# Author:       David Tschan
# Institute:    Paul-Scherrer Institute, Laboratory for Atmospheric Chemistry, Multiphase Chemistry Group, 
#               Forschungsstrasse 111, 5232 Villigen PSI, Switzerland 
# Role:         Master Student
# License:      n/a
# Date:         2026

### ----------------------------------------------------------------------------------------------------------------------------------- ###
### SUBMODULE DOCSTRING

"""The docstring for this submodule
"""

### ----------------------------------------------------------------------------------------------------------------------------------- ###
### IMPORTS AND CONFIGURATIONS

import numpy as np
import pandas as pd
from defs.Helper import DATFile
from defs.SMPS import OFRAnalyser

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### OzoneAnalyser.py

class OzoneAnalyser():

    """The docstring for this class
    
    Description: Bla bla bla

    Attributes
    ----------
    word: str
        A word

    Methods
    -------
    __init__: HelloWorld
        The class constructor
    """

    def __init__(self, file: DATFile, on_time_interval):

        self.file = file
        self.on_time_interval = on_time_interval
        self.averaged_data = None

    def GetData(self):

        self.file.read()

    def AverageWithSMPS(self, smps_analyser: OFRAnalyser, cut_edges: int, subtract_baseline = ['yes', 5], normalise_flow = True):

        avg_ozone = []
        mprr_data = []
        vflo_data = []

        local_smps_data = smps_analyser.data[(smps_analyser.data['Starting Time (-)'] >= smps_analyser.on_time_interval[0]) &
                                             (smps_analyser.data['Starting Time (-)'] <= smps_analyser.on_time_interval[1])].reset_index()

        baseline = None

        for index, data in local_smps_data.iterrows():

            start = data['Starting Time (-)']

            if index == 0:

                if subtract_baseline[0] == 'yes':
                    baseline_start = start - pd.Timedelta(minutes = int(subtract_baseline[1]))
                    baseline_ozone_data = self.file.data[(self.file.data['timestamp'] >= baseline_start) &
                                                         (self.file.data['timestamp'] <= start)]
                    baseline = np.average(baseline_ozone_data['ozone'])

            if index in list(range(cut_edges)):
                    continue

            stop = start + pd.Timedelta(seconds = data['Sampling Time (s)'])

            mprr_data.append(data['Mass Production Rate (kg/s)'])
            vflo_data.append(data['Filter Flow (m^3/s)'] * 1E-3 / 60)

            local_ozone_data = self.file.data[(self.file.data['timestamp'] >= start) &
                                              (self.file.data['timestamp'] <= stop)]

            avg_ozone.append(np.average(local_ozone_data['ozone']))

        for sub_index in list(range(cut_edges)):
                del avg_ozone[-(sub_index + 1)]
                del mprr_data[-(sub_index + 1)]
                del vflo_data[-(sub_index + 1)]

        out_mprr_data = np.array(mprr_data)

        if subtract_baseline[0] == 'yes':
                out_avg_ozone = np.array([baseline - value for value in avg_ozone])
        elif subtract_baseline[0] != 'yes':
                out_avg_ozone = np.array([value for value in avg_ozone])

        if normalise_flow == True:
            out_mprr_data = np.divide(out_mprr_data, np.array(vflo_data))
            key = 'Density (kg m^-3)'
        else:
            key = 'Mass Production Rate (kg s^-1)'

        self.averaged_data = pd.DataFrame({'Ozone (ppb)': out_avg_ozone, key: out_mprr_data})