pixie16.plot module

pixie16.plot.CFD(events: Iterable[Event], setting: Settings, w: float = 0.3125, N: int | None = None, randomize: bool = False, persistent: bool = False, title: str | None = None, ax: matplotlib.axes.Axes | None = None, pdf: matplotlib.backends.backend_pdf.PdfPages | None = None, plot_options: str | None = None) → None

Plot CFD and FastFilter several traces

Parameters:

  • events – List of events (perhaps already filtered) from read.read_list_mode_data() Need to be from a single channel.

  • setting – A read.Settings object

  • w – The w used for the CFD calculation

  • N – Number of traces to plot, if None all traces will be plotted.

  • randomize – if True and N is smaller than the total amount of traces, randomly pick traces

  • persistent – Use datashader-like plot of traces

  • title – Title to add to plot

  • ax – a matplotlib axes to plot on. If None, we create a new figure and show it at the end.

  • pdf – if present, we save the plot in the pdf

  • plot_options – only plot some type of traces. Example: “CFD,trace,FF” will plot all 3

pixie16.plot.MCA(data: numpy.ndarray, ax: matplotlib.axes.Axes | None = None, rebin: int = 1, title: str | None = None, label: str | None = None, pdf: matplotlib.backends.backend_pdf.PdfPages | None = None, log: bool = False) → None

Plot MCA spectra

If pdf is given, it will save the figure inside the pdf (to be used with PdfPages) for multi page pdfs.

Parameters:

  • data – Data from ReadHistogramFromModule

  • ax – The axis to plot on, if None, we create a new figure and axis

  • rebin – Rebin the data by summing over this amount of bins

  • title – Add this title to the plot

  • pdf – if present, we save the plot in the pdf

  • log – plot y-axis in log scale

pixie16.plot.advindexing_roll(A: numpy.ndarray, r: numpy.ndarray) → numpy.ndarray

Shift trace matrix A by r indices.

Traces and r must be > 1D numpy arrays

pixie16.plot.create_persistent_plot(traces: numpy.ndarray, x_range: Sequence[float] | None = None, ax: matplotlib.axes.Axes | None = None) → None

Create persistence plot

Parameters:

  • traces – 2D numpy array (N, M) where N is the number of traces and M the length of the traces

  • x_range – desired range of time axis. If None, it goes from 0 to 2*M in ns

  • ax – a matplotlib axes to plot on. If None, we create a new figure and show it at the end. Otherwise this needs to be an array of matplotlib axis objects: one axis for each channel present in events.

pixie16.plot.create_title_page(title: str | list[str], pdf: matplotlib.backends.backend_pdf.PdfPages | None = None) → None

Helper function to create a title page

The function will create a title centered in a plot. If pdf is given, it will save the figure inside the pdf (to be used with PdfPages) for multi page pdfs.

Parameters:

  • title – The title, if a list of str, then each will be put on a single line

  • pdf – The pdf where the title page should be added to.

pixie16.plot.energy_sums(events: Iterable[Event], setting: Settings, stats: Stats, N: int | None = None, randomize: bool = False, persistent: bool = True, title: str | None = None, ax: matplotlib.axes.Axes | None = None, pdf: matplotlib.backends.backend_pdf.PdfPages | None = None) → None

Plots traces aligned to position of the energy sums.

Plots vertical lines at the position of the energy sums only if all events are from a single channel.

Parameters:

  • events – List of events (perhaps already filtered) from read.read_list_mode_data()

  • setting – A read.Settings object

  • N – Number of traces to plot, if None all traces will be plotted.

  • randomize – if True and N is smaller than the total amount of traces, randomly pick traces

  • persistent – Use datashader-like plot of traces

  • title – Title to add to plot

  • ax – a matplotlib axes to plot on. If None, we create a new figure and show it at the end.

  • pdf – if present, we save the plot in the pdf

pixie16.plot.filter_events(events: Iterable[Event], N: int | None = None, randomize: bool = False) → Iterable[Event]

Pick N (random) events from a list of events

pixie16.plot.find_sums(traces: numpy.ndarray, SlowLength: Iterable[int], SlowGap: Iterable[int], trailsum: Iterable[int], gapsum: Iterable[int], leadsum: Iterable[int]) → tuple[numpy.ndarray, numpy.ndarray]

Find the location of the 3 reported sums in a trace that are used to calculate the energy.

For this to work, bit 12 of register A (CaptureSums) needs to be set for the channel.

Parameters:

  • traces – 2D numpy array (N, M) where N is the number of traces and M the length of the traces.

  • SlowLength – List of the length of the Ls for each trace in traces.

  • SlowGap – List of the length of the Gs for each trace in traces.

  • trailsum – Array/List of N values that contains the values of the first reported sum, which contains L values.

  • gapsum – Array/List of N values that contains the values of the second reported sum, which contains G values.

  • leadsum – Array/List of N values that contains the values of the third reported sum, which contains L values.

Returns:

  • sumIDX – numpy array of position of the start of the first sumIDX

  • Esums – numpy array of the three sums