rmellipse

Core components of the API are import and exposed to the top level module.

These components are also made available through their respective submodules.

Submodules

• rmellipse.arrschema
• rmellipse.propagators
• rmellipse.uobjects
• rmellipse.utils

Exceptions

RMEMeasFormatError	Error in formatting of data inside RMEMeas
MissingSchemaWarning	Base class for warnings generated by user code.
ValidationError	Common base class for all non-exit exceptions.

Classes

RMEMeas	Class that stores data along with uncertainties.
CovarianceDataArray	Store data with linear uncertainty mechanisms.
CovarianceStrMetadata	Stores metadata about linearuncertainty mechanisms.
MonteCarloDataArray	Store data with monte carlo samples.
RMEUncTuple
RMEProp	Microwave Uncertainty Framework uncertainty propagators.Propagator.
GroupSaveable	Interface for objects that can be saved as HDF5 or Exdir groups or files.
AnnotatedArray	Extension of xr.DataArray that is expected to conform to a specific schema.
ArraySchema	Specialized dict subclass to describe the shape an array.
CoordinateSchema	dict() -> new empty dictionary

Functions

load_object(→ any)	Construct Python object from group or dataset.
save_object(→ SAVED)	Save an object to a group.
load_file(path, **load_object_kwargs)	Load a GroupSaveable object from a file.
save_file(path, saveable, **save_object_kwargs)	Save a GroupSaveable object to a file.

Package Contents

class rmellipse.RMEMeas(name: str | None = None, cov: A | CovarianceDataArray = None, mc: A | MonteCarloDataArray | None = None, covdofs: xarray.DataArray | CovarianceDOFMetadata | None = None, covcats: xarray.DataArray | CovarianceStrMetadata | None = None, parent: rmellipse.utils.GroupSaveable = None, attrs: dict | None = None)

Bases: rmellipse.utils.GroupSaveable

Class that stores data along with uncertainties.

Stores linear uncertainties in the cov attribute and montecarlo uncertainties in the cov attribute.

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:

• name (str, optional) – Name of RMEMeas object. The default is ‘RMEMeas’.

• cov (A | CovarianceDataArray, 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.

• mc (A | MonteCarloDataArray, optional) –

  Montecarlo trials. 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 ‘sample_id’, and the first labels of the ‘sample_id’ must start at 0 and count up by 1 (i,e typical integer based indexing). The default is None.

• covdofs (xr.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.

• covcats (xr.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

The name of the object.

Type:

str

property cov

Linear uncertainty mechanisms.

Type:

xr.DataArray

property mc

samples of Monte-Carlo distributions.

Type:

xr.DataArray

property covdofs

The degrees of freedom on linear mechanisms.

Type:

xr.DataArray

property covcats

String metadata for linear uncertainty mechanisms.

Type:

xr.DataArray

property dims

property shape

property coords

property dtype

cast_umechids()

Cast any umech_id dimensions or other dimensions to the correct type.

Generally, umech_id and covcats should be ‘T’ (variable width strings).

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

Create a RMEMeas object with just a nominal dataset.

Parameters:

• name (str) – The name of the object to be created.

• nom (xr.DataArray) – Nominal data set.

Returns:

A RMEMeas object with only nominal values.

Return type:

‘RMEMeas’

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

Read an XML file from the Microwave Uncertainty Framework.

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

Parameters:

• path (str) – Path to xml header file.

• from_csv (callable) – Read function, takes a path to a copy of the data file and returns an xarray object.

• old_dir (str | None, 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_dir (str | None, optional) – Path string to replace old_dir with. The default is None.

Raises:

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

Returns:

new – MUFmeas object from the legacy format.

Return type:

MUFmeas

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

Save to a the Microwave Uncertainty Framework Format.

This does not preserve metadata in covdofs or covcats.

Parameters:

• target_directory (str) – Directory in which to save the support folder which stores all the copies of the data.

• to_csv (callable) – 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_extension (str, optional) – What to save the xml header file extension as. The default is ‘.meas’.

