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Update app.py
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app.py
CHANGED
@@ -512,11 +512,10 @@ def get_uncorrelated_portfolio_for_sim(Total_Sample_Size):
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return RandomPortfolio, maps_dict
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-
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dk_roo_raw = load_dk_player_projections()
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fd_roo_raw = load_fd_player_projections()
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t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
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site_slates = set_slate_teams()
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dkid_dict, fdid_dict = set_export_ids()
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static_exposure = pd.DataFrame(columns=['Player', 'count'])
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@@ -815,6 +814,8 @@ with tab1:
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overall_exposure = overall_exposure.sort_values(by='Exposure', ascending=False)
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overall_exposure['Exposure'] = overall_exposure['Exposure'].astype(float).map(lambda n: '{:.2%}'.format(n))
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with st.container():
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col1, col2 = st.columns([1, 6])
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@@ -865,7 +866,6 @@ with tab2:
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dk_roo_raw = load_dk_player_projections()
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fd_roo_raw = load_fd_player_projections()
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t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
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site_slates = set_slate_teams()
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dkid_dict, fdid_dict = set_export_ids()
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slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate', 'User'))
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@@ -882,6 +882,8 @@ with tab2:
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elif slate_var1 != 'User':
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raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var1)]
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raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
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st.info("If you are uploading a portfolio, note that there is an adjustments to projections and deviation mapping to prevent 'Projection Bias' and create a fair simulation")
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insert_port1 = st.selectbox("Are you uploading a portfolio?", ('No', 'Yes'), key='insert_port1')
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if insert_port1 == 'Yes':
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@@ -1248,10 +1250,10 @@ with tab2:
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SimVar += 1
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-
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# del best_lineup
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st.write('Contest simulation complete')
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# Initial setup
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@@ -1276,6 +1278,9 @@ with tab2:
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elif site_var1 == 'Fanduel':
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replace_dict = fdid_dict
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for col in columns_to_replace:
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st.session_state.Sim_Winner_Export[col].replace(replace_dict, inplace=True)
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return RandomPortfolio, maps_dict
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+
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dk_roo_raw = load_dk_player_projections()
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fd_roo_raw = load_fd_player_projections()
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t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
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dkid_dict, fdid_dict = set_export_ids()
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static_exposure = pd.DataFrame(columns=['Player', 'count'])
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overall_exposure = overall_exposure.sort_values(by='Exposure', ascending=False)
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overall_exposure['Exposure'] = overall_exposure['Exposure'].astype(float).map(lambda n: '{:.2%}'.format(n))
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del static_exposure
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with st.container():
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col1, col2 = st.columns([1, 6])
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dk_roo_raw = load_dk_player_projections()
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fd_roo_raw = load_fd_player_projections()
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t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
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dkid_dict, fdid_dict = set_export_ids()
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slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate', 'User'))
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elif slate_var1 != 'User':
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raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var1)]
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raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
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del dk_roo_raw
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del fd_roo_raw
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st.info("If you are uploading a portfolio, note that there is an adjustments to projections and deviation mapping to prevent 'Projection Bias' and create a fair simulation")
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insert_port1 = st.selectbox("Are you uploading a portfolio?", ('No', 'Yes'), key='insert_port1')
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if insert_port1 == 'Yes':
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SimVar += 1
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del vec_projection_map
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del vec_stdev_map
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del sample_arrays
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del final_array
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# del best_lineup
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st.write('Contest simulation complete')
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# Initial setup
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elif site_var1 == 'Fanduel':
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replace_dict = fdid_dict
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del dkid_dict
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del fdid_dict
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for col in columns_to_replace:
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st.session_state.Sim_Winner_Export[col].replace(replace_dict, inplace=True)
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