Skip to main content

A Python package to capture command/function execution context for reproducibility.

Project description

Capsula

PyPI conda-forge PyPI - License PyPI - Python Version Test Status codecov uv Ruff PyPI - Downloads

Capsula, a Latin word meaning box, is a Python package designed to help researchers and developers easily capture their command/function execution context for reproducibility. See the documentation for more information.

With Capsula, you can capture:

The captured contexts are dumped into JSON files for future reference and reproduction.

Usage example

For project-wide settings, prepare a capsula.toml file in the root directory of your project. An example of the capsula.toml file is as follows:

[pre-run]
contexts = [
    { type = "CwdContext" },
    { type = "CpuContext" },
    { type = "PlatformContext" },
    { type = "GitRepositoryContext", name = "capsula", path = ".", path_relative_to_project_root = true },
    { type = "CommandContext", command = "uv lock --locked", cwd = ".", cwd_relative_to_project_root = true },
    { type = "FileContext", path = "pyproject.toml", copy = true, path_relative_to_project_root = true },
    { type = "FileContext", path = "uv.lock", copy = true, path_relative_to_project_root = true },
    { type = "CommandContext", command = "uv export > requirements.txt", cwd = ".", cwd_relative_to_project_root = true },
    { type = "FileContext", path = "requirements.txt", move = true, path_relative_to_project_root = true },
    { type = "EnvVarContext", name = "HOME" },
]
reporters = [{ type = "JsonDumpReporter" }]

[in-run]
watchers = [{ type = "UncaughtExceptionWatcher" }, { type = "TimeWatcher" }]
reporters = [{ type = "JsonDumpReporter" }]

[post-run]
reporters = [{ type = "JsonDumpReporter" }]

Then, all you need to do is decorate your Python function with the @capsula.run() decorator. You can also use the @capsula.context() decorator to add a context specific to the function.

The following is an example of a Python script that estimates the value of π using the Monte Carlo method:

import random
import capsula

@capsula.run()
@capsula.context(capsula.FunctionContext.builder(), mode="pre")
@capsula.context(capsula.FileContext.builder("pi.txt", move=True), mode="post")
def calculate_pi(n_samples: int = 1_000, seed: int = 42) -> None:
    random.seed(seed)
    xs = (random.random() for _ in range(n_samples))
    ys = (random.random() for _ in range(n_samples))
    inside = sum(x * x + y * y <= 1.0 for x, y in zip(xs, ys))

    # You can record values to the capsule using the `record` method.
    capsula.record("inside", inside)

    pi_estimate = (4.0 * inside) / n_samples
    print(f"Pi estimate: {pi_estimate}")
    capsula.record("pi_estimate", pi_estimate)
    print(f"Run name: {capsula.current_run_name()}")

    with open("pi.txt", "w") as output_file:
        output_file.write(f"Pi estimate: {pi_estimate}.")

if __name__ == "__main__":
    calculate_pi(n_samples=1_000)

After running the script, a directory (calculate_pi_20240913_194900_2lxL in this example) will be created under the <project-root>/vault directory, and you will find the output files in the directory:

$ tree vault/calculate_pi_20240913_194900_2lxL
vault/calculate_pi_20240913_194900_2lxL
├── in-run-report.json    # Generated by the `JsonDumpReporter` in `capsula.toml` (`in-run` section)
├── pi.txt                # Moved by the `FileContext` specified with the decorator in the script
├── uv.lock           # Copied by the `FileContext` specified in `capsula.toml` (`pre-run` section)
├── post-run-report.json  # Generated by the `JsonDumpReporter` in `capsula.toml` (`post-run` section)
├── pre-run-report.json   # Generated by the `JsonDumpReporter` in `capsula.toml` (`pre-run` section)
├── pyproject.toml        # Copied by the `FileContext` specified in `capsula.toml` (`pre-run` section)
└── requirements.txt      # Moved by the `FileContext` specified in `capsula.toml` (`pre-run` section)

The contents of the JSON files are as follows:

Example of output pre-run-report.json:
{
  "cwd": "/Users/nomura/ghq/github.com/shunichironomura/capsula",
  "cpu": {
    "python_version": "3.8.20.final.0 (64 bit)",
    "cpuinfo_version": [
      9,
      0,
      0
    ],
    "cpuinfo_version_string": "9.0.0",
    "arch": "ARM_8",
    "bits": 64,
    "count": 16,
    "arch_string_raw": "arm64",
    "brand_raw": "Apple M3 Max"
  },
  "platform": {
    "machine": "arm64",
    "node": "MacBook-Pro.local",
    "platform": "macOS-14.6.1-arm64-arm-64bit",
    "release": "23.6.0",
    "version": "Darwin Kernel Version 23.6.0: Mon Jul 29 21:14:46 PDT 2024; root:xnu-10063.141.2~1/RELEASE_ARM64_T6031",
    "system": "Darwin",
    "processor": "arm",
    "python": {
      "executable_architecture": {
        "bits": "64bit",
        "linkage": ""
      },
      "build_no": "default",
      "build_date": "Sep  9 2024 22:25:40",
      "compiler": "Clang 18.1.8 ",
      "branch": "",
      "implementation": "CPython",
      "version": "3.8.20"
    }
  },
  "git": {
    "capsula": {
      "working_dir": "/Users/nomura/ghq/github.com/shunichironomura/capsula",
      "sha": "4ff5b9b9e5f6b527b0c2c660a5cb1a12937599b5",
      "remotes": {
        "origin": "ssh://git@github.com/shunichironomura/capsula.git"
      },
      "branch": "gitbutler/workspace",
      "is_dirty": true,
      "diff_file": "/Users/nomura/ghq/github.com/shunichironomura/capsula/vault/calculate_pi_20240913_194900_2lxL/capsula.diff"
    }
  },
  "command": {
    "uv lock --locked": {
      "command": "uv lock --locked",
      "cwd": "/Users/nomura/ghq/github.com/shunichironomura/capsula",
      "returncode": 0,
      "stdout": "",
      "stderr": "Resolved 73 packages in 0.35ms\n"
    },
    "uv export > requirements.txt": {
      "command": "uv export > requirements.txt",
      "cwd": "/Users/nomura/ghq/github.com/shunichironomura/capsula",
      "returncode": 0,
      "stdout": "",
      "stderr": "Resolved 73 packages in 0.32ms\n"
    }
  },
  "file": {
    "/Users/nomura/ghq/github.com/shunichironomura/capsula/pyproject.toml": {
      "copied_to": [
        "/Users/nomura/ghq/github.com/shunichironomura/capsula/vault/calculate_pi_20240913_194900_2lxL/pyproject.toml"
      ],
      "moved_to": null,
      "hash": {
        "algorithm": "sha256",
        "digest": "e331c7998167d64e4e90c9f2aa2c2fe9c9c3afe1cf8348f1d61998042b75040a"
      }
    },
    "/Users/nomura/ghq/github.com/shunichironomura/capsula/uv.lock": {
      "copied_to": [
        "/Users/nomura/ghq/github.com/shunichironomura/capsula/vault/calculate_pi_20240913_194900_2lxL/uv.lock"
      ],
      "moved_to": null,
      "hash": {
        "algorithm": "sha256",
        "digest": "62e5b7a5125778dd664ee2dc0cb3c10640d15db3e55b40240c4d652f8afe40fe"
      }
    },
    "/Users/nomura/ghq/github.com/shunichironomura/capsula/requirements.txt": {
      "copied_to": [],
      "moved_to": "/Users/nomura/ghq/github.com/shunichironomura/capsula/vault/calculate_pi_20240913_194900_2lxL",
      "hash": {
        "algorithm": "sha256",
        "digest": "3ba457abcefb0010a7b350e8a2567b8ac890726608b99ce85defbb5d06e197de"
      }
    }
  },
  "env": {
    "HOME": "/Users/nomura"
  },
  "function": {
    "calculate_pi": {
      "file_path": "examples/simple_decorator.py",
      "first_line_no": 15,
      "bound_args": {
        "n_samples": 1000,
        "seed": 42
      }
    }
  }
}
Example of output in-run-report.json:
{
  "inside": 782,
  "pi_estimate": 3.128,
  "time": {
    "execution_time": "0:00:00.000271"
  },
  "exception": {
    "exception": {
      "exc_type": null,
      "exc_value": null,
      "traceback": null
    }
  }
}
Example of output post-run-report.json:
{
  "file": {
    "pi.txt": {
      "copied_to": [],
      "moved_to": "/Users/nomura/ghq/github.com/shunichironomura/capsula/vault/calculate_pi_20240913_194900_2lxL",
      "hash": {
        "algorithm": "sha256",
        "digest": "a64c761cb6b6f9ef1bc1f6afa6ba44d796c5c51d14df0bdc9d3ab9ced7982a74"
      }
    }
  }
}

Installation

You can install Capsula via pip:

pip install capsula

Or via conda:

conda install conda-forge::capsula

Licensing

This project is licensed under the terms of the MIT.

Additionally, this project includes code derived from the Python programming language, which is licensed under the Python Software Foundation License Version 2 (PSF-2.0). For details, see the LICENSE file.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

capsula-0.8.0.tar.gz (109.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

capsula-0.8.0-py3-none-any.whl (38.6 kB view details)

Uploaded Python 3

File details

Details for the file capsula-0.8.0.tar.gz.

File metadata

  • Download URL: capsula-0.8.0.tar.gz
  • Upload date:
  • Size: 109.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.3

File hashes

Hashes for capsula-0.8.0.tar.gz
Algorithm Hash digest
SHA256 14a3e7e1e0becb0e0747eded993b5db194b7b34c7c5fc86dadb9bd16586cf06a
MD5 3680e4d59521c7bbe4824c27e37ac12c
BLAKE2b-256 894fb1087a08225660f66df24e7cc6a6283836ce8a9c1f5558e9d361c8b262b8

See more details on using hashes here.

File details

Details for the file capsula-0.8.0-py3-none-any.whl.

File metadata

  • Download URL: capsula-0.8.0-py3-none-any.whl
  • Upload date:
  • Size: 38.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.3

File hashes

Hashes for capsula-0.8.0-py3-none-any.whl
Algorithm Hash digest
SHA256 c0a7f9f12ad3b664458ee17dafe248fea59d8c30e0e1e39fc71d2b6c9e8c925f
MD5 c20f1b8724a862544cde7e6ee7542724
BLAKE2b-256 6bbda87cbbb6af4669eb78f1eaa4c7a60c4482a6037fb705945f404431deea4e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page