class TestHDFWrapper(unittest.TestCase):
def setUp(self):
self.fdir = os.path.join('.', 'test_files', 'panel')
months = ['1994_01', '1994_02', '1994_03']
frequency = 'monthly'
self.handler = HDFHandler('./test_files', 'panel', months, frequency)
def test_file_creation(self):
# _ = self.handler._select_stores(self.handler)
expected = [
os.path.join('test_files', 'panel', x)
for x in ('m1994_01.h5', 'm1994_02.h5', 'm1994_03.h5')
]
print(os.listdir('test_files'))
assert all([os.path.exists(x) for x in expected])
def test_create_from_list(self):
months = [['1994_01', '1994_02', '1994_03']]
frequency = 'Q'
handler = HDFHandler('./test_files',
kind='panel',
months=months,
frequency=frequency)
self.assertEqual(handler.stores.keys(), ['long_1994_Q1'])
handler.close()
def test_getitem(self):
result = self.handler['m1994_01']
expected = self.handler.stores['m1994_01']
self.assertIs(result, expected)
def test_write(self):
df = pd.DataFrame({'A': [1, 2, 3]})
self.handler.write(df, 'm1994_01', format='f', append=False)
res = self.handler.select('m1994_01')
tm.assert_frame_equal(df, res)
def test_iter(self):
result = [x for x in self.handler]
expected = ['m1994_01', 'm1994_02', 'm1994_03']
self.assertEqual(result, expected)
# def test_select_all(self):
# import ipdb; ipdb.set_trace()
# h = HDFHandler(self.settings, 'panel', frequency='monthly')
# assert len(h.stores) == 3
def test_sanitize_key(self):
result = self.handler._sanitize_key('1994-01')
expected = 'm1994_01'
self.assertEqual(result, expected)
result = self.handler._sanitize_key('1994_01')
self.assertEqual(result, expected)
result = self.handler._sanitize_key('m1994-01')
self.assertEqual(result, expected)
# With a different pre
settings = {'base_path': './test_files/'}
months = [['1994_01', '1994_02', '1994_03']]
frequency = 'Q'
# handler = HDFHandler(settings, kind='long', months=months,
# frequency=frequency)
# result = handler._sanitize_key('long_1996_Q1')
# print(handler.pre)
# expected = "long_1996_Q1"
# self.assertEqual(result, expected)
def test_iteritems(self):
g = self.handler.iteritems()
name, value = next(g)
self.assertEqual(name, 'm1994_01')
self.assertIs(value, None)
# getting a `is not a regular file` error.
def test_from_directory(self):
# setup should have created some.
os.mkdir('from_dir')
with open(os.path.join('from_dir', 'file5.h5'), 'w'):
pass
try:
handler = HDFHandler.from_directory('from_dir', kind='M')
self.assertEqual(len(handler.stores), 1)
handler.close()
except Exception as e:
print(e)
finally:
os.remove(os.path.join('from_dir', 'file5.h5'))
os.removedirs('from_dir')
# def test_map(self):
# res = self.handler.map(sum)
def test_apply(self):
# agg, groupby=None, level='stamp'
_write_df_to_handler(self)
result = self.handler.apply('mean', level='stamp', selector='A')
expected = pd.Series([2.5, 2.5, 2.5],
name='A',
index=pd.to_datetime(
['1994-01-01', '1994-02-01', '1994-03-01']))
tm.assert_series_equal(result, expected)
# agg, groupby=None, level='stamp', list selector
result = self.handler.apply('mean', level='stamp', selector=['A'])
expected = pd.DataFrame(expected)
expected.index.names = ['stamp']
tm.assert_frame_equal(result, expected)
# with select kwargs
result = self.handler.apply('mean',
level='stamp',
selector=['A'],
select_kwargs={'columns': ['A']})
expected = pd.DataFrame(expected)
expected.index.names = ['stamp']
tm.assert_frame_equal(result, expected)
# agg, groupby='B', level='None'
# will fail
# result = self.handler.apply('mean', groupby='B', selector=['A'])
# expected = pd.Series([1.5, 3.5], name='A', index=['a', 'b'])
# tm.assert_series_equal(result, expected)
# a list of aggs
result = self.handler.apply(['mean', 'count'],
level='stamp',
selector=['A'])
expected = pd.DataFrame(
[[2.5, 4], [2.5, 4], [2.5, 4]],
index=pd.to_datetime(['1994-01-01', '1994-02-01', '1994-03-01']))
expected.index.name = 'stamp'
expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'),
('A', 'count')])
tm.assert_frame_equal(result, expected)
def test_select(self):
df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
self.handler.write(df, 'm1994_01', format='table', append=False)
# actual test
result = self.handler.select('m1994_01')
tm.assert_frame_equal(result, df)
result = self.handler.select('m1994_01', columns=['A'])
tm.assert_frame_equal(result, df[['A']])
def test_select_all(self):
df = _write_df_to_handler(self, return_df=True).sort_index()
# actual test
result = self.handler.select_all()
tm.assert_frame_equal(result, df)
def tearDown(self):
self.handler.close()
def unlink(subdir):
for f in os.listdir(os.path.join('test_files', subdir)):
file_path = os.path.join('.', 'test_files', subdir, f)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except OSError:
pass
except Exception as e:
print(e)
os.rmdir(os.path.join('test_files', subdir))
unlink('panel')
# unlink('long')
os.rmdir('test_files')
def make_to_long(panel_h, settings, start=None, stop=None):
"""
Let's chunk by quarters.
