# -*- coding: utf-8 -*-

"""

Created on Mon Nov 25 15:30:54 2019

@author: bdai729

"""

"""

*********

intro to pyplot

*********

reference: https://matplotlib.org/tutorials/introductory/pyplot.html#sphx-glr-

tutorials-introductory-pyplot-py

https://matplotlib.org/gallery/index.html#mplot3d-examples-index

"""

import matplotlib.pyplot as plt

import numpy as np

# <codecell> simple plot

plt.plot([1, 2, 3, 4], [5, 6, 7, 8])

plt.ylabel("some numbers")

plt.xlabel("x axis label")

plt.show()

"""

*** Note::

the pyplot API is generally less-flexible than the object-oriented API.

Most of the function calls you see here can also be called as methods from

an Axes object.

TO ME, it might be the best practice to use an axes (or an axes_list) with

their methods. see below

"""

# <codecell> My preference

fig, axes = plt.subplots(1, 1, figsize=(9, 6))

fig.subplots_adjust(bottom=0.15, left=0.2)

axes.plot([1, 2, 3, 4], [1, 2, 3, 4])

axes.set_title("Plot using axes objects and their methods")

axes.set_xlabel("X label")

axes.set_ylabel("Y label")

axes.set_xlim(0, 4)

axes.set_ylim(1, 4)

axes.grid()

fig, ax_lst = plt.subplots(2, 2)

ax_lst[0, 1].plot([1, 2, 3, 4], [1, 2, 3, 4])

ax_lst[0, 1].set_title("subplot 2")

ax_lst[0, 1].set_xlabel("X label")

ax_lst[0, 1].set_ylabel("Y label")

ax_lst[0, 1].set_xlim(0, 4)

ax_lst[0, 1].set_ylim(1, 4)

ax_lst[0, 1].grid()

fig.suptitle("access subtitle of a figure object")

ax_lst[1, 0].scatter([1, 2, 3, 4], [1, 1.5, 3.5, 4])

# <codecell> formating plot style

# reference: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.

# plot.html#matplotlib.pyplot.plot

fig, axes = plt.subplots(1, 1)

axes.plot([1, 2, 3, 4], [1, 2, 3, 4], 'go--', linewidth=2, markersize=12)

axes.set_title("sub plot title")

axes.set_xlabel("X label")

axes.set_ylabel("Y label")

axes.set_xlim(0, 4)

axes.set_ylim(1, 4)

axes.grid()

# <codecell> Plotting with keyword strings

data = {'a': np.arange(50),

'c': np.random.randint(0, 50, 50),

'd': np.random.randn(50)}

data['b'] = data['a'] + 10 * np.random.randn(50)

data['d'] = np.abs(data['d']) * 100

plt.scatter('a', 'b', c='c', s='d', data=data)

plt.xlabel('entry a')

plt.ylabel('entry b')

plt.show()

# <codecell> Plotting with categorical variables

# It is also possible to create a plot using categorical variables.

# Matplotlib allows you to pass categorical variables directly to many

# plotting functions. For example:

names = ['group_a', 'group_b', 'group_c']

values = [1, 10, 100]

plt.figure(figsize=(9, 3))

plt.subplot(131)

plt.bar(names, values)

plt.subplot(132)

plt.scatter(names, values)

plt.subplot(133)

plt.plot(names, values, linewidth=5.0)

plt.suptitle('Categorical Plotting')

plt.show()

# <codecell> Working with multiple figures and axes

def f(t):

return np.exp(-t) * np.cos(2*np.pi*t)

t1 = np.arange(0.0, 5.0, 0.1)

t2 = np.arange(0.0, 5.0, 0.02)

plt.figure()

plt.subplot(211)

plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')

plt.subplot(212)

plt.plot(t2, np.cos(2*np.pi*t2), 'r--')

plt.show()

# <codecell> Working with multiple figures and axes

plt.figure(1) # the first figure

plt.subplot(211) # the first subplot in the first figure

plt.plot([1, 2, 3])

plt.subplot(212) # the second subplot in the first figure

plt.plot([4, 5, 6])

plt.figure(2) # a second figure

plt.plot([4, 5, 6]) # creates a subplot(111) by default

plt.figure(1) # figure 1 current; subplot(212) still current

plt.subplot(211) # make subplot(211) in figure1 current

plt.title('Easy as 1, 2, 3') # subplot 211 title

# <codecell> plot f(x)

# plot y = sin(x)

import numpy as np

fig, axes = plt.subplots(1, 1)

x = np.arange(-np.pi, np.pi, 0.2)

y = np.sin(x)

z = np.cos(x)

axes.plot(x, y)

axes.set_title("y = f(x) = sin(x)")

axes.set_xlabel("X label")

axes.set_ylabel("Y label")

axes.set_xlim(-np.pi, np.pi)

axes.set_ylim(-1, 1)

axes.grid()

plt.figure(1)

plt.subplot(211)

plt.plot(x, y)

plt.subplot(212)

plt.scatter(x, z)

plt.xlim(-5, 5)

plt.ylim(-2, 2)

z2 = 2*np.tan(x)

plt.figure(2)

plt.plot(x, z2)

