itcsimlib.mass_spec module

Extension of Ising classes for fitting mass spectrometry data.

class itcsimlib.mass_spec.MSExperiment(path, T=298.15, sigma=0.05, title=None, lattice_name='Lattice', ligand_name='Ligand')[source]

Bases: itcsimlib.itc_experiment.ITCExperimentBase

A class that represents a specific mass spec experiment, i.e. weights of different stoichiometry populations

chisq

If a fit has been generated, the reduced chi-squared goodness-of-fit value.

Type:float
T

The experimental temperature (in Kelvin).

Type:float
Lattice

The name of the lattice component (if it exists, read from the experiment file)

Type:string
Ligand

The name of the ligand component (if it exists, read from the experiment file)

Type:string
Concentrations

The concentrations of the components at each titration point

Type:list of dicts
PopIntens

The normalized intensities of each lattice+ligand stoichiometry at each set of component concentrations

Type:ndarray
PopSigmas

The uncertainty in the measured intensity of each lattice+ligand stoichiometry at each set of component concentrations

Type:ndarray
PopFits

The fitted intensities (if calculated) of each lattice+ligand stoichiometry at each set of component concentrations

Type:ndarray

Notes

This class abuses several of the usual ITCExperimentBase attributes in a rather dirty fashion - it won’t as a simple replacement for many itcsimlib routines, but basic fitting should be OK

make_plot(hardcopy=False, hardcopydir='.', hardcopyprefix='', hardcopytype='png')[source]

Generate a stacked plot of the experimental populations, the fitted populations, and the residuals between the two.

Parameters:
  • hardcopy (boolean) – Display the fit to the screen, or write it to a file?
  • hardcopydir (string) – The directory to write the hardcopy to.
  • hardcopyprefix (string) – A prefix to append to the experiment’s title when generating the output plot filename.
  • hardcopytype (string) – The output format for the plot (availability is dependent upon what backends matplotlib was compiled with).
Returns:
Return type:

None
make_population_plot(dataset='fit', hardcopy=False, hardcopydir='.', hardcopyprefix='', hardcopytype='png')[source]
export_to_file(path)[source]

Export the experimental data and any fit to the specified file path.

Parameters:path (string) – The filesystem path to write the output to.
Returns:
Return type:None
get_chisq(pops, writeback=False)[source]

Calculate the chi-square goodness-of-fit between the experimental population abundances and the fitted ones.

Parameters:
  • pops (ndarray) – The normalized abundances of each lattice+ligand stoichiometries at each of the provided component concentrations.
  • writeback (boolean) – Update the experiment’s simulated heat attribute (dQ_fit) with the provided Qs?
Returns:

The goodness of the fit, as a reduced chi-square.
Return type:

float

Notes

This method takes advantage of the fact that ITCSim doesn’t inspect the data that is returned by the model, and instead lets the associated experiment handle the goodness-of-fit. The only caveat of course is that model must return the same number of lattice+ligand stoichiometries as are present in the experimental results. Variances (sigma**2) must have been precomputed as self.PopSigmas (perhaps should be self.PopVariances?)

class itcsimlib.mass_spec.MSExperimentSynthetic(lattice_concs, ligand_concs, T=298.15, noise=0.02, title=None, lattice_name='Lattice', ligand_name='Ligand')[source]

Bases: itcsimlib.mass_spec.MSExperiment

A MSExperiment-derived class that can be used to simulate a mass spec experiment

get_chisq(pops, writeback=False)[source]

Extends the base MSExperiment class to overwrite the PopFits attribute on the first call

class itcsimlib.mass_spec.MSModel(model)[source]

Bases: itcsimlib.model_ising.Ising

Class used to convert an existing Ising-based binding model to one that returns lattice+ligand stoichiometry abundances suitable for fitting mass spec data.

Note #1: Inherits all attributes of the provided class instance, serves as a passthru using the provided class’ set_energies(). Note #2: If the provided class instance has overwritten any of the Ising class methods (other than set_energies), you’re best off not using this convertor class.

set_energies(T0, T)[source]

Update the parent model parameters with whatever we currently have, and set the parent model config energies

Q(T0, T, concentrations)[source]

Return a 2D numpy array consisting of the base model’s relative stoichiometries at each of the provided component concentrations.

Parameters:
  • T0 (float) – The reference temperature of the simulation.
  • T (float) – The temperature of the experiment to simulate.
  • concentrations (list of dicts) – The concentrations of each component at each titration point.
Returns:

The normalized abundances of each lattice+ligand stoichiometries at each of the provided component concentrations.
Return type:

ndarray