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import streamlit as st |
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st.set_page_config(layout="wide") |
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import numpy as np |
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import pandas as pd |
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import gspread |
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import pymongo |
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import time |
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@st.cache_resource |
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def init_conn(): |
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scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive'] |
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credentials = { |
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"type": "service_account", |
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"project_id": "model-sheets-connect", |
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"private_key_id": st.secrets['model_sheets_connect_pk'], |
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"private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvgIBADANBgkqhkiG9w0BAQEFAASCBKgwggSkAgEAAoIBAQDiu1v/e6KBKOcK\ncx0KQ23nZK3ZVvADYy8u/RUn/EDI82QKxTd/DizRLIV81JiNQxDJXSzgkbwKYEDm\n48E8zGvupU8+Nk76xNPakrQKy2Y8+VJlq5psBtGchJTuUSHcXU5Mg2JhQsB376PJ\nsCw552K6Pw8fpeMDJDZuxpKSkaJR6k9G5Dhf5q8HDXnC5Rh/PRFuKJ2GGRpX7n+2\nhT/sCax0J8jfdTy/MDGiDfJqfQrOPrMKELtsGHR9Iv6F4vKiDqXpKfqH+02E9ptz\nBk+MNcbZ3m90M8ShfRu28ebebsASfarNMzc3dk7tb3utHOGXKCf4tF8yYKo7x8BZ\noO9X4gSfAgMBAAECggEAU8ByyMpSKlTCF32TJhXnVJi/kS+IhC/Qn5JUDMuk4LXr\naAEWsWO6kV/ZRVXArjmuSzuUVrXumISapM9Ps5Ytbl95CJmGDiLDwRL815nvv6k3\nUyAS8EGKjz74RpoIoH6E7EWCAzxlnUgTn+5oP9Flije97epYk3H+e2f1f5e1Nn1d\nYNe8U+1HqJgILcxA1TAUsARBfoD7+K3z/8DVPHI8IpzAh6kTHqhqC23Rram4XoQ6\nzj/ZdVBjvnKuazETfsD+Vl3jGLQA8cKQVV70xdz3xwLcNeHsbPbpGBpZUoF73c65\nkAXOrjYl0JD5yAk+hmYhXr6H9c6z5AieuZGDrhmlFQKBgQDzV6LRXmjn4854DP/J\nI82oX2GcI4eioDZPRukhiQLzYerMQBmyqZIRC+/LTCAhYQSjNgMa+ZKyvLqv48M0\n/x398op/+n3xTs+8L49SPI48/iV+mnH7k0WI/ycd4OOKh8rrmhl/0EWb9iitwJYe\nMjTV/QxNEpPBEXfR1/mvrN/lVQKBgQDuhomOxUhWVRVH6x03slmyRBn0Oiw4MW+r\nrt1hlNgtVmTc5Mu+4G0USMZwYuOB7F8xG4Foc7rIlwS7Ic83jMJxemtqAelwOLdV\nXRLrLWJfX8+O1z/UE15l2q3SUEnQ4esPHbQnZowHLm0mdL14qSVMl1mu1XfsoZ3z\nJZTQb48CIwKBgEWbzQRtKD8lKDupJEYqSrseRbK/ax43DDITS77/DWwHl33D3FYC\nMblUm8ygwxQpR4VUfwDpYXBlklWcJovzamXpSnsfcYVkkQH47NuOXPXPkXQsw+w+\nDYcJzeu7F/vZqk9I7oBkWHUrrik9zPNoUzrfPvSRGtkAoTDSwibhoc5dAoGBAMHE\nK0T/ANeZQLNuzQps6S7G4eqjwz5W8qeeYxsdZkvWThOgDd/ewt3ijMnJm5X05hOn\ni4XF1euTuvUl7wbqYx76Wv3/1ZojiNNgy7ie4rYlyB/6vlBS97F4ZxJdxMlabbCW\n6b3EMWa4EVVXKoA1sCY7IVDE+yoQ1JYsZmq45YzPAoGBANWWHuVueFGZRDZlkNlK\nh5OmySmA0NdNug3G1upaTthyaTZ+CxGliwBqMHAwpkIRPwxUJpUwBTSEGztGTAxs\nWsUOVWlD2/1JaKSmHE8JbNg6sxLilcG6WEDzxjC5dLL1OrGOXj9WhC9KX3sq6qb6\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n", |
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"client_email": "[email protected]", |
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"client_id": "100369174533302798535", |
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"auth_uri": "https://accounts.google.com/o/oauth2/auth", |
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"token_uri": "https://oauth2.googleapis.com/token", |
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"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", |
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"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com" |
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} |
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credentials2 = { |
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"type": "service_account", |
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"project_id": "sheets-api-connect-378620", |
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"private_key_id": st.secrets['sheets_api_connect_pk'], |
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"private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", |
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"client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", |
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"client_id": "106625872877651920064", |
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"auth_uri": "https://accounts.google.com/o/oauth2/auth", |
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"token_uri": "https://oauth2.googleapis.com/token", |
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"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", |
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"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" |
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} |
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uri = st.secrets['mongo_uri'] |
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client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) |
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db = client["testing_db"] |
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NFL_Data = st.secrets['NFL_Data'] |
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gc = gspread.service_account_from_dict(credentials) |
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gc2 = gspread.service_account_from_dict(credentials2) |
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return gc, gc2, db, NFL_Data |
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gcservice_account, gcservice_account2, db, NFL_Data = init_conn() |
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percentages_format = {'Exposure': '{:.2%}'} |
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freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'} |
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dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count'] |
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fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count'] |
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@st.cache_data(ttl = 599) |
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def init_DK_seed_frames(): |
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collection = db["DK_NFL_SD_seed_frame"] |
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cursor = collection.find() |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']] |
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DK_seed = raw_display.to_numpy() |
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return DK_seed |
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@st.cache_data(ttl = 599) |
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def init_FD_seed_frames(): |
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collection = db["FD_NFL_SD_seed_frame"] |
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cursor = collection.find() |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']] |
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FD_seed = raw_display.to_numpy() |
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return FD_seed |
