import plotly.express as px
import plotly.graph_objects as go
import plotly.colors as pc
from scipy.stats import gaussian_kde
import numpy as np
# import pandas as pd
import polars as pl
import gradio as gr

from translate import max_pitch_types
from data import (
    df,
    pitch_stats, rhb_pitch_stats,lhb_pitch_stats,
    league_pitch_stats, rhb_league_pitch_stats, lhb_league_pitch_stats
)

# GRADIO FUNCTIONS

# location maps
def fit_pred_kde(data, X, Y):
  kde = gaussian_kde(data)
  return kde(np.stack((X, Y)).reshape(2, -1)).reshape(*X.shape)


plot_s = 256
sz_h = 200
sz_w = 160
h_h = 200 - 40*2
h_w = 160 - 32*2

kde_range = np.arange(-plot_s/2, plot_s/2, 1)
X, Y = np.meshgrid(
    kde_range,
    kde_range
)


def coordinatify(h, w):
  return dict(
      x0=-w/2,
      y0=-h/2,
      x1=w/2,
      y1=h/2
  )


colorscale = pc.sequential.OrRd
colorscale = [
    [0, 'rgba(0, 0, 0, 0)'],
] + [
    [i / len(colorscale), color] for i, color in enumerate(colorscale, start=1)
]


def plot_pitch_map(df, player=None, loc=None, pitch_type=None, pitch_name=None, all_pitches=False, min_pitches=2):
  assert not ((loc is None and player is None) or (loc is not None and player is not None)), 'exactly one of `player` or `loc` must be specified'

  if loc is None and player is not None:
    if all_pitches:
      assert not (pitch_type is not None or pitch_name is not None), 'cannot have `pitch_type` or `pitch_name` when `all_pitches` is `True`'
      # loc = df.set_index('name').sort_index().loc[player, ['plate_x', 'plate_z']]
      loc = df.filter(pl.col('name') == player).select(['plate_x', 'plate_z'])
    else:
      assert not ((pitch_type is None and pitch_name is None) or (pitch_type is not None and pitch_name is not None)), 'exactly one of `pitch_type` or `pitch_name` must be specified'
      pitch_val = pitch_type or pitch_name
      pitch_col = 'pitch_type' if pitch_type else 'pitch_name'
      # loc = df.sort_index().set_index(['name', pitch_col]).sort_index().loc[(player, pitch_val), ['plate_x', 'plate_z']]
      loc = df.filter((pl.col('name') == player) & (pl.col(pitch_col) == pitch_val)).select(['plate_x', 'plate_z'])

  fig = go.Figure()
  if len(loc) >= min_pitches:
    Z = fit_pred_kde(loc.to_numpy().T, X, Y)
    fig.add_shape(
        type="rect",
        **coordinatify(sz_h, sz_w),
        line_color='gray',
        # fillcolor='rgba(220, 220, 220, 0.75)', #gainsboro
    )
    fig.add_shape(
        type="rect",
        **coordinatify(h_h, h_w),
        line_color='dimgray',
    )
    fig.add_trace(go.Contour(
        z=Z,
        x=kde_range,
        y=kde_range,
        colorscale=colorscale,
        zmin=1e-5,
        zmax=Z.max(),
        contours={
            'start': 1e-5,
            'end': Z.max(),
            'size': Z.max() / 5
        },
        showscale=False
    ))
  else:
      fig.add_annotation(
          x=0,
          y=0,
          text=f'No visualization<br>as less than {min_pitches} pitches thrown',
          showarrow=False
      )
  fig.update_layout(
    xaxis=dict(range=[-plot_s/2, plot_s/2+1], showticklabels=False),
    yaxis=dict(range=[-plot_s/2, plot_s/2+1], scaleanchor='x', scaleratio=1, showticklabels=False),
    # width=384,
    # height=384
  )
  return fig


# velo distribution
def plot_pitch_velo(df=None, player=None, velos=None, pitch_type=None, pitch_name=None, min_pitches=2):
  assert not ((velos is None and player is None) or (velos is not None and player is not None)), 'exactly one of `player` or `velos` must be specified'

  if velos is None and player is not None:
    assert not ((pitch_type is None and pitch_name is None) or (pitch_type is not None and pitch_name is not None)), 'exactly one of `pitch_type` or `pitch_name` must be specified'
    assert df is not None, '`df` must be provided if `velos` not provided'
    pitch_val = pitch_type or pitch_name
    pitch_col = 'pitch_type' if pitch_type else 'pitch_name'
    # velos = df.set_index(['name', pitch_col]).sort_index().loc[(player, pitch_val), 'release_speed']
    velos = df.filter((pl.col('name') == player) & (pl.col(pitch_col) == pitch_val))['release_speed']

  if isinstance(velos, int):
    velos = [velos] # is this line still necessary after porting to polars?

