fig = plt.figure(figsize = (4,4), dpi = 200)
ax = plt.subplot(111)
pitch = Pitch(
    pitch_type='uefa',
    half=False,
    axis=True, 
    label=True, 
    tick=True,
    goal_type='box',
    positional=True
)
pitch.draw(ax = ax)

# Here we can get the positional dimensions
pos_x = pitch.dim.positional_x
pos_y = pitch.dim.positional_y

# We test the dimensions with the labels we got from the Analyst
counter = 1

for index_y, y in enumerate(pos_y):
    for index_x, x in enumerate(pos_x):
        try:
            label_y = (pos_y[index_y] + pos_y[index_y + 1])/2
            label_x = (pos_x[index_x] + pos_x[index_x + 1])/2
        except:
            continue
        ax.annotate(
            xy=(label_x, label_y),
            text=counter,
            ha='center',
            va='center'
        )
        counter += 1

test_df = data[data['team'] == 'Napoli']
test_df.head()

fig = plt.figure(figsize = (4,4), dpi = 200)
ax = plt.subplot(111)
plot_zone_dominance(ax, 459)

layout = '''
    ABCDE
    AFGHI
    AJKLM
    ANOPQ
    ARSTU
'''

fig = plt.figure(figsize=(15,8), dpi=300)
ax_dict = fig.subplot_mosaic(layout, gridspec_kw={'wspace':-0.45})

counter = 0
for k, ax in ax_dict.items():
    if k == 'A':
        continue
    team_id = data[data['team'] == team_order[counter]]['team_id'].iloc[0]
    plot_zone_dominance(ax, team_id)
    counter += 1

# We add the logos once we've reshaped the figure
counter = 0
for k, ax in ax_dict.items():
    if k == 'A':
        continue
    DC_to_FC = ax.transData.transform
    FC_to_NFC = fig.transFigure.inverted().transform
    # -- Take data coordinates and transform them to normalized figure coordinates
    DC_to_NFC = lambda x: FC_to_NFC(DC_to_FC(x))
    ax_coords = DC_to_NFC((65,71))
    ax_size = 0.023
    image_ax = fig.add_axes(
        [ax_coords[0], ax_coords[1], ax_size, ax_size],
        fc='None'
    )
    fotmob_url = 'https://images.fotmob.com/image_resources/logo/teamlogo/'
    club_icon = Image.open(urllib.request.urlopen(f'{fotmob_url}{team_logos[counter]:.0f}.png'))
    image_ax.imshow(club_icon)
    image_ax.axis('off')
    counter += 1

# Chart Legend and Title
ax_dict['A'].set_xlim(0,1)
ax_dict['A'].set_ylim(0,1)

ax_dict['A'].annotate(
    xy=(.01,.95),
    text='SERIE A\nZONES OF CONTROL',
    ha='left',
    va='center',
    weight='bold',
    size=20
)

ax_dict['A'].plot(
    [.01,.7],
    [.88, .88],
    color='grey'
)

string_label = '''
<Where on the pitch are teams touching the>
<ball more than their opponents in open play?>

Zones labelled <IN GREEN> means that the
<the team> gain more than 55% of total touches.

Zones labelled <IN VIOLET> show where <the>
<opposing team> gain more than 55% of
total touches, while the others 
are considered <CONTESTED>.

Viz by @sonofacorner.

Inspired by @petermckeever.

Data is from The Analyst.
'''

bbox_pad = 1.5
bboxprops = {'linewidth': 0, 'pad': bbox_pad}

ax_text(
    x=.01, y=.84,
    s=string_label,
    ax=ax_dict['A'],
    family='Karla',
    highlight_textprops=[
        {'weight':'bold'},
        {'weight':'bold'},
        {'color': 'w', 'weight': 'bold', 'bbox': {'facecolor':'#3E5641', **bboxprops}},
        {'weight':'bold'},
        {'color': 'w', 'weight': 'bold', 'bbox': {'facecolor':'#461220', **bboxprops}},
        {'weight':'bold'},
        {'weight':'bold'},
        {'color': 'black', 'weight': 'bold', 'bbox': {'facecolor':'#FCB9B2', **bboxprops}},
    ]
)

ax_dict['A'].set_axis_off()

plt.savefig(
	"figures/10242022_seriea_zones.png",
	dpi = 600,
	facecolor = "#EFE9E6",
	bbox_inches="tight",
    edgecolor="none",
	transparent = False
)

plt.savefig(
	"figures/10242022_seriea_zones_tr.png",
	dpi = 600,
	facecolor = "none",
	bbox_inches="tight",
    edgecolor="none",
	transparent = True
)