#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import numpy as np
import scipy.stats as stats
from scipy.stats import zscore
import matplotlib.pyplot as plt
from soccerplots.radar_chart import Radar
from mplsoccer import PyPizza, add_image, FontManager
import math
from PIL import Image
import imageio


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#Read in CSV file
df = pd.read_csv('HeartsDataset-statsbomb.csv')

#Duplciate and rename these three columns as we want them at the end
df['PLAYER NUMBER NAME'] = df['PLAYER NUMBER']
df['Age_'] = df['Age']


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#Dataset Preview
df.head()


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#Filter the dataset
#Minimum 500 mins
#27 years old or younger

df = df.loc[(df['Age']<28) & (df['Minutes Played']>500)]


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#Seperating dataframe for player key information

df1 = df.filter(['PLAYER NUMBER NAME','Competition','Season'], axis=1)


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#Convert all numeric values to Z-Scores, this is the maths distribution used to generate ratings
numeric_cols = df.select_dtypes(include=[np.number]).columns

df2 = df[numeric_cols].apply(zscore)


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#Combine both the player info dataframe and Z-Score data frame together
#Values used to standarise z-scores to allow ratings to be on a 100 scale

newdf = pd.concat([df1,df2 * 15 + 50], axis = 1)


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#Preview of dataframe with all zscores

newdf.head()


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# Weight and Ratings for metrics

#Overall minutes score
newdf['Minutes Score - (15%)'] = newdf['Minutes Played']

#Defending metrics
newdf['PAdjClearances'] = newdf['PAdj Clearances'] *0.1
newdf['PAdjTackles&Interceptions'] = newdf['PAdj Tackles & Interceptions'] *0.4
newdf['Blocks/Shot'] = newdf['Blocks/Shot'] *0.1
newdf['DribblesSuccessfullyDefended%'] = newdf['Dribbles Successfully Defended%'] *0.2
newdf['DefensiveActionRegains'] = newdf['Defensive Action Regains'] *0.2

#Overall defending score
newdf['Defending Score - (20%)'] = newdf['PAdjClearances'] + newdf['PAdjTackles&Interceptions'] + newdf['Blocks/Shot'] + newdf['DribblesSuccessfullyDefended%'] + newdf['DefensiveActionRegains']


#Aerial metrics
newdf['AerialWins'] = newdf['Aerial Wins'] *0.35
newdf['AerialWin%'] = newdf['Aerial Win%'] *0.65
#Overall aerial score
newdf['Aerial Score - (7.5%)'] = newdf['AerialWins'] + newdf['AerialWin%']


#Crossing metrics
newdf['SuccessfulCrosses'] = newdf['Successful Crosses'] *0.6
newdf['SuccessfulBoxCross%'] = newdf['Successful Box Cross%'] *0.4
#Overall crossing score
newdf['Crossing Score - (10%)'] = newdf['SuccessfulCrosses'] + newdf['SuccessfulBoxCross%']


#Dribbling metrixs
newdf['Dribble&CarryOBV'] = newdf['Dribble & Carry OBV'] *0.5
newdf['SuccessfulDribbles'] = newdf['Successful Dribbles'] *0.3
newdf['Carries_'] = newdf['Carries'] *0.2
#Overall dribbling score
newdf['Dribbling Score - (15%)'] = newdf['Dribble&CarryOBV'] + newdf['SuccessfulDribbles'] + newdf['Carries_']


#Creativity metrics
newdf['OBV_'] = newdf['OBV'] *0.5
newdf['OpenPlayxGAssisted'] = newdf['Open Play xG Assisted'] *0.3
newdf['OpenPlayKeyPasses'] = newdf['Open Play Key Passes'] *0.2
#Overall creativity score
newdf['Creativity Score - (20%)'] = newdf['OBV_'] + newdf['OpenPlayxGAssisted'] + newdf['OpenPlayKeyPasses']


#Ball Quality metrics
newdf['xGBuildup_'] = newdf['xGBuildup'] *0.4
newdf['DeepProgressions'] = newdf['Deep Progressions'] *0.3
newdf['OPPassesIntoBox'] = newdf['OP Passes Into Box'] *0.2
newdf['OpenPlayFinalThirdPasses'] = newdf['Open Play Final Third Passes'] *0.1
#Overall ball quality score
newdf['BallQuality Score - (12.5%)'] = newdf['xGBuildup_'] + newdf['DeepProgressions'] + newdf['OPPassesIntoBox'] + newdf['OpenPlayFinalThirdPasses']


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#Creating a new dataframe with all attributes
newdf.drop(newdf.columns.difference(['PLAYER NUMBER NAME','Competition','Season','Minutes Score - (15%)','Defending Score - (20%)','Aerial Score - (7.5%)','Crossing Score - (10%)','Dribbling Score - (15%)','Creativity Score - (20%)','BallQuality Score - (12.5%)']), 1, inplace=True)
df3 = newdf
df3.columns = ['Player', 'Competition','Season','PLAYER NUMBER NAME', 'Minutes Score - (15%)', 'Defending Score - (20%)', 'Aerial Score - (7.5%)', 'Crossing Score - (10%)', 'Dribbling Score - (15%)', 'Creativity Score - (20%)', 'BallQuality Score - (12.5%)']
df3 = df3.drop('PLAYER NUMBER NAME', axis=1)