"""
# need compensation for real wage
with open('../panel_construction/settings.txt', 'rt') as f:
settings = json.load(f)
analyzed = pd.HDFStore(settings['analyzed_path'])
comp = analyzed.select('bls_productivity_compensation')['compensation']
prod = analyzed.select('bls_productivity_compensation')['productivity']
keys = sorted(panel_h.stores.keys())
m0 = start or keys[0]
m0 = date_parser(m0)
mn = stop or keys[-1]
mn = date_parser(mn)
months = [
x.strftime('%Y_%m') for x in arrow.Arrow.range('month', m0, mn)
if x.strftime('m%Y_%m') in keys
]
# Getting some memory pressure. break into chunks, write each out.
# read proccessed chucnks.
# Chunking by quarter
month_chunks = chunk_quarters(months, 3)
month_chunks = [x for x in month_chunks if len(x) > 0]
p = pathlib.Path(str(settings['base_path']))
out_store = HDFHandler(str(p),
kind='long',
months=month_chunks,
frequency='Q')
earn_store = HDFHandler(str(p),
kind='earn',
months=month_chunks,
frequency='Q')
for chunk in month_chunks:
# need the three month chunks... maybe zip up with out_stoure.
# may need another dict.
df = read_to_long(panel_h, chunk)
name = make_chunk_name(chunk)
# out_store.write(df, name, format='table', append=False)
s = out_store.stores[name]
# add in real hourly wage
c = comp.reindex(df.index, level='stamp').fillna(method='ffill') / 100
# adjust weight decimals
df.loc[:, 'og_weight'] = df['og_weight'] / 10000
# CPS reports earnings in cents
df.loc[:, 'earnings'] = df['earnings'] / 100
df['real_hr_earns'] = (df['earnings'] / df['hours']) / c
df['real_hr_earns'] = df['real_hr_earns'].replace(np.inf,
np.nan) # div by 0
df = replace_categorical(df, kind='flow', inverse=True)
with pd.get_store(s.filename) as store:
df.to_hdf(store, name, format='table', append=False)
#----------------------------------------------------------------
# Also write out just earnings (nan issues so can't select later)
# need to make real hrs fisrt.
earn = df[~pd.isnull(df.real_hr_earns)]
earn = earn[(earn.hours > 0) & (earn.earnings > 0)]
s = earn_store.stores[name]
with pd.get_store(s.filename) as store:
earn.to_hdf(store,
name,
format='table',
append=False,
data_columns=True)
print("Finished " + str(chunk))
