msaf/plotting.py
def plot_labels(all_labels, gt_times, est_file, algo_ids=None, title=None,
                output_file=None):
    """Plots all the labels.

    Parameters
    ----------
    all_labels: list
        A list of np.arrays containing the labels of the boundaries, one array
        for each algorithm.
    gt_times: np.array
        Array with the ground truth boundaries.
    est_file: str
        Path to the estimated file (JSON file)
    algo_ids : list
        List of algorithm ids to to read boundaries from.
        If None, all algorithm ids are read.
    title : str
        Title of the plot. If None, the name of the file is printed instead.
    """
    import matplotlib.pyplot as plt
    N = len(all_labels)  # Number of lists of labels
    if algo_ids is None:
        algo_ids = io.get_algo_ids(est_file)

    # Translate ids
    for i, algo_id in enumerate(algo_ids):
        algo_ids[i] = translate_ids[algo_id]
    algo_ids = ["GT"] + algo_ids

    # Index the labels to normalize them
    for i, labels in enumerate(all_labels):
        all_labels[i] = mir_eval.util.index_labels(labels)[0]

    # Get color map
    cm = plt.get_cmap('gist_rainbow')
    max_label = max(max(labels) for labels in all_labels)

    # To intervals
    gt_inters = utils.times_to_intervals(gt_times)

    # Plot labels
    figsize = (6, 4)
    plt.figure(1, figsize=figsize, dpi=120, facecolor='w', edgecolor='k')
    for i, labels in enumerate(all_labels):
        for label, inter in zip(labels, gt_inters):
            plt.axvspan(inter[0], inter[1], ymin=i / float(N),
                        ymax=(i + 1) / float(N), alpha=0.6,
                        color=cm(label / float(max_label)))
        plt.axhline(i / float(N), color="k", linewidth=1)

    # Draw the boundary lines
    for bound in gt_times:
        plt.axvline(bound, color="g")

    # Format plot
    _plot_formatting(title, est_file, algo_ids, gt_times[-1], N,
                     output_file)
def plot_labels(all_labels, gt_times, est_file, algo_ids=None, title=None,
                output_file=None):
    """Plots all the labels.

    Parameters
    ----------
    all_labels: list
        A list of np.arrays containing the labels of the boundaries, one array
        for each algorithm.
    gt_times: np.array
        Array with the ground truth boundaries.
    est_file: str
        Path to the estimated file (JSON file)
    algo_ids : list
        List of algorithm ids to to read boundaries from.
        If None, all algorithm ids are read.
    title : str
        Title of the plot. If None, the name of the file is printed instead.
    """
    import matplotlib.pyplot as plt
    N = len(all_labels)  # Number of lists of labels
    if algo_ids is None:
        algo_ids = io.get_algo_ids(est_file)

    # Translate ids
    for i, algo_id in enumerate(algo_ids):
        algo_ids[i] = translate_ids[algo_id]
    algo_ids = ["GT"] + algo_ids

    # Index the labels to normalize them
    for i, labels in enumerate(all_labels):
        all_labels[i] = mir_eval.util.index_labels(labels)[0]

    # Get color map
    cm = plt.get_cmap('gist_rainbow')
    max_label = max(max(labels) for labels in all_labels)

    # To intervals
    gt_inters = utils.times_to_intervals(gt_times)

    # Plot labels
    figsize = (6, 4)
    plt.figure(1, figsize=figsize, dpi=120, facecolor='w', edgecolor='k')
    for i, labels in enumerate(all_labels):
        for label, inter in zip(labels, gt_inters):
            plt.axvspan(inter[0], inter[1], ymin=i / float(N),
                        ymax=(i + 1) / float(N), alpha=0.6,
                        color=cm(label / float(max_label)))
        plt.axhline(i / float(N), color="k", linewidth=1)

    # Draw the boundary lines
    for bound in gt_times:
        plt.axvline(bound, color="g")

    # Format plot
    _plot_formatting(title, est_file, algo_ids, gt_times[-1], N,
                     output_file)
def plot_labels ( all_labels , gt_times , est_file , algo_ids = None , title = None , output_file = None ) : import matplotlib . pyplot as plt N = len ( all_labels ) # Number of lists of labels if algo_ids is None : algo_ids = io . get_algo_ids ( est_file ) # Translate ids for i , algo_id in enumerate ( algo_ids ) : algo_ids [ i ] = translate_ids [ algo_id ] algo_ids = [ "GT" ] + algo_ids # Index the labels to normalize them for i , labels in enumerate ( all_labels ) : all_labels [ i ] = mir_eval . util . index_labels ( labels ) [ 0 ] # Get color map cm = plt . get_cmap ( 'gist_rainbow' ) max_label = max ( max ( labels ) for labels in all_labels ) # To intervals gt_inters = utils . times_to_intervals ( gt_times ) # Plot labels figsize = ( 6 , 4 ) plt . figure ( 1 , figsize = figsize , dpi = 120 , facecolor = 'w' , edgecolor = 'k' ) for i , labels in enumerate ( all_labels ) : for label , inter in zip ( labels , gt_inters ) : plt . axvspan ( inter [ 0 ] , inter [ 1 ] , ymin = i / float ( N ) , ymax = ( i + 1 ) / float ( N ) , alpha = 0.6 , color = cm ( label / float ( max_label ) ) ) plt . axhline ( i / float ( N ) , color = "k" , linewidth = 1 ) # Draw the boundary lines for bound in gt_times : plt . axvline ( bound , color = "g" ) # Format plot _plot_formatting ( title , est_file , algo_ids , gt_times [ - 1 ] , N , output_file )
Plots all the labels.

    Parameters
    ----------
    all_labels: list
        A list of np.arrays containing the labels of the boundaries, one array
        for each algorithm.
    gt_times: np.array
        Array with the ground truth boundaries.
    est_file: str
        Path to the estimated file (JSON file)
    algo_ids : list
        List of algorithm ids to to read boundaries from.
        If None, all algorithm ids are read.
    title : str
        Title of the plot. If None, the name of the file is printed instead.
Plots all the labels .