right = Node(4) # index: 4, value: 4
>>>
>>> root.pprint()
<BLANKLINE>
__1
/ \\
2 3
\\
4
<BLANKLINE>
>>> root.pprint(index=True) # Format: {index}-{value}
<BLANKLINE>
_____0-1_
/ \\
1-2_ 2-3
\\
4-4
<BLANKLINE>
.. note::
If you do not need level-order_ indexes in the output string, use
:func:`binarytree.Node.__str__` instead.
.. _level-order:
https://en.wikipedia.org/wiki/Tree_traversal#Breadth-first_search | [
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joowani/binarytree | binarytree/__init__.py | Node.validate | def validate(self):
"""Check if the binary tree is malformed.
:raise binarytree.exceptions.NodeReferenceError: If there is a
cyclic reference to a node in the binary tree.
:raise binarytree.exceptions.NodeTypeError: If a node is not an
instance of :class:`binarytree.Node`.
:raise binarytree.exceptions.NodeValueError: If a node value is not a
number (e.g. int, float).
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = root # Cyclic reference to root
>>>
>>> root.validate() # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
NodeReferenceError: cyclic node reference at index 0
"""
has_more_nodes = True
visited = set()
to_visit = [self]
index = 0
while has_more_nodes:
has_more_nodes = False
next_nodes = []
for node in to_visit:
if node is None:
next_nodes.extend((None, None))
else:
if node in visited:
raise NodeReferenceError(
'cyclic node reference at index {}'.format(index))
if not isinstance(node, Node):
raise NodeTypeError(
'invalid node instance at index {}'.format(index))
if not isinstance(node.value, numbers.Number):
raise NodeValueError(
'invalid node value at index {}'.format(index))
if node.left is not None or node.right is not None:
has_more_nodes = True
visited.add(node)
next_nodes.extend((node.left, node.right))
index += 1
to_visit = next_nodes | python | def validate(self):
"""Check if the binary tree is malformed.
:raise binarytree.exceptions.NodeReferenceError: If there is a
cyclic reference to a node in the binary tree.
:raise binarytree.exceptions.NodeTypeError: If a node is not an
instance of :class:`binarytree.Node`.
:raise binarytree.exceptions.NodeValueError: If a node value is not a
number (e.g. int, float).
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = root # Cyclic reference to root
>>>
>>> root.validate() # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
NodeReferenceError: cyclic node reference at index 0
"""
has_more_nodes = True
visited = set()
to_visit = [self]
index = 0
while has_more_nodes:
has_more_nodes = False
next_nodes = []
for node in to_visit:
if node is None:
next_nodes.extend((None, None))
else:
if node in visited:
raise NodeReferenceError(
'cyclic node reference at index {}'.format(index))
if not isinstance(node, Node):
raise NodeTypeError(
'invalid node instance at index {}'.format(index))
if not isinstance(node.value, numbers.Number):
raise NodeValueError(
'invalid node value at index {}'.format(index))
if node.left is not None or node.right is not None:
has_more_nodes = True
visited.add(node)
next_nodes.extend((node.left, node.right))
index += 1
to_visit = next_nodes | [
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:raise binarytree.exceptions.NodeReferenceError: If there is a
cyclic reference to a node in the binary tree.
:raise binarytree.exceptions.NodeTypeError: If a node is not an
instance of :class:`binarytree.Node`.
:raise binarytree.exceptions.NodeValueError: If a node value is not a
number (e.g. int, float).
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = root # Cyclic reference to root
>>>
>>> root.validate() # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
NodeReferenceError: cyclic node reference at index 0 | [
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joowani/binarytree | binarytree/__init__.py | Node.values | def values(self):
"""Return the `list representation`_ of the binary tree.
.. _list representation:
https://en.wikipedia.org/wiki/Binary_tree#Arrays
:return: List representation of the binary tree, which is a list of
node values in breadth-first order starting from the root (current
node). If a node is at index i, its left child is always at 2i + 1,
right child at 2i + 2, and parent at index floor((i - 1) / 2). None
indicates absence of a node at that index. See example below for an
illustration.
:rtype: [int | float | None]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.right = Node(4)
>>>
>>> root.values
[1, 2, 3, None, 4]
"""
current_nodes = [self]
has_more_nodes = True
values = []
while has_more_nodes:
has_more_nodes = False
next_nodes = []
for node in current_nodes:
if node is None:
values.append(None)
next_nodes.extend((None, None))
continue
if node.left is not None or node.right is not None:
has_more_nodes = True
values.append(node.value)
next_nodes.extend((node.left, node.right))
current_nodes = next_nodes
# Get rid of trailing None's
while values and values[-1] is None:
values.pop()
return values | python | def values(self):
"""Return the `list representation`_ of the binary tree.
.. _list representation:
https://en.wikipedia.org/wiki/Binary_tree#Arrays
:return: List representation of the binary tree, which is a list of
node values in breadth-first order starting from the root (current
node). If a node is at index i, its left child is always at 2i + 1,
right child at 2i + 2, and parent at index floor((i - 1) / 2). None
indicates absence of a node at that index. See example below for an
illustration.
:rtype: [int | float | None]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.right = Node(4)
>>>
>>> root.values
[1, 2, 3, None, 4]
"""
current_nodes = [self]
has_more_nodes = True
values = []
while has_more_nodes:
has_more_nodes = False
next_nodes = []
for node in current_nodes:
if node is None:
values.append(None)
next_nodes.extend((None, None))
continue
if node.left is not None or node.right is not None:
has_more_nodes = True
values.append(node.value)
next_nodes.extend((node.left, node.right))
current_nodes = next_nodes
# Get rid of trailing None's
while values and values[-1] is None:
values.pop()
return values | [
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:return: List representation of the binary tree, which is a list of
node values in breadth-first order starting from the root (current
node). If a node is at index i, its left child is always at 2i + 1,
right child at 2i + 2, and parent at index floor((i - 1) / 2). None
indicates absence of a node at that index. See example below for an
illustration.
:rtype: [int | float | None]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.right = Node(4)
>>>
>>> root.values
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joowani/binarytree | binarytree/__init__.py | Node.leaves | def leaves(self):
"""Return the leaf nodes of the binary tree.
A leaf node is any node that does not have child nodes.
:return: List of leaf nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.right = Node(4)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
\\
4
<BLANKLINE>
>>> root.leaves
[Node(3), Node(4)]
"""
current_nodes = [self]
leaves = []
while len(current_nodes) > 0:
next_nodes = []
for node in current_nodes:
if node.left is None and node.right is None:
leaves.append(node)
continue
if node.left is not None:
next_nodes.append(node.left)
if node.right is not None:
next_nodes.append(node.right)
current_nodes = next_nodes
return leaves | python | def leaves(self):
"""Return the leaf nodes of the binary tree.
A leaf node is any node that does not have child nodes.
:return: List of leaf nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.right = Node(4)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
\\
4
<BLANKLINE>
>>> root.leaves
[Node(3), Node(4)]
"""
current_nodes = [self]
leaves = []
while len(current_nodes) > 0:
next_nodes = []
for node in current_nodes:
if node.left is None and node.right is None:
leaves.append(node)
continue
if node.left is not None:
next_nodes.append(node.left)
if node.right is not None:
next_nodes.append(node.right)
current_nodes = next_nodes
return leaves | [
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A leaf node is any node that does not have child nodes.
:return: List of leaf nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.right = Node(4)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
\\
4
<BLANKLINE>
>>> root.leaves
[Node(3), Node(4)] | [
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joowani/binarytree | binarytree/__init__.py | Node.properties | def properties(self):
"""Return various properties of the binary tree.
:return: Binary tree properties.
:rtype: dict
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>> props = root.properties
>>>
>>> props['height'] # equivalent to root.height
2
>>> props['size'] # equivalent to root.size
5
>>> props['max_leaf_depth'] # equivalent to root.max_leaf_depth
2
>>> props['min_leaf_depth'] # equivalent to root.min_leaf_depth
1
>>> props['max_node_value'] # equivalent to root.max_node_value
5
>>> props['min_node_value'] # equivalent to root.min_node_value
1
>>> props['leaf_count'] # equivalent to root.leaf_count
3
>>> props['is_balanced'] # equivalent to root.is_balanced
True
>>> props['is_bst'] # equivalent to root.is_bst
False
>>> props['is_complete'] # equivalent to root.is_complete
True
>>> props['is_max_heap'] # equivalent to root.is_max_heap
False
>>> props['is_min_heap'] # equivalent to root.is_min_heap
True
>>> props['is_perfect'] # equivalent to root.is_perfect
False
>>> props['is_strict'] # equivalent to root.is_strict
True
"""
properties = _get_tree_properties(self)
properties.update({
'is_bst': _is_bst(self),
'is_balanced': _is_balanced(self) >= 0
})
return properties | python | def properties(self):
"""Return various properties of the binary tree.
