diff --git a/src/repurpose/img2ts.py b/src/repurpose/img2ts.py
index 13aa570..bd090ca 100644
--- a/src/repurpose/img2ts.py
+++ b/src/repurpose/img2ts.py
@@ -439,6 +439,8 @@ def calc(self):
 
         for img_stack_dict, timestamps in self.img_bulk():
             # =================================================================
+            logging.info(f"Finished reading bulk with {len(timestamps)} images")
+
             start_time = datetime.now()
 
             # temporally drop grids, due to issue when pickling them...
@@ -451,6 +453,7 @@ def calc(self):
 
             keys = list(img_stack_dict.keys())
             for key in keys:
+                #print(key)
                 # rename variable in output dataset
                 if self.variable_rename is None:
                     var_new_name = str(key)
@@ -473,6 +476,7 @@ def calc(self):
             ITER_KWARGS = {'cell': [], 'celldata': []}
 
             for cell in np.unique(target_grid.activearrcell):
+                #print(cell)
                 cell_idx = np.where(cells == cell)[0]
 
                 if len(cell_idx) == 0:
@@ -486,7 +490,6 @@ def calc(self):
                         np.atleast_2d(img_stack_dict[k])[:, cell_idx], 0, 1)
                     img_stack_dict[k] = np.delete(img_stack_dict[k], cell_idx,
                                                   axis=1)
-
                 cells = np.delete(cells, cell_idx)
 
                 ITER_KWARGS['celldata'].append(celldata)
diff --git a/src/repurpose/ts2img.py b/src/repurpose/ts2img.py
index 4e13462..fb3cbf0 100644
--- a/src/repurpose/ts2img.py
+++ b/src/repurpose/ts2img.py
@@ -42,8 +42,15 @@ def _convert(converter: 'Ts2Img',
         if ts is None:
             continue
         if preprocess_func is not None:
-            preprocess_kwargs = preprocess_kwargs or {}
-            ts = preprocess_func(ts, **preprocess_kwargs)
+            preprocess_func = np.atleast_1d(preprocess_func)
+            if preprocess_kwargs is None:
+                preprocess_kwargs = [{}] * len(preprocess_func)
+            preprocess_kwargs = np.atleast_1d(preprocess_kwargs)
+            if len(preprocess_func) != len(preprocess_kwargs):
+                raise ValueError("Length of preprocess_func and "
+                                 "preprocess_kwargs is different")
+            for func, kwargs in zip(preprocess_func, preprocess_kwargs):
+                ts = func(ts, **kwargs)
         if np.any(np.isin(ts.columns, Ts2Img._protected_vars)):
             raise ValueError(
                 f"Time series contains protected variables. "
@@ -263,7 +270,7 @@ def calc(self, path_out, format_out='slice', preprocess=None,
             - stack: write all time steps into one file. In this case if there
                 is a {datetime} placeholder in the fn_template, then the time
                 range is inserted.
-        preprocess: callable, optional (default: None)
+        preprocess: callable or list[Callable], optional (default: None)
             Function that is applied to each time series before converting it.
             The first argument is the data frame that the reader returns.
             Additional keyword arguments can be passed via `preprocess_kwargs`.
@@ -279,25 +286,25 @@ def preprocess_add(df: pd.DataFrame, **preprocess_kwargs) \
                 df['var3'] = df['var1'] + df['var2']
                 return df
             ```
-        preprocess_kwargs: dict, optional (default: None)
+        preprocess_kwargs: dict or list[dict], optional (default: None)
             Keyword arguments for the preprocess function. If None are given,
             then the preprocessing function is is called with only the input
             data frame and no additional arguments (see example above).
-        postprocess: Callable, optional (default: None)
-            Function that is applied to the image stack after loading the data
-            and before writing it to disk. The function must take xarray
+        postprocess: Callable or list[Callable], optional (default: None)
+            Function(s) applied to the image stack after loading the data
+            and before writing it to disk. The function must take an xarray
             Dataset as the first argument and return an xarray Dataset of the
-            same form as the input data.
+            same form.
             A simple example for a preprocessing function to add a new variable
             from the sum of two existing variables:
             ```
-            def preprocess_add(stack: xr.Dataset, **postprocess_kwargs) \
+            def postprocess_add(stack: xr.Dataset, **postprocess_kwargs) \
                     -> xr.Dataset
                 stack = stack.assign(var3=lambda x: x['var0'] + x['var2'])
                 return stack
             ```
-        postprocess_kwargs: dict, optional (default: None)
-            Keyword arguments for the postprocess function. If None are given,
+        postprocess_kwargs: dict or list[dict], optional (default: None)
+            Keyword arguments for the postprocess function(s). If None are given,
             then the postprocess function is called with only the input
             image stack and no additional arguments (see example above).
         fn_template: str, optional (default: "{datetime}.nc")
@@ -381,10 +388,6 @@ def preprocess_add(stack: xr.Dataset, **postprocess_kwargs) \
 
                 self.stack = self.stack.drop_isel(time=idx_empty)
 
-            if postprocess is not None:
-                postprocess_kwargs = postprocess_kwargs or {}
-                self.stack = postprocess(self.stack, **postprocess_kwargs)
-
             if var_fillvalues is not None:
                 for var, fillvalue in var_fillvalues.items():
                     self.stack[var].values = np.nan_to_num(
@@ -410,6 +413,19 @@ def preprocess_add(stack: xr.Dataset, **postprocess_kwargs) \
                 for var in var_attrs:
                     self.stack[var].attrs.update(var_attrs[var])
 
+            if postprocess is not None:
+                # this is done as late as possible
+                postprocess = np.atleast_1d(postprocess)
+                if postprocess_kwargs is None:
+                    postprocess_kwargs = [{}] * len(postprocess)
+                postprocess_kwargs = np.atleast_1d(postprocess_kwargs)
+                if len(postprocess) != len(postprocess_kwargs):
+                    raise ValueError("postprocess and postprocess_kwargs "
+                                     "have different lengths")
+
+                for func, kwargs in zip(postprocess, postprocess_kwargs):
+                    self.stack = func(self.stack, **kwargs)
+
             if self.stack['time'].size == 0:
                 warnings.warn("No images in stack to write to disk.")
                 self.stack = None