diff --git a/doc/BOZ analysis part II.1 Small data.ipynb b/doc/BOZ analysis part II.1 Small data.ipynb
index d0ea8a274321cc71814342a5fea2faa49d7705ac..157d1f292514cd2fa6c06fa06089a5855bb4e9ef 100644
--- a/doc/BOZ analysis part II.1 Small data.ipynb	
+++ b/doc/BOZ analysis part II.1 Small data.ipynb	
@@ -23,7 +23,6 @@
     "import dask\n",
     "print(f'dask: {dask.__version__}')\n",
     "\n",
-    "import netCDF4\n",
     "import xarray as xr\n",
     "\n",
     "from psutil import virtual_memory\n",
diff --git a/doc/BOZ analysis part II.2 Binning.ipynb b/doc/BOZ analysis part II.2 Binning.ipynb
index 19044e5fa092cc590bd536fd3eba13143e6fa0ef..0801e0524fb3769eebaf85d518e854e78aa2b7f3 100644
--- a/doc/BOZ analysis part II.2 Binning.ipynb	
+++ b/doc/BOZ analysis part II.2 Binning.ipynb	
@@ -23,7 +23,6 @@
     "import dask\n",
     "print(f'dask: {dask.__version__}')\n",
     "\n",
-    "import netCDF4\n",
     "import xarray as xr\n",
     "\n",
     "from psutil import virtual_memory\n",
diff --git a/src/toolbox_scs/routines/boz.py b/src/toolbox_scs/routines/boz.py
index 63e4d2b8885a71cd234c3c21942e086d034b2c53..b37726f5854078caa78444962a6929c52164f57b 100644
--- a/src/toolbox_scs/routines/boz.py
+++ b/src/toolbox_scs/routines/boz.py
@@ -1187,8 +1187,8 @@ def inspect_nl_fit(res_fit):
     ax.plot(1.0/np.sqrt(1e-8*r[:, 0]), c='C0')
     ax2.plot(r[:, 2], c='C1', ls='-.')
     ax.set_xlabel('# iteration')
-    ax.set_ylabel('SNR', c='C0')
-    ax2.set_ylabel('correction cost', c='C1')
+    ax.set_ylabel('SNR', color='C0')
+    ax2.set_ylabel('correction cost', color='C1')
     ax.set_yscale('log')
     ax2.set_yscale('log')
 
@@ -1337,8 +1337,7 @@ def inspect_correction(params, gain=None):
                 good_d[n].values.flatten()/good_d[r].values.flatten(),
                 [photon_scale, np.linspace(0.95, 1.05, 150)*m],
                 cmap='Blues',
-                vmax=200,
-                norm=LogNorm(),
+                norm=LogNorm(vmax=200),
                 # alpha=0.5 # make  the plot looks ugly with lots of white lines
                 )
             h, xedges, yedges, img2 = axs[l, k].hist2d(
@@ -1346,8 +1345,7 @@ def inspect_correction(params, gain=None):
                 sat_d[n].values.flatten()/sat_d[r].values.flatten(),
                 [photon_scale, np.linspace(0.95, 1.05, 150)*m],
                 cmap='Reds',
-                vmax=200,
-                norm=LogNorm(),
+                norm=LogNorm(vmax=200),
                 # alpha=0.5 # make  the plot looks ugly with lots of white lines
                 )
 
@@ -1468,7 +1466,7 @@ def inspect_saturation(data, gain, Nbins=200):
                 c=f'C{kk}', alpha=0.4)
 
     ax.text(0.6, 0.9, f"saturation: {sat_percent:.2f}%",
-             c='r', alpha=0.5, transform=plt.gca().transAxes)
+             color='r', alpha=0.5, transform=plt.gca().transAxes)
     ax.legend()
     ax.set_xlabel(r'10$^6$ ph')
     ax.set_ylabel('density')