# For setting parameters, we will need to use matplotlib (mpl) directly
import matplotlib as mpl

# This is the usual invocation of pyplot
import matplotlib.pyplot as plt

import numpy as np
import pandas as pd

# Set the random seed for consistency
np.random.seed(12)

# I happen to know that there are 7 default colors in matplotlib
for i in range(7):
    plt.plot(np.random.randn(1000).cumsum())

! sudo easy_install brewer2mpl
Searching for brewer2mpl
Reading http://pypi.python.org/simple/brewer2mpl/
Reading https://github.com/jiffyclub/brewer2mpl/wiki
Best match: brewer2mpl 1.3.1
Downloading http://pypi.python.org/packages/source/b/brewer2mpl/brewer2mpl-1.3.1.zip#md5=ae1e2cfc57e7e022e0208e2b5a994292
Processing brewer2mpl-1.3.1.zip
Writing /tmp/easy_install-9BiZaj/brewer2mpl-1.3.1/setup.cfg
Running brewer2mpl-1.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-9BiZaj/brewer2mpl-1.3.1/egg-dist-tmp-UKWbyr
zip_safe flag not set; analyzing archive contents...
brewer2mpl.brewer2mpl: module references __file__
Adding brewer2mpl 1.3.1 to easy-install.pth file

Installed /Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/brewer2mpl-1.3.1-py2.7.egg
Processing dependencies for brewer2mpl
Finished processing dependencies for brewer2mpl
! cat ~/.matplotlibrc | grep color_cycle

import brewer2mpl

# brewer2mpl.get_map args: set name  set type  number of colors
bmap = brewer2mpl.get_map('Set2', 'qualitative', 7)
colors = bmap.mpl_colors
print colors

# Set the random seed for consistency
np.random.seed(12)

# Change the default colors
mpl.rcParams['axes.color_cycle'] = colors

# I happen to know that there are 7 default colors in matplotlib
for i in range(7):
    plt.plot(np.random.randn(1000).cumsum())

# Set the random seed for consistency
np.random.seed(12)

# Change the default colors
#mpl.rcParams['axes.color_cycle'] = 
colors = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colors

#matplotlib.image.cmap = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colormap

# I happen to know that there are 7 default colors in matplotlib
for i, color in enumerate(colors):
    plt.scatter(np.random.randn(1000), np.random.randn(1000), 
    color=color)

# Set the random seed for consistency
np.random.seed(12)

# Change the default colors
#mpl.rcParams['axes.color_cycle'] = 
colors = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colors

#matplotlib.image.cmap = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colormap

# I happen to know that there are 7 default colors in matplotlib
for i, color in enumerate(colors):
    plt.scatter(np.random.randn(1000), np.random.randn(1000), 
    color=color, edgecolors='k')

# Set the random seed for consistency
np.random.seed(12)

# Change the default colors
#mpl.rcParams['axes.color_cycle'] = 
colors = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colors

#matplotlib.image.cmap = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colormap

# I happen to know that there are 7 default colors in matplotlib
for i, color in enumerate(colors):
    plt.scatter(np.random.randn(1000), np.random.randn(1000), 
    color=color,
        edgecolors='grey',linewidths=0.1)

# For some reason, this doesn't work with mpl
import matplotlib
matplotlib.matplotlib_fname()

%%bash
mkdir ~/.matplotlib
cd ~/.matplotlib
wget http://matplotlib.org/_static/matplotlibrc 
cat ~/.matplotlib/matplotlibrc

for color in colors:
    print mpl.colors.rgb2hex(color)

