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effectivepython/example_code/item_72.py at master · sharc007/effectivepython · GitHub
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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'
)
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