106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
import pandas as pd
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import h5py
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#import os
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import sys
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import numpy as np
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def is_wrapped(value):
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"""returns True if value is contained in a 1 by 1 array, or False otherwise."""
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if not isinstance(value,np.ndarray):
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return False
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elif sum(value.shape)==2:
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return True
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else:
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return False
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def read_hdf5_as_dataframe(filename):
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with h5py.File(filename,'r') as file:
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# Define group's attributes and datasets. This should hold
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# for all groups. TODO: implement verification and noncompliance error if needed.
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group_list = list(file.keys())
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group_attrs = list(file[group_list[0]].attrs.keys())
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#
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column_attr_names = [item[item.find('_')+1::] for item in group_attrs]
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column_attr_names_idx = [int(item[4:(item.find('_'))]) for item in group_attrs]
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group_datasets = list(file[group_list[0]].keys())
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#
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column_dataset_names = [file[group_list[0]][item].attrs['column_name'] for item in group_datasets]
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column_dataset_names_idx = [int(item[2:]) for item in group_datasets]
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# Define data_frame as group_attrs + group_datasets
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#pd_series_index = group_attrs + group_datasets
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pd_series_index = column_attr_names + column_dataset_names
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output_dataframe = pd.DataFrame(columns=pd_series_index,index=group_list)
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for group_key in group_list:
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# Print group_name
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#print(group_key)
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tmp_row = []
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for attr_key in group_attrs:
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#print(type(file[group_key].attrs[attr_key]))
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df_entry = file[group_key].attrs[attr_key][()]
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tmp_row.append(df_entry)
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for ds_key in group_datasets:
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# Check dataset's type by uncommenting the line below
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# print(type(file[group_key][ds_key][()]))
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# Append to list the value of the file at dataset /group/ds
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#tmp_row.append(file[group_key][ds_key][()])
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#tmp_row.append(file[group_key+'/'+ds_key][()])
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tmp_row.append(file[group_key+'/'+ds_key][()])
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# Create pandas Series/measurement
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row = pd.Series(data=tmp_row,index=pd_series_index, name = group_key)
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output_dataframe.loc[group_key,:] = row
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return output_dataframe
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def read_hdf5_as_dataframe_v2(filename):
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"""contructs dataframe by filling out entries columnwise. This way we can ensure homogenous data columns"""
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with h5py.File(filename,'r') as file:
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# Define group's attributes and datasets. This should hold
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# for all groups. TODO: implement verification and noncompliance error if needed.
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group_list = list(file.keys())
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group_attrs = list(file[group_list[0]].attrs.keys())
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#
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column_attr_names = [item[item.find('_')+1::] for item in group_attrs]
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column_attr_names_idx = [int(item[4:(item.find('_'))]) for item in group_attrs]
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group_datasets = list(file[group_list[0]].keys()) if not 'DS_EMPTY' in file[group_list[0]].keys() else []
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#
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column_dataset_names = [file[group_list[0]][item].attrs['column_name'] for item in group_datasets]
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column_dataset_names_idx = [int(item[2:]) for item in group_datasets]
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# Define data_frame as group_attrs + group_datasets
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#pd_series_index = group_attrs + group_datasets
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pd_series_index = column_attr_names + column_dataset_names
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output_dataframe = pd.DataFrame(columns=pd_series_index,index=group_list)
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tmp_col = []
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for meas_prop in group_attrs + group_datasets:
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if meas_prop in group_attrs:
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column_label = meas_prop[meas_prop.find('_')+1:]
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tmp_col = [file[group_key].attrs[meas_prop][()][0] for group_key in group_list]
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else:
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column_label = file[group_list[0] + '/' + meas_prop].attrs['column_name']
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tmp_col = [file[group_key + '/' + meas_prop][()][0] for group_key in group_list]
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output_dataframe.loc[:,column_label] = tmp_col
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return output_dataframe
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