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from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui | |
import datasets | |
from datasets import load_dataset | |
import pandas as pd | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
import numpy as np | |
from scipy.stats import gaussian_kde | |
import matplotlib | |
from matplotlib.ticker import MaxNLocator | |
from matplotlib.gridspec import GridSpec | |
from scipy.stats import zscore | |
import math | |
import matplotlib | |
from adjustText import adjust_text | |
import matplotlib.ticker as mtick | |
from shinywidgets import output_widget, render_widget | |
import pandas as pd | |
from configure import base_url | |
import shinyswatch | |
import inflect | |
from matplotlib.pyplot import text | |
exit_velo_df_codes_summ_batter = pd.read_csv('summary_batter.csv',index_col=[0]) | |
#exit_velo_df_codes_summ = pd.read_csv('summary_pitcher.csv',index_col=[0]) | |
exit_velo_df_codes_summ_non_level = pd.read_csv('summary_batter_level.csv',index_col=[0]).reset_index(drop=True) | |
exit_velo_df_codes_summ_non_level['levels'] = exit_velo_df_codes_summ_non_level.levels.str.split(', ') | |
exit_velo_df_codes_summ_non_level = exit_velo_df_codes_summ_non_level.rename(columns={'levels':'level'}) | |
print(exit_velo_df_codes_summ_batter.bb_minus_k_percent) | |
batter_dict_stat = { 'sweet_spot_percent':{'x_axis':'SweetSpot%','title':'SweetSpot%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100}, | |
'max_launch_speed':{'x_axis':'Max Exit Velocity','title':'Max Exit Velocity','flip_p':False,'decimal_format':'string_0','percent_adjust':1}, | |
'launch_speed_90':{'x_axis':'90th Percentile EV','title':'90th Percentile EV','flip_p':False,'decimal_format':'string_0','percent_adjust':1}, | |
'launch_speed':{'x_axis':'Exit Velocity','title':'Exit Velocity','flip_p':False,'decimal_format':'string_0','percent_adjust':1}, | |
'launch_angle':{'x_axis':'Launch Angle','title':'Launch Angle','flip_p':False,'decimal_format':'string_0','percent_adjust':100}, | |
'avg':{'x_axis':'AVG','title':'AVG','flip_p':False,'decimal_format':'string_3','percent_adjust':100}, | |
'obp':{'x_axis':'OBP','title':'OBP','flip_p':False,'decimal_format':'string_3','percent_adjust':100}, | |
'slg':{'x_axis':'SLG','title':'SLG','flip_p':False,'decimal_format':'string_3','percent_adjust':100}, | |
'ops':{'x_axis':'OPS','title':'OPS','flip_p':False,'decimal_format':'string_3','percent_adjust':100}, | |
'k_percent':{'x_axis':'K%','title':'K%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100}, | |
'bb_percent':{'x_axis':'BB%','title':'BB%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100}, | |
'bb_over_k_percent':{'x_axis':'BB/K','title':'BB/K','flip_p':False,'decimal_format':'string_1','percent_adjust':100}, | |
'bb_minus_k_percent':{'x_axis':'BB%-K%','title':'BB%-K%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100}, | |
'csw_percent':{'x_axis':'CSW%','title':'CSW%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100}, | |
'woba_percent':{'x_axis':'wOBA','title':'wOBA','flip_p':False,'decimal_format':'string_3','percent_adjust':100}, | |
'hard_hit_percent':{'x_axis':'HardHit%','title':'HardHit%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100}, | |
'barrel_percent':{'x_axis':'Barrel%','title':'Barrel%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100}, | |
'zone_contact_percent':{'x_axis':'Z-Contact%','title':'Z-Contact%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100}, | |
'zone_swing_percent':{'x_axis':'Z-Swing%','title':'Z-Swing%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100}, | |
'zone_percent':{'x_axis':'Zone%','title':'Zone%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100}, | |
'chase_percent':{'x_axis':'O-Swing%','title':'O-Swing%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100}, | |
'chase_contact':{'x_axis':'O-Contact%','title':'O-Contact%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100}, | |
'swing_percent':{'x_axis':'Swing%','title':'Swing%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100}, | |
'whiff_rate':{'x_axis':'Whiff%','title':'Whiff%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100}, | |
'swstr_rate':{'x_axis':'SwStr%','title':'SwStr%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100}, | |
} | |
batter_dict_stat_small = { 'sweet_spot_percent':'SweetSpot%', | |
'max_launch_speed':'Max Exit Velocity', | |
'launch_speed_90':'90th Percentile EV', | |
'launch_speed':'Exit Velocity', | |
'launch_angle':'Launch Angle', | |
'avg':'AVG', | |
'obp':'OBP', | |
'slg':'SLG', | |
'ops':'OPS', | |
'k_percent':'K%', | |
'bb_percent':'BB%', | |
'bb_over_k_percent':'BB/K', | |
'bb_minus_k_percent':'BB%-K%', | |
'csw_percent':'CSW%', | |
'woba_percent':'wOBA', | |
'hard_hit_percent':'HardHit%', | |
'barrel_percent':'Barrel%', | |
'zone_contact_percent':'Z-Contact%', | |
'zone_swing_percent':'Z-Swing%', | |
'zone_percent':'Zone%', | |
'chase_percent':'O-Swing%', | |
'chase_contact':'O-Contact%', | |
'swing_percent':'Swing%', | |
'whiff_rate':'Whiff%', | |
'swstr_rate':'SwStr%', | |
} | |
colour_palette = ['#FFB000','#648FFF','#785EF0', | |
'#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED'] | |
level_dict = {'MLB':'MLB','AAA':'AAA','AA':'AA','A+':'A+','A':'A','ROK':'ROK'} | |
batter_test_df = exit_velo_df_codes_summ_batter.sort_values(by='batter').drop_duplicates(subset='batter_id').reset_index(drop=True)[['batter_id','batter']]#['pitcher'].to_dict() | |
batter_test_df = batter_test_df.set_index('batter_id') | |
def decimal_format_assign(x): | |
if x['decimal_format'] == 'percent_1': | |
return mtick.PercentFormatter(1,decimals=1) | |
if x['decimal_format'] == 'string_3': | |
return mtick.FormatStrFormatter('%.3f') | |
if x['decimal_format'] == 'string_0': | |
return mtick.FormatStrFormatter('%.0f') | |
if x['decimal_format'] == 'string_1': | |
return mtick.FormatStrFormatter('%.1f') | |
#test_df = test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt']) | |
batter_dict = batter_test_df['batter'].to_dict() | |
exit_velo_df_codes_summ_batter.position = exit_velo_df_codes_summ_batter.position.replace(['LF','RF','CF','TWP'],['OF','OF','OF','DH']) | |
exit_velo_df_codes_summ_non_level.position = exit_velo_df_codes_summ_non_level.position.replace(['LF','RF','CF','TWP'],['OF','OF','OF','DH']) | |
position_list = ['All'] + list(exit_velo_df_codes_summ_batter.position.unique()) | |
team_list = ['All'] + sorted(list(exit_velo_df_codes_summ_batter.parent_org_abb.unique())) | |
def server(input,output,session): | |
def plot(): | |
sns.set_theme(style="whitegrid", palette="pastel") | |
print(input.level_id()) | |
print(input.n()) | |
print('we made it here',input.team_id(),input.position_id()) | |
if input.group_level(): | |
data_df = exit_velo_df_codes_summ_non_level.copy() | |
turth_list = [] | |
#turth_list_2 = [] | |
for x in range(0,len(data_df.level)): | |
turth_list_2 = [] | |
for y in range(0,len(data_df.level[x])): | |
#print(level_list[x][y]) | |
turth_list_2.append(data_df.level[x][y] in input.level_id()) | |
turth_list.append(turth_list_2) | |
