Skip to main content
SimilarExport ZIP
100%

Gist ef99831492181718c41fd4331d226a74

✓ Published0🌍 Public
CConorAspell
Last edited Dec 5, 2023
Created on Sep 12, 2023

This example shows the control flow of an AWS Step Functions state machine that checks a date before deciding whether to update a Fantasy Premier League team. A chain of AWS Lambda functions retrieves player odds and details, calculates transfers, and posts updates to the team. The visualization uses the state machine’s declarative JSON definition to map states and transitions, including a choice point that branches to a success state or continues to further tasks. It relies on the Step Functions API for orchestration, not on a separate plotting library.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist ed5c09d69d4e5d65c6ec5faafaea7e08

This example shows an AWS Step Functions state machine that automates a two-step data pipeline for controlling Fantasy Premier League updates. The workflow first invokes an `oddsGetter` Lambda function to fetch betting odds, then passes the result to a `storeFPLData` Lambda function, which persists the data and ends the process. The visualization uses the Step Functions JSON state machine definition to render the sequential task flow, with explicit `Resource` ARNs linking to the Lambda functions. The graph displays the `StartAt` and `End` states, making the linear execution path and the service integrations clear without showing runtime behavior or intermediate data handling.

CConorAspell
89% match
Loading thumbnail…

Gist 4523b841021e154daebbf9c9bccaae7b

This visualization is a linear flow diagram of an AWS Step Functions state machine that orchestrates a Fantasy Premier League update. It shows three sequential tasks—DateChecker, OddsGetter, and UpdateFPLTeam—each represented as a labeled node with a resource ARN. The diagram uses rectangular and rounded-rectangle shapes connected by arrows to indicate execution order, with the final node marked as terminal. The underlying JSON definition (visible in the accompanying file) defines the state transitions, while the rendered view likely employs a graph layout library such as D3.js or a similar tool to position nodes and edges. The data source is the embedded stateMachine.txt file, which exposes the exact Lambda function names and control flow.

CConorAspell
89% match
Loading thumbnail…

Gist 9bf3c1df30c761a2f4056979099ea7cd

This example shows an automated Fantasy Premier League workflow that posts a user’s finalized team sheet for a given gameweek. It retrieves the full team JSON from an S3 bucket and submits it to the league’s API via a POST request. The code uses the `requests` library for HTTP communication, `json` for data handling, and custom utility functions from `fpl_utils` to fetch gameweek parameters and storage details. Error handling triggers an SNS notification to a specified topic if the API call fails. The visualization highlights the integration between cloud storage, Lambda execution, and external sports data endpoints.

CConorAspell
69% match
Loading thumbnail…

Gist 99774fc136a57a61654393793e16488e

This example shows an automated transfer system for the Fantasy Premier League game, executing player swaps between gameweeks. A Python AWS Lambda function orchestrates the process by retrieving incoming and outgoing player data from JSON files stored in an S3 bucket, then posts the transfer request via the `requests` library to the league’s `https://fantasy.premierleague.com/api/transfers/` endpoint. The function constructs a transfer payload with player IDs, purchase and selling prices, and the user’s gameweek, along with authentication cookies. It uses the `fpl_utils` module to fetch gameweek information and manage failure notifications via SNS.

CConorAspell
69% match
Loading thumbnail…

Gist dca53ad15bb437468b52eb951a09f356

This visualization shows the data pipeline for managing Fantasy Premier League player data, fetching gameweek deadlines and team rosters from the official FPL API. It retrieves data using Python's `requests` library, converts JSON responses into DataFrames with `pandas`, and then persists the processed data to Amazon S3 through the `boto3` client. The code also reads configuration parameters from AWS SSM and sends notifications via SNS topics, demonstrating an automated workflow for updating and storing fantasy football statistics.

CConorAspell
68% match
Loading thumbnail…

Gist beac8531ee8bb5520c9a42f87e680e7a

This example shows how a Fantasy Premier League team is automatically updated each gameweek, selecting starters and substitutes based on player form and difficulty. The code calculates a starting weight for each player by combining their recent form and fixture difficulty, then sorts the squad to pick the strongest starting lineup. It uses pandas DataFrames to manipulate player data, boto3 to interact with S3 buckets, and AWS Lambda to run the logic as a serverless function. The final team sheet, including captain and vice-captain assignments, is uploaded as JSON for later retrieval.

CConorAspell
68% match
Loading thumbnail…

Gist a5e694c3852c3b56db4abcb02f364241

This example shows a Fantasy Premier League team update workflow that automatically selects a squad for the upcoming gameweek when the transfer deadline is within 24 hours. The code fetches live player and fixture data from the Fantasy Premier League API, merges team strength ratings with player records, and calculates a difficulty differential for each player based on home or away status. It uses pandas for data manipulation and merging, and the `requests` library to retrieve JSON responses from the API. The visualization is not graphical; instead, it demonstrates a programmatic data-processing pipeline with no charts or rendering, focusing solely on data preparation and conditional logic.

CConorAspell
68% match
Loading thumbnail…

Gist 1da59c7531b92d80b1087f8b51ac9d17

This visualization tracks fantasy Premier League player transfer momentum by calculating weighted "in" and "out" scores from fixture difficulty, player form, and selection chance. It uses a Python AWS Lambda function to fetch live data from the Fantasy Premier League API, merge it with bookmaker odds stored in an S3 bucket, and output a CSV of player weights per gameweek. The code relies on `pandas` for data manipulation, `boto3` for cloud storage access, and `requests` for API calls, with team strengths and fixture probabilities driving the transfer recommendations.

CConorAspell
67% match