SampleSet¶

class matk.sampleset.SampleSet(name, samples, parent, index_start=1, **kwargs)¶

MATK SampleSet class - Stores information related to a sample including parameter samples, associated responses, and sample indices

corner(bins=20, range=None, weights=None, color='k', smooth=None, smooth1d=None, labels=None, label_kwargs=None, show_titles=False, title_fmt='.2f', title_kwargs=None, truths=None, truth_color='#4682b4', scale_hist=False, quantiles=None, verbose=False, fig=None, max_n_ticks=5, top_ticks=False, use_math_text=False, hist_kwargs=None, **hist2d_kwargs)¶: Plot corner plot using the corner package written by Dan Foreman-Mackey (https://pypi.python.org/pypi/corner/1.0.0)

corr(type='pearson', plot=False, printout=True, plotvals=True, figsize=None, title=None, xrotation=0, filename=None, adjust_dict=None)¶

Calculate correlation coefficients of parameters and responses

Parameters:

type (str) – Type of correlation coefficient (pearson by default, spearman also avaialable)
plot (bool) – If True, plot correlation matrix
printout (bool) – If True, print correlation matrix with row and column headings
plotvals (bool) – If True, print correlation coefficients on plot matrix
figsize (tuple(fl64,fl64)) – Width and height of figure in inches
title (str) – Title of plot
xrotation – Rotation for x axis tick labels
filename (str) – Name of file to save plot. File ending determines plot type (pdf, png, ps, eps, etc.). Plot types available depends on the matplotlib backend in use on the system. Plot will not be displayed.
adjust_dict (dict) – Dictionary of kwargs to pass to plt.subplots_adjust. Keys and defaults are: left = 0.125, right = 0.9, bottom = 0.1, top = 0.9, wspace = 0.2, hspace = 0.2

Returns:

ndarray(fl64) – Correlation coefficients

index_start¶: Starting integer value for sample indices

indices¶: Array of sample indices

main_effects()¶: For each parameter, compile array of main effects.

mean(pretty_print=False)¶: Mean of samples

name¶: Sample set name

obsnames¶: Array of observation names

panels(type='pearson', alpha=0.2, figsize=None, title=None, tight=False, symbol='.', fontsize=None, corrfontsize=None, ms=5, mins=None, maxs=None, frequency=False, bins=10, ylim=None, labels=[], filename=None, xticks=2, yticks=2, color=None, cmap=None, edgecolors='face')¶

Plot histograms, scatterplots, and correlation coefficients in paired matrix

Parameters:

type (str) – Type of correlation coefficient (pearson by default, spearman also avaialable)
alpha (float) – Histogram color shading
figsize (tuple(fl64,fl64)) – Width and height of figure in inches
title (str) – Title of plot
tight (bool) – Use matplotlib tight layout
symbol (str) – matplotlib symbol for scatterplots
fontsize (fl64) – Size of font for axis labels
corrfontsize (fl64) – Size of font for correlation coefficients
ms (fl64) – Scatterplot marker size
frequency (bool) – If True, the first element of the return tuple will be the counts normalized by the length of data, i.e., n/len(x)
bins (int) – Number of bins in histograms
ylim (tuples - 2 element tuples with y limits for histograms) – y-axis limits for histograms.
labels (lst(str)) – Names to use instead of parameter names in plot
filename (str) – Name of file to save plot. File ending determines plot type (pdf, png, ps, eps, etc.). Plot types available depends on the matplotlib backend in use on the system. Plot will not be displayed.
xticks (int) – Number of ticks along x axes
yticks (int) – Number of ticks along y axes
color (str) – Name of parameter of observation to color points in colorplots by
cmap (matplotlib.colors.Colormap) – Colormap for color option
edgecolors (str) – Color of edges of markers in scatterplots

pardict(index)¶

Get parameter dictionary for sample with specified index

Parameters:	index (int) – Sample index
Returns:	dict(fl64)

parnames¶: Array of observation names

percentile(pct, interpolation='linear', pretty_print=False)¶

Percentile of samples

Parameters:	pct (fl64 or lst[fl64]) – Percentile in range [0,100] or list of percentiles interpolation (str - {'linear', 'lower', 'higher', 'midpoint', 'nearest'}) – Interpolation method to use when quantile lies between data points pretty_print (bool) – If True, print with row and column headings
Returns:	ndarray(fl64)

rank_parameter_frequencies()¶

Yields a printout of parameter value frequencies in the sample set

returns An array of tuples, each containing the parameter name tagged as min or max and a: second tuple containing the parameter value and the frequency of its appearance in the sample set.

rbd_fast(obsname='sse', M=10, print_to_console=True, problem={})¶

Perform RBD_Fast analysis on model output. This assumes that the sampleset has been run so that responses have been generated. This method calls functionality from the SALib package.

