id = Column(Integer, primary_key=True)
name = Column(String(255))
email = Column(String(255))
url = Column(String(255))
def __repr__(self):
return '<Person(name={}, email={}, url={})>'.format(
self.name, self.email, self.url)
@test_speed
def insert_all(max_records):
people = [random_person() for n in range(max_records)]
prnt('Records to create:', people)
for person in people:
# Don't need this prop for our example schema.
del person['address']
db_session.add(Person(**person))
if DEBUG:
with Section('MySQL - SQL Alchemy'):
Base.metadata.create_all(engine)
print_h2('Adding a bunch of records...')
run_trials(insert_all, trials=10)
print_h2('Reading all records...')
recs = db_session.query(Person).all()
ppr(recs)
'url': faker.url(),
'created': dt.now()
}
@test_speed
def insert_all(max_records):
global _count
global _testdata
for n in range(max_records):
_count += 1
data = make_person()
_testdata.append(data['name'])
collection.insert(data)
if DEBUG:
with Section('MongoDB (via pymongo)'):
# Clean up stale collection beforehand, if it exists.
collection.remove()
print(collection.count())
run_trials(insert_all, trials=10)
res = collection.find({})
# Assert all names are correct
mongo_res = []
for k, item in enumerate(res):
mongo_res.append(item['name'])
prnt('Result from MongoDB execution, names only: ', mongo_res)
# Check database count vs. local increment
assert sorted(mongo_res) == sorted(_testdata)
assert _count == collection.count()
res['tails'][1] = '~{}%'.format(res['tails'][0] // 10.0)
print(res)
return res
def _test(*args):
values = [rr(1, 999) for d in range(10)]
ppr(get_results(values))
if __name__ == '__main__':
with Section('Probability'):
values_rand = [rr(1, 999) for d in range(rr(1, 20))]
prnt(
('Probability of a given value:', values_rand),
Probability.of_single(values_rand))
run_trials(_test)
prnt(
('Probability of multiple given values:',
values_rand, values_rand[:2]),
Probability.of_group(values_rand, values_rand[:2], are_equal=True))
prnt(
('Probability of multiple given values:',
values_rand, values_rand[:4]),
Probability.of_group(values_rand, values_rand[:4], are_equal=True))
run_trials(test_coin_flip)
return num
else:
return fibo(num - 1) + fibo(num - 2)
print(fibo(num))
@test_speed
def o_log_n(max_amt=1000):
foo = max_amt
print('I am a O(log n) function')
while foo > 0:
foo = foo / 2
@test_speed
def o_n_factorial(factor=10):
# Basically a for loop nested `factor` times.
pass
if __name__ == '__main__':
with Section('BIG O Notation'):
o_1()
o_n()
o_n2()
o_n_comprehension()
run_trials(o_n3, trials=20)
run_trials(o_log_n, trials=10)
o_n_factorial()
o_2n(10)
res['heads'][1] = '~{}%'.format(res['heads'][0] // 10.0)
res['tails'][1] = '~{}%'.format(res['tails'][0] // 10.0)
print(res)
return res
def _test(*args):
values = [rr(1, 999) for d in range(10)]
ppr(get_results(values))
if __name__ == '__main__':
with Section('Probability'):
values_rand = [rr(1, 999) for d in range(rr(1, 20))]
prnt(('Probability of a given value:', values_rand),
Probability.of_single(values_rand))
run_trials(_test)
prnt(('Probability of multiple given values:', values_rand,
values_rand[:2]),
Probability.of_group(values_rand, values_rand[:2],
are_equal=True))
prnt(('Probability of multiple given values:', values_rand,
values_rand[:4]),
Probability.of_group(values_rand, values_rand[:4],
are_equal=True))
run_trials(test_coin_flip)
