latools/D_obj.py
def filter_clustering(self, analytes, filt=False, normalise=True,
                          method='meanshift', include_time=False,
                          sort=None, min_data=10, **kwargs):
        """
        Applies an n - dimensional clustering filter to the data.

        Available Clustering Algorithms

        * 'meanshift': The `sklearn.cluster.MeanShift` algorithm.
          Automatically determines number of clusters
          in data based on the `bandwidth` of expected
          variation.
        * 'kmeans': The `sklearn.cluster.KMeans` algorithm. Determines
          the characteristics of a known number of clusters
          within the data. Must provide `n_clusters` to specify
          the expected number of clusters.
        * 'DBSCAN': The `sklearn.cluster.DBSCAN` algorithm. Automatically
          determines the number and characteristics of clusters
          within the data based on the 'connectivity' of the
          data (i.e. how far apart each data point is in a
          multi - dimensional parameter space). Requires you to
          set `eps`, the minimum distance point must be from
          another point to be considered in the same cluster,
          and `min_samples`, the minimum number of points that
          must be within the minimum distance for it to be
          considered a cluster. It may also be run in automatic
          mode by specifying `n_clusters` alongside
          `min_samples`, where eps is decreased until the
          desired number of clusters is obtained.
        
        For more information on these algorithms, refer to the
        documentation.

        Parameters
        ----------
        analytes : str
            The analyte(s) that the filter applies to.
        filt : bool
            Whether or not to apply existing filters to the data before
            calculating this filter.
        normalise : bool
            Whether or not to normalise the data to zero mean and unit
            variance. Reccomended if clustering based on more than 1 analyte.
            Uses `sklearn.preprocessing.scale`.
        method : str
            Which clustering algorithm to use (see above).
        include_time : bool
            Whether or not to include the Time variable in the clustering
            analysis. Useful if you're looking for spatially continuous
            clusters in your data, i.e. this will identify each spot in your
            analysis as an individual cluster.
        sort : bool, str or array-like
            Whether or not to label the resulting clusters according to their
            contents. If used, the cluster with the lowest values will be
            labelled from 0, in order of increasing cluster mean value.analytes.
            The sorting rules depend on the value of 'sort', which can be the name
            of a single analyte (str), a list of several analyte names (array-like)
            or True (bool), to specify all analytes used to calcualte the cluster.
        min_data : int
            The minimum number of data points that should be considered by
            the filter. Default = 10.
        **kwargs
            Parameters passed to the clustering algorithm specified by
            `method`.

        Meanshift Parameters
        --------------------
        bandwidth : str or float
            The bandwith (float) or bandwidth method ('scott' or 'silverman')
            used to estimate the data bandwidth.
        bin_seeding : bool
            Modifies the behaviour of the meanshift algorithm. Refer to
            sklearn.cluster.meanshift documentation.

        K - Means Parameters
        ------------------
        n_clusters : int
            The number of clusters expected in the data.

        DBSCAN Parameters
        -----------------
        eps : float
            The minimum 'distance' points must be apart for them to be in the
            same cluster. Defaults to 0.3. Note: If the data are normalised
            (they should be for DBSCAN) this is in terms of total sample
            variance. Normalised data have a mean of 0 and a variance of 1.
        min_samples : int
            The minimum number of samples within distance `eps` required
            to be considered as an independent cluster.
        n_clusters : int
            The number of clusters expected. If specified, `eps` will be
            incrementally reduced until the expected number of clusters is
            found.
        maxiter : int
            The maximum number of iterations DBSCAN will run.

        Returns
        -------
        None
        """
        params = locals()
        del(params['self'])

        # convert string to list, if single analyte
        if isinstance(analytes, str):
            analytes = [analytes]

        setn = self.filt.maxset + 1

        # generate filter
        vals = np.vstack(nominal_values(list(self.focus.values())))
        if filt is not None:
            ind = (self.filt.grab_filt(filt, analytes) &
                   np.apply_along_axis(all, 0, ~np.isnan(vals)))
        else:
            ind = np.apply_along_axis(all, 0, ~np.isnan(vals))

        if sum(ind) > min_data:

            # get indices for data passed to clustering
            sampled = np.arange(self.Time.size)[ind]

            # generate data for clustering
            if include_time:
                extra = self.Time
            else:
                extra = None
            # get data as array
            ds = stack_keys(self.focus, analytes, extra)
            # apply filter, and get nominal values
            ds = nominal_values(ds[ind, :])

            if normalise | (len(analytes) > 1):
                ds = preprocessing.scale(ds)

            method_key = {'kmeans': clustering.cluster_kmeans,
                        #   'DBSCAN': clustering.cluster_DBSCAN,
                          'meanshift': clustering.cluster_meanshift}

            cfun = method_key[method]

            labels, core_samples_mask = cfun(ds, **kwargs)
            # return labels, and if DBSCAN core_sample_mask

            labels_unique = np.unique(labels)

            # label the clusters according to their contents
            if (sort is not None) & (sort is not False):

                if isinstance(sort, str):
                    sort = [sort]

                sanalytes = analytes

                # make boolean filter to select analytes
                if sort is True:
                    sortk = np.array([True] * len(sanalytes))
                else:
                    sortk = np.array([s in sort for s in sanalytes])

                # create per-point mean based on selected analytes.
                sd = np.apply_along_axis(sum, 1, ds[:, sortk])
                # calculate per-cluster means
                avs = [np.nanmean(sd[labels == lab]) for lab in labels_unique]
                # re-order the cluster labels based on their means
                order = [x[0] for x in sorted(enumerate(avs), key=lambda x:x[1])]
                sdict = dict(zip(order, labels_unique))
            else:
                sdict = dict(zip(labels_unique, labels_unique))

            filts = {}
            for ind, lab in sdict.items():
                filts[lab] = labels == ind

            # only applies to DBSCAN results.
            if not all(np.isnan(core_samples_mask)):
                filts['core'] = core_samples_mask

            resized = {}
            for k, v in filts.items():
                resized[k] = np.zeros(self.Time.size, dtype=bool)
                resized[k][sampled] = v

            namebase = '-'.join(analytes) + '_cluster-' + method
            info = '-'.join(analytes) + ' cluster filter.'

            if method == 'DBSCAN':
                for k, v in resized.items():
                    if isinstance(k, str):
                        name = namebase + '_core'
                    elif k < 0:
                        name = namebase + '_noise'
                    else:
                        name = namebase + '_{:.0f}'.format(k)
                    self.filt.add(name, v, info=info, params=params, setn=setn)
            else:
                for k, v in resized.items():
                    name = namebase + '_{:.0f}'.format(k)
                    self.filt.add(name, v, info=info, params=params, setn=setn)
        else:
            # if there are no data
            name = '-'.join(analytes) + '_cluster-' + method + '_0'
            info = '-'.join(analytes) + ' cluster filter failed.'

            self.filt.add(name, np.zeros(self.Time.size, dtype=bool),
                          info=info, params=params, setn=setn)

        return
def filter_clustering(self, analytes, filt=False, normalise=True,
                          method='meanshift', include_time=False,
                          sort=None, min_data=10, **kwargs):
        """
        Applies an n - dimensional clustering filter to the data.

        Available Clustering Algorithms

        * 'meanshift': The `sklearn.cluster.MeanShift` algorithm.
          Automatically determines number of clusters
          in data based on the `bandwidth` of expected
          variation.
        * 'kmeans': The `sklearn.cluster.KMeans` algorithm. Determines
          the characteristics of a known number of clusters
          within the data. Must provide `n_clusters` to specify
          the expected number of clusters.
        * 'DBSCAN': The `sklearn.cluster.DBSCAN` algorithm. Automatically
          determines the number and characteristics of clusters
          within the data based on the 'connectivity' of the
          data (i.e. how far apart each data point is in a
          multi - dimensional parameter space). Requires you to
          set `eps`, the minimum distance point must be from
          another point to be considered in the same cluster,
          and `min_samples`, the minimum number of points that
          must be within the minimum distance for it to be
          considered a cluster. It may also be run in automatic
          mode by specifying `n_clusters` alongside
          `min_samples`, where eps is decreased until the
          desired number of clusters is obtained.
        
        For more information on these algorithms, refer to the
        documentation.

