Add implementation of standard deviation on data

README.md

@@ -1,17 +0,0 @@
-# Introduction
-
-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).
-
-## Purpose
-
-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.
-
-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.
-
-## Tests
-
-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 tests should be run using `pytest`, which will be introduced during the workshop.

dist/inflammation-v1.1.0/inflammation-1.0.0.dist-info/METADATA

@@ -0,0 +1,18 @@
+Metadata-Version: 2.1
+Name: inflammation
+Version: 1.0.0
+Summary: 
+License: MIT
+Author: Yoyo Yiu
+Author-email: yoyo.yiu@ukaea.uk
+Requires-Python: >=3.10,<4.0
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Requires-Dist: matplotlib (>=3.9.0,<4.0.0)
+Requires-Dist: numpy (>=2.0.0,<3.0.0)
+Description-Content-Type: text/markdown
+
+

dist/inflammation-v1.1.0/inflammation-1.0.0.dist-info/RECORD

@@ -0,0 +1,8 @@
+inflammation/README.md,sha256=__27PY24jF0qIk22pnAuDj5M1lP_x_VpwbTpvaQj3SA,964
+inflammation/__init__.py,sha256=sB3D60ULiwjsYC5d4NjvSkmcAb-SI3igDBBQRFaXjCU,71
+inflammation/compute_data.py,sha256=bCq5h-dqcfaHi_coxo4OOL3iDnL-v9pn9wT_ZQp5a4I,984
+inflammation/models.py,sha256=SGaFY8OIzFqir-RT6JLQ_H20YRSiWB9-OTnFCR9sOT0,1652
+inflammation/views.py,sha256=D7B8J9JjkA4UN1_2yHURslhQGzs7kVkUJOd0YJQXoBY,649
+inflammation-1.0.0.dist-info/METADATA,sha256=U3VNEND10cG_GNFH8Sov5fU8VsWm8CDAB1_n2lbm1CE,532
+inflammation-1.0.0.dist-info/WHEEL,sha256=sP946D7jFCHeNz5Iq4fL4Lu-PrWrFsgfLXbbkciIZwg,88
+inflammation-1.0.0.dist-info/RECORD,,

dist/inflammation-v1.1.0/inflammation-1.0.0.dist-info/WHEEL

@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: poetry-core 1.9.0
+Root-Is-Purelib: true
+Tag: py3-none-any

dist/inflammation-v1.1.0/inflammation/README.md

@@ -0,0 +1,21 @@
+# Inflam
+Inflam is a data management system written in Python that manages trial data used in clinical inflammation studies.
+
+## Main features
+Here are some key features of Inflam:
+
+- 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 
+
+## Prerequisites
+Inflam requires the following Python packages:
+
+- [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
+
+The following optional packages are required to run Inflam's unit tests:
+
+- [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

dist/inflammation-v1.1.0/inflammation/__init__.py

@@ -0,0 +1 @@
+"""Package containing the bulk of code for the patient data system."""

dist/inflammation-v1.1.0/inflammation/compute_data.py

@@ -0,0 +1,31 @@
+"""Module containing mechanism for calculating standard deviation between datasets.
+"""
+
+import glob
+import os
+import numpy as np
+
+from inflammation import models, views
+
+
+def analyse_data(data_dir):
+    """Calculate the standard deviation by day between datasets
+
+    Gets all the inflammation csvs within a directory, works out the mean
+    inflammation value for each day across all datasets, then graphs the
+    standard deviation of these means."""
+    data_file_paths = glob.glob(os.path.join(data_dir, 'inflammation*.csv'))
+    if len(data_file_paths) == 0:
+        raise ValueError(f"No inflammation csv's found in path {data_dir}")
+    data = map(models.load_csv, data_file_paths)
+
+
+    means_by_day = map(models.daily_mean, data)
+    means_by_day_matrix = np.stack(list(means_by_day))
+
+    daily_standard_deviation = np.std(means_by_day_matrix, axis=0)
+
+    graph_data = {
+        'standard deviation by day': daily_standard_deviation,
+    }
+    views.visualize(graph_data)

dist/inflammation-v1.1.0/inflammation/models.py

@@ -0,0 +1,66 @@
+"""Module containing models representing patients and their data.
+
+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 
+and each column represents a single day across all patients.
+"""
+
+import json
+import numpy as np
+
+
+def load_csv(filename):
+    """Load a Numpy array from a CSV
+
+    :param filename: Filename of CSV to load
+    """
+    return np.loadtxt(fname=filename, delimiter=',')
+
+def load_json(filename):
+    """Load a numpy array from a JSON document.
+
+    Expected format:
+    [
+        {
+            observations: [0, 1]
+        },
+        {
+            observations: [0, 2]
+        }
+    ]
+
+    :param filename: Filename of CSV to load
+
+    """
+    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]
+
+
+
+def daily_mean(data):
+    """Calculate the daily mean of a 2d inflammation data array."""
+    return np.mean(data, axis=0)
+
+
+def daily_max(data):
+    """Calculate the daily max of a 2d inflammation data array."""
+    return np.max(data, axis=0)
+
+
+def daily_min(data):
+    """Calculate the daily min of a 2d inflammation data array."""
+    return np.min(data, axis=0)
+
+
+def s_dev(data):
+    """Computes and returns standard deviation for data."""
+    mmm = np.mean(data, axis=0)
+    devs = []
+    for entry in data:
+        devs.append((entry - mmm) * (entry - mmm))
+
+    s_dev2 = sum(devs) / len(data)
+    return {'standard deviation': s_dev2}

dist/inflammation-v1.1.0/inflammation/views.py

@@ -0,0 +1,25 @@
+"""Module containing code for plotting inflammation data."""
+
+from matplotlib import pyplot as plt
+import numpy as np
+
+
+def visualize(data_dict):
+    """Display plots of basic statistical properties of the inflammation data.
+
+    :param data_dict: Dictionary of name -> data to plot
+    """
+    # TODO(lesson-design) Extend to allow saving figure to file
+
+    num_plots = len(data_dict)
+    fig = plt.figure(figsize=((3 * num_plots) + 1, 3.0))
+
+    for i, (name, data) in enumerate(data_dict.items()):
+        axes = fig.add_subplot(1, num_plots, i + 1)
+
+        axes.set_ylabel(name)
+        axes.plot(data)
+
+    fig.tight_layout()
+
+    plt.show()

inflammation-analysis.py

@@ -27,7 +27,8 @@ 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.s_dev(inflammation_data))}
+
 
         views.visualize(view_data)
 

inflammation/README.md

@@ -0,0 +1,21 @@
+# Inflam
+Inflam is a data management system written in Python that manages trial data used in clinical inflammation studies.
+
+## Main features
+Here are some key features of Inflam:
+
+- 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 
+
+## Prerequisites
+Inflam requires the following Python packages:
+
+- [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
+
+The following optional packages are required to run Inflam's unit tests:
+
+- [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

inflammation/compute_data.py

@@ -29,3 +29,4 @@ def analyse_data(data_dir):
         'standard deviation by day': daily_standard_deviation,
     }
     views.visualize(graph_data)
+    return (daily_standard_deviation)

inflammation/models.py

@@ -54,3 +54,13 @@ def daily_min(data):
     """Calculate the daily min of a 2d inflammation data array."""
     return np.min(data, axis=0)
 
+
+def s_dev(data):
+    """Computes and returns standard deviation for data."""
+    mmm = np.mean(data, axis=0)
+    devs = []
+    for entry in data:
+        devs.append((entry - mmm) * (entry - mmm))
+
+    s_dev2 = sum(devs) / len(data)
+    return {'standard deviation': s_dev2}