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Source code for qiskit.visualization.counts_visualization

# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2018.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

# pylint: disable=import-error

"""
Visualization functions for measurement counts.
"""

from collections import Counter, OrderedDict
import functools
import numpy as np
from .matplotlib import HAS_MATPLOTLIB
from .exceptions import VisualizationError

if HAS_MATPLOTLIB:
    from matplotlib import get_backend
    import matplotlib.pyplot as plt
    from matplotlib.ticker import MaxNLocator


def hamming_distance(str1, str2):
    """Calculate the Hamming distance between two bit strings

    Args:
        str1 (str): First string.
        str2 (str): Second string.
    Returns:
        int: Distance between strings.
    Raises:
        VisualizationError: Strings not same length
    """
    if len(str1) != len(str2):
        raise VisualizationError('Strings not same length.')
    return sum(s1 != s2 for s1, s2 in zip(str1, str2))


VALID_SORTS = ['asc', 'desc', 'hamming']
DIST_MEAS = {'hamming': hamming_distance}


[docs]def plot_histogram(data, figsize=(7, 5), color=None, number_to_keep=None, sort='asc', target_string=None, legend=None, bar_labels=True, title=None, ax=None): """Plot a histogram of data. Args: data (list or dict): This is either a list of dictionaries or a single dict containing the values to represent (ex {'001': 130}) figsize (tuple): Figure size in inches. color (list or str): String or list of strings for histogram bar colors. number_to_keep (int): The number of terms to plot and rest is made into a single bar called 'rest'. sort (string): Could be 'asc', 'desc', or 'hamming'. target_string (str): Target string if 'sort' is a distance measure. legend(list): A list of strings to use for labels of the data. The number of entries must match the length of data (if data is a list or 1 if it's a dict) bar_labels (bool): Label each bar in histogram with probability value. title (str): A string to use for the plot title ax (matplotlib.axes.Axes): An optional Axes object to be used for the visualization output. If none is specified a new matplotlib Figure will be created and used. Additionally, if specified there will be no returned Figure since it is redundant. Returns: matplotlib.Figure: A figure for the rendered histogram, if the ``ax`` kwarg is not set. Raises: ImportError: Matplotlib not available. VisualizationError: When legend is provided and the length doesn't match the input data. Example: .. jupyter-execute:: from qiskit import QuantumCircuit, BasicAer, execute from qiskit.visualization import plot_histogram %matplotlib inline qc = QuantumCircuit(2, 2) qc.h(0) qc.cx(0, 1) qc.measure([0, 1], [0, 1]) backend = BasicAer.get_backend('qasm_simulator') job = execute(qc, backend) plot_histogram(job.result().get_counts(), color='midnightblue', title="New Histogram") """ if not HAS_MATPLOTLIB: raise ImportError('Must have Matplotlib installed.') if sort not in VALID_SORTS: raise VisualizationError("Value of sort option, %s, isn't a " "valid choice. Must be 'asc', " "'desc', or 'hamming'") if sort in DIST_MEAS.keys() and target_string is None: err_msg = 'Must define target_string when using distance measure.' raise VisualizationError(err_msg) if isinstance(data, dict): data = [data] if legend and len(legend) != len(data): raise VisualizationError("Length of legendL (%s) doesn't match " "number of input executions: %s" % (len(legend), len(data))) # Set bar colors if color is None: color = ['#648fff', '#dc267f', '#785ef0', '#ffb000', '#fe6100'] elif isinstance(color, str): color = [color] if ax is None: fig, ax = plt.subplots(figsize=figsize) else: fig = None labels = list(sorted( functools.reduce(lambda x, y: x.union(y.keys()), data, set()))) if number_to_keep is not None: labels.append('rest') if sort in DIST_MEAS.keys(): dist = [] for item in labels: dist.append(DIST_MEAS[sort](item, target_string)) labels = [list(x) for x in zip(*sorted(zip(dist, labels), key=lambda pair: pair[0]))][1] length = len(data) width = 1/(len(data)+1) # the width of the bars labels_dict, all_pvalues, all_inds = _plot_histogram_data(data, labels, number_to_keep) rects = [] for item, _ in enumerate(data): for idx, val in enumerate(all_pvalues[item]): label = None if not idx and legend: label = legend[item] if val >= 0: rects.append(ax.bar(idx+item*width, val, width, label=label, color=color[item % len(color)], zorder=2)) bar_center = (width / 2) * (length - 1) ax.set_xticks(all_inds[item] + bar_center) ax.set_xticklabels(labels_dict.keys(), fontsize=14, rotation=70) # attach some text labels if bar_labels: for rect in rects: for rec in rect: height = rec.get_height() if height >= 1e-3: ax.text(rec.get_x() + rec.get_width() / 2., 1.05 * height, '%.3f' % float(height), ha='center', va='bottom', zorder=3) else: ax.text(rec.get_x() + rec.get_width() / 2., 1.05 * height, '0', ha='center', va='bottom', zorder=3) # add some text for labels, title, and axes ticks ax.set_ylabel('Probabilities', fontsize=14) all_vals = np.concatenate(all_pvalues).ravel() ax.set_ylim([0., min([1.2, max([1.2 * val for val in all_vals])])]) if sort == 'desc': ax.invert_xaxis() ax.yaxis.set_major_locator(MaxNLocator(5)) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(14) plt.grid(which='major', axis='y', zorder=0, linestyle='--') if title: plt.title(title) if legend: ax.legend(loc='upper left', bbox_to_anchor=(1.01, 1.0), ncol=1, borderaxespad=0, frameon=True, fontsize=12) if fig: if get_backend() in ['module://ipykernel.pylab.backend_inline', 'nbAgg']: plt.close(fig) return fig
def _plot_histogram_data(data, labels, number_to_keep): """Generate the data needed for plotting counts. Parameters: 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'. 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 = [] for execution in data: if number_to_keep is not None: data_temp = dict(Counter(execution).most_common(number_to_keep)) data_temp["rest"] = sum(execution.values()) - sum(data_temp.values()) execution = data_temp values = [] for key in labels: if key not in execution: if number_to_keep is None: labels_dict[key] = 1 values.append(0) else: values.append(-1) else: labels_dict[key] = 1 values.append(execution[key]) values = np.array(values, dtype=float) where_idx = np.where(values >= 0)[0] pvalues = values[where_idx] / sum(values[where_idx]) all_pvalues.append(pvalues) numelem = len(values[where_idx]) ind = np.arange(numelem) # the x locations for the groups all_inds.append(ind) return labels_dict, all_pvalues, all_inds

© Copyright 2020, Qiskit Development Team. Last updated on 2021/02/18.

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