class Dataset(pd.DataFrame):
"""
An experimental phospho-proteomics dataset extending `pandas.DataFrame`_
Parameters
----------
midas : Absolute PATH to a MIDAS file
time : Data acquisition time-point for the early response
Attributes
----------
setup : :class:`caspo.core.setup.Setup`
clampings : :class:`caspo.core.clamping.ClampingList`
readouts : `pandas.DataFrame`_
.. _pandas.DataFrame: http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe
"""
def __init__(self, midas, time):
df = pd.read_csv(midas)
times = np.unique(df.filter(regex='^DA').values.flatten())
if time not in times:
raise ValueError("The time-point %s does not exists in the dataset. Available time-points are: %s" % (time, list(times)))
df.drop(df.columns[0], axis=1, inplace=True)
cond = True
for c in df.filter(regex='^DA').columns:
cond = cond & (df[c] == time)
super(Dataset, self).__init__(df[cond].reset_index(drop=True))
stimuli = map(lambda c: c[3:], filter(lambda c: self.is_stimulus(c), self.columns))
inhibitors = map(lambda c: c[3:-1], filter(lambda c: self.is_inhibitor(c), self.columns))
readouts = map(lambda c: c[3:], filter(lambda c: self.is_readout(c), self.columns))
self.setup = Setup(stimuli, inhibitors, readouts)
@property
def clampings(self):
clampings = []
for i, row in self.filter(regex='^TR').iterrows():
literals = []
for v,s in row.iteritems():
if self.is_stimulus(v):
literals.append(Literal(v[3:], 1 if s == 1 else -1))
else:
if s == 1:
literals.append(Literal(v[3:-1], -1))
clampings.append(Clamping(literals))
return ClampingList(clampings)
@property
def readouts(self):
return self.filter(regex='^DV').rename(columns=lambda c: c[3:]).astype(float)
def is_stimulus(self, name):
"""
Returns if the given species name is a stimulus or not
Parameters
----------
name : str
Returns
-------
boolean
True if the given name is a stimulus, False otherwise.
"""
return name.startswith('TR') and not name.endswith('i')
def is_inhibitor(self, name):
"""
Returns if the given species name is a inhibitor or not
Parameters
----------
name : str
Returns
-------
boolean
True if the given name is a inhibitor, False otherwise.
"""
return name.startswith('TR') and name.endswith('i')
def is_readout(self, name):
"""
Returns if the given species name is a readout or not
Parameters
----------
name : str
Returns
-------
boolean
True if the given name is a readout, False otherwise.
"""
return name.startswith('DV')
def to_funset(self, discrete):
"""
Converts the dataset to a set of `gringo.Fun`_ instances
Parameters
----------
discrete : callable
A discretization function
Returns
-------
set
Representation of the dataset as a set of `gringo.Fun`_ instances
.. _gringo.Fun: http://potassco.sourceforge.net/gringo.html#Fun
"""
fs = self.clampings.to_funset("exp")
fs = fs.union(self.setup.to_funset())
for i,row in self.readouts.iterrows():
for var,val in row.iteritems():
if not np.isnan(val):
fs.add(gringo.Fun('obs',[i,var,discrete(val)]))
return fs
