fixed stacking functions for S and P, name for setup.py

setup.py

@@ -7,6 +7,7 @@ version = '0.1'
 
 
 setup(
+    name='clusty',
     version=version,
     author='clusty collective',
     install_requires=[],

src/cc.py

@@ -304,10 +304,10 @@ def max_cc(coef_array):
     '''
     n_ph = num.shape(coef_array)[0]
     n_ev = num.shape(coef_array)[2]
-    array_max_cc = num.empty((n_ph,n_ev))
+    array_max_cc = num.empty((n_ph, n_ev, n_ev))
     for i_ph in range(n_ph):
-        array_max_cc[i_ph, :] = num.nanmax(coef_array,
-                            axis=1)
+        array_max_cc[i_ph,:,:] = num.nanmax(coef_array[i_ph],
+                            axis=0)
 
     return array_max_cc
 
@@ -329,7 +329,6 @@ def median_ccs(coef_array):
     # #fancy numpy
     # array_median_ccs2 = num.median(coef_array, axis=2)
 
-
     return  array_median_ccs
 
 
@@ -377,7 +376,7 @@ def product_combPS(coef_array):
                 cc_nonan_0 = coef_array[0, :,i,j][coef_array[0,:,i,j] != 0]
                 cc_nonan_1 = coef_array[1, :,i,j][coef_array[1,:,i,j] != 0]
                 try:
-                    array_prod_ccs[0,i,j] = (num.prod(cc_nonan_0)*num.prod(cc_nonan_1))**(1/(2*len(cc_nonan)))
+                    array_prod_ccs[0,i,j] = (num.prod(cc_nonan_0)*num.prod(cc_nonan_1))**(1/(len(cc_nonan_0)+len(cc_nonan_1)))
                 except ZeroDivisionError:
                     array_prod_ccs[0,i,j] = 0
                     continue
@@ -476,7 +475,8 @@ def stack_ccs_over_stations(coef_array, weights_array, az_stats_weights,
     '''
 
     method = netsim_settings.method_similarity_computation
-    print(method)
+    #print(coef_array[0])
+    #print(coef_array[1])
 
     if netsim_settings.apply_cc_station_thresh:
         apply_cc_station_threshs(coef_array, netsim_settings, n_ph)

src/clustering.py

@@ -64,7 +64,7 @@ def write_pretty_output(catalog, clustering, method):
     cl.dump(filename='clustering_%s.yaml' % method)
 
 
-def cluster_plots(network_similarity, clustering_list, catalog):
+def cluster_plots(network_similarity, clustering_list, catalog, method):
     '''
     plot results
     (a) network similarity matrix sorted by clusters
@@ -92,7 +92,7 @@ def cluster_plots(network_similarity, clustering_list, catalog):
 
     events_sorted = [s[2] for s in sorted_tuples]
     title = 'Network Similarity, events sorted by cluster'
-    fig_name = 'network_similarity_cluster.png'
+    fig_name = 'net_sim_cl_%s.png' % method
     small_imshow_plot(network_similarity_cluster, events_sorted, title,
                       cbarlabel=None, fig_name=fig_name)
 
@@ -126,5 +126,5 @@ def cluster_plots(network_similarity, clustering_list, catalog):
     cbar.ax.set_xticklabels(bounds, rotation=60)
 
     plt.tight_layout()
-    fig.savefig('cluster.png')
+    fig.savefig('cluster_%s.png' % method)
     plt.close()

src/clusty.py

@@ -126,28 +126,33 @@ def main():
             arrivals_array.fill(num.nan)
             print('Computing arrival times.')
             if cc_cnf.use_cake:
+                st_lats = num.asarray([st.lat for st in station_list])
+                st_lons = num.asarray([st.lon for st in station_list])
+
                 if cc_cnf.vmodel == '':
-                    vmodel = cake.load_model(
-                        crust2_profile=crust2x2.get_profile(
-                        cat[0].lat, cat[0].lon))
+                    vmodels = [cake.load_model(crust2_profile=crust2x2.get_profile(st.lat, st.lon))
+                                for st in station_list]
                 else:
                     vmodel = cake.load_model(cc_cnf.vmodel)
 
-                for i_ph, phase_name in enumerate(cc_cnf.phase):
+                for i_ev, ev in enumerate(cat):
+                    dists = orthodrome.distance_accurate50m_numpy(
+                        ev.lat, ev.lon, st_lats, st_lons)
 
-                    st_lats = num.asarray([st.lat for st in station_list])
-                    st_lons = num.asarray([st.lon for st in station_list])
+                    for i_st, st in enumerate(station_list):
+                        dist = dists[i_st]
 
-                    for i_ev, ev in enumerate(cat):
-                        dists = orthodrome.distance_accurate50m_numpy(
-                            ev.lat, ev.lon, st_lats, st_lons)
+                        if cc_cnf.vmodel == '':
+                            vmodel = vmodels[i_st]
+                        else:
+                            vmodel = vmodel
 
-                        for i_st, st in enumerate(station_list):
-                            dist = dists[i_st]
+                        for i_ph, phase_name in enumerate(cc_cnf.phase):
                             if phase_name == 'P' or phase_name == 'p':
                                 phase_names = ['p', 'P']
                             elif phase_name == 'S' or phase_name == 's':
                                 phase_names = ['s', 'S']
+
                             arrivals = vmodel.arrivals(distances=[dist*cake.m2d],
                                                       phases=phase_names,
                                                       zstart=ev.depth)
@@ -207,6 +212,7 @@ def main():
                 az_stats_weights = None
             # print(coef_array.shape)
             # print(weights_array.shape)
+        print('Similarity matrix ready.')
 
         n_ph = len(cc_cnf.phase)
         if not ns_cnf.use_precalc_net_sim:
@@ -227,25 +233,30 @@ def main():
                              fig_name=ns_cnf.method_similarity_computation+str(i_ph))
 
 
+        # make optional combining of P and S results or more methods to combine them
+        # in computation of network similarity!!!
+
+
         ### clusterinng ###
-        '''
-        # adjust for one more dimension of network_similarity array
-        if cl_cnf.method == 'dbscan':
-
-            clustering = cl.cluster_dbscan(cl_cnf.dbscan_eps,
-                                           cl_cnf.dbscan_min_samples,
-                                           network_similarity)
-        method_string = '%s_%s' % (ns_cnf.method_similarity_computation,
-                                cl_cnf.method)
-        #cl.write_pretty_output(cat, clustering, method_string)
-        cl.cluster_plots(network_similarity, clustering, cat)
+                
+        for i_ph in range(n_ph):
+
+            if cl_cnf.method == 'dbscan':
+                clustering = cl.cluster_dbscan(cl_cnf.dbscan_eps,
+                                               cl_cnf.dbscan_min_samples,
+                                               network_similarity[i_ph])
+            method_string = '%s_%s_%s' % (ns_cnf.method_similarity_computation,
+                                    cl_cnf.method, str(i_ph))
+            cl.write_pretty_output(cat, clustering, method_string)
+            cl.cluster_plots(network_similarity[i_ph], clustering, cat, method_string)
 
         #for ev, c in zip(cat, clustering):
         #    print(util.time_to_str(ev.time)[0:11], ev.magnitude, c)
-        '''
-
+        
+    print('Clusty run completed.')
 
 
 
 if __name__ == '__main__':
     main()
+