cvfe.data package#

Submodules#

cvfe.data.constant module#

cvfe.data.constant.CANADA_5257E_KEY_ABBREVIATION = {'Address': 'Addr', 'BGI2.VisaChoice1': 'noAuthStay', 'BGI2.VisaChoice2': 'refuseDeport', 'BGI3.Choice': 'criminalRec', 'BackgroundInfo': 'BGI', 'Contact': 'cntct', 'ContactInformation': 'CI', 'CountryWhereApplying': 'CWA', 'Current': 'Curr', 'Details.VisaChoice3': 'PrevApply', 'DetailsOfVisit': 'DOV', 'Education': 'Edu', 'GovPosition.Choice': 'witnessIllTreat', 'HowLongStay': 'HLS', 'Language': 'Lang', 'MaritalStatus': 'MS', 'Marriage': 'Marr', 'Married': 'Marr', 'Number': 'Num', 'Occ.Choice': 'politicViol', 'Occupation': 'Occ', 'Page': 'P', 'PageWrapper': 'PW', 'Passport': 'Psprt', 'PersonalDetails': 'PD', 'Phone': 'Phn', 'Previous': 'Prev', 'Previously': 'Prev', 'Purpose': 'Prps', 'Resident': 'Resi', 'Section': 'Sec', 'Signature': 'Sign', 'backgroundInfoCalc': 'otherThanMedic', 'contact': 'cntct'}#

Dict of abbreviation used to shortening length of KEYS in XML to CSV conversion

cvfe.data.constant.CANADA_5645E_KEY_ABBREVIATION = {'Address': 'Addr', 'Applicant': 'App', 'Child': 'Chd', 'Father': 'Fa', 'Mother': 'Mo', 'Occupation': 'Occ', 'Relationship': 'Rel', 'Section': 'Sec', 'Spouse': 'Sps', 'Yes': 'Accomp', 'page': 'p'}#

Dict of abbreviation used to shortening length of KEYS in XML to CSV conversion

cvfe.data.constant.CANADA_5257E_VALUE_ABBREVIATION = {'045': 'TURKEY', '223': 'IRAN', 'BIOMETRIC ENROLMENT': 'Bio'}#

Dict of abbreviation used to shortening length of VALUES in XML to CSV conversion

cvfe.data.constant.CANADA_5257E_DROP_COLUMNS = ['ns0:datasets.@xmlns:ns0', 'P1.Header.CRCNum', 'P1.FormVersion', 'P1.PD.UCIClientID', 'P1.PD.SecHeader.@ns0:dataNode', 'P1.PD.CurrCOR.Row1.@ns0:dataNode', 'P1.PD.PrevCOR.Row1.@ns0:dataNode', 'P1.PD.CWA.Row1.@ns0:dataNode', 'P1.PD.ApplicationValidatedFlag', 'P2.MS.SecA.SecHeader.@ns0:dataNode', 'P2.MS.SecA.PsprtSecHeader.@ns0:dataNode', 'P2.MS.SecA.Langs.languagesHeader.@ns0:dataNode', 'P2.natID.SecHeader.@ns0:dataNode', 'P2.USCard.SecHeader.@ns0:dataNode', 'P2.USCard.SecHeader.@ns0:dataNode', 'P2.CI.cntct.cntctInfoSecHeader.@ns0:dataNode', 'P3.SecHeader_DOV.@ns0:dataNode', 'P3.Edu.Edu_SecHeader.@ns0:dataNode', 'P3.Occ.SecHeader_CurrOcc.@ns0:dataNode', 'P3.BGI_SecHeader.@ns0:dataNode', 'P3.Sign.Consent0.Choice', 'P3.Sign.hand.@ns0:dataNode', 'P3.Sign.TextField2', 'P3.Disclosure.@ns0:dataNode', 'P3.ReaderInfo', 'Barcodes.@ns0:dataNode']#

List of columns to be dropped before doing any preprocessing

Note

This list has been determined manually.

cvfe.data.constant.CANADA_5645E_DROP_COLUMNS = {'formNum', 'p1.SecA.SecAdate', 'p1.SecA.SecAsignature', 'p1.SecA.Title.@xfa:dataNode', 'p1.SecB.SecBdate', 'p1.SecB.SecBsignature', 'p1.SecB.Title.@xfa:dataNode', 'p1.SecC.SecCsignature', 'p1.SecC.Subform2.@xfa:dataNode', 'p1.SecC.Title.@xfa:dataNode', 'xfa:datasets.@xmlns:xfa'}#

List of columns to be dropped before doing any preprocessing

Note

This list has been determined manually.

class cvfe.data.constant.DocTypes(value)[source]#

Bases: Enum

Contains all document types which can be used to customize ETL steps for each document type

Members follow the <country_name>_<document_type> naming convention. The value and its order are meaningless.

