def sort_visualize(array, desc=False, visualize=True, sleep_time=SLEEP_TIME, title='', xlabel='', ylabel='', xticks=[], yticks=[], color='b'):
"""
Function to visualize bubble sort.
Parameters:
array (arraylike): Array to be sorted.
desc (bool): Sort in ascending order if True, else sort in descending order.
visualize (bool): Generates visualization if True.
sleep_time (float): Time in ms between frames.
Other parameters are for the matplotlib plot.
Returns:
tuple: Tuple containing the sorted array and the time taken for sorting.
"""
# Sorting a copy of array so that the original array is unchanged
array_copy = np.array(array.copy())
# Plotting initial state
if(visualize == True):
fig, ax = plot.bar(array_copy, title=title, xlabel=xlabel, ylabel=ylabel, xticks=[], yticks=[], color=color)
plt.show(block=False)
plt.pause(sleep_time)
plt.close()
sort_time = 0 # Time for sorting (excluding plotting)
sorted_upto = -1 # Index upto which array_copy is sorted
while(not is_sorted.is_sorted(array_copy, desc=desc)):
for i in range(len(array_copy) - 2, sorted_upto, -1):
start_time = time.time()
# Sorting in ascending order
if(desc == False):
if(array_copy[i] > array_copy[i + 1]):
# Swapping array_copy[i] with array_copy[i + 1]
tmp = array_copy[i + 1]
array_copy[i + 1] = array_copy[i]
array_copy[i] = tmp
# Sorting in descending order
elif(desc == True):
if(array_copy[i] < array_copy[i + 1]):
# Swapping array_copy[i] with array_copy[i + 1]
tmp = array_copy[i + 1]
array_copy[i + 1] = array_copy[i]
array_copy[i] = tmp
stop_time = time.time()
# Adding time taken for iteration to sort_time
sort_time += (stop_time - start_time)
# Plotting
if(visualize == True):
fig, ax = plot.bar(array_copy, title=title, xlabel=xlabel, ylabel=ylabel, xticks=[], yticks=[], color=color)
ax.annotate(s=MARKER, xy=(i, 0), xytext=(i, MARKER_Y), xycoords='data', textcoords='data', fontsize=MARKER_SIZE, ha='center', fontweight=MARKER_WEIGHT, color=MARKER_COLOR)
plt.show(block=False)
plt.pause(sleep_time)
plt.close()
sorted_upto += 1
return array_copy, sort_time
def plot_figure(array, mid, sleep_time=SLEEP_TIME, title='', xlabel='', ylabel='', xticks=[], yticks=[], color='b'):
# Plotting
fig, ax = plot.bar(array, title=title, xlabel=xlabel, ylabel=ylabel, xticks=xticks, yticks=yticks, color=color)
ax.annotate(s=MARKER, xy=(mid, 0), xytext=(mid, MARKER_Y), xycoords='data', textcoords='data', fontsize=MARKER_SIZE, ha='center', fontweight=MARKER_WEIGHT, color=MARKER_COLOR)
plt.show(block=False)
plt.pause(sleep_time)
plt.close()
def plot_figure(array,
sleep_time=SLEEP_TIME,
title='',
xlabel='',
ylabel='',
xticks=[],
yticks=[],
color='b'):
# Plotting
fig, ax = plot.bar(array,
title=title,
xlabel=xlabel,
ylabel=ylabel,
xticks=xticks,
yticks=yticks,
color=color)
plt.show(block=False)
plt.pause(sleep_time)
plt.close()
def sort_visualize(array,
desc=False,
visualize=True,
sleep_time=SLEEP_TIME,
title='',
xlabel='',
ylabel='',
xticks=[],
yticks=[],
color='b'):
"""Function to visualize insertion sort. Docs same as for bubble sort."""
