From 59a46082348d0ea6cbc9ebc2b2c3aad956cd13dd Mon Sep 17 00:00:00 2001 From: Henry Wilde Date: Wed, 3 Apr 2024 12:07:33 +0100 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 1 + docs/_static/app-home-screenshot.png | Bin 0 -> 150988 bytes docs/reference/bloom_filters.html | 946 ++++++++ docs/reference/config.html | 703 ++++++ docs/reference/embedder.html | 1602 +++++++++++++ docs/reference/encryption.html | 918 ++++++++ docs/reference/features.html | 1291 +++++++++++ docs/reference/index.html | 687 ++++++ docs/reference/utils.html | 916 ++++++++ docs/tutorials/example-febrl.html | 848 +++++++ docs/tutorials/example-verknupfung.html | 831 +++++++ docs/tutorials/in-the-cloud.html | 1003 ++++++++ docs/tutorials/index.html | 746 ++++++ docs/tutorials/run-through.html | 958 ++++++++ index.html | 521 +++++ listings.json | 11 + search.json | 317 +++ site_libs/bootstrap/bootstrap-dark.min.css | 10 + site_libs/bootstrap/bootstrap-icons.css | 2018 +++++++++++++++++ site_libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 164168 bytes site_libs/bootstrap/bootstrap.min.css | 10 + site_libs/bootstrap/bootstrap.min.js | 7 + site_libs/clipboard/clipboard.min.js | 7 + site_libs/quarto-html/anchor.min.js | 9 + site_libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting-dark.css | 189 ++ .../quarto-syntax-highlighting.css | 203 ++ site_libs/quarto-html/quarto.js | 902 ++++++++ site_libs/quarto-html/tippy.css | 1 + site_libs/quarto-html/tippy.umd.min.js | 2 + site_libs/quarto-listing/list.min.js | 2 + site_libs/quarto-listing/quarto-listing.js | 243 ++ site_libs/quarto-nav/headroom.min.js | 7 + site_libs/quarto-nav/quarto-nav.js | 277 +++ site_libs/quarto-search/autocomplete.umd.js | 3 + site_libs/quarto-search/fuse.min.js | 9 + site_libs/quarto-search/quarto-search.js | 1140 ++++++++++ 37 files changed, 17344 insertions(+) create mode 100644 .nojekyll create mode 100644 docs/_static/app-home-screenshot.png create mode 100644 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zB|^4MX8I8K*EdM?hVq%wiAq)+gTr~{i5L+W)(=dS2+iy3+%e(IgWds4v@$q5wCAd4 zOP|%UYAg>hhNy)FUmBiCK$8cY2SW^wJD1qsg?hLnbEC@FHI$lY4mw^78AkXOpt6?? zZ=_!r`1A&1=4yuQhw#J>e_zwiiV8NPl=iAOtPZ_))HyGk9FI4^lW~oatFpM81?4yiJYWm!`xoC8Amt+&k$<5&*43SuJcaO`s8mU1 zz%`AiZdo01y^?>KR9f*66!Unh})7%Il^VN4sJ9>e5U9InODyYxH=_gZ|0j)&llAB58# z$19_Pf0@#K+|AOTk7mw4Or#x;rQ|4741nMU#B~17eE$g{qg$f_N8F)0Atn@N~ z@l$T0=Q=WY{H%|l5QpqefBPPMrx52JWlf< zMJ~0F6+k7OqhbyUdq=i{3a^cCZ1H<~aT_r=BucTH35h!xdR2qCV{q-qL5EijR#sR5 e6ujK=B}t&>=5*3c!hH$gXLIb-(UPBG*Zu>2lsvZp literal 0 HcmV?d00001 diff --git a/docs/reference/bloom_filters.html b/docs/reference/bloom_filters.html new file mode 100644 index 0000000..019ea37 --- /dev/null +++ b/docs/reference/bloom_filters.html @@ -0,0 +1,946 @@ + + + + + + + + + +`pprl` – bloom_filters + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+
+ + +
+ +
+ + +
+ + + +
+ + + +
+

bloom_filters

+

embedder.bloom_filters

+

Module for the Bloom filter encoder.

+
+

Classes

+ + + + + + + + + + + + + +
NameDescription
BloomFilterEncoderEncoder of tokens and features via hashing and a Bloom filter.
+
+

BloomFilterEncoder

+

embedder.bloom_filters.BloomFilterEncoder(self, size, num_hashes=3, offset=1, salt=None)

+

Encoder of tokens and features via hashing and a Bloom filter.

+

The process for creating a cryptographically secure Bloom filter encoding of a set of tokens is as follows:

+
    +
  1. Compute the hash digest for your tokens
    2. +
  2. Convert the digest bytes into integers
    3. +
  3. Map the integer to a bloom filter vector (modulo b, where b represents the length of the vector)
    4. +
+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
sizeintSize of the Bloom filter.required
num_hashesintNumber of hashes to perform. Defaults to three.3
offsetintOffset for Bloom filter indices to allow for masking. Defaults to one.1
saltstr | NoneCryptographic salt appended to tokens prior to hashing.None
+
+
+

Attributes

+ + + + + + + + + + + + + + + +
NameTypeDescription
hash_functionfuncHashing function (hashlib.sha1).
+
+
+

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameDescription
big_int_to_vecConvert an integer vector into indices for a Bloom vector.
bloom_filter_vectorConvert a feature vector into indices for a Bloom vector.
bloom_filter_vector_collision_fractionConvert a feature vector and return its collision fraction.
feature_to_big_int_reprConvert a feature vector into an integer vector.
+
+
big_int_to_vec
+

embedder.bloom_filters.BloomFilterEncoder.big_int_to_vec(feature_ints, offset=1)

+

Convert an integer vector into indices for a Bloom vector.

+

This conversion inserts 1 at the location derived from the integer vector, which is an integer representation of a deterministic hash value, modulo to the size of the Bloom filter.

+
+
Parameters
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
feature_intslistList of integer values representing the feature.required
offsetintAn offset to indices to allow for masking. Defaults to one.1
+
+
+
Returns
+ + + + + + + + + + + + + +
TypeDescription
listList of integers representing an index on the Bloom filter.
+
+
+
+
bloom_filter_vector
+

embedder.bloom_filters.BloomFilterEncoder.bloom_filter_vector(feature)

+

Convert a feature vector into indices for a Bloom vector.

+

The index vector uses an optional offset for masking.

+
+
Parameters
+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
featurelistList of features to be converted.required
+
+
+
Returns
+ + + + + + + + + + + + + +
TypeDescription
listIndex values used to create the Bloom filter vector.
+
+
+
+
bloom_filter_vector_collision_fraction
+

embedder.bloom_filters.BloomFilterEncoder.bloom_filter_vector_collision_fraction(feature)

+

Convert a feature vector and return its collision fraction.

+

The index vector uses an optional offset for masking.

+
+
Parameters
+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
featurelistList of features to be processed.required
+
+
+
Returns
+ + + + + + + + + + + + + + + + + +
TypeDescription
listIndex values used to create the Bloom filter vector.
floatProportion of repeated indices.
+
+
+
+
feature_to_big_int_repr
+

embedder.bloom_filters.BloomFilterEncoder.feature_to_big_int_repr(feature)

+

Convert a feature vector into an integer vector.

+

This conversion first generates a hash digest for each member of the feature vector and then converts them to an integer.

+
+
Parameters
+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
featurelistList of features to be processed.required
+
+
+
Returns
+ + + + + + + + + + + + + +
TypeDescription
listList of features as integers.
+ + +
+
+
+
+
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/reference/config.html b/docs/reference/config.html new file mode 100644 index 0000000..53785f2 --- /dev/null +++ b/docs/reference/config.html @@ -0,0 +1,703 @@ + + + + + + + + + +`pprl` – config + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + +
+

config

+

config

+

Functions for handling PPRL configuration.

+
+

Functions

+ + + + + + + + + + + + + +
NameDescription
load_environmentLoad the configuration file as a dictionary.
+
+

load_environment

+

config.load_environment(path=None)

+

Load the configuration file as a dictionary.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pathstrLocation of the configuration file to load. If not specified, try to load the configuration file from the root of the pprl installation called .env.None
+
+
+

Returns

+ ++++ + + + + + + + + + + + + +
TypeDescription
collections.collections.OrderedDictMapping of the key-value pairs in the configuration file.
+ + +
+
+
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/reference/embedder.html b/docs/reference/embedder.html new file mode 100644 index 0000000..ea46f4e --- /dev/null +++ b/docs/reference/embedder.html @@ -0,0 +1,1602 @@ + + + + + + + + + +`pprl` – embedder + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + +
+

embedder

+

embedder.embedder

+

Classes and functions for handling embedding objects.

+
+

Classes

+ + + + + + + + + + + + + + + + + + + + + +
NameDescription
EmbeddedDataFrameA data frame with a reference to an Embedder object.
EmbedderClass for embedding a dataset.
SimilarityArrayAugmented NumPy array of similarity scores with extra attributes.
+
+

EmbeddedDataFrame

+

embedder.embedder.EmbeddedDataFrame(self, data, embedder, update_norms=True, update_thresholds=False, *args, **kwargs)

+

A data frame with a reference to an Embedder object.

+

An EmbeddedDataFrame (EDF) instance wraps together a pandas.DataFrame with a reference to a pprl.embedder.Embedder object. An EDF also has a mandatory bf_indices column, describing the Bloom filter indices used for linkage.

+

The EDF instance can also calculate bf_norms and thresholds columns which are used in the Embedder.compare() method to compute pprl.embedder.SimilarityArray instances.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
datanumpy.numpy.ndarray | typing.Iterable | dict | pandas.pandas.DataFrameData to which to attach the embedder. Must include a bf_indices column with list data type.required
embedderpprl.embedder.embedder.EmbedderA compatible embedder object for the Bloom filter columns in data.required
update_normsboolWhether to update the Bloom filter norms on creation. Defaults to False.True
update_thresholdsboolWhether to update the similarity thresholds on creation. Defaults to True.False
*argsAdditional positional arguments to pass to pandas.DataFrame along with data.()
**kwargsAdditional keyword arguments to pass to pandas.DataFrame along with data.{}
+
+
+

Attributes

+ +++++ + + + + + + + + + + + + + + +
NameTypeDescription
embedder_checksumstrHexadecimal string digest from self.embedder.
+
+
+

Notes

+

An EDF instance is usually created from an existing Embedder object by calling the embedder.embed() method. It can also be initialised using an embedder and a pandas.DataFrame that already has a bf_indices column via EmbeddedDataFrame(df, embedder).

+

If using the second method it is up to the user to ensure that the Embedder instance is compatible with the bf_indices column (as well as bf_norms and thresholds, if present) in the data frame. If in doubt, call edf.update_norms() and edf.update_thresholds() to refresh them.

+
+
+

Methods

+ + + + + + + + + + + + + + + + + + + + + +
NameDescription
to_bloom_matrixConvert Bloom filter indices into a binary matrix.
update_normsGenerate vector norms (wrt. self.embedder) for each row.
update_thresholdsGenerate matching thresholds for each row of the data.
+
+
to_bloom_matrix
+

embedder.embedder.EmbeddedDataFrame.to_bloom_matrix()

+

Convert Bloom filter indices into a binary matrix.

+

The matrix has a row for each row in the EDF. The number of columns is equal to self.embedder.bf_size + self.embedder.offset. This representation is used in the Embedder.compare() method.

+
+
Returns
+ ++++ + + + + + + + + + + + + +
TypeDescription
numpy.numpy.ndarrayBinary array of size (len(self), self.bf_size + 1).
+
+
+
+
update_norms
+

embedder.embedder.EmbeddedDataFrame.update_norms()

+

Generate vector norms (wrt. self.embedder) for each row.

+

The vector norm is used to scale the (Soft) Cosine similarity scores.

+
+
Attributes
+ +++++ + + + + + + + + + + + + + + +
NameTypeDescription
data.bf_normslistColumn of vector norms for each row in the EDF.
+
+
+
+
update_thresholds
+

embedder.embedder.EmbeddedDataFrame.update_thresholds()

+

Generate matching thresholds for each row of the data.

+

The threshold is the minimum similarity score that will be matched. It is found by getting the pairwise similarities between each row and the other rows in the same EDF, and taking the maximum of these.

+
+
Attributes
+ +++++ + + + + + + + + + + + + + + +
NameTypeDescription
data.thresholdsnumpy.numpy.ndarrayColumn for maximum similarity of each row within the EDF.
+
+
+
+
+
+

Embedder

+

embedder.embedder.Embedder(self, feature_factory, ff_args=None, bf_size=2 ** 10, num_hashes=2, offset=1, salt=None)

+

Class for embedding a dataset.

+

Each instance of the Embedder class represents an embedding space on personal data features. An Embedder instance is defined by three things:

+
    +
  1. A set of Bloom filter parameters
    2. +
  2. A set of feature factory functions
    3. +
  3. An embedding matrix that corresponds to the above
    4. +
+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
feature_factorydictMapping from dataset columns to feature generation functions.required
ff_argsdict[str, dict] | NoneMapping from dataset columns to keyword arguments for their respective feature generation functions.None
bf_sizeintSize of the Bloom filter. Default is 2**10.2 ** 10
num_hashesintNumber of hashes to perform. Default is two.2
offsetintOffset for Bloom filter to enable masking. Default is one.1
saltstr | NoneCryptographic salt added to tokens from the data before hashing.None
+
+
+

Attributes

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
scm_matrixnumpy.numpy.ndarraySoft Cosine Measure matrix. Initialised as an identity matrix of size bf_size + offset.
freq_matr_matchednumpy.numpy.ndarrayMatched frequency matrix for computing scm_matrix. Initialised as an identity matrix of size bf_size + offset.
freq_matr_unmatchednumpy.numpy.ndarrayUnmatched frequency matrix for computing scm_matrix. Initialised as an identity matrix of size bf_size + offset.
checksumstrHexadecimal string digest of the feature factory, SCM matrix, and other embedding parameters. Used to check an embedder is compatible with an EmbeddedDataFrame.
+
+
+

Notes

+

When an instance is initialised in code, the embedding matrix is initialised as an identity matrix; the matrix can then be trained using a pair of datasets with known match status and the trained Embedder instance pickled to file. The pre-trained Embedder instance can then be reinitialised from the pickle file.

+

Both the untrained and trained instances provide embed() and compare() methods. Comparing datasets using an untrained Embedder instance is equivalent to calculating Cosine similarities on ordinary Bloom filters. Comparing datasets using a pre-trained Embedder calculates the Soft Cosine Measure between Bloom filters. The Soft Cosine Measure embedding matrix is trained using an experimental method.

+
+
+

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameDescription
compareCalculate a SimilarityArray on two EDFs.
embedEncode data columns into features from Bloom embedding.
from_pickleInitialise Embedder instance from pickle file.
to_pickleSave Embedder instance to pickle file.
trainFit Soft Cosine Measure matrix to two matched datasets.
+
+
compare
+

embedder.embedder.Embedder.compare(edf1, edf2, require_thresholds=True)

+

Calculate a SimilarityArray on two EDFs.

+

Given two EDFs, calculate all pairwise Soft Cosine Similarities between rows.

+
+
Parameters
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
edf1pprl.embedder.embedder.EmbeddedDataFrameAn EDF instance with N rows. Must have thresholds column unless require_thresholds=False.required
edf2pprl.embedder.embedder.EmbeddedDataFrameAn EDF instance with M rows. Must have thresholds column unless require_thresholds=False.required
require_thresholdsboolIf True (default), the comparison will fail if thresholds are not present. Must be explicitly set to False to allow comparison without thresholds.True
+
+
+
Returns
+ ++++ + + + + + + + + + + + + +
TypeDescription
pprl.embedder.embedder.SimilarityArrayAn N by M array containing the similarity matrix of pairwise Soft Cosine similarities between rows of edf1 and edf2.
+
+
+
Raises
+ ++++ + + + + + + + + + + + + +
TypeDescription
ValueErrorIf require_thresholds is True and both EDFs don’t have a thresholds column.
+
+
+
+
embed
+

embedder.embedder.Embedder.embed(df, colspec, update_norms=True, update_thresholds=False)

+

Encode data columns into features from Bloom embedding.

+
+
Parameters
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfpandas.pandas.DataFrameData frame to be embedded.required
colspecdictDictionary mapping columns in df to feature factory functions.required
update_normsboolWhether to calculate vector norms for SCM and add to EDF. False by default.True
update_thresholdsboolWhether to calculate similarity thresholds and add to EDF. Used as an outside option in matching. False by default.False
+
+
+
Returns
+ ++++ + + + + + + + + + + + + +
TypeDescription
pprl.embedder.embedder.EmbeddedDataFrameAn embedded data frame with its embedder.
+
+
+
+
from_pickle
+

embedder.embedder.Embedder.from_pickle(path=None, pickled=None)

+

Initialise Embedder instance from pickle file.

+
+
Parameters
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pathstrFile path from which to load the pickled embedder.None
pickledbytesByte-string containing the pickled embedder.None
+
+
+
Raises
+ ++++ + + + + + + + + + + + + +
TypeDescription
ValueErrorIf not exactly one of path and pickled are specified.
+
+
+
Returns
+ ++++ + + + + + + + + + + + + +
TypeDescription
pprl.embedder.embedder.EmbedderThe reformed instance of the Embedder class.
+
+
+
+
to_pickle
+

embedder.embedder.Embedder.to_pickle(path=None)

+

Save Embedder instance to pickle file.

+
+
Parameters
+ ++++++ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pathstrFile path at which to save the pickled embedder. If not specified, the pickled bytes string is returned.None
+
+
+
Returns
+ ++++ + + + + + + + + + + + + +
TypeDescription
bytes or NoneIf path is not specified, the pickled string comes back. Otherwise, nothing is returned.
+
+
+
+
train
+

embedder.embedder.Embedder.train(edf1, edf2, update=True, learning_rate=1.0, eps=0.01, random_state=None)

+

Fit Soft Cosine Measure matrix to two matched datasets.

+

This function updates the scm_matrix attribute in-place along with its constituent matrices, freq_matr_matched and freq_matr_unmatched.

+

Provide two datasets of pre-matched data. If update=True, the training is cumulative, so that train() can be called more than once, updating the same matrices each time by adding new frequency tables. Otherwise, all three matrices are reinitialised prior to training.

+
+
Parameters
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
edf1pprl.embedder.embedder.EmbeddedDataFrameAn embedded dataset.required
edf2pprl.embedder.embedder.EmbeddedDataFrameAn Embedded dataset of known matches in the same order as edf1.required
updateboolWhether to update the existing SCM matrix, or overwrite it. Defaults to True.True
epsfloatSmall non-negative constant to avoid -Inf in log of frequencies. Default is one.0.01
learning_ratefloatScaling factor to dampen matrix updates. Must be in the interval (0, 1]. Default is 0.01.1.0
random_stateNone | numpy.numpy.random.numpy.random.RandomStateRandom state to pass to dataset jumbler. Defaults to None.None
+
+
+
Attributes
+ +++++ + + + + + + + + + + + + + + +
NameTypeDescription
scm_matrixnumpy.numpy.ndarraySoft Cosine Measure matrix that is fitted cumulatively or afresh.
+
+
+
+
+
+

SimilarityArray

+

embedder.embedder.SimilarityArray()

+

Augmented NumPy array of similarity scores with extra attributes.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
input_arrayOriginal array of similarity score data.required
thresholds2-tuple of similarity score thresholds for each axis. These thresholds can be used as an outside option when generating a matching.required
embedder_checksumHexadecimal string digest of a pprl.embedder.Embedder object.required
+
+
+

Notes

+

SimilarityArray objects are usually initialised from an instance of pprl.embedder.Embedder via the embedder.compare() method.

+
+
+

Methods

+ + + + + + + + + + + + + +
NameDescription
matchCompute a matching.
+
+
match
+

embedder.embedder.SimilarityArray.match(abs_cutoff=0, rel_cutoff=0, hungarian=True, require_thresholds=True)

+

Compute a matching.

+

Given an array of similarity scores, compute a matching of its elements, using the Hungarian algorithm by default. If the SimilarityArray has thresholds, masking is used to ensure they are respected. An abs_cutoff (global minimum similarity score) can also be supplied.

+
+
Parameters
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
abs_cutoffint or floatA lower cutoff for the similarity score. No pairs with similarity below the absolute cutoff will be matched. By default, this is 0.0
rel_cutoffint or floatA margin above the row/column-specific threshold. Raises all thresholds by a constant. By default, this is 0.0
hungarianboolWhether to compute the unique matching using the Hungarian algorithm, filtered using thresholds and abs_cutoff. Default is True. If False, just return all pairs above the threshold.True
require_thresholdsboolIf True (default), the matching will fail if thresholds is not present and valid. Must be explicitly set to False to allow matching without similarity thresholds.True
+
+
+
Returns
+ ++++ + + + + + + + + + + + + +
TypeDescription
tuple[list[int], list[int]]2-tuple of indexes containing row and column indices of matched pairs eg. ([0, 1, ...], [0, 1, ...]).
+
+
+
Notes
+

If hungarian=False, the matching returns all pairs with similarity score above the abs_cutoff, respecting thresholds if present. This method does not guarantee no duplicates.

+
+
+
+
+
+
+

Functions

+ + + + + + + + + + + + + +
NameDescription
nearest_pos_semi_definiteCalculate nearest positive semi-definite version of a matrix.
+
+

nearest_pos_semi_definite

+

embedder.embedder.nearest_pos_semi_definite(X, eps=0.0)

+

Calculate nearest positive semi-definite version of a matrix.

+

This function achieves this by setting all negative eigenvalues of the matrix to zero, or a small positive value to give a positive definite matrix.

+

Graciously taken from this StackOverflow post

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
Xnumpy.numpy.ndarrayMatrix-like array.required
epsfloatUse a small positive constant to give a positive definite matrix. Default is 0 to give a positive semi-definite matrix.0.0
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
numpy.numpy.ndarrayA positive (semi-)definite matrix.
+ + +
+
+
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/reference/encryption.html b/docs/reference/encryption.html new file mode 100644 index 0000000..1bb5bf7 --- /dev/null +++ b/docs/reference/encryption.html @@ -0,0 +1,918 @@ + + + + + + + + + +`pprl` – encryption + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + +
+

encryption

+

encryption

+

Tools for performing envelope encryption on GCP.

+
+

Functions

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameDescription
decrypt_dataDecrypt a data frame with the provided key.
decrypt_dekDecrypt a data encryption key using an asymmetric key held on KMS.
encrypt_dataEncrypt a data frame.
encrypt_dekEncrypt the data encryption key.
+
+

decrypt_data

+

encryption.decrypt_data(encrypted, key)

+

Decrypt a data frame with the provided key.

+
+

Parameters

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
encryptedbytesData to be decrypted.required
keybytesKey used to encrypt the data.required
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
pandas.pandas.DataFrameDecrypted data frame.
+
+
+
+

decrypt_dek

+

encryption.decrypt_dek(encrypted, party, location='global', version=1, **kwargs)

+

Decrypt a data encryption key using an asymmetric key held on KMS.

+

Owing to the nature of the encryption key set-up of pprl this function is only really to be used in the GCP Confidential Space set up by the linkage administrator.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
encryptedbytesKey to be decrypted.required
partystrName of the party whose key we are decrypting.required
locationstrLocation of the keyring on which the key lives.'global'
versionint | strVersion of the asymmetric key to get from GCP. Default is 1.1
**kwargsdictKeyword arguments to pass when creating an instance of google.cloud.kms.KeyManagementServiceClient.{}
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
bytesDecrypted data encryption key.
+
+
+
+

encrypt_data

+

encryption.encrypt_data(data, key=None)

+

Encrypt a data frame.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
datapandas.pandas.DataFrameDataframe to encrypt.required
keybytesFernet key to encrypt data frame. If not specified, create one.None
+
+
+

Returns

+ + + + + + + + + + + + + + + + + +
TypeDescription
bytesEncrypted data frame.
bytesFernet key used to encrypt data frame.
+
+
+
+

encrypt_dek

+

encryption.encrypt_dek(dek, party, location='global', version=1, **kwargs)

+

Encrypt the data encryption key.

+

We encrypt the data encryption key using the public key portion of an asymmetric key retrieved from the GCP Key Management Service.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dekbytesData encryption key to be encrypted.required
partystrName of the party.required
locationstrLocation of the keyring on which the key lives.'global'
versionint | strVersion of the asymmetric key to get from GCP. Default is 1.1
**kwargsdictKeyword arguments to pass when creating an instance of google.cloud.kms.KeyManagementServiceClient.{}
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
bytesEncrypted data encryption key.
+ + +
+
+
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/reference/features.html b/docs/reference/features.html new file mode 100644 index 0000000..4aa52c5 --- /dev/null +++ b/docs/reference/features.html @@ -0,0 +1,1291 @@ + + + + + + + + + +`pprl` – features + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + +
+

features

+

embedder.features

+

Feature generation functions for various column types.

