def test_choice_distribution(self):
from faker.utils.distribution import choice_distribution
a = ('a', 'b', 'c', 'd')
p = (0.5, 0.2, 0.2, 0.1)
sample = choice_distribution(a, p)
self.assertTrue(sample in a)
with open(os.path.join(TEST_DIR, 'random_state.json'), 'r') as fh:
random_state = json.load(fh)
random_state[1] = tuple(random_state[1])
random.setstate(random_state)
samples = [choice_distribution(a, p) for i in range(100)]
a_pop = len([i for i in samples if i == 'a'])
b_pop = len([i for i in samples if i == 'b'])
c_pop = len([i for i in samples if i == 'c'])
d_pop = len([i for i in samples if i == 'd'])
boundaries = []
tolerance = 5
for probability in p:
boundaries.append([100 * probability + tolerance, 100 * probability - tolerance])
self.assertTrue(boundaries[0][0] > a_pop > boundaries[0][1])
self.assertTrue(boundaries[1][0] > b_pop > boundaries[1][1])
self.assertTrue(boundaries[2][0] > c_pop > boundaries[2][1])
self.assertTrue(boundaries[3][0] > d_pop > boundaries[3][1])
def horse_dob(self):
'''get a date of birth for a live horse, assuming horses live up to 40 years but distribution favours
younger horses'''
this_year = date.today().year
years = [y for y in range(this_year, this_year - 40, -1)]
# note that these do not add up to 100 and they are pure guesswork
p = [
0.06, 0.06, 0.06, 0.06, 0.05, 0.05, 0.05, 0.04, 0.04, 0.04, 0.03,
0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01,
0.01, 0.01, 0.01, 0.01, 0.01, 0.005, 0.005, 0.005, 0.001, 0.001,
0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001
]
# get a year - distributed around average horse age
year = choice_distribution(years, p)
# get a month - in northern hemisphere most horses are born in spring
#TODO: handle date of birth for southern hemisphere
months = [m for m in range(1, 13)]
p = [0.2, 0.2, 0.2, 0.2, 0.1, 0.5, 0.2, 0.1, 0.01, 0.01, 0.01, 0.01]
month = choice_distribution(months, p)
# get day of birth
dates = calendar.Calendar().itermonthdates(year, month)
dob = random.choice([date for date in dates if date.month == month])
# return in format YYYY-MM-DD
return str(dob)
def test_choice_distribution(self):
a = ('a', 'b', 'c', 'd')
p = (0.5, 0.2, 0.2, 0.1)
sample = choice_distribution(a, p)
self.assertTrue(sample in a)
with open(os.path.join(TEST_DIR, 'random_state.json'), 'r') as fh:
random_state = json.load(fh)
random_state[1] = tuple(random_state[1])
random.setstate(random_state)
samples = [choice_distribution(a, p) for i in range(100)]
a_pop = len([i for i in samples if i == 'a'])
b_pop = len([i for i in samples if i == 'b'])
c_pop = len([i for i in samples if i == 'c'])
d_pop = len([i for i in samples if i == 'd'])
boundaries = []
tolerance = 5
for probability in p:
boundaries.append(
[100 * probability + tolerance, 100 * probability - tolerance])
self.assertTrue(boundaries[0][0] > a_pop > boundaries[0][1])
self.assertTrue(boundaries[1][0] > b_pop > boundaries[1][1])
self.assertTrue(boundaries[2][0] > c_pop > boundaries[2][1])
self.assertTrue(boundaries[3][0] > d_pop > boundaries[3][1])
def age(cls, minor=False):
if minor:
# kids' ages are pretty evenly distributed..
return cls.random_int(0, 20)
random_range = choice_distribution(cls.age_ranges_US, cls.age_freq_US)
return random.randint(*random_range)
def random_element(self, elements=('a', 'b', 'c')):
"""
Returns a random element from a passed object.
If `elements` is a dictionary, the value will be used as
a weighting element. For example::
random_element({"{{variable_1}}": 0.5, "{{variable_2}}": 0.2, "{{variable_3}}": 0.2, "{{variable_4}}": 0.1})
will have the following distribution:
* `variable_1`: 50% probability
* `variable_2`: 20% probability
* `variable_3`: 20% probability
* `variable_4`: 10% probability
"""
if isinstance(elements, dict):
choices = elements.keys()
probabilities = elements.values()
return choice_distribution(
list(choices),
list(probabilities),
self.generator.random)
else:
return self.generator.random.choice(list(elements))
def ueln(self, country):
# choose breed society (PIO)
pio = choice_distribution(self.pios[country],
self.pios_distribution[country])
# create random id
id = randint(100000, 999999999)
return "%s-%s-%s" % (country, pio, id)
def horse_sex(self):
'''
Many male horses are gelded/neutered, so the code for sex includes this option. Rarely a female horse can also be neutered. The full list is:
00 - Not Known
10 - Male - entire/neutered not known
11 - Stallion - entire
12 - Gelding
20 - Female - neutered not known
21 - Mare
22 - Neutered female
30 - Hermaphrodite
:return:
'''
sex_choices = list(self.SEX.keys())
return choice_distribution(sex_choices, self.SEX_PROPORTIONS)
def random_element(cls, elements=('a', 'b', 'b')):
"""
Returns a random element from a passed object.
If `elements` is a dictionary, the value will be used as
a weighting element. For example::
random_element({"{{variable_1}}": 0.5, "{{variable_2}}": 0.2, "{{variable_3}}": 0.2, "{{variable_4}}": 0.1})
will have the following distribution:
* `variable_1`: 50% probability
* `variable_2`: 20% probability
* `variable_3`: 20% probability
* `variable_4`: 10% probability
"""
if isinstance(elements, dict):
choices = elements.keys()
probabilities = elements.values()
return choice_distribution(list(choices), list(probabilities))
else:
return random.choice(list(elements))
def GetFakerDataAsPercent(arr, arrp):
'''arr = ('a', 'b', 'c', 'd')
arrp = (0.5, 0.2, 0.2, 0.1)
'''
data = choice_distribution(arr, arrp)
return
def first_name_male(cls):
return choice_distribution(cls.first_names_male_US, cls.first_names_male_freq_US)
def last_name(cls):
return choice_distribution(cls.last_names_US, cls.last_name_freq_US)
def country_of_birth(self):
return choice_distribution(self.population,
self.population_distribution)
def horse_color(self):
#https://en.wikipedia.org/wiki/Equine_coat_color
#http://www.animalgenetics.us/Equine/CCalculator1.asp
return choice_distribution(self.COLORS, self.COLOURS_PROPORTIONS)
def city(self):
"""
:example 'Cork'
"""
return choice_distribution(self.population,
self.population_distribution)
