Full documentation here.
|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).|
Refer to the instructions here.
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()
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:
|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.