nans (encountered when integration fails) are changed to zeros. type : Can be 'yeast','phos','worm' onlyBest (Tru

Calls MATLAB code given in callString. outputFilename (None) : Looks for output and returns it if poss

Computes data for numPoints equally-spaced data points in the given timeInterval for both the given fittingModel and self.per

Returns JtJ, an approximation of the Hessian that uses analytical derivatives. (does not put on priors)

By default, add one timepoint for each independent parameter first, then increase the number of timepoints per independent parameter. Tim

See documentation for FittingProblem.fitAll. onlyCombine (False) : If True, only loop through existing fits

Returns indepParamsList,fittingData,fittingDataDerivs. Varies all indepParams in self.indepParamNames, which may include initial c

Generates SloppyCell parameter ensemble and saves to file. Returns ens,cost: ens = list of ensemble members cost = list of cost

Modify in place to create a stripped-down version of fpd that doesn't include the models.

Create a mock dataset with a single datapoint

some popular cmaps: pylab.cm.gray pylab.cm.copper pylab.cm.bone pylab.cm.jet Can also use kwargs for

Recompile the code SloppyCell uses to evaluate the model. This is sometimes necessary when loading models that were

Create a simple example dataset depending linearly on input and time, with N datapoints.

upperRangeMultiple (1.) : Each range of initial conditions is expanded by this factor by increasing

Defines a CTSN based on a connection list. A SloppyCell implementation of CTSNs: d X_i / d t = 1/tau_i * ( -X_i + sum_j=1^n w_i_

SloppyCell ('network') model that does not have any ODEs, but simply consists of an nth degree Laguerre polynomial [times e^(-t/2)], which

A SloppyCell implementation of planetary dynamics. Units of distance are rc = GM/(v0^2) Units of time are t0 = rc/v0 = GM/(v0^3)

SloppyCell ('network') model that does not have any ODEs, but simply consists of an nth degree polynomial. Currently produces one ou

Defines a power-law network based on a connection list. A SloppyCell implementation of Savageau's power law representation: d X

SloppyCell ('network') model that does not have any ODEs, but simply consists of a simple model for the phosphorylation example. Cur

SloppyCell ('network') model that does not have any ODEs, but implements a simple sinusoidal model. Produces outputs with names give

Creates SloppyCell transcription network with 4 species as in ZivNemWig07. Each of three transcription factors (X, Y, and Z) is regula

degreeList : should be the length of the number of parameters in the original SloppyCellNetwork, specifying th

useFullyConnected (False) : Treat complexityList as numSpeciesList and make fully connected m

Independent parameters must be a subset of the following list: S1_init,S2_init,S3_init,S4_init,N2_init,A3_init,S4ex_init,temperat

maxSVDeig (1e5) : Maximum singular value allowed in svdInverse minSVDeig (0.) : Minimum singular value allowed in svdInv

Planetary network set up as a powerLawNetwork.

The output measures the total phosphorylation. MichaelisMenten (True) : if True, each reaction rate is modified to

Units of distance are rc = GM/(v0^2) Units of time are t0 = rc/v0 = GM/(v0^3) where G = gravitational constant M =

output(t) = [outputName]_init - A sin(phi) + A sin(omega t + phi)

Models yeast oscillator as a 19-dimensional power-law network. See notes 2.6.2013 - 2.13.2013 and Ruoff_model_original.m.

Calculates approximation to the log-likelihood, including parameter sensitivities and priors. Jacobian calculation assumes 1

Returns string that evaluates to a piecewise interpolating function using the given xvals and yvals. The current default for

Index of best ensemble member for all tested models. As of 9.26.2012, the order of ensemble starting points is: Index 0 : Fit pa

testPerfect (False) : If True, look for fp.perfectCost. Returns 2*cost/numMeasurements. (2*cost should be chi^2.) Assumes

testPerfect (False) : If True, look for fp.perfectModel. Returns 2*cost/numMeasurements. (2*cost should be chi^2.)

Mean magnitude of the gradient with respect to parameters. (Should be small if we've converged)

Combine fittingProblems from multiple conditions saved in the parallel file structure into a single fittingProblemDict. Currently o

Print the current status of model fitting.

Returns JtJ, an approximation of the Hessian that uses analytical derivatives.