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: schrodinger.application.matsci.uq_utils.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

SAMPLE_SIZE = 1000¶

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 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

seedRandom(seed=None)¶

Seed the random number generator.

Parameters: seed (int or None) – the seed

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

setOriginalData(points)¶

Set the original data used to derive the histogram.

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

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

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

class schrodinger.application.matsci.uq_utils.HyperbolaHistogramData¶

Bases: schrodinger.application.matsci.uq_utils.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

SAMPLE_SIZE = 1000¶

__init__()¶: Create an instance.

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 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

seedRandom(seed=None)¶

Seed the random number generator.

Parameters: seed (int or None) – the seed

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

setOriginalData(points)¶

Set the original data used to derive the histogram.

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

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

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