⚠️ This library is no longer maintained. Please refer to the updated version: at https://github.com/kitamoto-lab/pyphoon2

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.

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.

Contents

Installation

Getting started

Load and visualize sequence

Experiments