#!/usr/bin/env python
from __future__ import division, absolute_import, print_function
import numpy as np

def spike(datin, thresh=7, toler=0, length=15):
    """
        spike filter which looks for spikes which have a first step > thresh and then a 'plateau' for <= length time steps and come back
        to original value within a tolerance of toler. (something like a rectangular shape, but the 'plateau' does not have to be flat,
        it just needs to exceed the threshold without crossing the value before the spike).
        Returns list with indices of detected spikes.


        Definition
        ----------
        def spike(datin, thresh=7, toler=0, length=15):


        Input
        -----
        datin      1d array or list


        Optional Input
        --------------
        thresh     spike threshold
        toler      tolerance value for the abs difference between first value before spike and first value after spike
        length     maximum length of spike plateau


        Output
        ------
        list with indices of all spikes detected


        Restrictions
        ------------
        --


        Examples
        --------
        >>> y = [1, 1, 20,1, 2, 1,15,1, 10, 11, 13, 10, 1.5, 1, 2, 1, 1, -7, -8, 1.4, 3, 1, 1, 1, -50, 1, 1, 1]
        >>> spike(y,thresh=2,toler=0.6,length=10)
        no. of spikes: 5
        no. of data points: 9
        [2, 6, 8, 9, 10, 11, 17, 18, 24]


        >>> y = [0.1, 0.1, 0.2, 0.1, 0.2, 0.1, 1.5, 0.1, 1.0, 1.1, 1.3, 1.0, 0.15, 0.1, 0.2, 0.1, 0.1, -0.7, -0.8, 0.14, 0.3, 0.1, 0.1, 0.1, -5.0, 0.1, 0.1, 0.1]
        >>> spike(y,thresh=0.2,toler=0.06,length=3)
        no. of spikes: 3
        no. of data points: 4
        [6, 17, 18, 24]


        >>> y = [0.1, 0.1, 0.2, 0.1, 0.2, 0.1, 1.5, 0.2, 1.0, 1.1, 1.3, 1.0, 0.15, 0.1, 0.2, 0.1, 0.1, -0.7, 0.8, 0.14, 0.3, 0.1, 0.1, 0.1, -5.0, 0.1, 0.1, 0.1]
        >>> spike(y,thresh=0.2,toler=0.06,length=3)
        no. of spikes: 1
        no. of data points: 1
        [24]

        License
        -------
        This file is part of the JAMS Python package, distributed under the MIT
        License. The JAMS Python package originates from the former UFZ Python library,
        Department of Computational Hydrosystems, Helmholtz Centre for Environmental
        Research - UFZ, Leipzig, Germany.

        Copyright (c) 2016 <NAME>

        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:

        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.

        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.


        History
        -------
        Written,  BD, Sep 2016
                  MC, Nov 2016 - ported to Python 3
                  DS, Aug 2017 - reduced verbosity 

    """
    debug = False
    cmp = lambda a, b: ((a > b) - (a < b)) # redefine cmp to be available in Python 3

    diff = [ datin[k+1]-datin[k] for k in range(len(datin)-1) ]  # differences
    ipos0 = [ k for k,a in enumerate(diff) if abs(a) > thresh ]  # get index position of spikes
    if len(ipos0)>0:
        ipos = ipos0[0]                                          # select first spike
        maxlen = length                                          # set max. length of spike plateau
        spike_pos_all = []
        while ipos < len(datin)-1:
            # if debug: print('ipos=',ipos)
            for i in range(1,maxlen+1):
                # if debug: print('i=',i)
                spike_pos = []
                if (ipos+i+1) > (len(datin)-1):
                    ipos = len(datin)
                    break
                tm = [ abs(datin[ipos]-datin[ipos+v]) for v in  list(range(2,i+2)) ] # diff between first val before spike and all candidates for first val after spike
                tms = [ datin[ipos]-datin[ipos+v] for v in  list(range(2,i+1)) ] # diff between first val before spike and all candidates for first val after spike (with sign)
                if len(tm)==1 and tm[0] < toler:
                    spike_pos = [ipos+1]
                    spike_pos_all.append(spike_pos)
                    # if debug: print('found peak 1 ', spike_pos)
                    break
                elif (len(tm)>1 and
                      all([ tt > thresh for tt in tm[0:-2] ]) and
                      all([ cmp(val,0) == cmp(datin[ipos]-datin[ipos+1],0) for n, val in enumerate(tms) ]) and
                      tm[-1] < toler): # check thresh, tolerance and no switching of sign
                    spike_pos = list(range(ipos+1,ipos+i+1))
                    spike_pos_all.append(spike_pos)
                    # if debug: print('found peak 2',spike_pos)
                    break
            ipos1 = [k for k,a in enumerate(diff) if abs(a) > thresh  and k >= ipos + i +1] # get index position of next spikes
            if len(ipos1)> 0:
                ipos = ipos1[0]
            else:
                break