CANADA = 1#
CANADA_5257E = 2#
CANADA_5645E = 3#
CANADA_LABEL = 4#
class cvfe.data.constant.CanadaCutoffTerms[source]#

Bases: object

Dict of cut off terms for different files that is can be used with :func:`vizard.data.functional.dict_summarizer

CA5645E = 'IMM_5645'#
CA5257E = 'form1'#
class cvfe.data.constant.CanadaFillna[source]#

Bases: object

Values used to fill None s depending on the form structure

Members follow the <field_name>_<form_name> naming convention. The value has been extracted by manually inspecting the documents. Hence, for each form, user must find and set this value manually.

Note

We do not use any heuristics here, we just follow what form used and only add another option which should be used as None state; i.e. None as a separate feature in categorical mode.

COUNTRY_CODE_5257E = 'Unknown'#
VISA_TYPE_5257E = 'OTHER'#
PLACE_BIRTH_CITY_5257E = 'OTHER'#
COUNTRY_5257E = 'IRAN'#
CITIZENSHIP_5257E = 'IRAN'#
RESIDENCY_STATUS_5257E = 6#
OTHER_DESCRIPTION_INDICATOR_5257E = False#
PREVIOUS_COUNTRY_5257E = 'OTHER'#
COUNTRY_WHERE_APPLYING_5257E = 'OTHER'#
MARRIAGE_TYPE_5257E = 'OTHER'#
PASSPORT_COUNTRY_5257E = 'OTHER'#
NATIVE_LANG_5257E = 'IRAN'#
LANGUAGES_ABLE_TO_COMMUNICATE_5257E = 'NEITHER'#
ID_COUNTRY_5257E = 'IRAN'#
PURPOSE_OF_VISIT_5257E = 7#
CONTACT_TYPE_5257E = 'OTHER'#
OCCUPATION_5257E = 'OTHER'#
INDICATOR_FIELD_5257E = False#
VISA_APPLICATION_TYPE_5645E = '0'#
CHILD_MARRIAGE_STATUS_5645E = 9#
CHILD_RELATION_5645E = 'OTHER'#
VISA_RESULT = 0#
cvfe.data.constant.DATEUTIL_DEFAULT_DATETIME = {'day': 1, 'month': 1, 'year': 1}#

A default date for the dateutil.parser.parse function when some part of date is not provided

cvfe.data.constant.T0 = '19000202T000000'#

a default meaningless time to fill the `None`s

class cvfe.data.constant.CustomNamingEnum(value)[source]#

Bases: Enum

Extends base enum.Enum to support custom naming for members

Note

Class attribute name has been overridden to return the name of a marital status that matches with the dataset and not the Enum naming convention of Python. For instance, COMMON_LAW -> common-law in case of Canada forms.

Note

Devs should subclass this class and add their desired members in newly created classes. E.g. see CanadaMarriageStatus

Note

Classes that subclass this, for values of their members should use enum.auto to demonstrate that chosen value is not domain-specific. Otherwise, any explicit value given to members should implicate a domain-specific (e.g. extracted from dataset) value. Values that are explicitly provided are the values used in original data. Hence, it should not be modified by any means as it is tied to dataset, transformation, and other domain-specific values. E.g. compare values in CanadaMarriageStatus and SiblingRelation.

classmethod get_member_names()[source]#
class cvfe.data.constant.CanadaMarriageStatus(value)[source]#

Bases: CustomNamingEnum

States of marriage in Canada forms

Note

Values for the members are the values used in original Canada forms. Hence, it should not be modified by any means as it is tied to dataset, transformation, and other domain-specific values.

