diff --git a/.ipynb_checkpoints/ptychoScopy-checkpoint.ipynb b/.ipynb_checkpoints/ptychoScopy-checkpoint.ipynb
index a56273b..1b4f582 100644
--- a/.ipynb_checkpoints/ptychoScopy-checkpoint.ipynb
+++ b/.ipynb_checkpoints/ptychoScopy-checkpoint.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "id": "7425242d-3c91-4c1e-a424-08625a38ee7a",
    "metadata": {},
    "outputs": [],
@@ -301,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 22,
    "id": "8055b802-cf83-4250-aea9-54e3e6b73db0",
    "metadata": {},
    "outputs": [
@@ -338,7 +338,7 @@
     "cl = ToggleButtons(options=opt.cameralengths(), value=12, description='Nominal camera length (cm)', layout=Layout(width='auto', grid_area='cl_set1'), **ali)\n",
     "camera = ToggleButtons(options=opt.detectors(),    description='Detector', layout=Layout(width='auto', grid_area='camera_set1'), style = {'description_width': '60px','button_width': str(100/len(opt.detectors())-len(opt.detectors()))+'%', 'font_weight': 'bold'})\n",
     "restriction = ToggleButtons(options=[('.',False), ('..',True)], value = False, description='PAAR',icons = ['times','check'], layout=Layout(width='auto', grid_area='camera_set2'),style = {'description_width': '80px','button_width': '48%', 'font_weight': 'bold'})\n",
-    "binning = ToggleButtons(options=[('.',1), ('2×2',2), ('4×4',4), ('6×6',6), ('8×8',8)], value=1, description='Binning', icons = ['times','',''], layout=Layout(width='auto', grid_area='camera_set3'), **ali)\n",
+    "binning = ToggleButtons(options=[('.',1), ('2×2',2), ('4×4',4), ('3×3',3), ('6×6',6), ('8×8',8), ('12×12',12), ('16×16',16), ('24×24',24), ('32×32',32), ('48×48',48)], value=1, description='Binning', icons = ['times','',''], layout=Layout(width='auto', grid_area='camera_set3'), **ali)\n",
     "\n",
     "beam_res = Label(value = f'λ (pm) '+ str(\"{:.1f}\".format(pty.get_wavelength(beam.value)*1e12)),layout=Layout(width='auto', grid_area='sidebar1'),)\n",
     "beam.observe(inte.show_wavelength, names='value')                  \n",
@@ -458,7 +458,6 @@
     " \n",
     "\n",
     "    ### SAMPLE PLANE ##########################################\n",
-    "    points = 5\n",
     "    yyy =  np.append(np.linspace(0,overlap,100), 200)\n",
     "    wid = np.array([-2, -1, 0, 1, 2]) * step_size_corr\n",
     "    fig5 = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
@@ -811,7 +810,7 @@
    "id": "1e474cb7-5fd4-4113-a3fa-ddf5ca152ce5",
    "metadata": {},
    "source": [
-    "![title](./ptychoscopy_logo.png)\n",
+    "![title](./logo2.png)\n",
     "\n",
     "Jupyter based interactive data acquisition tool designed for appropriate ptychographic data collection. It computes nessesary characteristics which play crutial role in final data reconstruction. \n",
     "You can chose of **Direct methods** (mainly Single Side Band ptychography) or **Iterative reconstruction** which takes probe defocus into account. With this tool, you can check for probe CTF, scanning step size, probe overlap, detector camera length a proper angular range collection, reconstructed probe size and many more.\n",
@@ -836,14 +835,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 23,
    "id": "7937f054-fcd0-4e67-a20f-7696f5903a94",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c62795511dd9485eba200c05200bbee1",
+       "model_id": "0a48e2bb85fd41a5931e183508ad4aa4",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/calibrations.xlsx b/calibrations.xlsx
index 4c10116..60ed35e 100644
Binary files a/calibrations.xlsx and b/calibrations.xlsx differ
diff --git a/logo2.png b/logo2.png
new file mode 100644
index 0000000..cba7a03
Binary files /dev/null and b/logo2.png differ
diff --git a/ptychoScopy.ipynb b/ptychoScopy.ipynb
index a56273b..1b4f582 100644
--- a/ptychoScopy.ipynb
+++ b/ptychoScopy.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "id": "7425242d-3c91-4c1e-a424-08625a38ee7a",
    "metadata": {},
    "outputs": [],
@@ -301,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 22,
    "id": "8055b802-cf83-4250-aea9-54e3e6b73db0",
    "metadata": {},
    "outputs": [
@@ -338,7 +338,7 @@
     "cl = ToggleButtons(options=opt.cameralengths(), value=12, description='Nominal camera length (cm)', layout=Layout(width='auto', grid_area='cl_set1'), **ali)\n",
     "camera = ToggleButtons(options=opt.detectors(),    description='Detector', layout=Layout(width='auto', grid_area='camera_set1'), style = {'description_width': '60px','button_width': str(100/len(opt.detectors())-len(opt.detectors()))+'%', 'font_weight': 'bold'})\n",
     "restriction = ToggleButtons(options=[('.',False), ('..',True)], value = False, description='PAAR',icons = ['times','check'], layout=Layout(width='auto', grid_area='camera_set2'),style = {'description_width': '80px','button_width': '48%', 'font_weight': 'bold'})\n",
-    "binning = ToggleButtons(options=[('.',1), ('2×2',2), ('4×4',4), ('6×6',6), ('8×8',8)], value=1, description='Binning', icons = ['times','',''], layout=Layout(width='auto', grid_area='camera_set3'), **ali)\n",
+    "binning = ToggleButtons(options=[('.',1), ('2×2',2), ('4×4',4), ('3×3',3), ('6×6',6), ('8×8',8), ('12×12',12), ('16×16',16), ('24×24',24), ('32×32',32), ('48×48',48)], value=1, description='Binning', icons = ['times','',''], layout=Layout(width='auto', grid_area='camera_set3'), **ali)\n",
     "\n",
     "beam_res = Label(value = f'λ (pm) '+ str(\"{:.1f}\".format(pty.get_wavelength(beam.value)*1e12)),layout=Layout(width='auto', grid_area='sidebar1'),)\n",
     "beam.observe(inte.show_wavelength, names='value')                  \n",
@@ -458,7 +458,6 @@
     " \n",
     "\n",
     "    ### SAMPLE PLANE ##########################################\n",
-    "    points = 5\n",
     "    yyy =  np.append(np.linspace(0,overlap,100), 200)\n",
     "    wid = np.array([-2, -1, 0, 1, 2]) * step_size_corr\n",
     "    fig5 = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
@@ -811,7 +810,7 @@
    "id": "1e474cb7-5fd4-4113-a3fa-ddf5ca152ce5",
    "metadata": {},
    "source": [
-    "![title](./ptychoscopy_logo.png)\n",
+    "![title](./logo2.png)\n",
     "\n",
     "Jupyter based interactive data acquisition tool designed for appropriate ptychographic data collection. It computes nessesary characteristics which play crutial role in final data reconstruction. \n",
     "You can chose of **Direct methods** (mainly Single Side Band ptychography) or **Iterative reconstruction** which takes probe defocus into account. With this tool, you can check for probe CTF, scanning step size, probe overlap, detector camera length a proper angular range collection, reconstructed probe size and many more.\n",
@@ -836,14 +835,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 23,
    "id": "7937f054-fcd0-4e67-a20f-7696f5903a94",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c62795511dd9485eba200c05200bbee1",
+       "model_id": "0a48e2bb85fd41a5931e183508ad4aa4",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/ptychoscopy_logo.png b/ptychoscopy_logo.png
deleted file mode 100644
index d1e817f..0000000
Binary files a/ptychoscopy_logo.png and /dev/null differ