formfyxer 0.0.7

A tool for learning about and pre-processing pdf forms.

FormFyxer

A Python package with a collection of functions for learning about and pre-processing pdf forms and associated form fields. This processing is done with an eye towards interoperability with the Suffolk LIT Lab's Document Assembly Line Project.

Installation and updating

Use the package manager pip to install FormFyxer. Rerun this command to check for and install updates directly from GitHub.

pip install git+https://github.com/SuffolkLITLab/FormFyxer

Functions

• reCase
• regex_norm_field
• reformat_field
• normalize_name
• vectorize
• spot
• parse_form
• cluster_screens
• set_fields
• rename_pdf_fields
• swap_pdf_page
• get_possible_fields
• auto_add_fields

formfyxer.reCase(text)

Reformats snake_case, camelCase, and similarly-formatted text into individual words.

Parameters:

• text : str

Returns:

A string where words combined by cases like snake_case are split back into individual words.

Example:

>>> import formfyxer
>>> formfyxer.reCase("Reformat snake_case, camelCase, and similarly-formatted text into individual words.")
'Reformat snake case, camel Case, and similarly formatted text into individual words.'

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formfyxer.regex_norm_field(text)

Given an auto-generated field name (e.g., those applied by a PDF editor's find form feilds function), this function uses regular expressions to replace common auto-generated field names for those found in our standard field names.

Parameters:

• text : str A string of words, such as that found in an auto-generated field name (e.g., those applied by a PDF editor's find form feilds function).

Returns:

Either the original string/field name, or if a standard field name is found, the standard field name.

Example:

>>> import formfyxer
>>> formfyxer.regex_norm_field("your name")
'users1_name'

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formfyxer.reformat_field(text,max_length=30)

Given a string of words, this function provides a summary of the string's semantic content by boiling it down to a few words. It then reformats these keywords into snake_case.

Parameters:

• text : str A string of words.
• max_length : int An integer setting the maximum length of your field name.

Returns:

A snake_case string summarizing the input sentence.

Example:

>>> import formfyxer
>>> reformat_field("this is a variable where you fill out your name")
'variable_fill_name'

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formfyxer.normalize_name(jur,group,n,per,last_field,this_field)

This function will use the above functions to produce a field name conforming to the format of our standard field names. It does this first by applying reCase() to the text of a field. It then applies regex_norm_field(). If a standard field name is NOT found, it makes use of a machine learning model we have trained to classify the text as one of our standard field names. If the model is confident in a classification, it changes the text to that field name. If it us uncertian, it applies reformat_field(). The end result is that you can feed in a field name and receive output that has been converted into either one of our standard fields or a string of similar formatting.

Parameters:

• jur : str The two-letter US postal jurisdiction code (e.g., MA).
• group : str Eventually this should be a LIST issue, but right now it can be anything.
• n : int The count of what number this field this is on its form (e.g., if it's the first field n=1)
• per : float {0-1) n divided by the total number of fields on this form. That is, the percentage of the form that completion of this field will result in
• last_field : str The normalized field name of the field that preceeded this one.
• this_field : str The un-normalized (raw) field name of the field you are looking to normalize.

Returns:

object

Example:

>>> import formfyxer
>>> normalize_name("UT",None,2,0.3,"null","Case Number")
('*docket_number', 1.0)

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formfyxer.spot(text,normalize=0)

A simple wrapper for Spacy's word2vec vectorization of a string.

Parameters:

• text : str Text.
• normalize : 0 or 1, default 1 If set to 1 vector will be normalized.

Returns:

A 300d vector for the text provided, using word2vec.

Example:

>>> import formfyxer
>>> 
>>> formfyxer.vectorize("my landlord kicked me out", normalize=1)
array([-5.26231120e-04,  2.24983986e-03, -8.35795340e-03,  4.02475413e-03,
        3.44079169e-03, -3.62503832e-03,  4.91300346e-04, -1.02481993e-02,
       -8.75018570e-05,  5.77801012e-02, -8.04772768e-03,  1.93668896e-03,
        1.61031034e-03, -4.88554112e-03, -7.56827288e-03, -3.22198853e-04,
        1.72684901e-03,  1.09334913e-02, -2.45365698e-03,  2.60785779e-03,
        3.31795751e-03, -1.82501888e-03, -5.17577020e-04,  9.05366796e-04,
       -1.88947119e-03, -1.41778216e-03, -2.19670966e-03, -2.33783632e-03,
        1.00638480e-03, -6.26632172e-03, -5.01368841e-04,  7.08620072e-03,
       -3.12600359e-03,  6.44426321e-03,  2.27485859e-04,  8.98271860e-04,
       -2.61456956e-03, -6.52393141e-04, -1.24109763e-03, -3.89325497e-03,
       -3.06367232e-03, -1.28471724e-03, -2.69054515e-03, -3.91209299e-03,
        1.32449560e-03,  4.50141250e-03, -1.75082921e-03, -3.78464401e-03,
        1.40550716e-03,  2.89970543e-03, -2.89665523e-03,  2.99134455e-03,
       -4.17377978e-03,  2.69617527e-03, -1.59275456e-04, -7.83068891e-04,
       -6.36623462e-04, -2.48208915e-03, -1.25590225e-03, -2.50187579e-04,
       -2.05267708e-03, -2.68196150e-03, -4.38043172e-05,  4.18123381e-03,
        6.31226077e-03, -3.65403513e-03,  2.65449648e-03,  2.05167042e-04,
       -1.30922426e-03,  8.00734152e-03,  1.38796774e-03,  1.76862839e-03,
        7.66475223e-03,  8.80032085e-04,  2.59798026e-03,  3.22615966e-03,
        9.56395168e-03, -4.76434876e-03, -4.72719918e-03,  6.81382797e-03,
       -4.05296658e-03,  8.91728523e-03, -4.68128831e-03, -5.16060328e-03,
        1.66344554e-03,  2.91576202e-03,  7.22084550e-03, -5.06330498e-03,
        1.33031214e-02,  4.79664028e-03, -1.07973688e-03,  3.88623763e-05,
       -1.23686194e-04,  3.57764797e-04,  2.40055549e-03,  1.39446255e-03,
        7.12260341e-03, -8.41163896e-04, -5.16814871e-04,  4.72550955e-03,
        1.33025948e-03, -4.51154774e-03,  8.67997034e-05, -5.33963124e-03,
       -2.16460094e-03, -1.51862293e-02,  5.61281296e-03,  4.78538005e-03,
       -4.88630835e-03, -2.68976954e-03,  6.91397406e-04, -7.68237538e-03,
        6.35172810e-03,  3.49582726e-04,  2.25623079e-03,  7.04172971e-04,
        3.25000592e-03, -1.08145548e-03,  5.15488738e-03, -2.57177091e-04,
        1.76189272e-03, -1.11836531e-04, -2.16169944e-04, -1.91144984e-03,
        3.43246164e-03,  3.58958300e-03, -5.56801994e-03,  1.46526319e-04,
       -3.65503599e-03,  1.21198749e-03, -4.71577666e-03,  1.32155107e-03,
       -7.55368584e-03,  4.21072481e-04,  3.84473030e-03,  7.37876031e-04,
        2.51003285e-03,  1.13599304e-02, -2.91033290e-03, -2.17953879e-03,
       -4.48384314e-02, -2.55559416e-03,  1.27762248e-03,  3.16679245e-04,
        1.73199350e-04,  1.50888037e-03, -4.79663957e-03,  5.17604744e-03,
       -3.98579436e-03, -1.39384994e-03, -1.60889822e-03,  6.14265860e-03,
        2.33297705e-04, -3.52656403e-04,  1.12852005e-03,  1.65170200e-03,
       -1.63151391e-03, -6.32309832e-04,  1.19076047e-03, -3.06317795e-03,
       -4.79622367e-03, -3.06260930e-04,  1.65514420e-03, -1.22830785e-03,
       -3.04184669e-03, -4.55402836e-03,  3.88777805e-03,  2.00426727e-03,
        2.14710762e-03,  1.68567815e-03, -6.47409360e-03, -6.46973133e-03,
       -5.74770121e-05, -5.43416808e-03, -6.19798278e-03, -1.14207302e-04,
       -1.01290520e-03,  9.50722038e-04, -4.10705805e-03,  4.52319444e-04,
        4.62367242e-03, -5.16681711e-03, -3.19836917e-03,  2.15407870e-03,
        2.97252097e-03,  1.56973282e-03, -3.55476436e-03,  1.62631717e-03,
        2.02915202e-03,  2.61959652e-03, -3.41814925e-04,  1.05913682e-03,
       -5.67106089e-03, -2.26600230e-04,  4.77448347e-04,  6.28645666e-03,
       -1.01146419e-03, -6.35369059e-03,  1.65369797e-04, -9.55562091e-04,
       -5.87664628e-03,  1.26514872e-03, -7.70650378e-03,  1.81958423e-03,
        1.19041591e-03,  3.69227689e-03,  6.81289417e-03,  2.34441887e-04,
        4.76372670e-03, -3.22644252e-03,  7.13130209e-04, -1.34127493e-03,
        3.49982077e-03,  3.36139797e-03, -2.95230863e-03,  5.77470678e-03,
       -1.91699363e-03, -1.58495209e-03, -1.37739101e-03,  2.91823707e-03,
       -2.54773333e-03,  1.70074190e-03, -9.05261306e-04, -1.14740142e-03,
        9.50051913e-04, -4.24206736e-03,  2.38089718e-04,  2.40279602e-03,
       -1.33007275e-03, -5.04937888e-03, -3.60123558e-03,  9.58420218e-04,
        3.30443339e-03,  1.58153860e-03,  4.72644728e-03, -3.60392571e-03,
       -3.44693427e-04, -2.36716859e-03,  4.83650924e-03,  2.43589561e-03,
        3.00392594e-03,  3.94615047e-03,  1.71704478e-03,  5.89554031e-03,
        2.18071977e-03, -6.37284036e-03,  5.64066123e-03, -4.59186528e-03,
       -1.58649190e-03,  6.56146175e-03, -1.04672343e-03, -6.28604576e-03,
       -7.47575222e-04,  2.12077705e-03,  5.26619347e-03,  2.89095824e-03,
       -1.62623299e-03, -5.51077501e-03, -9.67431517e-05,  3.44178292e-03,
       -6.74005028e-03,  3.24305482e-03,  1.03411332e-03, -1.69642029e-03,
        5.81055945e-03, -2.27511233e-03, -1.24854863e-03,  4.16209955e-04,
       -9.86948252e-04, -3.47459984e-03,  8.35964266e-03,  1.90435018e-03,
       -6.13537569e-04, -4.42874018e-03, -2.34987644e-03, -6.47533826e-04,
       -5.74400109e-03, -3.98190719e-03,  1.23454575e-03,  4.60058269e-03,
        1.13744318e-03, -1.43143558e-03, -3.46458480e-03,  1.70765680e-03,
       -5.39483022e-03, -2.10772697e-03,  9.33664766e-03, -2.98427860e-03,
       -6.75659472e-04, -3.30385404e-04, -3.49518099e-03,  1.54804617e-04,
        2.41326119e-03,  4.88547941e-03,  2.49007910e-03, -2.13324702e-04,
        5.94240817e-03,  4.13455749e-03,  1.64867480e-03, -1.49173268e-03])