:return: Binary tree properties.
:rtype: dict
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>> props = root.properties
>>>
>>> props['height'] # equivalent to root.height
2
>>> props['size'] # equivalent to root.size
5
>>> props['max_leaf_depth'] # equivalent to root.max_leaf_depth
2
>>> props['min_leaf_depth'] # equivalent to root.min_leaf_depth
1
>>> props['max_node_value'] # equivalent to root.max_node_value
5
>>> props['min_node_value'] # equivalent to root.min_node_value
1
>>> props['leaf_count'] # equivalent to root.leaf_count
3
>>> props['is_balanced'] # equivalent to root.is_balanced
True
>>> props['is_bst'] # equivalent to root.is_bst
False
>>> props['is_complete'] # equivalent to root.is_complete
True
>>> props['is_max_heap'] # equivalent to root.is_max_heap
False
>>> props['is_min_heap'] # equivalent to root.is_min_heap
True
>>> props['is_perfect'] # equivalent to root.is_perfect
False
>>> props['is_strict'] # equivalent to root.is_strict
True
"""
properties = _get_tree_properties(self)
properties.update({
'is_bst': _is_bst(self),
'is_balanced': _is_balanced(self) >= 0
})
return properties | [
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">=",... | Return various properties of the binary tree.
:return: Binary tree properties.
:rtype: dict
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>> props = root.properties
>>>
>>> props['height'] # equivalent to root.height
2
>>> props['size'] # equivalent to root.size
5
>>> props['max_leaf_depth'] # equivalent to root.max_leaf_depth
2
>>> props['min_leaf_depth'] # equivalent to root.min_leaf_depth
1
>>> props['max_node_value'] # equivalent to root.max_node_value
5
>>> props['min_node_value'] # equivalent to root.min_node_value
1
>>> props['leaf_count'] # equivalent to root.leaf_count
3
>>> props['is_balanced'] # equivalent to root.is_balanced
True
>>> props['is_bst'] # equivalent to root.is_bst
False
>>> props['is_complete'] # equivalent to root.is_complete
True
>>> props['is_max_heap'] # equivalent to root.is_max_heap
False
>>> props['is_min_heap'] # equivalent to root.is_min_heap
True
>>> props['is_perfect'] # equivalent to root.is_perfect
False
>>> props['is_strict'] # equivalent to root.is_strict
True | [
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] | 23cb6f1e60e66b96133259031e97ec03e932ba13 | https://github.com/joowani/binarytree/blob/23cb6f1e60e66b96133259031e97ec03e932ba13/binarytree/__init__.py#L1388-L1441 | train | 206,554 |
joowani/binarytree | binarytree/__init__.py | Node.inorder | def inorder(self):
"""Return the nodes in the binary tree using in-order_ traversal.
An in-order_ traversal visits left subtree, root, then right subtree.
.. _in-order: https://en.wikipedia.org/wiki/Tree_traversal
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.inorder
[Node(4), Node(2), Node(5), Node(1), Node(3)]
"""
node_stack = []
result = []
node = self
while True:
if node is not None:
node_stack.append(node)
node = node.left
elif len(node_stack) > 0:
node = node_stack.pop()
result.append(node)
node = node.right
else:
break
return result | python | def inorder(self):
"""Return the nodes in the binary tree using in-order_ traversal.
An in-order_ traversal visits left subtree, root, then right subtree.
.. _in-order: https://en.wikipedia.org/wiki/Tree_traversal
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.inorder
[Node(4), Node(2), Node(5), Node(1), Node(3)]
"""
node_stack = []
result = []
node = self
while True:
if node is not None:
node_stack.append(node)
node = node.left
elif len(node_stack) > 0:
node = node_stack.pop()
result.append(node)
node = node.right
else:
break
return result | [
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An in-order_ traversal visits left subtree, root, then right subtree.
.. _in-order: https://en.wikipedia.org/wiki/Tree_traversal
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.inorder
[Node(4), Node(2), Node(5), Node(1), Node(3)] | [
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joowani/binarytree | binarytree/__init__.py | Node.preorder | def preorder(self):
"""Return the nodes in the binary tree using pre-order_ traversal.
A pre-order_ traversal visits root, left subtree, then right subtree.
.. _pre-order: https://en.wikipedia.org/wiki/Tree_traversal
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.preorder
[Node(1), Node(2), Node(4), Node(5), Node(3)]
"""
node_stack = [self]
result = []
while len(node_stack) > 0:
node = node_stack.pop()
result.append(node)
if node.right is not None:
node_stack.append(node.right)
if node.left is not None:
node_stack.append(node.left)
return result | python | def preorder(self):
"""Return the nodes in the binary tree using pre-order_ traversal.
A pre-order_ traversal visits root, left subtree, then right subtree.
.. _pre-order: https://en.wikipedia.org/wiki/Tree_traversal
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.preorder
[Node(1), Node(2), Node(4), Node(5), Node(3)]
"""
node_stack = [self]
result = []
while len(node_stack) > 0:
node = node_stack.pop()
result.append(node)
if node.right is not None:
node_stack.append(node.right)
if node.left is not None:
node_stack.append(node.left)
return result | [
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A pre-order_ traversal visits root, left subtree, then right subtree.
.. _pre-order: https://en.wikipedia.org/wiki/Tree_traversal
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.preorder
[Node(1), Node(2), Node(4), Node(5), Node(3)] | [
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joowani/binarytree | binarytree/__init__.py | Node.postorder | def postorder(self):
"""Return the nodes in the binary tree using post-order_ traversal.
A post-order_ traversal visits left subtree, right subtree, then root.
.. _post-order: https://en.wikipedia.org/wiki/Tree_traversal
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.postorder
[Node(4), Node(5), Node(2), Node(3), Node(1)]
"""
node_stack = []
result = []
node = self
while True:
while node is not None:
if node.right is not None:
node_stack.append(node.right)
node_stack.append(node)
node = node.left
node = node_stack.pop()
if (node.right is not None and
len(node_stack) > 0 and
node_stack[-1] is node.right):
node_stack.pop()
node_stack.append(node)
node = node.right
else:
result.append(node)
node = None
if len(node_stack) == 0:
break
return result | python | def postorder(self):
"""Return the nodes in the binary tree using post-order_ traversal.
A post-order_ traversal visits left subtree, right subtree, then root.
.. _post-order: https://en.wikipedia.org/wiki/Tree_traversal
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.postorder
[Node(4), Node(5), Node(2), Node(3), Node(1)]
"""
node_stack = []
result = []
node = self
while True:
while node is not None:
if node.right is not None:
node_stack.append(node.right)
node_stack.append(node)
node = node.left
node = node_stack.pop()
if (node.right is not None and
len(node_stack) > 0 and
node_stack[-1] is node.right):
node_stack.pop()
node_stack.append(node)
node = node.right
else:
result.append(node)
node = None
if len(node_stack) == 0:
break
return result | [
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A post-order_ traversal visits left subtree, right subtree, then root.
.. _post-order: https://en.wikipedia.org/wiki/Tree_traversal
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.postorder
[Node(4), Node(5), Node(2), Node(3), Node(1)] | [
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joowani/binarytree | binarytree/__init__.py | Node.levelorder | def levelorder(self):
"""Return the nodes in the binary tree using level-order_ traversal.
A level-order_ traversal visits nodes left to right, level by level.
.. _level-order:
https://en.wikipedia.org/wiki/Tree_traversal#Breadth-first_search
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.levelorder
[Node(1), Node(2), Node(3), Node(4), Node(5)]
"""
current_nodes = [self]
result = []
while len(current_nodes) > 0:
next_nodes = []
for node in current_nodes:
result.append(node)
if node.left is not None:
next_nodes.append(node.left)
if node.right is not None:
next_nodes.append(node.right)
current_nodes = next_nodes
return result | python | def levelorder(self):
"""Return the nodes in the binary tree using level-order_ traversal.
A level-order_ traversal visits nodes left to right, level by level.
.. _level-order:
https://en.wikipedia.org/wiki/Tree_traversal#Breadth-first_search
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.levelorder
[Node(1), Node(2), Node(3), Node(4), Node(5)]
"""
current_nodes = [self]
result = []
while len(current_nodes) > 0:
next_nodes = []
for node in current_nodes:
result.append(node)
if node.left is not None:
next_nodes.append(node.left)
if node.right is not None:
next_nodes.append(node.right)
current_nodes = next_nodes
return result | [
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A level-order_ traversal visits nodes left to right, level by level.
