parpe.plotting

Plotting functions

Functions

correlation_coefficient(a, b)	Get correlation coefficient for flattened a and b
flatten_filter_nan(a, b)	Flatten arrays a and b removing entries for which either a or b is NaN
plotCorrelation(ymes, ysim[, ...])	Plot correlation of measured and simulated data
plotCorrelationBox(data)	Create boxplot of correlation by observable
plotCorrelationBoxMulti(datasets, labels[, ...])	Plot correlation boxplots of multi-start results for multiple datasets side by side
plotCorrelationDensity(ymes, ysim[, title, ...])	Plot correlation of measured and simulated data
plotCorrelationOld(ymes, ysim[, title, ...])	Plot correlation of measured and simulated data
plotCorrelations(ymes, ysim)	Plot correlation of measured and simulated data
plotCorrelationsByObsDensity(ymes, ysim)	Plot correlation of measured and simulated data
plotCostTrajectory(costTrajectory[, color, ...])	Plot the provided cost trajectory
plotDoseResponseCategorical(conc, mes, sim, ...)	e.g. # fig, ax = plt.subplots() plotDoseResponse(np.array([10, 3, 5]), np.array([1, 2, 3]), np.array([1, 3, 2]), 'test', ax).
plotDoseResponseLogDose(conc, mes, sim, ...)
plotTrajectoryFit(ymes, ysim, timepoints[, ...])	Create a plot with time-course for measured and simulated values for
plotTrajectoryFits(ymes, ysim, timepoints)	For each simulation condition create a plot with time-course for
plotWaterfall(finalCost)	Plot "waterfall plot"
square_plot_equal_ranges(ax[, lim])	Square plot with equal range

Functions

parpe.plotting.correlation_coefficient(a, b)

Get correlation coefficient for flattened a and b

parpe.plotting.flatten_filter_nan(a, b)

Flatten arrays a and b removing entries for which either a or b is NaN

parpe.plotting.plotCorrelation(ymes, ysim, observable_idx=None, title=None, alpha=1.0, legend=False, square=True, ax=None)

Plot correlation of measured and simulated data

Arguments:

ymes: @type numpy.ndarray

measured values n_condition x nt

ysim: @type numpy.ndarray

simulated values n_condition x nt

parpe.plotting.plotCorrelationBox(data)

Create boxplot of correlation by observable

Parameters:

data – Correlations as obtained from getCorrTable

parpe.plotting.plotCorrelationBoxMulti(datasets, labels, legend_loc='lower center')

Plot correlation boxplots of multi-start results for multiple datasets side by side

data1: nstarts x ny

parpe.plotting.plotCorrelationDensity(ymes, ysim, title=None, title_append_corr=True, normalize_percentile=100, nbins=50, contour=True)

Plot correlation of measured and simulated data

Arguments:

ymes: @type numpy.ndarray

measured values n_condition x nt

ysim: @type numpy.ndarray

simulated values n_condition x nt

parpe.plotting.plotCorrelationOld(ymes, ysim, title=None, alpha=1.0, legend=False, square=True, ax=None)

Plot correlation of measured and simulated data

Arguments:

ymes: @type numpy.ndarray

measured values n_condition x nt

ysim: @type numpy.ndarray

simulated values n_condition x nt

parpe.plotting.plotCorrelations(ymes, ysim)

Plot correlation of measured and simulated data

Arguments:

ymes: @type numpy.ndarray

measured values n_condition x nt x ny

ysim: @type numpy.ndarray

simulated values n_condition x nt x ny

parpe.plotting.plotCorrelationsByObsDensity(ymes, ysim)

Plot correlation of measured and simulated data

Arguments:

ymes: @type numpy.ndarray

measured values n_condition x nt x ny

ysim: @type numpy.ndarray

simulated values n_condition x nt x ny

parpe.plotting.plotCostTrajectory(costTrajectory, color=None, scaleToIteration=1, legend=True, legend_loc='upper right', ax=None, log=True)

Plot the provided cost trajectory

Arguments: costTrajectory: ndarray(numIterations x numStarts): cost over iterations for all optimizations

Returns:

ax

parpe.plotting.plotDoseResponseCategorical(conc, mes, sim, title, ax)

e.g. # fig, ax = plt.subplots() plotDoseResponse(np.array([10, 3, 5]), np.array([1, 2, 3]), np.array([1, 3, 2]), ‘test’, ax)

parpe.plotting.plotDoseResponseLogDose(conc, mes, sim, title, ax)

parpe.plotting.plotTrajectoryFit(ymes, ysim, timepoints, title=None)

Create a plot with time-course for measured and simulated values for

all observables.

Arguments:

ymes: measured values nt x n_observable per condition ysim: simulated values nt x n_observable per condition

parpe.plotting.plotTrajectoryFits(ymes, ysim, timepoints)

For each simulation condition create a plot with time-course for

measured and simulated values for all observables.

Arguments:

ymes: list of measured values nt x n_observable per condition ymes: list of simulated values nt x n_observable per condition

parpe.plotting.plotWaterfall(finalCost)

Plot “waterfall plot”

Sorted scatter plot of optimization results.

Arguments: finalCost: ndarray(numStarts) of final cost

Returns:

parpe.plotting.square_plot_equal_ranges(ax, lim=None)

Square plot with equal range