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@ -113,14 +113,22 @@ def find_guard_threshold(data_frame, parameters): |
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left_values = data_frame[parameters.left_guard_column] |
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left_values = data_frame[parameters.left_guard_column] |
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right_values = data_frame[parameters.right_guard_column] |
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right_values = data_frame[parameters.right_guard_column] |
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guard_values = left_values.append(right_values, ignore_index=True) |
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guard_values = left_values.append(right_values, ignore_index=True) |
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guard_data = numpy.histogram( |
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guard_data = numpy.histogram( |
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guard_values, bins=parameters.guard_histogram_bins |
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guard_values, bins=parameters.guard_histogram_bins |
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) |
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) |
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# The guard edges (x-axis values) enclose the counts (y-axis values) |
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# of the histogram. This means, there is one more guard edge than count. |
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# To construct a dataframe later, the number of items must be the same. |
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# Therefore a value of 0 counts is added before the "real counts" |
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# this also solves the problem of finding the first peak value later on |
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# if the first histogram bin contains the highest count. |
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guard_counts = numpy.concatenate([[0], guard_data[0]]).astype( |
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guard_counts = numpy.concatenate([[0], guard_data[0]]).astype( |
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numpy.float16 |
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numpy.float16 |
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) |
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) |
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guard_edges = guard_data[1] # edges enclose the counts |
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guard_edges = guard_data[1] |
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pyplot.clf() |
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pyplot.clf() |
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seaborn.lineplot(x=guard_edges, y=guard_counts) |
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seaborn.lineplot(x=guard_edges, y=guard_counts) |
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pyplot.title("Histogram of Guard Avarages (not filtered)") |
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pyplot.title("Histogram of Guard Avarages (not filtered)") |
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