+
+

Functions

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameDescription
gen_dateofbirth_featuresGenerate labelled date features from a series of dates of birth.
gen_double_metaphoneGenerate the double methaphones of a string.
gen_featuresGenerate string features of various types.
gen_misc_featuresGenerate miscellaneous categorical features for a series.
gen_misc_shingled_featuresGenerate shingled labelled features.
gen_name_featuresGenerate a features series for a series of names.
gen_ngramGenerate n-grams from a set of tokens.
gen_sex_featuresGenerate labelled sex features from a series of sexes.
gen_skip_gramsGenerate skip 2-grams from a set of tokens.
split_string_underscoreSplit and underwrap a string at typical punctuation marks.
+
+

gen_dateofbirth_features

+

embedder.features.gen_dateofbirth_features(dob, dayfirst=True, yearfirst=False, default=['day<01>', 'month<01>', 'year<2050>'])

+

Generate labelled date features from a series of dates of birth.

+

Features take the form ["day<dd>", "month<mm>", "year<YYYY>"]. Note that this feature generator can be used for any sort of date data, not just dates of birth.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dobpandas.pandas.SeriesSeries of dates of birth.required
dayfirstboolWhether the day comes first in the DOBs. Passed to pd.to_datetime() and defaults to True.True
yearfirstboolWhether the year comes first in the DOBs. Passed to pd.to_datetime() and defaults to False.False
defaultlist[str]Default date to fill in missing data in feature (list) form. Default is the feature form of 2050-01-01.['day<01>', 'month<01>', 'year<2050>']
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
pandas.pandas.SeriesSeries containing lists of date features.
+
+
+
+

gen_double_metaphone

+

embedder.features.gen_double_metaphone(string)

+

Generate the double methaphones of a string.

+

This function is a generator containing all the possible, non-empty double metaphones of a given string, separated by spaces. This function uses the metaphone.doublemetaphone() function under the hood, ignoring any empty strings. See their repository for details.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
stringstrString from which to derive double metaphones.required
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
strThe next double metaphone in the sequence.
+
+
+
+

gen_features

+

embedder.features.gen_features(string, ngram_length=[2, 3], use_gen_ngram=True, use_gen_skip_grams=False, use_double_metaphone=False)

+

Generate string features of various types.

+

This function is a generator capable of producing n-grams, skip 2-grams, and double metaphones from a single string. These outputs are referred to as features.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
stringstrBase string from which to generate features.required
ngram_lengthlistLengths of n-grams to make. Ignored if use_gen_ngram=False.[2, 3]
use_gen_ngramboolWhether to create n-grams. Default is True.True
use_gen_skip_gramsboolWhether to create skip 2-grams. Default is False.False
use_double_metaphoneboolWhether to create double metaphones. Default is False.False
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
strThe next feature in the sequence.
+
+
+
+

gen_misc_features

+

embedder.features.gen_misc_features(field, label=None)

+

Generate miscellaneous categorical features for a series.

+

Useful for keeping raw columns in the linkage data. All features use a label and take the form ["label<option>"] except for missing data, which are coded as "".

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
fieldpandas.pandas.SeriesSeries from which to generate our features.required
labelNone | str | typing.HashableLabel for the series. By default, the name of the series is used if available. Otherwise, if not specified, misc is used.None
+
+
+

Returns

+ ++++ + + + + + + + + + + + + +
TypeDescription
pandas.pandas.SeriesSeries containing lists of miscellaneous features.
+
+
+
+

gen_misc_shingled_features

+

embedder.features.gen_misc_shingled_features(field, ngram_length=[2, 3], use_gen_skip_grams=False, label=None)

+

Generate shingled labelled features.

+

Generate n-grams, with a label to distinguish them from (and ensure they’re hashed separately from) names. Like gen_name_features(), this function makes a call to gen_features() via pd.Series.apply().

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
fieldpandas.pandas.SeriesSeries of string data.required
ngram_lengthlistShingle sizes to generate. By default [2, 3].[2, 3]
use_gen_skip_gramsboolWhether to generate skip 2-grams. False by default.False
labelstrA label to differentiate from other shingled features. If field has no name, this defaults to zz.None
+
+
+

Returns

+ ++++ + + + + + + + + + + + + +
TypeDescription
pandas.pandas.SeriesSeries containing lists of shingled string features.
+
+
+
+

gen_name_features

+

embedder.features.gen_name_features(names, ngram_length=[2, 3], use_gen_ngram=True, use_gen_skip_grams=False, use_double_metaphone=False)

+

Generate a features series for a series of names.

+

Effectively, this function is a call to pd.Series.apply() using our gen_features() string feature generator function.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
namespandas.pandas.SeriesSeries of names.required
ngram_lengthlist[int]Lengths of n-grams to make. Ignored if use_gen_ngram=False.[2, 3]
use_gen_ngramboolWhether to create n-grams. Default is True.True
use_gen_skip_gramsboolWhether to create skip 2-grams. Default is False.False
use_double_metaphoneboolWhether to create double metaphones. Default is False.False
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
pandas.pandas.SeriesSeries containing lists of features.
+
+
+
+

gen_ngram

+

embedder.features.gen_ngram(split_tokens, ngram_length)

+

Generate n-grams from a set of tokens.

+

This is a generator function that contains a series of n-grams the size of the sliding window.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
split_tokenslistAll the split-up tokens from which to form n-grams.required
ngram_lengthlistDesired lengths of n-grams. For examples, ngram_length=[2, 3] would generate all 2-grams and 3-grams.required
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
strThe next n-gram in the sequence.
+
+
+
+

gen_sex_features

+

embedder.features.gen_sex_features(sexes)

+

Generate labelled sex features from a series of sexes.

+

Features take the form ["sex<option>"] or [""] for missing data.

+
+

Parameters

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
sexespandas.pandas.SeriesSeries of sex data.required
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
pandas.pandas.SeriesSeries containing lists of sex features.
+
+
+
+

gen_skip_grams

+

embedder.features.gen_skip_grams(split_tokens)

+

Generate skip 2-grams from a set of tokens.

+

This function is a generator that contains a series of skip 2-grams. For example:

+
>>> string = "dave james"
+>>> tokens = split_string_underscore(string)
+>>> skips = list(gen_skip_grams(tokens))
+>>> print(skips)
+["_a", "dv", "ae", "v_", "_a", "jm", "ae", "ms", "e_"]
+
+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
split_tokenslistAll the split-up tokens from which to form skip 2-grams.required
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
strThe next skip 2-gram in the sequence.
+
+
+
+

split_string_underscore

+

embedder.features.split_string_underscore(string)

+

Split and underwrap a string at typical punctuation marks.

+

Currently, we split at any combination of spaces, dashes, dots, commas, or underscores. For example:

+
>>> strings = ("dave  william johnson", "Francesca__Hogan-O'Malley")
+>>> for string in strings:
+...     print(split_string_underscore(string))
+...
+["_dave_", "_william_", "_johnson_"]
+["_Francesca_", "_Hogan_", "_O'Malley_"]
+
+
+

Parameters

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
stringstrString to split.required
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
list[str]List of the split and wrapped tokens.
+ + +
+
+
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/reference/index.html b/docs/reference/index.html new file mode 100644 index 0000000..c93ff7a --- /dev/null +++ b/docs/reference/index.html @@ -0,0 +1,687 @@ + + + + + + + + + +`pprl` – index + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + +
+

API reference

+
+

Embeddings

+

Tools for generating a Bloom filter embedding and its underlying features.

+ + + + + + + + + + + + + + + +
bloom_filtersModule for the Bloom filter encoder.
embedderClasses and functions for handling embedding objects.
featuresFeature generation functions for various column types.
+
+
+

Encryption

+

Functions for handling the data and key encryption processes.

+ + + + + + + +
encryptionTools for performing envelope encryption on GCP.
+
+
+

Configuration

+

Functions for working out and handling linkage configuration.

+ + + + + + + +
configFunctions for handling PPRL configuration.
+
+
+

Client-side app

+

Functions for the Flask application where users upload, process, and download their data.

+ + + + + + + +
utilsUtility functions for the party-side app.
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/reference/utils.html b/docs/reference/utils.html new file mode 100644 index 0000000..399ace7 --- /dev/null +++ b/docs/reference/utils.html @@ -0,0 +1,916 @@ + + + + + + + + + +`pprl` – utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ + + +
+

utils

+

app.utils

+

Utility functions for the party-side app.

+
+

Functions

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameDescription
assign_columnsAssign columns from a form to collections.
check_is_csvDetermine whether a file has the csv extension.
convert_dataframe_to_bfConvert a dataframe of features to a bloom filter.
download_filesSerialize, compress, and send a data frame with its embedder.
+
+

assign_columns

+

app.utils.assign_columns(form, feature_funcs)

+

Assign columns from a form to collections.

+

All columns belong to one of three collections: columns to drop, raw columns to keep, or a column feature factory specification.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
formdictForm from our column chooser page.required
feature_funcsdictMapping between column types and feature functions.required
+
+
+

Returns

+ ++++ + + + + + + + + + + + + + + + + + + + + +
TypeDescription
list[str]List of columns to drop.
list[str]List of columns to keep in their raw format.
dict[str, func]Mapping between column names and feature functions.
+
+
+
+

check_is_csv

+

app.utils.check_is_csv(path)

+

Determine whether a file has the csv extension.

+
+

Parameters

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pathstrPath to the file.required
+
+
+

Returns

+ ++++ + + + + + + + + + + + + +
TypeDescription
boolWhether the file name follows the pattern {name}.csv or not.
+
+
+
+

convert_dataframe_to_bf

+

app.utils.convert_dataframe_to_bf(df, colspec, other_columns=None, salt='')

+

Convert a dataframe of features to a bloom filter.

+

Convert the columns to features based on the colspec. The features are then combined and converted to Bloom filter indices with the Bloom filter norm also calculated.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfpandas.pandas.DataFrameData frame of features.required
colspecdictDictionary designating columns in the data frame as particular feature types to be processed as appropriate.required
other_columnsNone | listColumns to be returned as they appear in the data in addition to bf_indices and bf_norms.None
saltstrCryptographic salt to add to tokens before hashing.''
+
+
+

Returns

+ + + + + + + + + + + + + +
TypeDescription
pandas.pandas.DataFrameData frame of bloom-filtered data.
+
+
+
+

download_files

+

app.utils.download_files(dataframe, embedder, party, archive='archive')

+

Serialize, compress, and send a data frame with its embedder.

+
+

Parameters

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dataframepprl.embedder.embedder.EmbeddedDataFrameData frame to be downloaded.required
embedderpprl.embedder.embedder.EmbedderEmbedder used to embed dataframe.required
partystrName of the party.required
archivestrName of the archive. Default is "archive".'archive'
+
+
+

Returns

+ ++++ + + + + + + + + + + + + +
TypeDescription
flask.flask.ResponseResponse containing a ZIP archive with the data frame and its embedder.
+ + +
+
+
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/tutorials/example-febrl.html b/docs/tutorials/example-febrl.html new file mode 100644 index 0000000..d3e3cc8 --- /dev/null +++ b/docs/tutorials/example-febrl.html @@ -0,0 +1,848 @@ + + + + + + + + + + +pprl - Linking the FEBRL datasets + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Linking the FEBRL datasets

+
+ +
+
+ Using PPRL locally to link two well-known datasets +
+
+ + +
+ + + + +
+ + +
+ +

This tutorial shows how the package can be used locally to match the FEBRL datasets, included as example datasets in the recordlinkage package.

+
+
import os
+import time
+from functools import partial
+
+import pandas as pd
+import numpy as np
+from recordlinkage.datasets import load_febrl4
+
+from pprl import EmbeddedDataFrame, Embedder, config
+from pprl.embedder import features as feat
+
+datadir = config.DIR_DATA_INTERIM
+
+
+

Load the data

+

The datasets we are using are 5000 records across two datasets with no duplicates, and each of the records has a valid match in the other dataset.

+

After loading the data, we can parse the true matched ID number from the indices.

+
+
feb4a, feb4b = load_febrl4()
+
+feb4a["true_id"] = (
+    feb4a.index.str.extract("^rec-([0-9]*)-")
+    .iloc[:, 0].astype("int")
+    .to_list()
+)
+feb4b["true_id"] = (
+    feb4b.index.str.extract("^rec-([0-9]*)-")
+    .iloc[:, 0].astype("int")
+    .to_list()
+)
+
+feb4a.to_csv(os.path.join(datadir, "febrl_data_1.csv"))
+feb4b.to_csv(os.path.join(datadir, "febrl_data_2.csv"))
+
+
+
+

Create a feature factory

+

Define the feature processing functions that convert each field into a string so that they can be hashed into the Bloom filter. The dictionary of functions is called feature_factory.

+

If we want to pass the feature factory functions into the embedder with non-default parameters, we have two options:

+
    +
  1. Pass a dictionary of dictionaries of keyword arguments as an optional ff_args parameter (e.g. ff_args = {"dob": {"dayfirst": False, "yearfirst": True}}))
    2. +
  2. Use functools.partial(), as we have below.
    3. +
+
+
feature_factory = dict(
+    name=feat.gen_name_features,
+    dob=partial(feat.gen_dateofbirth_features, dayfirst=False, yearfirst=True),
+    misc=feat.gen_misc_features,
+    address=partial(
+        feat.gen_misc_shingled_features, label="addr", ngram_length=[4]
+    ),
+)
+
+
+
+

Initialise the embedder instance

+

This instance embeds each feature twice into a Bloom filter of length 1025 (with the offset).

+
+
embedder = Embedder(feature_factory, bf_size=2**10, num_hashes=2)
+
+
+
+

Embed the datasets

+

The column specification colspec is a dictionary that tells the embedder how to map feature-processing functions to columns of the data. Notice that we can map more than one column to the same function. This means that we can easily handle cases where fields we want to compare all together span several columns.

+

This process makes our Bloom filter robust to inconsistencies where, for example, surname and given name may be swapped, or addresses may be coded inconsistently.

+
+
+
+ +
+
+Note +
+
+
+

To ensure that different feature types hash into different buckets, the functions gen_misc_features() and gen_misc_shingled_features() will add a label to parsed string features, which will default to the data column name.

+

For example, to ensure suburb doesn’t collide with state (if they happened to be the same), gen_misc_features() would encode each of their tokens as suburb<token> and state<token>, respectively. If you want to map different columns into the same feature, such as address below, you can set the label explicitly when passing the function to the embedder.

+
+
+
+
colspec = dict(
+    given_name="name",
+    surname="name",
+    date_of_birth="dob",
+    street_number="misc",
+    state="misc",
+    soc_sec_id="misc",
+    postcode="misc",
+    suburb="misc",
+    address_1="address",
+    address_2="address",
+)
+
+edf1 = embedder.embed(feb4a, colspec=colspec)
+edf2 = embedder.embed(feb4b, colspec=colspec)
+
+

Store the embedded datasets and their embedder to file.

+
+
edf1.to_json(os.path.join(datadir, "party1_data.json"))
+edf2.to_json(os.path.join(datadir, "party2_data.json"))
+embedder.to_pickle(os.path.join(datadir, "embedder.pkl"))
+
+
+
+

Calculate similarity

+

Compute the row thresholds to provide a lower bound on matching similarity scores for each row. This operation is the most computationally intensive part of the whole process.

+
+
start = time.time()
+edf1.update_thresholds()
+edf2.update_thresholds()
+end = time.time()
+
+print(f"Updating thresholds took {end - start:.2f} seconds")
+
+
Updating thresholds took 3.00 seconds
+
+
+

Compute the matrix of similarity scores.

+
+
similarity_scores = embedder.compare(edf1,edf2)
+
+
+
+

Compute a match

+

Use the similarity scores to compute a match, using the Hungarian algorithm. First, we compute the match with the row thresholds.

+
+
matching = similarity_scores.match(require_thresholds=True)
+
+

Using the true IDs, evaluate the precision and recall of the match.

+
+
def get_results(edf1, edf2, matching):
+    """Get the results for a given matching."""
+
+    trueids_matched1 = edf1.iloc[matching[0], edf1.columns.get_loc("true_id")]
+    trueids_matched2 = edf2.iloc[matching[1], edf2.columns.get_loc("true_id")]
+
+    nmatches = len(matching[0])
+    truepos = sum(map(np.equal, trueids_matched1, trueids_matched2))
+    falsepos = nmatches - truepos
+
+    print(
+        f"True pos: {truepos} | False pos: {falsepos} | "
+        f"Precision: {truepos / nmatches:.1%} | Recall: {truepos / 5000:.1%}"
+    )
+
+    return nmatches, truepos, falsepos
+
+_ = get_results(edf1, edf2, matching)
+
+
True pos: 4963 | False pos: 0 | Precision: 100.0% | Recall: 99.3%
+
+
+

Then, we compute the match without using the row thresholds, calculating the same performance metrics:

+
+
matching = similarity_scores.match(require_thresholds=False)
+_ = get_results(edf1, edf2, matching)
+
+
True pos: 5000 | False pos: 0 | Precision: 100.0% | Recall: 100.0%
+
+
+

Without using the row thresholds, the number of false positives is larger, but the recall is much better. For some uses this balance may be preferable.

+

In testing, the use of local row thresholds provides a better trade-off between precision and recall, compared to using a single absolute threshold. It has the additional advantage, in a privacy-preserving setting, of being automatic and not requiring clerical review to set the level.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/tutorials/example-verknupfung.html b/docs/tutorials/example-verknupfung.html new file mode 100644 index 0000000..eca478a --- /dev/null +++ b/docs/tutorials/example-verknupfung.html @@ -0,0 +1,831 @@ + + + + + + + + + +pprl - Exploring a simple linkage example + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Exploring a simple linkage example

+
+ + + +
+ + + + +
+ + +
+ +

The Python package implements the Bloom filter linkage method (Schnell et al., 2009), and can also implement pretrained Hash embeddings (Miranda et al., 2022), if a suitable large, pre-matched corpus of data is available.

+

Let us consider a small example where we want to link two excerpts of data on bands. In this scenario, we are looking at some toy data on the members of a fictional, German rock trio called “Verknüpfung”. In this example we will see how to use untrained Bloom filters to match data.

+
+

Loading the data

+

First, we load our data into pandas.DataFrame objects. Here, the first records align, but the other two records should be swapped to have an aligned matching. We will use the toolkit to identify these matches.

+
+
import pandas as pd
+
+df1 = pd.DataFrame(
+    {
+        "first_name": ["Laura", "Kaspar", "Grete"],
+        "last_name": ["Daten", "Gorman", "Knopf"],
+        "gender": ["f", "m", "f"],
+        "date_of_birth": ["01/03/1977", "31/12/1975", "12/7/1981"],
+        "instrument": ["bass", "guitar", "drums"],
+    }
+)
+df2 = pd.DataFrame(
+    {
+        "name": ["Laura Datten", "Greta Knopf", "Casper Goreman"],
+        "sex": ["female", "female", "male"],
+        "main_instrument": ["bass guitar", "percussion", "electric guitar"],
+        "birth_date": ["1977-03-23", "1981-07-12", "1975-12-31"],
+    }
+)
+
+
+

[!NOTE] These datasets don’t have the same column names or follow the same encodings, and there are several spelling mistakes in the names of the band members, as well as a typo in the dates.

+

Thankfully, the pprl_toolkit is flexible enough to handle this!

+
+
+
+

Creating and assigning a feature factory

+

The next step is to decide how to process each of the columns in our datasets.

+

To do this, we define a feature factory that maps column types to feature generation functions, and a column specification for each dataset mapping our columns to column types in the factory.

+
+
from pprl.embedder import features
+from functools import partial
+
+factory = dict(
+    name=features.gen_name_features,
+    sex=features.gen_sex_features,
+    misc=features.gen_misc_features,
+    dob=features.gen_dateofbirth_features,
+    instrument=partial(features.gen_misc_shingled_features, label="instrument")
+)
+spec1 = dict(
+    first_name="name",
+    last_name="name",
+    gender="sex",
+    instrument="instrument",
+    date_of_birth="dob",
+)
+spec2 = dict(name="name", sex="sex", main_instrument="instrument", birth_date="dob")
+
+
+

[!TIP] The feature generation functions, features.gen_XXX_features have sensible default parameters, but sometimes have to be passed in to the feature factory with different parameters, such as to set a feature label in the example above. There are two ways to achieve this. Either use functools.partial to set parameters (as above), or pass keyword arguments as a dictionary of dictionaries to the Embedder as ff_args.

+
+
+
+

Embedding the data

+

With our specifications sorted out, we can get to creating our Bloom filter embedding. Before doing so, we need to decide on two parameters: the size of the filter and the number of hashes. By default, these are 1024 and 2, respectively.

+

Once we’ve decided, we can create our Embedder instance and use it to embed our data with their column specifications.

+
+
from pprl.embedder.embedder import Embedder
+
+embedder = Embedder(factory, bf_size=1024, num_hashes=2)
+edf1 = embedder.embed(df1, colspec=spec1, update_thresholds=True)
+edf2 = embedder.embed(df2, colspec=spec2, update_thresholds=True)
+
+
/Users/henrywilde/src/pprl_toolkit/src/pprl/embedder/features.py:284: UserWarning: Parsing dates in %Y-%m-%d format when dayfirst=True was specified. Pass `dayfirst=False` or specify a format to silence this warning.
+  datetimes = pd.to_datetime(dob, errors="coerce", dayfirst=dayfirst, yearfirst=yearfirst)
+
+
+

If we take a look at one of these embedded datasets, we can see that it has a whole bunch of new columns. There is a _features column for each of the original columns containing their pre-embedding string features, and there’s an all_features column that combines the features. Then there are three additional columns: bf_indices, bf_norms and thresholds.

+
+
edf1.columns
+
+
Index(['first_name', 'last_name', 'gender', 'date_of_birth', 'instrument',
+       'first_name_features', 'last_name_features', 'gender_features',
+       'instrument_features', 'date_of_birth_features', 'all_features',
+       'bf_indices', 'bf_norms', 'thresholds'],
+      dtype='object')
+
+
+

The bf_indices column contains the Bloom filters, represented compactly as a list of non-zero indices for each record. The bf_norms column contains the norm of each Bloom filter with respect to the Soft Cosine Measure (SCM) matrix. In this case since we are using an untrained model, the SCM matrix is an identity matrix, and the norm is just the Euclidean norm of the Bloom filter represented as a binary vector, which is equal to np.sqrt(len(bf_indices[i])) for record i. The norm is used to scale the similarity measures so that they take values between -1 and 1.

+

The thresholds column is calculated to provide, for each record, a threshold similarity score below which it will not be matched. It’s like a reserve price in an auction – it stops a record being matched to another record when the similarity isn’t high enough. In this feature, the method implemented here differs from other linkage methods, which typically only have one global threshold score for the entire dataset.

+ +
+
+

The processed features

+

Let’s take a look at how the features are processed into small text strings (shingles) before being hashed into the Bloom filter. The first record in the first dataset is the same person as the first record in the second dataset, although the data is not identical, so we can compare the processed features for these records to see how pprl_toolkit puts them into a format where they can be compared.

+

First, we’ll look at date of birth:

+
+
print(edf1.date_of_birth_features[0])
+print(edf2.birth_date_features[0])
+
+
['day<01>', 'month<03>', 'year<1977>']
+['day<23>', 'month<03>', 'year<1977>']
+
+
+

Python can parse the different formats easily. Although the dates are slightly different in the dataset, the year and month will still match, even though the day will not.

+

Then we’ll look at name:

+
+
print(edf1.first_name_features[0] + edf1.last_name_features[0])
+print(edf2.name_features[0])
+
+
['_l', 'la', 'au', 'ur', 'ra', 'a_', '_la', 'lau', 'aur', 'ura', 'ra_', '_d', 'da', 'at', 'te', 'en', 'n_', '_da', 'dat', 'ate', 'ten', 'en_']
+['_l', 'la', 'au', 'ur', 'ra', 'a_', '_d', 'da', 'at', 'tt', 'te', 'en', 'n_', '_la', 'lau', 'aur', 'ura', 'ra_', '_da', 'dat', 'att', 'tte', 'ten', 'en_']
+
+
+

The two datasets store the names differently, but this doesn’t matter for the Bloom filter method because it treats each record like a bag of features. By default, the name processor produces 2-grams, 3-grams and 4-grams.

+

The sex processing function just converts different formats to lowercase and takes the first letter. This will often be enough:

+
+
print(edf1.gender_features[0])
+print(edf2.sex_features[0])
+
+
['sex<f>']
+['sex<f>']
+
+
+

Finally, we’ll see how our instrument feature function (partial(features.gen_misc_shingled_features, label="instrument")) processed the data:

+
+
print(edf1.instrument_features[0])
+print(edf2.main_instrument_features[0])
+
+
['instrument<_b>', 'instrument<ba>', 'instrument<as>', 'instrument<ss>', 'instrument<s_>', 'instrument<_ba>', 'instrument<bas>', 'instrument<ass>', 'instrument<ss_>']
+['instrument<_b>', 'instrument<ba>', 'instrument<as>', 'instrument<ss>', 'instrument<s_>', 'instrument<_g>', 'instrument<gu>', 'instrument<ui>', 'instrument<it>', 'instrument<ta>', 'instrument<ar>', 'instrument<r_>', 'instrument<_ba>', 'instrument<bas>', 'instrument<ass>', 'instrument<ss_>', 'instrument<_gu>', 'instrument<gui>', 'instrument<uit>', 'instrument<ita>', 'instrument<tar>', 'instrument<ar_>']
+
+
+

Setting the label argument was important to ensure that the shingles match (and are hashed to the same slots) because the default behaviour of the function is to use the column name as a label: since the two columns have different names, the default wouldn’t have allowed the features to match to each other.