Parameters:

obsname (str) – Name of observation to perform analysis on. The default is to use the sum-of-squared errors of all observations. This requires that observation values were designated. An individual observation name can be used instead.
M (int) – The interference parameter, i.e., the number of harmonics to sum in the Fourier series decomposition
print_to_console (bool) – Print results directly to console
problem (dict) – Dictionary of model attributes used by sampler. For example, dictionary with a list with keyname ‘groups’ containing a list of length of the number of parameters with parameter group names can be used to group parameters with similar effects on the observation. This will reduce the number of samples required.

Returns:

Dictionary of rbd_fast analysis results

recarray¶: Structured (record) array of samples

run(cpus=1, workdir_base=None, save=True, reuse_dirs=False, outfile=None, logfile=None, restart_logfile=None, verbose=True, hosts={})¶

Run model using values in samples for parameter values If samples are not specified, LHS samples are produced

Parameters:

cpus (int,dict(lst)) – number of cpus; alternatively, dictionary of lists of processor ids keyed by hostnames to run models on (i.e. on a cluster); hostname provided as kwarg to model (hostname=<hostname>); processor id provided as kwarg to model (processor=<processor id>)
workdir_base (str) – Base name for model run folders, run index is appended to workdir_base
save (bool) – If True, model files and folders will not be deleted during parallel model execution
reuse_dirs (bool) – Will use existing directories if True, will return an error if False and directory exists
outfile (str) – File to write results to
logfile (str) – File to write details of run to during execution
restart_logfile (str) – Existing logfile containing completed runs, used to complete an incomplete sampling; Warning: sample indices are expected to match!
verbose (bool or str) – Prints results as calculated if True and Display progress bar in console if ‘progress’
hosts (lst(str)) – Option deprecated, use cpus instead

Returns:

tuple(ndarray(fl64),ndarray(fl64)) - (Matrix of responses from sampled model runs siz rows by npar columns, Parameter samples, same as input samples if provided)

savestats(outfile, q=[2.5, 5.0, 50.0, 95.0, 97.5], interpolation='linear')¶

Save statistical measures of sampleset to file

Parameters:	outfile (str) – Name of file q (fl64 or lst[fl64]) – percentile or list of percentiles to compute interpolation (str - {'linear', 'lower', 'higher', 'midpoint', 'nearest'}) – Interpolation method to use when quantile lies between data points

savetxt(outfile, sse=False)¶

Save sampleset to file

Parameters:	outfile (str) – Name of file where sampleset will be written sse (bool) – Print out sum-of-squared-errors instead of observations

sobol(obsname='sse', calc_second_order=True, print_to_console=True, num_resamples=100, conf_level=0.95, problem={})¶

Perform Sobol analysis on model output. This requires that the sampleset is a Saltelli sample and has been run. This method calls functionality from the SALib package.

Parameters:

obsname (str) – Name of observation to perform analysis on. The default is to use the sum-of-squared errors of all observations. This requires that observation values were designated. An individual observation name can be used instead.
calc_second_order (bool) – Calculate second-order sensitivities
num_resamples (int) – The number of resamples
conf_level (flt) – The confidence interval level
print_to_console (bool) – Print results directly to console
problem (dict) – Dictionary of model attributes used by sampler. For example, dictionary with a list with keyname ‘groups’ containing a list of length of the number of parameters with parameter group names can be used to group parameters with similar effects on the observation. This will reduce the number of samples required.

Returns:

Dictionary of sobol analysis results

sse(group=None)¶

Sum of squared errors (sse) for all samples

Parameters:	group (str) – Group name of observations; if not None, sse for observation group will be returned

std(pretty_print=False)¶: Standard deviation of samples

subset(boolfcn, field, *args, **kwargs)¶

Collect subset of samples based on parameter or response values, remove all others

Parameters:	boofcn – Function that returns true for samples to keep and false for samples to remove field (str) – Name of parameter or observations to apply boolfcn to args – Additional arguments to add to boolfcn kwargs – Keyword arguments to add to boolfcn

var(pretty_print=False)¶: Variance of samples

class matk.sampleset.DataSet(samples, names, mins=None, maxs=None)¶

MATK DataSet class used by SampleSet class to store samples (parameter combinations; ‘SampleSet.samples’) and responses (model outputs; ‘SampleSet.responses’)

corner(bins=20, range=None, weights=None, color='k', smooth=None, smooth1d=None, labels=None, label_kwargs=None, show_titles=False, title_fmt='.2f', title_kwargs=None, truths=None, truth_color='#4682b4', scale_hist=False, quantiles=None, verbose=False, fig=None, max_n_ticks=5, top_ticks=False, use_math_text=False, hist_kwargs=None, **hist2d_kwargs)¶: Plot corner plot using the corner package written by Dan Foreman-Mackey (https://pypi.python.org/pypi/corner/1.0.0)

corr(type='pearson', plot=False, printout=True, plotvals=True, figsize=None, title=None, xrotation=0, filename=None, adjust_dict=None)¶

