def test_comap_maplike():
cm = comap(op.add(1), (1, 2, 3))
assert isinstance(cm, map)
assert tuple(cm) == tuple(map(op.add(1), (1, 2, 3))) == (2, 3, 4)
dm = comap(op.add, (1, 2, 3), (1, 2, 3))
assert tuple(dm) == (2, 4, 6)
def make_indented_body(body_str):
"""
Helper for generating an indented string to use as the body of a function.
"""
return '\n'.join(
map(
add(" "),
dedent(body_str).splitlines(),
)
)
def make_indented_body(body_str):
"""
Helper for generating an indented string to use as the body of a function.
"""
return '\n'.join(
map(
add(" "),
dedent(body_str).splitlines(),
)
)
def test_comap_send():
cm = comap(op.add(1), co())
assert next(cm) == 2
for n in (2, 3):
assert cm.send(n) == n + 1
dm = comap(op.add, co(), co())
assert next(dm) == 2
for n in (2, 3):
assert dm.send(n) == 2 * n
def test_updates_lnotab():
@instance
class c(CodeTransformer):
@pattern(Ellipsis)
def _(self, instr):
yield type(instr)(instr.arg).steal(instr)
def f(): # pragma: no cover
# this function has irregular whitespace for testing the lnotab
a = 1
# intentional line
b = 2
# intentional line
c = 3
# intentional line
return a, b, c
original = Code.from_pyfunc(f)
post_transform = c.transform(original)
# check that something happened
assert original.lnotab != post_transform.lnotab
# check that we preserved the line numbers
assert (original.lnotab.keys() == post_transform.lnotab.keys() == set(
map(op.add(original.firstlineno), (2, 4, 6, 8))))
def sorted_instrs(lnotab):
order = sorted(lnotab.keys())
for idx in order:
yield lnotab[idx]
# check that the instrs are correct
assert all(
map(
Instruction.equiv,
sorted_instrs(original.lnotab),
sorted_instrs(post_transform.lnotab),
))
# sanity check that the function is correct
assert f() == c(f)()
def handle_exception(exc,
*,
_Done=Done,
_type=type,
_getframe=sys._getframe,
_isinstance=isinstance,
_Word=Word,
_clear_cstack=clear_cstack):
"""Handle exceptions that are raised during phorth operations.
Parameters
----------
exc : Exception
The exception that was raised.
Notes
-----
This normally just prints the exception and restarts jumps us to the start
of the repl with a clean stack. If ``exc`` is an instance of ``Done``, this
will reraise the exception and kill the phorth session.
"""
if _isinstance(exc, _Done):
# reraise the sentinel `Done` type
raise Done()
f = _getframe(1)
cstack = _clear_cstack(f)
print(
'traceback, most recent call last:\n %s\n%s: %s' % (
'\n '.join(map(
str,
concatv(
map(op.add(1), reversed(cstack)),
(exc.__traceback__.tb_lasti,),
))),
_type(exc).__name__,
exc,
),
)
def handle_exception(exc,
*,
_Done=Done,
_type=type,
_getframe=sys._getframe,
_isinstance=isinstance,
_Word=Word,
_clear_cstack=clear_cstack):
"""Handle exceptions that are raised during phorth operations.
Parameters
----------
exc : Exception
The exception that was raised.
Notes
-----
This normally just prints the exception and restarts jumps us to the start
of the repl with a clean stack. If ``exc`` is an instance of ``Done``, this
will reraise the exception and kill the phorth session.
