Basic data visualization



In [1]:

    
import matplotlib.pyplot as plt
import rasterio
from rasterio import plot



In [2]:

    
src = rasterio.open(r"../tests/data/RGB.byte.tif")

With a raster dataset, (band number) or numpy array we can display the raster



In [3]:

    
%matplotlib inline
plot.show(src)









    












    Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x87fbfd0>

We can also display a single band of a multiband image by passing a tuple (raster source, band)



In [4]:

    
world = rasterio.open(r"../tests/data/world.rgb.tif")
plot.show((world, 2), cmap='viridis')









    












    Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x8aeed30>

side by side:



In [5]:

    
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,7))
plot.show(src, ax=ax1)
plot.show((world, 2), cmap='viridis', ax=ax2)
fig.tight_layout()

Multiple bands side by side



In [6]:

    
fig, (axr, axg, axb) = plt.subplots(1,3, figsize=(21,7))
plot.show((src, 1), ax=axr, cmap='Reds', title='red channel')
plot.show((src, 2), ax=axg, cmap='Greens', title='green channel')
plot.show((src, 3), ax=axb, cmap='Blues', title='blue channel')









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x10c1f278>

How about the same thing but with the ability to navigate simultaneously?



In [7]:

    
%matplotlib notebook
fig, (axr, axg, axb) = plt.subplots(1,3, figsize=(12, 4), sharex=True, sharey=True)
plot.show((src, 1), ax=axr, cmap='Reds', title='red channel')
plot.show((src, 2), ax=axg, cmap='Greens', title='green channel')
plot.show((src, 3), ax=axb, cmap='Blues', title='blue channel')









    














    











    Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x11031470>

If we want to see a histogram of the data we use the plot.show_hist function



In [8]:

    
%matplotlib inline
plot.show_hist(src)

Any of the optional pyplot histogram parameters can be passed to tweak the representation



In [9]:

    
plot.show_hist(world, bins=50, lw=0.0, stacked=False, alpha=0.3, 
               histtype='stepfilled', title="World Histogram overlaid")



In [10]:

    
plot.show_hist(world, bins=20, lw=2.0, stacked=True,
               alpha=0.8, histtype='step', normed=True, 
               title="World Histogram stacked")

See matplotlib.pyplot.hist documentation at http://matplotlib.org/api/pyplot_api.html for a list of these parameters and available options



In [11]:

    
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,7))
plot.show(world, ax=ax1)
plot.show_hist(world, bins=50, lw=0.0, stacked=False, alpha=0.3, 
               histtype='stepfilled', title="World Histogram")
fig.tight_layout()

Show contours from a raster



In [13]:

    
fig, ax = plt.subplots(1, figsize=(12, 12))
plot.show((world, 1), cmap='Greys_r', interpolation='none', ax=ax)
ax.set_xlim(-50, 0)
ax.set_ylim(0, 40)

plot.show((world, 1), contour=True, ax=ax)









    



C:\Anaconda\lib\site-packages\matplotlib\contour.py:370: RuntimeWarning: invalid value encountered in true_divide
  dist = np.add.reduce(([(abs(s)[i] / L[i]) for i in range(xsize)]), -1)






    Out[13]:





<matplotlib.axes._subplots.AxesSubplot at 0x14c7cc88>

And of course there are many ways to easily customize this output ...



In [21]:

    
%matplotlib notebook
fig, ax = plt.subplots(1, figsize=(12, 12))
plot.show((world, 1), cmap='Greys_r', interpolation='none', ax=ax)
ax.set_xlim(-50, 0)
ax.set_ylim(0, 40)

plot.show((world, 1), contour=True, ax=ax, 
          levels=[25, 125], colors=['white', 'red'], linewidths=4,
         contour_label_kws=dict(fontsize=18, fmt="%1.0f", inline_spacing=15, use_clabeltext=True))









    














    











    Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x1d4debe0>

Add a rasterio raster to a cartopy geoaxes



In [12]:

    
import cartopy
import cartopy.crs as ccrs

fig = plt.figure(figsize=(20, 12))
ax = plt.axes(projection=ccrs.InterruptedGoodeHomolosine())

ax.set_global()
plot.show(world, origin='upper', transform=ccrs.PlateCarree(), interpolation=None, ax=ax)

ax.coastlines()
ax.add_feature(cartopy.feature.BORDERS)









    Out[12]:





<cartopy.mpl.feature_artist.FeatureArtist at 0x14df9ba8>






    



C:\Anaconda\lib\site-packages\matplotlib\artist.py:221: MatplotlibDeprecationWarning: This has been deprecated in mpl 1.5, please use the
axes property.  A removal date has not been set.
  warnings.warn(_get_axes_msg, mplDeprecation, stacklevel=1)



In [ ]: