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Merge pull request #442 from ThomasMBury/development
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Add feature to compute various measures of entropy
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@ThomasMBury
ThomasMBury committed May 19, 2024
2 parents 1ea2de2 + 8b4faa9 commit 098722d
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Showing 3 changed files with 408 additions and 14 deletions.
116 changes: 106 additions & 10 deletions ewstools/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@
import plotly.graph_objects as go
from plotly.subplots import make_subplots

import EntropyHub as EH

# For deprecating old functions
import deprecation
Expand All @@ -66,7 +67,8 @@

class TimeSeries:
"""
Univariate time series data on which to compute early warning signals.
Class to hold univariate time series data on which to
compute early warning signals.
Parameters
----------
Expand Down Expand Up @@ -116,7 +118,7 @@ def __init__(self, data, transition=None):
def detrend(self, method="Gaussian", bandwidth=0.2, span=0.2):
"""
Detrend the time series using a chosen method.
Add column to the dataframe for 'smoothing' and 'residuals'
Output is stored in the self.state dataframe.
Parameters
----------
Expand Down Expand Up @@ -182,8 +184,7 @@ def compute_var(self, rolling_window=0.25):
Compute variance over a rolling window.
If residuals have not been computed, computation will be
performed over state variable.
Put into 'ews' dataframe
Output is stored in the self.ews dataframe.
Parameters
----------
Expand Down Expand Up @@ -224,8 +225,8 @@ def compute_std(self, rolling_window=0.25):
Compute standard deviation over a rolling window.
If residuals have not been computed, computation will be
performed over state variable.
Output is stored in the self.ews dataframe.
Put into 'ews' dataframe
Parameters
----------
Expand Down Expand Up @@ -268,8 +269,7 @@ def compute_cv(self, rolling_window=0.25):
mean of the state variable.
If residuals have not been computed, computation will be
performed over state variable.
Put into 'ews' dataframe
Output is stored in the self.ews dataframe.
Parameters
----------
Expand Down Expand Up @@ -313,7 +313,10 @@ def compute_cv(self, rolling_window=0.25):

def compute_auto(self, rolling_window=0.25, lag=1):
"""
Compute autocorrelation over a rolling window. Add to dataframe.
Compute autocorrelation at a give lag over a rolling window.
If residuals have not been computed, computation will be
performed over state variable.
Output is stored in the self.ews dataframe.
Parameters
----------
Expand Down Expand Up @@ -364,8 +367,8 @@ def compute_skew(self, rolling_window=0.25):
Compute skew over a rolling window.
If residuals have not been computed, computation will be
performed over state variable.
Output is stored in the self.ews dataframe.
Add to dataframe.
Parameters
----------
Expand Down Expand Up @@ -406,8 +409,8 @@ def compute_kurt(self, rolling_window=0.25):
Compute kurtosis over a rolling window.
If residuals have not been computed, computation will be
performed over state variable.
Output is stored in the self.ews dataframe.
Add to dataframe.
Parameters
----------
Expand Down Expand Up @@ -443,6 +446,99 @@ def compute_kurt(self, rolling_window=0.25):

self.ews["kurtosis"] = kurt_values

def compute_entropy(self, rolling_window=0.25, method="sample", **kwargs):
"""
Compute entropy using a given method over a rolling window.
Uses EntropyHub https://www.entropyhub.xyz/Home.html.
If residuals have not been computed, computation will be
performed over state variable.
Output is stored in the self.ews dataframe.
Parameters
----------
rolling_window : float
Length of rolling window used to compute variance. Can be specified
as an absolute value or as a proportion of the length of the
data being analysed. Default is 0.25.
method : str
The method by which to compute entropy. Options include 'sample',
'approximate', and 'kolmogorov'
**kwargs
Keyword arguments for the EntropyHub function
Returns
-------
None.
"""

# Get time series data prior to transition
if self.transition:
df_pre = self.state[self.state.index <= self.transition]
else:
df_pre = self.state

# Get absolute size of rollling window if given as a proportion
if 0 < rolling_window <= 1:
rw_absolute = int(rolling_window * len(df_pre))
else:
rw_absolute = rolling_window

# Get data on which to compute entropy
if "residuals" in df_pre.columns:
ts_vals = df_pre["residuals"]
else:
ts_vals = df_pre["state"]

# Compute sample entropy
if method == "sample":

def entropy_func(series):
Samp, A, B = EH.SampEn(series.values, **kwargs)
return Samp

