def iter_timeseries_(exp, observable, conditions, size=None):
"""Iterator over :class:`TimeSeries` instances from lineages in exp.
TimeSeries are generated by browing Lineages instances from exp,
retrieving observable values as defined un Observable, under different
conditions defined in Conditions
Parameters
----------
exp : :class:`Experiment` instance
observable : :class:`Observable` or :class:`FunctionalObservable` instance
conditions : list of :class:`FilterSet` instances
size : int (default None)
when not None, limit the iterator to size items.
Yields
------
:class:`TimeSeries` instance
"""
all_filters = [exp.fset, ] + conditions
raw_obs, func_obs = set_observable_list(observable, filters=all_filters)
# run the iterator
if exp._counts is None:
exp.count_items()
n_lineages = exp._counts['lineages']
for lineage in tqdm(exp.iter_lineages(size=size), total=n_lineages, desc='sample iteration'):
ts = lineage.get_timeseries(observable,
raw_obs=raw_obs, func_obs=func_obs,
cset=conditions)
yield ts
def iter_timeseries_2(exp, obs1, obs2, conditions, size=None):
"""Iterator over couples :class:`TimeSeries` instances
:class:`TimeSeries` are generated by browing :class:`Lineage` instances
from exp, retrieving observable values as defined in obs1 and obs2,
under different conditions.
Parameters
----------
exp : :class:`Experiment` instance
obs1 : :class:`Observable` instance
obs2 : :class:`Observable` instance
conditions : list of :class:`FilterSet` instances
size : int (default None)
when not None, limit the iterator to size items.
Yields
------
Couple of :class:`TimeSeries` instances
"""
all_filters = [exp.fset, ] + conditions
raw_obs, func_obs = set_observable_list(obs1, obs2, filters=all_filters)
if exp._counts is None:
exp.count_items()
n_lineages = exp._counts['lineages']
for lineage in tqdm(exp.iter_lineages(size=size), total=n_lineages, desc='sample iteration'):
ts1 = lineage.get_timeseries(obs1,
raw_obs=raw_obs, func_obs=func_obs,
cset=conditions)
ts2 = lineage.get_timeseries(obs2,
raw_obs=raw_obs, func_obs=func_obs,
cset=conditions)
yield (ts1, ts2)
def make_plot(self, obs, **kwargs):
"""Produce Figure and save plotted data.
Parameters
----------
obs : :class:`Observable` isntance
observable to plot
kwargs : keyword arguments
check :fun:`plot_samples` doctring for valid keyword arguments
"""
self.obs = obs
raw_obs, func_obs = set_observable_list(obs, filters=self._all_filters)
raw_obs = raw_obs
func_obs = func_obs
samples = []
for lin in _unroll_samples(self._input_samples):
ts = lin.get_timeseries(obs, raw_obs, func_obs, self.cset)
samples.append((lin, ts))
self._samples = samples
fig = plot_samples(samples, obs,
conditions=self.cset,
parser=self.parser,
**kwargs)
self.fig = fig
return
def make_plot(self, obs, **kwargs):
"""Produce Figure and save plotted data.
Parameters
----------
obs : :class:`Observable` isntance
observable to plot
kwargs : keyword arguments
check :fun:`plot_samples` doctring for valid keyword arguments
"""
self.obs = obs
raw_obs, func_obs = set_observable_list(obs, filters=self._all_filters)
raw_obs = raw_obs
func_obs = func_obs
samples = []
for lin in _unroll_samples(self._input_samples):
ts = lin.get_timeseries(obs, raw_obs, func_obs, self.cset)
samples.append((lin, ts))
self._samples = samples
fig = plot_samples(samples,
obs,
conditions=self.cset,
parser=self.parser,
**kwargs)
self.fig = fig
return
def iter_timeseries_2(exp, obs1, obs2, conditions, size=None):
"""Iterator over couples :class:`TimeSeries` instances
:class:`TimeSeries` are generated by browing :class:`Lineage` instances
from exp, retrieving observable values as defined in obs1 and obs2,
under different conditions.
Parameters
----------
exp : :class:`Experiment` instance
obs1 : :class:`Observable` instance
obs2 : :class:`Observable` instance
conditions : list of :class:`FilterSet` instances
size : int (default None)
when not None, limit the iterator to size items.
Yields
------
Couple of :class:`TimeSeries` instances
"""
all_filters = [exp.fset, ] + conditions
raw_obs, func_obs = set_observable_list(obs1, obs2, filters=all_filters)
if exp._counts is None:
exp.count_items()
n_lineages = exp._counts['lineages']
for lineage in tqdm(exp.iter_lineages(size=size), total=n_lineages, desc='sample iteration'):
ts1 = lineage.get_timeseries(obs1,
raw_obs=raw_obs, func_obs=func_obs,
cset=conditions)
ts2 = lineage.get_timeseries(obs2,
raw_obs=raw_obs, func_obs=func_obs,
cset=conditions)
yield (ts1, ts2)
def iter_timeseries_(exp, observable, conditions, size=None):
"""Iterator over :class:`TimeSeries` instances from lineages in exp.
