diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index 61ae864..7631024 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -12,7 +12,7 @@ jobs: with: journal: joss # This should be the path to the paper within your repo. - paper-path: paper.md + paper-path: paper/paper.md - name: Upload uses: actions/upload-artifact@v1 with: @@ -20,4 +20,4 @@ jobs: # This is the output path where Pandoc will write the compiled # PDF. Note, this should be the same directory as the input # paper.md - path: paper.pdf \ No newline at end of file + path: paper/paper.pdf diff --git a/.zenodo.json b/.zenodo.json new file mode 100644 index 0000000..8451afe --- /dev/null +++ b/.zenodo.json @@ -0,0 +1,27 @@ +{ + "description": "A Python package for early warning signals of bifurcations in time series data", + "license": "CC BY 4.0", + "title": "ewstools: A Python package for early warning signals of bifurcations in time series data", + "version": "v2.1.1", + "upload_type": "software", + "publication_date": "2023-02-10", + "creators": [ + { + "affiliation": "McGill University", + "name": "Thomas Bury" + } + ], + "access_right": "open", + "related_identifiers": [ + { + "scheme": "url", + "identifier": "https://github.com/ThomasMBury/ewstools/tree/v2.1.1", + "relation": "isSupplementTo" + }, + { + "scheme": "doi", + "identifier": "10.5281/zenodo.2598517", + "relation": "isVersionOf" + } + ] +} \ No newline at end of file diff --git a/README.md b/README.md index ff8b2c5..ae8bbac 100644 --- a/README.md +++ b/README.md @@ -3,42 +3,40 @@ [![Documentation Status](https://readthedocs.org/projects/ewstools/badge/?version=latest)](https://ewstools.readthedocs.io/en/latest/?badge=latest) [![tests](https://github.com/ThomasMBury/ewstools/actions/workflows/tests.yml/badge.svg?branch=main)](https://github.com/ThomasMBury/ewstools/actions/workflows/tests.yml) [![codecov](https://codecov.io/gh/ThomasMBury/ewstools/branch/main/graph/badge.svg?token=Q5LGRV6TLF)](https://codecov.io/gh/ThomasMBury/ewstools) +[![DOI](https://joss.theoj.org/papers/10.21105/joss.05038/status.svg)](https://doi.org/10.21105/joss.05038) # ewstools **A Python package for early warning signals (EWS) of bifurcations in time series data.** ## Overview -Many systems across nature and society have the capacity to undergo an abrupt and profound change in their dynamics. From a dynamical systemes perspective, these events are often associated with the crossing of a bifurcation. Early warning signals (EWS) for bifurcations are therefore in high demand. Two commonly used EWS for bifurcations are variance and lag-1 autocorrelation, that are expected to increase prior to many bifurcations due to critical slowing down ([Scheffer et al. 2009](https://www.nature.com/articles/nature08227)). There now exist a wealth of other EWS based on changes in time series dynamics that are expected to occur prior to bifurcations (see e.g. [Clements & Ozgul 2018](https://onlinelibrary.wiley.com/doi/full/10.1111/ele.12948)). More recently, deep learning classifiers have been trained and applied to detect bifurcations, with promising results ([Bury et al. 2021](https://www.pnas.org/doi/10.1073/pnas.2106140118)). +Many systems in nature and society have the capacity to undergo critical transitions--sudden and profound changes in dynamics that are hard to reverse. Examples include the outbreak of disease, the collapse of an ecosystem, or the onset of a cardiac arrhythmia. From a mathematical perspective, these transitions may be understood as the crossing of a bifurcation (tipping point) in an appropriate dynamical system model. In 2009, Scheffer and colleagues proposed early warning signals (EWS) for bifurcations based on statistics of noisy fluctuations in time series data ([Scheffer et al. 2009](https://www.nature.com/articles/nature08227)). This spurred massive interest in the subject, resulting in a multitude of different EWS for anticipating bifurcations ([Clements & Ozgul 2018](https://onlinelibrary.wiley.com/doi/full/10.1111/ele.12948)). More recently, EWS from deep learning classifiers have outperformed conventional EWS on several model and empirical datasets, whilst also providing information on the type of bifurcation ([Bury et al. 2021](https://www.pnas.org/doi/10.1073/pnas.2106140118)). -The goal of this Python package is to provide a an accessible toolbox for computing, analysing and visulaising EWS in time series data. It complements an existing EWS package in R ([Dakos et al. 2012](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041010)). We hope that having an EWS toolbox in Python will allow for additional testing, and appeal to those who primarily work in Python. +`ewstools` is an accessible toolbox for computing, analysing and visualising EWS in time series data. It complements an existing EWS package in R ([Dakos et al. 2012](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041010)). Given the recent surge in popularity of the Python programming langauge ([PYPL, 2022](https://pypl.github.io/PYPL.html)), a Python-based implementation of EWS should be useful. -Current functionality of *ewstools* includes +The package provides: + - An intuitive, object-oriented framework for working with EWS in a given time series - Time series detrending methods using - A Gaussian kernel - LOWESS (Locally Weighted Scatterplot Smoothing) - - Computation of CSD-based early warning signals including: - Variance and associated metrics (standard deviation, coefficient of variation) - Autocorrelation (at specified lag times) - Higher-order statistical moments (skewness, kurtosis) - Power spectrum and associated metrics - - Computation of Kendall tau values to quantify trends - - - Application of deep learning classifiers for bifurcation prediction as in [Bury et al. (2022) PNAS](https://www.pnas.org/doi/10.1073/pnas.2106140118). - + - Application of deep learning classifiers for bifurcation prediction as in [Bury et al. 2021](https://www.pnas.org/doi/10.1073/pnas.2106140118). - Block-bootstrapping of time-series to obtain confidence intervals on EWS estimates - - Visualisation tools to display output + - Built-in theoretical models to test EWS -*ewstools* makes use of [pandas](https://pandas.pydata.org/) for dataframe handling, [numpy](https://numpy.org/) for fast numerical computing, [plotly](https://plotly.com/graphing-libraries/) for visuliastion, [lmfit](https://lmfit.github.io/lmfit-py/) for least-squares minimisation, [arch](https://github.com/bashtage/arch) for bootstrapping methods, [statsmodels](https://www.statsmodels.org/stable/index.html) and [scipy](https://scipy.org/) for detrending methods, and [TensorFlow](https://www.tensorflow.org/install) for deep learning. +`ewstools` makes use of [pandas](https://pandas.pydata.org/) for dataframe handling, [numpy](https://numpy.org/) for fast numerical computing, [plotly](https://plotly.com/graphing-libraries/) for visuliastion, [lmfit](https://lmfit.github.io/lmfit-py/) for least-squares minimisation, [arch](https://github.com/bashtage/arch) for bootstrapping methods, [statsmodels](https://www.statsmodels.org/stable/index.html) and [scipy](https://scipy.org/) for detrending methods, and [TensorFlow](https://www.tensorflow.org/install) for deep learning. ## Install -Requires Python 3.7 or later. You can install *ewstools* with pip using the commands +Requires Python 3.7 or later. You can install `ewstools` with pip using the commands ```bash pip install --upgrade pip @@ -49,7 +47,7 @@ pip install ewstools ```bash pip install jupyter notebook ``` -Package dependencies of *ewstools* are +Package dependencies are ```bash 'pandas>=0.23.0', 'numpy>=1.14.0', @@ -61,7 +59,7 @@ Package dependencies of *ewstools* are ``` and should be installed automatically. To use any of the deep learning functionality, you will need to install [TensorFlow](https://www.tensorflow.org/install) v2.0.0 or later. -To install the latest *development* version of *ewstools*, use the command +To install the latest *development* version, use the command ```bash pip install git+https://github.com/thomasmbury/ewstools.git#egg=ewstools ``` @@ -80,7 +78,7 @@ NB: the development version comes with the risk of undergoing continual changes, ## Quick demo -First we need to import *ewstools* and collect the data we wish to analyse. Here we will run a simulation of the Ricker model, one of the model functions stored in [`ewstools.models`](https://ewstools.readthedocs.io/en/latest/ewstools.html#ewstools-models-submodule). +First we need to import `ewstools` and collect data to analyse. Here we will run a simulation of the Ricker model, one of the models stored in [`ewstools.models`](https://ewstools.readthedocs.io/en/latest/ewstools.html#ewstools-models-submodule). ```python import ewstools from ewstools.models import simulate_ricker @@ -131,10 +129,6 @@ This work is currently supported by an FRQNT (Fonds de recherche du Québec - Na If you like the respoitory, please give it a star :D -If your research uses the deep learning functionality of *ewstools*, please cite - -Bury, Thomas M., et al. "[Deep learning for early warning signals of tipping points.](https://www.pnas.org/doi/abs/10.1073/pnas.2106140118)" *Proceedings of the National Academy of Sciences* 118.39 (2021): e2106140118. - -If your research computes spectral EWS using *ewstools*, please cite +If your research makes use of it, please cite -Bury, Thomas M., Chris T. Bauch, and Madhur Anand. "[Detecting and distinguishing tipping points using spectral early warning signals.](https://royalsocietypublishing.org/doi/full/10.1098/rsif.2020.0482)" *Journal of the Royal Society Interface* 17.170 (2020): 20200482. +Bury, Thomas M. "[ewstools: A Python package for early warning signals of bifurcations in time series data.](https://joss.theoj.org/papers/10.21105/joss.05038.pdf)" *Journal of Open Source Software* 8.82 (2023): 5038. diff --git a/codecov.yml b/codecov.yml index ca41197..11e2c81 100644 --- a/codecov.yml +++ b/codecov.yml @@ -7,4 +7,7 @@ coverage: default: target: 60% threshold: 30% + patch: + default: + target: 80% # new contributions should have a coverage at least equal to target diff --git a/docs/source/conf.py b/docs/source/conf.py index 6d37824..eaa5fe2 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -24,10 +24,9 @@ copyright = '2022, Thomas M Bury' author = 'Thomas M Bury' -# The short X.Y version -version = '1.0.1' -# The full version, including alpha/beta/rc tags -release = '1.0.1' +# Semantic version number +version = '2.0.1' + # -- General configuration --------------------------------------------------- diff --git a/ewstools/__init__.py b/ewstools/__init__.py index ead5e53..168119c 100644 --- a/ewstools/__init__.py +++ b/ewstools/__init__.py @@ -8,3 +8,4 @@ # Import specific classes and functions from .core import TimeSeries +from .core import MultiTimeSeries diff --git a/ewstools/core.py b/ewstools/core.py index e5b8ef2..47831bb 100644 --- a/ewstools/core.py +++ b/ewstools/core.py @@ -59,11 +59,11 @@ import deprecation - # --------------- # Classes # -------------- + class TimeSeries: """ Univariate time series data on which to compute early warning signals. @@ -80,29 +80,31 @@ class TimeSeries: """ def __init__(self, data, transition=None): - # Put data into a pandas DataFrame if type(data) in [list, np.ndarray]: - df_state = pd.DataFrame({'time': np.arange(len(data)), 'state': data}) - df_state.set_index('time', inplace=True) - + df_state = pd.DataFrame({"time": np.arange(len(data)), "state": data}) + df_state.set_index("time", inplace=True) + # If given as pandas series, carry index forward elif type(data) == pd.Series: - df_state = pd.DataFrame({'state': data.values}) + df_state = pd.DataFrame({"state": data.values}) df_state.index = data.index # Rename index if no name given if not df_state.index.name: - df_state.index.name = 'time' - + df_state.index.name = "time" + # If data is not provided as either of these, flag error else: - print('\nERROR: data has been provided as type {}'.format(type(data))) - print('Make sure to provide data as either a list, np.ndarray or pd.Series\n') - + print("\nERROR: data has been provided as type {}".format(type(data))) + print( + "Make sure to provide data as either a list, np.ndarray or pd.Series\n" + ) + return + # Set state and transition attributes self.state = df_state self.transition = float(transition) if transition else transition - + # Initialise other attributes self.ews = pd.DataFrame(index=df_state.index) self.dl_preds = pd.DataFrame() @@ -110,17 +112,16 @@ def __init__(self, data, transition=None): self.pspec = None self.pspec_fits = None self.ews_spec = pd.DataFrame() - - def detrend(self, method='Gaussian', bandwidth=0.2, span=0.2): - ''' + 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' Parameters ---------- method : str, optional - Method of detrending to use. + Method of detrending to use. Select from ['Gaussian', 'Lowess'] The default is 'Gaussian'. bandwidth : float, optional @@ -130,15 +131,15 @@ def detrend(self, method='Gaussian', bandwidth=0.2, span=0.2): such that the kernel has its quartiles at +/- 0.25*bandwidth. The default is 0.2. span : float, optional - Span of time-series data used for Lowess filtering. Provide as a - proportion of data length or as a number of data points. + Span of time-series data used for Lowess filtering. Provide as a + proportion of data length or as a number of data points. The default is 0.2. Returns ------- None. - ''' + """ # Get time series data prior to transition if self.transition: @@ -146,8 +147,7 @@ def detrend(self, method='Gaussian', bandwidth=0.2, span=0.2): else: df_pre = self.state - - if method == 'Gaussian': + if method == "Gaussian": # Get size of bandwidth in terms of num. datapoints if given as a proportion if 0 < bandwidth <= 1: bw_num = bandwidth * len(df_pre) @@ -158,35 +158,31 @@ def detrend(self, method='Gaussian', bandwidth=0.2, span=0.2): # Standard deviation of kernel given bandwidth # Note that for a Gaussian, quartiles are at +/- 0.675*sigma sigma = (0.25 / 0.675) * bw_num - smooth_values = gf(df_pre['state'].values, sigma=sigma, mode='reflect') + smooth_values = gf(df_pre["state"].values, sigma=sigma, mode="reflect") smooth_series = pd.Series(smooth_values, index=df_pre.index) - - if method == 'Lowess': + if method == "Lowess": # Convert span to a proportion of the length of the data if not 0 < span <= 1: span_prop = span / len(df_pre) else: span_prop = span - smooth_values = lowess(df_pre['state'].values, - df_pre.index.values, - frac=span_prop)[:,1] + smooth_values = lowess( + df_pre["state"].values, df_pre.index.values, frac=span_prop + )[:, 1] smooth_series = pd.Series(smooth_values, index=df_pre.index) - # Add smoothed data and residuals to the 'state' DataFrame self.state["smoothing"] = smooth_series self.state["residuals"] = self.state["state"] - self.state["smoothing"] - - 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 Parameters @@ -195,19 +191,19 @@ def compute_var(self, rolling_window=0.25): 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. - + 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)) @@ -215,22 +211,20 @@ def compute_var(self, rolling_window=0.25): rw_absolute = rolling_window # If residuals column exists, compute over residuals. - if 'residuals' in df_pre.columns: - var_values = df_pre['residuals'].rolling(window=rw_absolute).var() + if "residuals" in df_pre.columns: + var_values = df_pre["residuals"].rolling(window=rw_absolute).var() # Else, compute over state variable else: - var_values = df_pre['state'].rolling(window=rw_absolute).var() - - self.ews['variance'] = var_values - - - + var_values = df_pre["state"].rolling(window=rw_absolute).var() + + self.ews["variance"] = var_values + 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. - + Put into 'ews' dataframe Parameters @@ -239,19 +233,19 @@ def compute_std(self, rolling_window=0.25): 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. - + 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)) @@ -259,23 +253,22 @@ def compute_std(self, rolling_window=0.25): rw_absolute = rolling_window # If residuals column exists, compute over residuals. - if 'residuals' in df_pre.columns: - std_values = df_pre['residuals'].rolling(window=rw_absolute).std() + if "residuals" in df_pre.columns: + std_values = df_pre["residuals"].rolling(window=rw_absolute).std() # Else, compute over state variable else: - std_values = df_pre['state'].rolling(window=rw_absolute).std() - - self.ews['std'] = std_values + std_values = df_pre["state"].rolling(window=rw_absolute).std() + self.ews["std"] = std_values def compute_cv(self, rolling_window=0.25): - ''' + """ Compute coefficient of variation over a rolling window. This is the standard deviation of the residuals divided by the mean of the state variable. If residuals have not been computed, computation will be performed over state variable. - + Put into 'ews' dataframe Parameters @@ -284,19 +277,19 @@ def compute_cv(self, rolling_window=0.25): 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. - + 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)) @@ -305,26 +298,21 @@ def compute_cv(self, rolling_window=0.25): # Get standard deviation values # If residuals column exists, compute over residuals. - if 'residuals' in df_pre.columns: - std_values = df_pre['residuals'].rolling(window=rw_absolute).std() + if "residuals" in df_pre.columns: + std_values = df_pre["residuals"].rolling(window=rw_absolute).std() # Else, compute over state variable else: - std_values = df_pre['state'].rolling(window=rw_absolute).std() - - # Get mean values from state variable - mean_values = df_pre['state'].rolling(window=rw_absolute).mean() - - cv_values = std_values/mean_values - - self.ews['cv'] = cv_values - - + std_values = df_pre["state"].rolling(window=rw_absolute).std() + # Get mean values from state variable + mean_values = df_pre["state"].rolling(window=rw_absolute).mean() + cv_values = std_values / mean_values + self.ews["cv"] = cv_values def compute_auto(self, rolling_window=0.25, lag=1): - ''' + """ Compute autocorrelation over a rolling window. Add to dataframe. Parameters @@ -335,19 +323,19 @@ def compute_auto(self, rolling_window=0.25, lag=1): data being analysed. Default is 0.25. lag : int Lag of autocorrelation - + 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)) @@ -355,25 +343,28 @@ def compute_auto(self, rolling_window=0.25, lag=1): rw_absolute = rolling_window # If residuals column exists, compute over residuals. - if 'residuals' in df_pre.columns: - ac_values = df_pre['residuals'].rolling(window=rw_absolute).apply( - func=lambda x: pd.Series(x).autocorr(lag=lag), raw=True + if "residuals" in df_pre.columns: + ac_values = ( + df_pre["residuals"] + .rolling(window=rw_absolute) + .apply(func=lambda x: pd.Series(x).autocorr(lag=lag), raw=True) ) # Else, compute over state variable else: - ac_values = df_pre['state'].rolling(window=rw_absolute).apply( - func=lambda x: pd.Series(x).autocorr(lag=lag), raw=True - ) - - self.ews['ac{}'.format(lag)] = ac_values + ac_values = ( + df_pre["state"] + .rolling(window=rw_absolute) + .apply(func=lambda x: pd.Series(x).autocorr(lag=lag), raw=True) + ) + self.ews["ac{}".format(lag)] = ac_values 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. - + Add to dataframe. Parameters @@ -382,19 +373,19 @@ def compute_skew(self, rolling_window=0.25): 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. - + 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)) @@ -402,22 +393,20 @@ def compute_skew(self, rolling_window=0.25): rw_absolute = rolling_window # If residuals column exists, compute over residuals. - if 'residuals' in df_pre.columns: - skew_values = df_pre['residuals'].rolling(window=rw_absolute).skew() + if "residuals" in df_pre.columns: + skew_values = df_pre["residuals"].rolling(window=rw_absolute).skew() # Else, compute over state variable else: - skew_values = df_pre['state'].rolling(window=rw_absolute).skew() - - self.ews['skew'] = skew_values - + skew_values = df_pre["state"].rolling(window=rw_absolute).skew() + self.ews["skew"] = skew_values 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. - + Add to dataframe. Parameters @@ -426,19 +415,19 @@ def compute_kurt(self, rolling_window=0.25): 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. - + 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)) @@ -446,21 +435,18 @@ def compute_kurt(self, rolling_window=0.25): rw_absolute = rolling_window # If residuals column exists, compute over residuals. - if 'residuals' in df_pre.columns: - kurt_values = df_pre['residuals'].rolling(window=rw_absolute).kurt() + if "residuals" in df_pre.columns: + kurt_values = df_pre["residuals"].rolling(window=rw_absolute).kurt() # Else, compute over state variable else: - kurt_values = df_pre['state'].rolling(window=rw_absolute).kurt() - - self.ews['kurtosis'] = kurt_values - - + kurt_values = df_pre["state"].rolling(window=rw_absolute).kurt() + self.ews["kurtosis"] = kurt_values - def compute_ktau(self, tmin='earliest', tmax='latest'): - ''' + def compute_ktau(self, tmin="earliest", tmax="latest"): + """ Compute kendall tau values of CSD-based EWS. - Output is placed in the attribute *ktau*, which is a Python + Output is placed in the attribute *ktau*, which is a Python dictionary contianing Kendall tau values for each CSD-based EWS. Parameters @@ -473,35 +459,31 @@ def compute_ktau(self, tmin='earliest', tmax='latest'): taken as latest time point in EWS time series, which could be the end of the state time series, or a defined transtion point. - ''' - - + """ + # Get tmin and tmax values if using extrema - if tmin == 'earliest': + if tmin == "earliest": tmin = self.ews.dropna().index[0] - - if tmax == 'latest': + + if tmax == "latest": tmax = self.ews.dropna().index[-1] - + # Get cropped data - df_ews = self.ews[(self.ews.index >= tmin) &\ - (self.ews.index <= tmax)].copy() - + df_ews = self.ews[(self.ews.index >= tmin) & (self.ews.index <= tmax)].copy() + # Include smax in Kendall tau computation if it exists - if 'smax' in self.ews_spec.columns: - df_ews = df_ews.join(self.ews_spec['smax']) - + if "smax" in self.ews_spec.columns: + df_ews = df_ews.join(self.ews_spec["smax"]) + # Make a series with the time values time_values = pd.Series(data=df_ews.index, index=df_ews.index) ktau_out = df_ews.corrwith(time_values, method="kendall", axis=0) self.ktau = dict(ktau_out) - - def apply_classifier(self, classifier, tmin, tmax, - name='c1', verbose=1): - ''' + def apply_classifier(self, classifier, tmin, tmax, name="c1", verbose=1): + """ Apply a deep learning classifier to the residual time series from - tmin to tmax. + tmin to tmax. If time series has not been detrended, apply to the raw data. Predictions from the classifier are saved into the attribute dl_preds. @@ -518,55 +500,57 @@ def apply_classifier(self, classifier, tmin, tmax, verbose : int, optional Verbosity of update messages from TensorFlow. 0 = silent, 1 = progress bar, 2 = single line. The default is 1. - + Returns ------- None. - ''' - + """ + # Length of time series required as input to classifier input_len = classifier.layers[0].input_shape[1] - + # Get time series segment. Use residuals if detrending performed. # Otherwise use state variable. - if 'residuals' in self.state.columns: - series = self.state[(self.state.index >= tmin) &\ - (self.state.index < tmax)]['residuals'] + if "residuals" in self.state.columns: + series = self.state[(self.state.index >= tmin) & (self.state.index < tmax)][ + "residuals" + ] else: - series = self.state[(self.state.index >= tmin) &\ - (self.state.index < tmax)]['state'] - + series = self.state[(self.state.index >= tmin) & (self.state.index < tmax)][ + "state" + ] + # Get values in series data = series.values - + # If time series segment is larger than input dimension of classifier if len(data) > input_len: - print('ERROR: Length of time series segment is too long for the classifier. You can modify the tmin and tmax parameters to select a smaller segment.') + print( + "ERROR: Length of time series segment is too long for the classifier. You can modify the tmin and tmax parameters to select a smaller segment." + ) return - + # Normalise by mean of absolute value - data_norm = data/(abs(data).mean()) + data_norm = data / (abs(data).mean()) # Prepend with zeros to make appropriate length for classifier num_zeros = input_len - len(data_norm) - input_data = np.concatenate((np.zeros(num_zeros),data_norm)).reshape(1,-1, 1) - + input_data = np.concatenate((np.zeros(num_zeros), data_norm)).reshape(1, -1, 1) + # Get DL prediction dl_pred = classifier.predict(input_data, verbose=verbose)[0] # Put info into dataframe - dict_dl_pred = {i:val for (i,val) in zip(np.arange(len(dl_pred)), dl_pred)} - dict_dl_pred['time'] = series.index[-1] - dict_dl_pred['classifier'] = name + dict_dl_pred = {i: val for (i, val) in zip(np.arange(len(dl_pred)), dl_pred)} + dict_dl_pred["time"] = series.index[-1] + dict_dl_pred["classifier"] = name df_dl_pred = pd.DataFrame(dict_dl_pred, index=[len(self.dl_preds)]) # Append to dataframe contiaining DL predictions self.dl_preds = pd.concat([self.dl_preds, df_dl_pred], ignore_index=True) - - - def apply_classifier_inc(self, classifier, inc=10, name='c1', verbose=1): - ''' + def apply_classifier_inc(self, classifier, inc=10, name="c1", verbose=1): + """ Apply a deep learning classifier to incrementally increasing time series lengths. First prediction is made on time series segment from data point at index 0 to data point at index inc. Second prediction @@ -583,7 +567,7 @@ def apply_classifier_inc(self, classifier, inc=10, name='c1', verbose=1): name : str, optional Name assigned to the classifier. The default is 'c1'. verbose : int, optional - Verbosity of update messages from TensorFlow. + Verbosity of update messages from TensorFlow. 0 = silent, 1 = progress bar, 2 = single line. The default is 1. @@ -591,39 +575,40 @@ def apply_classifier_inc(self, classifier, inc=10, name='c1', verbose=1): ------- None. - ''' - - dt = self.state.index[1]-self.state.index[0] + """ + + dt = self.state.index[1] - self.state.index[0] tmin = self.state.index[0] tend = self.state.index[-1] if not self.transition else self.transition - tend += dt # Make transition point inclusive - + tend += dt # Make transition point inclusive + # Tmax values for each time series segment - tmax_vals = np.arange(tmin+inc, tend+dt, inc) + tmax_vals = np.arange(tmin + inc, tend + dt, inc) for tmax in tmax_vals: - self.apply_classifier(classifier, name=name, tmin=tmin, tmax=tmax, - verbose=verbose) - - - + self.apply_classifier( + classifier, name=name, tmin=tmin, tmax=tmax, verbose=verbose + ) def clear_dl_preds(self): - ''' + """ Clear the attribute *dl_preds* Returns ------- None. - ''' - - self.dl_preds = pd.DataFrame() - + """ + self.dl_preds = pd.DataFrame() - def compute_spectrum(self, rolling_window=0.25, ham_length=40, - ham_offset=0.5, pspec_roll_offset=20, - w_cutoff=1): - ''' + def compute_spectrum( + self, + rolling_window=0.25, + ham_length=40, + ham_offset=0.5, + pspec_roll_offset=20, + w_cutoff=1, + ): + """ Compute the power spectrum over a rolling window. Stores the power spectra as a DataFrame in TimeSeries.pspec @@ -634,10 +619,10 @@ def compute_spectrum(self, rolling_window=0.25, ham_length=40, as an absolute value or as a proportion of the length of the data being analysed. Default is 0.25. ham_length : int - Length of the Hamming window used to compute the power spectrum. + Length of the Hamming window used to compute the power spectrum. The default is 40. ham_offset : float - Hamming offset as a proportion of the Hamming window size. + Hamming offset as a proportion of the Hamming window size. The default is 0.5. pspec_roll_offset : int Rolling window offset used when computing power spectra. Power spectrum @@ -651,8 +636,8 @@ def compute_spectrum(self, rolling_window=0.25, ham_length=40, ------- None. - ''' - + """ + # Get time series data prior to transition if self.transition: df_pre = self.state[self.state.index <= self.transition] @@ -666,11 +651,11 @@ def compute_spectrum(self, rolling_window=0.25, ham_length=40, rw_absolute = rolling_window # If residuals column exists, compute over residuals. - if 'residuals' in df_pre.columns: - series = df_pre['residuals'] + if "residuals" in df_pre.columns: + series = df_pre["residuals"] # Else, compute over state variable else: - series = df_pre['state'] + series = df_pre["state"] # Number of components in the residual time-series num_comps = len(df_pre) @@ -685,7 +670,6 @@ def compute_spectrum(self, rolling_window=0.25, ham_length=40, # Loop through window locations shifted by roll_offset for k in np.arange(0, num_comps - (rw_absolute - 1), roll_offset): - # Select subset of series contained in window window_series = series.iloc[k : k + rw_absolute] # Asisgn the time value for the metrics (right end point of window) @@ -708,16 +692,14 @@ def compute_spectrum(self, rolling_window=0.25, ham_length=40, columns={"Power spectrum": "empirical"}, inplace=True ) # Include a column for the time-stamp - df_pspec_empirical['time'] = t_point * np.ones(len(pspec)) + df_pspec_empirical["time"] = t_point * np.ones(len(pspec)) list_pspec.append(df_pspec_empirical) - + # Concatenate power spectra dataframes and store in attribute pspec self.pspec = pd.concat(list_pspec) - - def compute_smax(self): - ''' + """ Compute Smax (the maximum power in the power spectrum). This can only be applied after applying compute_spectrum(). Stores Smax values in TimeSeries.ews_spec @@ -726,21 +708,20 @@ def compute_smax(self): ------- None. - ''' - + """ + if self.pspec is None: - print('ERROR: The power spectrum must be computed before computing\ + print( + "ERROR: The power spectrum must be computed before computing\ spectral EWS such as Smax. The power spectrum can be\ - computed using compute_pspec()') - - smax_values = self.pspec[['time','power']].groupby(['time']).max() - self.ews_spec['smax'] = smax_values - - + computed using compute_pspec()" + ) + smax_values = self.pspec[["time", "power"]].groupby(["time"]).max() + self.ews_spec["smax"] = smax_values def compute_spec_type(self, sweep=False): - ''' + """ Fit the analytical forms of the Fold, Hopf and Null power spectrum to the empirical power spectrum. Get Akaike Information Criterion (AIC) weights to determine best fit. @@ -759,31 +740,32 @@ def compute_spec_type(self, sweep=False): ------- None. - ''' - - + """ + if self.pspec is None: - print('ERROR: The power spectrum must be computed before computing\ + print( + "ERROR: The power spectrum must be computed before computing\ the spectrum type. The power spectrum can be\ - computed using compute_pspec()') - + computed using compute_pspec()" + ) + list_aic = [] list_pspec_fits = [] - + # Loop through time values - for time in self.pspec['time'].unique(): - pspec = self.pspec[self.pspec['time']==time] - pspec_series = pspec.set_index('frequency')['power'] - + for time in self.pspec["time"].unique(): + pspec = self.pspec[self.pspec["time"] == time] + pspec_series = pspec.set_index("frequency")["power"] + ## Compute the AIC values - metrics = helpers.pspec_metrics(pspec_series, ews=['aic'], sweep=sweep) + metrics = helpers.pspec_metrics(pspec_series, ews=["aic"], sweep=sweep) dict_aic = {} - dict_aic['fold'] = metrics['AIC fold'] - dict_aic['hopf'] = metrics['AIC hopf'] - dict_aic['null'] = metrics['AIC null'] - dict_aic['time'] = time + dict_aic["fold"] = metrics["AIC fold"] + dict_aic["hopf"] = metrics["AIC hopf"] + dict_aic["null"] = metrics["AIC null"] + dict_aic["time"] = time list_aic.append(dict_aic) - + # Generate data to plot the fitted power spectra # Create fine-scale frequency values @@ -806,8 +788,8 @@ def compute_spec_type(self, sweep=False): ## Put spectrum fits into a dataframe dict_fits = { - 'time': time * np.ones(len(wVals)), - 'frequency': wVals, + "time": time * np.ones(len(wVals)), + "frequency": wVals, "fit fold": pspec_fold, "fit hopf": pspec_hopf, "fit null": pspec_null, @@ -816,17 +798,14 @@ def compute_spec_type(self, sweep=False): list_pspec_fits.append(df_pspec_fits) # Concatenate - df_aic = pd.DataFrame(list_aic).set_index('time') + df_aic = pd.DataFrame(list_aic).set_index("time") df_pspec_fits = pd.concat(list_pspec_fits) self.ews_spec = self.ews_spec.join(df_aic) self.pspec_fits = df_pspec_fits - - - def make_plotly(self, kendall_tau=True, ens_avg=False): - ''' + """ Make an interactive Plotly figure to view all EWS computed Parameters @@ -836,15 +815,15 @@ def make_plotly(self, kendall_tau=True, ens_avg=False): Set as true to show Kendall tau values (if they have been computed) Default is True. ens_avg : bool, optional - Plot the ensenble average of DL predictions. + Plot the ensenble average of DL predictions. Default is False. Returns ------- Plotly figure - ''' - + """ + # Trace colours colours = px.colors.qualitative.Plotly # Count number of panels for Plotly subplots @@ -852,646 +831,511 @@ def make_plotly(self, kendall_tau=True, ens_avg=False): # Always a row for state varialbe row_count += 1 # Row for autocorrelation - ac_labels = [s for s in self.ews.columns if s[:2]=='ac'] - if len(ac_labels)>0: - row_count+=1 + ac_labels = [s for s in self.ews.columns if s[:2] == "ac"] + if len(ac_labels) > 0: + row_count += 1 # Row for each other EWS - row_count += len(self.ews.columns)-len(ac_labels) + row_count += len(self.ews.columns) - len(ac_labels) # Row for Smax if included - if 'smax' in self.ews_spec.columns: - row_count+=1 + if "smax" in self.ews_spec.columns: + row_count += 1 # Row for AIC weights if computed - if 'fold' in self.ews_spec.columns: - row_count+=1 + if "fold" in self.ews_spec.columns: + row_count += 1 # Row for DL predictions if computed - if len(self.dl_preds.columns)>0: - row_count+=1 - + if len(self.dl_preds.columns) > 0: + row_count += 1 + num_rows = row_count - row_count = 1 # reset row counter - + row_count = 1 # reset row counter + # Make Plotly subplots frame - fig = make_subplots(rows=num_rows, cols=1, - shared_xaxes=True, - x_title='Time', - vertical_spacing=0.02 - ) - + fig = make_subplots( + rows=num_rows, + cols=1, + shared_xaxes=True, + x_title="Time", + vertical_spacing=0.02, + ) + # Plot state variable fig.add_trace( - go.Scatter(x=self.state.index.values, - y=self.state['state'].values, - name='state', - ), - row=row_count, col=1 - ) - fig.update_yaxes(title='State', row=row_count) + go.Scatter( + x=self.state.index.values, + y=self.state["state"].values, + name="state", + ), + row=row_count, + col=1, + ) + fig.update_yaxes(title="State", row=row_count) - # Plot smoothing if computed - if 'smoothing' in self.state.columns: + if "smoothing" in self.state.columns: fig.add_trace( - go.Scatter(x=self.state.index.values, - y=self.state['smoothing'].values, - name='smoothing', - ), - row=row_count, col=1 - ) - + go.Scatter( + x=self.state.index.values, + y=self.state["smoothing"].values, + name="smoothing", + ), + row=row_count, + col=1, + ) + # Plot variance if computed - if 'variance' in self.ews.columns: + if "variance" in self.ews.columns: row_count += 1 - + # Add kendall tau to name - if kendall_tau and ('variance' in self.ktau.keys()): - ktau = self.ktau['variance'] - name = 'variance (ktau={:.2f})'.format(ktau) + if kendall_tau and ("variance" in self.ktau.keys()): + ktau = self.ktau["variance"] + name = "variance (ktau={:.2f})".format(ktau) else: - name = 'variance' - + name = "variance" + fig.add_trace( - go.Scatter(x=self.ews.index.values, - y=self.ews['variance'].values, - name=name, - ), - row=row_count, col=1 - ) - fig.update_yaxes(title='Variance', row=row_count) - + go.Scatter( + x=self.ews.index.values, + y=self.ews["variance"].values, + name=name, + ), + row=row_count, + col=1, + ) + fig.update_yaxes(title="Variance", row=row_count) + # Plot standard deviation if computed - if 'std' in self.ews.columns: + if "std" in self.ews.columns: row_count += 1 - + # Add kendall tau to name - if kendall_tau and ('std' in self.ktau.keys()): - ktau = self.ktau['std'] - name = 'std (ktau={:.2f})'.format(ktau) + if kendall_tau and ("std" in self.ktau.keys()): + ktau = self.ktau["std"] + name = "std (ktau={:.2f})".format(ktau) else: - name = 'std' - + name = "std" + fig.add_trace( - go.Scatter(x=self.ews.index.values, - y=self.ews['std'].values, - name=name, - ), - row=row_count, col=1 - ) - fig.update_yaxes(title='St. Dev.', row=row_count) - + go.Scatter( + x=self.ews.index.values, + y=self.ews["std"].values, + name=name, + ), + row=row_count, + col=1, + ) + fig.update_yaxes(title="St. Dev.", row=row_count) + # Plot coefficient of variation if computed - if 'cv' in self.ews.columns: + if "cv" in self.ews.columns: row_count += 1 - + # Add kendall tau to name - if kendall_tau and ('cv' in self.ktau.keys()): - ktau = self.ktau['cv'] - name = 'cv (ktau={:.2f})'.format(ktau) + if kendall_tau and ("cv" in self.ktau.keys()): + ktau = self.ktau["cv"] + name = "cv (ktau={:.2f})".format(ktau) else: - name = 'cv' - + name = "cv" + fig.add_trace( - go.Scatter(x=self.ews.index.values, - y=self.ews['cv'].values, - name=name, - ), - row=row_count, col=1 - ) - fig.update_yaxes(title='Coeff. of Var.', row=row_count) - - - + go.Scatter( + x=self.ews.index.values, + y=self.ews["cv"].values, + name=name, + ), + row=row_count, + col=1, + ) + fig.update_yaxes(title="Coeff. of Var.", row=row_count) + # Plot autocorrelation metrics if computed - if len(ac_labels)!=0: - row_count+=1 + if len(ac_labels) != 0: + row_count += 1 for ac_label in ac_labels: - # Add kendall tau to name if kendall_tau and (ac_label in self.ktau.keys()): ktau = self.ktau[ac_label] - name = '{} (ktau={:.2f})'.format(ac_label, ktau) + name = "{} (ktau={:.2f})".format(ac_label, ktau) else: - name = ac_label - + name = ac_label + fig.add_trace( - go.Scatter(x=self.ews.index.values, - y=self.ews[ac_label].values, - name=name, - ), - row=row_count, col=1 - ) - fig.update_yaxes(title='Autocorrelation', row=row_count) - - + go.Scatter( + x=self.ews.index.values, + y=self.ews[ac_label].values, + name=name, + ), + row=row_count, + col=1, + ) + fig.update_yaxes(title="Autocorrelation", row=row_count) + # Plot skew if computed - if 'skew' in self.ews.columns: + if "skew" in self.ews.columns: row_count += 1 - + # Add kendall tau to name - if kendall_tau and ('skew' in self.ktau.keys()): - ktau = self.ktau['skew'] - name = 'skew (ktau={:.2f})'.format(ktau) + if kendall_tau and ("skew" in self.ktau.keys()): + ktau = self.ktau["skew"] + name = "skew (ktau={:.2f})".format(ktau) else: - name = 'skew' - + name = "skew" + fig.add_trace( - go.Scatter(x=self.ews.index.values, - y=self.ews['skew'].values, - name=name, - ), - row=row_count, col=1 - ) - fig.update_yaxes(title='Skew', row=row_count) - + go.Scatter( + x=self.ews.index.values, + y=self.ews["skew"].values, + name=name, + ), + row=row_count, + col=1, + ) + fig.update_yaxes(title="Skew", row=row_count) + # Plot kurtosis if computed - if 'kurtosis' in self.ews.columns: + if "kurtosis" in self.ews.columns: row_count += 1 - + # Add kendall tau to name - if kendall_tau and ('kurtosis' in self.ktau.keys()): - ktau = self.ktau['kurtosis'] - name = 'kurtosis (ktau={:.2f})'.format(ktau) + if kendall_tau and ("kurtosis" in self.ktau.keys()): + ktau = self.ktau["kurtosis"] + name = "kurtosis (ktau={:.2f})".format(ktau) else: - name = 'kurtosis' - + name = "kurtosis" + fig.add_trace( - go.Scatter(x=self.ews.index.values, - y=self.ews['kurtosis'].values, - name=name, - ), - row=row_count, col=1 - ) - fig.update_yaxes(title='Kurtosis', row=row_count) - - + go.Scatter( + x=self.ews.index.values, + y=self.ews["kurtosis"].values, + name=name, + ), + row=row_count, + col=1, + ) + fig.update_yaxes(title="Kurtosis", row=row_count) + # Plot Smax if computd - if 'smax' in self.ews_spec.columns: + if "smax" in self.ews_spec.columns: row_count += 1 - + # Add kendall tau to name - if kendall_tau and ('smax' in self.ktau.keys()): - ktau = self.ktau['smax'] - name = 'smax (ktau={:.2f})'.format(ktau) + if kendall_tau and ("smax" in self.ktau.keys()): + ktau = self.ktau["smax"] + name = "smax (ktau={:.2f})".format(ktau) else: - name = 'smax' - + name = "smax" + fig.add_trace( - go.Scatter(x=self.ews_spec.index.values, - y=self.ews_spec['smax'].values, - name=name, - ), - row=row_count, col=1 - ) - fig.update_yaxes(title='Smax', row=row_count) - - + go.Scatter( + x=self.ews_spec.index.values, + y=self.ews_spec["smax"].values, + name=name, + ), + row=row_count, + col=1, + ) + fig.update_yaxes(title="Smax", row=row_count) + # Plot AIC weights if computd - if 'fold' in self.ews_spec.columns: + if "fold" in self.ews_spec.columns: row_count += 1 - aic_labels = ['fold', 'hopf', 'null'] + aic_labels = ["fold", "hopf", "null"] for aic_label in aic_labels: fig.add_trace( - go.Scatter(x=self.ews_spec.index.values, - y=self.ews_spec[aic_label].values, - name=aic_label, - ), - row=row_count, col=1 - ) - - fig.update_yaxes(title='AIC weights', row=row_count) - - - + go.Scatter( + x=self.ews_spec.index.values, + y=self.ews_spec[aic_label].values, + name=aic_label, + ), + row=row_count, + col=1, + ) + + fig.update_yaxes(title="AIC weights", row=row_count) + # Plot DL predictions if computed - if len(self.dl_preds)>0: + if len(self.dl_preds) > 0: row_count += 1 - class_labels = [s for s in self.dl_preds.columns if s not in ['time', 'classifier']] - classifiers = self.dl_preds['classifier'].unique() - + class_labels = [ + s for s in self.dl_preds.columns if s not in ["time", "classifier"] + ] + classifiers = self.dl_preds["classifier"].unique() + # If plotting predictions from every classifier if not ens_avg: # Loop through class labels and classifier names for idx, class_label in enumerate(class_labels): for idx2, classifier in enumerate(classifiers): - df_plot = self.dl_preds[self.dl_preds['classifier']==classifier] + df_plot = self.dl_preds[ + self.dl_preds["classifier"] == classifier + ] fig.add_trace( - go.Scatter(x=df_plot['time'].values, - y=df_plot[class_label].values, - name='DL class {}'.format(class_label), - legendgroup='DL class {}'.format(class_label), - showlegend=True if idx2==0 else False, - line=dict(color=colours[idx]) - ), - row=row_count, col=1 - ) - + go.Scatter( + x=df_plot["time"].values, + y=df_plot[class_label].values, + name="DL class {}".format(class_label), + legendgroup="DL class {}".format(class_label), + showlegend=True if idx2 == 0 else False, + line=dict(color=colours[idx]), + ), + row=row_count, + col=1, + ) + # If plotting ensemble average over classifiers else: - df_plot = self.dl_preds.groupby(['time']).mean().reset_index() + df = self.dl_preds.drop(["classifier"], axis=1) # JJV + df_plot = df.groupby(["time"]).mean().reset_index() # JJV + # Loop through class labels for idx, class_label in enumerate(class_labels): fig.add_trace( - go.Scatter(x=df_plot['time'].values, - y=df_plot[class_label].values, - name='DL class {}'.format(class_label), - line=dict(color=colours[idx]) - ), - row=row_count, col=1 - ) - - fig.update_yaxes(title='DL predictions', row=row_count) - - - - - # Set figure dimensions - fig.update_layout(height=200*num_rows, - width=800) - - # Separation between legend entries - fig.layout.legend.tracegroupgap = 0 - - return fig + go.Scatter( + x=df_plot["time"].values, + y=df_plot[class_label].values, + name="DL class {}".format(class_label), + line=dict(color=colours[idx]), + ), + row=row_count, + col=1, + ) + fig.update_yaxes(title="DL predictions", row=row_count) + # Set figure dimensions + fig.update_layout(height=200 * num_rows, width=800) + # Separation between legend entries + fig.layout.legend.tracegroupgap = 0 + return fig -# --------------- -# Functions -# --------------- - - -@deprecation.deprecated(deprecated_in="2.0", removed_in="2.1", - current_version="2.0", - details="Please use the ewstools.TimeSeries object and its associated methods to compute EWS instead. A tutorial on how to do so is provided in the tutorial_info ipython notebook at https://github.com/ThomasMBury/ewstools/tree/main/tutorials." - ) - -def ews_compute( - raw_series, - roll_window=0.4, - smooth="Lowess", - span=0.1, - band_width=0.2, - upto="Full", - ews=["var", "ac"], - lag_times=[1], - ham_length=40, - ham_offset=0.5, - pspec_roll_offset=20, - w_cutoff=1, - aic=["Fold", "Hopf", "Null"], - sweep=False, - ktau_time="Start", -): - +class MultiTimeSeries: """ - Compute temporal and spectral EWS from time-series data. - - Args - ---- - raw_series: pd.Series - Time-series data to analyse. Indexed by time. - roll_window: float - Rolling window size as a proportion of the length of the time-series - data. - smooth: {'Gaussian', 'Lowess', 'None'} - Type of detrending. - band_width: float - Bandwidth of Gaussian kernel. Provide as a proportion of data length - or as absolute value. As in the R function ksmooth - (used by the earlywarnings package in R), we define the bandwidth - such that the kernel has its quartiles at +/- 0.25*bandwidth. - span: float - Span of time-series data used for Lowess filtering. Taken as a - proportion of time-series length if in (0,1), otherwise taken as - absolute. - upto: int or 'Full' - Time up to which EWS are computed. Enter 'Full' to use - the entire time-series. Otherwise enter a time value. - ews: list of {'var', 'ac', 'sd', 'cv', 'skew', 'kurt', 'smax', 'cf', 'aic','smax/var', 'smax/mean'}. - List of EWS to compute. Options include variance ('var'), - autocorrelation ('ac'), standard deviation ('sd'), coefficient - of variation ('cv'), skewness ('skew'), kurtosis ('kurt'), peak in - the power spectrum ('smax'), coherence factor ('cf'), AIC weights ('aic'), - ratio of peak in power spectrum and variance ('smax/var'), ratio of - peak in power spectrum and mean of time series (smax/mean') - lag_times: list of int - List of lag times at which to compute autocorrelation. - ham_length: int - Length of the Hamming window used to compute the power spectrum. - ham_offset: float - Hamming offset as a proportion of the Hamming window size. - pspec_roll_offset: int - Rolling window offset used when computing power spectra. Power spectrum - computation is relatively expensive so this is rarely taken as 1 - (as is the case for the other EWS). - w_cutoff: float - Cutoff frequency used in power spectrum. Given as a proportion of the - maximum permissable frequency in the empirical power spectrum. - aic: AIC values to compute - sweep: bool - If 'True', sweep over a range of intialisation - parameters when optimising to compute AIC scores, at the expense of - longer computation. If 'False', intialisation parameter is taken as the - 'best guess'. - ktau_time: float or 'Start' - Time from which to compute Kendall tau values. Defaults to the start of - the EWS data. - - Returns - -------- - dict of pd.DataFrames: - A dictionary with the following entries. - **'EWS metrics':** A DataFrame indexed by time with columns corresopnding - to each EWS. - **'Power spectrum':** A DataFrame of the measured power spectra and the best fits - used to give the AIC weights. Indexed by time. - **'Kendall tau':** A DataFrame of the Kendall tau values for each EWS metric. + Multivariate time series data on which to compute early warning signals. + Parameters + ---------- + data : pandas.DataFrame + Contains each time series as a column. Index represents time values and + is carried over. + transition : float + Time value at which transition occurs, if any. If defined, early warning signals + are only computed up to this point in the time series """ - # Initialise a DataFrame to store EWS data - indexed by time - df_ews = pd.DataFrame(raw_series) - df_ews.columns = ["State variable"] - df_ews.index.rename('time', inplace=True) - - # Select portion of data where EWS are evaluated (e.g only up to bifurcation) - if upto == "Full": - short_series = raw_series - else: - short_series = raw_series.loc[:upto] - - # ------Data detrending-------- - - # Compute the absolute size of the bandwidth if it is given as a proportion - if 0 < band_width <= 1: - bw_size = short_series.shape[0] * band_width - else: - bw_size = band_width + def __init__(self, data, transition=None): + # If data is not provided as a dataframe, flag error + if type(data) != pd.DataFrame: + print("\nERROR: data has been provided as type {}".format(type(data))) + print("Please provide data as a pandas DataFrame.