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Add a python equivalent to MATLABs smoothdata and use in Bayes plots #208

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Add a python equivalent to MATLABs smoothdata and use in Bayes plots #208

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31 changes: 28 additions & 3 deletions ratapi/utils/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -826,9 +826,7 @@ def plot_one_hist(
sd_y = np.std(parameter_chain)

if smooth:
if sigma is None:
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@StephenNneji StephenNneji May 21, 2026

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please update docstring to reflect switch to moving average, remove sigma. I also think we should expose the window size parameter since we don't know if 8 will work for all cases

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@StephenNneji StephenNneji May 21, 2026

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Exposing the window size means the moving average function will need a check for valid window sizes and a unit test will be helpful

sigma = sd_y / 2
counts = gaussian_filter1d(counts, sigma)
counts = moving_avg(counts)
axes.hist(
bins[:-1],
bins,
Expand Down Expand Up @@ -1233,3 +1231,30 @@ def plot_bayes(project: ratapi.Project, results: ratapi.outputs.BayesResults):
plot_corner(results)
else:
raise ValueError("Bayes plots are only available for the results of Bayesian analysis (NS or DREAM)")


def moving_avg(data: np.ndarray, window_size: int = 8) -> list[float]:
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@StephenNneji StephenNneji May 21, 2026

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I think the function can be called moving_average as its not too long and slightly improves readability

"""Calculate the moving average of an array with a given window size.

This is a python equivalent to MATLABs smoothdata(A, 'movmean')

Parameters
----------
data : np.ndarray
The input array to smooth
window_size : int
The window slides down the length of the vector,
computing an average over the elements within each window.

"""
i = 0
moving_averages = []

while i < len(data):
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@StephenNneji StephenNneji May 21, 2026

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This seems like it can be replaced by a for loop for i in range(len(data)) which I tend to prefer over a while loop but also means you can get rid of the index initialization and increment

``

start_window_ind = floor(float(i - window_size / 2)) if i - window_size / 2 > 0 else 0
end_window_ind = floor(float(i + window_size / 2)) if i + window_size / 2 < len(data) else len(data)
window_average = np.sum(data[start_window_ind:end_window_ind]) / (end_window_ind + 0 - start_window_ind)
moving_averages.append(window_average)
i += 1

return moving_averages
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