Package SloppyCell :: Package ReactionNetworks :: Module Trajectory_mod :: Class Trajectory

Class Trajectory

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Instance Methods

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__init__(self, net, key_column=None, is_sens=False, holds_dt=False, empty=False, const_vals=None)

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__len__(self)

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__getitem__(self, this_slice)

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time_slice(self, start, stop, eps=1e-6)
Return a new trajectory containing only times from start to stop.

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keys(self)

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get_times(self)

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add_event_info(self, net, eventinfo, time_traj_ended, include_extra_event_info=False)
Add information about the network state at each event execution

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append(self, other)

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get_var_typical_val(self, id)

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get_var_traj(self, id)

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_get_time_index(self, time, eps=1e-6)
Return the index of the stored value closest to the requested time.

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get_var_vals(self, time, eps=1e-6)
Return a KeyedList of the values of the trajectory's variables at the given time.

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get_var_val(self, var_id, time, eps=1e-6)
Return the value of the given variable at the given time.

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get_var_vals_index(self, index)
Return a KeyedList of the values of the trajectory's variables at the given index.

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get_var_val_index(self, var_id, index)
Return the value of the given variable at the given index.

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get_dynvar_vals_index(self, index)
Return a KeyedList of the values of the trajectory's dynamic variables at the given index.

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get_dynvar_vals(self, time, eps=1e-6)
Return a KeyedList of the values of the trajectory's dynamic variables at the given time.

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_make__assignment(self, net)

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_make__sens_assignment(self, net)

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appendFromODEINT(self, timepoints, odeint_array, holds_dt=False)

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appendSensFromODEINT(self, timepoints, odeint_array, holds_dt=False)

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copy_subset(self, keys=None)
Return a copy of this trajectory containing only the variables specified in keys.

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__getstate__(self)

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__setstate__(self, newdict)

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_sub_var_names(self, input)

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build_interpolated_traj(self)
Given that a trajectory exists, build_interpolated_traj will create the coefficients for the spline interpolatation.

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evaluate_interpolated_trajs(self, time, subinterval, der=0)
This is a version of evaluate_interpolated_traj that returns all the...

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evaluate_interpolated_traj(self, dv_id, time, subinterval=None, der=0)
Needs Trajectory.build_interpolated_traj() to be called first

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to_file(self, file_name, out_vars=None, separator=', ')
Output the given variables to a file.

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last_dynamic_var_values(self)
Return a list of the dynamic variable values at the last timepoint in the trajectory.

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getVariableTrajectory(self, id)

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Class Variables

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_known_structures = []

_known_function_bodies = []

_dynamic_funcs = ['_assignment', '_sens_assignment']

_common_namespace = {'log': scipy.log, 'log10': scipy.log10, '...

Imports: psyco

Method Details

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_get_time_index(self, time, eps=1e-6)

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Return the index of the stored value closest to the requested time.

Prints a warning if the difference between the requested time and the
stored time is greater than a fraction eps of the trajectory length.

get_var_vals(self, time, eps=1e-6)

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Return a KeyedList of the values of the trajectory's variables at the
given time.

Prints a warning if the difference between the requested time and the
stored time is greater than a fraction eps of the trajectory length.

get_var_val(self, var_id, time, eps=1e-6)

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Return the value of the given variable at the given time.

Prints a warning if the difference between the requested time and the
stored time is greater than a fraction eps of the trajectory length.

get_dynvar_vals(self, time, eps=1e-6)

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Return a KeyedList of the values of the trajectory's dynamic variables
at the given time.

Prints a warning if the difference between the requested time and the
stored time is greater than a fraction eps of the trajectory length.

copy_subset(self, keys=None)

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Return a copy of this trajectory containing only the variables specified
in keys.

If keys is None, all variables are included.

build_interpolated_traj(self)

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Given that a trajectory exists, build_interpolated_traj will create 
the coefficients for the spline interpolatation.
The spline can then be evaluated using 
Trajectory.evaluate_interpolated_traj or
Trajectory.evaluate_interpolated_trajs

evaluate_interpolated_trajs(self, time, subinterval, der=0)

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This is a version of evaluate_interpolated_traj that returns all the
values of the dynamic variables and requires you to pass in the
appropriate subinterval between events (that can be found in 
Trajectory.tcks.keys() )
Faster than calling evaluate_interpolated_traj repeatedly

evaluate_interpolated_traj(self, dv_id, time, subinterval=None, der=0)

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Needs Trajectory.build_interpolated_traj() to be called first

Arguments:
dvid         the name of the component of the trajectory you wish to 
             evaluate
time         a vector of times or a scalar
subinterval  an optional argument specifying the time interval 
             between events that the time argument lies (but given a 
             valid time, it will be found automatically)
der          the derivative of the spline function you want, the order
             of the derivative will be constrained by the order of the 
             interpolated spline
Outputs:
A single scalar value (if time input is a scalar)
or
(returned_times, interpolated_trajectory at those times) if times is a
vector

Note: It is necessary to have a returned_times argument too, in case 
      the times passed in happens to have a timepoint that corresponds 
      to an event time, which often has two trajectory values associated
      with it.

to_file(self, file_name, out_vars=None, separator=', ')

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Output the given variables to a file.

file_name   Name of the file to use for output (will be overwritten!)
out_vars    List of variable ids ot output. If None, default is
            'time', dynamic variables
separator   The separator to use between columns.

Class Variable Details

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_common_namespace

Value:

{'log': scipy.log, 'log10': scipy.log10, 'exp': scipy.exp, 'cos': scip
y.cos, 'sin': scipy.sin, 'tan': scipy.tan, 'acos': scipy.arccos, 'asin
': scipy.arcsin, 'atan': scipy.arctan, 'cosh': scipy.cosh, 'sinh': sci
py.sinh, 'tanh': scipy.tanh, 'arccosh': scipy.arccosh, 'arcsinh': scip
y.arcsinh, 'arctanh': scipy.arctanh, 'pow': scipy.power, 'sqrt': scipy
.sqrt, 'exponentiale': scipy.e, 'pi': scipy.pi,}