diff --git a/ewstools/core.py b/ewstools/core.py index 47831bb..739f2f6 100644 --- a/ewstools/core.py +++ b/ewstools/core.py @@ -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 @@ -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 ---------- @@ -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 ---------- @@ -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 ---------- @@ -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 ---------- @@ -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 ---------- @@ -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 ---------- @@ -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 ---------- @@ -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 ---------- @@ -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. diff --git a/tests/test_ewstools.py b/tests/test_ewstools.py index e8d00b1..3cfeec9 100644 --- a/tests/test_ewstools.py +++ b/tests/test_ewstools.py @@ -3,7 +3,6 @@ --------------- """ - import pytest import numpy as np import pandas as pd @@ -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 @@ -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) @@ -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() @@ -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 diff --git a/tutorials/tutorial_entropy.ipynb b/tutorials/tutorial_entropy.ipynb new file mode 100644 index 0000000..4e40758 --- /dev/null +++ b/tutorials/tutorial_entropy.ipynb @@ -0,0 +1,278 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 4: Computing entropy\n", + "\n", + "Entropy is a measure of disorder/uncertainty/complexity. It can be computed precisely from a probability distribution. Given data, we can obtain an approximation using several different measures. \n", + "\n", + "*ewstools* currently supports\n", + "- sample entropy\n", + "- approximate entropy\n", + "- kolmogorov entropy\n", + "\n", + "Entropy is expected to **decrease** prior to a bifurcation.\n", + "\n", + "*ewstools* makes use of the Python package [EntropyHub](https://www.entropyhub.xyz/index.html), where lots more information on entropy is available, including other measures.\n", + "\n", + "By the end of this tutorial you should know how to:\n", + "- Compute entropy measures as an early warning signal for a bifurcation\n", + "\n", + "Notebook run time : XX s on Macbook Air (M1, 2020)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import libraries\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Start timer to record execution time of notebook\n", + "import time\n", + "start_time = time.time()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "np.random.seed(0) # Set seed for reproducibility\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "from IPython.display import Image\n", + "\n", + "import ewstools\n", + "from ewstools.models import simulate_ricker" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulate a model\n", + "Let's simulate the Ricker model to give us some data on which to compute entropy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "series = simulate_ricker(tmax=1000, F=[0,2.7])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Detrend the data\n", + "ts = ewstools.TimeSeries(data=series, transition=860)\n", + "ts.detrend(method='Lowess', span=0.2)\n", + "ts.state[['state','smoothing']].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute sample entropy" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ts.compute_entropy(rolling_window=0.5, method='sample')\n", + "ts.ews.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute approximate entropy" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7FsKCp01gTMwyrWBlMYHd5zKB8+LJYHPUnbdwOxRug65jWqYuJ6OqyATc4t0QmQwL/wpb59Ut064v3PWN6R7xuSCxy+Fp/xwIUckw8ePG15P/o9n5xbSDJS/BFw/DuFmQefHhHUB1Cez6DiJT4Md3IG+tOWqpOFD/MjsOhlvehbDIE/30QohmCP2AXp+SvSagx6aeunWcDvtXm+6QLyaZVnDR9uAEZfrto9pA6R7z3O+BEY/AiEnNX77WZqeWlNl0WW815C6HrV9CVTHk3Gi6hioPwvzJkHOzOVk8+AHoeKGZx+8Dd5np1qrZ+fh95ryEEOK4NRbQQ/dXFdehtWvQMtr3NY/eN5nW7/t3mhO3HQdB8S6w2ExA93tM+dR6/88NU6p5wRxM/3nGMPM42vZvzQlXgG1fw7Uvmi6ZjR8f7kYafL+p+8aPYejvzI7H7zNdPs6Y46u3EOIYodtCP5cc2ADLXjbj4B/aWf95gFOtuticr0jIgHcmmla51QF9bobclYdHJSmLGWHjrTQt+n0rzbz3LgVnLNjCTn/dhTiLnJtdLucarU13x5kwxjx/nemGaX+eOWEKMO+/zciZPj813UOf/g5Wvw3R7aEs15RxxJoRQz2uPDyapnC7Oe/hiIE3rjFHKR0uMH32WRe3xqcTolVJQBdnJneFCdzP9zVDPJO6wqEtYI+A7ldCZBIsebHh+aPamrH/l/3ZjGQS4hxwbvahizNfzdHE3f8xgV1ZzciaTZ/Cmpmmb73jIOg0GNbOgVGPmXH6Kd3NdQUHN5oROBs/gfFzIH0I+NzmCMDnMn8t1papayAAJbvM6KC81eZaBVs4bP/GXBQWk9py6xLiBEkLXZyZSveZLpn+t5uhmA3JWwNzbjPDVR0xpu++Rko2/GJh/SNqtIYt88x1C9XF0Cbb9PmX7IbVM8BVakb0OONM/7/fa65rqNHrJ2YntOot8zquE9zwGmz6xFxN3GmQuYK4eFfdoaXHS2uzk7I7D7/n88i5hnOYdLmI0Fa6D36cYy76CouE5dPM3/I8yBpjRgJFJsKoJ8yonnXvm5Oxa2fVXU5YVDCAusDmhPb9zMlbi80E78xLzA5j71LYudDMkzrAXKD1/RRzwZkOmBO//W8zXUeL/2H6++PTzVW7Pa6CLx83gd7mMBeoRSbWrUfFQVPH3jeavEU7FkDfn5m6H9hghrG27wsD7jDXDkS3NdcwRCRIuoZzgAR0cW7xe00Qfvd22PSZSZ9QtAOSu5nrE7yVplzmJdDretMa3/a1uVgrIh6umNL4UM78dfD5QzDyUZM6QSmTfuLzSSan0MaPTdeRDpjybXqbtAuVBWCxmwRuNaLbmcBfdchcNJbYxQRzV8nhMik9zclhi9XsGFJ6mGGi1UVm5xGTZoautj8P+t1i8hTVtOjzfzRHHuveM0NDk7ubRHORSS24wcXpdFIBXSnVAXgDaIPJNPWK1vq5o8oo4DngcqAKuE1r/UNjy5WALk6rRS/Al49B9vUw/CFzAvZU9nnvXW6SvV3yB5MDJxAw5wX2/wAZw6Fwq0nVvPifJkgnZZmdircSul1hWuA7FpgW/cBfmu4bZTkia2a5CfIbPzZHG3Edzbj/6mKTxmLUf5sd1N+61V+/8HhzBDD2z5Ky4SxzsgG9HdBOa/2DUioaWAlcq7XecESZy4H7MQH9QuA5rfWFjS1XAro4rbQ2wa41xug3xl0O9kgTVN0VptUd1/HEluV1wTu3mnQR6UPNUUKN616G9IugaKc5kVyeb674jUk1J5TbZJvt43ObLhxxxjqpUS5a6zwgL/i8XCm1EUgFNhxR7BrgDW32DkuUUnFKqXbBeYVofUqdecEcTHbQ2udRJ3cdgd1phnBunWeCeeeRpntlyK8Pf/bYNMgYanZwS16EeY/CvwabxGuF2825h3u+N/n7S/aa8xJjnz4zrm8QTTquYYtKqXSgH7D0qEmpwJGp+XKD79UJ6Eqpu4G7ATp2PMFWiBCiYQkZcP1UM7In+7qGyykFg+4zJ1O/e8bkBKouNgH8793rli3dC1c9Z7qGxBmt2QFdKRUFvAf8Rmtd1lT5+mitXwFeAdPlciLLEEI0IefG5pcd+d9wwV2Hu1mWvGTONVz+F+h+lemX/+oJeGGAOYF83cuH+/H9Xpj1MzMqKLk7XPqU2UGczjtyiTqaNcpFKWUHPgHmaa3/Xs/0l4EFWuuZwdebgRGNdblIH7oQZyivq+6495I98Mn/g21fmROzyd0hItFcuLX962Pn/93mc6cf3usyo5nCIk7bKhvrQ2/y9HZwBMs0YGN9wTxoLnCrMgYCpdJ/LsRZ6shgDuYk7bhZZvhlUleTRG3XdyaYZ42BJ0tg6IOHy+8N9sj6fSZh3IpXzSifGoGAyTvUkgq3myOG06XmTmnvTDAjiT6fBJ/9l7noqxU1Z5TLRcB3wI9AzX/lUaAjgNb6pWDQ/wcwFjNs8XatdaPNb2mhC3GWOfLWjuUHYP37wQuogonUfB743zTThXPpU/DNU6Z/HszVs0ldTT/9/tWmX77fBHN9QGmuaeW2yTZltN9cdNXjarMzaerOYKX7YEpP6DLajNg5meGo+1ebFM/pF5nupsRMc4euI234yIwmqk/29fCTqXXrULAZEjqf2B3O6iEXFgkhTo9pl5oWelg0eMpNiuSUHvDdFJMXPzzetPBL99ZN05B5MWybX/8ye99k8uenBE/WBgKw+VMzjn//Klj6r8Nlk3uYjJ2dBte9ajbgN339ymJuUdkup/51PdvbdDEd6c5vzOifLx+DtPNNquqqQjMtJg3uXWyODla9CfOfNCOG2vUxATw8Ab7/u8kqesUzEJ9hLmTrNOj4tusRJKALIU6P/atNYLPYTOqE3jeZMfZHtu7BvPZWmeDpc5my6z80QV9ZTEBcM9PchhHM8mI7BNMxVMGWL+qut/MIc0HW5/9lXg/5jTmJu/UrE/Rzl5sx9januWo3MdMcEVjsZmRQXEfTKn++nwm+A39pbpIz9wFzb96SvWYZgeBVyD97x4wMSuhct/98zSxz796jJXQ2RyM1TuK+BRLQhRBnpwPrTfBf9ZbJf5O73LTsB94LuxdB2gBzNW7Ab/r+t8yD2bccvoNXjZpbNB4tup3J+XOkO74yOffB5NKpScB2zYuQOTqYG6iR1Amf/Ze5XeNlf4GVr5kum7Y55srh76eYMsMnwchHTmiTSEAXQoSGQMD0tzd2T9rC7fDju+bGKW16my6fjKGmtY6Ct39iyj2yz+TRyV9rAvDSl0zenGH/dTgdQtFO837HgY2P628uvw8WPQcZIyCt/wktQgK6EELUeHGw6Tr51fLWrskJkRtcCCFEjbsXtHYNThkJ6EKIc0sI3xxE8mYKIUSIkIAuhBAhQgK6EEKECOlDF+Ikeb1ecnNzcblcrV0VEUKcTidpaWnY7c1PGSABXYiTlJubS3R0NOnp6Si5SbNoAVprCgsLyc3NJSMjo9nzSZeLECfJ5XKRmJgowVy0GKUUiYmJx33UJwFdiBYgwVy0tBP5TklAF0KIECEBXQjRYi6//HJKSkpOejmrV6/ms88+O/kKNWHXrl3MmDHjlK/naDt37uTCCy8kMzOTm2++GY+nZW6MIQFdCFEvn8933PN89tlnxMXFnfS6z4SAfiKfv7kefvhhfvvb37Jt2zbi4+OZNm1aiyxXAroQIeLaa6+lf//+ZGdn88orrwAQFRXFb3/7W7Kzsxk9ejQFBQUAjBgxgl//+tf07duXXr16sWzZMgAmT57MhAkTGDJkCBMmTGDXrl2MGjWKnJwcRo8ezZ49eygtLaVbt25s3rwZgHHjxvHvf/8bgPT0dA4dOsSuXbvo3r07t912G127dmX8+PHMnz+fIUOGkJWVVbu+ZcuWMWjQIPr168fgwYPZvHkzHo+HJ554gtmzZ9O3b19mz55NZWUlP//5z7ngggvo168fH330Ub3bYPv27YwdO5b+/fszdOhQNm3aBMBtt93GAw88wODBg+ncuTPvvvsuAJMmTeK7776jb9++TJkyhenTp3P11VczatQoRo8eTVFREddeey05OTkMHDiQtWvX1tlOgwYNIisrq/bz33rrrXz44Ye19Rk/fvwxddVa880333DDDTcAMHHixDrznBStdas8+vfvr4UIBRs2bKh9PnnuOn3TS4ta9DF57rpm1aOwsFBrrXVVVZXOzs7Whw4d0oB+6623tNZa//73v9f33Xef1lrr4cOH6zvvvFNrrfV//vMfnZ2drbXW+sknn9TnnXeerqqq0lprfeWVV+rp06drrbWeNm2avuaaa7TWWn/55Zd64MCBeubMmXrMmDG1dejUqZMuKCjQO3fu1FarVa9du1b7/X593nnn6dtvv10HAgH94Ycf1i6ntLRUe71erbXWX331lb7++uu11lq/9tprtXXVWutHHnlEv/nmm1prrYuLi3VWVpauqKg4ZhuMGjVKb9myRWut9ZIlS/TIkSO11lpPnDhR33DDDdrv9+v169frLl26aK21/vbbb/UVV1xRO/9rr72mU1NTa7flr371Kz158mSttdZff/217tOnT+12ysnJ0VVVVbqgoECnpaXpffv26QULFtR+tpKSEp2enl77+WoUFBTUrl9rrffs2VO7/Y925HerBrBCNxBXZRy6ECHi+eef54MPPgBg7969bN26FYvFws033wzALbfcwvXXX19bfty4cQAMGzaMsrKy2r7vq6++mvBwc5/QxYsX8/777wMwYcIEHnroIQAuueQS5syZw3333ceaNWvqrU9GRga9e/cGqD1CUErRu3dvdu3aBUBpaSkTJ05k69atKKXweuu/0fOXX37J3LlzeeYZc49Sl8vFnj176NGjR22ZiooKFi1axI033lj7ntvtrn1+7bXXYrFY6NmzJwcOHGhwO15yySUkJJi7CX3//fe89957AIwaNYrCwkLKysyt86655hrCw8MJDw9n5MiRLFu2jGuvvZZ7772XgoIC3nvvPX7yk59gs52+MCsBXYgW9ORV2a2y3gULFjB//nwWL15MREQEI0aMqHcM85FD4Y4eFlfzOjIyssn1BQIBNm7cSEREBMXFxaSlpR1TxuFw1D63WCy1ry0WS23/9OOPP87IkSP54IMP2LVrFyNGjKh3fVpr3nvvPbp161bn/dtvv51Vq1bRvn17Zs2aRVxcHKtXr653GUfWRzdyH4jmfH5oePvdeuutvPXWW8yaNYvXXnsNgDFjxnDgwAEGDBjAv//9b0pKSvD5fNhsNnJzc0lNTW3WOpsifehChIDS0lLi4+OJiIhg06ZNLFmyBDCBt6a/eMaMGVx00UW188yePRswrdDY2FhiY2OPWe7gwYOZNWsWAG+//TZDhw4FYMqUKfTo0YMZM2Zw++23N9iybk69a4LZ9OnTa9+Pjo6mvLy89vWYMWN44YUXagPxqlWrAHjttddqT6DGxMSQkZHBnDlzABO0Gzp6aGg9Rxs6dChvv/02YHaaSUlJxMTEAPDRRx/hcrkoLCxkwYIFnH/++YDpr3/22WcB6NmzJwDz5s1j9erVTJ06FaUUI0eOrP2/vP7661xzzTVNb6xmkIAuRAgYO3YsPp+PHj16MGnSJAYOHAiY1uayZcvo1asX33zzDU888UTtPE6nk379+nHPPfc0OMrihRde4LXXXiMnJ4c333yT5557js2bNzN16lT+9re/MXToUIYNG8ZTTz11QvV+6KGHeOSRR+jXr1+dUSUjR45kw4YNtSdFH3/8cbxeLzk5OWRnZ/P444/Xu7y3336badOm0adPH7Kzsxs8eVojJycHq9VKnz59mDJlyjHTJ0+ezMqVK8nJyWHSpEm8/vrrdeYdOXIkAwcO5PHHH6d9+/YAtGnThh49enD77bc3uN4///nP/P3vfyczM5PCwkLuuOOORuvZXHILOiFO0saNG+v05Z5JoqKiqKioOOb9ESNG8MwzzzBgQL13MhNNmDx5MlFRUTz44IPHTKuqqqJ379788MMP9R71HI/6vluN3YJOWuhCCNFC5s+fT48ePbj//vtPOpifCDkpKkQIq691DqY/WJy4yZMn1/v+xRdfzO7du09vZY4gLXQhhAgREtCFECJESEAXQogQ0WRAV0q9qpQ6qJRa18D0WKXUx0qpNUqp9UqphsfqCCGEOGWa00KfDoxtZPp9wAatdR9gBPA3pVTYyVdNCHG2kfS5zfOPf/yDzMxMlFIcOnSoxZbbZEDXWi8EihorAkQrc91rVLDsqcs7KYQ4LSR97qkLY0OGDGH+/Pl06tSpRZfbEn3o/wB6APuBH4Ffa60D9RVUSt2tlFqhlFpRk8ZTCNEyJH3u2ZE+F6Bfv36kp6ef8P+6QQ2lYTzyAaQD6xqYdgMwBVBAJrATiGlqmZI+V4SKOilOP3tY61cvb9nHZw83qx6SPvfsSJ97pJrt1ZDWSJ97O/B0cEXblFI7ge7AshZYthCimSR9rqTPbYk17QFGA98ppdoA3YAdLbBcIc4+lz3dKquV9LlnT/rcqVOnNmv5J6I5wxZnAouBbkqpXKXUHUqpe5RS9wSL/BEYrJT6EfgaeFhr3XKnbYUQTZL0uWdP+txTqTmjXMZprdtpre1a6zSt9TSt9Uta65eC0/drrS/VWvfWWvfSWr91SmsshDiGpM81zpb0uc8//zxpaWnk5uaSk5PDnXfe2Wg9m0vS5wpxkiR97rlH0ucKIUSIk/S5QohTRtLnnhqSPlcIIcQpJQFdCCFChAR0IYQIERLQhRAiREhAF0K0GEmf2zzjx4+nW7du9OrVi5///OcnfGHW0SSgCyHqJelzT1363PHjx7Np0yZ+/PFHqqurW+wKUgnoQoQISZ979qTPvfzyy1FKoZTiggsuIDc398T/8UdqKA3jqX5I+lwRKo5Mcfr00qf1bZ/f1qKPp5c+3ax6SPrcsy99rsfj0f369dMLFy6sd3prpM8VQpwBJH3u2Zc+995772XYsGG1Sc9OlgR0IVrQwxc83CrrlfS5Z1/63N///vcUFBTw8ssvN2t9zSF96EKEAEmfe3alz506dSrz5s1j5syZWCwtF4YloAsRAiR9rnG2pM+95557OHDgAIMGDaJv37784Q9/aLSezSXpc4U4SZI+99wj6XOFECLESfpcIcQpI+lzTw1JnytECGutrksRuk7kOyUBXYiT5HQ6KSwslKAuWozWmsLCQpxO53HNJ10uQpykmpv91lxWL0RLcDqd9Y7vb4wEdCFOkt1uJyMjo7WrIYR0uQghRKiQgC6EECFCAroQQoQICehCCBEiJKALIUSIaDKgK6VeVUodVEqta6TMCKXUaqXUeqXUf1q2ikIIIZqjOS306cDYhiYqpeKAF4GrtdbZwI0NlRVCCHHqNBnQtdYLgaJGivwMeF9rvSdY/mAL1U0IIcRxaIk+9K5AvFJqgVJqpVLq1hZYphBCiOPUEleK2oD+wGggHFislFqitd5ydEGl1N3A3QAdO3ZsgVULIYSo0RIt9Fxgnta6Umt9CFgI9KmvoNb6Fa31AK31gOTk5BZYtRBCiBotEdA/Ai5SStmUUhHAhcDGFliuEEKI49Bkl4tSaiYwAkhSSuUCTwJ2AK31S1rrjUqpL4C1QACYqrVucIijEEKIU6PJgK61HteMMn8F/toiNRJCCHFC5EpRIYQIERLQhRAiREhAF0KIECEBXQghQoQEdCGECBES0IUQIkRIQBdCiBAhAV0IIUKEBHQhhAgREtCFECJESEAXQogQIQFdCCFChAR0IYQIERLQhRAiREhAF0KIECEBXQghQoQEdCGECBES0IUQIkRIQBdCiBAhAV0IIUKEBHQhhAgREtCFECJESEAXQogQIQFdCCFChAR0IYQIERLQhRAiREhAF0KIECEBXQghQkSTAV0p9apS6qBSal0T5c5XSvmUUje0XPWEEEI0V3Na6NOBsY0VUEpZgT8DX7ZAnYQQQpyAJgO61nohUNREsfuB94CDLVEpIYQQx++k+9CVUqnAdcC/mlH2bqXUCqXUioKCgpNdtRBCiCO0xEnRZ4GHtdaBpgpqrV/RWg/QWg9ITk5ugVULIYSoYWuBZQwAZimlAJKAy5VSPq31hy2wbCGEEM100gFda51R81wpNR34RIK5EEKcfk0GdKXUTGAEkKSUygWeBOwAWuuXTmnthBBCNFuTAV1rPa65C9Na33ZStRFCCHHC5EpRIYQIERLQhRAiREhAF0KIECEBXQghQoQEdCGECBES0IUQIkRIQBdCiBAhAV0IIUKEBPSzUKW3kmV5y8ivzOefq/9JiavktNdBa82Okh1orWvro7U+7fUQQhzWEsm5xGng8Xv4dMen7Czbydxtcyl0FeK0OnH5XawtWMtLF79EMEHaKefyuXhpzUtMWzeNUR1GsaZgDYWuQn6R8wt+1e9XVHor8Ws/MWExp6U+IrT4Aj5eWPUCF3e8mN7JvQFYkreEBGcCXeO7Njn/xsKNfLv3W36R8wusFuupru4xtNZ8s/cb1h9aT6wjlssyLiMlIuW0rFu1VqtqwIABesWKFa2y7rOJL+DjrQ1vMWPTDPIq8wCIdcSS6Ewkyh5Fv5R+vL7hdR4c8CB7y/fy0PkPEWYNO2Y5Hr+HNze8SefYzizNX0pSeBLegJeJPSeSX5lPRmwGSim01izNX8rqg6tZkreEDtEdWHdoHfHOeNw+NyXuEiLtkWws2li77L7JfUlwJrAgdwF39b6LV9e9il/7eWzgY/wk6ydsLd7KGxveoE9yH27qdhMA20u28/2+7xmaNpQV+StIdCaSV5nHluItRNojuaXnLfxtxd+4oesNZMZlNvsH4fV7sVvtAOwo3cHcbXPpldSLoWlDcVgdjc5b4irhjQ1v8NPuPyUlIoUqbxV/Wf4XspOyuSHrhmbvMAM6gEXJwe/x0lrj9rv509I/8eG2DwFIjUrF4/dQUF2ARVnIiMnAYrHwxMAn6JvSt97ljH1vLPsq9nFl5yvpHNuZ89ue32DZ5tZrYe5Cvt/3PSXuEh46/yGSI+pP/11YXchflv+Fz3Z+hkVZCOgAKeEp3J1zN90SutE3pS8LcxeSk5RDnDPuhOqjlFqptR5Q7zQJ6GcurTWPfv8on+z4hAvaXsAtPW4hLTqNSHsk7aPaA1DlreKSdy+hzFMGwB8G/4Hrsq6rs5yDVQe5/5v72VC4ocF1pcekk1eZR4IzoXbHcbSk8CTinfFsL9nO5EGTGZM+hvWF6xnQZgDF7mIuffdS3H43mXGZRNgiWHto7THL+Pel/yY2LJaJX0yk2lfd7G3x7MhnGZE2osEW14yNM2qPYOZcNYcEZwKXvnspJe4SABxWB3GOOLrGdyXCHkGJu4Sbut7EqoOrmL9nPv3b9Gdj4UZ2lO6gfWR7MuMz2V6ynX0V+2rXkRyezM96/Iyru1yNx+/hqSVPMSR1CFd0voIEZwLzd89n+vrp7CjZwT8v/if9UvrhC/hQqFZpKZ4NSt2lfLv3W2ZsnFGnkZDgTOCi1IsodZeyq2wXl3a6lNUFqwHTGIi0R/LBNR/U2UlrrXlmxTO8seGNOuuwW+z8z9D/4dJOlza5ow3oAArFmoI1fLLjE7wBL0vzltb5HoRZwhjcfjBjMsYwJn0Mi/cvprC6kO0l2/lg2wdUeau4K+cu7s65m83Fm7lv/n0UugoB6J7QnU1Fm7i52808NvCxE9pmEtDPIoXVhczYNAOv38vy/OWsK1zHL/v8knv73tvgPGsK1vDO5nf4ZMcntI9sz6MXPsrWkq38tNtPsVvt3DHvDjYVbeKePvfw8faPubHrjUxZOYWM2IzaH1Hf5L4kRyTj8rlw+90AjM0YS4IjgXJvOWPSx+CwOrAoCxWeCqLCoo6px8LchfxpyZ/46/C/kh6bzodbP2RNwRosykKYNYyv93xNpbcSgERnIs+OfJa52+eSW57LnvI9XJ91PTd3u5kXV7/IjE0zjln+qA6jOK/NeQxNG8rW4q28sOoF2ka0pW1kWz7a/hFWZcWv/YRZwkiPTWdL8RamXjoVf8DPwn0L2VW6ix2lO8irzCPcFl67Q8mKz2Jn6U7SotIY2WEkH23/CI/fQ0pECg+c9wArD6zku9zvCLeFs7FoIzZlw2lzUuGtAMBmsfHUkKeY9N2k2rraLXai7FEUu4tJiUjh6aFPY1VWvt37Lbf0uIU2kW1O8BtydluYu5A/LvkjozuOxqqsfLjtw9rGSI2bu93Mb/v/lkh7ZL3LWJK3hLu+vIuhqUP5Tf/f0Dayrenm2PMNTyx6gis7X8mTg56kwltBmaeM8Z+Or/1fXdD2Aib0nEB0WDS55bkUu4op9ZSSFJ7E+1vfZ1/FPhxWBxWeCjwBDwC9k3pzRecrGJ42nIAOcP8397OjdEe9desS24W/j/g7neM6176XV5HHkrwlFLoKee6H5wD49LpP6RjT8YS2oQT0s4TWmru+vIul+Utr3+sa35XZV87GZmn6dMfKAyu57Yvbal8nOhNJiUhhY9FG/jLsL1yWcVntNLffjd1iZ/r66QxLHUZmfGaLfpb6FLmKGP/peHIrcnl66NNc0fmKestprdlfuZ9EZyJOm5NVB1cxd/tc3t3ybp1yHaM7Uu2rpqC6gBFpI3h25LMsyVvCWxvf4vt93zO4/eB6zy14/V5KPaU89v1jnN/2fH7e6+cAteUCwZtvHd2a8wV8fJf7HU8uepI4ZxyPXfgYr294nYW5CwFoE9GGRy58BLfPzYxNM1hTsKbez9czsSdvXf4Wy/KWsTx/OWPSx+DyuzhYdZAh7YfUu7M8Hn9e9mfCbeE8cN4DJ7WcgA7w/A/P4/K7eOj8h5rVjZRXkUeZp4wDVQdIDE+kS2wXdpftJiUihSf+7wkW5C6oUz4nOYef9/o52YnZVHmrUEqRHpPeZPfWv9b8i5fWvIQFC37tR6Nrl/f62Nfr/F5KXCU8tfQp5u2a1+gyM2IzOC/lPDYUbiAhPIHfD/o9AR2gTWSbOp/9UPUh1hxcQ0F1Ae9vfZ9qXzWTLphEZlwmieGJjf5WP93xKSXuEsb3GN9oXRpzzgT0NQVr2FS4iWuzrmVF/gr6pfQjwh7RoutoaVprdpXtItwWzvL85Tz6/aNMumASF6VeRJwjDrvFflyfYWHuQj7a9hGXZ1zO3O1zOVR9iBEdRnBXzl2n8FM0X7GrGF/A12AfZEMCOsCzPzxL24i2zNkyh5iwGF68+EVcPhef7PiEG7reUKdF5/K5cFgdp+RE8dF95JuLNvPqule5pccttSfxAMo8Zaw/tJ7UqFSmrZvGwHYDcflcPLHoiQaX3SOhBwEdICosiih7FMM7DOeGrBvwBDxNngMAE2xGvjMSgGeGP8PAdgOJdcQCZps4bU4A/AE/br+79rvlD/jZWbqTcHs4i/cvZvH+xXy5+8s6y/71eb9mXPdx7CnbQ1p0Gv9e+2/CbeH8su8vcfvd2JSNcZ+Oq9N1UsOqrFiUhbt638Wg9oPYWrKVQe0G0S6y3Ql3RxW7ivnL8r/wyY5PAGgb2ZbPrvus9hzKkbTWBHSAb/Z+w5OLnqTcU052YjaPXPgIXr+X/ZX7uSzjMuyWY+dtTM1yT2eXWsgH9LyKPKatm8bszbMBakd/dE/ozhuXvcGGwg24fW4Gpw5ukfW1lM1Fm3nwPw+yq2xX7XtdYrvw7tXvNqtFfq7yBXxYlfW0jeppSVprnlz0JB9s+wCAMeljGmw5RtujKfeW174ekjqEZ4Y902gL/tV1rzJl5ZQ67w1qN4jM+ExmbprJqA6jGJsxlrc2vMXagrXcnXM3E7Mn8syKZ5izZU7tPJH2SLrFd6NTTCf+k/sfilxFgOnbLnIV1f7GAJ4c9CQvr30Zh9XB7rLdAEweNJk95Xv4fOfnRNoj6Z7QnYnZE+me0P0EtlrjtNbM3jyboWlDSY1KbdY8C/YuoG9y3xM+MdmaQjqg7yjZwYTPJ1DmKaN9ZHs6RHdgaf7S2i/ckV+8vw77K2PSx5wxgeC2L25jZ+lO7ut7H2D6z0d2HHlKvvTizLKvYh/vbXmPX/b9Jb6Aj4AOEGYNY/H+xYTbwkmLSiMlIoVnf3iWr/d8zbC0YczaNIshqUO4t8+97K/cT9uItlT7qokKi2J/xX4W7V/EnC1zGNhuIA/0e4CF+xby0pq6NxWzKRs+7QPMaKlSd2md6YPaDeJX/X5F76TedbqgPH4P4z4dR7gtnKu6XMXC3IWM7DCS/136v7XLq/HP0f9kWNqwU7j1zm0hF9CLXEW8vv51Vh5YydbirThtTl4Y9QLpsemEW8MpqC6gXWQ7ZmyawYbCDfRM7Mn09dPJr8ynV2IvBqcOpkdCD4Z3GH7ch1gna0/ZHv6x6h90iOnAK2tf4eHzH+aWnrec1jqIs9PsTbN5aulTDU53WB2kRqXy9NCn6ZFoum4mfTeJrLgsOkR34OJOF1PsKiavMo94ZzxtItowY+MM9lfup11kOyZmT2y0n7y+4ZjL85ejtaZfSj++2PUFr6x9hVlXzmrwhKY4eSEV0Ms8ZUz4bAK7y3bTNb4rYdYwHh/4ON0SujU6n8fvYc6WOczaNKu2i6N7Qnf+OOSPJ9wiLqgqoMRdQmZcJkopfAEfLp8Ll99FrCOWX3z1C67qfBXXZV1HqbuUOVvmMHPjTA5WHwQgMy6T2VfOrnfcuBD1WZG/gr3le8mIzeBQ9SHsFjuegIfUqFTSY9LP+HNG4uSFVED/ePvHPLHoCV6++GUuaHfBcc+vtWbtobVsLtrMi6tfpNRdyvAOw/nhwA/8z9D/4aLUi+qUL3GVsLNsJx2iO5AUnkSxq5gleUtYnr+cT3Z8QrWvmtSoVLLisvhu33f4tR+AlIgUDlaZwP2LnF8wd/tc8irzaBfZjt8N+B2xjljSY9JpG9n2uD+DEOLcFVIBHSC3PJe06LSTrkOpu5S/Lv8r8/fMp9JbSVZ8Fg/2f5AAAb7a/RUAn+/8vHa8cqQ9Eq/fiyfgIcIWwfAOwzm/7fm8s/kddpXuYnSn0aRGpbKmYA1L85bWWVfPxJ48euGj9Enuc9L1FkKcu0IuoJ8KX+z6gkkLJ9W2sBUKjWZkh5Fcm3ktL615ibzKPMakj+GqLlfRI6FHbVeJ1hq/9teOTHH73SzPX073hO68s/kdrs+6XlriQogWIQG9mco95SzLX8aS/Uu4r+99hFnDavskvQEvvoCPcFt4K9dSCHEuayygy2DnI0SHRTO642hGdxx9zDS7xX7aR8QIIcTxkJRwQggRIiSgCyFEiJCALoQQIUICuhBChIgmA7pS6lWl1EGl1LoGpo9XSq1VSv2olFqklJKB1kII0Qqa00KfDoxtZPpOYLjWujfwR+CVFqiXEEKI49TksEWt9UKlVHoj0xcd8XIJcPKXcAohhDhuLd2HfgfweUMTlVJ3K6VWKKVWFBQUtPCqhRDi3NZiFxYppUZiAvpFDZXRWr9CsEtGKVWglNrdUus/iyUBh1q7Emcg2S4Nk21Tv3Nlu3RqaEKLBHSlVA4wFbhMa13YnHm01sd3D7IQpZRa0dBlvOcy2S4Nk21TP9kuLdDlopTqCLwPTNBabzn5KgkhhDgRTbbQlVIzgRFAklIqF3gSsANorV8CngASgReDt6zynet7SSGEaA3NGeUyronpdwJ3tliNzj0yzLN+sl0aJtumfuf8dmm19LlCCCFallz6L4QQIUICuhBChAgJ6KeYUmpXMM/NaqXUiuB7CUqpr5RSW4N/44PvK6XU80qpbcH8OOe1bu1PHaVUnFLqXaXUJqXURqXUINkuoJTqFvyu1DzKlFK/kW1jKKV+q5Rar5Rap5SaqZRyKqUylFJLg9tgtlIqLFjWEXy9LTg9vZWrf8pJQD89Rmqt+x4x+mcS8LXWOgv4Ovga4DIgK/i4G/jXaa/p6fMc8IXWujvQB9iIbBe01puD35W+QH+gCvgA2TYopVKBB4ABWutegBX4KfBnYIrWOhMoxlzgSPBvcfD9KcFyoU1rLY9T+AB2AUlHvbcZaBd83g7YHHz+MjCuvnKh9ABiMUndlGyXRrfTpcD/ybap/WypwF4gATNC7xNgDObqUFuwzCBgXvD5PGBQ8LktWE61Rt1P10Na6KeeBr5USq1USt0dfK+N1jov+DwfaBN8XvOFrZEbfC/UZAAFwGtKqVVKqalKqUhkuxztp8DM4PNzfttorfcBzwB7gDygFFgJlGitfcFiR37+2m0TnF6KuWYmZElAP/Uu0lqfhzk0vk8pNezIido0H861saM24DzgX1rrfkAlh7sQgHN2u9QK9gNfDcw5etq5um2C5w2uwTQI2gORNJ7a+5wjAf0UC7Yq0FofxPSFXgAcUEq1Awj+PRgsvg/ocMTsacH3Qk0ukKu1Xhp8/S4mwJ/r2+VIlwE/aK0PBF/LtoGLgZ1a6wKttReTcmQIEKeUqrlI8sjPX7ttgtNjgWblmjpbSUA/hZRSkUqp6JrnmD7RdcBcYGKw2ETgo+DzucCtwZELA4HSIw6zQ4bWOh/Yq5TqFnxrNLCBc3y7HGUch7tbQLYNmK6WgUqpCGXyjNR8b74FbgiWOXrb1GyzG4Bvgkc3IUuuFD2FlFKdMa1yMN0MM7TWf1JKJQLvAB2B3cBNWuui4Jf0H5jDyCrgdq31ilao+imnlOqLydAZBuwAbsc0MM7p7QK1O/89QGetdWnwvXP+OwOglPo9cDPgA1Zh0o6kArMwJ0tXAbdord1KKSfwJtAPKAJ+qrXe0SoVP00koAshRIiQLhchhAgREtCFECJESEAXQogQIQFdCCFChAR0IYQIERLQxTkjmOHx3uDz9kqpd1u7TkK0JBm2KM4ZwfSpn2iTqU+IkNPkPUWFCCFPA12UUquBrUAPrXUvpdRtwLWY3CBZmARQYcAEwA1cHryIpwvwTyAZcxHPXVrrTaf7QwjREOlyEeeSScB2bXKN/9dR03oB1wPnA38CqoKJwxYDtwbLvALcr7XuDzwIvHg6Ki1Ec0kLXQjjW611OVCulCoFPg6+/yOQo5SKAgYDc8zV9gA4Tn81hWiYBHQhDPcRzwNHvA5gficWTN7tvqe5XkI0m3S5iHNJORB9IjNqrcuAnUqpG6H2Xp59WrJyQpwsCejinKG1LgT+Tym1DvjrCSxiPHCHUmoNsB5zswUhzhgybFEIIUKEtNCFECJESEAXQogQIQFdCCFChAR0IYQIERLQhRAiREhAF0KIECEBXQghQsT/B+pnG2tyz5rXAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ts.ews.drop(ts.ews.columns, inplace=True, axis=1)\n", + "ts.compute_entropy(rolling_window=0.5, method='approximate')\n", + "ts.ews.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute Kolmogorov entropy" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ts.ews.drop(ts.ews.columns, inplace=True, axis=1)\n", + "ts.compute_entropy(rolling_window=0.5, method='kolmogorov')\n", + "ts.ews.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Using keyword arguments from Entropy Hub" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Entropy Hub [API](https://www.entropyhub.xyz/python/Functions/Base.html) contains functions to compute entropy. The keyword arguments to these functions can be passed to ```compute_entropy()```. For example we can modify the embedding dimension ```m``` and the time delay ```tau``` that are arguments to the [K2En](https://www.entropyhub.xyz/python/Functions/Base.html#EntropyHub.K2En) function." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ts.ews.drop(ts.ews.columns, inplace=True, axis=1)\n", + "ts.compute_entropy(rolling_window=0.5, method='kolmogorov', m=3, tau=2)\n", + "ts.ews.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notebook took 23.4s to run\n" + ] + } + ], + "source": [ + "# Stop timer\n", + "end_time = time.time()\n", + "print('Notebook took {:.1f}s to run'.format(end_time-start_time))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ewstools", + "language": "python", + "name": "ewstools" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}