A notebook that should use a module#

import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib

A copy/paste of the code from on_modules:

def vol_means(image_fname):
    img = nib.load(image_fname)
    data = img.get_fdata()
    means = []
    for i in range(data.shape[-1]):
        vol = data[..., i]
        means.append(np.mean(vol))
    return np.array(means)

def detect_outliers(some_values, n_stds=2):
    overall_mean = np.mean(some_values)
    overall_std = np.std(some_values)
    thresh = overall_std * n_stds
    is_outlier = (some_values - overall_mean) < -thresh
    return np.where(is_outlier)[0]

We apply this code to another image:

# Load the function to fetch the data file we need.
import nipraxis
# Fetch the data file.
another_data_fname = nipraxis.fetch_file('ds114_sub009_t2r1.nii')
# Show the file name of the fetched data
another_data_fname

'/home/runner/.cache/nipraxis/0.5/ds114_sub009_t2r1.nii'

more_means = vol_means(another_data_fname)
plt.plot(more_means)

[<matplotlib.lines.Line2D at 0x7f2758d59780>]
_images/15014b21ae46881fb9d7095330bbf29d5fabd45331b33c6e43c1de158b72c5da.png

Apply the code:

detect_outliers(more_means)

array([], dtype=int64)

Oh no! It didn’t work? What’s the problem?

Back to on_modules

Back again#

Now we’ve worked out a better solution:

import volmeans

more_means_again = volmeans.vol_means(another_data_fname)
volmeans.detect_outliers_fixed(more_means_again)

array([0])