fxy

Imports and command fx with parameters to import libraries often used in research to emulate CAS software, or LAB software.

Introduction

People coming from R, love that you can start quickly using it as a CALCulator,
People coming from use of CAS tools like Maple, Mathematica have isympy, that is narrowly focused,
People coming from computing LAB languages Matlab and R may find that Python requires quite a few imports just to do equivalent computing in Python.

This package fxy is a shorthand to do the imports packages to approximate these domains (CALC, CAS, and LAB) you've got a command fx, that starts Python with needed packages pre-imported: so, you can start using Python like a calculator right away.

Installation

pip install fxy to get the import shortcuts.

Use as a calculator

`` $ fx ``

(pass, -i for IPython)

Usage as imports

from fxy.calc import * for quick CALC - basic mpmath calculator
from fxy.cas import * for basic CAS software ("Symbolic") emulation
from fxy.lab import * for LAB software ("Numeric") emulation
from fxy.plot import * for plotting imports.

Usage as command

The package defines the fx command, if you just want Python with something, run:

$ fx --calc starts Python with CALC imports (basic mpmath calculator)
$ fx --cas (or -x) starts Python with CAS (Computer Algebra System) imports (to emulate Maple, Matematica,..)
$ fx --lab (or -y) starts Python with LAB (Linear AlgeBra system) imports (to emulate MATLAB, R,...)
$ fx --plot (or -p) for plotting imports

So, for example, if you want LAB imports with plotting and in IPython, then you'd:

$ fx -ip --lab

The following are usage examples.

CALC

>>> from fxy.calc import *
>>> pi
<pi: 3.14159~>
>>> mp.dps = 250
>>> print(pi)

>>> from fxy.plot import *
>>> plt.plot([1, 2, 3, 4])
>>> plt.ylabel('some numbers')
>>> plt.show()

CAS

>>> from fxy.cas import *
>>> f = x**4 - 4*x**3 + 4*x**2 - 2*x + 3
>>> f.subs([(x, 2), (y, 4), (z, 0)])
-1
>>> plot(f)
>>> plot3d(x**2-y**2)

LAB

>>> from fxy.lab import *
>>> df = pandas.DataFrame({'x': numpy.arange(10), 'y': np.random.random(10)})
>>> df.sum()
x    45.000000
y     4.196558
dtype: float64

>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> neigh = sklearn.neighbors.KNeighborsClassifier(n_neighbors=3)
>>> neigh.fit(X, y)
>>> print(neigh.predict([[1.1]]))
[0]
>>> print(neigh.predict_proba([[0.9]]))
[[0.66666667 0.33333333]]

Suggestions

If you envy R users being able to start their 'calculator' with just one key, add something like the below to your ~/.zshrc:

function F() {
    . ~/.venv/bin/activate
    fx "$@"
}

Aliasing fx as F command.

wefindx/fxy

fxy