Yanked

This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 81.2.26 instead.

	    ___    __                          __         
	   /   |  / /___  _________  _________/ /__  _____
	  / /| | / / __ `/ ___/ __ \/ ___/ __  / _ \/ ___/
	 / ___ |/ / /_/ / /__/ /_/ / /  / /_/ /  __/ /    
	/_/  |_/_/\__,_/\___/\____/_/   \__,_/\___/_/     

		ALACORDER beta 73

Getting Started with Alacorder

^{GitHub | PyPI | Report an issue}

Alacorder processes case detail PDFs into data tables suitable for research purposes. Alacorder also generates compressed text archives from the source PDFs to speed future data collection from the same set of cases.

Installation

Alacorder can run on most devices. If your device can run Python 3.7 or later, it can run Alacorder.

To install on Windows and Mac, open Command Prompt (Terminal) and enter pip install alacorder or pip3 install alacorder.
- To start the interface, enter python -m alacorder or python3 -m alacorder.
On Mac, open the Terminal and enter pip install alacorder or pip3 -m alacorder.
- To start the interface, enter python -m alacorder or python3 -m alacorder.
Install Anaconda Distribution to install Alacorder if the above methods do not work, or if you would like to open an interactive browser notebook equipped with Alacorder on your desktop.
- After installation, create a virtual environment, open a terminal, and then repeat these instructions. If your copy of Alacorder is corrupted, use pip uninstall alacorder or pip3 uninstall alacorder and then reinstall it. There may be a newer version available.

Alacorder should automatically download and install missing dependencies upon setup, but you can also install them yourself with pip: pandas, numpy, PyPDF2, openpyxl, xlrd, xlwt, xarray, numexpr, click, and bottleneck. Recommended dependencies: xlsxwriter, jupyter, tabulate, matplotlib.

pip uninstall -y alacorder
pip install alacorder

Using the guided interface

Once you have a Python environment up and running, you can launch the guided interface in two ways:

Import the library from your command line: Depending on your Python configuration, enter python -m alacorder or python3 -m alacorder to launch the command line interface in module mode.
Import the alacorder module in Python: Use the import statement from alacorder import __main__ to start the command line interface.

Alacorder can be used without writing any code, and exports to common formats like Excel (`.xls`, `.xlsx`), Stata (`.dta`), CSV (`.csv`), and JSON (`.json`).

Alacorder compresses case text into pickle archives (.pkl.xz) to save storage and processing time. If you need to unpack a pickle archive without importing alac, use a .xz compression tool, then read the pickle into Python with the standard library module pickle.
Once installed, enter python -m alacorder or python3 -m alacorder to start the interface. If you are using iPython, launch the iPython shell and enter from alacorder import __main__ to launch the guided interface.

from alacorder import __main__

Special Queries with `alac`

For more advanced queries, the `alac` module can extract fields and tables from case records with just a few lines of code.

Call alac.config(input_path, table_path = '', archive_path = '') and assign it to a variable to hold your configuration object. This tells the imported Alacorder methods where and how to input and output. If table_path and archive_path are left blank, alac.parse…() methods will print to console and return the DataFrame object.
Call alac.writeArchive(config) to export a full text archive. It's recommended that you create a full text archive (.pkl.xz) file before making tables from your data. Full text archives can be scanned faster than PDF directories and require less storage. Full text archives can be imported to Alacorder the same way as PDF directories.
Call alac.parseTables(config) to export detailed case information tables. If export type is .xls or .xlsx, the cases, fees, and charges tables will be exported.
Call alac.parseCharges(config) to export charges table only.
Call alac.parseFees(config) to export fee tables only.
Call alac.parseCases(config) to export case information without charge information.

import warnings
warnings.filterwarnings('ignore')

from alacorder import alac

pdf_directory = "/Users/crimson/Desktop/Tutwiler/"
archive = "/Users/crimson/Desktop/Tutwiler.pkl.xz"
tables = "/Users/crimson/Desktop/Tutwiler.xlsx"

# make full text archive from PDF directory 
c = alac.config(pdf_directory, archive_path=archive)
alac.writeArchive(c)

print("Full text archive complete. Now processing case information into tables at " + tables)

# then scan full text archive for spreadsheet
d = alac.config(archive, table_path=tables)
alac.parseTables(d)

Custom Parsing with `alac.parse()`

If you need to conduct a custom search of case records, Alacorder has the tools you need to extract custom fields from case PDFs without any fuss. Try out `alac.parse()` to search thousands of cases in seconds.

