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Auto-generated via `{sandpaper}` Source : 1966412 Branch : main Author : Matthew <6177028+bielsnohr@users.noreply.github.com> Time : 2025-05-27 14:21:56 +0000 Message : Merge pull request #456 from carpentries-incubator/bielsnohr/ukaea-march-2025 Updates from UKAEA March 2025 Run
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32-software-architecture-design.md

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@@ -131,6 +131,8 @@ goal of having *maintainable* code, which is:
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Now that we know what goals we should aspire to, let us take a critical look at the code in our
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software project and try to identify ways in which it can be improved.
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## Designing a New Feature for Our Project
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Our software project contains a pre-existing branch `full-data-analysis` which contains code for a new feature of our
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inflammation analysis software, which we will consider as a contribution by another developer.
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Recall that you can see all your branches as follows:
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The new data analysis code is located in `compute_data.py` file within the `inflammation` directory
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in a function called `analyse_data()`.
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This function loads all the data files for a given a directory path, then
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calculates and compares standard deviation across all the data by day and finaly plots a graph.
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calculates and compares standard deviation across all the data by day and finally plots a graph.
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::::::::::::::::::::::::::::::::::::::: challenge
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34-code-refactoring.md

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Start from the skeleton test code below:
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```python
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from inflammation.compute_data import analyse_data
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def test_analyse_data():
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from inflammation.compute_data import analyse_data
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path = Path.cwd() / "../data"
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path = os.path.join( os.getcwd(), "../data")
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data_source = CSVDataSource(path)
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result = analyse_data(data_source)
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# TODO: add assert statement(s) to test the result value is as expected
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```
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result equals some random made-up data, observe the test fail initially and then
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copy and paste the correct result into the test.
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:::::::::::::::::::::::::
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::::::::::::::: solution
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```python
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import numpy.testing as npt
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from pathlib import Path
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from inflammation.compute_data import analyse_data
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def test_analyse_data():
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from inflammation.compute_data import analyse_data
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path = Path.cwd() / "../data"
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path = os.path.join( os.getcwd(), "../data")
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data_source = CSVDataSource(path)
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result = analyse_data(data_source)
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expected_output = [0.,0.22510286,0.18157299,0.1264423,0.9495481,0.27118211,

fig/mvc_arch.svg

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md5sum.txt

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