  fig = go.Figure()
  if len(velos) >= min_pitches:
    fig = fig.add_trace(go.Violin(x=velos, side='positive', hoveron='points', points=False, meanline_visible=True, name='Velocity Distribution'))
    median = velos.median()
    x_range = [median-25, median+25]
  else:
    fig.add_annotation(
        x=(170+125)/2,
        y=0.3/2,
        text=f'No visualization<br>as less than {min_pitches} pitches thrown',
        showarrow=False,
    )
    x_range = [125, 170]
  fig.update_layout(
    xaxis=dict(
        title='Velocity',
        range=x_range,
        scaleratio=2
    ),
    yaxis=dict(
        title='Frequency',
        range=[0, 0.3],
        scaleanchor='x',
        scaleratio=1,
        tickvals=np.linspace(0, 0.3, 3),
        ticktext=np.linspace(0, 0.3, 3),
    ),
    autosize=True,
    # width=512,
    # height=256,
    modebar_remove=['zoom', 'autoScale', 'resetScale'],
  )
  return fig


def plot_all_pitch_velo(df=None, player=None, player_df=None, pitch_counts=None, min_pitches=2):
  # assert not ((player is None and player_df is None) or (player is not None and player_df is not None)), 'exactly one of `player` or `player_df` must be specified'

  if player_df is None and player is not None:
    assert pitch_counts is None, '`pitch_counts` must be `None` if `player_df` is None'
    assert df is not None, '`df` must be provided if `player_df` is None'
    # player_df = df.set_index('name').sort_index().loc[player].sort_values('pitch_name').set_index('pitch_name')
    # pitch_counts = player_df.index.value_counts(ascending=True)
    player_df = df.filter((pl.col('name') == player) & (pl.col('release_speed').is_not_null()))
    pitch_counts = player_df['pitch_name'].value_counts().sort('count')


  # league_df = df.set_index('pitch_name').sort_index()
  league_df = df.filter(pl.col('release_speed').is_not_null())

  fig = go.Figure()

  velo_center = (player_df['release_speed'].min() + player_df['release_speed'].max()) / 2
  # for i, (pitch_name, count) in enumerate(pitch_counts.items()):
  for i, (pitch_name, count) in enumerate(pitch_counts.iter_rows()):
    # velos = player_df.loc[pitch_name, 'release_speed']
    # league_velos = league_df.loc[pitch_name, 'release_speed']
    velos = player_df.filter(pl.col('pitch_name') == pitch_name)['release_speed']
    league_velos = league_df.filter(pl.col('pitch_name') == pitch_name)['release_speed']
    fig.add_trace(go.Violin(
        x=league_velos,
        y=[pitch_name]*len(league_velos),
        line_color='gray',
        side='positive',
        orientation='h',
        meanline_visible=True,
        points=False,
        legendgroup='NPB',
        legendrank=1,
        # visible='legendonly',
        showlegend=False,
        name='NPB',
    ))
    if count >= min_pitches:
      fig.add_trace(go.Violin(
          x=velos,
          y=[pitch_name]*len(velos),
          side='positive',
          orientation='h',
          meanline_visible=True,
          points=False,
          legendgroup=pitch_name,
          legendrank=2+(len(pitch_counts) - i),
          name=pitch_name
      ))
    else:
      fig.add_trace(go.Scatter(
          x=[velo_center],
          y=[pitch_name],
          text=[f'No visualization as less than {min_pitches} pitches thrown'],
          textposition='top center',
          hovertext=False,
          mode="lines+text",
          legendgroup=pitch_name,
          legendrank=2+(len(pitch_counts) - i),
          name=pitch_name,
      ))

  fig.add_trace(go.Violin(
      x=league_df['release_speed'],
      y=[player]*len(league_df),
      line_color='gray',
      side='positive',
      orientation='h',
      meanline_visible=True,
      points=False,
      legendgroup='NPB',
      legendrank=1,
      # visible='legendonly',
      name='NPB',
  ))
  fig.add_trace(go.Violin(
      x=player_df['release_speed'],
      y=[player]*len(player_df),
      side='positive',
      orientation='h',
      meanline_visible=True,
      points=False,
      legendrank=0,
      name=player
  ))

  # fig.update_xaxes(title='Velocity', range=[player_df['release_speed'].dropna().min() - 2, player_df['release_speed'].dropna().max() + 2])
  fig.update_xaxes(title='Velocity', range=[player_df['release_speed'].min() - 2, player_df['release_speed'].max() + 2])
  fig.update_yaxes(range=[0, len(pitch_counts)+1-0.25], visible=False)
  fig.update_layout(violingap=0, violingroupgap=0, legend=dict(orientation='h', y=-0.15, yanchor='top'))

  return fig


def get_data(player, handedness):
  player_name = f'# {player}'