# Compute approxiamte entropy
if method == "approximate":

def entropy_func(series):
Ap, Phi = EH.ApEn(series.values, **kwargs)
return Ap

# Compute komogorov entropy
if method == "kolmogorov":

def entropy_func(series):
K2, Ci = EH.K2En(series.values, **kwargs)
return K2

list_times = []
list_entropy = []
# Set up rolling window (cannot use pandas.rolling as multi-output fn)
for k in np.arange(0, len(ts_vals) - (rw_absolute - 1)):
# Select subset of series contained in window
window_series = ts_vals.iloc[k : k + rw_absolute]
# Assign the time value for the metrics (right end point of window)
t_point = ts_vals.index[k + (rw_absolute - 1)]
# Compute entropy (value for each channel)
entropy = entropy_func(window_series)

list_times.append(t_point)
list_entropy.append(entropy)

ar_entropy = np.array(list_entropy)

df_entropy = pd.DataFrame()
df_entropy["time"] = list_times
num_embedding_dims = ar_entropy.shape[1]
for dim in range(num_embedding_dims):
df_entropy["{}-entropy-{}".format(method, dim)] = ar_entropy[:, dim]
df_entropy.set_index("time", inplace=True)

# Add info to self.ews dataframe
for col in df_entropy.columns:
self.ews[col] = df_entropy[col]
# self.ews = pd.merge(self.ews, df_entropy, how="left", on="time")

def compute_ktau(self, tmin="earliest", tmax="latest"):
"""
Compute kendall tau values of CSD-based EWS.
Expand Down
28 changes: 24 additions & 4 deletions tests/test_ewstools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,6 @@
---------------
"""


import pytest
import numpy as np
import pandas as pd
Expand Down Expand Up @@ -145,10 +144,18 @@ def test_TimeSeries_ews():
ts.compute_auto(lag=5, rolling_window=rolling_window)
ts.compute_skew(rolling_window=rolling_window)
ts.compute_kurt(rolling_window=rolling_window)
ts.compute_entropy(rolling_window=rolling_window, method="sample")
ts.compute_entropy(rolling_window=rolling_window, method="approximate")
ts.compute_entropy(rolling_window=rolling_window, method="kolmogorov")

assert type(ts.ews) == pd.DataFrame
assert "variance" in ts.ews.columns
assert "ac5" in ts.ews.columns
assert "cv" in ts.ews.columns
assert "sample-entropy-0" in ts.ews.columns
assert "sample-entropy-2" in ts.ews.columns
assert "approximate-entropy-1" in ts.ews.columns
assert "kolmogorov-entropy-1" in ts.ews.columns

# Compute EWS on time series without transition
rolling_window = 0.5
Expand All @@ -159,10 +166,17 @@ def test_TimeSeries_ews():
ts2.compute_auto(lag=5, rolling_window=rolling_window)
ts2.compute_skew(rolling_window=rolling_window)
ts2.compute_kurt(rolling_window=rolling_window)
ts2.compute_entropy(rolling_window=rolling_window, method="sample")
ts2.compute_entropy(rolling_window=rolling_window, method="approximate")
ts2.compute_entropy(rolling_window=rolling_window, method="kolmogorov")
assert type(ts2.ews) == pd.DataFrame
assert "variance" in ts2.ews.columns
assert "ac5" in ts2.ews.columns
assert "cv" in ts2.ews.columns
assert "sample-entropy-0" in ts2.ews.columns
assert "sample-entropy-2" in ts2.ews.columns
assert "approximate-entropy-1" in ts2.ews.columns
assert "kolmogorov-entropy-1" in ts2.ews.columns

# Detrend data using Gaussian and Lowess filter
ts.detrend("Gaussian", bandwidth=0.2)
Expand All @@ -181,16 +195,24 @@ def test_TimeSeries_ews():
ts.compute_auto(lag=5, rolling_window=rolling_window)
ts.compute_skew(rolling_window=rolling_window)
ts.compute_kurt(rolling_window=rolling_window)
ts.compute_entropy(rolling_window=rolling_window, method="sample")
ts.compute_entropy(rolling_window=rolling_window, method="approximate")
ts.compute_entropy(rolling_window=rolling_window, method="kolmogorov")

assert type(ts.ews) == pd.DataFrame
assert "variance" in ts.ews.columns
assert "ac5" in ts.ews.columns
assert "sample-entropy-0" in ts.ews.columns
assert "sample-entropy-2" in ts.ews.columns
assert "approximate-entropy-1" in ts.ews.columns
assert "kolmogorov-entropy-1" in ts.ews.columns

# Test kendall tau computation
ts.compute_ktau()
assert type(ts.ktau) == dict
assert "variance" in ts.ktau.keys()
assert "ac5" in ts.ktau.keys()
assert "sample-entropy-0" in ts.ktau.keys()

# Make plotly fig
fig = ts.make_plotly()
Expand Down Expand Up @@ -333,9 +355,7 @@ def test_shopf_init():
[sigma, mu, w0] = helpers.shopf_init(smax, stot, wdom)

# Values that smax, stot should attain (+/- 1dp)
smax_assert = (sigma**2 / (4 * np.pi * mu**2)) * (
1 + (mu**2 / (mu**2 + 4 * w0**2))
)
smax_assert = (sigma**2 / (4 * np.pi * mu**2)) * (1 + (mu**2 / (mu**2 + 4 * w0**2)))
stot_assert = -(sigma**2) / (2 * mu)
wdom_assert = w0

Expand Down
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