Getting Started

Installation

immlib is available on PyPI and can be installed using pip:

$ pip install immlib

Using immlib

The immlib library is intended to be used in writing APIs (i.e., in your scientific libraries and tooling) and in a REPL or notebook interface to perform occasional utility work. Once you have installed immlib, you should be able to import and use it freely.

Simple Example

import immlib as il
import torch

# Create a calculation that computes a normalized vector `u` and a length
# `xlen` given an unnormalized vector `x`.
@il.tensor_args(keep_arrays=True)
@il.calc('u', 'xlen')
def normalize_vector(x):
    print("Normalizing vector...")
    xlen = torch.sqrt(torch.sum(x**2))
    u = x / xlen
    return (u, xlen)

# Create another calculation that finds the signed distance between a point
# `y` and the vector `x`, as well as the point of intersection.
@il.tensor_args(keep_arrays=True)
@il.calc('distance', 'nearest_point')
def point_vec_intersection(u, y):
    print('Calculating distance...')
    d = torch.dot(u, y)
    return (d, u*d)

# Declare the plan by putting together all the calculations:
distance_plan = il.plan(
    normalize_step=normalize_vector,
    calculate_step=point_vec_intersection)
                
# Make a plandict of the results:
pd = distance_plan(x=[0.0, -10.0], y=[2.0, 2.0])

print('xlen:',     pd['xlen'])
print('distance:', pd['distance'])
print('u:',        pd['u'])
print('nearest:',  pd['nearest_point'])

Normalizing vector...
xlen: 10.0
Calculating distance...
distance: -2.0
u: [ 0. -1.]
nearest: [-0.  2.]