# Sorting a copy of array so that the original array is unchanged
array_copy = np.array(array.copy())
# Plotting initial state
if (visualize == True):
fig, ax = plot.bar(array_copy,
title=title,
xlabel=xlabel,
ylabel=ylabel,
xticks=[],
yticks=[],
color=color)
plt.show(block=False)
plt.pause(sleep_time)
plt.close()
sort_time = 0 # Time for sorting (excluding plotting)
for i in range(1, len(array_copy)):
start_time = time.time()
j = i - 1
element = array_copy[i]
stop_time = time.time()
# Adding time taken for initialization to sort_time
sort_time += (stop_time - start_time)
while (j >= 0):
# Plotting
if (visualize == True):
fig, ax = plot.bar(array_copy,
title=title,
xlabel=xlabel,
ylabel=ylabel,
xticks=[],
yticks=[],
color=color)
ax.annotate(s=MARKER,
xy=(j, 0),
xytext=(j, MARKER_Y),
xycoords='data',
textcoords='data',
fontsize=MARKER_SIZE,
ha='center',
fontweight=MARKER_WEIGHT,
color=MARKER_COLOR)
plt.show(block=False)
plt.pause(sleep_time)
plt.close()
start_time = time.time()
# Sorting in ascending order
if (desc == False):
if (array_copy[j] > element):
array_copy[j + 1] = array_copy[j]
j -= 1
else:
break
# Sorting in descending order
elif (desc == True):
if (array_copy[j] < element):
array_copy[j + 1] = array_copy[j]
j -= 1
else:
break
stop_time = time.time()
# Adding time taken for iteration to sort_time
sort_time += (stop_time - start_time)
array_copy[j + 1] = element
return array_copy, sort_time
def sort_visualize(array,
desc=False,
visualize=True,
sleep_time=SLEEP_TIME,
title='',
xlabel='',
ylabel='',
xticks=[],
yticks=[],
color='b'):
"""Function to visualize selection sort. Docs same as for bubble sort."""
# Sorting a copy of array so that the original array is unchanged
array_copy = np.array(array.copy())
sort_time = 0 # Time for sorting (excluding plotting)
i = 0 # Iteration index
while (not is_sorted.is_sorted(array_copy, desc=desc)):
# Part 1: Finding min_index/max_index in array_copy[i:]
min_index = i
max_index = i
for j in range(i + 1, len(array_copy)):
start_time = time.time()
# Sorting in ascending order
if (desc == False):
if (array_copy[j] < array_copy[min_index]):
min_index = j
# Sorting in descending order
elif (desc == True):
if (array_copy[j] > array_copy[max_index]):
max_index = j
stop_time = time.time()
# Adding time taken for finding min_index/max_index to sort_time
sort_time += (stop_time - start_time)
# Plotting
if (visualize == True):
fig, ax = plot.bar(array_copy,
title=title,
xlabel=xlabel,
ylabel=ylabel,
color=color)
ax.annotate(s=MARKER,
xy=(j, 0),
xytext=(j, MARKER_Y),
xycoords='data',
textcoords='data',
fontsize=MARKER_SIZE,
ha='center',
fontweight=MARKER_WEIGHT,
color=MARKER_COLOR)
plt.show(block=False)
plt.pause(sleep_time)
plt.close()
# Part 2: Swapping array_copy[i] with array_copy[min_index]/
# array_copy[max_index]
start_time = time.time()
# Sorting in ascending order
if (desc == False):
tmp = array_copy[min_index]
array_copy[min_index] = array_copy[i]
array_copy[i] = tmp
i += 1
# Sorting in descending order
elif (desc == True):
tmp = array_copy[max_index]
array_copy[max_index] = array_copy[i]
array_copy[i] = tmp
i += 1
stop_time = time.time()
# Adding time taken for finding min_index/max_index to sort_time
sort_time += (stop_time - start_time)
# Plotting after last swap
if (visualize == True):
fig, ax = plot.bar(array_copy,
title=title,
xlabel=xlabel,
ylabel=ylabel,
color=color)
ax.annotate(s=MARKER,
xy=(j, 0),
xytext=(j, MARKER_Y),
xycoords='data',
textcoords='data',
fontsize=MARKER_SIZE,
ha='center',
fontweight=MARKER_WEIGHT,
color=MARKER_COLOR)
plt.show(block=False)
plt.pause(sleep_time)
plt.close()
return (array_copy, sort_time)