"""
if _isinstance(exc, _Done):
# reraise the sentinel `Done` type
raise Done()
f = _getframe(1)
cstack = _clear_cstack(f)
print(
'traceback, most recent call last:\n %s\n%s: %s' % (
'\n '.join(
map(
str,
concatv(
map(op.add(1), reversed(cstack)),
(exc.__traceback__.tb_lasti, ),
))),
_type(exc).__name__,
exc,
), )
def _expected_data(self, asset_finder):
sids = {
symbol: asset_finder.lookup_symbol(
symbol,
self.asset_start,
).sid
for symbol in self.symbols
}
def per_symbol(symbol):
df = pd.read_csv(
test_resource_path('quandl_samples', symbol + '.csv.gz'),
parse_dates=['Date'],
index_col='Date',
usecols=[
'Open',
'High',
'Low',
'Close',
'Volume',
'Date',
'Ex-Dividend',
'Split Ratio',
],
na_values=['NA'],
).rename(columns={
'Open': 'open',
'High': 'high',
'Low': 'low',
'Close': 'close',
'Volume': 'volume',
'Date': 'date',
'Ex-Dividend': 'ex_dividend',
'Split Ratio': 'split_ratio',
})
df['sid'] = sids[symbol]
return df
all_ = pd.concat(map(per_symbol, self.symbols)).set_index(
'sid',
append=True,
).unstack()
# fancy list comprehension with statements
@list
@apply
def pricing():
for column in self.columns:
vs = all_[column].values
if column == 'volume':
vs = np.nan_to_num(vs)
yield vs
# the first index our written data will appear in the files on disk
start_idx = (
self.calendar.all_sessions.get_loc(self.asset_start, 'ffill') + 1
)
# convert an index into the raw dataframe into an index into the
# final data
i = op.add(start_idx)
def expected_dividend_adjustment(idx, symbol):
sid = sids[symbol]
return (
1 -
all_.ix[idx, ('ex_dividend', sid)] /
all_.ix[idx - 1, ('close', sid)]
)
adjustments = [
# ohlc
{
# dividends
i(24): [Float64Multiply(
first_row=0,
last_row=i(24),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(24, 'AAPL'),
)],
i(87): [Float64Multiply(
first_row=0,
last_row=i(87),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(87, 'AAPL'),
)],
i(150): [Float64Multiply(
first_row=0,
last_row=i(150),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(150, 'AAPL'),
)],
i(214): [Float64Multiply(
first_row=0,
last_row=i(214),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(214, 'AAPL'),
)],
i(31): [Float64Multiply(
first_row=0,
last_row=i(31),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(31, 'MSFT'),
)],
i(90): [Float64Multiply(
first_row=0,
last_row=i(90),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(90, 'MSFT'),
)],
i(222): [Float64Multiply(
first_row=0,
last_row=i(222),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(222, 'MSFT'),
)],
# splits
i(108): [Float64Multiply(
first_row=0,
last_row=i(108),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=1.0 / 7.0,
)],
},
] * (len(self.columns) - 1) + [
# volume
{
i(108): [Float64Multiply(
first_row=0,
last_row=i(108),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=7.0,
)],
}
]
return pricing, adjustments
def _expected_data(self, asset_finder):
sids = {
symbol: asset_finder.lookup_symbol(
symbol,
None,
).sid
for symbol in self.symbols
}
# Load raw data from quandl test resources.
data = load_data_table(
file=test_resource_path(
'quandl_samples',
'QUANDL_ARCHIVE.zip'
),
index_col='date'
)
data['sid'] = pd.factorize(data.symbol)[0]
all_ = data.set_index(
'sid',
append=True,
).unstack()
# fancy list comprehension with statements
@list
@apply
def pricing():
for column in self.columns:
vs = all_[column].values
if column == 'volume':
vs = np.nan_to_num(vs)
yield vs
# the first index our written data will appear in the files on disk
start_idx = (
self.calendar.all_sessions.get_loc(self.start_date, 'ffill') + 1
)
# convert an index into the raw dataframe into an index into the
# final data
i = op.add(start_idx)
def expected_dividend_adjustment(idx, symbol):
sid = sids[symbol]
return (
1 -
all_.ix[idx, ('ex_dividend', sid)] /
all_.ix[idx - 1, ('close', sid)]
)
adjustments = [
# ohlc
{
# dividends
i(24): [Float64Multiply(
