In [1]:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')

In [2]:
import phuzzy as ph
from phuzzy.mpl import mix_mpl
from phuzzy.mpl.plots import plot_xy

In [3]:
x = ph.Trapezoid(alpha0=[-2.5, 2], alpha1=[1, 1.8], number_of_alpha_levels=15)
mix_mpl(x)
x.plot()


Out[3]:
True

In [4]:
y = x * x
fig, axs = plot_xy(x, y)



In [5]:
y = x ** 2
fig, axs = plot_xy(x, y)



In [6]:
y = x ** .5
fig, axs = plot_xy(x, y)
y.df


Out[6]:
alpha l r
0 0.000000 0.000000 1.414214
1 0.071429 0.000000 1.409154
2 0.142857 0.000000 1.404076
3 0.214286 0.000000 1.398979
4 0.285714 0.000000 1.393864
5 0.357143 0.000000 1.388730
6 0.428571 0.000000 1.383577
7 0.500000 0.000000 1.378405
8 0.571429 0.000000 1.373213
9 0.642857 0.000000 1.368002
10 0.714286 0.000000 1.362770
11 0.785714 0.500000 1.357519
12 0.857143 0.707107 1.352247
13 0.928571 0.866025 1.346954
14 1.000000 1.000000 1.341641

In [7]:
y = abs(x)
fig, axs = plot_xy(x, y)
y.df


Out[7]:
alpha l r
0 0.000000 0.00 2.500000
1 0.071429 0.00 2.250000
2 0.142857 0.00 2.000000
3 0.214286 0.00 1.957143
4 0.285714 0.00 1.942857
5 0.357143 0.00 1.928571
6 0.428571 0.00 1.914286
7 0.500000 0.00 1.900000
8 0.571429 0.00 1.885714
9 0.642857 0.00 1.871429
10 0.714286 0.00 1.857143
11 0.785714 0.25 1.842857
12 0.857143 0.50 1.828571
13 0.928571 0.75 1.814286
14 1.000000 1.00 1.800000

In [8]:
x.convert_df(5)
y = -x
fig, axs = plot_xy(x, y)
y.df


Out[8]:
alpha l r
0 0.00 -2.00 2.500
1 0.25 -1.95 1.625
2 0.50 -1.90 0.750
3 0.75 -1.85 -0.125
4 1.00 -1.80 -1.000

In [9]:
y = x - 3
fig, axs = plot_xy(x, y)
y.df


Out[9]:
alpha l r
0 0.00 -5.500 -1.00
1 0.25 -4.625 -1.05
2 0.50 -3.750 -1.10
3 0.75 -2.875 -1.15
4 1.00 -2.000 -1.20

In [10]:
y = x / 2
fig, axs = plot_xy(x, y)
y.df


Out[10]:
alpha l r
0 0.00 -1.2500 1.000
1 0.25 -0.8125 0.975
2 0.50 -0.3750 0.950
3 0.75 0.0625 0.925
4 1.00 0.5000 0.900

In [11]:
y = x ** 0
fig, axs = plot_xy(x, y)
y.df


Out[11]:
alpha l r
0 0.00 1.0 1.0
1 0.25 1.0 1.0
2 0.50 1.0 1.0
3 0.75 1.0 1.0
4 1.00 1.0 1.0

In [12]:
y = 0 ** x
y.name = "0^x"
fig, axs = plot_xy(x, y)
y.df


Out[12]:
alpha l r
0 0.00 0.0 0.0
1 0.25 0.0 0.0
2 0.50 0.0 0.0
3 0.75 0.0 0.0
4 1.00 0.0 0.0

In [ ]: