diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 4afe5d7..cd27c66 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -4,7 +4,6 @@ on: push: branches: - main - - bug/crystal_conv_saving pull_request: branches: - main @@ -20,18 +19,39 @@ jobs: steps: - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - - name: Install dependencies + + # Linux/macOS - Install dependencies + - name: Install dependencies (Linux/macOS) + if: runner.os != 'Windows' # Only run on Linux/macOS + env: + PYTHONIOENCODING: utf-8 # Ensure Python uses UTF-8 encoding run: | python -m pip install --upgrade pip - pip install -e .[test] + python -m pip install -e .[test] + shell: bash + + # Windows - Install dependencies + - name: Install dependencies (Windows) + if: runner.os == 'Windows' # Only run on Windows + env: + PYTHONIOENCODING: utf-8 # Ensure Python uses UTF-8 encoding + PYTHONUTF8: 1 # Force Python to use UTF-8 mode + run: | + chcp 65001 # Change code page to UTF-8 + python -m pip install --upgrade pip setuptools + python -m pip install -e .[test] + shell: pwsh + - name: Code formatting run: | pip install "black[jupyter]==24.4.2" black --check . + - name: Test with pytest run: | - pytest --color=yes \ No newline at end of file + pytest --color=yes diff --git a/.gitignore b/.gitignore index a4cfde3..e325d98 100644 --- a/.gitignore +++ b/.gitignore @@ -180,8 +180,10 @@ build.py /pickle_files **/pickles **/pickle_files -tests/files/models/my-test-gnn/* +tests/files/models/* tests/files/test.pickle.gz +tests/files/bdb/* +tests/files/kloppy/* examples/models/* /models diff --git a/README.md b/README.md index 7b93dfd..4b9172b 100644 --- a/README.md +++ b/README.md @@ -17,25 +17,35 @@ The **unravelsports** package aims to aid researchers, analysts and enthusiasts 🌀 Features ----- -âš― Convert **positional soccer data** into graphs to train **graph neural networks** by leveraging the powerful [**Kloppy**](https://github.com/PySport/kloppy/tree/master) data conversion standard and [**Spektral**](https://github.com/danielegrattarola/spektral) - a flexible framework for training graph neural networks. - -âš― **Randomize** and **split** data into train, test and validation sets along matches, sequences or possessions to avoid leakage and improve model quality. - -âš― **Train**, **validate** and **test** your (custom) Graph model(s) and easily **predict** on new data. - -âš― Leverage the power of **Kloppy** standardization and **unravelsports** to execute the these features for _Metrica_, _Sportec_, _Tracab (CyronHego)_, _SecondSpectrum_, _SkillCorner_ and _StatsPerform_ tracking data. +### **Convert** + +âš― **Soccer positional tracking data** into [Graphs](examples/graphs_faq.md) to train **graph neural networks** by leveraging the powerful [**Kloppy**](https://github.com/PySport/kloppy) data conversion standard for + - _Metrica_ + - _Sportec_ + - _Tracab (CyronHego)_ + - _SecondSpectrum_ + - _SkillCorner_ + - _StatsPerform_ + +🏈 **BigDataBowl American football positional tracking data** into [Graphs](examples/graphs_faq.md) to train **graph neural networks** by leveraging [**Polars**](https://github.com/pola-rs/polars). + +### **Graph Neural Networks** +These [Graphs](examples/graphs_faq.md) can be used with [**Spektral**](https://github.com/danielegrattarola/spektral) - a flexible framework for training graph neural networks. +`unravelsports` allows you to **randomize** and **split** data into train, test and validation sets along matches, sequences or possessions to avoid leakage and improve model quality. And finally, **train**, **validate** and **test** your (custom) Graph model(s) and easily **predict** on new data. ⌛ ***More to come soon...!*** 🌀 Quick Start ----- -📖 The [**Quick Start Jupyter Notebook**](examples/0_quick_start_guide.ipynb) explains how to convert any positional tracking data from **Kloppy** to **Spektral GNN** in a few easy steps while walking you through the most important features and documentation. +📖 âš― The [**Quick Start Jupyter Notebook**](examples/0_quick_start_guide.ipynb) explains how to convert any positional tracking data from **Kloppy** to **Spektral GNN** in a few easy steps while walking you through the most important features and documentation. + +📖 âš― The [**Graph Converter Tutorial Jupyter Notebook**](examples/1_kloppy_gnn_train.ipynb) gives an in-depth walkthrough. -📖 The [**Graph Converter Tutorial Jupyter Notebook**](examples/1_kloppy_gnn_train.ipynb) gives an in-depth walkthrough. +📖 🏈 The [**BigDataBowl Converter Tutorial Jupyter Notebook**](examples/2_big_data_bowl_guide.ipynb) gives an guide on how to convert the BigDataBowl data into Graphs. 🌀 Documentation ----- -For now, follow the [**Graph Converter Tutorial**](examples/1_kloppy_gnn_train.ipynb), more documentation will follow! +For now, follow the [**Graph Converter Tutorial**](examples/1_kloppy_gnn_train.ipynb) and check the [**Graph FAQ**](examples/graphs_faq.md), more documentation will follow! Additional reading: @@ -55,6 +65,7 @@ spektral==1.20.0 tensorflow==2.14.0 keras==2.14.0 kloppy==3.15.0 +polars==1.2.1 ``` These dependencies come pre-installed with the package. It is advised to create a [virtual environment](https://virtualenv.pypa.io/en/latest/). diff --git a/examples/0_quick_start_guide.ipynb b/examples/0_quick_start_guide.ipynb index 73787e3..3065c20 100644 --- a/examples/0_quick_start_guide.ipynb +++ b/examples/0_quick_start_guide.ipynb @@ -51,7 +51,7 @@ "\n", "1. Load [Kloppy](https://github.com/PySport/kloppy) dataset. \n", " See [in-depth Tutorial](1_kloppy_gnn_train.ipynb) on how do processes multiple match files, and to see an overview of all possible settings.\n", - "2. Convert to Graph format using `GraphConverter`\n", + "2. Convert to Graph format using `SoccerGraphConverter`\n", "3. Create dataset for easy processing with [Spektral](https://graphneural.network/) using `CustomSpektralDataset`" ] }, @@ -61,7 +61,7 @@ "metadata": {}, "outputs": [], "source": [ - "from unravel.soccer import GraphConverter\n", + "from unravel.soccer import SoccerGraphConverter\n", "from unravel.utils import CustomSpektralDataset\n", "\n", "from kloppy import skillcorner\n", @@ -77,7 +77,9 @@ "\n", "# Initialize the Graph Converter, with dataset and labels\n", "# Here we use the default settings\n", - "converter = GraphConverter(dataset=kloppy_dataset, labels=dummy_labels(kloppy_dataset))\n", + "converter = SoccerGraphConverter(\n", + " dataset=kloppy_dataset, labels=dummy_labels(kloppy_dataset)\n", + ")\n", "\n", "# Compute the graphs and add them to the CustomSpektralDataset\n", "dataset = CustomSpektralDataset(graphs=converter.to_spektral_graphs())" diff --git a/examples/1_kloppy_gnn_train.ipynb b/examples/1_kloppy_gnn_train.ipynb index 85b1a3f..be6bd2e 100644 --- a/examples/1_kloppy_gnn_train.ipynb +++ b/examples/1_kloppy_gnn_train.ipynb @@ -27,7 +27,7 @@ "source": [ "In this in-depth walkthrough we'll discuss everything the `unravelsports` package has to offer for converting a [Kloppy](https://github.com/PySport/kloppy) dataset of soccer tracking data into graphs for training binary classification graph neural networks using the [Spektral](https://graphneural.network/) library.\n", "\n", - "This walkthrough will touch on a lot of the concepts from [A Graph Neural Network Deep-dive into Successful Counterattacks {A. Sahasrabudhe & J. Bekkers}](https://github.com/USSoccerFederation/ussf_ssac_23_soccer_gnn). It is strongly advised to first read the [research paper (pdf)](https://ussf-ssac-23-soccer-gnn.s3.us-east-2.amazonaws.com/public/Sahasrabudhe_Bekkers_SSAC23.pdf). Some concepts are also explained in the [Graphs FAQ](graphs_faq.ipynb).\n", + "This walkthrough will touch on a lot of the concepts from [A Graph Neural Network Deep-dive into Successful Counterattacks {A. Sahasrabudhe & J. Bekkers}](https://github.com/USSoccerFederation/ussf_ssac_23_soccer_gnn). It is strongly advised to first read the [research paper (pdf)](https://ussf-ssac-23-soccer-gnn.s3.us-east-2.amazonaws.com/public/Sahasrabudhe_Bekkers_SSAC23.pdf). Some concepts are also explained in the [Graphs FAQ](graphs_faq.md).\n", "\n", "Step by step we'll show how this package can be used to load soccer positional (tracking) data with `kloppy`, how to convert this data into \"graphs\", train a Graph Neural Network with `spektral`, evaluate it's performance, save and load the model and finally apply the model to unseen data to make predictions.\n", "\n", @@ -57,7 +57,7 @@ " - [7.4 Evaluate Model](#74-evaluate-model)\n", " - [7.5 Predict on New Data](#75-predict-on-new-data)\n", "\n", - "â„đïļ [**Graphs FAQ**](graphs_faq.ipynb)\n", + "â„đïļ [**Graphs FAQ**](graphs_faq.md)\n", "\n", "-----" ] @@ -68,7 +68,7 @@ "source": [ "### 1. Imports\n", "\n", - "We import `GraphConverter` to help us convert from Kloppy positional tracking frames to graphs.\n", + "We import `SoccerGraphConverter` to help us convert from Kloppy positional tracking frames to graphs.\n", "\n", "With the power of **Kloppy** we can also load data from many providers by importing `metrica`, `sportec`, `tracab`, `secondspectrum`, or `statsperform` from `kloppy`." ] @@ -79,7 +79,7 @@ "metadata": {}, "outputs": [], "source": [ - "from unravel.soccer import GraphConverter\n", + "from unravel.soccer import SoccerGraphConverter\n", "\n", "from kloppy import skillcorner" ] @@ -97,7 +97,7 @@ "source": [ "### 2. Public SkillCorner Data\n", "\n", - "The `GraphConverter` class allows processing data from every tracking data provider supported by [PySports Kloppy](https://github.com/PySport/kloppy), namely:\n", + "The `SoccerGraphConverter` class allows processing data from every tracking data provider supported by [PySports Kloppy](https://github.com/PySport/kloppy), namely:\n", "- Sportec\n", "- Tracab\n", "- SecondSpectrum\n", @@ -132,12 +132,12 @@ "\n", "â„đïļ For more information on:\n", "- What a Graph is, check out [Graph FAQ Section A](graphs_faq.ipynb)\n", - "- What parameters we can pass to the `GraphConverter`, check out [Graph FAQ Section B](graphs_faq.ipynb)\n", + "- What parameters we can pass to the `SoccerGraphConverter`, check out [Graph FAQ Section B](graphs_faq.ipynb)\n", "- What features each Graph has, check out [Graph FAQ Section C](graphs_faq.ipynb)\n", "\n", "---\n", "\n", - "To get started with the `GraphConverter` we need to pass one _required_ parameter:\n", + "To get started with the `SoccerGraphConverter` we need to pass one _required_ parameter:\n", "- `dataset` (of type `TrackingDataset` (Kloppy)) \n", "\n", "And one parameter that's required when we're converting for training purposes (more on this later):\n", @@ -148,7 +148,7 @@ "⚠ïļ As mentioned before you will need to create your own labels! In this example we'll use `dummy_labels(dataset)` to generate a fake label for each frame.\n", "\n", "#### Graph Identifier(s):\n", - "When training a model on tracking data it's highly recommended to split data into test/train(/validation) sets by match or period such that all data end up in the same test, train or validation set. This should be done to avoid leaking information between test, train and validation sets. To make this simple, there are two _optional_ parameters we can pass to `GraphConverter`, namely:\n", + "When training a model on tracking data it's highly recommended to split data into test/train(/validation) sets by match or period such that all data end up in the same test, train or validation set. This should be done to avoid leaking information between test, train and validation sets. To make this simple, there are two _optional_ parameters we can pass to `SoccerGraphConverter`, namely:\n", "- `graph_id`. This is a single identifier (str or int) for a whole match, for example the unique match id.\n", "- `graph_ids`. This is a dictionary with the same keys as `labels`, but the values are now the unique identifiers. This option can be used if we want to split by sequence or possession_id. For example: {frame_id: 'matchId-sequenceId', frame_id: 'match_Id-sequenceId2'} etc. You will need to create your own ids. Note, if `labels` and `graph_ids` don't have the exact same keys it will throw an error.\n", "\n", @@ -176,7 +176,7 @@ "Important things to note:\n", "- We import `dummy_labels` to randomly generate binary labels. Training with these random labels will not create a good model.\n", "- We import `dummy_graph_ids` to generate fake graph labels.\n", - "- The `GraphConverter` handles all necessary steps (like setting the correct coordinate system, and left-right normalization).\n", + "- The `SoccerGraphConverter` handles all necessary steps (like setting the correct coordinate system, and left-right normalization).\n", "- We will end up with fewer than 2,000 eventhough we set `limit=500` frames because we set `include_empty_frames=False` and all frames without ball coordinates are automatically ommited.\n", "- When using other providers always set `include_empty_frames=False` or `only_alive=True`.\n", "- We store the data as individual compressed pickle files, one file for per match. The data that gets stored in the pickle is a list of dictionaries, one dictionary per frame. Each dictionary has keys for the adjacency matrix, node features, edge features, label and graph id." @@ -223,7 +223,7 @@ " )\n", "\n", " # Initialize the Graph Converter, with dataset, labels and settings\n", - " converter = GraphConverter(\n", + " converter = SoccerGraphConverter(\n", " dataset=dataset,\n", " # create fake labels\n", " labels=dummy_labels(dataset),\n", @@ -254,7 +254,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "â„đïļ For a full table of parameters we can pass to the `GraphConverter` check out [Graph FAQ Section B](graphs_faq.ipynb)\n", + "â„đïļ For a full table of parameters we can pass to the `SoccerGraphConverter` check out [Graph FAQ Section B](graphs_faq.ipynb)\n", "\n", "-----" ] @@ -303,7 +303,7 @@ "Our `dataset` object has two custom methods to help split the data into train, test and validation sets.\n", "Either use `dataset.split_test_train()` if we don't need a validation set, or `dataset.split_test_train_validation()` if we do also require a validation set.\n", "\n", - "We can split our data 'by_graph_id' if we have provided Graph Ids in our `GraphConverter` using the 'graph_id' or 'graph_ids' parameter.\n", + "We can split our data 'by_graph_id' if we have provided Graph Ids in our `SoccerGraphConverter` using the 'graph_id' or 'graph_ids' parameter.\n", "\n", "The 'split_train', 'split_test' and 'split_validation' parameters can either be ratios, percentages or relative size compared to total. \n", "\n", @@ -338,7 +338,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "🗒ïļ We can see that, because we are splitting by only 4 different graph_ids here (the 4 match_ids) the ratio's aren't perfectly 4 to 1 to 1. If you change the `graph_id=match_id` parameter in the `GraphConverter` to `graph_ids=dummy_graph_ids(dataset)` you'll see that it's easier to get close to the correct ratios, simply because we have a lot more graph_ids to split a cross. " + "🗒ïļ We can see that, because we are splitting by only 4 different graph_ids here (the 4 match_ids) the ratio's aren't perfectly 4 to 1 to 1. If you change the `graph_id=match_id` parameter in the `SoccerGraphConverter` to `graph_ids=dummy_graph_ids(dataset)` you'll see that it's easier to get close to the correct ratios, simply because we have a lot more graph_ids to split a cross. " ] }, { @@ -582,7 +582,7 @@ "\n", "1. Load new, unseen data from the SkillCorner dataset.\n", "2. Convert this data, making sure we use the exact same settings as in step 1.\n", - "3. If we set `prediction=True` we do not have to supply labels to the `GraphConverter`." + "3. If we set `prediction=True` we do not have to supply labels to the `SoccerGraphConverter`." ] }, { @@ -597,7 +597,7 @@ " limit=500,\n", ")\n", "\n", - "preds_converter = GraphConverter(\n", + "preds_converter = SoccerGraphConverter(\n", " dataset=kloppy_dataset,\n", " prediction=True,\n", " ball_carrier_treshold=25.0,\n", @@ -772,7 +772,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": ".venv311", "language": "python", "name": "python3" }, diff --git a/examples/2_big_data_bowl_guide.ipynb b/examples/2_big_data_bowl_guide.ipynb new file mode 100644 index 0000000..faf20ea --- /dev/null +++ b/examples/2_big_data_bowl_guide.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 🌀 unravel BigDataBowl data!\n", + "\n", + "First run `pip install unravelsports` if you haven't already!\n", + "\n", + "-------" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install unravelsports --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This basic guide shows the differences between loading and converting the BigDataBowl data using the `AmericanFootballGraphConverter` compared to the `SoccerGraphConverter`. For the remaining functionalities please reference the [Quick-Start Guide](0_quick_start_guide.ipynb) or [Section 5](1_kloppy_gnn_train.ipynb#5-creating-a-custom-graph-dataset) and beyond of the Walkthrough.\n", + "\n", + "If anything is unclear, please read through [the Soccer example](1_kloppy_gnn_train.ipynb) in it's interity. \n", + "\n", + "-------\n", + "\n", + "### BigDataBowl data\n", + "\n", + "Before you can get started you have to download the BigDataBowl files from [Kaggle](https://www.kaggle.com/competitions/nfl-big-data-bowl-2025/data) after agreeing to the terms and conditions of the BigDataBowl. \n", + "\n", + "Please note development of this implementation started prior to the BigDataBowl 2025 data release and was done using BigDataBowl 2023 data. This implementation supports the 2025 dataset, but the 2025 tracking data files have 7x as many frames (1,100,000 compared to 7,100,000), because of the BEFORE_SNAP and AFTER_SNAP frame types. It is highly adviced to walk through this notebook with 2023 data first, because it will run much faster (roughly ~2 compared to ~15 minutes for one tracking data csv on an M1 MacBook). After you've tested it feel free to progress with the 2025 dataset.\n", + "\n", + "\n", + "-------\n", + "\n", + "### American Football as Graphs\n", + "\n", + "Although a lot of the functionality is similar to the Soccer implementation, the American Football implemenation has some clear differences, because of the different file structure, the lack of a [Kloppy](https://github.com/PySport/kloppy)-like library for American Football data, and both sports having different requirements. And most importantly the American Football implementation uses Polars under the hood! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contents:\n", + "\n", + "- [**1. BigDataBowlDataset**](#1-bigdatabowldataset)\n", + "- [**2. AmericanFootballGraphConverter**](#2-americanfootballgraphconverter)\n", + "- [**3. Spektral Graphs**](#3-spektral-graphs)\n", + "\n", + "â„đïļ [**Graphs FAQ**](graphs_faq.md)\n", + "\n", + "-------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1. BigDataBowlDataset\n", + "\n", + "- The `AmericanFootballGraphConverter` expects a Polars DataFrame. You can load this with the `BigDataBowlDataset` class\n", + "- The `BigDataBowlDataset`-class takes 3 parameters, namely the paths to the tracking, players and plays files (str) respectively. \n", + "- The `BigDataBowlDataset` standardizes every play attacking from left to right, and it adjusts orientation (`o`) and direction (`dir`) accordingly. It also joins the `plays.csv` and `players.csv` to the tracking data to use the in possession team, and player positions (QB identifier), weight and height respectively. It also converts weight to kilograms and height to centimeters.\n", + "- We add `graph_id`s to the `BigDataBowlDataset` by calling `.add_graph_ids()`. We don't have to use dummy graph ids as we do for âš― because the data comes with `playId`s. It's recommended to assign graph identifiers at least by `gameId` and `playId` (the default behaviour). \n", + "- We add dummy labels by calling `.add_dummy_labels()`. It's still up to the user to define the _actual_ labels they can use to train on. The `add_dummy_labels` adds a column `\"label\"` to the Polars DataFrame. To add your own labels simply remove the `.add_dummy_labels()` call and add your own label column. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (1_118_122, 22)\n", + "┌────────────┮────────┮─────────┮─────────┮───┮───────────┮────────────────┮───────────────┮───────┐\n", + "│ gameId ┆ playId ┆ nflId ┆ frameId ┆ â€Ķ ┆ weight_kg ┆ possessionTeam ┆ graph_id ┆ label │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ f64 ┆ i64 ┆ ┆ f64 ┆ str ┆ str ┆ i64 │\n", + "╞════════════╩════════╩═════════╩═════════╩═══╩═══════════╩════════════════╩═══════════════╩═══════╡\n", + "│ 2021090900 ┆ 97 ┆ 25511.0 ┆ 1 ┆ â€Ķ ┆ 102.0582 ┆ TB ┆ 2021090900-97 ┆ 1 │\n", + "│ 2021090900 ┆ 97 ┆ 25511.0 ┆ 2 ┆ â€Ķ ┆ 102.0582 ┆ TB ┆ 2021090900-97 ┆ 1 │\n", + "│ 2021090900 ┆ 97 ┆ 25511.0 ┆ 3 ┆ â€Ķ ┆ 102.0582 ┆ TB ┆ 2021090900-97 ┆ 1 │\n", + "│ 2021090900 ┆ 97 ┆ 25511.0 ┆ 4 ┆ â€Ķ ┆ 102.0582 ┆ TB ┆ 2021090900-97 ┆ 0 │\n", + "│ 2021090900 ┆ 97 ┆ 25511.0 ┆ 5 ┆ â€Ķ ┆ 102.0582 ┆ TB ┆ 2021090900-97 ┆ 1 │\n", + "│ â€Ķ ┆ â€Ķ ┆ â€Ķ ┆ â€Ķ ┆ â€Ķ ┆ â€Ķ ┆ â€Ķ ┆ â€Ķ ┆ â€Ķ │\n", + "│ 2021091300 ┆ 4845 ┆ -9999.9 ┆ 30 ┆ â€Ķ ┆ null ┆ LV ┆ 2021091300-48 ┆ 1 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 45 ┆ │\n", + "│ 2021091300 ┆ 4845 ┆ -9999.9 ┆ 31 ┆ â€Ķ ┆ null ┆ LV ┆ 2021091300-48 ┆ 0 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 45 ┆ │\n", + "│ 2021091300 ┆ 4845 ┆ -9999.9 ┆ 32 ┆ â€Ķ ┆ null ┆ LV ┆ 2021091300-48 ┆ 1 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 45 ┆ │\n", + "│ 2021091300 ┆ 4845 ┆ -9999.9 ┆ 33 ┆ â€Ķ ┆ null ┆ LV ┆ 2021091300-48 ┆ 0 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 45 ┆ │\n", + "│ 2021091300 ┆ 4845 ┆ -9999.9 ┆ 34 ┆ â€Ķ ┆ null ┆ LV ┆ 2021091300-48 ┆ 1 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 45 ┆ │\n", + "└────────────â”ī────────â”ī─────────â”ī─────────â”ī───â”ī───────────â”ī────────────────â”ī───────────────â”ī───────┘" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from unravel.american_football import BigDataBowlDataset, AmericanFootballGraphConverter\n", + "\n", + "bdb = BigDataBowlDataset(\n", + " tracking_file_path=\".data/nfl-big-data-bowl-2023/week1.csv\",\n", + " players_file_path=\".data/nfl-big-data-bowl-2023/players.csv\",\n", + " plays_file_path=\".data/nfl-big-data-bowl-2023/plays.csv\",\n", + ")\n", + "bdb.load()\n", + "bdb.add_graph_ids(by=[\"gameId\", \"playId\"], column_name=\"graph_id\")\n", + "bdb.add_dummy_labels(by=[\"gameId\", \"playId\", \"frameId\"], column_name=\"label\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. AmericanFootballGraphConverter\n", + "\n", + "â„đïļ For more information on:\n", + "- What a Graph is, check out [Graph FAQ Section A](graphs_faq.ipynb)\n", + "- What features each Graph has, check out [Graph FAQ Section C](graphs_faq.ipynb)\n", + "\n", + "#### Parameters\n", + "- To learn what parameters we can pass to the `AmericanFootballGraphConverter`, check out [Graph FAQ Section B](graphs_faq.ipynb)\n", + "- We pass `dataset` as a `BigDataBowlDataset` object.\n", + "- The `AmericanFootballGraphConverter` also takes an optional `label_col` and `graph_id_col` parameter. These default to \"label\" and \"graph_id\" respectively and only need to be specified when they have been changed in the steps prior.\n", + "- Although we convert weight and height to kilograms and centimeters, the coordinate system, speed and acceleration remains in the provided formats. Hence, `max_*_speed` and `max_*_acceleration` are in yards/second and yards/second^2 respectively. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "converter = AmericanFootballGraphConverter(\n", + " dataset=bdb,\n", + " label_col=\"label\",\n", + " graph_id_col=\"graph_id\",\n", + " max_player_speed=8.0,\n", + " max_ball_speed=28.0,\n", + " max_player_acceleration=10.0,\n", + " max_ball_acceleration=10.0,\n", + " self_loop_ball=True,\n", + " adjacency_matrix_connect_type=\"ball\",\n", + " adjacency_matrix_type=\"split_by_team\",\n", + " label_type=\"binary\",\n", + " defending_team_node_value=0.0,\n", + " attacking_non_qb_node_value=0.1,\n", + " random_seed=False,\n", + " pad=False,\n", + " verbose=False,\n", + " chunk_size=10_000,\n", + ")\n", + "spektral_graphs = converter.to_graph_frames()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Spektral Graphs\n", + "\n", + "The `converter` allows for transformation in 3 different ways:\n", + "1. `converter.to_graph_frames()` returns a list of dictionary objects, one dict per frame. Each dictionary has the keys `x`, `e`, `a`, `y` and `graph_id`\n", + "2. `converter.to_spektral_graphs()` returns a list of Spektral `Graph` objects that can be loaded directly into `CustomSpektralDataset(graphs=converter.to_spektral_graphs())`\n", + "3. `converter.to_pickle(file_path=file_path)` stores the converted frames into a `pickle` file. You can load all pickle files directly with `CustomSpektralDataset(pickle_folder=pickle_folder)`\n", + "- For a comprehensive list of American Football node and edge features please reference [Graph FAQ Section C](graphs_faq.md)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now have enough information to continue on recreating [Section 5](examples/1_kloppy_gnn_train.ipynb#5-creating-a-custom-graph-dataset) and beyond of the Walkthrough with this American Football!\n", + "\n", + "Warning: As mentioned the data files for the 2025 BigDataBowl are pretty huge, which means the converted (pickle) files will be even bigger. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv311", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/graphs_faq.ipynb b/examples/graphs_faq.ipynb deleted file mode 100644 index 20f6a7d..0000000 --- a/examples/graphs_faq.ipynb +++ /dev/null @@ -1,263 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### A. What is a Graph?\n", - "\n", - "
\n", - " 🌀 Expand for an short explanations on Graphs \n", - "
\n", - "
\n", - "\n", - "Before we continue it might be good to briefly explain what a Graph even in is!\n", - "\n", - "A Graph is a data structure consisting of:\n", - "- Nodes: Individual elements in the graph\n", - "- Edges: Connections between nodes\n", - "\n", - "The graph is typically represented by:\n", - "- [Adjacency matrix](https://en.wikipedia.org/wiki/Adjacency_matrix): Shows connections between nodes\n", - "- Node features: Attributes or properties of each node\n", - "- Edge features: Attributes of the connections between nodes\n", - "\n", - "The image on the right represents a stylized version of a frame of tracking data in soccer.\n", - "\n", - "In section 6.1 we can see what this looks like in Python.\n", - "\n", - "
\n", - "
\n", - "\n", - "![Graph representation](https://github.com/UnravelSports/unravelsports.github.io/blob/main/imgs/what-is-a-graph-4.png?raw=true)\n", - "\n", - "
\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### B. What are all GraphConverter settings?\n", - "\n", - "
\n", - " 🌀 Expand for a full table of additional optional GraphConverter parameters
\n", - "\n", - "| Parameter | Type | Description | Default |\n", - "|-----------|------|-------------|---------|\n", - "| `prediction` | bool | When True use the converter to create Graph dataset to apply a pre-trained model to, no labels required. Defaults to False. | False |\n", - "| `ball_carrier_threshold` | float | The distance threshold to determine the ball carrier in meters. If no ball carrier within ball_carrier_threshold, we skip the frame. | 25.0 |\n", - "| `max_player_speed` | float | The maximum speed of a player in meters per second. Used for normalizing node features. | 12.0 |\n", - "| `max_ball_speed` | float | The maximum speed of the ball in meters per second. Used for normalizing node features. | 28.0 |\n", - "| `boundary_correction` | float | A correction factor for boundary calculations, used to correct out of bounds as a percentage (Used as 1+boundary_correction, i.e., 0.05). Not setting this might lead to players outside the pitch markings to have values that fall slightly outside of our normalization range. When we set boundary_correction, any players outside the pitch will be moved to be on the closest line. | None |\n", - "| `self_loop_ball` | bool | Flag to indicate if the ball node should have a self-loop, aka be connected with itself and not only player(s) | True |\n", - "| `adjacency_matrix_connect_type` | str | The type of connection used in the adjacency matrix, typically related to the ball. Choose from 'ball', 'ball_carrier' or 'no_connection' | 'ball' |\n", - "| `adjacency_matrix_type` | str | The type of adjacency matrix, indicating how connections are structured, such as split by team. Choose from 'delaunay', 'split_by_team', 'dense', 'dense_ap' or 'dense_dp' | 'split_by_team' |\n", - "| `infer_ball_ownership` | bool | Infers 'attacking_team' if no 'ball_owning_team' exist (in Kloppy TrackingDataset) by finding the player closest to the ball using ball xyz, uses 'ball_carrier_threshold' as a cut-off. | True |\n", - "| `infer_goalkeepers` | bool | Set True if no GK label is provided, set False for incomplete (broadcast tracking) data that might not have a GK in every frame. | True |\n", - "| `defending_team_node_value` | float | Value for the node feature when player is on defending team. Should be between 0 and 1 including. | 0.1 |\n", - "| `non_potential_receiver_node_value` | float | Value for the node feature when player is NOT a potential receiver of a pass (when on opposing team or in possession of the ball). Should be between 0 and 1 including. | 0.1 |\n", - "| `label_type` | str | The type of prediction label used. Currently only supports 'binary' | 'binary' |\n", - "| `random_seed` | int, bool | When a random_seed is given, it will randomly shuffle an individual Graph without changing the underlying structure. When set to True, it will shuffle every frame differently; False won't shuffle. Advised to set True when creating an actual dataset to support Permutation Invariance. | False |\n", - "| `pad` | bool | True pads to a total amount of 22 players and ball (so 23x23 adjacency matrix). It dynamically changes the edge feature padding size based on the combination of AdjacencyMatrixConnectType and AdjacencyMatrixType, and self_loop_ball. No need to set padding because smaller and larger graphs can all be used in the same dataset. | False |\n", - "| `verbose` | bool | The converter logs warnings / error messages when specific frames have no coordinates, or other missing information. False mutes all of these warnings. | False |\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### C. What features does each Graph have?\n", - "\n", - "
\n", - " 🌀 Expand for a full list of features \n", - "
\n", - " \n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### D. What is a CustomGraphDataset?\n", - "\n", - "
\n", - " 🌀 Expand for a short explanation on CustomSpektralDataset
\n", - "\n", - "Let's have a look at the internals of our `CustomSpektralDataset`. This dataset class contains a list of graphs, available through `dataset.graphs`.\n", - "\n", - "The first item in our dataset has 23 nodes, 12 features per node and 7 features per edge.\n", - "\n", - "
\n", - "\n", - "```python\n", - "dataset.graphs[0]\n", - "\n", - ">>> Graph(n_nodes=23, n_node_features=12, n_edge_features=7, n_labels=1)\n", - "```\n", - "\n", - "The `CustomSpektralDataset` also allows us to split our data into train and test sets (and validation set if required) by using either:\n", - "- `dataset.split_test_train_validation()`\n", - "- `dataset.split_test_train()`\n", - "\n", - "
\n", - "
\n", - "
\n", - "
\n", - " 🌀 Expand for a short explanation on the representation of adjacency matrix
\n", - "\n", - "##### Adjacency Matrix\n", - "The **adjacency matrix** is represented as a [compressed sparse row matrix](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html#scipy.sparse.csr_matrix), as required by Spektral. A 'normal' version of this same matrix would be of shape 23x23 filled with zero's and one's in places where two players (or ball) are connected. \n", - "\n", - "Because we set `adjacency_matrix_type='split_by_team'` and `adjacency_matrix_connect_type=\"ball\"` this results in a total of 287 connections (ones), namely between:\n", - "- `adjacency_matrix_type='split_by_team'`:\n", - " - All players on team A (11 * 11) \n", - " - All players on team B (11 * 11)\n", - " - Ball connected to ball (1)\n", - "- `adjacency_matrix_connect_type=\"ball\"`\n", - " - All players and the ball (22) \n", - " - The ball and all players (22)\n", - "\n", - "
\n", - "\n", - "```python\n", - "dataset.graphs[0].a\n", - ">>> \n", - "```\n", - "
\n", - "
\n", - "
\n", - "
\n", - " 🌀 Expand for a short explanation on the representation of node feature matrix
\n", - "\n", - "##### Node Features\n", - "The **node features** are described using a regular Numpy array. Each column represents one feature and every row represents one player. \n", - "\n", - "The ball is presented in the last row, unless we set `random_seed=True` then every Graph gets randomly shuffled (while leaving connections in tact).\n", - "\n", - "See the bullet points in **5. Load Kloppy Data, Convert and Store** to learn which column represents which feature.\n", - "\n", - "The rows filled with zero's are 'empty' players created because we set `pad=True`. Graph Neural Networks are flexible enough to deal with all sorts of different graph shapes in the same dataset, normally it's not actually necessary to add these empty players, even for incomplete data with only a couple players in frame.\n", - "\n", - "
\n", - "\n", - "```python\n", - "dataset.graphs[0].x\n", - ">>> [[-0.163 -0.135 0.245 -0.97 0.007 0.289 0.959 0.191 0.059 0.376 1. 1. ]\n", - " [-0.332 0.011 -0.061 0.998 0.02 0.76 1.015 0.177 0.029 0.009 1. 0.1 ]\n", - " [ 0.021 -0.072 0.987 -0.162 0.017 0.474 0.88 0.203 0.121 0.468 1. 1. ]\n", - " [-0.144 0.232 0.343 0.939 0.024 0.694 0.924 0.186 0.077 0.638 1. 1. ]\n", - " [-0.252 0.302 0.99 0.141 0.032 0.523 0.964 0.176 0.078 0.741 1. 1. ]\n", - " [ 0.012 0.573 0.834 -0.551 0.035 0.407 0.842 0.191 0.19 0.646 1. 1. ]\n", - " [-0.293 0.686 0.999 -0.045 0.044 0.493 0.966 0.163 0.182 0.761 1. 1. ]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", - " ...\n", - " [ 0.202 0.124 -0.874 0.486 0.024 0.919 0.791 0.214 0.197 0.524 0.1 0.1 ]\n", - " [ 0.404 0.143 -0.997 0.08 0.029 0.987 0.709 0.23 0.281 0.519 0.1 0.1 ]\n", - " [ 0.195 -0.391 0.48 -0.877 0.014 0.33 0.847 0.218 0.222 0.417 0.1 0.1 ]\n", - " [ 0.212 -0.063 0.982 -0.187 0.009 0.47 0.804 0.217 0.2 0.483 0.1 0.1 ]\n", - " [-0.03 0.248 -0.996 0.091 0.021 0.986 0.876 0.194 0.116 0.591 0.1 0.1 ]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n", - " [-0.262 0.016 0.937 -0.35 0.036 0.443 0.986 0.044 0. 0. 0. 0. ]]\n", - "\n", - " \n", - "dataset.graphs[0].x.shape\n", - ">>> (23, 12)\n", - "```\n", - "
