schrodinger.application.matsci.uq_utils module¶

Utilities for uncertainty quantification.

schrodinger.application.matsci.uq_utils.X_BIAS_F(x)¶

schrodinger.application.matsci.uq_utils.check_property_sign(prop, points)¶

Return True if the property has the correct sign.

Parameters:

prop (float) – the property
points (list) – (x, y) pair tuples of data

Return type:

bool

Returns:

True if the property has the correct sign

schrodinger.application.matsci.uq_utils.get_nonzero_data(xs, ys)¶

Return nonzero data.

Parameters:

xs (list) – the domain
ys (list) – the range

Return type:

list, list

Returns:

the domain and range

schrodinger.application.matsci.uq_utils.get_bilinear_regions(original_data)¶

Gets the initial early and late regions such that the R squared values are maximized.

Parameters:: original_data (list) – contains pair tuples of data points
Return type:: tuple, tuple
Returns:: (min, max) tuples for early and late regions

schrodinger.application.matsci.uq_utils.get_points_in_region(data, region)¶

Determine which data points fall into a region given its lower and upper bound.

Parameters:

data (list) – contains pair tuples of data points
region (tuple) – a tuple containing the lower and upper boundaries points of the region

Returns:

all points within the region

Return type:

list

schrodinger.application.matsci.uq_utils.get_bilinear_regression(data)¶

Return the regression information.

Parameters:: data (list) – contains pair tuples of data points
Return type:: tuple
Returns:: (slope, intercept, R^2)

schrodinger.application.matsci.uq_utils.get_bilinear_prop(early_regression, late_regression)¶

Returns the bilinear property if both regressions are available.

Parameters:

early_regression (tuple) – (slope, intercept, R^2) for early part
late_regression (tuple) – (slope, intercept, R^2) for late part

Returns:

the property

Return type:

float

schrodinger.application.matsci.uq_utils.get_bilinear_fit_property(x, x_intersection, early_regression, late_regression, invert_y=True)¶

Return the fit property given the x-value.

Parameters:

x (float) – the x-value
x_intersection (float) – the x intersection point
early_regression (tuple) – (slope, intercept, R^2) for early part
late_regression (tuple) – (slope, intercept, R^2) for late part
invert_y (bool) – whether to invert the y-values

Return type:

float or None

Returns:

the fit property value or None if there isn’t one

schrodinger.application.matsci.uq_utils.get_bilinear_prop_from_points(points, early_region=None, late_region=None)¶

Return a bilinear property using the given points.

Parameters:

points (list) – list of (x,y) tuples
early_region (tuple or None) – (min, max) tuple for early region, if None then automatically determined
late_region (tuple or None) – (min, max) tuple for late region, if None then automatically determined

Return type:

float or None

Returns:

the property or None if there isn’t one

schrodinger.application.matsci.uq_utils.get_min_max_avg(data)¶

Return (min, max, avg) of the given data.

Parameters:: data (list) – the data
Return type:: float, float, float
Returns:: the min, max, avg

schrodinger.application.matsci.uq_utils.get_hyperbola_regression(data, avg_x)¶

Return the regression information.

Parameters:

data (list) – contains pair tuples of data points
avg_x (float) – the average x-value

Return type:

tuple

Returns:

(t0, a, b, c, p0) parameters

schrodinger.application.matsci.uq_utils.calc_property(x_scaled, t0, a, b, c, p0)¶

Calculate the hyperbolic property.

Parameters:

x_scaled (float) – the scaled x-value
t0 (float) – the scaled T_0 factor in polynomial
a (float) – a factor in polynomial
b (float) – b factor in polynomial
c (float) – c factor in polynomial
p0 (float) – p_0 factor in polynomial

schrodinger.application.matsci.uq_utils.get_hyperbola_fit_property(x, regression)¶

Return the fit property given the x-value.

Parameters:

x (float) – the x-value
regression (tuple) – (t0, a_coeff, b_coeff, c_coeff, p0)

Return type:

float or None

Returns:

the fit property value or None if there isn’t one

schrodinger.application.matsci.uq_utils.get_hyperbola_prop_from_points(points)¶

Return a hyperbola property using the given points.