        Parameters
        ----------
        analytes : str
            The analyte(s) that the filter applies to.
        filt : bool
            Whether or not to apply existing filters to the data before
            calculating this filter.
        normalise : bool
            Whether or not to normalise the data to zero mean and unit
            variance. Reccomended if clustering based on more than 1 analyte.
            Uses `sklearn.preprocessing.scale`.
        method : str
            Which clustering algorithm to use (see above).
        include_time : bool
            Whether or not to include the Time variable in the clustering
            analysis. Useful if you're looking for spatially continuous
            clusters in your data, i.e. this will identify each spot in your
            analysis as an individual cluster.
        sort : bool, str or array-like
            Whether or not to label the resulting clusters according to their
            contents. If used, the cluster with the lowest values will be
            labelled from 0, in order of increasing cluster mean value.analytes.
            The sorting rules depend on the value of 'sort', which can be the name
            of a single analyte (str), a list of several analyte names (array-like)
            or True (bool), to specify all analytes used to calcualte the cluster.
        min_data : int
            The minimum number of data points that should be considered by
            the filter. Default = 10.
        **kwargs
            Parameters passed to the clustering algorithm specified by
            `method`.

        Meanshift Parameters
        --------------------
        bandwidth : str or float
            The bandwith (float) or bandwidth method ('scott' or 'silverman')
            used to estimate the data bandwidth.
        bin_seeding : bool
            Modifies the behaviour of the meanshift algorithm. Refer to
            sklearn.cluster.meanshift documentation.

        K - Means Parameters
        ------------------
        n_clusters : int
            The number of clusters expected in the data.

        DBSCAN Parameters
        -----------------
        eps : float
            The minimum 'distance' points must be apart for them to be in the
            same cluster. Defaults to 0.3. Note: If the data are normalised
            (they should be for DBSCAN) this is in terms of total sample
            variance. Normalised data have a mean of 0 and a variance of 1.
        min_samples : int
            The minimum number of samples within distance `eps` required
            to be considered as an independent cluster.
        n_clusters : int
            The number of clusters expected. If specified, `eps` will be
            incrementally reduced until the expected number of clusters is
            found.
        maxiter : int
            The maximum number of iterations DBSCAN will run.

        Returns
        -------
        None
        """
        params = locals()
        del(params['self'])

        # convert string to list, if single analyte
        if isinstance(analytes, str):
            analytes = [analytes]

        setn = self.filt.maxset + 1

        # generate filter
        vals = np.vstack(nominal_values(list(self.focus.values())))
        if filt is not None:
            ind = (self.filt.grab_filt(filt, analytes) &
                   np.apply_along_axis(all, 0, ~np.isnan(vals)))
        else:
            ind = np.apply_along_axis(all, 0, ~np.isnan(vals))

        if sum(ind) > min_data:

            # get indices for data passed to clustering
            sampled = np.arange(self.Time.size)[ind]

            # generate data for clustering
            if include_time:
                extra = self.Time
            else:
                extra = None
            # get data as array
            ds = stack_keys(self.focus, analytes, extra)
            # apply filter, and get nominal values
            ds = nominal_values(ds[ind, :])

            if normalise | (len(analytes) > 1):
                ds = preprocessing.scale(ds)

            method_key = {'kmeans': clustering.cluster_kmeans,
                        #   'DBSCAN': clustering.cluster_DBSCAN,
                          'meanshift': clustering.cluster_meanshift}

            cfun = method_key[method]

            labels, core_samples_mask = cfun(ds, **kwargs)
            # return labels, and if DBSCAN core_sample_mask

            labels_unique = np.unique(labels)

            # label the clusters according to their contents
            if (sort is not None) & (sort is not False):

                if isinstance(sort, str):
                    sort = [sort]

                sanalytes = analytes

                # make boolean filter to select analytes
                if sort is True:
                    sortk = np.array([True] * len(sanalytes))
                else:
                    sortk = np.array([s in sort for s in sanalytes])

                # create per-point mean based on selected analytes.
                sd = np.apply_along_axis(sum, 1, ds[:, sortk])
                # calculate per-cluster means
                avs = [np.nanmean(sd[labels == lab]) for lab in labels_unique]
                # re-order the cluster labels based on their means
                order = [x[0] for x in sorted(enumerate(avs), key=lambda x:x[1])]
                sdict = dict(zip(order, labels_unique))
            else:
                sdict = dict(zip(labels_unique, labels_unique))

            filts = {}
            for ind, lab in sdict.items():
                filts[lab] = labels == ind

