- Finish up motifs backend

- show spinner - explainer - sample query - fix test
murthylab · Jul 15, 2023 · 7649743 · 7649743
1 parent 7dba20c
commit 7649743
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Showing 5 changed files with 22 additions and 52 deletions.
diff --git a/src/blueprints/app.py b/src/blueprints/app.py
@@ -1429,22 +1429,10 @@ def motifs():
         motifs_query = MotifSearchQuery.from_form_query(
             request.args, NeuronDataFactory.instance()
         )
-        try:
-            search_results = motifs_query.search()
-            log_activity(
-                f"Motif search with {motifs_query} found {len(search_results)} matches"
-            )
-        except Exception as e:
-            error = f"Error searching for motif: {e=}"
-            log_error(error)
-            return render_template(
-                "motif_search.html",
-                regions=list(REGIONS.keys()),
-                results=search_results,
-                error=error,
-                NEURO_TRANSMITTER_NAMES=NEURO_TRANSMITTER_NAMES,
-                query=request.args,
-            )
+        search_results = motifs_query.search()
+        log_activity(
+            f"Motif search with {motifs_query} found {len(search_results)} matches"
+        )
 
     return render_template(
         "motif_search.html",

diff --git a/src/service/motif_search.py b/src/service/motif_search.py
@@ -442,10 +442,9 @@ def row_satisfies_constraints(row, edge_constraints):
         return True
 
     @staticmethod
-    def _feasible_pairs(
+    def feasible_pairs(
         neuron_db, x_candidates, y_candidates, xy_constraints, yx_constraints
     ):
-        print("+++ here!")
         ins, outs = neuron_db.input_output_partners_with_synapse_counts()
 
         if not xy_constraints and not yx_constraints:
@@ -503,7 +502,7 @@ def _feasible_pairs(
         else:  # both edges are specified
             for x_rid in x_candidates:
                 y_candidates_filtered = y_candidates.intersection(
-                    set(ins[x_rid].keys()).intersection(set(outs[x_rid]).keys())
+                    set(ins[x_rid].keys()).intersection(set(outs[x_rid].keys()))
                 )
                 rows = neuron_db.connections_.rows_for_cell(x_rid)
                 from_x_tuples = defaultdict(list)
@@ -575,16 +574,6 @@ def _search_triplets(
         assert all(
             [isinstance(c, set) for c in [x_candidates, y_candidates, z_candidates]]
         )
-        print(
-            f"+++ x, y, z: {len(x_candidates)}, {len(y_candidates)}, {len(z_candidates)}"
-        )
-
-        print(f"+++ xy_ec: {xy_edge_constraints}")
-        print(f"+++ xz_ec: {xz_edge_constraints}")
-        print(f"+++ yx_ec: {yx_edge_constraints}")
-        print(f"+++ yz_ec: {yz_edge_constraints}")
-        print(f"+++ zx_ec: {zx_edge_constraints}")
-        print(f"+++ zy_ec: {zy_edge_constraints}")
 
         x_query, y_query, z_query = [], [], []
         MotifSearchQuery.append_edge_queries(xy_edge_constraints, x_query, y_query)
@@ -604,37 +593,27 @@ def _search_triplets(
             neuron_db, z_candidates, z_query
         )
 
-        print(
-            f"+++ x, y, z after edge queries: {len(x_candidates)}, {len(y_candidates)}, {len(z_candidates)}"
-        )
-
         ins, outs = neuron_db.input_output_partner_sets()
 
         def z_satisfies_constraints(x_rid, y_rid, z_rid):
             constraints_satisfied = set()
-
-            print("+++ checking if z satisfies constraints")
             if not xz_edge_constraints:
                 if z_rid in outs[x_rid]:
-                    print("+++ xz should not be an edge")
                     return False
                 else:
                     constraints_satisfied.add("xz")
             if not zx_edge_constraints:
                 if x_rid in outs[z_rid]:
-                    print("+++ zx should not be an edge")
                     return False
                 else:
                     constraints_satisfied.add("zx")
             if not yz_edge_constraints:
                 if z_rid in outs[y_rid]:
-                    print("+++ yz should not be an edge")
                     return False
                 else:
                     constraints_satisfied.add("yz")
             if not zy_edge_constraints:
                 if y_rid in outs[z_rid]:
-                    print("+++ zy should not be an edge")
                     return False
                 else:
                     constraints_satisfied.add("zy")
@@ -643,7 +622,7 @@ def z_satisfies_constraints(x_rid, y_rid, z_rid):
                 return True
 
             z_rows = neuron_db.connections_.rows_for_cell(z_rid)
-            print("+++ checking if z satisfies rows")
+
             for r in z_rows:
                 if (
                     r[0] == x_rid
@@ -659,7 +638,7 @@ def z_satisfies_constraints(x_rid, y_rid, z_rid):
                 if (
                     r[0] == y_rid
                     and r[1] == z_rid
-                    and not MotifSearchQuery.row_satisfies_constraints(
+                    and MotifSearchQuery.row_satisfies_constraints(
                         r, yz_edge_constraints
                     )
                 ):
@@ -670,7 +649,7 @@ def z_satisfies_constraints(x_rid, y_rid, z_rid):
                 if (
                     r[0] == z_rid
                     and r[1] == x_rid
-                    and not MotifSearchQuery.row_satisfies_constraints(
+                    and MotifSearchQuery.row_satisfies_constraints(
                         r, zx_edge_constraints
                     )
                 ):
@@ -681,7 +660,7 @@ def z_satisfies_constraints(x_rid, y_rid, z_rid):
                 if (
                     r[0] == z_rid
                     and r[1] == y_rid
-                    and not MotifSearchQuery.row_satisfies_constraints(
+                    and MotifSearchQuery.row_satisfies_constraints(
                         r, zy_edge_constraints
                     )
                 ):
@@ -705,24 +684,19 @@ def filtered_z_candidates(x_rid, y_rid):
             return res
 
         matches = []
-        print("+++ starting the loop")
-        for _x_rid, _y_rid in MotifSearchQuery._feasible_pairs(
+        for _x_rid, _y_rid in MotifSearchQuery.feasible_pairs(
             neuron_db=neuron_db,
             x_candidates=x_candidates,
             y_candidates=y_candidates,
             xy_constraints=xy_edge_constraints,
             yx_constraints=yx_edge_constraints,
         ):
-            print(f"+++ examining {_x_rid}, {_y_rid}")
-
             z_candidates_filtered = filtered_z_candidates(_x_rid, _y_rid)
 
             for _z_rid in z_candidates_filtered:
                 if _z_rid == _x_rid or _z_rid == _y_rid:
-                    print("+++ z is equal to y or x")
                     continue
                 if z_satisfies_constraints(x_rid=_x_rid, y_rid=_y_rid, z_rid=_z_rid):
-                    print(f"+++ appending result, have {len(matches)} need {limit}")
                     matches.append(
                         MotifSearchQuery.make_match_dict(
                             neuron_db=neuron_db,

diff --git a/src/templates/motif_search.html b/src/templates/motif_search.html
@@ -82,7 +82,7 @@
         {% endfor %}
 
         <div class="d-flex justify-content-center">
-          <button type="submit" class="btn btn-primary">Search</button>
+          <button type="submit" class="btn btn-primary" onclick="loading(event);">Search</button>
         </div>
       </form>
     </div>

diff --git a/static/js/motif_search.js b/static/js/motif_search.js
@@ -28,9 +28,16 @@ function ExplainerCard() {
     <div class="card" style=${{ margin: "15px" }}>
       <div class="card-header" sttyle=${{ color: "purple" }}>What is this?</div>
       <div class="card-body">
-        [TODO: explanatory text and demo query]
+        With this tool you can search for specific motifs (sub-graphs) of size 3 in the connectome network. With A, B, C
+        denoting the motif node names, you can specify a filter for each node (same query language as in search) as well
+        as connectivity of each pair of nodes (not connected, connected one way,
+        connected both ways). Additionally you can apply neurotransmitter / brain region / min synapse count constraints
+        for every pair of connected nodes. Matching
+        motifs (if found) will be presented as a network along with 3D rendering of the corresponding cells.
         <br /><br />
-        <button class="btn btn btn-outline-success my-2 my-sm-0" type="button">Try Example Query</button>
+        <a class="btn btn btn-outline-success my-2 my-sm-0" href="/app/motifs/?queryA=720575940613052200&queryB=DL1_dorsal&queryC=DL1_dorsal&enabledAB=on&regionAB=Any&minSynapseCountAB=20&ntTypeAB=GLUT&enabledBA=on&regionBA=Any&minSynapseCountBA=20&ntTypeBA=GLUT&enabledAC=on&regionAC=Any&minSynapseCountAC=15&ntTypeAC=GLUT&enabledCA=on&regionCA=Any&minSynapseCountCA=15&ntTypeCA=GLUT&enabledBC=on&regionBC=Any&minSynapseCountBC=&ntTypeBC=GLUT&enabledCB=on&regionCB=Any&minSynapseCountCB=&ntTypeCB=GLUT">
+            Try Example Query
+        </a>
       </div>
     </div>
   </div>`;

diff --git a/tests/unit/test_motif_search.py b/tests/unit/test_motif_search.py
@@ -306,6 +306,7 @@ def test_search_size_3(self):
         msq.add_node("b", "id == 720575940620181077")
         msq.add_node("c", "id == 720575940626634789")
         msq.add_edge("a", "b", regions=None, min_synapse_count=0, nt_type=None)
+        msq.add_edge("b", "a", regions=None, min_synapse_count=0, nt_type=None)
         msq.add_edge("b", "c", regions=None, min_synapse_count=0, nt_type=None)
         msq.add_edge("a", "c", regions=None, min_synapse_count=0, nt_type=None)
         res = msq.search(limit=limit)