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formfyxer.spot(text,lower=0.25,pred=0.5,upper=0.6,verbose=0)

A simple wrapper for the LIT Lab's NLP issue spotter Spot. In order to use this feature you must edit the spot_token.txt file found in this package to contain your API token. You can sign up for an account and get your token on the Spot website.

Given a string, this function will return a list of LIST entities/issues found in the text. Items are filtered by estimates of how likely they are to be present. The values dictating this filtering are controlled by the optional lower, pred, and upper parameters. These refer to the lower bound of the predicted likelihood that an entity is present, the predicted likelihood it is present, and the upper-bound of this prediction respectively.

Parameters:

• text : str Text describing some fact pattern.
• lower : float between 0 and 1, default 0.25 Defines the cutoff for the lower bound of a prediction (lower) necessary to trigger inclusion in the results. That is, the lower bound of a prediction must exceed lower for it to appear in the results.
• pred : float between 0 and 1, default 0.5 Defines the cutoff for the prediction (pred) necessary to trigger inclusion in the results. That is, the prediction must exceed pred for it to appear in the results.
• upper : float between 0 and 1, default 0.6 Defines the cutoff for the upper bound of a prediction (upper) necessary to trigger inclusion in the results. That is, the upper bound of a prediction must exceed upper for it to appear in the results.
• verbose : 0 or 1, default 0 If set to 0 will return only a list of LIST IDs. If set to 1, will return a full set of Spot results.

Returns:

A list of LIST entities/issues found in the text.

Example:

>>> import formfyxer
>>> formfyxer.spot("my landlord kicked me out")
['HO-02-00-00-00', 'HO-00-00-00-00', 'HO-05-00-00-00', 'HO-06-00-00-00']

>>> formfyxer.spot("my landlord kicked me out", verbose=1)
{'build': 9,
 'query-id': '1efa5a098bc24f868684339f638ab7eb',
 'text': 'my landlord kicked me out',
 'save-text': 0,
 'cutoff-lower': 0.25,
 'cutoff-pred': 0.5,
 'cutoff-upper': 0.6,
 'labels': [{'id': 'HO-00-00-00-00',
   'name': 'Housing',
   'lower': 0.6614134886446631,
   'pred': 0.7022160833303629,
   'upper': 0.7208275781222152,
   'children': [{'id': 'HO-02-00-00-00',
     'name': 'Eviction from a home',
     'lower': 0.4048013980740931,
     'pred': 0.5571460102525152,
     'upper': 0.6989976788434928},
    {'id': 'HO-05-00-00-00',
     'name': 'Problems with living conditions',
     'lower': 0.3446066253503793,
     'pred': 0.5070074487913626,
     'upper': 0.6326627767849852},
    {'id': 'HO-06-00-00-00',
     'name': 'Renting or leasing a home',
     'lower': 0.6799417713794678,
     'pred': 0.8984004824420323,
     'upper': 0.9210222500232965,
     'children': [{'id': 'HO-02-00-00-00',
       'name': 'Eviction from a home',
       'lower': 0.4048013980740931,
       'pred': 0.5571460102525152,
       'upper': 0.6989976788434928}]}]}]}

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formfyxer.parse_form(fileloc,title=None,jur=None,cat=None,normalize=1,use_spot=0,rewrite=0)

Read in a pdf with pre-existing form fields, pull out basic stats, attempt to normalize its field names, and re-write the file with the new fields (if rewrite=1).

Parameters:

• fileloc : the location of the pdf file to be processed.
• title : str, default None The title of the form. If set to None the parser will make a best guess.
• jur : str, default None The two-letter US postal jurisdiction code (e.g., MA).
• cat: list, default None Eventually this should be a LIST issue, but right now it can be anything.
• normalize : {0 or 1}, default 1 O will not attempt to normalize the form fields. 1 will.
• use_spot : {0 or 1}, default 0 1 will use spot to guess at LIST issues covered by this form. 0 will skip this.
• rewrite : {0 or 1}, rewrite 0 1 will attempt to write over the original file with the normalized fields (assuming normalize set to 1). O will leave the original file as is.

Returns:

Object containing a set of stats for the form. See below

Example:

>>> import formfyxer
>>> stats = formfyxer.parse_form("sample.pdf",title=None,jur="UT",cat=None,normalize=0,use_spot=0,rewrite=0)
>>> stats
{'title': 'Waiver of',
 'category': None,
 'pages': 2,
 'reading grade level': 7.5,
 'list': [],
 'avg fields per page': 0.0,
 'fields': [],
 'fields_conf': [],
 'fields_old': [],
 'text': 'Waiver of . Rights. . Approved Board of District Court Judges . December 17, 2010. . Revised . November 26. , 2019. . Page . 1. . of . 2. . . . . Name. . . . . Address. . . . . City, State, Zip. . . . . Phone. . . . Check your email. . You will receive information and . documents at this email address. . . . Email. . In the District Court of Utah. . Judicial District Count. y. . Court Address . . In the Matter of the Adoption of. . . . . (. . ) . . Waiver . of Rights. . . . . . Case Number. . . . . . Jud. ge. . . . . . Commissioner. . Do not sign this document without reading it. . Do not sign it unless everything . stated is true and correct. . If you have any questions, . talk with. . an attorney. . . . . . You have the right to be notified of hearings and . to be served with papers in this . matter. You have the right to intervene and oppose the adoption. . By signing this . document you are giving up . these. . rights. . . . . . If you . want to waive your rights. , complete this form, sign it, . and . return. . it . to the . Petitioner. . . . . . If yo. u . want to intervene and . oppose the adoption, . file a motion to intervene . with . this. . court. . within 30 days after the . Notice of Petition to Adopt. . was served on you. . . 1. . . . I make this statement free from . duress. . . . Waiver of . Rights. . Approved Board of District Court Judges . December 17, 2010. . Revised . November 26. , 2019. . Page . 2. . of . 2. . . 2. . . . I am the . adoptee. . . [ ] . Guardian. . without the right. . to consent to the adoption. . [ ] . Custodian. . [ ] . S. p. ouse. . 3. . . . I understand that. : . . . . I have the right to be notified of hearings and to be served with papers in this . matter. . . . . I have the right to intervene and oppose the adoption. . . . . By signing this document . I am. . givin. g up . these. . rights. . . . 4. . . . Understanding all of this, . I . voluntarily . waive my right to . be notified of hearings . and served with papers in this matter. , and. . I voluntarily waive my right to . intervene in this matter. . . Do not sign this document without reading it. . Do n. ot sign it unless everything . stated is true and correct. . If you have any questions, . talk with. . an attorney. . . . . . I declare under . criminal . penalty . under the law of Utah. . that everything stated . in this document is true. . . . Signed at . (city, and state or country) . . . . Sign. atu. . . . Date. . Printed Name'}

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formfyxer.cluster_screens(fields,damping=0.7)

This function will take a list of snake_case field names and group them by semantic similarity.