first_row=0,
last_row=i(24),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(24, 'AAPL'),
)],
i(87): [Float64Multiply(
first_row=0,
last_row=i(87),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(87, 'AAPL'),
)],
i(150): [Float64Multiply(
first_row=0,
last_row=i(150),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(150, 'AAPL'),
)],
i(214): [Float64Multiply(
first_row=0,
last_row=i(214),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(214, 'AAPL'),
)],
i(31): [Float64Multiply(
first_row=0,
last_row=i(31),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(31, 'MSFT'),
)],
i(90): [Float64Multiply(
first_row=0,
last_row=i(90),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(90, 'MSFT'),
)],
i(158): [Float64Multiply(
first_row=0,
last_row=i(158),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(158, 'MSFT'),
)],
i(222): [Float64Multiply(
first_row=0,
last_row=i(222),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(222, 'MSFT'),
)],
# splits
i(108): [Float64Multiply(
first_row=0,
last_row=i(108),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=1.0 / 7.0,
)],
},
] * (len(self.columns) - 1) + [
# volume
{
i(108): [Float64Multiply(
first_row=0,
last_row=i(108),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=7.0,
)],
}
]
return pricing, adjustments
def _expected_data(self, asset_finder):
sids = {
symbol: asset_finder.lookup_symbol(
symbol,
self.asset_start,
).sid
for symbol in self.symbols
}
def per_symbol(symbol):
df = pd.read_csv(
test_resource_path('quandl_samples', symbol + '.csv.gz'),
parse_dates=['Date'],
index_col='Date',
usecols=[
'Open',
'High',
'Low',
'Close',
'Volume',
'Date',
'Ex-Dividend',
'Split Ratio',
],
na_values=['NA'],
).rename(columns={
'Open': 'open',
'High': 'high',
'Low': 'low',
'Close': 'close',
'Volume': 'volume',
'Date': 'date',
'Ex-Dividend': 'ex_dividend',
'Split Ratio': 'split_ratio',
})
df['sid'] = sids[symbol]
return df
all_ = pd.concat(map(per_symbol, self.symbols)).set_index(
'sid',
append=True,
).unstack()
# fancy list comprehension with statements
@list
@apply
def pricing():
for column in self.columns:
vs = all_[column].values
if column == 'volume':
vs = np.nan_to_num(vs)
yield vs
# the first index our written data will appear in the files on disk
start_idx = self.calendar.get_loc(self.asset_start, 'ffill') + 1
# convert an index into the raw dataframe into an index into the
# final data
i = op.add(start_idx)
def expected_dividend_adjustment(idx, symbol):
sid = sids[symbol]
return (
1 -
all_.ix[idx, ('ex_dividend', sid)] /
all_.ix[idx - 1, ('close', sid)]
)
adjustments = [
# ohlc
{
# dividends
i(24): [Float64Multiply(
first_row=0,
last_row=i(24),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(24, 'AAPL'),
)],
i(87): [Float64Multiply(
first_row=0,
last_row=i(87),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(87, 'AAPL'),
)],
i(150): [Float64Multiply(
first_row=0,
last_row=i(150),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(150, 'AAPL'),
)],
i(214): [Float64Multiply(
first_row=0,
last_row=i(214),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(214, 'AAPL'),
)],
i(31): [Float64Multiply(
first_row=0,
last_row=i(31),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(31, 'MSFT'),
)],
i(90): [Float64Multiply(
first_row=0,
last_row=i(90),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(90, 'MSFT'),
)],
i(222): [Float64Multiply(
first_row=0,
last_row=i(222),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(222, 'MSFT'),
)],
# splits
i(108): [Float64Multiply(
first_row=0,
last_row=i(108),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=1.0 / 7.0,
)],
},
] * (len(self.columns) - 1) + [
# volume
{
i(108): [Float64Multiply(
first_row=0,
last_row=i(108),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=7.0,
)],
}
]
return pricing, adjustments
def _expected_data(self, asset_finder):
