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_boundaries(all_boundaries,
est_file,
algo_ids=None,
title=None,
output_file=None):
"""Plots all the boundaries.
Parameters
----------
all_boundaries: list
A list of np.arrays containing the times of the boundaries, one array
for each algorithm.
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_boundaries) # Number of lists of boundaries
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
figsize = (6, 4)
plt.figure(1, figsize=figsize, dpi=120, facecolor='w', edgecolor='k')
for i, boundaries in enumerate(all_boundaries):
color = "b"
if i == 0:
color = "g"
for b in boundaries:
plt.axvline(b, i / float(N), (i + 1) / float(N), color=color)
plt.axhline(i / float(N), color="k", linewidth=1)
# Format plot
_plot_formatting(title, est_file, algo_ids, all_boundaries[0][-1], N,
output_file)
def plot_boundaries(all_boundaries, est_file, algo_ids=None, title=None,
output_file=None):
"""Plots all the boundaries.
Parameters
----------
all_boundaries: list
A list of np.arrays containing the times of the boundaries, one array
for each algorithm.
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_boundaries) # Number of lists of boundaries
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
figsize = (6, 4)
plt.figure(1, figsize=figsize, dpi=120, facecolor='w', edgecolor='k')
for i, boundaries in enumerate(all_boundaries):
color = "b"
if i == 0:
color = "g"
for b in boundaries:
plt.axvline(b, i / float(N), (i + 1) / float(N), color=color)
plt.axhline(i / float(N), color="k", linewidth=1)
# Format plot
_plot_formatting(title, est_file, algo_ids, all_boundaries[0][-1], N,
output_file)
