nectarchain.makers.component.spe.spe_algorithm.SPEnominalalgorithm#

class SPEnominalalgorithm(*args: t.Any, **kwargs: t.Any)[source]#

Bases: SPEalgorithm

SPE fitting algorithm at nominal voltage.

Runs a multi-pixel SPE fit using iminuit with pp and n as free parameters. Supports both single-process and multi-process execution.

Initializes the FlatFieldSingleHHVSPEMaker object.

Parameters:

chargenp.ma.masked_array or array-like: The charge data.
countsnp.ma.masked_array or array-like: The counts data.
``*args``: Additional positional arguments.
``**kwargs``: Additional keyword arguments.

Methods

`add_traits`	Dynamically add trait attributes to the HasTraits instance.
`class_config_rst_doc`	Generate rST documentation for this class' config options.
`class_config_section`	Get the config section for this class.
`class_get_help`	Get the help string for this class in ReST format.
`class_get_trait_help`	Get the helptext string for a single trait.
`class_own_trait_events`	Get a dict of all event handlers defined on this class, not a parent.
`class_own_traits`	Get a dict of all the traitlets defined on this class, not a parent.
`class_print_help`	Get the help string for a single trait and print it.
`class_trait_names`	Get a list of all the names of this class' traits.
`class_traits`	Get a `dict` of all the traits of this class.
`create_from_chargesContainer`	Creates an instance of FlatFieldSingleHHVSPEMaker using charge and counts data from a ChargesContainer object.
`display`	Display and save the plot for each specified pixel ID.
`from_name`	Obtain an instance of a subclass via its name
`get_current_config`	return the current configuration as a dict (e.g. the values of all traits, even if they were not set during configuration).
`has_trait`	Returns True if the object has a trait with the specified name.
`hold_trait_notifications`	Context manager for bundling trait change notifications and cross validation.
`non_abstract_subclasses`	Get a dict of all non-abstract subclasses of this class.
`notify_change`	Notify observers of a change event
`observe`	Setup a handler to be called when a trait changes.
`on_trait_change`	DEPRECATED: Setup a handler to be called when a trait changes.
`plot_single_matplotlib`	Generate a plot of the data and a model fit for a specific pixel.
`plot_single_pyqtgraph`	Plot the SPE fit result for a single pixel using pyqtgraph.
`read_param_from_yaml`	Reads parameters from a YAML file and updates the internal parameters of the FlatFieldSPEMaker class.
`run`	Run the SPE fit for the specified (or all) pixels.
`run_fit`	Perform a fit on a specific pixel using the Minuit package.
`section_names`	return section names as a list
`set_trait`	Forcibly sets trait attribute, including read-only attributes.
`setup_instance`
`trait_defaults`	Return a trait's default value or a dictionary of them
`trait_events`	Get a `dict` of all the event handlers of this class.
`trait_has_value`	Returns True if the specified trait has a value.
`trait_metadata`	Get metadata values for trait by key.
`trait_names`	Get a list of all the names of this class' traits.
`trait_values`	A `dict` of trait names and their values.
`traits`	Get a `dict` of all the traits of this class.
`unobserve`	Remove a trait change handler.
`unobserve_all`	Remove trait change handlers of any type for the specified name.
`update_config`	Update config and load the new values

Attributes

`charge`	Returns a deep copy of the `__charge` attribute.
`chunksize`	The chunk size for multi-processing
`config`	A trait whose value must be an instance of a specified class.
`counts`	Returns a deep copy of the `__counts` attribute.
`cross_validation_lock`	A contextmanager for running a block with our cross validation lock set to True.
`display_toggle`	Enable/disable display of SPE fit results
`multiproc`	flag to active multi-processing
`npixels`	Returns the total number of pixels.
`nproc`	The Number of cpu used for SPE fit
`order`	The order of the polynome used in the savgol filter algorithm
`parameters`	Returns a deep copy of the internal `Parameters` object.
`parameters_file`	The name of the SPE fit parameters file
`parent`	A trait whose value must be an instance of a specified class.
`pixels_id`	Returns a deep copy of the pixel ID array.
`results`	Returns a deep copy of the `SPEfitContainer` result table.
`tol`	The tolerance used for minuit
`window_length`	The windows leght used for the savgol filter algorithm

property charge#: Returns a deep copy of the __charge attribute.

chunksize#: The chunk size for multi-processing

property counts#: Returns a deep copy of the __counts attribute.

classmethod create_from_chargesContainer(signal: ChargesContainer, config=None, parent=None, **kwargs)[source]#

Creates an instance of FlatFieldSingleHHVSPEMaker using charge and counts data from a ChargesContainer object.