+
+
+

Performing the linkage

+

We can now perform the linkage by comparing these Bloom filter embeddings. We use the Soft Cosine Measure (which in this untrained model, is equivalent to a normal cosine similarity metric) to calculate record-wise similarity and an adapted Hungarian algorithm to match the records based on those similarities.

+
+
similarities = embedder.compare(edf1, edf2)
+similarities
+
+
SimilarityArray([[0.79385141, 0.16741128, 0.09035655],
+                 [0.37587466, 0.17460757, 0.61256385],
+                 [0.12240719, 0.55043024, 0.10971801]])
+
+
+

This SimilarityArray object is an augmented numpy.ndarray that can perform our matching. The matching itself can optionally be called with an absolute threshold score, but it doesn’t need one.

+
+
matching = similarities.match()
+matching
+
+
(array([0, 1, 2]), array([0, 2, 1]))
+
+
+

So, all three of the records in each dataset were matched correctly. Excellent!

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/tutorials/in-the-cloud.html b/docs/tutorials/in-the-cloud.html new file mode 100644 index 0000000..b869f7c --- /dev/null +++ b/docs/tutorials/in-the-cloud.html @@ -0,0 +1,1003 @@ + + + + + + + + + + +pprl - Working in the cloud + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Working in the cloud

+
+ +
+
+

Get you and your collaborators performing linkage in the cloud

+
+
+ + +
+ + + + +
+ + +
+ +

This tutorial provides an overview of how to use pprl_toolkit on Google Cloud Platform (GCP). We go over how to assemble and assign roles in a linkage team, how to set up everybody’s projects, and end with executing the linkage itself.

+
+
+

+
A diagram of the PPRL cloud architecture, with the secure enclave and key management services
+
+
+

Above is a diagram showing the PPRL cloud architecture. The cloud demo uses a Google Cloud Platform (GCP) Confidential Space compute instance, which is a virtual machine (VM) using AMD Secure Encrypted Virtualisation (AMD-SEV) technology to encrypt data in-memory. The Confidential Space VM can also provide cryptographically signed documents, called attestations, which the server can use to prove that it is running in a secure environment before gaining access to data.

+
+

Assembling a linkage team

+

There are four roles to fill in any PPRL project: two data-owning parties, a workload author, and a workload operator. A workload is how we refer to the resources for the linkage operation itself (i.e. the containerised linkage code and the environment in which to run it.)

+

These roles need not be fulfilled by four separate people. It is perfectly possible to perform PPRL on your own, or perhaps you are working under a trust model that allows one of the data-owning parties to author the workload while the other is the operator.

+
+
+
+ +
+
+Tip +
+
+
+

In fact, pprl_toolkit is set up to allow any configuration of these roles among up to four people.

+
+
+

In any case, you must decide who will be doing what from the outset. Each role comes with different responsibilities, but all roles require a GCP account and access to the gcloud command-line tool. Additionally, everyone in the linkage project will need to install pprl_toolkit.

+
+

Data-owning party

+

Often referred to as just a party, a data owner is responsible for the storage and preparation of some confidential data. During set-up, each party also sets up a storage bucket, a key management service, and a workload identity pool that allows the party to share permissions with the server during the linkage operation.

+

They create a Bloom filter embedding of their confidential data using an agreed configuration, and then upload that to GCP for processing. Once the workload operator is finished, the parties are able to retrieve their linkage results.

+
+
+

Workload author

+

The workload author is responsible for building a Docker image containing the cloud-based linkage code and uploading it to a GCP Artifact Registry. This image is the workload to be run by the operator.

+
+
+

Workload operator

+

The workload operator runs the linkage itself using some embedded data from the parties and an image from the author. They are responsible for setting up and running a Confidential Space in which to perform the linkage. This setting ensures that nobody ever has access to all the data at once, and that the data can only be accessed via the linkage code itself.

+
+
+
+

Creating your GCP projects

+

Once you have decided who will be playing which role(s), you need to decide on a naming structure and make some GCP projects. You will need a project for each member of the linkage project - not one for each role. The names of these projects will be used throughout the cloud implementation, from configuration files to buckets. As such, they need to be descriptive and unique.

+
+
+
+ +
+
+Warning +
+
+
+

Since Google Cloud bucket names must be globally unique, we highly recommend using a hash in your project names to ensure that they are unique. This will ensure that bucket names are also globally unique.

+

Our aim is to create a globally unique name (and thus ID) for each project.

+
+
+

For example, say the US Census Bureau and UK Office for National Statistics (ONS) are looking to link some data on ex-patriated residents with PPRL. Then they might use us-cb and uk-ons as their party names, which are succinct and descriptive. However, they are generic and rule out future PPRL projects with the same names.

+

As a remedy, they could make a hash of their project description to create an identifier:

+
$ echo -n "pprl us-cb uk-ons ex-pats-analysis" | sha256sum
+d59a50241dc78c3f926b565937b99614b7bb7c84e44fb780440718cb2b0ddc1b  -
+

This is very long. You might only want to use the first few characters of this hash. Note that Google Cloud bucket names also can’t be more than 63 characters long without dots.

+

You can trim it down like so:

+
$ echo -n "pprl us-cb uk-ons ex-pats-analysis" | sha256sum | cut -c 1-7
+d59a502
+

So, our names would be: uk-ons-d59a502, us-cb-d59a502. If they had a third-party linkage administrator (authoring and operating the workload), they would have a project called something like admin-d59a502.

+
+
+

Setting up your projects

+

Once you have decided on a naming structure, it is time to create the GCP projects. Each project will need specific Identity and Access Management (IAM) roles granted to them by the project owner’s GCP Administrator. Which IAM roles depends on the linkage role they are playing. If someone is fulfilling more than one role, they should follow all the relevant sections below.

+
+
+
+ +
+
+Tip +
+
+
+

If you have Administrator permissions for your GCP project, you can grant these roles using the gcloud command-line tool:

+
gcloud projects add-iam-policy-binding <project-name> \
+  --member=user:<user-email> \
+  --role=<role-code>
+
+
+
+

Data-owning parties

+

Each data-owning party requires the following IAM roles:

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TitleCodePurpose
Cloud KMS Adminroles/cloudkms.adminManaging encryption keys
IAM Workload Identity Pool Adminroles/iam.workloadIdentityPoolAdminManaging an impersonation service
Service Usage Adminroles/serviceusage.serviceUsageAdminManaging access to other APIs
Service Account Adminroles/iam.serviceAccountAdminManaging a service account
Storage Adminroles/storage.adminManaging a bucket for their data
+
+
+

Workload author

+

The workload author only requires one IAM role:

+ +++++ + + + + + + + + + + + + + + +
TitleCodePurpose
Artifact Registry Administratorroles/artifactregistry.adminManaging the registry for the workload
+
+
+

Workload operator

+

The workload operator requires three IAM roles:

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + +
TitleCodePurpose
Compute Adminroles/compute.adminManaging the virtual machine
Security Adminroles/iam.securityAdminAbility to set and get IAM policies
Storage Adminroles/storage.adminManaging a shared bucket
+
+
+
+

Configuring pprl_toolkit

+

Now your linkage team has its projects made up, you need to configure pprl_toolkit. This configuration tells the package where to look and what to call things; we do this with a single environment file containing a short collection of key-value pairs.

+

We have provided an example environment file in .env.example. Copy or rename that file to .env in the root of the pprl_toolkit installation. Then, fill in your project details as necessary.

+

For our example above, let’s say the ONS will be the workload author and the US Census Bureau will be the workload operator. The environment file would look something like this:

+
PARTY_1_PROJECT=us-cb-d59a502
+PARTY_1_KEY_VERSION=1
+
+PARTY_2_PROJECT=uk-ons-d59a502
+PARTY_2_KEY_VERSION=1
+
+WORKLOAD_AUTHOR_PROJECT=uk-ons-d59a502
+WORKLOAD_AUTHOR_PROJECT_REGION=europe-west2
+
+WORKLOAD_OPERATOR_PROJECT=us-cb-d59a502
+WORKLOAD_OPERATOR_PROJECT_ZONE=us-east4-a
+
+
+
+ +
+
+Important +
+
+
+

Your environment file should be identical among all the members of your linkage project.

+
+
+
+
+

Creating the other resources

+

The last step in setting up your linkage project is to create and configure the other resources on GCP. To make things straightforward for users, we have packaged up the steps to do this into a number of bash scripts. These scripts are located in the scripts/ directory and are numbered. You and your team must execute them from the scripts/ directory in their named order according to which role(s) each member is fulfilling in the linkage project.

+
+
+
+ +
+
+Tip +
+
+
+

Make sure you have set up gcloud on the command line. Once you’ve installed it, log in and set the application default:

+
gcloud auth login
+gcloud auth application-default login
+
+
+
    +

  1. The data-owning parties set up: a key encryption key; a bucket in which to store their encrypted data, data encryption key and results; a service account for accessing said bucket and key; and a workload identity pool to allow impersonations under stringent conditions.

    +
    sh ./01-setup-party-resources.sh <name-of-party-project>
    2. +

  2. The workload operator sets up a bucket for the parties to put their (non-sensitive) attestation credentials, and a service account for running the workload.

    +
    sh ./02-setup-workload-operator.sh
    3. +

  3. The workload author sets up an Artifact Registry on GCP, creates a Docker image and uploads that image to their registry.

    +
    sh ./03-setup-workload-author.sh
    4. +

  4. The data-owning parties authorise the workload operator’s service account to use the workload identity pool to impersonate their service account in a Confidential Space.

    +
    sh ./04-authorise-workload.sh <name-of-party-project>
    5. +
+
+
+

Processing and uploading the results

+
+
+
+ +
+
+Important +
+
+
+

This section only applies to data-owning parties. The workload author is finished now, and the workload operator should wait for this section to be completed before moving on to the next section.

+
+
+ +

Now that all the cloud infrastructure has been set up, we are ready to start the first step in doing the actual linkage. That is, to create a Bloom filter embedding of their data, encrypt it, and upload that to GCP.

+

For users who prefer a graphical user interface, we have included a Flask app to handle the processing and uploading of data behind the scenes. This app will also be used to download the results once the linkage has completed.

+

To launch the app, run the following in your terminal:

+
python -m flask --app src/pprl/app run
+

You should now be able to find the app in your browser of choice at 127.0.0.1:5000. It should look something like this:

+
+
+

+
A screenshot of the app
+
+
+

From here, the process to upload your data is as follows:

+
    +
  1. Choose which party you are uploading for. Click Submit.
    2. +
  2. Select Upload local file and click Choose file to open your file browser. Navigate to and select your dataset. Click Submit.
    3. +
  3. Assign types to each column in your dataset. Enter the agreed salt.
    4. +
  4. Click Upload file to GCP.
    5. +
+
+
+
+ +
+
+Note +
+
+
+

If you choose to use the Flask app to process your data, you will use a set of defaults for processing the confidential data before it gets embedded. If you want more control, then you’ll have to agree an embedding configuration with the other data-owning party and do the processing directly.

+
+
+

Once you have worked through the selection, processing, and GCP upload portions of the app, you will be at a holding page. This page can be updated by clicking the button, and when your results are ready you will be taken to another page where you can download them.

+
+
+

Running the linkage

+
+
+
+ +
+
+Important +
+
+
+

This section only applies to the workload operator.

+
+
+

Once the data-owning parties have uploaded their processed data, you are able to begin the linkage. To do so, run the 05-run-workload.sh bash script from scripts/:

+
cd /path/to/pprl_toolkit/scripts
+sh ./05-run-workload.sh
+

You can follow the progress of the workload from the Logs Explorer on GCP. Once it is complete, the data-owning parties will be able to download their results.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/tutorials/index.html b/docs/tutorials/index.html new file mode 100644 index 0000000..c623306 --- /dev/null +++ b/docs/tutorials/index.html @@ -0,0 +1,746 @@ + + + + + + + + + +pprl - Tutorials + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Tutorials

+
+ + + +
+ + + + +
+ + +
+ +

These tutorials walk you through some of the essential workflows for pprl. The purpose of these documents is for you to learn how to use the pprl package for your own linkage projects.

+


+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Title + +Description + +Reading Time +
+Embedder API run-through + +This article shows the main classes, methods and functionality of the Embedder API. + +5 min +
+Exploring a simple linkage example + +The Python package implements the Bloom filter linkage method (Schnell et al., 2009), and can also implement pretrained Hash embeddings (Miranda et al., 2022), if a suitable large, pre-matched corpus… + +6 min +
+Linking the FEBRL datasets + +Using PPRL locally to link two well-known datasets + +4 min +
+Working in the cloud + +Get you and your collaborators performing linkage in the cloud + +11 min +
+
+No matching items +
+
+ +
+ + + + + \ No newline at end of file diff --git a/docs/tutorials/run-through.html b/docs/tutorials/run-through.html new file mode 100644 index 0000000..c62344c --- /dev/null +++ b/docs/tutorials/run-through.html @@ -0,0 +1,958 @@ + + + + + + + + + +pprl - Embedder API run-through + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Embedder API run-through

+
+ + + +
+ + + + +
+ + +
+ +

This article shows the main classes, methods and functionality of the Embedder API.

+

First, we’ll import a few modules, including:

+
    +
  • the features module, that has functions for processing data into features for embedding
    • +
  • the config module, which includes our package configuration (such as the location of data directories)
    • +
  • some classes from the main embedder module
    • +
+
+
import os
+
+import pandas as pd
+
+from pprl import EmbeddedDataFrame, Embedder, config
+from pprl.embedder import features as feat
+
+filestem = config.DIR_DATA_INTERIM
+
+
+

Data set-up

+

For this demo we’ll create a really minimal pair of datasets. Notice that they don’t have to have the same structure or field names.

+
+
df1 = pd.DataFrame(
+    dict(
+        id=[1,2,3],
+        forename=["Henry", "Sally", "Ina"],
+        surname = ["Tull", "Brown", "Lawrey"],
+        dob=["1/1/2001", "2/1/2001", "4/10/1995"],
+        gender=["male", "Male", "Female"],
+    )
+)
+
+df2 = pd.DataFrame(
+    dict(
+        personid=[4,5,6],
+        full_name=["Harry Tull", "Sali Brown", "Ina Laurie"],
+        date_of_birth=["2/1/2001", "2/1/2001", "4/11/1995"],
+        sex=["M", "M", "F"],
+    )
+)
+
+

Features are extracted as different kinds of string objects from each field, ready to be hash embedded into the Bloom filters. We need to specify the feature extraction functions we’ll need.

+

In this case we’ll need one extractor for names, one for dates of birth, and one for sex/gender records. We create a dict with the functions we need. We create another dict to store any keyword arguments we want to pass in to each function (in this case we use all the default arguments so the keyword argument dictionaries are empty):

+
+
feature_factory = dict(
+    name=feat.gen_name_features,
+    dob=feat.gen_dateofbirth_features,
+    sex=feat.gen_sex_features,
+)
+
+ff_args = dict(name={}, sex={}, dob={})
+
+
+
+

Embedding

+

Now we can create an Embedder object. We want our Bloom filter vectors to have a length of 1024 elements (actually 1025 because of an offset), and we choose to hash each feature two times. These choices seem to work ok, but we haven’t explored them systematically.

+
+
embedder = Embedder(feature_factory,
+                    ff_args,
+                    bf_size = 2**10,
+                    num_hashes=2,
+                    )
+
+

Now we can hash embed the dataset into an EmbeddedDataFrame (EDF). For this we need to pass a column specification colspec that maps each column of the data into the feature_factory functions. Any columns not mapped will not contribute to the embedding.

+
+
edf1 = embedder.embed(
+    df1, colspec=dict(forename="name", surname="name", dob="dob", gender="sex")
+)
+edf2 = embedder.embed(
+    df2, colspec=dict(full_name="name", date_of_birth="dob", sex="sex")
+)
+
+print(edf1)
+print(edf2)
+
+
   id forename surname        dob  gender  \
+0   1    Henry    Tull   1/1/2001    male   
+1   2    Sally   Brown   2/1/2001    Male   
+2   3      Ina  Lawrey  4/10/1995  Female   
+
+                                   forename_features  \
+0  [_h, he, en, nr, ry, y_, _he, hen, enr, nry, ry_]   
+1  [_s, sa, al, ll, ly, y_, _sa, sal, all, lly, ly_]   
+2                    [_i, in, na, a_, _in, ina, na_]   
+
+                                    surname_features  \
+0           [_t, tu, ul, ll, l_, _tu, tul, ull, ll_]   
+1  [_b, br, ro, ow, wn, n_, _br, bro, row, own, wn_]   
+2  [_l, la, aw, wr, re, ey, y_, _la, law, awr, wr...   
+
+                       dob_features gender_features  \
+0  [day<01>, month<01>, year<2001>]        [sex<m>]   
+1  [day<02>, month<01>, year<2001>]        [sex<m>]   
+2  [day<04>, month<10>, year<1995>]        [sex<f>]   
+
+                                        all_features  \
+0  [en, _h, he, hen, _tu, day<01>, tul, ll_, nry,...   
+1  [lly, wn, _sa, _br, ll, own, y_, wn_, al, mont...   
+2  [rey, wre, _la, day<04>, _in, _l, na, na_, a_,...   
+
+                                          bf_indices  bf_norms  
+0  [13, 399, 271, 147, 277, 25, 537, 802, 547, 29...  6.782330  
+1  [259, 647, 521, 781, 147, 533, 918, 151, 408, ...  7.071068  
+2  [898, 391, 655, 531, 148, 663, 151, 668, 413, ...  6.928203  
+   personid   full_name date_of_birth sex  \
+0         4  Harry Tull      2/1/2001   M   
+1         5  Sali Brown      2/1/2001   M   
+2         6  Ina Laurie     4/11/1995   F   
+
+                                  full_name_features  \
+0  [_h, ha, ar, rr, ry, y_, _t, tu, ul, ll, l_, _...   
+1  [_s, sa, al, li, i_, _b, br, ro, ow, wn, n_, _...   
+2  [_i, in, na, a_, _l, la, au, ur, ri, ie, e_, _...   
+
+             date_of_birth_features sex_features  \
+0  [day<02>, month<01>, year<2001>]     [sex<m>]   
+1  [day<02>, month<01>, year<2001>]     [sex<m>]   
+2  [day<04>, month<11>, year<1995>]     [sex<f>]   
+
+                                        all_features  \
+0  [_h, _ha, _tu, ar, tul, ll_, ull, ry_, rry, ll...   
+1  [li_, li, wn, _sa, _br, own, wn_, al, month<01...   
+2  [e_, ur, _la, day<04>, _l, _in, na, na_, a_, l...   
+
+                                          bf_indices  bf_norms  
+0  [259, 13, 399, 147, 277, 918, 547, 293, 805, 1...  6.855655  
+1  [259, 515, 6, 647, 521, 781, 143, 15, 147, 533...  6.782330  
+2  [385, 771, 391, 647, 136, 908, 653, 786, 531, ...  6.928203
+
+
+
+
+

Training

+

Discuss this at this stage

+
+
+

Computing the similarity scores and the matching

+

Now we have two embedded datasets, we can compare them and compute all the pairwise Cosine similarity scores.

+

First, we have to compute the vector norms of each Bloom vector (for scaling the Cosine similarity) and the thresholds (thresholds are explained here [link]). Computing the thresholds can be time-consuming for a larger dataset, because it essentially computes all pairwise comparisons of the data to itself.

+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
personidfull_namedate_of_birthsexfull_name_featuresdate_of_birth_featuressex_featuresall_featuresbf_indicesbf_normsthresholds
04Harry Tull2/1/2001M[_h, ha, ar, rr, ry, y_, _t, tu, ul, ll, l_, _...[day<02>, month<01>, year<2001>][sex<m>][_h, _ha, _tu, ar, tul, ll_, ull, ry_, rry, ll...[259, 13, 399, 147, 277, 918, 547, 293, 805, 1...6.8556550.172053
15Sali Brown2/1/2001M[_s, sa, al, li, i_, _b, br, ro, ow, wn, n_, _...[day<02>, month<01>, year<2001>][sex<m>][li_, li, wn, _sa, _br, own, wn_, al, month<01...[259, 515, 6, 647, 521, 781, 143, 15, 147, 533...6.7823300.172053
26Ina Laurie4/11/1995F[_i, in, na, a_, _l, la, au, ur, ri, ie, e_, _...[day<04>, month<11>, year<1995>][sex<f>][e_, ur, _la, day<04>, _l, _in, na, na_, a_, l...[385, 771, 391, 647, 136, 908, 653, 786, 531, ...6.9282030.021281
+ +
+
+
+

NB: there’s also a flag to compute these at the same time as the embedding, but it doesn’t by default because, depending on the workflow, you may wish to compute the norms and thresholds at different times (e.g. on the server).

+

Now you can compute the similarities:

+
+
similarities = embedder.compare(edf1,edf2)
+
+print(similarities)
+
+
[[0.66670521 0.13043478 0.02128141]
+ [0.24754111 0.7923548  0.02041242]
+ [0.10526899 0.04256282 0.54166665]]
+
+
+

Finally, you can compute the matching:

+
+
matching = similarities.match(abs_cutoff=0.5)
+
+print(matching)
+
+
(array([0, 1, 2]), array([0, 1, 2]))
+
+
+
+
+

Serialisation and file I/O

+

That’s how to do the workflow in one session. However, this demo follows a multi-stage workflow, so we need to be able to pass objects around. There are a couple of methods that enable file I/O and serialisation.

+

First, the Embedder object itself needs to be written to file and loaded. The idea is to train it, share it to the data owning parties, and also to the matching server. For this purpose, it’s possible to pickle the entire Embedder object.

+
+
embedder.to_pickle(os.path.join(filestem, "embedder.pkl"))
+
+embedder_copy = Embedder.from_pickle(os.path.join(filestem, "embedder.pkl"))
+
+

The copy has the same functionality as the original:

+
+
similarities = embedder_copy.compare(edf1,edf2)
+
+print(similarities)
+
+
[[0.66670521 0.13043478 0.02128141]
+ [0.24754111 0.7923548  0.02041242]
+ [0.10526899 0.04256282 0.54166665]]
+
+
+

NB: This won’t work if two datasets were embedded with different Embedder instances, even if they’re identical. The compare() method checks for the same embedder object memory reference so it won’t work if one was embedded with the original and the other with the copy. The way to fix this is to re-initialise the EmbeddedDataFrame with the new Embedder object.

+
+
edf2_copy = EmbeddedDataFrame(edf2, embedder_copy)
+
+

In this case, be careful that the Embedder is compatible with the Bloom filter vectors in the EDF (i.e. uses the same parameters and feature factories), because while you can refresh the norms and thresholds, you can’t refresh the ‘bf_indices’ without reembedding the data frame.

+
+
+

Serialising the data

+

The EDF objects are just a thin wrapper around pandas.DataFrame instances, so you can serialise to JSON using the normal methods.

+
+
edf1.to_json(os.path.join(filestem, "edf1.json"))
+
+edf1_copy = pd.read_json(os.path.join(filestem, "edf1.json"))
+
+print(isinstance(edf1_copy,EmbeddedDataFrame))
+print(isinstance(edf1_copy,pd.DataFrame))
+
+
False
+True
+
+
+

The bf_indices, bf_norms and thresholds columns will be preserved. However, this demotes the data frames back to normal pandas.DataFrame instances and loses the link to an Embedder instance.

+

To fix this, just re-initialise them:

+
+
edf1_copy = EmbeddedDataFrame(edf1_copy, embedder_copy)
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..7467b68 --- /dev/null +++ b/index.html @@ -0,0 +1,521 @@ + + + + + + + + + +pprl - Welcome to the pprl documentation! + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+
+
+
+
+
+

Welcome to the pprl documentation!

+
+
+
+
+
+

What is this and why does it exist?

+

This package, pprl, implements a method for performing privacy-preserving record linkage. This linkage can be done locally or through Google Cloud Platform.

+
+
+

Where do I go now?

+

If you’re looking to get stuck in with pprl, head over to our tutorials.

+

For more focused, technical details of how this all works, see our API reference.