final_check_list = [True if True in x else False for x in turth_list] | |
data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())&(final_check_list)] | |
else: | |
data_df = exit_velo_df_codes_summ_batter.copy() | |
data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())&(data_df.level.isin(input.level_id()))] | |
print(data_df) | |
if 'All' in input.team_id(): | |
print('nice')#data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())].reset_index(drop=True) | |
else: | |
data_df = data_df[(data_df.parent_org_abb.isin(input.team_id()))].reset_index(drop=True) | |
if 'All' in input.position_id(): | |
print('nice')#data_df = data_df[(data_df.level.isin(input.level_id()))&(data_df.pa >= input.n())&(data_df.age <= input.n_age())].reset_index(drop=True) | |
else: | |
data_df = data_df[(data_df.position.isin(input.position_id()))].reset_index(drop=True) | |
#print('we made it here') | |
print(data_df) | |
data_df = data_df.sort_values(by='level').reset_index(drop=True) | |
print(batter_dict_stat[input.stat_x()]['flip_p']) | |
x_flip = batter_dict_stat[input.stat_x()]['flip_p'] | |
y_flip = batter_dict_stat[input.stat_y()]['flip_p'] | |
cbr_flip = batter_dict_stat[input.stat_z()]['flip_p'] | |
data_df[input.stat_x()+'_percent'] = data_df[input.stat_x()].rank(pct=True,ascending=abs(x_flip-1)) | |
data_df[input.stat_y()+'_percent'] = data_df[input.stat_y()].rank(pct=True,ascending=abs(y_flip-1)) | |
data_df[input.stat_z()+'_percent'] = data_df[input.stat_z()].rank(pct=True,ascending=abs(cbr_flip-1)) | |
fig, ax = plt.subplots(1, 1, figsize=(9, 9)) | |
#data_df['bb_over_obp'] = data_df['bb']/data_df['k'] | |
#data_df[input.stat_z()]= data_df[input.stat_z()].fillna(-100000) | |
if cbr_flip: | |
cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[0],colour_palette[3],colour_palette[1]]) | |
norm = plt.Normalize(data_df[input.stat_z()].min(), data_df[input.stat_z()].max()) | |
else: | |
cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[1],colour_palette[3],colour_palette[0]]) | |
norm = plt.Normalize(data_df[input.stat_z()].min(), data_df[input.stat_z()].max()) | |
sm = plt.cm.ScalarMappable(cmap=cmap_hue, norm=norm) | |
print('we made it here') | |
# sns.regplot(x = stat_x, y = stat_y, data=data_df, color = colour_palette[6],ax=ax,scatter=False, | |
# line_kws=dict(alpha=0.3,linewidth=2,zorder=1)) | |
# scatter_plot = sns.scatterplot(x = stat_x, y = stat_y, data=data_df, color = colour_palette[0],ax=ax,hue=stat_z,palette=cmap_hue) | |
# r, p = sp.stats.pearsonr(data_df[input.stat_x()], data_df[input.stat_y()]) | |
# ax = plt.gca() | |
# # ax.text(.25, 0.3, 'r={:.2f}, p={:.2g}'.format(r, p), | |
# # transform=ax.transAxes, fontsize=12) | |
# ax.annotate('R²={:.2f}'.format(r, p), ( math.ceil(data_df[input.stat_x()].max()*batter_dict_stat[input.stat_x()]['percent_adjust']/5)*5/batter_dict_stat[input.stat_x()]['percent_adjust']*(1-batter_dict_stat[input.stat_x()]['flip_p']), | |
# math.floor(data_df[input.stat_y()].min()*batter_dict_stat[input.stat_y()]['percent_adjust']/5)*5/batter_dict_stat[input.stat_y()]['percent_adjust']*(1-batter_dict_stat[input.stat_y()]['flip_p'])), | |
# fontsize=18,fontname='Century Gothic',ha='right') | |
if input.group_level(): | |
scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = '#b3b3b3') | |
#ax.get_legend().remove() | |
scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = colour_palette[0],ax=ax,hue=input.stat_z(),palette=cmap_hue) | |
else: | |
scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = '#b3b3b3',style='level') | |