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@st.cache_data(ttl = 599) |
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def init_baselines(): |
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try: |
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sh = gcservice_account.open_by_url(NFL_Data) |
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except: |
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sh = gcservice_account2.open_by_url(NFL_Data) |
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worksheet = sh.worksheet('DK_SD_ROO') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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load_display['STDev'] = load_display['Median'] / 4 |
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load_display = load_display.drop_duplicates(subset=['Player'], keep='first') |
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dk_raw = load_display.dropna(subset=['Median']) |
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worksheet = sh.worksheet('FD_SD_ROO') |
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load_display = pd.DataFrame(worksheet.get_all_records()) |
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load_display.replace('', np.nan, inplace=True) |
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load_display['STDev'] = load_display['Median'] / 4 |
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load_display = load_display.drop_duplicates(subset=['Player'], keep='first') |
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fd_raw = load_display.dropna(subset=['Median']) |
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return dk_raw, fd_raw |
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@st.cache_data |
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def convert_df(array): |
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array = pd.DataFrame(array, columns=column_names) |
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return array.to_csv().encode('utf-8') |
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@st.cache_data |
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def calculate_DK_value_frequencies(np_array): |
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unique, counts = np.unique(np_array[:, :6], return_counts=True) |
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frequencies = counts / len(np_array) |
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combined_array = np.column_stack((unique, frequencies)) |
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return combined_array |
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@st.cache_data |
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def calculate_FD_value_frequencies(np_array): |
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unique, counts = np.unique(np_array[:, :5], return_counts=True) |
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frequencies = counts / len(np_array) |
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combined_array = np.column_stack((unique, frequencies)) |
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return combined_array |
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@st.cache_data |
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def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size): |
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SimVar = 1 |
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Sim_Winners = [] |
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fp_array = seed_frame[:sharp_split, :] |
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vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__) |
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vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) |
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st.write('Simulating contest on frames') |
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while SimVar <= Sim_size: |
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fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)] |
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sample_arrays1 = np.c_[ |
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fp_random, |
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np.sum(np.random.normal( |
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loc=vec_projection_map(fp_random[:, :-6]), |
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scale=vec_stdev_map(fp_random[:, :-6])), |
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axis=1) |
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] |
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sample_arrays = sample_arrays1 |
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final_array = sample_arrays[sample_arrays[:, 7].argsort()[::-1]] |
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best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]] |
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Sim_Winners.append(best_lineup) |
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SimVar += 1 |
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return Sim_Winners |
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DK_seed = init_DK_seed_frames() |
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FD_seed = init_FD_seed_frames() |
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dk_raw, fd_raw = init_baselines() |
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tab1, tab2 = st.tabs(['Contest Sims', 'Data Export']) |
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with tab2: |
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col1, col2 = st.columns([1, 7]) |
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with col1: |
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if st.button("Load/Reset Data", key='reset1'): |
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st.cache_data.clear() |
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for key in st.session_state.keys(): |
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del st.session_state[key] |
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DK_seed = init_DK_seed_frames() |
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FD_seed = init_FD_seed_frames() |
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dk_raw, fd_raw = init_baselines() |
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slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Other Showdown')) |
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site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) |
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if site_var1 == 'Draftkings': |
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raw_baselines = dk_raw |
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column_names = dk_columns |
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team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1') |
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if team_var1 == 'Specific Teams': |
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team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique()) |