+ + +
+
+
+ + + GitHub + +
+
+
+
+ + +
+ + + + + \ No newline at end of file diff --git a/listings.json b/listings.json new file mode 100644 index 0000000..d37934d --- /dev/null +++ b/listings.json @@ -0,0 +1,11 @@ +[ + { + "listing": "/docs/tutorials/index.html", + "items": [ + "/docs/tutorials/run-through.html", + "/docs/tutorials/example-verknupfung.html", + "/docs/tutorials/example-febrl.html", + "/docs/tutorials/in-the-cloud.html" + ] + } +] \ No newline at end of file diff --git a/search.json b/search.json new file mode 100644 index 0000000..0344d98 --- /dev/null +++ b/search.json @@ -0,0 +1,317 @@ +[ + { + "objectID": "index.html", + "href": "index.html", + "title": "Welcome to the pprl documentation!", + "section": "", + "text": "This package, pprl, implements a method for performing privacy-preserving record linkage. This linkage can be done locally or through Google Cloud Platform." + }, + { + "objectID": "index.html#what-is-this-and-why-does-it-exist", + "href": "index.html#what-is-this-and-why-does-it-exist", + "title": "Welcome to the pprl documentation!", + "section": "", + "text": "This package, pprl, implements a method for performing privacy-preserving record linkage. This linkage can be done locally or through Google Cloud Platform." + }, + { + "objectID": "index.html#where-do-i-go-now", + "href": "index.html#where-do-i-go-now", + "title": "Welcome to the pprl documentation!", + "section": "Where do I go now?", + "text": "Where do I go now?\nIf you’re looking to get stuck in with pprl, head over to our tutorials.\nFor more focused, technical details of how this all works, see our API reference." + }, + { + "objectID": "docs/reference/utils.html", + "href": "docs/reference/utils.html", + "title": "utils", + "section": "", + "text": "app.utils\nUtility functions for the party-side app.\n\n\n\n\n\nName\nDescription\n\n\n\n\nassign_columns\nAssign columns from a form to collections.\n\n\ncheck_is_csv\nDetermine whether a file has the csv extension.\n\n\nconvert_dataframe_to_bf\nConvert a dataframe of features to a bloom filter.\n\n\ndownload_files\nSerialize, compress, and send a data frame with its embedder.\n\n\n\n\n\napp.utils.assign_columns(form, feature_funcs)\nAssign columns from a form to collections.\nAll columns belong to one of three collections: columns to drop, raw columns to keep, or a column feature factory specification.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nform\ndict\nForm from our column chooser page.\nrequired\n\n\nfeature_funcs\ndict\nMapping between column types and feature functions.\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist[str]\nList of columns to drop.\n\n\nlist[str]\nList of columns to keep in their raw format.\n\n\ndict[str, func]\nMapping between column names and feature functions.\n\n\n\n\n\n\n\napp.utils.check_is_csv(path)\nDetermine whether a file has the csv extension.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\npath\nstr\nPath to the file.\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nbool\nWhether the file name follows the pattern {name}.csv or not.\n\n\n\n\n\n\n\napp.utils.convert_dataframe_to_bf(df, colspec, other_columns=None, salt='')\nConvert a dataframe of features to a bloom filter.\nConvert the columns to features based on the colspec. The features are then combined and converted to Bloom filter indices with the Bloom filter norm also calculated.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndf\npandas.pandas.DataFrame\nData frame of features.\nrequired\n\n\ncolspec\ndict\nDictionary designating columns in the data frame as particular feature types to be processed as appropriate.\nrequired\n\n\nother_columns\nNone | list\nColumns to be returned as they appear in the data in addition to bf_indices and bf_norms.\nNone\n\n\nsalt\nstr\nCryptographic salt to add to tokens before hashing.\n''\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.DataFrame\nData frame of bloom-filtered data.\n\n\n\n\n\n\n\napp.utils.download_files(dataframe, embedder, party, archive='archive')\nSerialize, compress, and send a data frame with its embedder.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndataframe\npprl.embedder.embedder.EmbeddedDataFrame\nData frame to be downloaded.\nrequired\n\n\nembedder\npprl.embedder.embedder.Embedder\nEmbedder used to embed dataframe.\nrequired\n\n\nparty\nstr\nName of the party.\nrequired\n\n\narchive\nstr\nName of the archive. Default is \"archive\".\n'archive'\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nflask.flask.Response\nResponse containing a ZIP archive with the data frame and its embedder." + }, + { + "objectID": "docs/reference/utils.html#functions", + "href": "docs/reference/utils.html#functions", + "title": "utils", + "section": "", + "text": "Name\nDescription\n\n\n\n\nassign_columns\nAssign columns from a form to collections.\n\n\ncheck_is_csv\nDetermine whether a file has the csv extension.\n\n\nconvert_dataframe_to_bf\nConvert a dataframe of features to a bloom filter.\n\n\ndownload_files\nSerialize, compress, and send a data frame with its embedder.\n\n\n\n\n\napp.utils.assign_columns(form, feature_funcs)\nAssign columns from a form to collections.\nAll columns belong to one of three collections: columns to drop, raw columns to keep, or a column feature factory specification.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nform\ndict\nForm from our column chooser page.\nrequired\n\n\nfeature_funcs\ndict\nMapping between column types and feature functions.\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist[str]\nList of columns to drop.\n\n\nlist[str]\nList of columns to keep in their raw format.\n\n\ndict[str, func]\nMapping between column names and feature functions.\n\n\n\n\n\n\n\napp.utils.check_is_csv(path)\nDetermine whether a file has the csv extension.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\npath\nstr\nPath to the file.\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nbool\nWhether the file name follows the pattern {name}.csv or not.\n\n\n\n\n\n\n\napp.utils.convert_dataframe_to_bf(df, colspec, other_columns=None, salt='')\nConvert a dataframe of features to a bloom filter.\nConvert the columns to features based on the colspec. The features are then combined and converted to Bloom filter indices with the Bloom filter norm also calculated.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndf\npandas.pandas.DataFrame\nData frame of features.\nrequired\n\n\ncolspec\ndict\nDictionary designating columns in the data frame as particular feature types to be processed as appropriate.\nrequired\n\n\nother_columns\nNone | list\nColumns to be returned as they appear in the data in addition to bf_indices and bf_norms.\nNone\n\n\nsalt\nstr\nCryptographic salt to add to tokens before hashing.\n''\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.DataFrame\nData frame of bloom-filtered data.\n\n\n\n\n\n\n\napp.utils.download_files(dataframe, embedder, party, archive='archive')\nSerialize, compress, and send a data frame with its embedder.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndataframe\npprl.embedder.embedder.EmbeddedDataFrame\nData frame to be downloaded.\nrequired\n\n\nembedder\npprl.embedder.embedder.Embedder\nEmbedder used to embed dataframe.\nrequired\n\n\nparty\nstr\nName of the party.\nrequired\n\n\narchive\nstr\nName of the archive. Default is \"archive\".\n'archive'\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nflask.flask.Response\nResponse containing a ZIP archive with the data frame and its embedder." + }, + { + "objectID": "docs/reference/index.html", + "href": "docs/reference/index.html", + "title": "API reference", + "section": "", + "text": "Tools for generating a Bloom filter embedding and its underlying features.\n\n\n\nbloom_filters\nModule for the Bloom filter encoder.\n\n\nembedder\nClasses and functions for handling embedding objects.\n\n\nfeatures\nFeature generation functions for various column types.\n\n\n\n\n\n\nFunctions for handling the data and key encryption processes.\n\n\n\nencryption\nTools for performing envelope encryption on GCP.\n\n\n\n\n\n\nFunctions for working out and handling linkage configuration.\n\n\n\nconfig\nFunctions for handling PPRL configuration.\n\n\n\n\n\n\nFunctions for the Flask application where users upload, process, and download their data.\n\n\n\nutils\nUtility functions for the party-side app." + }, + { + "objectID": "docs/reference/index.html#embeddings", + "href": "docs/reference/index.html#embeddings", + "title": "API reference", + "section": "", + "text": "Tools for generating a Bloom filter embedding and its underlying features.\n\n\n\nbloom_filters\nModule for the Bloom filter encoder.\n\n\nembedder\nClasses and functions for handling embedding objects.\n\n\nfeatures\nFeature generation functions for various column types." + }, + { + "objectID": "docs/reference/index.html#encryption", + "href": "docs/reference/index.html#encryption", + "title": "API reference", + "section": "", + "text": "Functions for handling the data and key encryption processes.\n\n\n\nencryption\nTools for performing envelope encryption on GCP." + }, + { + "objectID": "docs/reference/index.html#configuration", + "href": "docs/reference/index.html#configuration", + "title": "API reference", + "section": "", + "text": "Functions for working out and handling linkage configuration.\n\n\n\nconfig\nFunctions for handling PPRL configuration." + }, + { + "objectID": "docs/reference/index.html#client-side-app", + "href": "docs/reference/index.html#client-side-app", + "title": "API reference", + "section": "", + "text": "Functions for the Flask application where users upload, process, and download their data.\n\n\n\nutils\nUtility functions for the party-side app." + }, + { + "objectID": "docs/reference/features.html", + "href": "docs/reference/features.html", + "title": "features", + "section": "", + "text": "embedder.features\nFeature generation functions for various column types.\n\n\n\n\n\nName\nDescription\n\n\n\n\ngen_dateofbirth_features\nGenerate labelled date features from a series of dates of birth.\n\n\ngen_double_metaphone\nGenerate the double methaphones of a string.\n\n\ngen_features\nGenerate string features of various types.\n\n\ngen_misc_features\nGenerate miscellaneous categorical features for a series.\n\n\ngen_misc_shingled_features\nGenerate shingled labelled features.\n\n\ngen_name_features\nGenerate a features series for a series of names.\n\n\ngen_ngram\nGenerate n-grams from a set of tokens.\n\n\ngen_sex_features\nGenerate labelled sex features from a series of sexes.\n\n\ngen_skip_grams\nGenerate skip 2-grams from a set of tokens.\n\n\nsplit_string_underscore\nSplit and underwrap a string at typical punctuation marks.\n\n\n\n\n\nembedder.features.gen_dateofbirth_features(dob, dayfirst=True, yearfirst=False, default=['day<01>', 'month<01>', 'year<2050>'])\nGenerate labelled date features from a series of dates of birth.\nFeatures take the form [\"day<dd>\", \"month<mm>\", \"year<YYYY>\"]. Note that this feature generator can be used for any sort of date data, not just dates of birth.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndob\npandas.pandas.Series\nSeries of dates of birth.\nrequired\n\n\ndayfirst\nbool\nWhether the day comes first in the DOBs. Passed to pd.to_datetime() and defaults to True.\nTrue\n\n\nyearfirst\nbool\nWhether the year comes first in the DOBs. Passed to pd.to_datetime() and defaults to False.\nFalse\n\n\ndefault\nlist[str]\nDefault date to fill in missing data in feature (list) form. Default is the feature form of 2050-01-01.\n['day<01>', 'month<01>', 'year<2050>']\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.Series\nSeries containing lists of date features.\n\n\n\n\n\n\n\nembedder.features.gen_double_metaphone(string)\nGenerate the double methaphones of a string.\nThis function is a generator containing all the possible, non-empty double metaphones of a given string, separated by spaces. This function uses the metaphone.doublemetaphone() function under the hood, ignoring any empty strings. See their repository for details.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nstring\nstr\nString from which to derive double metaphones.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nstr\nThe next double metaphone in the sequence.\n\n\n\n\n\n\n\nembedder.features.gen_features(string, ngram_length=[2, 3], use_gen_ngram=True, use_gen_skip_grams=False, use_double_metaphone=False)\nGenerate string features of various types.\nThis function is a generator capable of producing n-grams, skip 2-grams, and double metaphones from a single string. These outputs are referred to as features.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nstring\nstr\nBase string from which to generate features.\nrequired\n\n\nngram_length\nlist\nLengths of n-grams to make. Ignored if use_gen_ngram=False.\n[2, 3]\n\n\nuse_gen_ngram\nbool\nWhether to create n-grams. Default is True.\nTrue\n\n\nuse_gen_skip_grams\nbool\nWhether to create skip 2-grams. Default is False.\nFalse\n\n\nuse_double_metaphone\nbool\nWhether to create double metaphones. Default is False.\nFalse\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nstr\nThe next feature in the sequence.\n\n\n\n\n\n\n\nembedder.features.gen_misc_features(field, label=None)\nGenerate miscellaneous categorical features for a series.\nUseful for keeping raw columns in the linkage data. All features use a label and take the form [\"label<option>\"] except for missing data, which are coded as \"\".\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfield\npandas.pandas.Series\nSeries from which to generate our features.\nrequired\n\n\nlabel\nNone | str | typing.Hashable\nLabel for the series. By default, the name of the series is used if available. Otherwise, if not specified, misc is used.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.Series\nSeries containing lists of miscellaneous features.\n\n\n\n\n\n\n\nembedder.features.gen_misc_shingled_features(field, ngram_length=[2, 3], use_gen_skip_grams=False, label=None)\nGenerate shingled labelled features.\nGenerate n-grams, with a label to distinguish them from (and ensure they’re hashed separately from) names. Like gen_name_features(), this function makes a call to gen_features() via pd.Series.apply().\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfield\npandas.pandas.Series\nSeries of string data.\nrequired\n\n\nngram_length\nlist\nShingle sizes to generate. By default [2, 3].\n[2, 3]\n\n\nuse_gen_skip_grams\nbool\nWhether to generate skip 2-grams. False by default.\nFalse\n\n\nlabel\nstr\nA label to differentiate from other shingled features. If field has no name, this defaults to zz.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.Series\nSeries containing lists of shingled string features.\n\n\n\n\n\n\n\nembedder.features.gen_name_features(names, ngram_length=[2, 3], use_gen_ngram=True, use_gen_skip_grams=False, use_double_metaphone=False)\nGenerate a features series for a series of names.\nEffectively, this function is a call to pd.Series.apply() using our gen_features() string feature generator function.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nnames\npandas.pandas.Series\nSeries of names.\nrequired\n\n\nngram_length\nlist[int]\nLengths of n-grams to make. Ignored if use_gen_ngram=False.\n[2, 3]\n\n\nuse_gen_ngram\nbool\nWhether to create n-grams. Default is True.\nTrue\n\n\nuse_gen_skip_grams\nbool\nWhether to create skip 2-grams. Default is False.\nFalse\n\n\nuse_double_metaphone\nbool\nWhether to create double metaphones. Default is False.\nFalse\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.Series\nSeries containing lists of features.\n\n\n\n\n\n\n\nembedder.features.gen_ngram(split_tokens, ngram_length)\nGenerate n-grams from a set of tokens.\nThis is a generator function that contains a series of n-grams the size of the sliding window.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsplit_tokens\nlist\nAll the split-up tokens from which to form n-grams.\nrequired\n\n\nngram_length\nlist\nDesired lengths of n-grams. For examples, ngram_length=[2, 3] would generate all 2-grams and 3-grams.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nstr\nThe next n-gram in the sequence.\n\n\n\n\n\n\n\nembedder.features.gen_sex_features(sexes)\nGenerate labelled sex features from a series of sexes.\nFeatures take the form [\"sex<option>\"] or [\"\"] for missing data.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsexes\npandas.pandas.Series\nSeries of sex data.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.Series\nSeries containing lists of sex features.\n\n\n\n\n\n\n\nembedder.features.gen_skip_grams(split_tokens)\nGenerate skip 2-grams from a set of tokens.\nThis function is a generator that contains a series of skip 2-grams. For example:\n>>> string = \"dave james\"\n>>> tokens = split_string_underscore(string)\n>>> skips = list(gen_skip_grams(tokens))\n>>> print(skips)\n[\"_a\", \"dv\", \"ae\", \"v_\", \"_a\", \"jm\", \"ae\", \"ms\", \"e_\"]\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsplit_tokens\nlist\nAll the split-up tokens from which to form skip 2-grams.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nstr\nThe next skip 2-gram in the sequence.\n\n\n\n\n\n\n\nembedder.features.split_string_underscore(string)\nSplit and underwrap a string at typical punctuation marks.\nCurrently, we split at any combination of spaces, dashes, dots, commas, or underscores. For example:\n>>> strings = (\"dave william johnson\", \"Francesca__Hogan-O'Malley\")\n>>> for string in strings:\n... print(split_string_underscore(string))\n...\n[\"_dave_\", \"_william_\", \"_johnson_\"]\n[\"_Francesca_\", \"_Hogan_\", \"_O'Malley_\"]\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nstring\nstr\nString to split.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist[str]\nList of the split and wrapped tokens." + }, + { + "objectID": "docs/reference/features.html#functions", + "href": "docs/reference/features.html#functions", + "title": "features", + "section": "", + "text": "Name\nDescription\n\n\n\n\ngen_dateofbirth_features\nGenerate labelled date features from a series of dates of birth.\n\n\ngen_double_metaphone\nGenerate the double methaphones of a string.\n\n\ngen_features\nGenerate string features of various types.\n\n\ngen_misc_features\nGenerate miscellaneous categorical features for a series.\n\n\ngen_misc_shingled_features\nGenerate shingled labelled features.\n\n\ngen_name_features\nGenerate a features series for a series of names.\n\n\ngen_ngram\nGenerate n-grams from a set of tokens.\n\n\ngen_sex_features\nGenerate labelled sex features from a series of sexes.\n\n\ngen_skip_grams\nGenerate skip 2-grams from a set of tokens.\n\n\nsplit_string_underscore\nSplit and underwrap a string at typical punctuation marks.\n\n\n\n\n\nembedder.features.gen_dateofbirth_features(dob, dayfirst=True, yearfirst=False, default=['day<01>', 'month<01>', 'year<2050>'])\nGenerate labelled date features from a series of dates of birth.\nFeatures take the form [\"day<dd>\", \"month<mm>\", \"year<YYYY>\"]. Note that this feature generator can be used for any sort of date data, not just dates of birth.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndob\npandas.pandas.Series\nSeries of dates of birth.\nrequired\n\n\ndayfirst\nbool\nWhether the day comes first in the DOBs. Passed to pd.to_datetime() and defaults to True.\nTrue\n\n\nyearfirst\nbool\nWhether the year comes first in the DOBs. Passed to pd.to_datetime() and defaults to False.\nFalse\n\n\ndefault\nlist[str]\nDefault date to fill in missing data in feature (list) form. Default is the feature form of 2050-01-01.\n['day<01>', 'month<01>', 'year<2050>']\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.Series\nSeries containing lists of date features.\n\n\n\n\n\n\n\nembedder.features.gen_double_metaphone(string)\nGenerate the double methaphones of a string.\nThis function is a generator containing all the possible, non-empty double metaphones of a given string, separated by spaces. This function uses the metaphone.doublemetaphone() function under the hood, ignoring any empty strings. See their repository for details.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nstring\nstr\nString from which to derive double metaphones.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nstr\nThe next double metaphone in the sequence.\n\n\n\n\n\n\n\nembedder.features.gen_features(string, ngram_length=[2, 3], use_gen_ngram=True, use_gen_skip_grams=False, use_double_metaphone=False)\nGenerate string features of various types.\nThis function is a generator capable of producing n-grams, skip 2-grams, and double metaphones from a single string. These outputs are referred to as features.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nstring\nstr\nBase string from which to generate features.\nrequired\n\n\nngram_length\nlist\nLengths of n-grams to make. Ignored if use_gen_ngram=False.\n[2, 3]\n\n\nuse_gen_ngram\nbool\nWhether to create n-grams. Default is True.\nTrue\n\n\nuse_gen_skip_grams\nbool\nWhether to create skip 2-grams. Default is False.\nFalse\n\n\nuse_double_metaphone\nbool\nWhether to create double metaphones. Default is False.\nFalse\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nstr\nThe next feature in the sequence.\n\n\n\n\n\n\n\nembedder.features.gen_misc_features(field, label=None)\nGenerate miscellaneous categorical features for a series.\nUseful for keeping raw columns in the linkage data. All features use a label and take the form [\"label<option>\"] except for missing data, which are coded as \"\".\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfield\npandas.pandas.Series\nSeries from which to generate our features.\nrequired\n\n\nlabel\nNone | str | typing.Hashable\nLabel for the series. By default, the name of the series is used if available. Otherwise, if not specified, misc is used.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.Series\nSeries containing lists of miscellaneous features.\n\n\n\n\n\n\n\nembedder.features.gen_misc_shingled_features(field, ngram_length=[2, 3], use_gen_skip_grams=False, label=None)\nGenerate shingled labelled features.\nGenerate n-grams, with a label to distinguish them from (and ensure they’re hashed separately from) names. Like gen_name_features(), this function makes a call to gen_features() via pd.Series.apply().\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfield\npandas.pandas.Series\nSeries of string data.\nrequired\n\n\nngram_length\nlist\nShingle sizes to generate. By default [2, 3].\n[2, 3]\n\n\nuse_gen_skip_grams\nbool\nWhether to generate skip 2-grams. False by default.\nFalse\n\n\nlabel\nstr\nA label to differentiate from other shingled features. If field has no name, this defaults to zz.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.Series\nSeries containing lists of shingled string features.\n\n\n\n\n\n\n\nembedder.features.gen_name_features(names, ngram_length=[2, 3], use_gen_ngram=True, use_gen_skip_grams=False, use_double_metaphone=False)\nGenerate a features series for a series of names.\nEffectively, this function is a call to pd.Series.apply() using our gen_features() string feature generator function.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nnames\npandas.pandas.Series\nSeries of names.\nrequired\n\n\nngram_length\nlist[int]\nLengths of n-grams to make. Ignored if use_gen_ngram=False.\n[2, 3]\n\n\nuse_gen_ngram\nbool\nWhether to create n-grams. Default is True.\nTrue\n\n\nuse_gen_skip_grams\nbool\nWhether to create skip 2-grams. Default is False.\nFalse\n\n\nuse_double_metaphone\nbool\nWhether to create double metaphones. Default is False.\nFalse\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.Series\nSeries containing lists of features.\n\n\n\n\n\n\n\nembedder.features.gen_ngram(split_tokens, ngram_length)\nGenerate n-grams from a set of tokens.\nThis is a generator function that contains a series of n-grams the size of the sliding window.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsplit_tokens\nlist\nAll the split-up tokens from which to form n-grams.\nrequired\n\n\nngram_length\nlist\nDesired lengths of n-grams. For examples, ngram_length=[2, 3] would generate all 2-grams and 3-grams.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nstr\nThe next n-gram in the sequence.\n\n\n\n\n\n\n\nembedder.features.gen_sex_features(sexes)\nGenerate labelled sex features from a series of sexes.\nFeatures take the form [\"sex<option>\"] or [\"\"] for missing data.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsexes\npandas.pandas.Series\nSeries of sex data.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.Series\nSeries containing lists of sex features.\n\n\n\n\n\n\n\nembedder.features.gen_skip_grams(split_tokens)\nGenerate skip 2-grams from a set of tokens.\nThis function is a generator that contains a series of skip 2-grams. For example:\n>>> string = \"dave james\"\n>>> tokens = split_string_underscore(string)\n>>> skips = list(gen_skip_grams(tokens))\n>>> print(skips)\n[\"_a\", \"dv\", \"ae\", \"v_\", \"_a\", \"jm\", \"ae\", \"ms\", \"e_\"]\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsplit_tokens\nlist\nAll the split-up tokens from which to form skip 2-grams.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nstr\nThe next skip 2-gram in the sequence.\n\n\n\n\n\n\n\nembedder.features.split_string_underscore(string)\nSplit and underwrap a string at typical punctuation marks.\nCurrently, we split at any combination of spaces, dashes, dots, commas, or underscores. For example:\n>>> strings = (\"dave william johnson\", \"Francesca__Hogan-O'Malley\")\n>>> for string in strings:\n... print(split_string_underscore(string))\n...\n[\"_dave_\", \"_william_\", \"_johnson_\"]\n[\"_Francesca_\", \"_Hogan_\", \"_O'Malley_\"]\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nstring\nstr\nString to split.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist[str]\nList of the split and wrapped tokens." + }, + { + "objectID": "docs/tutorials/in-the-cloud.html", + "href": "docs/tutorials/in-the-cloud.html", + "title": "Working in the cloud", + "section": "", + "text": "This tutorial provides an overview of how to use pprl_toolkit on Google Cloud Platform (GCP). We go over how to assemble and assign roles in a linkage team, how to set up everybody’s projects, and end with executing the linkage itself.\nAbove is a diagram showing the PPRL cloud architecture. The cloud demo uses a Google Cloud Platform (GCP) Confidential Space compute instance, which is a virtual machine (VM) using AMD Secure Encrypted Virtualisation (AMD-SEV) technology to encrypt data in-memory. The Confidential Space VM can also provide cryptographically signed documents, called attestations, which the server can use to prove that it is running in a secure environment before gaining access to data." + }, + { + "objectID": "docs/tutorials/in-the-cloud.html#assembling-a-linkage-team", + "href": "docs/tutorials/in-the-cloud.html#assembling-a-linkage-team", + "title": "Working in the cloud", + "section": "Assembling a linkage team", + "text": "Assembling a linkage team\nThere are four roles to fill in any PPRL project: two data-owning parties, a workload author, and a workload operator. A workload is how we refer to the resources for the linkage operation itself (i.e. the containerised linkage code and the environment in which to run it.)\nThese roles need not be fulfilled by four separate people. It is perfectly possible to perform PPRL on your own, or perhaps you are working under a trust model that allows one of the data-owning parties to author the workload while the other is the operator.\n\n\n\n\n\n\nTip\n\n\n\nIn fact, pprl_toolkit is set up to allow any configuration of these roles among up to four people.\n\n\nIn any case, you must decide who will be doing what from the outset. Each role comes with different responsibilities, but all roles require a GCP account and access to the gcloud command-line tool. Additionally, everyone in the linkage project will need to install pprl_toolkit.\n\nData-owning party\nOften referred to as just a party, a data owner is responsible for the storage and preparation of some confidential data. During set-up, each party also sets up a storage bucket, a key management service, and a workload identity pool that allows the party to share permissions with the server during the linkage operation.\nThey create a Bloom filter embedding of their confidential data using an agreed configuration, and then upload that to GCP for processing. Once the workload operator is finished, the parties are able to retrieve their linkage results.\n\n\nWorkload author\nThe workload author is responsible for building a Docker image containing the cloud-based linkage code and uploading it to a GCP Artifact Registry. This image is the workload to be run by the operator.