# In iPython or the iPython notebook, it's easiest to use the pylab magic, which
# imports matplotlib, numpy, and scipy.

# The inline flag means that images will be shown here in the notebooks, rather
# than in pop-up windows.

%pylab inline

# If you are using 'regular' Python, however, you'll want the following. You'll
# need to also separately import numpy and any other packages that you might need.

#import matplotlib.pyplot as plt

# Make some data to plot
x = np.linspace(0, 2*np.pi)
y1 = np.sin(x)
y2 = np.cos(x)

# First, create an empty figure with 2 subplots
# - The arguments (1, 2) indicate 1 row and 2 cols
# - The function plt.subplots returns an object for the figure and for each axes
# - There are multiple ways to accomplish this same goal, but this is probably the
#   simplest - notice that each subplot is associated with one of the axes objects.
fig, (ax1, ax2) = plt.subplots(1, 2)

# Next, put one line on the first axis and both lines on the second axis
# - On the second axes, add a legend to distinguish the two lines
ax1.plot(x, y1)

ax2.plot(x, y1, label='sin')  # The labels are what appear in the legend
ax2.plot(x, y2, label='cos')
ax2.legend()

# Finally, save the figure as a png file
fig.savefig('myfig.png')

#ax1.plot?

# Make some data to plot
x = np.arange(0, 100)
y = np.random.rand(100)  # 100 random numbers

# Make a figure with 6 subplots and axes
fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2)

# Add data to each axis. Optional arguments to each method will customize each plot.
ax1.plot(x, y)
ax2.hist(y)
ax3.scatter(x, y)
ax4.boxplot(y)
ax5.loglog(x, y)
ax6.semilogx(x, y)

# Read an image file for first subplot, generate random array for second
img1 = plt.imread('lena.png')
img2 = np.random.rand(128, 128)

# Make figure
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(img1)
ax2.imshow(img2)

ax1.set_axis_off()  # Hide "spines" on first axis

# Try it here...
%loadpy http://matplotlib.org/mpl_examples/pylab_examples/contour_demo.py

# Make some data to plot
x = np.linspace(0, 2*np.pi)
y1 = np.sin(x)
y2 = np.cos(x)

# First, create an empty figure with 1 subplot
fig, ax1 = plt.subplots(1, 1)

# Add title and labels
ax1.set_title('My Plot')
ax1.set_xlabel('x')
ax1.set_ylabel('y')

# Change axis limits
ax1.set_xlim([0,2])
ax1.set_ylim([-1, 2])

# Add the lines, changing their color, style, and marker
ax1.plot(x, y1, 'k--o', label='sin') # Black line, dashed, with 'o' markers
ax1.plot(x, y2, 'r-^', label='cos') # Red line, solid, with triangle-up markers

# Adjust tick marks and get rid of 'box'
ax1.tick_params(direction='out', top=False, right=False) # Turn ticks out
ax1.spines['top'].set_visible(False) # Get rid of top axis line
ax1.spines['right'].set_visible(False) #  Get rid of bottom axis line

# Add subplot letter
ax1.annotate('(a)', (0.01, 0.96), size=12, xycoords='figure fraction')

# Add legend
ax1.legend()

# Finally, save the figure as a png file
fig.savefig('myfig-formatted.png')

# View rcParams
matplotlib.rcParams

# Save default rcParams so we can reset them later
# WARNING: Do not run this cell after changing rcParams, as it will overwrite the
# defaults that we are trying to preserve.
rcdef = plt.rcParams.copy()

# Make sure rcParams is at default settings, since we're messing with it
plt.rcParams.update(rcdef)

# Make a simple figure with default formatting
fig, axall = plt.subplots(1, 2)  # axall is the tuple containing both axis objects

for ax in axall:
    ax.plot(np.random.rand(100), 'k-o', label='Random')
    ax.set_ylim([0, 1.2])
    ax.set_ylabel('Value')
    ax.legend()

# Choose a bunch of new parameter values
# In practice, you'll try modifying these, running the code and saving the figure,
# looking at the figure, then making more modifications until you're happy.
newparams = {'axes.labelsize': 14, 'axes.linewidth': 1, 'savefig.dpi': 300, 
             'lines.linewidth': 1.5, 'figure.figsize': (8, 3),
             'figure.subplot.wspace': 0.4,
             'ytick.labelsize': 12, 'xtick.labelsize': 12,
             'ytick.major.pad': 5, 'xtick.major.pad': 5,
             'legend.fontsize': 12, 'legend.frameon': False, 
             'legend.handlelength': 1.5}

# Update the global rcParams dictionary with the new parameter choices
# Before doing this, we reset rcParams to its default again, just in case
plt.rcParams.update(rcdef)
plt.rcParams.update(newparams)

# Make the new figure with new formatting
fig, axall = plt.subplots(1, 2)

for ax in axall:
    ax.plot(np.random.rand(100), 'k-o', label='Random')
    ax.set_ylim([0, 1.2])
    ax.set_ylabel('Value')
    ax.legend()
    
fig.savefig('myfig-advanced.png')

# Put rcParams back to default
plt.rcParams.update(rcdef)