! cat ~/.matplotlib/matplotlibrc | grep axes.color_cycle

! cat ~/.matplotlib/matplotlibrc | grep axes.color_cycle

from matplotlib.colors import LogNorm
from pylab import *

#normal distribution center at x=0 and y=5
x = randn(100000)
y = randn(100000)+5

hist2d(x, y, bins=40, norm=LogNorm())
colorbar()
show()

from matplotlib.colors import LogNorm
from pylab import *

#normal distribution center at x=0 and y=5
x = randn(100000)
y = randn(100000)+5

hist2d(x, y, bins=40, norm=LogNorm(), 
    cmap=brewer2mpl.get_map('Greens', 'sequential', 8).mpl_colormap)
colorbar()
show()

from matplotlib.colors import LogNorm
from pylab import *

#normal distribution center at x=0 and y=5
x = randn(100000)
y = randn(100000)+5

# norm=LogNorm() tells the function to use a logscale for the z-values
hist2d(x, y, bins=40, norm=LogNorm(), 
    cmap=brewer2mpl.get_map('Greys', 'sequential', 8).mpl_colormap)
colorbar()
show()

from numpy.random import uniform, seed
from matplotlib.mlab import griddata
import matplotlib.pyplot as plt
import numpy as np
# make up data.
#npts = int(raw_input('enter # of random points to plot:'))
seed(0)
npts = 200
x = uniform(-2,2,npts)
y = uniform(-2,2,npts)
z = x*np.exp(-x**2-y**2)
# define grid.
xi = np.linspace(-2.1,2.1,100)
yi = np.linspace(-2.1,2.1,200)
# grid the data.
zi = griddata(x,y,z,xi,yi,interp='linear')
# contour the gridded data, plotting dots at the nonuniform data points.
CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.rainbow,
                  vmax=abs(zi).max(), vmin=-abs(zi).max())
plt.colorbar() # draw colorbar
# plot data points.
plt.scatter(x,y,marker='o',c='b',s=5,zorder=10)
plt.xlim(-2,2)
plt.ylim(-2,2)
plt.title('griddata test (%d points)' % npts)

from numpy.random import uniform, seed
from matplotlib.mlab import griddata
import matplotlib.pyplot as plt
import numpy as np
# make up data.
#npts = int(raw_input('enter # of random points to plot:'))
seed(0)
npts = 200
x = uniform(-2,2,npts)
y = uniform(-2,2,npts)
z = x*np.exp(-x**2-y**2)
# define grid.
xi = np.linspace(-2.1,2.1,100)
yi = np.linspace(-2.1,2.1,200)
# grid the data.
zi = griddata(x,y,z,xi,yi,interp='linear')
# contour the gridded data, plotting dots at the nonuniform data points.
CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')

# ---- This is the line we changed ---- #
CS = plt.contourf(xi,yi,zi,15,
    cmap=brewer2mpl.get_map('RdBu', 'diverging', 8, reverse=True).mpl_colormap,
                  vmax=abs(zi).max(), vmin=-abs(zi).max())

plt.colorbar() # draw colorbar
# plot data points.
plt.scatter(x,y,marker='o',c='b',s=5,zorder=10)
plt.xlim(-2,2)
plt.ylim(-2,2)
plt.title('griddata test (%d points)' % npts)


from numpy.random import uniform, seed
from matplotlib.mlab import griddata
import matplotlib.pyplot as plt
import numpy as np
# make up data.
#npts = int(raw_input('enter # of random points to plot:'))
seed(0)
npts = 200
x = uniform(-2,2,npts)
y = uniform(-2,2,npts)
z = x*np.exp(-x**2-y**2)
# define grid.
xi = np.linspace(-2.1,2.1,100)
yi = np.linspace(-2.1,2.1,200)
# grid the data.
zi = griddata(x,y,z,xi,yi,interp='linear')
# contour the gridded data, plotting dots at the nonuniform data points.
CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')

# ---- This is the line we changed ---- #
CS = plt.contourf(xi,yi,zi,15,
    cmap=brewer2mpl.get_map('PRGn', 'diverging', 8, reverse=True).mpl_colormap,
                  vmax=abs(zi).max(), vmin=-abs(zi).max())

plt.colorbar() # draw colorbar
# plot data points.
plt.scatter(x,y,marker='o',c='b',s=5,zorder=10)
plt.xlim(-2,2)
plt.ylim(-2,2)
plt.title('griddata test (%d points)' % npts)

