def print(self, distribution: Dict[Player, float]):
palette = sns.color_palette(None, len(distribution))
labels = []
sizes = []
colors = []
i = 0
for player in sorted(distribution.keys(), key=lambda item: item.value):
likelihood = distribution[player]
if likelihood != 0:
labels.append(get_name(player))
sizes.append(likelihood)
colors.append(palette[i])
i += 1
sizes_sum = sum(sizes)
sizes = saferound([size / sizes_sum for size in sizes],
self.__PRECISION)
wedges, names, percentages = plt.pie(sizes,
labels=labels,
colors=colors,
autopct='%1.1f%%')
kw = dict(arrowprops=dict(arrowstyle="-"), zorder=0, va="center")
previous_angle = None
for i, it in enumerate(zip(wedges, names, percentages, sizes)):
wedge, name, percentage, size = it
if size < self.__THRESHOLD_LIKELIHOOD:
name.update({'visible': False})
percentage.update({'visible': False})
angle = (wedge.theta2 - wedge.theta1) / 2 + wedge.theta1
text_angle = angle
if previous_angle is not None and self.__angle_distance(
angle,
previous_angle) < self.__THRESHOLD_MIN_ANGLE_INC:
text_angle = (previous_angle +
self.__THRESHOLD_MIN_ANGLE_INC + 360) % 360
x, y = np.cos(np.deg2rad(angle)), np.sin(np.deg2rad(angle))
x_text = (1 + self.__THRESHOLD_LINE_LENGTH) * np.cos(
np.deg2rad(text_angle))
y_text = (1 + self.__THRESHOLD_LINE_LENGTH) * np.sin(
np.deg2rad(text_angle))
horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
plt.annotate(name.get_text() + ": " + percentage.get_text(),
xy=(x, y),
xytext=(x_text, y_text),
horizontalalignment=horizontalalignment,
**kw)
previous_angle = angle
if self.__file_name is None:
plt.show()
else:
plt.savefig(self.__file_name)
plt.clf()
def print(self, distribution: Dict[Player, float]):
distribution = {
player: value * self.__multiplier
for player, value in distribution.items()
}
distribution = saferound(distribution, self.__precision)
sorted_distribution = []
for player, likelihood in distribution.items():
sorted_distribution.append((get_name(player), likelihood))
sorted_distribution.sort(key=lambda x: x[1], reverse=True)
for row in sorted_distribution:
player_name = row[0]
likelihood = row[1]
print(player_name + ": " + str(likelihood))
# Show at which frames each player occurs in a given episode.
from Data.PlayerData import get_name
from Layers.Appearance.VideoParser import VideoParser, ParsedVideo
SEASON = 17
EPISODE = 5
parsed_video = VideoParser.load_parsed_video(SEASON, EPISODE)
player_occurrences = parsed_video.player_occurrences
alive_players = parsed_video.alive_players
for player, occurrences in player_occurrences.items():
if player in alive_players:
print(get_name(player))
print(sorted(list(occurrences)))
total_face_frames = 0
for player, occurrences in player_occurrences.items():
if player in alive_players:
total_face_frames += len(occurrences)
for player, occurrences in player_occurrences.items():
if player in alive_players:
print(get_name(player))
print(len(occurrences) / total_face_frames)