COMMON_LAW = 2#
DIVORCED = 3#
SEPARATED = 4#
MARRIED = 5#
SINGLE = 7#
WIDOWED = 8#
UNKNOWN = 9#
class cvfe.data.constant.CanadaContactRelation(value)[source]#

Bases: CustomNamingEnum

Contact relation in Canada data

F1 = 1#
F2 = 2#
HOTEL = 3#
WORK = 4#
FRIEND = 5#
UKN = 6#
class cvfe.data.constant.CanadaResidencyStatus(value)[source]#

Bases: CustomNamingEnum

Residency status in a country in Canada data

CITIZEN = 1#
VISITOR = 3#
OTHER = 6#
class cvfe.data.constant.Sex(value)[source]#

Bases: CustomNamingEnum

Sex types in general

FEMALE = 1#
MALE = 2#

cvfe.data.functional module#

cvfe.data.functional.dict_summarizer(d, cutoff_term, KEY_ABBREVIATION_DICT=None, VALUE_ABBREVIATION_DICT=None)[source]#

Takes a flattened dictionary and shortens its keys

Parameters:

  • d (dict) – The dictionary to be shortened

  • cutoff_term (str) – The string that used to find in keys and remove anything behind it

  • KEY_ABBREVIATION_DICT (dict, optional) – A dictionary containing abbreviation mapping for keys. Defaults to None.

  • VALUE_ABBREVIATION_DICT (dict, optional) – A dictionary containing abbreviation mapping for values. Defaults to None.

Returns:

A dict with shortened keys by throwing away some part and using a abbreviation dictionary for both keys and values.

Return type:

dict

cvfe.data.functional.dict_to_csv(d, path)[source]#

Takes a flattened dictionary and writes it to a CSV file.

Parameters:

  • d (dict) – A dictionary

  • path (str) – Path to the output file (will be created if not exist)

Return type:

None

cvfe.data.functional.column_dropper(dataframe, string, exclude=None, regex=False, inplace=True)[source]#

Takes a Pandas Dataframe and drops columns matching a pattern

Parameters:

  • dataframe (pandas.DataFrame) – Pandas dataframe to be processed

  • string (str) – string to look for in dataframe columns

  • exclude (Optional[str], optional) – string to exclude a subset of columns from being dropped. Defaults to None.

  • regex (bool, optional) – compile string as regex. Defaults to False.

  • inplace (bool, optional) – whether or not use and inplace operation. Defaults to True.

Returns:

Takes a Pandas Dataframe and searches for columns containing string in them either raw string or regex (in latter case, use regex=True) and after exclude ing a subset of them, drops the remaining in-place.

Return type:

Optional[pandas.DataFrame]

cvfe.data.functional.fillna_datetime(dataframe, col_base_name, date, type, one_sided=False, inplace=False)[source]#

Takes names of two columns of dates (start, end) and fills them with a predefined value

Parameters:

  • dataframe (pandas.DataFrame) – Pandas Dataframe to be processed

  • col_base_name (str) – Base column name that accepts 'From' and 'To' for extracting dates of same category

  • date (str) – The desired date

  • type (DocTypes) –

    Different ways of filling empty date columns:

    1. 'right': Uses the current_date as the final time

    2. 'left': Uses the reference_date as the starting time

  • one_sided (str | bool, optional) – whether or not use an inplace operation. Defaults to False.

  • inplace (bool, optional) – DocTypes used to use rules for matching tags and filling appropriately. Defaults to False.

Note

In transformation operations such as aggregate_datetime() function, this would be converted to period of zero. It is useful for filling periods of non existing items (e.g. age of children for single person).

Returns:

A Pandas Dataframe that two columns of dates that had no value (None) which was filled to the same date via date.

Return type:

pandas.DataFrame

cvfe.data.functional.aggregate_datetime(dataframe, col_base_name, new_col_name, type, if_nan='skip', one_sided=None, reference_date=None, current_date=None, **kwargs)[source]#

Takes two columns of dates in string form and calculates the period of them

Parameters:

  • dataframe (pandas.DataFrame) – Pandas dataframe to be processed

  • col_base_name (str) – Base column name that accepts 'From' and 'To' for extracting dates of same category

  • new_col_name (str) – The column name that extends col_base_name and will be the final column containing the period.

  • type (DocTypes) – document type used to use rules for matching tags and filling appropriately. See DocTypes.

  • if_nan (Optional[str | Callable], optional) –

    What to do with None s (NaN). Could be a function or predefined states as follow:

    1. 'skip': do nothing (i.e. ignore None``s). Defaults to ``'skip'.

  • one_sided (Optional[str], optional) –

    Different ways of filling empty date columns. Defaults to None. Could be one of the following:

    1. 'right': Uses the current_date as the final time

    2. 'left': Uses the reference_date as the starting time

  • reference_date (Optional[str], optional) – Assumed reference_date (t0<t1). Defaults to None.

  • current_date (Optional[str], optional) – Assumed current_date (t1>t0). Defaults to None.

  • default_datetime – accepts datetime.datetime to set default date for dateutil.parser.parse.

Returns:

A Pandas Dataframe calculate the period of two columns of dates and represent it in integer form. The two columns used will be dropped.

Return type:

pandas.DataFrame

cvfe.data.functional.tag_to_regex_compatible(string, type)[source]#

Takes a string and makes it regex compatible for XML parsed string

Note

This is specialized method and it may be better to override it for your own case.

Parameters:

  • string (str) – input string to get manipulated

  • type (DocTypes) – specified DocTypes to determine regex rules

Returns:

A modified string

Return type:

str

cvfe.data.functional.change_dtype(dataframe, col_name, dtype, if_nan='skip', **kwargs)[source]#

Changes the data type of a column with ability to fill None s

Parameters:

  • dataframe (pandas.DataFrame) – Dataframe that column_name will be searched on

  • col_name (str) – Desired column name of the dataframe

  • dtype (Callable) – target data type as a function e.g. np.float32

  • if_nan (str, Callable, optional) –

    What to do with None s (NaN). Defaults to 'skip'. Could be a function or predefined states as follow:

    1. 'skip': do nothing (i.e. ignore None s)

    2. 'value': fill the None with value argument via kwargs

  • default_datetime (optional) – accepts datetime.datetime to set default date for dateutil.parser.parse

Raises:

ValueError – if string mode passed to if_nan does not exist. It won’t raise if if_nan is Callable.

Returns:

A Pandas Dataframe calculate the period of two columns of dates and represent it in integer form. The two columns used will be dropped.

Return type:

pandas.DataFrame

cvfe.data.functional.flatten_dict(d)[source]#

Takes a (nested) multilevel dictionary and flattens it

Parameters:

d (dict) – A dictionary (could be multilevel)

References

  1. https://stackoverflow.com/a/67744709/18971263

Returns:

Flattened dictionary where keys and values of returned dict are:

  • new_keys[i] = f'{old_leys[level]}.{old_leys[level+1]}.[...].{old_leys[level+n]}'

  • new_value = old_value

Return type:

dict

cvfe.data.functional.xml_to_flattened_dict(xml)[source]#

Takes a (nested) XML and flattens it to a dict via flatten_dict()

Parameters:

xml (str) – A XML string

Returns:

A flattened dictionary of given XML

Return type:

dict

cvfe.data.functional.create_directory_structure_tree(src, shallow=False)[source]#

Takes a path to directory and creates a dictionary of its directory structure tree

Parameters:

  • src (str) – Path to source directory

  • shallow (bool, optional) – Whether or not just dive to root dir’s subdir. Defaults to False.

References

  1. https://stackoverflow.com/a/25226267/18971263

Returns:

Dictionary of all dirs (and subdirs) where keys are path and values are 0

Return type:

dict

cvfe.data.functional.dump_directory_structure_csv(src, shallow=True)[source]#

Saves a tree structure of a directory in csv file

Takes a src directory path, creates a tree of dir structure and writes it down to a csv file with name 'label.csv' with default value of '0' for each path

Note

This has been used to manually extract and record labels.

Parameters:

  • src (str) – Source directory path

  • shallow (bool, optional) – If only dive one level of depth (False: recursive). Defaults to True.

Return type:

None

cvfe.data.functional.process_directory(src_dir, dst_dir, compose, file_pattern='*')[source]#

Transforms all files that match pattern in given dir and saves new files preserving dir structure

Note

A methods used for handling files from manually processed dataset to raw-dataset see FileTransform for more information.

References

  1. https://stackoverflow.com/a/24041933/18971263

Parameters:

  • src_dir (str) – Source directory to be processed

  • dst_dir (str) – Destination directory to write processed files

  • compose (FileTransformCompose) – An instance of transform composer. see Compose.

  • file_pattern (str, optional) – pattern to match files, default to '*' for all files. Defaults to '*'.

