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024 Image display : Answers to exercises

Exercise 1

  • Plot the first datasets in data_MCD15A3H as subplots in a 2 x 2 shape.

Hint: Use a loop for the keys of data_MCD15A3H. Set up the 2 x 2 subplots with:

fig, axs = plt.subplots(2,2,figsize=(x_size,y_size))
axs = axs.flatten()

then you can refer to the subplot axes as ax[0], ax[1], ax[2] and ax[3] when you loop over the keys. Don't forget to increase x_size,y_size appropriately.

# ANSWER
# Plot the first datasets in `data_MCD15A3H` as subplots in a 2 x 2 shape.

import matplotlib.pyplot as plt
from  geog0111.modisUtils import getModisTiledata

kwargs = {
    'product'    : 'MCD15A3H',
    'tile'       : 'h17v03',
    'year'       : 2019,
    'doy'        : 41
}

data_MCD15A3H = getModisTiledata(verbose=False,timeout=300,**kwargs)

# this is most neatly done with a 
# loop over the keys we want
# Notice [:4] in array selection of the first 4 items
keys = list(data_MCD15A3H.keys())[:4]

name = f'{kwargs["product"]} tile {kwargs["tile"]}'
# plot size : play with these to get the right size for your plot
x_size,y_size = 16,12
shape = (2,2)

fig, axs = plt.subplots(*shape,figsize=(x_size,y_size))

# Hint: Use a loop for the keys of `data_MCD15A3H`. Set up the 2 x 2 subplots with:
#
#    fig, axs = plt.subplots(2,2,figsize=(x_size,y_size))
#    axs = axs.flatten()   
# then you can refer to the subplot axes as 
# `ax[0]`, `ax[1]`, `ax[2]` and `ax[3]` when you 
# loop over the keys. 
# Don't forget to increase `x_size,y_size` appropriately.

if shape[0] == 1 and shape[1] == 1:  
    axs = [axs]
else:
    axs = axs.flatten()# set the figure title
fig.suptitle(name)

# the loop
for i,k in enumerate(keys):
    # plot image data
    im = axs[i].imshow(data_MCD15A3H[k],\
                   vmin=0,vmax=100,\
                   interpolation='nearest')
    axs[i].set_title(k)
    fig.colorbar(im, ax=axs[i])

png

Exercise 2

  • write a function called im_display that takes as input:
    • a data dictionary
    • a list of keywords of datasets to plot
    • optionally:
      • a title
      • a colourmap name
      • lower and upper limits for plot data (vmin, vmax)
      • x_size,y_size
      • subplots shape : e.g. (2,2)

You should assume some default values for the optional items if not given. For the subplots shape, assume it is (n,1) where n is the length of the keyword list.

You should set the default values of vmin and vmax to None, as this just then takes the dataset default minimum and maximum.

Your code should be well-documented.

  • test your code

Note that you will have to experiment a bit with the x_size,y_size values to get a good plot. It is not easy to automate that.

# ANSWER
# write a function called im_display that takes as input:
#   a data dictionary
#   a list of keywords of datasets to plot

import matplotlib.pyplot as plt
from  geog0111.modisUtils import getModisTiledata

kwargs = {
    'product'    : 'MCD15A3H',
    'tile'       : 'h17v03',
    'year'       : 2019,
    'doy'        : 41
}

data_MCD15A3H = getModisTiledata(verbose=False,timeout=300,**kwargs)# first,load the dataset

# optionally:
#   a title
#   a colourmap name
#   lower and upper limits for plot data (vmin, vmax)
#   x_size,y_size
#   subplots shape : e.g. (2,2)


# You should assume some default values for the optional 
# items if not given. For the subplots shape, assume it is `(n,1)` 
# where `n` is the length of the keyword list.

# You should set the default values of `vmin` and `vmax` to `None`, 
# as this just then takes the dataset default minimum and maximum.
def im_display(data,names,\
               title=None,colourmap=None,\
               vmin=None,vmax=None,\
               x_size=12,y_size=8,shape=None):
    '''
    Image display utility 

    a function called im_display that takes as input:
        data  :  a data dictionary
        names :  a list of keywords of datasets to plot

        optionally:
            title=None       : a title
            colourmap=None   : a colourmap name
            vmin,vmax        : lower and upper limits for plot data
            x_size=16        : plot x size * shape[0]
            y_size=12        : plot y size * shape[1]
            shape=None       : subplots shape : e.g. (2,2)
    '''
    # Your code should be well-documented.

