# Observable¶

This module provides API class definition to define observables.

## Classes¶

class tunacell.base.observable.FunctionalObservable(name=None, f=None, observables=[])

Combination of Observable instances

Parameters: name (str) – user defined name for this observable f (callable) – the function to apply to observables observables (list of Observable instances) – parameters of the function f to be applied

Warning

Contrary to Observable, instances of FunctionalObservable cannot be represented as a string using repr(), that could be turned into a new instance with identical parameters using eval(). This is due to the applied function, difficult to serialize as a string AND keeping a human-readable format to read its definition.

label

get unique string identifier

latexify(show_variable=True, plus_delta=False, shorten_time_variable=False, prime_time=False, as_description=False, use_name=None)

Latexify observable name

mode

Returns mode depending on observables passed as parameters

timing

Return timing depending on observables passed as parameters

class tunacell.base.observable.Observable(name=None, from_string=None, raw=None, differentiate=False, scale='linear', local_fit=False, time_window=0.0, join_points=3, mode='dynamics', timing='t', tref=None)

Defines how to retrieve observables.

Parameters: name (str) – user name for this observable (can be one of the raw observable) raw (str (default None)) – raw name of the observable: must be a column name of raw data, i.e. first element of one entry of Experiment.datatype differentiate (boolean (default False)) – whether to differentiate raw observable scale (str {'linear', 'log'}) – expected scaling form as a function of time (used for extrapolating values at boundaries, including in the local_fit procedure) local_fit (boolean (default False)) – whether to perform local fit procedure time_window (float (default 20.)) – Time window over which local fit procedure is applied (used only when local_fit is activated) join_points (int (default 3)) – number of points over which extrapolation procedure is led by fitting linearly [the scale of] the observable w.r.t time mode (str {'dynamics', 'birth', 'division', 'net_increase', 'rate',) – ‘average’} mode used to retrieve data: ’dynamics’: all timepoints are retrieved ’birth’: only birth value is retrieved ’division’: only division value is retrieved ’net-increase-additive’: difference between division value and birth value ’net-increase-multiplicative’: ratio between division value and birth value ’rate’: rate of linear fit of [scale of] observable ’average’: average of observable over cell cycle timing (str {'t', 'b', 'd', 'm', 'g'}) – set the time at which cell cycle observable is associated: ’t’ : time-lapse timing (associated to mode ‘dynamics’) ’b’ : cell cycle birth time ’d’ : cell cycle division time ’m’ : cell cycle midpoint (half time) ’g’ : cell cycle generation index tref (float or 'root' (default None)) – when timing is set to ‘g’, sets the 0th generation to the cell that bounds this reference time when timing is set to ‘t’ (time-lapse timing), allows to translate time values by substracting floating point value (if given as a float), or aligns to the colony root cell last time value as origin.
as_latex_string

Export as LaTeX string. Old format, replaced by latexify

as_string_table()

Human readable output as a table.

as_timelapse()

Convert current observable to its dynamic counterpart

This is needed when computing cell-cycle observables.

label

Label is outputing a unique string representation

This method creates a string label that specifies each parameter to re-construct the Observable. The output string is (kind-of) human readable. More importantly, it is suitable to be a filename (only alphanumeric caracters, and underscores), and in fact serves to name directories in analysis folder.

Note

__repr__() : returns another string representation, that can be called by the built-in eval(), to instantiate a new object with identical functional parameters.

latexify(show_variable=True, plus_delta=False, shorten_time_variable=False, prime_time=False, as_description=False, use_name=None)

Returns a latexified string for observable

Parameters: show_variable (bool) – whether to print out time/generation variable plus_delta (bool) – whether to add a $Delta$ to time/generation variable; used for auto- and cross-correlation labeling shorten_time_variable (bool) – when active, will display only $t$/$g$ prime_time (bool) – whether to set a prime on the time/generation variable as_description (bool (default False)) – sets up the description of the observable from rules to compute it (derivatives, log, and raw label) use_name (str (default None)) – when the observable name is too cumbersome to be printed, and the user wants to choose a specific name for such a printout
load_from_string(codestring)

Set Observable instance from string code created by label method

Parameters: codestring (str) – must follow some rules for parsing
tunacell.base.observable.set_observable_list(*args, **kwargs)

Make raw, and functional observable lists for running analyses

Parameters: *args – Variable length argument list of Observable or FunctionalObservable instances **kwargs – Accepted keyword arguments: ‘filters=[]’ with a list of FilterSet or FilterGeneral instance (must have a .obs attribute) lists of raw observables, functional observables (correctly ordered) raw_obs, func_obs
tunacell.base.observable.unroll_func_obs(obs)

Returns flattened list of FunctionalObservable instances

It inspect recursively the observable content of the argument to yield all nested FunctionalObservable instances. They are ordered from lower to deeper layers in nested-ness. If you need to compute f(g(h(x))), where x is a raw Observable, the generator yields h, g, and f lastly, so that evaluation can be performed in direct order.

Parameters: obs (FunctionalObservable instance) – the observable to inspect FunctionalObservable instance – The generator yields funcObs instance in appropriate order (from lower to higher level in nested-ness).
tunacell.base.observable.unroll_raw_obs(obs)

Returns a generator over flattened list of Observable instances

Parameters: obs ((list of) Observable or FunctionalObservable instances) – flatten – Observable instances found in argument list, going into nested layers in the case of nested list, or for FunctionalObservable instances