def make_category_scatter_figure():
categories = get_data()
# Build a figure and place a single axes instance in it.
fig = plt.figure(figsize=(4,3))
ax1 = fig.add_subplot(1,1,1)
# Use our helper function to do the category scatter plot.
cs = CategoryScatter( ax1, categories )
# Locate the axes spines on the left and bottom.
spine_placer(ax1, location='left,bottom' )
# Finalize the category scatter plot (stuff that can only be done
# after the spines are placed).
cs.finalize()
# Add standard y label.
ax1.set_ylabel('y value (units)')
# Now, add a final few touchups.
ax1.spines['bottom'].set_color('none') # don't draw bottom spine
ax1.yaxis.set_major_locator( mticker.MaxNLocator(nbins=4) )
auto_reduce_spine_bounds( ax1 )
fig.tight_layout()
return fig
def make_many_timeseries_figure():
# Generate some fake data in 4 timeseries
times = np.arange( 0, 200.0, 0.01 )
nt = len(times)
ys = []
for i in range(4):
ys.append( np.sin( times*2*np.pi*0.02 + i*20 ) + 0.13*np.random.randn(nt) )
# Build a figure and place a two axes instances in it.
fig = plt.figure(figsize=(8,6))
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
# Use our helper function to plot the multiple timeseries.
mts1 = ManyTimeseries( ax1, times, ys, show=['all','mean'] )
# Use our helper function to plot the multiple timeseries.
mts2 = ManyTimeseries( ax2, times, ys, show=['mean','std'] )
# Locate the axes spines.
spine_placer(ax1, location='left' )
spine_placer(ax2, location='left,bottom' )
# Finalize the plots (stuff that can only be done
# after the spines are placed).
mts1.finalize()
mts2.finalize()
# Add standard axis labels.
ax1.set_ylabel('y1 value (units)')
ax2.set_ylabel('y2 value (units)')
ax2.set_xlabel('time (seconds)')
# Now, add a final few touchups.
ax1.yaxis.set_major_locator( mticker.MaxNLocator(nbins=4) )
ax1.set_xticks([])
ax2.yaxis.set_major_locator( mticker.MaxNLocator(nbins=4) )
auto_reduce_spine_bounds( ax1 )
auto_reduce_spine_bounds( ax2 )
fig.tight_layout()
return fig