Calculate correlation coefficients of dataset values

Parameters:

type (str) – Type of correlation coefficient (pearson by default, spearman also avaialable)
plot (bool) – If True, plot correlation matrix
plotvals (bool) – If True, print correlation coefficients on plot matrix
printout (bool) – If True, print correlation matrix with row and column headings
figsize (tuple(fl64,fl64)) – Width and height of figure in inches
title (str) – Title of plot
xrotation – Rotation for x axis tick labels
filename (str) – Name of file to save plot. File ending determines plot type (pdf, png, ps, eps, etc.). Plot types available depends on the matplotlib backend in use on the system. Plot will not be displayed.
adjust_dict (dict) – Dictionary of kwargs to pass to plt.subplots_adjust. Keys and defaults are: left = 0.125, right = 0.9, bottom = 0.1, top = 0.9, wspace = 0.2, hspace = 0.2

Returns:

ndarray(fl64) – Correlation coefficients

hist(ncols=4, alpha=0.2, figsize=None, title=None, tight=False, mins=None, maxs=None, frequency=False, bins=10, ylim=None, printout=True, labels=[], filename=None, fontsize=None, xticks=3)¶

Plot histograms of dataset

Parameters:

ncols (int) – Number of columns in plot matrix
alpha (float) – Histogram color shading
figsize (tuple(fl64,fl64)) – Width and height of figure in inches
title (str) – Title of plot
tight (bool) – Use matplotlib tight layout
frequency (bool) – If True, the first element of the return tuple will be the counts normalized by the length of data, i.e., n/len(x)
bins (int) – Number of bins in histograms
ylim (tuple - 2 element tuple with y limits for histograms) – y-axis limits for histograms.
printout (bool) – If True, histogram values are printed to the terminal
labels (lst(str)) – Names to use instead of parameter names in plot
filename (str) – Name of file to save plot. File ending determines plot type (pdf, png, ps, eps, etc.). Plot types available depends on the matplotlib backend in use on the system. Plot will not be displayed.
fontsize (fl64) – Size of font
xticks (int) – Number of ticks along x axes

Returns:

dict(lst(int),lst(fl64)) - dictionary of histogram data (counts,bins) keyed by name

mean(pretty_print=False)¶: Mean of samples

names¶: Array of parameter names

panels(type='pearson', alpha=0.2, figsize=None, title=None, tight=False, symbol='.', fontsize=None, corrfontsize=None, ms=5, mins=None, maxs=None, frequency=False, bins=10, ylim=None, labels=[], filename=None, xticks=2, yticks=2, color=None, cmap=None, edgecolors='face')¶

Plot histograms, scatterplots, and correlation coefficients in paired matrix

Parameters:

type (str) – Type of correlation coefficient (pearson by default, spearman also avaialable)
alpha (float) – Histogram color shading
figsize (tuple(fl64,fl64)) – Width and height of figure in inches
title (str) – Title of plot
tight (bool) – Use matplotlib tight layout
symbol (str) – matplotlib symbol for scatterplots
corrfontsize (fl64) – Size of font for correlation coefficients
fontsize (fl64) – Size of font for axis labels
ms (fl64) – Scatterplot marker size
frequency (bool) – If True, the first element of the return tuple will be the counts normalized by the length of data, i.e., n/len(x)
bins (int or lst(lst(int))) – If an integer is given, bins + 1 bin edges are returned. Unequally spaced bins are supported if bins is a list of sequences for each histogram.
ylim (tuple - 2 element tuples with y limits for histograms) – y-axis limits for histograms.
labels (lst(str)) – Names to use instead of parameter names in plot
filename (str) – Name of file to save plot. File ending determines plot type (pdf, png, ps, eps, etc.). Plot types available depends on the matplotlib backend in use on the system. Plot will not be displayed.
xticks (int) – Number of ticks along x axes
yticks (int) – Number of ticks along y axes
color (str) – Name of parameter of observation to color points in colorplots by
cmap (matplotlib.colors.Colormap) – Colormap for color option
edgecolors (str) – Color of edges of markers in scatterplots

percentile(pct, interpolation='linear', pretty_print=False)¶

Percentile of samples

Parameters:	pct (fl64 or lst[fl64]) – Percentile in range [0,100] or list of percentiles interpolation (str - {'linear', 'lower', 'higher', 'midpoint', 'nearest'}) – Interpolation method to use when quantile lies between data points pretty_print (bool) – If True, print with row and column headings
Returns:	ndarray(fl64)

recarray¶: Structured (record) array of samples

savestats(outfile, q=[2.5, 5.0, 50.0, 95.0, 97.5], interpolation='linear')¶

Save statistical measures to file

Parameters:	outfile (str) – Name of file q (fl64 or lst[fl64]) – percentile or list of percentiles to compute interpolation (str - {'linear', 'lower', 'higher', 'midpoint', 'nearest'}) – Interpolation method to use when quantile lies between data points

std(pretty_print=False)¶: Standard deviation of samples

values¶: Ndarray of parameter samples, rows are samples, columns are parameters in order of MATKobject.parlist

var(pretty_print=False)¶: Variance of samples

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SampleSet¶