\n\n\nWorkload operator\nThe workload operator runs the linkage itself using some embedded data from the parties and an image from the author. They are responsible for setting up and running a Confidential Space in which to perform the linkage. This setting ensures that nobody ever has access to all the data at once, and that the data can only be accessed via the linkage code itself." + }, + { + "objectID": "docs/tutorials/in-the-cloud.html#creating-your-gcp-projects", + "href": "docs/tutorials/in-the-cloud.html#creating-your-gcp-projects", + "title": "Working in the cloud", + "section": "Creating your GCP projects", + "text": "Creating your GCP projects\nOnce you have decided who will be playing which role(s), you need to decide on a naming structure and make some GCP projects. You will need a project for each member of the linkage project - not one for each role. The names of these projects will be used throughout the cloud implementation, from configuration files to buckets. As such, they need to be descriptive and unique.\n\n\n\n\n\n\nWarning\n\n\n\nSince Google Cloud bucket names must be globally unique, we highly recommend using a hash in your project names to ensure that they are unique. This will ensure that bucket names are also globally unique.\nOur aim is to create a globally unique name (and thus ID) for each project.\n\n\nFor example, say the US Census Bureau and UK Office for National Statistics (ONS) are looking to link some data on ex-patriated residents with PPRL. Then they might use us-cb and uk-ons as their party names, which are succinct and descriptive. However, they are generic and rule out future PPRL projects with the same names.\nAs a remedy, they could make a hash of their project description to create an identifier:\n$ echo -n \"pprl us-cb uk-ons ex-pats-analysis\" | sha256sum\nd59a50241dc78c3f926b565937b99614b7bb7c84e44fb780440718cb2b0ddc1b -\nThis is very long. You might only want to use the first few characters of this hash. Note that Google Cloud bucket names also can’t be more than 63 characters long without dots.\nYou can trim it down like so:\n$ echo -n \"pprl us-cb uk-ons ex-pats-analysis\" | sha256sum | cut -c 1-7\nd59a502\nSo, our names would be: uk-ons-d59a502, us-cb-d59a502. If they had a third-party linkage administrator (authoring and operating the workload), they would have a project called something like admin-d59a502." + }, + { + "objectID": "docs/tutorials/in-the-cloud.html#setting-up-your-projects", + "href": "docs/tutorials/in-the-cloud.html#setting-up-your-projects", + "title": "Working in the cloud", + "section": "Setting up your projects", + "text": "Setting up your projects\nOnce you have decided on a naming structure, it is time to create the GCP projects. Each project will need specific Identity and Access Management (IAM) roles granted to them by the project owner’s GCP Administrator. Which IAM roles depends on the linkage role they are playing. If someone is fulfilling more than one role, they should follow all the relevant sections below.\n\n\n\n\n\n\nTip\n\n\n\nIf you have Administrator permissions for your GCP project, you can grant these roles using the gcloud command-line tool:\ngcloud projects add-iam-policy-binding <project-name> \\\n --member=user:<user-email> \\\n --role=<role-code>\n\n\n\nData-owning parties\nEach data-owning party requires the following IAM roles:\n\n\n\n\n\n\n\n\nTitle\nCode\nPurpose\n\n\n\n\nCloud KMS Admin\nroles/cloudkms.admin\nManaging encryption keys\n\n\nIAM Workload Identity Pool Admin\nroles/iam.workloadIdentityPoolAdmin\nManaging an impersonation service\n\n\nService Usage Admin\nroles/serviceusage.serviceUsageAdmin\nManaging access to other APIs\n\n\nService Account Admin\nroles/iam.serviceAccountAdmin\nManaging a service account\n\n\nStorage Admin\nroles/storage.admin\nManaging a bucket for their data\n\n\n\n\n\nWorkload author\nThe workload author only requires one IAM role:\n\n\n\n\n\n\n\n\nTitle\nCode\nPurpose\n\n\n\n\nArtifact Registry Administrator\nroles/artifactregistry.admin\nManaging the registry for the workload\n\n\n\n\n\nWorkload operator\nThe workload operator requires three IAM roles:\n\n\n\n\n\n\n\n\nTitle\nCode\nPurpose\n\n\n\n\nCompute Admin\nroles/compute.admin\nManaging the virtual machine\n\n\nSecurity Admin\nroles/iam.securityAdmin\nAbility to set and get IAM policies\n\n\nStorage Admin\nroles/storage.admin\nManaging a shared bucket" + }, + { + "objectID": "docs/tutorials/in-the-cloud.html#configuring-pprl_toolkit", + "href": "docs/tutorials/in-the-cloud.html#configuring-pprl_toolkit", + "title": "Working in the cloud", + "section": "Configuring pprl_toolkit", + "text": "Configuring pprl_toolkit\nNow your linkage team has its projects made up, you need to configure pprl_toolkit. This configuration tells the package where to look and what to call things; we do this with a single environment file containing a short collection of key-value pairs.\nWe have provided an example environment file in .env.example. Copy or rename that file to .env in the root of the pprl_toolkit installation. Then, fill in your project details as necessary.\nFor our example above, let’s say the ONS will be the workload author and the US Census Bureau will be the workload operator. The environment file would look something like this:\nPARTY_1_PROJECT=us-cb-d59a502\nPARTY_1_KEY_VERSION=1\n\nPARTY_2_PROJECT=uk-ons-d59a502\nPARTY_2_KEY_VERSION=1\n\nWORKLOAD_AUTHOR_PROJECT=uk-ons-d59a502\nWORKLOAD_AUTHOR_PROJECT_REGION=europe-west2\n\nWORKLOAD_OPERATOR_PROJECT=us-cb-d59a502\nWORKLOAD_OPERATOR_PROJECT_ZONE=us-east4-a\n\n\n\n\n\n\nImportant\n\n\n\nYour environment file should be identical among all the members of your linkage project." + }, + { + "objectID": "docs/tutorials/in-the-cloud.html#creating-the-other-resources", + "href": "docs/tutorials/in-the-cloud.html#creating-the-other-resources", + "title": "Working in the cloud", + "section": "Creating the other resources", + "text": "Creating the other resources\nThe last step in setting up your linkage project is to create and configure the other resources on GCP. To make things straightforward for users, we have packaged up the steps to do this into a number of bash scripts. These scripts are located in the scripts/ directory and are numbered. You and your team must execute them from the scripts/ directory in their named order according to which role(s) each member is fulfilling in the linkage project.\n\n\n\n\n\n\nTip\n\n\n\nMake sure you have set up gcloud on the command line. Once you’ve installed it, log in and set the application default:\ngcloud auth login\ngcloud auth application-default login\n\n\n\nThe data-owning parties set up: a key encryption key; a bucket in which to store their encrypted data, data encryption key and results; a service account for accessing said bucket and key; and a workload identity pool to allow impersonations under stringent conditions.\nsh ./01-setup-party-resources.sh <name-of-party-project>\nThe workload operator sets up a bucket for the parties to put their (non-sensitive) attestation credentials, and a service account for running the workload.\nsh ./02-setup-workload-operator.sh\nThe workload author sets up an Artifact Registry on GCP, creates a Docker image and uploads that image to their registry.\nsh ./03-setup-workload-author.sh\nThe data-owning parties authorise the workload operator’s service account to use the workload identity pool to impersonate their service account in a Confidential Space.\nsh ./04-authorise-workload.sh <name-of-party-project>" + }, + { + "objectID": "docs/tutorials/in-the-cloud.html#processing-and-uploading-the-results", + "href": "docs/tutorials/in-the-cloud.html#processing-and-uploading-the-results", + "title": "Working in the cloud", + "section": "Processing and uploading the results", + "text": "Processing and uploading the results\n\n\n\n\n\n\nImportant\n\n\n\nThis section only applies to data-owning parties. The workload author is finished now, and the workload operator should wait for this section to be completed before moving on to the next section.\n\n\n\nNow that all the cloud infrastructure has been set up, we are ready to start the first step in doing the actual linkage. That is, to create a Bloom filter embedding of their data, encrypt it, and upload that to GCP.\nFor users who prefer a graphical user interface, we have included a Flask app to handle the processing and uploading of data behind the scenes. This app will also be used to download the results once the linkage has completed.\nTo launch the app, run the following in your terminal:\npython -m flask --app src/pprl/app run\nYou should now be able to find the app in your browser of choice at 127.0.0.1:5000. It should look something like this:\n\n\n\nA screenshot of the app\n\n\nFrom here, the process to upload your data is as follows:\n\nChoose which party you are uploading for. Click Submit.\nSelect Upload local file and click Choose file to open your file browser. Navigate to and select your dataset. Click Submit.\nAssign types to each column in your dataset. Enter the agreed salt.\nClick Upload file to GCP.\n\n\n\n\n\n\n\nNote\n\n\n\nIf you choose to use the Flask app to process your data, you will use a set of defaults for processing the confidential data before it gets embedded. If you want more control, then you’ll have to agree an embedding configuration with the other data-owning party and do the processing directly.\n\n\nOnce you have worked through the selection, processing, and GCP upload portions of the app, you will be at a holding page. This page can be updated by clicking the button, and when your results are ready you will be taken to another page where you can download them." + }, + { + "objectID": "docs/tutorials/in-the-cloud.html#running-the-linkage", + "href": "docs/tutorials/in-the-cloud.html#running-the-linkage", + "title": "Working in the cloud", + "section": "Running the linkage", + "text": "Running the linkage\n\n\n\n\n\n\nImportant\n\n\n\nThis section only applies to the workload operator.\n\n\nOnce the data-owning parties have uploaded their processed data, you are able to begin the linkage. To do so, run the 05-run-workload.sh bash script from scripts/:\ncd /path/to/pprl_toolkit/scripts\nsh ./05-run-workload.sh\nYou can follow the progress of the workload from the Logs Explorer on GCP. Once it is complete, the data-owning parties will be able to download their results." + }, + { + "objectID": "docs/tutorials/run-through.html", + "href": "docs/tutorials/run-through.html", + "title": "Embedder API run-through", + "section": "", + "text": "This article shows the main classes, methods and functionality of the Embedder API.\nFirst, we’ll import a few modules, including:\nimport os\n\nimport pandas as pd\n\nfrom pprl import EmbeddedDataFrame, Embedder, config\nfrom pprl.embedder import features as feat\n\nfilestem = config.DIR_DATA_INTERIM" + }, + { + "objectID": "docs/tutorials/run-through.html#data-set-up", + "href": "docs/tutorials/run-through.html#data-set-up", + "title": "Embedder API run-through", + "section": "Data set-up", + "text": "Data set-up\nFor this demo we’ll create a really minimal pair of datasets. Notice that they don’t have to have the same structure or field names.\n\ndf1 = pd.DataFrame(\n dict(\n id=[1,2,3],\n forename=[\"Henry\", \"Sally\", \"Ina\"],\n surname = [\"Tull\", \"Brown\", \"Lawrey\"],\n dob=[\"1/1/2001\", \"2/1/2001\", \"4/10/1995\"],\n gender=[\"male\", \"Male\", \"Female\"],\n )\n)\n\ndf2 = pd.DataFrame(\n dict(\n personid=[4,5,6],\n full_name=[\"Harry Tull\", \"Sali Brown\", \"Ina Laurie\"],\n date_of_birth=[\"2/1/2001\", \"2/1/2001\", \"4/11/1995\"],\n sex=[\"M\", \"M\", \"F\"],\n )\n)\n\nFeatures are extracted as different kinds of string objects from each field, ready to be hash embedded into the Bloom filters. We need to specify the feature extraction functions we’ll need.\nIn this case we’ll need one extractor for names, one for dates of birth, and one for sex/gender records. We create a dict with the functions we need. We create another dict to store any keyword arguments we want to pass in to each function (in this case we use all the default arguments so the keyword argument dictionaries are empty):\n\nfeature_factory = dict(\n name=feat.gen_name_features,\n dob=feat.gen_dateofbirth_features,\n sex=feat.gen_sex_features,\n)\n\nff_args = dict(name={}, sex={}, dob={})" + }, + { + "objectID": "docs/tutorials/run-through.html#embedding", + "href": "docs/tutorials/run-through.html#embedding", + "title": "Embedder API run-through", + "section": "Embedding", + "text": "Embedding\nNow we can create an Embedder object. We want our Bloom filter vectors to have a length of 1024 elements (actually 1025 because of an offset), and we choose to hash each feature two times. These choices seem to work ok, but we haven’t explored them systematically.\n\nembedder = Embedder(feature_factory,\n ff_args,\n bf_size = 2**10,\n num_hashes=2,\n )\n\nNow we can hash embed the dataset into an EmbeddedDataFrame (EDF). For this we need to pass a column specification colspec that maps each column of the data into the feature_factory functions. Any columns not mapped will not contribute to the embedding.\n\nedf1 = embedder.embed(\n df1, colspec=dict(forename=\"name\", surname=\"name\", dob=\"dob\", gender=\"sex\")\n)\nedf2 = embedder.embed(\n df2, colspec=dict(full_name=\"name\", date_of_birth=\"dob\", sex=\"sex\")\n)\n\nprint(edf1)\nprint(edf2)\n\n id forename surname dob gender \\\n0 1 Henry Tull 1/1/2001 male \n1 2 Sally Brown 2/1/2001 Male \n2 3 Ina Lawrey 4/10/1995 Female \n\n forename_features \\\n0 [_h, he, en, nr, ry, y_, _he, hen, enr, nry, ry_] \n1 [_s, sa, al, ll, ly, y_, _sa, sal, all, lly, ly_] \n2 [_i, in, na, a_, _in, ina, na_] \n\n surname_features \\\n0 [_t, tu, ul, ll, l_, _tu, tul, ull, ll_] \n1 [_b, br, ro, ow, wn, n_, _br, bro, row, own, wn_] \n2 [_l, la, aw, wr, re, ey, y_, _la, law, awr, wr... \n\n dob_features gender_features \\\n0 [day<01>, month<01>, year<2001>] [sex<m>] \n1 [day<02>, month<01>, year<2001>] [sex<m>] \n2 [day<04>, month<10>, year<1995>] [sex<f>] \n\n all_features \\\n0 [en, _h, he, hen, _tu, day<01>, tul, ll_, nry,... \n1 [lly, wn, _sa, _br, ll, own, y_, wn_, al, mont... \n2 [rey, wre, _la, day<04>, _in, _l, na, na_, a_,... \n\n bf_indices bf_norms \n0 [13, 399, 271, 147, 277, 25, 537, 802, 547, 29... 6.782330 \n1 [259, 647, 521, 781, 147, 533, 918, 151, 408, ... 7.071068 \n2 [898, 391, 655, 531, 148, 663, 151, 668, 413, ... 6.928203 \n personid full_name date_of_birth sex \\\n0 4 Harry Tull 2/1/2001 M \n1 5 Sali Brown 2/1/2001 M \n2 6 Ina Laurie 4/11/1995 F \n\n full_name_features \\\n0 [_h, ha, ar, rr, ry, y_, _t, tu, ul, ll, l_, _... \n1 [_s, sa, al, li, i_, _b, br, ro, ow, wn, n_, _... \n2 [_i, in, na, a_, _l, la, au, ur, ri, ie, e_, _... \n\n date_of_birth_features sex_features \\\n0 [day<02>, month<01>, year<2001>] [sex<m>] \n1 [day<02>, month<01>, year<2001>] [sex<m>] \n2 [day<04>, month<11>, year<1995>] [sex<f>] \n\n all_features \\\n0 [_h, _ha, _tu, ar, tul, ll_, ull, ry_, rry, ll... \n1 [li_, li, wn, _sa, _br, own, wn_, al, month<01... \n2 [e_, ur, _la, day<04>, _l, _in, na, na_, a_, l... \n\n bf_indices bf_norms \n0 [259, 13, 399, 147, 277, 918, 547, 293, 805, 1... 6.855655 \n1 [259, 515, 6, 647, 521, 781, 143, 15, 147, 533... 6.782330 \n2 [385, 771, 391, 647, 136, 908, 653, 786, 531, ... 6.928203" + }, + { + "objectID": "docs/tutorials/run-through.html#training", + "href": "docs/tutorials/run-through.html#training", + "title": "Embedder API run-through", + "section": "Training", + "text": "Training\nDiscuss this at this stage" + }, + { + "objectID": "docs/tutorials/run-through.html#computing-the-similarity-scores-and-the-matching", + "href": "docs/tutorials/run-through.html#computing-the-similarity-scores-and-the-matching", + "title": "Embedder API run-through", + "section": "Computing the similarity scores and the matching", + "text": "Computing the similarity scores and the matching\nNow we have two embedded datasets, we can compare them and compute all the pairwise Cosine similarity scores.\nFirst, we have to compute the vector norms of each Bloom vector (for scaling the Cosine similarity) and the thresholds (thresholds are explained here [link]). Computing the thresholds can be time-consuming for a larger dataset, because it essentially computes all pairwise comparisons of the data to itself.\n\n\n\n\n\n\n\n\n\npersonid\nfull_name\ndate_of_birth\nsex\nfull_name_features\ndate_of_birth_features\nsex_features\nall_features\nbf_indices\nbf_norms\nthresholds\n\n\n\n\n0\n4\nHarry Tull\n2/1/2001\nM\n[_h, ha, ar, rr, ry, y_, _t, tu, ul, ll, l_, _...\n[day<02>, month<01>, year<2001>]\n[sex<m>]\n[_h, _ha, _tu, ar, tul, ll_, ull, ry_, rry, ll...\n[259, 13, 399, 147, 277, 918, 547, 293, 805, 1...\n6.855655\n0.172053\n\n\n1\n5\nSali Brown\n2/1/2001\nM\n[_s, sa, al, li, i_, _b, br, ro, ow, wn, n_, _...\n[day<02>, month<01>, year<2001>]\n[sex<m>]\n[li_, li, wn, _sa, _br, own, wn_, al, month<01...\n[259, 515, 6, 647, 521, 781, 143, 15, 147, 533...\n6.782330\n0.172053\n\n\n2\n6\nIna Laurie\n4/11/1995\nF\n[_i, in, na, a_, _l, la, au, ur, ri, ie, e_, _...\n[day<04>, month<11>, year<1995>]\n[sex<f>]\n[e_, ur, _la, day<04>, _l, _in, na, na_, a_, l...\n[385, 771, 391, 647, 136, 908, 653, 786, 531, ...\n6.928203\n0.021281\n\n\n\n\n\n\n\nNB: there’s also a flag to compute these at the same time as the embedding, but it doesn’t by default because, depending on the workflow, you may wish to compute the norms and thresholds at different times (e.g. on the server).\nNow you can compute the similarities:\n\nsimilarities = embedder.compare(edf1,edf2)\n\nprint(similarities)\n\n[[0.66670521 0.13043478 0.02128141]\n [0.24754111 0.7923548 0.02041242]\n [0.10526899 0.04256282 0.54166665]]\n\n\nFinally, you can compute the matching:\n\nmatching = similarities.match(abs_cutoff=0.5)\n\nprint(matching)\n\n(array([0, 1, 2]), array([0, 1, 2]))" + }, + { + "objectID": "docs/tutorials/run-through.html#serialisation-and-file-io", + "href": "docs/tutorials/run-through.html#serialisation-and-file-io", + "title": "Embedder API run-through", + "section": "Serialisation and file I/O", + "text": "Serialisation and file I/O\nThat’s how to do the workflow in one session. However, this demo follows a multi-stage workflow, so we need to be able to pass objects around. There are a couple of methods that enable file I/O and serialisation.\nFirst, the Embedder object itself needs to be written to file and loaded. The idea is to train it, share it to the data owning parties, and also to the matching server. For this purpose, it’s possible to pickle the entire Embedder object.\n\nembedder.to_pickle(os.path.join(filestem, \"embedder.pkl\"))\n\nembedder_copy = Embedder.from_pickle(os.path.join(filestem, \"embedder.pkl\"))\n\nThe copy has the same functionality as the original:\n\nsimilarities = embedder_copy.compare(edf1,edf2)\n\nprint(similarities)\n\n[[0.66670521 0.13043478 0.02128141]\n [0.24754111 0.7923548 0.02041242]\n [0.10526899 0.04256282 0.54166665]]\n\n\nNB: This won’t work if two datasets were embedded with different Embedder instances, even if they’re identical. The compare() method checks for the same embedder object memory reference so it won’t work if one was embedded with the original and the other with the copy. The way to fix this is to re-initialise the EmbeddedDataFrame with the new Embedder object.\n\nedf2_copy = EmbeddedDataFrame(edf2, embedder_copy)\n\nIn this case, be careful that the Embedder is compatible with the Bloom filter vectors in the EDF (i.e. uses the same parameters and feature factories), because while you can refresh the norms and thresholds, you can’t refresh the ‘bf_indices’ without reembedding the data frame." + }, + { + "objectID": "docs/tutorials/run-through.html#serialising-the-data", + "href": "docs/tutorials/run-through.html#serialising-the-data", + "title": "Embedder API run-through", + "section": "Serialising the data", + "text": "Serialising the data\nThe EDF objects are just a thin wrapper around pandas.DataFrame instances, so you can serialise to JSON using the normal methods.\n\nedf1.to_json(os.path.join(filestem, \"edf1.json\"))\n\nedf1_copy = pd.read_json(os.path.join(filestem, \"edf1.json\"))\n\nprint(isinstance(edf1_copy,EmbeddedDataFrame))\nprint(isinstance(edf1_copy,pd.DataFrame))\n\nFalse\nTrue\n\n\nThe bf_indices, bf_norms and thresholds columns will be preserved. However, this demotes the data frames back to normal pandas.DataFrame instances and loses the link to an Embedder instance.\nTo fix this, just re-initialise them:\n\nedf1_copy = EmbeddedDataFrame(edf1_copy, embedder_copy)" + }, + { + "objectID": "docs/tutorials/example-febrl.html", + "href": "docs/tutorials/example-febrl.html", + "title": "Linking the FEBRL datasets", + "section": "", + "text": "This tutorial shows how the package can be used locally to match the FEBRL datasets, included as example datasets in the recordlinkage package.\nimport os\nimport time\nfrom functools import partial\n\nimport pandas as pd\nimport numpy as np\nfrom recordlinkage.datasets import load_febrl4\n\nfrom pprl import EmbeddedDataFrame, Embedder, config\nfrom pprl.embedder import features as feat\n\ndatadir = config.DIR_DATA_INTERIM" + }, + { + "objectID": "docs/tutorials/example-febrl.html#load-the-data", + "href": "docs/tutorials/example-febrl.html#load-the-data", + "title": "Linking the FEBRL datasets", + "section": "Load the data", + "text": "Load the data\nThe datasets we are using are 5000 records across two datasets with no duplicates, and each of the records has a valid match in the other dataset.\nAfter loading the data, we can parse the true matched ID number from the indices.\n\nfeb4a, feb4b = load_febrl4()\n\nfeb4a[\"true_id\"] = (\n feb4a.index.str.extract(\"^rec-([0-9]*)-\")\n .iloc[:, 0].astype(\"int\")\n .to_list()\n)\nfeb4b[\"true_id\"] = (\n feb4b.index.str.extract(\"^rec-([0-9]*)-\")\n .iloc[:, 0].astype(\"int\")\n .to_list()\n)\n\nfeb4a.to_csv(os.path.join(datadir, \"febrl_data_1.csv\"))\nfeb4b.to_csv(os.path.join(datadir, \"febrl_data_2.csv\"))" + }, + { + "objectID": "docs/tutorials/example-febrl.html#create-a-feature-factory", + "href": "docs/tutorials/example-febrl.html#create-a-feature-factory", + "title": "Linking the FEBRL datasets", + "section": "Create a feature factory", + "text": "Create a feature factory\nDefine the feature processing functions that convert each field into a string so that they can be hashed into the Bloom filter. The dictionary of functions is called feature_factory.\nIf we want to pass the feature factory functions into the embedder with non-default parameters, we have two options:\n\nPass a dictionary of dictionaries of keyword arguments as an optional ff_args parameter (e.g. ff_args = {\"dob\": {\"dayfirst\": False, \"yearfirst\": True}}))\nUse functools.partial(), as we have below.\n\n\nfeature_factory = dict(\n name=feat.gen_name_features,\n dob=partial(feat.gen_dateofbirth_features, dayfirst=False, yearfirst=True),\n misc=feat.gen_misc_features,\n address=partial(\n feat.gen_misc_shingled_features, label=\"addr\", ngram_length=[4]\n ),\n)" + }, + { + "objectID": "docs/tutorials/example-febrl.html#initialise-the-embedder-instance", + "href": "docs/tutorials/example-febrl.html#initialise-the-embedder-instance", + "title": "Linking the FEBRL datasets", + "section": "Initialise the embedder instance", + "text": "Initialise the embedder instance\nThis instance embeds each feature twice into a Bloom filter of length 1025 (with the offset).\n\nembedder = Embedder(feature_factory, bf_size=2**10, num_hashes=2)" + }, + { + "objectID": "docs/tutorials/example-febrl.html#embed-the-datasets", + "href": "docs/tutorials/example-febrl.html#embed-the-datasets", + "title": "Linking the FEBRL datasets", + "section": "Embed the datasets", + "text": "Embed the datasets\nThe column specification colspec is a dictionary that tells the embedder how to map feature-processing functions to columns of the data. Notice that we can map more than one column to the same function. This means that we can easily handle cases where fields we want to compare all together span several columns.\nThis process makes our Bloom filter robust to inconsistencies where, for example, surname and given name may be swapped, or addresses may be coded inconsistently.\n\n\n\n\n\n\nNote\n\n\n\nTo ensure that different feature types hash into different buckets, the functions gen_misc_features() and gen_misc_shingled_features() will add a label to parsed string features, which will default to the data column name.\nFor example, to ensure suburb doesn’t collide with state (if they happened to be the same), gen_misc_features() would encode each of their tokens as suburb<token> and state<token>, respectively. If you want to map different columns into the same feature, such as address below, you can set the label explicitly when passing the function to the embedder.\n\n\n\ncolspec = dict(\n given_name=\"name\",\n surname=\"name\",\n date_of_birth=\"dob\",\n street_number=\"misc\",\n state=\"misc\",\n soc_sec_id=\"misc\",\n postcode=\"misc\",\n suburb=\"misc\",\n address_1=\"address\",\n address_2=\"address\",\n)\n\nedf1 = embedder.embed(feb4a, colspec=colspec)\nedf2 = embedder.embed(feb4b, colspec=colspec)\n\nStore the embedded datasets and their embedder to file.\n\nedf1.to_json(os.path.join(datadir, \"party1_data.json\"))\nedf2.to_json(os.path.join(datadir, \"party2_data.json\"))\nembedder.to_pickle(os.path.join(datadir, \"embedder.pkl\"))" + }, + { + "objectID": "docs/tutorials/example-febrl.html#calculate-similarity", + "href": "docs/tutorials/example-febrl.html#calculate-similarity", + "title": "Linking the FEBRL datasets", + "section": "Calculate similarity", + "text": "Calculate similarity\nCompute the row thresholds to provide a lower bound on matching similarity scores for each row. This operation is the most computationally intensive part of the whole process.\n\nstart = time.time()\nedf1.update_thresholds()\nedf2.update_thresholds()\nend = time.time()\n\nprint(f\"Updating thresholds took {end - start:.2f} seconds\")\n\nUpdating thresholds took 3.00 seconds\n\n\nCompute the matrix of similarity scores.\n\nsimilarity_scores = embedder.compare(edf1,edf2)" + }, + { + "objectID": "docs/tutorials/example-febrl.html#compute-a-match", + "href": "docs/tutorials/example-febrl.html#compute-a-match", + "title": "Linking the FEBRL datasets", + "section": "Compute a match", + "text": "Compute a match\nUse the similarity scores to compute a match, using the Hungarian algorithm. First, we compute the match with the row thresholds.\n\nmatching = similarity_scores.match(require_thresholds=True)\n\nUsing the true IDs, evaluate the precision and recall of the match.\n\ndef get_results(edf1, edf2, matching):\n \"\"\"Get the results for a given matching.\"\"\"\n\n trueids_matched1 = edf1.iloc[matching[0], edf1.columns.get_loc(\"true_id\")]\n trueids_matched2 = edf2.iloc[matching[1], edf2.columns.get_loc(\"true_id\")]\n\n nmatches = len(matching[0])\n truepos = sum(map(np.equal, trueids_matched1, trueids_matched2))\n falsepos = nmatches - truepos\n\n print(\n f\"True pos: {truepos} | False pos: {falsepos} | \"\n f\"Precision: {truepos / nmatches:.1%} | Recall: {truepos / 5000:.1%}\"\n )\n\n return nmatches, truepos, falsepos\n\n_ = get_results(edf1, edf2, matching)\n\nTrue pos: 4963 | False pos: 0 | Precision: 100.0% | Recall: 99.3%\n\n\nThen, we compute the match without using the row thresholds, calculating the same performance metrics:\n\nmatching = similarity_scores.match(require_thresholds=False)\n_ = get_results(edf1, edf2, matching)\n\nTrue pos: 5000 | False pos: 0 | Precision: 100.0% | Recall: 100.0%\n\n\nWithout using the row thresholds, the number of false positives is larger, but the recall is much better. For some uses this balance may be preferable.\nIn testing, the use of local row thresholds provides a better trade-off between precision and recall, compared to using a single absolute threshold. It has the additional advantage, in a privacy-preserving setting, of being automatic and not requiring clerical review to set the level." + }, + { + "objectID": "docs/tutorials/example-verknupfung.html", + "href": "docs/tutorials/example-verknupfung.html", + "title": "Exploring a simple linkage example", + "section": "", + "text": "The Python package implements the Bloom filter linkage method (Schnell et al., 2009), and can also implement pretrained Hash embeddings (Miranda et al., 2022), if a suitable large, pre-matched corpus of data is available.