qiskit/tools/qcvv/fitters.py
def plot_rb_data(xdata, ydatas, yavg, yerr, fit, survival_prob, ax=None,
                 show_plt=True):
    """Plot randomized benchmarking data.

    Args:
        xdata (list): list of subsequence lengths
        ydatas (list): list of lists of survival probabilities for each
            sequence
        yavg (list): mean of the survival probabilities at each sequence
            length
        yerr (list): error of the survival
        fit (list): fit parameters
        survival_prob (callable): function that computes survival probability
        ax (Axes or None): plot axis (if passed in)
        show_plt (bool): display the plot.

    Raises:
        ImportError: If matplotlib is not installed.
    """
    # pylint: disable=invalid-name

    if not HAS_MATPLOTLIB:
        raise ImportError('The function plot_rb_data needs matplotlib. '
                          'Run "pip install matplotlib" before.')
    if ax is None:
        plt.figure()
        ax = plt.gca()

    # Plot the result for each sequence
    for ydata in ydatas:
        ax.plot(xdata, ydata, color='gray', linestyle='none', marker='x')
    # Plot the mean with error bars
    ax.errorbar(xdata, yavg, yerr=yerr, color='r', linestyle='--', linewidth=3)

    # Plot the fit
    ax.plot(xdata, survival_prob(xdata, *fit), color='blue', linestyle='-', linewidth=2)
    ax.tick_params(labelsize=14)
    # ax.tick_params(axis='x',labelrotation=70)

    ax.set_xlabel('Clifford Length', fontsize=16)
    ax.set_ylabel('Z', fontsize=16)
    ax.grid(True)

    if show_plt:
        plt.show()
def plot_rb_data(xdata, ydatas, yavg, yerr, fit, survival_prob, ax=None,
                 show_plt=True):
    # pylint: disable=invalid-name

    if not HAS_MATPLOTLIB:
        raise ImportError('The function plot_rb_data needs matplotlib. '
                          'Run "pip install matplotlib" before.')
    if ax is None:
        plt.figure()
        ax = plt.gca()

    # Plot the result for each sequence
    for ydata in ydatas:
        ax.plot(xdata, ydata, color='gray', linestyle='none', marker='x')
    # Plot the mean with error bars
    ax.errorbar(xdata, yavg, yerr=yerr, color='r', linestyle='--', linewidth=3)

    # Plot the fit
    ax.plot(xdata, survival_prob(xdata, *fit), color='blue', linestyle='-', linewidth=2)
    ax.tick_params(labelsize=14)
    # ax.tick_params(axis='x',labelrotation=70)

    ax.set_xlabel('Clifford Length', fontsize=16)
    ax.set_ylabel('Z', fontsize=16)
    ax.grid(True)

    if show_plt:
        plt.show()
def plot_rb_data ( xdata , ydatas , yavg , yerr , fit , survival_prob , ax = None , show_plt = True ) : # pylint: disable=invalid-name if not HAS_MATPLOTLIB : raise ImportError ( 'The function plot_rb_data needs matplotlib. ' 'Run "pip install matplotlib" before.' ) if ax is None : plt . figure ( ) ax = plt . gca ( ) # Plot the result for each sequence for ydata in ydatas : ax . plot ( xdata , ydata , color = 'gray' , linestyle = 'none' , marker = 'x' ) # Plot the mean with error bars ax . errorbar ( xdata , yavg , yerr = yerr , color = 'r' , linestyle = '--' , linewidth = 3 ) # Plot the fit ax . plot ( xdata , survival_prob ( xdata , * fit ) , color = 'blue' , linestyle = '-' , linewidth = 2 ) ax . tick_params ( labelsize = 14 ) # ax.tick_params(axis='x',labelrotation=70) ax . set_xlabel ( 'Clifford Length' , fontsize = 16 ) ax . set_ylabel ( 'Z' , fontsize = 16 ) ax . grid ( True ) if show_plt : plt . show ( )
Plot randomized benchmarking data.

    Args:
        xdata (list): list of subsequence lengths
        ydatas (list): list of lists of survival probabilities for each
            sequence
        yavg (list): mean of the survival probabilities at each sequence
            length
        yerr (list): error of the survival
        fit (list): fit parameters
        survival_prob (callable): function that computes survival probability
        ax (Axes or None): plot axis (if passed in)
        show_plt (bool): display the plot.

    Raises:
        ImportError: If matplotlib is not installed.
Plot randomized benchmarking data .