DM-42888: Incorporate amp-to-amp correlation plots in analysis_tools for camera diagnostics #210
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enourbakhsh
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| yAxisTickValues = np.arange(matrix.shape[1] + shift) | ||
| xAxisTickLabels = {key + shift: str(val) for key, val in zip(range(matrix.shape[0]), comp1[0, :])} | ||
| yAxisTickLabels = {key + shift: str(val) for key, val in zip(range(matrix.shape[1]), comp2[:, 0])} | ||
| xAxisTickValues = np.arange(matrix.shape[1] + shift) |
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Can you explain why you flipped indices here? I don't think it matters, but I was curious.
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Yeah, it doesn't quite matter here since we're dealing with square matrices for correlations. Just thought it might be useful for future-proofing, in case someone comes along with a non-square matrix to plot. I thought the flip from matrix.shape[0] to matrix.shape[1] for the x-axis made sense, as in a 2D matrix (array), matrix.shape[0] represents the number of rows (which can be thought of as the matrix's height?), and matrix.shape[1] represents the number of columns (which can be thought of as the matrix's width?), unless I'm totally mistaken.
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That all makes sense to me. Thanks for the quick explanation!
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