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. # 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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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
- parpe.plotting.correlation_coefficient(a, b)[source]¶
Get correlation coefficient for flattened a and b
- parpe.plotting.flatten_filter_nan(a, b)[source]¶
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)[source]¶
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)[source]¶
Create boxplot of correlation by observable
- Parameters:
data – Correlations as obtained from getCorrTable
- 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
- 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
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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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)[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)
- parpe.plotting.plotTrajectoryFit(ymes, ysim, timepoints, title=None)[source]¶
- 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)[source]¶
- 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