44 lines
1.1 KiB
Python
44 lines
1.1 KiB
Python
import h5py
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import pandas as pd
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file = "ltcc_current.h5"
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def print_attrs(name, obj):
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print(f"\nAttributes for {name}:")
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for key, val in obj.attrs.items():
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print(f" {key}: {val}")
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with h5py.File(file, "r") as h5_file:
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h5_file.visititems(print_attrs)
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# Dict to hold DFs 'sex', 'tag' & 'spid'
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dfs_by_sex_tag_spid = {}
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with h5py.File(file, "r") as h5_file:
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for eid in h5_file.keys():
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attributes = h5_file[eid].attrs
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sex = attributes.get("sex")
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tag = attributes.get("tag")
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spid = attributes.get("spid")
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# Creates a unique key for dict based on sex, tag & spid
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key = f"{sex}_{tag}_{spid}"
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if key not in dfs_by_sex_tag_spid:
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dfs_by_sex_tag_spid[key] = pd.DataFrame()
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row_data = {"experiment_id": eid, "sex": sex, "tag": tag, "spid": spid}
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temp_df = pd.DataFrame([row_data])
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# Append the DF to the appropriate dict entry
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dfs_by_sex_tag_spid[key] = pd.concat(
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[dfs_by_sex_tag_spid[key], temp_df], ignore_index=True
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)
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for key, df in dfs_by_sex_tag_spid.items():
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print(f"DataFrame for {key}:")
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print(df)
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print()
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