save/load IFCB class scores

class_scores.py

import h5py as h5

import numpy as np
from scipy.io import savemat

def save_class_scores(path, bin_id, scores, roi_numbers, class_labels):
    assert scores.shape[0] == len(roi_numbers), 'wrong number of ROI numbers'
    assert scores.shape[1] == len(class_labels), 'wrong number of class labels'
    with h5.File(path,'w') as f:
        ds = f.create_dataset('scores', data=scores, compression='gzip', dtype='f4')
        ds.attrs['bin_id'] = bin_id
        ds.attrs['class_labels'] = [l.encode('ascii') for l in class_labels]
        f.create_dataset('roi_numbers', data=roi_numbers, compression='gzip', dtype='i2')

def save_class_scores_v1(out_path, bin_id, scores, roi_numbers, class_labels):
    d = {
        'version': 2,
        'roinum': roi_numbers,
        'TBscores': scores.astype('f4'),
        'class2useTB': np.asarray(class_labels + ['unclassified'], dtype='object')
    }
    savemat(out_path, d, do_compression=True)

def load_class_scores(path):
    with h5.File(path) as f:
        ds = f['scores']
        scores = ds[:]
        bin_id = ds.attrs['bin_id']
        class_labels = [l.decode('ascii') for l in ds.attrs['class_labels']]
        roi_numbers = f['roi_numbers'][:]
    return bin_id, scores, roi_numbers, class_labels

load_class_scores.m

function [bin_id, scores, roi_numbers, class_labels] = load_class_scores(path)

scores = h5read(path,'/scores')';

bin_id = h5readatt(path,'/scores','bin_id');
roi_numbers = h5read(path,'/roi_numbers');
class_labels = h5readatt(path,'/scores','class_labels');

end