def isnull(obj):
'''
Replacement for numpy.isnan / -numpy.isfinite which is suitable
for use on object arrays.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
'''
if lib.isscalar(obj):
return lib.checknull(obj)
from pandas.core.generic import PandasObject
if isinstance(obj, np.ndarray):
return _isnull_ndarraylike(obj)
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
elif isinstance(obj, list) or hasattr(obj, '__array__'):
return _isnull_ndarraylike(obj)
else:
return obj is None
def isnull(obj):
'''
Replacement for numpy.isnan / -numpy.isfinite which is suitable
for use on object arrays.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
'''
if lib.isscalar(obj):
return lib.checknull(obj)
from pandas.core.generic import PandasObject
if isinstance(obj, np.ndarray):
return _isnull_ndarraylike(obj)
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
elif hasattr(obj, '__array__'):
return _isnull_ndarraylike(obj)
else:
return obj is None
def _format(x):
if self.na_rep is not None and lib.checknull(x):
if x is None:
return 'None'
return self.na_rep
else:
# object dtype
return '%s' % formatter(x)
def _format(x):
if self.na_rep is not None and lib.checknull(x):
if x is None:
return 'None'
return self.na_rep
else:
# object dtype
return '%s' % formatter(x)
def _isnull_new(obj):
if is_scalar(obj):
return lib.checknull(obj)
# hack (for now) because MI registers as ndarray
elif isinstance(obj, ABCMultiIndex):
raise NotImplementedError("isnull is not defined for MultiIndex")
elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass)):
return _isnull_ndarraylike(obj)
elif isinstance(obj, ABCGeneric):
return obj._constructor(obj._data.isnull(func=isnull))
elif isinstance(obj, list) or hasattr(obj, '__array__'):
return _isnull_ndarraylike(np.asarray(obj))
else:
return obj is None
def _isnull_new(obj):
if is_scalar(obj):
return lib.checknull(obj)
# hack (for now) because MI registers as ndarray
elif isinstance(obj, ABCMultiIndex):
raise NotImplementedError("isnull is not defined for MultiIndex")
elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass)):
return _isnull_ndarraylike(obj)
elif isinstance(obj, ABCGeneric):
return obj._constructor(obj._data.isnull(func=isnull))
elif isinstance(obj, list) or hasattr(obj, '__array__'):
return _isnull_ndarraylike(np.asarray(obj))
else:
return obj is None
def _isnull_new(obj):
if lib.isscalar(obj):
return lib.checknull(obj)
from pandas.core.generic import PandasObject
if isinstance(obj, np.ndarray):
return _isnull_ndarraylike(obj)
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
elif isinstance(obj, list) or hasattr(obj, '__array__'):
return _isnull_ndarraylike(obj)
else:
return obj is None
def update_last_sale(self, event):
# NOTE, PerformanceTracker already vetted as TRADE type
sid = event.sid
if sid not in self.positions:
return 0
price = event.price
if checknull(price):
return 0
pos = self.positions[sid]
pos.last_sale_date = event.dt
pos.last_sale_price = price
def _isnull_new(obj):
if lib.isscalar(obj):
return lib.checknull(obj)
from pandas.core.generic import PandasObject
if isinstance(obj, np.ndarray):
return _isnull_ndarraylike(obj)
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
elif isinstance(obj, list) or hasattr(obj, '__array__'):
return _isnull_ndarraylike(obj)
else:
return obj is None
def update_last_sale(self, event):
# NOTE, PerformanceTracker already vetted as TRADE type
sid = event.sid
if sid not in self.positions:
return
price = event.price
if not checknull(price):
pos = self.positions[sid]
pos.last_sale_date = event.dt
pos.last_sale_price = price
self._position_last_sale_prices[sid] = price
self._position_values = None # invalidate cache
sid = event.sid
price = event.price
def update_last_sale(self, event):
sid = event.sid
if sid not in self.positions:
return
if event.type != TRADE_TYPE:
return
price = event.price
if not checknull(price):
pos = self.positions[sid]
pos.last_sale_date = event.dt
pos.last_sale_price = price
self._position_last_sale_prices[sid] = price
self._position_values = None # invalidate cache
def update_last_sale(self, event):
# NOTE, PerformanceTracker already vetted as TRADE type
sid = event.sid
if sid not in self.positions:
return
price = event.price
if not checknull(price):
pos = self.positions[sid]
pos.last_sale_date = event.dt
pos.last_sale_price = price
self._position_last_sale_prices[sid] = price
self._position_values = None # invalidate cache
sid = event.sid
price = event.price
def update_last_sale(self, event):
sid = event.sid
if sid not in self.positions:
return
if event.type != TRADE_TYPE:
return
price = event.price
if not checknull(price):
pos = self.positions[sid]
pos.last_sale_date = event.dt
pos.last_sale_price = price
self._position_last_sale_prices[sid] = price
self._position_values = None # invalidate cache
def update_last_sale(self, event):
# NOTE, PerformanceTracker already vetted as TRADE type,注意，PerformanceTracker已经审核为TRADE类型
sid = event.sid
#print sid,self.positions
if sid not in self.positions:
return 0
price = event.price
if checknull(price):
return 0
pos = self.positions[sid]
#print u'进入up_date_last_sale'
#print pos
pos.last_sale_date = event.dt
pos.last_sale_price = price
def update_last_sale(self, event):
# NOTE, PerformanceTracker already vetted as TRADE type
sid = event.sid
if sid not in self.positions:
return 0
price = event.price
if checknull(price):
return 0
pos = self.positions[sid]
old_price = pos.last_sale_price
pos.last_sale_date = event.dt
pos.last_sale_price = price
# Calculate cash adjustment on assets with multipliers
return ((price - old_price) * self._position_payout_multipliers[sid]
* pos.amount)
def update_last_sale(self, event):
# NOTE, PerformanceTracker already vetted as TRADE type
sid = event.sid
if sid not in self.positions:
return 0
price = event.price
if checknull(price):
return 0
pos = self.positions[sid]
old_price = pos.last_sale_price
pos.last_sale_date = event.dt
pos.last_sale_price = price
# Calculate cash adjustment on assets with multipliers
return ((price - old_price) * self._position_payout_multipliers[sid] *
pos.amount)
def isnull(obj):
'''
Replacement for numpy.isnan / -numpy.isfinite which is suitable
for use on object arrays.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
'''
if lib.isscalar(obj):
return lib.checknull(obj)
from pandas.core.generic import PandasObject
from pandas import Series
if isinstance(obj, np.ndarray):
if obj.dtype.kind in ('O', 'S'):
# Working around NumPy ticket 1542
shape = obj.shape
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(obj.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
elif obj.dtype == np.dtype('M8[ns]'):
# this is the NaT pattern
result = np.array(obj).view('i8') == lib.iNaT
else:
result = -np.isfinite(obj)
return result
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
else:
return obj is None
def isnull(obj):
'''
Replacement for numpy.isnan / -numpy.isfinite which is suitable
for use on object arrays.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
'''
if lib.isscalar(obj):
return lib.checknull(obj)
from pandas.core.generic import PandasObject
from pandas import Series
if isinstance(obj, np.ndarray):
if obj.dtype.kind in ('O', 'S'):
# Working around NumPy ticket 1542
shape = obj.shape
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(obj.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
elif obj.dtype == np.dtype('M8[ns]'):
# this is the NaT pattern
result = np.array(obj).view('i8') == lib.iNaT
else:
result = -np.isfinite(obj)
return result
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
else:
return obj is None
def _format_value(self, val):
if lib.checknull(val):
val = self.na_rep
if self.float_format is not None and com.is_float(val):
val = float(self.float_format % val)
return val
def _my_helper_csv(self, writer, na_rep=None, cols=None,
header=True, index=True,
index_label=None, float_format=None, write_dtypes=None):
if cols is None:
cols = self.columns
series = {}
for k, v in self._series.iteritems():
series[k] = v.values
has_aliases = isinstance(header, (tuple, list, np.ndarray))
if has_aliases or header:
if index:
# should write something for index label
if index_label is not False:
if index_label is None:
if isinstance(self.index, MultiIndex):
index_label = []
for i, name in enumerate(self.index.names):
if name is None:
name = ''
index_label.append(name)
else:
index_label = self.index.name
if index_label is None:
index_label = ['']
else:
index_label = [index_label]
elif not isinstance(index_label, (list, tuple, np.ndarray)):
# given a string for a DF with Index
index_label = [index_label]
encoded_labels = list(index_label)
else:
encoded_labels = []
if has_aliases:
if len(header) != len(cols):
raise ValueError(('Writing %d cols but got %d aliases'
% (len(cols), len(header))))
else:
write_cols = header
else:
write_cols = cols
encoded_cols = list(write_cols)
if write_dtypes:
for j, col in enumerate(cols):
encoded_cols[j] = "{0}:{1}".format(col, self._series[col].dtype)
writer.writerow(encoded_labels + encoded_cols)
else:
if write_dtypes:
for j, col in enumerate(cols):
encoded_cols[j] = "{0}:{1}".format(col, self._series[col].dtype)
writer.writerow(encoded_cols)
encoded_cols = list(cols)
writer.writerow(encoded_cols)
data_index = self.index
if isinstance(self.index, PeriodIndex):
data_index = self.index.to_timestamp()
nlevels = getattr(data_index, 'nlevels', 1)
for j, idx in enumerate(data_index):
row_fields = []
if index:
if nlevels == 1:
row_fields = [idx]
else: # handle MultiIndex
row_fields = list(idx)
for i, col in enumerate(cols):
val = series[col][j]
if lib.checknull(val):
val = na_rep
if float_format is not None and com.is_float(val):
val = float_format % val
elif isinstance(val, np.datetime64):
val = lib.Timestamp(val)._repr_base
row_fields.append(val)
writer.writerow(row_fields)
def _format_value(self, val):
if lib.checknull(val):
val = self.na_rep
if self.float_format is not None and com.is_float(val):
val = float(self.float_format % val)
return val