def pressurepreprocesstest(pressure,res_pressure,fcutA1,fcutB1,fcutB2,fcutA2,fs,order,Atm_OST,stormname,begintime,endtime,plot=None):
    
    
    ### Calibration from Volt to Pascal ###
    #seperated calibration for each column: !! fill in calibration factors !!
    
    pressure_cal = pressure.copy(deep=True) #to avoid overwriting the original pressure dataframe (A1,B1,B2,A2)
    calibrationlist = [7082445.026, 7076486.04, 7201259.142, 7226444.017,   7200909.636, 7282744.547, 7303428.346, 7315431.079,  7141262.614, 7066365.568, 7098082.242, 7102981.852,
                       7209403.272, 7165352.954, 7184994.007, 7095088.034]
    intersectionpointlist = [ -29639.74618 ,  -29601.42945 , -32664.24549 ,-32754.60478, -31204.01428, -33014.60364 , -33870.19357, -33691.06106,  -27616.95765,  -28568.86298, -29043.3229, -29745.91237, -31386.73127 , -30248.676, -30383.69895, -28766.79008 ]
    #fill in calibration factors for every sensor 
    for i,j in zip(pressure_cal,range(16)):      #multiply every column with its calibration factor
        pressure_cal[i] = calibrationlist[j]*pressure_cal[i] + intersectionpointlist[j]
        
  
    

    
    if stormname == &#39;Storm 25/02/2023&#39;:
    ##  SPECIFIC PER STORM: 25/02/2023
        Atm_Ost_selected = Atm_OST[12:37].reset_index(drop=True)

    if stormname == &#39;Storm 10/03/2023&#39;:
    ##  SPECIFIC PER STORM: 10/03/2023
        Atm_Ost_selected = Atm_OST[72:115].reset_index(drop=True)

    if stormname == &#39;Storm 25/03/2023&#39;:
    ##  SPECIFIC PER STORM: 10/03/2023
        Atm_Ost_selected = Atm_OST[72:115].reset_index(drop=True)
        
    if stormname == &#39;Storm 23/11/2023&#39;:
    ##  SPECIFIC PER STORM: 10/03/2023
        Atm_Ost_selected = Atm_OST[108:171].reset_index(drop=True)
    if stormname == &#39;Storm 24/11/2023&#39;:
    ##  SPECIFIC PER STORM: 10/03/2023
        Atm_Ost_selected = Atm_OST[177:244].reset_index(drop=True)
    
    #TimeOST =[datetime.strptime(begintime,&quot;%d-%m-%Y %H:%M&quot;) + timedelta(minutes=i*10) for i in Atm_OST_selected.index]
    res_pressure[&#39;p_atm Ostend&#39;] = np.nan
    
    
    i=0
    j=0
    while j &lt; len(Atm_Ost_selected)-1:
        res_pressure.iloc[i,6] = Atm_Ost_selected.iloc[j,1] 
        i = i + 10*60*10
        j = j + 1
    
    res_pressure[&#39;p_atm Ostend&#39;] = res_pressure[&#39;p_atm Ostend&#39;].interpolate(method=&#39;linear&#39;)
    
    #from absolute to relative pressure
    p_atm_Ost = res_pressure[&#39;p_atm Ostend&#39;]*100
   
    
    pressure_rel = pressure_cal.copy(deep=True)
    
    pressure_rel.columns = [&#39;A1P1&#39;,&#39;A1P2&#39;,&#39;A1P3&#39;,&#39;A1P4&#39;,&#39;B1P1&#39;,&#39;B1P2&#39;,&#39;B1P3&#39;,&#39;B1P4&#39;,&#39;B2P1&#39;, &#39;B2P2&#39;, &#39;B2P3&#39;, &#39;B2P4&#39;,&#39;A2P1&#39;,&#39;A2P2&#39;,&#39;A2P3&#39;,&#39;A2P4&#39;]       
   
