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#### MATPLOTLIBRC FORMAT

## This is a sample matplotlib configuration file - you can find a copy
## of it on your system in
## site-packages/matplotlib/mpl-data/matplotlibrc.  If you edit it
## there, please note that it will be overwritten in your next install.
## If you want to keep a permanent local copy that will not be
## overwritten, place it in the following location:
## unix/linux:
##      $HOME/.config/matplotlib/matplotlibrc or

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"""
===================
Anatomy of a figure
===================

This figure shows the name of several matplotlib elements composing a figure
"""

import numpy as np
import matplotlib.pyplot as plt

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import numpy as np
import matplotlib.pyplot as plt
from scipy.special import comb, perm
import math


def plt_distribution(distribution_data_list, show=False):

    fig, ax = plt.subplots()
    xys, figtitle, figname = distribution_data_list

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import numpy as np
import matplotlib.pyplot as plt


def area_polygons(n, l):  # n: the number of edges in polygons, l: length of edges
    apothem = l / (2 * np.tan(np.pi / n))
    area = apothem * n * l / 2
    return area

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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as pli


image_data = pli.imread("lindan_smash.jpg")
I = image_data.copy()


def _mirror(image_array):

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import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(5, 4))

x = np.linspace(1, 5, 100)
y1 = np.ones(len(x))
y2 = np.ones(len(x)) * 2
y3 = np.ones(len(x)) * 3
y4 = np.ones(len(x)) * 4

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import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots(3, 2, figsize=(10, 8))

x = np.linspace(1, 2 * np.pi, 100)

# y=sin(x)
i, j = 0, 0
ax[i][j].plot(x, np.sin(x), 'k-')

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import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(5, 4))

x = np.linspace(1, 5, 100)
y1 = np.ones(len(x))
y2 = np.ones(len(x)) * 2
y3 = np.ones(len(x)) * 3
y4 = np.ones(len(x)) * 4

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import numpy as np
import matplotlib.pyplot as plt

fig, axes = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(14, 10))
x = np.linspace(1, 2 * np.pi, 100)

# for i, row in enumerate(axes):
#    for j, cell in enumerate(row):
#        cell.plot(x,np.sin((i+1)*x))

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import numpy as np
def f(x):
    return x**3-3
def fdot(x):
    return 3*x**2

def NewtonMethod(x0,threshold):
    x_next=x0-f(x0)/fdot(x0)
    diff_x=np.abs(x_next-x0)
    x0=x_next