#Round overall attributes

df3 = df3.round({'Minutes Score - (15%)': 2})
df3 = df3.round({'Defending Score - (20%)': 2})
df3 = df3.round({'Aerial Score - (7.5%)': 2})
df3 = df3.round({'Crossing Score - (10%)': 2})
df3 = df3.round({'Dribbling Score - (15%)': 2})
df3 = df3.round({'Creativity Score - (20%)': 2})
df3 = df3.round({'BallQuality Score - (12.5%)': 2})

df3.head()


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#Seperating dataframe from player key information

dfviz = df3.filter(['Player','Minutes Score - (15%)', 'Defending Score - (20%)', 'Aerial Score - (7.5%)', 'Crossing Score - (10%)', 'Dribbling Score - (15%)', 'Creativity Score - (20%)', 'BallQuality Score - (12.5%)'], axis=1)


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#Rating Breakdown visual

#get parameters
params = list(dfviz.columns)
print(params)

#drop the first list team because we don't need player name. Start at first metric
params = params[1:]

#Input player name or number here
values = dfviz.loc[dfviz['Player']==77].reset_index()
values = list(values.loc[0])
values = values[2:]


#Getting the min and max values for the ranges for the data visual

min_range = []
max_range = []

for x in params:
    a = min(dfviz[params][x])
    
    b = max(dfviz[params][x])

    min_range.append((a))
    max_range.append((b))
    
#Creating pizza chart for rating breakdown visual

baker = PyPizza(
    params=params,
    background_color="white", straight_line_color="#000000",
    min_range=min_range,        # min range values
    max_range=max_range,        # max range values
    last_circle_color="#000000", last_circle_lw=2.5, straight_line_lw=1,
    other_circle_lw=0, other_circle_color="#000000", inner_circle_size=16.5,
)


# plot pizza
fig, ax = baker.make_pizza(
    values,                          # list of values
    figsize=(10, 10),                # adjust figsize according to your need
    color_blank_space="same",        # use same color to fill blank space
    blank_alpha=0.4,                 # alpha for blank-space colors
    kwargs_slices=dict(
        facecolor="#660000",edgecolor="white", zorder=2, linewidth=1
    ),                               # values to be used when plotting slices
    kwargs_params=dict(
        color="#000000", fontsize=13,
    ),                               # values to be used when adding parameter
    kwargs_values=dict(
        color="white", fontsize=13,
        bbox=dict(
            edgecolor="white", facecolor="#660000",
            boxstyle="round,pad=0.2", lw=1
        )
    )                                # values to be used when adding parameter-values
)

# Add Title / Edit Player Name and Club
fig.text(
    0.515, 0.975, "Player Number 77 - Premiership 2021/2022", size=18, fontweight='bold',
    ha="center", color="#000000"
)

# Add Subtitle
fig.text(
    0.515, 0.956,
    "Fullback / Wingback Rating Breakdown | Hearts Dataset",
    size=14,
    ha="center", color="#000000"
)

# add credits
CREDIT_1 = "Graphic: @HenshawAnalysis"
CREDIT_2 = "Data is via Statsbomb"
CREDIT_3 = "Template: Fullback / Wingback Rating Breakdown"
CREDIT_4 = "Notes: Value in brackets is the weighting for that attribute"

fig.text(
    0.935, 0.000, f"{CREDIT_1}\n{CREDIT_2}\n{CREDIT_3}\n{CREDIT_4}", size=9,
    color="#545454",
    ha="right"
)

fig.text (0.511, 0.509, "Overall\nRating", size=12, ha="center", fontweight='bold')
fig.text (0.511, 0.48, "65.31", size=16, ha="center", fontweight='bold')



# add image

im2 = imageio.imread('HeartsLogo.png')
ax_image = add_image(
    im2, fig, left=0.09, bottom=-0.01, width=0.13, height=0.127
)   # these values might differ when you are plotting

plt.show()


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#Weight the overall ratings
df3['Minutes Score - (15%)'] = df3['Minutes Score - (15%)'] *0.15
df3['Defending Score - (20%)'] = df3['Defending Score - (20%)'] *0.2
df3['Aerial Score - (7.5%)'] = df3['Aerial Score - (7.5%)'] *0.075
df3['Crossing Score - (10%)'] = df3['Crossing Score - (10%)'] *0.1
df3['Dribbling Score - (15%)'] = df3['Dribbling Score - (15%)'] *0.15
df3['Creativity Score - (20%)'] = df3['Creativity Score - (20%)'] *0.2
df3['BallQuality Score - (12.5%)'] = df3['BallQuality Score - (12.5%)'] *0.125



df3['FBWB_Rating'] = df3['Minutes Score - (15%)'] + df3['Defending Score - (20%)'] + df3['Aerial Score - (7.5%)'] + df3['Crossing Score - (10%)'] + df3['Creativity Score - (20%)'] + df3['BallQuality Score - (12.5%)']

