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pyphoon


Developed as part of the Digital Typhoon project from Kitamoto-sensei. Provides a set of tools to enable easy and pythonic interaction the Digital Typhoon dataset.

Full documentation here.

Contents

Section Description
pyphoon Library for Digital Typhoon project
docs Library documentation files
notebooks Basic code examples. (will be removed in near future)
scripts Some example scripts using library tools
sampledata Sample data from Digital Typhoon, used in
experiments Data and files related to specific applications of pyphoon library (includes notebooks).

Installation

Refer to the instructions here.

Getting started

Load and visualize sequence

# Load a sequence
from pyphoon.io.h5 import read_source_images
from pyphoon.io.utils import get_image_ids
images = read_source_images('sampledata/datasets/image/200717')
images_ids = get_image_ids('sampledata/datasets/image/200717')

# Display sequence
from pyphoon.visualise import DisplaySequence
DisplaySequence(
    images=images,
    images_ids=images_ids,
    name='200717',
    interval=100
).run()

Experiments

pyphoon was mainly conceived to assist researchers in Machine Learning/Deep Learning experiments. To this end, this repository provides examples of experiments carried by Kitamoto-lab interns:

Section Description
tcxtc Tropical cyclone vs Extratropical cyclone binary classifier.
multiclass Classification of Topical cyclone intensity in four categories.
pressure regression Regression of the centre pressure in Tropical cyclones

Note: All models have been implemented using keras.