Visualization of Merge sort using Matplotlib

28 Jul, 2020
Prerequisites: Introduction to Matplotlib, Merge Sort
Visualizing algorithms makes it easier to understand them by analyzing and comparing the number of operations that took place to compare and swap the elements. For this we will use matplotlib, to plot bar graphs to represent the elements of the array,
Approach:
We will generate an array with random elements.The algorithm will be called on that array and yield statement will be used instead of a return statement for visualization purposes.We will yield the current states of the array after comparing and swapping. Hence the algorithm will return a generator object.Matplotlib animation will be used to visualize the comparing and swapping of the array.The array will be stored in a matplotlib bar container object (‘bar_rects’), where the size of each bar will be equal to the corresponding value of the element in the array.The inbuilt FuncAnimation method of matplotlib animation will pass the container and generator objects to the function used to create animation. Each frame of the animation corresponds to a single iteration of the generator.The animation function is repeatedly called will set the height of the rectangle equal to the value of the elements.
We will generate an array with random elements.
The algorithm will be called on that array and yield statement will be used instead of a return statement for visualization purposes.
We will yield the current states of the array after comparing and swapping. Hence the algorithm will return a generator object.
Matplotlib animation will be used to visualize the comparing and swapping of the array.
The array will be stored in a matplotlib bar container object (‘bar_rects’), where the size of each bar will be equal to the corresponding value of the element in the array.
The inbuilt FuncAnimation method of matplotlib animation will pass the container and generator objects to the function used to create animation. Each frame of the animation corresponds to a single iteration of the generator.
The animation function is repeatedly called will set the height of the rectangle equal to the value of the elements.
Below is the implementation of the above approach.
Python3
# import all the modulesimport matplotlib.pyplot as pltfrom matplotlib.animation import FuncAnimationfrom mpl_toolkits.mplot3d import axes3dimport matplotlib as mpimport numpy as npimport random    # function to recursively divide the arradef mergesort(A, start, end):    if end <= start:        return      mid = start + ((end - start + 1) // 2) - 1          # yield from statements have been used to yield     # the array from the functions     yield from mergesort(A, start, mid)    yield from mergesort(A, mid + 1, end)    yield from merge(A, start, mid, end)  # function to merge the arraydef merge(A, start, mid, end):    merged = []    leftIdx = start    rightIdx = mid + 1      while leftIdx <= mid and rightIdx <= end:        if A[leftIdx] < A[rightIdx]:            merged.append(A[leftIdx])            leftIdx += 1        else:            merged.append(A[rightIdx])            rightIdx += 1      while leftIdx <= mid:        merged.append(A[leftIdx])        leftIdx += 1      while rightIdx <= end:        merged.append(A[rightIdx])        rightIdx += 1      for i in range(len(merged)):        A[start + i] = merged[i]        yield A  # function to plot barsdef showGraph():          # for random unique values    n=20    a=[i for i in range(1, n+1)]    random.shuffle(a)    datasetName='Random'          # generator object returned by the function    generator = mergesort(a, 0, len(a)-1)    algoName='Merge Sort'          # style of the chart    plt.style.use('fivethirtyeight')          # set colors of the bars    data_normalizer = mp.colors.Normalize()    color_map = mp.colors.LinearSegmentedColormap(        "my_map",        {            "red": [(0, 1.0, 1.0),                    (1.0, .5, .5)],            "green": [(0, 0.5, 0.5),                      (1.0, 0, 0)],            "blue": [(0, 0.50, 0.5),                     (1.0, 0, 0)]        }    )      fig, ax = plt.subplots()          # bar container     bar_rects = ax.bar(range(len(a)), a, align="edge",                        color=color_map(data_normalizer(range(n))))          # setting the limits of x and y axes    ax.set_xlim(0, len(a))    ax.set_ylim(0, int(1.1*len(a)))    ax.set_title("ALGORITHM : "+algoName+"\n"+"DATA SET : "+datasetName,                  fontdict={'fontsize': 13, 'fontweight': 'medium',                            'color' : '#E4365D'})          # the text to be shown on the upper left    # indicating the number of iterations    # transform indicates the position with     # relevance to the axes coordinates.    text = ax.text(0.01, 0.95, "", transform=ax.transAxes,                    color="#E4365D")    iteration = [0]      def animate(A, rects, iteration):        for rect, val in zip(rects, A):                          # setting the size of each bar equal             # to the value of the elements            rect.set_height(val)        iteration[0] += 1        text.set_text("iterations : {}".format(iteration[0]))          # call animate function repeatedly    anim = FuncAnimation(fig, func=animate,        fargs=(bar_rects, iteration), frames=generator, interval=50,        repeat=False)    plt.show()  showGraph()
Output:
Merge Sort
Python-matplotlib
Python
Merge Sort
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