nsmpy 1.0.3

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NsmPy Version 1.0.3: What's new and how do I use it?

NsmPy is a big little python library designed to make scientific, mathematical, and numerical equations easier to grasp. This module, as of this version (release 1.0.3) only contains one datatype - matricies. (more will be comming soon hopefully.)

Installation

NsmPy is availiable via the pip command. However, there is one other library needed - NumPy. I highly recommend using NumPy version 1.16.1 because it is the most stable. Installing this version will be covered in the installation steps.

Install with:

(mac/linux)
#$ sudo pip uninstall numpy
#$ sudo pip install numpy==1.16.1
#$ sudo pip install nsmpy==1.0.2
(PC)
#C:> pip uninstall numpy
#C:> pip install numpy==1.16.1
#C:> pip install nsmpy==1.0.2

What's new?

This update does not add much to the library other than bug fixes. datatype.

Using the library

The NsmPy library offers lots of different features for the matrix datatype - you can run callbacks on the elements, you can reshape matricies, and you can even treat the class like a list!

First off, how do you create a matrix? It's as simple as typing

from nsmpy import matrix
x = matrix([1,2,3,4],shape=(2,2))

Just like that we just created a 2x2 matrix! It's that simple! We can also do matrix multiplication - a common mathematical practice when working with matricies. We can do this with the following code:

from nsmpy import matrix
x = matrix([2,3,4,5],shape=(2,2))
y = matrix([3,4,5,6],shape=(2,2))

z = x.matrix_mult(y) # matrix_mult(n) -> matrix()

That's it! When you actually compare this library to the NumPy library, you will see that it is a much better option for matricies. Here is an example of defining matricies in both modules. NumPy:

mat = np.array([[2,2],
                [2,2]])

NsmPy:

mat = matrix([2,2,2,2],(2,2))

As you can see, the NumPy version, while being excellent, just doesn't look as good as the NsmPy version. It also is less efficient due to more typing being needed.

Method documentation

matrix(array, shape)                  -> matrix
matrix.reshape(new_shape)             -> matrix{new shape}
matrix.flatten()                      -> matrix{new shape}
matrix.add_element(element,new_shape) -> matrix{el+1, new shape}
matrix.get_type()                     -> dtype
matrix.matrix_mult(b_mat,a_mat=None)  -> matrix{new shape, new values}
matrix.as_numpy_array()               -> np.array(matrix)
matrix.multiply(mat_b,mat_a=None)     -> matrix{new values, a_matrix shape}
matrix.convert_to_list()              -> list{matrix elements}
matrix.inverse()                      -> matrix{inverse elements}
matrix.generate_identity(r,c,start)   -> matrix{identity matrix}
matrix.run_function_on_elements(func) -> matrix{other elements}
matrix.convert_type(new_type)         -> matrix{new type}
matrix.shape                          -> shape
matrix.as_array                       -> array-type matrix
matrix.temp                           -> nothing to see here
matrix.type                           -> the current matrix type.

Have fun!