   

    for column in pressure_rel:
        pressure_rel[column] = pressure_rel[column].subtract(p_atm_Ost,axis=0)
        print(pressure_rel[column])

    ### Low pass filtering ###
    
    #default: Butterworth filter
    P_A1 = pressure_rel[[&#39;A1P1&#39;,&#39;A1P2&#39;,&#39;A1P3&#39;,&#39;A1P4&#39;]]
    P_A1_filt = pp.df_LPFiltButter(P_A1, fcutA1,fs,order)
    P_B1 = pressure_rel[[&#39;B1P1&#39;,&#39;B1P2&#39;,&#39;B1P3&#39;,&#39;B1P4&#39;]]
    P_B1_filt = pp.df_LPFiltButter(P_B1, fcutB1,fs,order)
    P_B2 = pressure_rel[[&#39;B2P1&#39;,&#39;B2P2&#39;,&#39;B2P3&#39;,&#39;B2P4&#39;]]
    P_B2_filt = pp.df_LPFiltButter(P_B2, fcutB2,fs,order)
    P_A2 = pressure_rel[[&#39;A2P1&#39;,&#39;A2P2&#39;,&#39;A2P3&#39;,&#39;A2P4&#39;]]
    P_A2_filt = pp.df_LPFiltButter(P_A2,fcutA2,fs,order)
    
 
    ### Averaging over the four pressure sensors per overtopping box ###
    
    P_A1_avg = P_A1_filt.mean(axis=1)
    P_B1_avg = P_B1_filt.mean(axis=1)
    P_B2_avg = P_B2_filt.mean(axis=1)
    P_A2_avg = P_A2_filt.mean(axis=1)

    # add different averaged & filtered pressure signals into one DataFrame
    filtpressure = pd.concat([P_A1_filt,P_B1_filt,P_B2_filt,P_A2_filt],axis=1)
    filtpressure.columns = [&#39;A1P1&#39;,&#39;A1P2&#39;,&#39;A1P3&#39;,&#39;A1P4&#39;,&#39;B1P1&#39;,&#39;B1P2&#39;,&#39;B1P3&#39;,&#39;B1P4&#39;,&#39;B2P1&#39;, &#39;B2P2&#39;, &#39;B2P3&#39;, &#39;B2P4&#39;,&#39;A2P1&#39;,&#39;A2P2&#39;,&#39;A2P3&#39;,&#39;A2P4&#39;]
    filtpressure_avg = pd.concat([P_A1_avg,P_B1_avg,P_B2_avg,P_A2_avg],axis=1)
    filtpressure_avg.columns = [&#39;P_A1&#39;,&#39;P_B1&#39;,&#39;P_B2&#39;,&#39;P_A2&#39;]
 
    
    ### Optional plotting ###
    time_data = pd.to_datetime(Atm_Ost_selected.iloc[:, 0])
    pressure_data = Atm_Ost_selected.iloc[:, 1]

    # Define time steps 
    fs = 10  # sampling frequency
     Time = [begintime + timedelta(sec for i in range(len(pressure))]

    if plot == True:
        # Calculate the time difference between begintime and endtime
        time_diff = endtime - begintime
        num_hours = int(time_diff.total_seconds() / 3600)
    
        # Generate x-axis ticks with appropriate spacing
        x_ticks = [begintime + timedelta(hours=i) for i in range(num_hours+1)]
        
        # Plot both air pressure data and calibrated, filtered, and averaged pressure data on the same graph
        plt.figure(figsize=(12, 8))
        
        # Plot air pressure data
         plt.plot(time_data, pressure_data, color=&#39;purple&#39;, line label=&#39;Air Pressure Data&#39;)
        