sids = {
symbol: asset_finder.lookup_symbol(
symbol,
None,
).sid
for symbol in self.symbols
}
# Load raw data from quandl test resources.
data = load_data_table(
file=test_resource_path("quandl_samples", "QUANDL_ARCHIVE.zip"),
index_col="date",
)
data["sid"] = pd.factorize(data.symbol)[0]
all_ = data.set_index(
"sid",
append=True,
).unstack()
# fancy list comprehension with statements
@list
@apply
def pricing():
for column in self.columns:
vs = all_[column].values
if column == "volume":
vs = np.nan_to_num(vs)
yield vs
# the first index our written data will appear in the files on disk
start_idx = self.calendar.all_sessions.get_loc(self.start_date,
"ffill") + 1
# convert an index into the raw dataframe into an index into the
# final data
i = op.add(start_idx)
def expected_dividend_adjustment(idx, symbol):
sid = sids[symbol]
return (1 - all_.iloc[idx]["ex_dividend", sid] /
all_.iloc[idx - 1]["close", sid])
adjustments = [
# ohlc
{
# dividends
i(24): [
Float64Multiply(
first_row=0,
last_row=i(24),
first_col=sids["AAPL"],
last_col=sids["AAPL"],
value=expected_dividend_adjustment(24, "AAPL"),
)
],
i(87): [
Float64Multiply(
first_row=0,
last_row=i(87),
first_col=sids["AAPL"],
last_col=sids["AAPL"],
value=expected_dividend_adjustment(87, "AAPL"),
)
],
i(150): [
Float64Multiply(
first_row=0,
last_row=i(150),
first_col=sids["AAPL"],
last_col=sids["AAPL"],
value=expected_dividend_adjustment(150, "AAPL"),
)
],
i(214): [
Float64Multiply(
first_row=0,
last_row=i(214),
first_col=sids["AAPL"],
last_col=sids["AAPL"],
value=expected_dividend_adjustment(214, "AAPL"),
)
],
i(31): [
Float64Multiply(
first_row=0,
last_row=i(31),
first_col=sids["MSFT"],
last_col=sids["MSFT"],
value=expected_dividend_adjustment(31, "MSFT"),
)
],
i(90): [
Float64Multiply(
first_row=0,
last_row=i(90),
first_col=sids["MSFT"],
last_col=sids["MSFT"],
value=expected_dividend_adjustment(90, "MSFT"),
)
],
i(158): [
Float64Multiply(
first_row=0,
last_row=i(158),
first_col=sids["MSFT"],
last_col=sids["MSFT"],
value=expected_dividend_adjustment(158, "MSFT"),
)
],
i(222): [
Float64Multiply(
first_row=0,
last_row=i(222),
first_col=sids["MSFT"],
last_col=sids["MSFT"],
value=expected_dividend_adjustment(222, "MSFT"),
)
],
# splits
i(108): [
Float64Multiply(
first_row=0,
last_row=i(108),
first_col=sids["AAPL"],
last_col=sids["AAPL"],
value=1.0 / 7.0,
)
],
},
] * (len(self.columns) - 1) + [
# volume
{
i(108): [
Float64Multiply(
first_row=0,
last_row=i(108),
first_col=sids["AAPL"],
last_col=sids["AAPL"],
value=7.0,
)
],
}
]
return pricing, adjustments
def _expected_data(self, asset_finder):
sids = {
symbol: asset_finder.lookup_symbol(
symbol,
None,
).sid
for symbol in self.symbols
}
# Load raw data local db.
data = _raw_data(self.symbols, self.start_date, self.end_date,
self.columns)
all_ = data.set_index(
'sid',
append=True,
).unstack()
# fancy list comprehension with statements
@list
@apply
def pricing():
for column in self.columns:
vs = all_[column].values
if column == 'volume':
vs = np.nan_to_num(vs)
yield vs
# the first index our written data will appear in the files on disk
start_idx = (
self.calendar.all_sessions.get_loc(self.start_date, 'ffill') + 1)
######修改到此处
# convert an index into the raw dataframe into an index into the
# final data
i = op.add(start_idx)
def expected_dividend_adjustment(idx, symbol):
sid = sids[symbol]
return (1 -
all_.ix[idx,
('ex_dividend', sid)] / all_.ix[idx - 1,
('close', sid)])
adjustments = [
# ohlc
{
# dividends
i(24): [
Float64Multiply(
first_row=0,
last_row=i(24),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(24, 'AAPL'),
)
],
i(87): [
Float64Multiply(
first_row=0,
last_row=i(87),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(87, 'AAPL'),
)
],
i(150): [
Float64Multiply(
first_row=0,
last_row=i(150),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(150, 'AAPL'),
)
],
i(214): [
Float64Multiply(
first_row=0,
last_row=i(214),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=expected_dividend_adjustment(214, 'AAPL'),
)
],
i(31): [
Float64Multiply(
first_row=0,
last_row=i(31),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(31, 'MSFT'),
)
],
i(90): [
Float64Multiply(
first_row=0,
last_row=i(90),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(90, 'MSFT'),
)
],
i(158): [
Float64Multiply(
first_row=0,
last_row=i(158),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(158, 'MSFT'),
)
],
i(222): [
Float64Multiply(
first_row=0,
last_row=i(222),
first_col=sids['MSFT'],
last_col=sids['MSFT'],
value=expected_dividend_adjustment(222, 'MSFT'),
)
],
# splits
i(108): [
Float64Multiply(
first_row=0,
last_row=i(108),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=1.0 / 7.0,
)
],
},
] * (len(self.columns) - 1) + [
# volume
{
i(108): [
Float64Multiply(
first_row=0,
last_row=i(108),
first_col=sids['AAPL'],
last_col=sids['AAPL'],
value=7.0,
)
],
}
]
return pricing, adjustments