.. _level-order:
https://en.wikipedia.org/wiki/Tree_traversal#Breadth-first_search
:return: List of nodes.
:rtype: [binarytree.Node]
**Example**:
.. doctest::
>>> from binarytree import Node
>>>
>>> root = Node(1)
>>> root.left = Node(2)
>>> root.right = Node(3)
>>> root.left.left = Node(4)
>>> root.left.right = Node(5)
>>>
>>> print(root)
<BLANKLINE>
__1
/ \\
2 3
/ \\
4 5
<BLANKLINE>
>>> root.levelorder
[Node(1), Node(2), Node(3), Node(4), Node(5)] | [
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bennylope/django-organizations | organizations/backends/__init__.py | invitation_backend | def invitation_backend(backend=None, namespace=None):
# type: (Optional[Text], Optional[Text]) -> BaseBackend
"""
Returns a specified invitation backend
Args:
backend: dotted path to the invitation backend class
namespace: URL namespace to use
Returns:
an instance of an InvitationBackend
"""
backend = backend or ORGS_INVITATION_BACKEND
class_module, class_name = backend.rsplit(".", 1)
mod = import_module(class_module)
return getattr(mod, class_name)(namespace=namespace) | python | def invitation_backend(backend=None, namespace=None):
# type: (Optional[Text], Optional[Text]) -> BaseBackend
"""
Returns a specified invitation backend
Args:
backend: dotted path to the invitation backend class
namespace: URL namespace to use
Returns:
an instance of an InvitationBackend
"""
backend = backend or ORGS_INVITATION_BACKEND
class_module, class_name = backend.rsplit(".", 1)
mod = import_module(class_module)
return getattr(mod, class_name)(namespace=namespace) | [
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backend: dotted path to the invitation backend class
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bennylope/django-organizations | organizations/backends/__init__.py | registration_backend | def registration_backend(backend=None, namespace=None):
# type: (Optional[Text], Optional[Text]) -> BaseBackend
"""
Returns a specified registration backend
Args:
backend: dotted path to the registration backend class
namespace: URL namespace to use
Returns:
an instance of an RegistrationBackend
"""
backend = backend or ORGS_REGISTRATION_BACKEND
class_module, class_name = backend.rsplit(".", 1)
mod = import_module(class_module)
return getattr(mod, class_name)(namespace=namespace) | python | def registration_backend(backend=None, namespace=None):
# type: (Optional[Text], Optional[Text]) -> BaseBackend
"""
Returns a specified registration backend
Args:
backend: dotted path to the registration backend class
namespace: URL namespace to use
Returns:
an instance of an RegistrationBackend
"""
backend = backend or ORGS_REGISTRATION_BACKEND
class_module, class_name = backend.rsplit(".", 1)
mod = import_module(class_module)
return getattr(mod, class_name)(namespace=namespace) | [
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bennylope/django-organizations | organizations/backends/forms.py | org_registration_form | def org_registration_form(org_model):
"""
Generates a registration ModelForm for the given organization model class
"""
class OrganizationRegistrationForm(forms.ModelForm):
"""Form class for creating new organizations owned by new users."""
email = forms.EmailField()
class Meta:
model = org_model
exclude = ("is_active", "users")
def save(self, *args, **kwargs):
self.instance.is_active = False
super(OrganizationRegistrationForm, self).save(*args, **kwargs)
return OrganizationRegistrationForm | python | def org_registration_form(org_model):
"""
Generates a registration ModelForm for the given organization model class
"""
class OrganizationRegistrationForm(forms.ModelForm):
"""Form class for creating new organizations owned by new users."""
email = forms.EmailField()
class Meta:
model = org_model
exclude = ("is_active", "users")
def save(self, *args, **kwargs):
self.instance.is_active = False
super(OrganizationRegistrationForm, self).save(*args, **kwargs)
return OrganizationRegistrationForm | [
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bennylope/django-organizations | organizations/forms.py | OrganizationUserAddForm.save | def save(self, *args, **kwargs):
"""
The save method should create a new OrganizationUser linking the User
matching the provided email address. If not matching User is found it
should kick off the registration process. It needs to create a User in
order to link it to the Organization.
"""
try:
user = get_user_model().objects.get(
email__iexact=self.cleaned_data["email"]
)
except get_user_model().MultipleObjectsReturned:
raise forms.ValidationError(
_("This email address has been used multiple times.")
)
except get_user_model().DoesNotExist:
user = invitation_backend().invite_by_email(
self.cleaned_data["email"],
**{
"domain": get_current_site(self.request),
"organization": self.organization,
"sender": self.request.user,
}
)
# Send a notification email to this user to inform them that they
# have been added to a new organization.
invitation_backend().send_notification(
user,
**{
"domain": get_current_site(self.request),
"organization": self.organization,
"sender": self.request.user,
}
)
return OrganizationUser.objects.create(
user=user,
organization=self.organization,
is_admin=self.cleaned_data["is_admin"],
) | python | def save(self, *args, **kwargs):
"""
The save method should create a new OrganizationUser linking the User
matching the provided email address. If not matching User is found it
should kick off the registration process. It needs to create a User in
order to link it to the Organization.
"""
try:
user = get_user_model().objects.get(
email__iexact=self.cleaned_data["email"]
)
except get_user_model().MultipleObjectsReturned:
raise forms.ValidationError(
_("This email address has been used multiple times.")
)
except get_user_model().DoesNotExist:
user = invitation_backend().invite_by_email(
self.cleaned_data["email"],
**{
"domain": get_current_site(self.request),
"organization": self.organization,
"sender": self.request.user,
}
)
# Send a notification email to this user to inform them that they
# have been added to a new organization.
invitation_backend().send_notification(
user,
**{
"domain": get_current_site(self.request),
"organization": self.organization,
"sender": self.request.user,
}
)
return OrganizationUser.objects.create(
user=user,
organization=self.organization,
is_admin=self.cleaned_data["is_admin"],
) | [
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bennylope/django-organizations | organizations/forms.py | OrganizationAddForm.save | def save(self, **kwargs):
"""
Create the organization, then get the user, then make the owner.
"""
is_active = True
try:
user = get_user_model().objects.get(email=self.cleaned_data["email"])
except get_user_model().DoesNotExist:
user = invitation_backend().invite_by_email(
self.cleaned_data["email"],
**{
"domain": get_current_site(self.request),
"organization": self.cleaned_data["name"],
"sender": self.request.user,
"created": True,
}
)
is_active = False
return create_organization(
user,
self.cleaned_data["name"],
self.cleaned_data["slug"],
is_active=is_active,
) | python | def save(self, **kwargs):
"""
Create the organization, then get the user, then make the owner.
"""
is_active = True
try:
user = get_user_model().objects.get(email=self.cleaned_data["email"])
except get_user_model().DoesNotExist:
user = invitation_backend().invite_by_email(
self.cleaned_data["email"],
**{
"domain": get_current_site(self.request),
"organization": self.cleaned_data["name"],
"sender": self.request.user,
"created": True,
}
)
is_active = False
return create_organization(
user,
self.cleaned_data["name"],
self.cleaned_data["slug"],
is_active=is_active,
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bennylope/django-organizations | organizations/backends/modeled.py | ModelInvitation.invite_by_email | def invite_by_email(self, email, user, organization, **kwargs):
# type: (Text, AbstractUser, AbstractBaseOrganization) -> OrganizationInvitationBase
"""
Primary interface method by which one user invites another to join
Args:
email:
request:
**kwargs:
Returns:
an invitation instance
Raises:
MultipleObjectsReturned if multiple matching users are found
"""
try:
invitee = self.user_model.objects.get(email__iexact=email)
except self.user_model.DoesNotExist:
invitee = None
# TODO allow sending just the OrganizationUser instance
user_invitation = self.invitation_model.objects.create(
invitee=invitee,
invitee_identifier=email.lower(),
invited_by=user,
organization=organization,
)
self.send_invitation(user_invitation)
return user_invitation | python | def invite_by_email(self, email, user, organization, **kwargs):
# type: (Text, AbstractUser, AbstractBaseOrganization) -> OrganizationInvitationBase
"""
Primary interface method by which one user invites another to join
Args:
email:
request:
**kwargs:
Returns:
an invitation instance
Raises:
MultipleObjectsReturned if multiple matching users are found
"""
try:
invitee = self.user_model.objects.get(email__iexact=email)
except self.user_model.DoesNotExist:
invitee = None
# TODO allow sending just the OrganizationUser instance
user_invitation = self.invitation_model.objects.create(
invitee=invitee,
invitee_identifier=email.lower(),
invited_by=user,
organization=organization,
)
self.send_invitation(user_invitation)
return user_invitation | [
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bennylope/django-organizations | organizations/backends/modeled.py | ModelInvitation.send_invitation | def send_invitation(self, invitation, **kwargs):
# type: (OrganizationInvitationBase) -> bool
"""
Sends an invitation message for a specific invitation.
