def test_correct_number_of_rows_are_generated():
df = gen.generate(
props={
'region':
gen.choice(data=['EMEA', 'LATAM', 'NAM', 'APAC'],
weights=[0.1, 0.1, 0.3, 0.5]),
'sic_range':
gen.sic_range(),
'sic':
gen.sic_industry(sic_range_field='sic_range'),
'country':
gen.country_codes(region_field='region'),
'client_name':
gen.company_namer(field='sic',
field_type='sic',
countrycode_field='country')
},
count=50,
randomstate=np.random.RandomState(13031981)).to_dataframe()
df['sic_range'] = df['sic_range'].apply(lambda x: x.name)
df['sic'] = df['sic'].apply(lambda x: x.name)
df['country'] = df['country'].apply(lambda x: x.alpha3_code)
print(df)
def test_countries_are_in_nam_target_region():
result = gen.generate(
props={
"region": gen.choice(['NAM']),
"country": gen.country_codes(region_field="region")
},
count=100,
randomstate=np.random.RandomState(13031981)).to_dataframe()
country_list = data.countries(region="NAM")
for row in result['country']:
assert row in country_list
def test_correct_number_of_rows_are_generated():
df = gen.generate(
props={
'region':
gen.choice(data=['EMEA', 'LATAM', 'NAM', 'APAC'],
weights=[0.1, 0.1, 0.3, 0.5]),
"country":
gen.country_codes(region_field='region'),
"contact_name":
gen.person(country_field='country')
},
count=50,
randomstate=np.random.RandomState(13031981)).to_dataframe()
def run(seed=130319810):
regions = ['NAM', 'EMEA', 'APAC', 'LATAM']
region_weights = [0.5, 0.3, 0.1, 0.1]
df = gen.generate(
props={
'region': gen.choice(
data=regions,
weights=region_weights),
'country': gen.country_codes(
region_field='region'),
'secondary-region': gen.choice(
data=regions,
weights=region_weights),
'secondary-country': gen.country_codes(
region_field='secondary-region'),
'industry': gen.sic_range(),
'industry_code': gen.sic_industry('industry'),
'legal-name': gen.company_namer(
field='industry_code',
countrycode_field='country'),
'lei_code': gen.lei_code()
},
count=50,
randomstate=np.random.RandomState(seed)
).to_dataframe()
# Cleanup the country and add the CCY
df['prefered_ccy'] = df['country'].apply(lambda x: x.currency)
df['country'] = df['country'].apply(lambda x: x.alpha3_code)
df['secondary_ccy'] = df['secondary-country'].apply(lambda x: x.currency)
df['secondary-country'] = df['secondary-country'].apply(lambda x: x.alpha3_code)
print(df)
return df
def test_correct_number_of_rows_are_generated():
df = gen.generate(
props={
'region':
gen.choice(data=['EMEA', 'LATAM', 'NAM', 'APAC'],
weights=[0.1, 0.1, 0.3, 0.5]),
"country":
gen.country_codes(region_field='region'),
"client_type":
gen.choice(data=data.client_types()),
"client_name":
gen.company_namer(field='client_type',
field_type='client_type',
countrycode_field='country')
},
count=50,
randomstate=np.random.RandomState(13031981)).to_dataframe()
def test_address_generation():
df = gen.generate(
props={
'region': gen.choice(['NAM', 'EMEA', 'APAC', 'LATAM']),
'country': gen.country_codes(region_field='region'),
'address': gen.address('country')
},
count=100,
randomstate=np.random.RandomState(13031981)).to_dataframe()
## post process, adress is an object
df['country'] = df['country'].apply(lambda x: x.alpha2_code)
df['address_1'] = df['address'].apply(lambda x: x.address_1)
df['address_2'] = df['address'].apply(lambda x: x.address_2)
df['city'] = df['address'].apply(lambda x: x.city)
df['state'] = df['address'].apply(lambda x: x.state)
df['postal)code'] = df['address'].apply(lambda x: x.postal_code)
del df['address']
def test_normal_sampler_bound():
df = gen.generate_from_model(
props={
'country': gen.country_codes(sampler=gen.bound_normal)
},
count=100000,
randomstate=np.random.RandomState(13031981)).to_dataframe()
df['country'] = df['country'].map(lambda x: x.alpha2_code)
ax = df['country'].value_counts().plot(kind='bar')
ax = df['country'].value_counts().plot(x='month',
linestyle='-',
marker='o',
ax=ax)
ax.set_xlabel("Country")
ax.set_ylabel("Count")
plt.show()
def test_correct_number_of_rows_are_generated():
df = gen.generate(props={
'firm_account':
gen.choice(data=['A', 'B', 'C']),
'region':
gen.choice(data=['NAM', 'EMEA', 'LATAM', 'APAC']),
'country':
gen.country_codes(region_field="region"),
'intraday_pnl':
gen.random_range(low=-1000, high=1000, round_dp=2),
'trade_year':
gen.choice(
data=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019])
},
count=50,
randomstate=np.random.RandomState(
13031981)).to_dataframe()
df['ccy'] = df['country'].apply(lambda x: x.currency)
df['country'] = df['country'].apply(lambda x: x.alpha3_code)
df['trade_date'] = df['trade_year'].apply(lambda x: f"{x}-01-01")
import numpy as np
import datahub_core.generators as gen
from datahub_core.models import MarkovModel
RS = np.random.RandomState(13031981)
MODEL = MarkovModel(filename='./tests/client_data.json', randomstate=RS)
RESULT = gen.generate_from_model(props={
"country":
gen.country_codes(region_field='region'),
"ev":
gen.random_range(high=100000, low=10000000),
"address":
gen.address(country_field='country'),
"contact_name":
gen.person(country_field='country'),
"client_name":
gen.company_namer(field='client_type',
field_type='client_type',
countrycode_field='country')
},
count=50,
model=MODEL)
def test_correct_number_of_rows_are_generated():
df = RESULT.to_dataframe()
# remap obects into flat table
df['country'] = df['country'].map(lambda x: x.alpha3_code)
df['city'] = df['address'].map(lambda x: x.city)