# Turning on inline plots -- just for use in ipython notebooks.
%matplotlib inline

import matplotlib
print matplotlib.__version__
print matplotlib.get_backend()

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()

fig = plt.figure(figsize=(10, 4))
fig.gca()  # needed for the ipython-inline to display anything
plt.show()

# Twice as tall
fig = plt.figure(figsize=plt.figaspect(2.0))
fig.gca()
plt.show()

fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot([1, 2, 3, 4], [10, 20, 25, 30])
ax.set_xlim(0.5, 4.5)
plt.show()

plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.xlim(0.5, 4.5)
plt.show()

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=plt.figaspect(0.5))
ax1.plot([-10, -5, 0, 5, 10, 15], [-1.2, 2, 3.5, -0.3, -4, 1])
ax2.scatter([-10, -5, 0, 5, 10, 15], [-1.2, 2, 3.5, -0.3, -4, 1])
plt.show()

# Good -- setting limits after plotting is done
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=plt.figaspect(0.5))
ax1.plot([-10, -5, 0, 5, 10, 15], [-1.2, 2, 3.5, -0.3, -4, 1])
ax2.scatter([-10, -5, 0, 5, 10, 15], [-1.2, 2, 3.5, -0.3, -4, 1])
ax1.set_ylim(bottom=-10)
ax2.set_xlim(right=25)
plt.show()

# Bad -- Setting limits before plotting is done
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=plt.figaspect(0.5))
ax1.set_ylim(bottom=-10)
ax2.set_xlim(right=25)
ax1.plot([-10, -5, 0, 5, 10, 15], [-1.2, 2, 3.5, -0.3, -4, 1])
ax2.scatter([-10, -5, 0, 5, 10, 15], [-1.2, 2, 3.5, -0.3, -4, 1])
plt.show()

%load exercises/1.1-limits.py

fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot([1, 2, 3, 4], [10, 20, 25, 30], label='Philadelphia')
ax.plot([1, 2, 3, 4], [30, 23, 13, 4], label='Boston')
ax.set_ylabel('Temperature (deg C)')
ax.set_xlabel('Time')
ax.set_title("A tale of two cities")
ax.legend()
plt.show()

fig, ax = plt.subplots(1, 1)
ax.bar([1, 2, 3, 4], [10, 20, 25, 30], label="Foobar")
ax.plot([1, 2, 3, 4], [10, 20, 25, 30], label="_nolegend_")
ax.legend()
plt.show()

fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot([1, 2, 3, 4], [10, 20, 25, 30])
ax.xaxis.set_ticks(range(1, 5))               # Set ticks at 1, 2, 3, 4
ax.xaxis.set_ticklabels([3, 100, -12, "foo"]) # Label ticks as "3", "100", "-12", and "foo"
ax.tick_params(axis='y', direction='inout', length=10)
plt.show()

fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(121)
ax.plot([1, 2, 3, 4], [10, 20, 25, 30], label='Philadelphia')
ax.plot([1, 2, 3, 4], [30, 23, 13, 4], label='Boston')
ax.set_title('A tale of two cities')
ax.legend()

t = np.linspace(0, 7, 25)
z = 2 * np.sin(t) + 5
ax = fig.add_subplot(122)
ax.scatter(t, z, label='Philadelphia')
ax.set_title("Observed Tide")
ax.legend()

fig.suptitle('A title for the whole figure')
plt.show()

fig, axes = plt.subplots(2, 2, figsize=(9, 9))
plt.subplots_adjust(wspace=0.5, hspace=0.3,
                    left=0.125, right=0.9,
                    top=0.9,    bottom=0.1)
plt.show()

def example_plot(ax):
    ax.plot([1, 2])
    ax.set_xlabel('x-label', fontsize=16)
    ax.set_ylabel('y-label', fontsize=8)
    ax.set_title('Title', fontsize=24)

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)
example_plot(ax1)
example_plot(ax2)
example_plot(ax3)
example_plot(ax4)
#plt.tight_layout()
plt.show()

# going out of inline mode to demonstrate the zooming and panning
%matplotlib

fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
ax1.plot([1, 2, 3, 4], [1, 2, 3, 4])
ax2.plot([3, 4, 5, 6], [6, 5, 4, 3])
plt.show()

# Go back to inline mode
%matplotlib inline

fig, ax1 = plt.subplots(1, 1)
ax1.plot([1, 2, 3, 4], [1, 2, 3, 4])
ax2 = ax1.twinx()
ax2.scatter([1, 2, 3, 4], [60, 50, 40, 30])
ax1.set_xlabel('X')
ax1.set_ylabel('First scale')
ax2.set_ylabel('Other scale')
plt.show()

fig, ax = plt.subplots(1, 1)
ax.plot([-2, 2, 3, 4], [-10, 20, 25, 5])
ax.spines['top'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')  # no ticklines at the top
ax.spines['right'].set_visible(False)
ax.yaxis.set_ticks_position('left')  # no ticklines on the right

# "outward"
# Move the two remaining spines "out" away from the plot by 10 points
ax.spines['bottom'].set_position(('outward', 10))
ax.spines['left'].set_position(('outward', 10))

# "data"
# Have the spines stay intersected at (0,0)
#ax.spines['bottom'].set_position(('data', 0))
#ax.spines['left'].set_position(('data', 0))

# "axes"
# Have the two remaining spines placed at a fraction of the axes
#ax.spines['bottom'].set_position(('axes', 0.75))
#ax.spines['left'].set_position(('axes', 0.25))

plt.show()

%load exercises/1.2-spines.py

y, x = np.ogrid[-6:6:20j, -10:10:30j]
z = np.hypot(x, y)
plt.imshow(z)
plt.colorbar()
plt.show()

plt.imshow(z)
plt.colorbar(orientation='horizontal', shrink=0.75)  # We can make colorbars do all sorts of things!
plt.show()

plt.imshow(z)
cbar = plt.colorbar(extend='both', aspect=10)
cbar.set_label('distance')                           # And we can even add a label to it
plt.show()

fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(z)
im = ax2.imshow(z) # Note, due to a bug, you will need to save the
                   # returned image object when calling imshow() from an Axes
                   # and pass that to plt.colorbar() so that it knows what
                   # image to build a colorbar from. This will be fixed for v1.3.1.
plt.colorbar(im)
plt.show()

fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(z)
im = ax2.imshow(z)
plt.colorbar(im, ax=[ax1, ax2], shrink=0.5)
plt.show()