Updated CaCaMgFe for Opx and Cpx after Matt lowens issue.

Author: PennyWieser

Testing_and_saving_Pickles/Calibrating_Jorgenson/Jorg21_Cpx_only_Press.onnx

@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "9c1800f4-464c-4f17-937c-9347762a7ffb",
    "metadata": {},
    "outputs": [],
@@ -129,49 +129,6 @@
     "with open(\"Jorg21_Cpx_only_Press.onnx\", \"wb\") as f:\n",
     "    f.write(model_onnx_P.SerializeToString())"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d6ae8543-ce65-4455-9cba-3b8dc72feb0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'ExtraTreesRegressor' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[1], line 8\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Scaling\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m#scaler = StandardScaler().fit(x_train)\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m#x_train_scaled = scaler.transform(x_train)\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# Using the parameters from Petrelli supplement for P. Don't actually present a regression for T, \u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# but we add it here.\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m regr_T \u001b[38;5;241m=\u001b[39m ExtraTreesRegressor(n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m201\u001b[39m, criterion\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msquared_error\u001b[39m\u001b[38;5;124m'\u001b[39m, max_features\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6\u001b[39m, \n\u001b[0;32m      9\u001b[0m                            \n\u001b[0;32m     10\u001b[0m                              random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m120\u001b[39m) \u001b[38;5;66;03m# random_state fixed for reproducibility\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# Define the regressor, in our case the Extra Tree Regressor\u001b[39;00m\n\u001b[0;32m     13\u001b[0m regr_P \u001b[38;5;241m=\u001b[39m ExtraTreesRegressor(n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m201\u001b[39m, criterion\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msquared_error\u001b[39m\u001b[38;5;124m'\u001b[39m, max_features\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6\u001b[39m, \n\u001b[0;32m     14\u001b[0m                              \n\u001b[0;32m     15\u001b[0m                              random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m120\u001b[39m) \u001b[38;5;66;03m# random_state fixed for reproducibility\u001b[39;00m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'ExtraTreesRegressor' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "# Scaling\n",
-    "#scaler = StandardScaler().fit(x_train)\n",
-    "#x_train_scaled = scaler.transform(x_train)\n",
-    "\n",
-    "# Define the regressor, in our case the Extra Tree Regressor\n",
-    "# Using the parameters from Petrelli supplement for P. Don't actually present a regression for T, \n",
-    "# but we add it here.\n",
-    "regr_T = ExtraTreesRegressor(n_estimators=201, criterion='squared_error', max_features=6, \n",
-    "                           \n",
-    "                             random_state=120) # random_state fixed for reproducibility\n",
-    "\n",
-    "# Define the regressor, in our case the Extra Tree Regressor\n",
-    "regr_P = ExtraTreesRegressor(n_estimators=201, criterion='squared_error', max_features=6, \n",
-    "                             \n",
-    "                             random_state=120) # random_state fixed for reproducibility\n",
-    "\n",
-    "# Train the model\n",
-    "regr_T.fit(x_train, y_train_T.ravel())\n",
-    "regr_P.fit(x_train, y_train_P.ravel())\n",
-    "\n",
-    "\n",
-    "#x_test_scaled=scaler.transform(x_test)"
-   ]
   }
  ],
  "metadata": {

Testing_and_saving_Pickles/Calibrating_Jorgenson/Jorg21_Cpx_only_Temp.onnx

@@ -1,11 +1,14 @@
 ================================================
 Change Log
 ================================================
+Version 1.0.60 - April 15th, 2025.
+=====================================
+Following Matt Loewen's request, removed duplicate Ca_CaMgFe for Opx and Cpx, now called Ca_CaMgFe_Opx and Ca_CaMgFe_Cpx, changed in all calibration files.
 
 Version 1.0.58 - April 2nd, 2025.
 =====================================
 Added liscence with GUI clause
-updated machine learning pkls and onnx files for latest sklearn as stopped working on VICTOR 
+updated machine learning pkls and onnx files for latest sklearn as stopped working on VICTOR