Module pylars.plotting.plotwaveforms

Functions

def plot_pulses(waveform: numpy.ndarray, pulse_list: list, ax: Union[matplotlib.axes._axes.Axes, NoneType] = None) ‑> matplotlib.axes._axes.Axes

Plot the identified pulses in a waveform.

Args

waveform : np.ndarray

waveform array. Elements of the array correspond to the sample of the waveform and its value the recorded ADC counts.

pulse_list : list

list of pulses, as the output of pulse_processing.find_pulses_simple.

ax : plt.Axes, optional

axes to plot into. Defaults to None.

Returns

plt.Axes

axes with plot.

Expand source code

def plot_pulses(waveform: np.ndarray, pulse_list: list,
                ax: Optional[plt.Axes] = None) -> plt.Axes:
    """Plot the identified pulses in a waveform.

    Args:
        waveform (np.ndarray): waveform array. Elements of the array
            correspond to the sample of the waveform and its value the recorded ADC
            counts.
        pulse_list (list): list of pulses, as the output of
            pulse_processing.find_pulses_simple.
        ax (plt.Axes, optional): axes to plot into. Defaults to None.

    Returns:
        plt.Axes: axes with plot.
    """
    if ax is None:
        fig, ax = plt.subplots(1, 1)

    ax = plot_waveform(waveform, ax=ax)
    for pulse in pulse_list:
        if len(pulse) > 1:
            ax.fill_betweenx(y=np.linspace(0, 16000, 100),
                             x1=pulse[0], x2=pulse[-1],
                             alpha=0.2, color='cyan')
    return ax

def plot_waveform(waveform_array: numpy.ndarray, full_y: bool = False, full_x: bool = True, pe: bool = False, ax: Union[matplotlib.axes._axes.Axes, NoneType] = None) ‑> matplotlib.axes._axes.Axes

Plot a waveform from its array.

Args

waveform_array : np.ndarray

waveform array. Elements of the array correspond to the sample of the waveform and its value the recorded ADC counts.

full_y : bool, optional

plot full range of ADC amplitude. Defaults to False.

full_x : bool, optional

plot full range of ADC samples. Defaults to True.

pe : bool, optional

parse that the waveform is in PE/s (for peaks). Defaults to False.

ax : plt.Axes, optional

axes to plot into. Defaults to None.

Returns

plt.Axes

axes with plot.

Expand source code

def plot_waveform(waveform_array: np.ndarray, full_y: bool = False,
                  full_x: bool = True, pe: bool = False,
                  ax: Optional[plt.Axes] = None) -> plt.Axes:
    """Plot a waveform from its array.

    Args:
        waveform_array (np.ndarray): waveform array. Elements of the array
            correspond to the sample of the waveform and its value the recorded ADC
            counts.
        full_y (bool, optional): plot full range of ADC amplitude. Defaults
            to False.
        full_x (bool, optional): plot full range of ADC samples. Defaults
            to True.
        pe (bool, optional): parse that the waveform is in PE/s (for peaks).
            Defaults to False.
        ax (plt.Axes, optional): axes to plot into. Defaults to None.

    Returns:
        plt.Axes: axes with plot.
    """
    if ax is None:
        fig, ax = plt.subplots(1, 1)
    n_samples = len(waveform_array)
    x = np.arange(0, n_samples)
    ax.plot(x, waveform_array)
    baseline_rough = np.median(waveform_array[:50])
    std_rough = np.std(waveform_array[:50])
    ax.axhline(baseline_rough, ls='--', c='gray')
    ax.axhline(baseline_rough - std_rough, ls='--', c='orange')
    ax.axhline(baseline_rough + std_rough, ls='--', c='orange')
    ax.axhline(baseline_rough - std_rough * 5, ls='--', c='red')
    ax.axhline(baseline_rough + std_rough * 5, ls='--', c='red')
    if full_x != True:
        ax.set_xlim(full_x)
    if full_y:
        ax.set_ylim(0, 2**14)
    else:
        ax.set_ylim(
            min(waveform_array) -
            std_rough *
            6,
            max(waveform_array) +
            std_rough *
            6)
    ax.set_xlabel('Sample number')
    ax.set_ylabel('ADC counts / sample')
    if pe == True:
        ax.set_ylabel('PE/s')
    return ax