# finally, chunk by quarter and write out.
df = earn_store.select_all().drop(['occupation', 'actual_hours'], axis=1)
df = df.dropna(how='any', subset=['edu', 'age', 'flow', 'expr'])
df = quarterize(df)
df['productivity'] = prod.reindex(df.index, level='qmonth')
df['real_hr_earns'] = df.real_hr_earns.replace(np.inf, np.nan)
# df = add_demo_dummies(df)
# model, res = construct_wage_index(df)
# df.loc[:, 'wage_index_res'] = res.resid
cln_path = '/Volumes/HDD/Users/tom/DataStorage/CPS/analyzed/clean.h5'
with pd.get_store(cln_path) as store:
df.to_hdf(store, 'cleaned', format='f', append=False)
out_store.close()
analyzed.close()
earn_store.close()
class TestHDFWrapper(unittest.TestCase):
def setUp(self):
self.fdir = os.path.join('.', 'test_files', 'panel')
months = ['1994_01', '1994_02', '1994_03']
frequency = 'monthly'
self.handler = HDFHandler('./test_files', 'panel', months, frequency)
def test_file_creation(self):
# _ = self.handler._select_stores(self.handler)
expected = [os.path.join('test_files', 'panel', x) for x in
('m1994_01.h5', 'm1994_02.h5', 'm1994_03.h5')]
print(os.listdir('test_files'))
assert all([os.path.exists(x) for x in expected])
def test_create_from_list(self):
months = [['1994_01', '1994_02', '1994_03']]
frequency = 'Q'
handler = HDFHandler('./test_files', kind='panel', months=months,
frequency=frequency)
self.assertEqual(handler.stores.keys(), ['long_1994_Q1'])
handler.close()
def test_getitem(self):
result = self.handler['m1994_01']
expected = self.handler.stores['m1994_01']
self.assertIs(result, expected)
def test_write(self):
df = pd.DataFrame({'A': [1, 2, 3]})
self.handler.write(df, 'm1994_01', format='f', append=False)
res = self.handler.select('m1994_01')
tm.assert_frame_equal(df, res)
def test_iter(self):
result = [x for x in self.handler]
expected = ['m1994_01', 'm1994_02', 'm1994_03']
self.assertEqual(result, expected)
# def test_select_all(self):
# import ipdb; ipdb.set_trace()
# h = HDFHandler(self.settings, 'panel', frequency='monthly')
# assert len(h.stores) == 3
def test_sanitize_key(self):
result = self.handler._sanitize_key('1994-01')
expected = 'm1994_01'
self.assertEqual(result, expected)
result = self.handler._sanitize_key('1994_01')
self.assertEqual(result, expected)
result = self.handler._sanitize_key('m1994-01')
self.assertEqual(result, expected)
# With a different pre
settings = {'base_path': './test_files/'}
months = [['1994_01', '1994_02', '1994_03']]
frequency = 'Q'
# handler = HDFHandler(settings, kind='long', months=months,
# frequency=frequency)
# result = handler._sanitize_key('long_1996_Q1')
# print(handler.pre)
# expected = "long_1996_Q1"
# self.assertEqual(result, expected)
def test_iteritems(self):
g = self.handler.iteritems()
name, value = next(g)
self.assertEqual(name, 'm1994_01')
self.assertIs(value, None)
# getting a `is not a regular file` error.
def test_from_directory(self):
# setup should have created some.
os.mkdir('from_dir')
with open(os.path.join('from_dir', 'file5.h5'), 'w'):
pass
try:
handler = HDFHandler.from_directory('from_dir', kind='M')
self.assertEqual(len(handler.stores), 1)
handler.close()
except Exception as e:
print(e)
finally:
os.remove(os.path.join('from_dir', 'file5.h5'))
os.removedirs('from_dir')
# def test_map(self):
# res = self.handler.map(sum)
def test_apply(self):
# agg, groupby=None, level='stamp'
_write_df_to_handler(self)
result = self.handler.apply('mean', level='stamp', selector='A')
expected = pd.Series([2.5, 2.5, 2.5], name='A',
index=pd.to_datetime(['1994-01-01',
'1994-02-01',
'1994-03-01']))
tm.assert_series_equal(result, expected)
# agg, groupby=None, level='stamp', list selector
result = self.handler.apply('mean', level='stamp', selector=['A'])
expected = pd.DataFrame(expected)
expected.index.names = ['stamp']
tm.assert_frame_equal(result, expected)
# with select kwargs
result = self.handler.apply('mean', level='stamp', selector=['A'],
select_kwargs={'columns': ['A']})
expected = pd.DataFrame(expected)
expected.index.names = ['stamp']
tm.assert_frame_equal(result, expected)
# agg, groupby='B', level='None'
# will fail
# result = self.handler.apply('mean', groupby='B', selector=['A'])
# expected = pd.Series([1.5, 3.5], name='A', index=['a', 'b'])
# tm.assert_series_equal(result, expected)
# a list of aggs
result = self.handler.apply(['mean', 'count'], level='stamp',
selector=['A'])
expected = pd.DataFrame([[2.5, 4], [2.5, 4], [2.5, 4]],
index=pd.to_datetime(['1994-01-01',
'1994-02-01',
'1994-03-01']))
expected.index.name = 'stamp'
expected.columns = pd.MultiIndex.from_tuples([('A', 'mean'), ('A', 'count')])
tm.assert_frame_equal(result, expected)
def test_select(self):
df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
self.handler.write(df, 'm1994_01', format='table', append=False)
# actual test
result = self.handler.select('m1994_01')
tm.assert_frame_equal(result, df)
result = self.handler.select('m1994_01', columns=['A'])
tm.assert_frame_equal(result, df[['A']])
def test_select_all(self):
df = _write_df_to_handler(self, return_df=True).sort_index()
# actual test
result = self.handler.select_all()
tm.assert_frame_equal(result, df)
def tearDown(self):
self.handler.close()
def unlink(subdir):
for f in os.listdir(os.path.join('test_files', subdir)):
file_path = os.path.join('.', 'test_files', subdir, f)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except OSError:
pass
except Exception as e:
print(e)
os.rmdir(os.path.join('test_files', subdir))
unlink('panel')
# unlink('long')
os.rmdir('test_files')
def make_to_long(panel_h, settings, start=None, stop=None):
"""
Let's chunk by quarters.
