Metadata-Version: 2.1
Name: ewstools
Version: 0.0.2
Summary: A package with tools to compute early warning signals (EWS) 
    from time-series data
Home-page: https://github.com/ThomasMBury/ewstools
Author: Thomas M Bury
Author-email: tombury182@gmail.com
License: UNKNOWN
Description: # ewstools
        A module to compute early warning signals (EWS) from time-series data.
        Dependencies include:
          - standard python libraries: numpy, pandas, scipy, matplotlib
          - LMFIT: download [here](https://lmfit.github.io/lmfit-py/installation.html)
        
        
        ## ews_compute.py
        File for function `ews_compute`.  
        `ews_compute` takes in Series data and outputs user-specified EWS in a DataFrame.
        
        
        **Input** (default value)
        - *raw_series* : pandas Series indexed by time 
        - *roll_window* (0.25) : size of the rolling window (as a proportion of the length of the data)
        - *smooth* (True) : if True, series data is detrended with a Gaussian kernel
        - *band_width* (0.2) : bandwidth of Gaussian kernel
        - *ews* ( ['*var*', '*ac*', '*smax*'] ) : list of strings corresponding to the desired EWS. Options include
          - '*var*'   : Variance
          - '*ac*'    : Autocorrelation
          - '*sd*'    : Standard deviation
          - '*cv*'    : Coefficient of variation
          - '*skew*'  : Skewness
          - '*kurt*'  : Kurtosis
          - '*smax*'  : Peak in the power spectrum
          - '*cf*'    : Coherence factor
          - '*aic*'   : AIC weights
        - *lag_times* ( [1] ) : list of integers corresponding to the desired lag times for AC
        - *ham_length* (40) : length of the Hamming window (used to compute power spectrum)
        - *ham_offset* (0.5) : offset of Hamming windows as a proportion of *ham_length*
        - *w_cutoff* (1) : cutoff frequency (as a proportion of maximum frequency attainable from data)
            
        **Output**
        - DataFrame indexed by time with columns corresponding to each EWS
        
        
        
        ## ews_compute_run.py
        An example script that runs `ews_compute` on times-series data of a stochastic simulation of May's 
        harvesting model. It also shows how to compute kendall tau values and plot results. This
        can be used as a template for EWS of times-series data.
        
        
        ## ews_compute_runMulti.py
        An example script that runs `ews_compute` on multiple time-series data and outputs
        EWS as a distribution over realisations.
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
