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Add correlation between ions extracted and time

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rasmusvt 2022-10-23 20:23:59 +02:00
parent f7ba19b103
commit cefd7a2edd
1 changed files with 29 additions and 7 deletions
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36
nafuma/xanes/io.py
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@ -400,10 +400,12 @@ def read_metadata(data: dict, options={}) -> dict:
timestamps = new_times
metadata = {'time': timestamps, 'temperature': temperatures}
# Match timestamps against electrochemistry-data
# TODO This could be generalised to match up against any other dataset with timestamps.
if 'cycles' in data.keys():
ions = []
ions, specific_capacity = [], []
i = 0
for timestamp in timestamps:
if timestamp < 0:
@ -420,26 +422,46 @@ def read_metadata(data: dict, options={}) -> dict:
if all([x==x for x in closest_chg]):
print(f'Charge, cycle {i}')
ions.append(np.mean([data['cycles'][i][0]['ions'].loc[data['cycles'][i][0].index == closest_chg[0]], data['cycles'][i][0]['ions'].loc[data['cycles'][i][0].index == closest_chg[1]]]))
specific_capacity.append(np.mean([data['cycles'][i][0]['specific_capacity'].loc[data['cycles'][i][0].index == closest_chg[0]], data['cycles'][i][0]['specific_capacity'].loc[data['cycles'][i][0].index == closest_chg[1]]]))
continue
elif all([x==x for x in closest_dchg]):
print(f'Discharge, cycle {i}')
ions.append(np.mean([data['cycles'][i][1]['ions'].loc[data['cycles'][i][1].index == closest_dchg[0]], data['cycles'][i][1]['ions'].loc[data['cycles'][i][1].index == closest_dchg[1]]]))
specific_capacity.append(np.mean([data['cycles'][i][1]['specific_capacity'].loc[data['cycles'][i][1].index == closest_dchg[0]], data['cycles'][i][1]['specific_capacity'].loc[data['cycles'][i][1].index == closest_dchg[1]]]))
continue
elif (closest_chg[1]!=closest_chg[1]) and (closest_dchg[0]!=closest_dchg[0]):
print('Rest step!')
elif aux.isnan(closest_chg[1]) and aux.isnan(closest_dchg[0]):
ions.append(np.nan)
specific_capacity.append(np.nan)
continue
else:
print('Rest step, new cycle!')
ions.append(np.nan)
specific_capacity.append(np.nan)
i += 1
if i > len(data['cycles'])-1:
break
for i, (ion, cap) in enumerate(zip(ions, specific_capacity)):
if aux.isnan(ion): # if a resting step, assign a meaningful value
if i < len(ions)-1: # if resting step in the middle of the run, take the mean between the last of previous and first of next run
ions[i] = np.mean([ions[i-1], ions[i+1]])
else: # If last element, set to last values plus the delta between the last two previous measurements
ions[i] = ions[i-1] + (ions[i-1]-ions[i-2])
if aux.isnan(cap) and i < len(specific_capacity)-1: # do same thing for specific capacity
if i < len(specific_capacity)-1:
specific_capacity[i] = np.mean([specific_capacity[i-1], specific_capacity[i+1]])
else:
specific_capacity[i] = specific_capacity[i-1] + (specific_capacity[i-1]-specific_capacity[i-2])
metadata = {'time': timestamps, 'temperature': temperatures}
metadata['ions'] = ions
metadata['specific_capacity'] = specific_capacity
return metadata