            # only applies to DBSCAN results.
            if not all(np.isnan(core_samples_mask)):
                filts['core'] = core_samples_mask

            resized = {}
            for k, v in filts.items():
                resized[k] = np.zeros(self.Time.size, dtype=bool)
                resized[k][sampled] = v

            namebase = '-'.join(analytes) + '_cluster-' + method
            info = '-'.join(analytes) + ' cluster filter.'

            if method == 'DBSCAN':
                for k, v in resized.items():
                    if isinstance(k, str):
                        name = namebase + '_core'
                    elif k < 0:
                        name = namebase + '_noise'
                    else:
                        name = namebase + '_{:.0f}'.format(k)
                    self.filt.add(name, v, info=info, params=params, setn=setn)
            else:
                for k, v in resized.items():
                    name = namebase + '_{:.0f}'.format(k)
                    self.filt.add(name, v, info=info, params=params, setn=setn)
        else:
            # if there are no data
            name = '-'.join(analytes) + '_cluster-' + method + '_0'
            info = '-'.join(analytes) + ' cluster filter failed.'

            self.filt.add(name, np.zeros(self.Time.size, dtype=bool),
                          info=info, params=params, setn=setn)

        return
def filter_clustering ( self , analytes , filt = False , normalise = True , method = 'meanshift' , include_time = False , sort = None , min_data = 10 , * * kwargs ) : params = locals ( ) del ( params [ 'self' ] ) # convert string to list, if single analyte if isinstance ( analytes , str ) : analytes = [ analytes ] setn = self . filt . maxset + 1 # generate filter vals = np . vstack ( nominal_values ( list ( self . focus . values ( ) ) ) ) if filt is not None : ind = ( self . filt . grab_filt ( filt , analytes ) & np . apply_along_axis ( all , 0 , ~ np . isnan ( vals ) ) ) else : ind = np . apply_along_axis ( all , 0 , ~ np . isnan ( vals ) ) if sum ( ind ) > min_data : # get indices for data passed to clustering sampled = np . arange ( self . Time . size ) [ ind ] # generate data for clustering if include_time : extra = self . Time else : extra = None # get data as array ds = stack_keys ( self . focus , analytes , extra ) # apply filter, and get nominal values ds = nominal_values ( ds [ ind , : ] ) if normalise | ( len ( analytes ) > 1 ) : ds = preprocessing . scale ( ds ) method_key = { 'kmeans' : clustering . cluster_kmeans , #   'DBSCAN': clustering.cluster_DBSCAN, 'meanshift' : clustering . cluster_meanshift } cfun = method_key [ method ] labels , core_samples_mask = cfun ( ds , * * kwargs ) # return labels, and if DBSCAN core_sample_mask labels_unique = np . unique ( labels ) # label the clusters according to their contents if ( sort is not None ) & ( sort is not False ) : if isinstance ( sort , str ) : sort = [ sort ] sanalytes = analytes # make boolean filter to select analytes if sort is True : sortk = np . array ( [ True ] * len ( sanalytes ) ) else : sortk = np . array ( [ s in sort for s in sanalytes ] ) # create per-point mean based on selected analytes. sd = np . apply_along_axis ( sum , 1 , ds [ : , sortk ] ) # calculate per-cluster means avs = [ np . nanmean ( sd [ labels == lab ] ) for lab in labels_unique ] # re-order the cluster labels based on their means order = [ x [ 0 ] for x in sorted ( enumerate ( avs ) , key = lambda x : x [ 1 ] ) ] sdict = dict ( zip ( order , labels_unique ) ) else : sdict = dict ( zip ( labels_unique , labels_unique ) ) filts = { } for ind , lab in sdict . items ( ) : filts [ lab ] = labels == ind # only applies to DBSCAN results. if not all ( np . isnan ( core_samples_mask ) ) : filts [ 'core' ] = core_samples_mask resized = { } for k , v in filts . items ( ) : resized [ k ] = np . zeros ( self . Time . size , dtype = bool ) resized [ k ] [ sampled ] = v namebase = '-' . join ( analytes ) + '_cluster-' + method info = '-' . join ( analytes ) + ' cluster filter.' if method == 'DBSCAN' : for k , v in resized . items ( ) : if isinstance ( k , str ) : name = namebase + '_core' elif k < 0 : name = namebase + '_noise' else : name = namebase + '_{:.0f}' . format ( k ) self . filt . add ( name , v , info = info , params = params , setn = setn ) else : for k , v in resized . items ( ) : name = namebase + '_{:.0f}' . format ( k ) self . filt . add ( name , v , info = info , params = params , setn = setn ) else : # if there are no data name = '-' . join ( analytes ) + '_cluster-' + method + '_0' info = '-' . join ( analytes ) + ' cluster filter failed.' self . filt . add ( name , np . zeros ( self . Time . size , dtype = bool ) , info = info , params = params , setn = setn ) return
Applies an n - dimensional clustering filter to the data.