• header_directory (str | None, optional) – Location in which to store the header file. If None, defaults to the target_directory. Default is None.

Raises:

Exception – If no dataformat provided and one cannot be inferred.

Return type:

None.

copy() → RMEMeas

Make a copy of a RMEMeas object.

Returns:

Copied object.

Return type:

RMEMeas

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:

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

Return type:

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.

Return type:

None.

add_mc_sample(sample: xarray.DataArray)

Add a Monte Carlo sample to the distribution.

Parameters:

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

Return type:

None.

add_umech(name: str, value: xarray.DataArray, dof: float = np.inf, category: dict = {'Type': 'B'}, add_uid=None)

Add a linear mechanisms to covariance data.

Note:

Parameters:

• name (str) – Name of new mechanism, must be unique.

• value (xr.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, optional) – Append a UID to name to guarantee uniqueness.

Return type:

None.

property nom

Get the nominal values of the RMEMeas object.

Raises:

Exception – if no data is store.

Returns:

Nominal values in an xr.DataArray

Return type:

xr.DataArray

property umech_id

Get the names of uncertainty mechanisms, excluding the nominal.

Returns:

List of uncertainty mechanism strings.

Return type:

List

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:

• percent (float) – 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.

• deg (bool, optional) – If you are calculated confidence intervals on degrees. The default is False.

• rad (bool, optional) – If you are calculating confidence intervals on radians. The default is False.

Returns:

• lower (xr.DataArray) – Lower bounds on the confidence interval.

• upper (xr.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:

• k (float, 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:

• covunc (xr.DataArray) – xr.DataArray of std unc from covariance data.

• mcunc (xr.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:

• k (float, 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:

• mechanisms (list[str]) – 1-d List of mechanisms to be assigned a category

• categories (list[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.

Return type:

None.

assign_categories_to_all(**categories: dict)

Assign categories to all the linear uncertainty mechanisms.

Parameters:

**categories (list[str]) – Keyword argument pairs of category names : designation to assign to all linear uncertainty mechanisms in a variable.

Return type:

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:

category (list[str]) – List of categories to create. If they already exist, they are not added.

Return type:

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:

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

Return type:

RMEMeas

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:

• coords (dict, 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.

• method (str, optional) –

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

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

• assume_sorted (bool, 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.

• kwargs (dict, 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_kwargs (dict like) – The keyword arguments form of coords. One of coords or coords_kwargs must be provided..

Return type:

None.

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

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

Parameters:

• umech_id (iter[str], optional) – Linear uncertainty mechanisms to look at. The default is None.

• sample_id (iter[int], optional) – Monte Carlo samples to look at. The default is None.

Raises:

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

Returns:

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

Return type:

‘RMEMeas’

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:

• indexers (TYPE, 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.

• method (str, 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.

• tolerance (float, 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_kwargs (dict) – The keyword arguments form of indexers. One of indexers or indexers_kwargs must be provided.

Raises:

ValueError – DESCRIPTION.

Returns:

out – View into indexed RMEMeas object.

Return type:

RMEMeas

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:

• indexers (dict, 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.

• drop (bool, optional) – drop coordinates variables indexed by integers instead of making them scalar. The default is False.

• missing_dims (str, 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_kwargs (TYPE) – The keyword arguments form of indexers..

Raises:

ValueError – DESCRIPTION.

Returns:

out – View into indexed RMEMeas object.

Return type:

RMEMeas

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

Apply a polynominal fit along dim.

Parameters:

• dim (str) – Name of dimension to fit along.

• deg (int) – Degree of fit

• aoo

Returns:

Coefficients of data.

Return type:

RMEMeas

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:

• coord (xr.DataArray) – Coordinate to fit along.

• degree_dim (str) – Polynomial degree dimension.

Returns:

Coefficients of data.

Return type:

RMEMeas

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

• coords (str | 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.

• func (callable) – 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.

• skipna (bool, optional) – Whether to skip missing values when fitting. Default is True.

• p0 (dict, 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

• bounds (dict, 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_names (Sequence | 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.

• errors (str, 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.