This could be overridden to do other things, such as sending a confirmation
email to the sender.
Args:
invitation:
Returns:
"""
return self.email_message(
invitation.invitee_identifier,
self.invitation_subject,
self.invitation_body,
invitation.invited_by,
**kwargs
).send() | python | def send_invitation(self, invitation, **kwargs):
# type: (OrganizationInvitationBase) -> bool
"""
Sends an invitation message for a specific invitation.
This could be overridden to do other things, such as sending a confirmation
email to the sender.
Args:
invitation:
Returns:
"""
return self.email_message(
invitation.invitee_identifier,
self.invitation_subject,
self.invitation_body,
invitation.invited_by,
**kwargs
).send() | [
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bennylope/django-organizations | organizations/backends/modeled.py | ModelInvitation.email_message | def email_message(
self,
recipient, # type: Text
subject_template, # type: Text
body_template, # type: Text
sender=None, # type: Optional[AbstractUser]
message_class=EmailMessage,
**kwargs
):
"""
Returns an invitation email message. This can be easily overridden.
For instance, to send an HTML message, use the EmailMultiAlternatives message_class
and attach the additional conent.
"""
from_email = "%s %s <%s>" % (
sender.first_name,
sender.last_name,
email.utils.parseaddr(settings.DEFAULT_FROM_EMAIL)[1],
)
reply_to = "%s %s <%s>" % (sender.first_name, sender.last_name, sender.email)
headers = {"Reply-To": reply_to}
kwargs.update({"sender": sender, "recipient": recipient})
subject_template = loader.get_template(subject_template)
body_template = loader.get_template(body_template)
subject = subject_template.render(
kwargs
).strip() # Remove stray newline characters
body = body_template.render(kwargs)
return message_class(subject, body, from_email, [recipient], headers=headers) | python | def email_message(
self,
recipient, # type: Text
subject_template, # type: Text
body_template, # type: Text
sender=None, # type: Optional[AbstractUser]
message_class=EmailMessage,
**kwargs
):
"""
Returns an invitation email message. This can be easily overridden.
For instance, to send an HTML message, use the EmailMultiAlternatives message_class
and attach the additional conent.
"""
from_email = "%s %s <%s>" % (
sender.first_name,
sender.last_name,
email.utils.parseaddr(settings.DEFAULT_FROM_EMAIL)[1],
)
reply_to = "%s %s <%s>" % (sender.first_name, sender.last_name, sender.email)
headers = {"Reply-To": reply_to}
kwargs.update({"sender": sender, "recipient": recipient})
subject_template = loader.get_template(subject_template)
body_template = loader.get_template(body_template)
subject = subject_template.render(
kwargs
).strip() # Remove stray newline characters
body = body_template.render(kwargs)
return message_class(subject, body, from_email, [recipient], headers=headers) | [
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bennylope/django-organizations | organizations/base.py | OrgMeta.update_org | def update_org(cls, module):
"""
Adds the `users` field to the organization model
"""
try:
cls.module_registry[module]["OrgModel"]._meta.get_field("users")
except FieldDoesNotExist:
cls.module_registry[module]["OrgModel"].add_to_class(
"users",
models.ManyToManyField(
USER_MODEL,
through=cls.module_registry[module]["OrgUserModel"].__name__,
related_name="%(app_label)s_%(class)s",
),
)
cls.module_registry[module]["OrgModel"].invitation_model = cls.module_registry[
module
][
"OrgInviteModel"
] | python | def update_org(cls, module):
"""
Adds the `users` field to the organization model
"""
try:
cls.module_registry[module]["OrgModel"]._meta.get_field("users")
except FieldDoesNotExist:
cls.module_registry[module]["OrgModel"].add_to_class(
"users",
models.ManyToManyField(
USER_MODEL,
through=cls.module_registry[module]["OrgUserModel"].__name__,
related_name="%(app_label)s_%(class)s",
),
)
cls.module_registry[module]["OrgModel"].invitation_model = cls.module_registry[
module
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bennylope/django-organizations | organizations/base.py | OrgMeta.update_org_users | def update_org_users(cls, module):
"""
Adds the `user` field to the organization user model and the link to
the specific organization model.
"""
try:
cls.module_registry[module]["OrgUserModel"]._meta.get_field("user")
except FieldDoesNotExist:
cls.module_registry[module]["OrgUserModel"].add_to_class(
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USER_MODEL,
related_name="%(app_label)s_%(class)s",
on_delete=models.CASCADE,
),
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try:
cls.module_registry[module]["OrgUserModel"]._meta.get_field("organization")
except FieldDoesNotExist:
cls.module_registry[module]["OrgUserModel"].add_to_class(
"organization",
models.ForeignKey(
cls.module_registry[module]["OrgModel"],
related_name="organization_users",
on_delete=models.CASCADE,
),
) | python | def update_org_users(cls, module):
"""
Adds the `user` field to the organization user model and the link to
the specific organization model.
"""
try:
cls.module_registry[module]["OrgUserModel"]._meta.get_field("user")
except FieldDoesNotExist:
cls.module_registry[module]["OrgUserModel"].add_to_class(
"user",
models.ForeignKey(
USER_MODEL,
related_name="%(app_label)s_%(class)s",
on_delete=models.CASCADE,
),
)
try:
cls.module_registry[module]["OrgUserModel"]._meta.get_field("organization")
except FieldDoesNotExist:
cls.module_registry[module]["OrgUserModel"].add_to_class(
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models.ForeignKey(
cls.module_registry[module]["OrgModel"],
related_name="organization_users",
on_delete=models.CASCADE,
),
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bennylope/django-organizations | organizations/base.py | OrgMeta.update_org_owner | def update_org_owner(cls, module):
"""
Creates the links to the organization and organization user for the owner.
"""
try:
cls.module_registry[module]["OrgOwnerModel"]._meta.get_field(
"organization_user"
)
except FieldDoesNotExist:
cls.module_registry[module]["OrgOwnerModel"].add_to_class(
"organization_user",
models.OneToOneField(
cls.module_registry[module]["OrgUserModel"],
on_delete=models.CASCADE,
),
)
try:
cls.module_registry[module]["OrgOwnerModel"]._meta.get_field("organization")
except FieldDoesNotExist:
cls.module_registry[module]["OrgOwnerModel"].add_to_class(
"organization",
models.OneToOneField(
cls.module_registry[module]["OrgModel"],
related_name="owner",
on_delete=models.CASCADE,
),
) | python | def update_org_owner(cls, module):
"""
Creates the links to the organization and organization user for the owner.
"""
try:
cls.module_registry[module]["OrgOwnerModel"]._meta.get_field(
"organization_user"
)
except FieldDoesNotExist:
cls.module_registry[module]["OrgOwnerModel"].add_to_class(
"organization_user",
models.OneToOneField(
cls.module_registry[module]["OrgUserModel"],
on_delete=models.CASCADE,
),
)
try:
cls.module_registry[module]["OrgOwnerModel"]._meta.get_field("organization")
except FieldDoesNotExist:
cls.module_registry[module]["OrgOwnerModel"].add_to_class(
"organization",
models.OneToOneField(
cls.module_registry[module]["OrgModel"],
related_name="owner",
on_delete=models.CASCADE,
),
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bennylope/django-organizations | organizations/base.py | OrgMeta.update_org_invite | def update_org_invite(cls, module):
"""
Adds the links to the organization and to the organization user
"""
try:
cls.module_registry[module]["OrgInviteModel"]._meta.get_field("invited_by")
except FieldDoesNotExist:
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USER_MODEL,
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on_delete=models.CASCADE,
),
)
try:
cls.module_registry[module]["OrgInviteModel"]._meta.get_field("invitee")
except FieldDoesNotExist:
cls.module_registry[module]["OrgInviteModel"].add_to_class(
"invitee",
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USER_MODEL,
null=True,
blank=True,
related_name="%(app_label)s_%(class)s_invitations",
on_delete=models.CASCADE,
),
)
try:
cls.module_registry[module]["OrgInviteModel"]._meta.get_field(
"organization"
)
except FieldDoesNotExist:
cls.module_registry[module]["OrgInviteModel"].add_to_class(
"organization",
models.ForeignKey(
cls.module_registry[module]["OrgModel"],
related_name="organization_invites",
on_delete=models.CASCADE,
),
) | python | def update_org_invite(cls, module):
"""
Adds the links to the organization and to the organization user
"""
try:
cls.module_registry[module]["OrgInviteModel"]._meta.get_field("invited_by")
except FieldDoesNotExist:
cls.module_registry[module]["OrgInviteModel"].add_to_class(
"invited_by",
models.ForeignKey(
USER_MODEL,
related_name="%(app_label)s_%(class)s_sent_invitations",
on_delete=models.CASCADE,
),
)
try:
cls.module_registry[module]["OrgInviteModel"]._meta.get_field("invitee")
except FieldDoesNotExist:
cls.module_registry[module]["OrgInviteModel"].add_to_class(
"invitee",
models.ForeignKey(
USER_MODEL,
null=True,
blank=True,
related_name="%(app_label)s_%(class)s_invitations",
on_delete=models.CASCADE,
),
)
try:
cls.module_registry[module]["OrgInviteModel"]._meta.get_field(
"organization"
)
except FieldDoesNotExist:
cls.module_registry[module]["OrgInviteModel"].add_to_class(
"organization",
models.ForeignKey(
cls.module_registry[module]["OrgModel"],
related_name="organization_invites",
on_delete=models.CASCADE,
),
) | [
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bennylope/django-organizations | organizations/base.py | AbstractBaseOrganization.user_relation_name | def user_relation_name(self):
"""
Returns the string name of the related name to the user.