"""
# need compensation for real wage
with open('../panel_construction/settings.txt', 'rt') as f:
settings = json.load(f)
analyzed = pd.HDFStore(settings['analyzed_path'])
comp = analyzed.select('bls_productivity_compensation')['compensation']
prod = analyzed.select('bls_productivity_compensation')['productivity']
keys = sorted(panel_h.stores.keys())
m0 = start or keys[0]
m0 = date_parser(m0)
mn = stop or keys[-1]
mn = date_parser(mn)
months = [x.strftime('%Y_%m') for x in arrow.Arrow.range('month', m0, mn)
if x.strftime('m%Y_%m') in keys]
# Getting some memory pressure. break into chunks, write each out.
# read proccessed chucnks.
# Chunking by quarter
month_chunks = chunk_quarters(months, 3)
month_chunks = [x for x in month_chunks if len(x) > 0]
p = pathlib.Path(str(settings['base_path']))
out_store = HDFHandler(str(p), kind='long', months=month_chunks,
frequency='Q')
earn_store = HDFHandler(str(p), kind='earn', months=month_chunks,
frequency='Q')
for chunk in month_chunks:
# need the three month chunks... maybe zip up with out_stoure.
# may need another dict.
df = read_to_long(panel_h, chunk)
name = make_chunk_name(chunk)
# out_store.write(df, name, format='table', append=False)
s = out_store.stores[name]
# add in real hourly wage
c = comp.reindex(df.index, level='stamp').fillna(method='ffill') / 100
# adjust weight decimals
df.loc[:, 'og_weight'] = df['og_weight'] / 10000
# CPS reports earnings in cents
df.loc[:, 'earnings'] = df['earnings'] / 100
df['real_hr_earns'] = (df['earnings'] / df['hours']) / c
df['real_hr_earns'] = df['real_hr_earns'].replace(np.inf, np.nan) # div by 0
df = replace_categorical(df, kind='flow', inverse=True)
with pd.get_store(s.filename) as store:
df.to_hdf(store, name, format='table', append=False)
#----------------------------------------------------------------
# Also write out just earnings (nan issues so can't select later)
# need to make real hrs fisrt.
earn = df[~pd.isnull(df.real_hr_earns)]
earn = earn[(earn.hours > 0) & (earn.earnings > 0)]
s = earn_store.stores[name]
with pd.get_store(s.filename) as store:
earn.to_hdf(store, name, format='table', append=False,
data_columns=True)
print("Finished " + str(chunk))
# finally, chunk by quarter and write out.
df = earn_store.select_all().drop(['occupation', 'actual_hours'], axis=1)
df = df.dropna(how='any', subset=['edu', 'age', 'flow', 'expr'])
df = quarterize(df)
df['productivity'] = prod.reindex(df.index, level='qmonth')
df['real_hr_earns'] = df.real_hr_earns.replace(np.inf, np.nan)
# df = add_demo_dummies(df)
# model, res = construct_wage_index(df)
# df.loc[:, 'wage_index_res'] = res.resid
cln_path = '/Volumes/HDD/Users/tom/DataStorage/CPS/analyzed/clean.h5'
with pd.get_store(cln_path) as store:
df.to_hdf(store, 'cleaned', format='f', append=False)
out_store.close()
analyzed.close()
earn_store.close()