max_epicenter_dist 500; max pair dist 15;

Author: zhuwq0

ADLoc

@@ -268,7 +268,7 @@ def extract_template_numpy(
 
 
 # %%
-# def generate_pairs(picks, events, stations, max_pair_dist=10, max_neighbors=50, fname="pairs.txt"):
+# def generate_pairs(picks, events, stations, max_pair_dist=15, max_neighbors=50, fname="pairs.txt"):
 #     ncpu = min(32, mp.cpu_count())
 #     neigh = NearestNeighbors(radius=max_pair_dist, n_neighbors=max_neighbors, n_jobs=ncpu)
 #     neigh.fit(events[["x_km", "y_km", "z_km"]].values)
@@ -350,7 +350,7 @@ def cut_templates(jdays, root_path, region, config, bucket, protocol, token):
     time_window = max((time_before_p + time_after_p), (time_before_s + time_after_s))
     nt = int(round(time_window * sampling_rate))
     max_epicenter_dist = 500.0
-    max_pair_dist = 10
+    max_pair_dist = 15
     max_neighbors = 50
     min_cc_score = 0.5
     min_obs = 8

CCTorch

@@ -18,7 +18,7 @@
 
 
 # %%
-def generate_pairs(picks, events, stations, max_pair_dist=10, max_neighbors=50, fname="pairs.txt"):
+def generate_pairs(picks, events, stations, max_pair_dist=15, max_neighbors=50, fname="pairs.txt"):
     ncpu = min(32, mp.cpu_count())
     neigh = NearestNeighbors(radius=max_pair_dist, n_neighbors=max_neighbors, n_jobs=ncpu)
     neigh.fit(events[["x_km", "y_km", "z_km"]].values)
@@ -92,18 +92,19 @@ def generate_pairs(picks, events, stations, max_pair_dist=10, max_neighbors=50,
     # with open(args.config, "r") as fp:
     #     config = json.load(fp)
     config.update(vars(args))
-    print(json.dumps(config, indent=4, sort_keys=True))
 
     # %% CCTorch
-    # config["max_pair_dist_km"] = 10
-    # config["max_neighbors"] = 50
-    # config["max_obs"] = 100
-    # config["min_obs"] = 8
+    config["max_pair_dist_km"] = 15
+    config["max_neighbors"] = 50
+    config["max_obs"] = 100
+    config["min_obs"] = 8
     # config["nt"] = 400
     # config["maxdepth"] = 60
     # config["mindepth"] = 0
     # config["max_epicenter_dist_km"] = 200.0
 
+    print(json.dumps(config, indent=4, sort_keys=True))
+
     # %%
     def plot_templates(templates, events, picks):
         templates = templates - np.nanmean(templates, axis=(-1), keepdims=True)

GaMMA

@@ -182,7 +182,7 @@ def parse_args():
     # picks = pd.read_csv(f"{root_path}/{result_path}/picks.csv", parse_dates=["phase_time"])
 
     # %%
-    MAX_PAIR_DIST = 10  # km
+    MAX_PAIR_DIST = 15  # km
     MAX_NEIGHBORS = 50
     MIN_NEIGHBORS = 8
     MIN_OBS = 8

examples/california/cut_templates_cc.py

@@ -55,8 +55,8 @@ def parse_args():
 ## based on GPU memory
 
 batch = 1_024
-block_size1 = 1000_000
-block_size2 = 1000_000
+block_size1 = 2_000_000
+block_size2 = 2_000_000
 
 ########## DEBUG by day ##############
 # base_cmd = (

examples/california/cut_templates_merge.py

@@ -18,9 +18,9 @@ cd $root_path/$region/hypodd
 
 if [ ! -d "HypoDD" ]; then
   git clone https://github.com/zhuwq0/HypoDD.git
-  export PATH=$PATH:$PWD/HypoDD
-  make -C HypoDD/src/
 fi
+export PATH=$PATH:$PWD/HypoDD
+make -C HypoDD/src/
 
 cat <<EOF > cc.inp
 * RELOC.INP:
@@ -54,7 +54,7 @@ hypodd.src
 * IPHA: 1= P; 2= S; 3= P&S
 * DIST:max dist [km] between cluster centroid and station 
 * IDAT   IPHA   DIST
-    1     3     120
+    1     3     500
 *
 *--- event clustering:
 * OBSCC:    min # of obs/pair for crosstime data (0= no clustering)

examples/california/generate_pairs.py

@@ -232,7 +232,7 @@ def extract_template_numpy(
 
 
 # %%
-def generate_pairs(picks, events, stations, max_pair_dist=10, max_neighbors=50, fname="pairs.txt"):
+def generate_pairs(picks, events, stations, max_pair_dist=15, max_neighbors=50, fname="pairs.txt"):
     ncpu = min(32, mp.cpu_count())
     neigh = NearestNeighbors(radius=max_pair_dist, n_neighbors=max_neighbors, n_jobs=ncpu)
     neigh.fit(events[["x_km", "y_km", "z_km"]].values)
@@ -301,8 +301,8 @@ def cut_templates(root_path, region, config):
 
     time_window = max((time_before_p + time_after_p), (time_before_s + time_after_s))
     nt = int(round(time_window * sampling_rate))
-    max_epicenter_dist = 200.0
-    max_pair_dist = 10
+    max_epicenter_dist = 500.0
+    max_pair_dist = 15
     max_neighbors = 50
     min_cc_score = 0.5
     min_obs = 8

examples/california/run_cctorch.py

@@ -237,7 +237,7 @@ def extract_template_numpy(
 
 
 # %%
-def generate_pairs(picks, events, stations, max_pair_dist=10, max_neighbors=50, fname="pairs.txt"):
+def generate_pairs(picks, events, stations, max_pair_dist=15, max_neighbors=50, fname="pairs.txt"):
     ncpu = min(32, mp.cpu_count())
     neigh = NearestNeighbors(radius=max_pair_dist, n_neighbors=max_neighbors, n_jobs=ncpu)
     neigh.fit(events[["x_km", "y_km", "z_km"]].values)
@@ -311,7 +311,7 @@ def cut_templates(root_path, region, config):
     time_window = max((time_before_p + time_after_p), (time_before_s + time_after_s))
     nt = int(round(time_window * sampling_rate))
     max_epicenter_dist = 500.0
-    max_pair_dist = 10
+    max_pair_dist = 15
     max_neighbors = 50
     min_cc_score = 0.5
     min_obs = 8