This provides a consistent interface across different organization
model classes.
"""
return "{0}_{1}".format(
self._meta.app_label.lower(), self.__class__.__name__.lower()
) | python | def user_relation_name(self):
"""
Returns the string name of the related name to the user.
This provides a consistent interface across different organization
model classes.
"""
return "{0}_{1}".format(
self._meta.app_label.lower(), self.__class__.__name__.lower()
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bennylope/django-organizations | organizations/base.py | AbstractBaseInvitation.activate | def activate(self, user):
"""
Updates the `invitee` value and saves the instance
Provided as a way of extending the behavior.
Args:
user: the newly created user
Returns:
the linking organization user
"""
org_user = self.organization.add_user(user, **self.activation_kwargs())
self.invitee = user
self.save()
return org_user | python | def activate(self, user):
"""
Updates the `invitee` value and saves the instance
Provided as a way of extending the behavior.
Args:
user: the newly created user
Returns:
the linking organization user
"""
org_user = self.organization.add_user(user, **self.activation_kwargs())
self.invitee = user
self.save()
return org_user | [
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bennylope/django-organizations | organizations/views/mixins.py | OrganizationUserMixin.get_object | def get_object(self):
""" Returns the OrganizationUser object based on the primary keys for both
the organization and the organization user.
"""
if hasattr(self, "organization_user"):
return self.organization_user
organization_pk = self.kwargs.get("organization_pk", None)
user_pk = self.kwargs.get("user_pk", None)
self.organization_user = get_object_or_404(
self.get_user_model().objects.select_related(),
user__pk=user_pk,
organization__pk=organization_pk,
)
return self.organization_user | python | def get_object(self):
""" Returns the OrganizationUser object based on the primary keys for both
the organization and the organization user.
"""
if hasattr(self, "organization_user"):
return self.organization_user
organization_pk = self.kwargs.get("organization_pk", None)
user_pk = self.kwargs.get("user_pk", None)
self.organization_user = get_object_or_404(
self.get_user_model().objects.select_related(),
user__pk=user_pk,
organization__pk=organization_pk,
)
return self.organization_user | [
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bennylope/django-organizations | organizations/backends/tokens.py | RegistrationTokenGenerator.check_token | def check_token(self, user, token):
"""
Check that a password reset token is correct for a given user.
"""
# Parse the token
try:
ts_b36, hash = token.split("-")
except ValueError:
return False
try:
ts = base36_to_int(ts_b36)
except ValueError:
return False
# Check that the timestamp/uid has not been tampered with
if not constant_time_compare(self._make_token_with_timestamp(user, ts), token):
return False
# Check the timestamp is within limit
if (self._num_days(self._today()) - ts) > REGISTRATION_TIMEOUT_DAYS:
return False
return True | python | def check_token(self, user, token):
"""
Check that a password reset token is correct for a given user.
"""
# Parse the token
try:
ts_b36, hash = token.split("-")
except ValueError:
return False
try:
ts = base36_to_int(ts_b36)
except ValueError:
return False
# Check that the timestamp/uid has not been tampered with
if not constant_time_compare(self._make_token_with_timestamp(user, ts), token):
return False
# Check the timestamp is within limit
if (self._num_days(self._today()) - ts) > REGISTRATION_TIMEOUT_DAYS:
return False
return True | [
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bennylope/django-organizations | organizations/utils.py | create_organization | def create_organization(
user,
name,
slug=None,
is_active=None,
org_defaults=None,
org_user_defaults=None,
**kwargs
):
"""
Returns a new organization, also creating an initial organization user who
is the owner.
The specific models can be specified if a custom organization app is used.
The simplest way would be to use a partial.
>>> from organizations.utils import create_organization
>>> from myapp.models import Account
>>> from functools import partial
>>> create_account = partial(create_organization, model=Account)
"""
org_model = kwargs.pop("model", None) or kwargs.pop(
"org_model", None
) or default_org_model()
kwargs.pop("org_user_model", None) # Discard deprecated argument
org_owner_model = org_model.owner.related.related_model
try:
# Django 1.9
org_user_model = org_model.organization_users.rel.related_model
except AttributeError:
# Django 1.8
org_user_model = org_model.organization_users.related.related_model
if org_defaults is None:
org_defaults = {}
if org_user_defaults is None:
if "is_admin" in model_field_names(org_user_model):
org_user_defaults = {"is_admin": True}
else:
org_user_defaults = {}
if slug is not None:
org_defaults.update({"slug": slug})
if is_active is not None:
org_defaults.update({"is_active": is_active})
org_defaults.update({"name": name})
organization = org_model.objects.create(**org_defaults)
org_user_defaults.update({"organization": organization, "user": user})
new_user = org_user_model.objects.create(**org_user_defaults)
org_owner_model.objects.create(
organization=organization, organization_user=new_user
)
return organization | python | def create_organization(
user,
name,
slug=None,
is_active=None,
org_defaults=None,
org_user_defaults=None,
**kwargs
):
"""
Returns a new organization, also creating an initial organization user who
is the owner.
The specific models can be specified if a custom organization app is used.
The simplest way would be to use a partial.
>>> from organizations.utils import create_organization
>>> from myapp.models import Account
>>> from functools import partial
>>> create_account = partial(create_organization, model=Account)
"""
org_model = kwargs.pop("model", None) or kwargs.pop(
"org_model", None
) or default_org_model()
kwargs.pop("org_user_model", None) # Discard deprecated argument
org_owner_model = org_model.owner.related.related_model
try:
# Django 1.9
org_user_model = org_model.organization_users.rel.related_model
except AttributeError:
# Django 1.8
org_user_model = org_model.organization_users.related.related_model
if org_defaults is None:
org_defaults = {}
if org_user_defaults is None:
if "is_admin" in model_field_names(org_user_model):
org_user_defaults = {"is_admin": True}
else:
org_user_defaults = {}
if slug is not None:
org_defaults.update({"slug": slug})
if is_active is not None:
org_defaults.update({"is_active": is_active})
org_defaults.update({"name": name})
organization = org_model.objects.create(**org_defaults)
org_user_defaults.update({"organization": organization, "user": user})
new_user = org_user_model.objects.create(**org_user_defaults)
org_owner_model.objects.create(
organization=organization, organization_user=new_user
)
return organization | [
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>>> from organizations.utils import create_organization
>>> from myapp.models import Account
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bennylope/django-organizations | organizations/utils.py | model_field_attr | def model_field_attr(model, model_field, attr):
"""
Returns the specified attribute for the specified field on the model class.
"""
fields = dict([(field.name, field) for field in model._meta.fields])
return getattr(fields[model_field], attr) | python | def model_field_attr(model, model_field, attr):
"""
Returns the specified attribute for the specified field on the model class.
"""
fields = dict([(field.name, field) for field in model._meta.fields])
return getattr(fields[model_field], attr) | [
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bennylope/django-organizations | organizations/backends/defaults.py | BaseBackend.get_form | def get_form(self, **kwargs):
"""Returns the form for registering or inviting a user"""
if not hasattr(self, "form_class"):
raise AttributeError(_("You must define a form_class"))
return self.form_class(**kwargs) | python | def get_form(self, **kwargs):
"""Returns the form for registering or inviting a user"""
if not hasattr(self, "form_class"):
raise AttributeError(_("You must define a form_class"))
return self.form_class(**kwargs) | [
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bennylope/django-organizations | organizations/backends/defaults.py | BaseBackend.activate_organizations | def activate_organizations(self, user):
"""
Activates the related organizations for the user.
