%matplotlib inline
import numpy
import matplotlib.pyplot as plt

"""
Explanation: Plotting with matplotlib
The most common facility for plotting with the Python numerical suite is to use the matplotlib package.  We will cover a few of the basic approaches to plotting figures.  If you are interested in learning more about matplotlib or are looking to see how you might create a particular plot check out the matplotlib gallery for inspiration.
A MATLAB style interface
To start, matplotlib was designed to look very similar to MATLAB's plotting commands, here are a few examples that are along these lines:
End of explanation
"""


x = numpy.linspace(-5, 5, 100)
y = x**2 + 2 * x + 3
plt.plot(x, y)

"""
Explanation: These commands import the necessary functions for NumPy and matplotlib for our demos.  The first command is so that the IPython notebook will make our plots and display them in the notebook.
First off, lets plot a simple quadratic function $f(x) = x^2 + 2x + 3$:
End of explanation
"""


plt.plot(x, y, 'r--')
plt.xlabel("time (s)")
plt.ylabel("Tribble Population")
plt.title("Tribble Growth")
plt.xlim([0, 4])
plt.ylim([0, 25])

"""
Explanation: We can also label our plot (please do this), set the bounds and change the style of the plot:
End of explanation
"""


x = numpy.linspace(-1, 1, 100)
y = numpy.linspace(-1, 1, 100)
X, Y = numpy.meshgrid(x, y)
F = numpy.sin(X**2) + numpy.cos(Y**2)
plt.pcolor(X, Y, F)
plt.show()

"""
Explanation: The othe major way to plot data is via a pseudo-color plot.  A pseudo-color plot takes 3-dimensional data (or 2-dimensional depending on how you look at it) and plots it using color to represent the values of the function.  Say we have a function $f(x, y)$, we can plot a pseudo-color by using the following commands.
End of explanation
"""


color_map = plt.get_cmap("Oranges")
plt.gcf().set_figwidth(plt.gcf().get_figwidth() * 2)
plt.subplot(1, 2, 1, aspect="equal")
plt.pcolor(X, Y, F, cmap=color_map)

plt.colorbar()
plt.xlabel("x (m)")
plt.ylabel("y (m)")
plt.title("Tribble Density ($N/m^2$)")

plt.subplot(1, 2, 2, aspect="equal")
plt.pcolor(X, Y, X*Y, cmap=plt.get_cmap("RdBu"))
plt.xlabel("x (m)")
plt.ylabel("y (m)")
plt.title("Klignon Population ($N$)")
plt.colorbar()

plt.autoscale(enable=True, tight=False)
plt.show()

"""
Explanation: Here we have used a new way to create NumPy ndarrays so that we can easily evaluate 2-dimensional functions.  The function meshgrid takes two 1-dimensional arrays and turns them into 2, 2-dimensional arrays whose matching indices will provide an easy to use set of arrays that can be evaluate 2-dimensional functions.
Now as pretty as this rainbow plot is, it does not actually communicate as effectively as it could.  This default colormap (a colormap is how we decide which values are colored which way) called "jet" is fairly ubiquitous in scientific and engineering visualization.  Unfortunately the jet colormap tends to emphasize only certain parts of the range of values.  I highly suggest looking at this explanation of how to choose a colormap if you are interested.  The bottom line is, do not use jet if you can help it.
Anyway, let us make this plot also more useful.
End of explanation
"""


x = numpy.linspace(-5, 5, 100)

fig = plt.figure()
axes = fig.add_subplot(1, 1, 1)
growth_curve = axes.plot(x, x**2 + 2 * x + 3, 'r--')
axes.set_xlabel("time (s)")
axes.set_ylabel("Tribble Population")
axes.set_title("Tribble Growth")
axes.set_xlim([0, 4])
axes.set_ylim([0, 25])

"""
Explanation: A better way...
A much better way to "build" plots is to use a more object-oriented approach.  In this case, we create the objects and manipulate them which allows us to have more control over how we create plots.  Here we will create the annotated plots so that we have examples on how to do the same things (notice that they are very similar).  The basic premise of this approach is that you generate objects that can be manipulated and remain persistent.
End of explanation
"""


x = numpy.linspace(-1, 1, 100)
y = numpy.linspace(-1, 1, 100)
X, Y = numpy.meshgrid(x, y)

fig = plt.figure()
fig.set_figwidth(fig.get_figwidth() * 2)

axes = fig.add_subplot(1, 2, 1, aspect='equal')
tribble_density = axes.pcolor(X, Y, numpy.sin(X**2) + numpy.cos(Y**2), cmap=plt.get_cmap("Oranges"))
axes.set_xlabel("x (km)")
axes.set_ylabel("y (km)")
axes.set_title("Tribble Density ($N/km^2$)")
cbar = fig.colorbar(tribble_density, ax=axes)
cbar.set_label("$1/km^2$")

axes = fig.add_subplot(1, 2, 2, aspect='equal')
klingon_population_density = axes.pcolor(X, Y, X * Y + 1, cmap=plt.get_cmap("RdBu"))
axes.set_xlabel("x (km)")
axes.set_ylabel("y (km)")
axes.set_title("Klingon Population ($N$)")
cbar = fig.colorbar(klingon_population_density, ax=axes)
cbar.set_label("Number (thousands)")

plt.show()

"""
Explanation: Here is the other example with a few extra tricks added in.
End of explanation
"""


