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{"files": {"cubestat/__init__.py": {"path": "cubestat/__init__.py", "size": 0, "checksum": "d41d8cd98f00b204e9800998ecf8427e", "processing_timestamp": "2024-10-11T11:58:51.337819", "approx_tokens": 0, "processing_result": "An empty initialization file for the cubestat package."}, "cubestat/colors.py": {"path": "cubestat/colors.py", "size": 2093, "checksum": "31cc4d0df018c6811453c95f816d40f9", "processing_timestamp": "2024-10-11T11:58:51.337819", "approx_tokens": 622, "processing_result": "A module for handling colors and color themes in the cubestat application. It defines a ColorTheme enum and a Colorschemes class for managing different color schemes. The get_theme function returns a theme based on the metric name and color mode."}, "cubestat/common.py": {"path": "cubestat/common.py", "size": 2019, "checksum": "1146e937d2b2600215a991cb9bd8f577", "processing_timestamp": "2024-10-11T11:58:51.337819", "approx_tokens": 586, "processing_result": "A module containing common utility functions and classes for the cubestat application. It defines the DisplayMode enum, the SimpleMode class, and various functions for formatting measurements and labels."}, "cubestat/cubestat.py": {"path": "cubestat/cubestat.py", "size": 5193, "checksum": "692b1f2c77c1d6de9a7658b29fd4f78e", "processing_timestamp": "2024-10-11T11:58:51.337819", "approx_tokens": 1165, "processing_result": "The main entry point of the cubestat application. It defines the Cubestat class, which initializes the application, handles user input, and updates the display. The start function is the main function that sets up the curses screen and starts the application."}, "cubestat/data.py": {"path": "cubestat/data.py", "size": 966, "checksum": "1be3766d0065b282b955704a2f18f0b2", "processing_timestamp": "2024-10-11T11:58:51.337819", "approx_tokens": 206, "processing_result": "A module for managing data in the cubestat application. It defines the DataManager class, which stores and updates data from various metrics."}, "cubestat/input.py": {"path": "cubestat/input.py", "size": 2808, "checksum": "9ca4f347dcfb3f94dfa8e09e10f4cfcb", "processing_timestamp": "2024-10-11T11:58:51.337819", "approx_tokens": 632, "processing_result": "A module for handling user input in the cubestat application. It defines the InputHandler class, which processes keyboard input and updates the application state accordingly."}, "cubestat/metrics/__init__.py": {"path": "cubestat/metrics/__init__.py", "size": 0, "checksum": "d41d8cd98f00b204e9800998ecf8427e", "processing_timestamp": "2024-10-11T11:58:51.337819", "approx_tokens": 0, "processing_result": "An empty initialization file for the cubestat.metrics package."}, "cubestat/metrics/accel.py": {"path": "cubestat/metrics/accel.py", "size": 1478, "checksum": "350a1b47472cbde9a9314b3f06a484b5", "processing_timestamp": "2024-10-11T11:58:51.337819", "approx_tokens": 393, "processing_result": "A module for the ane_metric, which measures the utilization of the Apple Neural Engine (ANE) on macOS systems. It defines the ane_metric class, which inherits from the base_metric class."}, "cubestat/metrics/base_metric.py": {"path": "cubestat/metrics/base_metric.py", "size": 1405, "checksum": "7aaebf385f3f41a2b48f32f63d52b69f", "processing_timestamp": "2024-10-11T11:58:51.337819", "approx_tokens": 249, "processing_result": "A module containing the base class for metrics in the cubestat application. It defines the base_metric class, which provides a basic implementation for metrics and can be inherited by specific metric classes."}, "cubestat/metrics/cpu.py": {"path": "cubestat/metrics/cpu.py", "size": 2770, "checksum": "c3d357276bb5cdfc8eb5e35ebab2cea3", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 689, "processing_result": "This file defines a metric for CPU usage. The metric has three modes: all cores, cumulative by cluster, or both. The CPU usage is calculated using the psutil library. There are two classes: cpu_metric, which is the base class, and psutil_cpu_metric and macos_cpu_metric, which are platform-specific implementations. The metric also has a configure method that allows configuration of the CPU mode using the --cpu argument.\n\nRelationships: This file is related to the cubestat.metrics.base_metric and cubestat.metrics_registry files, which define the base metric class and the metrics registry, respectively."}, "cubestat/metrics/disk.py": {"path": "cubestat/metrics/disk.py", "size": 1554, "checksum": "75da171fe3028b01b9c35cbc78dde3fb", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 358, "processing_result": "This file defines a metric for disk usage. The metric has two modes: show or hide. The disk usage is calculated using the psutil library. There are two classes: disk_metric, which is the base class, and macos_disc_metric and linux_disc_metric, which are platform-specific implementations. The metric also has a configure method that allows configuration of the disk mode using the --disk argument.\n\nRelationships: This file is related to the cubestat.common file, which defines utility functions, and the cubestat.metrics.base_metric and cubestat.metrics_registry files, which define the base metric class and the metrics registry, respectively."}, "cubestat/metrics/gpu.py": {"path": "cubestat/metrics/gpu.py", "size": 2810, "checksum": "7a28f8c2e4e7b3077b7f3332907f15a1", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 716, "processing_result": "This file defines a metric for GPU usage. The metric has three modes: collapsed, load only, or load and vram usage. The GPU usage is calculated using the pynvml library on Linux and the sysctl command on MacOS. There are two classes: gpu_metric, which is the base class, and nvidia_gpu_metric and macos_gpu_metric, which are platform-specific implementations. The metric also has a configure method that allows configuration of the GPU mode using the --gpu argument.\n\nRelationships: This file is related to the cubestat.common file, which defines utility functions, and the cubestat.metrics.base_metric and cubestat.metrics_registry files, which define the base metric class and the metrics registry, respectively."}, "cubestat/metrics/memory.py": {"path": "cubestat/metrics/memory.py", "size": 1810, "checksum": "7f088bb8b7c4c171ceaf9e12f0656736", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 460, "processing_result": "This file defines a metric for memory usage. The metric has two modes: percent or all. The memory usage is calculated using the psutil library. There are two classes: ram_metric, which is the base class, and ram_metric_macos and ram_metric_linux, which are platform-specific implementations. The metric also has a configure method that allows configuration of the RAM mode.\n\nRelationships: This file is related to the cubestat.common file, which defines utility functions, and the cubestat.metrics.base_metric and cubestat.metrics_registry files, which define the base metric class and the metrics registry, respectively."}, "cubestat/metrics/mock.py": {"path": "cubestat/metrics/mock.py", "size": 673, "checksum": "d31774179be9a26735fdbdc604f7d7ed", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 177, "processing_result": "This file defines a mock metric for testing purposes. The metric has a configure method that initializes a value, and a read method that returns a dictionary with the mock value. The metric also has a pre and format method that are not implemented.\n\nRelationships: This file is related to the cubestat.metrics.base_metric and cubestat.metrics_registry files, which define the base metric class and the metrics registry, respectively."}, "cubestat/metrics/network.py": {"path": "cubestat/metrics/network.py", "size": 1568, "checksum": "9110c98b166444f05a270239350695bf", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 352, "processing_result": "This file defines a metric for network usage. The metric has two modes: show or hide. The network usage is calculated using the psutil library. There are two classes: network_metric, which is the base class, and macos_network_metric and linux_network_metric, which are platform-specific implementations. The metric also has a configure method that allows configuration of the network mode using the --network argument.\n\nRelationships: This file is related to the cubestat.common file, which defines utility functions, and the cubestat.metrics.base_metric and cubestat.metrics_registry files, which define the base metric class and the metrics registry, respectively."}, "cubestat/metrics/power.py": {"path": "cubestat/metrics/power.py", "size": 1536, "checksum": "5039ca0f9edf12a6d860c0e518284d60", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 372, "processing_result": "This file defines a metric for power usage. The metric has three modes: combined, all, or off. The power usage is calculated using the sysctl command on MacOS. There is one class: macos_power_metric, which is the platform-specific implementation. The metric also has a configure method that allows configuration of the power mode using the --power argument.\n\nRelationships: This file is related to the cubestat.common file, which defines utility functions, and the cubestat.metrics.base_metric and cubestat.metrics_registry files, which define the base metric class and the metrics registry, respectively."}, "cubestat/metrics/swap.py": {"path": "cubestat/metrics/swap.py", "size": 2719, "checksum": "f437e594b4f8b3dae653c9863ad54a44", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 631, "processing_result": "This file defines a metric for swap usage. The metric has two modes: show or hide. The swap usage is calculated using the sysctl command on MacOS and the /proc/meminfo file on Linux. There are two classes: swap_metric, which is the base class, and macos_swap_metric and linux_swap_metric, which are platform-specific implementations. The metric also has a configure method that allows configuration of the swap mode using the --swap argument.\n\nRelationships: This file is related to the cubestat.common file, which defines utility functions, and the cubestat.metrics.base_metric and cubestat.metrics_registry files, which define the base metric class and the metrics registry, respectively."}, "cubestat/metrics_registry.py": {"path": "cubestat/metrics_registry.py", "size": 994, "checksum": "7e1b2106f5ab651998bfdbcbcd4c381e", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 213, "processing_result": "This file defines a registry for metrics. It provides a decorator @cubestat_metric that allows registering metrics for specific platforms. It also provides a function metrics_configure_argparse that configures the argparse parser for all registered metrics.\n\nRelationships: This file is related to all metric files, which use the @cubestat_metric decorator to register their metrics."}, "cubestat/platforms/__init__.py": {"path": "cubestat/platforms/__init__.py", "size": 0, "checksum": "d41d8cd98f00b204e9800998ecf8427e", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 0, "processing_result": "This file is an empty __init__.py file that makes the cubestat.platforms package.\n\nRelationships: This file is related to the cubestat.platforms.factory file, which is used to get a platform-specific implementation."}, "cubestat/platforms/factory.py": {"path": "cubestat/platforms/factory.py", "size": 404, "checksum": "91a76fa91eea703b6f4b9273e5934be4", "processing_timestamp": "2024-10-11T12:03:26.010041", "approx_tokens": 93, "processing_result": "This file provides a function get_platform that returns a platform-specific implementation of a platform. It currently supports MacOS and Linux platforms.\n\nRelationships: This file is related to the cubestat.platforms.linux and cubestat.platforms.macos files, which define the platform-specific implementations."}, "cubestat/platforms/linux.py": {"path": "cubestat/platforms/linux.py", "size": 555, "checksum": "4ddcf57acf346c6bfdcd9e08cf77ce2e", "processing_timestamp": "2024-10-11T12:05:35.046552", "approx_tokens": 128, "processing_result": "This is a Python module that defines a class `LinuxPlatform` for Linux platform-specific functionality. It provides a `loop` method that runs an infinite loop, sleeping for a specified interval between iterations, and calls a callback function `do_read_cb` with a `None` argument. The module also imports the `time` module for timing-related functions."}, "cubestat/platforms/macos.py": {"path": "cubestat/platforms/macos.py", "size": 1228, "checksum": "2ad747d2ca314301fc15415f05cf9e7b", "processing_timestamp": "2024-10-11T12:05:35.046552", "approx_tokens": 265, "processing_result": "This is a Python module that defines a class `MacOSPlatform` for macOS platform-specific functionality. It provides a `loop` method that runs an infinite loop, reading output from a subprocess running the `powermetrics` command, and calls a callback function `do_read_cb` with a plist-formatted data. The module also imports the `subprocess` and `plistlib` modules for subprocess management and plist parsing, respectively."}, "cubestat/screen.py": {"path": "cubestat/screen.py", "size": 2947, "checksum": "d5fdf34f52752b603d815af8110bec5c", "processing_timestamp": "2024-10-11T12:05:35.046552", "approx_tokens": 740, "processing_result": "This is a Python module that defines a class `Screen` for managing the terminal screen. It provides methods for rendering the screen, writing strings and characters, and managing the screen's layout. The module also imports the `curses` module for terminal management."