rmellipse.uobjects

Exceptions

RMEMeasFormatError

Error in formatting of data inside RMEMeas

Classes

UObj	Generic Uncertain Object.
RMEMeas	Class that stores monte-carlo and linear sensitivity uncertainty information.

Package Contents

class rmellipse.uobjects.UObj

Bases: abc.ABC

Generic Uncertain Object.

Defines the interface that all uncertainty objects should inherit from.

classmethod copy()

Abstractmethod:

Return a copy of a uncertain object.

Returns:

None.

abstract property stdunc

Get the standard uncertainty with expansion factor k.

Return type should be the natural typing for the object being evauluated.

Parameters:

kfloat, optional

Expansion factor The default is 1.

Returns:

Standard uncertainty.

abstract property nom

Get the nominal value of the uncertain object.

Return type should be the natural typing for the object being evauluated.

Returns:

Nominal value.

class rmellipse.uobjects.RMEMeas(name: str = 'RMEMeas', cov: xarray.DataArray = None, mc: xarray.DataArray = None, covdofs: xarray.DataArray = None, covcats: xarray.DataArray = None)

Bases: rmellipse.uobjects.UObj, rmellipse.utils.GroupSaveable

Class that stores monte-carlo and linear sensitivity uncertainty information.

Used with the rmellipse.RMEProp propagator.

Initialize a RMEMeas object.

Please refer to xarray’s documentation for information about how to use DataArrays, and how to define coordinates and dimensions.

If covdofs is not provided, distributions of linear uncertainty mechanisms are assumed to have infinite degrees of freedom (Gaussian).

Parameters:

namestr, optional

Name of RMEMeas object. The default is ‘RMEMeas’.

covxr.DataArray, optional

Covariance data for linear sensitivity analysis. Copies of data set are stored along the first dimension (axis 0) of the DataArray, where the first index of axis 0 is the nominal data set, and the rest of the indexes along that dimension are perturbed by one standard deviation (i.e. 1 standard uncertainty).

The first dimension must be called ‘umech_id’, and the first label of the ‘parameter_dimensions’ coordinate must be ‘nominal’. The remaining labels for the ‘umech_id’ coordinate should be strings corresponding the the uncertainty mechanism. The default is None.

mcxr.DataArray, optional

Montecarlo data for linear sensitivity analysis. Samples of the data sets distribution are stored along the first dimension (axis 0) of the DataArray, where the first index of axis 0 is the nominal data set, and the rest of the indexes are samples of the distribution.

The first dimension must be called ‘umech_id’, and the first labels of the ‘parameter_dimensions’ must start at 0 and count up by 1 (i,e typical integer based indexing). The default is None.

covdofsxr.DataArray, optional

DataArray that stores the degrees of freedom for each linear uncertainty mechanism in cov. It should be a 1 dimensional DataArray with the dimension called ‘umech_id’ and the coordinate set should be identical to the ‘umech_id’ coordinate in cov. If covariance data is provided and covdofs is not, one will be created that assumes all linear mechanisms have infinite degrees of freedom (i.e. gaussian distributions). The default is None.

covcatsxr.DataArray, optional

DataArray that stores the categories of each uncertainty mechanism. Expected to be have dimensions (‘umech_id’,’categories’), If not provided, all umech_id are assigned a ‘Type’ category of ‘B’. If a mechanism doesn’t have a category, it should be an empty string.

property name

str: The name of the object.

property cov

xr.DataArray: Linear uncertainty mechanisms.

property mc

xr.DataArray: samples of Monte-Carlo distributions.

property covdofs

xr.DataArray: The degrees of freedom on linear mechanisms.

property covcats

xr.DataArray: String metadata for linear uncertainty mechanisms.

classmethod from_nom(name: str, nom: xarray.DataArray) → RMEMeas

Create a RMEMeas object with just a nominal dataset.

Parameters:

namestr

The name of the object to be created.

nomxr.DataArray

Nominal data set.