• **kwargs (optional) – 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:

• func (callable) – callable function

• coords (xr.DataArray) – coordinates.

class rmellipse.CovarianceDataArray(*args, **kwargs)

Bases: rmellipse.arrschema.AnnotatedArray

Store data with linear uncertainty mechanisms.

The first label of the umech_id coordinate is expected to be ‘nominal’, and represents the nominal (i.e. expected value) of the data set. Each label of the umech_id coordinate after that represents the nominal dataset perturbed by 1 standard deviation of that uncertainty mechanism, where the label is a universally unique ID that identifies the uncertainty mechanism.

schema

class rmellipse.CovarianceStrMetadata(*args, **kwargs)

Bases: rmellipse.arrschema.AnnotatedArray

Stores metadata about linearuncertainty mechanisms.

The ‘nominal’ should not be included in the umech_id dim.

schema

class rmellipse.MonteCarloDataArray(*args, **kwargs)

Bases: rmellipse.arrschema.AnnotatedArray

Store data with monte carlo samples.

The first label of the sample_id coordinate is the expected value of the distribution the data set.

Each sample_id after that represents a sample from the underlying probability distribution.

schema

exception rmellipse.RMEMeasFormatError(message)

Bases: Exception

Error in formatting of data inside RMEMeas

Parameters:

• Exception (_type_) – _description_

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

message

class rmellipse.RMEUncTuple

Bases: tuple

cov

mc

class rmellipse.RMEProp(montecarlo_sims: int = 0, sensitivity: bool = False, handle_common_grid_method: str = None, common_grid: str = 'frequency', common_coords: dict = {}, interp_kwargs: dict = {}, verbose: bool = False, vectorize: bool = True, set_active: bool = True)

Bases: rmellipse.propagators.Propagator

Microwave Uncertainty Framework uncertainty propagators.Propagator.

Stores perturbations to data sets and samples of a Monte Carlo distribution along a dimension called ‘umech_id’ for the cov and mc attributes respectively.

This class is used to represent data sets with uncertainty, and the provided propagators.Propagator wrapper and combine function are used to propagate those uncertainties through arbitrary functions using first order linear sensitivity analysis, or monte carlo simulations.

The class structure and algorithms are designed to support vectorized operations, and label based indexing with numpy/xarray libraries in the functions being propagated, enabling larger data sets/large numbers of uncertainties to be propagated efficiently without parallelization.

The class also supports the automatic handling of data sets with a common grid/dimensions, like frequency points.

Creates a RMEMeas propagators.Propagator initialized with the defined settings. Keyword arguments are initialized into a settings dictionary that can be modified on runtime.

Parameters:

• montecarlo_sims (int, optional) – How many Monte Carlo trials to run. 0 turns off. The default is 0.

• sensitivity (bool, optional) – If true, performs a sensitivity analysis, linear first order via finite differences. The default is False.

• handle_common_grid_method (str, optional) – How to select common dimensions on RMEMeas inputs, done automatically by propagated functions. See RMEMeas.handle_common_grid for more info. None turns off.

• common_grid (str, optional) – Name of the common dimension to handle. The default is ‘frequency’.

• common_coords (dict, optional) – Coordinates to pair RMEMeas inputs down to. Used for certain handle_common_grid_method values.See RMEMeas.handle_common_grid The default is {}.

• verbose (bool, optional) – IF true, propagators.Propagator prints information about operations as they happen. The default is False.

• vectorize (bool, optional;) – IF true, propagators.Propagator will loop over uncertainty mechanisms and repeatedly call the propagating function. The default is False.

• set_active (bool, optional) – Sets this as the active propagators.Propagator, used for some magic methods that need to infer what propagators.Propagator to use. The default is True.

settings

Stores the current settings of the propagator.

Type:

dict

handle_common_grid(process_args: tuple, process_kwargs: dict, dim: str, handle_method: str) → tuple[tuple, dict]

Handle common grids on RMEMeas objects in process_args or process_kwargs.

This function is called automatically by propagate to align and select common grid elements of RMEMeas objects so they are suitable for arithemetic and linear algebra.

Parameters:

• process_args (tuple) – DESCRIPTION.

• process_kwargs (dict) – DESCRIPTION.

• dim (str) – Name of the dimensions being handled.

• handle_method (str) –

  Name of the handle method. Valid options are: “common”,”interp_smallest”,”interp_common” “common” will only use values along dim that are shared among ALL the inputs.

  ”interp_common” will interpolate (1D) to the the provided frequency list in the common_coords dictionary of the propagators.Propagators settings. The common_coords settings is expected to be a dictionary of key value pairs with {dim:array} where dim is the name of the dimension and array is the 1d set of indexes.

Raises:

Exception – If a handle common grid method is provided that has not been defined.

Returns:

• tuple – Modified positional arguments with common grid handled.

• dict – Modified key worded arguments with common grid handled.

propagate(fun)

Decorate to make function automatically pass itself through propagate.

Assumes that all the RMEMeas arguments are passed as positional arguments. Any positional arguments that are not RMEMeas instances are turned into RMEMeas objects without any covariance or nominal data, and named ‘auto_arg’. The __name__ property of the function is assigned as the name of the output RMEMeas object.

Returns:

RMEMeas object of output.

Return type:

RMEMeas

combine(*measurements: rmellipse.uobjects.RMEMeas, error_of_mean: bool = False, n_single_values: float | int = None, combine_basename: str = 'combined', add_uuid: bool = True, combine_categories: dict[str] = {'Type': 'A'}) → rmellipse.uobjects.RMEMeas

Combine repeated measurements with uncertainty into a single measurement.

Additional uncertainty mechanisms are created with the ‘combine_basename’ as the name of the mechanisms + an iterated integer. Principal component analysis is used to create the additional mechanisms.

Parameters:

• *measurements (RMEMeas) – DESCRIPTION.

• error_of_mean (bool, optional) – If true, uses the error of the mean when creating the new uncertainty mechanisms. The default is False.

• n_single_values (Union[float,int], optional) – Describes how many of the singular values to keep as error mechanisms when performing the PCA.If n_single_values<1, will provide the min number of values to describe n_single_values ratio of the total variance described by the SVD. If n_single_values> 1, will utilize the integer n_single_values number of singular values. If None, will use all the singular values available. Useful for reducing the size of data sets when large numbers of repeated measurements are used.The default is None.

• combine_basename (dict[str], optional) – Base name usd when creating new uncertainty mechanisms. Uncertainty mechanisms are named with <basename>+_+<int>, int is iterated for each new mechanism. The default is ‘combined’.

• add_uid (str, optional) – If true, adds a UID to the combine_basename to make it unique. The default is True.

Returns:

out – Returns a RMEMeas object with combined uncertainties.

Return type:

RMEMeas

combine_across_dim(measurement: rmellipse.uobjects.RMEMeas, dim: str, error_of_mean: bool = False, n_single_values: float | int = None, combine_basename: str = 'combined', add_uuid: bool = True, combine_categories: dict[str] = {'Type': 'A'}) → rmellipse.uobjects.RMEMeas

Combine repeated measurements that are within a single RMEMeas object across a dimension with uncertainty into a single measurement.

Additional uncertainty mechanisms are created with the ‘combine_basename’ as the name of the mechanisms + an iterated integer. Principal component analysis is used to create the additional mechanisms.

Parameters:

• *measurements (RMEMeas) – DESCRIPTION.

• error_of_mean (bool, optional) – If true, uses the error of the mean when creating the new uncertainty mechanisms. The default is False.

• n_single_values (Union[float,int], optional) – Describes how many of the singular values to keep as error mechanisms when performing the PCA.If n_single_values<1, will provide the min number of values to describe n_single_values ratio of the total variance described by the SVD. If n_single_values> 1, will utilize the integer n_single_values number of singular values. If None, will use all the singular values available. Useful for reducing the size of data sets when large numbers of repeated measurements are used.The default is None.

• combine_basename (dict[str], optional) – Base name usd when creating new uncertainty mechanisms. Uncertainty mechanisms are named with <basename>+_+<int>, int is iterated for each new mechanism. The default is ‘combined’.

• add_uid (str, optional) – If true, adds a UID to the combine_basename to make it unique. The default is True.

Returns:

out – Returns a RMEMeas object with combined uncertainties.

Return type:

RMEMeas

rmellipse.load_object(saved_object: GROUP | DATASET, parent: GROUP_SAVEABLE = None, load_big_objects: bool = True, vlen_object_encoding: str = str) → any

Construct Python object from group or dataset.

Parameters:

• saved_object (Union[GROUP, DATASET]) – Group or datset that contains Python object.

• parent (GROUP_SAVEABLE, optional) – Parent of this object (Python object). The default is None.

• load_big_objects (bool, optional) – If True, fully load all objects into memory. If False, only the attributes of big objects will be loaded. The default is True.

• vlen_object_encoding (str, optional) – Variable length byte objects (np.dtype(‘O’)) are cast into this type when they are read into numpy arrays. The default is str.

Returns:

A Python object.

Return type:

any

rmellipse.save_object(group: GROUP, name: str, o: any, verbose: bool = False) → SAVED

Save an object to a group.

Parameters:

• group (GROUP) – Group where object will be saved.

• name (str) – Name the object will have in the group.

• o (any) – Object to save.

Returns:

The newly-created saved object.

Return type:

SAVED

rmellipse.load_file(path: str | pathlib.Path, **load_object_kwargs)

Load a GroupSaveable object from a file.

Follows the convention if there must be a single GroupSaveable object at the root of the file.

Parameters:

• path (str | Path) – File to read.

• **load_object_kwargs (any) – Any additional key word arguments that may be passed off to load_object.

rmellipse.save_file(path: str | pathlib.Path, saveable: GroupSaveable, **save_object_kwargs)

Save a GroupSaveable object to a file.

Overwrites the target file if it already exists.

Parameters:

• path (str | Path) – File to save to.

• saveable (GroupSaveable) – GroupSaveable object.

• **save_object_kwargs (any) – Any additional key word arguments that may be passed off to save_object.

class rmellipse.GroupSaveable(name: str = None, parent: GROUP_SAVEABLE = None, attrs: dict = None, **kwargs)

Bases: GROUP_SAVEABLE

Interface for objects that can be saved as HDF5 or Exdir groups or files.

These objects are organized in a tree-like structure to avoid data duplication. Specifically, a group-saveable object is a node in a tree graph. It stores references to its children, and also to its parent.

Each node also has a lookup table that stores the paths to data objects below it.

Save strategy

• Group saveable objects can be saved to groups and initialized from

groups. There should be a 1-1 mapping of objects to groups.

• After initialization, the group saveable object is independent

from the goup that it was initialized from, and the group(s) it was saved to. So, changing the group saveable object does not change either the group it was initialized from, or the group(s) it was saved to.

Recomendations for derived classes

• All attributes should be saveable types (see module description)

• The names of all attributes match the keywords of contructor

keyword arguments. Ex. if the object has an attribute called “foo”, the constructor will take a keyword argument called “foo”.

• In the constructor, you use self.add_child to initialize object

attributes. Big objects should be marked with is_big_object = True.

• Any modules that define classes derived from group_saveable are

in sys.path, so that import <module_name> works.

• If you plan on saving an object attribute as a group attribute, add

it to self.attrs.

• For best performance, do not store any data in a LIST_SAVEABLE type

(list, set, or tuple) if it can be stored in an array. Arrays are stored as datasets (efficient), while list-likes are stored in a custom format (inelegant, inefficient).

Tree structure

• Group saveable objects have a unique id. If you know an object’s id,

you can retrieve the object from a tree.

• Any group_saveable object can serve as the root of a tree. The only

thing that makes the root special is that it has no parent. Consequently, roots can be assigned parents, and children can be detached from their parents.