\nLet us consider a small example where we want to link two excerpts of data on bands. In this scenario, we are looking at some toy data on the members of a fictional, German rock trio called “Verknüpfung”. In this example we will see how to use untrained Bloom filters to match data.\n\nLoading the data\nFirst, we load our data into pandas.DataFrame objects. Here, the first records align, but the other two records should be swapped to have an aligned matching. We will use the toolkit to identify these matches.\n\nimport pandas as pd\n\ndf1 = pd.DataFrame(\n {\n \"first_name\": [\"Laura\", \"Kaspar\", \"Grete\"],\n \"last_name\": [\"Daten\", \"Gorman\", \"Knopf\"],\n \"gender\": [\"f\", \"m\", \"f\"],\n \"date_of_birth\": [\"01/03/1977\", \"31/12/1975\", \"12/7/1981\"],\n \"instrument\": [\"bass\", \"guitar\", \"drums\"],\n }\n)\ndf2 = pd.DataFrame(\n {\n \"name\": [\"Laura Datten\", \"Greta Knopf\", \"Casper Goreman\"],\n \"sex\": [\"female\", \"female\", \"male\"],\n \"main_instrument\": [\"bass guitar\", \"percussion\", \"electric guitar\"],\n \"birth_date\": [\"1977-03-23\", \"1981-07-12\", \"1975-12-31\"],\n }\n)\n\n\n[!NOTE] These datasets don’t have the same column names or follow the same encodings, and there are several spelling mistakes in the names of the band members, as well as a typo in the dates.\nThankfully, the pprl_toolkit is flexible enough to handle this!\n\n\n\nCreating and assigning a feature factory\nThe next step is to decide how to process each of the columns in our datasets.\nTo do this, we define a feature factory that maps column types to feature generation functions, and a column specification for each dataset mapping our columns to column types in the factory.\n\nfrom pprl.embedder import features\nfrom functools import partial\n\nfactory = dict(\n name=features.gen_name_features,\n sex=features.gen_sex_features,\n misc=features.gen_misc_features,\n dob=features.gen_dateofbirth_features,\n instrument=partial(features.gen_misc_shingled_features, label=\"instrument\")\n)\nspec1 = dict(\n first_name=\"name\",\n last_name=\"name\",\n gender=\"sex\",\n instrument=\"instrument\",\n date_of_birth=\"dob\",\n)\nspec2 = dict(name=\"name\", sex=\"sex\", main_instrument=\"instrument\", birth_date=\"dob\")\n\n\n[!TIP] The feature generation functions, features.gen_XXX_features have sensible default parameters, but sometimes have to be passed in to the feature factory with different parameters, such as to set a feature label in the example above. There are two ways to achieve this. Either use functools.partial to set parameters (as above), or pass keyword arguments as a dictionary of dictionaries to the Embedder as ff_args.\n\n\n\nEmbedding the data\nWith our specifications sorted out, we can get to creating our Bloom filter embedding. Before doing so, we need to decide on two parameters: the size of the filter and the number of hashes. By default, these are 1024 and 2, respectively.\nOnce we’ve decided, we can create our Embedder instance and use it to embed our data with their column specifications.\n\nfrom pprl.embedder.embedder import Embedder\n\nembedder = Embedder(factory, bf_size=1024, num_hashes=2)\nedf1 = embedder.embed(df1, colspec=spec1, update_thresholds=True)\nedf2 = embedder.embed(df2, colspec=spec2, update_thresholds=True)\n\n/Users/henrywilde/src/pprl_toolkit/src/pprl/embedder/features.py:284: UserWarning: Parsing dates in %Y-%m-%d format when dayfirst=True was specified. Pass `dayfirst=False` or specify a format to silence this warning.\n datetimes = pd.to_datetime(dob, errors=\"coerce\", dayfirst=dayfirst, yearfirst=yearfirst)\n\n\nIf we take a look at one of these embedded datasets, we can see that it has a whole bunch of new columns. There is a _features column for each of the original columns containing their pre-embedding string features, and there’s an all_features column that combines the features. Then there are three additional columns: bf_indices, bf_norms and thresholds.\n\nedf1.columns\n\nIndex(['first_name', 'last_name', 'gender', 'date_of_birth', 'instrument',\n 'first_name_features', 'last_name_features', 'gender_features',\n 'instrument_features', 'date_of_birth_features', 'all_features',\n 'bf_indices', 'bf_norms', 'thresholds'],\n dtype='object')\n\n\nThe bf_indices column contains the Bloom filters, represented compactly as a list of non-zero indices for each record. The bf_norms column contains the norm of each Bloom filter with respect to the Soft Cosine Measure (SCM) matrix. In this case since we are using an untrained model, the SCM matrix is an identity matrix, and the norm is just the Euclidean norm of the Bloom filter represented as a binary vector, which is equal to np.sqrt(len(bf_indices[i])) for record i. The norm is used to scale the similarity measures so that they take values between -1 and 1.\nThe thresholds column is calculated to provide, for each record, a threshold similarity score below which it will not be matched. It’s like a reserve price in an auction – it stops a record being matched to another record when the similarity isn’t high enough. In this feature, the method implemented here differs from other linkage methods, which typically only have one global threshold score for the entire dataset.\n\n\n\nThe processed features\nLet’s take a look at how the features are processed into small text strings (shingles) before being hashed into the Bloom filter. The first record in the first dataset is the same person as the first record in the second dataset, although the data is not identical, so we can compare the processed features for these records to see how pprl_toolkit puts them into a format where they can be compared.\nFirst, we’ll look at date of birth:\n\nprint(edf1.date_of_birth_features[0])\nprint(edf2.birth_date_features[0])\n\n['day<01>', 'month<03>', 'year<1977>']\n['day<23>', 'month<03>', 'year<1977>']\n\n\nPython can parse the different formats easily. Although the dates are slightly different in the dataset, the year and month will still match, even though the day will not.\nThen we’ll look at name:\n\nprint(edf1.first_name_features[0] + edf1.last_name_features[0])\nprint(edf2.name_features[0])\n\n['_l', 'la', 'au', 'ur', 'ra', 'a_', '_la', 'lau', 'aur', 'ura', 'ra_', '_d', 'da', 'at', 'te', 'en', 'n_', '_da', 'dat', 'ate', 'ten', 'en_']\n['_l', 'la', 'au', 'ur', 'ra', 'a_', '_d', 'da', 'at', 'tt', 'te', 'en', 'n_', '_la', 'lau', 'aur', 'ura', 'ra_', '_da', 'dat', 'att', 'tte', 'ten', 'en_']\n\n\nThe two datasets store the names differently, but this doesn’t matter for the Bloom filter method because it treats each record like a bag of features. By default, the name processor produces 2-grams, 3-grams and 4-grams.\nThe sex processing function just converts different formats to lowercase and takes the first letter. This will often be enough:\n\nprint(edf1.gender_features[0])\nprint(edf2.sex_features[0])\n\n['sex<f>']\n['sex<f>']\n\n\nFinally, we’ll see how our instrument feature function (partial(features.gen_misc_shingled_features, label=\"instrument\")) processed the data:\n\nprint(edf1.instrument_features[0])\nprint(edf2.main_instrument_features[0])\n\n['instrument<_b>', 'instrument<ba>', 'instrument<as>', 'instrument<ss>', 'instrument<s_>', 'instrument<_ba>', 'instrument<bas>', 'instrument<ass>', 'instrument<ss_>']\n['instrument<_b>', 'instrument<ba>', 'instrument<as>', 'instrument<ss>', 'instrument<s_>', 'instrument<_g>', 'instrument<gu>', 'instrument<ui>', 'instrument<it>', 'instrument<ta>', 'instrument<ar>', 'instrument<r_>', 'instrument<_ba>', 'instrument<bas>', 'instrument<ass>', 'instrument<ss_>', 'instrument<_gu>', 'instrument<gui>', 'instrument<uit>', 'instrument<ita>', 'instrument<tar>', 'instrument<ar_>']\n\n\nSetting the label argument was important to ensure that the shingles match (and are hashed to the same slots) because the default behaviour of the function is to use the column name as a label: since the two columns have different names, the default wouldn’t have allowed the features to match to each other.\n\n\nPerforming the linkage\nWe can now perform the linkage by comparing these Bloom filter embeddings. We use the Soft Cosine Measure (which in this untrained model, is equivalent to a normal cosine similarity metric) to calculate record-wise similarity and an adapted Hungarian algorithm to match the records based on those similarities.\n\nsimilarities = embedder.compare(edf1, edf2)\nsimilarities\n\nSimilarityArray([[0.79385141, 0.16741128, 0.09035655],\n [0.37587466, 0.17460757, 0.61256385],\n [0.12240719, 0.55043024, 0.10971801]])\n\n\nThis SimilarityArray object is an augmented numpy.ndarray that can perform our matching. The matching itself can optionally be called with an absolute threshold score, but it doesn’t need one.\n\nmatching = similarities.match()\nmatching\n\n(array([0, 1, 2]), array([0, 2, 1]))\n\n\nSo, all three of the records in each dataset were matched correctly. Excellent!" + }, + { + "objectID": "docs/tutorials/index.html", + "href": "docs/tutorials/index.html", + "title": "Tutorials", + "section": "", + "text": "These tutorials walk you through some of the essential workflows for pprl. The purpose of these documents is for you to learn how to use the pprl package for your own linkage projects.\n\n\n\n\n\n\n\n\n\n\n\nTitle\n\n\nDescription\n\n\nReading Time\n\n\n\n\n\n\nEmbedder API run-through\n\n\n\n\n\n5 min\n\n\n\n\nExploring a simple linkage example\n\n\n\n\n\n6 min\n\n\n\n\nLinking the FEBRL datasets\n\n\nUsing PPRL locally to link two well-known datasets\n\n\n4 min\n\n\n\n\nWorking in the cloud\n\n\nGet you and your collaborators performing linkage in the cloud \n\n\n11 min\n\n\n\n\n\n\nNo matching items" + }, + { + "objectID": "docs/reference/config.html", + "href": "docs/reference/config.html", + "title": "config", + "section": "", + "text": "config\nFunctions for handling PPRL configuration.\n\n\n\n\n\nName\nDescription\n\n\n\n\nload_environment\nLoad the configuration file as a dictionary.\n\n\n\n\n\nconfig.load_environment(path=None)\nLoad the configuration file as a dictionary.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\npath\nstr\nLocation of the configuration file to load. If not specified, try to load the configuration file from the root of the pprl installation called .env.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\ncollections.collections.OrderedDict\nMapping of the key-value pairs in the configuration file." + }, + { + "objectID": "docs/reference/config.html#functions", + "href": "docs/reference/config.html#functions", + "title": "config", + "section": "", + "text": "Name\nDescription\n\n\n\n\nload_environment\nLoad the configuration file as a dictionary.\n\n\n\n\n\nconfig.load_environment(path=None)\nLoad the configuration file as a dictionary.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\npath\nstr\nLocation of the configuration file to load. If not specified, try to load the configuration file from the root of the pprl installation called .env.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\ncollections.collections.OrderedDict\nMapping of the key-value pairs in the configuration file." + }, + { + "objectID": "docs/reference/encryption.html", + "href": "docs/reference/encryption.html", + "title": "encryption", + "section": "", + "text": "encryption\nTools for performing envelope encryption on GCP.\n\n\n\n\n\nName\nDescription\n\n\n\n\ndecrypt_data\nDecrypt a data frame with the provided key.\n\n\ndecrypt_dek\nDecrypt a data encryption key using an asymmetric key held on KMS.\n\n\nencrypt_data\nEncrypt a data frame.\n\n\nencrypt_dek\nEncrypt the data encryption key.\n\n\n\n\n\nencryption.decrypt_data(encrypted, key)\nDecrypt a data frame with the provided key.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nencrypted\nbytes\nData to be decrypted.\nrequired\n\n\nkey\nbytes\nKey used to encrypt the data.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.DataFrame\nDecrypted data frame.\n\n\n\n\n\n\n\nencryption.decrypt_dek(encrypted, party, location='global', version=1, **kwargs)\nDecrypt a data encryption key using an asymmetric key held on KMS.\nOwing to the nature of the encryption key set-up of pprl this function is only really to be used in the GCP Confidential Space set up by the linkage administrator.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nencrypted\nbytes\nKey to be decrypted.\nrequired\n\n\nparty\nstr\nName of the party whose key we are decrypting.\nrequired\n\n\nlocation\nstr\nLocation of the keyring on which the key lives.\n'global'\n\n\nversion\nint | str\nVersion of the asymmetric key to get from GCP. Default is 1.\n1\n\n\n**kwargs\ndict\nKeyword arguments to pass when creating an instance of google.cloud.kms.KeyManagementServiceClient.\n{}\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nbytes\nDecrypted data encryption key.\n\n\n\n\n\n\n\nencryption.encrypt_data(data, key=None)\nEncrypt a data frame.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndata\npandas.pandas.DataFrame\nDataframe to encrypt.\nrequired\n\n\nkey\nbytes\nFernet key to encrypt data frame. If not specified, create one.\nNone\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nbytes\nEncrypted data frame.\n\n\nbytes\nFernet key used to encrypt data frame.\n\n\n\n\n\n\n\nencryption.encrypt_dek(dek, party, location='global', version=1, **kwargs)\nEncrypt the data encryption key.\nWe encrypt the data encryption key using the public key portion of an asymmetric key retrieved from the GCP Key Management Service.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndek\nbytes\nData encryption key to be encrypted.\nrequired\n\n\nparty\nstr\nName of the party.\nrequired\n\n\nlocation\nstr\nLocation of the keyring on which the key lives.\n'global'\n\n\nversion\nint | str\nVersion of the asymmetric key to get from GCP. Default is 1.\n1\n\n\n**kwargs\ndict\nKeyword arguments to pass when creating an instance of google.cloud.kms.KeyManagementServiceClient.\n{}\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nbytes\nEncrypted data encryption key." + }, + { + "objectID": "docs/reference/encryption.html#functions", + "href": "docs/reference/encryption.html#functions", + "title": "encryption", + "section": "", + "text": "Name\nDescription\n\n\n\n\ndecrypt_data\nDecrypt a data frame with the provided key.\n\n\ndecrypt_dek\nDecrypt a data encryption key using an asymmetric key held on KMS.\n\n\nencrypt_data\nEncrypt a data frame.\n\n\nencrypt_dek\nEncrypt the data encryption key.\n\n\n\n\n\nencryption.decrypt_data(encrypted, key)\nDecrypt a data frame with the provided key.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nencrypted\nbytes\nData to be decrypted.\nrequired\n\n\nkey\nbytes\nKey used to encrypt the data.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npandas.pandas.DataFrame\nDecrypted data frame.\n\n\n\n\n\n\n\nencryption.decrypt_dek(encrypted, party, location='global', version=1, **kwargs)\nDecrypt a data encryption key using an asymmetric key held on KMS.\nOwing to the nature of the encryption key set-up of pprl this function is only really to be used in the GCP Confidential Space set up by the linkage administrator.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nencrypted\nbytes\nKey to be decrypted.\nrequired\n\n\nparty\nstr\nName of the party whose key we are decrypting.\nrequired\n\n\nlocation\nstr\nLocation of the keyring on which the key lives.\n'global'\n\n\nversion\nint | str\nVersion of the asymmetric key to get from GCP. Default is 1.\n1\n\n\n**kwargs\ndict\nKeyword arguments to pass when creating an instance of google.cloud.kms.KeyManagementServiceClient.\n{}\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nbytes\nDecrypted data encryption key.\n\n\n\n\n\n\n\nencryption.encrypt_data(data, key=None)\nEncrypt a data frame.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndata\npandas.pandas.DataFrame\nDataframe to encrypt.\nrequired\n\n\nkey\nbytes\nFernet key to encrypt data frame. If not specified, create one.\nNone\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nbytes\nEncrypted data frame.\n\n\nbytes\nFernet key used to encrypt data frame.\n\n\n\n\n\n\n\nencryption.encrypt_dek(dek, party, location='global', version=1, **kwargs)\nEncrypt the data encryption key.\nWe encrypt the data encryption key using the public key portion of an asymmetric key retrieved from the GCP Key Management Service.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndek\nbytes\nData encryption key to be encrypted.\nrequired\n\n\nparty\nstr\nName of the party.\nrequired\n\n\nlocation\nstr\nLocation of the keyring on which the key lives.\n'global'\n\n\nversion\nint | str\nVersion of the asymmetric key to get from GCP. Default is 1.\n1\n\n\n**kwargs\ndict\nKeyword arguments to pass when creating an instance of google.cloud.kms.KeyManagementServiceClient.\n{}\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nbytes\nEncrypted data encryption key." + }, + { + "objectID": "docs/reference/embedder.html", + "href": "docs/reference/embedder.html", + "title": "embedder", + "section": "", + "text": "embedder.embedder\nClasses and functions for handling embedding objects.\n\n\n\n\n\nName\nDescription\n\n\n\n\nEmbeddedDataFrame\nA data frame with a reference to an Embedder object.\n\n\nEmbedder\nClass for embedding a dataset.\n\n\nSimilarityArray\nAugmented NumPy array of similarity scores with extra attributes.\n\n\n\n\n\nembedder.embedder.EmbeddedDataFrame(self, data, embedder, update_norms=True, update_thresholds=False, *args, **kwargs)\nA data frame with a reference to an Embedder object.\nAn EmbeddedDataFrame (EDF) instance wraps together a pandas.DataFrame with a reference to a pprl.embedder.Embedder object. An EDF also has a mandatory bf_indices column, describing the Bloom filter indices used for linkage.\nThe EDF instance can also calculate bf_norms and thresholds columns which are used in the Embedder.compare() method to compute pprl.embedder.SimilarityArray instances.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndata\nnumpy.numpy.ndarray | typing.Iterable | dict | pandas.pandas.DataFrame\nData to which to attach the embedder. Must include a bf_indices column with list data type.\nrequired\n\n\nembedder\npprl.embedder.embedder.Embedder\nA compatible embedder object for the Bloom filter columns in data.\nrequired\n\n\nupdate_norms\nbool\nWhether to update the Bloom filter norms on creation. Defaults to False.\nTrue\n\n\nupdate_thresholds\nbool\nWhether to update the similarity thresholds on creation. Defaults to True.\nFalse\n\n\n*args\n\nAdditional positional arguments to pass to pandas.DataFrame along with data.\n()\n\n\n**kwargs\n\nAdditional keyword arguments to pass to pandas.DataFrame along with data.\n{}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nembedder_checksum\nstr\nHexadecimal string digest from self.embedder.\n\n\n\n\n\n\nAn EDF instance is usually created from an existing Embedder object by calling the embedder.embed() method. It can also be initialised using an embedder and a pandas.DataFrame that already has a bf_indices column via EmbeddedDataFrame(df, embedder).\nIf using the second method it is up to the user to ensure that the Embedder instance is compatible with the bf_indices column (as well as bf_norms and thresholds, if present) in the data frame. If in doubt, call edf.update_norms() and edf.update_thresholds() to refresh them.\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nto_bloom_matrix\nConvert Bloom filter indices into a binary matrix.\n\n\nupdate_norms\nGenerate vector norms (wrt. self.embedder) for each row.\n\n\nupdate_thresholds\nGenerate matching thresholds for each row of the data.\n\n\n\n\n\nembedder.embedder.EmbeddedDataFrame.to_bloom_matrix()\nConvert Bloom filter indices into a binary matrix.\nThe matrix has a row for each row in the EDF. The number of columns is equal to self.embedder.bf_size + self.embedder.offset. This representation is used in the Embedder.compare() method.\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nnumpy.numpy.ndarray\nBinary array of size (len(self), self.bf_size + 1).\n\n\n\n\n\n\n\nembedder.embedder.EmbeddedDataFrame.update_norms()\nGenerate vector norms (wrt. self.embedder) for each row.\nThe vector norm is used to scale the (Soft) Cosine similarity scores.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ndata.bf_norms\nlist\nColumn of vector norms for each row in the EDF.\n\n\n\n\n\n\n\nembedder.embedder.EmbeddedDataFrame.update_thresholds()\nGenerate matching thresholds for each row of the data.\nThe threshold is the minimum similarity score that will be matched. It is found by getting the pairwise similarities between each row and the other rows in the same EDF, and taking the maximum of these.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ndata.thresholds\nnumpy.numpy.ndarray\nColumn for maximum similarity of each row within the EDF.\n\n\n\n\n\n\n\n\n\nembedder.embedder.Embedder(self, feature_factory, ff_args=None, bf_size=2 ** 10, num_hashes=2, offset=1, salt=None)\nClass for embedding a dataset.\nEach instance of the Embedder class represents an embedding space on personal data features. An Embedder instance is defined by three things:\n\nA set of Bloom filter parameters\nA set of feature factory functions\nAn embedding matrix that corresponds to the above\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfeature_factory\ndict\nMapping from dataset columns to feature generation functions.\nrequired\n\n\nff_args\ndict[str, dict] | None\nMapping from dataset columns to keyword arguments for their respective feature generation functions.\nNone\n\n\nbf_size\nint\nSize of the Bloom filter. Default is 2**10.\n2 ** 10\n\n\nnum_hashes\nint\nNumber of hashes to perform. Default is two.\n2\n\n\noffset\nint\nOffset for Bloom filter to enable masking. Default is one.\n1\n\n\nsalt\nstr | None\nCryptographic salt added to tokens from the data before hashing.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nscm_matrix\nnumpy.numpy.ndarray\nSoft Cosine Measure matrix. Initialised as an identity matrix of size bf_size + offset.\n\n\nfreq_matr_matched\nnumpy.numpy.ndarray\nMatched frequency matrix for computing scm_matrix. Initialised as an identity matrix of size bf_size + offset.\n\n\nfreq_matr_unmatched\nnumpy.numpy.ndarray\nUnmatched frequency matrix for computing scm_matrix. Initialised as an identity matrix of size bf_size + offset.\n\n\nchecksum\nstr\nHexadecimal string digest of the feature factory, SCM matrix, and other embedding parameters. Used to check an embedder is compatible with an EmbeddedDataFrame.\n\n\n\n\n\n\nWhen an instance is initialised in code, the embedding matrix is initialised as an identity matrix; the matrix can then be trained using a pair of datasets with known match status and the trained Embedder instance pickled to file. The pre-trained Embedder instance can then be reinitialised from the pickle file.\nBoth the untrained and trained instances provide embed() and compare() methods. Comparing datasets using an untrained Embedder instance is equivalent to calculating Cosine similarities on ordinary Bloom filters. Comparing datasets using a pre-trained Embedder calculates the Soft Cosine Measure between Bloom filters. The Soft Cosine Measure embedding matrix is trained using an experimental method.\n\n\n\n\n\n\nName\nDescription\n\n\n\n\ncompare\nCalculate a SimilarityArray on two EDFs.\n\n\nembed\nEncode data columns into features from Bloom embedding.\n\n\nfrom_pickle\nInitialise Embedder instance from pickle file.\n\n\nto_pickle\nSave Embedder instance to pickle file.\n\n\ntrain\nFit Soft Cosine Measure matrix to two matched datasets.\n\n\n\n\n\nembedder.embedder.Embedder.compare(edf1, edf2, require_thresholds=True)\nCalculate a SimilarityArray on two EDFs.\nGiven two EDFs, calculate all pairwise Soft Cosine Similarities between rows.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nedf1\npprl.embedder.embedder.EmbeddedDataFrame\nAn EDF instance with N rows. Must have thresholds column unless require_thresholds=False.\nrequired\n\n\nedf2\npprl.embedder.embedder.EmbeddedDataFrame\nAn EDF instance with M rows. Must have thresholds column unless require_thresholds=False.\nrequired\n\n\nrequire_thresholds\nbool\nIf True (default), the comparison will fail if thresholds are not present. Must be explicitly set to False to allow comparison without thresholds.\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npprl.embedder.embedder.SimilarityArray\nAn N by M array containing the similarity matrix of pairwise Soft Cosine similarities between rows of edf1 and edf2.\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nValueError\nIf require_thresholds is True and both EDFs don’t have a thresholds column.\n\n\n\n\n\n\n\nembedder.embedder.Embedder.embed(df, colspec, update_norms=True, update_thresholds=False)\nEncode data columns into features from Bloom embedding.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndf\npandas.pandas.DataFrame\nData frame to be embedded.\nrequired\n\n\ncolspec\ndict\nDictionary mapping columns in df to feature factory functions.\nrequired\n\n\nupdate_norms\nbool\nWhether to calculate vector norms for SCM and add to EDF. False by default.\nTrue\n\n\nupdate_thresholds\nbool\nWhether to calculate similarity thresholds and add to EDF. Used as an outside option in matching. False by default.\nFalse\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npprl.embedder.embedder.EmbeddedDataFrame\nAn embedded data frame with its embedder.\n\n\n\n\n\n\n\nembedder.embedder.Embedder.from_pickle(path=None, pickled=None)\nInitialise Embedder instance from pickle file.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\npath\nstr\nFile path from which to load the pickled embedder.\nNone\n\n\npickled\nbytes\nByte-string containing the pickled embedder.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nValueError\nIf not exactly one of path and pickled are specified.\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npprl.embedder.embedder.Embedder\nThe reformed instance of the Embedder class.\n\n\n\n\n\n\n\nembedder.embedder.Embedder.to_pickle(path=None)\nSave Embedder instance to pickle file.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\npath\nstr\nFile path at which to save the pickled embedder. If not specified, the pickled bytes string is returned.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nbytes or None\nIf path is not specified, the pickled string comes back. Otherwise, nothing is returned.\n\n\n\n\n\n\n\nembedder.embedder.Embedder.train(edf1, edf2, update=True, learning_rate=1.0, eps=0.01, random_state=None)\nFit Soft Cosine Measure matrix to two matched datasets.\nThis function updates the scm_matrix attribute in-place along with its constituent matrices, freq_matr_matched and freq_matr_unmatched.\nProvide two datasets of pre-matched data. If update=True, the training is cumulative, so that train() can be called more than once, updating the same matrices each time by adding new frequency tables. Otherwise, all three matrices are reinitialised prior to training.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nedf1\npprl.embedder.embedder.EmbeddedDataFrame\nAn embedded dataset.\nrequired\n\n\nedf2\npprl.embedder.embedder.EmbeddedDataFrame\nAn Embedded dataset of known matches in the same order as edf1.\nrequired\n\n\nupdate\nbool\nWhether to update the existing SCM matrix, or overwrite it. Defaults to True.\nTrue\n\n\neps\nfloat\nSmall non-negative constant to avoid -Inf in log of frequencies. Default is one.\n0.01\n\n\nlearning_rate\nfloat\nScaling factor to dampen matrix updates. Must be in the interval (0, 1]. Default is 0.01.\n1.0\n\n\nrandom_state\nNone | numpy.numpy.random.numpy.random.RandomState\nRandom state to pass to dataset jumbler. Defaults to None.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nscm_matrix\nnumpy.numpy.ndarray\nSoft Cosine Measure matrix that is fitted cumulatively or afresh.\n\n\n\n\n\n\n\n\n\nembedder.embedder.SimilarityArray()\nAugmented NumPy array of similarity scores with extra attributes.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ninput_array\n\nOriginal array of similarity score data.\nrequired\n\n\nthresholds\n\n2-tuple of similarity score thresholds for each axis. These thresholds can be used as an outside option when generating a matching.\nrequired\n\n\nembedder_checksum\n\nHexadecimal string digest of a pprl.embedder.Embedder object.\nrequired\n\n\n\n\n\n\nSimilarityArray objects are usually initialised from an instance of pprl.embedder.Embedder via the embedder.compare() method.\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nmatch\nCompute a matching.\n\n\n\n\n\nembedder.embedder.SimilarityArray.match(abs_cutoff=0, rel_cutoff=0, hungarian=True, require_thresholds=True)\nCompute a matching.\nGiven an array of similarity scores, compute a matching of its elements, using the Hungarian algorithm by default. If the SimilarityArray has thresholds, masking is used to ensure they are respected. An abs_cutoff (global minimum similarity score) can also be supplied.