Return type:

None

cvfe.data.functional.search_dict(string, dic, if_nan)[source]#

Converts a string to another given a dictionary to search for

Note

This could be used to convert non-standard country codes to their names

Parameters:

  • string (str) – input string

  • dic (dict) – dictionary to be searched for string in its keys

  • if_nan (str) – if string could not be found in dic, return if_nan

Returns:

Converted string

Return type:

str

cvfe.data.functional.config_csv_to_dict(path)[source]#

Takes a config CSV and return a dictionary of key and values

Note

Configs of our use case can be found in cvfe.configs

Parameters:

path (str) – string path to config file

Returns:

A dictionary of converted csv

Return type:

dict

cvfe.data.pdf module#

class cvfe.data.pdf.PDFIO[source]#

Bases: object

Base class for dealing with PDF files

For each type of PDF, let’s say XFA files, one needs to extend this class and abstract methods like extract_raw_content() to generate a string of the content of the PDF in a format that can be used by the other classes (e.g. XML). For instance, see XFAPDF for the extension of this class.

__init__()[source]#
extract_raw_content(pdf_path)[source]#

Extracts unprocessed data from a PDF file

Parameters:

pdf_path (str) – Path to the pdf file

Return type:

str

find_in_dict(needle, haystack)[source]#

Looks for the value of a key inside a nested dictionary

Parameters:

  • needle (Any) – Key to look for

  • haystack (Any) – Dictionary to look in. Can be a dict inside another dict

Returns:

The value of key needle

Return type:

Any

class cvfe.data.pdf.XFAPDF[source]#

Bases: PDFIO

Contains functions and utility tools for dealing with XFA PDF documents.

__init__()[source]#
extract_raw_content(pdf_path)[source]#

Extracts RAW content of XFA PDF files which are in XML format

Parameters:

pdf_path (str) – path to the pdf file

Reference:

Returns:

XFA object of the pdf file in XML format

Return type:

str

clean_xml_for_csv(xml, type)[source]#

Cleans the XML file extracted from XFA forms

Since each form has its own format and issues, this method needs to be implemented uniquely for each unique file/form which needs to be specified using argument type that can be populated from DocTypes.

Parameters:

  • xml (str) – XML content

  • type (Enum) – type of the document defined in DocTypes

Returns:

cleaned XML content to be used in CSV file

Return type:

str

flatten_dict_basic(d)[source]#

Takes a (nested) dictionary and flattens it

ref: https://stackoverflow.com/questions/38852822/how-to-flatten-xml-file-in-python :type d: dict :param d: A dictionary :param return: An ordered dict

Return type:

dict

flatten_dict(d)[source]#

Takes a (nested) multilevel dictionary and flattens it

The final keys are key.key... and values are the leaf values of dictionary

Parameters:

d (dict) – A dictionary

References

Returns:

A flattened dictionary

Return type:

dict

xml_to_flattened_dict(xml)[source]#

Takes a (nested) XML and converts it to a flattened dictionary

The final keys are key.key... and values are the leaf values of XML tree

Parameters:

xml (str) – A XML string

Returns:

A flattened dictionary

Return type:

dict

class cvfe.data.pdf.CanadaXFA[source]#

Bases: XFAPDF

Handles Canada XFA PDF files

__init__()[source]#
clean_xml_for_csv(xml, type)[source]#

Hardcoded cleaning of Canada XFA XML files to be XML compatible with CSV

Parameters:

  • xml (str) – XML content

  • type (Enum) – type of the document defined in DocTypes

Returns:

cleaned XML content to be used in CSV file

Return type:

str

cvfe.data.preprocessor module#

class cvfe.data.preprocessor.DataframePreprocessor(dataframe=None)[source]#

Bases: object

A wrapper around builtin Pandas functions to make it easier for our data values

A class that contains methods for dealing with dataframes regarding transformation of data such as filling missing values, dropping columns, or aggregating multiple columns into a single more meaningful one.

This class needs to be extended for file specific preprocessing where tags are unique and need to be done entirely manually. In this case, file_specific_basic_transform() needs to be implemented.

__init__(dataframe=None)[source]#
Parameters:

dataframe (pandas.DataFrame, optional) – Main dataframe to be preprocessed. Defaults to None.

column_dropper(string, exclude=None, regex=False, inplace=True)[source]#

See cvfe.data.functional.column_dropper() for more information

Return type:

Optional[DataFrame]

fillna_datetime(col_base_name, type, one_sided, date=None, inplace=False)[source]#

See cvfe.data.functional.fillna_datetime() for more details

Return type:

Optional[DataFrame]

aggregate_datetime(col_base_name, new_col_name, type, if_nan=None, one_sided=None, reference_date=None, current_date=None)[source]#

See cvfe.data.functional.aggregate_datetime() for more details

Return type:

DataFrame

file_specific_basic_transform(type, path)[source]#

Takes a specific file then does data type fixing, missing value filling, discretization, etc.

