Refactor init_baselines function in app.py to accept a slate variable, allowing dynamic selection of slate data for both DK and FD seed frame initializations. This change enhances flexibility in data retrieval and improves accuracy in contest simulations by ensuring the correct slate is used throughout the application.

Update sharp_split values in app.py for strength categories to improve accuracy in contest simulations. Adjusted thresholds for 'Not Very', 'Below Average', 'Average', 'Above Average', and 'Very' to enhance data handling and ensure more precise calculations.

Refactor app.py to update database collection names for DK and FD secondary seed frame initializations. This change improves clarity and consistency in data retrieval by aligning collection names with their respective functionalities, enhancing overall data handling during contest simulations.

Enhance app.py by adding name mapping functionality in DK seed frame initialization. This update retrieves player names from the database and maps them to the corresponding columns in the output DataFrame, improving data visibility and accuracy during contest simulations.

Refactor app.py to improve FD seed frame initialization by removing unnecessary table displays and adding a mapping for player names using a dictionary from the database. This enhances data handling and visibility during contest simulations, ensuring player names are correctly represented in the output DataFrame.

Refactor FD seed frame initialization in app.py to retrieve all data from the database and construct a names dictionary using key-value pairs. This change enhances data handling and improves the display of player names in the output, ensuring better visibility during contest simulations.

Enhance app.py by adding a display of player names in the FD seed frame initialization. This update improves data visibility by presenting the names in a structured format using st.table, facilitating better understanding during contest simulations.

Refactor app.py to improve the display of the working seed table by replacing head(100) with a DataFrame constructor that explicitly sets column names. This change enhances data visibility during contest simulations by ensuring the correct structure of the displayed data.

Limit displayed rows of the working seed table to the first 100 entries in app.py, enhancing data visibility during contest simulations.

Enhance app.py by adding a display of the working seed table in the simulation section, improving data visibility during contest simulations. Removed commented-out code for better readability.

Update FD seed frame initialization in app.py to exclude the _id field from the database query, improving data handling and readability.

Enhance FD seed frame initialization in app.py by adding a mapping for player names using a dictionary from the database. This improves data readability and ensures that player names are correctly displayed in the output DataFrame.

Refactor app.py to remove Google Sheets integration and streamline database connection. The init_conn function now only returns the MongoDB client, simplifying the connection process and enhancing code maintainability.

Refactor seed frame initialization functions in app.py to return the full DK and FD seed arrays instead of a sliced version based on 'sharp_split'. This change simplifies the data handling process and ensures that all relevant data is available for further processing.

adding query limiting

Update lineup input constraints in app.py to limit user input for 'sharp_split_var' to a range between 10,000 and 500,000, enhancing data validation and user experience.

Refactor seed frame initialization and contest simulation to accept 'sharp_split' parameter, allowing dynamic lineup size input. Update logic to conditionally load seed frames based on user-selected slate and site, enhancing flexibility and user experience.

Revert

Refactor seed frame initialization functions to accept 'sharp_split' parameter, enhancing flexibility in data handling. Update contest simulation logic to conditionally load seed frames based on selected slate and site, improving user experience and code maintainability.

Refactor data export logic to utilize list comprehension for mapping player IDs, improving performance and readability

Enhance data export functionality by initializing and filtering 'working_seed' based on team and stack variables in session state

Refactor data export preparation to use correct indexing for DataFrame columns

fixed ownership in sim

replacing player values with ID values

added player IDs to exports

Added secondary slates

added STDev calc

fixed "cei" error

Removing need for NFL_Data source and using strictly mongo

added FD_seed

fixing some initial querying

Fixed removal of DK/FD option for some reason

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changed TTL as a test

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