#ax.get_legend().remove() | |
scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = colour_palette[0],ax=ax,hue=input.stat_z(),palette=cmap_hue,style='level') | |
sns.set_theme(style="whitegrid", palette="pastel") | |
fig.set_facecolor('#F0F0F0') | |
ax.set_facecolor('white') | |
print('we made it here') | |
# for i in range(0,len(pitch_group_unique)): | |
# data_df = elly_zone_df[elly_zone_df.pitch_group==pitch_group_unique[i]] | |
# len_df.append(len(data_df)) | |
# sns.lineplot(x=range(1,len(data_df)+1),y=data_df.swings.rolling(window=rolling_window_input).sum()/data_df.pitches.rolling(window=rolling_window_input).sum(),color=colour_palette[i],linewidth=3,ax=ax, | |
# label=f'{pitch_group_unique[i]} (Season Average {float(data_df.swings.sum()/data_df.pitches.sum()):.1%})',zorder=i+10) | |
# ax.hlines(xmin=0,xmax=len(elly_zone_df),y=data_df.swings.sum()/data_df.pitches.sum(),color=colour_palette[i],linewidth=3,linestyle='-.',alpha=0.4,zorder=i) | |
ts=[] | |
print(input.player_id()) | |
print(len(data_df)) | |
if input.names(): | |
for i in range(len(data_df)): | |
if (data_df[input.stat_x()+'_percent'].values[i] < input.n_percent_bot_x() or data_df[input.stat_x()+'_percent'].values[i] > 1 - input.n_percent_top_x() ) \ | |
or (data_df[input.stat_y()+'_percent'].values[i] < input.n_percent_bot_y() or data_df[input.stat_y()+'_percent'].values[i] > 1 -input.n_percent_top_y()) \ | |
or (data_df[input.stat_z()+'_percent'].values[i] < input.n_percent_bot_z() or data_df[input.stat_z()+'_percent'].values[i] > 1 -input.n_percent_top_z() )\ | |
or (str(data_df.batter_id[i]) in (input.player_id())): | |
# print(data_df.batter[i]) | |
# ax.annotate(data_df.batter[i], xy=((data_df[input.stat_x()][i])+0.025/batter_dict_stat[input.stat_x()]['percent_adjust'], data_df[input.stat_y()][i]+0.01/batter_dict_stat[input.stat_x()]['percent_adjust']), xytext=(-20,20), | |
# textcoords='offset points', ha='center', va='bottom',fontsize=7, | |
# bbox=dict(boxstyle='round,pad=0', fc=colour_palette[6], alpha=0.0), | |
# arrowprops=dict(arrowstyle='->', connectionstyle="angle,angleA=-90,angleB=-10,rad=2", | |
# color=colour_palette[8])) | |
#if data_df['batter'][i] != 'Jo Adell': | |
# ax.annotate(data_df.batter[i], (data_df[input.stat_x()][i]-len(data_df.batter[i])*0.00025, data_df[input.stat_y()][i]+0.001),fontsize=8) | |
ts.append(ax.text(data_df[input.stat_x()][i], data_df[input.stat_y()][i], data_df.batter[i],fontsize=8)) | |
ax.hlines(xmin=(math.floor((data_df[input.stat_x()].min()*batter_dict_stat[input.stat_x()]['percent_adjust']-0.01)/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'], | |
xmax= (math.ceil((data_df[input.stat_x()].max()*batter_dict_stat[input.stat_x()]['percent_adjust']+0.01)/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'], | |
y=data_df[input.stat_y()].mean(),color='gray',linewidth=3,linestyle='dotted',alpha=0.4) | |
print('we made it here') | |
ax.vlines(ymin=(math.floor((data_df[input.stat_y()].min()*batter_dict_stat[input.stat_y()]['percent_adjust']-0.01)/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'], | |
ymax= (math.ceil((data_df[input.stat_y()].max()*batter_dict_stat[input.stat_y()]['percent_adjust']+0.01)/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'], | |
x=data_df[input.stat_x()].mean(),color='gray',linewidth=3,linestyle='dotted',alpha=0.4) | |
print(data_df[input.stat_x()].min()) | |
print(batter_dict_stat[input.stat_x()]['percent_adjust']) | |
