""""""

# TODO: move img_px and scaling factors to config file.

img_px = 1364

scaling_factor = dataset.MAP_SCALE_FACTOR[dataset.match.map]

colours = ['magenta', 'y', 'cyan', 'lime']

dpi = 96

fig, ax = plt.subplots(figsize=(img_px/dpi, img_px/dpi), dpi=dpi)

ax.axis('off')

# TODO: match shouldn't be under dataset?

img = plt.imread(dataset.MAP_IMG[dataset.match.map])

ax.imshow(img)

# add zone circles

for (x, y, r) in dataset.zones.values():

ax.add_artist(

plt.Circle(

(x / scaling_factor, y / scaling_factor), r / scaling_factor,

fill=False,

ec='white'

)

)

for i, player in enumerate(players):

ax.plot(

np.array(dataset.player_positions[player].x) / scaling_factor,

np.array(dataset.player_positions[player].y) / scaling_factor,

ls='-', lw=1

)#, color=colours[i], label=player

ax.grid(False)

plt.xlim(0, img_px)

plt.ylim(img_px, 0)

fig.subplots_adjust(bottom=0, top=1, left=0, right=1)

output_path = 'c:/workspace/pubg-analytics/output/location_history_{}.png'.format(dataset.match_id)

""""""

all_participants = dataset.player_positions.keys()

plt.style.use("dark_background")

fig, ax = plt.subplots(figsize=(14, 9))

for i, player in enumerate(all_participants):

zed = dataset.zone_edge_distance_t(player)

ax.plot(zed[:, 0] / 60, zed[:, 1] / dataset.MAP_SCALE_FACTOR[dataset.match.map]) # convert x time values to minutes

_, x_max = plt.xlim()

plt.xlim(0, x_max)

ax.hlines(0, 0, max(dataset.game_states.elapsed_time_s) / 60, 'white', lw=0.5)

y_min, y_max = plt.ylim()

ax.vlines(np.array(list(dataset.zones.keys())) / 60, y_min, y_max, 'white', lw=1.5)

# ax.grid(False)

ax.grid(color='white', ls='--', lw=1, alpha=0.25)

plt.title("Player Distance from Safety Zone Edge")

plt.xlabel("Time (m)\nVertical lines are new zone timepoints")

plt.ylabel("Distance from Safety Zone Edge")

plt.tight_layout()

output_path = 'c:/workspace/pubg-analytics/output/distance_from_zone_all_{}.png'.format(dataset.match_id)

plt.savefig(output_path)

""""""

all_participants = dataset.player_positions.keys()

winners = dataset.get_winners() # TODO: make property?

player_specified = False if name is None else True

if player_specified:

team = dataset.get_team(name)

else:

team = []

winner_label_used = False

rest_label_used = False

teammate_label_used = False

plt.style.use("dark_background")

# greys = ['#262626', '#333333', '#404040']

fig, ax = plt.subplots(figsize=(14, 9))

for i, player in enumerate(all_participants):

zed = dataset.zone_edge_distance_t(player)

# TODO: use slightly differnt variation on the colours to tell apart players

if player in winners:

# http://www.flatuicolorpicker.com/category/orange

# colour = '#F5AB35' # lightning yellow

colour = '#F62459' # radical red

zorder = 200

label = 'winners'

alpha = 0.75

if winner_label_used:

label = None

winner_label_used = True

elif player == name:

colour = '#22A7F0' # picton blue

zorder = 300

label = player

alpha = 1.0

elif player in team:

colour = '#2ECC71' # shamrock

zorder = 250

label = 'teammates'

alpha = 0.75

if teammate_label_used:

label = None

teammate_label_used = True

else:

# colour = greys[i % 3]

colour = '#666666'

zorder = i

label = 'rest'

alpha = 0.3

if rest_label_used:

label = None

rest_label_used = True

ax.plot(

zed[:, 0] / 60,

zed[:, 1] / dataset.MAP_SCALE_FACTOR[dataset.match.map],

color=colour, zorder=zorder, label=label, alpha=alpha

)

# plt.legend(facecolor='inherit')

leg = ax.legend()

leg.remove()

ax.add_artist(leg)

_, x_max = plt.xlim()

plt.xlim(0, x_max)

ax.hlines(0, 0, max(dataset.game_states.elapsed_time_s) / 60, 'white', lw=0.5)

y_min, y_max = plt.ylim()

ax.vlines(np.array(list(dataset.zones.keys())) / 60, y_min, y_max, 'white', lw=1.5)

# ax.grid(False)

ax.grid(color='white', ls='--', lw=1, alpha=0.10)

plt.title("Player Distance from Safety Zone Edge")

plt.xlabel("Time (m)\nVertical lines are new zone timepoints")

plt.ylabel("Distance from Safety Zone Edge")

plt.tight_layout()

output_path = 'c:/workspace/pubg-analytics/output/distance_from_zone_comparison_{}.png'.format(dataset.match_id)

plt.savefig(output_path)