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import streamlit as st
import pandas as pd
import numpy as np
from pymongo import MongoClient
from database import db
@st.cache_data(ttl = 600)
def init_DK_seed_frames(slate_var, sharp_split):
if slate_var == 'Main Slate':
collection = db['DK_NFL_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = db[f"DK_NFL_seed_frame_Main Slate"]
elif slate_var == 'Secondary Slate':
collection = db['DK_NFL_Secondary_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = db[f"DK_NFL_seed_frame_Secondary Slate"]
elif slate_var == 'Late Slate':
collection = db['DK_NFL_Late_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = db[f"DK_NFL_seed_frame_Late Slate"]
cursor = collection.find().limit(sharp_split)
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
for col in dict_columns:
raw_display[col] = raw_display[col].map(names_dict)
DK_seed = raw_display.to_numpy()
return DK_seed
@st.cache_data(ttl = 599)
def init_FD_seed_frames(slate_var, sharp_split):
if slate_var == 'Main Slate':
collection = db['FD_NFL_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = db[f"FD_NFL_seed_frame_Main Slate"]
cursor = collection.find().limit(sharp_split)
elif slate_var == 'Secondary Slate':
collection = db['FD_NFL_Secondary_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = db[f"FD_NFL_seed_frame_Secondary Slate"]
cursor = collection.find().limit(sharp_split)
elif slate_var == 'Late Slate':
collection = db['FD_NFL_Late_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = db[f"FD_NFL_seed_frame_Late Slate"]
cursor = collection.find().limit(sharp_split)
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
for col in dict_columns:
raw_display[col] = raw_display[col].map(names_dict)
FD_seed = raw_display.to_numpy()
return FD_seed
@st.cache_data(ttl = 599)
def init_baselines(slate_var):
collection = db["DK_NFL_ROO"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display[raw_display['slate'] == slate_var]
raw_display = raw_display[raw_display['version'] == 'overall']
dk_raw = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']]
dk_raw['STDev'] = (dk_raw['Ceiling'] - dk_raw['Floor']) / 4
collection = db["FD_NFL_ROO"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display[raw_display['slate'] == slate_var]
raw_display = raw_display[raw_display['version'] == 'overall']
fd_raw = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']]
fd_raw['STDev'] = (fd_raw['Ceiling'] - fd_raw['Floor']) / 4
return dk_raw, fd_raw
@st.cache_data
def convert_df(array):
array = pd.DataFrame(array, columns=column_names)
return array.to_csv().encode('utf-8')
@st.cache_data
def calculate_DK_value_frequencies(np_array):
unique, counts = np.unique(np_array[:, :9], return_counts=True)
frequencies = counts / len(np_array) # Normalize by the number of rows
combined_array = np.column_stack((unique, frequencies))
return combined_array
@st.cache_data
def calculate_FD_value_frequencies(np_array):
unique, counts = np.unique(np_array[:, :9], return_counts=True)
frequencies = counts / len(np_array) # Normalize by the number of rows
combined_array = np.column_stack((unique, frequencies))
return combined_array
@st.cache_data
def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
SimVar = 1
Sim_Winners = []
fp_array = seed_frame.copy()
# Pre-vectorize functions
vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
st.write('Simulating contest on frames')
while SimVar <= Sim_size:
fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
sample_arrays1 = np.c_[
fp_random,
np.sum(np.random.normal(
loc=vec_projection_map(fp_random[:, :-7]),
scale=vec_stdev_map(fp_random[:, :-7])),
axis=1)
]
sample_arrays = sample_arrays1
final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
Sim_Winners.append(best_lineup)
SimVar += 1
return Sim_Winners |