def __init__(self, operations, search, insert, delete, bm_start, bm_length,
bm_interval):
"""Performs the operations sequence listed, producing the plot points listed."""
super(MixedSIDBenchmarkPlot, self).__init__()
self.operations = operations
self.search = search
self.insert = insert
self.delete = delete
self.bm_start = bm_start
self.bm_length = bm_length
self.bm_interval = bm_interval
# First, let's go up to bm_start.
for i in range(bm_start):
op = operations[i]
self.doOperation(delete, insert, search, op)
# Now, we'll do the standard benchmark loop....
# For the remainder of the benchmark cycle, we will use next_benchmark to track when to start a benchmark
# and p and q to represent the start and end, respectively, of a range that we're calculating.
p = bm_start
while p < len(operations):
# Don't overshoot stop when benchmarking.
q = min(p + bm_length, len(operations))
time = Plot.benchmark(
lambda x: self.doOperation(delete, insert, search, x),
operations, p, q)
self.coordinates.append((p, time))
next_benchmark = p + bm_interval
# Then, advance to the next benchmark
p = q
q = min(next_benchmark, len(operations))
for i in range(p, q):
self.doOperation(delete, insert, search, operations[i])
# Before looping, update p.
p = q
def _doBenchmark(
self,
search,
insert,
delete,
search_samples,
insert_samples,
delete_samples,
stop,
bm_start,
bm_length,
bm_interval,
):
"""Helper function. Performs the benchmark routine described elsewhere."""
# Initialize indices.
search_index = insert_index = delete_index = 0
# 0. Set next_benchmark as bm_start.
next_benchmark = bm_start
# 1. Call insert on the items in insert_samples with indices [0, bm_start).
print("Inserting up to {}".format(bm_start))
for i in range(0, bm_start):
insert(insert_samples[insert_index])
insert_index += 1
# 2. While insert_index < stop:
while insert_index < stop:
# a. set last_benchmark as next_benchmark.
last_benchmark = next_benchmark
# b. Set next_benchmark as min(next_benchmark + bm_interval, stop).
next_benchmark = min(next_benchmark + bm_interval, stop)
# c. Benchmark: time bm_length searches on items in search_samples[search_index]. Divide the total time by
# bm_length and save (data structure size, time) as a plot point for insert.
print("Benchmarking: search for items {} to {}".format(
search_index, search_index + bm_length))
self.search_plot.plotPoint(
insert_index,
Plot.benchmark(search, search_samples, search_index,
search_index + bm_length))
search_index += bm_length
# d. Benchmark: time bm_length inserts on items in insert_samples[insert_index]. Divide the total time by
# bm_length and save (data structure size before timed insertions, time) as a plot point for insert.
print("Benchmarking: insert items {} to {}".format(
insert_index, insert_index + bm_length))
self.insert_plot.plotPoint(
insert_index,
Plot.benchmark(insert, insert_samples, insert_index,
insert_index + bm_length))
insert_index += bm_length
# e. Insert subsequent items from insert_samples, incrementing insert_index, until
# insert_index == next_benchmark. These insertions are not timed.
print("Inserting intermediate values up to {}".format(
next_benchmark))
while insert_index < next_benchmark:
insert(insert_samples[insert_index])
insert_index += 1
# 3. Set next_benchmark = last_benchmark + bm_length.
next_benchmark = last_benchmark + bm_length
# 4. While data structure size > bm_start:
# We could reuse insert_index, but this is VASTLY clearer.
data_structure_size = insert_index
while data_structure_size > bm_start:
# a. Delete items from delete_samples[delete_index], incrementing delete_index, until
# data structure size == next_benchmark.
print("Deleting vlues down to {}".format(next_benchmark))
while data_structure_size > next_benchmark:
delete(delete_samples[delete_index])
delete_index += 1
data_structure_size -= 1
# b. Benchmark: time bm_length deletions on items in delete_samples[delete_index], incrementing
# delete_index each time. Divide the total time by bm_length and save
# (data structure size after timed deletions, time) as a plot point for delete.
print("Benchmarking: delete items {} to {}".format(
delete_index, delete_index + bm_length))
data_structure_size -= bm_length
self.delete_plot.plotPoint(
data_structure_size,
Plot.benchmark(delete, delete_samples, delete_index,
delete_index + bm_length))
delete_index += bm_length
# c. Decrease next_benchmark by bm_interval.
next_benchmark -= bm_interval