Example: nameLHS=S1, sign=-1, factor='k2', exponentList=[('S2A',2)] Sets degradation term of dS1/dt to k2*S2A**2.

Returns SloppyCell 'model' that contains the given network and the given data (also setting fixed scale factors). Here the d

maxIndex (-3) : If the best model has an index above maxIndex, return None. (Use negative number -N to force

stopFittingN (inf) : Only include names of models up to stopFittingN past the maxLogLikelihoodName

usePreviousParams : if True, use the previous model's parameters as a starting point.

Calculates G given Pg and H given Ph.

Transforms fittingData and fittingDataDerivs into matrices that can be used in log-linear fitting. Integrates at current par

Returns 2D list of axes. numCols (None) : 3.17.2013 set to 1 to plot all indepParams on a single

retTheta (False) : Return thetaMatrixG and thetaMatrixH to be used with _derivProblem_regression

Returns times,14-dimensional data for each time (7 concentrations,7 derivatives). (S1,S2,S3,S4,N2,A3,S4ex) and their derivatives in the same

Given a reaction's 'center site' (the site modified by the reaction), its 'order' (number of involved sites), and a list of all interact

Also includes the initialParameters as the last set of parameters.

Returns the typical ranges of initial conditions for the parameters in self.indepParamNames. Useful when creating random ini

What happens with naming convention when there are inputs? I think the species nodes (non-input nodes) start at index #inputs.

Uses Levenberg-Marquardt to find local best fit.

Uses pygraphviz to create a DOT file from the given networkList. prog ('neato') :'neato','fdp', showWeights (False) : True

filterNans (False) : If True, NaNs are removed from the data before taking the mean.

indices (None) : If a list of indepParamsList indices, plots only these indices. Otherwise plots

Remove factors with an exponent of zero from right-hand-sides.

Note: complexity != numParameters (Only works for 1 <= maxConnection <= 2) Careful with random orderings, which have the pos

A faster way to evaluate derivatives when you don't need to integrate. (To fix: Shouldn't actually need fittingDataDerivs)

Takes two matrices of shape (# indepParams + # species + 1)x(# species) and returns a single flat array of length (total # params).

Returns parameter matrix Pg or Ph with shape (#indepParams + 1 + #species)x(#species) weightMatrix (None) : A matrix the

Returns model output as array: output[0] = time series for species 1, output[1] = time series for species 2, ...

Returns JtJ, an approximation of the Hessian that uses analytical derivatives. (includes no data, only priors) (Bug: Should

(As of 9.19.2012, does not support fittingDataDerivs) otherStartingPoint : passed to self.perfectModel.fitToData

Uses SloppyCell's built-in mpi4py support to run ensemble generation (generateEnsemble) in parallel.

Uses mpi4py to run many local fits (localFitToData) in parallel.

Calculate log-likelihood estimate based on cost (usu. sums of squared residuals), the singular values of the Hessian, and the

Create a simple mock SloppyCell Network object

See correlationWithPerfectModel. Returns list of shape (numTests, # variables).

Computes data and derivatives for both the given fittingModel and self.perfectFittingModel, and returns the Pearson correlation coe

See getBestModel and plotModelResults

Find data subsets (N) that have models that have been fit to all conditions. onlyNew (False) : Optionally include only subse

Returns SloppyCell network with the given independent parameters.

Modifies given SloppyCell net to "clamp" all species except unclampedSpeciesID to the given data. Hopefully useful for faste

Example s-system power law network from SavVoi87 (p. 98).

Creates BioNetGen network 'filename.net'. Returns list of names of network parameters.

Returns J^T.J approximation to the Hessian, with shape (# parameters)x(#parameters). Note: (# parameters) is equal to the nu

Returns Jacobian of shape (# residuals)x(# parameters). Note: (# parameters) is equal to the number of optimizable parameter

Uses an alternating log-linear routine to fit the power law network given data on concentrations AND their time derivatives.

Returns list of length (# variables). varList (None) : List of variables to test. Defaults

Find all variable names NOT used to calculate the variables in data.keys() in the given SloppyCell network 'net'.

Find all variable names used to calculate the variables in data.keys() in the given SloppyCell network 'net'.

To update old versions: indepParams -> indepParamsList

Finds a filename that won't be overwriting anything (hopefully).

Test whether the model can be evaluated successfully at the current parameters.