diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml new file mode 100644 index 00000000..e6625100 --- /dev/null +++ b/.github/workflows/main.yml @@ -0,0 +1,38 @@ +name: CI + +# We can specify which Github events will trigger a CI build +on: push + +# now define a single job 'build' (but could define more) +jobs: + + build: + strategy: + matrix: + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: ["3.8", "3.9", "3.10"] + + runs-on: ${{ matrix.os }} + + # a job is a seq of steps + steps: + + # Next we need to checkout out repository, and set up Python + # A 'name' is just an optional label shown in the log - helpful to clarify progress - and can be anything + - name: Checkout repository + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Install Python dependencies + run: | + python3 -m pip install --upgrade pip + pip3 install -r requirements.txt + + - name: Test with PyTest + run: | + python -m pytest --cov=inflammation.models tests/test_models.py +... diff --git a/.gitignore b/.gitignore index 0e0aa744..3a56602e 100644 --- a/.gitignore +++ b/.gitignore @@ -12,3 +12,8 @@ *.pyc *.egg-info .pytest_cache + + +# Virtual environments +venv/ +.venv/ diff --git a/README.md b/README.md index c9506464..875af9e2 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,62 @@ -# Introduction +# Inflam +![Continuous Integration build in GitHub Actions](https://github.com/mjjq/python-intermediate-inflammation/workflows/CI/badge.svg?branch=main) -This is a template software project repository used by the [Intermediate Research Software Development Skills In Python](https://github.com/carpentries-incubator/python-intermediate-development). +Inflam is a data management system written in Python that manages trial data used in clinical inflammation studies. -## Purpose +## Main features +Here are some key features of Inflam: -This repository is intended to be used as a code template which is copied by learners at [Intermediate Research Software Development Skills In Python](https://github.com/carpentries-incubator/python-intermediate-development) course. -This can be done using the `Use this template` button towards the top right of this repo's GitHub page. +- Provide basic statistical analyses over clinical trial data +- Ability to work on trial data in Comma-Separated Value (CSV) format +- Generate plots of trial data +- Analytical functions and views can be easily extended based on its Model-View-Controller architecture -This software project is not finished, is currently failing to run and contains some code style issues. It is used as a starting point for the course - issues will be fixed and code will be added in a number of places during the course by learners in their own copies of the repository, as course topics are introduced. +## Prerequisites +Inflam requires the following Python packages: -## Tests +- [NumPy](https://www.numpy.org/) - makes use of NumPy's statistical functions +- [Matplotlib](https://matplotlib.org/stable/index.html) - uses Matplotlib to generate statistical plots -Several tests have been implemented already, some of which are currently failing. -These failing tests set out the requirements for the additional code to be implemented during the workshop. +The following optional packages are required to run Inflam's unit tests: -The tests should be run using `pytest`, which will be introduced during the workshop. +- [pytest](https://docs.pytest.org/en/stable/) - Inflam's unit tests are written using pytest +- [pytest-cov](https://pypi.org/project/pytest-cov/) - Adds test coverage stats to unit testing + +## Installation +Clone the repository + +Install requirements (preferably in a virtual environment) + +``` +python -m pip3 install requirements.txt +``` + +## Usage +Open a terminal to the root of the repository on your local machine. Run the code with + +``` +python3 inflammation-analysis.py +``` + +## License +MIT License + +Copyright (c) 2024 Marcus Quantrill + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/inflammation-analysis.py b/inflammation-analysis.py index e1564317..f6c4c2a6 100644 --- a/inflammation-analysis.py +++ b/inflammation-analysis.py @@ -1,5 +1,8 @@ #!