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#!/usr/bin/env PYTHONHASHSEED=1234 python3

# Copyright 2014-2019 Brett Slatkin, Pearson Education Inc.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# Reproduce book environment

import random

random.seed(1234)

import logging

from pprint import pprint

from sys import stdout as STDOUT

# Write all output to a temporary directory

import atexit

import gc

import io

import os

import tempfile

TEST_DIR = tempfile.TemporaryDirectory()

atexit.register(TEST_DIR.cleanup)

# Make sure Windows processes exit cleanly

OLD_CWD = os.getcwd()

atexit.register(lambda: os.chdir(OLD_CWD))

os.chdir(TEST_DIR.name)

def close_open_files():

everything = gc.get_objects()

for obj in everything:

if isinstance(obj, io.IOBase):

obj.close()

atexit.register(close_open_files)

# Example 1

data = list(range(10**5))

index = data.index(91234)

assert index == 91234

# Example 2

def find_closest(sequence, goal):

for index, value in enumerate(sequence):

if goal < value:

return index

raise ValueError(f'{goal} is out of bounds')

index = find_closest(data, 91234.56)

assert index == 91235

try:

find_closest(data, 100000000)

except ValueError:

pass # Expected

else:

assert False

# Example 3

from bisect import bisect_left

index = bisect_left(data, 91234) # Exact match

assert index == 91234

index = bisect_left(data, 91234.56) # Closest match

assert index == 91235

# Example 4

import random

import timeit

size = 10**5

iterations = 1000

data = list(range(size))

to_lookup = [random.randint(0, size)

for _ in range(iterations)]

def run_linear(data, to_lookup):

for index in to_lookup:

data.index(index)

def run_bisect(data, to_lookup):

for index in to_lookup:

bisect_left(data, index)

baseline = timeit.timeit(

stmt='run_linear(data, to_lookup)',

globals=globals(),

number=10)

print(f'Linear search takes {baseline:.6f}s')

comparison = timeit.timeit(

stmt='run_bisect(data, to_lookup)',

globals=globals(),

number=10)

print(f'Bisect search takes {comparison:.6f}s')

slowdown = 1 + ((baseline - comparison) / comparison)

print(f'{slowdown:.1f}x time')