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nabs_cutoff\nint or float\nA lower cutoff for the similarity score. No pairs with similarity below the absolute cutoff will be matched. By default, this is 0.\n0\n\n\nrel_cutoff\nint or float\nA margin above the row/column-specific threshold. Raises all thresholds by a constant. By default, this is 0.\n0\n\n\nhungarian\nbool\nWhether to compute the unique matching using the Hungarian algorithm, filtered using thresholds and abs_cutoff. Default is True. If False, just return all pairs above the threshold.\nTrue\n\n\nrequire_thresholds\nbool\nIf True (default), the matching will fail if thresholds is not present and valid. Must be explicitly set to False to allow matching without similarity thresholds.\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\ntuple[list[int], list[int]]\n2-tuple of indexes containing row and column indices of matched pairs eg. ([0, 1, ...], [0, 1, ...]).\n\n\n\n\n\n\nIf hungarian=False, the matching returns all pairs with similarity score above the abs_cutoff, respecting thresholds if present. This method does not guarantee no duplicates.\n\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nnearest_pos_semi_definite\nCalculate nearest positive semi-definite version of a matrix.\n\n\n\n\n\nembedder.embedder.nearest_pos_semi_definite(X, eps=0.0)\nCalculate nearest positive semi-definite version of a matrix.\nThis function achieves this by setting all negative eigenvalues of the matrix to zero, or a small positive value to give a positive definite matrix.\nGraciously taken from this StackOverflow post\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nX\nnumpy.numpy.ndarray\nMatrix-like array.\nrequired\n\n\neps\nfloat\nUse a small positive constant to give a positive definite matrix. Default is 0 to give a positive semi-definite matrix.\n0.0\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nnumpy.numpy.ndarray\nA positive (semi-)definite matrix." + }, + { + "objectID": "docs/reference/embedder.html#classes", + "href": "docs/reference/embedder.html#classes", + "title": "embedder", + "section": "", + "text": "Name\nDescription\n\n\n\n\nEmbeddedDataFrame\nA data frame with a reference to an Embedder object.\n\n\nEmbedder\nClass for embedding a dataset.\n\n\nSimilarityArray\nAugmented NumPy array of similarity scores with extra attributes.\n\n\n\n\n\nembedder.embedder.EmbeddedDataFrame(self, data, embedder, update_norms=True, update_thresholds=False, *args, **kwargs)\nA data frame with a reference to an Embedder object.\nAn EmbeddedDataFrame (EDF) instance wraps together a pandas.DataFrame with a reference to a pprl.embedder.Embedder object. An EDF also has a mandatory bf_indices column, describing the Bloom filter indices used for linkage.\nThe EDF instance can also calculate bf_norms and thresholds columns which are used in the Embedder.compare() method to compute pprl.embedder.SimilarityArray instances.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndata\nnumpy.numpy.ndarray | typing.Iterable | dict | pandas.pandas.DataFrame\nData to which to attach the embedder. Must include a bf_indices column with list data type.\nrequired\n\n\nembedder\npprl.embedder.embedder.Embedder\nA compatible embedder object for the Bloom filter columns in data.\nrequired\n\n\nupdate_norms\nbool\nWhether to update the Bloom filter norms on creation. Defaults to False.\nTrue\n\n\nupdate_thresholds\nbool\nWhether to update the similarity thresholds on creation. Defaults to True.\nFalse\n\n\n*args\n\nAdditional positional arguments to pass to pandas.DataFrame along with data.\n()\n\n\n**kwargs\n\nAdditional keyword arguments to pass to pandas.DataFrame along with data.\n{}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nembedder_checksum\nstr\nHexadecimal string digest from self.embedder.\n\n\n\n\n\n\nAn EDF instance is usually created from an existing Embedder object by calling the embedder.embed() method. It can also be initialised using an embedder and a pandas.DataFrame that already has a bf_indices column via EmbeddedDataFrame(df, embedder).\nIf using the second method it is up to the user to ensure that the Embedder instance is compatible with the bf_indices column (as well as bf_norms and thresholds, if present) in the data frame. If in doubt, call edf.update_norms() and edf.update_thresholds() to refresh them.\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nto_bloom_matrix\nConvert Bloom filter indices into a binary matrix.\n\n\nupdate_norms\nGenerate vector norms (wrt. self.embedder) for each row.\n\n\nupdate_thresholds\nGenerate matching thresholds for each row of the data.\n\n\n\n\n\nembedder.embedder.EmbeddedDataFrame.to_bloom_matrix()\nConvert Bloom filter indices into a binary matrix.\nThe matrix has a row for each row in the EDF. The number of columns is equal to self.embedder.bf_size + self.embedder.offset. This representation is used in the Embedder.compare() method.\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nnumpy.numpy.ndarray\nBinary array of size (len(self), self.bf_size + 1).\n\n\n\n\n\n\n\nembedder.embedder.EmbeddedDataFrame.update_norms()\nGenerate vector norms (wrt. self.embedder) for each row.\nThe vector norm is used to scale the (Soft) Cosine similarity scores.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ndata.bf_norms\nlist\nColumn of vector norms for each row in the EDF.\n\n\n\n\n\n\n\nembedder.embedder.EmbeddedDataFrame.update_thresholds()\nGenerate matching thresholds for each row of the data.\nThe threshold is the minimum similarity score that will be matched. It is found by getting the pairwise similarities between each row and the other rows in the same EDF, and taking the maximum of these.\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\ndata.thresholds\nnumpy.numpy.ndarray\nColumn for maximum similarity of each row within the EDF.\n\n\n\n\n\n\n\n\n\nembedder.embedder.Embedder(self, feature_factory, ff_args=None, bf_size=2 ** 10, num_hashes=2, offset=1, salt=None)\nClass for embedding a dataset.\nEach instance of the Embedder class represents an embedding space on personal data features. An Embedder instance is defined by three things:\n\nA set of Bloom filter parameters\nA set of feature factory functions\nAn embedding matrix that corresponds to the above\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfeature_factory\ndict\nMapping from dataset columns to feature generation functions.\nrequired\n\n\nff_args\ndict[str, dict] | None\nMapping from dataset columns to keyword arguments for their respective feature generation functions.\nNone\n\n\nbf_size\nint\nSize of the Bloom filter. Default is 2**10.\n2 ** 10\n\n\nnum_hashes\nint\nNumber of hashes to perform. Default is two.\n2\n\n\noffset\nint\nOffset for Bloom filter to enable masking. Default is one.\n1\n\n\nsalt\nstr | None\nCryptographic salt added to tokens from the data before hashing.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nscm_matrix\nnumpy.numpy.ndarray\nSoft Cosine Measure matrix. Initialised as an identity matrix of size bf_size + offset.\n\n\nfreq_matr_matched\nnumpy.numpy.ndarray\nMatched frequency matrix for computing scm_matrix. Initialised as an identity matrix of size bf_size + offset.\n\n\nfreq_matr_unmatched\nnumpy.numpy.ndarray\nUnmatched frequency matrix for computing scm_matrix. Initialised as an identity matrix of size bf_size + offset.\n\n\nchecksum\nstr\nHexadecimal string digest of the feature factory, SCM matrix, and other embedding parameters. Used to check an embedder is compatible with an EmbeddedDataFrame.\n\n\n\n\n\n\nWhen an instance is initialised in code, the embedding matrix is initialised as an identity matrix; the matrix can then be trained using a pair of datasets with known match status and the trained Embedder instance pickled to file. The pre-trained Embedder instance can then be reinitialised from the pickle file.\nBoth the untrained and trained instances provide embed() and compare() methods. Comparing datasets using an untrained Embedder instance is equivalent to calculating Cosine similarities on ordinary Bloom filters. Comparing datasets using a pre-trained Embedder calculates the Soft Cosine Measure between Bloom filters. The Soft Cosine Measure embedding matrix is trained using an experimental method.\n\n\n\n\n\n\nName\nDescription\n\n\n\n\ncompare\nCalculate a SimilarityArray on two EDFs.\n\n\nembed\nEncode data columns into features from Bloom embedding.\n\n\nfrom_pickle\nInitialise Embedder instance from pickle file.\n\n\nto_pickle\nSave Embedder instance to pickle file.\n\n\ntrain\nFit Soft Cosine Measure matrix to two matched datasets.\n\n\n\n\n\nembedder.embedder.Embedder.compare(edf1, edf2, require_thresholds=True)\nCalculate a SimilarityArray on two EDFs.\nGiven two EDFs, calculate all pairwise Soft Cosine Similarities between rows.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nedf1\npprl.embedder.embedder.EmbeddedDataFrame\nAn EDF instance with N rows. Must have thresholds column unless require_thresholds=False.\nrequired\n\n\nedf2\npprl.embedder.embedder.EmbeddedDataFrame\nAn EDF instance with M rows. Must have thresholds column unless require_thresholds=False.\nrequired\n\n\nrequire_thresholds\nbool\nIf True (default), the comparison will fail if thresholds are not present. Must be explicitly set to False to allow comparison without thresholds.\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npprl.embedder.embedder.SimilarityArray\nAn N by M array containing the similarity matrix of pairwise Soft Cosine similarities between rows of edf1 and edf2.\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nValueError\nIf require_thresholds is True and both EDFs don’t have a thresholds column.\n\n\n\n\n\n\n\nembedder.embedder.Embedder.embed(df, colspec, update_norms=True, update_thresholds=False)\nEncode data columns into features from Bloom embedding.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ndf\npandas.pandas.DataFrame\nData frame to be embedded.\nrequired\n\n\ncolspec\ndict\nDictionary mapping columns in df to feature factory functions.\nrequired\n\n\nupdate_norms\nbool\nWhether to calculate vector norms for SCM and add to EDF. False by default.\nTrue\n\n\nupdate_thresholds\nbool\nWhether to calculate similarity thresholds and add to EDF. Used as an outside option in matching. False by default.\nFalse\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npprl.embedder.embedder.EmbeddedDataFrame\nAn embedded data frame with its embedder.\n\n\n\n\n\n\n\nembedder.embedder.Embedder.from_pickle(path=None, pickled=None)\nInitialise Embedder instance from pickle file.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\npath\nstr\nFile path from which to load the pickled embedder.\nNone\n\n\npickled\nbytes\nByte-string containing the pickled embedder.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nValueError\nIf not exactly one of path and pickled are specified.\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\npprl.embedder.embedder.Embedder\nThe reformed instance of the Embedder class.\n\n\n\n\n\n\n\nembedder.embedder.Embedder.to_pickle(path=None)\nSave Embedder instance to pickle file.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\npath\nstr\nFile path at which to save the pickled embedder. If not specified, the pickled bytes string is returned.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nbytes or None\nIf path is not specified, the pickled string comes back. Otherwise, nothing is returned.\n\n\n\n\n\n\n\nembedder.embedder.Embedder.train(edf1, edf2, update=True, learning_rate=1.0, eps=0.01, random_state=None)\nFit Soft Cosine Measure matrix to two matched datasets.\nThis function updates the scm_matrix attribute in-place along with its constituent matrices, freq_matr_matched and freq_matr_unmatched.\nProvide two datasets of pre-matched data. If update=True, the training is cumulative, so that train() can be called more than once, updating the same matrices each time by adding new frequency tables. Otherwise, all three matrices are reinitialised prior to training.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nedf1\npprl.embedder.embedder.EmbeddedDataFrame\nAn embedded dataset.\nrequired\n\n\nedf2\npprl.embedder.embedder.EmbeddedDataFrame\nAn Embedded dataset of known matches in the same order as edf1.\nrequired\n\n\nupdate\nbool\nWhether to update the existing SCM matrix, or overwrite it. Defaults to True.\nTrue\n\n\neps\nfloat\nSmall non-negative constant to avoid -Inf in log of frequencies. Default is one.\n0.01\n\n\nlearning_rate\nfloat\nScaling factor to dampen matrix updates. Must be in the interval (0, 1]. Default is 0.01.\n1.0\n\n\nrandom_state\nNone | numpy.numpy.random.numpy.random.RandomState\nRandom state to pass to dataset jumbler. Defaults to None.\nNone\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nscm_matrix\nnumpy.numpy.ndarray\nSoft Cosine Measure matrix that is fitted cumulatively or afresh.\n\n\n\n\n\n\n\n\n\nembedder.embedder.SimilarityArray()\nAugmented NumPy array of similarity scores with extra attributes.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ninput_array\n\nOriginal array of similarity score data.\nrequired\n\n\nthresholds\n\n2-tuple of similarity score thresholds for each axis. These thresholds can be used as an outside option when generating a matching.\nrequired\n\n\nembedder_checksum\n\nHexadecimal string digest of a pprl.embedder.Embedder object.\nrequired\n\n\n\n\n\n\nSimilarityArray objects are usually initialised from an instance of pprl.embedder.Embedder via the embedder.compare() method.\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nmatch\nCompute a matching.\n\n\n\n\n\nembedder.embedder.SimilarityArray.match(abs_cutoff=0, rel_cutoff=0, hungarian=True, require_thresholds=True)\nCompute a matching.\nGiven an array of similarity scores, compute a matching of its elements, using the Hungarian algorithm by default. If the SimilarityArray has thresholds, masking is used to ensure they are respected. An abs_cutoff (global minimum similarity score) can also be supplied.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nabs_cutoff\nint or float\nA lower cutoff for the similarity score. No pairs with similarity below the absolute cutoff will be matched. By default, this is 0.\n0\n\n\nrel_cutoff\nint or float\nA margin above the row/column-specific threshold. Raises all thresholds by a constant. By default, this is 0.\n0\n\n\nhungarian\nbool\nWhether to compute the unique matching using the Hungarian algorithm, filtered using thresholds and abs_cutoff. Default is True. If False, just return all pairs above the threshold.\nTrue\n\n\nrequire_thresholds\nbool\nIf True (default), the matching will fail if thresholds is not present and valid. Must be explicitly set to False to allow matching without similarity thresholds.\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\ntuple[list[int], list[int]]\n2-tuple of indexes containing row and column indices of matched pairs eg. ([0, 1, ...], [0, 1, ...]).\n\n\n\n\n\n\nIf hungarian=False, the matching returns all pairs with similarity score above the abs_cutoff, respecting thresholds if present. This method does not guarantee no duplicates." + }, + { + "objectID": "docs/reference/embedder.html#functions", + "href": "docs/reference/embedder.html#functions", + "title": "embedder", + "section": "", + "text": "Name\nDescription\n\n\n\n\nnearest_pos_semi_definite\nCalculate nearest positive semi-definite version of a matrix.\n\n\n\n\n\nembedder.embedder.nearest_pos_semi_definite(X, eps=0.0)\nCalculate nearest positive semi-definite version of a matrix.\nThis function achieves this by setting all negative eigenvalues of the matrix to zero, or a small positive value to give a positive definite matrix.\nGraciously taken from this StackOverflow post\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nX\nnumpy.numpy.ndarray\nMatrix-like array.\nrequired\n\n\neps\nfloat\nUse a small positive constant to give a positive definite matrix. Default is 0 to give a positive semi-definite matrix.\n0.0\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nnumpy.numpy.ndarray\nA positive (semi-)definite matrix." + }, + { + "objectID": "docs/reference/bloom_filters.html", + "href": "docs/reference/bloom_filters.html", + "title": "bloom_filters", + "section": "", + "text": "embedder.bloom_filters\nModule for the Bloom filter encoder.\n\n\n\n\n\nName\nDescription\n\n\n\n\nBloomFilterEncoder\nEncoder of tokens and features via hashing and a Bloom filter.\n\n\n\n\n\nembedder.bloom_filters.BloomFilterEncoder(self, size, num_hashes=3, offset=1, salt=None)\nEncoder of tokens and features via hashing and a Bloom filter.\nThe process for creating a cryptographically secure Bloom filter encoding of a set of tokens is as follows:\n\nCompute the hash digest for your tokens\nConvert the digest bytes into integers\nMap the integer to a bloom filter vector (modulo b, where b represents the length of the vector)\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsize\nint\nSize of the Bloom filter.\nrequired\n\n\nnum_hashes\nint\nNumber of hashes to perform. Defaults to three.\n3\n\n\noffset\nint\nOffset for Bloom filter indices to allow for masking. Defaults to one.\n1\n\n\nsalt\nstr | None\nCryptographic salt appended to tokens prior to hashing.\nNone\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nhash_function\nfunc\nHashing function (hashlib.sha1).\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nbig_int_to_vec\nConvert an integer vector into indices for a Bloom vector.\n\n\nbloom_filter_vector\nConvert a feature vector into indices for a Bloom vector.\n\n\nbloom_filter_vector_collision_fraction\nConvert a feature vector and return its collision fraction.\n\n\nfeature_to_big_int_repr\nConvert a feature vector into an integer vector.\n\n\n\n\n\nembedder.bloom_filters.BloomFilterEncoder.big_int_to_vec(feature_ints, offset=1)\nConvert an integer vector into indices for a Bloom vector.\nThis conversion inserts 1 at the location derived from the integer vector, which is an integer representation of a deterministic hash value, modulo to the size of the Bloom filter.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfeature_ints\nlist\nList of integer values representing the feature.\nrequired\n\n\noffset\nint\nAn offset to indices to allow for masking. Defaults to one.\n1\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist\nList of integers representing an index on the Bloom filter.\n\n\n\n\n\n\n\nembedder.bloom_filters.BloomFilterEncoder.bloom_filter_vector(feature)\nConvert a feature vector into indices for a Bloom vector.\nThe index vector uses an optional offset for masking.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfeature\nlist\nList of features to be converted.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist\nIndex values used to create the Bloom filter vector.\n\n\n\n\n\n\n\nembedder.bloom_filters.BloomFilterEncoder.bloom_filter_vector_collision_fraction(feature)\nConvert a feature vector and return its collision fraction.\nThe index vector uses an optional offset for masking.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfeature\nlist\nList of features to be processed.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist\nIndex values used to create the Bloom filter vector.\n\n\nfloat\nProportion of repeated indices.\n\n\n\n\n\n\n\nembedder.bloom_filters.BloomFilterEncoder.feature_to_big_int_repr(feature)\nConvert a feature vector into an integer vector.\nThis conversion first generates a hash digest for each member of the feature vector and then converts them to an integer.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfeature\nlist\nList of features to be processed.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist\nList of features as integers." + }, + { + "objectID": "docs/reference/bloom_filters.html#classes", + "href": "docs/reference/bloom_filters.html#classes", + "title": "bloom_filters", + "section": "", + "text": "Name\nDescription\n\n\n\n\nBloomFilterEncoder\nEncoder of tokens and features via hashing and a Bloom filter.\n\n\n\n\n\nembedder.bloom_filters.BloomFilterEncoder(self, size, num_hashes=3, offset=1, salt=None)\nEncoder of tokens and features via hashing and a Bloom filter.\nThe process for creating a cryptographically secure Bloom filter encoding of a set of tokens is as follows:\n\nCompute the hash digest for your tokens\nConvert the digest bytes into integers\nMap the integer to a bloom filter vector (modulo b, where b represents the length of the vector)\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsize\nint\nSize of the Bloom filter.\nrequired\n\n\nnum_hashes\nint\nNumber of hashes to perform. Defaults to three.\n3\n\n\noffset\nint\nOffset for Bloom filter indices to allow for masking. Defaults to one.\n1\n\n\nsalt\nstr | None\nCryptographic salt appended to tokens prior to hashing.\nNone\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nhash_function\nfunc\nHashing function (hashlib.sha1).\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nbig_int_to_vec\nConvert an integer vector into indices for a Bloom vector.\n\n\nbloom_filter_vector\nConvert a feature vector into indices for a Bloom vector.\n\n\nbloom_filter_vector_collision_fraction\nConvert a feature vector and return its collision fraction.\n\n\nfeature_to_big_int_repr\nConvert a feature vector into an integer vector.\n\n\n\n\n\nembedder.bloom_filters.BloomFilterEncoder.big_int_to_vec(feature_ints, offset=1)\nConvert an integer vector into indices for a Bloom vector.\nThis conversion inserts 1 at the location derived from the integer vector, which is an integer representation of a deterministic hash value, modulo to the size of the Bloom filter.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfeature_ints\nlist\nList of integer values representing the feature.\nrequired\n\n\noffset\nint\nAn offset to indices to allow for masking. Defaults to one.\n1\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist\nList of integers representing an index on the Bloom filter.\n\n\n\n\n\n\n\nembedder.bloom_filters.BloomFilterEncoder.bloom_filter_vector(feature)\nConvert a feature vector into indices for a Bloom vector.\nThe index vector uses an optional offset for masking.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfeature\nlist\nList of features to be converted.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist\nIndex values used to create the Bloom filter vector.\n\n\n\n\n\n\n\nembedder.bloom_filters.BloomFilterEncoder.bloom_filter_vector_collision_fraction(feature)\nConvert a feature vector and return its collision fraction.\nThe index vector uses an optional offset for masking.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfeature\nlist\nList of features to be processed.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist\nIndex values used to create the Bloom filter vector.\n\n\nfloat\nProportion of repeated indices.\n\n\n\n\n\n\n\nembedder.bloom_filters.BloomFilterEncoder.feature_to_big_int_repr(feature)\nConvert a feature vector into an integer vector.\nThis conversion first generates a hash digest for each member of the feature vector and then converts them to an integer.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nfeature\nlist\nList of features to be processed.\nrequired\n\n\n\n\n\n\n\n\n\nType\nDescription\n\n\n\n\nlist\nList of features as integers." + } +] \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-dark.min.css b/site_libs/bootstrap/bootstrap-dark.min.css new file mode 100644 index 0000000..a305fa3 --- /dev/null +++ b/site_libs/bootstrap/bootstrap-dark.min.css @@ -0,0 +1,10 @@ +/*! + * Bootstrap v5.1.3 (https://getbootstrap.com/) + * Copyright 2011-2021 The Bootstrap Authors + * Copyright 2011-2021 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + 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t?t:{}},a(K,t,Y),t}_handleSwipe(){const t=Math.abs(this.touchDeltaX);if(t<=40)return;const e=t/this.touchDeltaX;this.touchDeltaX=0,e&&this._slide(e>0?J:Z)}_addEventListeners(){this._config.keyboard&&j.on(this._element,"keydown.bs.carousel",(t=>this._keydown(t))),"hover"===this._config.pause&&(j.on(this._element,"mouseenter.bs.carousel",(t=>this.pause(t))),j.on(this._element,"mouseleave.bs.carousel",(t=>this.cycle(t)))),this._config.touch&&this._touchSupported&&this._addTouchEventListeners()}_addTouchEventListeners(){const t=t=>this._pointerEvent&&("pen"===t.pointerType||"touch"===t.pointerType),e=e=>{t(e)?this.touchStartX=e.clientX:this._pointerEvent||(this.touchStartX=e.touches[0].clientX)},i=t=>{this.touchDeltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this.touchStartX},n=e=>{t(e)&&(this.touchDeltaX=e.clientX-this.touchStartX),this._handleSwipe(),"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((t=>this.cycle(t)),500+this._config.interval))};V.find(".carousel-item img",this._element).forEach((t=>{j.on(t,"dragstart.bs.carousel",(t=>t.preventDefault()))})),this._pointerEvent?(j.on(this._element,"pointerdown.bs.carousel",(t=>e(t))),j.on(this._element,"pointerup.bs.carousel",(t=>n(t))),this._element.classList.add("pointer-event")):(j.on(this._element,"touchstart.bs.carousel",(t=>e(t))),j.on(this._element,"touchmove.bs.carousel",(t=>i(t))),j.on(this._element,"touchend.bs.carousel",(t=>n(t))))}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=tt[t.key];e&&(t.preventDefault(),this._slide(e))}_getItemIndex(t){return this._items=t&&t.parentNode?V.find(".carousel-item",t.parentNode):[],this._items.indexOf(t)}_getItemByOrder(t,e){const i=t===Q;return v(this._items,e,i,this._config.wrap)}_triggerSlideEvent(t,e){const i=this._getItemIndex(t),n=this._getItemIndex(V.findOne(nt,this._element));return j.trigger(this._element,"slide.bs.carousel",{relatedTarget:t,direction:e,from:n,to:i})}_setActiveIndicatorElement(t){if(this._indicatorsElement){const e=V.findOne(".active",this._indicatorsElement);e.classList.remove(it),e.removeAttribute("aria-current");const i=V.find("[data-bs-target]",this._indicatorsElement);for(let e=0;e{j.trigger(this._element,et,{relatedTarget:o,direction:d,from:s,to:r})};if(this._element.classList.contains("slide")){o.classList.add(h),u(o),n.classList.add(c),o.classList.add(c);const t=()=>{o.classList.remove(c,h),o.classList.add(it),n.classList.remove(it,h,c),this._isSliding=!1,setTimeout(f,0)};this._queueCallback(t,n,!0)}else n.classList.remove(it),o.classList.add(it),this._isSliding=!1,f();a&&this.cycle()}_directionToOrder(t){return[J,Z].includes(t)?m()?t===Z?G:Q:t===Z?Q:G:t}_orderToDirection(t){return[Q,G].includes(t)?m()?t===G?Z:J:t===G?J:Z:t}static carouselInterface(t,e){const i=st.getOrCreateInstance(t,e);let{_config:n}=i;"object"==typeof e&&(n={...n,...e});const s="string"==typeof e?e:n.slide;if("number"==typeof e)i.to(e);else if("string"==typeof s){if(void 0===i[s])throw new TypeError(`No method named "${s}"`);i[s]()}else n.interval&&n.ride&&(i.pause(),i.cycle())}static jQueryInterface(t){return this.each((function(){st.carouselInterface(this,t)}))}static dataApiClickHandler(t){const e=n(this);if(!e||!e.classList.contains("carousel"))return;const i={...U.getDataAttributes(e),...U.getDataAttributes(this)},s=this.getAttribute("data-bs-slide-to");s&&(i.interval=!1),st.carouselInterface(e,i),s&&st.getInstance(e).to(s),t.preventDefault()}}j.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",st.dataApiClickHandler),j.on(window,"load.bs.carousel.data-api",(()=>{const t=V.find('[data-bs-ride="carousel"]');for(let e=0,i=t.length;et===this._element));null!==s&&o.length&&(this._selector=s,this._triggerArray.push(e))}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return rt}static get NAME(){return ot}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t,e=[];if(this._config.parent){const t=V.find(ut,this._config.parent);e=V.find(".collapse.show, .collapse.collapsing",this._config.parent).filter((e=>!t.includes(e)))}const i=V.findOne(this._selector);if(e.length){const n=e.find((t=>i!==t));if(t=n?pt.getInstance(n):null,t&&t._isTransitioning)return}if(j.trigger(this._element,"show.bs.collapse").defaultPrevented)return;e.forEach((e=>{i!==e&&pt.getOrCreateInstance(e,{toggle:!1}).hide(),t||H.set(e,"bs.collapse",null)}));const n=this._getDimension();this._element.classList.remove(ct),this._element.classList.add(ht),this._element.style[n]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static jQueryInterface(t){return this.each((function(){const e={};"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1);const 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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in 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t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

