fxy 0.3.7

Convenience imports and scientific functions.

fxy

Just a convenience imports for scientific functions and packages for calculation.

• pip3 install fxy to get the import shortcuts.

• pip3 install fxy[main] to install all libraries except xgboost,

• pip3 install fxy[all] (slow) to install all libraries for which the shortcuts exist.

If you are in existing environment of some kind, just do, to import:

• from fxy.n import *, if you need mpmath and plotting.

• from fxy.s import *, if you need isympy imports.

• from fxy.a import *, if you need numpy, pandas, xarray, scipy, statsmodels and matplotlib, seaborn.

• from fxy.p import *, if you need matplotlib and seaborn.

• from fxy.l import *, if you need sklearn.* as sklearn and xgboost as xgb.

Usage

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

• $ fx -[n|s|a|p|l] - with IPython

• $ fx -b[n|s|a|p|l] - with BPython

Examples

• fx – IPython with n(mpmath) and plotting included.

• fx -s – IPython with n(mpmath) + s(isympy) on top.

• fx -bs– BPython with n(mpmath) + s(isympy) on top.

• fx -ap– IPython with n9`mpmath <https://github.com/esamattis/slimux>`__) + a(numpy, pandas, xarray, scipy, statsmodels), p(matplotlib, seaborn)

• fx -bap– BPython with n(mpmath) + a(numpy, pandas, xarray, scipy, statsmodels), p(matplotlib, seaborn)

• fx -sap – IPython with n(mpmath) + s(isympy) + a(numpy, pandas, xarray, scipy, statsmodels), p(matplotlib, seaborn)

• fx -salp – IPython with n(mpmath) + s(isympy) + a(numpy, pandas, xarray, scipy, statsmodels), l(sklearn.* as sklearn, xgboost as xgb), p(matplotlib, seaborn)

If you are using vim with tmux with slimux, suggest adding ~/.zshrc:

fxy() {
   if [ -n "$1" ]
     then
       mkdir -p "/home/mindey/Projects/Research/mindey/$1"
       cd "/home/mindey/Projects/Research/mindey/$1"
       touch main.py
       tmux new -s "$1-research" 'zsh' \; send-keys "vim main.py" Enter \; splitw -hd "python3 -mvenv .env && . .env/bin/activate; fx -bap"
     else
       echo "No project name selected."
   fi
}

This way, running something like fxy project-name makes a project folder and starts Python environment with packages fx -bap (BPython + Acturial + Plotting).

Or simply use fxy as shortcut for some custom initialization that you often use, like:

fxy() {
    fx -bnsalp
}

This way, you created a command fxy that imports:

>>> from fxy.n import * # Numeric: from mpmath import *
>>> from fxy.s import * # Symbolic: import sympy; exec(sympy.interactive.session.preexec_source)
>>> from fxy.a import * # Actuarial: numpy, np, pandas, pd, xarray, xr, scipy, sp, scipy.stats, st, statsmodels, sm, statsmodels.formula.api, smf
>>> from fxy.l import * # Learning: sklearn, xgboost, xgb
>>> from fxy.p import * # Plotting: matplotlib.pyplot, plt, matplotlib, seaborn, sns
>>>

About

This package may be useful for computing basic things, doing things to emulate Python’s capabilities in computational and symbolic mathematics and statistics, so this package will introduce just convenient imports so that one doesn’t have to configure Jupyter notebook profile, to have those imports every time, and works well as an on-the-go calculator.

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.n import * (394 functions)
>>> pi
<pi: 3.14159~>

# Symbolic (sympy.*)
>>> from fxy.s 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.a 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)
>>> from fxy.l import *
>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> neigh = skl.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.p import *
>>> plt.plot([1, 2, 3, 4])
>>> plt.ylabel('some numbers')
>>> plt.show()
<image>

I often collect convenient computations and functions in various fields, like what WolframAlpha does cataloguing implementations of advanced computations to be reused.