Parameters:

signalChargesContainer: The ChargesContainer object.
``**kwargs``: Additional keyword arguments.

Returns:

FlatFieldSingleHHVSPEMaker: An instance of FlatFieldSingleHHVSPEMaker.

display(pixels_id: ndarray, package='pyqtgraph', **kwargs) → None[source]#

Display and save the plot for each specified pixel ID.

Parameters:

pixels_id: np.ndarray: An array of pixel IDs.
package: str: the package used to plot, can be matplotlib or pyqtgraph. Default to pyqtgraph
kwargs: Additional keyword arguments. figpath : str The path to save the generated plot figures. Defaults to /tmp/NectarGain_pid{os.getpid()}.

multiproc#: flag to active multi-processing

nproc#: The Number of cpu used for SPE fit

parameters_file#: The name of the SPE fit parameters file

plot_single_matplotlib(charge: ndarray, counts: ndarray, pp: float, resolution: float, gain: float, gain_error: float, n: float, pedestal: float, pedestalWidth: float, luminosity: float, likelihood: float, **kwargs) → tuple[source]#

Generate a plot of the data and a model fit for a specific pixel. The different parameters are explained in Caroff et al. (2019).

Parameters:

pixel_id: int: The ID of the pixel for which the plot is generated.
charge: np.ndarray: An array of charge values.
counts: np.ndarray: An array of event counts corresponding to the charge values.
pp: float: The value of the pp parameter.
resolution: float: The value of the resolution parameter.
gain: float: The value of the gain parameter.
gain_error: float: The value of the gain_error parameter.
n: float: The value of the n parameter.
pedestal: float: The value of the pedestal parameter.
pedestalWidth: float: The value of the pedestalWidth parameter.
luminosity: float: The value of the luminosity parameter.
likelihood: float: The value of the likelihood parameter.

Returns:

: tuple: A tuple containing the generated plot figure and the axes of the plot.

plot_single_pyqtgraph(charge: ndarray, counts: ndarray, pp: float, resolution: float, gain: float, gain_error: float, n: float, pedestal: float, pedestalWidth: float, luminosity: float, likelihood: float) → tuple[source]#

Plot the SPE fit result for a single pixel using pyqtgraph.

Parameters:

pixel_idint: The pixel ID.
chargenp.ndarray: Charge bin centres.
countsnp.ndarray: Event counts per charge bin.
ppfloat: The pp parameter.
resolutionfloat: The resolution parameter.
gainfloat: The SPE gain.
gain_errorfloat: The error on the SPE gain.
nfloat: The n parameter.
pedestalfloat: The pedestal value.
pedestalWidthfloat: The pedestal width.
luminosityfloat: The luminosity parameter.
likelihoodfloat: The fit likelihood value.

Returns:

pyqtgraph.GraphicsLayoutWidget: The generated pyqtgraph window containing the plot.

run(pixels_id: ndarray = None, **kwargs) → ndarray[source]#

Run the SPE fit for the specified (or all) pixels.

Parameters:

pixels_idnp.ndarray, optional: Array of pixel IDs to fit. If None, all pixels are fitted.
``**kwargs``: Additional keyword arguments passed to the fit machinery. Accepted keys include tol (Minuit tolerance), nproc (number of processes), and chunksize.

Returns:

np.ndarray: Array of fit status objects for each fitted pixel.

static run_fit(i: int, tol: float) → dict[source]#

Perform a fit on a specific pixel using the Minuit package.

Parameters:

iint: The index of the pixel to perform the fit on.

Returns:

: dict: A dictionary containing the fit values and errors for the specified pixel. The keys are values_i and errors_i, where i is the index of the pixel.

tol#: The tolerance used for minuit