diff --git a/docs/Examples/1_getting_started.ipynb b/docs/Examples/1_getting_started.ipynb
index 59a1d67..778ab8b 100644
--- a/docs/Examples/1_getting_started.ipynb
+++ b/docs/Examples/1_getting_started.ipynb
@@ -50,19 +50,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'llepe'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-2c8cdd51efc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mllepe\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLLEPE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m opt_dict = {'Nd(H(A)2)3(org)_h0': {'upper_element_name': 'species',\n\u001b[1;32m      3\u001b[0m                               \u001b[0;34m'upper_attrib_name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'name'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                               \u001b[0;34m'upper_attrib_value'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Nd(H(A)2)3(org)'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                               \u001b[0;34m'lower_element_name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'h0'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'llepe'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from llepe import LLEPE\n",
     "opt_dict = {'Nd(H(A)2)3(org)_h0': {'upper_element_name': 'species',\n",
@@ -73,7 +61,7 @@
     "                              'lower_attrib_value': None,\n",
     "                              'input_format': '{0}',\n",
     "                              'input_value': -4.7e6}}\n",
-    "llepe_parameters = {'exp_csv_filename': '../../data/csvs/Nd_exp_data.csv',\n",
+    "llepe_parameters = {'exp_data': '../../data/csvs/Nd_exp_data.csv',\n",
     "                       'phases_xml_filename': '../../data/xmls/twophase.xml',\n",
     "                       'opt_dict': opt_dict,\n",
     "                       'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],\n",
@@ -143,6 +131,7 @@
        "      <th>Nd_aq_i</th>\n",
        "      <th>Nd_aq_eq</th>\n",
        "      <th>Nd_d_eq</th>\n",
+       "      <th>Nd_org_eq</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -155,6 +144,7 @@
        "      <td>0.050001</td>\n",
        "      <td>0.0239</td>\n",
        "      <td>1.0921</td>\n",
+       "      <td>0.026101</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -165,6 +155,7 @@
        "      <td>0.099998</td>\n",
        "      <td>0.0683</td>\n",
        "      <td>0.4641</td>\n",
+       "      <td>0.031698</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -175,6 +166,7 @@
        "      <td>0.150006</td>\n",
        "      <td>0.1170</td>\n",
        "      <td>0.2821</td>\n",
+       "      <td>0.033006</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -185,6 +177,7 @@
        "      <td>0.200004</td>\n",
        "      <td>0.1680</td>\n",
        "      <td>0.1905</td>\n",
+       "      <td>0.032004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -195,18 +188,19 @@
        "      <td>0.300011</td>\n",
        "      <td>0.2637</td>\n",
        "      <td>0.1377</td>\n",
+       "      <td>0.036311</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    h_i      h_eq  z_i      z_eq   Nd_aq_i  Nd_aq_eq  Nd_d_eq\n",
-       "0  0.01  0.088304    1  0.921696  0.050001    0.0239   1.0921\n",
-       "1  0.01  0.105094    1  0.904906  0.099998    0.0683   0.4641\n",
-       "2  0.01  0.109017    1  0.900983  0.150006    0.1170   0.2821\n",
-       "3  0.01  0.106012    1  0.903988  0.200004    0.1680   0.1905\n",
-       "4  0.01  0.118934    1  0.891066  0.300011    0.2637   0.1377"
+       "    h_i      h_eq  z_i      z_eq   Nd_aq_i  Nd_aq_eq  Nd_d_eq  Nd_org_eq\n",
+       "0  0.01  0.088304    1  0.921696  0.050001    0.0239   1.0921   0.026101\n",
+       "1  0.01  0.105094    1  0.904906  0.099998    0.0683   0.4641   0.031698\n",