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage

from matplotlib._png import read_png
import matplotlib.colors
from matplotlib.cbook import get_sample_data

class RibbonBox(object):

    original_image = read_png(get_sample_data("Minduka_Present_Blue_Pack.png",
                                              asfileobj=False))
    cut_location = 70
    b_and_h = original_image[:,:,2]
    color = original_image[:,:,2] - original_image[:,:,0]
    alpha = original_image[:,:,3]
    nx = original_image.shape[1]

    def __init__(self, color):
        rgb = matplotlib.colors.colorConverter.to_rgb(color)

        im = np.empty(self.original_image.shape,
                      self.original_image.dtype)


        im[:,:,:3] = self.b_and_h[:,:,np.newaxis]
        im[:,:,:3] -= self.color[:,:,np.newaxis]*(1.-np.array(rgb))
        im[:,:,3] = self.alpha

        self.im = im


    def get_stretched_image(self, stretch_factor):
        stretch_factor = max(stretch_factor, 1)
        ny, nx, nch = self.im.shape
        ny2 = int(ny*stretch_factor)

        stretched_image = np.empty((ny2, nx, nch),
                                   self.im.dtype)
        cut = self.im[self.cut_location,:,:]
        stretched_image[:,:,:] = cut
        stretched_image[:self.cut_location,:,:] = \
                self.im[:self.cut_location,:,:]
        stretched_image[-(ny-self.cut_location):,:,:] = \
                self.im[-(ny-self.cut_location):,:,:]

        self._cached_im = stretched_image
        return stretched_image



class RibbonBoxImage(BboxImage):
    zorder = 1

    def __init__(self, bbox, color,
                 cmap = None,
                 norm = None,
                 interpolation=None,
                 origin=None,
                 filternorm=1,
                 filterrad=4.0,
                 resample = False,
                 **kwargs
                 ):

        BboxImage.__init__(self, bbox,
                           cmap = cmap,
                           norm = norm,
                           interpolation=interpolation,
                           origin=origin,
                           filternorm=filternorm,
                           filterrad=filterrad,
                           resample = resample,
                           **kwargs
                           )

        self._ribbonbox = RibbonBox(color)
        self._cached_ny = None


    def draw(self, renderer, *args, **kwargs):

        bbox = self.get_window_extent(renderer)
        stretch_factor = bbox.height / bbox.width

        ny = int(stretch_factor*self._ribbonbox.nx)
        if self._cached_ny != ny:
            arr = self._ribbonbox.get_stretched_image(stretch_factor)
            self.set_array(arr)
            self._cached_ny = ny

        BboxImage.draw(self, renderer, *args, **kwargs)


if 1:
    from matplotlib.transforms import Bbox, TransformedBbox
    from matplotlib.ticker import ScalarFormatter

    fig = plt.gcf()
    fig.clf()
    ax = plt.subplot(111)

    years = np.arange(2004, 2009)
    box_colors = [(0.8, 0.2, 0.2),
                  (0.2, 0.8, 0.2),
                  (0.2, 0.2, 0.8),
                  (0.7, 0.5, 0.8),
                  (0.3, 0.8, 0.7),
                  ]
    heights = np.random.random(years.shape) * 7000 + 3000

    fmt = ScalarFormatter(useOffset=False)
    ax.xaxis.set_major_formatter(fmt)

    for year, h, bc in zip(years, heights, box_colors):
        bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h)
        bbox = TransformedBbox(bbox0, ax.transData)
        rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic")

        ax.add_artist(rb_patch)

        ax.annotate(r"%d" % (int(h/100.)*100),
                    (year, h), va="bottom", ha="center")