Parameters:: points (list) – list of (x,y) tuples
Return type:: float or None
Returns:: the property or None if there isn’t one

class schrodinger.application.matsci.uq_utils.BootstrapNoiseHistogramData¶

Bases: object

Manage data of a histogram of a quantity derived from a parametric bootstrap analysis of uncorrelated Gaussian white noise.

SAMPLE_SIZE = 1000¶

__init__()¶: Create an instance.

setOriginalData(points)¶

Set the original data used to derive the histogram.

Parameters:: points (list) – list of (x,y) tuples

setFitFunction(fit_function)¶

Set the fit function, i.e. the function that was fit to the original data.

Parameters:: fit_function (function) – the fit function

setXBiasFunction(x_bias_function=None)¶

Set a biasing function for the original x data.

Parameters:: x_bias_function (function or None) – a biasing function for the original x data or None if there isn’t one

seedRandom(seed=None)¶

Seed the random number generator.

Parameters:: seed (int or None) – the seed

setSampleSize(size=None)¶

Set the sample size.

Parameters:: size (int or None) – the sample size, if None then the default of SAMPLE_SIZE is used

setTargetFunction(target_function)¶

Set the target function, i.e. the function used to obtain the target quantity from points.

Parameters:: target_function (function) – the target function

getHistogramData()¶

Return the histogram data which is just a list of sampled target values.

Raises:: RuntimeError – if required variables are not defined
Return type:: list or None
Returns:: list of sampled target values or None if there isn’t one

static getSampleHistogram(points, fit_function, target_function, x_bias_function=None, seed=None, size=None)¶

Return a sample of histogram data using the given points, fit function, and target function.

Parameters:

points (list) – list of (x,y) tuples
fit_function (function) – the fit function
target_function (function) – the target function
x_bias_function (function or None) – a biasing function for the original x data or None if there isn’t one
seed (int or None) – the seed
size (int or None) – the sample size, if None then the default of SAMPLE_SIZE is used

Return type:

list or None

Returns:

list of sampled target values or None if there isn’t one

static getNormalParameters(data)¶

Return the mean, standard deviation, covariance matrix, and figure of merit parameters from a normal fit of the given data.

Parameters:: data (list) – contains (x, y) tuples
Return type:: float, float, numpy.array, float or None, None, None, and None
Returns:: the mean, standard deviation, covariance matrix, and figure of merit or None, None, None, and None if there aren’t any

class schrodinger.application.matsci.uq_utils.BilinearHistogramData(invert_y=True)¶

Bases: BootstrapNoiseHistogramData

Manage a histogram using bilinear data.

__init__(invert_y=True)¶

Create an instance.

Parameters:: invert_y (bool) – whether to invert the y-values

static getSampleHistogram(points, fit_function, target_function, x_bias_function=None, seed=None, size=None, invert_y=True)¶

Return a sample of histogram data using the given points, fit function, and target function.

Parameters:

points (list) – list of (x,y) tuples, if using invert_y then this data should be uninverted
fit_function (function) – the fit function, if using invert_y then expected to return uninverted data
target_function (function) – the target function, if using invert_y then expects the input data to already be inverted
x_bias_function (function or None) – a biasing function for the original x data or None if there isn’t one
seed (int or None) – the seed
size (int or None) – the sample size, if None then the default of SAMPLE_SIZE is used
invert_y (bool) – whether to invert the y-values

Return type:

list or None

Returns:

list of sampled target values or None if there isn’t one

class schrodinger.application.matsci.uq_utils.HyperbolaHistogramData¶

Bases: BootstrapNoiseHistogramData

Manage a histogram using hyperbola data.

static getSampleHistogram(points, fit_function, target_function, x_bias_function=None, seed=None, size=None)¶

Return a sample of histogram data using the given points, fit function, and target function.

Parameters:

points (list) – list of (x,y) tuples
fit_function (function) – the fit function
target_function (function) – the target function
x_bias_function (function or None) – a biasing function for the original x data or None if there isn’t one
seed (int or None) – the seed
size (int or None) – the sample size, if None then the default of SAMPLE_SIZE is used

Return type:

list or None

Returns:

list of sampled target values or None if there isn’t one