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elif team_var1 == 'Full Slate': |
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team_var2 = dk_raw.Team.values.tolist() |
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stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1') |
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if stack_var1 == 'Specific Stack Sizes': |
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stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0]) |
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elif stack_var1 == 'Full Slate': |
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stack_var2 = [5, 4, 3, 2, 1, 0] |
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elif site_var1 == 'Fanduel': |
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raw_baselines = fd_raw |
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column_names = fd_columns |
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team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1') |
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if team_var1 == 'Specific Teams': |
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team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique()) |
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elif team_var1 == 'Full Slate': |
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team_var2 = fd_raw.Team.values.tolist() |
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stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1') |
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if stack_var1 == 'Specific Stack Sizes': |
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stack_var2 = st.multiselect('Which stack sizes do you want?', options = [4, 3, 2, 1, 0]) |
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elif stack_var1 == 'Full Slate': |
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stack_var2 = [4, 3, 2, 1, 0] |
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if st.button("Prepare data export", key='data_export'): |
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data_export = st.session_state.working_seed.copy() |
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st.download_button( |
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label="Export optimals set", |
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data=convert_df(data_export), |
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file_name='NFL_SD_optimals_export.csv', |
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mime='text/csv', |
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) |
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with col2: |
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if st.button("Load Data", key='load_data'): |
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if site_var1 == 'Draftkings': |
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if 'working_seed' in st.session_state: |
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)] |
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)] |
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) |
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elif 'working_seed' not in st.session_state: |
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st.session_state.working_seed = DK_seed.copy() |
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)] |
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)] |
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) |
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elif site_var1 == 'Fanduel': |
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if 'working_seed' in st.session_state: |
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)] |
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)] |
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) |
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elif 'working_seed' not in st.session_state: |
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st.session_state.working_seed = FD_seed.copy() |
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)] |
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st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)] |
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) |
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with st.container(): |
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if 'data_export_display' in st.session_state: |
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st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True) |
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with tab1: |
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col1, col2 = st.columns([1, 7]) |
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with col1: |
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if st.button("Load/Reset Data", key='reset2'): |
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st.cache_data.clear() |
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for key in st.session_state.keys(): |
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del st.session_state[key] |
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DK_seed = init_DK_seed_frames() |
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FD_seed = init_FD_seed_frames() |
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dk_raw, fd_raw = init_baselines() |
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sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'), key='sim_slate_var1') |
|
|
sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1') |
|
|
if sim_site_var1 == 'Draftkings': |
|
|
raw_baselines = dk_raw |
|
|
column_names = dk_columns |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
raw_baselines = fd_raw |
|
|
column_names = fd_columns |
|
|
|
|
|
contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom')) |
|
|
if contest_var1 == 'Small': |
|
|
Contest_Size = 1000 |
|
|
elif contest_var1 == 'Medium': |
|
|
Contest_Size = 5000 |
|
|
elif contest_var1 == 'Large': |
|
|
Contest_Size = 10000 |
|
|
elif contest_var1 == 'Custom': |
|
|
Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...") |
|
|
strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very')) |
|
|
if strength_var1 == 'Not Very': |
|
|
sharp_split = 500000 |
|
|
elif strength_var1 == 'Below Average': |
|
|
sharp_split = 400000 |
|
|
elif strength_var1 == 'Average': |
|
|
sharp_split = 300000 |
|
|
elif strength_var1 == 'Above Average': |
|
|
sharp_split = 200000 |
|
|
elif strength_var1 == 'Very': |
|
|
sharp_split = 100000 |
|
|
|
|
|
|
|
|
with col2: |
|
|
if st.button("Run Contest Sim"): |
|
|
if 'working_seed' in st.session_state: |
|
|
maps_dict = { |
|
|
'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), |
|
|
'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), |
|
|
'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), |
|
|
'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), |
|
|
'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), |
|
|
'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)) |
|
|
} |
|
|
Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size) |
|
|
Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) |
|
|
Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 |
|
|
Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str) |
|
|
Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) |
|
|
|
|
|
|
|
|
type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32} |
|
|
Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) |
|
|
st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() |
|
|
|
|
|
else: |
|
|
if sim_site_var1 == 'Draftkings': |
|
|
st.session_state.working_seed = DK_seed.copy() |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
st.session_state.working_seed = FD_seed.copy() |
|
|
maps_dict = { |
|
|
'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), |
|
|
'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), |
|
|
'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), |
|
|
'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), |
|
|
'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), |
|
|
'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)) |
|
|
} |
|
|
Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size) |
|
|
Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) |
|
|
Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 |
|
|
Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str) |
|
|
Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) |
|
|
|
|
|
|
|
|
type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32} |
|
|
Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) |
|
|
st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() |
|
|
|
|
|
|
|
|
st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() |
|
|
freq_copy = st.session_state.Sim_Winner_Display |
|
|
|
|
|
if sim_site_var1 == 'Draftkings': |
|
|
freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:5].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
freq_working['Freq'] = freq_working['Freq'].astype(int) |
|
|
freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map']) |
|
|
freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5 |
|
|
freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100 |
|
|
freq_working['Exposure'] = freq_working['Freq']/(1000) |
|
|
freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own'] |
|
|
freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map']) |
|
|
st.session_state.player_freq = freq_working.copy() |
|
|
|
|
|
if sim_site_var1 == 'Draftkings': |
|
|
cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
cpt_working['Freq'] = cpt_working['Freq'].astype(int) |
|
|
cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map']) |
|
|
cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) |
|
|
cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['Own_map']) / 400 |
|
|
cpt_working['Exposure'] = cpt_working['Freq']/(1000) |
|
|
cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own'] |
|
|
cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map']) |
|
|
st.session_state.sp_freq = cpt_working.copy() |
|
|
|
|
|
if sim_site_var1 == 'Draftkings': |
|
|
flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:5].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
flex_working['Freq'] = flex_working['Freq'].astype(int) |
|
|
flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map']) |
|
|
flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) |
|
|
flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['Own_map']) / 400) |
|
|
flex_working['Exposure'] = flex_working['Freq']/(1000) |
|
|
flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own'] |
|
|
flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map']) |
|
|
st.session_state.flex_freq = flex_working.copy() |
|
|
|
|
|
if sim_site_var1 == 'Draftkings': |
|
|
team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,8:9].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,7:8].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
team_working['Freq'] = team_working['Freq'].astype(int) |
|
|
team_working['Exposure'] = team_working['Freq']/(1000) |
|
|
st.session_state.team_freq = team_working.copy() |
|
|
|
|
|
if sim_site_var1 == 'Draftkings': |
|
|
stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,10:11].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
elif sim_site_var1 == 'Fanduel': |
|
|
stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,9:10].values, return_counts=True)), |
|
|
columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) |
|
|
stack_working['Freq'] = stack_working['Freq'].astype(int) |
|
|
stack_working['Exposure'] = stack_working['Freq']/(1000) |
|
|
st.session_state.stack_freq = stack_working.copy() |
|
|
|
|
|
with st.container(): |
|
|
if st.button("Reset Sim", key='reset_sim'): |
|
|
for key in st.session_state.keys(): |
|
|
del st.session_state[key] |
|
|
if 'player_freq' in st.session_state: |
|
|
player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2') |
|
|
if player_split_var2 == 'Specific Players': |
|
|
find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique()) |
|
|
elif player_split_var2 == 'Full Players': |
|
|
find_var2 = st.session_state.player_freq.Player.values.tolist() |
|
|
|
|
|
if player_split_var2 == 'Specific Players': |
|
|
st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)] |
|
|
if player_split_var2 == 'Full Players': |
|
|
st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame |
|
|
if 'Sim_Winner_Display' in st.session_state: |
|
|
st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) |
|
|
if 'Sim_Winner_Export' in st.session_state: |
|
|
st.download_button( |
|
|
label="Export Full Frame", |
|
|
data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'), |
|
|
file_name='NFL_SD_consim_export.csv', |
|
|
mime='text/csv', |
|
|
) |
|
|
|
|
|
with st.container(): |
|
|
tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures', 'Team Exposures']) |
|
|
with tab1: |
|
|
if 'player_freq' in st.session_state: |
|
|
|
|
|
st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) |
|
|
st.download_button( |
|
|
label="Export Exposures", |
|
|
data=st.session_state.player_freq.to_csv().encode('utf-8'), |
|
|
file_name='player_freq_export.csv', |
|
|
mime='text/csv', |
|
|
key='overall' |
|
|
) |
|
|
with tab2: |
|
|
if 'sp_freq' in st.session_state: |
|
|
|
|
|
st.dataframe(st.session_state.sp_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) |
|
|
st.download_button( |
|
|
label="Export Exposures", |
|
|
data=st.session_state.sp_freq.to_csv().encode('utf-8'), |
|
|
file_name='cpt_freq.csv', |
|
|
mime='text/csv', |
|
|
key='sp' |
|
|
) |
|
|
with tab3: |
|
|
if 'flex_freq' in st.session_state: |
|
|
|
|
|
st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) |
|
|
st.download_button( |
|
|
label="Export Exposures", |
|
|
data=st.session_state.flex_freq.to_csv().encode('utf-8'), |
|
|
file_name='flex_freq.csv', |
|
|
mime='text/csv', |
|
|
key='flex' |
|
|
) |
|
|
with tab4: |
|
|
if 'team_freq' in st.session_state: |
|
|
|
|
|
st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True) |
|
|
st.download_button( |
|
|
label="Export Exposures", |
|
|
data=st.session_state.team_freq.to_csv().encode('utf-8'), |
|
|
file_name='team_freq.csv', |
|
|
mime='text/csv', |
|
|
key='team' |
|
|
) |
