diff --git a/core/cell.py b/core/cell.py
index 0d1c48eab0fdecc4dde511a8113fa3839c069f9c..f96f30d53e8bc462044eb40bb6417d1454c3f742 100644
--- a/core/cell.py
+++ b/core/cell.py
@@ -18,5 +18,6 @@ def growth_rate(data:Series, alg=None, filt = 'savgol'):
     if alg is None:
         alg='standard'
 
+    
 
     
diff --git a/examples/testing.py b/examples/testing.py
index 70150066b5245a60bc908bd27d64ac6e0bc24008..885e34f62d8f86335dd08c01c0f9e8faf7d14c28 100644
--- a/examples/testing.py
+++ b/examples/testing.py
@@ -7,53 +7,67 @@ from pandas import Series
 from postprocessor.core.postprocessor import PostProcessor
 from postprocessor.core.tracks import non_uniform_savgol
 
-pp = PostProcessor(source=19831)
+pp = PostProcessor(source=19916)  # 19916
 pp.load_tiler_cells()
-f = '/home/alan/Documents/libs/extraction/extraction/examples/gluStarv_2_0_x2_dual_phl_ura8_00/extraction'
-pp.load_extraction('/home/alan/Documents/libs/extraction/extraction/examples/' + pp.expt.name + '/extraction/')
+# f = '/home/alan/Documents/libs/extraction/extraction/examples/gluStarv_2_0_x2_dual_phl_ura8_00/extraction'
+f = "/home/alan/Documents/libs/extraction/extraction/examples/pH_calibration_dual_phl__ura8__by4741__01"
+pp.load_extraction(
+    "/home/alan/Documents/libs/extraction/extraction/examples/"
+    + pp.expt.name
+    + "/extraction/"
+)
 
-tmp=pp.extraction['phl_ura8_002']
+tmp = pp.extraction[pp.expt.positions[0]]
 
 # prepare data
-test = tmp[('GFPFast', np.maximum, 'mean')]
+test = tmp[("GFPFast", np.maximum, "mean")]
 clean = test.loc[test.notna().sum(axis=1) > 30]
 
 window = 9
 degree = 3
-savgol_on_srs = lambda x: Series(non_uniform_savgol(x.dropna().index, x.dropna().values,
-                                                  window, degree), index=x.dropna().index)
+savgol_on_srs = lambda x: Series(
+    non_uniform_savgol(x.dropna().index, x.dropna().values, window, degree),
+    index=x.dropna().index,
+)
 
 smooth = clean.apply(savgol_on_srs, axis=1)
 
 from random import randint
 
 x = randint(0, len(smooth))
-plt.plot(clean.iloc[x], 'b')
-plt.plot(smooth.iloc[x], 'r')
+plt.plot(clean.iloc[x], "b")
+plt.plot(smooth.iloc[x], "r")
 plt.show()
 
-def growth_rate(data:Series, alg=None, filt = {'kind':'savgol','window':9, 'degree':3}):
+
+def growth_rate(
+    data: Series, alg=None, filt={"kind": "savgol", "window": 9, "degree": 3}
+):
     if alg is None:
-        alg='standard'
+        alg = "standard"
+
+    if filt:  # TODO add support for multiple algorithms
+        data = Series(
+            non_uniform_savgol(
+                data.dropna().index, data.dropna().values, window, degree
+            ),
+            index=data.dropna().index,
+        )
 
-    if filt: #TODO add support for multiple algorithms
-        data = Series(non_uniform_savgol(data.dropna().index, data.dropna().values,
-                                         window, degree), index = data.dropna().index)
+    return Series(np.convolve(data, diff_kernel, "same"), index=data.dropna().index)
 
-        
-    
-    return Series(np.convolve(data,diff_kernel ,'same'), index=data.dropna().index)
 
 import numpy as np
-diff_kernel = np.array([1,-1])
+
+diff_kernel = np.array([1, -1])
 gr = clean.apply(growth_rate, axis=1)
-    
 
-def sort_df(df, by='first', rev=True):
+
+def sort_df(df, by="first", rev=True):
     nona = df.notna()
-    if by=='len':
+    if by == "len":
         idx = nona.sum(axis=1)
-    elif by=='first':
+    elif by == "first":
         idx = nona.idxmax(axis=1)
     idx = idx.sort_values().index
 
@@ -62,23 +76,26 @@ def sort_df(df, by='first', rev=True):
 
     return df.loc[idx]
 
-test = tmp[('GFPFast', np.maximum, 'median')]
-test2 = tmp[('pHluorin405', np.maximum, 'median')]
-ph = test/test2
+
+test = tmp[("GFPFast", np.maximum, "median")]
+test2 = tmp[("pHluorin405", np.maximum, "median")]
+ph = test / test2
 ph = ph.stack().reset_index(1)
-ph.columns = ['tp', 'fl']
+ph.columns = ["tp", "fl"]
+
 
 def m2p5_med(ext, ch, red=np.maximum):
-    m2p5pc = ext[(ch, red, 'max2p5pc')]
-    med = ext[(ch, red, 'median')]
+    m2p5pc = ext[(ch, red, "max2p5pc")]
+    med = ext[(ch, red, "median")]
 
-    result = m2p5pc/med
+    result = m2p5pc / med
 
     return result
 
+
 def plot_avg(df):
     df = df.stack().reset_index(1)
-    df.columns = ['tp', 'val']
+    df.columns = ["tp", "val"]
 
-    sns.relplot(x=df['tp'],y=df['val'], kind='line')
+    sns.relplot(x=df["tp"], y=df["val"], kind="line")
     plt.show()