diff --git a/pysp2/util/deadtime.py b/pysp2/util/deadtime.py
index 5888529..1474faf 100644
--- a/pysp2/util/deadtime.py
+++ b/pysp2/util/deadtime.py
@@ -1,21 +1,22 @@
 import numpy as np
 import xarray as xr
 
+
 def deadtime(my_binary, my_ini):
     """
     Function to calculate Deadtime Bias in the SP2 data as published in
-    Joshua. P. Schwarz, J. M. Katich, S. L. Lee, D. S. Thomson & L. A. Watts 
-    (2022) “Invisible bias” in the single particle soot photometer due to 
-    trigger deadtime, Aerosol Science and Technology, 56:7, 623-635, 
-    DOI: 10.1080/02786826.2022.2064265 
-    
+    Joshua. P. Schwarz, J. M. Katich, S. L. Lee, D. S. Thomson & L. A. Watts
+    (2022) “Invisible bias” in the single particle soot photometer due to
+    trigger deadtime, Aerosol Science and Technology, 56:7, 623-635,
+    DOI: 10.1080/02786826.2022.2064265
+
 
     Parameters
     ----------
     my_binary : xarrray Dataset
         xarray Dataset that is the output of pysp2.util.gaussian_fit(my_sp2b, my_ini)
         Dataset with gaussian fits, peak heights etc.
-        
+
     my_ini : dict
         The .ini file loaded as a dict. my_ini=pysp2.io.read_config(ini_file)
 
