def std(self, background=None):
"""Standard deviation of the score of a motif."""
if background is None:
background = dict.fromkeys(self._letters, 1.0)
else:
background = dict(background)
total = sum(background.values())
for letter in self._letters:
background[letter] /= total
variance = 0.0
for i in range(self.length):
sx = 0.0
sxx = 0.0
for letter in self._letters:
logodds = self[letter, i]
if _isnan(logodds):
continue
if _isinf(logodds) and logodds < 0:
continue
b = background[letter]
p = b * math.pow(2, logodds)
sx += p*logodds
sxx += p*logodds*logodds
sxx -= sx*sx
variance += sxx
variance = max(variance, 0) # to avoid roundoff problems
return math.sqrt(variance)
def std(self, background=None):
"""Standard deviation of the score of a motif."""
if background is None:
background = dict.fromkeys(self._letters, 1.0)
else:
background = dict(background)
total = sum(background.values())
for letter in self._letters:
background[letter] /= total
variance = 0.0
for i in range(self.length):
sx = 0.0
sxx = 0.0
for letter in self._letters:
logodds = self[letter, i]
if _isnan(logodds):
continue
if _isinf(logodds) and logodds < 0:
continue
b = background[letter]
p = b * math.pow(2, logodds)
sx += p * logodds
sxx += p * logodds * logodds
sxx -= sx * sx
variance += sxx
variance = max(variance, 0) # to avoid roundoff problems
return math.sqrt(variance)
def mean(self, background=None):
"""Expected value of the score of a motif."""
if background is None:
background = dict.fromkeys(self._letters, 1.0)
else:
background = dict(background)
total = sum(background.values())
for letter in self._letters:
background[letter] /= total
sx = 0.0
for i in range(self.length):
for letter in self._letters:
logodds = self[letter, i]
if _isnan(logodds):
continue
b = background[letter]
p = b * math.pow(2, logodds)
sx += p * logodds
return sx
def mean(self, background=None):
"""Expected value of the score of a motif."""
if background is None:
background = dict.fromkeys(self._letters, 1.0)
else:
background = dict(background)
total = sum(background.values())
for letter in self._letters:
background[letter] /= total
sx = 0.0
for i in range(self.length):
for letter in self._letters:
logodds = self[letter,i]
if _isnan(logodds):
continue
b = background[letter]
p = b * math.pow(2,logodds)
sx += p * logodds
return sx
def _parse(value, except_value=float("NaN"), nan_is_numeric=True):
"""Returns value parsed to float (via the function float(value))
with more freedom to control the returned value.
Parameters:
- except_value (NaN by default) is the value returned on errors, e.g., when trying to parse dicts (float({})).
- nan_is_numeric tells whether nan values should be treated as numeric (this is the default)
or should be treated as errors (in this case nan values will return except_value)
Note that this function is handy in that it can be used also to get if value is either
a number or a numeric string via, e.g.:
if _parse(value, None, False) is not None
"""
try:
val = float(value)
if not nan_is_numeric and _isnan(val):
return except_value
else:
return val
except:
return except_value
def _parse(value, except_value=float('NaN'), nan_is_numeric=True):
"""Returns value parsed to float (via the function float(value))
with more freedom to control the returned value.
Parameters:
- except_value (NaN by default) is the value returned on errors, e.g., when trying to parse dicts (float({})).
- nan_is_numeric tells whether nan values should be treated as numeric (this is the default)
or should be treated as errors (in this case nan values will return except_value)
Note that this function is handy in that it can be used also to get if value is either
a number or a numeric string via, e.g.:
if _parse(value, None, False) is not None
"""
try:
val = float(value)
if not nan_is_numeric and _isnan(val):
return except_value
else:
return val
except:
return except_value
def isnan(value):
try:
return _isnan(value)
except TypeError:
return False
def isnan(value):
try:
return _isnan(value)
except Exception:
return False