# <codecell> working with text

import matplotlib

import matplotlib.pyplot as plt

fig = plt.figure()

fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')

ax = fig.add_subplot(111)

fig.subplots_adjust(top=0.85)

ax.set_title('axes title')

ax.set_xlabel('xlabel')

ax.set_ylabel('ylabel')

ax.text(3, 8, 'boxed italics text in data coords', style='italic',

bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})

ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)

ax.text(3, 2, 'unicode: Institut für Festkörperphysik')

ax.text(0.95, 0.01, 'colored text in axes coords',

verticalalignment='bottom', horizontalalignment='right',

transform=ax.transAxes,

color='green', fontsize=15)

ax.plot([2], [1], 'o')

ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),

arrowprops=dict(facecolor='black', shrink=0.05))

ax.axis([0, 10, 0, 10])

plt.show()

# <codecell> working with text (cont.)

mu, sigma = 100, 15

x = mu + sigma * np.random.randn(10000)

# the histogram of the data

n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)

plt.xlabel('Smarts')

plt.ylabel('Probability')

plt.title('Histogram of IQ')

plt.text(60, .025, r'$\mu=100,\ \sigma=15$')

plt.axis([40, 160, 0, 0.03])

plt.grid(True)

plt.show()

# <codecell> font

from matplotlib.font_manager import FontProperties

font = FontProperties()

font.set_family('serif')

font.set_name('Times New Roman')

font.set_style('italic')

fig, ax = plt.subplots(figsize=(5, 3))

fig.subplots_adjust(bottom=0.15, left=0.2)

ax.plot(x1, y1)

ax.set_xlabel('time [s]', fontsize='large', fontweight='bold')

ax.set_ylabel('Damped oscillation [V]', fontproperties=font)

plt.show()

# <codecell> TeX rendering

x1 = np.linspace(0.0, 5.0, 100)

y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)

fig, ax = plt.subplots(figsize=(9, 6))

fig.subplots_adjust(bottom=0.2, left=0.2)

ax.plot(x1, np.cumsum(y1**2))

ax.set_xlabel('time [s] \n This was a long experiment')

ax.set_ylabel(r'$\int\ Y^2\ dt\ \ [V^2 s]$')

plt.show()

# <codecell> anotating text

ax = plt.subplot(111)

t = np.arange(0.0, 5.0, 0.01)

s = np.cos(2*np.pi*t)

line, = plt.plot(t, s, lw=2)

plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),

arrowprops=dict(facecolor='black', shrink=0.05),

)

plt.annotate('local min', xy=(2.5, -1), xytext=(3, -1.5),

arrowprops=dict(facecolor='black', shrink=0.05),

)

plt.ylim(-2, 2)

plt.show()

# <codecell> logarithmic scale

from matplotlib.ticker import NullFormatter # useful for `logit` scale

# Fixing random state for reproducibility

np.random.seed(19680801)

# make up some data in the interval ]0, 1[

y = np.random.normal(loc=0.5, scale=0.4, size=1000)

y = y[(y > 0) & (y < 1)]

y.sort()

x = np.arange(len(y))

# plot with various axes scales

plt.figure()

# linear

plt.subplot(221)

plt.plot(x, y)

plt.yscale('linear')

plt.title('linear')

plt.grid(True)

# log

plt.subplot(222)

plt.plot(x, y)

plt.yscale('log')

plt.title('log')

plt.grid(True)

# symmetric log

plt.subplot(223)

plt.plot(x, y - y.mean())

plt.yscale('symlog', linthreshy=0.01)

plt.title('symlog')

plt.grid(True)

# logit

plt.subplot(224)

plt.plot(x, y)

plt.yscale('logit')

plt.title('logit')

plt.grid(True)

# Format the minor tick labels of the y-axis into empty strings with

# `NullFormatter`, to avoid cumbering the axis with too many labels.

plt.gca().yaxis.set_minor_formatter(NullFormatter())

# Adjust the subplot layout, because the logit one may take more space

# than usual, due to y-tick labels like "1 - 10^{-3}"

plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,

wspace=0.35)

plt.show()

# <codecell> Legend

# reference: https://matplotlib.org/gallery/text_labels_and_annotations/

# figlegend_demo.html#sphx-glr-gallery-text-labels-and-annotations-figlegend-demo-py

import numpy as np

import matplotlib.pyplot as plt

fig, axs = plt.subplots(1, 2)

x = np.arange(0.0, 2.0, 0.02)

y1 = np.sin(2 * np.pi * x)

y2 = np.exp(-x)

l1, = axs[0].plot(x, y1)

l2, = axs[0].plot(x, y2, marker='o')

y3 = np.sin(4 * np.pi * x)

y4 = np.exp(-2 * x)

l3, = axs[1].plot(x, y3, color='tab:green')

l4, = axs[1].plot(x, y4, color='tab:red', marker='^')

fig.legend((l1, l2), ('Line 1', 'Line 2'), 'upper left')

fig.legend((l3, l4), ('Line 3', 'Line 4'), 'upper right')

plt.tight_layout()

plt.show()