from alacorder import alac
import re

archive = "/Users/crimson/Desktop/Tutwiler.pkl.xz"
tables = "/Users/crimson/Desktop/Tutwiler.xlsx"

def findName(text):
    name = ""
    if bool(re.search(r'(?a)(VS\.|V\.{1})(.+)(Case)*', text, re.MULTILINE)) == True:
        name = re.search(r'(?a)(VS\.|V\.{1})(.+)(Case)*', text, re.MULTILINE).group(2).replace("Case Number:","").
	strip()
    else:
        if bool(re.search(r'(?:DOB)(.+)(?:Name)', text, re.MULTILINE)) == True:
            name = re.search(r'(?:DOB)(.+)(?:Name)', text, re.MULTILINE).group(1).replace(":","").replace("Case Number:","").strip()
    return name

c = alac.config(archive, table_path=tables)

alac.parse(c, findName)

Method	Description
`getPDFText(path) -> text`	Returns full text of case
`getCaseInfo(text) -> [case_number, name, alias, date_of_birth, race, sex, address, phone]`	Returns basic case details
`getFeeSheet(text, cnum = '') -> [total_amtdue, total_balance, total_d999, feecodes_w_bal, all_fee_codes, table_string, feesheet: pd.DataFrame()]`	Returns fee sheet and summary as `str` and `pd.DataFrame`
`getCharges(text, cnum = '') -> [convictions_string, disposition_charges, filing_charges, cerv_eligible_convictions, pardon_to_vote_convictions, permanently_disqualifying_convictions, conviction_count, charge_count, cerv_charge_count, pardontovote_charge_count, permanent_dq_charge_count, cerv_convictions_count, pardontovote_convictions_count, charge_codes, conviction_codes, all_charges_string, charges: pd.DataFrame()]`	Returns charges table and summary as `str`, `int`, and `pd.DataFrame`
`getCaseNumber(text) -> case_number`	Returns case number
`getName(text) -> name`	Returns name
`getFeeTotals(text) -> [total_row, tdue, tpaid, tbal, tdue]`	Return totals without parsing fee sheet

Working with case data in Python

Out of the box, Alacorder exports to `.xls`, `.xlsx`, `.csv`, `.json`, and `.dta`. But you can use `alac`, `pandas`, and other python libraries to create your own data collection workflows and design custom exports.

The snippet below prints the fee sheets from a directory of case PDFs as it reads them.

from alacorder import alac

c = alac.config("/Users/crimson/Desktop/Tutwiler/","/Users/crimson/Desktop/Tutwiler.xls")

for path in c['contents']:
    text = alac.getPDFText(path)
    cnum = alac.getCaseNumber(text)
    charges_outputs = alac.getCharges(text, cnum)
    if len(charges_outputs[0]) > 1:
        print(charges_outputs[0])

Extending Alacorder with `pandas` and other tools

Alacorder runs on pandas, a python library you can use to perform calculations, process text data, and make tables and charts. pandas can read from and write to all major data storage formats. It can connect to a wide variety of services to provide for easy export. When Alacorder table data is exported to .pkl.xz, it is stored as a pd.DataFrame and can be imported into other python modules and scripts with pd.read_pickle() like below:

import pandas as pd
contents = pd.read_pickle("/path/to/pkl")

If you would like to visualize data without exporting to Excel or another format, create a jupyter notebook and import a data visualization library like matplotlib to get started. The resources below can help you get started. jupyter is a Python kernel you can use to create interactive notebooks for data analysis and other purposes. It can be installed using pip install jupyter or pip3 install jupyter and launched using jupyter notebook. Your device may already be equipped to view .ipynb notebooks.

Resources

License
- OSI Approved :: MIT License
Operating System
- OS Independent
Programming Language
- Python :: 3

Release files for alacorder 73

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for alacorder 73
File	Size	Uploaded
alacorder-73.tar.gz	21.9 kB	Feb 16, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for alacorder 73
File	Interpreter	ABI	Platform	Reset
alacorder-73-py3-none-any.whl 18.7 kB Feb 16, 2023	Python 3	none	any	Details

Total release size: 40.6 kB

Release files / alacorder-73.tar.gz

Download URL	alacorder-73.tar.gz
Size	21.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`e60d3aa2da3a7b1a127be5f92588369665c7ff00df942b3d2cdc79ab65b27315`
BLAKE2b-256 checksum How to use checksums	`2138c73d018e02913f18f663c24bcc3a65469dd42c39751bfbbdb43aadaf1d50`
Upload date	Feb 16, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.11.1`

Release files / alacorder-73-py3-none-any.whl

Download URL	alacorder-73-py3-none-any.whl
Size	18.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`35a8e513cc2ef430e1cc888861656a148eaaf9f73f402121371aafd455d6a6ec`
BLAKE2b-256 checksum How to use checksums	`f3b0f4987b4542576af19ecbf2ae5a06a696f80b3ea2f2b7d55b6fb9fdbbe632`
Upload date	Feb 16, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.11.1`