Pulling hydrophone data, not tested

compute_spectrogram_lf.py

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+import os
+import sys
+
+#cwd = os.getcwd()
+#ooipy_dir = os.path.dirname(cwd) + '/ooipy'
+#sys.path.append(ooipy_dir)
+
+from matplotlib import pyplot as plt
+import datetime
+import numpy as np
+from obspy import read,Stream, Trace
+from obspy.core import UTCDateTime
+
+from obspy import read
+import pickle
+import scipy
+from gwpy.timeseries import TimeSeries
+import seaborn as sns
+
+from ooipy.request import hydrophone_request
+import gwpy
+
+from datetime import timedelta
+
+import xarray
+
+nodes = ['Axial_Base', 'Central_Caldera','Eastern_Caldera','Frequency','Slope_Base','Southern_Hydrate']
+years = [2015, 2016, 2017, 2018, 2019, 2022] # Add 2023 for full year
+
+master_file_names = []
+
+kstart=0
+
+for node_count, node in enumerate(nodes):
+    for year_count, year in enumerate(years):
+        start_time_first = datetime.datetime(year,1,1,0,0,0)
+
+        # set num of hours in year depending on leap
+        if (year % 4) == 0:
+            num_hours = 8784
+        else:
+            num_hours = 8760
+
+        if (node_count == 0) & (year_count==0):
+            start_index = kstart
+        else:
+            start_index = 0
+        for k in range(start_index,num_hours):
+            start_time = start_time_first + timedelta(hours=k)
+            end_time = start_time_first + timedelta(hours=k+1)
+            print(f'Computing Spectrogram for {node} | {start_time} - {end_time}: {k}')
+            print('   Downloading Data...')
+            hdata = hydrophone_request.get_acoustic_data_LF(start_time, end_time, node, channel='HNZ')
+            print('   Computing Spectrogram...')
+            try:
+                # 1 minute average PSD
+                spec = hdata.compute_spectrogram(avg_time=60, L=512, average_type='mean')
+            except:
+                print('   No Data for time segment')
+                spec = None
+            print('   Writing to File...')
+
+            # Write to Pickle File
+            # Adjust filename to work with local machine's directories
+            filename = "/Volumes/Ocean_Acoustics/Spectrograms/seismometers/" +node+ "/" + str(year) +f"/spectrogram{k:03}.pkl"
+            with open(filename,'wb') as f:
+                pickle.dump(spec, f)
+            master_file_names.append(filename)
+
+
+## Part 2 - Converting data to zarr files
+
+## Iterates over files, reads pkl data (dictonary). Then, creates an xarray object for the data
+## and makes it into a zarr file using the method: to_zarr. 
+for i in master_file_names:
+    with open(i, 'r') as f: 
+        data = pickle.load(f)
+        temp = xarray.data.to_xarray()
+        xarray.temp.to_zarr(append_dim="")  # append_dim to add to same diretory
+
+
+