komanawa.BASE.generators.pseudo_uniform_change_generator#

created matt_dumont on: 1/12/23

Classes#

`ParentPathChange`	A base class for the path change generators which defines the KS test for the path change generator.
`PseudoUniformChangeGenerator`	A base class for the path change generators which defines the KS test for the path change generator.

Module Contents#

class ParentPathChange#

A base class for the path change generators which defines the KS test for the path change generator.

comptest(sample, test=kstest, n_gen=None, random_state=None, **kwargs)#

Calculate the scipy test for the path change generator. The test is calculated for each time step in the path. The first time step is the starting point of the path, the second time step is the difference between the first and second points in the path, the third time step is the difference between the second and third points in the path, etc.

The test can be any of the scipy tests like kstest, Mann-Whitney U test, wasserstein_distance_nd, etc.

Parameters:

sample – sample to test shape=(nsims, self.n)
n_gen – Number of samples to generate for the KS test, if None use the length of the sample
kwargs – other keyword arguments to pass to scipy.stats test

Returns:

[estResult, …] for each time step in the path (see scipy function)

plot_test(test_results, ax, x=None, plot_stats=('statistic', 'pvalue'), plot_on_twinx=True, colors=None, **kwargs)#

Plot the results of the KS test for the path change generator.

Parameters:

ktest_results – [testResult, …] for each time step in the path
ax – axis to plot on or None (if None a new figure is created)
plot_stats – tuple subset of result values to plot or None (assumes results are float)
plot_on_twinx – if True plot the first of plot_stats on the ax and then each subsequent statistic on individual twinx axes
colors – dictionary of colors for each statistic or None (if None use default colors)
kwargs – other keyword arguments to pass to the ax.plot

Returns:

fig, ax, (handles, labels)

class PseudoUniformChangeGenerator(start_bounds, low, up, scale_low, scale_up, uniform_up, uniform_low, n)#

Bases: komanawa.BASE.generators.parent_path_change.ParentPathChange

Inheritance diagram of komanawa.BASE.generators.pseudo_uniform_change_generator.PseudoUniformChangeGenerator

A base class for the path change generators which defines the KS test for the path change generator.

Generate a random path of length n, with a starting point in the interval start_bounds and then a series of n-1 deltas picked from a uniform distribution bounded by lower and upper half normal distributions.

The process is first pick a value out of the uniform distribution (”*start_bounds”), then for each additional point in the series pick a value from a distribution (equally probable) defined by:

-inf -> uniform_low : half normal distribution with scale scale_low
uniform_low -> uniform_up : uniform distribution
uniform_up -> inf : half normal distribution with scale scale_up

Parameters:

start_bounds – (low, high) bounds for the starting point
up – upper bound for the path, can be a scalar or a list of length n-1 (one for change)
low – lower bound for the path, can be a scalar or a list of length n-1 (one for change)
scale_low – scale (always postive) for the half normal distribution from -inf -> uniform_low can be a scalar or a list of length n-1 (one for change)
scale_up – scale (always postive) for the half normal distribution from uniform_up -> inf can be a scalar or a list of length n-1 (one for change)
uniform_up – upper bound for the uniform distribution from uniform_low -> uniform_up can be a scalar or a list of length n-1 (one for change)
uniform_low – lower bound for the uniform distribution from uniform_low -> uniform_up can be a scalar or a list of length n-1 (one for change)
n – length of the path

comptest(sample, test=kstest, n_gen=None, random_state=None, **kwargs)#

The test can be any of the scipy tests like kstest, Mann-Whitney U test, wasserstein_distance_nd, etc.

Parameters:

sample – sample to test shape=(nsims, self.n)
n_gen – Number of samples to generate for the KS test, if None use the length of the sample
kwargs – other keyword arguments to pass to scipy.stats test

Returns:

[estResult, …] for each time step in the path (see scipy function)

logpdf(q0)#

Calculate the log probability of the path (q0) given the parameters of the generator.

Parameters:: q0
Returns:

plot_path(x=None, nsigmas=2, ax=None, ax_delta=None, round_lab=2, lab_rotation=0)#

Plot the path generated by the generator

Parameters:

x – None or an array of length n to use as the x axis if None the x axis will be the integers from 0 to n
nsigmas – number of standard deviations to plot around the center path (default 2, 95th percentile)
ax – None or an axis to plot the paths on
ax_delta – None or an axis to plot the deltas on
round_lab – number of decimal places to round the labels to
lab_rotation – rotation of the x axis labels

Returns:

fig, ax

plot_test(test_results, ax, x=None, plot_stats=('statistic', 'pvalue'), plot_on_twinx=True, colors=None, **kwargs)#

Plot the results of the KS test for the path change generator.

Parameters:

ktest_results – [testResult, …] for each time step in the path
ax – axis to plot on or None (if None a new figure is created)
plot_stats – tuple subset of result values to plot or None (assumes results are float)
plot_on_twinx – if True plot the first of plot_stats on the ax and then each subsequent statistic on individual twinx axes
colors – dictionary of colors for each statistic or None (if None use default colors)
kwargs – other keyword arguments to pass to the ax.plot

Returns:

fig, ax, (handles, labels)

rvs(random_state=None)#

Generate a random path of length n, with a starting point in the interval start_bounds process is first pick a value out of the uniform distribution (”*start_bounds”), then for each additional point in the series pick a value from a normal distribution centered on the previous value with a scale of scale. If the picked value is outside the interval set to the upper and lower bounds then the value is truncated to the bounds value.

Parameters:: random_state – None, use the global random state, or an integer to seed the random state. As this is a markov chain process the random state is used to generate a suite of integer seeds to make each delta deterministic.
Returns: