microcalorimetry.math.unc_models

This module contains functions that define uncertainty models for operating calorimeters.

These models can be used as part of the calculate eta function to apply uncertainty models post correction.

Typically these uncertainty models are inherited from uncertiainties called out in tech notes, papers, and internal memos.

Functions

uA_24mm(f)	Historical repeatability of NIST's 2.4 mm Type A uncertainty.
uA_CN(frequencies[, RLead, RThermistor])	Get the uncertainties associated with the Type N calorimeter.
uB_CN(frequencies[, RLead, RThermistor])	Get the uncertainties associated with the Type N calorimeter.

Module Contents

microcalorimetry.math.unc_models.uA_24mm(f)

Historical repeatability of NIST’s 2.4 mm Type A uncertainty.

Accepts frequency in GHz.

Taken from the calorimeter matlab code “effcalc.m” on March 2026

Parameters:

farray

Frequency in GHz.

Returns:

uncarray

Type A uncertainty same shape as f.

microcalorimetry.math.unc_models.uA_CN(frequencies, RLead: float = 0, RThermistor: float = 200)

Get the uncertainties associated with the Type N calorimeter.

This function is envoked by the underlying models.

This function taken from pat of the effcalc.m matlab function on March 2026.

Parameters:

frequenciesarray

Array of frequencies

RLeadfloat, optional

Lead resistance of the sensor. The default is 0.

RThermistor :float , optional

Resistance of the thermistor. The default is 200.

Returns:

uAnp.array

Type A uncertainty.

uBnp.array

Type B uncertainty.

Utotnp.array

k = 2 total uncertainty.

microcalorimetry.math.unc_models.uB_CN(frequencies, RLead: float = 0, RThermistor: float = 200)

Get the uncertainties associated with the Type N calorimeter.

This function is envoked by the underlying models.

This function taken from pat of the effcalc.m matlab function on March 2026.

Parameters:

frequenciesarray

Array of frequencies

RLeadfloat, optional

Lead resistance of the sensor. The default is 0.

RThermistor :float , optional

Resistance of the thermistor. The default is 200.

Returns:

uAnp.array

Type A uncertainty.

uBnp.array

Type B uncertainty.

Utotnp.array

k = 2 total uncertainty.