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.
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ALACORDER beta 7.4.9.9.9.9.9.9.9
Getting Started with Alacorder
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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, open Command Prompt and enter
pip install alacorder.- To start the interface, enter
python -m alacorderorpython3 -m alacorder.
- To start the interface, enter
- On Mac, open the Terminal and enter
pip3 install alacorderthenpython3 -m alacorder.- To start the interface, enter
python3 -m alacorderorpython -m alacorder.
- To start the interface, enter
- Install Anaconda Distribution to install Alacorder if the above methods do not work, or if you would like to open and edit this interactive tutorial 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 alacorderorpip3 uninstall alacorderand then reinstall it. There may be a newer version.
- After installation, create a virtual environment, open a terminal, and then repeat these instructions. If your copy of Alacorder is corrupted, use
Alacorder should automatically download and install dependencies upon setup, but you can also install the full list of dependencies yourself with
pip:pandas,numpy,PyPDF2,openpyxl,xlrd,xlwt,build,setuptools,xarray,jupyter,numexpr,bottleneck.
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 module from your command line: Depending on your Python configuration, enter
python -m alacorderorpython3 -m alacorderto launch the command line interface. -
Import the
alacordermodule in Python: Use the import statementfrom alacorder import __main__to run the command line interface.
Alacorder can be used without writing any code, and exports to common formats like Excel (.xls), Stata (.dta), CSV (.csv), and JSON (.json).
-
Alacorder compresses case text into
picklearchives (.pkl.xz) to save storage and processing time. If you need to unpack apicklearchive without thealacmodule, use a.xzcompression tool, then read thepickleinto Python with the standard library modulepickle. -
Once installed, enter
python -m alacorderorpython3 -m alacorderto start the interface. If you are usingiPython, launch theiPythonshell and enterfrom 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 only a few lines of code.
-
Call
alac.config(in_path: str, out_path='', flags='', print_log=True, warn=False)and assign it to a variable to hold your configuration object. This tells the imported Alacorder methods where and how to input and output. Ifout_pathis left blank,alac.writemethods will print to console instead of export. -
Call
alac.writeArchive(config)to export a full text archive. It's recommended that you create a full text archive and save it as a.pklfile before making tables from your data. Full text archives can be scanned faster than PDF directories and require much less storage. Full text archives can be used just like PDF directories. -
Call
alac.writeTables(config)to export detailed case information tables. If export type is.xls, thecases,fees, andchargestables will all be exported. Otherwise, you can select which table you would like to export. -
Call
alac.writeCharges(config)to exportchargestable only. -
Call
alac.writeFees(config)to exportfeetables only.
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.xls"
# make full text archive from PDF directory
c = alac.config(pdf_directory, 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, tables)
alac.writeTables(d)
Custom Parsing with alac.write()
If you need to conduct a custom search of Alacorder records, the alac module has the tools you need to extract custom fields from case PDFs without any fuss. Try out alac.write() and alac.search() to search thousands of cases in just a few minutes.
from alacorder import alac
import pandas as pd
import re
archive = "/Users/crimson/Desktop/Tutwiler.pkl.xz"
tables = "/Users/crimson/Desktop/Tutwiler.xls"
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, tables)
alac.write(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: str, 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: str, 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: str |
Returns case number |
getName(text) -> name: str |
Returns name |
getFeeTotals(text) -> [total_row: str, tdue: str, tpaid: str, tbal: str, tdue: str] |
Return totals without parsing fee sheet |
Working with case data in Python
Out of the box, Alacorder exports to .xls, .csv, .json, .dta, .pkl.xz, and .txt. But you can use alac, pandas, and other python modules 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 module 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 expand the capability of Alacorder data. 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 libraries 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 pandas tutorials and documentation 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 jupyter notebooks.
Resources
pandascheat sheet- regex cheat sheet
- anaconda (tutorials on python data analysis)
- The Python Tutorial
© 2023 Sam Robson
1 maintainer
Metadata
1 maintainer
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