    patch_gradient = BboxImage(ax.bbox,
                               interpolation="bicubic",
                               zorder=0.1,
                               )
    gradient = np.zeros((2, 2, 4), dtype=np.float)
    gradient[:,:,:3] = [1, 1, 0.]
    gradient[:,:,3] = [[0.1, 0.3],[0.3, 0.5]] # alpha channel
    patch_gradient.set_array(gradient)
    ax.add_artist(patch_gradient)


    ax.set_xlim(years[0]-0.5, years[-1]+0.5)
    ax.set_ylim(0, 10000)

    fig.savefig('ribbon_box.png')
    plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage

from matplotlib._png import read_png
import matplotlib.colors
from matplotlib.cbook import get_sample_data

class RibbonBox(object):

    original_image = read_png(get_sample_data("Minduka_Present_Blue_Pack.png",
                                              asfileobj=False))
    cut_location = 70
    b_and_h = original_image[:,:,2]
    color = original_image[:,:,2] - original_image[:,:,0]
    alpha = original_image[:,:,3]
    nx = original_image.shape[1]

    def __init__(self, color):
        rgb = matplotlib.colors.colorConverter.to_rgb(color)

        im = np.empty(self.original_image.shape,
                      self.original_image.dtype)


        im[:,:,:3] = self.b_and_h[:,:,np.newaxis]
        im[:,:,:3] -= self.color[:,:,np.newaxis]*(1.-np.array(rgb))
        im[:,:,3] = self.alpha

        self.im = im


    def get_stretched_image(self, stretch_factor):
        stretch_factor = max(stretch_factor, 1)
        ny, nx, nch = self.im.shape
        ny2 = int(ny*stretch_factor)

        stretched_image = np.empty((ny2, nx, nch),
                                   self.im.dtype)
        cut = self.im[self.cut_location,:,:]
        stretched_image[:,:,:] = cut
        stretched_image[:self.cut_location,:,:] = \
                self.im[:self.cut_location,:,:]
        stretched_image[-(ny-self.cut_location):,:,:] = \
                self.im[-(ny-self.cut_location):,:,:]

        self._cached_im = stretched_image
        return stretched_image



class RibbonBoxImage(BboxImage):
    zorder = 1

    def __init__(self, bbox, color,
                 cmap = None,
                 norm = None,
                 interpolation=None,
                 origin=None,
                 filternorm=1,
                 filterrad=4.0,
                 resample = False,
                 **kwargs
                 ):

        BboxImage.__init__(self, bbox,
                           cmap = cmap,
                           norm = norm,
                           interpolation=interpolation,
                           origin=origin,
                           filternorm=filternorm,
                           filterrad=filterrad,
                           resample = resample,
                           **kwargs
                           )

        self._ribbonbox = RibbonBox(color)
        self._cached_ny = None


    def draw(self, renderer, *args, **kwargs):

        bbox = self.get_window_extent(renderer)
        stretch_factor = bbox.height / bbox.width

        ny = int(stretch_factor*self._ribbonbox.nx)
        if self._cached_ny != ny:
            arr = self._ribbonbox.get_stretched_image(stretch_factor)
            self.set_array(arr)
            self._cached_ny = ny

        BboxImage.draw(self, renderer, *args, **kwargs)


if 1:
    from matplotlib.transforms import Bbox, TransformedBbox
    from matplotlib.ticker import ScalarFormatter

    fig = plt.gcf()
    fig.clf()
    ax = plt.subplot(111)

    years = np.arange(2004, 2009)
    box_colors = [(0.8, 0.2, 0.2),
                  (0.2, 0.8, 0.2),
                  (0.2, 0.2, 0.8),
                  (0.7, 0.5, 0.8),
                  (0.3, 0.8, 0.7),
                  ]
    heights = np.random.random(years.shape) * 7000 + 3000

    fmt = ScalarFormatter(useOffset=False)
    ax.xaxis.set_major_formatter(fmt)

    for year, h, bc in zip(years, heights, box_colors):
        bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h)
        bbox = TransformedBbox(bbox0, ax.transData)
        rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic")

        ax.add_artist(rb_patch)

        ax.annotate(r"%d" % (int(h/100.)*100),
                    (year, h), va="bottom", ha="center")