It only activates the related organizations by model type - that is, if
there are multiple types of organizations then only organizations in
the provided model class are activated.
"""
try:
relation_name = self.org_model().user_relation_name
except TypeError:
# No org_model specified, raises a TypeError because NoneType is
# not callable. This the most sensible default:
relation_name = "organizations_organization"
organization_set = getattr(user, relation_name)
for org in organization_set.filter(is_active=False):
org.is_active = True
org.save() | python | def activate_organizations(self, user):
"""
Activates the related organizations for the user.
It only activates the related organizations by model type - that is, if
there are multiple types of organizations then only organizations in
the provided model class are activated.
"""
try:
relation_name = self.org_model().user_relation_name
except TypeError:
# No org_model specified, raises a TypeError because NoneType is
# not callable. This the most sensible default:
relation_name = "organizations_organization"
organization_set = getattr(user, relation_name)
for org in organization_set.filter(is_active=False):
org.is_active = True
org.save() | [
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bennylope/django-organizations | organizations/backends/defaults.py | BaseBackend.activate_view | def activate_view(self, request, user_id, token):
"""
View function that activates the given User by setting `is_active` to
true if the provided information is verified.
"""
try:
user = self.user_model.objects.get(id=user_id, is_active=False)
except self.user_model.DoesNotExist:
raise Http404(_("Your URL may have expired."))
if not RegistrationTokenGenerator().check_token(user, token):
raise Http404(_("Your URL may have expired."))
form = self.get_form(
data=request.POST or None, files=request.FILES or None, instance=user
)
if form.is_valid():
form.instance.is_active = True
user = form.save()
user.set_password(form.cleaned_data["password"])
user.save()
self.activate_organizations(user)
user = authenticate(
username=form.cleaned_data["username"],
password=form.cleaned_data["password"],
)
login(request, user)
return redirect(self.get_success_url())
return render(request, self.registration_form_template, {"form": form}) | python | def activate_view(self, request, user_id, token):
"""
View function that activates the given User by setting `is_active` to
true if the provided information is verified.
"""
try:
user = self.user_model.objects.get(id=user_id, is_active=False)
except self.user_model.DoesNotExist:
raise Http404(_("Your URL may have expired."))
if not RegistrationTokenGenerator().check_token(user, token):
raise Http404(_("Your URL may have expired."))
form = self.get_form(
data=request.POST or None, files=request.FILES or None, instance=user
)
if form.is_valid():
form.instance.is_active = True
user = form.save()
user.set_password(form.cleaned_data["password"])
user.save()
self.activate_organizations(user)
user = authenticate(
username=form.cleaned_data["username"],
password=form.cleaned_data["password"],
)
login(request, user)
return redirect(self.get_success_url())
return render(request, self.registration_form_template, {"form": form}) | [
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bennylope/django-organizations | organizations/backends/defaults.py | BaseBackend.send_reminder | def send_reminder(self, user, sender=None, **kwargs):
"""Sends a reminder email to the specified user"""
if user.is_active:
return False
token = RegistrationTokenGenerator().make_token(user)
kwargs.update({"token": token})
self.email_message(
user, self.reminder_subject, self.reminder_body, sender, **kwargs
).send() | python | def send_reminder(self, user, sender=None, **kwargs):
"""Sends a reminder email to the specified user"""
if user.is_active:
return False
token = RegistrationTokenGenerator().make_token(user)
kwargs.update({"token": token})
self.email_message(
user, self.reminder_subject, self.reminder_body, sender, **kwargs
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bennylope/django-organizations | organizations/backends/defaults.py | BaseBackend.email_message | def email_message(
self,
user,
subject_template,
body_template,
sender=None,
message_class=EmailMessage,
**kwargs
):
"""
Returns an email message for a new user. This can be easily overridden.
For instance, to send an HTML message, use the EmailMultiAlternatives message_class
and attach the additional conent.
"""
if sender:
try:
display_name = sender.get_full_name()
except (AttributeError, TypeError):
display_name = sender.get_username()
from_email = "%s <%s>" % (
display_name, email.utils.parseaddr(settings.DEFAULT_FROM_EMAIL)[1]
)
reply_to = "%s <%s>" % (display_name, sender.email)
else:
from_email = settings.DEFAULT_FROM_EMAIL
reply_to = from_email
headers = {"Reply-To": reply_to}
kwargs.update({"sender": sender, "user": user})
subject_template = loader.get_template(subject_template)
body_template = loader.get_template(body_template)
subject = subject_template.render(
kwargs
).strip() # Remove stray newline characters
body = body_template.render(kwargs)
return message_class(subject, body, from_email, [user.email], headers=headers) | python | def email_message(
self,
user,
subject_template,
body_template,
sender=None,
message_class=EmailMessage,
**kwargs
):
"""
Returns an email message for a new user. This can be easily overridden.
For instance, to send an HTML message, use the EmailMultiAlternatives message_class
and attach the additional conent.
"""
if sender:
try:
display_name = sender.get_full_name()
except (AttributeError, TypeError):
display_name = sender.get_username()
from_email = "%s <%s>" % (
display_name, email.utils.parseaddr(settings.DEFAULT_FROM_EMAIL)[1]
)
reply_to = "%s <%s>" % (display_name, sender.email)
else:
from_email = settings.DEFAULT_FROM_EMAIL
reply_to = from_email
headers = {"Reply-To": reply_to}
kwargs.update({"sender": sender, "user": user})
subject_template = loader.get_template(subject_template)
body_template = loader.get_template(body_template)
subject = subject_template.render(
kwargs
).strip() # Remove stray newline characters
body = body_template.render(kwargs)
return message_class(subject, body, from_email, [user.email], headers=headers) | [
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bennylope/django-organizations | organizations/backends/defaults.py | RegistrationBackend.register_by_email | def register_by_email(self, email, sender=None, request=None, **kwargs):
"""
Returns a User object filled with dummy data and not active, and sends
an invitation email.
"""
try:
user = self.user_model.objects.get(email=email)
except self.user_model.DoesNotExist:
user = self.user_model.objects.create(
username=self.get_username(),
email=email,
password=self.user_model.objects.make_random_password(),
)
user.is_active = False
user.save()
self.send_activation(user, sender, **kwargs)
return user | python | def register_by_email(self, email, sender=None, request=None, **kwargs):
"""
Returns a User object filled with dummy data and not active, and sends
an invitation email.
"""
try:
user = self.user_model.objects.get(email=email)
except self.user_model.DoesNotExist:
user = self.user_model.objects.create(
username=self.get_username(),
email=email,
password=self.user_model.objects.make_random_password(),
)
user.is_active = False
user.save()
self.send_activation(user, sender, **kwargs)
return user | [
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bennylope/django-organizations | organizations/backends/defaults.py | RegistrationBackend.send_activation | def send_activation(self, user, sender=None, **kwargs):
"""
Invites a user to join the site
"""
if user.is_active:
return False
token = self.get_token(user)
kwargs.update({"token": token})
self.email_message(
user, self.activation_subject, self.activation_body, sender, **kwargs
).send() | python | def send_activation(self, user, sender=None, **kwargs):
"""
Invites a user to join the site
"""
if user.is_active:
return False
token = self.get_token(user)
kwargs.update({"token": token})
self.email_message(
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bennylope/django-organizations | organizations/backends/defaults.py | RegistrationBackend.create_view | def create_view(self, request):
"""
Initiates the organization and user account creation process
"""
try:
if request.user.is_authenticated():
return redirect("organization_add")
except TypeError:
if request.user.is_authenticated:
return redirect("organization_add")
form = org_registration_form(self.org_model)(request.POST or None)
if form.is_valid():
try:
user = self.user_model.objects.get(email=form.cleaned_data["email"])
except self.user_model.DoesNotExist:
user = self.user_model.objects.create(
username=self.get_username(),
email=form.cleaned_data["email"],
password=self.user_model.objects.make_random_password(),
)
user.is_active = False
user.save()
else:
return redirect("organization_add")
organization = create_organization(
user,
form.cleaned_data["name"],
form.cleaned_data["slug"],
is_active=False,
)
return render(
request,
self.activation_success_template,
{"user": user, "organization": organization},
)
return render(request, self.registration_form_template, {"form": form}) | python | def create_view(self, request):
"""
Initiates the organization and user account creation process
"""
try:
if request.user.is_authenticated():
return redirect("organization_add")
except TypeError:
if request.user.is_authenticated:
return redirect("organization_add")
form = org_registration_form(self.org_model)(request.POST or None)
if form.is_valid():
try:
user = self.user_model.objects.get(email=form.cleaned_data["email"])
except self.user_model.DoesNotExist:
user = self.user_model.objects.create(
username=self.get_username(),
email=form.cleaned_data["email"],
password=self.user_model.objects.make_random_password(),
)
user.is_active = False
user.save()
else:
return redirect("organization_add")
organization = create_organization(
user,
form.cleaned_data["name"],
form.cleaned_data["slug"],
is_active=False,
)
return render(
request,
self.activation_success_template,
{"user": user, "organization": organization},
)
return render(request, self.registration_form_template, {"form": form}) | [
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bennylope/django-organizations | organizations/backends/defaults.py | InvitationBackend.invite_by_email | def invite_by_email(self, email, sender=None, request=None, **kwargs):
"""Creates an inactive user with the information we know and then sends
an invitation email for that user to complete registration.
