# load arrays with coordinates of points on a unit circle, then plot them.
# this file is simple_python_example.py 

# George Gollin, University of Illinois
# January 2022

# import the numpy and matplotlib.pyplot libraries
import numpy as np
import matplotlib.pyplot as plt 

# close any already-open graphics windows
plt.close("all")

# create the arrays. first is angle, running from [0,2pi). Note the use of
# endpoint = False in linspace to make this interval open on the right. The
# first two arguments in linspace are the beginning and end of the interval
# of uniformly spaced points. The third is the total number of points.
NumberOfPoints = 72
ThetaArray = np.linspace(0, 2*np.pi, NumberOfPoints, endpoint=False)

# cos and sin in numpy can act on all elements in an array. Note that the 
# output is also an array.
x = np.cos(ThetaArray)
y = np.sin(ThetaArray)

# set the size for figures so that they are square. (Units: inches???)
plt.figure(figsize=(8.0, 8.0))

# also set the x and y axis limits
plt.xlim(-1.2, 1.2)
plt.ylim(-1.2, 1.2)

# plot the x,y points, connecting successive points with lines
plt.plot(x,y)

# now plot the points again, on the same axes, but using red + signs: 
plt.plot(x,y, 'r+')

# now redo the array of angles (and x,y points) to include the 2pi endpoint.
# make a smaller circle this time...
ThetaArray = np.linspace(0, 2*np.pi, NumberOfPoints, endpoint=True)
x = 0.8*np.cos(ThetaArray)
y = 0.8*np.sin(ThetaArray)

#plot it. note how the line color changes...
plt.plot(x,y)

# now make an oval by down-scaling the x, y arrays, then plot
# it using rather large, filled blue circles.
x = 0.7 * x
y = 0.4 * y
plt.plot(x,y, 'bo', markersize=6)

# now put a title onto the plot, then label the axes
plt.title("Some circles and ovals-- George Gollin")
plt.xlabel("X")
plt.ylabel("Y")

# now save plot to a png (portable network graphics) file
plt.savefig("CirclePlotOne.png")