• Nodes can’t store information about nodes that are not their

children or their parent. Otherwise, we would have to define a root.

attrs = None

parent = None

lookup_table

children

is_big_object

classmethod load(group: GROUP, parent: GROUP_SAVEABLE = None, load_big_objects: bool = False) → GROUP_SAVEABLE

Initialize GROUP_SAVEABLE object from a group.

The group_saveable class is designed to be used as an archive, and may store many large data sets. So, to save space in memory, some objects must be open explicitly using the load_big_objects argument.

The attribute “is_big_object” determines if the object is fully loaded or not. If an object is not loaded, a placeholder with the same attributes will be added.

Parameters:

• group (GROUP) – An hdf5 (or equivalent) group.

• parent (GROUP_SAVEABLE, optional) – The parent of this object. The default is None.

• load_big_objects (bool, optional) – If False, attributes marked as big objects are not loaded into memory. The default is False.

Returns:

new_object – New data tree object loaded from group.

Return type:

GROUP_SAVEABLE

get_root() → GROUP_SAVEABLE

Get the root of the data tree.

Returns:

The root (the tree with no parents).

Return type:

GROUP_SAVEABLE

look_up_node(unique_id: str) → GROUP_SAVEABLE

Find the node that holds an object by unique id.

Parameters:

unique_id (str) – The hex representation of the unique id of an object stored in a subtree.

Returns:

The data tree that holds the object with that unique id.

Return type:

GROUP_SAVEABLE

update_lookup_table(unique_id: str, path: str = '') → bool

Try to update lookup table with path to data stored in a subtree.

This method will also recursively try to update the parent’s lookup tables.

If there is already an object with the same unique id in a parent’s lookup table, then that is the real one, and this is a copy. In that case, do not upate the lookup table.

Parameters:

• unique_id (str) – The hex representation of the unique id of an object stored in a subtree.

• path (str, optional) – Used for recursion. Do not change. The default is “”.

Returns:

True if object is successfully added (does not already exist in parents’ lookup table)

Return type:

bool

save(parent: GROUP, name: str = None, verbose: bool = False)

Save a group_saveable object as a group.

Parameters:

• parent (GROUP) – Parent of group to be created.

• name (str, optional) – If not None, overwrite name attribute of the group. The default is None.

• verbose (bool, optional) – if True, prints information about what is being saved

Return type:

None.

add_child(key: str = None, data: SAVEABLE = None, is_big_object: bool = False)

Add child to this node.

If the new data is a GROUP_SAVEABLE object, enforce that data.attrs[“name”] == key.

Parameters:

• key (str, optional) – Name of child. If None, check if the data has a name. The defualt is None.

• data (SAVEABLE, optional) – Data to add. If None, initialize an empty group_saveable object. The defualt is None.

• is_big_object (bool, optional) – If True, when this object is read from a file, it will be ignored if the load_big_objects argument is set to False. The default is False.

Return type:

None.

update_parents()

Recursively update the parents lookup tables all of this object’s children.

Return type:

None.

exception rmellipse.MissingSchemaWarning

Bases: UserWarning, RuntimeWarning

Base class for warnings generated by user code.

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

class rmellipse.AnnotatedArray(*args, **kwargs)

Bases: xarray.DataArray

Extension of xr.DataArray that is expected to conform to a specific schema.

Schema is defined by an ArraySchema class.

schema: ArraySchema

validate()

Validate array data against the schema of this type.

classmethod from_dataarray(array: xarray.DataArray) → Self

Cast an array into an annotated array.

Parameters:

array (xr.DataArray) – Array that can be cast into this format.

Returns:

New array.

Return type:

Self

classmethod zeros(attrs: Mapping | None = None, **coords: numpy.ndarray | xarray.DataArray) → AnnotatedArray

Generate an empty array of zeros based on the schema.

If extra coordinates are supplied they will be inserted at the first arbitrary dimension specificier in the AnnotatedArray’s schema (’…’). Coordinates with set values can be ignored, and will be automatically inserted.

Zero array is initialized with numpy.zeros.

Parameters:

• attrs (Mapping | None = None) – Provided metadata to instantiate the AnnotatedArray with. The values in the supplied metadata are shallow copied onto the instantiated AnnotatedArrays’s attrs.

