def load_data(self, customer_path):
customer_index = 0
self.customer = os.path.dirname(customer_path)
for subdir, dirs, files in os.walk(customer_path):
for projdir in dirs:
# TODO log
try:
self.projects[projdir] = ProjectData.CustomerProject(
self.inactive_delay, self.low_cpi)
self.projects[projdir].load_data(
os.path.join(customer_path, projdir))
adder.add_project_data(self.data,
self.projects[projdir].data,
customer_index,
CustomerData.column_to_average)
customer_index += 1
except ... as e:
logging.error(
"Could not data for customer {!s}:{!s} [{!s}]".format(
projdir, self.customer, e))
def test_multiple_simple_addition(self):
total = pd.DataFrame(columns=['X', 'CPI'], dtype=float)
date_today = datetime.now()
invoice_weeks = pd.date_range(date_today, periods=3, freq='W')
project = pd.DataFrame(index=invoice_weeks,
data={
'X': [10, 20, 30],
'CPI': [10, 20, 30]
})
for idx in range(5):
adder.add_project_data(total,
project,
idx,
column_to_average=('CPI'))
expected_result = pd.DataFrame(index=invoice_weeks,
data={
'X': [10 * 5, 20 * 5, 30 * 5],
'CPI': [10, 20, 30]
},
dtype=float)
expected_result.sort_index(inplace=True, axis=1)
self.assertTrue(total.equals(expected_result))
def test_finishing_late_no_overlap(self):
total = pd.DataFrame(columns=['X', 'Y', 'CPI'], dtype=float)
date_today = datetime.now()
project1_invoice_weeks = pd.date_range(date_today, periods=3, freq='W')
project1 = pd.DataFrame(index=project1_invoice_weeks,
data={
'X': [40, 50, 60],
'Y': [10000, 20000, 30000],
'CPI': [10, 20, 30]
})
project2_invoice_weeks = pd.date_range(date_today +
pd.Timedelta(3, unit='W'),
periods=3,
freq='W')
project2 = pd.DataFrame(index=project2_invoice_weeks,
data={
'X': [10, 10, 10],
'Y': [30, 40, 50],
'CPI': [100, 20, 100]
})
for idx, project in enumerate((project1, project2)):
adder.add_project_data(total,
project,
idx,
column_to_average=('CPI'))
expected_results_weeks = project1_invoice_weeks.union(
project2_invoice_weeks)
expected_result = pd.DataFrame(
index=expected_results_weeks,
data={
'X': [40, 50, 60, 70, 70, 70],
'Y': [10000, 20000, 30000, 30000 + 30, 30000 + 40, 30000 + 50],
'CPI':
[10, 20, 30, (100 + 30) / 2, (20 + 30) / 2, (100 + 30) / 2]
},
dtype=float)
expected_result.sort_index(inplace=True, axis=1)
self.assertTrue(total.equals(expected_result))
def test_date_gap2(self):
total = pd.DataFrame(columns=['X', 'Y', 'CPI'], dtype=float)
date_today = datetime.now()
invoice_weeks = pd.date_range(date_today, periods=6, freq='W')
project1 = pd.DataFrame(index=invoice_weeks,
data={
'X': [40, 50, 60, 70, 80, 90],
'Y': [10000, 20000, 30000, 100, 300, 300],
'CPI': [10, 20, 30, 40, 50, 60]
})
project2 = pd.DataFrame(index=invoice_weeks,
data={
'X': [1, 2, 3, 4, 5, 6],
'Y': [7, 8, 9, 10, 11, 12],
'CPI': [13, 14, 15, 16, 17, 18]
})
project2.drop(project2.index[2], inplace=True)
project2.drop(project2.index[2], inplace=True)
for idx, project in enumerate((project1, project2)):
adder.add_project_data(total,
project,
idx,
column_to_average=('CPI'))
expected_result = pd.DataFrame(
index=invoice_weeks,
data={
'X': [40 + 1, 50 + 2, 60, 70, 80 + 5, 90 + 6],
'Y': [10000 + 7, 20000 + 8, 30000, 100, 300 + 11, 300 + 12],
'CPI': [(10 + 13) / 2, (20 + 14) / 2, 30, 40, (50 + 17) / 2,
(60 + 18) / 2]
},
dtype=float)
expected_result.sort_index(inplace=True, axis=1)
self.assertTrue(total.equals(expected_result))
def test_simple_addition(self):
total = pd.DataFrame(columns=['X', 'Y', 'CPI'], dtype=float)
date_today = datetime.now()
invoice_weeks = pd.date_range(date_today, periods=3, freq='W')
project1 = pd.DataFrame(index=invoice_weeks,
data={
'X': [40, 50, 60],
'Y': [10000, 20000, 30000],
'CPI': [10, 20, 30]
})
project2 = pd.DataFrame(index=invoice_weeks,
data={
'X': [10, 10, 10],
'Y': [30, 40, 50],
'CPI': [100, 20, 100]
})
for idx, project in enumerate((project1, project2)):
adder.add_project_data(total,
project,
idx,
column_to_average=('CPI'))
expected_result = pd.DataFrame(index=invoice_weeks,
data={
'X': [50, 60, 70],
'Y': [10030, 20040, 30050],
'CPI': [55, 20, 65]
},
dtype=float)
expected_result.sort_index(inplace=True, axis=1)
self.assertTrue(total.equals(expected_result))