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Initial version of data flagging app and some utility functions

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florez_j
2024-08-16 10:20:36 +02:00
parent 0c1c0d04da
commit a017f1fb12
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data_flagging_utils.py Normal file
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import dima.src.hdf5_data_extraction as h5de
from plotly.subplots import make_subplots
import plotly.graph_objs as go
flags_dict = {
"000" : {"flag_label": 'V', "flag_description": "Valid measurement"},
"100" : {"flag_label": 'V', "flag_description": "Checked by data originator. Valid measurement, overrides any invalid flags"},
"110" : {"flag_label": 'V', "flag_description": "Episode data checked and accepted by data originator. Valid measurement"},
"111" : {"flag_label": 'V', "flag_description": "Irregular data checked and accepted by data originator. Valid measurement"},
"456" : {"flag_label": 'I', "flag_description": "Invalidated by data originator"},
"460" : {"flag_label": 'I', "flag_description": "Contamination suspected"},
"559" : {"flag_label": 'V', "flag_description": "Unspecified contamination or local influence, but considered valid"},
"599" : {"flag_label": 'I', "flag_description": "Unspecified contamination or local influence"},
"652" : {"flag_label": 'V', "flag_description": "construction/activity nearby"},
"659" : {"flag_label": 'I', "flag_description": "Unspecified instrument/sampling anomaly"},
"660" : {"flag_label": 'V', "flag_description": "Unspecified instrument/sampling anomaly"},
"999" : {"flag_label": 'I', "flag_description": "Missing measurement, unspecified reason"}
}
def create_loaded_file_figure(file_path):
DataOpsAPI = h5de.HDF5DataOpsManager(file_path)
target_channels = DataOpsAPI.file_obj.attrs['target_channels']['names'][0].decode().split(',')
target_loc = DataOpsAPI.file_obj.attrs['target_channels']['location'][0].decode()
diagnostic_channels = DataOpsAPI.file_obj.attrs['diagnostic_channels']['names'][0].decode().split(',')
diagnostic_loc = DataOpsAPI.file_obj.attrs['diagnostic_channels']['location'][0].decode()
fig = make_subplots(rows=(len(target_channels+diagnostic_channels)-2), cols=1, shared_xaxes=True)
traces = []
trace_idx = 1
dataset = DataOpsAPI.file_obj[target_loc]
time_column = DataOpsAPI.reformat_datetime_column(target_loc,target_channels[0],'%d.%m.%Y %H:%M:%S.%f')
for i in range(1,len(target_channels)):
fig.add_trace(go.Scatter(x = time_column,
y = dataset[target_channels[i]][:],
mode = 'lines',
name = target_channels[i]), row=trace_idx, col=1)
trace_idx = trace_idx + 1
dataset = DataOpsAPI.file_obj[diagnostic_loc]
time_column = DataOpsAPI.reformat_datetime_column(diagnostic_loc,diagnostic_channels[0],'%d.%m.%Y %H:%M:%S')
for i in range(1,len(diagnostic_channels)):
fig.add_trace(go.Scatter(x = time_column,
y = dataset[diagnostic_channels[i]][:],
mode = 'lines',
name = diagnostic_channels[i]), row=trace_idx, col=1)
fig.update_yaxes(row=trace_idx, col=1, type="log")
trace_idx = trace_idx + 1
DataOpsAPI.close_file()
return fig