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

"""

Created on Tue Oct 6 19:54:34 2020

@author: mahme026

"""

#%matplotlib inline

import matplotlib as mpl

mpl.get_backend()

import matplotlib.pyplot as plt

#plt.plot?

plt.plot(3, 2)

plt.plot(3, 2, '.')

# First let's set the backend without using mpl.use() from the scripting layer

from matplotlib.backends.backend_agg import FigureCanvasAgg

from matplotlib.figure import Figure

# create a new figure

fig = Figure()

# associate fig with the backend

canvas = FigureCanvasAgg(fig)

# add a subplot to the fig

ax = fig.add_subplot(111)

# plot the point (3,2)

ax.plot(3, 2, '.')

# save the figure to test.png

# you can see this figure in your Jupyter workspace afterwards by going to

# https://hub.coursera-notebooks.org/

canvas.print_png('test.png')

# create a new figure

plt.figure()

# plot the point (3,2) using the circle marker

plt.plot(3, 2, 'o')

# get the current axes

ax = plt.gca()

# Set axis properties [xmin, xmax, ymin, ymax]

ax.axis([0,6,0,10])

# create a new figure

plt.figure()

# plot the point (1.5, 1.5) using the circle marker

plt.plot(1.5, 1.5, 'o')

# plot the point (2, 2) using the circle marker

plt.plot(2, 2, 'o')

# plot the point (2.5, 2.5) using the circle marker

plt.plot(2.5, 2.5, 'o')

# get current axes

ax = plt.gca()

# get all the child objects the axes contains

ax.get_children()

import numpy as np

x = np.array([1,2,3,4,5,6,7,8])

y = x

plt.figure()

plt.scatter(x, y) # similar to plt.plot(x, y, '.'), but the underlying child objects in the axes are not Line2D

x = np.array([1,2,3,4,5,6,7,8])

y = x

# create a list of colors for each point to have

# ['green', 'green', 'green', 'green', 'green', 'green', 'green', 'red']

colors = ['green']*(len(x)-1)

colors.append('red')

plt.figure()

# plot the point with size 100 and chosen colors

plt.scatter(x, y, s=50, c=colors)

# convert the two lists into a list of pairwise tuples

zip_generator = zip([1,2,3,4,5], [6,7,8,9,10])

print(list(zip_generator))

zip_generator = zip([1,2,3,4,5], [6,7,8,9,10])

# The single star * unpacks a collection into positional arguments

print(*zip_generator)

print(list(zip((1, 6), (2, 7), (3, 8), (4, 9), (5, 10))))

zip_generator = zip([1,2,3,4,5], [6,7,8,9,10])

# let's turn the data back into 2 lists

x, y = zip(*zip_generator) # This is like calling zip((1, 6), (2, 7), (3, 8), (4, 9), (5, 10))

print(x)

print(y)

plt.figure()

# plot a data series 'Tall students' in red using the first two elements of x and y

plt.scatter(x[:2], y[:2], s=100, c='red', label='Tall students')

# plot a second data series 'Short students' in blue using the last three elements of x and y

plt.scatter(x[2:], y[2:], s=100, c='blue', label='Short students')

# add a label to the x axis

plt.xlabel('The number of times the child kicked a ball')

# add a label to the y axis

plt.ylabel('The grade of the student')

# add a title

plt.title('Relationship between ball kicking and grades')

# add a legend (uses the labels from plt.scatter)

plt.legend()

# add the legend to loc=4 (the lower right hand corner), also gets rid of the frame and adds a title

plt.legend(loc=4, frameon=False, title='Legend')

# get children from current axes (the legend is the second to last item in this list)

plt.gca().get_children()

# get the legend from the current axes

legend = plt.gca().get_children()[-2]

# you can use get_children to navigate through the child artists

#legend.get_children()[0].get_children()[1].get_children()[0].get_children()

from matplotlib.artist import Artist

def rec_gc(art, depth=0):

rec_gc(child, depth+2)

# Call this function on the legend artist to see what the legend is made up of

rec_gc(plt.legend())

linear_data = np.array([1,2,3,4,5,6,7,8])

exponential_data = linear_data**2

plt.figure()

# plot the linear data and the exponential data

plt.plot(linear_data, '-o', exponential_data, '-o')

plt.plot([22,44,55], '--r')

plt.xlabel('Some data')

plt.ylabel('Some other data')

plt.title('A title')

# add a legend with legend entries (because we didn't have labels when we plotted the data series)

plt.legend(['Baseline', 'Competition', 'Us'])

# fill the area between the linear data and exponential data

plt.gca().fill_between(range(len(linear_data)),

linear_data, exponential_data,

facecolor='blue',

alpha=0.25)

plt.figure()

observation_dates = np.arange('2017-01-01', '2017-01-09', dtype='datetime64[D]')

plt.plot(observation_dates, linear_data, '-o', observation_dates, exponential_data, '-o')

import pandas as pd

plt.figure()

observation_dates = np.arange('2017-01-01', '2017-01-09', dtype='datetime64[D]')

observation_dates = map(pd.to_datetime, observation_dates) # trying to plot a map will result in an error

plt.plot(observation_dates, linear_data, '-o', observation_dates, exponential_data, '-o')

plt.figure()

observation_dates = np.arange('2017-01-01', '2017-01-09', dtype='datetime64[D]')

observation_dates = list(map(pd.to_datetime, observation_dates)) # convert the map to a list to get rid of the error

plt.plot(observation_dates, linear_data, '-o', observation_dates, exponential_data, '-o')

x = plt.gca().xaxis

# rotate the tick labels for the x axis

for item in x.get_ticklabels():

item.set_rotation(45)

ax = plt.gca()

ax.set_xlabel('Date')

ax.set_ylabel('Units')

ax.set_title('Exponential vs. Linear performance')

# you can add mathematical expressions in any text element

ax.set_title("Exponential ($x^2$) vs. Linear (x) performance")

#

plt.figure()

xvals = range(len(linear_data))

plt.bar(xvals, linear_data, width = 0.3)

new_xvals = []

# plot another set of bars, adjusting the new xvals to make up for the first set of bars plotted

for item in xvals:

new_xvals.append(item+0.3)

plt.bar(new_xvals, exponential_data, width = 0.3 ,color='red')

from random import randint

linear_err = [randint(0,15) for x in range(len(linear_data))]

# This will plot a new set of bars with errorbars using the list of random error values

plt.bar(xvals, linear_data, width = 0.3, yerr=linear_err)

# stacked bar charts are also possible

plt.figure()

xvals = range(len(linear_data))

plt.bar(xvals, linear_data, width = 0.3, color='b')

plt.bar(xvals, exponential_data, width = 0.3, bottom=linear_data, color='r')

# or use barh for horizontal bar charts

plt.figure()

xvals = range(len(linear_data))

plt.barh(xvals, linear_data, height = 0.3, color='b')

plt.barh(xvals, exponential_data, height = 0.3, left=linear_data, color='r')