fix small bugs

quakeflow_nc.py

@@ -21,6 +21,7 @@ import h5py
 import numpy as np
 import torch
 from typing import Dict, List, Optional, Tuple, Union
+import fsspec
 
 import datasets
 
@@ -52,6 +53,7 @@ _LICENSE = ""
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _REPO = "https://huggingface.co/datasets/AI4EPS/quakeflow_nc/resolve/main/data"
 _FILENAMES = ["NC1970-1989.h5", "NC1990-1994.h5", "NC1995-1999.h5", "NC2000-2004.h5", "NC2005-2009.h5", "NC2010.h5", "NC2011.h5", "NC2012.h5", "NC2013.h5", "NC2014.h5", "NC2015.h5", "NC2016.h5", "NC2017.h5", "NC2018.h5", "NC2019.h5", "NC2020.h5"]
+# _FILENAMES = ["NC2020.h5"]
 _URLS = {
     "station": [f"{_REPO}/{x}" for x in _FILENAMES],
     "event": [f"{_REPO}/{x}" for x in _FILENAMES],
@@ -107,8 +109,8 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
         if self.config.name=="station":
             features=datasets.Features(
                 {
-                    "waveform": datasets.Array3D(shape=(3, self.nt), dtype='float32'),
-                    "phase_pick": datasets.Array3D(shape=(3, self.nt), dtype='float32'),
+                    "waveform": datasets.Array2D(shape=(3, self.nt), dtype='float32'),
+                    "phase_pick": datasets.Array2D(shape=(3, self.nt), dtype='float32'),
                     "event_location": datasets.Sequence(datasets.Value("float32")),
                     "station_location": datasets.Sequence(datasets.Value("float32")),
                 })
@@ -186,54 +188,58 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
 
         for file in filepath:
-            with h5py.File(file, "r") as fp:
-                # for event_id in sorted(list(fp.keys())):
-                for event_id in fp.keys():
-                    event = fp[event_id]
-                    station_ids = list(event.keys())
-                    if self.config.name=="station":
-                        waveforms = np.zeros([3, self.nt], dtype="float32")
-                        phase_pick = np.zeros_like(waveforms)
-                        attrs = event.attrs
-                        event_location = [attrs["longitude"], attrs["latitude"], attrs["depth_km"], attrs["event_time_index"]]
-
-                        for i, sta_id in enumerate(station_ids):
-                            waveforms[:, :self.nt] = event[sta_id][:,:self.nt]
-                            attrs = event[sta_id].attrs
-                            p_picks = attrs["phase_index"][attrs["phase_type"] == "P"]
-                            s_picks = attrs["phase_index"][attrs["phase_type"] == "S"]
-                            phase_pick[:, :self.nt] = generate_label([p_picks, s_picks], nt=self.nt)
-                            station_location = [attrs["longitude"], attrs["latitude"], -attrs["elevation_m"]/1e3]
-
-                            yield f"{event_id}/{sta_id}", {
+            with fsspec.open(file, "rb") as fs:
+                with h5py.File(fs, "r") as fp:
+                    # for event_id in sorted(list(fp.keys())):
+                    event_ids = list(fp.keys())
+                    for event_id in event_ids:
+                        event = fp[event_id]
+                        station_ids = list(event.keys())
+                        if self.config.name=="station":
+                            waveforms = np.zeros([3, self.nt], dtype="float32")
+                            phase_pick = np.zeros_like(waveforms)
+                            attrs = event.attrs
+                            event_location = [attrs["longitude"], attrs["latitude"], attrs["depth_km"], attrs["event_time_index"]]
+
+                            for i, sta_id in enumerate(station_ids):
+                                # waveforms[:, :self.nt] = event[sta_id][:,:self.nt]
+                                waveforms[:, :self.nt] = event[sta_id][:self.nt,:].T
+                                attrs = event[sta_id].attrs
+                                p_picks = attrs["phase_index"][attrs["phase_type"] == "P"]
+                                s_picks = attrs["phase_index"][attrs["phase_type"] == "S"]
+                                # phase_pick[:, :self.nt] = generate_label([p_picks, s_picks], nt=self.nt)
+                                station_location = [attrs["longitude"], attrs["latitude"], -attrs["elevation_m"]/1e3]
+
+                                yield f"{event_id}/{sta_id}", {
+                                    "waveform": torch.from_numpy(waveforms).float(),
+                                    "phase_pick": torch.from_numpy(phase_pick).float(),
+                                    "event_location": torch.from_numpy(np.array(event_location)).float(),
+                                    "station_location": torch.from_numpy(np.array(station_location)).float(),
+                                }
+
+                        elif self.config.name=="event":
+                            waveforms = np.zeros([len(station_ids), 3, self.nt], dtype="float32")
+                            phase_pick = np.zeros_like(waveforms)
+                            attrs = event.attrs
+                            event_location = [attrs["longitude"], attrs["latitude"], attrs["depth_km"], attrs["event_time_index"]]
+                            station_location = []
+                        
+                            for i, sta_id in enumerate(station_ids):
+                                # waveforms[i, :, :self.nt] = event[sta_id][:,:self.nt]
+                                waveforms[i, :, :self.nt] = event[sta_id][:self.nt,:].T
+                                attrs = event[sta_id].attrs
+                                p_picks = attrs["phase_index"][attrs["phase_type"] == "P"]
+                                s_picks = attrs["phase_index"][attrs["phase_type"] == "S"]
+                                phase_pick[i, :, :] = generate_label([p_picks, s_picks], nt=self.nt)
+                                station_location.append([attrs["longitude"], attrs["latitude"], -attrs["elevation_m"]/1e3])
+                            
+                            yield event_id, {
                                 "waveform": torch.from_numpy(waveforms).float(),
                                 "phase_pick": torch.from_numpy(phase_pick).float(),
                                 "event_location": torch.from_numpy(np.array(event_location)).float(),
                                 "station_location": torch.from_numpy(np.array(station_location)).float(),
                             }
 
-                    elif self.config.name=="event":
-                        waveforms = np.zeros([len(station_ids), 3, self.nt], dtype="float32")
-                        phase_pick = np.zeros_like(waveforms)
-                        attrs = event.attrs
-                        event_location = [attrs["longitude"], attrs["latitude"], attrs["depth_km"], attrs["event_time_index"]]
-                        station_location = []
-                    
-                        for i, sta_id in enumerate(station_ids):
-                            waveforms[i, :, :self.nt] = event[sta_id][:,:self.nt]
-                            attrs = event[sta_id].attrs
-                            p_picks = attrs["phase_index"][attrs["phase_type"] == "P"]
-                            s_picks = attrs["phase_index"][attrs["phase_type"] == "S"]
-                            phase_pick[i, :, :] = generate_label([p_picks, s_picks], nt=self.nt)
-                            station_location.append([attrs["longitude"], attrs["latitude"], -attrs["elevation_m"]/1e3])
-                        
-                        yield event_id, {
-                            "waveform": torch.from_numpy(waveforms).float(),
-                            "phase_pick": torch.from_numpy(phase_pick).float(),
-                            "event_location": torch.from_numpy(np.array(event_location)).float(),
-                            "station_location": torch.from_numpy(np.array(station_location)).float(),
-                        }
-
 
 def generate_label(phase_list, label_width=[150, 150], nt=8192):