        # print('no. of spikes: '+str(len(spike_pos_all)))
        spike_pos_all = [item for sublist in spike_pos_all for item in sublist]    # create list without sublists
        # print('no. of data points: '+str(len(spike_pos_all)))
        return spike_pos_all

    else:
        return []



if __name__ == '__main__':
    import doctest
    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)

#!/usr/bin/env python
from __future__ import division, absolute_import, print_function
import numpy as np

def spike(datin, thresh=7, toler=0, length=15):
    """
        spike filter which looks for spikes which have a first step > thresh and then a 'plateau' for <= length time steps and come back
        to original value within a tolerance of toler. (something like a rectangular shape, but the 'plateau' does not have to be flat,
        it just needs to exceed the threshold without crossing the value before the spike).
        Returns list with indices of detected spikes.


        Definition
        ----------
        def spike(datin, thresh=7, toler=0, length=15):


        Input
        -----
        datin      1d array or list


        Optional Input
        --------------
        thresh     spike threshold
        toler      tolerance value for the abs difference between first value before spike and first value after spike
        length     maximum length of spike plateau


        Output
        ------
        list with indices of all spikes detected


        Restrictions
        ------------
        --


        Examples
        --------
        >>> y = [1, 1, 20,1, 2, 1,15,1, 10, 11, 13, 10, 1.5, 1, 2, 1, 1, -7, -8, 1.4, 3, 1, 1, 1, -50, 1, 1, 1]
        >>> spike(y,thresh=2,toler=0.6,length=10)
        no. of spikes: 5
        no. of data points: 9
        [2, 6, 8, 9, 10, 11, 17, 18, 24]


        >>> y = [0.1, 0.1, 0.2, 0.1, 0.2, 0.1, 1.5, 0.1, 1.0, 1.1, 1.3, 1.0, 0.15, 0.1, 0.2, 0.1, 0.1, -0.7, -0.8, 0.14, 0.3, 0.1, 0.1, 0.1, -5.0, 0.1, 0.1, 0.1]
        >>> spike(y,thresh=0.2,toler=0.06,length=3)
        no. of spikes: 3
        no. of data points: 4
        [6, 17, 18, 24]


        >>> y = [0.1, 0.1, 0.2, 0.1, 0.2, 0.1, 1.5, 0.2, 1.0, 1.1, 1.3, 1.0, 0.15, 0.1, 0.2, 0.1, 0.1, -0.7, 0.8, 0.14, 0.3, 0.1, 0.1, 0.1, -5.0, 0.1, 0.1, 0.1]
        >>> spike(y,thresh=0.2,toler=0.06,length=3)
        no. of spikes: 1
        no. of data points: 1
        [24]

        License
        -------
        This file is part of the JAMS Python package, distributed under the MIT
        License. The JAMS Python package originates from the former UFZ Python library,
        Department of Computational Hydrosystems, Helmholtz Centre for Environmental
        Research - UFZ, Leipzig, Germany.

        Copyright (c) 2016 <NAME>

        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:

        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.

        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.


        History
        -------
        Written,  BD, Sep 2016
                  MC, Nov 2016 - ported to Python 3
                  DS, Aug 2017 - reduced verbosity 

    """
    debug = False
    cmp = lambda a, b: ((a > b) - (a < b)) # redefine cmp to be available in Python 3

    diff = [ datin[k+1]-datin[k] for k in range(len(datin)-1) ]  # differences
    ipos0 = [ k for k,a in enumerate(diff) if abs(a) > thresh ]  # get index position of spikes
    if len(ipos0)>0:
        ipos = ipos0[0]                                          # select first spike
        maxlen = length                                          # set max. length of spike plateau
        spike_pos_all = []
        while ipos < len(datin)-1:
            # if debug: print('ipos=',ipos)
            for i in range(1,maxlen+1):
                # if debug: print('i=',i)
                spike_pos = []
                if (ipos+i+1) > (len(datin)-1):
                    ipos = len(datin)
                    break
                tm = [ abs(datin[ipos]-datin[ipos+v]) for v in  list(range(2,i+2)) ] # diff between first val before spike and all candidates for first val after spike
                tms = [ datin[ipos]-datin[ipos+v] for v in  list(range(2,i+1)) ] # diff between first val before spike and all candidates for first val after spike (with sign)
                if len(tm)==1 and tm[0] < toler:
                    spike_pos = [ipos+1]
                    spike_pos_all.append(spike_pos)
                    # if debug: print('found peak 1 ', spike_pos)
                    break
                elif (len(tm)>1 and
                      all([ tt > thresh for tt in tm[0:-2] ]) and
                      all([ cmp(val,0) == cmp(datin[ipos]-datin[ipos+1],0) for n, val in enumerate(tms) ]) and
                      tm[-1] < toler): # check thresh, tolerance and no switching of sign
                    spike_pos = list(range(ipos+1,ipos+i+1))
                    spike_pos_all.append(spike_pos)
                    # if debug: print('found peak 2',spike_pos)
                    break
            ipos1 = [k for k,a in enumerate(diff) if abs(a) > thresh  and k >= ipos + i +1] # get index position of next spikes
            if len(ipos1)> 0:
                ipos = ipos1[0]
            else:
                break

