diff --git a/py/bayesian_inference.py b/py/bayesian_inference.py
deleted file mode 100644
index 01ef6b0..0000000
--- a/py/bayesian_inference.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# To add a new cell, type '#%%'
-# To add a new markdown cell, type '#%% [markdown]'
-
-#%%
-
-import numpy as np
-
-np.arange(16)
-

diff --git a/py/bayesian_inference.py b/py/bayesian_inference.py
deleted file mode 100644
index 01ef6b0..0000000
--- a/py/bayesian_inference.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# To add a new cell, type '#%%'
-# To add a new markdown cell, type '#%% [markdown]'
-
-#%%
-
-import numpy as np
-
-np.arange(16)
-
diff --git a/py/spike_train_visualization.py b/py/spike_train_visualization.py
new file mode 100644
index 0000000..09488d9
--- /dev/null
+++ b/py/spike_train_visualization.py
@@ -0,0 +1,33 @@
+#%% [markdown]
+# This snippet plots a spiking train in a raster plot.
+# `spike_times` contains the timestamp for each even in the spike train.
+
+
+#%%
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Set the theme
+sns.set_style("white")
+
+# generate random timestamps (0 to 100s)
+spike_times = np.random.random([8,50]) * 100
+
+
+#* creating the plot. It also accepts color and linelength as arrays for colors and lengths.
+plt.eventplot(spike_times, 
+              color="blue",
+              linelength=0.9)
+
+# title and axis
+plt.title("Sample spike train plot")
+plt.xlabel("time")
+# plt.ylabel("channels")
+#plt.axis('off')
+plt.yticks(np.arange(0, 8),[f'Channel {l+1}' for l in range(8)])
+
+
+plt.show()
+
+#%%

diff --git a/py/bayesian_inference.py b/py/bayesian_inference.py
deleted file mode 100644
index 01ef6b0..0000000
--- a/py/bayesian_inference.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# To add a new cell, type '#%%'
-# To add a new markdown cell, type '#%% [markdown]'
-
-#%%
-
-import numpy as np
-
-np.arange(16)
-
diff --git a/py/spike_train_visualization.py b/py/spike_train_visualization.py
new file mode 100644
index 0000000..09488d9
--- /dev/null
+++ b/py/spike_train_visualization.py
@@ -0,0 +1,33 @@
+#%% [markdown]
+# This snippet plots a spiking train in a raster plot.
+# `spike_times` contains the timestamp for each even in the spike train.
+
+
+#%%
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Set the theme
+sns.set_style("white")
+
+# generate random timestamps (0 to 100s)
+spike_times = np.random.random([8,50]) * 100
+
+
+#* creating the plot. It also accepts color and linelength as arrays for colors and lengths.
+plt.eventplot(spike_times, 
+              color="blue",
+              linelength=0.9)
+
+# title and axis
+plt.title("Sample spike train plot")
+plt.xlabel("time")
+# plt.ylabel("channels")
+#plt.axis('off')
+plt.yticks(np.arange(0, 8),[f'Channel {l+1}' for l in range(8)])
+
+
+plt.show()
+
+#%%
diff --git a/py/stan.py b/py/stan.py
index c4884ed..738f7fc 100644
--- a/py/stan.py
+++ b/py/stan.py
@@ -167,4 +167,3 @@
 
 plt.title("Posterior distribution of alpha")
 plt.show()
-#%%