Returns:

‘RMEMeas’

A RMEMeas object with only nominal values.

classmethod from_dist(name: str, nom: float, std: float, dist='gaussian', mechanism_name: str = None, samples: float = 100, categories: dict[str] = {'Type': 'B'}, use_sample_mean: bool = False) → RMEMeas

Generate a RMEMeas object from a probability distribution.

Parameters:

namestr

Name of the object.

nomfloat

Expected value of the distribution.

stdfloat

Standard deviation of the distribution.

diststr, {‘gaussian’, ‘normal’, ‘uniform’, ‘rectangular’}

Name of the distribution to use. Supports ‘gaussian’ or ‘uniform’. The default is ‘gaussian’.

mechanism_namestr, optional

What to name the linear uncertainty mechanisms associated with the distribution. The default is None.

samplesfloat, optional

How many monte-carlo samples to draw from. The default is 100.

categoriesdict[str], optional

What to categorize the linear uncertainty mechanism as Should be {category:value} pairs. The default is {‘Type’:’B’}.

use_sample_mean_stdbool, optional

If true, uses the mean and standard deviation of random samples drawn from the defined distribution for the linear sensitivity analysis and as the nominal and standard uncertainty values. If False, uses the provided nominal and standard deviation of the distribution. The default is True.

Returns:

‘RMEMeas’

RMEMeas based on defined distribution.

Raises:

Exception

Unsupported distribution.

classmethod dict_from_group(file: str, group_path: str = None, verbose: bool = False) → dict[RMEMeas]

Read any RMEMeas objects saved in an hdf5 group.

Parameters:

filestr

path to the hdf5 file.

group_pathstr, optional

path to group. The default is None.

verbosebool , optional

If True, prints information about attempts to read files. The default is False.

Returns:

dict[‘RMEMeas’]

Dictionary where keys are names of the RMEMeas objects and values are the RMEMeas objects themselves.

classmethod from_h5(group: h5py.Group, nominal_only: bool = False, keep_attrs: bool = True) → RMEMeas

Read a RMEMeas object from HDF5.

Parameters:

group:

HDF5 Group object

nominal_only: bool, optional

If true, will only load the nominal value of the data set. Saves time and memory if you don’t need that data.

keep_attrs: bool, optional

If true, copies over all metadata in attrs. Otherwise, only keeps metadata required for class instantiation.

Returns:

RMEMeas

RMEMeas object.

to_h5(group: h5py.Group, override: bool = False, name: str = None, verbose: bool = False)

Save to an hdf5 object. Wrapper for the group_saveable method ‘save’.

Parameters:

grouph5py.Group

hdf5 group to save under.

override: bool,

if True, will delete the group object being saved to if it already exists. Default is false.

name:str,

If provided will change Name of RMEMeas object prior to saving. Equivalent to calling self.name = name prior to calling save.

Returns:

None.

classmethod from_xml(path: str, from_csv: callable, old_dir: str = None, new_dir: str = None, verbose: bool = False) → RMEMeas

Read a legacy xml MUFmeas object (xml .meas format).

covcats and covdofs metadata will be loaded as the default values.

Parameters:

pathstr

Path to xml header file.

from_csvcallable

Read function, takes a path to a copy of the data file and returns an xarray object.

old_dirstr, optional

Name of old path stored in xml file. Will be swapped with new_dir if provided. Old XML format isn’t portable, and the paths need to be manually swapped when the files are moved around. The default is None.

new_dirstr, optional

Path string to replace old_dir with. The default is None.

Returns:

newMUFmeas

MUFmeas object from the legacy format.

Raises:

Exception

If old_dir/new_dir are not both provided, but one is.

to_xml(target_directory: str, to_csv: callable, data_extension: str, header_extension: str = '.meas', header_directory: str = None)

Save to a the Microwave Uncertainty Framework Format.

This does not preserve metadata in covdofs or covcats.

Parameters:

target_directorystr

Directory in which to save the support folder which stores all the copies of the data.

to_csvcallable

Dataformat of the underlying cov-data. Will be inferred if left as None. The default is None.

data_extension: str

What to save the datafile extensions as (e.g. .s1p, .csv, etc)

header_extensionstr, optional

What to save the xml header file extension as. The default is ‘.meas’.

header_directorystr, optional

Location in which to store the header file. If None, defaults to the target_directory. Default is None.