#    patch_gradient = BboxImage(ax.bbox,
#                               interpolation="bicubic",
#                               zorder=0.1,
#                               )
#    gradient = np.zeros((2, 2, 4), dtype=np.float)
#    gradient[:,:,:3] = [1, 1, 0.]
#    gradient[:,:,3] = [[0.1, 0.3],[0.3, 0.5]] # alpha channel
#    patch_gradient.set_array(gradient)
#    ax.add_artist(patch_gradient)


    ax.set_xlim(years[0]-0.5, years[-1]+0.5)
    ax.set_ylim(0, 10000)

    fig.savefig('ribbon_box.png')
    plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage

from matplotlib._png import read_png
import matplotlib.colors
from matplotlib.cbook import get_sample_data

if 1:
    from matplotlib.transforms import Bbox, TransformedBbox
    from matplotlib.ticker import ScalarFormatter

    fig = plt.gcf()
    fig.clf()
    ax = plt.subplot(111)

    years = np.arange(2004, 2009)
    box_colors = [(0.8, 0.2, 0.2),
                  (0.2, 0.8, 0.2),
                  (0.2, 0.2, 0.8),
                  (0.7, 0.5, 0.8),
                  (0.3, 0.8, 0.7),
                  ]
    heights = np.random.random(years.shape) * 7000 + 3000

    fmt = ScalarFormatter(useOffset=False)
    ax.xaxis.set_major_formatter(fmt)

    for year, h, bc in zip(years, heights, box_colors):
#        bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h)
#       bbox = TransformedBbox(bbox0, ax.transData)
#        rb_patch = BboxImage(bbox, interpolation='bicubic')
#        rb_ptch = RibbonBoxImage(bbox, bc, interpolation="bicubic")

#        ax.add_artist(rb_patch)
#        ax.add_artist(bbox)

        # --- this is the line we changed --- #
        ax.bar(year, h, color=bc)

        ax.annotate(r"%d" % (int(h/100.)*100),
                    (year, h), va="bottom", ha="center")


    ax.set_xlim(years[0]-0.5, years[-1]+0.5)
    ax.set_ylim(0, 10000)
    
    fig.savefig('ribbon_box_no_ribbons.png')
    plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage

from matplotlib._png import read_png
import matplotlib.colors
from matplotlib.cbook import get_sample_data

if 1:
    from matplotlib.transforms import Bbox, TransformedBbox
    from matplotlib.ticker import ScalarFormatter

    fig = plt.gcf()
    fig.clf()
    ax = plt.subplot(111)

    years = np.arange(2004, 2009)
    box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors
#    box_colors = [(0.8, 0.2, 0.2),
#                  (0.2, 0.8, 0.2),
#                  (0.2, 0.2, 0.8),
#                  (0.7, 0.5, 0.8),
#                  (0.3, 0.8, 0.7),
#                  ]
    heights = np.random.random(years.shape) * 7000 + 3000

    fmt = ScalarFormatter(useOffset=False)
    ax.xaxis.set_major_formatter(fmt)

    for year, h, bc in zip(years, heights, box_colors):
        # --- this is the line we changed --- #
        ax.bar(year-0.4, h, color =bc)

        ax.annotate(r"%d" % (int(h/100.)*100),
                    (year, h), va="bottom", ha="center")


    ax.set_xlim(years[0]-0.5, years[-1]+0.5)
    ax.set_ylim(0, 10000)
    
    fig.savefig('ribbon_box_no_ribbons.png')
    plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage

from matplotlib._png import read_png
import matplotlib.colors
from matplotlib.cbook import get_sample_data

if 1:
    from matplotlib.transforms import Bbox, TransformedBbox
    from matplotlib.ticker import ScalarFormatter

    fig = plt.gcf()
    fig.clf()
    ax = plt.subplot(111)