\n") + return - # Compute the Lowess span as a proportion if given as absolute - if not 0 < span <= 1: - span = span / short_series.shape[0] - else: - span = span + # Set state and transition attributes + self.state = data + self.transition = float(transition) if transition else transition + self.var_names = data.columns - # Compute smoothed data and residuals - if smooth == "Gaussian": - # Use the gaussian_filter function provided by Scipy - # Standard deviation of kernel given bandwidth - # Note that for a Gaussian, quartiles are at +/- 0.675*sigma - sigma = (0.25 / 0.675) * bw_size - smooth_data = gf(short_series.values, sigma=sigma, mode="reflect") - smooth_series = pd.Series(smooth_data, index=short_series.index) - residuals = short_series.values - smooth_data - resid_series = pd.Series(residuals, index=short_series.index) - - # Add smoothed data and residuals to the EWS DataFrame - df_ews["Smoothing"] = smooth_series - df_ews["Residuals"] = resid_series + # Initialise other attributes + self.ews = pd.DataFrame(index=data.index) + self.ktau = dict() - if smooth == "Lowess": - smooth_data = lowess(short_series.values, short_series.index.values, frac=span)[ - :, 1 - ] - smooth_series = pd.Series(smooth_data, index=short_series.index) - residuals = short_series.values - smooth_data - resid_series = pd.Series(residuals, index=short_series.index) + 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' for each variable - # Add smoothed data and residuals to the EWS DataFrame - df_ews["Smoothing"] = smooth_series - df_ews["Residuals"] = resid_series + Parameters + ---------- + method : str, optional + Method of detrending to use. + Select from ['Gaussian', 'Lowess'] + The default is 'Gaussian'. + bandwidth : float, optional + Bandwidth of Gaussian kernel. Provide as a proportion of data length + or as a number of data points. As in the R function ksmooth + (used by the earlywarnings package in R), we define the bandwidth + such that the kernel has its quartiles at +/- 0.25*bandwidth. + The default is 0.2. + span : float, optional + Span of time-series data used for Lowess filtering. Provide as a + proportion of data length or as a number of data points. + The default is 0.2. - # Use the short_series EWS if smooth='None'. Otherwise use reiduals. - eval_series = short_series if smooth == "None" else resid_series + Returns + ------- + None. - # Compute the rolling window size (integer value) - rw_size = int(np.floor(roll_window * raw_series.shape[0])) + """ - # ------------ Compute temporal EWS---------------# + # Error messages + if method not in ["Lowess", "Gaussian"]: + print("ERROR: {} is not a valid detrending method.\n".format(method)) - # Compute standard deviation as a Series and add to the DataFrame - if "sd" in ews: - roll_sd = eval_series.rolling(window=rw_size).std() - df_ews["Standard deviation"] = roll_sd + # Get time series data prior to transition + if self.transition: + df_pre = self.state[self.state.index <= self.transition] + else: + df_pre = self.state - # Compute variance as a Series and add to the DataFrame - if "var" in ews: - roll_var = eval_series.rolling(window=rw_size).var() - df_ews["Variance"] = roll_var + if method == "Gaussian": + # Get size of bandwidth in terms of num. datapoints if given as a proportion + if 0 < bandwidth <= 1: + bw_num = bandwidth * len(df_pre) + else: + bw_num = bandwidth - # Compute autocorrelation for each lag in lag_times and add to the DataFrame - if "ac" in ews: - for i in range(len(lag_times)): - roll_ac = eval_series.rolling(window=rw_size).apply( - func=lambda x: pd.Series(x).autocorr(lag=lag_times[i]), raw=True - ) - df_ews["Lag-" + str(lag_times[i]) + " AC"] = roll_ac + # Use the gaussian_filter function provided by Scipy + # Standard deviation of kernel given bandwidth + # Note that for a Gaussian, quartiles are at +/- 0.675*sigma + sigma = (0.25 / 0.675) * bw_num - # Compute Coefficient of Variation (C.V) and add to the DataFrame - if "cv" in ews: - # mean of raw_series - roll_mean = raw_series.rolling(window=rw_size).mean() - # standard deviation of residuals - roll_std = eval_series.rolling(window=rw_size).std() - # coefficient of variation - roll_cv = roll_std.divide(roll_mean) - df_ews["Coefficient of variation"] = roll_cv + # Do guassian smoothing on each variable + for var_name in self.var_names: + smooth_values = gf(df_pre[var_name].values, sigma=sigma, mode="reflect") + smooth_series = pd.Series(smooth_values, index=df_pre.index) + # Add smoothed data and residuals to the 'state' DataFrame + self.state["{}_smoothing".format(var_name)] = smooth_series + self.state["{}_residuals".format(var_name)] = ( + self.state[var_name] - self.state["{}_smoothing".format(var_name)] + ) - # Compute skewness and add to the DataFrame - if "skew" in ews: - roll_skew = eval_series.rolling(window=rw_size).skew() - df_ews["Skewness"] = roll_skew + if method == "Lowess": + # Convert span to a proportion of the length of the data + if not 0 < span <= 1: + span_prop = span / len(df_pre) + else: + span_prop = span - # Compute Kurtosis and add to DataFrame - if "kurt" in ews: - roll_kurt = eval_series.rolling(window=rw_size).kurt() - df_ews["Kurtosis"] = roll_kurt + # Do Lowess smoothing on each variable + for var_name in self.var_names: + smooth_values = lowess( + df_pre[var_name].values, df_pre.index.values, frac=span_prop + )[:, 1] + smooth_series = pd.Series(smooth_values, index=df_pre.index) + # Add smoothed data and residuals to the 'state' DataFrame + self.state["{}_smoothing".format(var_name)] = smooth_series + self.state["{}_residuals".format(var_name)] = ( + self.state[var_name] - self.state["{}_smoothing".format(var_name)] + ) - # ------------Compute spectral EWS-------------# + def compute_covar(self, rolling_window=0.25, leading_eval=False): + """ + Compute the covariance matrix over a rolling window. + If residuals have not been computed, computation will be + performed over state variable. - """ In this section we compute newly proposed EWS based on the power spectrum - of the time-series computed over a rolling window """ + Put covariance matrices into self.covar + Put leading eigenvalue into self.ews - # Empty DataFrame for storage of power spectra - df_pspec = pd.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. + leading_eval : bool + Whether to compute the leading eigenvalue of the covariance matrix. + This has been suggested as an early warning signal (Carpenter et al. 2008, Ecol. Letters) - # If any of the spectral metrics are listed in the ews vector, compute power spectra: - if ("smax" or "cf" or "aic" or "smax/var" or "smax/mean") in ews: + Returns + ------- + None. - # Number of components in the residual time-series - num_comps = len(eval_series) - # Rolling window offset (can make larger to save on computation time) - roll_offset = int(pspec_roll_offset) - # Time separation between data points (need for frequency values of power spectrum) - dt = eval_series.index[1] - eval_series.index[0] + """ - # Initialise a list for the spectral EWS - list_metrics_append = [] - # Initialise a list for the power spectra - list_spec_append = [] + # Get time series data prior to transition + if self.transition: + df_pre = self.state[self.state.index <= self.transition] + else: + df_pre = self.state - # Loop through window locations shifted by roll_offset - for k in np.arange(0, num_comps - (rw_size - 1), roll_offset): + # 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 - # Select subset of series contained in window - window_series = eval_series.iloc[k : k + rw_size] - # Asisgn the time value for the metrics (right end point of window) - t_point = eval_series.index[k + (rw_size - 1)] + # If residuals column exists, compute over residuals. + if "{}_residuals".format(self.var_names[0]) in df_pre.columns: + col_names_to_compute = [ + "{}_residuals".format(var) for var in self.var_names + ] + else: + col_names_to_compute = self.var_names - ## Compute the power spectrum using function pspec_welch - pspec = helpers.pspec_welch( - window_series, - dt, - ham_length=ham_length, - ham_offset=ham_offset, - w_cutoff=w_cutoff, - scaling="spectrum", - ) + # Compute covariance matrix + df_covar = df_pre[col_names_to_compute].rolling(window=rw_absolute).cov() + self.covar = df_covar - ## Create DataFrame for empirical power spectrum - df_pspec_empirical = pspec.to_frame().reset_index() - # Rename column - df_pspec_empirical.rename( - columns={"Power spectrum": "Empirical"}, inplace=True - ) - # Include a column for the time-stamp - df_pspec_empirical['time'] = t_point * np.ones(len(pspec)) - # Use a multi-index of ['time','Frequency'] - df_pspec_empirical.set_index(['time', 'frequency'], inplace=True) - - ## Compute the spectral EWS using pspec_metrics (dictionary) - metrics = helpers.pspec_metrics(pspec, ews, aic, sweep) - # Add the time-stamp - metrics['time'] = t_point - # Add metrics (dictionary) to the list - list_metrics_append.append(metrics) - - ## Store the power spectra fits (if aic computed) - if "aic" in ews: - # Create fine-scale frequency values - wVals = np.linspace(min(pspec.index), max(pspec.index), 100) - # Fold fit - pspec_fold = helpers.psd_fold( - wVals, - metrics["Params fold"]["sigma"], - metrics["Params fold"]["lam"], - ) - # Flip fit - pspec_flip = helpers.psd_flip( - wVals, metrics["Params flip"]["sigma"], metrics["Params flip"]["r"] - ) - # Hopf fit - pspec_hopf = helpers.psd_hopf( - wVals, - metrics["Params hopf"]["sigma"], - metrics["Params hopf"]["mu"], - metrics["Params hopf"]["w0"], - ) - # Null fit - pspec_null = helpers.psd_null(wVals, metrics["Params null"]["sigma"]) - - ## Put spectrum fits into a dataframe - dic_temp = { - 'time': t_point * np.ones(len(wVals)), - 'frequency': wVals, - "Fit fold": pspec_fold, - "Fit flip": pspec_flip, - "Fit hopf": pspec_hopf, - "Fit null": pspec_null, - } - df_pspec_fits = pd.DataFrame(dic_temp) - # Set the multi-index - df_pspec_fits.set_index(['time', 'frequency'], inplace=True) - - # Concatenate empirical PS with fits - df_pspec_temp = ( - pd.concat([df_pspec_empirical, df_pspec_fits], axis=1) - if "aic" in ews - else df_pspec_empirical + # Compute leading eigenvalue + if leading_eval: + covar_matrices = df_covar.values.reshape( + -1, len(self.var_names), len(self.var_names) ) - # Store DataFrame in list - list_spec_append.append(df_pspec_temp) - - # Concatenate the list of power spectra DataFrames to form a single DataFrame - df_pspec = pd.concat(list_spec_append) - - # Create a DataFrame out of the multiple dictionaries consisting of the spectral metrics - df_spec_metrics = pd.DataFrame(list_metrics_append) - df_spec_metrics.set_index('time', inplace=True) - - # Join the spectral EWS DataFrame to the main EWS DataFrame - df_ews = df_ews.join(df_spec_metrics) + ar_evals = np.zeros(len(df_pre)) + for idx, mat in enumerate(covar_matrices): + # Only compute eval if there are no NaN entries + if not np.any(np.isnan(mat)): + evals, evecs = np.linalg.eig(mat) + ar_evals[idx] = max(evals) + else: + ar_evals[idx] = np.nan + series_evals = pd.Series(ar_evals, index=df_pre.index) + self.ews["covar_leading_eval"] = series_evals + + def compute_corr(self, rolling_window=0.25): + """ + Compute the (Pearson) correlation matrix over a rolling window. + If residuals have not been computed, computation will be + performed over state variable. - # Compute smax/var and add to the DataFrame - if "smax/var" in ews: - df_ews["Smax/Var"] = df_ews["Smax"] / df_ews["Variance"] + Put correlation matrices into self.corr - # Compute smax normalised by mean and add to the DataFrame - if "smax/mean" in ews: - df_ews["Smax/Mean"] = df_ews["Smax"] / roll_mean + 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. - # ------------Compute Kendall tau coefficients----------------# + Returns + ------- + None. - """ In this section we compute the kendall correlation coefficients for each EWS - with respect to time. Values close to one indicate high correlation (i.e. EWS - increasing with time), values close to zero indicate no significant correlation, - and values close to negative one indicate high negative correlation (i.e. EWS - decreasing with time).""" + """ - # Time at which to start computng Kendall tau values - if ktau_time == "Start": - ktau_time = df_ews.index[0] + # Get time series data prior to transition + if self.transition: + df_pre = self.state[self.state.index <= self.transition] + else: + df_pre = self.state - else: - ktau_time = df_ews.index[np.argmin(abs(df_ews.index - ktau_time))] + # 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 - # Put time values as their own series for correlation computation - time_vals = pd.Series( - df_ews.loc[ktau_time:].index, index=df_ews.loc[ktau_time:].index - ) + # If residuals column exists, compute over residuals. + if "{}_residuals".format(self.var_names[0]) in df_pre.columns: + col_names_to_compute = [ + "{}_residuals".format(var) for var in self.var_names + ] + else: + col_names_to_compute = self.var_names - # List of EWS that can be used for Kendall tau computation - ktau_metrics = [ - "Variance", - "Standard deviation", - "Skewness", - "Kurtosis", - "Coefficient of variation", - "Smax", - "Smax/Var", - "Smax/Mean", - ] + ["Lag-" + str(i) + " AC" for i in lag_times] - # Find intersection with this list and EWS computed - ews_list = df_ews.columns.values.tolist() - ktau_metrics = list(set(ews_list) & set(ktau_metrics)) - - # Find Kendall tau for each EWS and store in a DataFrame - dic_ktau = { - x: df_ews[x].loc[ktau_time:].corr(time_vals, method="kendall") - for x in ktau_metrics - } # temporary dictionary - df_ktau = pd.DataFrame( - dic_ktau, index=[0] - ) # DataFrame (easier for concatenation purposes) - - # -------------Organise final output and return--------------# - - # Ouptut a dictionary containing EWS DataFrame, power spectra DataFrame, and Kendall tau values - output_dic = { - "EWS metrics": df_ews, - "Power spectrum": df_pspec, - "Kendall tau": df_ktau, - } - - return output_dic + # Compute correlation matrix + df_corr = df_pre[col_names_to_compute].rolling(window=rw_absolute).corr() + self.corr = df_corr # ----------------------------- # Eigenvalue reconstruction # ------------------------------ + def eval_recon_rolling( df_in, roll_window=0.4, @@ -1564,7 +1408,6 @@ def eval_recon_rolling( if smooth == "Gaussian": # Loop through variables for var in var_names: - smooth_data = gf(df_pre[var].values, sigma=bw_size, mode="reflect") smooth_series = pd.Series(smooth_data, index=df_pre.index) residuals = df_pre[var].values - smooth_data @@ -1576,7 +1419,6 @@ def eval_recon_rolling( if smooth == "Lowess": # Loop through variabless for var in var_names: - smooth_data = lowess(df_pre[var].values, df_pre.index.values, frac=span)[ :, 1 ] @@ -1602,7 +1444,6 @@ def eval_recon_rolling( # Loop through window locations shifted by roll_offset for k in np.arange(0, num_comps - (rw_size - 1), roll_offset): - # Select subset of residuals contained in window df_window = df_pre[[var + "_r" for var in var_names]].iloc[k : k + rw_size] # Asisgn the time value for the metrics (right end point of window) @@ -1611,13 +1452,13 @@ def eval_recon_rolling( # Do eigenvalue reconstruction on residuals dic_eval_recon = helpers.eval_recon(df_window) # Add time component - dic_eval_recon['time'] = t_point + dic_eval_recon["time"] = t_point # Add them to list list_evaldata.append(dic_eval_recon) # Create dataframe from list of dicts of eval data df_evaldata = pd.DataFrame(list_evaldata) - df_evaldata.set_index('time', inplace=True) + df_evaldata.set_index("time", inplace=True) # Create output dataframe that merges all useful info df_out = pd.concat( @@ -1640,7 +1481,6 @@ def eval_recon_rolling( def block_bootstrap(series, n_samples, bs_type="Stationary", block_size=10): - """ Computes block-bootstrap samples of series. @@ -1674,9 +1514,8 @@ def block_bootstrap(series, n_samples, bs_type="Stationary", block_size=10): # Count for sample number count = 1 for data in bs.bootstrap(n_samples): - df_temp = pd.DataFrame( - {"sample": count, 'time': series.index.values, "x": data[0][0]} + {"sample": count, "time": series.index.values, "x": data[0][0]} ) list_samples.append(df_temp) count += 1 @@ -1687,16 +1526,15 @@ def block_bootstrap(series, n_samples, bs_type="Stationary", block_size=10): # Count for sample number count = 1 for data in bs.bootstrap(n_samples): - df_temp = pd.DataFrame( - {"sample": count, 'time': series.index.values, "x": data[0][0]} + {"sample": count, "time": series.index.values, "x": data[0][0]} ) list_samples.append(df_temp) count += 1 # Concatenate list of samples df_samples = pd.concat(list_samples) - df_samples.set_index(["sample", 'time'], inplace=True) + df_samples.set_index(["sample", "time"], inplace=True) # Output DataFrame of samples return df_samples @@ -1712,7 +1550,6 @@ def roll_bootstrap( bs_type="Stationary", block_size=10, ): - """ Smooths raw_series and computes residuals over a rolling window. Bootstraps each segment and outputs samples. @@ -1789,7 +1626,6 @@ def roll_bootstrap( # Loop through window locations shifted by roll_offset for k in np.arange(0, num_comps - (rw_size - 1), roll_offset): - # Select subset of series contained in window window_series = resid_series.iloc[k : k + rw_size] # Asisgn the time value for the metrics (right end point of window) @@ -1798,15 +1634,15 @@ def roll_bootstrap( # Compute bootstrap samples of residauls within rolling window df_samples_temp = block_bootstrap(window_series, n_samples, bs_type, block_size) df_samples_temp.reset_index(inplace=True) - df_samples_temp['wintime'] = df_samples_temp['time'] - + df_samples_temp["wintime"] = df_samples_temp["time"] + # Add column with real time (end of window) - df_samples_temp['time'] = t_point + df_samples_temp["time"] = t_point # Reorganise index - + df_samples_temp.reset_index(inplace=True) - df_samples_temp.set_index(['time', "sample", 'wintime'], inplace=True) + df_samples_temp.set_index(["time", "sample", "wintime"], inplace=True) # Append the list of samples list_samples.append(df_samples_temp) diff --git a/paper.bib b/paper.bib deleted file mode 100644 index 4e14304..0000000 --- a/paper.bib +++ /dev/null @@ -1,43 +0,0 @@ -@article{scheffer2009early, - title={Early-warning signals for critical transitions}, - author={Scheffer, Marten and Bascompte, Jordi and Brock, William A and Brovkin, Victor and Carpenter, Stephen R and Dakos, Vasilis and Held, Hermann and Van Nes, Egbert H and Rietkerk, Max and Sugihara, George}, - journal={Nature}, - volume={461}, - number={7260}, - pages={53--59}, - year={2009}, - publisher={Nature Publishing Group} -} - -@article{clements2018indicators, - title={Indicators of transitions in biological systems}, - author={Clements, Christopher F and Ozgul, Arpat}, - journal={Ecology letters}, - volume={21}, - number={6}, - pages={905--919}, - year={2018}, - publisher={Wiley Online Library} -} - -@article{bury2020detecting, - title={Detecting and distinguishing tipping points using spectral early warning signals}, - author={Bury, Thomas M and Bauch, Chris T and Anand, Madhur}, - journal={Journal of the Royal Society Interface}, - volume={17}, - number={170}, - pages={20200482}, - year={2020}, - publisher={The Royal Society} -} - -@article{bury2021deep, - title={Deep learning for early warning signals of tipping points}, - author={Bury, Thomas M and Sujith, RI and Pavithran, Induja and Scheffer, Marten and Lenton, Timothy M and Anand, Madhur and Bauch, Chris T}, - journal={Proceedings of the National Academy of Sciences}, - volume={118}, - number={39}, - pages={e2106140118}, - year={2021}, - publisher={National Acad Sciences} -} \ No newline at end of file diff --git a/paper.md b/paper.md deleted file mode 100644 index 548fd0f..0000000 --- a/paper.md +++ /dev/null @@ -1,52 +0,0 @@ ---- -title: 'ewstools: A Python package for early warning signals of bifurcations in time series data.' -tags: - - Python - - time series - - early warning signal - - tipping point - - dynamical system - - bifurcation -authors: - - name: Thomas M. Bury - equal-contrib: false - orcid: 0000-0003-1595-9444 - affiliation: "1, 2" # (Multiple affiliations must be quoted) -affiliations: - - name: Department of Physiology, McGill University, Montréal, Canada - index: 1 - - name: Department of Applied Mathematics, University of Waterloo, Waterloo, Canada - index: 2 -date: 17 August 2022 -bibliography: paper.bib ---- - - -# Summary - -Many systems across nature and society have the capacity to undergo an abrupt and -profound change in their dynamics. From a dynamical systemes perspective, these events -are often associated with the crossing of a bifurcation. Early warning signals (EWS) -for bifurcations are therefore in high demand. Two commonly used EWS for bifurcations -are variance and lag-1 autocorrelation, that are expected to increase prior to many -bifurcations due to critical slowing down [@scheffer2009early]. There now exist a -wealth of other EWS based on changes in time series dynamics that are expected to occur -prior to bifurcations [@clements2018indicators]. More recently, deep learning -classifiers have been trained and applied to detect bifurcations, with promising -results [@bury2021deep] - - -``EWStools`` is a Python package for computing, analysing, and visualising -early warning signals in time series data. - - -# Acknowledgements - -TMB acknowledges contributions from Chris T. Bauch (University of Waterloo) -on code for training deep learning classifiers. This project is currently supported by the -Fonds de recherche du Québec (FRQ) -and has received funding in the past from the -Natural Sciences and Engineering Research Council of Canada (NSERC). - - -# References \ No newline at end of file diff --git a/paper/figure1.png b/paper/figure1.png new file mode 100644 index 0000000..293caec Binary files /dev/null and b/paper/figure1.png differ diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 0000000..7cd186b --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,184 @@ +%% This BibTeX bibliography file was created using BibDesk. +%% https://bibdesk.sourceforge.io/ + +%% Created for Thomas Bury at 2022-12-14 12:22:07 -0500 + + +%% Saved with string encoding Unicode (UTF-8) + + + +@article{scheffer2009early, + author = {Scheffer, Marten and Bascompte, Jordi and Brock, William A and Brovkin, Victor and Carpenter, Stephen R and Dakos, Vasilis and Held, Hermann and Van Nes, Egbert H and Rietkerk, Max and Sugihara, George}, + journal = {Nature}, + number = {7260}, + pages = {53--59}, + publisher = {Nature Publishing Group}, + title = {Early-warning signals for critical transitions}, + volume = {461}, + year = {2009}, + doi = {10.1038/nature08227}} + +@article{clements2018indicators, + author = {Clements, Christopher F and Ozgul, Arpat}, + journal = {Ecology letters}, + number = {6}, + pages = {905--919}, + publisher = {Wiley Online Library}, + title = {Indicators of transitions in biological systems}, + volume = {21}, + year = {2018}, + doi = {10.1111/ele.12948}} + +@article{bury2020detecting, + author = {Bury, Thomas M and Bauch, Chris T and Anand, Madhur}, + journal = {Journal of the Royal Society Interface}, + number = {170}, + pages = {20200482}, + publisher = {The Royal Society}, + title = {Detecting and distinguishing tipping points using spectral early warning signals}, + volume = {17}, + year = {2020}, + doi = {10.1098/rsif.2020.0482}} + +@article{bury2021deep, + author = {Bury, Thomas M and Sujith, RI and Pavithran, Induja and Scheffer, Marten and Lenton, Timothy M and Anand, Madhur and Bauch, Chris T}, + journal = {Proceedings of the National Academy of Sciences}, + number = {39}, + pages = {e2106140118}, + publisher = {National Acad Sciences}, + title = {Deep learning for early warning signals of tipping points}, + volume = {118}, + year = {2021}, + doi = {10.1073/pnas.2106140118}} + +@article{dakos2012methods, + author = {Dakos, Vasilis and Carpenter, Stephen R and Brock, William A and Ellison, Aaron M and Guttal, Vishwesha and Ives, Anthony R and K{\'e}fi, Sonia and Livina, Valerie and Seekell, David A and {van Nes}, Egbert H and others}, + journal = {PloS one}, + number = {7}, + pages = {e41010}, + publisher = {Public Library of Science San Francisco, USA}, + title = {Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data}, + volume = {7}, + year = {2012}, + doi = {10.1371/journal.pone.0041010}} + +@article{genin2018monitoring, + author = {G{\'e}nin, Alexandre and Majumder, Sabiha and Sankaran, Sumithra and Danet, Alain and Guttal, Vishwesha and Schneider, Florian D and K{\'e}fi, Sonia}, + journal = {Methods in Ecology and Evolution}, + number = {10}, + pages = {2067--2075}, + publisher = {Wiley Online Library}, + title = {Monitoring ecosystem degradation using spatial data and the {R} package spatialwarnings}, + volume = {9}, + year = {2018}, + doi = {10.1111/2041-210X.13058}} + +@article{harris2020array, + author = {Harris, Charles R and Millman, K Jarrod and Van Der Walt, St{\'e}fan J and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J and others}, + journal = {Nature}, + number = {7825}, + pages = {357--362}, + publisher = {Nature Publishing Group}, + title = {Array programming with {N}um{P}y}, + volume = {585}, + year = {2020}, + doi = {10.1038/s41586-020-2649-2}} + + +@MISC{newville2016lmfit, + author = {{Newville}, Matthew and {Stensitzki}, Till and {Allen}, Daniel B. and {Rawlik}, Michal and {Ingargiola}, Antonino and {Nelson}, Andrew}, + title = "Lmfit: Non-Linear Least-Square Minimization and Curve-Fitting for {P}ython", + keywords = {Software}, + howpublished = {Astrophysics Source Code Library, record ascl:1606.014}, + year = 2016, + month = jun, + eid = {ascl:1606.014}, + pages = {ascl:1606.014}, +archivePrefix = {ascl}, + eprint = {1606.014}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2016ascl.soft06014N}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@InProceedings{ mckinney2010data, + author = { {W}es {M}c{K}inney }, + title = { {D}ata {S}tructures for {S}tatistical {C}omputing in {P}ython }, + booktitle = { {P}roceedings of the 9th {P}ython in {S}cience {C}onference }, + pages = { 56 - 61 }, + year = { 2010 }, + editor = { {S}t\'efan van der {W}alt and {J}arrod {M}illman }, + doi = { 10.25080/Majora-92bf1922-00a } +} + +@software{reback2020pandas, + author = {{The pandas development team}}, + title = {pandas-dev/pandas: Pandas}, + month = feb, + year = 2020, + publisher = {Zenodo}, + version = {latest}, + doi = {10.5281/zenodo.3509134}, + url = {https://doi.org/10.5281/zenodo.3509134} +} + +@online{plotly, + address = {Montreal, QC}, + author = {{Plotly Technologies Inc.}}, + publisher = {Plotly Technologies Inc.}, + title = {Collaborative data science}, + url = {https://plotly.com}, + year = {2015}, + bdsk-url-1 = {https://plotly.com}} + +@software{sheppard_2015_15681, + author = {Sheppard, Kevin}, + doi = {10.5281/zenodo.15681}, + month = feb, + note = {{GitHub repository: https://github.com/bashtage/arch}}, + publisher = {Zenodo}, + title = {ARCH Toolbox for {P}ython}, + url = {https://doi.org/10.5281/zenodo.15681}, + year = 2015, + bdsk-url-1 = {https://doi.org/10.5281/zenodo.15681}} + +@inproceedings{seabold2010statsmodels, + author = {Seabold, Skipper and Perktold, Josef}, + booktitle = {9th Python in Science Conference}, + title = {statsmodels: Econometric and statistical modeling with {P}ython}, + year = {2010}, + doi = {10.25080/Majora-92bf1922-011}} + +@article{virtanen2020scipy, + author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and others}, + journal = {Nature methods}, + number = {3}, + pages = {261--272}, + publisher = {Nature Publishing Group}, + title = {SciPy 1.0: fundamental algorithms for scientific computing in {P}ython}, + volume = {17}, + year = {2020}, + doi = {10.1038/s41592-020-0772-5}} + +@inproceedings{abadi2016tensorflow, + author = {Abadi, Mart{\'\i}n and Barham, Paul and Chen, Jianmin and Chen, Zhifeng and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Irving, Geoffrey and Isard, Michael and others}, + booktitle = {12th USENIX symposium on operating systems design and implementation (OSDI 16)}, + pages = {265--283}, + title = {Tensor{F}low: A system for large-scale machine learning}, + year = {2016}, + doi = {10.48550/arXiv.1605.08695}} + +@software{chollet2015keras, + title={Keras}, + author={Chollet, Fran\c{c}ois and others}, + year={2015}, + publisher={GitHub}, + howpublished={\url{https://github.com/fchollet/keras}}, + } + +@online{pypl, + author = {PYPL}, + title = {Popularity of Programming Language Index}, + url = {https://pypl.github.io/PYPL.html}, + year = {2022}, + bdsk-url-1 = {https://pypl.github.io/PYPL.html}} diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 0000000..245f83a --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,167 @@ +--- +title: 'ewstools: A Python package for early warning signals of bifurcations in time series data' +tags: + - Python + - time series + - early warning signal + - tipping point + - dynamical system + - bifurcation +authors: + - name: Thomas M. Bury + equal-contrib: false + orcid: 0000-0003-1595-9444 + affiliation: "1, 2" # (Multiple affiliations must be quoted) +affiliations: + - name: Department of Physiology, McGill University, Montréal, Canada + index: 1 + - name: Department of Applied Mathematics, University of Waterloo, Waterloo, Canada + index: 2 +date: 17 August 2022 +bibliography: paper.bib +--- + + +# Summary + +Many systems in nature and society have the capacity to undergo critical transitions: +sudden and profound changes in dynamics that are hard to reverse. +Examples include the outbreak of disease, the collapse of an ecosystem, and the onset +of a cardiac arrhythmia. +From a mathematical perspective, these transitions may be understood as the +crossing of a bifurcation (tipping point) in an appropriate dynamical system model. +In 2009, Scheffer and colleagues proposed early warning