        Available Clustering Algorithms

        * 'meanshift': The `sklearn.cluster.MeanShift` algorithm.
          Automatically determines number of clusters
          in data based on the `bandwidth` of expected
          variation.
        * 'kmeans': The `sklearn.cluster.KMeans` algorithm. Determines
          the characteristics of a known number of clusters
          within the data. Must provide `n_clusters` to specify
          the expected number of clusters.
        * 'DBSCAN': The `sklearn.cluster.DBSCAN` algorithm. Automatically
          determines the number and characteristics of clusters
          within the data based on the 'connectivity' of the
          data (i.e. how far apart each data point is in a
          multi - dimensional parameter space). Requires you to
          set `eps`, the minimum distance point must be from
          another point to be considered in the same cluster,
          and `min_samples`, the minimum number of points that
          must be within the minimum distance for it to be
          considered a cluster. It may also be run in automatic
          mode by specifying `n_clusters` alongside
          `min_samples`, where eps is decreased until the
          desired number of clusters is obtained.
        
        For more information on these algorithms, refer to the
        documentation.

        Parameters
        ----------
        analytes : str
            The analyte(s) that the filter applies to.
        filt : bool
            Whether or not to apply existing filters to the data before
            calculating this filter.
        normalise : bool
            Whether or not to normalise the data to zero mean and unit
            variance. Reccomended if clustering based on more than 1 analyte.
            Uses `sklearn.preprocessing.scale`.
        method : str
            Which clustering algorithm to use (see above).
        include_time : bool
            Whether or not to include the Time variable in the clustering
            analysis. Useful if you're looking for spatially continuous
            clusters in your data, i.e. this will identify each spot in your
            analysis as an individual cluster.
        sort : bool, str or array-like
            Whether or not to label the resulting clusters according to their
            contents. If used, the cluster with the lowest values will be
            labelled from 0, in order of increasing cluster mean value.analytes.
            The sorting rules depend on the value of 'sort', which can be the name
            of a single analyte (str), a list of several analyte names (array-like)
            or True (bool), to specify all analytes used to calcualte the cluster.
        min_data : int
            The minimum number of data points that should be considered by
            the filter. Default = 10.
        **kwargs
            Parameters passed to the clustering algorithm specified by
            `method`.

        Meanshift Parameters
        --------------------
        bandwidth : str or float
            The bandwith (float) or bandwidth method ('scott' or 'silverman')
            used to estimate the data bandwidth.
        bin_seeding : bool
            Modifies the behaviour of the meanshift algorithm. Refer to
            sklearn.cluster.meanshift documentation.

        K - Means Parameters
        ------------------
        n_clusters : int
            The number of clusters expected in the data.

        DBSCAN Parameters
        -----------------
        eps : float
            The minimum 'distance' points must be apart for them to be in the
            same cluster. Defaults to 0.3. Note: If the data are normalised
            (they should be for DBSCAN) this is in terms of total sample
            variance. Normalised data have a mean of 0 and a variance of 1.
        min_samples : int
            The minimum number of samples within distance `eps` required
            to be considered as an independent cluster.
        n_clusters : int
            The number of clusters expected. If specified, `eps` will be
            incrementally reduced until the expected number of clusters is
            found.
        maxiter : int
            The maximum number of iterations DBSCAN will run.

        Returns
        -------
        None
Applies an n - dimensional clustering filter to the data .