fxy 0.5.5

Convenience imports and scientific functions.

fxy

Mnemonic imports and command fx with parameters to import libraries often used in research.

• c (CALC)

• m (MATH)

• f (F(PH)YSICS)

• s (STATISTICS)

Introduction

The people who come from tools like Maple, Matlab, Mathematica, and R, may find that Python requires a lot of mathematical imports just to start doing basic stuff. So, I tried to simplify it – simply pip install fxy, and you’ve got a command fx, that starts Python with mpmath stuff pre-imported: so, you can start using Python like a calculator right away. If you need more, like symbolics, or statistics, or machine learning, – these things are import-able with extra parameter, e.g., fx -s for isympy (shorter than typing isympy), or just by running import import shortcuts described below:

Installation

• pip install fxy to get the import shortcuts.

Usage

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

• $ fx -[c|m|f|s] - plain Python (i: “IPython off”)

Examples

In command line

• $ fx -c – imports useful for numeric math functions (mpmath)

• $ fx -m – imports useful for symbolic math functions (isympy)

• $ fx -f – imports usful for physics (scipy+)

• $ fx -s – imports usful for statistics (scikit-learn+)

• $ fx -p – imports usful for plotting (matplotlib+seaborn)

Additions:

• $ fx – calculator (equivalent to $fx -c

• $ fx -i – calculator + IPython + explicit imports.

• $ fx -ip – calculator + plotting, with IPython.

E.g.,:

• $ fx -imp - math with IPython, and plotting imports

• $ fx -isp - stats with IPython, and plotting imports

Within notebooks and Python code

NB: This package does not assume versions of the imported packages, it just performs the basic imports, assuming that those namespaces within those packages will exist for a long time to come, so it is dependencies-agnostic.

# Numeric (mpmath.*)
>>> from fxy.calc import * (394 functions)
>>> pi
<pi: 3.14159~>

# Symbolic (sympy.*)
>>> from fxy.math import * (915 functions, and "isympy" imports)
>>> f = x**4 - 4*x**3 + 4*x**2 - 2*x + 3
>>> f.subs([(x, 2), (y, 4), (z, 0)])
-1
>>> plot(f)

# Actuarial (np: numpy, pd: pandas, sm: statsmodels.api, sp: scipy, st: scipy.stats, smf: statsmodels.formula.api, statsmodels)
>>> from fxy.stats import *
>>> df = pandas.DataFrame({'x': numpy.arange(10), 'y': np.random.random(10)})
>>> df.sum()
x    45.000000
y     4.196558
dtype: float64

# Learning (sklearn.* as sklearn)
>>> 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]]

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

Suggestions

If you use some initialization commonly, we suggest adding ~/.zshrc, something like, for example:

alias f=". ~/.venv/bin/activate && fx -[something]"

This way, running something like f makes a project folder and starts Python environment with packages fx -ap (IPython + Acturial + Plotting).