If your project uses email in a different way then you should make to
extend this method as it only checks the `email` attribute for Users.
"""
try:
user = self.user_model.objects.get(email=email)
except self.user_model.DoesNotExist:
# TODO break out user creation process
if "username" in inspect.getargspec(
self.user_model.objects.create_user
).args:
user = self.user_model.objects.create(
username=self.get_username(),
email=email,
password=self.user_model.objects.make_random_password(),
)
else:
user = self.user_model.objects.create(
email=email, password=self.user_model.objects.make_random_password()
)
user.is_active = False
user.save()
self.send_invitation(user, sender, **kwargs)
return user | python | def invite_by_email(self, email, sender=None, request=None, **kwargs):
"""Creates an inactive user with the information we know and then sends
an invitation email for that user to complete registration.
If your project uses email in a different way then you should make to
extend this method as it only checks the `email` attribute for Users.
"""
try:
user = self.user_model.objects.get(email=email)
except self.user_model.DoesNotExist:
# TODO break out user creation process
if "username" in inspect.getargspec(
self.user_model.objects.create_user
).args:
user = self.user_model.objects.create(
username=self.get_username(),
email=email,
password=self.user_model.objects.make_random_password(),
)
else:
user = self.user_model.objects.create(
email=email, password=self.user_model.objects.make_random_password()
)
user.is_active = False
user.save()
self.send_invitation(user, sender, **kwargs)
return user | [
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bennylope/django-organizations | organizations/backends/defaults.py | InvitationBackend.send_invitation | def send_invitation(self, user, sender=None, **kwargs):
"""An intermediary function for sending an invitation email that
selects the templates, generating the token, and ensuring that the user
has not already joined the site.
"""
if user.is_active:
return False
token = self.get_token(user)
kwargs.update({"token": token})
self.email_message(
user, self.invitation_subject, self.invitation_body, sender, **kwargs
).send()
return True | python | def send_invitation(self, user, sender=None, **kwargs):
"""An intermediary function for sending an invitation email that
selects the templates, generating the token, and ensuring that the user
has not already joined the site.
"""
if user.is_active:
return False
token = self.get_token(user)
kwargs.update({"token": token})
self.email_message(
user, self.invitation_subject, self.invitation_body, sender, **kwargs
).send()
return True | [
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bennylope/django-organizations | organizations/backends/defaults.py | InvitationBackend.send_notification | def send_notification(self, user, sender=None, **kwargs):
"""
An intermediary function for sending an notification email informing
a pre-existing, active user that they have been added to a new
organization.
"""
if not user.is_active:
return False
self.email_message(
user, self.notification_subject, self.notification_body, sender, **kwargs
).send()
return True | python | def send_notification(self, user, sender=None, **kwargs):
"""
An intermediary function for sending an notification email informing
a pre-existing, active user that they have been added to a new
organization.
"""
if not user.is_active:
return False
self.email_message(
user, self.notification_subject, self.notification_body, sender, **kwargs
).send()
return True | [
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bennylope/django-organizations | organizations/abstract.py | AbstractOrganization.add_user | def add_user(self, user, is_admin=False):
"""
Adds a new user and if the first user makes the user an admin and
the owner.
"""
users_count = self.users.all().count()
if users_count == 0:
is_admin = True
# TODO get specific org user?
org_user = self._org_user_model.objects.create(
user=user, organization=self, is_admin=is_admin
)
if users_count == 0:
# TODO get specific org user?
self._org_owner_model.objects.create(
organization=self, organization_user=org_user
)
# User added signal
user_added.send(sender=self, user=user)
return org_user | python | def add_user(self, user, is_admin=False):
"""
Adds a new user and if the first user makes the user an admin and
the owner.
"""
users_count = self.users.all().count()
if users_count == 0:
is_admin = True
# TODO get specific org user?
org_user = self._org_user_model.objects.create(
user=user, organization=self, is_admin=is_admin
)
if users_count == 0:
# TODO get specific org user?
self._org_owner_model.objects.create(
organization=self, organization_user=org_user
)
# User added signal
user_added.send(sender=self, user=user)
return org_user | [
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bennylope/django-organizations | organizations/abstract.py | AbstractOrganization.remove_user | def remove_user(self, user):
"""
Deletes a user from an organization.
"""
org_user = self._org_user_model.objects.get(user=user, organization=self)
org_user.delete()
# User removed signal
user_removed.send(sender=self, user=user) | python | def remove_user(self, user):
"""
Deletes a user from an organization.
"""
org_user = self._org_user_model.objects.get(user=user, organization=self)
org_user.delete()
# User removed signal
user_removed.send(sender=self, user=user) | [
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bennylope/django-organizations | organizations/abstract.py | AbstractOrganization.get_or_add_user | def get_or_add_user(self, user, **kwargs):
"""
Adds a new user to the organization, and if it's the first user makes
the user an admin and the owner. Uses the `get_or_create` method to
create or return the existing user.
`user` should be a user instance, e.g. `auth.User`.
Returns the same tuple as the `get_or_create` method, the
`OrganizationUser` and a boolean value indicating whether the
OrganizationUser was created or not.
"""
is_admin = kwargs.pop("is_admin", False)
users_count = self.users.all().count()
if users_count == 0:
is_admin = True
org_user, created = self._org_user_model.objects.get_or_create(
organization=self, user=user, defaults={"is_admin": is_admin}
)
if users_count == 0:
self._org_owner_model.objects.create(
organization=self, organization_user=org_user
)
if created:
# User added signal
user_added.send(sender=self, user=user)
return org_user, created | python | def get_or_add_user(self, user, **kwargs):
"""
Adds a new user to the organization, and if it's the first user makes
the user an admin and the owner. Uses the `get_or_create` method to
create or return the existing user.
`user` should be a user instance, e.g. `auth.User`.
Returns the same tuple as the `get_or_create` method, the
`OrganizationUser` and a boolean value indicating whether the
OrganizationUser was created or not.
"""
is_admin = kwargs.pop("is_admin", False)
users_count = self.users.all().count()
if users_count == 0:
is_admin = True
org_user, created = self._org_user_model.objects.get_or_create(
organization=self, user=user, defaults={"is_admin": is_admin}
)
if users_count == 0:
self._org_owner_model.objects.create(
organization=self, organization_user=org_user
)
if created:
# User added signal
user_added.send(sender=self, user=user)
return org_user, created | [
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bennylope/django-organizations | organizations/abstract.py | AbstractOrganization.change_owner | def change_owner(self, new_owner):
"""
Changes ownership of an organization.
"""
old_owner = self.owner.organization_user
self.owner.organization_user = new_owner
self.owner.save()
# Owner changed signal
owner_changed.send(sender=self, old=old_owner, new=new_owner) | python | def change_owner(self, new_owner):
"""
Changes ownership of an organization.
"""
old_owner = self.owner.organization_user
self.owner.organization_user = new_owner
self.owner.save()
# Owner changed signal
owner_changed.send(sender=self, old=old_owner, new=new_owner) | [
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bennylope/django-organizations | organizations/abstract.py | AbstractOrganization.is_admin | def is_admin(self, user):
"""
Returns True is user is an admin in the organization, otherwise false
"""
return True if self.organization_users.filter(
user=user, is_admin=True
) else False | python | def is_admin(self, user):
"""
Returns True is user is an admin in the organization, otherwise false
"""
return True if self.organization_users.filter(
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) else False | [
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] | 85f753a8f7a8f0f31636c9209fb69e7030a5c79a | https://github.com/bennylope/django-organizations/blob/85f753a8f7a8f0f31636c9209fb69e7030a5c79a/organizations/abstract.py#L189-L195 | train | 206,592 |
bennylope/django-organizations | organizations/abstract.py | AbstractOrganizationUser.delete | def delete(self, using=None):
"""
If the organization user is also the owner, this should not be deleted
unless it's part of a cascade from the Organization.
If there is no owner then the deletion should proceed.
