APOG_public/acsmnode
0
0
Fork 0
mirror of https://gitea.psi.ch/APOG/acsmnode.git synced 2025-06-25 16:55:44 +02:00
Code Issues Packages Projects Releases Wiki Activity

Added correct calibration parameters to calibration factors table

Browse Source
This commit is contained in:
florez_j
2025-03-03 14:23:45 +01:00
parent d3a491ea85
commit def67a0562
1 changed files with 30 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
pipelines/steps/apply_calibration_factors.py
View File

@ -61,13 +61,31 @@ def compute_calibration_factors(data_table, datetime_var_name, calibration_param
calibration_factors_dict = {}
calibration_factors_dict = {datetime_var_name : data_table[datetime_var_name].to_numpy()}
#calibration_factors_dict = {datetime_var_name : data_table[datetime_var_name].to_numpy()}
# Create table to store the factors and parameters
column_params = [datetime_var_name]
column_factors = []
for name in list(calibration_params['default_params'].keys()) + list(calibration_factors['variables'].keys()):
if '_tofware' in name:
column_params.append(name.replace('_tofware','_correct'))
else:
column_factors.append(f'factor_{name}')
print(datetime_var_name, data_table[datetime_var_name].size, len(column_params+column_factors))
tmp_df = pd.DataFrame(data=np.full(shape=(data_table[datetime_var_name].size, len(column_params)+len(column_factors)), fill_value=np.nan),columns=column_params+column_factors)
tmp_df[datetime_var_name] = data_table[datetime_var_name].to_numpy()
# print(tmp_df.head())
for variable_name in calibration_factors['variables']:
#tmp = np.empty(shape=data_table[datetime_var_name].to_numpy().shape)
print(variable_name)
tmp = np.full(shape=data_table[datetime_var_name].shape, fill_value=np.nan)
#tmp = np.full(shape=data_table[datetime_var_name].shape, fill_value=np.nan)
for interval_idx, interval_params in calibration_params['calibration_intervals'].items(): # Fixed typo
@ -84,13 +102,18 @@ def compute_calibration_factors(data_table, datetime_var_name, calibration_param
if denominator == 0:
raise ZeroDivisionError(f"Denominator is zero for '{variable_name}' in interval {t1} - {t2}")
tmp[t1_idx:t2_idx] = numerator / denominator
tmp_df.loc[t1_idx:t2_idx, f'factor_{variable_name}'] = numerator / denominator
for param in column_params:
if param in interval_params:
tmp_df.loc[t1_idx:t2_idx, param] = interval_params[param]
else:
raise ValueError(f"Invalid calibration interval: start_datetime {t1} must be before end_datetime {t2}")
calibration_factors_dict[f'factor_{variable_name}'] = tmp
#calibration_factors_dict[f'factor_{variable_name}'] = tmp
return pd.DataFrame(data=calibration_factors_dict)
#return pd.DataFrame(data=calibration_factors_dict)
return tmp_df
def load_calibration_file(calibration_file):