Parameters:

• files : list A list of snake_case field names.
• damping : float A number between 0.5 and 1 controlling how similar members of a group need to be.

Returns:

An object grouping together similar field names.

Example:

>>> import formfyxer
>>> fields= [
        "users1_name",
        "users1_birthdate",
        "users1_address_line_one",
        "users1_address_line_two",
        "users1_address_city",
        "users1_address_state",
        "users1_address_zip",
        "users1_phone_number",
        "users1_email",
        "plantiffs1_name",
        "defendants1_name",
        "petitioners1_name",
        "respondents1_name",
        "docket_number",
        "trial_court_county",
        "users1_signature",
        "signature_date"
        ]
>>> cluster_screens(fields,damping=0.7)
{'screen_0': ['users1_name',
  'users1_birthdate',
  'users1_address_line_one',
  'users1_address_line_two',
  'users1_address_city',
  'users1_address_state',
  'users1_address_zip',
  'users1_phone_number',
  'users1_email',
  'users1_signature'],
 'screen_1': ['plantiffs1_name',
  'defendants1_name',
  'petitioners1_name',
  'respondents1_name'],
 'screen_2': ['docket_number'],
 'screen_3': ['trial_court_county'],
 'screen_4': ['signature_date']}

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formfyxer.set_fields

This function adds fields to an input PDF, writing the new PDF to a new file.

Parameters:

• in_file: Union[str, Path, BinaryIO]: the input file name or path of the PDF that we're adding the fields to
• out_file: Union[str, Path, BinaryIO]: the output file name or path where the new version of in_file will be written. Doesn't need to exist.
• fields_per_page: Iterable[Iterable[FormField]]: for each page, a series of fields that should be added to that page.
• overwrite:bool: if the input file already has some fields (AcroForm fields specifically) and this value is true, it will erase those existing fields and just add fields_per_page. If not true and the input file has fields, we won't generate a PDF, since we don't currently have a way to merge AcroForm fields from different PDFs at the moment.

Returns:

Nothing

Example:

set_fields('no_fields.pdf', 'four_fields_on_second_page.pdf', 
      [
        [],  # nothing on the first page
        [ # Second page
          FormField('new_field', 'text', 110, 105, configs={'width': 200, 'height': 30}),
          # Choice needs value to be one of the possible options, and options to be a list of strings or tuples
          FormField('new_choices', 'choice', 110, 400, configs={'value': 'Option 1', 'options': ['Option 1', 'Option 2']}),
          # Radios need to have the same name, with different values
          FormField('new_radio1', 'radio', 110, 600, configs={'value': 'option a'}),
          FormField('new_radio1', 'radio', 110, 500, configs={'value': 'option b'})
        ] 
      ]
)

formfyxer.rename_pdf_fields

Given a dictionary that maps existing PDF field names to the corresponding desired names, this function renames the PDF fields from an input file.

Parameters:

• in_file: str: the file name of an input file
• out_file: str: the output file name. Doesn't need to exist, and will be overwritten if it does exist.
• mapping: Mapping: a python dict from a current field name to the desired name

Returns:

Nothing.

Example:

rename_pdf_fields('current.pdf', 'new_field_names.pdf', 
    {'abc123': 'user1_name', 'abc124': 'user1_address_city'})

formfyxer.swap_pdf_page

Copies the AcroForm fields from one PDF to another PDF (without AcroForm fields). Useful for getting started with an updated PDF form, where the old fields are pretty close to where they should go on the new document.