        # print('no. of spikes: '+str(len(spike_pos_all)))
        spike_pos_all = [item for sublist in spike_pos_all for item in sublist]    # create list without sublists
        # print('no. of data points: '+str(len(spike_pos_all)))
        return spike_pos_all

    else:
        return []



if __name__ == '__main__':
    import doctest
    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)

#!/usr/bin/env python spike filter which looks for spikes which have a first step > thresh and then a 'plateau' for <= length time steps and come back         to original value within a tolerance of toler. (something like a rectangular shape, but the 'plateau' does not have to be flat,         it just needs to exceed the threshold without crossing the value before the spike).         Returns list with indices of detected spikes.           Definition         ----------         def spike(datin, thresh=7, toler=0, length=15):           Input         -----         datin      1d array or list           Optional Input         --------------         thresh     spike threshold         toler      tolerance value for the abs difference between first value before spike and first value after spike         length     maximum length of spike plateau           Output         ------         list with indices of all spikes detected           Restrictions         ------------         --           Examples         --------         >>> y = [1, 1, 20,1, 2, 1,15,1, 10, 11, 13, 10, 1.5, 1, 2, 1, 1, -7, -8, 1.4, 3, 1, 1, 1, -50, 1, 1, 1]         >>> spike(y,thresh=2,toler=0.6,length=10)         no. of spikes: 5         no. of data points: 9         [2, 6, 8, 9, 10, 11, 17, 18, 24]           >>> y = [0.1, 0.1, 0.2, 0.1, 0.2, 0.1, 1.5, 0.1, 1.0, 1.1, 1.3, 1.0, 0.15, 0.1, 0.2, 0.1, 0.1, -0.7, -0.8, 0.14, 0.3, 0.1, 0.1, 0.1, -5.0, 0.1, 0.1, 0.1]         >>> spike(y,thresh=0.2,toler=0.06,length=3)         no. of spikes: 3         no. of data points: 4         [6, 17, 18, 24]           >>> y = [0.1, 0.1, 0.2, 0.1, 0.2, 0.1, 1.5, 0.2, 1.0, 1.1, 1.3, 1.0, 0.15, 0.1, 0.2, 0.1, 0.1, -0.7, 0.8, 0.14, 0.3, 0.1, 0.1, 0.1, -5.0, 0.1, 0.1, 0.1]         >>> spike(y,thresh=0.2,toler=0.06,length=3)         no. of spikes: 1         no. of data points: 1         [24]          License         -------         This file is part of the JAMS Python package, distributed under the MIT         License. The JAMS Python package originates from the former UFZ Python library,         Department of Computational Hydrosystems, Helmholtz Centre for Environmental         Research - UFZ, Leipzig, Germany.          Copyright (c) 2016 <NAME>          Permission is hereby granted, free of charge, to any person obtaining a copy         of this software and associated documentation files (the "Software"), to deal         in the Software without restriction, including without limitation the rights         to use, copy, modify, merge, publish, distribute, sublicense, and/or sell         copies of the Software, and to permit persons to whom the Software is         furnished to do so, subject to the following conditions:          The above copyright notice and this permission notice shall be included in all         copies or substantial portions of the Software.          THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR         IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,         FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE         AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER         LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,         OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE         SOFTWARE.           History         -------         Written,  BD, Sep 2016                   MC, Nov 2016 - ported to Python 3                   DS, Aug 2017 - reduced verbosity # redefine cmp to be available in Python 3 # differences # get index position of spikes # select first spike # set max. length of spike plateau # if debug: print('ipos=',ipos) # if debug: print('i=',i) # diff between first val before spike and all candidates for first val after spike # diff between first val before spike and all candidates for first val after spike (with sign) # if debug: print('found peak 1 ', spike_pos) # check thresh, tolerance and no switching of sign # if debug: print('found peak 2',spike_pos) # get index position of next spikes # print('no. of spikes: '+str(len(spike_pos_all))) # create list without sublists # print('no. of data points: '+str(len(spike_pos_all)))