bactoscoop.signalcorrelation¶

Classes and public operations¶

SignalCorrelation¶

class bactoscoop.signalcorrelation.SignalCorrelation(df, channel1, channel2, feature, method_name, prepared_feature=None)¶

Select an implemented relationship between two channel-prefixed feature columns.

The feature must exist for both channels. calculate mutates the supplied DataFrame; prepared_feature can cache the numerical input arrays.

Parameters:

df – DataFrame to inspect, transform, or augment. Correlation mutates it in place and returns it.
channel1 – First channel prefix for the matching feature.
channel2 – Second channel prefix for the matching feature.
feature – Unprefixed shared feature name; corresponding channel-prefixed columns must exist.
method_name – Implemented correlation selector, such as pearson, manders, or ratio.
prepared_feature – Optional cached signal arrays and validity mask for a channel/feature pair.

Returns:

None (no explicit return statement).

Assigned state: self.df, self.channel1, self.channel2, self.feature, self.method_name, self.prepared_feature, self.method.

View current source, line 16.

SignalCorrelation.get_method¶

SignalCorrelation.get_method()¶

Get method.

Return expressions in source:

methods[self.method_name]

Explicitly raised exceptions: ValueError. Errors from called functions can also propagate or be recorded.

View current source, line 35.

SignalCorrelation.validate_inputs¶

SignalCorrelation.validate_inputs()¶

Validate inputs.

Returns:: None (no explicit return statement).

Explicitly raised exceptions: ValueError. Errors from called functions can also propagate or be recorded.

View current source, line 60.

SignalCorrelation.calculate¶

SignalCorrelation.calculate()¶

Calculate the selected signal relationship row by row.

Requires matching prefixed columns. Adds a function-name-suffixed column to df and returns that same DataFrame.

Return expressions in source:

self.df

Assigned state: self.df.

View current source, line 71.

SignalCorrelation.prepare_feature_pair¶

SignalCorrelation.prepare_feature_pair(df, channel1, channel2, feature)¶

Prepare feature pair.

Parameters:

df – DataFrame to inspect, transform, or augment. Correlation mutates it in place and returns it.
channel1 – First channel prefix for the matching feature.
channel2 – Second channel prefix for the matching feature.
feature – Unprefixed shared feature name; corresponding channel-prefixed columns must exist.

Return expressions in source:

{'signals1': signals1, 'signals2': signals2, 'valid_mask': valid_mask}

View current source, line 101.

SignalCorrelation.is_valid_signal¶

SignalCorrelation.is_valid_signal(signal)¶

Is valid signal.

Parameters:: signal – Input signal consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

bool(signal_array.size and np.isfinite(signal_array).all())
False

View current source, line 117.

SignalCorrelation.ratio¶

SignalCorrelation.ratio(signal1, signal2)¶

Ratio.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
signal1 / signal2

View current source, line 125.

SignalCorrelation.manders_overlap_coefficient¶

SignalCorrelation.manders_overlap_coefficient(signal1, signal2)¶

Manders overlap coefficient.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
np.sum(signal1 * signal2) / np.sqrt(np.sum(signal1 ** 2) * np.sum(signal2 ** 2))

View current source, line 137.

SignalCorrelation.pearson_correlation_coefficient¶

SignalCorrelation.pearson_correlation_coefficient(signal1, signal2)¶

Pearson correlation coefficient.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
stats.pearsonr(signal1, signal2)[0]

View current source, line 150.

SignalCorrelation.li_icq¶

SignalCorrelation.li_icq(signal1, signal2)¶

Li icq.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
(np.sum(product) / len(signal1) - 0.5) * 2

View current source, line 159.

SignalCorrelation.spearman_rank_correlation¶

SignalCorrelation.spearman_rank_correlation(signal1, signal2)¶

Spearman rank correlation.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
stats.spearmanr(signal1, signal2)[0]

View current source, line 172.

SignalCorrelation.kendall_tau¶

SignalCorrelation.kendall_tau(signal1, signal2)¶

Kendall tau.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
corr

View current source, line 181.

SignalCorrelation.distance_correlation¶

SignalCorrelation.distance_correlation(signal1, signal2)¶

Biased sample distance correlation; undefined profiles return NaN.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
float(np.sqrt(np.clip(correlation_squared, 0, 1)))

View current source, line 191.

Original docstring (legacy wording may differ from the current signature)

Biased sample distance correlation; undefined profiles return NaN.

SignalCorrelation.covariance¶

SignalCorrelation.covariance(signal1, signal2)¶

Covariance.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
np.cov(signal1, signal2)[0, 1]

View current source, line 235.

SignalCorrelation.normalized_cross_correlation¶

SignalCorrelation.normalized_cross_correlation(signal1, signal2)¶

Normalized cross correlation.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
np.correlate(signal1 - np.mean(signal1), signal2 - np.mean(signal2))[0] / (len(signal1) * np.std(signal1) * np.std(signal2))

View current source, line 244.

SignalCorrelation.entropy_difference¶

SignalCorrelation.entropy_difference(signal1, signal2)¶

Entropy difference.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
np.abs(entropy1 - entropy2)

View current source, line 255.

SignalCorrelation.kurtosis_ratio¶

SignalCorrelation.kurtosis_ratio(signal1, signal2)¶

Kurtosis ratio.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
np.abs(kurtosis1 / kurtosis2) if kurtosis2 != 0 else np.nan

View current source, line 266.

SignalCorrelation.skewness_product¶

SignalCorrelation.skewness_product(signal1, signal2)¶

Skewness product.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
skew1 * skew2

View current source, line 277.

SignalCorrelation.zero_crossings_difference¶

SignalCorrelation.zero_crossings_difference(signal1, signal2)¶

Zero crossings difference.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
np.abs(zero_crossings1 - zero_crossings2)

View current source, line 288.

SignalCorrelation.fft_peak_ratio¶

SignalCorrelation.fft_peak_ratio(signal1, signal2)¶

Fft peak ratio.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
peak1 / peak2 if peak2 != 0 else np.nan

View current source, line 299.

SignalCorrelation.fft_energy_ratio¶

SignalCorrelation.fft_energy_ratio(signal1, signal2)¶

Fft energy ratio.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
energy1 / energy2 if energy2 != 0 else np.nan

View current source, line 312.

SignalCorrelation.histogram_intersection¶

SignalCorrelation.histogram_intersection(signal1, signal2, bins=10)¶

Intersect probability histograms using shared bins for both profiles.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
bins – Input bins consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
float(np.minimum(hist1 / count1, hist2 / count2).sum())

View current source, line 330.

Original docstring (legacy wording may differ from the current signature)

Intersect probability histograms using shared bins for both profiles.

SignalCorrelation.cosine_similarity¶

SignalCorrelation.cosine_similarity(signal1, signal2)¶

Cosine similarity.

Parameters:

signal1 – Input signal1 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.
signal2 – Input signal2 consumed by this implementation; its exact use is visible in the linked source below. No maintained parameter description is present in the original docstring.

Return expressions in source:

np.nan
np.dot(signal1, signal2) / (np.linalg.norm(signal1) * np.linalg.norm(signal2))

View current source, line 360.