+       "2  0.01  0.109017    1  0.900983  0.150006    0.1170   0.2821   0.033006\n",
+       "3  0.01  0.106012    1  0.903988  0.200004    0.1680   0.1905   0.032004\n",
+       "4  0.01  0.118934    1  0.891066  0.300011    0.2637   0.1377   0.036311"
       ]
      },
      "execution_count": 2,
@@ -215,7 +209,7 @@
     }
    ],
    "source": [
-    "searcher.get_exp_df()"
+    "estimator.get_exp_df()"
    ]
   },
   {
@@ -263,19 +257,19 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[<cantera.composite.Solution object at 0x0000017D4FF9F128>, <cantera.composite.Solution object at 0x0000017D4FF9F748>]\n"
+      "[<cantera.composite.Solution object at 0x00000278E83A15F8>, <cantera.composite.Solution object at 0x00000278E83A1CF8>]\n"
      ]
     }
    ],
    "source": [
-    "print(searcher.get_phases())"
+    "print(estimator.get_phases())"
    ]
   },
   {
@@ -304,7 +298,7 @@
     }
    ],
    "source": [
-    "for phase in searcher.get_phases():\n",
+    "for phase in estimator.get_phases():\n",
     "    print(phase.name)\n",
     "    print(phase.species_names)"
    ]
@@ -436,13 +430,15 @@
       "            Iterations: 4\n",
       "            Function evaluations: 16\n",
       "            Gradient evaluations: 4\n",
-      "{'Nd(H(A)2)3(org)': {'h0': -4704699.156668724}}\n"
+      "{'Nd(H(A)2)3(org)_h0': {'upper_element_name': 'species', 'upper_attrib_name': 'name', 'upper_attrib_value': 'Nd(H(A)2)3(org)', 'lower_element_name': 'h0', 'lower_attrib_name': None, 'lower_attrib_value': None, 'input_format': '{0}', 'input_value': -4704699.156668724}}\n",
+      "0.025193288852542232\n"
      ]
     }
    ],
    "source": [
-    "est_enthalpy = searcher.fit()\n",
-    "print(est_enthalpy)"
+    "est_enthalpy, obj_value = estimator.fit()\n",
+    "print(est_enthalpy)\n",
+    "print(obj_value)"
    ]
   },
   {
@@ -472,7 +468,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "searcher.update_xml(est_enthalpy)"
+    "estimator.update_xml(est_enthalpy)"
    ]
   },
   {
@@ -496,17 +492,30 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 743.226x572.185 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 743.226x572.185 with 1 Axes>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x278e4f1df48>)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "searcher.parity_plot()"
+    "estimator.parity_plot(\"Nd_aq_eq\", print_r_squared=True)"
    ]
   },
   {
@@ -518,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -530,7 +539,7 @@
     }
    ],
    "source": [
-    "print(searcher.r_squared())"
+    "print(estimator.r_squared())"
    ]
   },
   {
@@ -557,7 +566,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,
diff --git a/llepe/llepe.py b/llepe/llepe.py
index 29d8ba8..ad32334 100644
--- a/llepe/llepe.py
+++ b/llepe/llepe.py
@@ -1267,6 +1267,7 @@ class LLEPE:
                 default_title = '{0} complex eq. conc. (mol/L)'.format(
                     compared_species)
         fig, ax = plt.subplots()
+
         if isinstance(data_labels, list):
             unique_labels = list(set(data_labels))
             for label in unique_labels:
@@ -1287,17 +1288,14 @@ class LLEPE:
             if c_label is not None:
                 c_bar.set_label(c_label, rotation=270, labelpad=20)
         else:
-            ax.scatter(meas, pred, color="r",
-                       legend=False)
-        ax.plot(min_max_data, min_max_data, color="r", label="")
+            ax.scatter(meas, pred, c="r", label="")
+
+        ax.plot(min_max_data, min_max_data, color="b", label="")
 
         if print_r_squared:
             ax.text(min_max_data[0],
                     min_max_data[1] * 0.9,
                     '$R^2$={0:.2f}'.format(self.r_squared(compared_value)))
-            # plt.legend(loc='lower right')
-        # else:
-        # plt.legend()
 
         ax.set(xlabel='Measured', ylabel='Predicted')
         if plot_title is None: