enhancements and results download

src/.streamlit/config.toml

@@ -2,4 +2,7 @@
 base="light"
 primaryColor="#7ab0c7"
 secondaryBackgroundColor="#7ab0c7"
-textColor="#000000"
+textColor="#000000"
\ No newline at end of file
+
+[server]
+maxUploadSize = 200

src/Class_Mod/Miscellaneous.py

@@ -55,6 +55,7 @@ def reg_plot( meas, pred, train_idx, test_idx):
     ax.set_xlabel('Measured values')
     plt.legend()
     plt.margins(0)
+    # fig.savefig('./Report/figures/measured_vs_predicted.png')
     return fig
 
 @st.cache_data
@@ -84,16 +85,17 @@ def resid_plot( meas, pred, train_idx, test_idx):
     for i, txt  in enumerate(train_idx):
         #plt.annotate(txt ,(np.array(meas[0]).reshape(-1)[i],ec[i]))
         if np.abs(e[0][i])> np.mean(e[0])+ 3*np.std(e[0]):
-            plt.annotate(txt ,(np.array(meas[0]).reshape(-1)[i],e[0][i]))
+            plt.annotate(txt ,(np.array(pred[0]).reshape(-1)[i],e[0][i]))
 
     for i, txt  in enumerate(test_idx):
         if np.abs(e[1][i])> np.mean(e[1])+ 3*np.std(e[1]):
-            plt.annotate(txt ,(np.array(meas[1]).reshape(-1)[i],e[1][i]))
+            plt.annotate(txt ,(np.array(pred[1]).reshape(-1)[i],e[1][i]))
     ax.set_xlabel(f'{ train_idx.shape}')
     ax.set_ylabel('Residuals')
     ax.set_xlabel('Predicted values')
     plt.legend()
     plt.margins(0)
+    # fig.savefig('./Report/figures/residuals_plot.png')
     return fig
 
 
@@ -137,6 +139,7 @@ def desc_stats(x):
 
 
 def hash_data(data):
+    import xxhash
     """Hash various data types using MD5."""
     
     # Convert to a string representation
@@ -164,6 +167,6 @@ def hash_data(data):
     data_bytes = data_str.encode()
     
     # Compute the MD5 hash
-    md5_hash = hashlib.md5(data_bytes).hexdigest()
+    md5_hash = xxhash.xxh32(data_bytes).hexdigest()
     
-    return md5_hash
+    return str(md5_hash)
\ No newline at end of file