Plotting matrices
There are a number of methods available for plotting grids of data in matplotlib. Start off by saving this file somewhere on your machine.
Let’s start by opening an image - we have to use the scipy.misc package for this:
from scipy import misc
image=misc.imread('/path/to/image/flower.jpg')
We have now created a numpy array version of the image (you can check this by typing type(image)).
To plot this, we need to use matplotlib.pylot’s imshow() function. Now type the following:
import matplotlib.pyplot as plt
plt.imshow(image)
plt.show()
This image is RGB - i.e. the array consists of 3 bands where each band holds values between 0-255 (see [here(https://en.wikipedia.org/wiki/RGB_color_model) for more info) making up the red, green and blue components of the image. Remember that you can check the shape of the image array using:
image.shape
As the image has RGB information, we can;t override the colour scheme. To do this, we must not present RGB info and we can do this by accessing just one band of the image by slicing the array:
image=image[:,:,0]
Now, let’s change the colour scheme - we can do this by setting the cmap option - the potential colour schemes can be found here:
plt.imshow(image, cmap='cool')
plt.show()
As with the scatter plot (see here), you can also access the following to edit the axis and titles:
plt.imshow(image, cmap='cool')
plt.title("Flower ")
plt.xlabel("x dimension")
plt.ylabel("y dimension")
To save the image, you can use the following code to which you pass in the output file which we will associate with a variable call ofile (you can call this what you like though):
ofile='flower_cool.jpg'
plt.savefig(ofile)
When saving figures, you can also alter a variety of parameters including the dots per inch (dpi) (remember to recreate your figure instance before doing this otherwise the image you save will be empty) e.g.:
ofile='flower_cool_300dpi.jpg'
plt.savefig(ofile, dpi=300)
The plt.savefig() has a variety of options such as the ability to set transparency so have a look at the documentation to ensure you take full advantage of it.
Plotting a DEM
Now let’s plot a digital elevation model. Save this file on your machine.
Read it in as an array:
dem=misc.imread("/path/to/file/dem.tif")
Create a plot object and apply a sequential colormap (to show the transition from low to high elevations - see here for more info):
plt.imshow(dem, cmap='afmhot')
plt.title('North Greenland Bathymetry')
plt.xlabel('Easting')
plt.ylabel('Northing')
plt.show()
The coordinates of the DEM represented by the DEM are:
top left (x min): -889595
top right (y max): -900955
bottom right (x max): -600459
bottom left (y min): -607972
We can use this information to set the tick labels of the image by making use of the extent option in plt.imshow() e.g.:
x_min=-889595
y_max=-900955
x_max=-600459
y_min=-607972
extent_dims=[x_min, x_max, y_min, y_max]
plt.imshow(dem, cmap='afmhot', extent=extent_dims)
This image represents elevation, so let’s make a colorbar to provide useful information to those viewing the image:
cbar=plt.colorbar()
cbar.set_label("Elevation (m a.s.l.)", labelpad=+2)
Then just show the plot:
plt.show()
You should end up with something like this:
Have a look at the other options available with plt.imshow() and see what else you can do with this figure.