Minor bug fix for flash making

env-nompi.yaml

@@ -12,4 +12,3 @@ dependencies:
     - h5flow>=0.1.0
     - adc64format>=0.1.1
     - git+https://github.com/cuddandr/pylandau.git
-    - dbscan1d

env.yaml

@@ -12,4 +12,3 @@ dependencies:
     - h5flow>=0.1.0
     - adc64format>=0.1.1
     - git+https://github.com/cuddandr/pylandau.git
-    - dbscan1d

setup.py

@@ -23,6 +23,5 @@
                      'scikit-learn',
                      'h5flow>=0.2.0',
                      'pylandau @ git+https://github.com/cuddandr/pylandau.git#egg=pylandau',
-                     'dbscan1d',
                  ]
                  )

src/proto_nd_flow/reco/light/flash_finder.py

@@ -5,7 +5,7 @@
 from h5flow.core import H5FlowStage, resources
 from h5flow.data import dereference
 
-from dbscan1d.core import DBSCAN1D
+import sklearn.cluster as cluster
 
 import proto_nd_flow.util.units as units
 
@@ -74,7 +74,7 @@ def init(self, source_name):
         self.sum_hits_dset = self.data_manager.get_dset(self.sum_hits_dset_name)
         self.sipm_hits_dset = self.data_manager.get_dset(self.sipm_hits_dset_name)
 
-        self.dbs = DBSCAN1D(eps=self.eps, min_samples=self.min_samples)
+        self.dbs = cluster.DBSCAN(eps=self.eps, min_samples=self.min_samples)
 
         # get waveform shape information
         self.nadc = cwvfm_dset.dtype['samples'].shape[0]
@@ -117,6 +117,11 @@ def get_tpc_channels(self,itpc):
 
         return(return_arr)
 
+    def get_extrema(self, input_array):
+            return np.column_stack((
+                input_array.min(),
+                input_array.max()))
+
     def run(self, source_name, source_slice, cache):
         super(FlashFinder, self).run(source_name, source_slice, cache)
         events = cache[source_name]
@@ -153,9 +158,12 @@ def run(self, source_name, source_slice, cache):
                 tpc_hits = sum_hits[i,tpc_mask]
                 tpc_hits_idx = sum_hits_idx[i,tpc_mask]
                 if np.any(tpc_mask):
-                    labels = self.dbs.fit_predict(tpc_hits["sample_idx"])
+                    labels = self.dbs.fit_predict(tpc_hits["sample_idx"].reshape(-1,1)) # single feature
 
                     n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
+                    # skip if there's no light hits
+                    if n_clusters == 0:
+                        continue
                     n_noise = np.count_nonzero(labels == -1)
                     tpc_flashes = np.empty((n_clusters+n_noise),dtype=self.flash_dtype)
                     ev_ref = np.empty((n_clusters+n_noise),dtype='u4') #ev ID for each flash
@@ -176,9 +184,9 @@ def run(self, source_name, source_slice, cache):
                         tpc_flashes[cl]["n_sum_hits"] = np.count_nonzero(labels==cl)
 
                         #Timing information
-                        tpc_flashes[cl]["sample_range"] = get_extrema(tpc_hits[labels==cl]["sample_idx"])
-                        tpc_flashes[cl]['hit_time_range'] = get_extrema(tpc_hits[labels==cl]["busy_ns"])
-                        tpc_flashes[cl]['rising_spline_range'] = get_extrema(tpc_hits[labels==cl]["busy_ns"]
+                        tpc_flashes[cl]["sample_range"] = self.get_extrema(tpc_hits[labels==cl]["sample_idx"])
+                        tpc_flashes[cl]['hit_time_range'] = self.get_extrema(tpc_hits[labels==cl]["busy_ns"])
+                        tpc_flashes[cl]['rising_spline_range'] = self.get_extrema(tpc_hits[labels==cl]["busy_ns"]
                                                                              +tpc_hits[labels==cl]["rising_spline"])
 
                         #Hit intensity information
@@ -197,43 +205,36 @@ def run(self, source_name, source_slice, cache):
                             cwvfms[i,ch_idx[...,0], ch_idx[...,1], flash_slice],
                             axis=-1)
 
-                    #Handle Noise events
-                        # NOT NEEDED IF min_sample==1
-
+                    # when it gets here, there must be at least one non-noisy clusters
                     ev_ref[:] = np.r_[source_slice][i]
-                    sum_ref[:,0] = tpc_hits_idx
-                    sum_ref[:,1] = labels
+                    sum_ref[:,0] = tpc_hits_idx[labels>=0]
+                    sum_ref[:,1] = labels[labels>=0]
 
                     flash_list.append(tpc_flashes)
                     ev_ref_list.append(ev_ref)
                     sum_ref_list.append(sum_ref)
-
-        flash_data = np.concatenate(flash_list)
-
-        # save data
-        flash_slice = self.data_manager.reserve_data(
-            self.flash_dset_name, len(flash_data))
-        if len(flash_data):
-            flash_data['id'] = np.r_[flash_slice]
-        self.data_manager.write_data(self.flash_dset_name, flash_slice, flash_data)
-
-        # save references
-        ev_ref_data = np.concatenate(ev_ref_list)
-        ref = np.array([(ev_idx,flash_data[flash_slice_idx]['id']) for flash_slice_idx, ev_idx in enumerate(ev_ref_data)])
-        self.data_manager.write_ref(source_name, self.flash_dset_name, ref)
-
-        flash_list_struc = [arr.shape[0] for arr in flash_list]
-        flash_list = np.split(flash_data, np.cumsum(flash_list_struc)[:-1])
-        for j, tpc_flash_slice in enumerate(flash_list):
-            sum_ref_list[j] = np.c_[sum_ref_list[j][:,0], tpc_flash_slice["id"][sum_ref_list[j][:,1]]]
-        ref_sum = np.concatenate(sum_ref_list)
-        self.data_manager.write_ref(
-            self.sum_hits_dset_name, self.flash_dset_name, ref_sum)
-        #self.data_manager.write_ref(
-        #    self.sum_hits_dset_name, self.flash_dset_name, ref_sipm)
-
-@staticmethod
-def get_extrema(input_array):
-        return np.column_stack((
-            input_array.min(),
-            input_array.max()))
+
+        if len(flash_list) and len(ev_ref_list) and len(sum_ref_list):
+            flash_data = np.concatenate(flash_list)
+
+            # save data
+            flash_slice = self.data_manager.reserve_data(
+                self.flash_dset_name, len(flash_data))
+            if len(flash_data):
+                flash_data['id'] = np.r_[flash_slice]
+            self.data_manager.write_data(self.flash_dset_name, flash_slice, flash_data)
+
+            # save references
+            ev_ref_data = np.concatenate(ev_ref_list)
+            ref = np.array([(ev_idx,flash_data[flash_slice_idx]['id']) for flash_slice_idx, ev_idx in enumerate(ev_ref_data)])
+            self.data_manager.write_ref(source_name, self.flash_dset_name, ref)
+
+            flash_list_struc = [arr.shape[0] for arr in flash_list]
+            flash_list = np.split(flash_data, np.cumsum(flash_list_struc)[:-1])
+            for j, tpc_flash_slice in enumerate(flash_list):
+                sum_ref_list[j] = np.c_[sum_ref_list[j][:,0], tpc_flash_slice["id"][sum_ref_list[j][:,1]]]
+            ref_sum = np.concatenate(sum_ref_list)
+            self.data_manager.write_ref(
+                self.sum_hits_dset_name, self.flash_dset_name, ref_sum)
+            #self.data_manager.write_ref(
+            #    self.sum_hits_dset_name, self.flash_dset_name, ref_sipm)