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Hi, I do not know any "qcodes" way... If speed is really a concern, I would do something like this: import sqlite3
import json
databaseAbsPath = '' # Your database absolute path
runId = 1 # Your run id
conn = sqlite3.connect(databaseAbsPath)
conn.row_factory = sqlite3.Row
cur = conn.cursor()
# Get runs infos
cur.execute("SELECT snapshot FROM 'runs' WHERE run_id="+str(runId))
row = cur.fetchall()[0]
# Create nice dict object from a string
d = json.loads(row['snapshot'])
cur.close()
conn.close() From their, d should contain a python dict that you can handle. |
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I have a .db file with over 30,000 datasets.
I need to extract some information from the snapshot and metadata of each data set.
The fastest method I have found is to load the dataset:
experiment.load_dataset()
This operation takes 0.2 seconds for each dataset, which is takes 30 minutes for 10,000 datasets.
I am only extracting one number from the snapshot, and one from the metadata, so it might not be necessary to load the entire dataset each time.
Is there a way to speed this up?
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