print((math.floor((data_df[input.stat_x()].min()*batter_dict_stat[input.stat_x()]['percent_adjust']-0.01)/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust']) | |
ax.set_xlim((math.floor((data_df[input.stat_x()].min()*batter_dict_stat[input.stat_x()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'], | |
(math.ceil((data_df[input.stat_x()].max()*batter_dict_stat[input.stat_x()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust']) | |
ax.set_ylim((math.floor((data_df[input.stat_y()].min()*batter_dict_stat[input.stat_y()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'], | |
(math.ceil((data_df[input.stat_y()].max()*batter_dict_stat[input.stat_y()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust']) | |
title_level = str([x .strip("\'")for x in input.level_id()]).strip('[').strip(']').replace("'",'') | |
if title_level == 'AAA, AA, A+, A': | |
title_level='MiLB' | |
#title_level = input.level_id()[0] | |
if input.n_age() >= 50: | |
title_spot = f'{title_level} Batter {batter_dict_stat[input.stat_y()]["title"]} vs {batter_dict_stat[input.stat_x()]["title"]} (min. {input.n()} PA)' | |
else: | |
title_spot = f'{title_level} Batter {batter_dict_stat[input.stat_y()]["title"]} vs {batter_dict_stat[input.stat_x()]["title"]} (min. {input.n()} PA, Max Age {input.n_age()})' | |
ax.set_title(title_spot, fontsize=24/(len(title_spot)*0.03),fontname='Century Gothic') | |
# #vals = ax.get_yticks() | |
ax.set_xlabel(batter_dict_stat[input.stat_x()]['x_axis'], fontsize=16,fontname='Century Gothic') | |
ax.set_ylabel(batter_dict_stat[input.stat_y()]['x_axis'], fontsize=16,fontname='Century Gothic') | |
if input.group_level(): | |
ax.get_legend().remove() | |
if not input.group_level(): | |
if len(input.level_id()) > 1: | |
h,l = scatter.get_legend_handles_labels() | |
l[-(len(input.level_id())+1)] = 'Level' | |
ax.legend(h[-(len(input.level_id())+1):],l[-(len(input.level_id())+1):], borderaxespad=0.1,loc=0) | |
else: | |
ax.get_legend().remove() | |
#plt.show(g) | |
# ax.figure.colorbar(sm, ax=ax) | |
cbar = ax.figure.colorbar(sm, ax=ax,format=decimal_format_assign(x=batter_dict_stat[input.stat_z()]),orientation='vertical',aspect=30) | |
cbar.set_label(batter_dict_stat[input.stat_z()]['x_axis']) | |
#fig.axes[0].invert_yaxis() | |
print('we made it here5') | |
fig.subplots_adjust(wspace=.02, hspace=.02) | |
# ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x))) | |
#ax.set_yticks([0,0.1,0.2,0.3,0.4,0.5]) | |
# fig.colorbar(plot_dist, ax=ax) | |
# fig.colorbar(plot_dist) | |
if batter_dict_stat[input.stat_x()]['flip_p']: | |
fig.axes[0].invert_xaxis() | |
if batter_dict_stat[input.stat_y()]['flip_p']: | |
fig.axes[0].invert_yaxis() | |
# ax.xaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=0)) | |
# ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) | |
print('we made it here6') | |
ax.xaxis.set_major_formatter(decimal_format_assign(x=batter_dict_stat[input.stat_x()])) | |
ax.yaxis.set_major_formatter(decimal_format_assign(x=batter_dict_stat[input.stat_y()])) | |
print('we made it here7') | |
# ax.text(0.5, 0.5, '/u/tomstoms', transform=ax.transAxes, | |
# fontsize=60, color='gray', alpha=0.075, | |
# ha='center', va='center', rotation=45) | |
print(ts) | |
if len(ts) > 0: | |
adjust_text(ts, | |
arrowprops=dict(arrowstyle="-", color=colour_palette[4], lw=1),ax=ax) | |
#ax.legend(fontsize='16') | |
fig.text(x=0.03,y=0.02,s='By: @TJStats',fontname='Century Gothic') | |
fig.text(x=1-0.03,y=0.02,s='Data: MLB',ha='right',fontname='Century Gothic') | |
fig.tight_layout() | |
#matplotlib.rcParams["figure.dpi"] = 600 | |
#plt.show() | |
