diff --git a/ratios.py b/ratios.py
index 5911c44..5a6f6a0 100644
--- a/ratios.py
+++ b/ratios.py
@@ -1,6 +1,6 @@
 import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
+import matplotlib.pyplot as plt
+import numpy as np
 
 # Read the CSV file into a Pandas DataFrame
 file_path = "texts_by_period_and_location_saparated_by_periods_in_columns.csv"
@@ -12,21 +12,24 @@ columns_to_summarize = ["ancient", "old", "middle", "new", "late"]
 # Group the data by the "place" column and calculate sums within each group
 grouped = df.groupby("place")[columns_to_summarize].sum().reset_index()
 
-# Create a bar chart using Plotly
-fig = go.Figure()
+# Create a list of colors for the inner divisions
+colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd"]
 
-# Add a bar trace for each category
-for col in columns_to_summarize:
-    fig.add_trace(go.Bar(x=grouped["place"], y=grouped[col], name=col))
+# Create the figure and axis for the bar chart
+fig, ax = plt.subplots()
 
-# Customize the layout
-fig.update_layout(
-    title="Summary of Categories by Place with Filtering",
-    xaxis_title="Place",
-    yaxis_title="Total Count",
-    xaxis=dict(categoryorder='total descending'),
-    barmode='group'
-)
+# Create bars for each place with inner divisions
+bottom = np.zeros(len(grouped))
+for i, col in enumerate(columns_to_summarize):
+    ax.bar(grouped["place"], grouped[col], 0.6, bottom=bottom, color=colors[i], label=col)
+    bottom += grouped[col]
 
-# Show the interactive graph
-fig.show()
+# Customize the plot
+ax.set_ylabel("Total Count")
+ax.set_title("Summary of Categories by Place with Inner Divisions")
+ax.set_xticks(np.arange(len(grouped["place"])))
+ax.set_xticklabels(grouped["place"], rotation=45, ha="right")
+ax.legend(loc='upper right')
+
+# Display the plot
+plt.show()