diff --git a/README.md b/README.md
index 1963d1b..bdf35ce 100644
--- a/README.md
+++ b/README.md
@@ -111,7 +111,8 @@ plots_info =  {
 	"xlim_min": ...,
 	"xlim_max": ...,
 	"ylim_min": ...,
-	"ylim_max": ...
+	"ylim_max": ...,
+	"color_map": ...
 }
 ```
 To calculate the refined detector center of raw data frame as a numpy array using the following methods: 
diff --git a/bblib/methods.py b/bblib/methods.py
index 1b9d02d..039f1b1 100755
--- a/bblib/methods.py
+++ b/bblib/methods.py
@@ -122,14 +122,14 @@ def _run_centering(self, **kwargs) -> tuple:
                 ax1.imshow(
                     self.visual_data * self.mask_for_center_of_mass,
                     norm=LogNorm(),
-                    cmap="spring",
+                    cmap=self.plots_info["color_map"],
                     origin="lower",
                 )
             else: 
                 ax1.imshow(
                     self.visual_data * self.mask_for_center_of_mass,
                     norm=LogNorm(self.plots_info["value_min"], self.plots_info["value_max"]),
-                    cmap="spring",
+                    cmap=self.plots_info["color_map"],
                     origin="lower",
                 )
             ax1.scatter(
@@ -274,14 +274,14 @@ def _run_centering(self, **kwargs) -> tuple:
                     self.visual_data * self.mask_for_circle_detection,
                     norm=LogNorm(),
                     origin="lower",
-                    cmap="spring",
+                    cmap=self.plots_info["color_map"],
                 )
             else:
                 ax1.imshow(
                     self.visual_data * self.mask_for_circle_detection,
                     norm=LogNorm(self.plots_info["value_min"], self.plots_info["value_max"]),
                     origin="lower",
-                    cmap="spring",
+                    cmap=self.plots_info["color_map"],
                 )
             ax1.scatter(
                 self.initial_detector_center[0],
@@ -562,14 +562,14 @@ def _run_centering(self, **kwargs) -> tuple:
                     self.visual_data * self.mask_for_fwhm_min,
                     norm=LogNorm(),
                     origin="lower",
-                    cmap="spring",
+                    cmap=self.plots_info["color_map"],
                 )
             else:
                 ax1.imshow(
                     self.visual_data * self.mask_for_fwhm_min,
                     norm=LogNorm(self.plots_info["value_min"], self.plots_info["value_max"]),
                     origin="lower",
-                    cmap="spring",
+                    cmap=self.plots_info["color_map"],
                 )
             ax1.scatter(
                 self.initial_detector_center[0],
@@ -802,11 +802,11 @@ def _run_centering(self, **kwargs) -> tuple:
             fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
             if self.plots_info["value_auto"]:
                 pos = ax1.imshow(
-                    self.visual_data, norm=LogNorm(), cmap="spring", origin="lower"
+                    self.visual_data, norm=LogNorm(), cmap=self.plots_info["color_map"], origin="lower"
                 )
             else:
                 pos = ax1.imshow(
-                    self.visual_data, norm=LogNorm(self.plots_info["value_min"], self.plots_info["value_max"]), cmap="spring", origin="lower"
+                    self.visual_data, norm=LogNorm(self.plots_info["value_min"], self.plots_info["value_max"]), cmap=self.plots_info["color_map"], origin="lower"
                 )
             ax1.scatter(
                 self.initial_detector_center[0],
@@ -874,11 +874,11 @@ def _run_centering(self, **kwargs) -> tuple:
             fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
             if self.plots_info["value_auto"]:
                 pos = ax1.imshow(
-                    self.visual_data, norm=LogNorm(), cmap="spring", origin="lower"
+                    self.visual_data, norm=LogNorm(), cmap=self.plots_info["color_map"], origin="lower"
                 )
             else:
                 pos = ax1.imshow(
-                    self.visual_data, norm=LogNorm(self.plots_info["value_min"], self.plots_info["value_max"]), cmap="spring", origin="lower"
+                    self.visual_data, norm=LogNorm(self.plots_info["value_min"], self.plots_info["value_max"]), cmap=self.plots_info["color_map"], origin="lower"
                 )
                 
             ax1.scatter(