\n", - "
\n", - "
\n", - "
\n", - " 🌀 Expand for a short explanation on the representation of edge feature matrix
\n", - "\n", - "##### Edge Features\n", - "The **edge features** are also represented in a regular Numpy array. Again, each column represents one feature, and every row decribes the connection between two players, or player and ball.\n", - "\n", - "We saw before how the **adjacency matrix** was presented in a Sparse Row Matrix with 287 rows. It is no coincidence this lines up perfectly with the **edge feature matrix**. \n", - "\n", - "
\n", - "\n", - "```python\n", - "dataset.graphs[0].e\n", - ">>> [[ 0. 0. 1. 0.5 0.5 1. 0. ]\n", - " [ 0.081 0.006 0.936 0.255 0.21 0.907 1. ]\n", - " [ 0.079 0.004 0.012 0.391 0. 0.515 1. ]\n", - " [ 0.1 0.007 0.46 0.002 0.005 0.571 1. ]\n", - " [ 0.125 0.011 0.65 0.023 0.474 0.999 0. ]\n", - " [ 0.206 0.012 0.322 0.033 0.535 0.999 0. ]\n", - " [ 0.23 0.016 0.619 0.014 0.567 0.996 0. ]\n", - " [ 0. 0. 0. 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. 0. 0. 0. ]\n", - " ...\n", - " [ 0.197 -0.025 0.005 0.426 0.929 0.757 1. ]\n", - " [ 0.281 -0.023 0.004 0.439 0.959 0.699 1. ]\n", - " [ 0.222 -0.03 0.067 0.75 0.979 0.643 1. ]\n", - " [ 0.2 -0.032 0.003 0.554 0.982 0.633 1. ]\n", - " [ 0.116 -0.026 0.08 0.229 0.82 0.884 1. ]\n", - " [ 0. 0. 0. 0. 0. 0. 1. ]\n", - " [ 0. 0. 0. 0. 0. 0. 1. ]\n", - " [ 0. 0. 0. 0. 0. 0. 1. ]\n", - " [ 0. 0. 0. 0. 0. 0. 1. ]\n", - " [ 0. 0. 1. 0.5 0.5 1. 1. ]]\n", - "\n", - " dataset.graphs[0].e.shape\n", - " (287, 7)\n", - "```\n", - "
\n", - "
\n", - "\n" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/graphs_faq.md b/examples/graphs_faq.md new file mode 100644 index 0000000..8ea6495 --- /dev/null +++ b/examples/graphs_faq.md @@ -0,0 +1,269 @@ +#### A. What is a Graph? + +
+ 🌀 Expand for an short explanations on Graphs +
+
+ +Before we continue it might be good to briefly explain what a Graph even in is! + +A Graph is a data structure consisting of: +- Nodes: Individual elements in the graph +- Edges: Connections between nodes + +The graph is typically represented by: +- [Adjacency matrix](https://en.wikipedia.org/wiki/Adjacency_matrix): Shows connections between nodes +- Node features: Attributes or properties of each node +- Edge features: Attributes of the connections between nodes + +The image on the right represents a stylized version of a frame of tracking data in soccer. + +In section 6.1 we can see what this looks like in Python. + +
+
+ +![Graph representation](https://github.com/UnravelSports/unravelsports.github.io/blob/main/imgs/what-is-a-graph-4.png?raw=true) + +
+
+
+ +#### B. What are all GraphConverter settings? + +
+ 🌀 âš― 🏈 Expand for a full table of additional optional GraphConverter parameters
+ +| Parameter | Type | Description | Default | Sport | +|-------------------------------------|-----------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------|-----------------| +| `prediction` | bool | When True use the converter to create Graph dataset to apply a pre-trained model to, no labels required. Defaults to False. | False | âš― 🏈 | +| `adjacency_matrix_connect_type` | str | The type of connection used in the adjacency matrix, typically related to the ball. Choose from 'ball', 'ball_carrier' or 'no_connection' | 'ball' | âš― 🏈 | +| `adjacency_matrix_type` | str | The type of adjacency matrix, indicating how connections are structured, such as split by team. Choose from 'delaunay' , 'split_by_team', 'dense', 'dense_ap' or 'dense_dp' | 'split_by_team' | âš― 🏈 | +| `self_loop_ball` | bool | Flag to indicate if the ball node should have a self-loop, aka be connected with itself and not only player(s) | True | âš― 🏈 | +| `label_type` | str | The type of prediction label used. Currently only supports 'binary' | 'binary' | âš― 🏈 | +| `random_seed` | int, bool | When a random_seed is given, it will randomly shuffle an individual Graph without changing the underlying structure. When set to True, it will shuffle every frame differently; False won't shuffle. Advised to set True when creating an actual dataset to support Permutation Invariance. | False | âš― 🏈 | +| `pad` | bool | True pads to a total amount of 22 players and ball (so 23x23 adjacency matrix). It dynamically changes the edge feature padding size based on the combination of AdjacencyMatrixConnectType and AdjacencyMatrixType, and self_loop_ball. No need to set padding because smaller and larger graphs can all be used in the same dataset. | False | âš― 🏈 | +| `verbose` | bool | The converter logs warnings / error messages when specific frames have no coordinates, or other missing information. False mutes all of these warnings. | False | âš― 🏈 | +| `defending_team_node_value` | float | Value for the node feature when player is on defending team. Should be between 0 and 1 including. | 0.1 | âš― 🏈 +| `max_player_speed` | float | The maximum speed of a player in meters or yards per second. Used for normalizing node features. | 12.0 | âš― 🏈 | +| `max_ball_speed` | float | The maximum speed of the ball in meters or yards per second. Used for normalizing node features. | 28.0 | âš― 🏈 | +| `max_player_acceleration` | float | The maximum speed of a player in yards per second. Used for normalizing node features. | 10.0 | 🏈 | +| `max_ball_acceleration` | float | The maximum speed of the ball in yards per second squared. Used for normalizing node features. | 10.0 | 🏈 | +| `attacking_non_qb_node_value` | float | Value for the node feature when player is NOT the QB, but is on the attacking team | 0.1 | 🏈 | +| `chunk_size` | int | Set to determine size of conversions from Polars to Graphs. Preferred setting depends on available computing power | 2_000 | 🏈 | +| `ball_carrier_threshold` | float | The distance threshold to determine the ball carrier in meters. If no ball carrier within ball_carrier_threshold, we skip the frame. | 25.0 | âš― | +| `boundary_correction` | float | A correction factor for boundary calculations, used to correct out of bounds as a percentage (Used as 1+boundary_correction, i.e., 0.05). Not setting this might lead to players outside the pitch markings to have values that fall slightly outside of our normalization range. When we set boundary_correction, any players outside the pitch will be moved to be on the closest line. | None | âš― | +| `infer_ball_ownership` | bool | Infers 'attacking_team' if no 'ball_owning_team' exist (in Kloppy TrackingDataset) by finding the player closest to the ball using ball xyz, uses 'ball_carrier_threshold' as a cut-off. | True | âš― | +| `infer_goalkeepers` | bool | Set True if no GK label is provided, set False for incomplete (broadcast tracking) data that might not have a GK in every frame. | True | âš― | +| `non_potential_receiver_node_value` | float | Value for the node feature when player is NOT a potential receiver of a pass (when on opposing team or in possession of the ball). Should be between 0 and 1 including. | 0.1 | âš― | + + + +
+ +#### C. What features does each Graph have? + +
+ 🌀 âš― Expand for a full list of Soccer features + +| Variable | Datatype | Index | Features | +|----------|-----------------------------------|-------|---------------------------------------------------------------------------------------------------------------------------------| +| a | np.array of shape (players+ball, players+ball) | | - | +| x | np.array of shape (n_nodes, n_node_features) | 0 | normalized x-coordinate | +| | | 1 | normalized y-coordinate | +| | | 2 | x component of the velocity unit vector | +| | | 3 | y component of the velocity unit vector | +| | | 4 | normalized speed | +| | | 5 | normalized angle of velocity vector | +| | | 6 | normalized distance to goal | +| | | 7 | normalized angle to goal | +| | | 8 | normalized distance to ball | +| | | 9 | normalized angle to ball | +| | | 10 | attacking (1) or defending team (`defending_team_node_value`) | +| | | 11 | potential receiver (1) else `non_potential_receiver_node_value` | +| e | np.array of shape (np.non_zero(a), n_edge_features) | 0 | normalized inter-player distance | +| | | 1 | normalized inter-player speed difference | +| | | 2 | inter-player angle cosine | +| | | 3 | inter-player angle sine | +| | | 4 | inter-player velocity vector cosine | +| | | 5 | inter-player velocity vector sine | +| | | 6 | optional: 1 if two players are connected else 0 according to delaunay adjacency matrix. Only if adjacency_matrix_type is NOT 'delauney' | +| y | np.array | | - | +| id | int, str, None | | - | + +
+
+
+ 🌀 🏈 Expand for a full list of American Football features + +| Variable | Datatype | Index | Features | +|----------|-----------------------------------|-------|----------------------------------------------------------------------------------------------------------------------------------| +| a | np.array of shape (players+ball, players+ball) | | - | +| x | np.array of shape (n_nodes, n_node_features) | 0 | normalized x-coordinate | +| | | 1 | normalized y-coordinate | +| | | 2 | x component of the speed unit vector | +| | | 3 | y component of the speed unit vector | +| | | 4 | normalized speed | +| | | 5 | x component of the acceleration unit vector | +| | | 6 | y component of the acceleration unit vector | +| | | 7 | normalized acceleration | +| | | 8 | sine of the normalized direction (angle) | +| | | 9 | cosine of the normalized direction (angle) | +| | | 10 | sine of the normalized orientation (angle) | +| | | 11 | cosine of the normalized orientation (angle) | +| | | 12 | normalized distance to goal | +| | | 13 | normalized distance to ball | +| | | 14 | normalized distance to end zone | +| | | 15 | possession team or defending team (`defending_team_node_value`) indicator | +| | | 16 | quarterback indicator or `attacking_non_qb_node_value` or 0 (defending team) | +| | | 17 | ball indicator | +| | | 18 | normalized weight | +| | | 19 | normalized height | +| e | np.array of shape (np.non_zero(a), n_edge_features) | 0 | inter-player distance | +| | | 1 | inter-player speed difference | +| | | 2 | inter-player acceleration difference | +| | | 3 | cosine of the inter-player positional angle | +| | | 4 | sine of the inter-player positional angle | +| | | 5 | cosine of the inter-player direction angle | +| | | 6 | sine of the inter-player direction angle | +| | | 7 | cosine of the inter-player orientation angle | +| | | 8 | sine of the inter-player orientation angle | +| y | np.array | | - | +| id | int, str, None | | - | + +
+ +#### D. What is a CustomGraphDataset? + +
+ 🌀 Expand for a short explanation on CustomSpektralDataset
+ +Let's have a look at the internals of our `CustomSpektralDataset`. This dataset class contains a list of graphs, available through `dataset.graphs`. + +The first item in our dataset has 23 nodes, 12 features per node and 7 features per edge. + +
+ +```python +dataset.graphs[0] + +>>> Graph(n_nodes=23, n_node_features=12, n_edge_features=7, n_labels=1) +``` + +The `CustomSpektralDataset` also allows us to split our data into train and test sets (and validation set if required) by using either: +- `dataset.split_test_train_validation()` +- `dataset.split_test_train()` + +
+
+
+
+ 🌀 Expand for a short explanation on the representation of adjacency matrix
+ +##### Adjacency Matrix +The **adjacency matrix** is represented as a [compressed sparse row matrix](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html#scipy.sparse.csr_matrix), as required by Spektral. A 'normal' version of this same matrix would be of shape 23x23 filled with zero's and one's in places where two players (or ball) are connected. + +Because we set `adjacency_matrix_type='split_by_team'` and `adjacency_matrix_connect_type="ball"` this results in a total of 287 connections (ones), namely between: +- `adjacency_matrix_type='split_by_team'`: + - All players on team A (11 * 11) + - All players on team B (11 * 11) + - Ball connected to ball (1) +- `adjacency_matrix_connect_type="ball"` + - All players and the ball (22) + - The ball and all players (22) + +
+ +```python +dataset.graphs[0].a +>>> +``` +
+
+
+
+ 🌀 Expand for a short explanation on the representation of node feature matrix
+ +##### Node Features +The **node features** are described using a regular Numpy array. Each column represents one feature and every row represents one player. + +The ball is presented in the last row, unless we set `random_seed=True` then every Graph gets randomly shuffled (while leaving connections in tact). + +See the bullet points in **5. Load Kloppy Data, Convert and Store** to learn which column represents which feature. + +The rows filled with zero's are 'empty' players created because we set `pad=True`. Graph Neural Networks are flexible enough to deal with all sorts of different graph shapes in the same dataset, normally it's not actually necessary to add these empty players, even for incomplete data with only a couple players in frame. + +
+ +```python +dataset.graphs[0].x +>>> [[-0.163 -0.135 0.245 -0.97 0.007 0.289 0.959 0.191 0.059 0.376 1. 1. ] + [-0.332 0.011 -0.061 0.998 0.02 0.76 1.015 0.177 0.029 0.009 1. 0.1 ] + [ 0.021 -0.072 0.987 -0.162 0.017 0.474 0.88 0.203 0.121 0.468 1. 1. ] + [-0.144 0.232 0.343 0.939 0.024 0.694 0.924 0.186 0.077 0.638 1. 1. ] + [-0.252 0.302 0.99 0.141 0.032 0.523 0.964 0.176 0.078 0.741 1. 1. ] + [ 0.012 0.573 0.834 -0.551 0.035 0.407 0.842 0.191 0.19 0.646 1. 1. ] + [-0.293 0.686 0.999 -0.045 0.044 0.493 0.966 0.163 0.182 0.761 1. 1. ] + [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] + [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] + [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] + ... + [ 0.202 0.124 -0.874 0.486 0.024 0.919 0.791 0.214 0.197 0.524 0.1 0.1 ] + [ 0.404 0.143 -0.997 0.08 0.029 0.987 0.709 0.23 0.281 0.519 0.1 0.1 ] + [ 0.195 -0.391 0.48 -0.877 0.014 0.33 0.847 0.218 0.222 0.417 0.1 0.1 ] + [ 0.212 -0.063 0.982 -0.187 0.009 0.47 0.804 0.217 0.2 0.483 0.1 0.1 ] + [-0.03 0.248 -0.996 0.091 0.021 0.986 0.876 0.194 0.116 0.591 0.1 0.1 ] + [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] + [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] + [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] + [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] + [-0.262 0.016 0.937 -0.35 0.036 0.443 0.986 0.044 0. 0. 0. 0. ]] + + +dataset.graphs[0].x.shape +>>> (23, 12) +``` +
+
+
+
+ 🌀 Expand for a short explanation on the representation of edge feature matrix
+ +##### Edge Features +The **edge features** are also represented in a regular Numpy array. Again, each column represents one feature, and every row decribes the connection between two players, or player and ball. + +We saw before how the **adjacency matrix** was presented in a Sparse Row Matrix with 287 rows. It is no coincidence this lines up perfectly with the **edge feature matrix**. + +
+ +```python +dataset.graphs[0].e +>>> [[ 0. 0. 1. 0.5 0.5 1. 0. ] + [ 0.081 0.006 0.936 0.255 0.21 0.907 1. ] + [ 0.079 0.004 0.012 0.391 0. 0.515 1. ] + [ 0.1 0.007 0.46 0.002 0.005 0.571 1. ] + [ 0.125 0.011 0.65 0.023 0.474 0.999 0. ] + [ 0.206 0.012 0.322 0.033 0.535 0.999 0. ] + [ 0.23 0.016 0.619 0.014 0.567 0.996 0. ] + [ 0. 0. 0. 0. 0. 0. 0. ] + [ 0. 0. 0. 0. 0. 0. 0. ] + [ 0. 0. 0. 0. 0. 0. 0. ] + ... + [ 0.197 -0.025 0.005 0.426 0.929 0.757 1. ] + [ 0.281 -0.023 0.004 0.439 0.959 0.699 1. ] + [ 0.222 -0.03 0.067 0.75 0.979 0.643 1. ] + [ 0.2 -0.032 0.003 0.554 0.982 0.633 1. ] + [ 0.116 -0.026 0.08 0.229 0.82 0.884 1. ] + [ 0. 0. 0. 0. 0. 0. 1. ] + [ 0. 0. 0. 0. 0. 0. 1. ] + [ 0. 0. 0. 0. 0. 0. 1. ] + [ 0. 0. 0. 0. 0. 0. 1. ] + [ 0. 0. 1. 0.5 0.5 1. 1. ]] + + dataset.graphs[0].e.shape + (287, 7) +``` +
+
+ diff --git a/requirements.txt b/requirements.txt index 1c76f25..72295da 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,7 @@ +numpy==1.26.4 spektral==1.2.0 kloppy==3.15.0 tensorflow>=2.14.0; platform_machine != 'arm64' or platform_system != 'Darwin' tensorflow-macos>=2.14.0; platform_machine == 'arm64' and platform_system == 'Darwin' -keras==2.14.0 \ No newline at end of file +keras==2.14.0 +polars==1.2.1 \ No newline at end of file diff --git a/setup.py b/setup.py index 9f09948..9f4fa76 100644 --- a/setup.py +++ b/setup.py @@ -1,12 +1,13 @@ from setuptools import setup, find_packages import os import re +import codecs # Read the version from the __init__.py file def read_version(): version_file = os.path.join(os.path.dirname(__file__), "unravel", "__init__.py") - with open(version_file, "r") as f: + with codecs.open(version_file, "r", "utf-8") as f: version_match = re.search(r'^__version__ = ["\']([^"\']*)["\']', f.read(), re.M) if version_match: return version_match.group(1) @@ -35,6 +36,7 @@ def read_version(): "tensorflow>=2.14.0;platform_machine != 'arm64' or platform_system != 'Darwin'", "tensorflow-macos>=2.14.0;platform_machine == 'arm64' and platform_system == 'Darwin'", "keras==2.14.0", + "polars==1.2.1", ], extras_require={ "test": [ diff --git a/tests/files/bdb_coords-1.csv b/tests/files/bdb_coords-1.csv new file mode 100644 index 0000000..43ff093 --- /dev/null +++ b/tests/files/bdb_coords-1.csv @@ -0,0 +1,6050 @@ +gameId,playId,nflId,frameId,time,jerseyNumber,team,playDirection,x,y,s,a,dis,o,dir,event +2021091300,4845,33131.0,1,2021-09-14T03:54:17.700,93.0,BAL,left,39.83,21.46,0.0,0.0,0.01,104.04,297.88, +2021091300,4845,33131.0,2,2021-09-14T03:54:17.800,93.0,BAL,left,39.83,21.46,0.0,0.0,0.0,104.77,292.34, +2021091300,4845,33131.0,3,2021-09-14T03:54:17.900,93.0,BAL,left,39.83,21.45,0.0,0.0,0.01,104.77,277.11, +2021091300,4845,33131.0,4,2021-09-14T03:54:18.000,93.0,BAL,left,39.83,21.45,0.01,0.16,0.0,104.77,191.25, 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+2021091300,4845,37240.0,16,2021-09-14T03:54:19.200,90.0,BAL,left,41.48,20.05,3.8,2.02,0.37,81.92,41.82, +2021091300,4845,37240.0,17,2021-09-14T03:54:19.300,90.0,BAL,left,41.73,20.34,3.88,1.51,0.38,86.07,39.96, +2021091300,4845,37240.0,18,2021-09-14T03:54:19.400,90.0,BAL,left,41.97,20.64,3.92,1.12,0.39,89.09,38.37, +2021091300,4845,37240.0,19,2021-09-14T03:54:19.500,90.0,BAL,left,42.19,20.96,3.96,1.36,0.39,93.46,34.65, +2021091300,4845,37240.0,20,2021-09-14T03:54:19.600,90.0,BAL,left,42.42,21.27,3.88,1.02,0.39,97.35,34.34, +2021091300,4845,37240.0,21,2021-09-14T03:54:19.700,90.0,BAL,left,42.63,21.59,3.78,1.15,0.38,99.49,33.3, +2021091300,4845,37240.0,22,2021-09-14T03:54:19.800,90.0,BAL,left,42.84,21.89,3.65,1.26,0.37,105.78,33.43, +2021091300,4845,37240.0,23,2021-09-14T03:54:19.900,90.0,BAL,left,43.04,22.18,3.49,1.55,0.35,118.91,35.65, +2021091300,4845,37240.0,24,2021-09-14T03:54:20.000,90.0,BAL,left,43.25,22.45,3.23,2.31,0.33,120.96,38.62, +2021091300,4845,37240.0,25,2021-09-14T03:54:20.100,90.0,BAL,left,43.45,22.65,2.8,3.26,0.29,124.41,46.48, +2021091300,4845,37240.0,26,2021-09-14T03:54:20.200,90.0,BAL,left,43.67,22.8,2.53,3.59,0.26,126.11,56.37, +2021091300,4845,37240.0,27,2021-09-14T03:54:20.300,90.0,BAL,left,43.89,22.89,2.32,4.46,0.24,124.07,70.85, +2021091300,4845,37240.0,28,2021-09-14T03:54:20.400,90.0,BAL,left,44.11,22.93,2.29,4.82,0.23,124.77,85.27, +2021091300,4845,37240.0,29,2021-09-14T03:54:20.500,90.0,BAL,left,44.35,22.93,2.4,4.58,0.24,124.77,97.27,autoevent_passforward +2021091300,4845,37240.0,30,2021-09-14T03:54:20.600,90.0,BAL,left,44.6,22.88,2.65,4.15,0.25,120.56,105.97,pass_forward +2021091300,4845,37240.0,31,2021-09-14T03:54:20.700,90.0,BAL,left,44.86,22.79,2.93,3.57,0.28,119.6,111.74, +2021091300,4845,37240.0,32,2021-09-14T03:54:20.800,90.0,BAL,left,45.14,22.67,3.17,2.92,0.3,117.52,115.59, +2021091300,4845,37240.0,33,2021-09-14T03:54:20.900,90.0,BAL,left,45.44,22.52,3.38,2.21,0.33,113.72,117.8, +2021091300,4845,37240.0,34,2021-09-14T03:54:21.000,90.0,BAL,left,45.74,22.36,3.52,1.46,0.34,106.48,118.54, +2021091300,4845,40042.0,1,2021-09-14T03:54:17.700,98.0,BAL,left,39.97,24.26,0.0,0.0,0.01,106.35,326.6, +2021091300,4845,40042.0,2,2021-09-14T03:54:17.800,98.0,BAL,left,39.97,24.26,0.0,0.0,0.01,108.22,325.1, +2021091300,4845,40042.0,3,2021-09-14T03:54:17.900,98.0,BAL,left,39.98,24.26,0.0,0.0,0.0,111.9,335.01, +2021091300,4845,40042.0,4,2021-09-14T03:54:18.000,98.0,BAL,left,39.99,24.27,0.0,0.0,0.01,107.54,9.71, +2021091300,4845,40042.0,5,2021-09-14T03:54:18.100,98.0,BAL,left,39.99,24.27,0.05,0.77,0.01,104.89,63.03, +2021091300,4845,40042.0,6,2021-09-14T03:54:18.200,98.0,BAL,left,40.02,24.28,0.3,2.27,0.02,103.02,72.97,autoevent_ballsnap +2021091300,4845,40042.0,7,2021-09-14T03:54:18.300,98.0,BAL,left,40.06,24.29,0.56,2.75,0.04,98.09,76.11,ball_snap +2021091300,4845,40042.0,8,2021-09-14T03:54:18.400,98.0,BAL,left,40.13,24.3,0.88,2.99,0.07,94.96,77.58, +2021091300,4845,40042.0,9,2021-09-14T03:54:18.500,98.0,BAL,left,40.26,24.32,1.36,3.4,0.13,85.95,80.13, +2021091300,4845,40042.0,10,2021-09-14T03:54:18.600,98.0,BAL,left,40.42,24.34,1.72,3.06,0.16,63.35,81.83, +2021091300,4845,40042.0,11,2021-09-14T03:54:18.700,98.0,BAL,left,40.6,24.37,1.99,2.6,0.18,70.24,82.32, +2021091300,4845,40042.0,12,2021-09-14T03:54:18.800,98.0,BAL,left,40.8,24.39,2.19,1.95,0.21,82.72,83.68, +2021091300,4845,40042.0,13,2021-09-14T03:54:18.900,98.0,BAL,left,41.04,24.41,2.39,1.56,0.23,93.95,85.45, +2021091300,4845,40042.0,14,2021-09-14T03:54:19.000,98.0,BAL,left,41.28,24.43,2.48,0.92,0.24,101.3,85.78, +2021091300,4845,40042.0,15,2021-09-14T03:54:19.100,98.0,BAL,left,41.53,24.44,2.53,0.45,0.25,101.3,86.42, +2021091300,4845,40042.0,16,2021-09-14T03:54:19.200,98.0,BAL,left,41.78,24.45,2.53,0.35,0.25,105.23,87.75, +2021091300,4845,40042.0,17,2021-09-14T03:54:19.300,98.0,BAL,left,42.04,24.46,2.5,0.39,0.25,106.57,86.28, +2021091300,4845,40042.0,18,2021-09-14T03:54:19.400,98.0,BAL,left,42.28,24.48,2.49,0.55,0.25,110.72,85.13, +2021091300,4845,40042.0,19,2021-09-14T03:54:19.500,98.0,BAL,left,42.53,24.51,2.52,0.6,0.25,113.63,83.51, +2021091300,4845,40042.0,20,2021-09-14T03:54:19.600,98.0,BAL,left,42.79,24.54,2.54,0.54,0.25,111.85,82.61, +2021091300,4845,40042.0,21,2021-09-14T03:54:19.700,98.0,BAL,left,43.03,24.57,2.49,0.56,0.25,118.57,81.8, +2021091300,4845,40042.0,22,2021-09-14T03:54:19.800,98.0,BAL,left,43.28,24.61,2.52,0.54,0.25,129.13,79.94, +2021091300,4845,40042.0,23,2021-09-14T03:54:19.900,98.0,BAL,left,43.53,24.66,2.57,0.41,0.25,143.64,79.12, +2021091300,4845,40042.0,24,2021-09-14T03:54:20.000,98.0,BAL,left,43.78,24.71,2.65,0.28,0.26,159.14,80.61, +2021091300,4845,40042.0,25,2021-09-14T03:54:20.100,98.0,BAL,left,44.04,24.75,2.71,0.39,0.26,166.83,80.47, +2021091300,4845,40042.0,26,2021-09-14T03:54:20.200,98.0,BAL,left,44.31,24.79,2.77,0.54,0.27,173.52,79.83, +2021091300,4845,40042.0,27,2021-09-14T03:54:20.300,98.0,BAL,left,44.59,24.85,2.9,0.69,0.28,175.36,78.33, +2021091300,4845,40042.0,28,2021-09-14T03:54:20.400,98.0,BAL,left,44.87,24.91,2.96,0.74,0.29,168.48,76.11, +2021091300,4845,40042.0,29,2021-09-14T03:54:20.500,98.0,BAL,left,45.16,24.98,3.04,0.75,0.3,160.33,75.11,autoevent_passforward +2021091300,4845,40042.0,30,2021-09-14T03:54:20.600,98.0,BAL,left,45.46,25.07,3.17,0.88,0.31,159.02,73.8,pass_forward +2021091300,4845,40042.0,31,2021-09-14T03:54:20.700,98.0,BAL,left,45.77,25.16,3.28,0.86,0.33,159.02,72.43, +2021091300,4845,40042.0,32,2021-09-14T03:54:20.800,98.0,BAL,left,46.09,25.26,3.31,0.67,0.33,168.76,71.28, +2021091300,4845,40042.0,33,2021-09-14T03:54:20.900,98.0,BAL,left,46.4,25.38,3.32,0.69,0.33,176.78,69.43, +2021091300,4845,40042.0,34,2021-09-14T03:54:21.000,98.0,BAL,left,46.7,25.49,3.25,0.57,0.33,184.05,69.49, +2021091300,4845,41265.0,1,2021-09-14T03:54:17.700,4.0,LV,left,44.88,23.77,0.09,0.08,0.02,279.75,243.63, +2021091300,4845,41265.0,2,2021-09-14T03:54:17.800,4.0,LV,left,44.89,23.77,0.07,0.07,0.01,279.75,242.03, +2021091300,4845,41265.0,3,2021-09-14T03:54:17.900,4.0,LV,left,44.91,23.77,0.06,0.06,0.02,279.75,239.74, +2021091300,4845,41265.0,4,2021-09-14T03:54:18.000,4.0,LV,left,44.93,23.77,0.04,0.04,0.02,279.09,233.85, +2021091300,4845,41265.0,5,2021-09-14T03:54:18.100,4.0,LV,left,44.97,23.78,0.02,0.02,0.04,278.41,219.54, +2021091300,4845,41265.0,6,2021-09-14T03:54:18.200,4.0,LV,left,45.01,23.78,0.02,0.02,0.04,277.77,183.79,autoevent_ballsnap +2021091300,4845,41265.0,7,2021-09-14T03:54:18.300,4.0,LV,left,45.03,23.77,0.07,1.15,0.02,281.24,106.13,ball_snap +2021091300,4845,41265.0,8,2021-09-14T03:54:18.400,4.0,LV,left,45.05,23.77,0.28,2.18,0.03,289.55,94.67, +2021091300,4845,41265.0,9,2021-09-14T03:54:18.500,4.0,LV,left,45.11,23.77,0.7,3.45,0.06,294.85,94.74, +2021091300,4845,41265.0,10,2021-09-14T03:54:18.600,4.0,LV,left,45.21,23.75,1.14,3.93,0.1,296.03,96.72, +2021091300,4845,41265.0,11,2021-09-14T03:54:18.700,4.0,LV,left,45.36,23.73,1.67,4.03,0.15,299.34,97.66, +2021091300,4845,41265.0,12,2021-09-14T03:54:18.800,4.0,LV,left,45.56,23.73,2.12,3.72,0.2,306.18,94.51, +2021091300,4845,41265.0,13,2021-09-14T03:54:18.900,4.0,LV,left,45.8,23.71,2.58,3.54,0.25,310.49,93.61, +2021091300,4845,41265.0,14,2021-09-14T03:54:19.000,4.0,LV,left,46.08,23.7,2.95,3.2,0.28,317.53,92.07, +2021091300,4845,41265.0,15,2021-09-14T03:54:19.100,4.0,LV,left,46.4,23.7,3.28,2.78,0.32,328.16,89.87, +2021091300,4845,41265.0,16,2021-09-14T03:54:19.200,4.0,LV,left,46.76,23.72,3.71,2.82,0.36,334.98,87.34, +2021091300,4845,41265.0,17,2021-09-14T03:54:19.300,4.0,LV,left,47.15,23.74,4.03,2.57,0.39,339.88,85.5, +2021091300,4845,41265.0,18,2021-09-14T03:54:19.400,4.0,LV,left,47.56,23.79,4.3,2.26,0.42,344.23,83.87, +2021091300,4845,41265.0,19,2021-09-14T03:54:19.500,4.0,LV,left,48.01,23.84,4.51,1.81,0.44,347.87,82.73, +2021091300,4845,41265.0,20,2021-09-14T03:54:19.600,4.0,LV,left,48.46,23.9,4.68,1.49,0.46,350.04,81.37, +2021091300,4845,41265.0,21,2021-09-14T03:54:19.700,4.0,LV,left,48.93,23.98,4.76,0.99,0.47,353.83,80.35, +2021091300,4845,41265.0,22,2021-09-14T03:54:19.800,4.0,LV,left,49.4,24.06,4.75,0.75,0.48,357.79,79.14, +2021091300,4845,41265.0,23,2021-09-14T03:54:19.900,4.0,LV,left,49.86,24.15,4.68,0.78,0.47,359.49,79.0, +2021091300,4845,41265.0,24,2021-09-14T03:54:20.000,4.0,LV,left,50.31,24.24,4.52,1.34,0.46,359.49,77.83, +2021091300,4845,41265.0,25,2021-09-14T03:54:20.100,4.0,LV,left,50.74,24.34,4.32,1.84,0.44,0.29,76.89, +2021091300,4845,41265.0,26,2021-09-14T03:54:20.200,4.0,LV,left,51.14,24.44,4.06,2.25,0.42,1.7,76.1, +2021091300,4845,41265.0,27,2021-09-14T03:54:20.300,4.0,LV,left,51.52,24.52,3.74,2.57,0.38,349.3,77.41, +2021091300,4845,41265.0,28,2021-09-14T03:54:20.400,4.0,LV,left,51.87,24.59,3.45,2.72,0.36,330.31,78.62, +2021091300,4845,41265.0,29,2021-09-14T03:54:20.500,4.0,LV,left,52.19,24.65,3.15,2.79,0.33,295.52,80.24,autoevent_passforward +2021091300,4845,41265.0,30,2021-09-14T03:54:20.600,4.0,LV,left,52.49,24.69,2.88,2.74,0.3,265.72,82.73,pass_forward +2021091300,4845,41265.0,31,2021-09-14T03:54:20.700,4.0,LV,left,52.77,24.71,2.69,2.58,0.28,242.56,87.52, +2021091300,4845,41265.0,32,2021-09-14T03:54:20.800,4.0,LV,left,53.03,24.71,2.56,2.39,0.27,231.94,92.39, +2021091300,4845,41265.0,33,2021-09-14T03:54:20.900,4.0,LV,left,53.3,24.68,2.54,2.09,0.26,224.11,97.61, +2021091300,4845,41265.0,34,2021-09-14T03:54:21.000,4.0,LV,left,53.55,24.63,2.53,2.03,0.26,218.79,103.8, +2021091300,4845,42547.0,1,2021-09-14T03:54:17.700,83.0,LV,left,43.08,14.47,0.0,0.0,0.0,264.72,120.73, +2021091300,4845,42547.0,2,2021-09-14T03:54:17.800,83.0,LV,left,43.08,14.47,0.0,0.0,0.0,264.72,118.96, +2021091300,4845,42547.0,3,2021-09-14T03:54:17.900,83.0,LV,left,43.08,14.47,0.0,0.0,0.0,263.95,129.34, +2021091300,4845,42547.0,4,2021-09-14T03:54:18.000,83.0,LV,left,43.08,14.47,0.0,0.0,0.0,263.95,141.11, +2021091300,4845,42547.0,5,2021-09-14T03:54:18.100,83.0,LV,left,43.08,14.47,0.0,0.0,0.0,263.95,147.78, +2021091300,4845,42547.0,6,2021-09-14T03:54:18.200,83.0,LV,left,43.08,14.47,0.0,0.0,0.0,263.95,199.87,autoevent_ballsnap +2021091300,4845,42547.0,7,2021-09-14T03:54:18.300,83.0,LV,left,43.07,14.47,0.01,0.29,0.01,262.83,263.92,ball_snap +2021091300,4845,42547.0,8,2021-09-14T03:54:18.400,83.0,LV,left,43.07,14.47,0.11,1.35,0.01,261.03,263.74, +2021091300,4845,42547.0,9,2021-09-14T03:54:18.500,83.0,LV,left,43.04,14.46,0.33,2.15,0.03,261.03,262.92, +2021091300,4845,42547.0,10,2021-09-14T03:54:18.600,83.0,LV,left,42.99,14.44,0.66,2.7,0.06,261.98,253.75, +2021091300,4845,42547.0,11,2021-09-14T03:54:18.700,83.0,LV,left,42.89,14.4,1.13,3.47,0.11,263.44,247.92, +2021091300,4845,42547.0,12,2021-09-14T03:54:18.800,83.0,LV,left,42.77,14.35,1.5,3.36,0.13,262.02,246.68, +2021091300,4845,42547.0,13,2021-09-14T03:54:18.900,83.0,LV,left,42.62,14.27,1.87,3.43,0.17,258.4,242.59, +2021091300,4845,42547.0,14,2021-09-14T03:54:19.000,83.0,LV,left,42.42,14.16,2.32,3.33,0.22,257.07,240.34, +2021091300,4845,42547.0,15,2021-09-14T03:54:19.100,83.0,LV,left,42.2,14.02,2.68,3.0,0.25,249.15,238.32, +2021091300,4845,42547.0,16,2021-09-14T03:54:19.200,83.0,LV,left,41.96,13.88,2.98,2.49,0.29,240.42,238.28, +2021091300,4845,42547.0,17,2021-09-14T03:54:19.300,83.0,LV,left,41.69,13.71,3.26,2.07,0.32,236.63,237.8, +2021091300,4845,42547.0,18,2021-09-14T03:54:19.400,83.0,LV,left,41.39,13.54,3.52,1.91,0.34,238.79,238.96, +2021091300,4845,42547.0,19,2021-09-14T03:54:19.500,83.0,LV,left,41.07,13.36,3.75,1.89,0.37,243.24,241.93, +2021091300,4845,42547.0,20,2021-09-14T03:54:19.600,83.0,LV,left,40.73,13.19,3.93,1.89,0.38,245.76,244.61, +2021091300,4845,42547.0,21,2021-09-14T03:54:19.700,83.0,LV,left,40.36,13.02,4.07,2.12,0.4,248.52,247.98, +2021091300,4845,42547.0,22,2021-09-14T03:54:19.800,83.0,LV,left,39.98,12.88,4.15,2.37,0.41,251.9,251.7, +2021091300,4845,42547.0,23,2021-09-14T03:54:19.900,83.0,LV,left,39.58,12.76,4.2,2.54,0.42,254.73,255.58, +2021091300,4845,42547.0,24,2021-09-14T03:54:20.000,83.0,LV,left,39.17,12.66,4.22,2.88,0.42,259.8,260.06, +2021091300,4845,42547.0,25,2021-09-14T03:54:20.100,83.0,LV,left,38.75,12.61,4.24,2.92,0.42,263.36,264.69, +2021091300,4845,42547.0,26,2021-09-14T03:54:20.200,83.0,LV,left,38.32,12.6,4.29,3.69,0.43,270.31,272.3, +2021091300,4845,42547.0,27,2021-09-14T03:54:20.300,83.0,LV,left,37.89,12.65,4.36,4.07,0.43,283.51,279.15, +2021091300,4845,42547.0,28,2021-09-14T03:54:20.400,83.0,LV,left,37.46,12.74,4.43,4.52,0.44,294.7,286.4, +2021091300,4845,42547.0,29,2021-09-14T03:54:20.500,83.0,LV,left,37.05,12.9,4.51,4.63,0.45,300.52,293.63,autoevent_passforward +2021091300,4845,42547.0,30,2021-09-14T03:54:20.600,83.0,LV,left,36.64,13.1,4.65,4.54,0.46,307.06,300.11,pass_forward +2021091300,4845,42547.0,31,2021-09-14T03:54:20.700,83.0,LV,left,36.24,13.36,4.82,4.65,0.47,312.73,306.63, +2021091300,4845,42547.0,32,2021-09-14T03:54:20.800,83.0,LV,left,35.87,13.68,5.01,4.77,0.49,319.68,313.3, +2021091300,4845,42547.0,33,2021-09-14T03:54:20.900,83.0,LV,left,35.51,14.05,5.28,4.53,0.52,325.52,319.36, +2021091300,4845,42547.0,34,2021-09-14T03:54:21.000,83.0,LV,left,35.18,14.49,5.52,4.25,0.54,327.62,325.02, +2021091300,4845,43362.0,1,2021-09-14T03:54:17.700,23.0,LV,left,45.5,21.83,0.0,0.0,0.0,279.03,223.28, +2021091300,4845,43362.0,2,2021-09-14T03:54:17.800,23.0,LV,left,45.5,21.83,0.0,0.0,0.0,279.03,224.47, +2021091300,4845,43362.0,3,2021-09-14T03:54:17.900,23.0,LV,left,45.5,21.83,0.0,0.0,0.0,279.03,232.73, +2021091300,4845,43362.0,4,2021-09-14T03:54:18.000,23.0,LV,left,45.49,21.83,0.0,0.0,0.0,279.03,237.6, +2021091300,4845,43362.0,5,2021-09-14T03:54:18.100,23.0,LV,left,45.49,21.84,0.0,0.0,0.0,279.03,255.82, +2021091300,4845,43362.0,6,2021-09-14T03:54:18.200,23.0,LV,left,45.49,21.84,0.0,0.0,0.0,279.03,268.57,autoevent_ballsnap +2021091300,4845,43362.0,7,2021-09-14T03:54:18.300,23.0,LV,left,45.49,21.84,0.0,0.0,0.0,279.03,295.28,ball_snap +2021091300,4845,43362.0,8,2021-09-14T03:54:18.400,23.0,LV,left,45.49,21.85,0.05,0.92,0.01,279.03,338.1, +2021091300,4845,43362.0,9,2021-09-14T03:54:18.500,23.0,LV,left,45.48,21.87,0.26,2.17,0.02,280.35,340.34, +2021091300,4845,43362.0,10,2021-09-14T03:54:18.600,23.0,LV,left,45.47,21.91,0.6,3.07,0.05,281.11,340.05, +2021091300,4845,43362.0,11,2021-09-14T03:54:18.700,23.0,LV,left,45.44,22.0,1.02,3.46,0.09,282.61,340.65, +2021091300,4845,43362.0,12,2021-09-14T03:54:18.800,23.0,LV,left,45.4,22.12,1.43,3.52,0.13,283.93,341.88, +2021091300,4845,43362.0,13,2021-09-14T03:54:18.900,23.0,LV,left,45.36,22.3,1.9,3.78,0.18,287.16,346.07, +2021091300,4845,43362.0,14,2021-09-14T03:54:19.000,23.0,LV,left,45.33,22.52,2.32,3.49,0.22,291.79,349.91, +2021091300,4845,43362.0,15,2021-09-14T03:54:19.100,23.0,LV,left,45.32,22.78,2.69,3.12,0.26,299.48,354.13, +2021091300,4845,43362.0,16,2021-09-14T03:54:19.200,23.0,LV,left,45.35,23.07,3.02,3.1,0.29,313.31,1.19, +2021091300,4845,43362.0,17,2021-09-14T03:54:19.300,23.0,LV,left,45.38,23.38,3.28,3.05,0.31,328.88,6.29, +2021091300,4845,43362.0,18,2021-09-14T03:54:19.400,23.0,LV,left,45.44,23.72,3.55,3.47,0.35,348.32,11.57, +2021091300,4845,43362.0,19,2021-09-14T03:54:19.500,23.0,LV,left,45.54,24.08,3.8,3.86,0.37,4.17,19.17, +2021091300,4845,43362.0,20,2021-09-14T03:54:19.600,23.0,LV,left,45.69,24.44,4.03,3.99,0.39,16.32,25.36, +2021091300,4845,43362.0,21,2021-09-14T03:54:19.700,23.0,LV,left,45.88,24.81,4.23,3.82,0.41,24.85,30.23, +2021091300,4845,43362.0,22,2021-09-14T03:54:19.800,23.0,LV,left,46.12,25.17,4.39,3.73,0.43,29.88,36.22, +2021091300,4845,43362.0,23,2021-09-14T03:54:19.900,23.0,LV,left,46.41,25.51,4.59,3.57,0.45,40.12,41.54, +2021091300,4845,43362.0,24,2021-09-14T03:54:20.000,23.0,LV,left,46.73,25.86,4.79,3.09,0.48,48.75,45.12, +2021091300,4845,43362.0,25,2021-09-14T03:54:20.100,23.0,LV,left,47.08,26.18,4.81,2.46,0.48,56.61,48.25, +2021091300,4845,43362.0,26,2021-09-14T03:54:20.200,23.0,LV,left,47.45,26.5,4.83,1.94,0.48,59.7,50.64, +2021091300,4845,43362.0,27,2021-09-14T03:54:20.300,23.0,LV,left,47.79,26.78,4.48,1.91,0.45,84.43,51.85, +2021091300,4845,43362.0,28,2021-09-14T03:54:20.400,23.0,LV,left,48.13,27.05,4.17,2.53,0.43,101.18,52.26, +2021091300,4845,43362.0,29,2021-09-14T03:54:20.500,23.0,LV,left,48.44,27.29,3.68,3.81,0.39,113.53,51.1,autoevent_passforward +2021091300,4845,43362.0,30,2021-09-14T03:54:20.600,23.0,LV,left,48.7,27.51,3.18,4.36,0.35,121.56,49.85,pass_forward +2021091300,4845,43362.0,31,2021-09-14T03:54:20.700,23.0,LV,left,48.92,27.7,2.58,4.71,0.29,129.38,47.2, +2021091300,4845,43362.0,32,2021-09-14T03:54:20.800,23.0,LV,left,49.09,27.86,2.09,4.82,0.23,137.79,43.1, +2021091300,4845,43362.0,33,2021-09-14T03:54:20.900,23.0,LV,left,49.21,28.0,1.62,4.49,0.18,142.71,36.48, +2021091300,4845,43362.0,34,2021-09-14T03:54:21.000,23.0,LV,left,49.28,28.12,1.27,3.93,0.14,149.09,27.14, +2021091300,4845,44828.0,1,2021-09-14T03:54:17.700,44.0,BAL,left,39.89,16.47,0.01,0.01,0.01,152.21,178.36, +2021091300,4845,44828.0,2,2021-09-14T03:54:17.800,44.0,BAL,left,39.88,16.47,0.01,0.01,0.01,162.93,192.86, +2021091300,4845,44828.0,3,2021-09-14T03:54:17.900,44.0,BAL,left,39.88,16.47,0.01,0.01,0.0,169.1,203.22, +2021091300,4845,44828.0,4,2021-09-14T03:54:18.000,44.0,BAL,left,39.86,16.48,0.01,0.01,0.02,171.24,233.4, +2021091300,4845,44828.0,5,2021-09-14T03:54:18.100,44.0,BAL,left,39.84,16.49,0.02,0.01,0.03,167.81,258.36, +2021091300,4845,44828.0,6,2021-09-14T03:54:18.200,44.0,BAL,left,39.78,16.49,0.03,0.03,0.06,158.89,265.35,autoevent_ballsnap +2021091300,4845,44828.0,7,2021-09-14T03:54:18.300,44.0,BAL,left,39.72,16.51,0.04,0.04,0.06,146.26,273.95,ball_snap +2021091300,4845,44828.0,8,2021-09-14T03:54:18.400,44.0,BAL,left,39.69,16.53,0.05,0.27,0.04,130.52,280.3, +2021091300,4845,44828.0,9,2021-09-14T03:54:18.500,44.0,BAL,left,39.65,16.56,0.21,2.09,0.05,88.74,297.57, +2021091300,4845,44828.0,10,2021-09-14T03:54:18.600,44.0,BAL,left,39.62,16.58,0.58,3.65,0.03,81.05,303.35, +2021091300,4845,44828.0,11,2021-09-14T03:54:18.700,44.0,BAL,left,39.56,16.62,1.02,3.8,0.08,76.26,309.05, +2021091300,4845,44828.0,12,2021-09-14T03:54:18.800,44.0,BAL,left,39.47,16.71,1.48,3.67,0.13,62.51,313.77, +2021091300,4845,44828.0,13,2021-09-14T03:54:18.900,44.0,BAL,left,39.35,16.85,1.97,3.56,0.18,54.05,318.35, +2021091300,4845,44828.0,14,2021-09-14T03:54:19.000,44.0,BAL,left,39.21,17.02,2.35,3.1,0.22,40.55,320.49, +2021091300,4845,44828.0,15,2021-09-14T03:54:19.100,44.0,BAL,left,39.04,17.25,2.85,3.07,0.29,10.46,323.08, +2021091300,4845,44828.0,16,2021-09-14T03:54:19.200,44.0,BAL,left,38.85,17.5,3.19,2.62,0.31,5.31,323.47, +2021091300,4845,44828.0,17,2021-09-14T03:54:19.300,44.0,BAL,left,38.63,17.77,3.54,2.49,0.35,353.15,322.53, +2021091300,4845,44828.0,18,2021-09-14T03:54:19.400,44.0,BAL,left,38.39,18.07,3.86,2.16,0.38,339.86,322.34, +2021091300,4845,44828.0,19,2021-09-14T03:54:19.500,44.0,BAL,left,38.14,18.39,4.17,1.95,0.41,319.81,321.94, +2021091300,4845,44828.0,20,2021-09-14T03:54:19.600,44.0,BAL,left,37.87,18.73,4.43,1.8,0.44,309.98,321.33, +2021091300,4845,44828.0,21,2021-09-14T03:54:19.700,44.0,BAL,left,37.55,19.09,4.79,2.09,0.47,313.17,318.13, +2021091300,4845,44828.0,22,2021-09-14T03:54:19.800,44.0,BAL,left,37.21,19.44,5.04,2.18,0.5,313.99,315.88, +2021091300,4845,44828.0,23,2021-09-14T03:54:19.900,44.0,BAL,left,36.82,19.81,5.39,2.53,0.53,307.13,312.78, +2021091300,4845,44828.0,24,2021-09-14T03:54:20.000,44.0,BAL,left,36.4,20.18,5.63,2.3,0.55,305.94,310.6, +2021091300,4845,44828.0,25,2021-09-14T03:54:20.100,44.0,BAL,left,35.96,20.55,5.86,2.22,0.58,306.89,308.8, +2021091300,4845,44828.0,26,2021-09-14T03:54:20.200,44.0,BAL,left,35.47,20.91,6.14,2.49,0.61,312.56,306.04, +2021091300,4845,44828.0,27,2021-09-14T03:54:20.300,44.0,BAL,left,34.96,21.28,6.41,2.24,0.63,313.77,304.44, +2021091300,4845,44828.0,28,2021-09-14T03:54:20.400,44.0,BAL,left,34.41,21.64,6.69,2.12,0.66,314.41,303.09, +2021091300,4845,44828.0,29,2021-09-14T03:54:20.500,44.0,BAL,left,33.84,22.01,6.87,1.65,0.68,312.72,301.94,autoevent_passforward +2021091300,4845,44828.0,30,2021-09-14T03:54:20.600,44.0,BAL,left,33.24,22.38,7.13,1.28,0.71,319.32,301.62,pass_forward +2021091300,4845,44828.0,31,2021-09-14T03:54:20.700,44.0,BAL,left,32.62,22.77,7.34,0.95,0.73,319.32,301.51, +2021091300,4845,44828.0,32,2021-09-14T03:54:20.800,44.0,BAL,left,31.98,23.16,7.49,0.6,0.75,322.15,301.21, +2021091300,4845,44828.0,33,2021-09-14T03:54:20.900,44.0,BAL,left,31.33,23.56,7.62,0.24,0.76,323.54,301.54, +2021091300,4845,44828.0,34,2021-09-14T03:54:21.000,44.0,BAL,left,30.67,23.98,7.81,0.54,0.78,323.54,302.7, +2021091300,4845,44849.0,1,2021-09-14T03:54:17.700,7.0,LV,left,41.61,16.46,0.0,0.0,0.0,274.76,153.46, +2021091300,4845,44849.0,2,2021-09-14T03:54:17.800,7.0,LV,left,41.6,16.46,0.0,0.0,0.0,274.76,154.91, +2021091300,4845,44849.0,3,2021-09-14T03:54:17.900,7.0,LV,left,41.6,16.46,0.0,0.0,0.0,275.86,159.3, +2021091300,4845,44849.0,4,2021-09-14T03:54:18.000,7.0,LV,left,41.6,16.46,0.0,0.1,0.01,275.86,287.71, +2021091300,4845,44849.0,5,2021-09-14T03:54:18.100,7.0,LV,left,41.59,16.46,0.07,0.84,0.0,273.76,307.21, +2021091300,4845,44849.0,6,2021-09-14T03:54:18.200,7.0,LV,left,41.58,16.48,0.23,1.46,0.02,273.76,312.78,autoevent_ballsnap +2021091300,4845,44849.0,7,2021-09-14T03:54:18.300,7.0,LV,left,41.55,16.51,0.52,2.29,0.05,272.96,312.92,ball_snap +2021091300,4845,44849.0,8,2021-09-14T03:54:18.400,7.0,LV,left,41.49,16.58,0.92,3.02,0.09,272.96,318.11, +2021091300,4845,44849.0,9,2021-09-14T03:54:18.500,7.0,LV,left,41.37,16.68,1.52,3.78,0.16,275.0,313.29, +2021091300,4845,44849.0,10,2021-09-14T03:54:18.600,7.0,LV,left,41.23,16.81,2.06,4.18,0.2,278.83,313.5, +2021091300,4845,44849.0,11,2021-09-14T03:54:18.700,7.0,LV,left,41.05,17.0,2.64,4.25,0.26,285.57,315.0, +2021091300,4845,44849.0,12,2021-09-14T03:54:18.800,7.0,LV,left,40.84,17.21,3.11,4.07,0.29,287.66,315.34, +2021091300,4845,44849.0,13,2021-09-14T03:54:18.900,7.0,LV,left,40.58,17.47,3.77,4.34,0.37,289.7,314.91, +2021091300,4845,44849.0,14,2021-09-14T03:54:19.000,7.0,LV,left,40.29,17.76,4.29,4.18,0.41,299.15,315.08, +2021091300,4845,44849.0,15,2021-09-14T03:54:19.100,7.0,LV,left,39.96,18.08,4.84,3.97,0.46,304.65,314.81, +2021091300,4845,44849.0,16,2021-09-14T03:54:19.200,7.0,LV,left,39.6,18.44,5.27,3.41,0.51,299.3,314.87, +2021091300,4845,44849.0,17,2021-09-14T03:54:19.300,7.0,LV,left,39.2,18.83,5.7,3.02,0.56,291.95,314.53, +2021091300,4845,44849.0,18,2021-09-14T03:54:19.400,7.0,LV,left,38.76,19.24,6.15,3.02,0.6,313.49,312.68, +2021091300,4845,44849.0,19,2021-09-14T03:54:19.500,7.0,LV,left,38.28,19.67,6.5,2.67,0.64,319.92,311.34, +2021091300,4845,44849.0,20,2021-09-14T03:54:19.600,7.0,LV,left,37.77,20.1,6.79,2.26,0.67,326.73,310.16, +2021091300,4845,44849.0,21,2021-09-14T03:54:19.700,7.0,LV,left,37.23,20.55,7.1,2.04,0.7,330.74,308.98, +2021091300,4845,44849.0,22,2021-09-14T03:54:19.800,7.0,LV,left,36.65,21.0,7.49,2.24,0.74,340.14,307.65, +2021091300,4845,44849.0,23,2021-09-14T03:54:19.900,7.0,LV,left,36.04,21.46,7.7,1.94,0.76,342.93,306.59, +2021091300,4845,44849.0,24,2021-09-14T03:54:20.000,7.0,LV,left,35.41,21.92,7.89,1.47,0.78,345.59,305.81, +2021091300,4845,44849.0,25,2021-09-14T03:54:20.100,7.0,LV,left,34.75,22.39,8.06,1.06,0.8,349.06,305.21, +2021091300,4845,44849.0,26,2021-09-14T03:54:20.200,7.0,LV,left,34.08,22.86,8.28,0.94,0.82,359.4,304.85, +2021091300,4845,44849.0,27,2021-09-14T03:54:20.300,7.0,LV,left,33.4,23.34,8.38,0.56,0.83,4.08,304.62, +2021091300,4845,44849.0,28,2021-09-14T03:54:20.400,7.0,LV,left,32.71,23.81,8.47,0.34,0.84,6.47,304.34, +2021091300,4845,44849.0,29,2021-09-14T03:54:20.500,7.0,LV,left,32.0,24.29,8.54,0.25,0.85,6.47,304.23,autoevent_passforward +2021091300,4845,44849.0,30,2021-09-14T03:54:20.600,7.0,LV,left,31.29,24.79,8.71,0.2,0.87,11.19,304.83,pass_forward +2021091300,4845,44849.0,31,2021-09-14T03:54:20.700,7.0,LV,left,30.58,25.29,8.75,0.42,0.87,13.8,305.2, +2021091300,4845,44849.0,32,2021-09-14T03:54:20.800,7.0,LV,left,29.86,25.79,8.78,0.56,0.88,12.14,305.55, +2021091300,4845,44849.0,33,2021-09-14T03:54:20.900,7.0,LV,left,29.15,26.31,8.83,0.61,0.88,9.99,305.78, +2021091300,4845,44849.0,34,2021-09-14T03:54:21.000,7.0,LV,left,28.43,26.83,8.91,0.53,0.89,9.1,306.07, +2021091300,4845,44972.0,1,2021-09-14T03:54:17.700,72.0,LV,left,41.56,25.49,0.0,0.0,0.0,267.4,153.32, +2021091300,4845,44972.0,2,2021-09-14T03:54:17.800,72.0,LV,left,41.56,25.49,0.01,0.08,0.0,268.73,157.8, +2021091300,4845,44972.0,3,2021-09-14T03:54:17.900,72.0,LV,left,41.56,25.49,0.03,0.23,0.01,268.73,162.88, +2021091300,4845,44972.0,4,2021-09-14T03:54:18.000,72.0,LV,left,41.56,25.47,0.11,0.49,0.01,268.73,171.09, +2021091300,4845,44972.0,5,2021-09-14T03:54:18.100,72.0,LV,left,41.57,25.45,0.24,0.79,0.03,267.98,168.01, +2021091300,4845,44972.0,6,2021-09-14T03:54:18.200,72.0,LV,left,41.58,25.41,0.37,0.82,0.04,267.98,166.9,autoevent_ballsnap +2021091300,4845,44972.0,7,2021-09-14T03:54:18.300,72.0,LV,left,41.59,25.35,0.53,0.85,0.06,267.98,167.59,ball_snap +2021091300,4845,44972.0,8,2021-09-14T03:54:18.400,72.0,LV,left,41.6,25.29,0.64,0.72,0.06,266.4,167.5, +2021091300,4845,44972.0,9,2021-09-14T03:54:18.500,72.0,LV,left,41.64,25.21,0.77,0.61,0.09,265.47,162.54, +2021091300,4845,44972.0,10,2021-09-14T03:54:18.600,72.0,LV,left,41.69,25.13,0.88,0.57,0.1,267.91,154.29, +2021091300,4845,44972.0,11,2021-09-14T03:54:18.700,72.0,LV,left,41.76,25.05,0.96,0.57,0.1,269.24,146.88, +2021091300,4845,44972.0,12,2021-09-14T03:54:18.800,72.0,LV,left,41.86,24.98,1.08,0.86,0.12,267.16,135.69, +2021091300,4845,44972.0,13,2021-09-14T03:54:18.900,72.0,LV,left,41.96,24.91,1.19,0.96,0.13,267.16,128.24, +2021091300,4845,44972.0,14,2021-09-14T03:54:19.000,72.0,LV,left,42.1,24.83,1.41,1.73,0.16,270.86,121.01, +2021091300,4845,44972.0,15,2021-09-14T03:54:19.100,72.0,LV,left,42.22,24.75,1.56,1.7,0.14,268.27,117.02, +2021091300,4845,44972.0,16,2021-09-14T03:54:19.200,72.0,LV,left,42.37,24.68,1.73,1.28,0.17,272.25,113.65, +2021091300,4845,44972.0,17,2021-09-14T03:54:19.300,72.0,LV,left,42.54,24.61,1.83,0.74,0.18,271.45,111.77, +2021091300,4845,44972.0,18,2021-09-14T03:54:19.400,72.0,LV,left,42.74,24.54,1.99,0.59,0.21,272.48,108.31, +2021091300,4845,44972.0,19,2021-09-14T03:54:19.500,72.0,LV,left,42.94,24.49,2.0,0.53,0.2,275.39,105.48, +2021091300,4845,44972.0,20,2021-09-14T03:54:19.600,72.0,LV,left,43.14,24.45,2.02,0.71,0.21,285.48,102.35, +2021091300,4845,44972.0,21,2021-09-14T03:54:19.700,72.0,LV,left,43.35,24.42,1.99,0.85,0.21,292.2,99.99, +2021091300,4845,44972.0,22,2021-09-14T03:54:19.800,72.0,LV,left,43.54,24.39,1.94,0.98,0.2,297.83,97.43, +2021091300,4845,44972.0,23,2021-09-14T03:54:19.900,72.0,LV,left,43.74,24.37,1.92,1.01,0.2,302.9,94.28, +2021091300,4845,44972.0,24,2021-09-14T03:54:20.000,72.0,LV,left,43.97,24.36,2.03,0.79,0.22,311.56,91.9, +2021091300,4845,44972.0,25,2021-09-14T03:54:20.100,72.0,LV,left,44.17,24.36,2.01,0.69,0.2,320.15,90.13, +2021091300,4845,44972.0,26,2021-09-14T03:54:20.200,72.0,LV,left,44.38,24.36,2.05,0.57,0.21,327.91,88.45, +2021091300,4845,44972.0,27,2021-09-14T03:54:20.300,72.0,LV,left,44.6,24.38,2.1,0.61,0.22,337.38,85.12, +2021091300,4845,44972.0,28,2021-09-14T03:54:20.400,72.0,LV,left,44.81,24.41,2.15,0.57,0.22,341.38,83.29, +2021091300,4845,44972.0,29,2021-09-14T03:54:20.500,72.0,LV,left,45.04,24.44,2.19,0.59,0.22,350.12,81.15,autoevent_passforward +2021091300,4845,44972.0,30,2021-09-14T03:54:20.600,72.0,LV,left,45.24,24.49,2.13,0.78,0.21,355.62,78.5,pass_forward +2021091300,4845,44972.0,31,2021-09-14T03:54:20.700,72.0,LV,left,45.46,24.54,2.14,0.87,0.22,358.82,75.89, +2021091300,4845,44972.0,32,2021-09-14T03:54:20.800,72.0,LV,left,45.66,24.59,2.05,1.04,0.21,359.86,74.16, +2021091300,4845,44972.0,33,2021-09-14T03:54:20.900,72.0,LV,left,45.85,24.66,1.94,1.23,0.2,1.47,71.66, +2021091300,4845,44972.0,34,2021-09-14T03:54:21.000,72.0,LV,left,46.03,24.72,1.83,1.32,0.19,3.2,70.36, +2021091300,4845,44999.0,1,2021-09-14T03:54:17.700,36.0,BAL,left,39.63,29.19,0.03,0.03,0.02,82.84,70.48, +2021091300,4845,44999.0,2,2021-09-14T03:54:17.800,36.0,BAL,left,39.63,29.19,0.02,0.02,0.0,81.03,69.04, +2021091300,4845,44999.0,3,2021-09-14T03:54:17.900,36.0,BAL,left,39.63,29.18,0.02,0.02,0.02,81.03,81.55, +2021091300,4845,44999.0,4,2021-09-14T03:54:18.000,36.0,BAL,left,39.65,29.19,0.02,0.02,0.02,81.03,71.72, +2021091300,4845,44999.0,5,2021-09-14T03:54:18.100,36.0,BAL,left,39.66,29.18,0.02,0.02,0.01,81.03,77.95, +2021091300,4845,44999.0,6,2021-09-14T03:54:18.200,36.0,BAL,left,39.67,29.17,0.02,0.02,0.02,81.03,84.15,autoevent_ballsnap +2021091300,4845,44999.0,7,2021-09-14T03:54:18.300,36.0,BAL,left,39.68,29.17,0.02,0.02,0.01,81.03,86.54,ball_snap +2021091300,4845,44999.0,8,2021-09-14T03:54:18.400,36.0,BAL,left,39.69,29.15,0.02,0.02,0.02,82.25,93.56, +2021091300,4845,44999.0,9,2021-09-14T03:54:18.500,36.0,BAL,left,39.7,29.15,0.07,0.73,0.01,82.25,129.29, +2021091300,4845,44999.0,10,2021-09-14T03:54:18.600,36.0,BAL,left,39.72,29.13,0.23,1.32,0.02,83.68,135.9, +2021091300,4845,44999.0,11,2021-09-14T03:54:18.700,36.0,BAL,left,39.74,29.11,0.42,1.63,0.04,82.05,134.41, +2021091300,4845,44999.0,12,2021-09-14T03:54:18.800,36.0,BAL,left,39.81,29.06,0.78,2.19,0.08,85.88,129.17, +2021091300,4845,44999.0,13,2021-09-14T03:54:18.900,36.0,BAL,left,39.89,29.0,1.11,2.63,0.11,86.85,127.01, +2021091300,4845,44999.0,14,2021-09-14T03:54:19.000,36.0,BAL,left,40.01,28.93,1.45,2.64,0.14,86.85,121.15, +2021091300,4845,44999.0,15,2021-09-14T03:54:19.100,36.0,BAL,left,40.14,28.85,1.67,2.2,0.15,88.98,120.98, +2021091300,4845,44999.0,16,2021-09-14T03:54:19.200,36.0,BAL,left,40.3,28.78,1.84,1.83,0.17,93.6,114.97, +2021091300,4845,44999.0,17,2021-09-14T03:54:19.300,36.0,BAL,left,40.47,28.71,1.95,1.53,0.19,98.61,112.67, +2021091300,4845,44999.0,18,2021-09-14T03:54:19.400,36.0,BAL,left,40.64,28.64,1.87,1.05,0.18,103.06,108.82, +2021091300,4845,44999.0,19,2021-09-14T03:54:19.500,36.0,BAL,left,40.81,28.6,1.75,1.47,0.18,105.14,101.57, +2021091300,4845,44999.0,20,2021-09-14T03:54:19.600,36.0,BAL,left,40.98,28.58,1.63,1.84,0.16,108.46,93.11, +2021091300,4845,44999.0,21,2021-09-14T03:54:19.700,36.0,BAL,left,41.13,28.59,1.53,2.1,0.15,112.79,82.98, +2021091300,4845,44999.0,22,2021-09-14T03:54:19.800,36.0,BAL,left,41.28,28.62,1.44,2.0,0.15,123.46,75.93, +2021091300,4845,44999.0,23,2021-09-14T03:54:19.900,36.0,BAL,left,41.4,28.66,1.3,1.86,0.13,131.92,69.85, +2021091300,4845,44999.0,24,2021-09-14T03:54:20.000,36.0,BAL,left,41.51,28.7,1.15,1.6,0.12,139.77,66.05, +2021091300,4845,44999.0,25,2021-09-14T03:54:20.100,36.0,BAL,left,41.6,28.73,0.91,1.53,0.09,150.16,68.05, +2021091300,4845,44999.0,26,2021-09-14T03:54:20.200,36.0,BAL,left,41.68,28.75,0.69,1.71,0.08,154.78,74.78, +2021091300,4845,44999.0,27,2021-09-14T03:54:20.300,36.0,BAL,left,41.72,28.75,0.4,2.12,0.04,156.37,95.34, +2021091300,4845,44999.0,28,2021-09-14T03:54:20.400,36.0,BAL,left,41.73,28.72,0.28,2.51,0.03,157.4,166.1, +2021091300,4845,44999.0,29,2021-09-14T03:54:20.500,36.0,BAL,left,41.72,28.68,0.53,2.61,0.05,160.82,204.51,autoevent_passforward +2021091300,4845,44999.0,30,2021-09-14T03:54:20.600,36.0,BAL,left,41.68,28.63,0.74,2.37,0.06,166.12,217.03,pass_forward +2021091300,4845,44999.0,31,2021-09-14T03:54:20.700,36.0,BAL,left,41.63,28.57,0.91,1.96,0.08,174.34,224.26, +2021091300,4845,44999.0,32,2021-09-14T03:54:20.800,36.0,BAL,left,41.55,28.51,1.09,1.76,0.1,181.63,231.66, +2021091300,4845,44999.0,33,2021-09-14T03:54:20.900,36.0,BAL,left,41.45,28.45,1.21,1.49,0.11,188.44,239.7, +2021091300,4845,44999.0,34,2021-09-14T03:54:21.000,36.0,BAL,left,41.34,28.4,1.34,1.5,0.13,197.69,248.85, +2021091300,4845,46084.0,1,2021-09-14T03:54:17.700,74.0,LV,left,41.78,20.66,0.0,0.0,0.0,263.0,272.44, +2021091300,4845,46084.0,2,2021-09-14T03:54:17.800,74.0,LV,left,41.78,20.66,0.0,0.0,0.0,263.0,275.7, +2021091300,4845,46084.0,3,2021-09-14T03:54:17.900,74.0,LV,left,41.78,20.66,0.0,0.0,0.0,263.0,274.93, +2021091300,4845,46084.0,4,2021-09-14T03:54:18.000,74.0,LV,left,41.78,20.66,0.0,0.0,0.0,264.44,276.6, +2021091300,4845,46084.0,5,2021-09-14T03:54:18.100,74.0,LV,left,41.79,20.66,0.03,0.5,0.01,265.26,107.0, 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+2021091300,4845,46259.0,19,2021-09-14T03:54:19.500,32.0,BAL,left,43.21,29.43,6.13,3.01,0.6,109.25,113.42, +2021091300,4845,46259.0,20,2021-09-14T03:54:19.600,32.0,BAL,left,43.79,29.18,6.42,2.27,0.63,110.91,112.82, +2021091300,4845,46259.0,21,2021-09-14T03:54:19.700,32.0,BAL,left,44.39,28.93,6.61,1.49,0.65,108.82,112.71, +2021091300,4845,46259.0,22,2021-09-14T03:54:19.800,32.0,BAL,left,45.01,28.68,6.75,1.12,0.67,110.32,111.2, +2021091300,4845,46259.0,23,2021-09-14T03:54:19.900,32.0,BAL,left,45.65,28.43,6.87,0.75,0.69,113.22,110.75, +2021091300,4845,46259.0,24,2021-09-14T03:54:20.000,32.0,BAL,left,46.29,28.2,6.84,0.81,0.68,114.39,109.91, +2021091300,4845,46259.0,25,2021-09-14T03:54:20.100,32.0,BAL,left,46.94,27.98,6.79,1.22,0.68,119.88,108.55, +2021091300,4845,46259.0,26,2021-09-14T03:54:20.200,32.0,BAL,left,47.59,27.79,6.77,1.76,0.68,123.34,106.24, +2021091300,4845,46259.0,27,2021-09-14T03:54:20.300,32.0,BAL,left,48.25,27.61,6.77,1.98,0.68,137.05,104.36, +2021091300,4845,46259.0,28,2021-09-14T03:54:20.400,32.0,BAL,left,48.9,27.45,6.61,2.2,0.67,158.05,102.92, +2021091300,4845,46259.0,29,2021-09-14T03:54:20.500,32.0,BAL,left,49.54,27.32,6.5,2.01,0.66,212.83,101.81,autoevent_passforward +2021091300,4845,46259.0,30,2021-09-14T03:54:20.600,32.0,BAL,left,50.17,27.19,6.35,1.93,0.64,266.79,101.15,pass_forward +2021091300,4845,46259.0,31,2021-09-14T03:54:20.700,32.0,BAL,left,50.78,27.07,6.14,2.16,0.62,290.09,100.31, +2021091300,4845,46259.0,32,2021-09-14T03:54:20.800,32.0,BAL,left,51.37,26.97,5.87,2.43,0.6,300.06,99.71, +2021091300,4845,46259.0,33,2021-09-14T03:54:20.900,32.0,BAL,left,51.94,26.88,5.6,2.65,0.57,305.95,98.9, +2021091300,4845,46259.0,34,2021-09-14T03:54:21.000,32.0,BAL,left,52.48,26.8,5.27,2.92,0.54,309.7,97.75, +2021091300,4845,47920.0,1,2021-09-14T03:54:17.700,87.0,LV,left,41.52,28.57,0.0,0.0,0.0,254.98,345.43, +2021091300,4845,47920.0,2,2021-09-14T03:54:17.800,87.0,LV,left,41.52,28.57,0.0,0.0,0.0,254.98,344.47, +2021091300,4845,47920.0,3,2021-09-14T03:54:17.900,87.0,LV,left,41.52,28.57,0.0,0.0,0.01,254.98,348.66, +2021091300,4845,47920.0,4,2021-09-14T03:54:18.000,87.0,LV,left,41.52,28.57,0.0,0.06,0.0,253.91,353.78, +2021091300,4845,47920.0,5,2021-09-14T03:54:18.100,87.0,LV,left,41.52,28.57,0.01,0.16,0.0,253.91,266.55, +2021091300,4845,47920.0,6,2021-09-14T03:54:18.200,87.0,LV,left,41.52,28.57,0.08,0.57,0.01,255.69,214.47,autoevent_ballsnap +2021091300,4845,47920.0,7,2021-09-14T03:54:18.300,87.0,LV,left,41.51,28.55,0.24,1.13,0.02,257.45,205.5,ball_snap +2021091300,4845,47920.0,8,2021-09-14T03:54:18.400,87.0,LV,left,41.5,28.52,0.41,1.48,0.03,259.81,200.09, +2021091300,4845,47920.0,9,2021-09-14T03:54:18.500,87.0,LV,left,41.48,28.47,0.6,1.75,0.05,262.41,199.08, +2021091300,4845,47920.0,10,2021-09-14T03:54:18.600,87.0,LV,left,41.46,28.41,0.74,1.53,0.06,265.62,196.09, +2021091300,4845,47920.0,11,2021-09-14T03:54:18.700,87.0,LV,left,41.45,28.34,0.8,1.02,0.07,268.59,192.14, +2021091300,4845,47920.0,12,2021-09-14T03:54:18.800,87.0,LV,left,41.44,28.26,0.81,0.65,0.08,272.74,186.95, +2021091300,4845,47920.0,13,2021-09-14T03:54:18.900,87.0,LV,left,41.44,28.2,0.69,0.88,0.07,277.33,174.87, +2021091300,4845,47920.0,14,2021-09-14T03:54:19.000,87.0,LV,left,41.45,28.13,0.58,1.17,0.06,282.27,164.95, +2021091300,4845,47920.0,15,2021-09-14T03:54:19.100,87.0,LV,left,41.47,28.08,0.54,1.44,0.06,286.08,151.48, +2021091300,4845,47920.0,16,2021-09-14T03:54:19.200,87.0,LV,left,41.5,28.04,0.4,1.83,0.05,283.0,125.63, +2021091300,4845,47920.0,17,2021-09-14T03:54:19.300,87.0,LV,left,41.54,28.03,0.47,2.22,0.04,283.0,88.25, +2021091300,4845,47920.0,18,2021-09-14T03:54:19.400,87.0,LV,left,41.59,28.04,0.57,2.15,0.05,281.8,68.36, +2021091300,4845,47920.0,19,2021-09-14T03:54:19.500,87.0,LV,left,41.65,28.07,0.74,1.87,0.07,280.52,59.79, +2021091300,4845,47920.0,20,2021-09-14T03:54:19.600,87.0,LV,left,41.72,28.11,0.98,1.74,0.09,274.28,60.45, +2021091300,4845,47920.0,21,2021-09-14T03:54:19.700,87.0,LV,left,41.81,28.17,1.07,1.18,0.11,263.73,61.19, +2021091300,4845,47920.0,22,2021-09-14T03:54:19.800,87.0,LV,left,41.91,28.22,1.07,0.88,0.11,255.58,69.84, +2021091300,4845,47920.0,23,2021-09-14T03:54:19.900,87.0,LV,left,42.02,28.25,1.1,1.27,0.11,252.0,79.79, +2021091300,4845,47920.0,24,2021-09-14T03:54:20.000,87.0,LV,left,42.12,28.26,1.02,1.37,0.11,242.62,87.48, +2021091300,4845,47920.0,25,2021-09-14T03:54:20.100,87.0,LV,left,42.22,28.26,0.97,1.54,0.1,236.66,96.73, +2021091300,4845,47920.0,26,2021-09-14T03:54:20.200,87.0,LV,left,42.31,28.24,0.9,1.57,0.09,235.57,106.26, +2021091300,4845,47920.0,27,2021-09-14T03:54:20.300,87.0,LV,left,42.39,28.21,0.83,1.53,0.09,230.49,117.66, +2021091300,4845,47920.0,28,2021-09-14T03:54:20.400,87.0,LV,left,42.46,28.17,0.68,1.54,0.07,224.47,129.93, +2021091300,4845,47920.0,29,2021-09-14T03:54:20.500,87.0,LV,left,42.5,28.12,0.55,1.76,0.06,220.32,147.33,autoevent_passforward +2021091300,4845,47920.0,30,2021-09-14T03:54:20.600,87.0,LV,left,42.51,28.08,0.41,1.8,0.04,218.95,171.27,pass_forward +2021091300,4845,47920.0,31,2021-09-14T03:54:20.700,87.0,LV,left,42.5,28.05,0.41,1.9,0.04,218.95,206.97, +2021091300,4845,47920.0,32,2021-09-14T03:54:20.800,87.0,LV,left,42.47,28.01,0.52,2.05,0.05,221.04,236.21, +2021091300,4845,47920.0,33,2021-09-14T03:54:20.900,87.0,LV,left,42.42,27.99,0.65,1.89,0.06,223.54,254.39, +2021091300,4845,47920.0,34,2021-09-14T03:54:21.000,87.0,LV,left,42.35,27.99,0.78,1.62,0.07,227.44,264.54, +2021091300,4845,47932.0,1,2021-09-14T03:54:17.700,13.0,LV,left,42.73,17.52,0.0,0.0,0.0,273.14,173.98, +2021091300,4845,47932.0,2,2021-09-14T03:54:17.800,13.0,LV,left,42.73,17.52,0.0,0.0,0.0,273.14,177.77, +2021091300,4845,47932.0,3,2021-09-14T03:54:17.900,13.0,LV,left,42.73,17.52,0.0,0.0,0.0,272.57,186.32, +2021091300,4845,47932.0,4,2021-09-14T03:54:18.000,13.0,LV,left,42.73,17.52,0.0,0.0,0.0,272.57,194.85, +2021091300,4845,47932.0,5,2021-09-14T03:54:18.100,13.0,LV,left,42.72,17.52,0.0,0.0,0.01,271.87,233.27, +2021091300,4845,47932.0,6,2021-09-14T03:54:18.200,13.0,LV,left,42.72,17.52,0.04,0.68,0.0,271.87,278.41,autoevent_ballsnap +2021091300,4845,47932.0,7,2021-09-14T03:54:18.300,13.0,LV,left,42.71,17.52,0.19,1.56,0.01,273.07,272.14,ball_snap +2021091300,4845,47932.0,8,2021-09-14T03:54:18.400,13.0,LV,left,42.67,17.53,0.49,2.52,0.04,271.68,274.93, +2021091300,4845,47932.0,9,2021-09-14T03:54:18.500,13.0,LV,left,42.59,17.53,0.84,3.1,0.07,271.0,274.55, +2021091300,4845,47932.0,10,2021-09-14T03:54:18.600,13.0,LV,left,42.48,17.53,1.24,3.38,0.11,270.14,270.75, +2021091300,4845,47932.0,11,2021-09-14T03:54:18.700,13.0,LV,left,42.32,17.51,1.68,3.42,0.16,270.14,266.18, +2021091300,4845,47932.0,12,2021-09-14T03:54:18.800,13.0,LV,left,42.12,17.46,2.11,3.48,0.2,268.84,259.75, +2021091300,4845,47932.0,13,2021-09-14T03:54:18.900,13.0,LV,left,41.9,17.41,2.49,3.58,0.24,265.15,255.64, +2021091300,4845,47932.0,14,2021-09-14T03:54:19.000,13.0,LV,left,41.63,17.33,2.87,3.25,0.27,260.69,252.86, +2021091300,4845,47932.0,15,2021-09-14T03:54:19.100,13.0,LV,left,41.33,17.23,3.3,3.17,0.32,263.32,250.39, +2021091300,4845,47932.0,16,2021-09-14T03:54:19.200,13.0,LV,left,40.98,17.08,3.83,3.45,0.38,254.85,247.35, +2021091300,4845,47932.0,17,2021-09-14T03:54:19.300,13.0,LV,left,40.6,16.91,4.27,3.28,0.42,244.12,245.08, +2021091300,4845,47932.0,18,2021-09-14T03:54:19.400,13.0,LV,left,40.18,16.72,4.68,2.91,0.46,235.26,244.68, +2021091300,4845,47932.0,19,2021-09-14T03:54:19.500,13.0,LV,left,39.73,16.51,5.05,2.51,0.5,230.34,244.99, +2021091300,4845,47932.0,20,2021-09-14T03:54:19.600,13.0,LV,left,39.24,16.3,5.43,2.51,0.53,223.62,246.06, +2021091300,4845,47932.0,21,2021-09-14T03:54:19.700,13.0,LV,left,38.73,16.08,5.7,2.16,0.56,222.67,246.79, +2021091300,4845,47932.0,22,2021-09-14T03:54:19.800,13.0,LV,left,38.16,15.85,6.18,2.63,0.61,225.36,248.92, +2021091300,4845,47932.0,23,2021-09-14T03:54:19.900,13.0,LV,left,37.57,15.64,6.42,2.36,0.63,225.36,250.46, +2021091300,4845,47932.0,24,2021-09-14T03:54:20.000,13.0,LV,left,36.96,15.43,6.59,2.32,0.65,226.48,252.83, +2021091300,4845,47932.0,25,2021-09-14T03:54:20.100,13.0,LV,left,36.31,15.26,6.74,2.07,0.67,228.26,255.14, +2021091300,4845,47932.0,26,2021-09-14T03:54:20.200,13.0,LV,left,35.63,15.11,6.99,2.29,0.7,240.9,258.29, +2021091300,4845,47932.0,27,2021-09-14T03:54:20.300,13.0,LV,left,34.92,14.99,7.16,2.49,0.71,252.36,260.95, +2021091300,4845,47932.0,28,2021-09-14T03:54:20.400,13.0,LV,left,34.21,14.89,7.26,2.44,0.72,259.98,263.33, +2021091300,4845,47932.0,29,2021-09-14T03:54:20.500,13.0,LV,left,33.48,14.82,7.36,2.2,0.73,264.24,265.07,autoevent_passforward +2021091300,4845,47932.0,30,2021-09-14T03:54:20.600,13.0,LV,left,32.74,14.75,7.39,1.38,0.74,267.53,265.39,pass_forward +2021091300,4845,47932.0,31,2021-09-14T03:54:20.700,13.0,LV,left,32.0,14.69,7.36,0.6,0.74,262.62,264.37, +2021091300,4845,47932.0,32,2021-09-14T03:54:20.800,13.0,LV,left,31.28,14.61,7.27,1.13,0.73,252.95,263.89, +2021091300,4845,47932.0,33,2021-09-14T03:54:20.900,13.0,LV,left,30.56,14.51,7.18,2.01,0.72,242.26,261.07, +2021091300,4845,47932.0,34,2021-09-14T03:54:21.000,13.0,LV,left,29.86,14.38,7.06,2.79,0.71,228.86,257.71, +2021091300,4845,48235.0,1,2021-09-14T03:54:17.700,68.0,LV,left,41.08,23.78,0.0,0.0,0.0,263.93,124.69, +2021091300,4845,48235.0,2,2021-09-14T03:54:17.800,68.0,LV,left,41.08,23.78,0.0,0.07,0.0,263.24,129.78, +2021091300,4845,48235.0,3,2021-09-14T03:54:17.900,68.0,LV,left,41.08,23.78,0.01,0.09,0.0,263.24,57.23, +2021091300,4845,48235.0,4,2021-09-14T03:54:18.000,68.0,LV,left,41.09,23.79,0.06,0.33,0.01,262.57,20.47, +2021091300,4845,48235.0,5,2021-09-14T03:54:18.100,68.0,LV,left,41.1,23.79,0.12,0.44,0.01,261.15,56.34, +2021091300,4845,48235.0,6,2021-09-14T03:54:18.200,68.0,LV,left,41.13,23.79,0.24,0.65,0.03,260.06,75.39,autoevent_ballsnap +2021091300,4845,48235.0,7,2021-09-14T03:54:18.300,68.0,LV,left,41.17,23.79,0.39,0.95,0.04,257.96,88.99,ball_snap +2021091300,4845,48235.0,8,2021-09-14T03:54:18.400,68.0,LV,left,41.24,23.77,0.59,1.18,0.07,254.74,97.89, +2021091300,4845,48235.0,9,2021-09-14T03:54:18.500,68.0,LV,left,41.31,23.76,0.78,1.36,0.08,250.5,101.17, +2021091300,4845,48235.0,10,2021-09-14T03:54:18.600,68.0,LV,left,41.41,23.74,1.02,1.43,0.1,241.76,103.88, +2021091300,4845,48235.0,11,2021-09-14T03:54:18.700,68.0,LV,left,41.53,23.71,1.2,1.2,0.12,235.89,103.71, +2021091300,4845,48235.0,12,2021-09-14T03:54:18.800,68.0,LV,left,41.67,23.67,1.43,1.23,0.14,226.73,106.46, +2021091300,4845,48235.0,13,2021-09-14T03:54:18.900,68.0,LV,left,41.81,23.63,1.59,1.0,0.15,225.85,106.66, +2021091300,4845,48235.0,14,2021-09-14T03:54:19.000,68.0,LV,left,41.96,23.59,1.62,0.42,0.16,225.85,106.35, +2021091300,4845,48235.0,15,2021-09-14T03:54:19.100,68.0,LV,left,42.13,23.55,1.65,0.07,0.17,225.85,104.82, +2021091300,4845,48235.0,16,2021-09-14T03:54:19.200,68.0,LV,left,42.3,23.5,1.74,0.15,0.18,226.89,104.87, +2021091300,4845,48235.0,17,2021-09-14T03:54:19.300,68.0,LV,left,42.48,23.45,1.78,0.26,0.18,226.31,105.54, +2021091300,4845,48235.0,18,2021-09-14T03:54:19.400,68.0,LV,left,42.67,23.39,1.87,0.21,0.2,225.47,105.66, +2021091300,4845,48235.0,19,2021-09-14T03:54:19.500,68.0,LV,left,42.87,23.35,1.95,0.27,0.21,221.72,104.1, +2021091300,4845,48235.0,20,2021-09-14T03:54:19.600,68.0,LV,left,43.07,23.31,1.99,0.35,0.21,216.35,101.72, +2021091300,4845,48235.0,21,2021-09-14T03:54:19.700,68.0,LV,left,43.27,23.28,2.0,0.42,0.2,208.54,100.21, +2021091300,4845,48235.0,22,2021-09-14T03:54:19.800,68.0,LV,left,43.48,23.25,2.02,0.6,0.21,202.49,97.49, +2021091300,4845,48235.0,23,2021-09-14T03:54:19.900,68.0,LV,left,43.69,23.24,2.07,0.69,0.22,193.42,94.6, +2021091300,4845,48235.0,24,2021-09-14T03:54:20.000,68.0,LV,left,43.9,23.22,2.02,0.83,0.2,188.45,93.08, +2021091300,4845,48235.0,25,2021-09-14T03:54:20.100,68.0,LV,left,44.12,23.23,2.05,0.92,0.23,182.48,89.68, +2021091300,4845,48235.0,26,2021-09-14T03:54:20.200,68.0,LV,left,44.33,23.24,1.99,1.05,0.21,174.57,87.92, +2021091300,4845,48235.0,27,2021-09-14T03:54:20.300,68.0,LV,left,44.54,23.24,1.94,1.08,0.21,166.49,86.88, +2021091300,4845,48235.0,28,2021-09-14T03:54:20.400,68.0,LV,left,44.75,23.27,1.93,0.96,0.21,155.83,84.75, +2021091300,4845,48235.0,29,2021-09-14T03:54:20.500,68.0,LV,left,44.95,23.29,1.92,0.72,0.2,136.3,82.49,autoevent_passforward +2021091300,4845,48235.0,30,2021-09-14T03:54:20.600,68.0,LV,left,45.15,23.32,1.98,0.49,0.2,120.42,81.11,pass_forward +2021091300,4845,48235.0,31,2021-09-14T03:54:20.700,68.0,LV,left,45.35,23.35,1.98,0.32,0.2,102.41,81.17, +2021091300,4845,48235.0,32,2021-09-14T03:54:20.800,68.0,LV,left,45.56,23.37,2.19,0.18,0.22,64.13,84.39, +2021091300,4845,48235.0,33,2021-09-14T03:54:20.900,68.0,LV,left,45.79,23.39,2.3,0.21,0.23,38.24,84.68, 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+2021091300,4845,52506.0,13,2021-09-14T03:54:18.900,40.0,BAL,left,40.37,14.44,2.54,3.98,0.25,72.4,54.8, +2021091300,4845,52506.0,14,2021-09-14T03:54:19.000,40.0,BAL,left,40.62,14.62,3.15,4.23,0.3,75.84,53.97, +2021091300,4845,52506.0,15,2021-09-14T03:54:19.100,40.0,BAL,left,40.91,14.83,3.72,4.08,0.36,76.43,54.11, +2021091300,4845,52506.0,16,2021-09-14T03:54:19.200,40.0,BAL,left,41.24,15.08,4.31,4.24,0.41,73.68,53.53, +2021091300,4845,52506.0,17,2021-09-14T03:54:19.300,40.0,BAL,left,41.61,15.35,4.79,3.8,0.46,72.72,53.62, +2021091300,4845,52506.0,18,2021-09-14T03:54:19.400,40.0,BAL,left,42.01,15.64,5.25,3.62,0.5,77.1,52.87, +2021091300,4845,52506.0,19,2021-09-14T03:54:19.500,40.0,BAL,left,42.45,15.98,5.66,3.01,0.55,79.53,52.99, +2021091300,4845,52506.0,20,2021-09-14T03:54:19.600,40.0,BAL,left,42.92,16.33,6.0,2.37,0.59,78.52,53.62, +2021091300,4845,52506.0,21,2021-09-14T03:54:19.700,40.0,BAL,left,43.41,16.69,6.25,1.73,0.61,77.08,53.94, +2021091300,4845,52506.0,22,2021-09-14T03:54:19.800,40.0,BAL,left,43.94,17.06,6.48,1.37,0.64,75.64,55.12, +2021091300,4845,52506.0,23,2021-09-14T03:54:19.900,40.0,BAL,left,44.49,17.44,6.73,1.09,0.67,75.64,55.37, +2021091300,4845,52506.0,24,2021-09-14T03:54:20.000,40.0,BAL,left,45.06,17.84,6.96,1.06,0.7,74.39,55.29, +2021091300,4845,52506.0,25,2021-09-14T03:54:20.100,40.0,BAL,left,45.64,18.24,7.12,0.71,0.71,72.43,55.37, +2021091300,4845,52506.0,26,2021-09-14T03:54:20.200,40.0,BAL,left,46.23,18.66,7.2,0.34,0.72,66.99,54.63, +2021091300,4845,52506.0,27,2021-09-14T03:54:20.300,40.0,BAL,left,46.81,19.08,7.22,0.46,0.72,63.06,54.01, +2021091300,4845,52506.0,28,2021-09-14T03:54:20.400,40.0,BAL,left,47.39,19.51,7.18,0.93,0.72,58.91,53.32, +2021091300,4845,52506.0,29,2021-09-14T03:54:20.500,40.0,BAL,left,47.96,19.94,7.05,1.48,0.71,54.89,52.34,autoevent_passforward +2021091300,4845,52506.0,30,2021-09-14T03:54:20.600,40.0,BAL,left,48.51,20.37,6.91,1.84,0.7,50.24,51.57,pass_forward +2021091300,4845,52506.0,31,2021-09-14T03:54:20.700,40.0,BAL,left,49.03,20.8,6.66,2.39,0.68,42.21,50.1, +2021091300,4845,52506.0,32,2021-09-14T03:54:20.800,40.0,BAL,left,49.52,21.22,6.37,2.83,0.65,38.02,48.51, +2021091300,4845,52506.0,33,2021-09-14T03:54:20.900,40.0,BAL,left,49.98,21.64,6.07,3.04,0.62,32.74,47.24, +2021091300,4845,52506.0,34,2021-09-14T03:54:21.000,40.0,BAL,left,50.39,22.05,5.7,3.41,0.58,27.13,44.89, +2021091300,4845,52517.0,1,2021-09-14T03:54:17.700,76.0,LV,left,41.82,22.29,0.0,0.0,0.0,252.29,341.71, +2021091300,4845,52517.0,2,2021-09-14T03:54:17.800,76.0,LV,left,41.82,22.29,0.0,0.0,0.0,252.29,343.79, +2021091300,4845,52517.0,3,2021-09-14T03:54:17.900,76.0,LV,left,41.82,22.29,0.0,0.0,0.0,252.29,348.13, +2021091300,4845,52517.0,4,2021-09-14T03:54:18.000,76.0,LV,left,41.82,22.29,0.0,0.0,0.0,252.29,355.33, +2021091300,4845,52517.0,5,2021-09-14T03:54:18.100,76.0,LV,left,41.83,22.29,0.02,0.25,0.0,251.58,87.58, +2021091300,4845,52517.0,6,2021-09-14T03:54:18.200,76.0,LV,left,41.83,22.29,0.08,0.66,0.01,251.58,104.09,autoevent_ballsnap +2021091300,4845,52517.0,7,2021-09-14T03:54:18.300,76.0,LV,left,41.85,22.28,0.23,1.1,0.02,251.58,109.45,ball_snap +2021091300,4845,52517.0,8,2021-09-14T03:54:18.400,76.0,LV,left,41.89,22.27,0.38,1.12,0.04,251.58,107.0, +2021091300,4845,52517.0,9,2021-09-14T03:54:18.500,76.0,LV,left,41.94,22.26,0.56,1.09,0.06,251.58,103.25, +2021091300,4845,52517.0,10,2021-09-14T03:54:18.600,76.0,LV,left,42.03,22.24,0.77,1.21,0.08,250.54,101.82, +2021091300,4845,52517.0,11,2021-09-14T03:54:18.700,76.0,LV,left,42.12,22.22,0.94,1.0,0.1,249.0,101.75, +2021091300,4845,52517.0,12,2021-09-14T03:54:18.800,76.0,LV,left,42.25,22.2,1.13,0.86,0.12,246.66,100.21, +2021091300,4845,52517.0,13,2021-09-14T03:54:18.900,76.0,LV,left,42.39,22.19,1.3,0.76,0.14,246.66,97.46, +2021091300,4845,52517.0,14,2021-09-14T03:54:19.000,76.0,LV,left,42.53,22.17,1.41,0.5,0.15,244.63,98.14, +2021091300,4845,52517.0,15,2021-09-14T03:54:19.100,76.0,LV,left,42.69,22.15,1.51,0.31,0.16,246.41,96.89, +2021091300,4845,52517.0,16,2021-09-14T03:54:19.200,76.0,LV,left,42.85,22.13,1.55,0.11,0.16,248.65,96.51, +2021091300,4845,52517.0,17,2021-09-14T03:54:19.300,76.0,LV,left,43.0,22.11,1.53,0.26,0.15,249.47,96.83, +2021091300,4845,52517.0,18,2021-09-14T03:54:19.400,76.0,LV,left,43.15,22.09,1.52,0.47,0.16,253.89,97.22, +2021091300,4845,52517.0,19,2021-09-14T03:54:19.500,76.0,LV,left,43.32,22.08,1.51,0.6,0.16,258.88,94.94, +2021091300,4845,52517.0,20,2021-09-14T03:54:19.600,76.0,LV,left,43.47,22.07,1.48,0.66,0.16,264.05,93.89, +2021091300,4845,52517.0,21,2021-09-14T03:54:19.700,76.0,LV,left,43.65,22.07,1.54,0.56,0.18,270.13,91.98, +2021091300,4845,52517.0,22,2021-09-14T03:54:19.800,76.0,LV,left,43.82,22.07,1.57,0.51,0.17,274.68,89.43, +2021091300,4845,52517.0,23,2021-09-14T03:54:19.900,76.0,LV,left,44.0,22.08,1.64,0.32,0.18,282.15,88.84, +2021091300,4845,52517.0,24,2021-09-14T03:54:20.000,76.0,LV,left,44.17,22.07,1.7,0.16,0.17,291.0,89.23, +2021091300,4845,52517.0,25,2021-09-14T03:54:20.100,76.0,LV,left,44.37,22.09,1.85,0.44,0.2,299.78,85.75, +2021091300,4845,52517.0,26,2021-09-14T03:54:20.200,76.0,LV,left,44.58,22.13,2.01,0.74,0.21,306.31,81.8, +2021091300,4845,52517.0,27,2021-09-14T03:54:20.300,76.0,LV,left,44.79,22.18,2.15,0.84,0.22,311.95,77.94, +2021091300,4845,52517.0,28,2021-09-14T03:54:20.400,76.0,LV,left,45.03,22.24,2.31,0.83,0.24,316.86,74.9, +2021091300,4845,52517.0,29,2021-09-14T03:54:20.500,76.0,LV,left,45.25,22.34,2.39,1.04,0.24,319.77,68.67,autoevent_passforward +2021091300,4845,52517.0,30,2021-09-14T03:54:20.600,76.0,LV,left,45.49,22.46,2.54,1.2,0.27,317.39,64.65,pass_forward +2021091300,4845,52517.0,31,2021-09-14T03:54:20.700,76.0,LV,left,45.73,22.58,2.64,1.18,0.27,318.54,61.53, +2021091300,4845,52517.0,32,2021-09-14T03:54:20.800,76.0,LV,left,45.97,22.74,2.78,1.18,0.29,327.34,57.13, +2021091300,4845,52517.0,33,2021-09-14T03:54:20.900,76.0,LV,left,46.21,22.92,2.88,1.24,0.3,329.55,52.62, +2021091300,4845,52517.0,34,2021-09-14T03:54:21.000,76.0,LV,left,46.42,23.13,2.91,1.48,0.3,323.62,46.87, +2021091300,4845,53446.0,1,2021-09-14T03:54:17.700,70.0,LV,left,41.49,27.14,0.0,0.0,0.0,270.62,181.37, +2021091300,4845,53446.0,2,2021-09-14T03:54:17.800,70.0,LV,left,41.49,27.14,0.0,0.0,0.0,270.62,180.59, +2021091300,4845,53446.0,3,2021-09-14T03:54:17.900,70.0,LV,left,41.49,27.14,0.0,0.0,0.0,270.62,179.12, +2021091300,4845,53446.0,4,2021-09-14T03:54:18.000,70.0,LV,left,41.48,27.14,0.0,0.0,0.0,270.62,183.47, +2021091300,4845,53446.0,5,2021-09-14T03:54:18.100,70.0,LV,left,41.48,27.14,0.0,0.08,0.0,270.62,227.0, +2021091300,4845,53446.0,6,2021-09-14T03:54:18.200,70.0,LV,left,41.48,27.14,0.0,0.04,0.0,271.35,261.49,autoevent_ballsnap +2021091300,4845,53446.0,7,2021-09-14T03:54:18.300,70.0,LV,left,41.49,27.14,0.03,0.1,0.0,271.35,139.58,ball_snap +2021091300,4845,53446.0,8,2021-09-14T03:54:18.400,70.0,LV,left,41.49,27.13,0.09,0.28,0.01,271.35,146.8, +2021091300,4845,53446.0,9,2021-09-14T03:54:18.500,70.0,LV,left,41.5,27.12,0.13,0.3,0.01,271.35,144.26, +2021091300,4845,53446.0,10,2021-09-14T03:54:18.600,70.0,LV,left,41.51,27.09,0.23,0.48,0.03,271.35,152.89, +2021091300,4845,53446.0,11,2021-09-14T03:54:18.700,70.0,LV,left,41.53,27.05,0.38,0.67,0.05,272.32,157.35, +2021091300,4845,53446.0,12,2021-09-14T03:54:18.800,70.0,LV,left,41.55,27.0,0.51,0.92,0.05,271.39,156.29, +2021091300,4845,53446.0,13,2021-09-14T03:54:18.900,70.0,LV,left,41.58,26.93,0.69,0.99,0.08,267.49,155.47, +2021091300,4845,53446.0,14,2021-09-14T03:54:19.000,70.0,LV,left,41.63,26.85,0.9,0.98,0.1,262.59,153.3, +2021091300,4845,53446.0,15,2021-09-14T03:54:19.100,70.0,LV,left,41.68,26.75,1.07,0.89,0.11,258.44,153.03, +2021091300,4845,53446.0,16,2021-09-14T03:54:19.200,70.0,LV,left,41.74,26.64,1.23,0.82,0.13,248.38,151.53, +2021091300,4845,53446.0,17,2021-09-14T03:54:19.300,70.0,LV,left,41.85,26.5,1.54,1.12,0.17,235.05,145.7, +2021091300,4845,53446.0,18,2021-09-14T03:54:19.400,70.0,LV,left,41.96,26.35,1.79,1.16,0.19,223.19,143.26, +2021091300,4845,53446.0,19,2021-09-14T03:54:19.500,70.0,LV,left,42.1,26.19,2.05,1.16,0.21,211.68,140.62, +2021091300,4845,53446.0,20,2021-09-14T03:54:19.600,70.0,LV,left,42.24,26.02,2.17,0.82,0.22,204.82,139.22, +2021091300,4845,53446.0,21,2021-09-14T03:54:19.700,70.0,LV,left,42.39,25.85,2.25,0.5,0.23,200.15,137.91, +2021091300,4845,53446.0,22,2021-09-14T03:54:19.800,70.0,LV,left,42.57,25.64,2.52,0.44,0.28,178.27,138.18, +2021091300,4845,53446.0,23,2021-09-14T03:54:19.900,70.0,LV,left,42.74,25.44,2.59,0.34,0.26,164.44,138.16, +2021091300,4845,53446.0,24,2021-09-14T03:54:20.000,70.0,LV,left,42.93,25.23,2.72,0.09,0.28,158.81,138.19, +2021091300,4845,53446.0,25,2021-09-14T03:54:20.100,70.0,LV,left,43.13,25.02,2.81,0.19,0.29,154.96,137.55, +2021091300,4845,53446.0,26,2021-09-14T03:54:20.200,70.0,LV,left,43.32,24.81,2.83,0.38,0.29,151.2,137.03, +2021091300,4845,53446.0,27,2021-09-14T03:54:20.300,70.0,LV,left,43.52,24.6,2.79,0.59,0.28,152.79,136.68, +2021091300,4845,53446.0,28,2021-09-14T03:54:20.400,70.0,LV,left,43.71,24.4,2.74,0.92,0.28,152.18,136.21, +2021091300,4845,53446.0,29,2021-09-14T03:54:20.500,70.0,LV,left,43.9,24.21,2.69,1.09,0.28,156.04,135.08,autoevent_passforward +2021091300,4845,53446.0,30,2021-09-14T03:54:20.600,70.0,LV,left,44.1,24.01,2.67,1.09,0.28,161.51,134.9,pass_forward +2021091300,4845,53446.0,31,2021-09-14T03:54:20.700,70.0,LV,left,44.29,23.82,2.61,1.15,0.27,165.46,135.59, +2021091300,4845,53446.0,32,2021-09-14T03:54:20.800,70.0,LV,left,44.46,23.63,2.49,1.34,0.26,165.46,136.5, +2021091300,4845,53446.0,33,2021-09-14T03:54:20.900,70.0,LV,left,44.62,23.43,2.43,1.44,0.26,160.2,139.9, +2021091300,4845,53446.0,34,2021-09-14T03:54:21.000,70.0,LV,left,44.76,23.25,2.26,1.66,0.23,160.2,142.7, +2021091300,4845,53460.0,1,2021-09-14T03:54:17.700,99.0,BAL,left,39.49,27.84,0.02,0.02,0.01,102.26,90.57, +2021091300,4845,53460.0,2,2021-09-14T03:54:17.800,99.0,BAL,left,39.49,27.83,0.02,0.02,0.01,102.26,94.41, +2021091300,4845,53460.0,3,2021-09-14T03:54:17.900,99.0,BAL,left,39.48,27.83,0.01,0.01,0.01,100.86,97.22, +2021091300,4845,53460.0,4,2021-09-14T03:54:18.000,99.0,BAL,left,39.49,27.84,0.01,0.01,0.01,101.64,93.16, +2021091300,4845,53460.0,5,2021-09-14T03:54:18.100,99.0,BAL,left,39.5,27.83,0.02,0.11,0.01,101.64,103.57, +2021091300,4845,53460.0,6,2021-09-14T03:54:18.200,99.0,BAL,left,39.51,27.82,0.08,0.68,0.02,101.64,124.87,autoevent_ballsnap +2021091300,4845,53460.0,7,2021-09-14T03:54:18.300,99.0,BAL,left,39.53,27.81,0.23,1.49,0.02,98.03,130.26,ball_snap +2021091300,4845,53460.0,8,2021-09-14T03:54:18.400,99.0,BAL,left,39.56,27.78,0.51,2.11,0.05,99.04,130.45, +2021091300,4845,53460.0,9,2021-09-14T03:54:18.500,99.0,BAL,left,39.63,27.72,0.92,2.75,0.09,99.04,131.55, +2021091300,4845,53460.0,10,2021-09-14T03:54:18.600,99.0,BAL,left,39.73,27.61,1.47,3.57,0.15,102.1,133.35, +2021091300,4845,53460.0,11,2021-09-14T03:54:18.700,99.0,BAL,left,39.87,27.49,1.93,3.67,0.18,100.38,132.73, +2021091300,4845,53460.0,12,2021-09-14T03:54:18.800,99.0,BAL,left,40.05,27.32,2.48,3.8,0.25,95.75,133.45, +2021091300,4845,53460.0,13,2021-09-14T03:54:18.900,99.0,BAL,left,40.25,27.13,2.92,3.67,0.28,93.24,133.69, +2021091300,4845,53460.0,14,2021-09-14T03:54:19.000,99.0,BAL,left,40.47,26.9,3.34,3.61,0.32,90.46,135.01, +2021091300,4845,53460.0,15,2021-09-14T03:54:19.100,99.0,BAL,left,40.71,26.65,3.64,2.88,0.35,86.67,136.37, +2021091300,4845,53460.0,16,2021-09-14T03:54:19.200,99.0,BAL,left,40.96,26.38,3.82,2.02,0.37,83.24,138.53, +2021091300,4845,53460.0,17,2021-09-14T03:54:19.300,99.0,BAL,left,41.21,26.08,3.94,1.46,0.39,78.85,140.52, +2021091300,4845,53460.0,18,2021-09-14T03:54:19.400,99.0,BAL,left,41.45,25.77,3.97,1.24,0.39,75.38,142.52, +2021091300,4845,53460.0,19,2021-09-14T03:54:19.500,99.0,BAL,left,41.69,25.46,3.95,1.17,0.39,73.54,144.39, +2021091300,4845,53460.0,20,2021-09-14T03:54:19.600,99.0,BAL,left,41.91,25.14,3.88,1.14,0.39,71.44,145.32, +2021091300,4845,53460.0,21,2021-09-14T03:54:19.700,99.0,BAL,left,42.14,24.83,3.75,1.16,0.38,68.27,144.54, +2021091300,4845,53460.0,22,2021-09-14T03:54:19.800,99.0,BAL,left,42.35,24.53,3.59,1.43,0.37,66.32,143.72, +2021091300,4845,53460.0,23,2021-09-14T03:54:19.900,99.0,BAL,left,42.57,24.27,3.38,1.85,0.35,72.73,141.08, +2021091300,4845,53460.0,24,2021-09-14T03:54:20.000,99.0,BAL,left,42.79,24.02,3.18,2.28,0.33,70.92,137.15, +2021091300,4845,53460.0,25,2021-09-14T03:54:20.100,99.0,BAL,left,43.01,23.82,2.97,2.62,0.3,69.72,132.06, +2021091300,4845,53460.0,26,2021-09-14T03:54:20.200,99.0,BAL,left,43.23,23.63,2.78,2.6,0.28,68.93,128.03, +2021091300,4845,53460.0,27,2021-09-14T03:54:20.300,99.0,BAL,left,43.45,23.47,2.61,2.38,0.27,67.56,124.51, 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+2021092612,54,43376.0,37,2021-09-26T20:26:36.200,59.0,MIN,left,79.0,31.02,5.52,1.89,0.55,13.7,335.19, +2021092612,54,43376.0,38,2021-09-26T20:26:36.300,59.0,MIN,left,78.79,31.53,5.54,2.19,0.55,16.73,338.61, +2021092612,54,43376.0,39,2021-09-26T20:26:36.400,59.0,MIN,left,78.61,32.05,5.55,2.42,0.56,19.8,342.04, +2021092612,54,43376.0,40,2021-09-26T20:26:36.500,59.0,MIN,left,78.47,32.59,5.48,3.01,0.55,22.38,346.98, +2021092612,54,43376.0,41,2021-09-26T20:26:36.600,59.0,MIN,left,78.38,33.13,5.44,3.46,0.55,30.17,351.85, +2021092612,54,43376.0,42,2021-09-26T20:26:36.700,59.0,MIN,left,78.33,33.67,5.38,3.65,0.54,33.54,356.38, +2021092612,54,43376.0,43,2021-09-26T20:26:36.800,59.0,MIN,left,78.32,34.2,5.29,3.81,0.53,37.1,1.25, +2021092612,54,43376.0,44,2021-09-26T20:26:36.900,59.0,MIN,left,78.35,34.72,5.16,3.61,0.52,39.24,5.08, +2021092612,54,43376.0,45,2021-09-26T20:26:37.000,59.0,MIN,left,78.41,35.23,5.04,3.23,0.51,42.2,8.41,pass_forward +2021092612,54,43376.0,46,2021-09-26T20:26:37.100,59.0,MIN,left,78.49,35.72,4.86,2.9,0.49,47.37,11.17, +2021092612,54,43376.0,47,2021-09-26T20:26:37.200,59.0,MIN,left,78.58,36.18,4.62,2.58,0.47,56.89,12.4, +2021092612,54,43376.0,48,2021-09-26T20:26:37.300,59.0,MIN,left,78.65,36.61,4.31,2.52,0.44,64.38,10.66, +2021092612,54,43376.0,49,2021-09-26T20:26:37.400,59.0,MIN,left,78.71,37.03,4.0,3.01,0.42,125.1,7.27, +2021092612,54,43376.0,50,2021-09-26T20:26:37.500,59.0,MIN,left,78.75,37.41,3.66,3.82,0.38,171.78,2.72, +2021092612,54,43694.0,1,2021-09-26T20:26:32.600,58.0,MIN,left,84.0,25.97,0.02,0.02,0.01,96.34,272.55, +2021092612,54,43694.0,2,2021-09-26T20:26:32.700,58.0,MIN,left,84.01,25.97,0.02,0.01,0.01,95.23,276.83, +2021092612,54,43694.0,3,2021-09-26T20:26:32.800,58.0,MIN,left,84.01,25.98,0.01,0.01,0.0,95.23,283.0,man_in_motion +2021092612,54,43694.0,4,2021-09-26T20:26:32.900,58.0,MIN,left,84.01,25.98,0.01,0.17,0.0,97.12,348.09, 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+2021092612,54,43694.0,14,2021-09-26T20:26:33.900,58.0,MIN,left,84.58,24.96,2.51,2.1,0.24,87.44,158.78, +2021092612,54,43694.0,15,2021-09-26T20:26:34.000,58.0,MIN,left,84.66,24.71,2.74,1.93,0.27,86.07,161.88, +2021092612,54,43694.0,16,2021-09-26T20:26:34.100,58.0,MIN,left,84.75,24.44,2.95,1.83,0.28,84.67,164.68, +2021092612,54,43694.0,17,2021-09-26T20:26:34.200,58.0,MIN,left,84.82,24.14,3.15,1.84,0.3,83.8,167.3, +2021092612,54,43694.0,18,2021-09-26T20:26:34.300,58.0,MIN,left,84.89,23.83,3.33,1.63,0.32,83.8,169.3,play_action +2021092612,54,43694.0,19,2021-09-26T20:26:34.400,58.0,MIN,left,84.94,23.49,3.48,1.38,0.34,81.84,171.39, +2021092612,54,43694.0,20,2021-09-26T20:26:34.500,58.0,MIN,left,84.99,23.14,3.5,0.86,0.35,78.98,172.4, +2021092612,54,43694.0,21,2021-09-26T20:26:34.600,58.0,MIN,left,85.04,22.8,3.52,0.53,0.35,76.83,172.99, +2021092612,54,43694.0,22,2021-09-26T20:26:34.700,58.0,MIN,left,85.08,22.45,3.46,0.49,0.35,74.37,172.91, +2021092612,54,43694.0,23,2021-09-26T20:26:34.800,58.0,MIN,left,85.13,22.11,3.39,0.73,0.34,70.94,172.25, +2021092612,54,43694.0,24,2021-09-26T20:26:34.900,58.0,MIN,left,85.17,21.81,3.1,1.57,0.31,66.67,172.77, +2021092612,54,43694.0,25,2021-09-26T20:26:35.000,58.0,MIN,left,85.19,21.52,2.77,2.25,0.28,59.37,174.23, +2021092612,54,43694.0,26,2021-09-26T20:26:35.100,58.0,MIN,left,85.22,21.28,2.38,2.84,0.25,53.72,174.08, +2021092612,54,43694.0,27,2021-09-26T20:26:35.200,58.0,MIN,left,85.24,21.06,1.99,3.17,0.21,51.11,174.74, +2021092612,54,43694.0,28,2021-09-26T20:26:35.300,58.0,MIN,left,85.25,20.89,1.59,3.34,0.17,48.27,176.2, +2021092612,54,43694.0,29,2021-09-26T20:26:35.400,58.0,MIN,left,85.25,20.75,1.23,3.29,0.14,45.68,182.61, +2021092612,54,43694.0,30,2021-09-26T20:26:35.500,58.0,MIN,left,85.22,20.64,1.02,3.09,0.12,37.81,197.99, +2021092612,54,43694.0,31,2021-09-26T20:26:35.600,58.0,MIN,left,85.17,20.55,0.89,2.93,0.1,35.2,216.77, +2021092612,54,43694.0,32,2021-09-26T20:26:35.700,58.0,MIN,left,85.1,20.49,0.92,2.72,0.09,32.61,238.7, +2021092612,54,43694.0,33,2021-09-26T20:26:35.800,58.0,MIN,left,85.0,20.46,1.09,2.57,0.11,29.29,254.97, +2021092612,54,43694.0,34,2021-09-26T20:26:35.900,58.0,MIN,left,84.88,20.43,1.31,2.35,0.12,25.8,263.63, +2021092612,54,43694.0,35,2021-09-26T20:26:36.000,58.0,MIN,left,84.74,20.43,1.49,2.02,0.14,19.62,268.49, +2021092612,54,43694.0,36,2021-09-26T20:26:36.100,58.0,MIN,left,84.58,20.43,1.65,1.72,0.16,14.36,273.64, +2021092612,54,43694.0,37,2021-09-26T20:26:36.200,58.0,MIN,left,84.41,20.45,1.83,1.58,0.18,9.23,278.28, +2021092612,54,43694.0,38,2021-09-26T20:26:36.300,58.0,MIN,left,84.21,20.48,2.04,1.53,0.2,5.45,281.46, +2021092612,54,43694.0,39,2021-09-26T20:26:36.400,58.0,MIN,left,84.0,20.54,2.25,1.98,0.22,3.55,288.93, +2021092612,54,43694.0,40,2021-09-26T20:26:36.500,58.0,MIN,left,83.79,20.62,2.42,2.33,0.23,3.55,295.64, +2021092612,54,43694.0,41,2021-09-26T20:26:36.600,58.0,MIN,left,83.56,20.74,2.58,2.32,0.25,3.55,301.38, +2021092612,54,43694.0,42,2021-09-26T20:26:36.700,58.0,MIN,left,83.34,20.9,2.8,2.69,0.27,356.97,307.92, +2021092612,54,43694.0,43,2021-09-26T20:26:36.800,58.0,MIN,left,83.12,21.09,3.01,2.96,0.29,355.36,314.01, +2021092612,54,43694.0,44,2021-09-26T20:26:36.900,58.0,MIN,left,82.91,21.31,3.22,2.86,0.31,352.94,318.12, +2021092612,54,43694.0,45,2021-09-26T20:26:37.000,58.0,MIN,left,82.69,21.56,3.46,2.71,0.33,352.94,321.94,pass_forward +2021092612,54,43694.0,46,2021-09-26T20:26:37.100,58.0,MIN,left,82.48,21.85,3.71,2.63,0.36,352.12,325.94, +2021092612,54,43694.0,47,2021-09-26T20:26:37.200,58.0,MIN,left,82.27,22.17,3.97,2.48,0.38,349.41,328.57, +2021092612,54,43694.0,48,2021-09-26T20:26:37.300,58.0,MIN,left,82.07,22.52,4.19,2.18,0.41,345.94,330.66, +2021092612,54,43694.0,49,2021-09-26T20:26:37.400,58.0,MIN,left,81.86,22.9,4.37,1.72,0.43,342.34,331.93, 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+2021092612,54,,9,2021-09-26T20:26:33.400,,football,left,86.56,25.92,0.96,4.17,0.23,,, +2021092612,54,,10,2021-09-26T20:26:33.500,,football,left,86.72,25.85,1.56,4.94,0.17,,, +2021092612,54,,11,2021-09-26T20:26:33.600,,football,left,86.86,25.79,1.86,4.66,0.15,,, +2021092612,54,,12,2021-09-26T20:26:33.700,,football,left,87.18,25.68,2.87,1.95,0.34,,, +2021092612,54,,13,2021-09-26T20:26:33.800,,football,left,87.52,25.56,3.28,0.41,0.36,,, +2021092612,54,,14,2021-09-26T20:26:33.900,,football,left,87.89,25.43,3.5,0.61,0.39,,, +2021092612,54,,15,2021-09-26T20:26:34.000,,football,left,88.28,25.3,3.37,0.37,0.42,,, +2021092612,54,,16,2021-09-26T20:26:34.100,,football,left,88.6,25.2,3.3,0.79,0.33,,, +2021092612,54,,17,2021-09-26T20:26:34.200,,football,left,88.96,25.12,3.29,1.08,0.36,,, +2021092612,54,,18,2021-09-26T20:26:34.300,,football,left,89.26,25.03,3.17,1.29,0.32,,,play_action +2021092612,54,,19,2021-09-26T20:26:34.400,,football,left,89.66,25.01,3.31,1.14,0.4,,, +2021092612,54,,20,2021-09-26T20:26:34.500,,football,left,90.0,24.94,3.25,0.96,0.35,,, +2021092612,54,,21,2021-09-26T20:26:34.600,,football,left,90.59,25.04,3.61,1.42,0.6,,, +2021092612,54,,22,2021-09-26T20:26:34.700,,football,left,90.94,25.0,3.3,4.41,0.35,,, +2021092612,54,,23,2021-09-26T20:26:34.800,,football,left,91.51,25.25,3.08,5.75,0.62,,, +2021092612,54,,24,2021-09-26T20:26:34.900,,football,left,92.16,25.64,2.84,6.3,0.76,,, +2021092612,54,,25,2021-09-26T20:26:35.000,,football,left,92.66,26.02,2.44,6.04,0.63,,, +2021092612,54,,26,2021-09-26T20:26:35.100,,football,left,92.87,26.07,1.88,5.04,0.22,,, +2021092612,54,,27,2021-09-26T20:26:35.200,,football,left,93.13,26.5,1.5,4.24,0.5,,, +2021092612,54,,28,2021-09-26T20:26:35.300,,football,left,93.25,26.54,1.12,3.31,0.13,,, +2021092612,54,,29,2021-09-26T20:26:35.400,,football,left,93.38,27.15,0.87,2.66,0.62,,, +2021092612,54,,30,2021-09-26T20:26:35.500,,football,left,93.45,27.18,0.64,2.12,0.08,,, 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+2021092612,54,,42,2021-09-26T20:26:36.700,,football,left,92.04,34.36,1.99,4.81,0.19,,, +2021092612,54,,43,2021-09-26T20:26:36.800,,football,left,91.96,34.57,2.47,4.45,0.22,,, +2021092612,54,,44,2021-09-26T20:26:36.900,,football,left,91.87,34.82,2.87,3.56,0.27,,, +2021092612,54,,45,2021-09-26T20:26:37.000,,football,left,91.77,35.1,3.18,2.67,0.3,,,pass_forward +2021092612,54,,46,2021-09-26T20:26:37.100,,football,left,88.56,35.98,22.45,0.19,3.33,,, +2021092612,54,,47,2021-09-26T20:26:37.200,,football,left,86.37,35.47,22.37,1.39,2.25,,, +2021092612,54,,48,2021-09-26T20:26:37.300,,football,left,84.2,34.99,22.18,2.43,2.23,,, +2021092612,54,,49,2021-09-26T20:26:37.400,,football,left,82.05,34.5,21.9,3.33,2.21,,, +2021092612,54,,50,2021-09-26T20:26:37.500,,football,left,79.93,34.01,21.54,4.1,2.17,,, +2021101011,54,34472.0,1,2021-10-10T20:26:21.500,50.0,SF,left,85.48,23.78,0.02,0.15,0.0,264.9,149.89, +2021101011,54,34472.0,2,2021-10-10T20:26:21.600,50.0,SF,left,85.49,23.77,0.11,0.44,0.01,265.57,154.91, +2021101011,54,34472.0,3,2021-10-10T20:26:21.700,50.0,SF,left,85.5,23.76,0.18,0.49,0.02,266.17,144.22, +2021101011,54,34472.0,4,2021-10-10T20:26:21.800,50.0,SF,left,85.53,23.75,0.27,0.62,0.03,266.82,121.0, +2021101011,54,34472.0,5,2021-10-10T20:26:21.900,50.0,SF,left,85.58,23.73,0.46,0.96,0.05,267.64,114.98, +2021101011,54,34472.0,6,2021-10-10T20:26:22.000,50.0,SF,left,85.62,23.7,0.58,0.94,0.06,267.64,116.51,ball_snap +2021101011,54,34472.0,7,2021-10-10T20:26:22.100,50.0,SF,left,85.69,23.67,0.76,1.01,0.08,266.82,114.35, +2021101011,54,34472.0,8,2021-10-10T20:26:22.200,50.0,SF,left,85.77,23.63,0.92,0.99,0.09,263.77,115.59, +2021101011,54,34472.0,9,2021-10-10T20:26:22.300,50.0,SF,left,85.88,23.56,1.18,1.24,0.12,260.6,120.44, +2021101011,54,34472.0,10,2021-10-10T20:26:22.400,50.0,SF,left,85.98,23.5,1.32,1.12,0.12,257.55,122.13, +2021101011,54,34472.0,11,2021-10-10T20:26:22.500,50.0,SF,left,86.11,23.41,1.51,1.02,0.15,252.41,125.47, +2021101011,54,34472.0,12,2021-10-10T20:26:22.600,50.0,SF,left,86.23,23.3,1.66,0.91,0.16,245.56,129.13, +2021101011,54,34472.0,13,2021-10-10T20:26:22.700,50.0,SF,left,86.38,23.19,1.83,0.78,0.19,238.21,129.8, +2021101011,54,34472.0,14,2021-10-10T20:26:22.800,50.0,SF,left,86.53,23.06,1.94,0.66,0.19,230.81,131.29, +2021101011,54,34472.0,15,2021-10-10T20:26:22.900,50.0,SF,left,86.68,22.93,2.03,0.38,0.2,225.78,132.55, +2021101011,54,34472.0,16,2021-10-10T20:26:23.000,50.0,SF,left,86.83,22.79,2.04,0.28,0.21,219.86,132.93, +2021101011,54,34472.0,17,2021-10-10T20:26:23.100,50.0,SF,left,86.98,22.65,2.0,0.57,0.2,213.94,132.68, +2021101011,54,34472.0,18,2021-10-10T20:26:23.200,50.0,SF,left,87.13,22.52,1.97,0.75,0.2,208.8,132.31, +2021101011,54,34472.0,19,2021-10-10T20:26:23.300,50.0,SF,left,87.28,22.39,1.91,0.94,0.2,205.34,131.84, +2021101011,54,34472.0,20,2021-10-10T20:26:23.400,50.0,SF,left,87.42,22.27,1.79,1.18,0.18,201.37,129.34, +2021101011,54,34472.0,21,2021-10-10T20:26:23.500,50.0,SF,left,87.57,22.17,1.7,1.24,0.18,197.51,126.59, +2021101011,54,34472.0,22,2021-10-10T20:26:23.600,50.0,SF,left,87.7,22.07,1.6,1.25,0.17,193.17,124.52, +2021101011,54,34472.0,23,2021-10-10T20:26:23.700,50.0,SF,left,87.84,21.98,1.54,1.12,0.16,189.78,122.5, +2021101011,54,34472.0,24,2021-10-10T20:26:23.800,50.0,SF,left,87.97,21.9,1.48,1.02,0.16,188.16,120.27, +2021101011,54,34472.0,25,2021-10-10T20:26:23.900,50.0,SF,left,88.09,21.82,1.41,0.88,0.15,183.98,121.84, +2021101011,54,34472.0,26,2021-10-10T20:26:24.000,50.0,SF,left,88.2,21.73,1.34,0.93,0.14,173.16,128.43, +2021101011,54,34472.0,27,2021-10-10T20:26:24.100,50.0,SF,left,88.3,21.64,1.27,1.03,0.13,166.49,132.1, +2021101011,54,34472.0,28,2021-10-10T20:26:24.200,50.0,SF,left,88.38,21.55,1.22,1.1,0.13,159.99,137.23, +2021101011,54,34472.0,29,2021-10-10T20:26:24.300,50.0,SF,left,88.46,21.45,1.19,1.19,0.12,155.94,145.16, +2021101011,54,34472.0,30,2021-10-10T20:26:24.400,50.0,SF,left,88.51,21.34,1.21,1.39,0.12,150.6,155.67, +2021101011,54,34472.0,31,2021-10-10T20:26:24.500,50.0,SF,left,88.55,21.22,1.3,1.52,0.13,148.63,166.43,run +2021101011,54,34472.0,32,2021-10-10T20:26:24.600,50.0,SF,left,88.56,21.07,1.44,1.51,0.15,146.58,176.19, +2021101011,54,34472.0,33,2021-10-10T20:26:24.700,50.0,SF,left,88.55,20.91,1.62,1.51,0.16,146.58,184.39, +2021101011,54,34472.0,34,2021-10-10T20:26:24.800,50.0,SF,left,88.52,20.73,1.8,1.55,0.18,149.65,191.5, +2021101011,54,34472.0,35,2021-10-10T20:26:24.900,50.0,SF,left,88.47,20.54,1.97,1.65,0.19,154.0,197.88, +2021101011,54,34472.0,36,2021-10-10T20:26:25.000,50.0,SF,left,88.38,20.36,2.07,1.61,0.2,155.17,204.89, +2021101011,54,35443.0,1,2021-10-10T20:26:21.500,71.0,SF,left,86.54,20.66,0.0,0.0,0.0,269.16,202.75, +2021101011,54,35443.0,2,2021-10-10T20:26:21.600,71.0,SF,left,86.55,20.66,0.0,0.0,0.0,269.78,199.22, +2021101011,54,35443.0,3,2021-10-10T20:26:21.700,71.0,SF,left,86.55,20.66,0.0,0.0,0.0,269.78,196.25, +2021101011,54,35443.0,4,2021-10-10T20:26:21.800,71.0,SF,left,86.55,20.66,0.0,0.0,0.0,269.78,187.29, +2021101011,54,35443.0,5,2021-10-10T20:26:21.900,71.0,SF,left,86.55,20.66,0.0,0.0,0.0,269.78,183.71, +2021101011,54,35443.0,6,2021-10-10T20:26:22.000,71.0,SF,left,86.55,20.66,0.0,0.0,0.0,269.78,174.54,ball_snap +2021101011,54,35443.0,7,2021-10-10T20:26:22.100,71.0,SF,left,86.55,20.66,0.0,0.0,0.0,269.78,176.24, +2021101011,54,35443.0,8,2021-10-10T20:26:22.200,71.0,SF,left,86.55,20.65,0.0,0.0,0.0,269.78,154.63, +2021101011,54,35443.0,9,2021-10-10T20:26:22.300,71.0,SF,left,86.55,20.65,0.0,0.0,0.0,268.95,150.93, +2021101011,54,35443.0,10,2021-10-10T20:26:22.400,71.0,SF,left,86.55,20.65,0.0,0.0,0.0,268.95,153.46, +2021101011,54,35443.0,11,2021-10-10T20:26:22.500,71.0,SF,left,86.56,20.64,0.0,0.0,0.01,268.95,155.46, +2021101011,54,35443.0,12,2021-10-10T20:26:22.600,71.0,SF,left,86.56,20.64,0.02,0.47,0.01,267.77,150.75, +2021101011,54,35443.0,13,2021-10-10T20:26:22.700,71.0,SF,left,86.57,20.62,0.17,1.38,0.02,266.9,144.67, +2021101011,54,35443.0,14,2021-10-10T20:26:22.800,71.0,SF,left,86.6,20.59,0.49,2.31,0.05,263.12,142.05, +2021101011,54,35443.0,15,2021-10-10T20:26:22.900,71.0,SF,left,86.66,20.52,0.88,3.08,0.08,260.61,139.84, +2021101011,54,35443.0,16,2021-10-10T20:26:23.000,71.0,SF,left,86.75,20.43,1.32,2.93,0.13,258.44,136.94, +2021101011,54,35443.0,17,2021-10-10T20:26:23.100,71.0,SF,left,86.87,20.31,1.7,2.84,0.17,254.71,135.21, +2021101011,54,35443.0,18,2021-10-10T20:26:23.200,71.0,SF,left,87.04,20.18,2.11,2.7,0.22,244.54,131.57, +2021101011,54,35443.0,19,2021-10-10T20:26:23.300,71.0,SF,left,87.22,20.03,2.41,2.26,0.23,233.62,129.12, +2021101011,54,35443.0,20,2021-10-10T20:26:23.400,71.0,SF,left,87.44,19.88,2.63,1.48,0.26,222.52,126.37, +2021101011,54,35443.0,21,2021-10-10T20:26:23.500,71.0,SF,left,87.67,19.74,2.76,0.91,0.28,212.59,123.38, +2021101011,54,35443.0,22,2021-10-10T20:26:23.600,71.0,SF,left,87.98,19.65,2.9,1.33,0.31,203.7,114.9, +2021101011,54,35443.0,23,2021-10-10T20:26:23.700,71.0,SF,left,88.27,19.57,2.93,1.68,0.3,199.1,108.67, +2021101011,54,35443.0,24,2021-10-10T20:26:23.800,71.0,SF,left,88.54,19.48,2.88,1.68,0.29,195.61,105.49, +2021101011,54,35443.0,25,2021-10-10T20:26:23.900,71.0,SF,left,88.83,19.43,2.83,1.91,0.29,189.72,100.82, +2021101011,54,35443.0,26,2021-10-10T20:26:24.000,71.0,SF,left,89.14,19.42,2.87,2.05,0.31,181.17,94.67, +2021101011,54,35443.0,27,2021-10-10T20:26:24.100,71.0,SF,left,89.41,19.42,2.71,2.23,0.27,182.04,89.23, +2021101011,54,35443.0,28,2021-10-10T20:26:24.200,71.0,SF,left,89.65,19.44,2.51,2.28,0.25,179.93,83.93, +2021101011,54,35443.0,29,2021-10-10T20:26:24.300,71.0,SF,left,89.88,19.49,2.31,2.29,0.23,177.13,78.71, +2021101011,54,35443.0,30,2021-10-10T20:26:24.400,71.0,SF,left,90.07,19.54,2.02,2.63,0.2,177.13,73.73, +2021101011,54,35443.0,31,2021-10-10T20:26:24.500,71.0,SF,left,90.23,19.59,1.65,3.03,0.17,178.51,69.12,run +2021101011,54,35443.0,32,2021-10-10T20:26:24.600,71.0,SF,left,90.36,19.64,1.28,3.08,0.14,180.55,64.65, +2021101011,54,35443.0,33,2021-10-10T20:26:24.700,71.0,SF,left,90.46,19.68,0.93,2.89,0.1,185.06,63.03, +2021101011,54,35443.0,34,2021-10-10T20:26:24.800,71.0,SF,left,90.52,19.7,0.55,2.6,0.06,190.16,67.1, +2021101011,54,35443.0,35,2021-10-10T20:26:24.900,71.0,SF,left,90.55,19.71,0.23,2.3,0.03,195.63,83.09, +2021101011,54,35443.0,36,2021-10-10T20:26:25.000,71.0,SF,left,90.55,19.68,0.18,2.0,0.02,200.44,188.17, +2021101011,54,35522.0,1,2021-10-10T20:26:21.500,98.0,ARI,left,84.37,25.68,0.0,0.0,0.0,92.6,41.73, +2021101011,54,35522.0,2,2021-10-10T20:26:21.600,98.0,ARI,left,84.38,25.68,0.0,0.0,0.0,91.23,44.3, +2021101011,54,35522.0,3,2021-10-10T20:26:21.700,98.0,ARI,left,84.38,25.68,0.0,0.0,0.0,91.23,46.22, +2021101011,54,35522.0,4,2021-10-10T20:26:21.800,98.0,ARI,left,84.38,25.68,0.0,0.0,0.0,91.23,45.87, +2021101011,54,35522.0,5,2021-10-10T20:26:21.900,98.0,ARI,left,84.38,25.68,0.0,0.0,0.0,89.5,43.64, +2021101011,54,35522.0,6,2021-10-10T20:26:22.000,98.0,ARI,left,84.38,25.69,0.0,0.06,0.01,86.1,56.59,ball_snap +2021101011,54,35522.0,7,2021-10-10T20:26:22.100,98.0,ARI,left,84.4,25.68,0.06,0.76,0.01,86.1,116.83, +2021101011,54,35522.0,8,2021-10-10T20:26:22.200,98.0,ARI,left,84.42,25.66,0.24,1.34,0.02,80.86,119.01, +2021101011,54,35522.0,9,2021-10-10T20:26:22.300,98.0,ARI,left,84.45,25.65,0.43,1.44,0.04,84.84,113.42, +2021101011,54,35522.0,10,2021-10-10T20:26:22.400,98.0,ARI,left,84.52,25.64,0.69,1.53,0.07,89.21,105.89, +2021101011,54,35522.0,11,2021-10-10T20:26:22.500,98.0,ARI,left,84.64,25.62,1.05,1.84,0.12,103.53,100.32, +2021101011,54,35522.0,12,2021-10-10T20:26:22.600,98.0,ARI,left,84.79,25.62,1.34,1.73,0.14,107.64,95.43, +2021101011,54,35522.0,13,2021-10-10T20:26:22.700,98.0,ARI,left,84.94,25.61,1.52,1.28,0.15,111.58,93.78, +2021101011,54,35522.0,14,2021-10-10T20:26:22.800,98.0,ARI,left,85.14,25.64,1.83,1.24,0.21,114.12,86.85, +2021101011,54,35522.0,15,2021-10-10T20:26:22.900,98.0,ARI,left,85.36,25.66,2.07,1.23,0.22,114.12,84.56, +2021101011,54,35522.0,16,2021-10-10T20:26:23.000,98.0,ARI,left,85.61,25.7,2.32,1.24,0.25,116.52,82.58, +2021101011,54,35522.0,17,2021-10-10T20:26:23.100,98.0,ARI,left,85.88,25.73,2.57,0.88,0.28,122.5,81.38, +2021101011,54,35522.0,18,2021-10-10T20:26:23.200,98.0,ARI,left,86.21,25.78,2.92,0.87,0.33,119.87,81.26, +2021101011,54,35522.0,19,2021-10-10T20:26:23.300,98.0,ARI,left,86.54,25.83,3.19,1.19,0.34,125.1,81.36, +2021101011,54,35522.0,20,2021-10-10T20:26:23.400,98.0,ARI,left,86.92,25.85,3.5,0.82,0.37,120.82,83.81, +2021101011,54,35522.0,21,2021-10-10T20:26:23.500,98.0,ARI,left,87.31,25.84,3.73,1.1,0.39,113.02,88.51, +2021101011,54,35522.0,22,2021-10-10T20:26:23.600,98.0,ARI,left,87.69,25.81,3.84,1.39,0.39,119.75,92.73, +2021101011,54,35522.0,23,2021-10-10T20:26:23.700,98.0,ARI,left,88.08,25.76,3.87,2.05,0.39,121.35,97.62, +2021101011,54,35522.0,24,2021-10-10T20:26:23.800,98.0,ARI,left,88.46,25.68,3.86,2.45,0.39,127.01,102.9, +2021101011,54,35522.0,25,2021-10-10T20:26:23.900,98.0,ARI,left,88.82,25.57,3.81,2.88,0.38,133.9,108.57, +2021101011,54,35522.0,26,2021-10-10T20:26:24.000,98.0,ARI,left,89.16,25.4,3.69,3.49,0.38,142.25,117.5, +2021101011,54,35522.0,27,2021-10-10T20:26:24.100,98.0,ARI,left,89.45,25.2,3.54,3.88,0.36,145.7,126.16, +2021101011,54,35522.0,28,2021-10-10T20:26:24.200,98.0,ARI,left,89.71,24.97,3.42,3.75,0.35,151.62,133.71, +2021101011,54,35522.0,29,2021-10-10T20:26:24.300,98.0,ARI,left,89.88,24.67,3.28,4.31,0.33,154.23,147.79, +2021101011,54,35522.0,30,2021-10-10T20:26:24.400,98.0,ARI,left,89.96,24.36,3.2,4.89,0.32,164.08,162.59, +2021101011,54,35522.0,31,2021-10-10T20:26:24.500,98.0,ARI,left,90.0,24.05,3.15,5.19,0.31,169.86,174.52,run +2021101011,54,35522.0,32,2021-10-10T20:26:24.600,98.0,ARI,left,89.99,23.74,3.18,4.64,0.32,173.54,185.08, +2021101011,54,35522.0,33,2021-10-10T20:26:24.700,98.0,ARI,left,89.88,23.43,3.29,5.23,0.33,177.45,200.67, +2021101011,54,35522.0,34,2021-10-10T20:26:24.800,98.0,ARI,left,89.7,23.14,3.47,5.47,0.34,185.72,213.82, +2021101011,54,35522.0,35,2021-10-10T20:26:24.900,98.0,ARI,left,89.48,22.87,3.63,4.81,0.35,227.69,222.62, +2021101011,54,35522.0,36,2021-10-10T20:26:25.000,98.0,ARI,left,89.2,22.63,3.72,3.85,0.37,238.84,230.98, +2021101011,54,37084.0,1,2021-10-10T20:26:21.500,99.0,ARI,left,84.25,27.95,0.0,0.0,0.0,73.84,346.25, 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+2021101011,54,40008.0,29,2021-10-10T20:26:24.300,23.0,ARI,left,72.64,42.81,4.34,0.94,0.43,178.02,282.77, +2021101011,54,40008.0,30,2021-10-10T20:26:24.400,23.0,ARI,left,72.2,42.9,4.47,0.98,0.45,178.02,281.2, +2021101011,54,40008.0,31,2021-10-10T20:26:24.500,23.0,ARI,left,71.76,42.98,4.52,0.86,0.45,179.32,280.08,run +2021101011,54,40008.0,32,2021-10-10T20:26:24.600,23.0,ARI,left,71.31,43.06,4.53,0.71,0.45,179.32,279.14, +2021101011,54,40008.0,33,2021-10-10T20:26:24.700,23.0,ARI,left,70.87,43.13,4.48,0.75,0.45,179.32,278.39, +2021101011,54,40008.0,34,2021-10-10T20:26:24.800,23.0,ARI,left,70.44,43.19,4.37,1.11,0.44,181.26,277.07, +2021101011,54,40008.0,35,2021-10-10T20:26:24.900,23.0,ARI,left,70.01,43.23,4.25,1.4,0.43,183.56,275.91, +2021101011,54,40008.0,36,2021-10-10T20:26:25.000,23.0,ARI,left,69.59,43.27,4.08,1.67,0.41,183.56,274.87, +2021101011,54,40078.0,1,2021-10-10T20:26:21.500,44.0,SF,left,85.64,43.1,0.0,0.0,0.0,297.27,248.58, +2021101011,54,40078.0,2,2021-10-10T20:26:21.600,44.0,SF,left,85.64,43.1,0.0,0.0,0.0,297.27,245.87, +2021101011,54,40078.0,3,2021-10-10T20:26:21.700,44.0,SF,left,85.64,43.1,0.0,0.0,0.0,297.98,243.24, +2021101011,54,40078.0,4,2021-10-10T20:26:21.800,44.0,SF,left,85.64,43.1,0.0,0.0,0.0,297.98,237.32, +2021101011,54,40078.0,5,2021-10-10T20:26:21.900,44.0,SF,left,85.64,43.1,0.0,0.0,0.0,296.9,233.57, +2021101011,54,40078.0,6,2021-10-10T20:26:22.000,44.0,SF,left,85.63,43.1,0.0,0.14,0.01,296.9,260.77,ball_snap +2021101011,54,40078.0,7,2021-10-10T20:26:22.100,44.0,SF,left,85.63,43.1,0.07,0.88,0.01,296.21,266.79, +2021101011,54,40078.0,8,2021-10-10T20:26:22.200,44.0,SF,left,85.6,43.1,0.29,1.91,0.02,295.14,263.13, +2021101011,54,40078.0,9,2021-10-10T20:26:22.300,44.0,SF,left,85.54,43.1,0.7,3.2,0.06,294.2,268.27, +2021101011,54,40078.0,10,2021-10-10T20:26:22.400,44.0,SF,left,85.41,43.09,1.31,4.18,0.13,294.2,267.73, +2021101011,54,40078.0,11,2021-10-10T20:26:22.500,44.0,SF,left,85.24,43.07,1.88,4.55,0.18,290.12,266.35, +2021101011,54,40078.0,12,2021-10-10T20:26:22.600,44.0,SF,left,85.0,43.06,2.48,4.63,0.24,285.76,266.95, +2021101011,54,40078.0,13,2021-10-10T20:26:22.700,44.0,SF,left,84.7,43.04,3.06,4.51,0.29,280.89,266.07, +2021101011,54,40078.0,14,2021-10-10T20:26:22.800,44.0,SF,left,84.35,43.02,3.67,4.63,0.35,279.57,266.02, +2021101011,54,40078.0,15,2021-10-10T20:26:22.900,44.0,SF,left,83.95,43.0,4.25,4.46,0.41,275.74,266.56, +2021101011,54,40078.0,16,2021-10-10T20:26:23.000,44.0,SF,left,83.49,42.97,4.77,4.07,0.46,274.99,266.41, +2021101011,54,40078.0,17,2021-10-10T20:26:23.100,44.0,SF,left,82.98,42.94,5.26,3.78,0.51,273.81,266.36, +2021101011,54,40078.0,18,2021-10-10T20:26:23.200,44.0,SF,left,82.43,42.9,5.71,3.25,0.55,276.77,266.18, +2021101011,54,40078.0,19,2021-10-10T20:26:23.300,44.0,SF,left,81.84,42.87,6.05,2.62,0.59,275.31,266.89, +2021101011,54,40078.0,20,2021-10-10T20:26:23.400,44.0,SF,left,81.22,42.83,6.3,1.9,0.62,274.26,267.28, +2021101011,54,40078.0,21,2021-10-10T20:26:23.500,44.0,SF,left,80.58,42.81,6.48,1.22,0.64,272.19,267.66, +2021101011,54,40078.0,22,2021-10-10T20:26:23.600,44.0,SF,left,79.93,42.79,6.58,0.64,0.66,271.57,268.17, +2021101011,54,40078.0,23,2021-10-10T20:26:23.700,44.0,SF,left,79.27,42.77,6.61,0.51,0.66,270.34,268.93, +2021101011,54,40078.0,24,2021-10-10T20:26:23.800,44.0,SF,left,78.61,42.77,6.57,0.9,0.66,269.57,269.86, +2021101011,54,40078.0,25,2021-10-10T20:26:23.900,44.0,SF,left,77.96,42.78,6.48,1.34,0.65,269.57,270.88, +2021101011,54,40078.0,26,2021-10-10T20:26:24.000,44.0,SF,left,77.32,42.8,6.33,1.76,0.64,268.25,272.22, +2021101011,54,40078.0,27,2021-10-10T20:26:24.100,44.0,SF,left,76.69,42.84,6.21,2.07,0.63,268.25,274.1, +2021101011,54,40078.0,28,2021-10-10T20:26:24.200,44.0,SF,left,76.07,42.9,6.11,2.12,0.62,268.25,275.75, +2021101011,54,40078.0,29,2021-10-10T20:26:24.300,44.0,SF,left,75.47,42.97,5.97,2.14,0.61,267.61,277.19, +2021101011,54,40078.0,30,2021-10-10T20:26:24.400,44.0,SF,left,74.88,43.04,5.83,1.96,0.59,266.76,277.39, +2021101011,54,40078.0,31,2021-10-10T20:26:24.500,44.0,SF,left,74.31,43.11,5.62,1.95,0.57,266.76,277.15,run +2021101011,54,40078.0,32,2021-10-10T20:26:24.600,44.0,SF,left,73.77,43.16,5.42,2.07,0.55,266.76,275.72, +2021101011,54,40078.0,33,2021-10-10T20:26:24.700,44.0,SF,left,73.24,43.2,5.21,2.21,0.53,266.76,274.87, +2021101011,54,40078.0,34,2021-10-10T20:26:24.800,44.0,SF,left,72.72,43.23,5.02,2.37,0.51,266.76,272.73, +2021101011,54,40078.0,35,2021-10-10T20:26:24.900,44.0,SF,left,72.23,43.24,4.79,2.5,0.49,266.76,270.79, +2021101011,54,40078.0,36,2021-10-10T20:26:25.000,44.0,SF,left,71.77,43.24,4.56,2.52,0.47,267.57,269.08, +2021101011,54,42371.0,1,2021-10-10T20:26:21.500,75.0,SF,left,86.08,22.17,0.0,0.0,0.0,28.79,101.08, +2021101011,54,42371.0,2,2021-10-10T20:26:21.600,75.0,SF,left,86.08,22.17,0.0,0.0,0.0,28.79,86.98, +2021101011,54,42371.0,3,2021-10-10T20:26:21.700,75.0,SF,left,86.08,22.17,0.0,0.0,0.0,28.79,73.65, +2021101011,54,42371.0,4,2021-10-10T20:26:21.800,75.0,SF,left,86.08,22.17,0.0,0.0,0.0,30.05,101.85, +2021101011,54,42371.0,5,2021-10-10T20:26:21.900,75.0,SF,left,86.09,22.17,0.01,0.17,0.0,30.05,122.22, +2021101011,54,42371.0,6,2021-10-10T20:26:22.000,75.0,SF,left,86.09,22.16,0.06,0.48,0.01,27.79,119.53,ball_snap +2021101011,54,42371.0,7,2021-10-10T20:26:22.100,75.0,SF,left,86.1,22.16,0.14,0.67,0.01,25.23,112.99, +2021101011,54,42371.0,8,2021-10-10T20:26:22.200,75.0,SF,left,86.13,22.15,0.28,0.98,0.03,17.81,117.32, +2021101011,54,42371.0,9,2021-10-10T20:26:22.300,75.0,SF,left,86.16,22.13,0.43,1.12,0.04,356.82,117.61, +2021101011,54,42371.0,10,2021-10-10T20:26:22.400,75.0,SF,left,86.23,22.1,0.69,1.32,0.07,325.7,114.47, +2021101011,54,42371.0,11,2021-10-10T20:26:22.500,75.0,SF,left,86.32,22.06,0.91,1.35,0.1,261.13,114.31, +2021101011,54,42371.0,12,2021-10-10T20:26:22.600,75.0,SF,left,86.42,22.01,1.1,1.04,0.11,238.36,115.59, +2021101011,54,42371.0,13,2021-10-10T20:26:22.700,75.0,SF,left,86.55,21.94,1.32,1.04,0.14,230.38,117.04, +2021101011,54,42371.0,14,2021-10-10T20:26:22.800,75.0,SF,left,86.68,21.89,1.47,0.89,0.15,234.4,114.56, +2021101011,54,42371.0,15,2021-10-10T20:26:22.900,75.0,SF,left,86.85,21.83,1.67,0.72,0.17,237.57,112.25, +2021101011,54,42371.0,16,2021-10-10T20:26:23.000,75.0,SF,left,87.03,21.75,1.84,0.47,0.19,235.85,111.97, +2021101011,54,42371.0,17,2021-10-10T20:26:23.100,75.0,SF,left,87.2,21.68,1.91,0.27,0.19,235.07,111.58, +2021101011,54,42371.0,18,2021-10-10T20:26:23.200,75.0,SF,left,87.38,21.62,1.89,0.28,0.19,234.14,109.9, +2021101011,54,42371.0,19,2021-10-10T20:26:23.300,75.0,SF,left,87.56,21.56,1.89,0.51,0.19,233.4,108.33, +2021101011,54,42371.0,20,2021-10-10T20:26:23.400,75.0,SF,left,87.75,21.5,1.89,0.6,0.2,231.95,107.58, +2021101011,54,42371.0,21,2021-10-10T20:26:23.500,75.0,SF,left,87.94,21.44,1.92,0.61,0.2,230.29,106.71, +2021101011,54,42371.0,22,2021-10-10T20:26:23.600,75.0,SF,left,88.12,21.39,1.85,0.79,0.19,229.5,104.74, +2021101011,54,42371.0,23,2021-10-10T20:26:23.700,75.0,SF,left,88.3,21.35,1.81,0.85,0.19,227.76,103.16, +2021101011,54,42371.0,24,2021-10-10T20:26:23.800,75.0,SF,left,88.49,21.31,1.79,0.8,0.19,222.91,102.24, +2021101011,54,42371.0,25,2021-10-10T20:26:23.900,75.0,SF,left,88.66,21.28,1.72,0.86,0.17,219.73,101.37, +2021101011,54,42371.0,26,2021-10-10T20:26:24.000,75.0,SF,left,88.81,21.24,1.61,0.94,0.16,215.6,102.09, +2021101011,54,42371.0,27,2021-10-10T20:26:24.100,75.0,SF,left,88.95,21.2,1.43,1.2,0.14,212.08,102.67, +2021101011,54,42371.0,28,2021-10-10T20:26:24.200,75.0,SF,left,89.08,21.16,1.29,1.32,0.13,211.06,106.2, +2021101011,54,42371.0,29,2021-10-10T20:26:24.300,75.0,SF,left,89.2,21.1,1.21,1.42,0.13,208.79,114.92, +2021101011,54,42371.0,30,2021-10-10T20:26:24.400,75.0,SF,left,89.29,21.04,1.07,1.58,0.11,204.43,122.75, +2021101011,54,42371.0,31,2021-10-10T20:26:24.500,75.0,SF,left,89.36,20.97,0.99,1.6,0.1,202.95,134.31,run +2021101011,54,42371.0,32,2021-10-10T20:26:24.600,75.0,SF,left,89.41,20.88,0.97,1.63,0.1,199.57,150.85, +2021101011,54,42371.0,33,2021-10-10T20:26:24.700,75.0,SF,left,89.45,20.77,1.05,1.66,0.11,201.74,162.66, +2021101011,54,42371.0,34,2021-10-10T20:26:24.800,75.0,SF,left,89.46,20.66,1.14,1.74,0.11,200.54,175.37, +2021101011,54,42371.0,35,2021-10-10T20:26:24.900,75.0,SF,left,89.46,20.55,1.2,1.36,0.12,200.54,183.67, +2021101011,54,42371.0,36,2021-10-10T20:26:25.000,75.0,SF,left,89.43,20.41,1.32,1.23,0.14,211.65,192.99, +2021101011,54,42427.0,1,2021-10-10T20:26:21.500,58.0,ARI,left,80.16,21.16,0.47,1.3,0.05,80.33,184.74, +2021101011,54,42427.0,2,2021-10-10T20:26:21.600,58.0,ARI,left,80.16,21.12,0.32,1.2,0.04,79.3,185.33, +2021101011,54,42427.0,3,2021-10-10T20:26:21.700,58.0,ARI,left,80.16,21.1,0.21,1.05,0.03,78.6,185.26, +2021101011,54,42427.0,4,2021-10-10T20:26:21.800,58.0,ARI,left,80.15,21.08,0.1,0.95,0.01,78.6,195.47, +2021101011,54,42427.0,5,2021-10-10T20:26:21.900,58.0,ARI,left,80.15,21.08,0.03,0.85,0.0,78.6,284.2, +2021101011,54,42427.0,6,2021-10-10T20:26:22.000,58.0,ARI,left,80.15,21.08,0.07,0.58,0.01,78.6,307.27,ball_snap +2021101011,54,42427.0,7,2021-10-10T20:26:22.100,58.0,ARI,left,80.14,21.09,0.1,0.38,0.01,78.6,315.74, +2021101011,54,42427.0,8,2021-10-10T20:26:22.200,58.0,ARI,left,80.13,21.09,0.07,0.16,0.01,82.01,254.83, +2021101011,54,42427.0,9,2021-10-10T20:26:22.300,58.0,ARI,left,80.13,21.07,0.17,0.57,0.01,84.74,203.71, +2021101011,54,42427.0,10,2021-10-10T20:26:22.400,58.0,ARI,left,80.12,21.05,0.33,0.91,0.03,85.96,198.84, +2021101011,54,42427.0,11,2021-10-10T20:26:22.500,58.0,ARI,left,80.09,21.0,0.6,1.43,0.06,88.73,201.54, +2021101011,54,42427.0,12,2021-10-10T20:26:22.600,58.0,ARI,left,80.06,20.92,0.84,1.68,0.08,88.73,202.3, +2021101011,54,42427.0,13,2021-10-10T20:26:22.700,58.0,ARI,left,80.02,20.83,1.08,1.81,0.1,89.9,203.0, +2021101011,54,42427.0,14,2021-10-10T20:26:22.800,58.0,ARI,left,79.97,20.72,1.33,1.87,0.12,90.53,205.55, +2021101011,54,42427.0,15,2021-10-10T20:26:22.900,58.0,ARI,left,79.89,20.6,1.55,1.81,0.15,88.42,210.24, +2021101011,54,42427.0,16,2021-10-10T20:26:23.000,58.0,ARI,left,79.81,20.46,1.69,1.56,0.16,86.92,213.49, +2021101011,54,42427.0,17,2021-10-10T20:26:23.100,58.0,ARI,left,79.7,20.32,1.81,1.35,0.17,85.5,217.17, +2021101011,54,42427.0,18,2021-10-10T20:26:23.200,58.0,ARI,left,79.59,20.18,1.88,1.12,0.18,84.35,221.0, +2021101011,54,42427.0,19,2021-10-10T20:26:23.300,58.0,ARI,left,79.45,20.06,1.86,1.43,0.18,81.61,229.6, 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+2021101011,54,44848.0,2,2021-10-10T20:26:21.600,3.0,ARI,left,66.41,25.47,1.19,0.28,0.12,82.45,238.39, +2021101011,54,44848.0,3,2021-10-10T20:26:21.700,3.0,ARI,left,66.31,25.41,1.19,0.18,0.12,81.79,237.36, +2021101011,54,44848.0,4,2021-10-10T20:26:21.800,3.0,ARI,left,66.2,25.34,1.18,0.19,0.12,81.06,238.74, +2021101011,54,44848.0,5,2021-10-10T20:26:21.900,3.0,ARI,left,66.1,25.28,1.17,0.2,0.12,81.06,239.39, +2021101011,54,44848.0,6,2021-10-10T20:26:22.000,3.0,ARI,left,66.0,25.23,1.16,0.21,0.12,81.06,240.74,ball_snap +2021101011,54,44848.0,7,2021-10-10T20:26:22.100,3.0,ARI,left,65.89,25.17,1.18,0.22,0.12,79.79,242.53, +2021101011,54,44848.0,8,2021-10-10T20:26:22.200,3.0,ARI,left,65.79,25.12,1.18,0.23,0.12,78.06,243.64, +2021101011,54,44848.0,9,2021-10-10T20:26:22.300,3.0,ARI,left,65.68,25.07,1.18,0.21,0.12,76.55,244.31, +2021101011,54,44848.0,10,2021-10-10T20:26:22.400,3.0,ARI,left,65.58,25.02,1.16,0.25,0.12,76.55,245.61, +2021101011,54,44848.0,11,2021-10-10T20:26:22.500,3.0,ARI,left,65.47,24.97,1.13,0.34,0.11,74.13,247.63, +2021101011,54,44848.0,12,2021-10-10T20:26:22.600,3.0,ARI,left,65.36,24.93,1.16,0.24,0.12,72.07,248.6, +2021101011,54,44848.0,13,2021-10-10T20:26:22.700,3.0,ARI,left,65.25,24.89,1.18,0.22,0.12,72.07,250.16, +2021101011,54,44848.0,14,2021-10-10T20:26:22.800,3.0,ARI,left,65.14,24.85,1.21,0.18,0.12,72.71,250.87, +2021101011,54,44848.0,15,2021-10-10T20:26:22.900,3.0,ARI,left,65.02,24.81,1.24,0.28,0.12,74.34,253.9, +2021101011,54,44848.0,16,2021-10-10T20:26:23.000,3.0,ARI,left,64.89,24.78,1.25,0.4,0.13,74.34,257.41, +2021101011,54,44848.0,17,2021-10-10T20:26:23.100,3.0,ARI,left,64.77,24.77,1.28,0.72,0.13,73.29,264.92, +2021101011,54,44848.0,18,2021-10-10T20:26:23.200,3.0,ARI,left,64.63,24.76,1.36,0.72,0.14,71.09,267.6, +2021101011,54,44848.0,19,2021-10-10T20:26:23.300,3.0,ARI,left,64.48,24.77,1.46,0.87,0.15,68.39,272.71, +2021101011,54,44848.0,20,2021-10-10T20:26:23.400,3.0,ARI,left,64.33,24.79,1.53,1.03,0.15,66.04,278.58, +2021101011,54,44848.0,21,2021-10-10T20:26:23.500,3.0,ARI,left,64.18,24.83,1.63,1.2,0.16,66.04,285.15, +2021101011,54,44848.0,22,2021-10-10T20:26:23.600,3.0,ARI,left,64.02,24.88,1.72,1.24,0.17,64.16,290.19, +2021101011,54,44848.0,23,2021-10-10T20:26:23.700,3.0,ARI,left,63.85,24.96,1.83,1.23,0.18,63.04,294.74, +2021101011,54,44848.0,24,2021-10-10T20:26:23.800,3.0,ARI,left,63.67,25.07,2.08,1.57,0.21,61.45,300.59, +2021101011,54,44848.0,25,2021-10-10T20:26:23.900,3.0,ARI,left,63.49,25.18,2.18,1.31,0.21,61.45,303.09, +2021101011,54,44848.0,26,2021-10-10T20:26:24.000,3.0,ARI,left,63.3,25.3,2.23,0.94,0.22,63.55,304.47, +2021101011,54,44848.0,27,2021-10-10T20:26:24.100,3.0,ARI,left,63.13,25.41,2.19,0.42,0.21,65.73,304.5, +2021101011,54,44848.0,28,2021-10-10T20:26:24.200,3.0,ARI,left,62.95,25.53,2.09,0.5,0.21,67.47,304.37, +2021101011,54,44848.0,29,2021-10-10T20:26:24.300,3.0,ARI,left,62.79,25.63,1.86,1.33,0.19,70.08,303.49, +2021101011,54,44848.0,30,2021-10-10T20:26:24.400,3.0,ARI,left,62.65,25.72,1.61,2.16,0.17,70.82,301.59, +2021101011,54,44848.0,31,2021-10-10T20:26:24.500,3.0,ARI,left,62.53,25.79,1.18,2.89,0.14,71.77,296.45,run +2021101011,54,44848.0,32,2021-10-10T20:26:24.600,3.0,ARI,left,62.44,25.83,0.78,3.22,0.09,73.94,286.97, +2021101011,54,44848.0,33,2021-10-10T20:26:24.700,3.0,ARI,left,62.4,25.83,0.32,3.61,0.05,81.2,253.66, +2021101011,54,44848.0,34,2021-10-10T20:26:24.800,3.0,ARI,left,62.39,25.81,0.36,3.64,0.02,83.55,163.59, +2021101011,54,44848.0,35,2021-10-10T20:26:24.900,3.0,ARI,left,62.44,25.77,0.75,3.6,0.06,80.41,124.99, +2021101011,54,44848.0,36,2021-10-10T20:26:25.000,3.0,ARI,left,62.53,25.73,1.16,3.43,0.1,80.41,113.77, +2021101011,54,45069.0,1,2021-10-10T20:26:21.500,60.0,SF,left,86.1,25.17,0.0,0.0,0.0,240.45,305.44, +2021101011,54,45069.0,2,2021-10-10T20:26:21.600,60.0,SF,left,86.1,25.16,0.0,0.0,0.0,240.45,303.92, +2021101011,54,45069.0,3,2021-10-10T20:26:21.700,60.0,SF,left,86.1,25.17,0.0,0.0,0.0,240.45,309.32, +2021101011,54,45069.0,4,2021-10-10T20:26:21.800,60.0,SF,left,86.1,25.17,0.0,0.0,0.0,241.12,320.7, +2021101011,54,45069.0,5,2021-10-10T20:26:21.900,60.0,SF,left,86.1,25.17,0.01,0.2,0.0,242.28,353.15, +2021101011,54,45069.0,6,2021-10-10T20:26:22.000,60.0,SF,left,86.1,25.18,0.05,0.45,0.0,242.93,347.0,ball_snap +2021101011,54,45069.0,7,2021-10-10T20:26:22.100,60.0,SF,left,86.1,25.18,0.07,0.33,0.0,244.5,337.84, +2021101011,54,45069.0,8,2021-10-10T20:26:22.200,60.0,SF,left,86.09,25.18,0.09,0.23,0.01,245.75,316.78, +2021101011,54,45069.0,9,2021-10-10T20:26:22.300,60.0,SF,left,86.07,25.18,0.15,0.44,0.02,248.68,273.45, +2021101011,54,45069.0,10,2021-10-10T20:26:22.400,60.0,SF,left,86.06,25.17,0.17,0.48,0.02,252.8,242.58, +2021101011,54,45069.0,11,2021-10-10T20:26:22.500,60.0,SF,left,86.04,25.15,0.25,0.56,0.03,257.81,229.12, +2021101011,54,45069.0,12,2021-10-10T20:26:22.600,60.0,SF,left,86.02,25.13,0.28,0.45,0.02,263.31,221.67, +2021101011,54,45069.0,13,2021-10-10T20:26:22.700,60.0,SF,left,86.01,25.11,0.3,0.33,0.02,270.73,215.18, +2021101011,54,45069.0,14,2021-10-10T20:26:22.800,60.0,SF,left,86.02,25.1,0.19,0.39,0.01,281.7,192.85, +2021101011,54,45069.0,15,2021-10-10T20:26:22.900,60.0,SF,left,86.06,25.11,0.19,0.98,0.04,294.67,109.38, +2021101011,54,45069.0,16,2021-10-10T20:26:23.000,60.0,SF,left,86.1,25.11,0.36,1.24,0.04,317.69,85.89, +2021101011,54,45069.0,17,2021-10-10T20:26:23.100,60.0,SF,left,86.16,25.13,0.6,1.43,0.07,339.49,75.66, +2021101011,54,45069.0,18,2021-10-10T20:26:23.200,60.0,SF,left,86.26,25.17,1.0,2.23,0.11,1.78,69.54, +2021101011,54,45069.0,19,2021-10-10T20:26:23.300,60.0,SF,left,86.39,25.22,1.39,2.78,0.13,27.98,68.09, +2021101011,54,45069.0,20,2021-10-10T20:26:23.400,60.0,SF,left,86.54,25.28,1.78,2.59,0.17,38.11,69.09, +2021101011,54,45069.0,21,2021-10-10T20:26:23.500,60.0,SF,left,86.74,25.35,2.16,2.16,0.21,45.97,70.76, +2021101011,54,45069.0,22,2021-10-10T20:26:23.600,60.0,SF,left,86.99,25.41,2.58,2.05,0.26,53.51,74.58, +2021101011,54,45069.0,23,2021-10-10T20:26:23.700,60.0,SF,left,87.26,25.47,2.88,1.91,0.28,68.97,77.58, +2021101011,54,45069.0,24,2021-10-10T20:26:23.800,60.0,SF,left,87.56,25.53,3.1,1.67,0.3,77.92,80.41, +2021101011,54,45069.0,25,2021-10-10T20:26:23.900,60.0,SF,left,87.89,25.57,3.31,1.35,0.33,87.55,83.02, +2021101011,54,45069.0,26,2021-10-10T20:26:24.000,60.0,SF,left,88.24,25.6,3.5,1.3,0.35,94.93,85.91, +2021101011,54,45069.0,27,2021-10-10T20:26:24.100,60.0,SF,left,88.59,25.61,3.56,1.21,0.35,100.46,88.47, +2021101011,54,45069.0,28,2021-10-10T20:26:24.200,60.0,SF,left,88.95,25.6,3.58,1.43,0.36,104.63,92.1, +2021101011,54,45069.0,29,2021-10-10T20:26:24.300,60.0,SF,left,89.32,25.57,3.58,1.63,0.36,113.39,95.5, +2021101011,54,45069.0,30,2021-10-10T20:26:24.400,60.0,SF,left,89.66,25.5,3.46,2.16,0.35,127.49,100.7, +2021101011,54,45069.0,31,2021-10-10T20:26:24.500,60.0,SF,left,89.99,25.43,3.33,2.44,0.34,137.95,104.67,run +2021101011,54,45069.0,32,2021-10-10T20:26:24.600,60.0,SF,left,90.29,25.33,3.09,2.77,0.31,155.34,109.23, +2021101011,54,45069.0,33,2021-10-10T20:26:24.700,60.0,SF,left,90.55,25.21,2.82,3.11,0.29,176.96,115.12, +2021101011,54,45069.0,34,2021-10-10T20:26:24.800,60.0,SF,left,90.77,25.08,2.47,3.63,0.26,229.02,123.63, +2021101011,54,45069.0,35,2021-10-10T20:26:24.900,60.0,SF,left,90.95,24.94,2.14,3.96,0.23,257.67,132.69, +2021101011,54,45069.0,36,2021-10-10T20:26:25.000,60.0,SF,left,91.07,24.79,1.84,4.01,0.19,272.79,146.3, +2021101011,54,46078.0,1,2021-10-10T20:26:21.500,69.0,SF,left,86.26,26.69,0.0,0.0,0.0,261.16,107.61, +2021101011,54,46078.0,2,2021-10-10T20:26:21.600,69.0,SF,left,86.26,26.69,0.0,0.0,0.0,261.16,116.77, +2021101011,54,46078.0,3,2021-10-10T20:26:21.700,69.0,SF,left,86.26,26.69,0.0,0.0,0.0,261.16,116.3, +2021101011,54,46078.0,4,2021-10-10T20:26:21.800,69.0,SF,left,86.26,26.69,0.0,0.0,0.0,261.16,128.24, +2021101011,54,46078.0,5,2021-10-10T20:26:21.900,69.0,SF,left,86.26,26.69,0.0,0.04,0.0,259.62,187.94, +2021101011,54,46078.0,6,2021-10-10T20:26:22.000,69.0,SF,left,86.26,26.69,0.02,0.28,0.0,260.72,43.88,ball_snap +2021101011,54,46078.0,7,2021-10-10T20:26:22.100,69.0,SF,left,86.26,26.69,0.08,0.47,0.01,261.5,36.69, +2021101011,54,46078.0,8,2021-10-10T20:26:22.200,69.0,SF,left,86.27,26.71,0.16,0.59,0.01,262.74,32.44, +2021101011,54,46078.0,9,2021-10-10T20:26:22.300,69.0,SF,left,86.28,26.73,0.27,0.73,0.03,263.45,26.9, +2021101011,54,46078.0,10,2021-10-10T20:26:22.400,69.0,SF,left,86.31,26.79,0.52,1.17,0.06,263.45,23.71, +2021101011,54,46078.0,11,2021-10-10T20:26:22.500,69.0,SF,left,86.34,26.87,0.78,1.46,0.09,266.1,24.07, +2021101011,54,46078.0,12,2021-10-10T20:26:22.600,69.0,SF,left,86.4,26.96,0.99,1.4,0.1,269.27,28.53, +2021101011,54,46078.0,13,2021-10-10T20:26:22.700,69.0,SF,left,86.47,27.05,1.17,1.18,0.12,274.89,34.14, +2021101011,54,46078.0,14,2021-10-10T20:26:22.800,69.0,SF,left,86.57,27.15,1.39,1.28,0.14,280.04,39.28, +2021101011,54,46078.0,15,2021-10-10T20:26:22.900,69.0,SF,left,86.71,27.28,1.68,1.52,0.18,277.38,45.32, +2021101011,54,46078.0,16,2021-10-10T20:26:23.000,69.0,SF,left,86.85,27.4,1.89,1.33,0.19,293.63,49.44, +2021101011,54,46078.0,17,2021-10-10T20:26:23.100,69.0,SF,left,87.02,27.53,2.08,1.07,0.21,300.71,53.06, +2021101011,54,46078.0,18,2021-10-10T20:26:23.200,69.0,SF,left,87.21,27.65,2.21,0.86,0.23,310.63,56.75, +2021101011,54,46078.0,19,2021-10-10T20:26:23.300,69.0,SF,left,87.41,27.77,2.29,0.68,0.23,322.21,58.65, +2021101011,54,46078.0,20,2021-10-10T20:26:23.400,69.0,SF,left,87.64,27.9,2.45,0.67,0.26,335.29,60.91, +2021101011,54,46078.0,21,2021-10-10T20:26:23.500,69.0,SF,left,87.88,28.02,2.57,0.64,0.27,340.94,63.54, +2021101011,54,46078.0,22,2021-10-10T20:26:23.600,69.0,SF,left,88.13,28.13,2.64,0.69,0.27,344.6,66.12, +2021101011,54,46078.0,23,2021-10-10T20:26:23.700,69.0,SF,left,88.39,28.23,2.68,0.81,0.28,349.14,68.76, +2021101011,54,46078.0,24,2021-10-10T20:26:23.800,69.0,SF,left,88.66,28.32,2.73,0.89,0.29,355.85,71.12, +2021101011,54,46078.0,25,2021-10-10T20:26:23.900,69.0,SF,left,88.94,28.39,2.74,0.99,0.28,3.35,73.91, +2021101011,54,46078.0,26,2021-10-10T20:26:24.000,69.0,SF,left,89.22,28.45,2.76,1.13,0.29,11.11,77.24, +2021101011,54,46078.0,27,2021-10-10T20:26:24.100,69.0,SF,left,89.5,28.5,2.79,1.23,0.29,18.37,81.04, +2021101011,54,46078.0,28,2021-10-10T20:26:24.200,69.0,SF,left,89.79,28.52,2.81,1.37,0.29,26.51,85.22, +2021101011,54,46078.0,29,2021-10-10T20:26:24.300,69.0,SF,left,90.11,28.51,2.94,1.52,0.31,29.45,90.49, +2021101011,54,46078.0,30,2021-10-10T20:26:24.400,69.0,SF,left,90.42,28.49,2.99,1.45,0.31,32.34,94.11, +2021101011,54,46078.0,31,2021-10-10T20:26:24.500,69.0,SF,left,90.72,28.46,2.98,1.43,0.3,42.43,97.63,run +2021101011,54,46078.0,32,2021-10-10T20:26:24.600,69.0,SF,left,91.01,28.39,2.94,1.56,0.3,60.31,102.0, +2021101011,54,46078.0,33,2021-10-10T20:26:24.700,69.0,SF,left,91.3,28.31,2.93,1.55,0.3,99.32,105.48, +2021101011,54,46078.0,34,2021-10-10T20:26:24.800,69.0,SF,left,91.58,28.23,2.88,1.45,0.3,135.74,107.64, +2021101011,54,46078.0,35,2021-10-10T20:26:24.900,69.0,SF,left,91.86,28.14,2.85,1.34,0.29,144.89,109.42, +2021101011,54,46078.0,36,2021-10-10T20:26:25.000,69.0,SF,left,92.13,28.04,2.78,1.37,0.29,156.3,111.58, +2021101011,54,46331.0,1,2021-10-10T20:26:21.500,82.0,SF,left,86.89,7.62,0.07,0.05,0.0,291.33,11.02, +2021101011,54,46331.0,2,2021-10-10T20:26:21.600,82.0,SF,left,86.89,7.63,0.06,0.05,0.0,291.33,13.31, +2021101011,54,46331.0,3,2021-10-10T20:26:21.700,82.0,SF,left,86.89,7.62,0.05,0.04,0.0,291.33,14.6, +2021101011,54,46331.0,4,2021-10-10T20:26:21.800,82.0,SF,left,86.89,7.61,0.03,0.03,0.01,290.76,15.57, +2021101011,54,46331.0,5,2021-10-10T20:26:21.900,82.0,SF,left,86.89,7.61,0.03,0.03,0.0,290.08,15.4, +2021101011,54,46331.0,6,2021-10-10T20:26:22.000,82.0,SF,left,86.89,7.61,0.02,0.02,0.0,290.08,16.41,ball_snap +2021101011,54,46331.0,7,2021-10-10T20:26:22.100,82.0,SF,left,86.88,7.61,0.02,0.02,0.0,290.8,11.96, +2021101011,54,46331.0,8,2021-10-10T20:26:22.200,82.0,SF,left,86.84,7.62,0.03,0.16,0.04,290.8,314.83, +2021101011,54,46331.0,9,2021-10-10T20:26:22.300,82.0,SF,left,86.81,7.63,0.11,0.99,0.03,291.91,285.48, +2021101011,54,46331.0,10,2021-10-10T20:26:22.400,82.0,SF,left,86.78,7.63,0.28,1.61,0.03,287.24,281.76, +2021101011,54,46331.0,11,2021-10-10T20:26:22.500,82.0,SF,left,86.71,7.64,0.67,2.78,0.07,288.19,279.04, +2021101011,54,46331.0,12,2021-10-10T20:26:22.600,82.0,SF,left,86.59,7.66,1.31,4.54,0.12,290.91,278.78, +2021101011,54,46331.0,13,2021-10-10T20:26:22.700,82.0,SF,left,86.43,7.68,1.88,4.92,0.16,289.07,277.5, +2021101011,54,46331.0,14,2021-10-10T20:26:22.800,82.0,SF,left,86.2,7.7,2.48,4.84,0.23,285.56,275.73, +2021101011,54,46331.0,15,2021-10-10T20:26:22.900,82.0,SF,left,85.91,7.72,3.11,4.69,0.29,282.09,274.54, +2021101011,54,46331.0,16,2021-10-10T20:26:23.000,82.0,SF,left,85.56,7.75,3.7,4.79,0.35,273.62,273.9, +2021101011,54,46331.0,17,2021-10-10T20:26:23.100,82.0,SF,left,85.17,7.77,4.19,4.32,0.39,273.62,273.52, +2021101011,54,46331.0,18,2021-10-10T20:26:23.200,82.0,SF,left,84.73,7.8,4.64,3.7,0.44,270.97,272.8, +2021101011,54,46331.0,19,2021-10-10T20:26:23.300,82.0,SF,left,84.25,7.82,4.96,2.83,0.48,270.97,272.83, +2021101011,54,46331.0,20,2021-10-10T20:26:23.400,82.0,SF,left,83.74,7.85,5.12,1.73,0.5,273.42,273.32, +2021101011,54,46331.0,21,2021-10-10T20:26:23.500,82.0,SF,left,83.23,7.89,5.17,0.66,0.51,278.76,273.98, +2021101011,54,46331.0,22,2021-10-10T20:26:23.600,82.0,SF,left,82.72,7.93,5.1,0.64,0.51,285.04,274.83, +2021101011,54,46331.0,23,2021-10-10T20:26:23.700,82.0,SF,left,82.23,7.98,4.81,2.12,0.49,293.88,277.54, +2021101011,54,46331.0,24,2021-10-10T20:26:23.800,82.0,SF,left,81.77,8.06,4.49,3.42,0.47,307.3,281.79, +2021101011,54,46331.0,25,2021-10-10T20:26:23.900,82.0,SF,left,81.35,8.16,4.19,3.99,0.43,314.97,285.91, +2021101011,54,46331.0,26,2021-10-10T20:26:24.000,82.0,SF,left,80.97,8.3,3.83,4.67,0.4,329.13,293.46, +2021101011,54,46331.0,27,2021-10-10T20:26:24.100,82.0,SF,left,80.67,8.48,3.47,5.63,0.36,346.1,305.5, +2021101011,54,46331.0,28,2021-10-10T20:26:24.200,82.0,SF,left,80.42,8.71,3.33,6.03,0.34,355.53,318.3, +2021101011,54,46331.0,29,2021-10-10T20:26:24.300,82.0,SF,left,80.23,8.98,3.33,5.89,0.33,8.22,329.7, +2021101011,54,46331.0,30,2021-10-10T20:26:24.400,82.0,SF,left,80.08,9.28,3.42,5.23,0.34,19.67,339.47, +2021101011,54,46331.0,31,2021-10-10T20:26:24.500,82.0,SF,left,79.99,9.64,3.72,4.89,0.37,28.3,348.34,run +2021101011,54,46331.0,32,2021-10-10T20:26:24.600,82.0,SF,left,79.94,10.02,3.99,4.51,0.39,28.3,355.63, +2021101011,54,46331.0,33,2021-10-10T20:26:24.700,82.0,SF,left,79.92,10.43,4.21,3.75,0.41,33.25,0.7, +2021101011,54,46331.0,34,2021-10-10T20:26:24.800,82.0,SF,left,79.94,10.86,4.39,2.95,0.43,36.11,4.3, +2021101011,54,46331.0,35,2021-10-10T20:26:24.900,82.0,SF,left,79.98,11.3,4.51,2.05,0.44,36.11,6.13, +2021101011,54,46331.0,36,2021-10-10T20:26:25.000,82.0,SF,left,80.03,11.75,4.56,1.19,0.45,37.77,7.1, +2021101011,54,47819.0,1,2021-10-10T20:26:21.500,19.0,SF,left,86.48,34.63,0.0,0.0,0.0,279.88,216.57, +2021101011,54,47819.0,2,2021-10-10T20:26:21.600,19.0,SF,left,86.48,34.62,0.0,0.0,0.0,279.88,214.32, +2021101011,54,47819.0,3,2021-10-10T20:26:21.700,19.0,SF,left,86.48,34.62,0.0,0.0,0.0,279.88,214.1, +2021101011,54,47819.0,4,2021-10-10T20:26:21.800,19.0,SF,left,86.48,34.62,0.0,0.0,0.0,279.88,218.23, +2021101011,54,47819.0,5,2021-10-10T20:26:21.900,19.0,SF,left,86.47,34.63,0.0,0.0,0.0,279.88,226.41, +2021101011,54,47819.0,6,2021-10-10T20:26:22.000,19.0,SF,left,86.47,34.63,0.01,0.32,0.0,278.9,287.64,ball_snap +2021101011,54,47819.0,7,2021-10-10T20:26:22.100,19.0,SF,left,86.47,34.63,0.08,0.7,0.01,278.9,280.71, +2021101011,54,47819.0,8,2021-10-10T20:26:22.200,19.0,SF,left,86.44,34.63,0.34,1.84,0.03,278.9,277.44, +2021101011,54,47819.0,9,2021-10-10T20:26:22.300,19.0,SF,left,86.39,34.64,0.62,2.4,0.05,278.9,279.69, +2021101011,54,47819.0,10,2021-10-10T20:26:22.400,19.0,SF,left,86.3,34.65,0.98,2.9,0.09,278.01,278.54, +2021101011,54,47819.0,11,2021-10-10T20:26:22.500,19.0,SF,left,86.18,34.67,1.32,2.9,0.12,278.01,277.02, +2021101011,54,47819.0,12,2021-10-10T20:26:22.600,19.0,SF,left,85.99,34.69,1.91,3.77,0.19,273.8,275.68, +2021101011,54,47819.0,13,2021-10-10T20:26:22.700,19.0,SF,left,85.77,34.7,2.38,3.94,0.22,271.87,274.66, +2021101011,54,47819.0,14,2021-10-10T20:26:22.800,19.0,SF,left,85.5,34.72,2.84,3.79,0.27,269.62,273.71, +2021101011,54,47819.0,15,2021-10-10T20:26:22.900,19.0,SF,left,85.18,34.75,3.35,3.7,0.32,271.1,273.8, +2021101011,54,47819.0,16,2021-10-10T20:26:23.000,19.0,SF,left,84.82,34.77,3.75,3.29,0.36,269.24,273.55, +2021101011,54,47819.0,17,2021-10-10T20:26:23.100,19.0,SF,left,84.43,34.8,4.09,2.92,0.39,269.24,273.04, +2021101011,54,47819.0,18,2021-10-10T20:26:23.200,19.0,SF,left,84.01,34.82,4.34,2.21,0.42,271.42,272.73, +2021101011,54,47819.0,19,2021-10-10T20:26:23.300,19.0,SF,left,83.57,34.84,4.42,1.19,0.44,270.74,271.86, +2021101011,54,47819.0,20,2021-10-10T20:26:23.400,19.0,SF,left,83.13,34.84,4.34,0.71,0.44,266.39,270.04, +2021101011,54,47819.0,21,2021-10-10T20:26:23.500,19.0,SF,left,82.7,34.84,4.23,1.39,0.43,258.51,267.76, +2021101011,54,47819.0,22,2021-10-10T20:26:23.600,19.0,SF,left,82.29,34.8,4.04,2.19,0.41,249.09,263.93, +2021101011,54,47819.0,23,2021-10-10T20:26:23.700,19.0,SF,left,81.91,34.73,3.8,3.16,0.39,238.55,257.39, +2021101011,54,47819.0,24,2021-10-10T20:26:23.800,19.0,SF,left,81.55,34.63,3.66,3.42,0.37,228.71,251.91, +2021101011,54,47819.0,25,2021-10-10T20:26:23.900,19.0,SF,left,81.21,34.47,3.62,3.84,0.37,211.84,243.28, +2021101011,54,47819.0,26,2021-10-10T20:26:24.000,19.0,SF,left,80.9,34.26,3.75,4.27,0.38,202.72,232.96, +2021101011,54,47819.0,27,2021-10-10T20:26:24.100,19.0,SF,left,80.61,34.0,3.85,4.15,0.38,202.72,225.5, +2021101011,54,47819.0,28,2021-10-10T20:26:24.200,19.0,SF,left,80.33,33.69,4.14,3.93,0.42,191.95,219.66, +2021101011,54,47819.0,29,2021-10-10T20:26:24.300,19.0,SF,left,80.07,33.35,4.39,3.45,0.43,185.46,215.05, +2021101011,54,47819.0,30,2021-10-10T20:26:24.400,19.0,SF,left,79.81,32.96,4.75,3.17,0.47,180.65,212.86, +2021101011,54,47819.0,31,2021-10-10T20:26:24.500,19.0,SF,left,79.54,32.55,5.05,2.69,0.49,176.91,211.96,run +2021101011,54,47819.0,32,2021-10-10T20:26:24.600,19.0,SF,left,79.26,32.11,5.39,2.24,0.52,174.54,212.54, +2021101011,54,47819.0,33,2021-10-10T20:26:24.700,19.0,SF,left,78.96,31.65,5.66,1.94,0.55,171.75,213.18, +2021101011,54,47819.0,34,2021-10-10T20:26:24.800,19.0,SF,left,78.63,31.17,5.9,1.64,0.58,171.75,214.49, +2021101011,54,47819.0,35,2021-10-10T20:26:24.900,19.0,SF,left,78.29,30.68,6.03,1.31,0.6,170.01,215.46, +2021101011,54,47819.0,36,2021-10-10T20:26:25.000,19.0,SF,left,77.92,30.19,6.15,1.3,0.61,168.59,217.5, +2021101011,54,47848.0,1,2021-10-10T20:26:21.500,94.0,ARI,left,83.93,20.96,0.01,0.01,0.0,125.62,130.69, +2021101011,54,47848.0,2,2021-10-10T20:26:21.600,94.0,ARI,left,83.93,20.96,0.01,0.01,0.01,125.62,119.17, +2021101011,54,47848.0,3,2021-10-10T20:26:21.700,94.0,ARI,left,83.93,20.97,0.01,0.01,0.0,125.62,114.98, +2021101011,54,47848.0,4,2021-10-10T20:26:21.800,94.0,ARI,left,83.94,20.96,0.01,0.01,0.01,122.78,121.45, +2021101011,54,47848.0,5,2021-10-10T20:26:21.900,94.0,ARI,left,83.94,20.94,0.01,0.01,0.02,117.51,139.81, +2021101011,54,47848.0,6,2021-10-10T20:26:22.000,94.0,ARI,left,83.96,20.92,0.07,0.66,0.03,100.24,146.89,ball_snap +2021101011,54,47848.0,7,2021-10-10T20:26:22.100,94.0,ARI,left,83.99,20.91,0.25,1.64,0.03,92.87,126.36, +2021101011,54,47848.0,8,2021-10-10T20:26:22.200,94.0,ARI,left,84.03,20.89,0.55,2.43,0.04,87.51,120.2, +2021101011,54,47848.0,9,2021-10-10T20:26:22.300,94.0,ARI,left,84.1,20.85,0.93,2.98,0.08,85.55,118.27, +2021101011,54,47848.0,10,2021-10-10T20:26:22.400,94.0,ARI,left,84.23,20.78,1.53,3.25,0.15,72.68,117.41, +2021101011,54,47848.0,11,2021-10-10T20:26:22.500,94.0,ARI,left,84.4,20.69,1.96,2.9,0.19,79.81,117.37, +2021101011,54,47848.0,12,2021-10-10T20:26:22.600,94.0,ARI,left,84.61,20.57,2.44,2.67,0.25,84.1,119.37, +2021101011,54,47848.0,13,2021-10-10T20:26:22.700,94.0,ARI,left,84.85,20.42,2.84,2.13,0.29,85.84,120.59, +2021101011,54,47848.0,14,2021-10-10T20:26:22.800,94.0,ARI,left,85.11,20.26,3.06,1.33,0.3,88.43,121.23, +2021101011,54,47848.0,15,2021-10-10T20:26:22.900,94.0,ARI,left,85.4,20.08,3.37,1.37,0.35,81.91,122.41, +2021101011,54,47848.0,16,2021-10-10T20:26:23.000,94.0,ARI,left,85.71,19.89,3.55,0.68,0.35,81.91,121.85, +2021101011,54,47848.0,17,2021-10-10T20:26:23.100,94.0,ARI,left,86.03,19.7,3.68,0.31,0.37,73.56,121.74, +2021101011,54,47848.0,18,2021-10-10T20:26:23.200,94.0,ARI,left,86.34,19.5,3.7,0.27,0.37,72.76,122.17, +2021101011,54,47848.0,19,2021-10-10T20:26:23.300,94.0,ARI,left,86.67,19.31,3.76,0.7,0.38,33.25,120.59, +2021101011,54,47848.0,20,2021-10-10T20:26:23.400,94.0,ARI,left,87.0,19.12,3.75,1.02,0.38,11.38,119.12, +2021101011,54,47848.0,21,2021-10-10T20:26:23.500,94.0,ARI,left,87.34,18.97,3.66,1.65,0.37,0.07,115.66, +2021101011,54,47848.0,22,2021-10-10T20:26:23.600,94.0,ARI,left,87.68,18.83,3.57,1.97,0.37,352.58,112.32, +2021101011,54,47848.0,23,2021-10-10T20:26:23.700,94.0,ARI,left,88.03,18.73,3.46,2.36,0.36,355.08,106.92, +2021101011,54,47848.0,24,2021-10-10T20:26:23.800,94.0,ARI,left,88.36,18.67,3.32,2.69,0.34,353.24,100.89, +2021101011,54,47848.0,25,2021-10-10T20:26:23.900,94.0,ARI,left,88.69,18.62,3.19,2.83,0.33,356.69,95.95, +2021101011,54,47848.0,26,2021-10-10T20:26:24.000,94.0,ARI,left,89.0,18.61,3.05,2.77,0.31,359.91,90.16, +2021101011,54,47848.0,27,2021-10-10T20:26:24.100,94.0,ARI,left,89.28,18.64,2.79,2.98,0.28,8.1,83.44, +2021101011,54,47848.0,28,2021-10-10T20:26:24.200,94.0,ARI,left,89.54,18.68,2.56,2.88,0.26,11.68,78.23, +2021101011,54,47848.0,29,2021-10-10T20:26:24.300,94.0,ARI,left,89.77,18.74,2.3,2.77,0.24,14.27,73.69, +2021101011,54,47848.0,30,2021-10-10T20:26:24.400,94.0,ARI,left,89.97,18.8,2.01,2.61,0.2,15.56,70.37, +2021101011,54,47848.0,31,2021-10-10T20:26:24.500,94.0,ARI,left,90.1,18.85,1.57,2.83,0.14,16.23,66.9,run +2021101011,54,47848.0,32,2021-10-10T20:26:24.600,94.0,ARI,left,90.18,18.88,1.05,3.13,0.09,10.93,61.86, +2021101011,54,47848.0,33,2021-10-10T20:26:24.700,94.0,ARI,left,90.24,18.9,0.59,3.18,0.06,10.93,61.73, +2021101011,54,47848.0,34,2021-10-10T20:26:24.800,94.0,ARI,left,90.25,18.9,0.14,3.12,0.02,9.46,66.47, +2021101011,54,47848.0,35,2021-10-10T20:26:24.900,94.0,ARI,left,90.25,18.88,0.26,2.83,0.02,7.7,218.73, +2021101011,54,47848.0,36,2021-10-10T20:26:25.000,94.0,ARI,left,90.21,18.85,0.58,2.49,0.05,3.07,226.86, +2021101011,54,52416.0,1,2021-10-10T20:26:21.500,9.0,ARI,left,80.39,14.75,0.44,0.12,0.04,59.64,207.45, +2021101011,54,52416.0,2,2021-10-10T20:26:21.600,9.0,ARI,left,80.37,14.71,0.45,0.06,0.04,61.25,208.2, +2021101011,54,52416.0,3,2021-10-10T20:26:21.700,9.0,ARI,left,80.35,14.67,0.51,0.25,0.05,63.63,211.99, +2021101011,54,52416.0,4,2021-10-10T20:26:21.800,9.0,ARI,left,80.32,14.63,0.52,0.2,0.05,65.56,212.7, +2021101011,54,52416.0,5,2021-10-10T20:26:21.900,9.0,ARI,left,80.29,14.58,0.55,0.13,0.05,64.48,213.56, +2021101011,54,52416.0,6,2021-10-10T20:26:22.000,9.0,ARI,left,80.26,14.54,0.55,0.04,0.06,64.48,211.67,ball_snap +2021101011,54,52416.0,7,2021-10-10T20:26:22.100,9.0,ARI,left,80.24,14.5,0.47,0.19,0.04,64.48,214.12, +2021101011,54,52416.0,8,2021-10-10T20:26:22.200,9.0,ARI,left,80.19,14.46,0.49,0.21,0.06,67.06,218.89, +2021101011,54,52416.0,9,2021-10-10T20:26:22.300,9.0,ARI,left,80.15,14.4,0.55,0.24,0.08,70.43,218.76, +2021101011,54,52416.0,10,2021-10-10T20:26:22.400,9.0,ARI,left,80.1,14.35,0.56,0.28,0.07,70.43,219.93, +2021101011,54,52416.0,11,2021-10-10T20:26:22.500,9.0,ARI,left,80.05,14.31,0.56,0.32,0.06,68.67,223.16, +2021101011,54,52416.0,12,2021-10-10T20:26:22.600,9.0,ARI,left,79.99,14.27,0.57,0.27,0.07,67.88,226.23, +2021101011,54,52416.0,13,2021-10-10T20:26:22.700,9.0,ARI,left,79.91,14.21,0.65,0.27,0.1,62.39,230.19, +2021101011,54,52416.0,14,2021-10-10T20:26:22.800,9.0,ARI,left,79.83,14.14,0.79,0.74,0.1,51.86,231.69, +2021101011,54,52416.0,15,2021-10-10T20:26:22.900,9.0,ARI,left,79.74,14.07,1.05,1.57,0.12,51.86,233.8, +2021101011,54,52416.0,16,2021-10-10T20:26:23.000,9.0,ARI,left,79.64,14.0,1.26,1.79,0.12,43.22,235.55, +2021101011,54,52416.0,17,2021-10-10T20:26:23.100,9.0,ARI,left,79.51,13.92,1.58,2.43,0.15,44.6,238.41, +2021101011,54,52416.0,18,2021-10-10T20:26:23.200,9.0,ARI,left,79.36,13.83,1.91,2.72,0.18,48.74,239.18, +2021101011,54,52416.0,19,2021-10-10T20:26:23.300,9.0,ARI,left,79.18,13.71,2.26,2.73,0.21,56.18,237.44, +2021101011,54,52416.0,20,2021-10-10T20:26:23.400,9.0,ARI,left,78.98,13.58,2.56,2.42,0.24,57.93,237.57, +2021101011,54,52416.0,21,2021-10-10T20:26:23.500,9.0,ARI,left,78.75,13.44,2.75,1.8,0.27,59.87,238.67, +2021101011,54,52416.0,22,2021-10-10T20:26:23.600,9.0,ARI,left,78.51,13.31,2.82,1.12,0.27,61.98,240.75, +2021101011,54,52416.0,23,2021-10-10T20:26:23.700,9.0,ARI,left,78.27,13.19,2.74,0.81,0.27,59.21,244.03, +2021101011,54,52416.0,24,2021-10-10T20:26:23.800,9.0,ARI,left,78.02,13.07,2.71,0.99,0.27,59.85,245.75, +2021101011,54,52416.0,25,2021-10-10T20:26:23.900,9.0,ARI,left,77.78,12.97,2.64,1.24,0.27,62.35,248.22, +2021101011,54,52416.0,26,2021-10-10T20:26:24.000,9.0,ARI,left,77.54,12.88,2.44,1.8,0.25,60.83,252.12, +2021101011,54,52416.0,27,2021-10-10T20:26:24.100,9.0,ARI,left,77.32,12.83,2.24,2.38,0.23,69.06,259.14, +2021101011,54,52416.0,28,2021-10-10T20:26:24.200,9.0,ARI,left,77.11,12.82,2.0,3.09,0.21,61.04,269.27, +2021101011,54,52416.0,29,2021-10-10T20:26:24.300,9.0,ARI,left,76.92,12.84,1.83,3.29,0.19,65.42,279.97, +2021101011,54,52416.0,30,2021-10-10T20:26:24.400,9.0,ARI,left,76.77,12.9,1.62,3.67,0.16,55.93,296.28, +2021101011,54,52416.0,31,2021-10-10T20:26:24.500,9.0,ARI,left,76.65,13.01,1.65,4.15,0.16,32.29,317.45,run +2021101011,54,52416.0,32,2021-10-10T20:26:24.600,9.0,ARI,left,76.55,13.15,1.82,3.96,0.18,35.26,331.51, +2021101011,54,52416.0,33,2021-10-10T20:26:24.700,9.0,ARI,left,76.47,13.35,2.26,4.19,0.21,31.77,342.49, +2021101011,54,52416.0,34,2021-10-10T20:26:24.800,9.0,ARI,left,76.41,13.61,2.86,4.37,0.27,24.14,349.75, +2021101011,54,52416.0,35,2021-10-10T20:26:24.900,9.0,ARI,left,76.37,13.92,3.3,4.12,0.31,30.72,353.91, +2021101011,54,52416.0,36,2021-10-10T20:26:25.000,9.0,ARI,left,76.33,14.29,3.85,3.88,0.37,28.41,355.62, +2021101011,54,52433.0,1,2021-10-10T20:26:21.500,11.0,SF,left,85.66,15.31,0.0,0.0,0.0,285.34,115.16, +2021101011,54,52433.0,2,2021-10-10T20:26:21.600,11.0,SF,left,85.65,15.31,0.0,0.0,0.0,285.34,111.21, +2021101011,54,52433.0,3,2021-10-10T20:26:21.700,11.0,SF,left,85.65,15.31,0.0,0.0,0.0,286.05,102.63, +2021101011,54,52433.0,4,2021-10-10T20:26:21.800,11.0,SF,left,85.66,15.31,0.0,0.0,0.0,286.97,76.2, +2021101011,54,52433.0,5,2021-10-10T20:26:21.900,11.0,SF,left,85.66,15.31,0.0,0.0,0.0,286.97,66.56, +2021101011,54,52433.0,6,2021-10-10T20:26:22.000,11.0,SF,left,85.66,15.32,0.0,0.0,0.0,286.97,32.37,ball_snap +2021101011,54,52433.0,7,2021-10-10T20:26:22.100,11.0,SF,left,85.65,15.32,0.0,0.14,0.01,286.08,301.14, +2021101011,54,52433.0,8,2021-10-10T20:26:22.200,11.0,SF,left,85.64,15.32,0.13,1.69,0.01,286.97,284.37, +2021101011,54,52433.0,9,2021-10-10T20:26:22.300,11.0,SF,left,85.61,15.33,0.47,3.5,0.03,287.83,279.09, +2021101011,54,52433.0,10,2021-10-10T20:26:22.400,11.0,SF,left,85.53,15.33,0.97,4.5,0.08,288.58,276.59, +2021101011,54,52433.0,11,2021-10-10T20:26:22.500,11.0,SF,left,85.37,15.35,1.76,5.76,0.16,288.58,275.4, +2021101011,54,52433.0,12,2021-10-10T20:26:22.600,11.0,SF,left,85.16,15.37,2.39,5.78,0.21,288.58,274.75, +2021101011,54,52433.0,13,2021-10-10T20:26:22.700,11.0,SF,left,84.89,15.39,2.96,5.34,0.27,287.51,274.39, +2021101011,54,52433.0,14,2021-10-10T20:26:22.800,11.0,SF,left,84.55,15.42,3.56,4.84,0.34,287.51,274.69, +2021101011,54,52433.0,15,2021-10-10T20:26:22.900,11.0,SF,left,84.14,15.44,4.24,4.88,0.41,290.52,274.28, +2021101011,54,52433.0,16,2021-10-10T20:26:23.000,11.0,SF,left,83.69,15.48,4.81,4.53,0.46,288.91,274.51, +2021101011,54,52433.0,17,2021-10-10T20:26:23.100,11.0,SF,left,83.18,15.52,5.31,4.04,0.51,286.14,274.84, +2021101011,54,52433.0,18,2021-10-10T20:26:23.200,11.0,SF,left,82.62,15.56,5.8,3.6,0.56,282.33,274.67, +2021101011,54,52433.0,19,2021-10-10T20:26:23.300,11.0,SF,left,82.02,15.61,6.24,3.24,0.6,283.34,274.53, +2021101011,54,52433.0,20,2021-10-10T20:26:23.400,11.0,SF,left,81.38,15.66,6.52,2.49,0.64,281.74,274.25, +2021101011,54,52433.0,21,2021-10-10T20:26:23.500,11.0,SF,left,80.72,15.71,6.74,1.71,0.66,275.93,274.36, +2021101011,54,52433.0,22,2021-10-10T20:26:23.600,11.0,SF,left,80.04,15.76,6.91,1.02,0.69,274.46,274.78, +2021101011,54,52433.0,23,2021-10-10T20:26:23.700,11.0,SF,left,79.35,15.82,6.94,0.44,0.69,277.23,275.45, +2021101011,54,52433.0,24,2021-10-10T20:26:23.800,11.0,SF,left,78.66,15.89,6.88,0.82,0.69,281.0,275.94, +2021101011,54,52433.0,25,2021-10-10T20:26:23.900,11.0,SF,left,77.98,15.96,6.76,1.45,0.68,287.2,276.82, +2021101011,54,52433.0,26,2021-10-10T20:26:24.000,11.0,SF,left,77.32,16.05,6.47,2.54,0.66,298.48,278.72, +2021101011,54,52433.0,27,2021-10-10T20:26:24.100,11.0,SF,left,76.7,16.16,6.15,3.37,0.63,306.46,280.96, +2021101011,54,52433.0,28,2021-10-10T20:26:24.200,11.0,SF,left,76.11,16.29,5.82,3.95,0.6,315.03,284.12, +2021101011,54,52433.0,29,2021-10-10T20:26:24.300,11.0,SF,left,75.58,16.45,5.39,4.77,0.56,325.95,289.26, +2021101011,54,52433.0,30,2021-10-10T20:26:24.400,11.0,SF,left,75.1,16.66,5.07,5.39,0.52,342.31,296.14, +2021101011,54,52433.0,31,2021-10-10T20:26:24.500,11.0,SF,left,74.67,16.9,4.81,5.77,0.49,349.43,303.55,run +2021101011,54,52433.0,32,2021-10-10T20:26:24.600,11.0,SF,left,74.3,17.2,4.64,5.92,0.47,355.27,312.25, +2021101011,54,52433.0,33,2021-10-10T20:26:24.700,11.0,SF,left,73.99,17.54,4.61,5.85,0.47,358.8,321.37, +2021101011,54,52433.0,34,2021-10-10T20:26:24.800,11.0,SF,left,73.73,17.94,4.8,5.7,0.48,5.98,329.87, +2021101011,54,52433.0,35,2021-10-10T20:26:24.900,11.0,SF,left,73.51,18.38,5.02,5.29,0.5,8.15,336.64, +2021101011,54,52433.0,36,2021-10-10T20:26:25.000,11.0,SF,left,73.33,18.87,5.24,4.62,0.52,12.18,342.31, +2021101011,54,52539.0,1,2021-10-10T20:26:21.500,90.0,ARI,left,84.22,22.69,0.0,0.01,0.0,124.72,313.34, +2021101011,54,52539.0,2,2021-10-10T20:26:21.600,90.0,ARI,left,84.23,22.69,0.0,0.0,0.01,124.72,315.94, +2021101011,54,52539.0,3,2021-10-10T20:26:21.700,90.0,ARI,left,84.23,22.69,0.0,0.0,0.0,124.72,316.36, +2021101011,54,52539.0,4,2021-10-10T20:26:21.800,90.0,ARI,left,84.23,22.69,0.0,0.0,0.0,125.63,314.6, +2021101011,54,52539.0,5,2021-10-10T20:26:21.900,90.0,ARI,left,84.23,22.68,0.0,0.15,0.01,125.63,210.64, +2021101011,54,52539.0,6,2021-10-10T20:26:22.000,90.0,ARI,left,84.24,22.67,0.06,0.79,0.01,125.63,148.17,ball_snap +2021101011,54,52539.0,7,2021-10-10T20:26:22.100,90.0,ARI,left,84.25,22.66,0.2,1.31,0.02,124.86,143.42, +2021101011,54,52539.0,8,2021-10-10T20:26:22.200,90.0,ARI,left,84.28,22.64,0.53,2.57,0.04,122.24,127.07, +2021101011,54,52539.0,9,2021-10-10T20:26:22.300,90.0,ARI,left,84.35,22.59,0.93,3.37,0.08,119.53,123.58, +2021101011,54,52539.0,10,2021-10-10T20:26:22.400,90.0,ARI,left,84.45,22.53,1.37,3.73,0.12,117.39,121.2, +2021101011,54,52539.0,11,2021-10-10T20:26:22.500,90.0,ARI,left,84.6,22.45,1.79,3.53,0.16,115.28,119.41, +2021101011,54,52539.0,12,2021-10-10T20:26:22.600,90.0,ARI,left,84.77,22.35,2.16,3.32,0.2,111.5,117.87, +2021101011,54,52539.0,13,2021-10-10T20:26:22.700,90.0,ARI,left,84.98,22.25,2.49,2.97,0.23,104.52,116.22, +2021101011,54,52539.0,14,2021-10-10T20:26:22.800,90.0,ARI,left,85.22,22.13,2.75,2.36,0.26,93.97,115.61, +2021101011,54,52539.0,15,2021-10-10T20:26:22.900,90.0,ARI,left,85.47,22.01,2.89,1.52,0.28,83.92,114.91, +2021101011,54,52539.0,16,2021-10-10T20:26:23.000,90.0,ARI,left,85.73,21.9,2.86,0.56,0.28,71.15,113.78, +2021101011,54,52539.0,17,2021-10-10T20:26:23.100,90.0,ARI,left,85.99,21.78,2.85,0.27,0.28,64.41,113.71, +2021101011,54,52539.0,18,2021-10-10T20:26:23.200,90.0,ARI,left,86.25,21.67,2.8,0.58,0.28,60.09,114.29, +2021101011,54,52539.0,19,2021-10-10T20:26:23.300,90.0,ARI,left,86.5,21.55,2.68,0.96,0.27,56.89,115.0, +2021101011,54,52539.0,20,2021-10-10T20:26:23.400,90.0,ARI,left,86.74,21.44,2.6,1.06,0.27,52.85,115.75, +2021101011,54,52539.0,21,2021-10-10T20:26:23.500,90.0,ARI,left,86.97,21.33,2.48,1.18,0.25,50.02,116.28, +2021101011,54,52539.0,22,2021-10-10T20:26:23.600,90.0,ARI,left,87.19,21.21,2.43,1.06,0.25,43.86,117.18, +2021101011,54,52539.0,23,2021-10-10T20:26:23.700,90.0,ARI,left,87.4,21.11,2.33,1.03,0.24,35.03,117.12, +2021101011,54,52539.0,24,2021-10-10T20:26:23.800,90.0,ARI,left,87.61,21.0,2.24,1.01,0.23,31.31,118.09, +2021101011,54,52539.0,25,2021-10-10T20:26:23.900,90.0,ARI,left,87.8,20.9,2.11,1.09,0.22,26.17,118.06, +2021101011,54,52539.0,26,2021-10-10T20:26:24.000,90.0,ARI,left,87.97,20.8,1.98,1.18,0.2,22.73,119.19, +2021101011,54,52539.0,27,2021-10-10T20:26:24.100,90.0,ARI,left,88.15,20.69,1.94,1.03,0.2,19.13,122.1, +2021101011,54,52539.0,28,2021-10-10T20:26:24.200,90.0,ARI,left,88.3,20.59,1.86,1.05,0.19,16.28,124.97, +2021101011,54,52539.0,29,2021-10-10T20:26:24.300,90.0,ARI,left,88.43,20.48,1.67,1.49,0.17,12.35,130.98, +2021101011,54,52539.0,30,2021-10-10T20:26:24.400,90.0,ARI,left,88.53,20.36,1.54,1.9,0.16,9.07,140.76, +2021101011,54,52539.0,31,2021-10-10T20:26:24.500,90.0,ARI,left,88.6,20.22,1.47,2.4,0.15,6.09,155.39,run +2021101011,54,52539.0,32,2021-10-10T20:26:24.600,90.0,ARI,left,88.64,20.08,1.45,2.74,0.14,359.84,166.78, +2021101011,54,52539.0,33,2021-10-10T20:26:24.700,90.0,ARI,left,88.66,19.93,1.51,2.89,0.15,354.84,180.98, +2021101011,54,52539.0,34,2021-10-10T20:26:24.800,90.0,ARI,left,88.64,19.78,1.58,2.6,0.15,352.72,190.83, +2021101011,54,52539.0,35,2021-10-10T20:26:24.900,90.0,ARI,left,88.59,19.63,1.57,2.57,0.15,336.25,204.44, +2021101011,54,52539.0,36,2021-10-10T20:26:25.000,90.0,ARI,left,88.51,19.49,1.68,2.82,0.16,325.91,216.73, +2021101011,54,53432.0,1,2021-10-10T20:26:21.500,5.0,SF,left,89.96,23.7,0.0,0.0,0.0,271.06,158.74, +2021101011,54,53432.0,2,2021-10-10T20:26:21.600,5.0,SF,left,89.96,23.7,0.0,0.0,0.0,271.06,159.09, +2021101011,54,53432.0,3,2021-10-10T20:26:21.700,5.0,SF,left,89.96,23.7,0.0,0.0,0.0,271.06,158.9, +2021101011,54,53432.0,4,2021-10-10T20:26:21.800,5.0,SF,left,89.97,23.69,0.0,0.0,0.0,271.06,144.76, +2021101011,54,53432.0,5,2021-10-10T20:26:21.900,5.0,SF,left,89.97,23.7,0.0,0.0,0.0,271.06,127.23, +2021101011,54,53432.0,6,2021-10-10T20:26:22.000,5.0,SF,left,89.97,23.7,0.0,0.0,0.0,271.06,105.06,ball_snap +2021101011,54,53432.0,7,2021-10-10T20:26:22.100,5.0,SF,left,89.97,23.7,0.0,0.0,0.0,270.45,104.67, +2021101011,54,53432.0,8,2021-10-10T20:26:22.200,5.0,SF,left,89.98,23.7,0.0,0.0,0.01,270.45,103.29, +2021101011,54,53432.0,9,2021-10-10T20:26:22.300,5.0,SF,left,89.98,23.69,0.03,0.67,0.0,269.44,110.01, +2021101011,54,53432.0,10,2021-10-10T20:26:22.400,5.0,SF,left,89.99,23.69,0.17,1.51,0.01,269.44,113.31, +2021101011,54,53432.0,11,2021-10-10T20:26:22.500,5.0,SF,left,90.02,23.67,0.44,2.39,0.03,271.14,117.35, +2021101011,54,53432.0,12,2021-10-10T20:26:22.600,5.0,SF,left,90.08,23.64,0.77,2.88,0.06,271.93,117.34, +2021101011,54,53432.0,13,2021-10-10T20:26:22.700,5.0,SF,left,90.16,23.6,1.06,2.79,0.09,273.42,116.68, +2021101011,54,53432.0,14,2021-10-10T20:26:22.800,5.0,SF,left,90.28,23.55,1.35,2.51,0.12,275.87,114.64, +2021101011,54,53432.0,15,2021-10-10T20:26:22.900,5.0,SF,left,90.41,23.5,1.59,2.13,0.15,281.81,112.92, +2021101011,54,53432.0,16,2021-10-10T20:26:23.000,5.0,SF,left,90.57,23.44,1.79,1.77,0.17,291.0,111.13, +2021101011,54,53432.0,17,2021-10-10T20:26:23.100,5.0,SF,left,90.75,23.38,1.93,1.34,0.18,304.04,109.05, +2021101011,54,53432.0,18,2021-10-10T20:26:23.200,5.0,SF,left,90.93,23.31,2.02,0.98,0.2,316.94,107.24, +2021101011,54,53432.0,19,2021-10-10T20:26:23.300,5.0,SF,left,91.12,23.26,2.01,0.54,0.2,326.71,105.71, +2021101011,54,53432.0,20,2021-10-10T20:26:23.400,5.0,SF,left,91.3,23.21,1.83,0.74,0.18,334.86,106.11, +2021101011,54,53432.0,21,2021-10-10T20:26:23.500,5.0,SF,left,91.46,23.15,1.62,1.33,0.17,337.88,108.61, +2021101011,54,53432.0,22,2021-10-10T20:26:23.600,5.0,SF,left,91.56,23.1,1.14,2.59,0.12,335.75,119.35, +2021101011,54,53432.0,23,2021-10-10T20:26:23.700,5.0,SF,left,91.63,23.04,0.77,3.26,0.09,333.68,137.15, +2021101011,54,53432.0,24,2021-10-10T20:26:23.800,5.0,SF,left,91.64,22.98,0.57,3.83,0.06,330.28,184.42, +2021101011,54,53432.0,25,2021-10-10T20:26:23.900,5.0,SF,left,91.58,22.93,0.88,4.25,0.08,324.98,228.75, +2021101011,54,53432.0,26,2021-10-10T20:26:24.000,5.0,SF,left,91.47,22.86,1.47,4.63,0.14,316.9,244.12, +2021101011,54,53432.0,27,2021-10-10T20:26:24.100,5.0,SF,left,91.29,22.8,2.04,4.64,0.19,312.33,252.5, +2021101011,54,53432.0,28,2021-10-10T20:26:24.200,5.0,SF,left,91.05,22.74,2.6,4.42,0.24,305.74,256.51, +2021101011,54,53432.0,29,2021-10-10T20:26:24.300,5.0,SF,left,90.76,22.7,3.15,4.31,0.3,295.35,260.23, +2021101011,54,53432.0,30,2021-10-10T20:26:24.400,5.0,SF,left,90.42,22.65,3.59,3.94,0.34,288.52,262.38, +2021101011,54,53432.0,31,2021-10-10T20:26:24.500,5.0,SF,left,90.05,22.6,3.99,3.47,0.38,286.71,263.66,run +2021101011,54,53432.0,32,2021-10-10T20:26:24.600,5.0,SF,left,89.63,22.57,4.31,2.9,0.42,285.05,266.6, +2021101011,54,53432.0,33,2021-10-10T20:26:24.700,5.0,SF,left,89.18,22.56,4.59,2.6,0.45,285.05,269.66, +2021101011,54,53432.0,34,2021-10-10T20:26:24.800,5.0,SF,left,88.71,22.57,4.82,2.58,0.47,280.22,273.07, +2021101011,54,53432.0,35,2021-10-10T20:26:24.900,5.0,SF,left,88.22,22.61,4.97,2.52,0.49,278.45,276.35, +2021101011,54,53432.0,36,2021-10-10T20:26:25.000,5.0,SF,left,87.72,22.71,5.13,3.33,0.51,282.06,282.58, +2021101011,54,53445.0,1,2021-10-10T20:26:21.500,25.0,ARI,left,80.11,30.85,0.23,0.43,0.02,100.62,182.15, +2021101011,54,53445.0,2,2021-10-10T20:26:21.600,25.0,ARI,left,80.11,30.83,0.16,0.55,0.02,100.62,182.61, +2021101011,54,53445.0,3,2021-10-10T20:26:21.700,25.0,ARI,left,80.1,30.82,0.15,0.42,0.02,100.03,182.38, +2021101011,54,53445.0,4,2021-10-10T20:26:21.800,25.0,ARI,left,80.1,30.8,0.17,0.2,0.02,96.33,181.56, +2021101011,54,53445.0,5,2021-10-10T20:26:21.900,25.0,ARI,left,80.1,30.78,0.16,0.13,0.02,92.84,181.08, +2021101011,54,53445.0,6,2021-10-10T20:26:22.000,25.0,ARI,left,80.11,30.76,0.16,0.09,0.02,88.35,174.37,ball_snap +2021101011,54,53445.0,7,2021-10-10T20:26:22.100,25.0,ARI,left,80.11,30.75,0.13,0.12,0.01,85.39,179.31, +2021101011,54,53445.0,8,2021-10-10T20:26:22.200,25.0,ARI,left,80.1,30.75,0.02,0.36,0.0,83.39,204.85, +2021101011,54,53445.0,9,2021-10-10T20:26:22.300,25.0,ARI,left,80.1,30.76,0.09,0.61,0.01,81.38,331.97, +2021101011,54,53445.0,10,2021-10-10T20:26:22.400,25.0,ARI,left,80.09,30.77,0.23,0.81,0.02,79.64,331.2, +2021101011,54,53445.0,11,2021-10-10T20:26:22.500,25.0,ARI,left,80.08,30.8,0.4,0.99,0.03,77.87,335.21, +2021101011,54,53445.0,12,2021-10-10T20:26:22.600,25.0,ARI,left,80.05,30.85,0.66,1.45,0.06,76.99,331.85, +2021101011,54,53445.0,13,2021-10-10T20:26:22.700,25.0,ARI,left,80.01,30.92,0.89,1.57,0.08,76.1,331.27, +2021101011,54,53445.0,14,2021-10-10T20:26:22.800,25.0,ARI,left,79.96,31.02,1.18,1.87,0.11,72.66,329.03, +2021101011,54,53445.0,15,2021-10-10T20:26:22.900,25.0,ARI,left,79.89,31.13,1.45,2.05,0.13,69.1,328.3, +2021101011,54,53445.0,16,2021-10-10T20:26:23.000,25.0,ARI,left,79.8,31.27,1.83,2.3,0.17,69.1,326.59, +2021101011,54,53445.0,17,2021-10-10T20:26:23.100,25.0,ARI,left,79.68,31.44,2.14,2.23,0.2,66.79,326.34, +2021101011,54,53445.0,18,2021-10-10T20:26:23.200,25.0,ARI,left,79.56,31.63,2.44,2.1,0.23,64.66,326.81, +2021101011,54,53445.0,19,2021-10-10T20:26:23.300,25.0,ARI,left,79.42,31.86,2.74,2.03,0.26,64.66,328.0, +2021101011,54,53445.0,20,2021-10-10T20:26:23.400,25.0,ARI,left,79.26,32.12,3.1,2.16,0.3,61.28,329.7, +2021101011,54,53445.0,21,2021-10-10T20:26:23.500,25.0,ARI,left,79.11,32.4,3.31,1.81,0.32,60.61,331.17, +2021101011,54,53445.0,22,2021-10-10T20:26:23.600,25.0,ARI,left,78.95,32.69,3.42,1.32,0.33,62.19,332.46, +2021101011,54,53445.0,23,2021-10-10T20:26:23.700,25.0,ARI,left,78.8,33.0,3.42,0.78,0.34,61.43,333.84, +2021101011,54,53445.0,24,2021-10-10T20:26:23.800,25.0,ARI,left,78.66,33.29,3.32,0.58,0.33,62.76,334.28, +2021101011,54,53445.0,25,2021-10-10T20:26:23.900,25.0,ARI,left,78.53,33.56,2.95,1.69,0.3,64.7,334.23, +2021101011,54,53445.0,26,2021-10-10T20:26:24.000,25.0,ARI,left,78.41,33.77,2.42,2.9,0.24,68.74,332.61, +2021101011,54,53445.0,27,2021-10-10T20:26:24.100,25.0,ARI,left,78.31,33.93,1.86,3.82,0.19,75.56,329.27, +2021101011,54,53445.0,28,2021-10-10T20:26:24.200,25.0,ARI,left,78.22,34.01,1.08,5.23,0.12,83.73,313.52, +2021101011,54,53445.0,29,2021-10-10T20:26:24.300,25.0,ARI,left,78.15,34.05,0.66,5.55,0.08,90.3,277.53, +2021101011,54,53445.0,30,2021-10-10T20:26:24.400,25.0,ARI,left,78.09,34.01,0.8,5.92,0.07,104.86,219.99, +2021101011,54,53445.0,31,2021-10-10T20:26:24.500,25.0,ARI,left,78.04,33.9,1.39,5.72,0.12,121.63,197.6,run +2021101011,54,53445.0,32,2021-10-10T20:26:24.600,25.0,ARI,left,77.98,33.73,2.17,5.69,0.19,131.57,197.13, +2021101011,54,53445.0,33,2021-10-10T20:26:24.700,25.0,ARI,left,77.91,33.49,2.8,5.36,0.25,143.56,195.77, +2021101011,54,53445.0,34,2021-10-10T20:26:24.800,25.0,ARI,left,77.82,33.19,3.37,4.86,0.31,153.3,194.98, +2021101011,54,53445.0,35,2021-10-10T20:26:24.900,25.0,ARI,left,77.73,32.84,3.87,4.15,0.37,161.31,195.4, +2021101011,54,53445.0,36,2021-10-10T20:26:25.000,25.0,ARI,left,77.61,32.45,4.28,3.46,0.41,170.65,196.99, +2021101011,54,53623.0,1,2021-10-10T20:26:21.500,25.0,SF,left,91.31,16.23,5.07,1.33,0.52,220.14,181.26, +2021101011,54,53623.0,2,2021-10-10T20:26:21.600,25.0,SF,left,91.3,15.73,4.94,1.48,0.5,224.13,181.9, +2021101011,54,53623.0,3,2021-10-10T20:26:21.700,25.0,SF,left,91.28,15.24,4.84,1.5,0.49,232.07,182.22, +2021101011,54,53623.0,4,2021-10-10T20:26:21.800,25.0,SF,left,91.26,14.75,4.76,1.44,0.49,238.64,183.03, +2021101011,54,53623.0,5,2021-10-10T20:26:21.900,25.0,SF,left,91.23,14.28,4.64,1.46,0.47,242.84,183.11, +2021101011,54,53623.0,6,2021-10-10T20:26:22.000,25.0,SF,left,91.21,13.82,4.53,1.41,0.46,248.11,183.72,ball_snap +2021101011,54,53623.0,7,2021-10-10T20:26:22.100,25.0,SF,left,91.18,13.37,4.43,1.38,0.45,250.91,184.24, +2021101011,54,53623.0,8,2021-10-10T20:26:22.200,25.0,SF,left,91.14,12.94,4.35,1.06,0.44,250.91,184.26, +2021101011,54,53623.0,9,2021-10-10T20:26:22.300,25.0,SF,left,91.11,12.51,4.3,0.95,0.43,250.12,184.41, +2021101011,54,53623.0,10,2021-10-10T20:26:22.400,25.0,SF,left,91.08,12.08,4.19,1.09,0.42,251.17,184.35, +2021101011,54,53623.0,11,2021-10-10T20:26:22.500,25.0,SF,left,91.04,11.67,4.17,0.95,0.42,253.26,184.85, +2021101011,54,53623.0,12,2021-10-10T20:26:22.600,25.0,SF,left,91.01,11.26,4.04,1.12,0.41,257.09,185.04, +2021101011,54,53623.0,13,2021-10-10T20:26:22.700,25.0,SF,left,90.97,10.86,3.9,1.31,0.4,264.97,185.44, +2021101011,54,53623.0,14,2021-10-10T20:26:22.800,25.0,SF,left,90.93,10.48,3.73,1.5,0.38,270.27,186.18, +2021101011,54,53623.0,15,2021-10-10T20:26:22.900,25.0,SF,left,90.89,10.12,3.59,1.57,0.37,276.91,186.96, +2021101011,54,53623.0,16,2021-10-10T20:26:23.000,25.0,SF,left,90.84,9.77,3.46,1.57,0.35,281.72,187.83, +2021101011,54,53623.0,17,2021-10-10T20:26:23.100,25.0,SF,left,90.79,9.43,3.3,1.62,0.34,289.48,188.72, +2021101011,54,53623.0,18,2021-10-10T20:26:23.200,25.0,SF,left,90.75,9.13,3.04,1.87,0.31,300.11,189.15, +2021101011,54,53623.0,19,2021-10-10T20:26:23.300,25.0,SF,left,90.69,8.84,2.84,1.92,0.29,305.75,190.29, +2021101011,54,53623.0,20,2021-10-10T20:26:23.400,25.0,SF,left,90.64,8.57,2.66,1.87,0.28,308.68,191.42, +2021101011,54,53623.0,21,2021-10-10T20:26:23.500,25.0,SF,left,90.58,8.32,2.45,1.9,0.25,310.06,193.33, +2021101011,54,53623.0,22,2021-10-10T20:26:23.600,25.0,SF,left,90.51,8.09,2.35,1.79,0.25,317.95,197.6, +2021101011,54,53623.0,23,2021-10-10T20:26:23.700,25.0,SF,left,90.43,7.87,2.25,1.62,0.23,320.43,200.54, +2021101011,54,53623.0,24,2021-10-10T20:26:23.800,25.0,SF,left,90.35,7.66,2.18,1.44,0.23,326.39,203.71, +2021101011,54,53623.0,25,2021-10-10T20:26:23.900,25.0,SF,left,90.26,7.47,2.11,1.22,0.22,326.98,205.98, +2021101011,54,53623.0,26,2021-10-10T20:26:24.000,25.0,SF,left,90.16,7.28,2.01,1.12,0.21,325.83,208.19, +2021101011,54,53623.0,27,2021-10-10T20:26:24.100,25.0,SF,left,90.08,7.12,1.85,1.19,0.19,321.81,209.63, +2021101011,54,53623.0,28,2021-10-10T20:26:24.200,25.0,SF,left,89.98,6.96,1.74,1.24,0.18,316.41,211.43, +2021101011,54,53623.0,29,2021-10-10T20:26:24.300,25.0,SF,left,89.89,6.83,1.55,1.55,0.16,317.01,217.17, +2021101011,54,53623.0,30,2021-10-10T20:26:24.400,25.0,SF,left,89.79,6.72,1.46,1.44,0.15,317.01,221.03, +2021101011,54,53623.0,31,2021-10-10T20:26:24.500,25.0,SF,left,89.69,6.62,1.33,1.65,0.14,309.44,229.04,run +2021101011,54,53623.0,32,2021-10-10T20:26:24.600,25.0,SF,left,89.59,6.55,1.2,1.95,0.12,306.23,241.08, +2021101011,54,53623.0,33,2021-10-10T20:26:24.700,25.0,SF,left,89.47,6.52,1.16,2.31,0.12,303.39,257.23, +2021101011,54,53623.0,34,2021-10-10T20:26:24.800,25.0,SF,left,89.36,6.51,1.17,2.65,0.11,299.19,274.9, +2021101011,54,53623.0,35,2021-10-10T20:26:24.900,25.0,SF,left,89.23,6.54,1.42,2.81,0.13,295.18,289.48, +2021101011,54,53623.0,36,2021-10-10T20:26:25.000,25.0,SF,left,89.09,6.61,1.76,3.21,0.16,292.64,300.0, +2021101011,54,,1,2021-10-10T20:26:21.500,,football,left,85.21,23.85,0.0,0.0,0.0,,, +2021101011,54,,2,2021-10-10T20:26:21.600,,football,left,85.21,23.85,0.0,0.0,0.0,,, +2021101011,54,,3,2021-10-10T20:26:21.700,,football,left,85.21,23.85,0.0,0.0,0.0,,, +2021101011,54,,4,2021-10-10T20:26:21.800,,football,left,85.21,23.85,0.0,0.0,0.0,,, +2021101011,54,,5,2021-10-10T20:26:21.900,,football,left,85.21,23.85,0.0,0.0,0.0,,, +2021101011,54,,6,2021-10-10T20:26:22.000,,football,left,85.58,23.51,6.49,0.42,0.5,,,ball_snap +2021101011,54,,7,2021-10-10T20:26:22.100,,football,left,86.25,23.49,6.49,0.9,0.67,,, +2021101011,54,,8,2021-10-10T20:26:22.200,,football,left,86.88,23.49,6.41,1.29,0.63,,, +2021101011,54,,9,2021-10-10T20:26:22.300,,football,left,87.51,23.46,6.24,1.75,0.63,,, +2021101011,54,,10,2021-10-10T20:26:22.400,,football,left,88.12,23.43,6.01,2.15,0.61,,, +2021101011,54,,11,2021-10-10T20:26:22.500,,football,left,88.66,23.41,5.31,3.59,0.54,,, +2021101011,54,,12,2021-10-10T20:26:22.600,,football,left,89.16,23.4,4.73,4.53,0.49,,, +2021101011,54,,13,2021-10-10T20:26:22.700,,football,left,89.58,23.42,4.17,4.65,0.43,,, +2021101011,54,,14,2021-10-10T20:26:22.800,,football,left,89.95,23.44,3.66,4.47,0.37,,, +2021101011,54,,15,2021-10-10T20:26:22.900,,football,left,90.3,23.47,3.24,4.28,0.35,,, +2021101011,54,,16,2021-10-10T20:26:23.000,,football,left,90.6,23.5,2.85,4.04,0.3,,, +2021101011,54,,17,2021-10-10T20:26:23.100,,football,left,90.86,23.55,2.49,3.58,0.26,,, +2021101011,54,,18,2021-10-10T20:26:23.200,,football,left,91.07,23.61,2.14,3.27,0.23,,, +2021101011,54,,19,2021-10-10T20:26:23.300,,football,left,91.25,23.68,1.82,2.99,0.19,,, +2021101011,54,,20,2021-10-10T20:26:23.400,,football,left,91.41,23.75,1.54,2.68,0.17,,, +2021101011,54,,21,2021-10-10T20:26:23.500,,football,left,91.47,23.78,1.15,2.43,0.07,,, +2021101011,54,,22,2021-10-10T20:26:23.600,,football,left,91.03,23.71,0.44,2.45,0.45,,, +2021101011,54,,23,2021-10-10T20:26:23.700,,football,left,91.02,23.33,0.83,2.59,0.38,,, +2021101011,54,,24,2021-10-10T20:26:23.800,,football,left,90.96,23.22,1.12,2.42,0.12,,, +2021101011,54,,25,2021-10-10T20:26:23.900,,football,left,90.87,23.12,1.38,2.16,0.13,,, +2021101011,54,,26,2021-10-10T20:26:24.000,,football,left,90.76,23.0,1.57,1.93,0.16,,, +2021101011,54,,27,2021-10-10T20:26:24.100,,football,left,90.65,22.88,1.75,1.65,0.16,,, +2021101011,54,,28,2021-10-10T20:26:24.200,,football,left,90.54,22.76,2.0,1.46,0.16,,, +2021101011,54,,29,2021-10-10T20:26:24.300,,football,left,90.4,22.61,2.12,1.02,0.21,,, +2021101011,54,,30,2021-10-10T20:26:24.400,,football,left,90.07,22.52,2.68,1.58,0.34,,, +2021101011,54,,31,2021-10-10T20:26:24.500,,football,left,89.58,22.59,3.5,2.84,0.5,,,run +2021101011,54,,32,2021-10-10T20:26:24.600,,football,left,89.11,22.67,3.96,2.66,0.48,,, +2021101011,54,,33,2021-10-10T20:26:24.700,,football,left,88.61,22.7,4.48,1.14,0.49,,, +2021101011,54,,34,2021-10-10T20:26:24.800,,football,left,88.14,22.75,4.63,1.26,0.47,,, +2021101011,54,,35,2021-10-10T20:26:24.900,,football,left,87.69,22.79,4.86,3.32,0.46,,, 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a/tests/files/models/my-test-gnn/variables/variables.data-00000-of-00001 and /dev/null differ diff --git a/tests/files/models/my-test-gnn/variables/variables.index b/tests/files/models/my-test-gnn/variables/variables.index deleted file mode 100644 index 78eecca..0000000 Binary files a/tests/files/models/my-test-gnn/variables/variables.index and /dev/null differ diff --git a/tests/files/test.pickle.gz b/tests/files/test.pickle.gz deleted file mode 100644 index b41c9ec..0000000 Binary files a/tests/files/test.pickle.gz and /dev/null differ diff --git a/tests/test_bigdb.py b/tests/test_bigdb.py new file mode 100644 index 0000000..adedd3e --- /dev/null +++ b/tests/test_bigdb.py @@ -0,0 +1,430 @@ +from pathlib import Path + +from typing import List + +import pytest + +import polars as pl +import pandas as pd +import numpy as np + +from os.path import join + +from datetime import datetime + +from spektral.data import Graph + +from unravel.american_football import ( + AmericanFootballGraphSettings, + BigDataBowlDataset, + AmericanFootballGraphConverter, + AmericanFootballPitchDimensions, +) +from unravel.utils import ( + add_graph_id_column, + add_dummy_label_column, + flatten_to_reshaped_array, + make_sparse, + CustomSpektralDataset, +) + +from kloppy.domain import Unit + + +class TestAmericanFootballDataset: + + @pytest.fixture + def coordinates(self, base_dir: Path) -> str: + return base_dir / "files" / "bdb_coords-1.csv" + + @pytest.fixture + def players(self, base_dir: Path) -> str: + return base_dir / "files" / "bdb_players-1.csv" + + @pytest.fixture + def plays(self, base_dir: Path) -> str: + return base_dir / "files" / "bdb_plays-1.csv" + + @pytest.fixture + def dataset(self, coordinates: str, players: str, plays: str): + bdb_dataset = BigDataBowlDataset( + tracking_file_path=coordinates, + players_file_path=players, + plays_file_path=plays, + ) + bdb_dataset.load() + bdb_dataset.add_graph_ids(by=["gameId", "playId"], column_name="graph_id") + bdb_dataset.add_dummy_labels( + by=["gameId", "playId", "frameId"], column_name="label" + ) + return bdb_dataset + + @pytest.fixture + def raw_dataset(self, coordinates: str): + return pd.read_csv(coordinates, parse_dates=["time"]) + + @pytest.fixture + def edge_feature_values(self): + item_idx = 260 + + assert_values = { + "dist": 0.031333127237586675, + "speed_diff": 0.0725, + "acc_diff": 0.017000000000000005, + "pos_cos": 0.21318726919535064, + "pos_sin": 0.0904411428764118, + "dir_cos": 0.9999911965824017, + "dir_sin": 0.5029670423148592, + "o_cos": 0.9724458937341698, + "o_sin": 0.6636914093461278, + } + return item_idx, assert_values + + @pytest.fixture + def adj_matrix_values(self): + return np.asarray( + [ + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1], + [1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], + ] + ) + + @pytest.fixture + def node_feature_values(self): + item_idx = 6 + + assert_values = { + "x_normed": 0.6679999999999999, + "y_normed": 0.6906191369606004, + "uv_sa[0]": 0.0006550334862428781, + "uv_sa[1]": 0.003179802408809971, + "s_normed": 0.0025, + "uv_aa[0]": 0.0012270197205202376, + "uv_aa[1]": 0.005956459242025523, + "a_normed": 0.001, + "dir_sin_normed": 0.9897173160115632, + "dir_cos_normed": 0.6008808723120034, + "o_sin_normed": 0.394422899008786, + "o_cos_normed": 0.9887263812669529, + "normed_dist_to_goal": 0.31312769316888, + "normed_dist_to_ball": 0.05817057703598108, + "normed_dist_to_end_zone": 0.2486666666666667, + "is_possession_team": 0.0, + "is_qb": 0.0, + "is_ball": 0.0, + "weight_normed": 0.21941714285714287, + "height_normed": 0.4722666666666665, + } + return item_idx, assert_values + + @pytest.fixture + def arguments(self): + return dict( + label_col="label", + graph_id_col="graph_id", + max_player_speed=8.0, + max_ball_speed=28.0, + max_player_acceleration=10.0, + max_ball_acceleration=10.0, + self_loop_ball=True, + adjacency_matrix_connect_type="ball", + adjacency_matrix_type="split_by_team", + label_type="binary", + defending_team_node_value=0.0, + attacking_non_qb_node_value=0.1, + random_seed=42, + pad=False, + verbose=False, + ) + + @pytest.fixture + def non_default_arguments(self): + return dict( + label_col="label", + graph_id_col="graph_id", + max_player_speed=12.0, + max_ball_speed=24.0, + max_player_acceleration=11.0, + max_ball_acceleration=12.0, + self_loop_ball=False, + adjacency_matrix_connect_type="ball", + adjacency_matrix_type="dense_ap", + label_type="binary", + defending_team_node_value=0.3, + attacking_non_qb_node_value=0.2, + random_seed=42, + pad=False, + ) + + @pytest.fixture + def gnnc(self, dataset, arguments): + return AmericanFootballGraphConverter(dataset=dataset, **arguments) + + @pytest.fixture + def gnnc_non_default(self, dataset, non_default_arguments): + return AmericanFootballGraphConverter(dataset=dataset, **non_default_arguments) + + def test_settings(self, gnnc_non_default, non_default_arguments): + settings = gnnc_non_default.settings + assert isinstance(settings, AmericanFootballGraphSettings) + + assert settings.pitch_dimensions.pitch_length == 120.0 + assert settings.pitch_dimensions.pitch_width == 53.3 + assert settings.pitch_dimensions.standardized == False + assert settings.pitch_dimensions.unit == Unit.YARDS + assert settings.pitch_dimensions.x_dim.max == 60.0 + assert settings.pitch_dimensions.x_dim.min == -60.0 + assert settings.pitch_dimensions.y_dim.max == 26.65 + assert settings.pitch_dimensions.y_dim.min == -26.65 + assert settings.pitch_dimensions.end_zone == 50.0 + + assert settings.ball_id == "football" + assert settings.qb_id == "QB" + assert settings.max_height == 225.0 + assert settings.min_height == 150.0 + assert settings.max_weight == 200.0 + assert settings.min_weight == 60.0 + assert settings.max_ball_speed == non_default_arguments["max_ball_speed"] + assert settings.max_ball_speed == non_default_arguments["max_ball_speed"] + assert ( + settings.max_player_acceleration + == non_default_arguments["max_player_acceleration"] + ) + assert ( + settings.max_ball_acceleration + == non_default_arguments["max_ball_acceleration"] + ) + assert settings.self_loop_ball == non_default_arguments["self_loop_ball"] + assert ( + settings.adjacency_matrix_connect_type + == non_default_arguments["adjacency_matrix_connect_type"] + ) + assert ( + settings.adjacency_matrix_type + == non_default_arguments["adjacency_matrix_type"] + ) + assert settings.label_type == non_default_arguments["label_type"] + assert ( + settings.defending_team_node_value + == non_default_arguments["defending_team_node_value"] + ) + assert ( + settings.attacking_non_qb_node_value + == non_default_arguments["attacking_non_qb_node_value"] + ) + + def test_raw_data(self, raw_dataset: pd.DataFrame): + row_10 = raw_dataset.loc[10] + + assert row_10["gameId"] == 2021091300 + assert row_10["playId"] == 4845 + assert row_10["nflId"] == 33131 + assert row_10["frameId"] == 11 + assert row_10["time"] == datetime(2021, 9, 14, 3, 54, 18, 700000) + assert row_10["jerseyNumber"] == 93 + assert row_10["team"] == "BAL" + assert row_10["playDirection"] == "left" + assert row_10["x"] == pytest.approx(40.23, rel=1e-9) + assert row_10["y"] == pytest.approx(21.73, rel=1e-9) + assert row_10["s"] == pytest.approx(1.5, rel=1e-9) + assert row_10["a"] == pytest.approx(2.13, rel=1e-9) + assert row_10["dis"] == pytest.approx(0.19, rel=1e-9) + assert row_10["o"] == pytest.approx(100.77, rel=1e-9) + assert row_10["dir"] == pytest.approx(55.29, rel=1e-9) + + def test_dataset_loader(self, dataset: tuple): + assert isinstance(dataset, BigDataBowlDataset) + assert isinstance(dataset.data, pl.DataFrame) + assert isinstance(dataset.pitch_dimensions, AmericanFootballPitchDimensions) + + assert dataset.pitch_dimensions.pitch_length == 120.0 + assert dataset.pitch_dimensions.pitch_width == 53.3 + assert dataset.pitch_dimensions.x_dim.max == 60.0 + assert dataset.pitch_dimensions.y_dim.max == 26.65 + assert dataset.pitch_dimensions.standardized == False + assert dataset.pitch_dimensions.unit == Unit.YARDS + + data = dataset.data + + assert len(data) == 6049 + + row_10 = data[10].to_dict() + + assert row_10["gameId"][0] == 2021091300 + assert row_10["playId"][0] == 4845 + assert row_10["nflId"][0] == 33131 + assert row_10["frameId"][0] == 11 + assert row_10["time"][0] == datetime(2021, 9, 14, 3, 54, 18, 700000) + assert row_10["jerseyNumber"][0] == 93 + assert row_10["team"][0] == "BAL" + assert row_10["playDirection"][0] == "left" + assert row_10["x"][0] == pytest.approx(19.770000000000003, rel=1e-9) + assert row_10["y"][0] == pytest.approx(4.919999999999998, rel=1e-9) + assert row_10["s"][0] == pytest.approx(1.5, rel=1e-9) + assert row_10["a"][0] == pytest.approx(2.13, rel=1e-9) + assert row_10["dis"][0] == pytest.approx(0.19, rel=1e-9) + assert row_10["o"][0] == pytest.approx(-1.3828243663551074, rel=1e-9) + assert row_10["dir"][0] == pytest.approx(-2.176600110162128, rel=1e-9) + assert row_10["event"][0] == None + assert row_10["officialPosition"][0] == "DE" + assert row_10["possessionTeam"][0] == "LV" + assert row_10["graph_id"][0] == "2021091300-4845" + assert "label" in data.columns + + def test_conversion( + self, + gnnc: AmericanFootballGraphConverter, + node_feature_values: tuple, + edge_feature_values: tuple, + adj_matrix_values: tuple, + ): + item_idx_x, node_feature_assert_values = node_feature_values + item_idx_e, edge_feature_assert_values = edge_feature_values + + results_df = gnnc._convert() + + assert len(results_df) == 263 + + row_4 = results_df[4].to_dict() + + x, x0, x1 = row_4["x"][0], row_4["x_shape_0"][0], row_4["x_shape_1"][0] + a, a0, a1 = row_4["a"][0], row_4["a_shape_0"][0], row_4["a_shape_1"][0] + e, e0, e1 = row_4["e"][0], row_4["e_shape_0"][0], row_4["e_shape_1"][0] + + assert e0 == 287 + assert e1 == len(edge_feature_assert_values.keys()) + assert x0 == 23 + assert x1 == len(node_feature_assert_values.keys()) + assert a0 == 23 + assert a1 == 23 + + x = flatten_to_reshaped_array(x, x0, x1) + a = flatten_to_reshaped_array(a, a0, a1) + e = flatten_to_reshaped_array(e, e0, e1) + + assert x.shape == tuple((x0, x1)) + assert a.shape == tuple((a0, a1)) + assert e.shape == tuple((e0, e1)) + + assert np.min(a) == 0 + assert np.max(1) == 1 + + for idx, node_feature in enumerate(node_feature_assert_values.keys()): + assert x[item_idx_x][idx] == pytest.approx( + node_feature_assert_values.get(node_feature), abs=1e-5 + ) + + for idx, edge_feature in enumerate(edge_feature_assert_values.keys()): + assert e[item_idx_e][idx] == pytest.approx( + edge_feature_assert_values.get(edge_feature), abs=1e-5 + ) + + np.testing.assert_array_equal(a, adj_matrix_values) + + def test_to_graph_frames( + self, gnnc: AmericanFootballGraphConverter, node_feature_values + ): + graph_frames = gnnc.to_graph_frames() + + data = graph_frames + assert len(data) == 263 + assert isinstance(data[0], dict) + # note: these shape tests fail if we add more features (ie. metabolicpower) + + item_idx_x, node_feature_assert_values = node_feature_values + + x = data[4]["x"] + assert x.shape == (23, len(node_feature_assert_values.keys())) + + for idx, node_feature in enumerate(node_feature_assert_values.keys()): + assert x[item_idx_x][idx] == pytest.approx( + node_feature_assert_values.get(node_feature), abs=1e-5 + ) + + def test_to_spektral_graph( + self, + gnnc: AmericanFootballGraphConverter, + node_feature_values: tuple, + edge_feature_values: tuple, + adj_matrix_values: tuple, + ): + """ + Test navigating (next/prev) through events + """ + spektral_graphs = gnnc.to_spektral_graphs() + + item_idx_x, node_feature_assert_values = node_feature_values + item_idx_e, edge_feature_assert_values = edge_feature_values + + assert 1 == 1 + + data = spektral_graphs + assert len(data) == 263 + assert isinstance(data[0], Graph) + # note: these shape tests fail if we add more features + + x = data[4].x + assert x.shape == (23, len(node_feature_assert_values.keys())) + + for idx, node_feature in enumerate(node_feature_assert_values.keys()): + assert x[item_idx_x][idx] == pytest.approx( + node_feature_assert_values.get(node_feature), abs=1e-5 + ) + + e = data[4].e + for idx, edge_feature in enumerate(edge_feature_assert_values.keys()): + assert e[item_idx_e][idx] == pytest.approx( + edge_feature_assert_values.get(edge_feature), abs=1e-5 + ) + + def __are_csr_matrices_equal(mat1, mat2): + return ( + mat1.shape == mat2.shape + and np.array_equal(mat1.data, mat2.data) + and np.array_equal(mat1.indices, mat2.indices) + and np.array_equal(mat1.indptr, mat2.indptr) + ) + + a = data[4].a + assert __are_csr_matrices_equal(a, make_sparse(adj_matrix_values)) + + dataset = CustomSpektralDataset(graphs=spektral_graphs) + N, F, S, n_out, n = dataset.dimensions() + assert N == 23 + assert F == len(node_feature_assert_values.keys()) + assert S == 9 + assert n_out == 1 + assert n == 263 + + def test_to_pickle(self, gnnc: AmericanFootballGraphConverter): + """ + Test navigating (next/prev) through events + """ + pickle_folder = join("tests", "files", "bdb") + + gnnc.to_pickle(file_path=join(pickle_folder, "test_bdb.pickle.gz")) + + data = CustomSpektralDataset(pickle_folder=pickle_folder) + + assert data.n_graphs == 263 diff --git a/tests/test_kloppy.py b/tests/test_kloppy.py index b0461ca..4fb2273 100644 --- a/tests/test_kloppy.py +++ b/tests/test_kloppy.py @@ -1,11 +1,10 @@ from pathlib import Path -from unravel.soccer import GraphConverter +from unravel.soccer import SoccerGraphConverter, GraphFrame, SoccerGraphSettings from unravel.utils import ( DefaultTrackingModel, dummy_labels, dummy_graph_ids, CustomSpektralDataset, - GraphFrame, ) from kloppy import skillcorner @@ -40,8 +39,8 @@ def dataset(self, match_data: str, structured_data: str) -> TrackingDataset: ) @pytest.fixture() - def gnnc(self, dataset: TrackingDataset) -> GraphConverter: - return GraphConverter( + def gnnc(self, dataset: TrackingDataset) -> SoccerGraphConverter: + return SoccerGraphConverter( dataset=dataset, labels=dummy_labels(dataset), graph_ids=dummy_graph_ids(dataset), @@ -63,8 +62,8 @@ def gnnc(self, dataset: TrackingDataset) -> GraphConverter: ) @pytest.fixture() - def gnnc_padding(self, dataset: TrackingDataset) -> GraphConverter: - return GraphConverter( + def gnnc_padding(self, dataset: TrackingDataset) -> SoccerGraphConverter: + return SoccerGraphConverter( dataset=dataset, labels=dummy_labels(dataset), graph_id=1234, @@ -86,8 +85,8 @@ def gnnc_padding(self, dataset: TrackingDataset) -> GraphConverter: ) @pytest.fixture() - def gnnc_padding_random(self, dataset: TrackingDataset) -> GraphConverter: - return GraphConverter( + def gnnc_padding_random(self, dataset: TrackingDataset) -> SoccerGraphConverter: + return SoccerGraphConverter( dataset=dataset, labels=dummy_labels(dataset), # settings @@ -108,7 +107,7 @@ def gnnc_padding_random(self, dataset: TrackingDataset) -> GraphConverter: verbose=False, ) - def test_conversion(self, gnnc: GraphConverter): + def test_conversion(self, gnnc: SoccerGraphConverter): data, label, frame_id, _ = gnnc._convert(gnnc.dataset[2]) assert isinstance(data, DefaultTrackingModel) @@ -153,7 +152,7 @@ def test_conversion(self, gnnc: GraphConverter): assert gid == "abcdefg" - def test_conversion_padding(self, gnnc_padding: GraphConverter): + def test_conversion_padding(self, gnnc_padding: SoccerGraphConverter): data, _, frame_id, graph_id = gnnc_padding._convert(gnnc_padding.dataset[2]) assert isinstance(data, DefaultTrackingModel) @@ -167,7 +166,7 @@ def test_conversion_padding(self, gnnc_padding: GraphConverter): assert len(data.home_players) == 11 assert len(data.away_players) == 11 - def test_to_spektral_graph(self, gnnc: GraphConverter): + def test_to_spektral_graph(self, gnnc: SoccerGraphConverter): """ Test navigating (next/prev) through events """ @@ -249,7 +248,7 @@ def test_to_spektral_graph(self, gnnc: GraphConverter): ) def test_to_spektral_graph_padding_random( - self, gnnc_padding_random: GraphConverter + self, gnnc_padding_random: SoccerGraphConverter ): """ Test navigating (next/prev) through events diff --git a/tests/test_spektral.py b/tests/test_spektral.py index ffb3ec5..b170970 100644 --- a/tests/test_spektral.py +++ b/tests/test_spektral.py @@ -1,5 +1,6 @@ from pathlib import Path -from unravel.soccer import GraphConverter +from unravel.soccer import SoccerGraphConverter +from unravel.american_football import BigDataBowlDataset, AmericanFootballGraphConverter from unravel.utils import dummy_labels, dummy_graph_ids, CustomSpektralDataset from unravel.classifiers import CrystalGraphClassifier @@ -20,8 +21,35 @@ import numpy as np import pandas as pd +from os.path import join + class TestSpektral: + @pytest.fixture + def coordinates(self, base_dir: Path) -> str: + return base_dir / "files" / "bdb_coords-1.csv" + + @pytest.fixture + def players(self, base_dir: Path) -> str: + return base_dir / "files" / "bdb_players-1.csv" + + @pytest.fixture + def plays(self, base_dir: Path) -> str: + return base_dir / "files" / "bdb_plays-1.csv" + + @pytest.fixture + def bdb_dataset(self, coordinates: str, players: str, plays: str): + bdb_dataset = BigDataBowlDataset( + tracking_file_path=coordinates, + players_file_path=players, + plays_file_path=plays, + ) + bdb_dataset.load() + bdb_dataset.add_graph_ids(by=["gameId", "playId"], column_name="graph_id") + bdb_dataset.add_dummy_labels( + by=["gameId", "playId", "frameId"], column_name="label" + ) + return bdb_dataset @pytest.fixture def match_data(self, base_dir: Path) -> str: @@ -32,7 +60,7 @@ def structured_data(self, base_dir: Path) -> str: return base_dir / "files" / "skillcorner_structured_data.json.gz" @pytest.fixture() - def dataset(self, match_data: str, structured_data: str) -> TrackingDataset: + def kloppy_dataset(self, match_data: str, structured_data: str) -> TrackingDataset: return skillcorner.load( raw_data=structured_data, meta_data=match_data, @@ -42,11 +70,11 @@ def dataset(self, match_data: str, structured_data: str) -> TrackingDataset: ) @pytest.fixture() - def converter(self, dataset: TrackingDataset) -> GraphConverter: - return GraphConverter( - dataset=dataset, - labels=dummy_labels(dataset), - graph_ids=dummy_graph_ids(dataset), + def soccer_converter(self, kloppy_dataset: TrackingDataset) -> SoccerGraphConverter: + return SoccerGraphConverter( + dataset=kloppy_dataset, + labels=dummy_labels(kloppy_dataset), + graph_ids=dummy_graph_ids(kloppy_dataset), ball_carrier_treshold=25.0, max_player_speed=12.0, max_ball_speed=28.0, @@ -65,9 +93,11 @@ def converter(self, dataset: TrackingDataset) -> GraphConverter: ) @pytest.fixture() - def converter_preds(self, dataset: TrackingDataset) -> GraphConverter: - return GraphConverter( - dataset=dataset, + def soccer_converter_preds( + self, kloppy_dataset: TrackingDataset + ) -> SoccerGraphConverter: + return SoccerGraphConverter( + dataset=kloppy_dataset, prediction=True, ball_carrier_treshold=25.0, max_player_speed=12.0, @@ -86,19 +116,64 @@ def converter_preds(self, dataset: TrackingDataset) -> GraphConverter: verbose=False, ) - def test_training(self, converter: GraphConverter): - train = CustomSpektralDataset(graphs=converter.to_spektral_graphs()) + @pytest.fixture + def bdb_converter( + self, bdb_dataset: BigDataBowlDataset + ) -> AmericanFootballGraphConverter: + return AmericanFootballGraphConverter( + dataset=bdb_dataset, + label_col="label", + graph_id_col="graph_id", + max_player_speed=8.0, + max_ball_speed=28.0, + max_player_acceleration=10.0, + max_ball_acceleration=10.0, + self_loop_ball=True, + adjacency_matrix_connect_type="ball", + adjacency_matrix_type="split_by_team", + label_type="binary", + defending_team_node_value=0.0, + random_seed=42, + pad=False, + verbose=False, + ) - cd = converter.to_custom_dataset() + @pytest.fixture + def bdb_converter_preds( + self, bdb_dataset: BigDataBowlDataset + ) -> AmericanFootballGraphConverter: + return AmericanFootballGraphConverter( + dataset=bdb_dataset, + prediction=True, + max_player_speed=8.0, + max_ball_speed=28.0, + max_player_acceleration=10.0, + max_ball_acceleration=10.0, + self_loop_ball=True, + adjacency_matrix_connect_type="ball", + adjacency_matrix_type="split_by_team", + label_type="binary", + defending_team_node_value=0.0, + random_seed=42, + pad=False, + verbose=False, + ) + + def test_soccer_training(self, soccer_converter: SoccerGraphConverter): + train = CustomSpektralDataset(graphs=soccer_converter.to_spektral_graphs()) + + cd = soccer_converter.to_custom_dataset() assert isinstance(cd, CustomSpektralDataset) - converter.to_pickle("tests/files/test.pickle.gz") + pickle_folder = join("tests", "files", "kloppy") + + soccer_converter.to_pickle(join(pickle_folder, "test.pickle.gz")) with pytest.raises( ValueError, match="Only compressed pickle files of type 'some_file_name.pickle.gz' are supported...", ): - converter.to_pickle("tests/files/test.pickle") + soccer_converter.to_pickle(join(pickle_folder, "test.pickle")) model = CrystalGraphClassifier() @@ -120,8 +195,7 @@ def test_training(self, converter: GraphConverter): steps_per_epoch=loader_tr.steps_per_epoch, verbose=0, ) - - model_path = "tests/files/models/my-test-gnn" + model_path = join("tests", "files", "models", "my-test-gnn") model.save(model_path) loaded_model = load_model(model_path) @@ -133,15 +207,14 @@ def test_training(self, converter: GraphConverter): assert np.allclose(pred, loaded_pred, atol=1e-8) - def test_prediction(self, converter_preds: GraphConverter): + def test_soccer_prediction(self, soccer_converter_preds: SoccerGraphConverter): pred_dataset = CustomSpektralDataset( - graphs=converter_preds.to_spektral_graphs() + graphs=soccer_converter_preds.to_spektral_graphs() ) loader_pred = DisjointLoader( pred_dataset, batch_size=32, epochs=1, shuffle=False ) - - model_path = "tests/files/models/my-test-gnn" + model_path = join("tests", "files", "models", "my-test-gnn") loaded_model = load_model(model_path) preds = loaded_model.predict(loader_pred.load()) @@ -154,3 +227,74 @@ def test_prediction(self, converter_preds: GraphConverter): assert df["frame_id"].iloc[0] == 1524 assert df["frame_id"].iloc[-1] == 1621 + + def test_bdb_training(self, bdb_converter: AmericanFootballGraphConverter): + train = CustomSpektralDataset(graphs=bdb_converter.to_spektral_graphs()) + + cd = bdb_converter.to_custom_dataset() + assert isinstance(cd, CustomSpektralDataset) + + pickle_folder = join("tests", "files", "bdb") + + bdb_converter.to_pickle(join(pickle_folder, "test_bdb.pickle.gz")) + + with pytest.raises( + ValueError, + match="Only compressed pickle files of type 'some_file_name.pickle.gz' are supported...", + ): + bdb_converter.to_pickle(join(pickle_folder, "test_bdb.pickle")) + + model = CrystalGraphClassifier() + + assert model.channels == 128 + assert model.drop_out == 0.5 + assert model.n_layers == 3 + assert model.n_out == 1 + + model.compile( + loss=BinaryCrossentropy(), + optimizer=Adam(), + metrics=[AUC(), BinaryAccuracy()], + ) + + loader_tr = DisjointLoader(train, batch_size=32) + model.fit( + loader_tr.load(), + epochs=1, + steps_per_epoch=loader_tr.steps_per_epoch, + verbose=0, + ) + + model_path = join("tests", "files", "models", "my_bdb-test-gnn") + model.save(model_path) + loaded_model = load_model(model_path) + + loader_te = DisjointLoader(train, batch_size=32, epochs=1, shuffle=False) + pred = model.predict(loader_te.load()) + + loader_te = DisjointLoader(train, batch_size=32, epochs=1, shuffle=False) + loaded_pred = loaded_model.predict(loader_te.load(), use_multiprocessing=True) + + assert np.allclose(pred, loaded_pred, atol=1e-8) + + def test_dbd_prediction(self, bdb_converter_preds: AmericanFootballGraphConverter): + pred_dataset = CustomSpektralDataset( + graphs=bdb_converter_preds.to_spektral_graphs() + ) + loader_pred = DisjointLoader( + pred_dataset, batch_size=32, epochs=1, shuffle=False + ) + + model_path = join("tests", "files", "models", "my_bdb-test-gnn") + loaded_model = load_model(model_path) + + preds = loaded_model.predict(loader_pred.load()) + + assert not np.any(np.isnan(preds.flatten())) + + df = pd.DataFrame( + {"frame_id": [x.id for x in pred_dataset], "y": preds.flatten()} + ) + + assert df["frame_id"].iloc[0] == "2021091300-4845" + assert df["frame_id"].iloc[-1] == "2021103108-54" diff --git a/unravel/__init__.py b/unravel/__init__.py index 1eeb93d..b0cda09 100644 --- a/unravel/__init__.py +++ b/unravel/__init__.py @@ -1,5 +1,6 @@ -__version__ = "0.1.2" +__version__ = "0.2.0" from .soccer import * +from .american_football import * from .utils import * from .classifiers import * diff --git a/unravel/american_football/__init__.py b/unravel/american_football/__init__.py new file mode 100644 index 0000000..e1858db --- /dev/null +++ b/unravel/american_football/__init__.py @@ -0,0 +1 @@ +from .graphs import * diff --git a/unravel/american_football/graphs/__init__.py b/unravel/american_football/graphs/__init__.py new file mode 100644 index 0000000..a45c95f --- /dev/null +++ b/unravel/american_football/graphs/__init__.py @@ -0,0 +1,7 @@ +from .graph_converter import AmericanFootballGraphConverter +from .graph_settings import ( + AmericanFootballGraphSettings, + AmericanFootballPitchDimensions, +) +from .dataset import BigDataBowlDataset +from .features import * diff --git a/unravel/american_football/graphs/dataset.py b/unravel/american_football/graphs/dataset.py new file mode 100644 index 0000000..93368f5 --- /dev/null +++ b/unravel/american_football/graphs/dataset.py @@ -0,0 +1,175 @@ +from dataclasses import dataclass, field + +from typing import List + +import polars as pl + +import numpy as np + +from .graph_settings import AmericanFootballPitchDimensions, Dimension, Unit +from ...utils import add_dummy_label_column, add_graph_id_column + + +@dataclass +class BigDataBowlDataset: + tracking_file_path: str + players_file_path: str + plays_file_path: str + pitch_dimensions: AmericanFootballPitchDimensions = field( + init=False, repr=False, default_factory=AmericanFootballPitchDimensions + ) + + def __post_init__(self): + if ( + not self.tracking_file_path + or not self.players_file_path + or not self.plays_file_path + ): + raise Exception("Missing data file path...") + + def load(self): + pitch_length = self.pitch_dimensions.pitch_length + pitch_width = self.pitch_dimensions.pitch_width + + df = pl.read_csv( + self.tracking_file_path, + separator=",", + encoding="utf8", + null_values=["NA", "NULL", ""], + try_parse_dates=True, + ) + + play_direction = "left" + + if "club" in df.columns: + df = df.with_columns(pl.col("club").alias("team")) + df = df.drop("club") + + df = ( + df.with_columns( + pl.when(pl.col("playDirection") == play_direction) + .then(pl.col("o") + 180) # rotate 180 degrees + .otherwise(pl.col("o")) + .alias("o"), + pl.when(pl.col("playDirection") == play_direction) + .then(pl.col("dir") + 180) # rotate 180 degrees + .otherwise(pl.col("dir")) + .alias("dir"), + ) + .with_columns( + [ + (pl.col("x") - (pitch_length / 2)).alias("x"), + (pl.col("y") - (pitch_width / 2)).alias("y"), + # convert to radian on (-pi, pi) range + (((pl.col("o") * np.pi / 180) + np.pi) % (2 * np.pi) - np.pi).alias( + "o" + ), + ( + ((pl.col("dir") * np.pi / 180) + np.pi) % (2 * np.pi) - np.pi + ).alias("dir"), + ] + ) + .with_columns( + [ + pl.when(pl.col("playDirection") == play_direction) + .then(pl.col("x") * -1.0) + .otherwise(pl.col("x")) + .alias("x"), + pl.when(pl.col("playDirection") == play_direction) + .then(pl.col("y") * -1.0) + .otherwise(pl.col("y")) + .alias("y"), + # set "football" to nflId -9999 for ordering purposes + pl.when(pl.col("team") == "football") + .then(-9999.9) + .otherwise(pl.col("nflId")) + .alias("nflId"), + ] + ) + ) + players = pl.read_csv( + self.players_file_path, + separator=",", + encoding="utf8", + null_values=["NA", "NULL", ""], + dtypes={"birthDate": pl.Date}, + ignore_errors=True, + ) + if "position" in players.columns: + players = players.with_columns(pl.col("position").alias("officialPosition")) + players = players.drop("position") + + players = players.with_columns( + pl.col("nflId").cast(pl.Float64, strict=False).alias("nflId") + ) + players = self._convert_weight_height_to_metric(df=players) + + plays = pl.read_csv( + self.plays_file_path, + separator=",", + encoding="utf8", + null_values=["NA", "NULL", ""], + try_parse_dates=True, + ) + + df = df.join( + (players.select(["nflId", "officialPosition", "height_cm", "weight_kg"])), + on="nflId", + how="left", + ) + df = df.join( + (plays.select(["gameId", "playId", "possessionTeam"])), + on=["gameId", "playId"], + how="left", + ) + self.data = df + + # update pitch dimensions to how it looks after loading + self.pitch_dimensions = AmericanFootballPitchDimensions( + x_dim=Dimension(min=-pitch_length / 2, max=pitch_length / 2), + y_dim=Dimension(min=-pitch_width / 2, max=pitch_width / 2), + standardized=False, + unit=Unit.YARDS, + pitch_length=pitch_length, + pitch_width=pitch_width, + ) + + return self.data, self.pitch_dimensions + + def add_dummy_labels( + self, + by: List[str] = ["gameId", "playId", "frameId"], + column_name: str = "label", + ) -> pl.DataFrame: + self.data = add_dummy_label_column(self.data, by, column_name) + return self.data + + def add_graph_ids( + self, by: List[str] = ["gameId", "playId"], column_name: str = "graph_id" + ) -> pl.DataFrame: + self.data = add_graph_id_column(self.data, by, column_name) + return self.data + + @staticmethod + def _convert_weight_height_to_metric(df: pl.DataFrame): + df = df.with_columns( + [ + pl.col("height") + .str.extract(r"(\d+)") + .cast(pl.Float64) + .alias("feet"), # Extract feet and cast to float + pl.col("height") + .str.extract(r"\d+-(\d+)", 1) + .cast(pl.Float64) + .alias("inches"), # Extract inches and cast to float + ] + ) + df = df.with_columns( + [ + (pl.col("feet") * 30.48 + pl.col("inches") * 2.54).alias("height_cm"), + (pl.col("weight") * 0.453592).alias( + "weight_kg" + ), # Convert pounds to kilograms + ] + ).drop(["height", "feet", "inches", "weight"]) + return df diff --git a/unravel/american_football/graphs/features/__init__.py b/unravel/american_football/graphs/features/__init__.py new file mode 100644 index 0000000..e43b292 --- /dev/null +++ b/unravel/american_football/graphs/features/__init__.py @@ -0,0 +1,3 @@ +from .adjacency_matrix import compute_adjacency_matrix +from .edge_features import compute_edge_features +from .node_features import compute_node_features diff --git a/unravel/american_football/graphs/features/adjacency_matrix.py b/unravel/american_football/graphs/features/adjacency_matrix.py new file mode 100644 index 0000000..130cd0f --- /dev/null +++ b/unravel/american_football/graphs/features/adjacency_matrix.py @@ -0,0 +1,42 @@ +import numpy as np + +from ....utils import AdjacencyMatrixType, AdjacenyMatrixConnectType + + +def compute_adjacency_matrix(team, possession_team, settings): + adjacency_matrix_type = settings.adjacency_matrix_type + adjacency_matrix_connect_type = settings.adjacency_matrix_connect_type + ball_id = settings.ball_id + + exclusion_ids = np.asarray([ball_id, *np.unique(possession_team)]) + defensive_team = np.setdiff1d(team, exclusion_ids)[0] + if adjacency_matrix_type == AdjacencyMatrixType.DENSE: + adjacency_matrix = np.ones((team.shape[0], team.shape[0])).astype(np.int32) + elif adjacency_matrix_type == AdjacencyMatrixType.DENSE_AP: + is_att = team == np.unique(possession_team)[0] + adjacency_matrix = np.outer(is_att, is_att).astype(int) + elif adjacency_matrix_type == AdjacencyMatrixType.DENSE_DP: + is_def = team == defensive_team + adjacency_matrix = np.outer(is_def, is_def).astype(int) + elif adjacency_matrix_type == AdjacencyMatrixType.SPLIT_BY_TEAM: + # Create a pairwise team comparison matrix + adjacency_matrix = np.equal(team[:, None], team[None, :]).astype(np.int32) + elif adjacency_matrix_type == AdjacencyMatrixType.DELAUNAY: + raise NotImplementedError( + "Delaunay matrix not implemented for American Football..." + ) + else: + raise NotImplementedError("Please specify an existing AdjacencyMatrixType...") + + if adjacency_matrix_connect_type: + if adjacency_matrix_connect_type == AdjacenyMatrixConnectType.BALL: + # Create a mask where either team is "football" + football_mask = (team[:, None] == ball_id) | (team[None, :] == ball_id) + # Set entries to 1 where either team is "football" + adjacency_matrix = np.where(football_mask, 1, adjacency_matrix) + elif adjacency_matrix_connect_type == AdjacenyMatrixConnectType.BALL_CARRIER: + raise NotImplementedError( + "No ball carrier information exists in the BigDataBowl dataset, please choose a different AdjacenyMatrixConnectType..." + ) + + return adjacency_matrix diff --git a/unravel/american_football/graphs/features/edge_features.py b/unravel/american_football/graphs/features/edge_features.py new file mode 100644 index 0000000..7ff3081 --- /dev/null +++ b/unravel/american_football/graphs/features/edge_features.py @@ -0,0 +1,81 @@ +import numpy as np + +from ....utils import ( + normalize_distance, + normalize_sincos, + non_zeros, + reindex, + normalize_speed_differences_nfl, + normalize_accelerations_nfl, +) + + +def compute_edge_features(adjacency_matrix, p, s, a, o, dir, team, settings): + # Compute pairwise distances using broadcasting + max_dist_to_player = np.sqrt( + settings.pitch_dimensions.pitch_length**2 + + settings.pitch_dimensions.pitch_width**2 + ) + + distances_between_players = np.linalg.norm(p[:, None, :] - p[None, :, :], axis=-1) + dist_matrix_normed = normalize_distance( + distances_between_players, max_distance=max_dist_to_player + ) # 11x11 + + speed_diff_matrix = np.nan_to_num(s[None, :] - s[:, None]) # NxNx1 + speed_diff_matrix_normed = normalize_speed_differences_nfl( + s=speed_diff_matrix, + team=team, + settings=settings, + ) + acc_diff_matrix = np.nan_to_num(a[None, :] - a[:, None]) # NxNx1 + acc_diff_matrix_normed = normalize_accelerations_nfl( + a=acc_diff_matrix, + team=team, + settings=settings, + ) + vect_to_player_matrix = p[:, None, :] - p[None, :, :] # NxNx2 + + # Angles between players in sin and cos + angle_pos_matrix = np.nan_to_num( + np.arctan2(vect_to_player_matrix[:, :, 1], vect_to_player_matrix[:, :, 0]) + ) + pos_cos_matrix = normalize_sincos(np.nan_to_num(np.cos(angle_pos_matrix))) + pos_sin_matrix = normalize_sincos(np.nan_to_num(np.sin(angle_pos_matrix))) + + dir_diff_matrix = dir[None, :] - dir[:, None] # NxNx1 + dir_cos_matrix = normalize_sincos(np.nan_to_num(np.cos(dir_diff_matrix))) + dir_sin_matrix = normalize_sincos(np.nan_to_num(np.sin(dir_diff_matrix))) + o_diff_matrix = o[None, :] - o[:, None] # NxNx1 + o_cos_matrix = normalize_sincos(np.nan_to_num(np.cos(o_diff_matrix))) + o_sin_matrix = normalize_sincos(np.nan_to_num(np.sin(o_diff_matrix))) + + nan_mask = np.isnan(distances_between_players) + non_zero_idxs, len_a = non_zeros(A=adjacency_matrix) + + dist_matrix_normed[nan_mask] = 0 + speed_diff_matrix_normed[nan_mask] = 0 + acc_diff_matrix[nan_mask] = 0 + pos_cos_matrix[nan_mask] = 0 + pos_sin_matrix[nan_mask] = 0 + dir_cos_matrix[nan_mask] = 0 + dir_sin_matrix[nan_mask] = 0 + o_cos_matrix[nan_mask] = 0 + o_sin_matrix[nan_mask] = 0 + + e_tuple = list( + [ + reindex(dist_matrix_normed, non_zero_idxs, len_a), + reindex(speed_diff_matrix_normed, non_zero_idxs, len_a), + reindex(acc_diff_matrix_normed, non_zero_idxs, len_a), + reindex(pos_cos_matrix, non_zero_idxs, len_a), + reindex(pos_sin_matrix, non_zero_idxs, len_a), + reindex(dir_cos_matrix, non_zero_idxs, len_a), + reindex(dir_sin_matrix, non_zero_idxs, len_a), + reindex(o_cos_matrix, non_zero_idxs, len_a), + reindex(o_sin_matrix, non_zero_idxs, len_a), + ] + ) + + e = np.concatenate(e_tuple, axis=1) + return np.nan_to_num(e) diff --git a/unravel/american_football/graphs/features/node_features.py b/unravel/american_football/graphs/features/node_features.py new file mode 100644 index 0000000..c723e21 --- /dev/null +++ b/unravel/american_football/graphs/features/node_features.py @@ -0,0 +1,140 @@ +import numpy as np + + +from ....utils import ( + normalize_coords, + normalize_speeds_nfl, + normalize_sincos, + normalize_distance, + unit_vector_from_angle, + normalize_speeds_nfl, + normalize_accelerations_nfl, + normalize_between, +) + + +def compute_node_features( + x, + y, + s, + a, + o, + dir, + team, + official_position, + possession_team, + height, + weight, + settings, +): + ball_id = settings.ball_id + + goal_mouth_position = ( + settings.pitch_dimensions.x_dim.max, + (settings.pitch_dimensions.y_dim.max + settings.pitch_dimensions.y_dim.min) / 2, + ) + max_dist_to_player = np.sqrt( + settings.pitch_dimensions.pitch_length**2 + + settings.pitch_dimensions.pitch_width**2 + ) + max_dist_to_goal = np.sqrt( + settings.pitch_dimensions.pitch_length**2 + + settings.pitch_dimensions.pitch_width**2 + ) + + position = np.stack((x, y), axis=-1) + + if len(np.where(team == ball_id)) >= 1: + ball_index = np.where(team == ball_id)[0] + ball_position = position[ball_index][0] + else: + ball_position = np.sarray([np.nan, np.nan]) + + x_normed = normalize_between( + value=x, + max_value=settings.pitch_dimensions.x_dim.max, + min_value=settings.pitch_dimensions.x_dim.min, + ) + y_normed = normalize_between( + value=y, + max_value=settings.pitch_dimensions.y_dim.max, + min_value=settings.pitch_dimensions.y_dim.min, + ) + uv_sa = unit_vector_from_angle(value=s, angle_radians=dir) + s_normed = normalize_speeds_nfl(s, team, settings) + + uv_aa = unit_vector_from_angle(value=a, angle_radians=dir) + a_normed = normalize_accelerations_nfl(a, team, settings) + + dir_sin_normed = normalize_sincos(np.nan_to_num(np.sin(dir))) + dir_cos_normed = normalize_sincos(np.nan_to_num(np.cos(dir))) + o_sin_normed = normalize_sincos(np.nan_to_num(np.sin(o))) + o_cos_normed = normalize_sincos(np.nan_to_num(np.cos(o))) + + dist_to_goal = np.linalg.norm(position - goal_mouth_position, axis=1) + normed_dist_to_goal = normalize_distance( + value=dist_to_goal, max_distance=max_dist_to_goal + ) + + dist_to_ball = np.linalg.norm(position - ball_position, axis=1) + normed_dist_to_ball = normalize_distance( + value=dist_to_ball, max_distance=max_dist_to_player + ) + + dist_to_end_zone = settings.pitch_dimensions.end_zone - x + normed_dist_to_end_zone = normalize_between( + value=dist_to_end_zone, + max_value=settings.pitch_dimensions.pitch_length, + min_value=0, + ) + + is_possession_team = np.where( + team == possession_team, 1, settings.defending_team_node_value + ) + is_qb = np.where( + official_position == settings.qb_id, # First condition + 1, # If true, set to 1 (indicating the player is a QB) + np.where( + team == possession_team, # Second condition inside the else of the first + settings.attacking_non_qb_node_value, # If true, set to attacking_non_qb_value + 0, # If false, set to 0 + ), + ) + + is_ball = np.where(team == ball_id, 1, 0) + weight_normed = normalize_between( + min_value=settings.min_weight, max_value=settings.max_weight, value=weight + ) + height_normed = normalize_between( + min_value=settings.min_height, max_value=settings.max_height, value=height + ) + + X = np.nan_to_num( + np.stack( + ( + x_normed, + y_normed, + uv_sa[0], + uv_sa[1], + s_normed, + uv_aa[0], + uv_aa[1], + a_normed, + dir_sin_normed, + dir_cos_normed, + o_sin_normed, + o_cos_normed, + normed_dist_to_goal, + normed_dist_to_ball, + normed_dist_to_end_zone, + is_possession_team, + is_qb, + is_ball, + weight_normed, + height_normed, + ), + axis=-1, + ) + ) + + return X diff --git a/unravel/american_football/graphs/graph_converter.py b/unravel/american_football/graphs/graph_converter.py new file mode 100644 index 0000000..2f7bfd9 --- /dev/null +++ b/unravel/american_football/graphs/graph_converter.py @@ -0,0 +1,307 @@ +from dataclasses import dataclass + +import polars as pl +import numpy as np + +from typing import List + +from spektral.data import Graph + +from .dataset import BigDataBowlDataset + +from .graph_settings import ( + AmericanFootballGraphSettings, + AmericanFootballPitchDimensions, +) +from .features import ( + compute_node_features, + compute_edge_features, + compute_adjacency_matrix, +) + +from ...utils import DefaultGraphConverter, flatten_to_reshaped_array, make_sparse + + +@dataclass(repr=True) +class AmericanFootballGraphConverter(DefaultGraphConverter): + """ + Converts our dataset TrackingDataset into an internal structure + + Attributes: + dataset (TrackingDataset): Kloppy TrackingDataset. + label_col (str): Column name that contains labels in the dataset.data Polars dataframe + graph_id_col (str): Column name that contains graph ids in the dataset.data Polars dataframe + + chunk_size (int): Used to batch convert Polars into Graphs + attacking_non_qb_node_value (float): Value between 0 and 1 to assign any attacking team player who is not the QB + """ + + def __init__( + self, + dataset: BigDataBowlDataset, + label_col: str = "label", + graph_id_col: str = "graph_id", + chunk_size: int = 2_000, + attacking_non_qb_node_value: float = 0.1, + **kwargs, + ): + super().__init__(**kwargs) + + if not isinstance(dataset, BigDataBowlDataset): + raise Exception("'dataset' should be an instance of BigDataBowlDataset") + + self.dataset: pl.DataFrame = dataset.data + self.pitch_dimensions: AmericanFootballPitchDimensions = ( + dataset.pitch_dimensions + ) + self.label_col = label_col + self.graph_id_col = graph_id_col + self.chunk_size = chunk_size + self.attacking_non_qb_node_value = attacking_non_qb_node_value + + self._sport_specific_checks() + + self.settings = self._apply_settings() + + def _sport_specific_checks(self): + + if not isinstance(self.label_col, str): + raise Exception("'label_col' should be of type string (str)") + + if not isinstance(self.graph_id_col, str): + raise Exception("'graph_id_col' should be of type string (str)") + + if not isinstance(self.chunk_size, int): + raise Exception("chunk_size should be of type integer (int)") + + if not isinstance(self.attacking_non_qb_node_value, (int, float)): + raise Exception( + "'attacking_non_qb_node_value' should be of type float or integer (int)" + ) + + if not self.label_col in self.dataset.columns and not self.prediction: + raise Exception( + "Please specify a 'label_col' and add that column to your 'dataset' or set 'prediction=True' if you want to use the converted dataset to make predictions on." + ) + + if not self.graph_id_col in self.dataset.columns: + raise Exception( + "Please specify a 'graph_id_col' and add that column to your 'dataset' ..." + ) + + def _apply_settings(self): + return AmericanFootballGraphSettings( + pitch_dimensions=self.pitch_dimensions, + max_player_speed=self.max_player_speed, + max_ball_speed=self.max_ball_speed, + max_ball_acceleration=self.max_ball_acceleration, + max_player_acceleration=self.max_player_acceleration, + self_loop_ball=self.self_loop_ball, + adjacency_matrix_connect_type=self.adjacency_matrix_connect_type, + adjacency_matrix_type=self.adjacency_matrix_type, + label_type=self.label_type, + defending_team_node_value=self.defending_team_node_value, + attacking_non_qb_node_value=self.attacking_non_qb_node_value, + random_seed=self.random_seed, + pad=self.pad, + verbose=self.verbose, + ) + + def _convert(self): + def __compute(args: List[pl.Series]) -> dict: + x = args[0].to_numpy() + y = args[1].to_numpy() + s = args[2].to_numpy() + a = args[3].to_numpy() + dis = args[4].to_numpy() + o = args[5].to_numpy() + dir = args[6].to_numpy() + team = args[7].to_numpy() + official_position = args[8].to_numpy() + possession_team = args[9].to_numpy() + height = args[10].to_numpy() + weight = args[11].to_numpy() + graph_id = args[12].to_numpy() + label = args[13].to_numpy() + + if not np.all(graph_id == graph_id[0]): + raise Exception( + "GraphId selection contains multiple different values. Make sure each GraphId is unique by at least playId and frameId..." + ) + + if not np.all(label == label[0]): + raise Exception( + "Label selection contains multiple different values for a single selection (group by) of playId and frameId, make sure this is not the case. Each group can only have 1 label." + ) + adjacency_matrix = compute_adjacency_matrix( + team=team, possession_team=possession_team, settings=self.settings + ) + edge_features = compute_edge_features( + adjacency_matrix=adjacency_matrix, + p=np.stack((x, y), axis=-1), + s=s, + a=a, + dir=dir, + o=o, # Shape will be (N, 2) + team=team, + settings=self.settings, + ) + node_features = compute_node_features( + x, + y, + s=s, + a=a, + dir=dir, + o=o, + team=team, + official_position=official_position, + possession_team=possession_team, + height=height, + weight=weight, + settings=self.settings, + ) + return { + "e": pl.Series( + [edge_features.tolist()], dtype=pl.List(pl.List(pl.Float64)) + ), + "x": pl.Series( + [node_features.tolist()], dtype=pl.List(pl.List(pl.Float64)) + ), + "a": pl.Series( + [adjacency_matrix.tolist()], dtype=pl.List(pl.List(pl.Int32)) + ), + "e_shape_0": edge_features.shape[0], + "e_shape_1": edge_features.shape[1], + "x_shape_0": node_features.shape[0], + "x_shape_1": node_features.shape[1], + "a_shape_0": adjacency_matrix.shape[0], + "a_shape_1": adjacency_matrix.shape[1], + self.graph_id_col: graph_id[0], + self.label_col: label[0], + } + + result_df = self.dataset.group_by( + ["gameId", "playId", "frameId"], maintain_order=True + ).agg( + pl.map_groups( + exprs=[ + "x", + "y", + "s", + "a", + "dis", + "o", + "dir", + "team", + "officialPosition", + "possessionTeam", + "height_cm", + "weight_kg", + self.graph_id_col, + self.label_col, + ], + function=__compute, + ).alias("result_dict") + ) + + graph_df = result_df.with_columns( + [ + pl.col("result_dict").struct.field("a").alias("a"), + pl.col("result_dict").struct.field("e").alias("e"), + pl.col("result_dict").struct.field("x").alias("x"), + pl.col("result_dict").struct.field("e_shape_0").alias("e_shape_0"), + pl.col("result_dict").struct.field("e_shape_1").alias("e_shape_1"), + pl.col("result_dict").struct.field("x_shape_0").alias("x_shape_0"), + pl.col("result_dict").struct.field("x_shape_1").alias("x_shape_1"), + pl.col("result_dict").struct.field("a_shape_0").alias("a_shape_0"), + pl.col("result_dict").struct.field("a_shape_1").alias("a_shape_1"), + pl.col("result_dict") + .struct.field(self.graph_id_col) + .alias(self.graph_id_col), + pl.col("result_dict") + .struct.field(self.label_col) + .alias(self.label_col), + ] + ) + + return graph_df.drop("result_dict") + + def to_graph_frames(self) -> List[dict]: + def __convert_to_graph_data_list(df): + lazy_df = df.lazy() + + graph_list = [] + + for chunk in lazy_df.collect().iter_slices(self.chunk_size): + chunk_graph_list = [ + { + "a": make_sparse( + flatten_to_reshaped_array( + arr=chunk["a"][i], + s0=chunk["a_shape_0"][i], + s1=chunk["a_shape_1"][i], + ) + ), + "x": flatten_to_reshaped_array( + arr=chunk["x"][i], + s0=chunk["x_shape_0"][i], + s1=chunk["x_shape_1"][i], + ), + "e": flatten_to_reshaped_array( + arr=chunk["e"][i], + s0=chunk["e_shape_0"][i], + s1=chunk["e_shape_1"][i], + ), + "y": np.asarray([chunk[self.label_col][i]]), + "id": chunk[self.graph_id_col][i], + } + for i in range(len(chunk["a"])) + ] + graph_list.extend(chunk_graph_list) + + return graph_list + + graph_df = self._convert() + self.graph_frames = __convert_to_graph_data_list(graph_df) + + return self.graph_frames + + def to_spektral_graphs(self) -> List[Graph]: + if not self.graph_frames: + self.to_graph_frames() + + return [ + Graph( + x=d["x"], + a=d["a"], + e=d["e"], + y=d["y"], + id=d["id"], + ) + for d in self.graph_frames + ] + + def to_pickle(self, file_path: str) -> None: + """ + We store the 'dict' version of the Graphs to pickle each graph is now a dict with keys x, a, e, and y + To use for training with Spektral feed the loaded pickle data to CustomDataset(data=pickled_data) + """ + if not file_path.endswith("pickle.gz"): + raise ValueError( + "Only compressed pickle files of type 'some_file_name.pickle.gz' are supported..." + ) + + if not self.graph_frames: + self.to_graph_frames() + + import pickle + import gzip + from pathlib import Path + + path = Path(file_path) + + directories = path.parent + directories.mkdir(parents=True, exist_ok=True) + + with gzip.open(file_path, "wb") as file: + pickle.dump(self.graph_frames, file) diff --git a/unravel/american_football/graphs/graph_settings.py b/unravel/american_football/graphs/graph_settings.py new file mode 100644 index 0000000..5de30aa --- /dev/null +++ b/unravel/american_football/graphs/graph_settings.py @@ -0,0 +1,48 @@ +from ...utils import DefaultGraphSettings + +from dataclasses import dataclass, field +from kloppy.domain import Dimension, Unit +from typing import Optional + +PITCH_LENGTH = 120.0 +PITCH_WIDTH = 53.3 + + +@dataclass +class AmericanFootballPitchDimensions: + pitch_length: float = PITCH_LENGTH + pitch_width: float = PITCH_WIDTH + standardized: bool = False + unit: Unit = Unit.YARDS + + x_dim: Dimension = field(default_factory=lambda: Dimension(min=0, max=PITCH_LENGTH)) + y_dim: Dimension = field(default_factory=lambda: Dimension(min=0, max=PITCH_WIDTH)) + end_zone: float = field(init=False) + + def __post_init__(self): + self.end_zone = self.x_dim.max - 10 # Calculated value + + +@dataclass +class AmericanFootballGraphSettings(DefaultGraphSettings): + pitch_dimensions: AmericanFootballPitchDimensions = None + ball_id: str = "football" + qb_id: str = "QB" + attacking_non_qb_node_value: float = 0.1 + max_height: float = 225.0 # in cm + min_height: float = 150.0 + max_weight: float = 200.0 # in kg + min_weight: float = 60.0 + + def __post_init__(self): + if not isinstance(self.pitch_dimensions, AmericanFootballPitchDimensions): + raise Exception( + "Incorrect pitch_dimension type... Should be of type AmericanFootballPitchDimensions" + ) + self._sport_specific_checks() + + def _sport_specific_checks(self): + if self.attacking_non_qb_node_value > 1: + self.attacking_non_qb_node_value = 1 + elif self.attacking_non_qb_node_value < 0: + self.attacking_non_qb_node_value = 0 diff --git a/unravel/soccer/graphs/__init__.py b/unravel/soccer/graphs/__init__.py index 4a5f9b8..905585c 100644 --- a/unravel/soccer/graphs/__init__.py +++ b/unravel/soccer/graphs/__init__.py @@ -1,2 +1,5 @@ -from .graph_converter import GraphConverter +from .graph_converter import SoccerGraphConverter +from .graph_settings import SoccerGraphSettings +from .graph_frame import GraphFrame from .exceptions import * +from .features import * diff --git a/unravel/soccer/graphs/features/__init__.py b/unravel/soccer/graphs/features/__init__.py new file mode 100644 index 0000000..0a8744a --- /dev/null +++ b/unravel/soccer/graphs/features/__init__.py @@ -0,0 +1,3 @@ +from .adjacency_matrix import adjacency_matrix, delaunay_adjacency_matrix +from .edge_features import edge_features +from .node_features import node_features diff --git a/unravel/utils/features/adjacency_matrix.py b/unravel/soccer/graphs/features/adjacency_matrix.py similarity index 67% rename from unravel/utils/features/adjacency_matrix.py rename to unravel/soccer/graphs/features/adjacency_matrix.py index 7abab6a..48fe1a6 100644 --- a/unravel/utils/features/adjacency_matrix.py +++ b/unravel/soccer/graphs/features/adjacency_matrix.py @@ -2,61 +2,7 @@ from scipy.spatial import Delaunay -from .utils import AdjacencyMatrixType, AdjacenyMatrixConnectType - - -def delaunay_adjacency_matrix( - attacking_players, - defending_players, - adjacency_matrix_connect_type: AdjacenyMatrixConnectType, - ball_carrier_idx: int = None, - self_loop_ball: bool = False, -): - """ - Computes the Delaunay triangulation of the given points - :param x:np.asarray of shape (num_nodes, 2) - :return: the computed adjacency matrix - """ - try: - from scipy.spatial import Delaunay - except ImportError: - raise ("No such package `scipy`... Please install using `pip install scipy`") - # ! att_players needs to always go first here, because _ball_carrier_idx is associated with att_players - pts = np.asarray([p.position for p in attacking_players + defending_players]) - - tri = Delaunay(pts) - edges_explicit = np.concatenate( - (tri.vertices[:, :2], tri.vertices[:, 1:], tri.vertices[:, ::2]), axis=0 - ) - adj = np.zeros((pts.shape[0], pts.shape[0])) - adj[edges_explicit[:, 0], edges_explicit[:, 1]] = 1.0 - A = np.clip(adj + adj.T, 0, 1) - - num_p = len(pts) - - if adjacency_matrix_connect_type: - if adjacency_matrix_connect_type == AdjacenyMatrixConnectType.BALL: - Z1 = np.ones((num_p, 1), dtype=int) # Create off-diagonal ones array - Z2 = np.ones((1, num_p), dtype=int) # Create off-diagonal ones array - elif adjacency_matrix_connect_type == AdjacenyMatrixConnectType.BALL_CARRIER: - Z1 = np.zeros((num_p, 1), dtype=int) # Create off-diagonal ones array - Z2 = np.zeros((1, num_p), dtype=int) # Create off-diagonal ones array - if not ball_carrier_idx: - # if we don't have a ball carrier, because ball is too far away from furthest player - # then set ball carrier index to 0 - Z1[ball_carrier_idx, :] = 0 - Z2[:, ball_carrier_idx] = 0 - else: - Z1[ball_carrier_idx, :] = 1 - Z2[:, ball_carrier_idx] = 1 - else: - Z1 = np.zeros((num_p, 1), dtype=int) # Create off-diagonal zeros array - Z2 = np.zeros((1, num_p), dtype=int) # Create off-diagonal zeros array - - ball_connect_val = 1 if self_loop_ball else 0 - b = np.asarray([[ball_connect_val]]) - A = np.asarray(np.bmat([[A, Z1], [Z2, b]])) - return A +from ....utils import AdjacencyMatrixType, AdjacenyMatrixConnectType def adjacency_matrix( @@ -79,10 +25,10 @@ def adjacency_matrix( if adjacency_matrix_type == AdjacencyMatrixType.DENSE: Z1 = np.ones((ap, dp), dtype=int) # Create off-diagonal ones array Z2 = np.ones((dp, ap), dtype=int) # Create off-diagonal ones array - elif adjacency_matrix_type == AdjacencyMatrixType.DENSE_ATTACKING_PLAYERS: + elif adjacency_matrix_type == AdjacencyMatrixType.DENSE_AP: Z1 = np.ones((ap, dp), dtype=int) # Create off-diagonal ones array Z2 = np.zeros((dp, ap), dtype=int) # Create off-diagonal zeros array - elif adjacency_matrix_type == AdjacencyMatrixType.DENSE_DEFENSIVE_PLAYERS: + elif adjacency_matrix_type == AdjacencyMatrixType.DENSE_DP: Z1 = np.zeros((ap, dp), dtype=int) # Create off-diagonal zeros array Z2 = np.ones((dp, ap), dtype=int) # Create off-diagonal ones array elif adjacency_matrix_type == AdjacencyMatrixType.SPLIT_BY_TEAM: diff --git a/unravel/utils/features/edge_features.py b/unravel/soccer/graphs/features/edge_features.py similarity index 99% rename from unravel/utils/features/edge_features.py rename to unravel/soccer/graphs/features/edge_features.py index bfe4c90..f354a24 100644 --- a/unravel/utils/features/edge_features.py +++ b/unravel/soccer/graphs/features/edge_features.py @@ -1,6 +1,6 @@ import numpy as np -from .utils import ( +from ....utils import ( normalize_distance, normalize_speed, normalize_sincos, diff --git a/unravel/utils/features/node_features.py b/unravel/soccer/graphs/features/node_features.py similarity index 99% rename from unravel/utils/features/node_features.py rename to unravel/soccer/graphs/features/node_features.py index 4009425..7127404 100644 --- a/unravel/utils/features/node_features.py +++ b/unravel/soccer/graphs/features/node_features.py @@ -1,7 +1,7 @@ import math import numpy as np -from .utils import ( +from ....utils import ( unit_vector, normalize_angles, normalize_distance, diff --git a/unravel/soccer/graphs/graph_converter.py b/unravel/soccer/graphs/graph_converter.py index 03dd7a9..4eded6e 100644 --- a/unravel/soccer/graphs/graph_converter.py +++ b/unravel/soccer/graphs/graph_converter.py @@ -26,6 +26,9 @@ KeyMismatchError, ) +from .graph_settings import SoccerGraphSettings +from .graph_frame import GraphFrame + from ...utils import * logger = logging.getLogger(__name__) @@ -35,7 +38,7 @@ @dataclass(repr=True) -class GraphConverter: +class SoccerGraphConverter(DefaultGraphConverter): """ Converts our dataset TrackingDataset into an internal structure @@ -56,105 +59,30 @@ class GraphConverter: Also infers ball_carrier within ball_carrier_threshold infer_goalkeepers (bool): set True if no GK label is provider, set False for incomplete (broadcast tracking) data that might not have a GK in every frame ball_carrier_threshold (float): The distance threshold to determine the ball carrier. Defaults to 25.0. - max_player_speed (float): The maximum speed of a player in meters per second. Defaults to 12.0. - max_ball_speed (float): The maximum speed of the ball in meters per second. Defaults to 28.0. boundary_correction (float): A correction factor for boundary calculations, used to correct out of bounds as a percentages (Used as 1+boundary_correction, ie 0.05). Defaults to None. - self_loop_ball (bool): Flag to indicate if the ball node should have a self-loop. Defaults to True. - adjacency_matrix_connect_type (AdjacencyMatrixConnectType): The type of connection used in the adjacency matrix, typically related to the ball. Defaults to AdjacenyMatrixConnectType.BALL. - adjacency_matrix_type (AdjacencyMatrixType): The type of adjacency matrix, indicating how connections are structured, such as split by team. Defaults to AdjacencyMatrixType.SPLIT_BY_TEAM. - label_type (PredictionLabelType): The type of prediction label used. Defaults to PredictionLabelType.BINARY. - random_seed (int, bool): When a random_seed is given it will randomly shuffle an individual Graph without changing the underlying structure. - When set to True it will shuffle every frame differently, False won't shuffle. Defaults to False. - Adviced to set True when creating actual dataset. - pad (bool): True pads to a total amount of 22 players and ball (so 23x23 adjacency matrix). - It dynamically changes the edge feature padding size based on the combination of AdjacenyMatrixConnectType and AdjacencyMatrixType, and self_loop_ball - Ie. AdjacenyMatrixConnectType.BALL and AdjacencyMatrixType.SPLIT_BY_TEAM has a maximum of 287 edges (11*11)*2 + (11+11)*2 + 1 - verbose (bool): The converter logs warnings / error messages when specific frames have no coordinates, or other missing information. False mutes all these warnings. + non_potential_receiver_node_value (float): Value between 0 and 1 to assign to the defing team players """ - dataset: TrackingDataset + dataset: TrackingDataset = None + labels: dict = None + labels: dict = None - prediction: bool = False graph_id: Union[str, int, dict] = None graph_ids: dict = None - ball_carrier_treshold: float = 25.0 - max_player_speed: float = 12.0 - max_ball_speed: float = 28.0 - boundary_correction: float = None - self_loop_ball: bool = False - adjacency_matrix_connect_type: Union[ - Literal["ball"], Literal["ball_carrier"], Literal["no_connection"] - ] = "ball" - adjacency_matrix_type: Union[ - Literal["delaunay"], - Literal["split_by_team"], - Literal["dense"], - Literal["dense_ap"], - Literal["dense_dp"], - ] = "split_by_team" - label_type: Literal["binary"] = "binary" - infer_ball_ownership: bool = True infer_goalkeepers: bool = True - defending_team_node_value: float = 0.1 - non_potential_receiver_node_value: float = 0.1 - random_seed: Union[bool, int] = False - pad: bool = False - verbose: bool = False + infer_ball_ownership: bool = True + boundary_correction: float = None + ball_carrier_treshold: float = 25.0 - graph_frames: dict = field(init=False, repr=False, default=None) - settings: GraphSettings = field( - init=False, repr=False, default_factory=GraphSettings - ) + non_potential_receiver_node_value: float = 0.1 def __post_init__(self): - if not self.labels and not self.prediction: - raise Exception( - "Please specify 'labels' or set 'prediction=True' if you want to use the converted dataset to make predictions on." - ) - - if self.graph_id is not None and self.graph_ids: - raise Exception("Please set either 'graph_id' or 'graph_ids', not both...") - - if self.graph_ids: - if not self.prediction: - if not set(list(self.labels.keys())) == set( - list(self.graph_ids.keys()) - ): - raise KeyMismatchError( - "When 'graph_id' is of type dict it needs to have the exact same keys as 'labels'..." - ) - if not self.graph_ids and self.prediction: - self.graph_ids = {x.frame_id: x.frame_id for x in self.dataset} - - if hasattr( - AdjacenyMatrixConnectType, self.adjacency_matrix_connect_type.upper() - ): - self.adjacency_matrix_connect_type = getattr( - AdjacenyMatrixConnectType, self.adjacency_matrix_connect_type.upper() - ) - else: - raise ValueError( - f"Invalid adjacency_matrix_connect_type: {self.adjacency_matrix_connect_type}. Should by of type 'ball', 'ball_carrier' or 'no_connection'" - ) - - if hasattr(AdjacencyMatrixType, self.adjacency_matrix_type.upper()): - self.adjacency_matrix_type = getattr( - AdjacencyMatrixType, self.adjacency_matrix_type.upper() - ) - else: - raise ValueError( - f"Invalid adjacency_matrix_type: {self.adjacency_matrix_type}. Should be of type 'delaunay', 'split_by_team', 'dense', 'dense_ap' or 'dense_dp'" - ) - - if hasattr(PredictionLabelType, self.label_type.upper()): - self.label_type = getattr(PredictionLabelType, self.label_type.upper()) - else: - raise ValueError( - f"Invalid label_type: {self.label_type}. Should be of type 'binary'" - ) + if not self.dataset: + raise Exception("Please provide a 'kloppy' dataset.") - self.settings = GraphSettings( + self._sport_specific_checks() + self.settings = SoccerGraphSettings( ball_carrier_treshold=self.ball_carrier_treshold, max_player_speed=self.max_player_speed, max_ball_speed=self.max_ball_speed, @@ -190,6 +118,56 @@ def __post_init__(self): self.settings.pitch_dimensions = self.dataset.metadata.pitch_dimensions + def _sport_specific_checks(self): + if not self.labels and not self.prediction: + raise Exception( + "Please specify 'labels' or set 'prediction=True' if you want to use the converted dataset to make predictions on." + ) + + if self.graph_id is not None and self.graph_ids: + raise Exception("Please set either 'graph_id' or 'graph_ids', not both...") + + if self.graph_ids: + if not self.prediction: + if not set(list(self.labels.keys())) == set( + list(self.graph_ids.keys()) + ): + raise KeyMismatchException( + "When 'graph_id' is of type dict it needs to have the exact same keys as 'labels'..." + ) + if not self.graph_ids and self.prediction: + self.graph_ids = {x.frame_id: x.frame_id for x in self.dataset} + + if self.labels and not isinstance(self.labels, dict): + raise Exception("'labels' should be of type dictionary (dict)") + + if self.graph_id and not isinstance(self.graph_id, (str, int, dict)): + raise Exception("'graph_id_col' should be of type {str, int, dict}") + + if self.graph_ids and not isinstance(self.graph_ids, dict): + raise Exception("chunk_size should be of type dictionary (dict") + + if not isinstance(self.infer_goalkeepers, bool): + raise Exception("'infer_goalkeepers' should be of type boolean (bool)") + + if not isinstance(self.infer_ball_ownership, bool): + raise Exception("'infer_ball_ownership' should be of type boolean (bool)") + + if self.boundary_correction and not isinstance(self.boundary_correction, float): + raise Exception("'boundary_correction' should be of type float") + + if self.ball_carrier_treshold and not isinstance( + self.ball_carrier_treshold, float + ): + raise Exception("'ball_carrier_treshold' should be of type float") + + if self.non_potential_receiver_node_value and not isinstance( + self.non_potential_receiver_node_value, float + ): + raise Exception( + "'non_potential_receiver_node_value' should be of type float" + ) + def _convert(self, frame: Frame): data = DefaultTrackingModel( frame, @@ -278,13 +256,6 @@ def to_spektral_graphs(self) -> List[Graph]: return [g.to_spektral_graph() for g in self.graph_frames] - def to_custom_dataset(self) -> CustomSpektralDataset: - """ - Spektral requires a spektral Dataset to load the data - for docs see https://graphneural.network/creating-dataset/ - """ - return CustomSpektralDataset(graphs=self.to_spektral_graphs()) - def to_pickle(self, file_path: str) -> None: """ We store the 'dict' version of the Graphs to pickle each graph is now a dict with keys x, a, e, and y diff --git a/unravel/soccer/graphs/graph_frame.py b/unravel/soccer/graphs/graph_frame.py new file mode 100644 index 0000000..a0c5b9f --- /dev/null +++ b/unravel/soccer/graphs/graph_frame.py @@ -0,0 +1,104 @@ +import numpy as np + +import warnings + +from typing import Union + +from dataclasses import dataclass, field + +from warnings import * + +from spektral.data import Graph + +from .features import ( + delaunay_adjacency_matrix, + adjacency_matrix, + node_features, + edge_features, +) +from ...utils import ( + DefaultGraphSettings, + DefaultTrackingModel, + DefaultGraphFrame, + AdjacencyMatrixType, + AdjacenyMatrixConnectType, + AdjcacenyMatrixTypeNotSetException, +) + + +@dataclass +class GraphFrame(DefaultGraphFrame): + + def to_spektral_graph(self) -> Graph: + if self.graph_data: + return Graph( + x=self.graph_data["x"], + a=self.graph_data["a"], + e=self.graph_data["e"], + y=self.graph_data["y"], + id=self.graph_id, + ) + else: + return None + + def _adjaceny_matrix(self): + """ + Create adjeceny matrices. If we specify the Adjaceny Matrix type to be Delaunay it's created as the 'general' A, + else we create a seperate one as A_delaunay. + This way we can use the Delaunay matrix in the Edge Features if it's not used as the Adj Matrix + """ + if not self.settings.adjacency_matrix_type: + raise AdjcacenyMatrixTypeNotSetException( + "AdjacencyMatrixTypeNotSet Error... Please set `adjacency_matrix_type`..." + ) + elif self.settings.adjacency_matrix_type == AdjacencyMatrixType.DELAUNAY: + A = delaunay_adjacency_matrix( + self.data.attacking_players, + self.data.defending_players, + self.settings.adjacency_matrix_connect_type, + self.data.ball_carrier_idx, + self.settings.self_loop_ball, + ) + A_delaunay = None + else: + A = adjacency_matrix( + self.data.attacking_players, + self.data.defending_players, + self.settings.adjacency_matrix_connect_type, + self.settings.adjacency_matrix_type, + self.data.ball_carrier_idx, + ) + A_delaunay = delaunay_adjacency_matrix( + self.data.attacking_players, + self.data.defending_players, + self.settings.adjacency_matrix_connect_type, + self.data.ball_carrier_idx, + self.settings.self_loop_ball, + ) + return A, A_delaunay + + def _node_features(self): + return node_features( + attacking_players=self.data.attacking_players, + defending_players=self.data.defending_players, + ball=self.data.ball, + max_player_speed=self.settings.max_player_speed, + max_ball_speed=self.settings.max_ball_speed, + ball_carrier_idx=self.data.ball_carrier_idx, + pitch_dimensions=self.settings.pitch_dimensions, + include_ball_node=True, + defending_team_node_value=self.settings.defending_team_node_value, + non_potential_receiver_node_value=self.settings.non_potential_receiver_node_value, + ) + + def _edge_features(self, A, A_delaunay): + return edge_features( + self.data.attacking_players, + self.data.defending_players, + self.data.ball, + self.settings.max_player_speed, + self.settings.max_ball_speed, + self.settings.pitch_dimensions, + A, + A_delaunay, + ) diff --git a/unravel/soccer/graphs/graph_settings.py b/unravel/soccer/graphs/graph_settings.py new file mode 100644 index 0000000..f066a2d --- /dev/null +++ b/unravel/soccer/graphs/graph_settings.py @@ -0,0 +1,27 @@ +from dataclasses import dataclass + +from ...utils import DefaultGraphSettings + +from kloppy.domain import MetricPitchDimensions + + +@dataclass +class SoccerGraphSettings(DefaultGraphSettings): + infer_goalkeepers: bool = True + boundary_correction: float = None + non_potential_receiver_node_value: float = 0.1 + ball_carrier_treshold: float = 25.0 + + @property + def pitch_dimensions(self) -> int: + return self._pitch_dimensions + + @pitch_dimensions.setter + def pitch_dimensions(self, pitch_dimensions: MetricPitchDimensions) -> None: + self._pitch_dimensions = pitch_dimensions + + def _sport_specific_checks(self): + if self.non_potential_receiver_node_value > 1: + self.non_potential_receiver_node_value = 1 + elif self.non_potential_receiver_node_value < 0: + self.non_potential_receiver_node_value = 0 diff --git a/unravel/utils/exceptions/exceptions.py b/unravel/utils/exceptions/exceptions.py index 762270e..cb5d134 100644 --- a/unravel/utils/exceptions/exceptions.py +++ b/unravel/utils/exceptions/exceptions.py @@ -1,26 +1,30 @@ -class MissingHomePlayerData(Exception): +class MissingHomePlayerDataException(Exception): pass -class MissingAwayPlayerData(Exception): +class MissingAwayPlayerDataException(Exception): pass -class MissingBallData(Exception): +class MissingBallDataException(Exception): pass -class InvalidAttackingTeam(Exception): +class InvalidAttackingTeamException(Exception): pass -class ModelParametersNotSet(Exception): +class ModelParametersNotSetException(Exception): pass -class InvalidAttackingTeamType(Exception): +class InvalidAttackingTeamTypeException(Exception): pass -class AdjcacenyMatrixTypeNotSet(Exception): +class AdjcacenyMatrixTypeNotSetException(Exception): + pass + + +class KeyMismatchException(Exception): pass diff --git a/unravel/utils/features/__init__.py b/unravel/utils/features/__init__.py index 8372fc3..16281fe 100644 --- a/unravel/utils/features/__init__.py +++ b/unravel/utils/features/__init__.py @@ -1,10 +1 @@ -from .adjacency_matrix import adjacency_matrix, delaunay_adjacency_matrix -from .node_features import node_features -from .edge_features import edge_features -from .utils import ( - AdjacencyMatrixType, - AdjacenyMatrixConnectType, - PredictionLabelType, - make_sparse, - Pad, -) +from .utils import * diff --git a/unravel/utils/features/utils.py b/unravel/utils/features/utils.py index 40a3682..d74c931 100644 --- a/unravel/utils/features/utils.py +++ b/unravel/utils/features/utils.py @@ -27,8 +27,8 @@ class AdjacencyMatrixType: DELAUNAY = "delaunay" SPLIT_BY_TEAM = "split_by_team" DENSE = "dense" - DENSE_ATTACKING_PLAYERS = "dense_ap" - DENSE_DEFENSIVE_PLAYERS = "dense_dp" + DENSE_AP = "dense_ap" + DENSE_DP = "dense_dp" class PredictionLabelType: @@ -54,6 +54,10 @@ def normalize_angles(angle): return (((angle - old_min) * new_range) / old_range) + new_min +def normalize_between(min_value, max_value, value): + return (value - min_value) / (max_value - min_value) + + def normalize_distance(value, max_distance): return value / max_distance @@ -69,16 +73,6 @@ def normalize_coords(value, max_value): return value / max_value -def normalize_speed(value, max_speed): - x = value / max_speed - try: - return 1 if x > 1 else 0 if x < 0 else x - except ValueError: - x[x < 0] = 0 - x[x > 1] = 1 - return x - - def normalize_sincos(value): return (value + 1) / 2 @@ -112,3 +106,69 @@ def reindex(m, non_zero_idxs, len_a): def make_sparse(a): A = sparse.csr_matrix(a) return np.nan_to_num(A) + + +def unit_vector_from_angle(value, angle_radians): + # Compute velocity components + value = np.nan_to_num(value, nan=0.0) + angle_radians = np.nan_to_num(angle_radians, nan=0.0) + + v_x = value * np.cos(angle_radians) + v_y = value * np.sin(angle_radians) + + # Create velocity vector + velocity = np.array([v_x, v_y]) + + # Normalize the vector (get unit vector) + norm = np.linalg.norm(velocity) + if norm == 0: + return np.zeros_like(velocity) + + return velocity / norm + + +def normalize_speed(value, max_speed): + x = value / max_speed + return np.clip(x, 0, 1) + + +def normalize_acceleration(value, max_acceleration): + x = value / max_acceleration + return np.clip(x, -1, 1) + + +def normalize_speeds_nfl(s, team, settings): + ball_mask = team == settings.ball_id + s_normed = np.zeros_like(s) + + s_normed[ball_mask] = normalize_speed(s[ball_mask], settings.max_ball_speed) + + s_normed[~ball_mask] = normalize_speed(s[~ball_mask], settings.max_player_speed) + return s_normed + + +def normalize_speed_differences_nfl(s, team, settings): + + return normalize_speeds_nfl(s, team, settings) * np.sign(s) + + +def normalize_accelerations_nfl(a, team, settings): + ball_mask = team == settings.ball_id + a_normed = np.zeros_like(a) + + a_normed[ball_mask] = normalize_acceleration( + a[ball_mask], settings.max_ball_acceleration + ) + + a_normed[~ball_mask] = normalize_acceleration( + a[~ball_mask], settings.max_player_acceleration + ) + return a_normed + + +def flatten_to_reshaped_array(arr, s0, s1, as_list=False): + # Convert the structure into a list of arrays + flattened_list = [item for sublist in arr for item in sublist] + # Concatenate the arrays into one single array + result_array = np.concatenate(flattened_list).reshape(s0, s1) + return result_array if not as_list else result_array.tolist() diff --git a/unravel/utils/objects/__init__.py b/unravel/utils/objects/__init__.py index 34fefc3..3940548 100644 --- a/unravel/utils/objects/__init__.py +++ b/unravel/utils/objects/__init__.py @@ -2,5 +2,6 @@ from .default_ball import DefaultBall from .default_tracking import DefaultTrackingModel from .custom_spektral_dataset import CustomSpektralDataset -from .graph_frame import GraphFrame -from .graph_settings import GraphSettings +from .default_graph_frame import DefaultGraphFrame +from .default_graph_settings import DefaultGraphSettings +from .default_graph_converter import DefaultGraphConverter diff --git a/unravel/utils/objects/custom_spektral_dataset.py b/unravel/utils/objects/custom_spektral_dataset.py index 563802e..dcc21a5 100644 --- a/unravel/utils/objects/custom_spektral_dataset.py +++ b/unravel/utils/objects/custom_spektral_dataset.py @@ -18,7 +18,7 @@ from spektral.data import Dataset, Graph -from .graph_frame import GraphFrame +from .default_graph_frame import DefaultGraphFrame from ..exceptions import NoGraphIdsWarning @@ -70,7 +70,7 @@ def __convert(self, data) -> List[Graph]: """ if isinstance(data[0], Graph): return data - elif isinstance(data[0], GraphFrame): + elif isinstance(data[0], DefaultGraphFrame): return [g.to_spektral_graph() for g in self.data] elif isinstance(data[0], dict): return [ diff --git a/unravel/utils/objects/default_graph_converter.py b/unravel/utils/objects/default_graph_converter.py new file mode 100644 index 0000000..dd3f3f5 --- /dev/null +++ b/unravel/utils/objects/default_graph_converter.py @@ -0,0 +1,183 @@ +import logging +import sys + +from dataclasses import dataclass, field, asdict + +import polars as pl + +from typing import List, Union, Dict, Literal + +from kloppy.domain import TrackingDataset + +from spektral.data import Graph + +from ..exceptions import ( + KeyMismatchException, +) +from ..features import ( + AdjacencyMatrixType, + AdjacenyMatrixConnectType, + PredictionLabelType, +) + +from .default_graph_settings import DefaultGraphSettings +from .custom_spektral_dataset import CustomSpektralDataset + +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) +stdout_handler = logging.StreamHandler(sys.stdout) +logger.addHandler(stdout_handler) + + +@dataclass(repr=True) +class DefaultGraphConverter: + """ + Converts our dataset TrackingDataset into an internal structure + + Attributes: + dataset (TrackingDataset): Kloppy TrackingDataset. + labels (dict): Dict with a key per frame_id, like so {frame_id: True/False/1/0} + graph_id (str, int): Set a single id for the whole Kloppy dataset. + graph_ids (dict): Frame level control over graph ids. + + The graph_ids will be used to assign each graph an identifier. This identifier allows us to split the CustomSpektralDataset such that + all graphs with the same id are either all in the test, train or validation set to avoid leakage. It is recommended to either set graph_id (int, str) as + a match_id, or pass a dictionary into 'graph_ids' with exactly the same keys as 'labels' for more granualar control over the graph ids. + The latter can be useful when splitting graphs by possession or sequence id. In this case the dict would be {frame_id: sequence_id/possession_id}. + Note that sequence_id/possession_id should probably be unique for the whole dataset. Perhaps like so {frame_id: 'match_id-sequence_id'}. Defaults to None. + + max_player_speed (float): The maximum speed of a player in meters per second. Defaults to 12.0. + max_ball_speed (float): The maximum speed of the ball in meters per second. Defaults to 28.0. + boundary_correction (float): A correction factor for boundary calculations, used to correct out of bounds as a percentages (Used as 1+boundary_correction, ie 0.05). Defaults to None. + self_loop_ball (bool): Flag to indicate if the ball node should have a self-loop. Defaults to True. + adjacency_matrix_connect_type (AdjacencyMatrixConnectType): The type of connection used in the adjacency matrix, typically related to the ball. Defaults to AdjacenyMatrixConnectType.BALL. + adjacency_matrix_type (AdjacencyMatrixType): The type of adjacency matrix, indicating how connections are structured, such as split by team. Defaults to AdjacencyMatrixType.SPLIT_BY_TEAM. + label_type (PredictionLabelType): The type of prediction label used. Defaults to PredictionLabelType.BINARY. + defending_team_node_value (float): Value between 0 and 1 to assign to the defending team nodes + random_seed (int, bool): When a random_seed is given it will randomly shuffle an individual Graph without changing the underlying structure. + When set to True it will shuffle every frame differently, False won't shuffle. Defaults to False. + Adviced to set True when creating actual dataset. + pad (bool): True pads to a total amount of 22 players and ball (so 23x23 adjacency matrix). + It dynamically changes the edge feature padding size based on the combination of AdjacenyMatrixConnectType and AdjacencyMatrixType, and self_loop_ball + Ie. AdjacenyMatrixConnectType.BALL and AdjacencyMatrixType.SPLIT_BY_TEAM has a maximum of 287 edges (11*11)*2 + (11+11)*2 + 1 + verbose (bool): The converter logs warnings / error messages when specific frames have no coordinates, or other missing information. False mutes all these warnings. + """ + + prediction: bool = False + + max_player_speed: float = 12.0 + max_ball_speed: float = 28.0 + max_player_acceleration: float = 6.0 + max_ball_acceleration: float = 13.5 + self_loop_ball: bool = False + adjacency_matrix_connect_type: Union[ + Literal["ball"], Literal["ball_carrier"], Literal["no_connection"] + ] = "ball" + adjacency_matrix_type: Union[ + Literal["delaunay"], + Literal["split_by_team"], + Literal["dense"], + Literal["dense_ap"], + Literal["dense_dp"], + ] = "split_by_team" + label_type: Literal["binary"] = "binary" + + defending_team_node_value: float = 0.1 + random_seed: Union[bool, int] = False + pad: bool = False + verbose: bool = False + + graph_frames: dict = field(init=False, repr=False, default=None) + settings: DefaultGraphSettings = field( + init=False, repr=False, default_factory=DefaultGraphSettings + ) + + def __post_init__(self): + if hasattr( + AdjacenyMatrixConnectType, self.adjacency_matrix_connect_type.upper() + ): + self.adjacency_matrix_connect_type = getattr( + AdjacenyMatrixConnectType, self.adjacency_matrix_connect_type.upper() + ) + else: + raise ValueError( + f"Invalid adjacency_matrix_connect_type: {self.adjacency_matrix_connect_type}. Should by of type 'ball', 'ball_carrier' or 'no_connection'" + ) + + if hasattr(AdjacencyMatrixType, self.adjacency_matrix_type.upper()): + self.adjacency_matrix_type = getattr( + AdjacencyMatrixType, self.adjacency_matrix_type.upper() + ) + else: + raise ValueError( + f"Invalid adjacency_matrix_type: {self.adjacency_matrix_type}. Should be of type 'delaunay', 'split_by_team', 'dense', 'dense_ap' or 'dense_dp'" + ) + + if hasattr(PredictionLabelType, self.label_type.upper()): + self.label_type = getattr(PredictionLabelType, self.label_type.upper()) + else: + raise ValueError( + f"Invalid label_type: {self.label_type}. Should be of type 'binary'" + ) + + if not isinstance(self.prediction, bool): + raise Exception("'prediction' should be of type boolean (bool)") + + if not isinstance(self.max_player_speed, (float, int)): + raise Exception("'max_player_speed' should be of type float or int") + + if not isinstance(self.max_ball_speed, (float, int)): + raise Exception("'max_ball_speed' should be of type float or int") + + if not isinstance(self.max_player_acceleration, (float, int)): + raise Exception("'max_player_acceleration' should be of type float or int") + + if not isinstance(self.max_ball_acceleration, (float, int)): + raise Exception("'max_ball_acceleration' should be of type float or int") + + if not isinstance(self.self_loop_ball, bool): + raise Exception("'self_loop_ball' should be of type boolean (bool)") + + if not isinstance(self.defending_team_node_value, (float, int)): + raise Exception( + "'defending_team_node_value' should be of type float or int" + ) + + if not isinstance(self.random_seed, (bool, int)): + raise Exception("'random_seed' should be of type boolean (bool) or int") + + if not isinstance(self.pad, bool): + raise Exception("'pad' should be of type boolean (bool)") + + if not isinstance(self.verbose, bool): + raise Exception("'verbose' should be of type boolean (bool)") + + def _sport_specific_checks(self): + raise NotImplementedError( + "No sport specific checks implementend... Make sure to check for existens of labels of some sort, and graph ids of some sort..." + ) + + def _apply_settings(self): + raise NotImplementedError() + + def _convert(self): + raise NotImplementedError() + + def to_graph_frames(self) -> dict: + raise NotImplementedError() + + def to_pickle(self) -> None: + raise NotImplementedError() + + def to_spektral_graphs(self) -> List[Graph]: + if not self.graph_frames: + self.to_graph_frames() + + return [g.to_spektral_graph() for g in self.graph_frames] + + def to_custom_dataset(self) -> CustomSpektralDataset: + """ + Spektral requires a spektral Dataset to load the data + for docs see https://graphneural.network/creating-dataset/ + """ + return CustomSpektralDataset(graphs=self.to_spektral_graphs()) diff --git a/unravel/utils/objects/graph_frame.py b/unravel/utils/objects/default_graph_frame.py similarity index 66% rename from unravel/utils/objects/graph_frame.py rename to unravel/utils/objects/default_graph_frame.py index 9f4d4b7..8a2cc37 100644 --- a/unravel/utils/objects/graph_frame.py +++ b/unravel/utils/objects/default_graph_frame.py @@ -6,29 +6,23 @@ from dataclasses import dataclass, field -from warnings import * +from ..exceptions.warnings import QualityCheckWarning from spektral.data import Graph -from ..exceptions import AdjcacenyMatrixTypeNotSet from ..features import ( - AdjacencyMatrixType, PredictionLabelType, - delaunay_adjacency_matrix, - adjacency_matrix, - node_features, - edge_features, make_sparse, ) -from .graph_settings import GraphSettings +from .default_graph_settings import DefaultGraphSettings from .default_tracking import DefaultTrackingModel @dataclass -class GraphFrame: +class DefaultGraphFrame: frame_id: int data: DefaultTrackingModel - settings: GraphSettings + settings: DefaultGraphSettings graph_id: Union[str, int] label: Union[int, bool] graph_data: dict = field(init=False, repr=False, default=None) @@ -78,74 +72,26 @@ def _adjaceny_matrix(self): else we create a seperate one as A_delaunay. This way we can use the Delaunay matrix in the Edge Features if it's not used as the Adj Matrix """ - if not self.settings.adjacency_matrix_type: - raise AdjcacenyMatrixTypeNotSet( - "AdjacencyMatrixTypeNotSet Error... Please set `adjacency_matrix_type`..." - ) - elif self.settings.adjacency_matrix_type == AdjacencyMatrixType.DELAUNAY: - A = delaunay_adjacency_matrix( - self.data.attacking_players, - self.data.defending_players, - self.settings.adjacency_matrix_connect_type, - self.data.ball_carrier_idx, - self.settings.self_loop_ball, - ) - A_delaunay = None - else: - A = adjacency_matrix( - self.data.attacking_players, - self.data.defending_players, - self.settings.adjacency_matrix_connect_type, - self.settings.adjacency_matrix_type, - self.data.ball_carrier_idx, - ) - A_delaunay = delaunay_adjacency_matrix( - self.data.attacking_players, - self.data.defending_players, - self.settings.adjacency_matrix_connect_type, - self.data.ball_carrier_idx, - self.settings.self_loop_ball, - ) - return A, A_delaunay + raise NotImplementedError() def _node_features(self): - return node_features( - attacking_players=self.data.attacking_players, - defending_players=self.data.defending_players, - ball=self.data.ball, - max_player_speed=self.settings.max_player_speed, - max_ball_speed=self.settings.max_ball_speed, - ball_carrier_idx=self.data.ball_carrier_idx, - pitch_dimensions=self.settings.pitch_dimensions, - include_ball_node=True, - defending_team_node_value=self.settings.defending_team_node_value, - non_potential_receiver_node_value=self.settings.non_potential_receiver_node_value, - ) + raise NotImplementedError() def _edge_features(self, A, A_delaunay): - return edge_features( - self.data.attacking_players, - self.data.defending_players, - self.data.ball, - self.settings.max_player_speed, - self.settings.max_ball_speed, - self.settings.pitch_dimensions, - A, - A_delaunay, - ) + raise NotImplementedError() def _quality_check(self, X, E): if self.settings.boundary_correction is not None: if (np.max(X) <= 1) or (np.min(X) >= -1): warnings.warn( f"""Node Feature(s) outside boundary for frame={self.frame_id}, skipping...""", - warnings.QualityCheckWarning, + QualityCheckWarning, ) return False if (np.max(E) <= 1) or (np.min(E) >= -1): warnings.warn( f"""Edge Feature(s) outside boundary for frame={self.frame_id}, skipping...""", - warnings.QualityCheckWarning, + QualityCheckWarning, ) return False return True diff --git a/unravel/utils/objects/graph_settings.py b/unravel/utils/objects/default_graph_settings.py similarity index 88% rename from unravel/utils/objects/graph_settings.py rename to unravel/utils/objects/default_graph_settings.py index 6ffec64..fd67519 100644 --- a/unravel/utils/objects/graph_settings.py +++ b/unravel/utils/objects/default_graph_settings.py @@ -1,7 +1,6 @@ import numpy as np from dataclasses import dataclass, field from typing import Union -from kloppy.domain import MetricPitchDimensions from ..features import ( AdjacencyMatrixType, @@ -12,7 +11,7 @@ @dataclass -class GraphSettings: +class DefaultGraphSettings: """ Configuration settings for a Graph Neural Network (GNN) applied in sports analytics. @@ -41,11 +40,10 @@ class GraphSettings: """ infer_ball_ownership: bool = True - infer_goalkeepers: bool = True - ball_carrier_treshold: float = 25.0 max_player_speed: float = 12.0 max_ball_speed: float = 28.0 - boundary_correction: float = None + max_player_acceleration: float = None + max_ball_acceleration: float = None self_loop_ball: bool = True adjacency_matrix_connect_type: AdjacenyMatrixConnectType = ( AdjacenyMatrixConnectType.BALL @@ -53,7 +51,6 @@ class GraphSettings: adjacency_matrix_type: AdjacencyMatrixType = AdjacencyMatrixType.SPLIT_BY_TEAM label_type: PredictionLabelType = PredictionLabelType.BINARY defending_team_node_value: float = 0.1 - non_potential_receiver_node_value: float = 0.1 random_seed: Union[int, bool] = False pad: bool = True verbose: bool = False @@ -67,11 +64,6 @@ def __post_init__(self): elif self.defending_team_node_value < 0: self.defending_team_node_value = 0 - if self.non_potential_receiver_node_value > 1: - self.non_potential_receiver_node_value = 1 - elif self.non_potential_receiver_node_value < 0: - self.non_potential_receiver_node_value = 0 - if self.pad: if self.adjacency_matrix_type == AdjacencyMatrixType.DELAUNAY: raise NotImplementedError( @@ -83,14 +75,6 @@ def __post_init__(self): def pad_settings(self) -> Pad: return self._pad_settings - @property - def pitch_dimensions(self) -> int: - return self._pitch_dimensions - - @pitch_dimensions.setter - def pitch_dimensions(self, pitch_dimensions: MetricPitchDimensions) -> None: - self._pitch_dimensions = pitch_dimensions - def __pad_settings(self): """ Compute maximum theoretical amount of padding for all different types of settings @@ -119,8 +103,8 @@ def __pad_settings(self): elif self.adjacency_matrix_type == AdjacencyMatrixType.DENSE: max_player_edges = (n_players + n_players) ** 2 elif self.adjacency_matrix_type in ( - AdjacencyMatrixType.DENSE_ATTACKING_PLAYERS, - AdjacencyMatrixType.DENSE_DEFENSIVE_PLAYERS, + AdjacencyMatrixType.DENSE_AP, + AdjacencyMatrixType.DENSE_DP, ): max_player_edges = n_players * n_players else: @@ -131,3 +115,6 @@ def __pad_settings(self): max_nodes=(n_players * 2) + n_ball, n_players=n_players, ) + + def _sport_specific_checks(self): + raise NotImplementedError() diff --git a/unravel/utils/objects/default_tracking.py b/unravel/utils/objects/default_tracking.py index a06dfad..e0fedc6 100644 --- a/unravel/utils/objects/default_tracking.py +++ b/unravel/utils/objects/default_tracking.py @@ -13,7 +13,7 @@ from . import DefaultPlayer, DefaultBall from ..exceptions import ( - InvalidAttackingTeamType, + InvalidAttackingTeamTypeException, MissingAttackingTeam, MissingCoordinates, NoNextFrameWarning, @@ -65,7 +65,7 @@ def attacking_players(self): Ground.HOME.value, Ground.AWAY.value, ]: - raise InvalidAttackingTeamType( + raise InvalidAttackingTeamTypeException( f"'attacking_team' should be of type {Ground.HOME} or {Ground.AWAY}" ) @@ -88,7 +88,7 @@ def defending_players(self): Ground.HOME.value, Ground.AWAY.value, ]: - raise InvalidAttackingTeamType( + raise InvalidAttackingTeamTypeException( f"'attacking_team' should be of type {Ground.HOME} or {Ground.AWAY}" ) diff --git a/unravel/utils/utils.py b/unravel/utils/utils.py index 7ed098a..66cbfab 100644 --- a/unravel/utils/utils.py +++ b/unravel/utils/utils.py @@ -1,4 +1,8 @@ -from typing import Dict +from typing import Dict, List, Literal + +import polars as pl + +import random from kloppy.domain import TrackingDataset @@ -9,7 +13,6 @@ def dummy_labels(dataset: TrackingDataset) -> Dict: """ if not isinstance(dataset, TrackingDataset): raise TypeError("dataset should be of type TrackingDataset (from kloppy)") - import random labels = dict() for frame in dataset: @@ -33,3 +36,27 @@ def dummy_graph_ids(dataset: TrackingDataset) -> Dict: fake_possession_id = i % 10 graph_ids[frame.frame_id] = f"{fake_match_id}-{fake_possession_id}" return graph_ids + + +def add_dummy_label_column( + dataset: pl.DataFrame, + by: List[str] = ["gameId", "playId", "frameId"], + column_name: str = "label", +): + + labels = dataset.group_by(by).agg( + [ + pl.col("a") + .map_elements(lambda _: random.choice([0, 1]), return_dtype=int) + .alias(column_name), + ] + ) + return dataset.join(labels, on=by, how="left") + + +def add_graph_id_column( + dataset: pl.DataFrame, + by: List[str] = ["gameId", "playId"], + column_name: str = "graph_id", +): + return dataset.with_columns([pl.concat_str(by, separator="-").alias(column_name)])