Parameters:

• source_pdf: Union[str, Path, Pdf]: a file name or path to a PDF that has AcroForm fields
• destination_pdf: Union[str, Path, Pdf]: a file name or path to a PDF without AcroForm fields. Existing fields will be removed.
• source_offset: int: the starting page that fields will be copied from. Defaults to 0
• destination_offset: int: the start page that fields will be copied to. Defaults to 0
• append_annotations: bool: controls whether formfyxer will try to append form fields instead of overwriting. Defaults to false; when enabled may lead to undefined behavior

Returns:

A pikepdf.Pdf with the new fields. If blank_pdf was a pikepdf.Pdf object, the same object is returned

Example:

new_pdf_with_fields = swap_pdf_page(source_pdf="old_pdf.pdf", destination_pdf="new_pdf_with_no_fields.pdf")
new_pdf_with_fields.save("new_pdf_with_fields.pdf")

formfyxer.get_possible_fields

Given an input PDF, runs a series of heuristics to predict where there might be places for user enterable information (i.e. PDF fields), and returns those predictions

Parameters:

in_pdf_file: Union[str, Path, bytes]: the input PDF

Returns:

For each page in the input PDF, a list of predicted form fields

Example:

fields = get_possible_fields('no_fields.pdf')
print(fields)
[[Type: FieldType.TEXT, Name: name, User name: , X: 67.68, Y: 666.0, Configs: {'fieldFlags': 'doNotScroll', 'width': 239.4, 'height': 16}, Type: FieldType.TEXT, Name: address, User name: , X: 67.68, Y: 638.28, Configs: {'fieldFlags': 'doNotScroll', 'width': 239.4, 'height': 16}, Type: FieldType.TEXT, Name: city__state__zip, User name: , X: 67.67999999999999, Y: 610.5600000000001, Configs: {'fieldFlags': 'doNotScroll', 'width': 239.4, 'height': 16}, Type: FieldType.TEXT, Name: phone, User name: , X: 67.67999999999999, Y: 582.84, Configs: {'fieldFlags': 'doNotScroll', 'width': 239.4, 'height': 16}, Type: FieldType.TEXT, Name: email, User name: , X: 67.67999999999999, Y: 552.6, Configs: {'fieldFlags': 'doNotScroll', 'width': 239.4, 'height': 16}, Type: FieldType.TEXT, Name: email, User name: , X: 62.28, Y: 536.76, Configs: {'fieldFlags': 'doNotScroll', 'width': 479.16, 'height': 16}, Type: FieldType.TEXT, Name: in_the_district_justice_court_of_utah, User name: , X: 304.56000000000006, Y: 481.68, Configs: {'fieldFlags': 'doNotScroll', 'width': 125.64000000000001, 'height': 16}, Type: FieldType.TEXT, Name: judicial_district_county, User name: , X: 125.64000000000001, Y: 481.68, Configs: {'fieldFlags': 'doNotScroll', 'width': 78.84, 'height': 16}, Type: FieldType.TEXT, Name: court_address, User name: , X: 161.28000000000003, Y: 453.59999999999997, Configs: {'fieldFlags': 'doNotScroll', 'width': 374.40000000000003, 'height': 16}, Type: FieldType.TEXT, Name: , User name: , X: 325.08, Y: 352.43999999999994, Configs: {'fieldFlags': 'doNotScroll', 'width': 211.32, 'height': 16}, Type: FieldType.TEXT, Name: , User name: , X: 67.32000000000001, Y: 348.84, Configs: {'fieldFlags': 'doNotScroll', 'width': 234.35999999999999, 'height': 16}, Type: FieldType.TEXT, Name: judge, User name: , X: 325.08, Y: 312.84, Configs: {'fieldFlags': 'doNotScroll', 'width': 211.32, 'height': 16}, Type: FieldType.TEXT, Name: respondent_name_and_address, User name: , X: 62.28, Y: 253.44, Configs: {'fieldFlags': 'doNotScroll', 'width': 479.16, 'height': 16}, Type: FieldType.TEXT, Name: court_appoint_name__as_your_guardian_to, User name: , X: 157.32, Y: 156.24, Configs: {'fieldFlags': 'doNotScroll', 'width': 203.4, 'height': 16}, Type: FieldType.TEXT, Name: page_1_of_4, User name: , X: 67.67999999999999, Y: 46.800000000000004, Configs: {'fieldFlags': 'doNotScroll', 'width': 478.08, 'height': 16}]]

formfyxer.auto_add_fields

This function uses get_possible_fields and set_fields to automatically add new detected fields to an input PDF.

Parameters:

• in_pdf_file: Union[str, Path]: the input file name or path of the PDF where we'll try to find possible fields.
• out_pdf_file: Union[str, Path]: the output file name or path of the PDF where a new version of in_pdf_file will be stored, with the new fields. Doesn't need to exist, but if a file does exist at that file name, it will be overwritten.

Returns:

Nothing.

Example:

auto_add_fields('no_fields.pdf', 'newly_add_fields.pdf')

License

MIT