  # _df = df.set_index('name').sort_index().loc[player]
  # _df.to_csv(f'files/npb.csv', index=False)
  # _df_by_pitch_name = _df.set_index('pitch_name').sort_index()
  _df = df.filter(pl.col('name') == player)
  league_df = df
  _pitch_stats = pitch_stats
  _league_pitch_stats = league_pitch_stats
  if handedness == 'Right':
    _df = _df.filter(pl.col('stand') == 'R')
    league_df = league_df.filter(pl.col('stand') == 'R')
    _pitch_stats = rhb_pitch_stats
    _league_pitch_stats = rhb_league_pitch_stats
  elif handedness == 'Left':
    _df = _df.filter(pl.col('stand') == 'L')
    league_df = league_df.filter(pl.col('stand') == 'L')
    _pitch_stats = lhb_pitch_stats
    _league_pitch_stats = lhb_league_pitch_stats

  handedness = gr.update(value=handedness, interactive=True)

  # usage_fig = px.pie(_df['pitch_name'], names='pitch_name')
  usage_fig = px.pie(_df.select('pitch_name'), names='pitch_name')

  usage_fig.update_traces(texttemplate='%{percent:.1%}', hovertemplate=f'<b>{player}</b><br>' + 'threw a <b>%{label}</b><br><b>%{percent:.1%}</b> of the time (<b>%{value}</b> pitches)')

  pitch_counts = _df['pitch_name'].value_counts().sort('count', descending=True)

  # pitch_velo_summary = plot_all_pitch_velo(player=player, player_df=_df_by_pitch_name, pitch_counts=pitch_counts.sort_values(ascending=True))
  pitch_velo_summary = plot_all_pitch_velo(df=league_df, player=player, player_df=_df.filter(pl.col('release_speed').is_not_null()), pitch_counts=pitch_counts.sort('count', descending=False))
  pitch_loc_summary = plot_pitch_map(df=_df, player=player, all_pitches=True)

  pitch_groups = []
  pitch_names = []
  pitch_infos = []
  pitch_velos = []
  pitch_maps = []

  # for pitch_name, count in pitch_counts.items():
  for pitch_name, count in pitch_counts.iter_rows():
    pitch_groups.append(gr.update(visible=True))
    pitch_names.append(gr.update(value=f'### {pitch_name}', visible=True))
    pitch_infos.append(gr.update(
        # value=pd.DataFrame([{
        #     'Whiff%': pitch_stats.loc[(player, pitch_name), 'Whiff%'].item(),
        #     'CSW%': pitch_stats.loc[(player, pitch_name), 'CSW%'].item()
        # }]),
        value=_pitch_stats.filter((pl.col('name') == player) & (pl.col('pitch_name') == pitch_name)).select(['Whiff%', 'CSW%']),
        visible=True
    ))

    pitch_velos.append(gr.update(
        # value=plot_pitch_velo(velos=_df_by_pitch_name.loc[pitch_name, 'release_speed']),
        value=plot_pitch_velo(velos=_df.filter(pl.col('pitch_name') == pitch_name)['release_speed']),
        visible=True
    ))
    pitch_maps.append(gr.update(
        value=plot_pitch_map(df=_df, player=player, pitch_name=pitch_name),
        label='Pitch location',
        visible=True
    ))

  for _ in range(max_pitch_types - len(pitch_names)):
    pitch_groups.append(gr.update(visible=False))
    pitch_names.append(gr.update(value=None, visible=False))
    pitch_infos.append(gr.update(value=None, visible=False))
  for _ in range(max_pitch_types - len(pitch_maps)):
    pitch_velos.append(gr.update(value=None, visible=False))
    pitch_maps.append(gr.update(value=None, visible=False))

  # velo_stats = pd.concat([pitch_stats.loc[player, 'Velocity'].rename('Avg. Velo'), league_pitch_stats['Velocity'].rename('League Avg. Velo')], join='inner', axis=1).rename_axis(['Pitch']).reset_index()
  velo_stats = (
      _pitch_stats
      .filter(pl.col('name') == player)
      .select(pl.col('pitch_name').alias('Pitch'), pl.col('Velocity').alias('Avg. Velo'), pl.col('Count'))
      .join(
          _league_pitch_stats.select(pl.col('pitch_name').alias('Pitch'), pl.col('Velocity').alias('League Avg. Velo')),
          on='Pitch',
          how='inner'
      )
      .sort('Count', descending=True)
      .drop('Count')
  )

  return player_name, handedness, 'files/npb.csv', usage_fig, pitch_velo_summary, pitch_loc_summary, *pitch_groups, *pitch_names, *pitch_infos, *pitch_velos, *pitch_maps, velo_stats