diff --git a/nuwavdet/nuwavdet.py b/nuwavdet/nuwavdet.py
index da1cd49..802aecf 100644
--- a/nuwavdet/nuwavdet.py
+++ b/nuwavdet/nuwavdet.py
@@ -278,7 +278,7 @@ class Observation:
         correction_poiss = np.random.poisson(corr*array, corr.shape)
         return array + correction_poiss
 
-    def wavdecomp(self, mode='gauss', thresh=False, occ_coeff=False):
+    def wavdecomp(self, mode='gauss', thresh=0, occ_coeff=False):
         """
         Performs a wavelet decomposition of image.
         """
@@ -338,11 +338,11 @@ class Observation:
             current_dir = os.path.dirname(__file__)
             pixpos = Table(fits.getdata(os.path.join(current_dir, 'pixpos', f'nu{self.det}pixpos20100101v007.fits'), i+1))
             pixpos = pixpos[pixpos['REF_DET1X'] != -1]
-            
+
             ref_condition = np.zeros(len(pixpos['REF_DET1X']), dtype=bool)
             for idx, (x, y) in enumerate(zip(pixpos['REF_DET1X'], pixpos['REF_DET1Y'])):
                 ref_condition[idx] = np.logical_and(np.equal(x, x_region), np.equal(y, y_region)).any()
-            
+
             positions = np.array((pixpos['RAWX'][ref_condition], pixpos['RAWY'][ref_condition]))
             if sum(ref_condition) != 0:
                 positions = np.unique(positions, axis=1)
@@ -392,7 +392,6 @@ def process(obs_path, thresh):
     Arguments: path to the file of interest and threshold,
     e.g. process('D:\\Data\\obs_cl.evt', (3, 2))
     """
-    bin_num = 6
 
     table = {
         'obs_id': [], 'detector': [], 'ra': [], 'dec': [],
@@ -407,17 +406,18 @@ def process(obs_path, thresh):
                              dec=obs.dec*u.deg,
                              frame='fk5').transform_to('galactic')
         lon, lat = sky_coord.l.value, sky_coord.b.value
-        rem_signal, rem_area, poiss_comp, rms = np.zeros((4, 2**bin_num))
+        useful_bin_num = 6
+        rem_signal, rem_area, poiss_comp, rms = np.zeros((4, 2**useful_bin_num))
         region = np.zeros(obs.data.shape, dtype=bool)
         region_raw = -1
         rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
         masked_obs = np.ma.masked_array(obs.data, mask=region)
-        good_lvl = np.zeros(bin_num, dtype=bool)
+        good_lvl = np.zeros(useful_bin_num, dtype=bool)
         if obs.exposure > 1000:
             wav_obs = obs.wavdecomp('atrous', thresh, occ_coeff=True)
             wav_sum = wav_obs[2:-1].sum(0)
             occ_coeff = obs.get_coeff()
-            binary_arr = binary_array(bin_num)
+            binary_arr = binary_array(useful_bin_num)
             good_idx = len(binary_arr) - 1
 
             for idx, lvl in enumerate(binary_arr):
@@ -456,7 +456,7 @@ def process(obs_path, thresh):
                         obs.dec,
                         lon,
                         lat,
-                        obs.time_start, 
+                        obs.time_start,
                         obs.exposure,
                         masked_obs.mean()/obs.exposure,  # count rate
                         1 - rem_region.sum()/np.logical_not(obs.data.mask).sum(),  # rem_area
@@ -482,7 +482,7 @@ def process(obs_path, thresh):
                         ]
 
         for key, value in zip(table.keys(), to_table):
-            table[key] = [value]
+            table[key] = value
         return table, region.astype(int), region_raw, wav_sum
     except TypeError:
         return obs_path, -1, -1, -1
@@ -603,14 +603,14 @@ def process_folder(input_folder=None, start_new_file=None, fits_folder=None,
                     num += 1
                     continue
 
-                obs_name = str(result['obs_id'][0])+result['detector'][0]
-                if result['exposure'][0] < 1000:
+                obs_name = str(result['obs_id'])+result['detector']
+                if result['exposure'] < 1000:
                     print(f'{num:>3} {obs_name} is skipped. Exposure < 1000')
-                    DataFrame(result).to_csv(os.path.join(fits_folder, 'overview_skipped.csv'), mode='a', header=False)
+                    DataFrame(result, index=[0]).to_csv(os.path.join(fits_folder, 'overview_skipped.csv'), mode='a', header=False)
                     num += 1
                     continue
 
-                DataFrame(result).to_csv(os.path.join(fits_folder, 'overview.csv'), mode='a', header=False)
+                DataFrame(result, index=[0]).to_csv(os.path.join(fits_folder, 'overview.csv'), mode='a', header=False)
                 save_region(region, os.path.join(region_folder, f'{obs_name}_region.fits'), overwrite=True)
                 region_raw.writeto(os.path.join(region_raw_folder, f'{obs_name}_reg_raw.fits'), overwrite=True)
                 fits.writeto(os.path.join(wav_sum_folder, f'{obs_name}_wav_sum.fits'), wav_sum, overwrite=True)