TimeSeries are generated by browing Lineages instances from exp,
retrieving observable values as defined un Observable, under different
conditions defined in Conditions
Parameters
----------
exp : :class:`Experiment` instance
observable : :class:`Observable` or :class:`FunctionalObservable` instance
conditions : list of :class:`FilterSet` instances
size : int (default None)
when not None, limit the iterator to size items.
Yields
------
:class:`TimeSeries` instance
"""
all_filters = [exp.fset, ] + conditions
raw_obs, func_obs = set_observable_list(observable, filters=all_filters)
# run the iterator
if exp._counts is None:
exp.count_items()
n_lineages = exp._counts['lineages']
for lineage in tqdm(exp.iter_lineages(size=size), total=n_lineages, desc='sample iteration'):
ts = lineage.get_timeseries(observable,
raw_obs=raw_obs, func_obs=func_obs,
cset=conditions)
yield ts
def test_unrolling():
length = Observable(name='length')
width = Observable(name='width')
area = FunctionalObservable(name='square-area', f=lambda x, y: x*y, observables=[length, width])
vol = FunctionalObservable(name='volume', f=lambda x, y: x*y, observables=[area, width])
raw_obs, func_obs = set_observable_list(vol, filters=[])
assert length in raw_obs
assert length not in func_obs
assert width in raw_obs
assert width not in func_obs
assert area in func_obs
assert area not in raw_obs
assert vol in func_obs
assert vol not in raw_obs
def test_unrolling():
length = Observable(name='length')
width = Observable(name='width')
area = FunctionalObservable(name='square-area',
f=lambda x, y: x * y,
observables=[length, width])
vol = FunctionalObservable(name='volume',
f=lambda x, y: x * y,
observables=[area, width])
raw_obs, func_obs = set_observable_list(vol, filters=[])
assert length in raw_obs
assert length not in func_obs
assert width in raw_obs
assert width not in func_obs
assert area in func_obs
assert area not in raw_obs
assert vol in func_obs
assert vol not in raw_obs
def prefilter(self, filt=None, verbose=False):
"""Filter at the cell level.
Parameters
----------
filt : Filter instance
has to accept Cell instance as argument
has to be callable
returns True when cell is acccepted, False when not
Notes
-----
This method acts of self.cells: it removes cells, and updates
properties of parent/daughters cells when appropriate.
Apply .prefilter only BEFORE building trees.
See also
--------
The .postfilter method that filters cells at the tree level: when trees
have been built after reading from file, they can be reconstructed
upon filtering with this other method.
"""
erased = []
if verbose:
msg = 'Prior to filter, we have {} cells.'.format(len(self.cells))
print(msg)
# check for supplementary observables to be computed
raw_obs, func_obs = set_observable_list(filters=[
filt,
])
# compute suppl obs for all cells
if raw_obs:
for cell in self.cells:
for obs in raw_obs:
cell.build(obs)
for cell in self.cells:
if filt is not None:
if not filt(cell):
erased.append(cell)
# make Colony.add_cell_recursive non functional
cell.bpointer = None
if cell.parent:
cell.parent.childs.remove(cell)
# make daughter cells new roots
for ch in cell.childs:
ch.bpointer = None
if verbose:
msg = '{} cells do not pass filter.'.format(len(erased))
print(msg)
for cell in erased:
self.cells.remove(cell) # otherwise would be considered root
# clean-up actions for computing extra obs
# extra obs computation depends on tree decomposition
# this will be done in lineage.get_timeseries()
for cell in self.cells:
for obs in raw_obs:
del cell._sdata[obs.label]
if verbose:
msg = 'After filtering, we get {} cells.'.format(len(self.cells))
print(msg)
# self.metadata.filters.append(repr(boofunc))
return
def prefilter(self, filt=None, verbose=False):
"""Filter at the cell level.
Parameters
----------
filt : Filter instance
has to accept Cell instance as argument
has to be callable
returns True when cell is acccepted, False when not
Notes
-----
This method acts of self.cells: it removes cells, and updates
properties of parent/daughters cells when appropriate.
Apply .prefilter only BEFORE building trees.
See also
--------
The .postfilter method that filters cells at the tree level: when trees
have been built after reading from file, they can be reconstructed
upon filtering with this other method.
"""
erased = []
if verbose:
msg = 'Prior to filter, we have {} cells.'.format(len(self.cells))
print(msg)
# check for supplementary observables to be computed
raw_obs, func_obs = set_observable_list(filters=[filt, ])
# compute suppl obs for all cells
if raw_obs:
for cell in self.cells:
for obs in raw_obs:
cell.build(obs)
for cell in self.cells:
if filt is not None:
if not filt(cell):
erased.append(cell)
# make Colony.add_cell_recursive non functional
cell.bpointer = None
if cell.parent:
cell.parent.childs.remove(cell)
# make daughter cells new roots
for ch in cell.childs:
ch.bpointer = None
if verbose:
msg = '{} cells do not pass filter.'.format(len(erased))
print(msg)
for cell in erased:
self.cells.remove(cell) # otherwise would be considered root
# clean-up actions for computing extra obs
# extra obs computation depends on tree decomposition
# this will be done in lineage.get_timeseries()
for cell in self.cells:
for obs in raw_obs:
del cell._sdata[obs.label]
if verbose:
msg = 'After filtering, we get {} cells.'.format(len(self.cells))
print(msg)
# self.metadata.filters.append(repr(boofunc))
return