    # sort out options
    n = len(names)
    if shape == None:
        shape = (n,1)

    # adaptive size
    x_size = x_size * shape[0]
    y_size = y_size * shape[1]


    fig, axs = plt.subplots(*shape,figsize=(x_size,y_size))
    if shape[0] == 1 and shape[1] == 1:  
        axs = [axs]
    else:
        axs = axs.flatten()

    # switch off ticks
    plt.setp(axs, xticks=[], yticks=[])

    # set the figure title
    # Note sub-plots have titles too, set in the loop
    if title:
        fig.suptitle(title)

    # loop over names (titles for sub-plots)
    for i,k in enumerate(names):
        # i is an index (from the enumerate) so
        # we can refer to axs[i] as the sub-plot i
        # k is the value of names[i] which is the title
        # for plot i

        # plot image data with vmin and vmax setting 
        # the upper and lower thresholds
        # Use interpolation="nearest" for clearest data plot
        im = axs[i].imshow(data[k],\
                   vmin=vmin,vmax=vmax,\
                   interpolation='nearest')

        # optionally set a colurmap
        # be careful of UK/US spellings!
        if colourmap:
            im.set_cmap(colourmap)

        # set title to sub-plot i
        axs[i].set_title(k)

        # colourbar for sub-plot i
        fig.colorbar(im, ax=axs[i])    

# test
import matplotlib.pyplot as plt
from  geog0111.modisUtils import getModisTiledata

kwargs = {
    'product'    : 'MCD15A3H',
    'tile'       : 'h17v03',
    'year'       : 2019,
    'doy'        : 41
}

data_MCD15A3H = getModisTiledata(verbose=False,timeout=300,**kwargs)
# first,load the dataset

# this is mnost neatly done with a 
# loop over the keys we want
keys = list(data_MCD15A3H.keys())[:4]

name = f'{kwargs["product"]} tile {kwargs["tile"]}'

im_display(data_MCD15A3H,keys,\
               title=None,colourmap=None,\
               x_size=4,y_size=14,shape=(4,1))

png

im_display(data_MCD15A3H,keys,\
               title=None,colourmap='gray',\
               x_size=4,y_size=3,shape=(2,2))

png

Exercise 3

  • Write a function called plot_lc that takes as input modis land cover dataset and plots the associated land cover map
  • You might use x_size,y_size as optional inputs to improve scaling
  • Show the function operating
# ANSWER
# this is a little long-winded, but just wraps up the codes above
import matplotlib
import matplotlib.patches
import matplotlib.pyplot as plt
import pandas as pd

def plot_lc(data,x_size=12,y_size=12):
    '''
    takes as input MODIS LC dataset
    plots the associated land cover map

    x_size,y_size as optional inputs
    '''
    lc_Type1 = pd.read_csv('data/LC_Type1_colour.csv')

    # generate matplotlib cmap and norm objects from these

    # get the LC codes, colours and classes
    # from LC_Type1_class and LC_Type1_colour
    cmap = matplotlib.colors.\
            ListedColormap(list(lc_Type1['colour']))
    norm = matplotlib.colors.\
            BoundaryNorm(list(lc_Type1['code']), len(lc_Type1['code']))

    # set up the legend
    legend_labels = dict(zip(list(lc_Type1['colour']),\
                             list(lc_Type1['class'])))
    patches = [matplotlib.patches.Patch(color=c, label=l)
               for c,l in legend_labels.items()]
    fig, axs = plt.subplots(1,figsize=(x_size,y_size))
    im = axs.imshow(data,cmap=cmap,norm=norm,interpolation='nearest')
    plt.legend(handles=patches,
              bbox_to_anchor=(1.4, 1),
              facecolor="white")
from  geog0111.modisUtils import getModisTiledata
# ANSWER


kwargs = {
    'product'    : 'MCD12Q1',
    'tile'       : 'h17v03',
    'year'       : 2019,
    'doy'        : 1
}

data_MCD12Q1 = getModisTiledata(verbose=False,timeout=300,**kwargs)

# the data we want here
plot_lc(data_MCD12Q1['LC_Type1'])

png


Last update: September 29, 2021