/usr/bin/env python3 -"""Software for managing and analysing patients' inflammation data in our imaginary hospital.""" +""" +Software for managing and analysing patients' +inflammation data in our imaginary hospital. +""" import argparse import os @@ -19,7 +22,6 @@ def main(args): if not isinstance(InFiles, list): InFiles = [args.infiles] - if args.full_data_analysis: analyse_data(os.path.dirname(InFiles[0])) return @@ -27,20 +29,26 @@ def main(args): for filename in InFiles: inflammation_data = models.load_csv(filename) - view_data = {'average': models.daily_mean(inflammation_data), 'max': models.daily_max(inflammation_data), 'min': models.daily_min(inflammation_data)} + view_data = {'average': models.daily_mean(inflammation_data), 'max': models.daily_max(inflammation_data), 'min': models.daily_min(inflammation_data), **(models.standard_deviation(inflammation_data))} + views.visualize(view_data) + if __name__ == "__main__": parser = argparse.ArgumentParser( - description='A basic patient inflammation data management system') + description="A basic patient inflammation data management system" + ) parser.add_argument( - 'infiles', - nargs='+', - help='Input CSV(s) containing inflammation series for each patient') + "infiles", + nargs="+", + help="Input CSV(s) containing inflammation series for each patient", + ) - parser.add_argument('--full-data-analysis', action='store_true', dest='full_data_analysis') + parser.add_argument( + "--full-data-analysis", action="store_true", dest="full_data_analysis" + ) args = parser.parse_args() diff --git a/inflammation/models.py b/inflammation/models.py index 94387ee3..9ae898f2 100644 --- a/inflammation/models.py +++ b/inflammation/models.py @@ -2,8 +2,8 @@ The Model layer is responsible for the 'business logic' part of the software. -Patients' data is held in an inflammation table (2D array) where each row contains -inflammation data for a single patient taken over a number of days +Patients' data is held in an inflammation table (2D array) where each row contains +inflammation data for a single patient taken over a number of days and each column represents a single day across all patients. """ @@ -11,12 +11,18 @@ import numpy as np +class Patient: + def __init__(self, name): + self.name = name + + def load_csv(filename): """Load a Numpy array from a CSV :param filename: Filename of CSV to load """ - return np.loadtxt(fname=filename, delimiter=',') + return np.loadtxt(fname=filename, delimiter=",") + def load_json(filename): """Load a numpy array from a JSON document. @@ -34,23 +40,85 @@ def load_json(filename): :param filename: Filename of CSV to load """ - with open(filename, 'r', encoding='utf-8') as file: + with open(filename, "r", encoding="utf-8") as file: data_as_json = json.load(file) - return [np.array(entry['observations']) for entry in data_as_json] - + return [np.array(entry["observations"]) for entry in data_as_json] def daily_mean(data): - """Calculate the daily mean of a 2d inflammation data array.""" + """Calculate the daily mean of a 2D inflammation data array. + + :param data: 2D array of values to perform mean + :returns: 1D array of values contains means along first axis of data + """ return np.mean(data, axis=0) def daily_max(data): - """Calculate the daily max of a 2d inflammation data array.""" + """Calculate the daily max of a 2D inflammation data array. + + :param data: Array of values to perform max + :returns: 1D array of values contains maxes along first axis of data + """ return np.max(data, axis=0) def daily_min(data): - """Calculate the daily min of a 2d inflammation data array.""" + """Calculate the daily min of a 2D inflammation data array. + + :param data: Array of values to perform min + :returns: 1D array of values contains mins along first axis of data + """ return np.min(data, axis=0) + +def patient_normalise(data): + """ + Normalise patient data from a 2D inflammation data array. + + NaN values are ignored, and normalised to 0. + + Negative values are rounded to 0. + + :param data: 2D array to be normalised + :returns: Normalised 2D array + """ + maxima = np.nanmax(data, axis=1) + with np.errstate(invalid="ignore", divide="ignore"): + normalised = data / maxima[:, np.newaxis] + normalised[np.isnan(normalised)] = 0 + normalised[normalised < 0] = 0 + return normalised + + +def standard_deviation(data): + """Computes and returns standard deviation for data.""" + if len(data) == 0: + return {"standard deviation": 0.0} + + mmm = np.mean(data, axis=0) + devs = [] + for entry in data: + devs.append((entry - mmm) * (entry - mmm)) + + standard_dev = sum(devs) / len(data) + return {"standard deviation": np.sqrt(standard_dev)} + + +def patient_normalise(data): + """ + Normalise patient data from a 2D inflammation data array. + + NaN values are ignored, and normalised to 0. + + Negative values are rounded to 0. + + :param data: 2D array to be normalised + :returns: Normalised 2D array + """ + maxima = np.nanmax(data, axis=1) + with np.errstate(invalid="ignore", divide="ignore"): + normalised = data / maxima[:, np.newaxis] + normalised[np.isnan(normalised)] = 0 + normalised[normalised < 0] = 0 + return normalised diff --git a/inflammation/views.py b/inflammation/views.py index 724d6bd3..4e85884f 100644 --- a/inflammation/views.py +++ b/inflammation/views.py @@ -1,7 +1,6 @@ """Module containing code for plotting inflammation data.""" from matplotlib import pyplot as plt -import numpy as np def visualize(data_dict): diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 00000000..c9677dd4 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,830 @@ +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. + +[[package]] +name = "colorama" +version = "0.4.6" +description = "Cross-platform colored terminal text." +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" +files = [ + {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, + {file = "colorama-0.4.6.tar.gz", hash = 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"more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] + +[metadata] +lock-version = "2.0" +python-versions = "^3.8" +content-hash = "d4ff2192fdc323ab9e7201aa12dbab5690db3b38d7bfeed7c4558908a9e1048c" diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..b444bc08 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,21 @@ +[tool.poetry] +name = "inflam" +version = "1.0.0" +description = "Analyse patient inflammation data" +authors = ["Marcus Quantrill "] +license = "MIT" +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.8" +numpy = "<2" +matplotlib = "^3.7.5" + + +[tool.poetry.group.dev.dependencies] +pytest = "^8.3.3" +pytest-cov = "^5.0.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..4483d8c7 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +numpy==1.24.4 +matplotlib==3.7.5 +pytest==8.3.3 +pytest-cov==5.0.0 diff --git a/tests/test_models.py b/tests/test_models.py index 292d00c4..f104f99f 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -3,32 +3,20 @@ import numpy as np import numpy.testing as npt import os +import pytest -def test_daily_mean_zeros(): - """Test that mean function works for an array of zeros.""" +@pytest.mark.parametrize( + "test, expected", + [ + ([ [0, 0], [0, 0], [0, 0] ], [0, 0]), + ([ [1, 2], [3, 4], [5, 6] ], [3, 4]), + ]) +def test_daily_mean(test, expected): + """Test mean function works for array of zeroes and positive integers.""" from inflammation.models import daily_mean + npt.assert_array_equal(daily_mean(np.array(test)), np.array(expected)) - test_input = np.array([[0, 0], - [0, 0], - [0, 0]]) - test_result = np.array([0, 0]) - - # Need to use Numpy testing functions to compare arrays - npt.assert_array_equal(daily_mean(test_input), test_result) - - -def test_daily_mean_integers(): - """Test that mean function works for an array of positive integers.""" - from inflammation.models import daily_mean - - test_input = np.array([[1, 2], - [3, 4], - [5, 6]]) - test_result = np.array([3, 4]) - - # Need to use Numpy testing functions to compare arrays - npt.assert_array_equal(daily_mean(test_input), test_result) def test_load_from_json(tmpdir): from inflammation.models import load_json @@ -37,3 +25,15 @@ def test_load_from_json(tmpdir): temp_json_file.write('[{"observations":[1, 2, 3]},{"observations":[4, 5, 6]}]') result = load_json(example_path) npt.assert_array_equal(result, [[1, 2, 3], [4, 5, 6]]) + + +@pytest.mark.parametrize('data, expected_standard_deviation', [ + ([0, 0, 0], 0.0), + ([1.0, 1.0, 1.0], 0), + ([0.0, 2.0], 1.0), + ([], 0.0) +]) +def test_daily_standard_deviation(data, expected_standard_deviation): + from inflammation.models import standard_deviation + result_data = standard_deviation(data)['standard deviation'] + npt.assert_approx_equal(result_data, expected_standard_deviation)