A 2D grid array plot can be a valuable visualization tool, e.g. in the area of agent-based simulation. In this post I want to give a brief tutorial in how you can visualize a 2D grid array, using matplotlib in Python. The coding example is below; relevant documentation has been added in the form of comments.

# to start with, we will need matplotlib.pyplot from matplotlib import pyplot # next, i will set up a 8 x 8 2d matrix, with random bits as elements (0 or 1); # for randomization of integers (0 or 1) I use the random module in Python; # for building each row in the 2d matrix I use list comprehension in Python import random data = [[random.randint(a=0,b=1) for x in range(0,8)], # row 1 [random.randint(a=0,b=1) for x in range(0,8)], # row 2 [random.randint(a=0,b=1) for x in range(0,8)], # row 3 [random.randint(a=0,b=1) for x in range(0,8)], # row 4 [random.randint(a=0,b=1) for x in range(0,8)], # row 5 [random.randint(a=0,b=1) for x in range(0,8)], # row 6 [random.randint(a=0,b=1) for x in range(0,8)], # row 7 [random.randint(a=0,b=1) for x in range(0,8)]] # row 8 # display the 2d data matrix data

[[1, 1, 0, 0, 1, 1, 1, 0], [1, 1, 1, 0, 1, 0, 1, 1], [0, 1, 0, 1, 0, 0, 0, 0], [1, 1, 0, 0, 1, 1, 0, 1], [0, 1, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 1, 1], [0, 0, 0, 1, 1, 0, 0, 1]]

# we will visualize the bits of this data matrix with matplot.pyplot; # the .imshow function from Python can do the job pyplot.figure(figsize=(5,5)) pyplot.imshow(data) pyplot.show()

# .imshow has a range of parameters that I can use for adjusting the 2D grid visualization # setting "alpha" result in defined transparency pyplot.imshow(data, alpha=0.75) pyplot.show()

# "cmap" allows for defining a defined coloring map; # for this we need to import colors from matplotlib from matplotlib import colors # using ListedColormap method from the colors package we can define a color map colormap = colors.ListedColormap(["red","green"]) # handing this problem over to pyplot.imshow(data, cmap=colormap) pyplot.show()

# as showing in my documentation on matplotlib in python, we can also e.g. adjust axis ticks or add labels; # adjusting figure size pyplot.figure(figsize=(10,10)) # adding labels to x and y axis pyplot.xlabel("x axis with ticks", size = 14) pyplot.ylabel("y axis with ticks", size= 14) # addding plot title pyplot.title("this is the title of the plot", size=28) # adjusting the ticks on both x and y axis pyplot.xticks(size=14, color = "red") pyplot.yticks(size=14, color = "red") # defining color map colormap = colors.ListedColormap(["darkblue","lightblue"]) # assigning color map when colling ".imshow" method pyplot.imshow(data, cmap=colormap)

<matplotlib.image.AxesImage at 0x16408e89488>

I will use this approach for visualizing iterations in some agent-based simulation studies to be published on my blog.

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