#Round overall ratings
df3 = df3.round({'FBWB_Rating': 2})

df3.sort_values("FBWB_Rating", ascending=False)



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#Download as ratings to CSV
df3.to_csv('HeartsDataTaskRatings.csv', index=False)


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#PIZZA CHART DATA VISUAL BELOW


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#Filter metrics for Pizza Chart

dfviz = df.filter(['PLAYER NUMBER','Successful Crosses','Successful Dribbles','Touches In Box','Dribble & Carry OBV','xGBuildup','Open Play xG Assisted','Open Play Key Passes','Deep Progressions','OP Passes Into Box','Passing%','PAdj Tackles & Interceptions','Dribbles Successfully Defended%','Defensive Action Regains','Aerial Win%','Aerial Wins'], axis=1)



# In[785]:


#Rename metrics for visual
dfviz.columns = ['PLAYER NUMBER','Successful Crosses','Successful Dribbles','Touches In Box','Dribble & Carry OBV','xGBuildup','Open Play xG\nAssisted','Open Play Key\nPasses','Deep Progressions','OP Passes Into Box','Passing%','PAdj Tackles &\nInterceptions','Dribbles Successfully\nDefended%','Defensive Action\nRegains','Aerial Win%','Aerial Wins']




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dfviz.head()


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#get parameters
params = list(dfviz.columns)

#drop the first list team because we don't need player name. Start at first metric
params = params[1:]


#NEW DATA FRAME FOR YOUR PLAYER / EDIT NAME
Player = dfviz.loc[dfviz['PLAYER NUMBER']==77].reset_index()
Player = list(Player.loc[0])
Player = Player[2:]

#Creating the percentile rank score
values = []
for x in range(len(params)):
    values.append(math.floor(stats.percentileofscore(dfviz[params[x]],Player[x])))
    
    
# color for the slices and text
slice_colors = ["#660000"] * 5 + ["#014F8A"] * 5 + ["grey"] * 5
text_colors = ["white"] * 15


# instantiate PyPizza class
baker = PyPizza(
    params=params,                  # list of parameters
    background_color='white',     # background color
    straight_line_color="#EBEBE9",  # color for straight lines
    straight_line_lw=1,             # linewidth for straight lines
    last_circle_lw=0,               # linewidth of last circle
    other_circle_lw=0,              # linewidth for other circles
    inner_circle_size=5            # size of inner circle
)

# plot pizza
fig, ax = baker.make_pizza(
    values,                          # list of values
    figsize=(10, 10),                # adjust figsize according to your need
    color_blank_space="same",        # use same color to fill blank space
    slice_colors=slice_colors,       # color for individual slices
    value_colors=text_colors,        # color for the value-text
    value_bck_colors=slice_colors,   # color for the blank spaces
    blank_alpha=0.4,                 # alpha for blank-space colors
    kwargs_slices=dict(
        edgecolor="#F2F2F2", zorder=2, linewidth=1
    ),                               # values to be used when plotting slices
    kwargs_params=dict(
        color="#000000", fontsize=11,
    ),                               # values to be used when adding parameter
    kwargs_values=dict(
        color="#000000", fontsize=12,
        bbox=dict(
            edgecolor="white", facecolor="cornflowerblue",
            boxstyle="round,pad=0.2", lw=1
        )
    )                                # values to be used when adding parameter-values
)

# add title
fig.text(
    0.515, 0.975, "Player Number 77 - Premiership 2021/2022", size=16, fontweight='bold',
    ha="center", color="#000000"
)

# add subtitle
fig.text(
    0.515, 0.956,
    "Percentile Rank vs Fullbacks / Wingbacks | Hearts Dataset",
    size=14,
    ha="center", color="#000000"
)

# add credits
CREDIT_1 = "Inspired by: @Worville, @FootballSlices, @somazerofc & @Soumyaj15209314"
CREDIT_2 = "Graphic: @HenshawAnalysis"
CREDIT_3 = "Template: Fullback / Wingback"
CREDIT_4 = "Data is via Statsbomb"

fig.text(
    0.94, 0.000, f"{CREDIT_1}\n{CREDIT_2}\n{CREDIT_3}\n{CREDIT_4}", size=9,
    color="#545454",
    ha="right"
)

# add text
fig.text(
    0.35, 0.93, "Attacking     Possession     Defending", size=14,
    color="#000000"
)

# add rectangles
fig.patches.extend([
    plt.Rectangle(
        (0.32, 0.9275), 0.025, 0.021, fill=True, color="#660000",
        transform=fig.transFigure, figure=fig
    ),
    plt.Rectangle(
        (0.445, 0.9275), 0.025, 0.021, fill=True, color="#014F8A",
        transform=fig.transFigure, figure=fig
    ),
    plt.Rectangle(
        (0.582, 0.9275), 0.025, 0.021, fill=True, color="grey",
        transform=fig.transFigure, figure=fig
    ),
])

im2 = imageio.imread('HeartsLogo.png')

# add image
ax_image = add_image(
    im2, fig, left=0.09, bottom=-0.01, width=0.13, height=0.127
)   # these values might differ when you are plotting

plt.show()


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