Note

Since each files has its own unique tags and requirements, it is expected that all these transformation being hardcoded for each file, hence this method exists to just improve readability without any generalization to other problems or even files.

Parameters:

  • type (DocTypes) – The input document type (see DocTypes)

  • path (str) – Path to the input document

Return type:

DataFrame

change_dtype(col_name, dtype, if_nan='skip', **kwargs)[source]#

See cvfe.data.functional.change_dtype() for more details

config_csv_to_dict(path)[source]#

Take a config CSV and return a dictionary of key and values

Parameters:

path (str) – string path to config file

Return type:

dict

class cvfe.data.preprocessor.CanadaDataframePreprocessor(dataframe=None)[source]#

Bases: DataframePreprocessor

__init__(dataframe=None)[source]#
Parameters:

dataframe (pandas.DataFrame, optional) – Main dataframe to be preprocessed. Defaults to None.

convert_country_code_to_name(string)[source]#

Converts the (custom and non-standard) code of a country to its name given the XFA docs LOV section.

Parameters:

string (str) – input code string

Return type:

str

file_specific_basic_transform(type, path)[source]#

Takes a specific file then does data type fixing, missing value filling, discretization, etc.

Note

Since each files has its own unique tags and requirements, it is expected that all these transformation being hardcoded for each file, hence this method exists to just improve readability without any generalization to other problems or even files.

Parameters:

  • type (DocTypes) – The input document type (see DocTypes)

  • path (str) – Path to the input document

Return type:

DataFrame

class cvfe.data.preprocessor.FileTransformCompose(transforms)[source]#

Bases: object

Composes several transforms operating on files together

The transforms should be tied to files with keyword and this will be only applying functions on files that match the keyword using a dictionary

Transformation dictionary over files in the following structure:

{
    FileTransform: 'filter_str',
    ...,
}

Note

Transforms will be applied in order of the keys in the dictionary

__init__(transforms)[source]#
Parameters:

transforms (dict[FileTransform, str]) – a dictionary of transforms, where the key is the instance of FileTransform and the value is the keyword that the transform will be applied to

Raises:

ValueError – if the keyword is not a string

__call__(src, dst, *args, **kwds)[source]#

Applies transforms in order

Parameters:

  • src (str) – source file path to be processed

  • dst (str) – destination to save the processed file

Return type:

Any

class cvfe.data.preprocessor.FileTransform[source]#

Bases: object

A base class for applying transforms as a composable object over files.

Any behavior over the files itself (not the content of files) must extend this class.

__init__()[source]#
__call__(src, dst, *args, **kwds)[source]#
Parameters:

  • src (str) – source file to be processed

  • dst (str) – the pass that the processed file to be saved

Return type:

Any

class cvfe.data.preprocessor.CopyFile(mode)[source]#

Bases: FileTransform

Only copies a file, a wrapper around shutil’s copying methods

Default is set to ‘cf’, i.e. shutil.copyfile. For more info see shutil documentation.

Reference:

  1. https://stackoverflow.com/a/30359308/18971263

__init__(mode)[source]#
__call__(src, dst, *args, **kwds)[source]#
Parameters:

  • src (str) – source file to be processed

  • dst (str) – the pass that the processed file to be saved

Return type:

Any

__check_mode(mode)#

Checks copying mode to be available in shutil

Parameters:

mode (str) – copying mode in shutil, one of ‘c’, ‘cf’, ‘c2’

class cvfe.data.preprocessor.MakeContentCopyProtectedMachineReadable[source]#

Bases: FileTransform

Reads a ‘content-copy’ protected PDF and removes this restriction

Removing the protection is done by saving a “printed” version of via pikepdf

References

  1. https://www.reddit.com/r/Python/comments/t32z2o/simple_code_to_unlock_all_readonly_pdfs_in/

  2. https://pikepdf.readthedocs.io/en/latest/

__init__()[source]#
__call__(src, dst, *args, **kwds)[source]#
Parameters:

  • src (str) – source file to be processed

  • dst (str) – destination to save the processed file

Returns:

None

Return type:

Any

Module contents#