batter_scatter = App(ui.page_fluid( | |
ui.tags.base(href=base_url), | |
ui.tags.div( | |
{"style": "width:90%;margin: 0 auto;max-width: 1600px;"}, | |
ui.tags.style( | |
""" | |
h4 { | |
margin-top: 1em;font-size:35px; | |
} | |
h2{ | |
font-size:25px; | |
} | |
""" | |
), | |
shinyswatch.theme.simplex(), | |
ui.tags.h4("TJStats"), | |
ui.tags.i("Baseball Analytics and Visualizations"), | |
ui.markdown("""<a href='https://www.patreon.com/tj_stats'>Support me on Patreon for Access to 2024 Apps</a><sup>1</sup>"""), | |
ui.navset_tab( | |
ui.nav_control( | |
ui.a( | |
"Home", | |
href="home/" | |
), | |
), | |
ui.nav_menu( | |
"Batter Charts", | |
ui.nav_control( | |
ui.a( | |
"Batting Rolling", | |
href="rolling_batter/" | |
), | |
ui.a( | |
"Spray & Damage", | |
href="spray/" | |
), | |
ui.a( | |
"Decision Value", | |
href="decision_value/" | |
), | |
# ui.a( | |
# "Damage Model", | |
# href="damage_model/" | |
# ), | |
ui.a( | |
"Batter Scatter", | |
href="batter_scatter/" | |
), | |
# ui.a( | |
# "EV vs LA Plot", | |
# href="ev_angle/" | |
# ), | |
ui.a( | |
"Statcast Compare", | |
href="statcast_compare/" | |
) | |
), | |
), | |
ui.nav_menu( | |
"Pitcher Charts", | |
ui.nav_control( | |
ui.a( | |
"Pitcher Rolling", | |
href="rolling_pitcher/" | |
), | |
ui.a( | |
"Pitcher Summary", | |
href="pitching_summary_graphic_new/" | |
), | |
ui.a( | |
"Pitcher Scatter", | |
href="pitcher_scatter/" | |
) | |
), | |
)),ui.row( | |
ui.layout_sidebar( | |
ui.panel_sidebar( | |
#ui.input_select("id", "Select Batter",batter_dict,selected=675911,width=1,size=1), | |
ui.row( | |
ui.column(4,ui.input_select("level_id", "Select Level",level_dict,width=1,size=1,multiple=True,selected='MLB',selectize=True),), | |
ui.column(4,ui.input_select("team_id", "Select Team",team_list,width=1,size=1,multiple=True,selected='All',selectize=True),), | |
ui.column(4,ui.input_select("position_id", "Select Position",position_list,width=1,size=1,selected='All',multiple=True,selectize=True))), | |
ui.row( | |
ui.column(6,ui.input_numeric("n", "Minimum PA", value=100)), | |
ui.column(6,ui.input_numeric("n_age", "Maximum Age", value=50))), | |
ui.row( | |
ui.column(4,ui.input_select("stat_x", "X-Axis",batter_dict_stat_small,selected='k_percent',width=1,size=1)), | |
ui.column(4,ui.input_select("stat_y", "Y-Axis",batter_dict_stat_small,selected='bb_percent',width=1,size=1)), | |
ui.column(4,ui.input_select("stat_z", "Colour-Bar Axis",batter_dict_stat_small,selected='bb_over_k_percent',width=1,size=1))), | |
ui.row( | |
ui.column(6,ui.input_numeric("n_percent_top_x", "Top 'n' Percentile X-Labels", value=0.01)), | |
ui.column(6,ui.input_numeric("n_percent_bot_x", "Bottom 'n' Percentile X-Labels", value=0.01))), | |
ui.row( | |
ui.column(6,ui.input_numeric("n_percent_top_y", "Top 'n' Percentile Y-Labels", value=0.01)), | |
ui.column(6,ui.input_numeric("n_percent_bot_y", "Bottom 'n' Percentile Y-Labels", value=0.01))), | |
ui.row( | |
ui.column(6,ui.input_numeric("n_percent_top_z", "Top 'n' Percentile Z-Labels", value=0.01)), | |
ui.column(6,ui.input_numeric("n_percent_bot_z", "Bottom 'n' Percentile Z-Labels", value=0.01))), | |
ui.input_select("player_id", "Label Player",batter_dict,width=1,size=1,multiple=True,selectize=True), | |
ui.row( | |
ui.input_switch("names", "Toggle Names"), | |
ui.input_switch("group_level", "Group Levels")), | |
ui.input_action_button("go", "Generate",class_="btn-primary"), | |
), | |
ui.panel_main( | |
ui.output_plot("plot",height = "1000px",width="1000px") | |
, | |
), | |
)),)),server) |