rminstr_specs.HP3458A ===================== .. py:module:: rminstr_specs.HP3458A Classes ------- .. autoapisummary:: rminstr_specs.HP3458A.DatasheetDCI rminstr_specs.HP3458A.DatasheetDCV rminstr_specs.HP3458A.DatasheetDCOhm Package Contents ---------------- .. py:class:: DatasheetDCI(name: str = 'HP3458ADCI', serial: str = 'NA', i_range: float = None, days_since_cal: int = None, delta_t_cal=None, acal: bool = None, time_zero: float = None, nplc: float = None, suppress_warnings: bool = False, **kwargs) Bases: :py:obj:`rminstr_specs.Specification` Class that defines the current datasheet uncertainties for the HP3458A. This model assumes a uniform distribution between the datasheet ranges, the functions output the standard deviation (1/sqrt(3)) of the posted errors. Initialize instance of DCV datasheet specs of HP3458A. :Parameters: **name** : str, Used in spec_manager to generate names for uncertainty mechanisms. Used for identifying instruments in warnings. The default is 'HP3458ADCI'. **serial** : str, Serial of instrument. Used to identify calibration history in log books. The default is 'NA'. **i_range** : float, optional Current measurement range. The default is None. **days_since_cal** : int, optional Days since the last calibration. The default is None. **delta_t_cal** : float, optional Ambient temperature of measurement. The default is None. **acal** : bool, optional Whether or not the instrument was autocalibrated before measurements. The default is None. **time_zero: float, optional** Used as comparison time when trying to infer days since cal from logbook. **nplc: float, optional** Power lince cycle integrations. Default is None. **suppress_warnings** : bool, optional Suppress warning output to the console. The default is False :Returns: None. .. .. !! processed by numpydoc !! .. py:attribute:: components .. py:attribute:: i_range :value: None .. py:attribute:: days_since_cal :value: None .. py:attribute:: delta_t_cal :value: None .. py:attribute:: acal :value: None .. py:attribute:: nplc :value: None .. py:method:: all_manufacturer_errors(readings: numpy.ndarray, addition_method: str = 'spec') Calculate all manufacturer errors and add them together. Add together all the manufacture errors using rules given by the datasheet. :Parameters: **readings** : _np.ndarray Readings to get errors for. **addition_method** : str, optional Specifies how the errors are added together. Linear will add all the standard deviations linearly, whereas quad will do a quadrature sum. Linear will give a worst case. Setting this to 'spec' will do whatever the specsheet says if stated, otherwise it will revert to linear. The default is 'spec'. :Returns: **err** : np.ndarray Total manufacturer errors. .. !! processed by numpydoc !! .. py:method:: accuracy_offset(readings: numpy.ndarray) Get the offset accuracy errors. .. !! processed by numpydoc !! .. py:method:: accuracy_slope(readings: numpy.ndarray) Get the slope accuracy errors. .. !! processed by numpydoc !! .. py:method:: temperature_coef_offset(readings: numpy.ndarray) Get the temperature coefficient errors. .. !! processed by numpydoc !! .. py:method:: temperature_coef_slope(readings: numpy.ndarray) Get the temperature coefficient slope errors. .. !! processed by numpydoc !! .. py:method:: powerline_cycles(readings: numpy.ndarray) Get errors associates with powerline cycle readings This is specific as Root mean square of noise, so presumably k=1 uncertainty. .. !! processed by numpydoc !! .. py:class:: DatasheetDCV(name: str = 'HP3458ADCV', serial: str = 'NA', v_range: float = None, days_since_cal: int = None, delta_t_cal: float = None, acal: bool = None, time_zero: float = None, nplc: float = None, suppress_warnings: bool = False, **kwargs) Bases: :py:obj:`rminstr_specs.Specification` Class that defines the datasheet uncertainties for the HP3458A. This model assumes a uniform distribution between the datasheet ranges, the functions output the standard deviation (1/sqrt(3)) of the posted errors. Initialize instance of DCV datasheet specs of HP3458A. :Parameters: **name** : str, Used in spec_manager to generate names for uncertainty mechanisms. Used for identifying instruments in warnings. The default is 'HP3458ADCV' **serial** : str, Serial of instrument. Used to identify calibration history in log books. The default is 'NA' **v_range** : float, optional Current measurement range. The default is None. **days_since_cal** : int, optional Days since the last calibration. The default is None. **delta_t_cal** : float, optional Ambient temperature of measurement. The default is None. **acal** : bool, optional Whether or not the instrument was auto calibrated before measurements. The default is None. **time_zero** : float, optional Used as comparison time when trying to infer days since cal from logbook **nplc** : float, optional Power lince cycle integrations. Default is None. **suppress_warnings** : bool, optional Suppress warning output to the console. The default is False :Returns: None. .. .. !! processed by numpydoc !! .. py:attribute:: components .. py:attribute:: v_range :value: None .. py:attribute:: days_since_cal :value: None .. py:attribute:: delta_t_cal :value: None .. py:attribute:: acal :value: None .. py:attribute:: nplc :value: None .. py:attribute:: suppress_warnings :value: False .. py:method:: all_manufacturer_errors(readings: numpy.ndarray, addition_method: str = 'spec') Return the sum of all manufacturer errors for readings. Ensure that errors are added in the way specified by the data sheet, if provided. The default is spec unless overridden by a child class. if spec, it will use the spec sheet addition which is linear if linear, it will add linearly if quadrature, it will add in quadrature :Parameters: **readings** : _np.ndarray Array of measurements to get uncertainties for. **addition_method** : str, optional Specifies how the errors are added together. Linear will add all the standard deviations linearly, whereas quad will do a quadrature sum. Linear will give a worst case. Setting this to 'spec' will do whatever the specsheet says if stated, otherwise it will revert to linear. The default is 'spec'. :Returns: np.ndarray Array of manufacturer errors of same shape as readings. .. !! processed by numpydoc !! .. py:method:: accuracy_offset(readings: numpy.ndarray) Get the offset accuracy errors. .. !! processed by numpydoc !! .. py:method:: accuracy_slope(readings: numpy.ndarray) Get the slope accuracy errors. .. !! processed by numpydoc !! .. py:method:: temperature_coef_offset(readings: numpy.ndarray) Get the temperature coefficient errors. .. !! processed by numpydoc !! .. py:method:: temperature_coef_slope(readings: numpy.ndarray) Get the temperature coefficient slope errors. .. !! processed by numpydoc !! .. py:method:: powerline_cycles(readings: numpy.ndarray) Get errors associates with powerline cycle readings This is specifiec as Root mean square of noise, so presumably k=1 uncertainty. .. !! processed by numpydoc !! .. py:class:: DatasheetDCOhm(name: str = 'HP3458ADCI', serial: str = 'NA', ohm_range: float = None, days_since_cal: int = None, delta_t_cal=None, acal: bool = None, time_zero: float = None, nplc: float = None, wires: int = None, suppress_warnings: bool = False, **kwargs) Bases: :py:obj:`rminstr_specs.Specification` Class that defines the current datasheet uncertainties for the HP3458A. This model assumes a uniform distribution between the datasheet ranges, the functions output the standard deviation (1/sqrt(3)) of the posted errors. Initialize instance of DCV datasheet specs of HP3458A. :Parameters: **name** : str, Used in spec_manager to generate names for uncertainty mechanisms. Used for identifying instruments in warnings. The default is 'HP3458ADCI'. **serial** : str, Serial of instrument. Used to identify calibration history in log books. The default is 'NA'. **ohm_range** : float, optional Current measurement range. The default is None. **days_since_cal** : int, optional Days since the last calibration. The default is None. **delta_t_cal** : float, optional Ambient temperature of measurement. The default is None. **acal** : bool, optional Whether or not the instrument was auto calibrated before measurements. The default is None. **time_zero: float, optional** Used as comparison time when trying to infer days since cal from logbook. **nplc: float, optional** Power line cycle integrations. Default is None. **wires: int, optional** How many wires measured with. The default is 2. **suppress_warnings** : bool, optional Suppress warning output to the console. The default is False :Returns: None. .. .. !! processed by numpydoc !! .. py:attribute:: components .. py:attribute:: i_range :value: None .. py:attribute:: days_since_cal :value: None .. py:attribute:: delta_t_cal :value: None .. py:attribute:: acal :value: None .. py:attribute:: nplc :value: None .. py:attribute:: wires :value: None .. py:method:: all_manufacturer_errors(readings: numpy.ndarray, addition_method: str = 'spec') Calculate all manufacturer errors and add them together. Add together all the manufacture errors using rules given by the datasheet. :Parameters: **readings** : _np.ndarray Readings to get errors for. **addition_method** : str, optional Specifies how the errors are added together. Linear will add all the standard deviations linearly, whereas quad will do a quadrature sum. Linear will give a worst case. Setting this to 'spec' will do whatever the specsheet says if stated, otherwise it will revert to linear. The default is 'spec'. :Returns: **err** : np.ndarray Total manufacturer errors. .. !! processed by numpydoc !! .. py:method:: accuracy_offset(readings: numpy.ndarray) Get the offset accuracy errors. .. !! processed by numpydoc !! .. py:method:: accuracy_2wire(readings: numpy.array) .. py:method:: accuracy_slope(readings: numpy.ndarray) Get the slope accuracy errors. .. !! processed by numpydoc !! .. py:method:: temperature_coef_offset(readings: numpy.ndarray) Get the temperature coefficient errors. .. !! processed by numpydoc !! .. py:method:: temperature_coef_slope(readings: numpy.ndarray) Get the temperature coefficient slope errors. .. !! processed by numpydoc !! .. py:method:: powerline_cycles(readings: numpy.ndarray) Get errors associates with powerline cycle readings This is specific as Root mean square of noise, so presumably k=1 uncertainty. .. !! processed by numpydoc !!