44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
import numpy as np
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from scipy.stats import gaussian_kde
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def shorten_species(name):
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genus, species = name.split('_')
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return genus[0] + '. ' + species
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def unsort_unique(array):
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values, inds = np.unique(array, return_index=True)
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return values[np.argsort(inds)]
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def get_kde(data, sigma, axis=None, n=1000, pad=10):
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if axis is None:
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axis = np.linspace(data.min() - pad * sigma, data.max() + pad * sigma, n)
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pdf = gaussian_kde(data, sigma)(axis)
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return pdf, axis
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def get_saturation(sigmoid, low=0.05, high=0.95, first=True, last=True,
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condense=None):
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if condense == 'norm' and sigmoid.ndim == 2:
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sigmoid = np.linalg.norm(sigmoid, axis=1)
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min_value = sigmoid[0] if first else sigmoid.min(axis=0)
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max_value = sigmoid[-1] if last else sigmoid.max(axis=0)
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span = max_value - min_value
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low_value = min_value + low * span
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high_value = min_value + high * span
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low_mask = sigmoid <= low_value
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high_mask = sigmoid <= high_value
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if sigmoid.ndim == 1:
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low_ind = np.nonzero(low_mask)[0][-1]
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high_ind = np.nonzero(high_mask)[0][-1]
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elif condense == 'all':
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low_ind = np.nonzero(low_mask.all(axis=1))[0][-1]
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high_ind = np.nonzero(high_mask.all(axis=1))[0][-1]
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else:
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low_ind, high_ind = [], []
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for i in range(sigmoid.shape[1]):
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low_ind.append(np.nonzero(low_mask[:, i])[0][-1])
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high_ind.append(np.nonzero(high_mask[:, i])[0][-1])
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return low_ind, high_ind
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