signals (EWS) for bifurcations +based on statistics of noisy fluctuations in time series data [@scheffer2009early]. +This spurred massive interest in the subject, resulting in a multitude of different +EWS for anticipating bifurcations [@clements2018indicators]. More recently, EWS +from deep learning classifiers have outperformed conventional EWS +on several model and empirical datasets, whilst also providing +information on the type of bifurcation [@bury2021deep]. +Software packages for EWS can facilitate the development and testing of EWS, +whilst also providing the scientific community with tools to rapidly apply +EWS to their own data. + + +`ewstools` is an accessible Python package for computing, analysing, and +visualising EWS in time series data. The package provides: + +- An intuitive, object-oriented framework for working with EWS in a given time series +- A suite of temporal EWS and associated methods [@dakos2012methods] +- A suite of spectral EWS [@bury2020detecting] +- Methods to use deep learning classifiers for EWS [@bury2021deep] +- Integrated plotting and evaluation functions to quickly check performance of EWS +- Built-in theoretical models to test EWS +- Interactive tutorials in the form of Jupyter notebooks + + +`ewstools` makes use of several open-source Python packages, including +pandas [@mckinney2010data; @reback2020pandas] for dataframe handling, +NumPy [@harris2020array] for fast numerical computing, +Plotly [@plotly] for visualisation, +LMFIT [@newville2016lmfit] for nonlinear least-squares minimisation, +ARCH [@sheppard_2015_15681] for bootstrapping methods, +statsmodels [@seabold2010statsmodels] and SciPy [@virtanen2020scipy] for detrending methods, +and Keras [@chollet2015keras] and TensorFlow [@abadi2016tensorflow] for deep learning. + + +# Statement of need + +Critical transitions are relevant to many disciplines, including ecology, medicine, +finance, and epidemiology, to name a few. As such, it is important that EWS are made +widely accessible. +To my knowledge, there are two other software packages developed for +computing EWS, namely +[earlywarnings](https://cran.r-project.org/web/packages/earlywarnings/index.html) by @dakos2012methods +and +[spatialwarnings](https://cran.r-project.org/web/packages/spatialwarnings/index.html) by @genin2018monitoring, +which both use the R programming language. +Given the recent surge in popularity of the Python programming language [@pypl], +a Python-based implementation of EWS should be useful. +`ewstools` also implements novel deep learning methods for EWS, which have +outperformed conventional EWS in several model and empirical systems [@bury2021deep]. +These new methods should be tried, tested, and developed for a variety of systems +and I hope that this package facilitates this endeavour. + + + + + +# Usage Example + +```python +import ewstools + +# Load data and get time series as a pandas Series object +df = pd.read_csv(‘data.csv’) +series = df['x'] + +# Initialise ewstools TimeSeries object and define transition time +ts = ewstools.TimeSeries(data=series, transition=440) + +# Detrend time series +ts.detrend(method='Lowess', span=0.2) + +# Compute desired EWS +ts.compute_var(rolling_window=0.5) +ts.compute_auto(lag=1, rolling_window=0.5) +ts.compute_auto(lag=2, rolling_window=0.5) + +# Compute performance metrics +ts.compute_ktau() + +# Plot results - can be saved as an interactive html file or as a static image +fig = ts.make_plotly() +``` + +![Output of plotting function in usage example.\label{fig:Figure 1}](figure1.png) + + +# Documentation + +Documentation for `ewstools` is available at +[https://ewstools.readthedocs.io/en/latest/](https://ewstools.readthedocs.io/en/latest/). +Tutorials in the form of Jupyter notebooks are available at +[https://github.com/ThomasMBury/ewstools/tree/main/tutorials](https://github.com/ThomasMBury/ewstools/tree/main/tutorials). + + + +# Acknowledgements + +This work is supported by the +Fonds de Recherche du Québec -- Nature et technologies (FRQNT) +and Compute Canada. Earlier versions were supported by the +Natural Sciences and Engineering Research Council of Canada (NSERC). + + +# References diff --git a/requirements_dev.txt b/requirements_dev.txt index 2699bd5..8e23971 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,7 +1,7 @@ pip==22.1.2 -setuptools==62.6.0 +setuptools==65.5.1 twine==4.0.1 -wheel==0.37.1 +wheel==0.38.1 pytest==7.1.2 pytest-cov==3.0.0 sphinx==5.0.2 @@ -13,5 +13,5 @@ plotly==5.9.0 lmfit==1.0.3 statsmodels==0.13.2 scipy==1.8.1 -tensorflow==2.9.1 +tensorflow==2.9.3 deprecation==2.1.0 \ No newline at end of file diff --git a/setup.py b/setup.py index a409638..63e3e9e 100644 --- a/setup.py +++ b/setup.py @@ -15,7 +15,7 @@ setuptools.setup( name="ewstools", - version="2.0.1", + version="2.1.0", author="Thomas M Bury", author_email="tombury182@gmail.com", description="""Python package to compute early warning signals (EWS) diff --git a/tests/test_ewstools.py b/tests/test_ewstools.py index 3b056c5..e8d00b1 100644 --- a/tests/test_ewstools.py +++ b/tests/test_ewstools.py @@ -11,76 +11,131 @@ from tensorflow.keras.models import load_model - # Import ewstools import ewstools from ewstools import core from ewstools import helpers - # # Import ewstools locally when testing locally # import sys -# sys.path.append('../') + +# sys.path.append("../") # import ewstools +def test_MultiTimeSeries_init(): + """ + Test the MultiTimeSeries initialisation process + """ + + # Simulate a time series + tVals = np.arange(0, 10, 0.1) + xVals = 5 + np.random.normal(0, 1, len(tVals)) + yVals = 2 + np.random.normal(0, 2, len(tVals)) + df = pd.DataFrame({"x": xVals, "y": yVals}, index=tVals) + df.index.name = "index_name" + + # Create MultiTimeSeries object + mts = ewstools.core.MultiTimeSeries(df, transition=8) + assert type(mts.state) == pd.DataFrame + assert type(mts.ews) == pd.DataFrame + assert type(mts.ktau) == dict + assert mts.state.index.name == "index_name" + assert mts.ews.index.name == "index_name" + assert type(mts.transition) == float + + +def test_MultiTimeSeries_ews(): + """ + Test the MultiTimeSeries EWS computations + """ + + # Simulate a time series + tVals = np.arange(0, 10, 0.1) + xVals = 5 + np.random.normal(0, 1, len(tVals)) + yVals = 2 + np.random.normal(0, 2, len(tVals)) + zVals = 1 + 0.5 * tVals + np.random.normal(0, 2, len(tVals)) + df = pd.DataFrame({"x": xVals, "y": yVals, "z": zVals}, index=tVals) + df.index.name = "index_name" + + # Create MultiTimeSeries object + mts = ewstools.MultiTimeSeries(np.array([1, 2, 3])) # invalid entry + mts = ewstools.MultiTimeSeries(df, transition=8) + mts.detrend(method="XXX", bandwidth=0.2) # invalid detrend method + mts.detrend(method="Gaussian", bandwidth=0.2) + mts.detrend(method="Gaussian", bandwidth=20) + mts.detrend(method="Lowess", span=0.2) + mts.detrend(method="Lowess", span=20) + + mts.compute_covar(rolling_window=0.25, leading_eval=True) + mts.compute_covar(rolling_window=20, leading_eval=False) + mts.compute_corr(rolling_window=0.25) + mts.compute_corr(rolling_window=20) + + assert type(mts.ews) == pd.DataFrame + assert type(mts.covar) == pd.DataFrame + assert type(mts.corr) == pd.DataFrame + assert "x_residuals" in mts.state.columns + assert "z_smoothing" in mts.state.columns + assert "covar_leading_eval" in mts.ews.columns + + def test_TimeSeries_init(): - ''' + """ Test the TimeSeries initialisation process - ''' - + """ + # Simulate a time series tVals = np.arange(0, 10, 0.1) xVals = 5 + np.random.normal(0, 1, len(tVals)) - + # Create TimeSeries object using np.ndarray + ts = ewstools.TimeSeries("hello") # invalid entry ts = ewstools.TimeSeries(xVals) assert type(ts.state) == pd.DataFrame assert type(ts.ews) == pd.DataFrame assert type(ts.ktau) == dict assert type(ts.dl_preds) == pd.DataFrame - assert ts.state.index.name == 'time' - assert ts.ews.index.name == 'time' - + assert ts.state.index.name == "time" + assert ts.ews.index.name == "time" + # Create TimeSeries object using list ts = ewstools.TimeSeries(list(xVals)) assert type(ts.state) == pd.DataFrame - + # Create TimeSeries object using pd.Series data = pd.Series(xVals, index=tVals) - data.index.name = 'test_index_name' + data.index.name = "test_index_name" ts = ewstools.TimeSeries(data) assert type(ts.state) == pd.DataFrame - assert ts.state.index.name == 'test_index_name' + assert ts.state.index.name == "test_index_name" # Create TimeSeries object using pd.Series data = pd.Series(xVals, index=tVals) ts = ewstools.TimeSeries(data, transition=80) assert type(ts.state) == pd.DataFrame - assert ts.state.index.name == 'time' + assert ts.state.index.name == "time" assert type(ts.transition) == float - + # Make plotly fig fig = ts.make_plotly() - assert type(fig)==plotly.graph_objs._figure.Figure - + assert type(fig) == plotly.graph_objs._figure.Figure def test_TimeSeries_ews(): - ''' + """ Test the TimeSeries methods that involve detrending and computing EWS - ''' - + """ + # Simulate a time series tVals = np.arange(0, 10, 0.1) xVals = 5 + np.random.normal(0, 1, len(tVals)) data = pd.Series(xVals, index=tVals) ts = ewstools.TimeSeries(data, transition=80) - ts2 = ewstools.TimeSeries(data) # time series without a transition - - + ts2 = ewstools.TimeSeries(data) # time series without a transition + # Compute EWS without detrending rolling_window = 0.5 ts.compute_var(rolling_window=rolling_window) @@ -91,11 +146,10 @@ def test_TimeSeries_ews(): ts.compute_skew(rolling_window=rolling_window) ts.compute_kurt(rolling_window=rolling_window) 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 "variance" in ts.ews.columns + assert "ac5" in ts.ews.columns + assert "cv" in ts.ews.columns + # Compute EWS on time series without transition rolling_window = 0.5 ts2.compute_var(rolling_window=rolling_window) @@ -106,18 +160,17 @@ def test_TimeSeries_ews(): ts2.compute_skew(rolling_window=rolling_window) ts2.compute_kurt(rolling_window=rolling_window) 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 "variance" in ts2.ews.columns + assert "ac5" in ts2.ews.columns + assert "cv" in ts2.ews.columns + # Detrend data using Gaussian and Lowess filter - ts.detrend('Gaussian', bandwidth=0.2) - ts.detrend('Gaussian', bandwidth=30) - ts.detrend('Lowess', span=0.2) - ts.detrend('Lowess', span=30) + ts.detrend("Gaussian", bandwidth=0.2) + ts.detrend("Gaussian", bandwidth=30) + ts.detrend("Lowess", span=0.2) + ts.detrend("Lowess", span=30) assert type(ts.state) == pd.DataFrame - assert 'residuals' in ts.state.columns + assert "residuals" in ts.state.columns # Compute EWS on detrended data using rolling window as fraction and absolute rolling_window = 0.2 @@ -130,26 +183,25 @@ def test_TimeSeries_ews(): ts.compute_kurt(rolling_window=rolling_window) assert type(ts.ews) == pd.DataFrame - assert 'variance' in ts.ews.columns - assert 'ac5' in ts.ews.columns + assert "variance" in ts.ews.columns + assert "ac5" 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 "variance" in ts.ktau.keys() + assert "ac5" in ts.ktau.keys() + # Make plotly fig fig = ts.make_plotly() - assert type(fig)==plotly.graph_objs._figure.Figure - + assert type(fig) == plotly.graph_objs._figure.Figure def test_TimeSeries_dl_preds(): - ''' + """ Test the TimeSeries methods that involve computing DL predictions - ''' + """ # Simulate a time series tVals = np.arange(0, 10, 0.1) @@ -159,41 +211,40 @@ def test_TimeSeries_dl_preds(): # Detrend time series ts.detrend() - + # Import a classifier - classifier_path = 'saved_classifiers/bury_pnas_21/len500/best_model_1_1_len500.pkl' + classifier_path = "saved_classifiers/bury_pnas_21/len500/best_model_1_1_len500.pkl" classifier = load_model(classifier_path) # Apply classifier with time bounds - ts.apply_classifier(classifier, name='c1', tmin=1, tmax=5) + ts.apply_classifier(classifier, name="c1", tmin=1, tmax=5) assert type(ts.dl_preds) == pd.DataFrame - assert 'time' in ts.dl_preds.columns - assert 'classifier' in ts.dl_preds.columns - + assert "time" in ts.dl_preds.columns + assert "classifier" in ts.dl_preds.columns + # Apply classifier many times on incrementally longer segments of time series ts.clear_dl_preds() - ts.apply_classifier_inc(classifier, inc=40, name='c1', verbose=1) - + ts.apply_classifier_inc(classifier, inc=40, name="c1", verbose=1) + # Make plotly fig fig = ts.make_plotly() - assert type(fig)==plotly.graph_objs._figure.Figure + assert type(fig) == plotly.graph_objs._figure.Figure # Import and apply a second classifier - classifier_path = 'saved_classifiers/bury_pnas_21/len500/best_model_1_2_len500.pkl' + classifier_path = "saved_classifiers/bury_pnas_21/len500/best_model_1_2_len500.pkl" classifier2 = load_model(classifier_path) - ts.apply_classifier_inc(classifier2, inc=40, name='c2', verbose=1) + ts.apply_classifier_inc(classifier2, inc=40, name="c2", verbose=1) # Make plotly fig using ensemble average fig = ts.make_plotly(ens_avg=True) - assert type(fig)==plotly.graph_objs._figure.Figure - - + assert type(fig) == plotly.graph_objs._figure.Figure + def test_TimeSeries_spec_ews(): - ''' + """ Test the TimeSeries methods that involve computing spectral EWS - - ''' + + """ # Simulate a time series tVals = np.arange(0, 10, 0.1) @@ -206,75 +257,19 @@ def test_TimeSeries_spec_ews(): # Compute power spectrum ts.compute_spectrum() - assert type(ts.pspec)==pd.DataFrame - assert 'frequency' in ts.pspec.columns - assert 'power' in ts.pspec.columns - + assert type(ts.pspec) == pd.DataFrame + assert "frequency" in ts.pspec.columns + assert "power" in ts.pspec.columns + # Compute smax ts.compute_smax() # Compute spectrum types indicated by AIC weights ts.compute_spec_type() - assert type(ts.ews_spec)==pd.DataFrame - assert 'smax' in ts.ews_spec.columns - assert 'fold' in ts.ews_spec.columns - assert 'hopf' in ts.ews_spec.columns - assert 'null' in ts.ews_spec.columns - - - - - - -def test_ews_compute(): - """ - Run a time-series through ews_compute and check everything is - functioning correctly. - """ - # Simulate a simple time-series - tVals = np.arange(0, 10, 0.1) - xVals = 5 + np.random.normal(0, 1, len(tVals)) - series = pd.Series(xVals, index=tVals) - - # Run through ews_compute with all possible EWS - ews = [ - "var", - "ac", - "sd", - "cv", - "skew", - "kurt", - "smax", - "aic", - "cf", - "smax/var", - "smax/mean", - ] - aic = ["Fold", "Hopf", "Null"] - lag_times = [1, 2, 3, 4, 5] - dict_ews = core.ews_compute( - series, ews=ews, aic=aic, lag_times=lag_times, sweep=True, ktau_time=5.232 - ) - - assert type(dict_ews) == dict - - # Obtain components of dict_ews - df_ews = dict_ews["EWS metrics"] - df_pspec = dict_ews["Power spectrum"] - df_ktau = dict_ews["Kendall tau"] - - # Check types - assert type(df_ews) == pd.DataFrame - assert type(df_pspec) == pd.DataFrame - assert type(df_ktau) == pd.DataFrame - - # Check index - assert df_ews.index.name == "time" - assert df_pspec.index.names == ["time", "frequency"] - - - - - + assert type(ts.ews_spec) == pd.DataFrame + assert "smax" in ts.ews_spec.columns + assert "fold" in ts.ews_spec.columns + assert "hopf" in ts.ews_spec.columns + assert "null" in ts.ews_spec.columns def test_pspec_welch(): diff --git a/tutorials/demo_ews_bootstrap.ipynb b/tutorials/demo_ews_bootstrap.ipynb deleted file mode 100644 index 1c779b2..0000000 --- a/tutorials/demo_ews_bootstrap.ipynb +++ /dev/null @@ -1,646 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Demo 2: \n", - "The objectives of this demo are as follows:\n", - "- Simulate a single stochastic trajectory of the Ricker model going through a Flip bifurcation\n", - "- Compute bootstrapped versions of segments of the time-series over a rolling window\n", - "- Compute EWS of the bootstrapped time-series\n", - "- Compute and display confidence intervals of the ensemble of EWS\n", - "- Run time < 3min" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import the standard Python libraries and ewstools" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# We will require the following standard Python packages for this analysis\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "# This is the package we use to compute the early warning signals\n", - "import ewstools.core as ewstools" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulate the Ricker model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we simulate a single trajectory of the Ricker model going through a Fold bifurcation. We will use this data to demonstrate the process of computing EWS. Alternatively, you could import your own data here. The importnat thing is that we end up with a Pandas DataFrame indexed by time." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Set simulation parameters**" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dt = 1 # time-step (using 1 since discrete-time system)\n", - "t0 = 0 # starting time\n", - "tmax = 1000 # end time\n", - "tburn = 100 # burn-in period preceding start-time\n", - "seed = 0 # random number generation seed (set for reproducibility)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Define model**\n", - "\n", - "We use the Ricker model with a Holling Type II harvesting term and additive white noise. It is given by\n", - "$$ N_{t+1} = N_t e^{(r(1-N_t/K) + \\sigma\\epsilon_t} ) - F\\frac{N_t^2}{N_t^2 + h^2}$$\n", - "where $N_t$ is the population size at time $t$, $r$ is the intrinsic growth rate, $K$ is the carrying capacity, $F$ is the maximum rate of harvesting, $h$ is the half saturation constant of the harvesting term, $\\sigma$ is the noise amplitude, and $\\epsilon_t$ is a normal random variable with zero mean and unit variance." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the model\n", - "def de_fun(x,r,k,f,h,xi):\n", - " return x*np.exp(r*(1-x/k)+xi) - f*x**2/(x**2+h**2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Set model parameters**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "f = 0 # harvesting rate\n", - "k = 10 # carrying capacity\n", - "h = 0.75 # half-saturation constant of harvesting function\n", - "bl = 0.5 # bifurcation parameter (growth rate) low\n", - "bh = 2.3 # bifurcation parameter (growth rate) high\n", - "bcrit = 2 # bifurcation point (computed using XPPAUT)\n", - "sigma = 0.02 # noise intensity\n", - "x0 = 0.8 # initial condition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Initialisation**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialise arrays for time and state values\n", - "t = np.arange(t0,tmax,dt)\n", - "x = np.zeros(len(t))\n", - "\n", - "# Bifurcation parameter values (increasing linearly in time)\n", - "b = pd.Series(np.linspace(bl,bh,len(t)),index=t) # bifurcation parameter values over time (linear increase)\n", - "\n", - "# Compute time at which bifurcation is crossed\n", - "tcrit = b[b > bcrit].index[1]\n", - "\n", - "# Array of noise values (normal random variables with variance sigma^2 dt)\n", - "dW_burn = np.random.normal(loc=0, scale=sigma*np.sqrt(dt), size = int(tburn/dt)) # burn-in period\n", - "dW = np.random.normal(loc=0, scale=sigma*np.sqrt(dt), size = len(t)) # monitored period" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Run simulation**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Run burn-in period starting from intiial condition x0\n", - "for i in range(int(tburn/dt)):\n", - " x0 = de_fun(x0,bl,k,f,h,dW_burn[i])\n", - "\n", - "# State value post burn-in period. Set as starting value.\n", - "x[0]=x0\n", - "\n", - "# Run simulation using recursion\n", - "for i in range(len(t)-1):\n", - " x[i+1] = de_fun(x[i],b.iloc[i],k,f,h,dW[i])\n", - " # Make sure that state variable stays >= 0\n", - " if x[i+1] < 0:\n", - " x[i+1] = 0\n", - " \n", - "# Store array data in a DataFrame indexed by time\n", - "sim_data = {'Time': t, 'x': x}\n", - "df_traj = pd.DataFrame(sim_data)\n", - "df_traj.set_index('Time', inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have a DataFrame df_traj, with our trajectory, indexed by time. We can check it out with a simple plot, using the command" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df_traj.plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bootstrap the time-series over a rolling window\n", - "\n", - "\n", - "\n", - "To obtain a more reliable estimate of the statistical metrics that consitute EWS in this system, we bootstrap the detrended time-series within each position of the rolling window. Specifically, we use a block-bootstrapping method where blocks of points are sampled randomly with replacement. The size of the block for each sample is taken from an exponential distribution with a chosen parameter. The block sizes used should be large enough to retain the significant temporal correlations in the time-series. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Set bootstrapping parameters**" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "rw = 0.4 # rolling window\n", - "span = 0.5 # Lowess span\n", - "block_size = 20 # characteristic size of blocks used to resample time-series\n", - "bs_type = 'Stationary' # type of bootstrapping\n", - "n_samples = 3 # number of bootstrapping samples to take\n", - "roll_offset = 20 # rolling window offset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Compute block-bootstrapped samples**\n", - "\n", - "We now construct a Dataframe of bootstrapped samples of the time-series, using the function *roll_bootstrap* within the *ewstools* package. Note that documentation of each function can be obtained using help(*function_name*)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "df_samples = ewstools.roll_bootstrap(df_traj['x'],\n", - " span = span,\n", - " roll_window = rw,\n", - " roll_offset = roll_offset,\n", - " upto = tcrit,\n", - " n_samples = n_samples,\n", - " bs_type = bs_type,\n", - " block_size = block_size\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For illustraion, here are 3 bootstrapped samples from the time-series within the rolling window at $t=459$." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df_samples.loc[459].loc[1:3]['x'].unstack(level=0).plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute EWS of the ensemble of bootstrap time-series\n", - "\n", - "Now we send each bootstrapped time-series through *ews_compute*. Note that detrending and extracting segments of the time-series has already been done, so there is no need to smooth the bootstrapped data, or use a rolling window." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**EWS parameters**" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "ews = ['var','ac','smax','aic']\n", - "lags = [1,2,3] # autocorrelation lag times\n", - "ham_length = 40 # number of data points in Hamming window\n", - "ham_offset = 0.5 # proportion of Hamming window to offset by upon each iteration\n", - "pspec_roll_offset = 20 # offset for rolling window when doing spectrum metrics\n", - "sweep = 'False' # whether to sweep over optimisation parameters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Initialisation**" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# List to store EWS DataFrames\n", - "list_df_ews = []\n", - "# List to store power spectra \n", - "list_pspec = []\n", - "\n", - "# Extract time and sample values to loop over\n", - "# Time values\n", - "tVals = np.array(df_samples.index.levels[0])\n", - "# Sample values\n", - "sampleVals = np.array(df_samples.index.levels[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Run ews_compute for each bootstrapped sample (takes a few minutes)**" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "EWS for t=399.00 complete\n", - "EWS for t=419.00 complete\n", - "EWS for t=439.00 complete\n", - "EWS for t=459.00 complete\n", - "EWS for t=479.00 complete\n", - "EWS for t=499.00 complete\n", - "EWS for t=519.00 complete\n", - "EWS for t=539.00 complete\n", - "EWS for t=559.00 complete\n", - "EWS for t=579.00 complete\n", - "EWS for t=599.00 complete\n", - "EWS for t=619.00 complete\n", - "EWS for t=639.00 complete\n", - "EWS for t=659.00 complete\n", - "EWS for t=679.00 complete\n", - "EWS for t=699.00 complete\n", - "EWS for t=719.00 complete\n", - "EWS for t=739.00 complete\n", - "EWS for t=759.00 complete\n", - "EWS for t=779.00 complete\n", - "EWS for t=799.00 complete\n", - "EWS for t=819.00 complete\n" - ] - } - ], - "source": [ - "# Loop over time (at end of rolling window)\n", - "for t in tVals:\n", - " # Loop over samples\n", - " for sample in sampleVals:\n", - " \n", - " # Extract series for this time and sample number\n", - " series_temp = df_samples.loc[t].loc[sample]['x']\n", - " \n", - " ews_dic = ewstools.ews_compute(series_temp,\n", - " roll_window = 1, # effectively no rolling window\n", - " band_width = 1, # effectively no detrending\n", - " ews = ews,\n", - " lag_times = lags,\n", - " upto='Full',\n", - " ham_length = ham_length,\n", - " ham_offset = ham_offset,\n", - " sweep = sweep)\n", - " \n", - " # The DataFrame of EWS\n", - " df_ews_temp = ews_dic['EWS metrics']\n", - " \n", - " # Include columns for sample value and realtime\n", - " df_ews_temp['Sample'] = sample\n", - " df_ews_temp['Time'] = t\n", - "\n", - " # Drop NaN values\n", - " df_ews_temp = df_ews_temp.dropna() \n", - " \n", - " # Append list_df_ews\n", - " list_df_ews.append(df_ews_temp)\n", - " \n", - " # Output power spectrum for just one of the samples (ow large file size)\n", - " df_pspec_temp = ews_dic['Power spectrum'][['Empirical']].dropna()\n", - " list_pspec.append(df_pspec_temp)\n", - " \n", - " # Print update\n", - " print('EWS for t=%.2f complete' % t)\n", - " \n", - "# Concatenate EWS DataFrames. Index [Realtime, Sample]\n", - "df_ews_boot = pd.concat(list_df_ews).reset_index(drop=True).set_index(['Time','Sample'])\n", - "\n", - "df_pspec_boot = pd.concat(list_pspec)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot EWS with 95% confidence intervals\n", - "\n", - "We use the Seaborn package here to make plots of the ensemble EWS as mean values with 95% confidence intervals." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Variance**" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.relplot(x='Time', y='Variance',\n", - " data=df_ews_boot.reset_index()[['Time','Variance']],\n", - " kind='line',\n", - " height=3,\n", - " aspect=2);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Autocorrelation**" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Structure the data for Seaborn\n", - "plot_data=df_ews_boot.reset_index()[['Time','Lag-1 AC','Lag-2 AC','Lag-3 AC']].melt(id_vars='Time',\n", - " value_vars=('Lag-1 AC','Lag-2 AC','Lag-3 AC'),\n", - " var_name='EWS', \n", - " value_name='Magnitude')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.relplot(x='Time', y='Smax',\n", - " data=df_ews_boot.reset_index()[['Time','Smax']],\n", - " kind='line',\n", - " height=3,\n", - " aspect=2);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**AIC weights**" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Structure the data for Seaborn\n", - "plot_data=df_ews_boot.reset_index()[['Time','AIC fold','AIC hopf','AIC null']].melt(id_vars='Time',\n", - " value_vars=('AIC fold','AIC hopf','AIC null'),\n", - " var_name='EWS', \n", - " value_name='Magnitude')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.relplot(x='Time', \n", - " y='Magnitude',\n", - " hue='EWS',\n", - " data=plot_data,\n", - " kind='line',\n", - " height=3,\n", - " aspect=2);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/demo_ews_fold.ipynb b/tutorials/demo_ews_fold.ipynb deleted file mode 100644 index ff61bd2..0000000 --- a/tutorials/demo_ews_fold.ipynb +++ /dev/null @@ -1,804 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Demo 1: EWS in the Ricker model\n", - "\n", - "The objectives of this demo are as follows:\n", - "- Simulate a single stochastic trajectory of the Ricker model going through a Fold bifurcation\n", - "- Show how to use the package *ewstools* to compute early warning signals\n", - "- Visualise the output of *ewstools* graphically\n", - "- Run time < 1 min\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import the standard Python libraries and ewstools" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# We will require the following standard Python packages for this analysis\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "# import seaborn as sns\n", - "\n", - "# This is the package we use to compute the early warning signals\n", - "import ewstools" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulate the Ricker model\n", - "\n", - "Here we simulate a single trajectory of the Ricker model going through a Fold bifurcation. We will use this data to demonstrate the process of computing EWS. Alternatively, you could import your own data here. The importnat thing is that we end up with a Pandas DataFrame indexed by time." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Set simulation parameters**" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "dt = 1 # time-step (using 1 since discrete-time system)\n", - "t0 = 0 # starting time\n", - "tmax = 500 # end time\n", - "tburn = 100 # burn-in period preceding start-time\n", - "seed = 1 # random number generation seed (set for reproducibility)\n", - "np.random.seed(seed)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Define model**\n", - "\n", - "We use the Ricker model with a Holling Type II harvesting term and additive white noise. It is given by\n", - "$$ N_{t+1} = N_t e^{(r(1-N_t/K) + \\sigma\\epsilon_t} ) - F\\frac{N_t^2}{N_t^2 + h^2}$$\n", - "where $N_t$ is the population size at time $t$, $r$ is the intrinsic growth rate, $K$ is the carrying capacity, $F$ is the maximum rate of harvesting, $h$ is the half saturation constant of the harvesting term, $\\sigma$ is the noise amplitude, and $\\epsilon_t$ is a normal random variable with zero mean and unit variance." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the model\n", - "def de_fun(x,r,k,f,h,xi):\n", - " return x*np.exp(r*(1-x/k)+xi) - f*x**2/(x**2+h**2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Set model parmaeters**" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "r = 0.75 # growth rate\n", - "k = 10 # carrying capacity\n", - "h = 0.75 # half-saturation constant of harvesting function\n", - "bl = 0 # bifurcation parameter (harvesting) low\n", - "bh = 2.7 # bifurcation parameter (harvesting) high\n", - "bcrit = 2.364 # bifurcation point (computed using XPPAUT)\n", - "sigma = 0.02 # noise intensity\n", - "x0 = 0.8 # initial condition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Initialisation**" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialise arrays for time and state values\n", - "t = np.arange(t0,tmax,dt)\n", - "x = np.zeros(len(t))\n", - "\n", - "# Bifurcation parameter values (increasing linearly in time)\n", - "b = pd.Series(np.linspace(bl,bh,len(t)),index=t) # bifurcation parameter values over time (linear increase)\n", - "\n", - "# Compute time at which bifurcation is crossed\n", - "tcrit = b[b > bcrit].index[1]\n", - "\n", - "# Array of noise values (normal random variables with variance sigma^2 dt)\n", - "dW_burn = np.random.normal(loc=0, scale=sigma*np.sqrt(dt), size = int(tburn/dt)) # burn-in period\n", - "dW = np.random.normal(loc=0, scale=sigma*np.sqrt(dt), size = len(t)) # monitored period" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Run simulation**" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Run burn-in period starting from intiial condition x0\n", - "for i in range(int(tburn/dt)):\n", - " x0 = de_fun(x0,r,k,bl,h,dW_burn[i])\n", - "\n", - "# State value post burn-in period. Set as starting value.