guide.rst

User's guide

User's guide

Below is an overview of the extensions NumPy String-Indexed offers. Functionality can be categorized into: array operations, computing arrays, and formatting arrays.

The examples below build on those from Index.

Array operations

Friendly matrix objects can be operated on just like NumPy ndarray s with minimal overhead. The package contains separate implementations of most of the relevant NumPy ndarray operations, taking advantage of labels. For example:

side_by_side = fm.concatenate((image1, image2), axis='left_to_right')

An optimized alternative is to perform label-less operations, by adding "_A" (for "array") to the operation name:

side_by_side_arr = fm.concatenate_A((image1, image2), axis='left_to_right')

If it becomes important to optimize within a particular scope, it's recommended to shed labels before operating:

for image in huge_list:
    image_processor(image.A)

Computing arrays

A friendly matrix is an ideal structure for storing and retrieving the results of computations over multiple variables. The compute_ndarray() function executes computations over all values of the input arrays and stores them in a new friendly matrix ndarray instance in a single step:

'''Collect samples from a variety of normal distributions'''

import numpy as np

n_samples_list = [1, 10, 100, 1000]
mean_list = list(range(-21, 21))
var_list = [1E1, 1E0, 1E-1, 1E-2, 1E-3]

results = fm.compute_ndarray(
    ['# Samples', 'Mean', 'Variance']
    n_samples_list,
    mean_list,
    var_list,
    normal_sampling_function,
    dtype=np.float32)

# friendly matrices can be sliced using dicts
print(results({
    '# Samples': 100,
    'Mean': 0,
    'Variance': 1,
}))

Formatting arrays

The formatted() function displays a friendly matrix as a nested list. This is useful for displaying the labels and values of smaller arrays or slice results:

mean_0_results = results({
    '# Samples': (1, 1000),
    'Mean': 0,
    'Variance': (10, 1, 0.1),
})
formatted = fm.formatted(
    mean_0_results,
    formatter=lambda n: round(n, 1))

print(formatted)

'''
Example output:

# Samples = 1:
    Variance = 10:
        2.2
    Variance = 1:
        -0.9
    Variance = 0.1:
        0.1
# Samples = 1000:
    Variance = 10:
        -0.2
    Variance = 1:
        -0.0
    Variance = 0.1:
        0.0
'''