"""
from organizations.exceptions import OwnershipRequired
try:
if self.organization.owner.organization_user.pk == self.pk:
raise OwnershipRequired(
_(
"Cannot delete organization owner "
"before organization or transferring ownership."
)
)
# TODO This line presumes that OrgOwner model can't be modified
except self._org_owner_model.DoesNotExist:
pass
super(AbstractBaseOrganizationUser, self).delete(using=using) | python | def delete(self, using=None):
"""
If the organization user is also the owner, this should not be deleted
unless it's part of a cascade from the Organization.
If there is no owner then the deletion should proceed.
"""
from organizations.exceptions import OwnershipRequired
try:
if self.organization.owner.organization_user.pk == self.pk:
raise OwnershipRequired(
_(
"Cannot delete organization owner "
"before organization or transferring ownership."
)
)
# TODO This line presumes that OrgOwner model can't be modified
except self._org_owner_model.DoesNotExist:
pass
super(AbstractBaseOrganizationUser, self).delete(using=using) | [
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bennylope/django-organizations | organizations/abstract.py | AbstractOrganizationOwner.save | def save(self, *args, **kwargs):
"""
Extends the default save method by verifying that the chosen
organization user is associated with the organization.
Method validates against the primary key of the organization because
when validating an inherited model it may be checking an instance of
`Organization` against an instance of `CustomOrganization`. Mutli-table
inheritence means the database keys will be identical though.
"""
from organizations.exceptions import OrganizationMismatch
if self.organization_user.organization.pk != self.organization.pk:
raise OrganizationMismatch
else:
super(AbstractBaseOrganizationOwner, self).save(*args, **kwargs) | python | def save(self, *args, **kwargs):
"""
Extends the default save method by verifying that the chosen
organization user is associated with the organization.
Method validates against the primary key of the organization because
when validating an inherited model it may be checking an instance of
`Organization` against an instance of `CustomOrganization`. Mutli-table
inheritence means the database keys will be identical though.
"""
from organizations.exceptions import OrganizationMismatch
if self.organization_user.organization.pk != self.organization.pk:
raise OrganizationMismatch
else:
super(AbstractBaseOrganizationOwner, self).save(*args, **kwargs) | [
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PX4/pyulog | pyulog/ulog2kml.py | _kml_default_colors | def _kml_default_colors(x):
""" flight mode to color conversion """
x = max([x, 0])
colors_arr = [simplekml.Color.red, simplekml.Color.green, simplekml.Color.blue,
simplekml.Color.violet, simplekml.Color.yellow, simplekml.Color.orange,
simplekml.Color.burlywood, simplekml.Color.azure, simplekml.Color.lightblue,
simplekml.Color.lawngreen, simplekml.Color.indianred, simplekml.Color.hotpink]
return colors_arr[x] | python | def _kml_default_colors(x):
""" flight mode to color conversion """
x = max([x, 0])
colors_arr = [simplekml.Color.red, simplekml.Color.green, simplekml.Color.blue,
simplekml.Color.violet, simplekml.Color.yellow, simplekml.Color.orange,
simplekml.Color.burlywood, simplekml.Color.azure, simplekml.Color.lightblue,
simplekml.Color.lawngreen, simplekml.Color.indianred, simplekml.Color.hotpink]
return colors_arr[x] | [
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PX4/pyulog | pyulog/ulog2kml.py | _kml_add_camera_triggers | def _kml_add_camera_triggers(kml, ulog, camera_trigger_topic_name, altitude_offset):
"""
Add camera trigger points to the map
"""
data = ulog.data_list
topic_instance = 0
cur_dataset = [elem for elem in data
if elem.name == camera_trigger_topic_name and elem.multi_id == topic_instance]
if len(cur_dataset) > 0:
cur_dataset = cur_dataset[0]
pos_lon = cur_dataset.data['lon']
pos_lat = cur_dataset.data['lat']
pos_alt = cur_dataset.data['alt']
sequence = cur_dataset.data['seq']
for i in range(len(pos_lon)):
pnt = kml.newpoint(name='Camera Trigger '+str(sequence[i]))
pnt.coords = [(pos_lon[i], pos_lat[i], pos_alt[i] + altitude_offset)] | python | def _kml_add_camera_triggers(kml, ulog, camera_trigger_topic_name, altitude_offset):
"""
Add camera trigger points to the map
"""
data = ulog.data_list
topic_instance = 0
cur_dataset = [elem for elem in data
if elem.name == camera_trigger_topic_name and elem.multi_id == topic_instance]
if len(cur_dataset) > 0:
cur_dataset = cur_dataset[0]
pos_lon = cur_dataset.data['lon']
pos_lat = cur_dataset.data['lat']
pos_alt = cur_dataset.data['alt']
sequence = cur_dataset.data['seq']
for i in range(len(pos_lon)):
pnt = kml.newpoint(name='Camera Trigger '+str(sequence[i]))
pnt.coords = [(pos_lon[i], pos_lat[i], pos_alt[i] + altitude_offset)] | [
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PX4/pyulog | pyulog/core.py | ULog.get_dataset | def get_dataset(self, name, multi_instance=0):
""" get a specific dataset.
example:
try:
gyro_data = ulog.get_dataset('sensor_gyro')
except (KeyError, IndexError, ValueError) as error:
print(type(error), "(sensor_gyro):", error)
:param name: name of the dataset
:param multi_instance: the multi_id, defaults to the first
:raises KeyError, IndexError, ValueError: if name or instance not found
"""
return [elem for elem in self._data_list
if elem.name == name and elem.multi_id == multi_instance][0] | python | def get_dataset(self, name, multi_instance=0):
""" get a specific dataset.
example:
try:
gyro_data = ulog.get_dataset('sensor_gyro')
except (KeyError, IndexError, ValueError) as error:
print(type(error), "(sensor_gyro):", error)
:param name: name of the dataset
:param multi_instance: the multi_id, defaults to the first
:raises KeyError, IndexError, ValueError: if name or instance not found
"""
return [elem for elem in self._data_list
if elem.name == name and elem.multi_id == multi_instance][0] | [
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example:
try:
gyro_data = ulog.get_dataset('sensor_gyro')
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print(type(error), "(sensor_gyro):", error)
:param name: name of the dataset
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PX4/pyulog | pyulog/core.py | ULog._add_message_info_multiple | def _add_message_info_multiple(self, msg_info):
""" add a message info multiple to self._msg_info_multiple_dict """
if msg_info.key in self._msg_info_multiple_dict:
if msg_info.is_continued:
self._msg_info_multiple_dict[msg_info.key][-1].append(msg_info.value)
else:
self._msg_info_multiple_dict[msg_info.key].append([msg_info.value])
else:
self._msg_info_multiple_dict[msg_info.key] = [[msg_info.value]] | python | def _add_message_info_multiple(self, msg_info):
""" add a message info multiple to self._msg_info_multiple_dict """
if msg_info.key in self._msg_info_multiple_dict:
if msg_info.is_continued:
self._msg_info_multiple_dict[msg_info.key][-1].append(msg_info.value)
else:
self._msg_info_multiple_dict[msg_info.key].append([msg_info.value])
else:
self._msg_info_multiple_dict[msg_info.key] = [[msg_info.value]] | [
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PX4/pyulog | pyulog/core.py | ULog._load_file | def _load_file(self, log_file, message_name_filter_list):
""" load and parse an ULog file into memory """
if isinstance(log_file, str):
self._file_handle = open(log_file, "rb")
else:
self._file_handle = log_file
# parse the whole file
self._read_file_header()
self._last_timestamp = self._start_timestamp
self._read_file_definitions()
if self.has_data_appended and len(self._appended_offsets) > 0:
if self._debug:
print('This file has data appended')
for offset in self._appended_offsets:
self._read_file_data(message_name_filter_list, read_until=offset)
self._file_handle.seek(offset)
# read the whole file, or the rest if data appended
self._read_file_data(message_name_filter_list)
self._file_handle.close()
del self._file_handle | python | def _load_file(self, log_file, message_name_filter_list):
""" load and parse an ULog file into memory """
if isinstance(log_file, str):
self._file_handle = open(log_file, "rb")
else:
self._file_handle = log_file
# parse the whole file
self._read_file_header()
self._last_timestamp = self._start_timestamp
self._read_file_definitions()
if self.has_data_appended and len(self._appended_offsets) > 0:
if self._debug:
print('This file has data appended')
for offset in self._appended_offsets:
self._read_file_data(message_name_filter_list, read_until=offset)
self._file_handle.seek(offset)
# read the whole file, or the rest if data appended
self._read_file_data(message_name_filter_list)
self._file_handle.close()
del self._file_handle | [
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