Returns:

None.

Raises:

Exception

If no dataformat provided and one cannot be inferred.

copy() → RMEMeas

Make a copy of a RMEMeas object.

Returns:

RMEMeas

Copied object.

cull_cov(tolerance: float = 0)

Remove trivial linear uncertainty mechanisms set by tolerance.

Any linear uncertainty mechanisms with a sum of all standard uncertainties < tolerance will be removed from the object.

For example if tolerance is 0.1, the nominal is [0,0,0] and the perturbed data for a mechanisms is [0.0,0.1,-0.2], then the function will evaluate the total standard uncertainty for the mechanism as 0.1 + 0.0 + 0.2 = 0.3, and not remove the mechanism.

This function is called by the auto-cull setting in the RME propagator.

Parameters:

tolerancefloat, optional

Maximum value a sum of standard uncertainties that is deemed trivial, used to determine what mechanisms should be removed. The default is 0.

Returns:

None.

make_umechs_unique(same_uid: bool = False)

Make parameter locations unique by adding a uuid4 string to end of each.

A uiid4 string is added to the end of each parameter_location string. Useful when reading data from different sources that have the same names of uncertainty files. (E.G, multiple device definitions come from calibration service formats with generic ‘ua,ub’ naming)

Parameters:

same_uid: bool, optional

If true, all parameter locations will have the same uid added to the end. Default is False.

Returns:

None.

add_mc_sample(sample: xarray.DataArray)

Add a Monte Carlo sample to the distribution.

Parameters:

samplexr.DataArray

Single sample of the probability distribution that represents the data set. Expected to be the same shape, dimensions, and coordinates of the nominal.

Returns:

None.

add_umech(name: str, value: xarray.DataArray, dof: float = np.inf, category: dict = {'Type': 'B'}, add_uid: bool = False)

Add a linear mechanisms to covariance data.

Parameters:

namestr

Name of new mechanism, must be unique.

valuexr.DataArray

Array of nominal+1 standard uncertainty of new mechanism. If value has the same size as the nominal data, the function will insert a new dimension at axis zero before concatenating to the covariance data.

dof: float,optional

Degrees of freedom associated with the uncertainty mechanism. Default is infinite.

category: dict,

Dictionary of key-value string pairs categorizing the uncertainty mechanisms. E.g. {‘Type’:’B’,’Origin’:’Datasheet’}. The default is {‘Type’:’B’}.

add_uid: bool,

If true, adds a uuid4 string to the uncertainty mechanism name to make it unique.

Returns:

None.

property nom

Get the nominal values of the RMEMeas object.

Returns:

xr.DataArray

Nominal values in an xr.DataArray

Raises:

Exception

if no data is store.

property umech_id

Get the names of uncertainty mechanisms, excluding the nominal.

Returns:

List

List of uncertainty mechanism strings.

confint(percent: float, deg: bool = False, rad: bool = False)

Generate the lower, upper confidence intervals for a fractional percent confidence.

Confidence intervals are generate using the linear sensitivity analysis data (.cov) using a student-t distribution with means of .nominal, and a scale of 1 standard uncertainty.

Parameters:

percentfloat

Percent confidence to calculate intervals on, centered about the nominal. For example, a value of 0.5 will calculate intervals such that 0.25 of samples are between the mean and the lower, and 0.25 of expected samples are between the mean and the upper.

degbool, optional

If you are calculated confidence intervals on degrees. The default is False.

radbool, optional

If you are calculating confidence intervals on radians. The default is False.

Returns:

lowerxr.DataArray

Lower bounds on the confidence interval.

upperxr.DataArray

Upper bounds on the confidence interval.

dof(deg: bool = False, rad: bool = False)

Get the dof of of the RMEMeas object’s covariance data.

Returns:

float

degrees of freedom of standard uncertainty from linear sensitivity.

float

degrees of freedom of standard uncertainty from monte-carlo

stdunc(k: float = 1, deg: bool = False, rad: bool = False)

Get the standard uncertainty with expansion factor k.

Supports uncertainties on angles via the deg/rad key word arguments.

