diff --git a/src/gstools/covmodel/fit.py b/src/gstools/covmodel/fit.py
index b420be70..8b19f497 100755
--- a/src/gstools/covmodel/fit.py
+++ b/src/gstools/covmodel/fit.py
@@ -214,6 +214,7 @@ def fit_variogram(
 def _pre_para(model, para_select, sill, anis):
     """Preprocess selected parameters."""
     var_last = False
+    var_tmp = 0.0  # init value
     for par in para_select:
         if par not in model.arg_bounds:
             raise ValueError(f"fit: unknown parameter in selection: {par}")
@@ -464,6 +465,7 @@ def _post_fitting(model, para, popt, anis, is_dir_vario):
     fit_para = {}
     para_skip = 0
     opt_skip = 0
+    var_tmp = 0.0  # init value
     for par in DEFAULT_PARA:
         if para[par]:
             if par == "var":  # set variance last
diff --git a/src/gstools/field/plot.py b/src/gstools/field/plot.py
index ab28c974..346ef602 100644
--- a/src/gstools/field/plot.py
+++ b/src/gstools/field/plot.py
@@ -291,6 +291,7 @@ def update_plane(label):
             s.vline.set_data(2 * [s.valinit], [-0.1, 1.1])
             s.reset()
         im.set_extent(ax_extents[p])
+        asp = 1.0  # init value
         if aspect == "quad":
             asp = ax_rngs[planes[p][0]] / ax_rngs[planes[p][1]]
         if aspect is not None:
diff --git a/src/gstools/krige/base.py b/src/gstools/krige/base.py
index 49a4f62f..78aa2a9f 100755
--- a/src/gstools/krige/base.py
+++ b/src/gstools/krige/base.py
@@ -337,6 +337,7 @@ def _get_krige_vecs(
         # determine the chunk size
         chunk_size = len(pos[0]) if chunk_slice[1] is None else chunk_slice[1]
         chunk_size -= chunk_slice[0]
+        chunk_pos = None  # init value
         res = np.empty((self.krige_size, chunk_size), dtype=np.double)
         if only_mean:
             # set points to limit of the covariance to only get the mean
diff --git a/src/gstools/transform/field.py b/src/gstools/transform/field.py
index 4a281564..a123e798 100644
--- a/src/gstools/transform/field.py
+++ b/src/gstools/transform/field.py
@@ -262,8 +262,7 @@ def binary(
     """
     if not process and divide is None:
         _check_for_default_normal(fld)
-    if divide is None:
-        mean = 0.0 if process and not keep_mean else fld.mean
+    mean = 0.0 if process and not keep_mean else fld.mean
     divide = mean if divide is None else divide
     upper = mean + np.sqrt(fld.model.sill) if upper is None else upper
     lower = mean - np.sqrt(fld.model.sill) if lower is None else lower