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

"""

Created on Sat Apr 1 13:56:57 2017

@author: Shabaka

"""

import numpy as np

import matplotlib.pyplot as plt

# ''''''''''' Specifying Label ''''''''''''''''#

# Specify the label 'Computer Science'

plt.plot(year, computer_science, color='red', label='Computer Science')

# Specify the label 'Physical Sciences'

plt.plot(year, physical_sciences, color='blue', label='Physical Sciences')

# Add a legend at the lower center

plt.legend(loc='lower center')

# Add axis labels and title

plt.xlabel('Year')

plt.ylabel('Enrollment (%)')

plt.title('Undergraduate enrollment of women')

plt.show()

# '''''''' ############# ;''''''''''''' ################## #

# ''''''''''''''''' Using Annotate ''''''''''''''#

# Plot with legend as before

plt.plot(year, computer_science, color='red', label='Computer Science')

plt.plot(year, physical_sciences, color='blue', label='Physical Sciences')

plt.legend(loc='bottom right')

# Compute the maximum enrollment of women in Computer Science: cs_max

cs_max = computer_science.max()

# Calculate the year in which there was maximum enrollment

# of women in Computer Science: yr_max

yr_max = year[computer_science.argmax()]

# Add a black arrow annotation

plt.annotate('Maximum', xy=(yr_max, cs_max), xytext=(yr_max+5, cs_max+5),

arrowprops=dict(facecolor='black'))

# Add axis labels and title

plt.xlabel('Year')

plt.ylabel('Enrollment (%)')

plt.title('Undergraduate enrollment of women')

plt.show()

# '''''''''''''''''' Modifying Plots '''''''''#

# Import matplotlib.pyplot

# Set the style to 'ggplot'

plt.style.use('ggplot')

print(plt.style.available)

# Create a figure with 2x2 subplot layout

plt.subplot(2, 2, 1)

# Plot the enrollment % of women in the Physical Sciences

plt.plot(year, physical_sciences, color='blue')

plt.title('Physical Sciences')

# Plot the enrollment % of women in Computer Science

plt.subplot(2, 2, 2)

plt.plot(year, computer_science, color='red')

plt.title('Computer Science')

# Add annotation

cs_max = computer_science.max()

yr_max = year[computer_science.argmax()]

plt.annotate('Maximum', xy=(yr_max, cs_max), xytext=(yr_max-1,

cs_max-10), arrowprops=dict(facecolor='black'))

# Plot the enrollmment % of women in Health professions

plt.subplot(2, 2, 3)

plt.plot(year, health, color='green')

plt.title('Health Professions')

# Plot the enrollment % of women in Education

plt.subplot(2, 2, 4)

plt.plot(year, education, color='yellow')

plt.title('Education')

# Improve spacing between subplots and display them

plt.tight_layout()

plt.show()

# '''''''''''''''' Creating a Meshed Fig ''''''''''' #

# Import numpy and matplotlib.pyplot

# Generate two 1-D arrays: u, v

u = np.linspace(-2, 2, 41)

v = np.linspace(-1, 1, 21)

# Generate 2-D arrays from u and v: X, Y

X, Y = np.meshgrid(u, v)

# Compute Z based on X and Y

Z = np.sin(3*np.sqrt(X**2 + Y**2))

# Display the resulting image with pcolor()

plt.pcolor(Z)

plt.show()

# Save the figure to 'sine_mesh.png'

plt.savefig('sine_mesh.png')

# '''''''''''' Visualising Bivariate Functions ''''''#

# ''''''''''' Contours and Filled Contours '''#

# Generate a default contour map of the array Z

plt.subplot(2, 2, 1)

plt.contour(X, Y, Z)

# Generate a contour map with 20 contours

plt.subplot(2, 2, 2)

plt.contour(X, Y, Z, 20)

# Generate a default filled contour map of the array Z

plt.subplot(2, 2, 3)

plt.contourf(X, Y, Z)

# Generate a contour map with 20 contours

plt.subplot(2, 2, 4)

plt.contourf(X, Y, Z, 20)

# Improve the spacing between subplots

plt.tight_layout()

# Display the figure

plt.show()

# ''''''########''########''''''''''##################### #

# ''''' Colour Map Modifier ''''''''''#

# Create a filled contour plot with a color map of 'viridis'

plt.subplot(2, 2, 1)

plt.contourf(X, Y, Z, 20, cmap='viridis')

plt.colorbar()

plt.title('Viridis')

# Create a filled contour plot with a color map of 'gray'

plt.subplot(2, 2, 2)

plt.contourf(X, Y, Z, 20, cmap='gray')

plt.colorbar()

plt.title('Gray')

# Create a filled contour plot with a color map of 'autumn'

plt.subplot(2, 2, 3)

plt.contourf(X, Y, Z, 20, cmap='autumn')

plt.colorbar()

plt.title('Autumn')

# Create a filled contour plot with a color map of 'winter'

plt.subplot(2, 2, 4)

plt.contourf(X, Y, Z, 20, cmap='winter')

plt.colorbar()

plt.title('Winter')

# Improve the spacing between subplots and display them

plt.tight_layout()

plt.show()

# '''''''' Using hist2d() '''''''''' #

# # Generate a 2-D histogram

_ = plt.hist2d(hp, mpg, bins=(20, 20), range=((40, 235), (8, 48)))

# Add a color bar to the histogram

_ = plt.colorbar()

# Add labels, title, and display the plot

plt.xlabel('Horse power [hp]')

plt.ylabel('Miles per gallon [mpg]')

plt.title('hist2d() plot')

plt.show()

# ''''''''''''' Plotting with hexbin() '''''''''''#

# # Generate a 2d histogram with hexagonal bins

_ = plt.hexbin(hp, mpg, gridsize=(15, 12), extent=((40, 235, 8, 48)))

# Add a color bar to the histogram

_ = plt.colorbar()

# Add labels, title, and display the plot

plt.xlabel('Horse power [hp]')

plt.ylabel('Miles per gallon [mpg]')

plt.title('hexbin() plot')

plt.show()

# ''''''''''''''' Loading and Viewing Images '''''''' #

# Load the image into an array: img

img = plt.imread('480px-Astronaut-EVA.jpg')

# Print the shape of the image

print(img.shape)

# Display the image

plt.imshow(img)

# Hide the axes

plt.axis('off')

plt.show()

# ''''''''''' Pseudocolor Plot from Image Data ''#

# Load the image into an array: img

img = plt.imread('480px-Astronaut-EVA.jpg')

# Print the shape of the image

print(img.shape)

# Compute the sum of the red, green and blue channels: intensity

intensity = img.sum(axis=2)

# Print the shape of the intensity

print(intensity.shape)

# Display the intensity with a colormap of 'gray'

plt.imshow(intensity, cmap='gray')

# Add a colorbar

plt.colorbar()

# Hide the axes and show the figure

plt.axis('off')

plt.show()

# # '''''''''''''Specifying Extents and Aspect Ratio '''''#

# Load the image into an array: img

img = plt.imread('480px-Astronaut-EVA.jpg')

# Specify the extent and aspect ratio of the top left subplot

plt.subplot(2, 2, 1)

plt.title('extent=(-1,1,-1,1),\naspect=0.5')

plt.xticks([-1, 0, 1])

plt.yticks([-1, 0, 1])

plt.imshow(img, extent=(-1, 1, -1, 1), aspect=0.5)

# Specify the extent and aspect ratio of the top right subplot

plt.subplot(2, 2, 2)

plt.title('extent=(-1,1,-1,1),\naspect=1')

plt.xticks([-1, 0, 1])

plt.yticks([-1, 0, 1])

plt.imshow(img, extent=(-1, 1, -1, 1), aspect=1)

# Specify the extent and aspect ratio of the bottom left subplot

plt.subplot(2, 2, 3)

plt.title('extent=(-1,1,-1,1),\naspect=2')

plt.xticks([-1, 0, 1])

plt.yticks([-1, 0, 1])

plt.imshow(img, extent=(-1, 1, -1, 1), aspect=2)

# Specify the extent and aspect ratio of the bottom right subplot

plt.subplot(2, 2, 4)

plt.title('extent=(-2,2,-1,1),\naspect=2')

plt.xticks([-2, -1, 0, 1, 2])

plt.yticks([-1, 0, 1])

plt.imshow(img, extent=(-2, 2, -1, 1), aspect=2)

# Improve spacing and display the figure

plt.tight_layout()

plt.show()

# '''''' Rescale Pixel Intensities '''''''''''''#

# Load the image into an array: image

image = plt.imread('640px-Unequalized_Hawkes_Bay_NZ.jpg')

# Extract minimum and maximum values from the image: pmin, pmax

pmin, pmax = image.min(), image.max()

print("The smallest & largest pixel intensities are %d & %d." % (pmin, pmax))

# Rescale the pixels: rescaled_image

rescaled_image = 256*(image - pmin) / (pmax - pmin)

print("The rescaled smallest & largest pixel intensities are %.1f & %.1f." %

(rescaled_image.min(), rescaled_image.max()))

# Display the original image in the top subplot

plt.subplot(2, 1, 1)

plt.title('original image')

plt.axis('off')

plt.imshow(image, extent=(-2, 2, -1, 1), aspect=2)

# Display the rescaled image in the bottom subplot

plt.subplot(2, 1, 2)

plt.title('rescaled image')

plt.axis('off')

plt.imshow(rescaled_image, extent=(-2, 2, -1, 1), aspect=2)

plt.show()