Parameters:

kfloat, optional

Expansion factor. The default is 1.

deg: bool, optional

If true, treats the values as angles and finds the minimum distance between the perturbed and un-perturbed data sets in degrees. The default is False

rad: bool, optional

If true, treats the values as angles and finds the minimum distance between the perturbed and un-perturbed data sets in radians. The default is False

Returns:

covuncxr.DataArray

xr.DataArray of std unc from covariance data.

mcuncxr.DataArray

standard uncertainty of montecarlo data.

uncbounds(k: float = 1, deg: bool = False, rad: bool = False)

Get uncertainty bounds (nominal + k*stdunc).

Supports uncertainties on angles via the deg/rad key word arguments.

Parameters:

kfloat, optional

Expansion factor. The default is 1.

deg: bool, optional

If true, treats the values as angles and finds the minimum distance between the perturbed and un-perturbed data sets in degrees. The default is False

rad: bool, optional

If true, treats the values as angles and finds the minimum distance between the perturbed and un-perturbed data sets in radians. The default is False

Returns:

xr.DataArray

Uncertainty bounds from cov data.

xr.DataArray

Uncertainty bounds from montecarlo data.

assign_categories(mechanisms: list[str], categories: list[str], designation: list[str])

Assign categories to mechanisms.

Mechanisms, categories, and designation should all be the same shape.

categories and designation correspond to key,

Parameters:

mechanismslist[str]

1-d List of mechanisms to be assigned a category

categorieslist[str]

1-d list of categories being assigned. Can be new categories that don’t already exist. Index corresponds to index of mechanisms.

designation: list[str]

1-d list of designation to assign each mechanism. Index corresponds to index of mechanisms.

Returns:

None.

assign_categories_to_all(**categories: dict)

Assign categories to all the linear uncertainty mechanisms.

Parameters:

**categorieslist[str]

Keyword argument pairs of category names : designation to assign to all linear uncertainty mechanisms in a variable.

Returns:

None.

create_empty_categories(categories: list[str])

Add empty categories to the covcats array.

If an element in categories already exists, it is ignored and not added.

Parameters:

categorylist[str]

List of categories to create. If they already exist, they are not added.

Returns:

None.

group_combine_mechanisms(deg: bool = False, rad: bool = False) → RMEMeas

Group linear uncertainty mechanism originating from the same combine call.

Preserves degrees of freedom does NOT preserve categorical information, used by the self.dof() function prior to running the welch-stat equation.

Returns:

RMEMeas:

New RMEMeas object with the linear uncertainty mechanisms originating from the same combine call (Type A Analysis) grouped together.

categorize_by(category: str, combine_uncategorized: bool = True, uncategorized_name: str = 'uncategorized', deg: bool = False, rad: bool = False, verbose: bool = False) → RMEMeas

Group linear uncertainty mechanisms into categories.

Mechanisms sharing a common category will be combined quadratically into a single mechanism. For example, if the ‘Type’ category is selected, all the mechanisms with the ‘Type’ = ‘A’ designation in the covcats attribute will be quadratically combined.

This function does NOT preserve the DOF’s or the category information of the uncertainty mechanisms after categorization, and is recommended to be used for debugging and presentation purposes only. It is NOT recommended for categorized RMEMeas objects to be saved.

Parameters:

categorystr

String identifying the category of uncertainty mechanism.

combine_uncategorized: bool,

If True, mechanisms that do not contain ‘category’ will all be combined into a single mechanism called uncategorized_name.

uncategorized_name:str,

String to call all uncategorized mechanisms IF they are being combined. The default is ‘uncategorized’.

deg: bool,

If true, calculates minimum angle differences for uncertainties as degrees.

rad: bool,

If true, calculates minimum angle differences for uncertainties as radians.

verbosebool, optional

Print information about grouping. The default is False.

Returns:

outRMEMeas

DESCRIPTION.

get_unique_categories()

Get list of unique categories.

print_categories()

Print out the unique categories.

interp(coords: dict = None, method: str = 'linear', assume_sorted: bool = False, kwargs: dict = None, **coords_kwargs)

Interpolate RMEMeas object.

