Initial commit and update

.streamlit/secrets.toml

@@ -0,0 +1 @@
+NBA_DATA="https://sheetdb.io/api/v1/mh9ep4ovqs45h"

Dockerfile

@@ -5,11 +5,25 @@ WORKDIR /app
 RUN apt-get update && apt-get install -y \
     build-essential \
     curl \
+    software-properties-common \
     git \
     && rm -rf /var/lib/apt/lists/*
 
 COPY requirements.txt ./
 COPY src/ ./src/
+COPY .streamlit/ ./.streamlit/
+
+
+
+ENV MONGO_URI="mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"
+ENV NBA_DATA="https://sheetdb.io/api/v1/mh9ep4ovqs45h"
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user\
+	PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+RUN pip install --no-cache-dir --upgrade pip
+COPY --chown=user . $HOME/app
 
 RUN pip3 install -r requirements.txt
 

requirements.txt

@@ -1,3 +1,8 @@
-altair
-pandas
-streamlit
+streamlit
+openpyxl
+matplotlib
+pulp
+docker
+plotly
+scipy
+pymongo

src/streamlit_app.py

@@ -1,40 +1,144 @@
-import altair as alt
+import streamlit as st
+st.set_page_config(layout="wide")
+
+for name in dir():
+    if not name.startswith('_'):
+        del globals()[name]
+
 import numpy as np
 import pandas as pd
 import streamlit as st
+import os
+import requests
+
+NBA_DATA = os.getenv('NBA_DATA')
+
+percentages_format = {'Pts% Boost': '{:.2%}', 'Reb% Boost': '{:.2%}', 'Ast% Boost': '{:.2%}', '3p% Boost': '{:.2%}',
+                      'Stl Boost%': '{:.2%}', 'Blk Boost%': '{:.2%}', 'TOV Boost%': '{:.2%}', 'FPPM Boost': '{:.2%}',
+                      'Team FPPM Boost': '{:.2%}'}
+
+@st.cache_resource(ttl = 600)
+def init_baselines():
+    
+    json_matchups = requests.get(NBA_DATA + '?sheet=DEM%20Matchups').json()
+    raw_display = pd.DataFrame(json_matchups)
+    raw_display = raw_display.reset_index(drop=True)
+    matchups = raw_display[raw_display['Var'] != ""]
+    matchups_dict = dict(zip(matchups['Team'], matchups['Opp']))
+    
+    json_pg_dem = requests.get(NBA_DATA + '?sheet=PG_DEM_Calc').json()
+    raw_display = pd.DataFrame(json_pg_dem)
+    raw_display = raw_display.reset_index(drop=True)
+    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
+    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
+    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
+    raw_display['position'] = 'Point Guard'
+    pg_dem = raw_display[raw_display['Acro'] != ""]
+
+    json_sg_dem = requests.get(NBA_DATA + '?sheet=SG_DEM_Calc').json()
+    raw_display = pd.DataFrame(json_sg_dem)
+    raw_display = raw_display.reset_index(drop=True)
+    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
+    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
+    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
+    raw_display['position'] = 'Shooting Guard'
+    sg_dem = raw_display[raw_display['Acro'] != ""]
+    
+    json_sf_dem = requests.get(NBA_DATA + '?sheet=SF_DEM_Calc').json()
+    raw_display = pd.DataFrame(json_sf_dem)
+    raw_display = raw_display.reset_index(drop=True)
+    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
+    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
+    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
+    raw_display['position'] = 'Small Forward'
+    sf_dem = raw_display[raw_display['Acro'] != ""]
+    
+    json_pf_dem = requests.get(NBA_DATA + '?sheet=PF_DEM_Calc').json()
+    raw_display = pd.DataFrame(json_pf_dem)
+    raw_display = raw_display.reset_index(drop=True)
+    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
+    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
+    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
+    raw_display['position'] = 'Power Forward'
+    pf_dem = raw_display[raw_display['Acro'] != ""]
+    
+    json_c_dem = requests.get(NBA_DATA + '?sheet=C_DEM_Calc').json()
+    raw_display = pd.DataFrame(json_c_dem)
+    raw_display = raw_display.reset_index(drop=True)
+    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
+    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
+    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
+    raw_display['position'] = 'Center'