• **coords (np.ndarray | xr.DataArray) – KeyValue pairs of coordinates. Must include the required coordinates of the AnnotatedArray.

Returns:

Array with supplied coordinates that conforms to the schema.

Return type:

AnnotatedArray

Raises:

KeyError – DESCRIPTION.

classmethod zeros_from(prototype: AnnotatedArray, drop_dims: list[str] | None = None, rename_dims: Mapping | None = None, use_coords: Mapping | None = None, reorder: bool = True, validate: bool = True, attrs: Mapping | None = None, **coords) → AnnotatedArray

Generate a new zeros array based on a prototype array.

Dimensions that are mapped from the prototype array to the output array are cast into the correct type. Otherwise, dimensions are inserted in the expected place.

Parameters:

• prototype (AnnotatedArray) – Array to base the new array off of.

• drop_dims (list[str], optional) – Drop these dimensions. The default is None.

• rename_dims (Mapping | dict, optional) – Mapping of dimensions on the prototype array that should be converted to dimensions of this type of array. The default is None.

• use_coords (Mapping | dict, optional) – Additional dimensions required for the new type, key is the dimension name and value is the new coordinate to use for that dimension.

• reorder (bool, optional) – Automatically try to reorder dimensions to conform to the specification.

• validate (bool, optional) – If true, validate after creation. Default is False

• attrs (Mapping | dict, optional) – If provided, supply metadata to be used as attributes.

• **coords (Mapping | None, optional) – Keyword version of use_coords. Is merged with ontop of use_coords.

Returns:

zeros – Zeros array in the new format.

Return type:

AnnotatedArray

class rmellipse.ArraySchema(shape: tuple[str | int | types.EllipsisType, Ellipsis], dims: tuple[str | types.EllipsisType, Ellipsis], dtype: type | None = None, units: str | None = None, coords: Mapping = {}, attrs: pydantic.BaseModel = None)

Bases: dict

Specialized dict subclass to describe the shape an array.

Initialize an ArraySchema.

Parameters:

• shape (tuple[str | int | EllipsisType, ...]) – Shape of structure. Ellipses indicate arbitrary dimensions, letters indicate a required dimension of unknown length, and integers indicate a required dimension of a required length.

• dims (tuple[str | EllipsisType, ...]) – Names assigned to dimensions specified by shape. Any required dimension must be names, and arbitrary dimensions must also be ellipses.

• dtype (type | None) – Type must be parseable by numpy’s dtype (e.g. f8, c8, u8, etc). None means no datatype requriement, can be Any.

• units (Mapping, optional) – Mapping of units to the array structure.

• coords (Mapping, optional) – Mapping of required dimensions to a coordinate space. Must provide at least a dtype and a single unit as a string. Optionally, if the coordinates are fixed (i.e. the row and column indices of stacks of 2-d matrices) then you may specify those coordinates here.

• attrs_schema (mapping, optional) – JSON Schema for validating metadata attributes.

Returns:

Dictionary conforming to an arrschema specification.

Return type:

dict

Raises:

Exception – If some logical inconsistency or is found, or the provided schema doesn’t follow the specification for an array schema.

validate(arr: AnnotatedArray, attach_schema: bool = True)

Test if array conforms to schema.

Parameters:

• arr (AnnotatedArray) – Array to check.

• attach_schema (bool, optional) – If True, the schema is dumped into a string and attatched to the attrs of the input data array. The default is True.

Raises:

ValidationError – If a discrepancy is found between the data and the schema.

Return type:

None.

class rmellipse.CoordinateSchema(values: list | None = None, dtype: type | None = None, units: str | None = None)

Bases: dict

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:

d = {} for k, v in iterable:

d[k] = v

dict(**kwargs) -> new dictionary initialized with the name=value pairs

in the keyword argument list. For example: dict(one=1, two=2)

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

exception rmellipse.ValidationError(*args, **kwargs)

Bases: Exception

Common base class for all non-exit exceptions.

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