See xarray interp documentation for more details.

Parameters:

coordsdict, optional

Mapping from dimension names to the new coordinates. New coordinate can be a scalar, array-like or DataArray. If DataArrays are passed as new coordinates, their dimensions are used for the broadcasting. Missing values are skipped. The default is None.

methodstr, optional

{“linear”, “nearest”, “zero”, “slinear”, “quadratic”, “cubic”,

“quintic”, “polynomial”, “pchip”, “barycentric”, “krogh”, “akima”, “makima”}) – Interpolation method. The default is ‘linear’.

assume_sortedbool, optional

If False, values of x can be in any order and they are sorted first. If True, x has to be an array of monotonically increasing values. The default is False.

kwargsdict, optional

Additional keyword arguments passed to scipy’s interpolator. Valid options and their behavior depend whether interp1d or interpn is used.. The default is None.

**coords_kwargsdict like

The keyword arguments form of coords. One of coords or coords_kwargs must be provided..

Returns:

None.

usel(umech_id: collections.abc.Iterable[str] = None, mcsamples: collections.abc.Iterable[int] = None) → RMEMeas

Get a view into specific uncertainty mechanisms or Monte Carlo samples.

Parameters:

umech_iditer[str], optional

Linear uncertainty mechanisms to look at. The default is None.

mcsamplesiter[int], optional

Monte Carlo samples to look at. The default is None.

Returns:

‘RMEMeas’

View into RMEMeas object with only the selected linear uncertainty mechanisms and Monte Carlo samples.

Raises:

ValueError

If ‘nominal’ is passed to umech_id, or 0 is passed to the mcsamples. Those represent the nominal values and are always included by default, since an uncertainty object should always have a nominal value.

property loc

Get view into underlying data with label based indexing.

Ignores the umech_id dimension, it cannot be indexed into with this function.Index into array assuming the umech_id dimensions doesn’t exist in cov (i.e. as if indexing into only the nominal)

Generates a view on the underlying cov,mc, covcats and covdofs array. Use .copy() to create an unconnected version.

sel(indexers: dict = None, method: str = None, tolerance: float = None, **indexers_kwargs) → RMEMeas

Get view into underlying data with label based selection.

Ignores the umech_id dimension, it cannot be indexed into with this function.Index into array assuming the umech_id dimensions doesn’t exist in cov (i.e. as if indexing into only the nominal)

Generates a view on the underlying cov,mc, covcats and covdofs array. Use .copy() to create an unconnected version.

See documentation on DataArray.sel in the xarray package for details.

Parameters:

indexersTYPE, optional

A dict with keys matching dimensions and values given by scalars, slices or arrays of tick labels. For dimensions with multi-index, the indexer may also be a dict-like object with keys matching index level names. umech_id can’t be provided.

methodstr, optional

Method to use for inexact matches: * None (default): only exact matches * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value The default is None.

tolerancefloat, optional

Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance.

**indexers_kwargsdict

The keyword arguments form of indexers. One of indexers or indexers_kwargs must be provided.

Returns:

outRMEMeas

View into indexed RMEMeas object.

Raises:

ValueError

DESCRIPTION.

isel(indexers: dict = None, drop: bool = False, missing_dims: str = 'raise', **indexers_kwargs) → RMEMeas

Get view into underlying data with integer based selection.

Ignores the umech_id dimension, it cannot be indexed into with this function.Index into array assuming the umech_id dimensions doesn’t exist in cov (i.e. as if indexing into only the nominal)

Generates a view on the underlying cov,mc, covcats and covdofs array. Use .copy() to create an unconnected version.

Parameters:

indexersdict, optional

A dict with keys matching dimensions and values given by integers, slice objects or arrays. indexer can be a integer, slice, array-like or DataArray. If DataArrays are passed as indexers, xarray-style indexing will be carried out.One of indexers or indexers_kwargs must be provided.. The default is None.

dropbool, optional

drop coordinates variables indexed by integers instead of making them scalar. The default is False.

missing_dimsstr, optional

What to do if dimensions that should be selected from are not present in the DataArray: - “raise”: raise an exception - “warn”: raise a warning, and ignore the missing dimensions - “ignore”: ignore the missing dimensions. The default is ‘raise’.

