API Reference
HoloViews defaults
Set convenient HoloViews defaults |
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Hook for disabling x-grid lines. |
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Hook for disabling x-grid lines. |
Visualization
Make a horizontal plot of centers/conf ints with error bars. |
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Create a filled region between two curves. |
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Generate a Q-Q plot. |
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Make a contour plot, possibly overlaid on an image. |
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Plot a predictive ECDF from samples. |
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Plot a predictive regression plot from samples. |
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Make a rank ECDF plot from simulation-based calibration. |
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Make a parallel coordinate plot of MCMC samples. |
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Make a trace plot of MCMC samples. |
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Make a corner plot of sampling results. |
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Get lines for a contour plot from (x, y) samples. |
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Convert discrete values of CDF to staircase for plotting. |
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Convert a quantitative value to a color. |
Bootstrap methods
Seed random number generators for Numpy and Numba'd functions. |
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Generate bootstrap replicates out of data using func. |
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Perform pairs bootstrap for single statistic. |
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Draw bootstrap replicates of maximum likelihood estimator. |
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Generate permutation replicates of func from data_1 and data_2 |
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Difference in means of two arrays. |
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Studentized difference in means of two arrays. |
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Compute the Pearson correlation coefficient between two samples. |
Stan utilities
Remove all .hpp, .o, .d, and executable files resulting from compilation of Stan models using CmdStanPy. |
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Determine CmdStan version |
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Convert a tidy data frame to a data dictionary for a hierarchical Stan model. |
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Convert ArviZ InferenceData to a Pandas data frame. |
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Check transitions that ended with a divergence. |
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Check transitions that ended prematurely due to maximum tree depth limit. |
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Checks the energy-Bayes fraction of missing information (E-BFMI) |
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Checks the effective sample size (ESS). |
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Checks the potential issues with scale reduction factors. |
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Checks all MCMC diagnostics |
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Parses warning code from check_all_diagnostics() into individual failures and prints results. |
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Perform simulation-based calibration on a Stan Model. |
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Context manager for disabling logging when doing MCMC sampling. |
Gaussian process utilities
Add the entries of X to Xstar, sort the result, and find indices in the results where the entries in X appear. |
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Linear kernel. |
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Polynomial kernel: (sigma_0^2 + sigma_p^2 x1 . |
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Squared exponential kernel. |
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Derivative of first variable of squared exponential kernel. |
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Derivative of second variable of squared exponential kernel. |
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Mixed second derivative of squared exponential kernel. |
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Matern kernel. |
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Periodic kernel. |
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Return covariance matrix for squared exponential kernel. |
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Return covariance matrix for squared exponential kernel differentiated by the first variable. |
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Return covariance matrix for squared exponential kernel differentiated once by the first variable and once by the second. |
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Return covariance matrix for a Matérn kernel. |
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Return covariance matrix for a perdioic kernel. |
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Return covariance matrix for specified kernel. |
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Compute the posterior mean vector and covariance matrix for a posterior Gaussian process derived from a Normal likelihood and Gaussian process prior. |
Image processing utilities
Display an image in a Bokeh figure. |
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Display and record mouse clicks on a Bokeh plot of an image. |
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Draw and record polygonal regions of interest on a plot of a Bokeh image. |
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Convert a ColumnDataSource outputted by draw_rois() to a Pandas DataFrame. |
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Merge channels to make RGB image. |
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Convert an RGB image to a 32 bit-encoded RGBA image. |
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Convert fractional RGB values to hexidecimal color string. |
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Load a collection of images. |
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Converts list of vertices to an ROI and ROI bounding box |
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Perform Costes colocalization analysis on a pair of images. |