\n", - "x[0]=x0\n", - "\n", - "# Run simulation using recursion\n", - "for i in range(len(t)-1):\n", - " x[i+1] = de_fun(x[i],r,k,b.iloc[i], h,dW[i])\n", - " # Make sure that state variable stays >= 0\n", - " if x[i+1] < 0:\n", - " x[i+1] = 0\n", - " \n", - "# Store array data in a DataFrame indexed by time\n", - "sim_data = {'Time': t, 'x': x}\n", - "df_traj = pd.DataFrame(sim_data)\n", - "df_traj.set_index('Time', inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have a DataFrame df_traj, with our trajectory, indexed by time. We can check it out with a simple plot, using the command" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df_traj.plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute EWS" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now use *ewstools* to compute early warning signals that precede this bifurcation. The package can compute the following statistical metrics. Simply put the label into the argument ews.\n", - "\n", - "| EWS | Label | Notes |\n", - "| ------------- | ------------- | -------------- |\n", - "| Variance | 'var' | Second moment of the data. Increases with critical slowing down. |\n", - "| Standard deviation | 'sd' | Root of the variance. Increases with critical slowing down. |\n", - "| Coefficient of variation | 'cv' | Ratio of standard deviation to the mean. Useful when mean of system varies over time. |\n", - "| Skewness | 'skew' | Third standardised moment of the data. Increases preceding Fold bifurcations. |\n", - "| Kurtosis | 'kurt' | Fourth standardised moment of the data |\n", - "| Autocorrelation | 'ac' | Correlation between data-points a given lag-time apart. Increases with critical slowing down for sufficiently small lags.|\n", - "| Smax | 'smax' | Peak in the power spectrum. Increases with critical slowing down. |\n", - "| Coherence factor | 'cf' | Ratio of height to width of peak in the power spectrum. Increases preceding a Hopf bifurcation. |\n", - "| AIC weights | 'aic' | Weights characterising the class of bifurcation |\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Set EWS parameters**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we must configure some imporant parameters that are used in the process of finding early warning signals. For more details on the description of these parameters, check out the *ewstools* documentation. \n", - "\n", - "Sensible choices of these parameters will depend on the resolution and scale of your data. Details on how to select these parameter values can be found in the mothodological paper by [Dakos et al.](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041010)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "438" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tcrit" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "rw = 0.5*(438/500) # rolling window\n", - "span = 0.2 # Lowess span\n", - "lags = [1,2,3] # autocorrelation lag times to compute\n", - "ews = ['var','sd','cv','skew','kurt','ac','smax','cf','aic'] # EWS to compute (let's do all of them)\n", - "ham_length = 80 # number of data points in Hamming window\n", - "ham_offset = 0.5 # proportion of Hamming window to offset by upon each iteration\n", - "pspec_roll_offset = 20 # offset for rolling window when doing spectrum metrics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Run ews_compute**\n", - "\n", - "ews_compute is the main function in the package *ewstools*. It takes in your time-series data, along with your parameter configurations, and outputs the specified EWS." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "ews_dic = ewstools.core.ews_compute(df_traj['x'],\n", - " smooth='Lowess',\n", - " roll_window = rw, \n", - " span = span,\n", - " lag_times = lags, \n", - " ews = ews,\n", - " upto=tcrit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Extract info**\n", - "\n", - "ews_compute outputs a dictionary containing three DataFrames\n", - "- *EWS metrics* : column for each EWS indexed by time\n", - "- *Power spectrum* : measured power spectrum and fitted power spectra indexed by time and frequency\n", - "- *Kendall tau* : Kendall tau values for each EWS" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# The DataFrame of EWS\n", - "df_ews = ews_dic['EWS metrics']\n", - "# The DataFrame of power spectra\n", - "df_pspec = ews_dic['Power spectrum']\n", - "# The DataFrame of ktau values\n", - "df_ktau = ews_dic['Kendall tau']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is a sample from the EWS DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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State variableSmoothingResidualsStandard deviationVarianceLag-1 ACLag-2 ACLag-3 ACCoefficient of variationSkewnessKurtosisSmaxCoherence factorAIC foldAIC hopfAIC nullParams foldParams flipParams hopfParams null
Time
2188.3536708.2610510.0926190.1989040.0395630.2398900.049045-0.0031180.021627-0.0051220.1772310.0028960.0014481.01.206526e-107.754067e-11{'sigma': 0.23729683193032602, 'lam': -1.96802...{'sigma': 0.09576836641914256, 'r': -1.9053869...{'sigma': 0.614797245738754, 'mu': -5.75089849...{'sigma': 0.09576783726059354}
2387.5243328.050742-0.5264100.2029880.0412040.2726540.045116-0.0119390.022471-0.0587860.1739450.0028560.0014281.01.305635e-119.902635e-12{'sigma': 0.2205917201804984, 'lam': -1.815424...{'sigma': 0.0942206433657914, 'r': -1.10367048...{'sigma': 0.6049032674465442, 'mu': -5.7472498...{'sigma': 0.09421993053039378}
2587.8016637.827556-0.0258930.2095220.0439000.3828650.1658860.0243080.023666-0.3628930.9090530.0029940.0000001.02.307153e-132.559770e-13{'sigma': 0.1998910763012033, 'lam': -1.492447...{'sigma': 0.09811985223229108, 'r': -7.3163697...{'sigma': 0.6349764570168284, 'mu': -5.7976249...{'sigma': 0.09811919736773222}
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" - ], - "text/plain": [ - " State variable Smoothing Residuals Standard deviation Variance \\\n", - "Time \n", - "218 8.353670 8.261051 0.092619 0.198904 0.039563 \n", - "238 7.524332 8.050742 -0.526410 0.202988 0.041204 \n", - "258 7.801663 7.827556 -0.025893 0.209522 0.043900 \n", - "\n", - " Lag-1 AC Lag-2 AC Lag-3 AC Coefficient of variation Skewness \\\n", - "Time \n", - "218 0.239890 0.049045 -0.003118 0.021627 -0.005122 \n", - "238 0.272654 0.045116 -0.011939 0.022471 -0.058786 \n", - "258 0.382865 0.165886 0.024308 0.023666 -0.362893 \n", - "\n", - " Kurtosis Smax Coherence factor AIC fold AIC hopf \\\n", - "Time \n", - "218 0.177231 0.002896 0.001448 1.0 1.206526e-10 \n", - "238 0.173945 0.002856 0.001428 1.0 1.305635e-11 \n", - "258 0.909053 0.002994 0.000000 1.0 2.307153e-13 \n", - "\n", - " AIC null Params fold \\\n", - "Time \n", - "218 7.754067e-11 {'sigma': 0.23729683193032602, 'lam': -1.96802... \n", - "238 9.902635e-12 {'sigma': 0.2205917201804984, 'lam': -1.815424... \n", - "258 2.559770e-13 {'sigma': 0.1998910763012033, 'lam': -1.492447... \n", - "\n", - " Params flip \\\n", - "Time \n", - "218 {'sigma': 0.09576836641914256, 'r': -1.9053869... \n", - "238 {'sigma': 0.0942206433657914, 'r': -1.10367048... \n", - "258 {'sigma': 0.09811985223229108, 'r': -7.3163697... \n", - "\n", - " Params hopf \\\n", - "Time \n", - "218 {'sigma': 0.614797245738754, 'mu': -5.75089849... \n", - "238 {'sigma': 0.6049032674465442, 'mu': -5.7472498... \n", - "258 {'sigma': 0.6349764570168284, 'mu': -5.7976249... \n", - "\n", - " Params null \n", - "Time \n", - "218 {'sigma': 0.09576783726059354} \n", - "238 {'sigma': 0.09421993053039378} \n", - "258 {'sigma': 0.09811919736773222} " - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_ews.dropna().head(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the spectral metrics are computed at a lower time-resolution, and so have many blank cells (Nan). The DataFrame of Kendall tau values for each EWS looks like" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Lag-1 ACCoefficient of variationLag-2 ACKurtosisSkewnessStandard deviationLag-3 ACSmaxVariance
00.8968330.9321270.738955-0.086960.2407240.4863020.4259980.8181820.486302
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" - ], - "text/plain": [ - " Lag-1 AC Coefficient of variation Lag-2 AC Kurtosis Skewness \\\n", - "0 0.896833 0.932127 0.738955 -0.08696 0.240724 \n", - "\n", - " Standard deviation Lag-3 AC Smax Variance \n", - "0 0.486302 0.425998 0.818182 0.486302 " - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_ktau" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df_ews[['State variable','Smoothing']].plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualise EWS\n", - "\n", - "We can visualise the EWS by constructing some plots using the Seaborn package." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Early warning signals**" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "## Plot of trajectory, smoothing and EWS of var (x or y)\n", - "fig1, axes = plt.subplots(nrows=5, ncols=1, figsize=(6,10), sharex=True)\n", - "df_ews[['State variable','Smoothing']].plot(ax=axes[0],\n", - " title='Early warning signals')\n", - "df_ews['Variance'].plot(ax=axes[1],legend=True)\n", - "df_ews[['Lag-1 AC','Lag-2 AC']].plot(ax=axes[2],legend=True)\n", - "df_ews['Smax'].plot(ax=axes[3],legend=True, marker='o')\n", - "df_ews[['AIC null','AIC fold','AIC hopf']].plot(ax=axes[4],legend=True, marker='o');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Power spectra**\n", - "\n", - "A grid plot to visualise the evolution of the power spectrum" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def plot_pspec_grid(tVals):\n", - " '''\n", - " Plot a grid of power spectra, their fits, and AIC values at different times leading up to the bifurcation\n", - " \n", - " Args\n", - " --------\n", - " tVals: An array of time values at which to plot the power spectrum\n", - " \n", - " Returns\n", - " ----------\n", - " A grid plot\n", - " \n", - " '''\n", - " g = sns.FacetGrid(df_pspec.loc[t_display].reset_index(), \n", - " col='Time',\n", - " col_wrap=3,\n", - " sharey=False,\n", - " aspect=1.5,\n", - " height=1.8\n", - " )\n", - "\n", - " g.map(plt.plot, 'Frequency', 'Empirical', color='k', linewidth=2)\n", - " g.map(plt.plot, 'Frequency', 'Fit fold', color='b', linestyle='dashed', linewidth=1)\n", - " g.map(plt.plot, 'Frequency', 'Fit hopf', color='r', linestyle='dashed', linewidth=1)\n", - " g.map(plt.plot, 'Frequency', 'Fit null', color='g', linestyle='dashed', linewidth=1)\n", - " # Axes properties\n", - " axes = g.axes\n", - " # Set y labels\n", - " for ax in axes[::3]:\n", - " ax.set_ylabel('Power')\n", - " # Set y limit as max power over all time\n", - " for ax in axes:\n", - " ax.set_ylim(top=1.05*max(df_pspec['Empirical']), bottom=0)\n", - "# ax.set_yscale('log')\n", - " \n", - " return g\n", - "\n", - "# Choose time values at which to display power spectrum\n", - "t_display = df_pspec.index.levels[0][::3].values\n", - "\n", - "plot_pspec = plot_pspec_grid(t_display)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the power spectrum conforms most closely to that of the Fold bifurcation, thus $w_{\\text{Fold}}$ is the AIC weight. We also see the rise in the peak of the power spectrum as the bifurcation is approached." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "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": 2 -} diff --git a/tutorials/readme.md b/tutorials/readme.md index 5628039..14f2b67 100644 --- a/tutorials/readme.md +++ b/tutorials/readme.md @@ -20,23 +20,3 @@ To run these tutorials, [Jupyter notebook](https://jupyter.org/install) must be - Import a TensorFlow classifier and obtain its predictions on a section of time series data - Compute predictions made from an ensemble of classifiers - - - -## Old tutorials - -These old tutorials use deprecated functions that will be removed in future versions of *ewstools*. - -### ews_fold.ipynb - Deprecated (uses deprecated functions in ewstools) -- Simulates a single stochastic trajectory of the Ricker model going through a Fold bifurcation -- Shows how to use *ewstools* to compute early warning signals -- Visualises the output of *ewstools* graphically -- Run time < 1 min - - -### ews_bootstrap.ipynb - Deprecated (uses deprecated functions ewstools) -- Simulates single stochastic trajectories of the Ricker model going through a Flip bifurcation -- Uses *ewstools* to compute bootstrapped time-series and the corresponding EWS. -- Visualises the output, comparing the two bifurcations. -- Run time < 3 mins - diff --git a/tutorials/tutorial_deep_learning.ipynb b/tutorials/tutorial_deep_learning.ipynb index 215bdc7..44da13f 100644 --- a/tutorials/tutorial_deep_learning.ipynb +++ b/tutorials/tutorial_deep_learning.ipynb @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "id": "c670076c", "metadata": {}, "outputs": [], @@ -360,14 +360,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-07-16 10:13:02.121813: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" + "2023-05-23 17:36:10.849166: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "1/1 [==============================] - 0s 335ms/step\n" + "1/1 [==============================] - 0s 339ms/step\n" ] } ], @@ -423,11 +423,11 @@ " \n", " \n", " 0\n", - " 0.751295\n", - " 0.030536\n", - " 0.21511\n", - " 0.003059\n", - " 400\n", + " 0.8281\n", + " 0.030163\n", + " 0.140126\n", + " 0.001611\n", + " 399.0\n", " c1\n", " \n", " \n", @@ -435,8 +435,8 @@ "" ], "text/plain": [ - " 0 1 2 3 time classifier\n", - "0 0.751295 0.030536 0.21511 0.003059 400 c1" + " 0 1 2 3 time classifier\n", + "0 0.8281 0.030163 0.140126 0.001611 399.0 c1" ] }, "execution_count": 15, @@ -515,47 +515,47 @@ " \n", " \n", " 0\n", - " 0.207065\n", - " 0.339513\n", - " 0.247485\n", - " 0.205937\n", - " 0.0\n", + " 0.213087\n", + " 0.385012\n", + " 0.208215\n", + " 0.193687\n", + " 9.0\n", " c1\n", " \n", " \n", " 1\n", - " 0.228072\n", - " 0.376060\n", - " 0.219008\n", - " 0.176861\n", - " 10.0\n", + " 0.249523\n", + " 0.303257\n", + " 0.259251\n", + " 0.187969\n", + " 19.0\n", " c1\n", " \n", " \n", " 2\n", - " 0.250696\n", - " 0.294206\n", - " 0.267425\n", - " 0.187674\n", - " 20.0\n", + " 0.221228\n", + " 0.141781\n", + " 0.269568\n", + " 0.367423\n", + " 29.0\n", " c1\n", " \n", " \n", " 3\n", - " 0.225095\n", - " 0.144681\n", - " 0.282058\n", - " 0.348166\n", - " 30.0\n", + " 0.189283\n", + " 0.043751\n", + " 0.297417\n", + " 0.469550\n", + " 39.0\n", " c1\n", " \n", " \n", " 4\n", - " 0.212830\n", - " 0.046121\n", - " 0.296307\n", - " 0.444743\n", - " 40.0\n", + " 0.165011\n", + " 0.031797\n", + " 0.215114\n", + " 0.588078\n", + " 49.0\n", " c1\n", " \n", " \n", @@ -564,11 +564,11 @@ ], "text/plain": [ " 0 1 2 3 time classifier\n", - "0 0.207065 0.339513 0.247485 0.205937 0.0 c1\n", - "1 0.228072 0.376060 0.219008 0.176861 10.0 c1\n", - "2 0.250696 0.294206 0.267425 0.187674 20.0 c1\n", - "3 0.225095 0.144681 0.282058 0.348166 30.0 c1\n", - "4 0.212830 0.046121 0.296307 0.444743 40.0 c1" + "0 0.213087 0.385012 0.208215 0.193687 9.0 c1\n", + "1 0.249523 0.303257 0.259251 0.187969 19.0 c1\n", + "2 0.221228 0.141781 0.269568 0.367423 29.0 c1\n", + "3 0.189283 0.043751 0.297417 0.469550 39.0 c1\n", + "4 0.165011 0.031797 0.215114 0.588078 49.0 c1" ] }, "execution_count": 17, @@ -598,7 +598,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "" ] @@ -644,7 +644,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] @@ -669,7 +669,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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