parpe.plotting¶
Plotting functions
Functions
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Get correlation coefficient for flattened a and b |
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Flatten arrays a and b removing entries for which either a or b is NaN |
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Plot correlation of measured and simulated data |
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Create boxplot of correlation by observable |
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Plot correlation boxplots of multi-start results for multiple datasets side by side |
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Plot correlation of measured and simulated data |
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Plot correlation of measured and simulated data |
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Plot correlation of measured and simulated data |
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Plot correlation of measured and simulated data |
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Plot the provided cost trajectory |
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e.g. |
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Create a plot with time-course for measured and simulated values for |
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For each simulation condition create a plot with time-course for |
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Plot “waterfall plot” |
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Square plot with equal range |
Functions
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parpe.plotting.
correlation_coefficient
(a, b)[source]¶ Get correlation coefficient for flattened a and b
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parpe.plotting.
flatten_filter_nan
(a, b)[source]¶ Flatten arrays a and b removing entries for which either a or b is NaN
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parpe.plotting.
plotCorrelation
(ymes, ysim, observable_idx=None, title=None, alpha=1.0, legend=False, square=True, ax=None)[source]¶ Plot correlation of measured and simulated data
- ymes: @type numpy.ndarray
measured values n_condition x nt
- ysim: @type numpy.ndarray
simulated values n_condition x nt
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parpe.plotting.
plotCorrelationBox
(data)[source]¶ Create boxplot of correlation by observable
- Arguments:
data: Correlations as obtained from getCorrTable
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parpe.plotting.
plotCorrelationBoxMulti
(datasets, labels, legend_loc='lower center')[source]¶ Plot correlation boxplots of multi-start results for multiple datasets side by side
data1: nstarts x ny
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parpe.plotting.
plotCorrelationDensity
(ymes, ysim, title=None, title_append_corr=True, normalize_percentile=100, nbins=50, contour=True)[source]¶ Plot correlation of measured and simulated data
- ymes: @type numpy.ndarray
measured values n_condition x nt
- ysim: @type numpy.ndarray
simulated values n_condition x nt
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parpe.plotting.
plotCorrelationOld
(ymes, ysim, title=None, alpha=1.0, legend=False, square=True, ax=None)[source]¶ Plot correlation of measured and simulated data
- ymes: @type numpy.ndarray
measured values n_condition x nt
- ysim: @type numpy.ndarray
simulated values n_condition x nt
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parpe.plotting.
plotCorrelations
(ymes, ysim)[source]¶ Plot correlation of measured and simulated data
- ymes: @type numpy.ndarray
measured values n_condition x nt x ny
- ysim: @type numpy.ndarray
simulated values n_condition x nt x ny
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parpe.plotting.
plotCorrelationsByObsDensity
(ymes, ysim)[source]¶ Plot correlation of measured and simulated data
- ymes: @type numpy.ndarray
measured values n_condition x nt x ny
- ysim: @type numpy.ndarray
simulated values n_condition x nt x ny
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parpe.plotting.
plotCostTrajectory
(costTrajectory, color=None, scaleToIteration=1, legend=True, legend_loc='upper right', ax=None, log=True)[source]¶ Plot the provided cost trajectory
Arguments: costTrajectory: ndarray(numIterations x numStarts): cost over iterations for all optimizations
- Returns:
ax
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parpe.plotting.
plotDoseResponseCategorical
(conc, mes, sim, title, ax)[source]¶ e.g. # fig, ax = plt.subplots() plotDoseResponse(np.array([10, 3, 5]), np.array([1, 2, 3]), np.array([1, 3, 2]), ‘test’, ax)
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parpe.plotting.
plotTrajectoryFit
(ymes, ysim, timepoints, title=None)[source]¶ - Create a plot with time-course for measured and simulated values for
all observables.
ymes: measured values nt x n_observable per condition ysim: simulated values nt x n_observable per condition
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parpe.plotting.
plotTrajectoryFits
(ymes, ysim, timepoints)[source]¶ - For each simulation condition create a plot with time-course for
measured and simulated values for all observables.
ymes: list of measured values nt x n_observable per condition ymes: list of simulated values nt x n_observable per condition