    years = np.arange(2004, 2009)
    box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors
#    box_colors = [(0.8, 0.2, 0.2),
#                  (0.2, 0.8, 0.2),
#                  (0.2, 0.2, 0.8),
#                  (0.7, 0.5, 0.8),
#                  (0.3, 0.8, 0.7),
#                  ]
    heights = np.random.random(years.shape) * 7000 + 3000

    fmt = ScalarFormatter(useOffset=False)
    ax.xaxis.set_major_formatter(fmt)

    for year, h, bc in zip(years, heights, box_colors):
        # --- this is the line we changed --- #
        ax.bar(year-0.4, h, color =bc)

        ax.annotate(r"%d" % (int(h/100.)*100),
                    (year, h), va="bottom", ha="center")


    ax.set_xlim(years[0]-0.5, years[-1]+0.5)
    ax.set_ylim(0, 10000)
    
    fig.savefig('ribbon_box_no_ribbons.png')
    plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage

from matplotlib._png import read_png
import matplotlib.colors
from matplotlib.cbook import get_sample_data

if 1:
    from matplotlib.transforms import Bbox, TransformedBbox
    from matplotlib.ticker import ScalarFormatter

    fig = plt.gcf()
    fig.clf()
    ax = plt.subplot(111)

    years = np.arange(2004, 2009)
    box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors
#    box_colors = [(0.8, 0.2, 0.2),
#                  (0.2, 0.8, 0.2),
#                  (0.2, 0.2, 0.8),
#                  (0.7, 0.5, 0.8),
#                  (0.3, 0.8, 0.7),
#                  ]
    heights = np.random.random(years.shape) * 7000 + 3000

    fmt = ScalarFormatter(useOffset=False)
    ax.xaxis.set_major_formatter(fmt)

    for year, h, bc in zip(years, heights, box_colors):
        # --- this is the line we changed --- #
        ax.bar(year-0.4, h, color =bc)

        ax.annotate(r"%d" % (int(h/100.)*100),
                    (year, h+100), va="bottom", ha="center")


    ax.set_xlim(years[0]-0.5, years[-1]+0.5)
    ax.set_ylim(0, 10000)
    
    fig.savefig('ribbon_box_no_ribbons.png')
    plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage

from matplotlib._png import read_png
import matplotlib.colors
from matplotlib.cbook import get_sample_data

if 1:
    from matplotlib.transforms import Bbox, TransformedBbox
    from matplotlib.ticker import ScalarFormatter

    fig = plt.gcf()
    fig.clf()
    ax = plt.subplot(111)

    years = np.arange(2004, 2009)
    
    # --- changed this line --- #
    box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors
    
    heights = np.random.random(years.shape) * 7000 + 3000

    fmt = ScalarFormatter(useOffset=False)
    ax.xaxis.set_major_formatter(fmt)

    for year, h, bc in zip(years, heights, box_colors):
        # --- this is the line we changed --- #
        ax.bar(year-0.4, h, color =bc)

        ax.annotate(r"%d" % (int(h/100.)*100),
                    (year, h+100), va="bottom", ha="center")


    ax.set_xlim(years[0]-0.5, years[-1]+0.5)
    ax.set_ylim(0, 10000)
    
    # --- Added this line --- #
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    
    fig.savefig('ribbon_box_no_ribbons.png')
    plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage

from matplotlib._png import read_png
import matplotlib.colors
from matplotlib.cbook import get_sample_data

if 1:
    from matplotlib.transforms import Bbox, TransformedBbox
    from matplotlib.ticker import ScalarFormatter

    fig = plt.gcf()
    fig.clf()
    ax = plt.subplot(111)

    years = np.arange(2004, 2009)
    