        # Plot calibrated, filtered, and averaged pressure data
        plt.plot(Time, pressure_rel[&#39;A1P1&#39;], linewidth=0.3, color=&#39;red&#39;, label=&#39;Calibrated raw data of sensor P1&#39;)
        plt.plot(Time, pressure[&#39;A1P1&#39;], linewidth=0.3, color=&#39;green&#39;, label=&#39;Raw data of sensor P1&#39;)
        plt.plot(Time, P_A1_filt[&#39;A1P1&#39;], linewidth=1.5, color=&#39;blue&#39;, label=&#39;Filtered pressures of sensor P1&#39;)
        plt.plot(Time, P_A1_avg, linewidth=1.5, color=&#39;black&#39;, label=&#39;Filtered pressures (average of the four sensors P1, P2, P3 and P4)&#39;)    
         plt.xlabel(&#39;Time&#39;, f
         plt.ylabel(&#39;Pressure&#39;, f
         plt.title(&#39;Comparison of Air Pressure Data with Calibrated, Filtered, and Averaged Pressure Data&#39;, f
         plt.xticks(x_ticks, rotati  # Rotate x-axis labels for better readability
        plt.gca().xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter(&#39;%Y-%m-%d %H:%M:%S&#39;)) # format the x-axis ticks
        plt.legend(prop={&#39;size&#39;: 10})
        plt.grid(True)
        plt.tight_layout()
        plt.show()
    return filtpressure,filtpressure_avg
    
#%%

def find_occurrences(series_of_sums, begintime, target_values, percentages, plot=False):
    # Initialize dictionaries to store the times when each target value is triggered
    occurrences_dict = {target_value: [] for target_value in target_values}
    range_triggered = {target_value: [] for target_value in target_values}
    
    # Calculate the time sampling frequency
    fs = 10
     Time = [begintime + timedelta(sec for i in range(len(series_of_sums))]
    
    # Initialize a list to store the total trigger status at each timestamp
    total_trigger_status = [0] * len(series_of_sums)
    
    # Iterate through each sum in the series
    for target_value_index, sum_value in enumerate(series_of_sums):
        # Check if the sum is close to any of the target values
        for target_value, percentage in zip(target_values, percentages):
            if abs(sum_value - target_value) &lt;= percentage * target_value:  # Check if the difference is within the specified percentage
                range_triggered[target_value].append(Time[target_value_index])  # Append the time
                occurrences_dict[target_value].append(Time[target_value_index])  # Append the time
                total_trigger_status[target_value_index] = 1  # Set total trigger status to 1
    
    # Find transitions in total trigger status
     transiti
    for i in range(1, len(total_trigger_status)):
        if total_trigger_status[i - 1] == 0 and total_trigger_status[i] != 0:
            transitions.append(i)  # Store index of transition from 0 to non-zero
        elif total_trigger_status[i - 1] != 0 and total_trigger_status[i] == 0:
            transitions.append(i - 1)  # Store index of transition from non-zero to 0 (adjusted index)
    
    if plot:
        plt.figure(figsize=(10, 6))
        #for target_value, occurrences in occurrences_dict.items():
            #plt.scatter(occurrences, [1] * len(occurrences), color=&#39;blue&#39;, marker=&#39;o&#39;, label=f&#39;Target Value {target_value}&#39;)
        
        # Plot vertical lines for transitions from 0 to non-zero
        for transition_index in transitions:
             transiti
            if total_trigger_status[transition_index-1] == 0:
                 plt.axvline(x=transition_time, color=&#39;green&#39;, line linewidth=0.5)  # Add vertical line for 0 to non-zero transition
            else:
                 plt.axvline(x=transition_time, color=&#39;red&#39;, line linewidth=0.5)  # Add vertical line for non-zero to 0 transition
        
        plt.xlabel(&#39;Time&#39;)
        plt.title(&#39;Transitions in Trigger Status from 0 to a Non-zero Number and Vice Versa&#39;)
        plt.grid(True)
         plt.xticks(rotati
        plt.gca().xaxis.set_major_formatter(mdates.DateFormatter(&#39;%Y-%m-%d %H:%M:%S&#39;))
        plt.tight_layout()
        plt.show()
         
    return occurrences_dict, range_triggered