This version of code is refactored by Vedant Mathur
TAR Software Package Repository
Code combining preprocessing, data collection, training and inference to generate automated disaster reports.
Key Files
tar_main.py- File that consolidates relevant functions to produce a report- date2template* - Files that do different collectiong/processing of USGIS data to be added to the briefings
classifiers.py- Calls classifiers (regression, SVN, GAN, CNN) and runs a majority vote to determine the final classification for sentences according to 4 categories (buildings, infrastructure, resilience, other)resilience_curve.py- Generates resilience curves, and calculates t0 and t1 (to calculate recovery time for disaster)config.ini- Set of parameters to control briefing generation- data - Folder containing log of earthquakes, tweets and news articles
Usage
Generating a report
To generate a report, run
python -m tear
This will iterate through earthquakes listed in the earthquake log and output a report to the "reports" directory.
Generating a resilience curve
To do this, call the generateResilience function in resilience_curve.py. It takes the following parameters -
ruptureTime- Reference time to when the earthquake happened (e.g. 2021-02-24 02:05:59)twitterFile- CSV with tweets for earthquakekeywords- keywords to filter tweets by
For example:
generateResilience("2021-02-24 02:05:59", "data/tweets/ArgentinaTweets.csv", ["electricity", "lights"])
1 maintainer
Metadata
1 maintainer
Release files for tear 0.0.2
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