}, "cubestat/tests/test_data_manager.py": {"path": "cubestat/tests/test_data_manager.py", "size": 2143, "checksum": "61a8ef7149a9ced6bfe1958e5d9c3221", "processing_timestamp": "2024-10-11T12:05:35.046552", "approx_tokens": 586, "processing_result": "This is a Python test module that tests the `DataManager` class. It provides several test cases for testing the ` DataManager` class's initialization, updating, getting slice, and generating data."}, "cubestat/tests/test_format_measurement.py": {"path": "cubestat/tests/test_format_measurement.py", "size": 594, "checksum": "12318e36076cd698fb6b2e998009debb", "processing_timestamp": "2024-10-11T12:05:35.046552", "approx_tokens": 156, "processing_result": "This is a Python test module that tests the `format_measurement` function. It provides several test cases for testing the function's formatting of measurements."}, "cubestat/tests/test_prepare_cells.py": {"path": "cubestat/tests/test_prepare_cells.py", "size": 412, "checksum": "3591da73d2add0d242960f256f52aa35", "processing_timestamp": "2024-10-11T12:05:35.046552", "approx_tokens": 88, "processing_result": "This is a Python test module that tests the `Colorschemes` class. It provides a test case for testing the class's preparation of cells."}, "scripts/apple_loadgen.py": {"path": "scripts/apple_loadgen.py", "size": 2075, "checksum": "d367ea22a614ab2fc75168883e2cbc61", "processing_timestamp": "2024-10-11T12:05:35.046552", "approx_tokens": 539, "processing_result": "This is a Python script that generates a load on Apple devices using the `coremltools` and `torch` libraries. It defines a neural network model and runs it on the device to generate a load."}, "scripts/cuda_loadgen.py": {"path": "scripts/cuda_loadgen.py", "size": 1179, "checksum": "23c1f88812ed3b854cb55d3e78cb3b0d", "processing_timestamp": "2024-10-11T12:05:35.046552", "approx_tokens": 283, "processing_result": "This is a Python script that generates a load on NVIDIA GPUs using the `torch` and `torch.nn` libraries. It defines a simple neural network model and runs it on the GPU to generate a load."}, "setup.py": {"path": "setup.py", "size": 669, "checksum": "8894acc16ccf94b51a54a7faacd5fef6", "processing_timestamp": "2024-10-11T12:05:35.046552", "approx_tokens": 189, "processing_result": "This is a Python setup script that defines the installation and dependencies for the `cubestat` package. It provides information about the package, its version, and its dependencies, and specifies the entry points for the package."}}, "dirs": {"cubestat/metrics": {"processing_result": "This directory contains a collection of metric modules for the cubestat application. Each module defines a specific metric, such as CPU usage, disk usage, GPU usage, memory usage, network usage, power usage, and swap usage. The modules are designed to be platform-independent, with platform-specific implementations provided for macOS and Linux.\n\nThe directory includes a base metric class (base_metric.py) that provides a basic implementation for metrics, which can be inherited by specific metric classes. The metric modules are related to each other and to other files in the cubestat application, such as the cubestat.common file, which defines utility functions, and the cubestat.metrics_registry file, which defines the metrics registry.\n\nThe metric modules have a consistent structure, with each module defining a base class and one or more platform-specific implementations. The modules also have a configure method that allows configuration of the metric mode using command-line arguments.\n\nThe directory includes the following metric modules:\n\n* ane_metric (accel.py): measures the utilization of the Apple Neural Engine (ANE) on macOS systems\n* cpu_metric (cpu.py): measures CPU usage, with three modes: all cores, cumulative by cluster, or both\n* disk_metric (disk.py): measures disk usage, with two modes: show or hide\n* gpu_metric (gpu.py): measures GPU usage, with three modes: collapsed, load only, or load and vram usage\n* memory_metric (memory.py): measures memory usage, with two modes: percent or all\n* mock_metric (mock.py): a mock metric for testing purposes\n* network_metric (network.py): measures network usage, with two modes: show or hide\n* power_metric (power.py): measures power usage, with three modes: combined, all, or off\n* swap_metric (swap.py): measures swap usage, with two modes: show or hide\n\nOverall, this directory provides a comprehensive set of metrics for monitoring system performance and usage.", "checksum": "968fd3254849f45cad44bdbe11113e5a"}, "cubestat/platforms": {"processing_result": "This directory contains platform-specific code for the cubestat application. It includes an empty __init__.py file to make it a package, and a factory file to get platform-specific implementations.\n\nThe factory file is related to the linux and macos files, which define the platform-specific implementations. The linux and macos files provide classes with a `loop` method that runs an infinite loop, with platform-specific functionality.\n\nThe directory provides a modular structure for supporting different platforms, with clear relationships between the files and modules. The factory file acts as an entry point to get the platform-specific implementation, making it easier to add support for new platforms in the future.\n\nRelationships: This directory is related to the cubestat application, which uses the platform-specific implementations provided by this directory.", "checksum": "62ffa01e1b6dca06df9b65f46239e65a"}, "cubestat/tests": {"processing_result": "This directory contains a collection of Python test modules for the `cubestat` project. It includes tests for specific classes and functions, such as `DataManager` and `format_measurement`. The tests are organized into individual modules, each containing multiple test cases. The modules test the functionality of the corresponding classes and functions, ensuring their correct initialization, updating, and formatting. Specifically, the directory includes test modules for testing data management, measurement formatting, and cell preparation. The presence of these test modules suggests that the `cubestat` project follows a test-driven development approach, emphasizing the importance of thorough testing for its components.", "checksum": "37795dc96f818bb5f0d84c24ba42ca83"}, "cubestat": {"processing_result": "This is the root directory of the cubestat application. It contains a collection of Python modules and files that provide various functionalities for the application. The directory includes platform-specific code, a metrics registry, and modules for handling user input, data management, and screen rendering.\n\nThe cubestat application is a system monitoring tool that provides real-time metrics and usage information. The directory structure is modular, with clear relationships between the files and modules. The application follows a test-driven development approach, with a separate directory for tests.\n\nThe directory includes the following subdirectories:\n\n* metrics: contains a collection of metric modules for the cubestat application, each defining a specific metric such as CPU usage, disk usage, and memory usage.\n* platforms: contains platform-specific code for the cubestat application, including an empty __init__.py file and a factory file to get platform-specific implementations.\n* tests: contains a collection of Python test modules for the cubestat project, testing specific classes and functions.\n\nThe directory also includes several files, including:\n\n* __init__.py: an empty initialization file for the cubestat package.\n* colors.py: a module for handling colors and color themes in the cubestat application.\n* common.py: a module containing common utility functions and classes for the cubestat application.\n* cubestat.py: the main entry point of the cubestat application, defining the Cubestat class.\n* data.py: a module for managing data in the cubestat application.\n* input.py: a module for handling user input in the cubestat application.\n* metrics_registry.py: a file defining a registry for metrics, providing a decorator @cubestat_metric for registering metrics.\n* screen.py: a module defining a class `Screen` for managing the terminal screen.\n\nOverall, this directory provides a comprehensive set of modules and files for the cubestat application, supporting its functionality as a system monitoring tool.", "checksum": "fbd0970cd5e7020a5647bcd1c2d3d6bb"}, "scripts": {"processing_result": "The scripts directory contains two Python scripts that generate loads on different hardware platforms. The directory is focused on load generation and testing for machine learning models. The two scripts, `apple_loadgen.py` and `cuda_loadgen.py`, share a common goal but target different devices: Apple devices and NVIDIA GPUs, respectively. Both scripts utilize popular machine learning libraries, `coremltools` and `torch`, to define and run neural network models. The scripts can be used to simulate real-world workloads and test the performance of these devices under different conditions.", "checksum": "d315e014ecff6e66dfdf3177c4a59f8c"}, "": {"processing_result": "n/a", "checksum": "ff726e76df89ee6d94f4f3ef9e4ae492", "skipped": true}}}