@@ -23,57 +24,67 @@ def deadtime(my_binary, my_ini):
     -------
     my_binary : xarrray Dataset
         Returns the input xarray dataset with one additional variable:
-        DeadtimeRelativeBias. This can be used to correct for Deadtime Bias 
-        in the SP2 at high concentrations. 
+        DeadtimeRelativeBias. This can be used to correct for Deadtime Bias
+        in the SP2 at high concentrations.
     """
-    #numpy array to store the relative bias in
-    bias = np.zeros(my_binary.sizes['event_index'], )
-    #scattering triggered
-    scatter_triggered = np.any(np.isfinite(np.vstack((my_binary['PkHt_ch0'].values,
-                                                    my_binary['PkHt_ch4'].values))), axis=0)
-    #incandesence triggered
-    incandesence_triggered = np.any(np.isfinite(np.vstack((my_binary['PkHt_ch1'].values,
-                                                    my_binary['PkHt_ch5'].values))), axis=0)
-    digitization_rate = int(my_ini['Acquisition']['Samples/Sec'])
-    buffer_length = int(my_ini['Acquisition']['Scan Length']) #how many data points in one buffer
-    buffer_length_seconds = buffer_length/digitization_rate #in seconds
-    event_length_points = int(my_ini['Acquisition']['Points per Event']) 
-    pretrigger_length_points = int(my_ini['Acquisition']['Pre-Trig Points']) 
-    #intentialanny skipped particles, notation from Eq. 2 in doi:10.1080/02786826.2022.2064265
-    S_S = int(my_ini['Acquisition']['1 of every']) 
-    #find locations where the buffer changes (i.e. where the TimeWave values changes)
-    #get unique time stamps
-    unique_buffer_time_stamps = np.unique(my_binary['TimeWave'].values) 
-    #find locations where those unique time stamps should be inserted
-    ind = np.searchsorted(my_binary['TimeWave'].values, unique_buffer_time_stamps) 
-    #add the end to the list of indexes if needed
-    if ind[-1] < my_binary.sizes['event_index']:
-        ind = np.append(ind, my_binary.sizes['event_index'])
-    for i in range(len(ind)-1):
-        #from index
+    # numpy array to store the relative bias in
+    bias = np.zeros(my_binary.sizes["event_index"], dtype="float32")
+    # scattering triggered
+    scatter_triggered = np.any(
+        np.isfinite(
+            np.vstack((my_binary["PkHt_ch0"].values, my_binary["PkHt_ch4"].values))
+        ),
+        axis=0,
+    )
+    # incandesence triggered
+    incandesence_triggered = np.any(
+        np.isfinite(
+            np.vstack((my_binary["PkHt_ch1"].values, my_binary["PkHt_ch5"].values))
+        ),
+        axis=0,
+    )
+    digitization_rate = float(my_ini["Acquisition"]["Samples/Sec"])
+    buffer_length = float(
+        my_ini["Acquisition"]["Scan Length"]
+    )  # how many data points in one buffer
+    buffer_length_seconds = buffer_length / digitization_rate  # in seconds
+    event_length_points = float(my_ini["Acquisition"]["Points per Event"])
+    pretrigger_length_points = float(my_ini["Acquisition"]["Pre-Trig Points"])
+    # intentialanny skipped particles, notation from Eq. 2 in doi:10.1080/02786826.2022.2064265
+    S_S = float(my_ini["Acquisition"]["1 of every"])
+    # find locations where the buffer changes (i.e. where the TimeWave values changes)
+    # get unique time stamps
+    unique_buffer_time_stamps = np.unique(my_binary["TimeWave"].values)
+    # find locations where those unique time stamps should be inserted
+    ind = np.searchsorted(my_binary["TimeWave"].values, unique_buffer_time_stamps)
+    # add the end to the list of indexes if needed
+    if ind[-1] < my_binary.sizes["event_index"]:
+        ind = np.append(ind, my_binary.sizes["event_index"])
+    for i in range(len(ind) - 1):
+        # from index
         fr = ind[i]
-        #to index
-        to = ind[i+1]
-        #number of scattering particle windows saved in the buffer
-        N_S = scatter_triggered[fr:to].sum()
-        #number of incandesence particle windows saved in the buffer
-        N_I = incandesence_triggered[fr:to].sum()
-        #length of time, in seconds, for a single measurement of the digitizer
-        t_b = 1. / digitization_rate
-        #"PW is the total number of data points (for each channel of data) in the window"
+        # to index
+        to = ind[i + 1]
+        # number of scattering particle windows saved in the buffer
+        N_S = scatter_triggered[fr:to].sum().astype('float64')
+        # number of incandesence particle windows saved in the buffer
+        N_I = incandesence_triggered[fr:to].sum().astype('float64')
+        # length of time, in seconds, for a single measurement of the digitizer
+        t_b = 1.0 / digitization_rate
+        # "PW is the total number of data points (for each channel of data) in the window"
         P_W = event_length_points
-        #Buffer length in seconds
-        T_B = buffer_length_seconds     
-        #Fraction triggered (Eq. 2) in doi:10.1080/02786826.2022.2064265
-        F_T = (N_S * S_S+N_I) * P_W * t_b / T_B
-        #pretrigger length data points
+        # Buffer length in seconds
+        T_B = buffer_length_seconds
+        # Fraction triggered (Eq. 2) in doi:10.1080/02786826.2022.2064265
+        F_T = (N_S * S_S + N_I) * P_W * t_b / T_B
+        # pretrigger length data points
         P_PT = pretrigger_length_points
-        #T_P is the total points in each window
+        # T_P is the total points in each window
         T_P = event_length_points
-        #relative bias as in Eq. 3 in doi:10.1080/02786826.2022.2064265
+        # relative bias as in Eq. 3 in doi:10.1080/02786826.2022.2064265
         B_rel = -(P_PT / T_P) * F_T
-        #save the bias value to the whole buffer
+        # save the bias value to the whole buffer
         bias[fr:to] = B_rel
-    #add the bias containing array to the xarray
-    my_binary['DeadtimeRelativeBias'] = xr.DataArray(bias, dims=['event_index'])
+    # add the bias containing array to the xarray
+    my_binary["DeadtimeRelativeBias"] = xr.DataArray(bias, dims=["event_index"])
     return my_binary