Function _plot_data

qiskit/visualization/counts_visualization.py:440–487

Generate the data needed for plotting counts. Args: data (list or dict): This is either a list of dictionaries or a single dict containing the values to represent (ex {'001': 130}) labels (list): The list of bitstring labels for the plot. number_to_keep (int)

(data, labels, number_to_keep, kind="counts")

def _plot_data(data, labels, number_to_keep, kind="counts"):
    """Generate the data needed for plotting counts.

    Args:
        data (list or dict): This is either a list of dictionaries or a single
            dict containing the values to represent (ex {'001': 130})
        labels (list): The list of bitstring labels for the plot.
        number_to_keep (int): The number of terms to plot and rest
            is made into a single bar called 'rest'.
        kind (str): One of 'counts' or 'distribution`

    Returns:
        tuple: tuple containing:
            (dict): The labels actually used in the plotting.
            (list): List of ndarrays for the bars in each experiment.
            (list): Indices for the locations of the bars for each
                    experiment.
    """
    labels_dict = OrderedDict()
    all_pvalues = []
    all_inds = []

    if isinstance(data, dict):
        data = [data]
    if number_to_keep is not None:
        data = _unify_labels(_keep_largest_items(execution, number_to_keep) for execution in data)

    for execution in data:
        values = []
        for key in labels:
            if key not in execution:
                if number_to_keep is None:
                    labels_dict[key] = 1
                    values.append(0)
            else:
                labels_dict[key] = 1
                values.append(execution[key])
        if kind == "counts":
            pvalues = np.array(values, dtype=int)
        else:
            pvalues = np.array(values, dtype=float)
            pvalues /= np.sum(pvalues)
        all_pvalues.append(pvalues)
        numelem = len(values)
        ind = np.arange(numelem)  # the x locations for the groups
        all_inds.append(ind)

    return labels_dict, all_pvalues, all_inds