**indexers_kwargsTYPE

The keyword arguments form of indexers..

Returns:

outRMEMeas

View into indexed RMEMeas object.

Raises:

ValueError

DESCRIPTION.

polyfit(dim: str, deg: int, apply_cov: bool = True) → RMEMeas

Apply a polynominal fit along dim.

Parameters:

dimstr

Name of dimension to fit along.

degint

Degree of fit

aoo

Returns:

RMEMeas

Coefficients of data.

polyval(coord: xarray.DataArray, degree_dim: str) → RMEMeas

Evaluate a polynomial fit along coord.

Assumes self is a measurement of fit-parameters, with the dimension of polynomial fits along degree_dim.

Parameters:

coordxr.DataArray

Coordinate to fit along.

degree_dimstr

Polynomial degree dimension.

Returns:

RMEMeas

Coefficients of data.

curvefit(coords: str | xarray.DataArray | Sequence[str] | Sequence[xarray.DataArray], func: callable, reduce_dims: str | collections.abc.Iterable | None = None, skipna: bool = True, p0: dict = None, bounds: dict = None, param_names: Sequence | None = None, errors: str = 'raise', **kwargs)

Simple binding to curvefit on xarray.

The nominal value is solved for first, than used as an initial guess for the perturbed datasets.

See https://docs.xarray.dev/en/stable/generated/xarray.DataArray.curvefit.html for more details.

Parameters:

coordsstr | xr.DataArray | Sequence[str] | Sequence[xr.DataArray], DataArray

Independent coordinate(s) over which to perform the curve fitting. Must share at least one dimension with the calling object. When fitting multi-dimensional functions, supply coords as a sequence in the same order as arguments in func. To fit along existing dimensions of the calling object, coords can also be specified as a str or sequence of strs.

funccallable

User specified function in the form f(x, *params) which returns a numpy array of length len(x). Params are the fittable parameters which are optimized by scipy curve_fit. x can also be specified as a sequence containing multiple coordinates, e.g. f((x0, x1), *params).

reduce_dims: str | Iterable | None, optional

Additional dimension(s) over which to aggregate while fitting. For example, calling ds.curvefit(coords=’time’, reduce_dims=[‘lat’, ‘lon’], …) will aggregate all lat and lon points and fit the specified function along the time dimension.

skipnabool, optional

Whether to skip missing values when fitting. Default is True.

p0dict, optional

Optional dictionary of parameter names to initial guesses passed to the curve_fit p0 arg. If the values are DataArrays, they will be appropriately broadcast to the coordinates of the array. If none or only some parameters are passed, the rest will be assigned initial values following the default scipy behavior

boundsdict, optional

Optional dictionary of parameter names to tuples of bounding values passed to the curve_fit bounds arg. If any of the bounds are DataArrays, they will be appropriately broadcast to the coordinates of the array. If none or only some parameters are passed, the rest will be unbounded following the default scipy behavior.

param_namesSequence | None, optional

Sequence of names for the fittable parameters of func. If not supplied, this will be automatically determined by arguments of func. param_names should be manually supplied when fitting a function that takes a variable number of parameters.

errorsstr, optional

If ‘raise’, any errors from the scipy.optimize_curve_fit optimization will raise an exception. If ‘ignore’, the coefficients and covariances for the coordinates where the fitting failed will be NaN.

**kwargsoptional

Additional keyword arguments passed to scipy curve_fit.

curveval(func: callable, coords: xarray.DataArray)

Evaluate the output of curvefit.

Assumes there is a dimension called ‘param’ that coresponds. to the fit coefficients of func. Assumes function is nonlinear and iterates over each function evaluation, which can be slow.

Parameters:

funccallable

callable function

coordsxr.DataArray

coordinates.

exception rmellipse.uobjects.RMEMeasFormatError(message)

Bases: Exception

Error in formatting of data inside RMEMeas

Parameters

Exception_type_

_description_

Initialize self. See help(type(self)) for accurate signature.

message