+    c_dem = raw_display[raw_display['Acro'] != ""]
+    
+    overall_dem = pd.concat([pg_dem, sg_dem, sf_dem, pf_dem, c_dem])
+    overall_dem = overall_dem[['Acro', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
+                               'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'position']]
+    overall_dem['Team'] = overall_dem['Acro'] + '-' + overall_dem['position']
+    overall_dem['Team FPPM Boost'] = overall_dem.groupby('Acro', sort=False)['FPPM Boost'].transform('mean')
+    overall_dem = overall_dem.reset_index()
+    
+    
+    export_dem = overall_dem[['Team', 'Acro', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
+                               'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'Team FPPM Boost', 'position']]
+    
+
+    return export_dem, matchups, matchups_dict
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+overall_dem, matchups, matchups_dict = init_baselines()
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+col1, col2 = st.columns([1, 9])
+with col1:
+    if st.button("Reset Data", key='reset1'):
+              st.cache_data.clear()
+              overall_dem, matchups, matchups_dict = init_baselines()
+    split_var1 = st.radio("View all teams or just this main slate's matchups?", ('Slate Matchups', 'All'), key='split_var1')
+    if split_var1 == 'Slate Matchups':
+        view_var1 = matchups.Opp.values.tolist()
+        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
+        if split_var2 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
+        elif split_var2 == 'All':
+            team_var1 = view_var1
+        split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3')
+        if split_var3 == 'Specific Positions':
+            pos_var1 = st.multiselect('Which teams would you like to include in the tables?', options = overall_dem['position'].unique(), key='pos_var1')
+        elif split_var3 == 'All':
+            pos_var1 = overall_dem.position.values.tolist()
+    if split_var1 == 'All':
+        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
+        if split_var2 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = overall_dem['Acro'].unique(), key='team_var1')
+        elif split_var2 == 'All':
+            team_var1 = overall_dem.Acro.values.tolist()
+        split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3')
+        if split_var3 == 'Specific Positions':
+            pos_var1 = st.multiselect('Which teams would you like to include in the tables?', options = overall_dem['position'].unique(), key='pos_var1')
+        elif split_var3 == 'All':
+            pos_var1 = overall_dem.position.values.tolist()
+with col2:
+    if split_var1 == 'Slate Matchups':
+        dem_display = overall_dem[overall_dem['Acro'].isin(view_var1)]
+        dem_display['Team (Getting Boost)'] = dem_display['Acro'].map(matchups_dict)
+        dem_display.rename(columns={"Acro": "Opp (Giving Boost)"}, inplace = True)
+        dem_display = dem_display[['Team (Getting Boost)', 'Opp (Giving Boost)', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
+                                   'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'Team FPPM Boost', 'position']]
+        dem_display = dem_display[dem_display['Team (Getting Boost)'].isin(team_var1)]
+        dem_display = dem_display[dem_display['position'].isin(pos_var1)]
+        dem_display = dem_display.sort_values(by='FPPM Boost', ascending=False)
+    elif split_var1 == 'All':
+        dem_display = overall_dem[overall_dem['Acro'].isin(team_var1)]
+        dem_display = dem_display[dem_display['position'].isin(pos_var1)]
+        dem_display = dem_display.sort_values(by='FPPM Boost', ascending=False)
+        dem_display.rename(columns={"Team": "Team (Giving Boost)"}, inplace = True)
+        dem_display = dem_display.set_index('Team (Giving Boost)')
+    st.dataframe(dem_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+    st.download_button(
+        label="Export DEM Numbers",
+        data=convert_df_to_csv(overall_dem),
+        file_name='DEM_export.csv',
+        mime='text/csv',
+    )