    # --- changed this line --- #
    box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors
    
    heights = np.random.random(years.shape) * 7000 + 3000

    fmt = ScalarFormatter(useOffset=False)
    ax.xaxis.set_major_formatter(fmt)

    for year, h, bc in zip(years, heights, box_colors):
        # --- this is the line we changed --- #
        ax.bar(year-0.4, h, color =bc)

        ax.annotate(r"%d" % (int(h/100.)*100),
                    (year, h+100), va="bottom", ha="center")


    ax.set_xlim(years[0]-0.5, years[-1]+0.5)
    ax.set_ylim(0, 10000)
    
    # --- Added this line --- #
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    
    # --- Added this line --- #
    ax.yaxis.set_ticks_position('left')
    ax.xaxis.set_ticks_position('bottom')
    
    fig.savefig('ribbon_box_no_ribbons.png')
    plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage

from matplotlib._png import read_png
import matplotlib.colors
from matplotlib.cbook import get_sample_data

if 1:
    from matplotlib.transforms import Bbox, TransformedBbox
    from matplotlib.ticker import ScalarFormatter

    fig = plt.gcf()
    fig.clf()
    ax = plt.subplot(111)

    years = np.arange(2004, 2009)
    
    # --- changed this line --- #
    box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors
    
    heights = np.random.random(years.shape) * 7000 + 3000

    fmt = ScalarFormatter(useOffset=False)
    ax.xaxis.set_major_formatter(fmt)

    for year, h, bc in zip(years, heights, box_colors):
        # --- this is the line we changed --- #
        ax.bar(year-0.4, h, color =bc)

        ax.annotate(r"%d" % (int(h/100.)*100),
                    (year, h+100), va="bottom", ha="center")


    ax.set_xlim(years[0]-0.5, years[-1]+0.5)
    ax.set_ylim(0, 10000)
    
    # --- Added this line --- #
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_visible(False)
    
    # --- Added this line --- #
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
    
    ax.grid(axis = 'y', color ='white', linestyle='-')
    
    fig.savefig('ribbon_box_no_ribbons.png')
    plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage

from matplotlib._png import read_png
import matplotlib.colors
from matplotlib.cbook import get_sample_data

if 1:
    from matplotlib.transforms import Bbox, TransformedBbox
    from matplotlib.ticker import ScalarFormatter

    fig = plt.gcf()
    fig.clf()
    ax = plt.subplot(111)

    years = np.arange(2004, 2009)
    
    # --- changed this line --- #
    box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors
    
    heights = np.random.random(years.shape) * 7000 + 3000

    fmt = ScalarFormatter(useOffset=False)
    ax.xaxis.set_major_formatter(fmt)

    for year, h, bc in zip(years, heights, box_colors):
        # --- this is the line we changed --- #
        ax.bar(year-0.4, h, color=bc, linewidth=0)

        ax.annotate(r"%d" % (int(h/100.)*100),
                    (year, h+100), va="bottom", ha="center")


    ax.set_xlim(years[0]-0.5, years[-1]+0.5)
    ax.set_ylim(0, 10000)
    
    # --- Added this line --- #
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_visible(False)
    
    # --- Added this line --- #
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
    
    ax.grid(axis = 'y', color ='white', linestyle='-')
    
    fig.savefig('ribbon_box_no_ribbons.png')
    plt.show()

# Find where my iPython directory is
! ipython locate

# Show the contents of my custom.css file, which I created using the above tutorial
! cat /Users/olga/.ipython/profile_customcss/static/css/custom.css

from bokeh.mpl import PlotClient
p = PlotClient(username='defaultuser', serverloc="http://portcon:5006",userapikey="nokey")
p.use_doc('example')
p.notebooksources()


x = np.arange(100) / 6.0
y = np.sin(x)
z = np.cos(x)
data_source = p.make_source(idx=range(100), x=x, y=y, z=z)
p.hold('off')
plot1 = p.plot('x', 'y', 'orange', data_source=data_source)
plot2 = p.plot('x', 'z', 'blue', data_source=data_source)
grid = p.grid([[plot1,plot2]])
grid