    ',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=Y(t,{elementContext:"reference"}),s=Y(t,{altBoundary:!0}),f=he(a,r),c=he(s,o,i),p=me(f),u=me(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},ge=K({defaultModifiers:[Z,$,ne,re]}),ye=[Z,$,ne,re,oe,pe,le,de,ve],be=K({defaultModifiers:ye});e.applyStyles=re,e.arrow=de,e.computeStyles=ne,e.createPopper=be,e.createPopperLite=ge,e.defaultModifiers=ye,e.detectOverflow=Y,e.eventListeners=Z,e.flip=pe,e.hide=ve,e.offset=oe,e.popperGenerator=K,e.popperOffsets=$,e.preventOverflow=le,Object.defineProperty(e,"__esModule",{value:!0})})); + diff --git a/site_libs/quarto-html/quarto-syntax-highlighting-dark.css b/site_libs/quarto-html/quarto-syntax-highlighting-dark.css new file mode 100644 index 0000000..343d2f8 --- /dev/null +++ b/site_libs/quarto-html/quarto-syntax-highlighting-dark.css @@ -0,0 +1,189 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-al-color: #f07178; + --quarto-hl-an-color: #d4d0ab; + --quarto-hl-at-color: #00e0e0; + --quarto-hl-bn-color: #d4d0ab; + --quarto-hl-bu-color: #abe338; + --quarto-hl-ch-color: #abe338; + --quarto-hl-co-color: #f8f8f2; + --quarto-hl-cv-color: #ffd700; + --quarto-hl-cn-color: #ffd700; + --quarto-hl-cf-color: #ffa07a; + --quarto-hl-dt-color: #ffa07a; + --quarto-hl-dv-color: #d4d0ab; + --quarto-hl-do-color: #f8f8f2; + --quarto-hl-er-color: #f07178; + --quarto-hl-ex-color: #00e0e0; + --quarto-hl-fl-color: #d4d0ab; + --quarto-hl-fu-color: #ffa07a; + --quarto-hl-im-color: #abe338; + --quarto-hl-in-color: #d4d0ab; + --quarto-hl-kw-color: #ffa07a; + --quarto-hl-op-color: #ffa07a; + --quarto-hl-ot-color: #00e0e0; + --quarto-hl-pp-color: #dcc6e0; + --quarto-hl-re-color: #00e0e0; + --quarto-hl-sc-color: #abe338; + --quarto-hl-ss-color: #abe338; + --quarto-hl-st-color: #abe338; + --quarto-hl-va-color: #00e0e0; + --quarto-hl-vs-color: #abe338; + --quarto-hl-wa-color: #dcc6e0; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +code span.al { + background-color: #2a0f15; + font-weight: bold; + color: #f07178; +} + +code span.an { + color: #d4d0ab; +} + +code span.at { + color: #00e0e0; +} + +code span.bn { + color: #d4d0ab; +} + +code span.bu { + color: #abe338; +} + +code span.ch { + color: #abe338; +} + +code span.co { + font-style: italic; + color: #f8f8f2; +} + +code span.cv { + color: #ffd700; +} + +code span.cn { + color: #ffd700; +} + +code span.cf { + font-weight: bold; + color: #ffa07a; +} + +code span.dt { + color: #ffa07a; +} + +code span.dv { + color: #d4d0ab; +} + +code span.do { + color: #f8f8f2; +} + +code span.er { + color: #f07178; + text-decoration: underline; +} + +code span.ex { + font-weight: bold; + color: #00e0e0; +} + +code span.fl { + color: #d4d0ab; +} + +code span.fu { + color: #ffa07a; +} + +code span.im { + color: #abe338; +} + +code span.in { + color: #d4d0ab; +} + +code span.kw { + font-weight: bold; + color: #ffa07a; +} + +pre > code.sourceCode > span { + color: #f8f8f2; +} + +code span { + color: #f8f8f2; +} + +code.sourceCode > span { + color: #f8f8f2; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #f8f8f2; +} + +code span.op { + color: #ffa07a; +} + +code span.ot { + color: #00e0e0; +} + +code span.pp { + color: #dcc6e0; +} + +code span.re { + background-color: #f8f8f2; + color: #00e0e0; +} + +code span.sc { + color: #abe338; +} + +code span.ss { + color: #abe338; +} + +code span.st { + color: #abe338; +} + +code span.va { + color: #00e0e0; +} + +code span.vs { + color: #abe338; +} + +code span.wa { + color: #dcc6e0; +} + +.prevent-inlining { + content: " code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/site_libs/quarto-listing/list.min.js new file mode 100644 index 0000000..8131881 --- /dev/null +++ b/site_libs/quarto-listing/list.min.js @@ -0,0 +1,2 @@ +var List;List=function(){var t={"./src/add-async.js":function(t){t.exports=function(t){return function e(r,n,s){var i=r.splice(0,50);s=(s=s||[]).concat(t.add(i)),r.length>0?setTimeout((function(){e(r,n,s)}),1):(t.update(),n(s))}}},"./src/filter.js":function(t){t.exports=function(t){return t.handlers.filterStart=t.handlers.filterStart||[],t.handlers.filterComplete=t.handlers.filterComplete||[],function(e){if(t.trigger("filterStart"),t.i=1,t.reset.filter(),void 0===e)t.filtered=!1;else{t.filtered=!0;for(var r=t.items,n=0,s=r.length;nv.page,a=new g(t[s],void 0,n),v.items.push(a),r.push(a)}return v.update(),r}m(t.slice(0),e)}},this.show=function(t,e){return this.i=t,this.page=e,v.update(),v},this.remove=function(t,e,r){for(var n=0,s=0,i=v.items.length;s-1&&r.splice(n,1),v},this.trigger=function(t){for(var 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",valueNames:["page","dotted"],searchClass:"pagination-search-that-is-not-supposed-to-exist",sortClass:"pagination-sort-that-is-not-supposed-to-exist"});s.bind(n.listContainer,"click",(function(e){var r=e.target||e.srcElement,n=t.utils.getAttribute(r,"data-page"),s=t.utils.getAttribute(r,"data-i");s&&t.show((s-1)*n+1,n)})),t.on("updated",(function(){r(n,e)})),r(n,e)}}},"./src/parse.js":function(t,e,r){t.exports=function(t){var e=r("./src/item.js")(t),n=function(r,n){for(var s=0,i=r.length;s0?setTimeout((function(){e(r,s)}),1):(t.update(),t.trigger("parseComplete"))};return t.handlers.parseComplete=t.handlers.parseComplete||[],function(){var e=function(t){for(var e=t.childNodes,r=[],n=0,s=e.length;n]/g.exec(t)){var e=document.createElement("tbody");return e.innerHTML=t,e.firstElementChild}if(-1!==t.indexOf("<")){var r=document.createElement("div");return r.innerHTML=t,r.firstElementChild}}},a=function(e,r,n){var s=void 0,i=function(e){for(var r=0,n=t.valueNames.length;r=1;)t.list.removeChild(t.list.firstChild)},function(){var r;if("function"!=typeof t.item){if(!(r="string"==typeof t.item?-1===t.item.indexOf("<")?document.getElementById(t.item):i(t.item):s()))throw new Error("The list needs to have at least one item on init otherwise you'll have to add a template.");r=n(r,t.valueNames),e=function(){return r.cloneNode(!0)}}else e=function(e){var r=t.item(e);return i(r)}}()};t.exports=function(t){return new e(t)}},"./src/utils/classes.js":function(t,e,r){var n=r("./src/utils/index-of.js"),s=/\s+/;Object.prototype.toString;function i(t){if(!t||!t.nodeType)throw new Error("A DOM element reference is required");this.el=t,this.list=t.classList}t.exports=function(t){return new i(t)},i.prototype.add=function(t){if(this.list)return this.list.add(t),this;var e=this.array();return~n(e,t)||e.push(t),this.el.className=e.join(" "),this},i.prototype.remove=function(t){if(this.list)return this.list.remove(t),this;var e=this.array(),r=n(e,t);return~r&&e.splice(r,1),this.el.className=e.join(" "),this},i.prototype.toggle=function(t,e){return this.list?(void 0!==e?e!==this.list.toggle(t,e)&&this.list.toggle(t):this.list.toggle(t),this):(void 0!==e?e?this.add(t):this.remove(t):this.has(t)?this.remove(t):this.add(t),this)},i.prototype.array=function(){var t=(this.el.getAttribute("class")||"").replace(/^\s+|\s+$/g,"").split(s);return""===t[0]&&t.shift(),t},i.prototype.has=i.prototype.contains=function(t){return this.list?this.list.contains(t):!!~n(this.array(),t)}},"./src/utils/events.js":function(t,e,r){var n=window.addEventListener?"addEventListener":"attachEvent",s=window.removeEventListener?"removeEventListener":"detachEvent",i="addEventListener"!==n?"on":"",a=r("./src/utils/to-array.js");e.bind=function(t,e,r,s){for(var o=0,l=(t=a(t)).length;o32)return!1;var a=n,o=function(){var t,r={};for(t=0;t=p;b--){var j=o[t.charAt(b-1)];if(C[b]=0===m?(C[b+1]<<1|1)&j:(C[b+1]<<1|1)&j|(v[b+1]|v[b])<<1|1|v[b+1],C[b]&d){var x=l(m,b-1);if(x<=u){if(u=x,!((c=b-1)>a))break;p=Math.max(1,2*a-c)}}}if(l(m+1,a)>u)break;v=C}return!(c<0)}},"./src/utils/get-attribute.js":function(t){t.exports=function(t,e){var r=t.getAttribute&&t.getAttribute(e)||null;if(!r)for(var n=t.attributes,s=n.length,i=0;i=48&&t<=57}function i(t,e){for(var i=(t+="").length,a=(e+="").length,o=0,l=0;o=i&&l=a?-1:l>=a&&o=i?1:i-a}i.caseInsensitive=i.i=function(t,e){return i((""+t).toLowerCase(),(""+e).toLowerCase())},Object.defineProperties(i,{alphabet:{get:function(){return e},set:function(t){r=[];var s=0;if(e=t)for(;s { + if (categoriesLoaded) { + activateCategory(category); + setCategoryHash(category); + } +}; + +window["quarto-listing-loaded"] = () => { + // Process any existing hash + const hash = getHash(); + + if (hash) { + // If there is a category, switch to that + if (hash.category) { + activateCategory(hash.category); + } + // Paginate a specific listing + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + const page = hash[getListingPageKey(listingId)]; + if (page) { + showPage(listingId, page); + } + } + } + + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + // The actual list + const list = window["quarto-listings"][listingId]; + + // Update the handlers for pagination events + refreshPaginationHandlers(listingId); + + // Render any visible items that need it + renderVisibleProgressiveImages(list); + + // Whenever the list is updated, we also need to + // attach handlers to the new pagination elements + // and refresh any newly visible items. + list.on("updated", function () { + renderVisibleProgressiveImages(list); + setTimeout(() => refreshPaginationHandlers(listingId)); + + // Show or hide the no matching message + toggleNoMatchingMessage(list); + }); + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Attach click handlers to categories + const categoryEls = window.document.querySelectorAll( + ".quarto-listing-category .category" + ); + + for (const categoryEl of categoryEls) { + const category = categoryEl.getAttribute("data-category"); + categoryEl.onclick = () => { + activateCategory(category); + setCategoryHash(category); + }; + } + + // Attach a click handler to the category title + // (there should be only one, but since it is a class name, handle N) + const categoryTitleEls = window.document.querySelectorAll( + ".quarto-listing-category-title" + ); + for (const categoryTitleEl of categoryTitleEls) { + categoryTitleEl.onclick = () => { + activateCategory(""); + setCategoryHash(""); + }; + } + + categoriesLoaded = true; +}); + +function toggleNoMatchingMessage(list) { + const selector = `#${list.listContainer.id} .listing-no-matching`; + const noMatchingEl = window.document.querySelector(selector); + if (noMatchingEl) { + if (list.visibleItems.length === 0) { + noMatchingEl.classList.remove("d-none"); + } else { + if (!noMatchingEl.classList.contains("d-none")) { + noMatchingEl.classList.add("d-none"); + } + } + } +} + +function setCategoryHash(category) { + setHash({ category }); +} + +function setPageHash(listingId, page) { + const currentHash = getHash() || {}; + currentHash[getListingPageKey(listingId)] = page; + setHash(currentHash); +} + +function getListingPageKey(listingId) { + return `${listingId}-page`; +} + +function refreshPaginationHandlers(listingId) { + const listingEl = window.document.getElementById(listingId); + const paginationEls = listingEl.querySelectorAll( + ".pagination li.page-item:not(.disabled) .page.page-link" + ); + for (const paginationEl of paginationEls) { + paginationEl.onclick = (sender) => { + setPageHash(listingId, sender.target.getAttribute("data-i")); + showPage(listingId, sender.target.getAttribute("data-i")); + return false; + }; + } +} + +function renderVisibleProgressiveImages(list) { + // Run through the visible items and render any progressive images + for (const item of list.visibleItems) { + const itemEl = item.elm; + if (itemEl) { + const progressiveImgs = itemEl.querySelectorAll( + `img[${kProgressiveAttr}]` + ); + for (const progressiveImg of progressiveImgs) { + const srcValue = progressiveImg.getAttribute(kProgressiveAttr); + if (srcValue) { + progressiveImg.setAttribute("src", srcValue); + } + progressiveImg.removeAttribute(kProgressiveAttr); + } + } + } +} + +function getHash() { + // Hashes are of the form + // #name:value|name1:value1|name2:value2 + const currentUrl = new URL(window.location); + const hashRaw = currentUrl.hash ? currentUrl.hash.slice(1) : undefined; + return parseHash(hashRaw); +} + +const kAnd = "&"; +const kEquals = "="; + +function parseHash(hash) { + if (!hash) { + return undefined; + } + const hasValuesStrs = hash.split(kAnd); + const hashValues = hasValuesStrs + .map((hashValueStr) => { + const vals = hashValueStr.split(kEquals); + if (vals.length === 2) { + return { name: vals[0], value: vals[1] }; + } else { + return undefined; + } + }) + .filter((value) => { + return value !== undefined; + }); + + const hashObj = {}; + hashValues.forEach((hashValue) => { + hashObj[hashValue.name] = decodeURIComponent(hashValue.value); + }); + return hashObj; +} + +function makeHash(obj) { + return Object.keys(obj) + .map((key) => { + return `${key}${kEquals}${obj[key]}`; + }) + .join(kAnd); +} + +function setHash(obj) { + const hash = makeHash(obj); + window.history.pushState(null, null, `#${hash}`); +} + +function showPage(listingId, page) { + const list = window["quarto-listings"][listingId]; + if (list) { + list.show((page - 1) * list.page + 1, list.page); + } +} + +function activateCategory(category) { + // Deactivate existing categories + const activeEls = window.document.querySelectorAll( + ".quarto-listing-category .category.active" + ); + for (const activeEl of activeEls) { + activeEl.classList.remove("active"); + } + + // Activate this category + const categoryEl = window.document.querySelector( + `.quarto-listing-category .category[data-category='${category}'` + ); + if (categoryEl) { + categoryEl.classList.add("active"); + } + + // Filter the listings to this category + filterListingCategory(category); +} + +function filterListingCategory(category) { + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + const list = window["quarto-listings"][listingId]; + if (list) { + if (category === "") { + // resets the filter + list.filter(); + } else { + // filter to this category + list.filter(function (item) { + const itemValues = item.values(); + if (itemValues.categories !== null) { + const categories = itemValues.categories.split(","); + return categories.includes(category); + } else { + return false; + } + }); + } + } + } +} diff --git a/site_libs/quarto-nav/headroom.min.js b/site_libs/quarto-nav/headroom.min.js new file mode 100644 index 0000000..b08f1df --- /dev/null +++ b/site_libs/quarto-nav/headroom.min.js @@ -0,0 +1,7 @@ +/*! + * headroom.js v0.12.0 - Give your page some headroom. Hide your header until you need it + * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,277 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver( + updateDocumentOffsetWithoutAnimation + ); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 0000000..619c57c --- /dev/null +++ b/site_libs/quarto-search/autocomplete.umd.js @@ -0,0 +1,3 @@ +/*! @algolia/autocomplete-js 1.7.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */ +!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof 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0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n1&&void 0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..f5d852d --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1140 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query !== null && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query !== null && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = () => { + setIsOpen(false); + }; + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.map((event) => { + const maxEvents = event.objectIDs.slice(0, 20); + return { + ...event, + objectIDs: maxEvents, + }; + }); + + insights.viewedObjectIDs(...events); + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + // create fuse index + const options = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, + }; + const fuse = new window.Fuse([], options); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard(createElement, icon, title, section, text, href) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + [iconEl, titleEl] + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text"].forEach((keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + }); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +function fuseSearch(query, fuse, fuseOptions) { + return fuse.search(query, fuseOptions).map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + }; + }); +}